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Title: robust risk-sensitive reinforcement learning with conditional value-at-risk.

Abstract: Robust Markov Decision Processes (RMDPs) have received significant research interest, offering an alternative to standard Markov Decision Processes (MDPs) that often assume fixed transition probabilities. RMDPs address this by optimizing for the worst-case scenarios within ambiguity sets. While earlier studies on RMDPs have largely centered on risk-neutral reinforcement learning (RL), with the goal of minimizing expected total discounted costs, in this paper, we analyze the robustness of CVaR-based risk-sensitive RL under RMDP. Firstly, we consider predetermined ambiguity sets. Based on the coherency of CVaR, we establish a connection between robustness and risk sensitivity, thus, techniques in risk-sensitive RL can be adopted to solve the proposed problem. Furthermore, motivated by the existence of decision-dependent uncertainty in real-world problems, we study problems with state-action-dependent ambiguity sets. To solve this, we define a new risk measure named NCVaR and build the equivalence of NCVaR optimization and robust CVaR optimization. We further propose value iteration algorithms and validate our approach in simulation experiments.

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State and Transition Models: Theory, Applications, and Challenges

• Open Access
• First Online: 14 April 2017

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• Brandon T. Bestelmeyer 5 ,
• Andrew Ash 6 ,
• Joel R. Brown 7 ,
• Bulgamaa Densambuu 8 ,
• María Fernández-Giménez 9 ,
• Jamin Johanson 10 ,
• Matthew Levi 5 ,
• Dardo Lopez 11 ,
• Raul Peinetti 12 ,
• Libby Rumpff 13 &
• Patrick Shaver 14  

Part of the book series: Springer Series on Environmental Management ((SSEM))

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State and transition models (STMs) are used to organize and communicate information regarding ecosystem change, especially the implications for management. The fundamental premise that rangelands can exhibit multiple states is now widely accepted and has deeply pervaded management thinking, even in the absence of formal STM development. The current application of STMs for management, however, has been limited by both the science and the ability of institutions to develop and use STMs. In this chapter, we provide a comprehensive and contemporary overview of STM concepts and applications at a global level. We first review the ecological concepts underlying STMs with the goal of bridging STMs to recent theoretical developments in ecology. We then provide a synthesis of the history of STM development and current applications in rangelands of Australia, Argentina, the United States, and Mongolia, exploring why STMs have been limited in their application for management. Challenges in expanding the use of STMs for management are addressed and recent advances that may improve STMs, including participatory approaches in model development, the use of STMs within a structured decision-making process, and mapping of ecological states, are described. We conclude with a summary of actions that could increase the utility of STMs for collaborative adaptive management in the face of global change.

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1 Introduction

State and transition models (STMs) were conceived as a means to organize and communicate information about ecosystem change as a basis for management . While some authors regard “the state and transition model” as a specific theory about how ecosystems respond to disturbance (see review in Pulsford et al. 2014 ), we take the view that STMs are not a theory per se, but are a flexible way of organizing information about ecosystem change that may draw on a wide range of concepts about ecosystem dynamics (Westoby et al. 1989 ). The value of STMs for rangeland managers is in fostering a general understanding of how rangelands function and respond to management actions, thereby leading to more efficient and effective allocation of management efforts.

The fundamental idea is simple (see the Caldenal STM at http://jornada.nmsu.edu/esd/international/argentina ). Vegetation , a commonly used indicator of ecosystem conditions, is described according to discrete plant communities (such as an open Prosopis caldenia forest with grassy understory). In doing so, we develop a logic for distinguishing different communities so that stakeholders can communicate effectively about them. Next, we describe the multiple plant communities that can occur on a particular site. The key problem in this step is to define the characteristics of the “site”—its climate, soils, and topographic position. Otherwise we might conclude erroneously that a set of plant communities are alternative states of a specific site when in fact they exist on different sites. Finally, we identify the causes of transitions between communities and the constraints to recovery of particular communities , including succession, event-driven change, and persistent transitions to alternative stable states (Briske et al. 2003 ). The causes and constraints to change are often incompletely understood, but they can be tested by monitoring the effects of management and restoration actions.

These steps allow managers to link information about plant community composition collected during inventory with concepts of ecosystem dynamics to develop management plans aimed at long-term stewardship. For instance, management actions may seek to maintain a desired plant community with high forage quality, to restore native plants and animals that formerly occupied the site, or to create a mosaic of different plant communities favoring wildlife. In this way, STMs can help specify management objectives for a site, and serve as guides to maintain and restore ecosystem services.

The diagrammatic and narrative portions of STMs synthesize various sources of knowledge about ecosystem change, including scientific results, historical anecdotes, and local knowledge. The synthesis is used to develop predictions for how ecosystems respond to natural events and management actions (Bestelmeyer et al. 2009b ). Conceptual STMs can be expanded into quantitative models by including estimates of the likelihood of change.

Well-developed STMs can serve as a basis for collaborative adaptive management (i.e., management by iterative hypothesis testing, involving multiple stakeholders; Susskind et al. 2012 ) (Chap. 1 , this volume). These guidelines can be updated based on monitoring and new knowledge. In this way, STMs can facilitate a shift from rigid prescriptions based on a one-way relationship between science and management toward a constantly evolving set of recommendations based on collaborative learning and adaptation. Collaborative adaptive management is likely to be more effective than rigid rules of thumb as a basis for environmental stewardship, especially as global climate continues to change.

Because of the potential for STMs to link science to management, they are being developed with increasing frequency in rangelands and other ecosystems on several continents (Hobbs and Suding 2009 ). While some STMs were never intended to be used for management, others were developed as a basis for outreach and decision support . The linkage of STMs to on-the-ground decision-making, however, remains limited for a number of reasons, including a lack of adequate detail and specificity in STMs and the inability of institutions to develop and use STMs. Moreover, it is inherently difficult to determine the likelihood of transitions, especially given time lags and long timeframes needed to observe some transitions. Nonetheless, there is continued optimism that STMs can provide useful tools for bridging the science-management divide (Knapp et al. 2011b ).

Our approach in this chapter is to (1) review the ecological basis for STMs, (2) outline the fundamental components of STMs, (3) review the experiences in several countries with the development and use of STMs (Australia, Argentina, the United States, and Mongolia), (4) identify and address challenges to the use of STMs for management, and (5) describe recent technical advances that may improve STMs, including participatory approaches in model construction, the use of STMs within a structured decision-making process, and mapping of ecological states. We conclude with a summary of strategies to improve the utility of STMs for collaborative adaptive management.

2 Conceptual Advances in the Ecology of State Transitions

Primary author B. Bestelmeyer.

The publications of seminal papers on ecosystem resilience and event-driven vegetation dynamics in rangelands catalyzed a significant shift in thought among scientists and managers beginning in the 1970s (Westoby 1980 ; Walker and Westoby 2011 ) (Chap. 6 , this volume). Prior to this time, the notions of climax vegetation and orderly succession following disturbance, stemming from early American plant ecology, were used to interpret vegetation dynamics , even in systems where vegetation change is now known to be discontinuous and irreversible (e.g., Campbell 1929 ). It is now widely acknowledged that (1) vegetation change in response to grazing or weather variations may not occur along a single continuum but rather may produce multiple stable plant communities; (2) vegetation change is not necessarily reversible; and (3) vegetation change can be discontinuous and sudden. While recognition of these patterns occurred prior to the development of STMs, the formalization of “state-and-transition” thinking via the models promoted a broadened view of how vegetation can change (Westoby et al. 1989 ).

In spite of the impact of STMs on general thought, the continuing challenge is to represent accurately the patterns, timescales, and drivers of change among states in particular settings. To this end, it is important to distinguish transient dynamics from persistent transitions between alternative states (Bestelmeyer et al. 2003 ; Stringham et al. 2003 ). Transient dynamics , driven by disturbance or weather events, produce significant but temporary changes in vegetation composition or production that can be reversed in a few years to several decades (e.g., via moderation of disturbance, succession, or weather events). State transitions , on the other hand, involve persistent changes in vegetation such that recovery of the former state is dependent on unacceptably long recovery times, active restoration, extreme events, or a reversal of climatic change that occurs over several decades or never occurs (Suding and Hobbs 2009 ). Below, we review the conceptual distinction between these types of dynamics, acknowledging that it may be difficult to distinguish them in practice.

2.1 Transient Dynamics

Whether a system undergoes transient dynamics or a state transition following a disturbance is influenced by a variety of factors, including plant traits that evolved in response to disturbance, the ability of alternative plant species to colonize a site, and the resistance of soils to degradation (Seybold et al. 1999 ; Cingolani et al. 2005 ). For example, in the Chihuahuan Desert where most historical grasslands have converted to eroding shrublands, grasslands dominated by the perennial grass tobosa ( Pleuraphis mutica ) have been comparatively resilient to drought and overgrazing episodes owing to its low palatability, vegetative reproduction via rhizomes that are protected below ground, and its dominance on landforms that receive water runoff and sediment from upslope positions (Herbel and Gibbens 1989 ; Yao et al. 2006 ). While disturbances such as continuous heavy grazing can cause significant change in vegetation cover and composition in many rangelands, recovery can be rapid, taking only a few growing seasons in productive settings (Fig. 9.1a ), or occur slowly, taking decades in resource-limited environments (Miriti et al. 2007 ; Lewis et al. 2010 ). Species having slow recruitment and growth rates may exhibit significant time lags in recovery. Nonetheless, adjustments to the management strategy or disturbance regime (e.g., via reduced stocking rates or reestablishment of natural fire disturbance regimes to remove woody plants) can be used to initiate recovery.

Examples of transient dynamics and state transitions in rangelands. ( a ) Transient dynamics featuring a reversible shift between communities dominated by western wheatgrass, Pascopyrum smithii, ( 1 ) and blue grama grass, Bouteloua gracilis ( 2 ), in the northern Great Plains of North Dakota, USA; recovery of the more productive P. smithi community can occur in several years with changes to grazing management (courtesy of Jeff Printz). ( b ) Transient dynamics on the Santa Rita Experimental Range in the Sonoran Desert of Arizona, USA, starting with cholla cactus ( Opuntia imbricata ) dominance in 1948 ( 1 ), then burroweed ( Ambrosia dumosa ) dominance in 1962 ( 2 ), and increasing dominance of blue palo verde ( Parkinsonia florida ) from 1988–2007, which might represent a state transition ( 3 and 4 ; courtesy of Mitch McClaran). ( c ) A state transition from grassland to shrubland on the Jornada Experimental Range in the Chihuahuan Desert of New Mexico, USA, starting with high cover of black grama grass, Bouteloua eriopoda , ( 1 ) that may be reduced ( 2 ) and subsequently recovered, unless a threshold is crossed ( 3 ) after which B. eriopoda goes extinct and mesquite ( Prosopis glandulosa ) dominates ( 4 ). This change results in an eroding shrubland state that experiences infrequent co-dominance by another perennial grass, Sporobolus spp. ( 5 ), during periods of high rainfall

Weather variations are especially important causes of transient dynamics in rangelands featuring high interannual rainfall variability. For example, high winter precipitation initiates recruitment of burroweed ( Isocoma tenuisecta ) in the Sonoran Desert and burroweed dominance can be sustained for one to two decades until dry periods and senescence cause declines in density (Fig. 9.1b ; McClaran et al. 2010 ; Bagchi et al. 2012 ). Such vegetation changes can be abrupt, but they do not necessarily represent a transition between alternative stable states. This is because vegetation change is predictably related to recent environmental conditions and it can be reversed via plant senescence or subsequent, common weather events (Jackson and Bartolome 2002 ; McClaran et al. 2010 ).

2.2 State Transitions

The hallmark of a state transition (sometimes referred to as a “regime shift ”; Scheffer and Carpenter ( 2003 )) is long-term persistence of new plant communities, or a new range of variation among plant communities that differs from that of the previous state. The persistence of new states can be caused by mechanisms that are internal to the ecosystem, such as competitive dominance of invaders or plant-environment feedbacks favoring new species under the same soil and climate conditions. In addition, directional changes in external environmental drivers , such as climate change, can cause the persistence of new states.

State transitions in rangelands have been described with the following sequence in some STMs produced in the United States (Fig. 9.1c ). Weather variations or disturbances can cause transient dynamics within a historical or “reference” state resulting in two (or more) distinct communities (Bestelmeyer et al. 2003 ; Stringham et al. 2003 ). Certain of these communities may have low resilience and be susceptible to a state transition (called an “at-risk community” ; Briske et al. 2008 ). Recovery to communities less likely to undergo a transition can occur with the return of favorable weather or reduced disturbance frequency or intensity. Alternatively, an intensification of adverse weather or disturbance can cause the plant community to cross a threshold (often called a “tipping point ”) into a new state. The new state may be stable with respect to the dominance of key plant species, but still exhibit transient dynamics among a set of plant communities that did not exist in the previous state (Friedel 1991 ).

The persistence of alternative states can be caused by invaders that are superior competitors when given a foothold in a community (Seabloom et al. 2003 ). Alternatively, the cessation of natural disturbances can lead to the dominance of superior competitors . For example, the cessation of fire in prairie grasslands can lead to increases in woody plant density and size. When the density of woody plants limits grass (and fuel) continuity and fire spread, and when woody plants grow to a size that limits their mortality in response to fire, then reintroduction of fire can no longer recover the grassland state ( Twidwell et al. 2013b ) (Chap. 2 , this volume). These two types of state transitions involve changes in dominant plants, but not necessarily a change in plant production or other ecosystem properties. While production and soil carbon levels may be maintained (or even increased) with such transitions (Barger et al. 2011 ), the provision of other ecosystem services (e.g., forage for livestock production) is often reduced (Eldridge et al. 2011 ) (Chap. 14 , this volume).

Plant production can be reduced when the loss of dominant perennial plants leads to a reduction in soil water infiltration, accelerated erosion that reduces soil fertility, or rising water tables resulting in salinization (D’Odorico et al. 2013 ). In arid and semiarid rangelands, there may be thresholds in plant patch organization below which positive feedbacks between plant patches, resource acquisition, and plant survival and reproduction break down, resulting in a persistent low-productivity/high bare ground state (Kéfi et al. 2011 ). In other words, if larger plant patches become fragmented too much, the plants occupying those patches suffer due to increased soil erosion and decreased resource availability (Svejcar et al. 2015 ). State transitions associated with soil degradation are often called “desertification.”

State transitions often have multiple, interacting causes (Fig 9.2 ; Walker and Salt 2012 ). Drivers that are external to the system can cause a gradual or abrupt change in controlling (or “slow”) variables . The controlling variables directly determine the state variables of interest. An example would be a change in the intensity and duration of grazing periods (the driver) that gradually reduces grass root mass, basal cover, and soil organic matter (controlling variables) to affect plant foliar cover, production, and composition (state variables). Triggering events occurring over relatively short time periods, such as an extreme drought, can amplify the rate and magnitude of change in controlling variables and may produce abrupt changes in state variables.

A schematic illustrating the pattern and interaction of variables over time involved in state transitions. Elements include external drivers that are returned to pre-transition levels, discrete triggering events occurring over short periods that exacerbate the effects of changing drivers, responses of internal controlling (a.k.a slow) variables that may exhibit feedbacks with state variables, and transitions in state variables. The position of different states and the threshold between states are noted. Dashed lines indicate that changes in controlling and state variables need not be abrupt

Abrupt changes can occur in both transient dynamics and state transitions. In state transitions, however, feedbacks among controlling variables and state variables can lead to persistence of the new state. For example, reduced plant cover leads to increased soil erosion and reduced litter inputs, accelerating the loss of soil organic matter and the ability of the soil to store moisture. Modifications to one or more feedbacks can produce an abrupt change in state variables (i.e., community structure and composition) to create an alternative state, even after the driver has returned to previous levels. The threshold between states is the period in time when changes in controlling variables, and possibly feedbacks, lead to persistent changes in state variables.

State transitions need not always be abrupt, however. Abrupt changes in controlling variables can cause strongly lagged, nearly linear responses in state variables. For example, long-lived plants can persist long after the environment required for their establishment has disappeared, leading to a gradual transition after the threshold is crossed. Alternatively, controlling variables may change gradually and be tracked by gradual changes in plant composition, such as with climate change (see the dashed lines in Fig. 9.2 ; Hughes et al. 2013 ). Even irreversible state transitions can occur gradually .

2.3 Distinguishing Transient Dynamics from State Transitions

The criteria used to distinguish transient dynamics from state transitions depend on the length of time needed for recovery and the implications of these timelines for management. Recovery that does not occur within an acceptable management timeframe without intensive effort is often categorized as a state transition (Watson and Novelly 2012 ). What is deemed “acceptable” varies among users and contexts, but should ideally be based on measurable recovery criteria. For example, recovery that takes longer than 3 years following a change in grazing management is treated as a state transition in Mongolia by the Mongolian government (National Agency for Meteorology and Environmental Monitoring and Ministry of Environment 2015 ). For the US government, changes are called state transitions when they are irreversible or take “several decades” for recovery of the former state (Caudle et al. 2013 ).

It is also important to realize that the type of dynamics recognized might depend on the specific plant functional groups considered. For example, in the Calden ( Prosopis caldenia ) forests of central Argentina, herbaceous plants can exhibit transient dynamics and be managed over multi-year timescales (Llorens 1995 ), even as gradual shrub and tree encroachment over decades increasingly constrains herbaceous cover and composition, representing a state transition (Dussart et al. 1998 ). Unlike the simpler models of the past, transient dynamics and state transitions can be represented simultaneously in STMs.

3 Development of State and Transition Models

STMs should be designed to serve land managers and policymakers by: (1) communicating locally relevant indicators of transient dynamics and state transitions and their consequences for ecosystem services; (2) describing the drivers and environmental conditions affecting susceptibility to transitions; (3) recommending management to avoid undesirable transitions (i.e., resilience management) and to obtain desired ecosystem services; and (4) identifying realistic restoration or adaptation options for alternative states (Bestelmeyer et al. 2009a ).

Assembling the evidence to support an STM can be accomplished in most cases using a combination of sources . Monitoring data, historical records, comparisons of plant communities and surface soil characteristics among sites with different management histories, published experiments, and local knowledge can be combined to infer vegetation dynamics (Bestelmeyer et al. 2009b ). In any case, it is important to recognize that the dynamics represented in STMs are hypotheses that should be tested through the outcomes of management decisions.

The structure of STMs represented in the literature to date is highly diverse. Different authors have used different conventions to develop model diagrams and narratives . Models can be entirely qualitative/descriptive (Knapp and Fernandez-Gimenez 2009 ; Kachergis et al. 2013 ), quantify only properties of states (Bestelmeyer et al. 2010 ; Miller et al. 2011 ), or quantify states and/or transitions (Jackson and Bartolome 2002 ; Czembor and Vesk 2009 ; Rumpff et al. 2011 ). Across all model types, however, there are a set of common elements that define an STM.

3.1 Define the “Site”

An STM should focus on the alternative states and dynamics of an environmentally uniform area (Peterson 1984 ). STMs focus on temporal dynamics, so inclusion of significant ecosystem differences due to inherent differences in soil or climate confuses space and time and may lead to flawed interpretations. In rangelands and forests, terrestrial land units such as ecological sites or potential vegetation types approximate areas of environmental uniformity and can define the spatial extent of individual STMs (Bestelmeyer et al. 2003 ; Yospin et al. 2014 ). Attempts to define STMs at too fine a spatial scale, however, may result in an unwieldy number of STMs and make comparisons among environmental contexts difficult. For this reason, STMs can be developed at a relatively broad spatial extent, such as a landscape, and the effects of varying soil and climate context within the landscape can be described as a narrative for transitions. Grouping land areas according to “disturbance response groups” in the northwestern USA similarly seeks to produce more general STMs that sacrifice spatial precision for greater efficiency of development and use (T. K. Stringham, pers. comm.).

3.2 Define the Alternative States

Each state that is possible for a site is described. In some instances, plant communities linked via transient dynamics are represented as “states” in the broad sense (Jackson and Bartolome 2002 ; Bagchi et al. 2012 ) and in other cases, alternative stable states in the narrow sense are emphasized and transient dynamics within states are described separately or ignored (Miller et al. 2011 ).

Descriptions of transient dynamics have been based on differences in species composition of plant communities that are relevant to management, such as grazing use or wildlife habitat value. Descriptions of alternative states tend to focus on the relationships of vegetation structure to the processes maintaining that structure, such as erosion, fire frequency, or nitrogen fixation (Petersen et al. 2009 ; Kachergis et al. 2011 ). Some STMs depict both alternative states and transient dynamics within states by using boxes for plant communities and separating certain communities using irreversible transitions across a threshold boundary, signifying a state transition (Oliva et al. 1998 ). US agencies developing STMs for Ecological Site Description s (see Sect. 9.4.3 ; Fig. 9.3 ) identify transient dynamics among communities within a state (called “community phases”) as smaller boxes connected by reversible arrows, that are nested within larger boxes representing alternative states (USDA Natural Resources Conservation Service 2014 ).

An example of an STM developed for the Gravelly ecological site, including soils that are loamy-skeletal Haplocalcids and non-carbonatic Petrocalcids in the 200–250 mm precipitation zone of the Southern Desertic Basins, Plains, Mountains Major Land Resource Area (MLRA 42) of New Mexico and west Texas, USA. Following conventions used by US federal land management agencies, rapidly reversible community phases are small boxes whereas states defined by important management and ecological thresholds are defined by large boxes. Each phase is characterized by foliar cover values for dominant or key plant species or functional groups that distinguish it from other phases. In the abbreviated narrative, T signifies an unintentional transition whereas R signifies a transition caused by restoration action (that can have unintended consequences)

Each community or state is typically given a narrative to describe its characteristics and, in some cases, the important ecosystem services it provides. Numerical values allow quantitative distinction of states (Fig. 9.3 ). It is useful to describe the management actions or natural processes that maintain or weaken the resilience of each state and the conditions characterizing low resilience (Standish et al. 2014 ). Alternative states may exhibit variations in resilience, such that undesirable shifts can be avoided (Briske et al. 2008 ) and opportunities for restoration toward desirable states can be exploited (Holmgren and Scheffer 2001 ).

3.3 Describe Transitions

Each transition, represented by arrows, is given a narrative. Transient dynamics are typically attributed to perturbations such as grazing or fire, rainy periods or droughts, or to succession. As described in Sect. 9.2.2 and Fig. 9.2 , state transitions can be described using four basic elements. First, the mechanisms causing a shift among states are described, including external drivers or triggering events, changes in controlling variables and feedbacks, and indicators of change based on controlling variables (e.g., evidence of soil erosion) or state variables (changes in plant composition). Timelines for transitions can be described, such as whether they are gradual or abrupt relative to management timeframes. Second, the constraints to recovery of the former state can be described (sometimes referred to as a threshold ), including how altered feedbacks or environmental conditions preclude the appearance of some plant communities. Third, strategies for the reversal of transitions through restoration actions can be described. Fourth, context dependence in space (such as soils or climate) or time (such as weather conditions) that affects the likelihood of undesirable transitions or restoration success can be described.

4 Development and Applications of STMs in Rangeland Management

Although many STMs have been created, four countries have produced groups of STMs to support rangeland management. How these efforts originated and progressed (or didn’t progress) provide important lessons for future efforts. Below, authors familiar with the history of STM development in Australia, Argentina, the United States, and Mongolia offer accounts representing a variety of global contexts.

4.1 Australia

4.1.1 history.

Australia was an early adopter of STMs , particularly in their application to rangeland management. This early interest stems from two development s. First, Australian rangeland ecologists were at the forefront of considering how concepts of nonequilibrium dynamics and thresholds were applicable to the management of arid rangelands (Westoby 1980 ; Friedel 1991 ). Second, unlike the United States where formal monitoring of rangelands had been instituted based on the range condition and trend concept (Dyksterhuis 1949 ; Shiflet 1973 ), Australia had no single or dominant institutionalized model for rangeland monitoring and, consequently, a number of approaches were developed (e.g., Watson et al. 2007 ).

The absence of a widely accepted framework for describing plant community dynamics in Australia, coupled with the appeal of the state-and-transition format, led to keen interest from the rangeland research community. Adoption was particularly rapid in tropical Australia where the research and management of tropical grazing lands was moving away from a long phase of pasture agronomy associated with the use of introduced species to one based on sustainable utilization of the largely intact, native savannas (Ash et al. 1994 ; Brown and Ash 1996 ). STMs provided an effective approach for describing the dynamics of many plant communities in tropical rangelands. This resulted in a special edition of the journal Tropical Grasslands on STMs (Taylor et al. 1994 ).

In addition to providing qualitative STMs for the major plant communities used for livestock production across northern Australia, the journal issue raised a number of concerns about the broader use of STMs in rangeland management. Major concerns included strategies for communication using models and their role in management; the ability (or inability) to define quantitatively both states and transitions for specific plant communities; and incorporation of spatial processes, such as water flow (Brown 1994 ; Grice and MacLeod 1994 ; Scanlan 1994 ). Shortly afterward, Watson et al. ( 1996 ) questioned the strong focus on event-driven processes and abrupt change and suggested that a model of more continuous, cumulative change was just as appropriate to describe vegetation dynamics in many systems. Further, they suggested an emphasis on the management of vegetation within an ecological state to either prime it for a desired transition or protect it from an undesired transition.

Acceptance of STMs was also evident in southern Australia, such as the original bladder saltbush model used by Westoby et al. ( 1989 ), as well as in arid rangelands, particularly where piosphere effects can lead to alternative vegetation states within a management unit (Hunt 1992 ). A strong interest from ecologists in the fragmented and remnant temperate woodlands drove further conceptual development of STMs, primarily in the context of restoration (Price and Morgan 2008 ; Hobbs and Suding 2009 ; Rumpff et al. 2011 ).

4.1.2 Current Applications

The early interest in developing and applying STMs was not followed by a well-resourced or formal approach to embedding STMs in management of rangelands used for livestock grazing. STM development was carried out via research projects, or by informal approaches in land management or extension agencies, often driven by enterprising individuals, but rarely through systematic institutional initiatives.

One of the limitations in using STMs has been a robust approach to defining states and the thresholds between states. There is a lack of quantitative data for the majority of plant communities and descriptions of dynamics have tended to be qualitative. Moreover, defining and applying threshold concepts in practical management can be problematic because of the potential misinterpretation of management needs (Bestelmeyer 2006 ). Thus, a quantitative basis for distinguishing state transitions from transient dynamics is immensely important.

There were early efforts in Australia to describe transitions quantitatively using Markov models (Scanlan 1994 ). The use of Bayesian belief networks to better incorporate uncertainty and expert knowledge has provided an improved conceptual basis for defining states (Bashari et al. 2009 ) but to date has had limited application. Other approaches included simulation/scenario modeling based on historical rainfall, understory grassland growth, and utilization rates by livestock (Hill et al. 2005 ). While the simulation modeling approach has proved useful in research for understanding system dynamics, it has not translated well to practical application. Another approach to testing the applicability of the state transition concept is to monitor how frequently transitions are occurring. Watson and Novelly ( 2012 ) used an extensive, long-term monitoring dataset from Western Australia to determine how often predefined thresholds were crossed. During a 17-year evaluation period 11 % of grassland sites and 1 % of shrubland sites were judged to have undergone a transition. More recently, a study in semiarid wetlands in Australia provided a robust approach for quantifying the causes of state transitions and using logistic models to generate future transition scenarios (Bino et al. 2015 ). While there has been a range of quantitative approaches tested, a consistent, structured approach to defining and testing for state transitions is still lacking.

Following the initial interest in the STM approach and continued, sporadic development of models for different plant communities (e.g., Phelps and Bosch 2002 ), there is little evidence that STMs have been formally incorporated into pastoral management in Australia, by either individual producers or by land management agencies (Watson and Novelly 2012 ). There is, however, anecdotal evidence that STMs have influenced how rangeland professionals communicate with land managers. One argument is that this “mindset change” is sufficient and that institutionalizing a highly proscribed approach to STMs will stifle flexibility. However, this may be outweighed by the risk of not having a consistent, institutionalized approach to vegetation management in an environment where there are declining resources and capacity in management agencies to proactively assist land managers.

Why has the development of STMs slowed in Australia while it has gained momentum in other countries, most notably the USA? Australia lacks the critical mass of research and extension personnel to develop a comprehensive catalogue of STMs for plant communities at a spatial scale relevant to management. In addition, there is a paucity of robust information on the management-scale distribution of soil properties and accompanying plant community dynamics, exacerbated by the absence of a well-supported and consistent national approach to field-based rangeland monitoring. While that deficiency is being overcome to some extent through a more coordinated national approach to synthesizing information on rangeland condition and trend (Bastin et al. 2009 ), Australia still lacks a widely applied, ecologically based site classification system such as “ecological sites” in the USA (Brown 2010 ) which underpins the development of spatially specific STMs.

The lack of formalized STMs does not mean that rangeland management is occurring in the absence of general principles and locally explicit guidelines. Many rangeland professionals working in land management agencies across Australia have been exposed to STMs and either implicitly or explicitly use STM concepts when engaging with producers. In addition, considerable effort has been expended on developing grazing land management education courses for producers, with the most visible example being in northern and central Australia (Quirk and McIvor 2003 ). However, in an effort to simplify concepts of land condition and its interaction with grazing management, STMs within these educational courses have been replaced by a simple four-level (A[best], B, C, D[worst]) land condition class scheme (e.g., Bartley et al. 2014 ). While this has been effective as a communication tool, it has tended to de-emphasize the importance of processes responsible for long-term vegetation change. For example, Bartley et al. ( 2014 ) showed that even with recommended grazing management practices over a 10 year period, the improvement from class “C” to “B” was proceeding very slowly. This might indicate a state transition related to soil degradation and/or the presence of an exotic grass that was limiting native perennial grass re-establishment. The land condition classes cannot distinguish transient from state transition dynamics or capture the mechanisms involved.

Having been the leaders in the initial development of STMs, rangeland ecologists and land administrators in Australia should consider how development of STMs has progressed elsewhere in the world to see what innovations in application might be relevant to Australia. Recent approaches provide useful frameworks for incorporating STMs into practical management (Bestelmeyer et al. 2009b ; Suding and Hobbs 2009 ). These frameworks go well beyond the development of STMs themselves to include aspects of empirical data to support development of STMs, monitoring protocols, and adaptive management. Ultimately, success will be judged by the utility and relevance of STMs to rangeland managers.

4.2 Argentina

4.2.1 history.

Interest in STMs began in the early 1990s following the publication of the seminal paper by Westoby et al. ( 1989 ). STMs were motivated in large part by the need for a new framework to describe plant community dynamics . A series of STMs developed for the arid Patagonian region were the earliest examples (Paruelo et al. 1993 ). Models for arid environments usually involved the effects of grazing, initially causing a loss of palatable grass species but eventually causing a reduction in total grass cover associated with increasing bare soil and erosion rates. Following these models, a decline in plant cover results in a reduction of soil water holding capacity and plant production, causing a feedback to water and wind erosion that further inhibits reestablishment of grass species (Cesa and Paruelo 2011 ) (Chap. 3 , this volume). State transitions were regarded as irreversible or difficult to reverse. This sequence corresponds to most STMs developed for the Patagonian steppe in Paruelo et al. ( 1993 ).

STMs were developed for more humid environments later in the 1990s, including montane grasslands (Barrera and Frangi 1997 ; Pucheta et al. 1997 ), Pampean grasslands (Aguilera et al. 1998 ; Laterra et al. 1998 ; León and Burkart 1998 ), and herbaceous vegetation of the Caldenal/Espinal ecoregion (Llorens 1995 ). These STMs emphasized changes in species composition rather than large decreases in total plant cover. In these models, grazing did not produce noticeable changes in soil physical properties through erosion as observed in the Patagonian region because total plant cover is usually not greatly reduced by grazing.

A third type of STM described state transitions in “mallines,” a local name for meadows with high productivity and biodiversity within the Patagonian steppe, and which are an important source of forage for livestock (Paruelo et al. 1993 ). Overgrazing and trampling by livestock in mallines produces a transition to an alternative state due to the loss of plant cover that promotes increased runoff and/or soil salinization. Increased runoff and erosion result in gully formation. Consequently, altered hydrology causes a shift in plant communities. Similar hydrologically based state transitions are observed in alluvial floodplains of the Chaco region (Menghi and Herrera 1998 ).

In contrast to early expectations, these STMs had little impact on science and management in Argentina. Exploring the reasons why interest and activity waned may provide insights for improving the usefulness of the STM framework in Argentina and elsewhere. First, STMs developed in the 1990s did not feature adequate detail. STMs described drivers associated with transitions but provided little description on processes and mechanisms controlled by the drivers. Narratives did not contain information on thresholds and processes controlling the functions of alternative states (i.e., feedbacks). Transitions identified in these models were rarely experimentally tested (López 2011 ). Most models were superficial representations of community dynamics that did not provide useful predictions.

Second, STMs were used to synthesize general regional information on ecosystem dynamics but lacked the site specificity needed for practical applications. They contained few recommendations on management practices or restoration actions to reverse undesirable transitions. Similar to Australia, the lack of a land classification system tied to STMs such as ecological sites (Bestelmeyer et al. 2009b ) led to confusion about the spatial domain to which a particular model applied.

Third, important types of state change were simply not addressed by existing models. Tree and shrub encroachment and “thicketization” of woody plants represents one of the most important kinds of state change occurring in several ecosystems in the central and northern parts of the country (Brown et al. 2006 ). The thicketization of forests and grasslands has received a great deal of attention in basic and applied sciences (e.g., Dussart et al. 1998 ), including information about management practices, but there have been very few cases in which this understanding was incorporated in STMs.

Finally, there have been few incentives for scientists to expand development of STMs. Modern Argentinean ecological science , as directed by funding and reward systems over the last few decades, has been focused on short-term studies that yield rapid publication and career advancement (Farji-Brener and Ruggiero 2010 ). In this environment, there was little incentive for integration across different case studies at a regional level or long-term studies to support STM development.

4.2.2 Current Applications

There is a substantial demand from society for responsible natural resource management, in part due to the alarming deforestation rates of the last 10–15 years in the semiarid and humid forests of Argentina (Gasparri et al. 2013 ). Societal demand for rational management forced the establishment of new federal regulations on the use of natural resources. To apply these regulations, policymakers have recognized that a new suite of management decision tools and a basis for assessment and monitoring are required, leading to a renewed interest in STMs manifest in the recent Argentinean Rangeland Congress in 2013 ( http://inta.gob.ar/documentos/jornadas-taller-post-congreso-argentino-mercosur-de-pastizales-cap2013 ). At this meeting, there was general consensus among participants that STMs associated with ecological site concepts should be explored as an option to organize the available information under a common framework for both rangelands and forests. A research network was proposed. It is hoped that this network will serve as a platform for interactions between different research groups and thereby stimulate the production of systematically structured STMs and ecological site classifications across the nation. As yet, funding the network and motivating coordination among researchers via a network remains a significant challenge.

4.3 United States

4.3.1 history.

The official adoption of STMs in 1997 as a component of land evaluation can be considered a paradigm shift in US rangeland science. Clementsian, or succession-based, concepts of community dynamics originating in the early twentieth century provided acceptable explanations for observed vegetation changes in rangelands, particularly in response to livestock grazing. Succession concepts embodied in the “range succession” or “range condition” model (Westoby 1980 ; Joyce 1993 ) worked fairly well in highly resilient prairie ecosystems where much of the grazing livestock and conservation efforts were concentrated. Even leading proponents of the range condition model (Dyksterhuis 1958 ; Passey and Hugie 1962 ), however, noted that the scope of this model was limited to forage for domestic livestock and climax plant communities dominated by perennial, herbaceous species.

In spite of these caveats, use of the range condition model spread throughout US rangelands and was linked to evaluation procedures and financial and technical assistance from federal land management agencies . A relatively well-trained and mature workforce able to detect discrepancies between model predictions and actual conditions, and make ad hoc adjustments to management prescriptions (Shiflet 1973 ), created a sense of complacency among adherents (Joyce 1993 ). Strong connections among universities, agencies, and managers strengthened the ability of the rangeland profession to adapt to these inconsistencies (Svejcar and Brown 1991 ). However, as the application of the range condition concepts spread into diverse rangeland settings, such as those experiencing long-term shrub encroachment, significant limitations in model application became apparent.

As the applicability of the range condition model began to be questioned, theoretical ecologists were developing alternatives to the Clementsian model to explain how ecosystems, and specifically rangelands, behave (Holling 1973 ; May 1977 ). The multiple stable state model was less deterministic than the range condition model and multiple trajectories were possible, better matching observations of rangeland change. Soon afterward in the 1980s, concern about the appropriateness of range condition as a universal metric of rangeland function surfaced within US land management agencies. The inability to link non-forage values to the range condition model was now recognized as a major limitation of assessment procedures (Society for Range Management 1983 ). By the end of the decade, there was widespread dissatisfaction with the application of the range condition model to all rangeland ecosystems (Lauenroth and Laycock 1989 ; Pieper and Beck 1990 ).

In this context, the impact of the first publication on STMs (Westoby et al. 1989 ) was rapid and substantial. Following this paper, there was a flurry of experimental and review papers exploring the application of STMs to particular rangeland ecosystems, both within and outside of the USA. Federal land management agencies undertook extensive reviews of the use of the range condition model as a basis for technical and financial assistance versus implementation of an STM-based approach, culminating in publications by the US National Research Council (National Research Council 1994 ) and the Society for Range Management (Task Group on Unity in Concepts and Terminology Committee Members 1995 ). The two reviews called for standardization of rangeland evaluation approaches and replacement of the range condition model with a model that could account for multiple stable states. Different plant communities could have distinct values to society and call for different management approaches, but a primary focus was to preserve “site potential”—the option to sustain desired plant communities and services—by avoiding accelerated soil erosion.

These two reviews were catalysts for adoption of STM concepts by natural resource agencies. Beginning in late 1990s, STMs began to be developed and used by rangeland specialists, primarily those associated with USDA Natural Resource Conservation Service (NRCS), for communication with ranchers about management needs and to provide guidance in administering federal financial assistance. The policy implications of the latter led to a systematic approach to STM development within NRCS . Widespread development of STMs, however, was delayed because they had to be linked to “ecological site descriptions ” (ESDs; formerly called “range site descriptions ”). ESDs are documents that had long served as the site-specific basis for management recommendations by federal land management agencies. ESDs are linked to soil survey databases through the connection of ecological sites to soil maps maintained by the NRCS . Application of the range condition model via ESDs involved the calculation of plant community similarity between an observed and a single, historical climax plant community identified for each ecological site (Dyksterhuis 1949 ). In order for the rangeland condition model to be replaced, thousands of STMs would have to be developed for ecological sites across the USA, each requiring the description of multiple plant communities.

4.3.2 Current Applications

Acceleration of STM development represents a major logistical challenge because of the large number of STMs needed, particularly in the eastern half of the USA. Added to these logistical concerns, there has been a lack of clear institutional guidance on how to structure STMs that were developed in the late 1990s and early 2000s. Few agency employees have been dedicated to ESD and STM development, and in some locations, contracts were awarded to private enterprises to work on STMs. In most locations, however, STM development was an added duty for existing federal agency staff. Much of this work, although creative, lacked coordination. In some cases, transitions featuring overwhelming indicators of persistence were presented as transient dynamics following the range condition model. In other systems that feature transient behavior, community variations were presented as alternative stable states. The resulting inaccuracies in some STMs have elicited criticism of how they are produced ( Twidwell et al. 2013a ).

In spite of these problems, STMs have gained greater visibility and are increasingly viewed as useful tools for communicating research and management recommendations. New definitions of STM components, scale considerations, and a greater variety of ecosystem attributes linked to STMs (Briske et al. 2008 ; Bestelmeyer et al. 2010 ; Holmes and Miller 2010 ) have emerged. Systematic approaches to the development, evaluation, and refinement of STMs (Bestelmeyer et al. 2009b ; Bestelmeyer and Brown 2010 ), informed by the successes and limitations of early model development efforts, have been incorporated in recent US government guidelines (Caudle et al. 2013 ; USDA Natural Resources Conservation Service 2014 ). These comprehensive guidelines address priority setting, resource allocation, and progress reporting. They also incorporate recent scientific literature, diverse agency policies, and user needs. Nonetheless, significant challenges remain, particularly (1) funding and expertise required to accelerate STM development and deliver STMs to the public, (2) inclusion of information pertaining to ecosystem services other than livestock production, such as climate change mitigation and adaptation, hydrology, and species of conservation concern, (3) how to make STM development more participatory and inclusive to support adaptive management, and (4) how to address the impending effects of climate change in models developed with a high degree of spatial specificity (Knapp et al. 2011b ; Twidwell et al. 2013a ). Current NRCS and interagency efforts are focused on these concerns.

4.4 Mongolia

4.4.1 history.

Mongolia is dominated by rangelands, and livestock production is a critical component of the national economy and cultural traditions . Nonetheless, Mongolia never adopted well-defined or universally accepted rangeland evaluation concepts or procedures. The shift from a nomadic or transhumant, subsistence herding system into a market economy in 1993 led to dramatic increases in livestock numbers and loss of herder mobility (Fernández-Giménez 2002 ). The perception of widespread rangeland degradation associated with overgrazing (Bruegger et al. 2014 ; Hilker et al. 2014 ) motivated interest in rangeland evaluation and monitoring procedures. A systematic approach was needed because assertions about rangeland degradation have been challenged within the Mongolian government and the broader academic community (Addison et al. 2012 ), creating conflict about the need for interventions to reduce stocking rates versus calls by some officials to encourage larger livestock numbers. Beginning in 2004, Green Gold Mongolia (GG), a project funded by the Swiss Agency for Development and Cooperation, initiated efforts to build a national capacity for reporting on the present state and future trend of Mongolian rangelands. In addition, GG sought to develop tools to facilitate rangeland management at local, regional, and national levels . Following exposure to the concept of ESD-based STMs from US scientists and land managers in the mid-2000s, GG and its government partners undertook an effort to develop ESDs for Mongolia.

4.4.2 Current Applications

In many ways, the relatively recent Mongolian experience with STMs takes advantage of what was learned in the early development efforts of Australia, Argentina, and the USA. STM development began in concert in 2008 with the development of a standard methodology for vegetation measurement, based on procedures used by US government agencies (Herrick et al. 2005 ). These procedures were officially adopted by the Mongolian government in 2011. In addition to providing a sound basis for reporting trends in rangeland vegetation, adoption of a unified measurement method ensured that cover and production values reported in STMs were comparable to monitoring data produced by the National Agency for Meteorology and Environmental Monitoring (NAMEM ). Training for a GG research team on methods to develop STMs and database management began in the USA in early 2009, followed by data collection co-occurring with training in Mongolia from 2009 to 2014. Following recommendations adopted by US agencies (Bestelmeyer et al. 2009b ; Knapp et al. 2011a ; USDA Natural Resources Conservation Service 2014 ), inventory of vegetation and soils was conducted at over 600 sites across Mongolia, coupled to workshops aimed at eliciting local knowledge about reference conditions, the presumed causes of vegetation change, and to identify informative sites for inventory. These data are the basis for STMs that were included as a report for the Mongolian government in 2015 ( https://www.eda.admin.ch/content/dam/countries/countries-content/mongolia/en/Mongolia-Rangeland-health-Report_EN.pdf ).

A National Ecological Site Core Group was established in 2011 composed of experienced plant community ecologists representing different ecoregions across Mongolia as well as decision-makers of key institutes able to develop shared interpretations of inventory data. The National Core group (1) reviews published materials to establish reference conditions and causes of state change, (2) works in close collaboration with the GG research team in developing STMs, and (3) performs outreach to encourage adoption of materials by local government and herder cooperatives.

Because of the magnitude of the project, the limited budget, and the need for landscape-level information matched to herding and transhumance patterns, the decision was made to produce broad-level concepts for ecological sites based primarily on landforms and large differences in soil texture or hydrology. These classes, called Ecological Site Groups (ESG), combine finer-level soil classes that are equivalent to ecological sites in the USA (e.g., Moseley et al. 2010 ). STMs are developed for each ESG, resulting in 3–5 STMs per ecoregion and 25 total STMs for Mongolia ( http://jornada.nmsu.edu/files/STM_Mongolian-catalogue-revised_2015.pdf ). Because vegetation dynamics do not differ strongly across ecological sites within an ESG, the general models are deemed adequate for evaluation and management recommendations.

The specification of rangeland management strategies to maintain or recover perennial grasses is a primary objective of the STM development effort. In most of the sites sampled, the presence of well-distributed, remnant perennial grasses suggests that plant community recovery could occur in a few years to several decades with changes to grazing management (Khishigbayar et al. 2015 ). Thus, STMs are being designed to contain detailed information about recommended stocking rates and grazing deferment periods, tailored to the objectives of either maintaining a state or recovering a former state. Recommendations and expectations are linked to specific vegetation cover indicators that can be monitored.

In addition to their use as rangeland management guides by local governments and herder groups, STMs are being embedded in the activities of two government agencies. NAMEM has responsibility for monitoring 1550 plots across Mongolia to report on national rangeland trends. A lack of information about reference conditions and trends in monitoring data has precluded clear statements about rangeland health. Based on STMs drafted for most common rangeland communities in different ecoregions of Mongolia, NAMEM was able to conclude, preliminarily, that Mongolian rangeland communities are in general altered from historical reference states but that relatively rapid recovery was possible in the majority of cases.

STMs can provide a link between monitoring interpretations and management recommendations at the local level. The Agency for Land Affairs, Geodesy and Cartography (ALAGAC) is responsible for land management planning and its implementation nationally. STM concepts are being integrated into participatory rangeland management plans in several pilot areas. These pilot programs will provide a test of the value of the information content of STMs and therefore how they should be refined. As of 2015, expectations are high. Herder groups are using maps based on STMs (including information about recent forage availability and desired community change) to plan grazing and resting periods. It is encouraging that STMs are being used as a basis for such specific management actions .

4.5 Summary of STM Applications

The cases described above suggest that major efforts to develop STMs have taken different trajectories following the introduction of the concept in 1989. In Australia and Argentina, initial enthusiasm and progress was not sustained due to limitations in the data available to develop STMs, the dearth of land classification systems as a basis for STMs, and lack of resources and incentives for scientists and managers. In the USA, these limitations were overcome to varying degrees by the linkage of STMs to rangeland evaluation systems and financial assistance programs supported by government agencies. The vast scientific and administrative infrastructure provided by well-funded US government agencies has supported the nationwide development of numerous STMs. While this strategy has dramatically accelerated STM development compared to Australia and Argentina, it also introduced logistical difficulties associated with managing such a large number of STMs.

The Mongolian effort takes advantage of recent advances and lessons learned. STM development there was motivated by national concerns over rangeland degradation that attracted international development support. A dedicated team of scientists worked with government agencies to develop a relatively simple land classification system as a basis for STMs and employed a broadly collaborative approach to develop STMs. Furthermore, the STMs and related educational materials were purpose-built for collaborative rangeland management at broad spatial scales characteristic of transhumant and nomadic grazing systems of the country. The Mongolian experience may provide a useful model for STM development efforts for many parts of the world.

5 Knowledge Gaps

Primary author is B. Bestelmeyer.

The limitations to STM use highlighted above and recent evaluations of STMs in the USA (Knapp et al. 2011b ; Twidwell et al. 2013a ) suggest several overarching challenges that must be addressed in order to develop more useful STMs and better employ them for management. Below, we describe the main challenges and strategies for responding to them.

5.1 Reference States, History, and Novel Ecosystems

STMs, such as those used in the USA and Mongolia, often define a reference state that represents historical or a “healthy” set of ecosystem conditions for society, such that a primary goal of management is to maintain the reference state or to restore it (Fulé et al. 1997 ; Stoddard et al. 2006 ). Reference states are usually ascertained using historical information or measurements gathered in areas that have not been transformed relative to historical conditions. In many ecosystems, the societal significance and desirability of the reference state is straightforward when that state is well known and when it supports a set of ecosystem services valued by stakeholders.

In other cases, however, there can be difficulties in identifying a meaningful reference state. Historical conditions may be poorly understood, such that there is controversy about the plant communities present and the nature of disturbance regimes (Whipple et al. 2011 ; Lanner 2012 ). This may be especially problematic for plant and animal species that rely on a variety of states (Fuhlendorf et al. 2012 ). For example, a persistent, low plant cover state associated with prairie dog disturbance is necessary to support some native bird species in shortgrass steppe ecosystems (Augustine and Derner 2012 ). Thus, areas that may appear degraded to some observers, and with respect to some ecosystem functions, may support biodiversity and valued species.

Furthermore, the recent concept of “novel ecosystems ” acknowledges that it may not be practical to target a historical state as a management goal if the likelihood for restoration success is low or the costs high (Hobbs et al. 2009 ) (Chap. 13 , this volume). In such cases, the costs of restoration should be evaluated relative to the ecosystem services provided by different states (Belnap et al. 2012 ). In some cases, it may be preferable to manage for alternative states. For some scientists, however, evaluations based on ecosystem services rather than historical fidelity are controversial (Doak et al. 2014 ).

The designation of reference conditions should be based on a broadly collaborative process and take into consideration several factors including history (both recent and evolutionary), the physical processes affecting potential plant communities (climate, soils, and topography), a recognition of specific time scales for disturbance and other processes, practicality of use, and the variety of ecosystem services of interest in particular ecosystems. Similarly, management objectives should be defined in a circumspect and collaborative manner. Managing toward reference conditions may be preferred in some locations, while managing for alternative states may be useful in others.

5.2 Broader Representation of Ecosystem Services

Given that STMs are principally used for communication with particular sets of managers, grazing managers for example, they often emphasize a relatively narrow set of ecosystem services ( Twidwell et al. 2013a ) (Chap. 14 , this volume). Minimal recognition of other ecosystem services, including biodiversity and the regulation of water supply, will limit the utility of such STMs for other users. Quantitative interpretations about the different ecosystem services provided by ecological states could be added to STMs (Brown and MacLeod 2011 ; Koniak et al. 2011 ). Such information could be used to evaluate the financial costs of restoring a historical state against the change in benefits relative to the current state. Similarly, trade-offs among ecosystem services associated with transitions between states can be communicated in terms of specific variables such as forage provision, species losses, and changes to groundwater recharge rates. As noted above, such exercises may reveal that states considered to be degraded by some observers offer important ecosystem services to others (Mascaro et al. 2012 ). They may also clarify the trade-offs between specific services, such as forage production vs. biodiversity (Fuhlendorf et al. 2012 ).

Although it is useful to communicate about states in terms of ecosystem services, it is prudent to acknowledge our limited ability to comprehensively measure all of them effectively. Certain attributes of reference states will be overlooked if they are not adequately measured, especially the biodiversity of organisms that are not the focus of management (Bullock et al. 2011 ; Reyers et al. 2012 ). Historical states will continue to be valued for this reason.

5.3 Climate Change

STMs often implicitly assume that long-term climate properties and potential vegetation are stable (i.e., stationarity). This assumption leads to an emphasis on recent history in designating alternative states ( Twidwell et al. 2013a ) (Chap. 7 , this volume). Given that climate change is likely to cause directional changes in environmental conditions, plant community responses to management observed in the recent past may become less informative in the future. At present, however, forecasts of climate change effects on vegetation, especially at the resolution of STMs, are not well developed (Settele et al. 2014 ). STMs could benefit from linkages to species distribution models (Bradley 2010 ) and models examining the role of soil profile properties in mediating water availability (Zhang 2005 ). Narratives highlighting the consequences of recent extreme events, such as the tree die-off during an extreme drought in the southwestern USA (Breshears et al. 2005 ), could be readily included in STMs. Particularly in arid rangelands, management strategies aimed at promoting resilience to known extreme events (especially water deficits) would be similar to strategies implemented to adapt to climate change, at least over the next decade or two (Ash et al. 2012 ).

5.4 Testable Mechanisms

The inclusion of sufficient detail on mechanisms of vegetation change has been a primary limitation of STMs (Knapp et al. 2011b ; Svejcar et al. 2014 , Sect. 9.4 ). For example, transitions in some grassland STMs are sometimes ascribed only to the driver (e.g., continuous heavy grazing) without more detailed analysis of the mechanisms by which transitions occur. Information on plant demography (plant death, lack of recruitment), the timeframe for transitions (1 year or several decades), specific indicators of the risk of transition (reduced reproduction rates, indications of erosion), and the management strategies used to prevent transitions given the processes (proper timing of defoliation to permit successful reproduction during favorable years) are often not described in STMs. Richness of detail may be lacking because (1) the information is believed to be too complicated to include and therefore best left to direct interactions between managers and extension specialists; (2) simple lack of effort on the part of model developers; or (3) a lack of detailed knowledge.

These reasons notwithstanding, model developers should strive to include details in a systematic way (e.g., Sect. 9.3 ; the Caldenal STM at http://jornada.nmsu.edu/esd/international/argentina ) in order for STMs to be used and, more importantly, be tested and improved via adaptive management (Briske et al. 2008 ; Bestelmeyer et al. 2010 ) (Chap. 9 , this volume). Even when the specific mechanisms of state transitions (or resilience of a state) are not well understood, they can be postulated by blending local knowledge with the rich body of work in ecological science (Kachergis et al. 2013 ). This can be aided by the development of general STMs at the level of broad ecosystem types that can be refined, if needed, to finer-grained land units such as ecological sites. Analysis of historical treatments and new monitoring data can then be used to revisit the hypotheses. For example, shrub-dominated coppice dune states of sandy soils in the Chihuahuan Desert were believed to resist widespread perennial grass recovery based on historical observations and the notion that high erosion rates precluded grass establishment. An unusual sequence of years with high precipitation, and other poorly understood factors, led to a flush of grass recruitment that was unexpected (Peters et al. 2012 ). The STM for this system has been modified to include this new information. In this way, STMs can be regarded as theoretical constructs that synthesize what is known, use that knowledge to generate management hypotheses, and are updated as new knowledge is acquired.

5.5 Information Delivery and Use

If STMs are to be used as tools for long-term environmental stewardship, then the information presented in STMs must be accessible to land managers and/or become integrated in outreach and management activities. Developing and conveying the information in STMs to users such that they can guide management decisions is a multifaceted problem that should be carefully considered by the institutions developing STMs (and see Sect. 9.4 ). General approaches to information transfer include (1) collaborative development of STMs that include the managers who will use them (see Sect. 9.6.1 ; Knapp et al. 2011a ), (2) initiation of collaborative adaptive management projects at the scale of landscapes that include STM development and use as key components (Bestelmeyer and Briske 2012 ), (3) the use of web-based technologies and mobile devices to link users to STMs pertaining to specific localities (Herrick et al. 2013 ), and (4) the distillation of STM information into simple presentation materials (such as pictorial field guides, web-based materials) and the use of field-based workshops to enable understanding of these materials. The use of STMs for management will require concerted efforts by scientists, government agencies, educators, and technical experts and cannot be limited to the production of reports, publications, and associated databases by a handful of managers and ecologists .

6 Future Perspectives

Three emerging approaches are currently transforming how STMs are developed and used, including participatory development of STMs with stakeholders as part of community-based management approaches, structured decision-making via STMs, and the use of digital mapping approaches to provide spatially explicit information on ecological states. Here we summarize the current status and future goals of these three approaches.

6.1 Participatory Approaches to Model Development

Participatory and collaborative STM development approaches emerged for two practical reasons. First, available field data rarely cover the landscape adequately at a sufficiently fine resolution, or over timescales sufficient to detect transitions and calculate their probabilities. Key types and combinations of management and environmental drivers often are not represented in the available data. Second, models based solely on the knowledge of individual scientists or land management professionals may rely too heavily on a single person’s observations and experiences, which can result in biases similar to using monitoring data from only a few locations on a landscape or points in time. These limitations suggest that a more inclusive and participatory approach that integrates multiple knowledge sources may be a pragmatic solution to the challenges inherent in STM development (Kachergis et al. 2013 ) (Chap. 11 , this volume).

Perhaps even more important, participatory approaches will increase the utility, credibility, and use of STMs by managers. Recent surveys have shown that many ranchers and natural resource professionals have little knowledge or experience with STMs when they are available (Kelley 2010 ). Engaging these potential “end-user s” of STMs in the process of developing the models increases STM awareness and acceptance, and thus the likelihood that the models will be used to guide and refine management. An acknowledged limitation of many existing STMs is a focus on a narrow set of ecological attributes and management practices to characterize states and transitions, and a limited suite of management interpretations emphasizing livestock production (Sect. 9.5.2 ; Knapp et al. 2011b ). If STMs are to represent multiple ecosystem values and services, and not just changes in vegetation composition and production for a single or narrow range of uses (e.g., forage production), then multiple disciplines and perspectives are needed.

Participatory or collaborative STM development has taken a variety of forms. The most familiar in the USA is the “technical team ,” an interdisciplinary collaboration of specialists (e.g., rangeland ecology, soils, hydrology, fire, wildlife, geographic information systems, and cultural resources), often involving several natural resources agencies and academic experts, convened to develop STMs for a particular area. In some areas, such technical teams have been expanded to include landowners or ranchers (Johanson and Fernandez-Gimenez 2015 ). Collaborative STM development usually takes place over a period of months to a few years and may involve multiple meetings and field trips. The “model development workshop ” is another type of participatory approach in which a multi-stakeholder group with diverse knowledge and interests in a particular ecological site or set of sites is brought together for a single workshop or series of workshops to develop or refine STMs (Knapp et al. 2011a ). Such workshops often have an explicit aim to include the local knowledge of long-time residents in an area as well as professional and scientific knowledge. Kachergis et al. ( 2013 ) proposed a hybrid approach that involves a diverse set of stakeholders and a combination of literature review, workshops, and field sampling. When it is not possible or practical to bring diverse stakeholders together in one location, or when knowledge documentation is an objective, interviews or surveys with stakeholders can provide a means of recording valuable information that can inform model development (Knapp et al. 2010 ; Runge 2011 ).

There is no one best way to facilitate a collaborative or participatory STM development process, but several groups with experience using different collaborative approaches have described the processes that have worked for them (Knapp et al. 2011a ; Kachergis et al. 2013 ; T. K. Stringham, pers. comm.). The process outlined by T. K. Stringham (pers. comm.), which follows the expanded technical team model, focuses on assembling a core team of highly experienced and committed disciplinary experts and inviting participation from a broader group of agency specialists. The workshop model (Knapp et al. 2011a ) and integrated literature, workshop, and field sampling approach (Kachergis et al. 2013 ) draw from a wider array of stakeholders and emphasize the value of including long-term residents and those whose knowledge is derived from land-based livelihoods. All three of these processes begin with a draft graphical model that serves as the basis for initial discussions and feedback from the group.

Johanson and Fernandez-Gimenez ( 2015 ) drew on these experiences together with those of participants in 16 collaborative ESD and STM development projects in the USA to identify common outcomes, challenges, and keys to success. Most efforts were successful in producing an STM or portion of an ESD. Additional outputs included publications, applications of the models to management, workshops, and databases. Many benefits beyond these tangible outputs were also identified, such as improved working relationships and communication among participants from different organizations, decreased conflict, increased efficiency of STM development, greater use of STMs, and improved data credibility.

Participatory processes are never without challenges. The most frequently cited concerns were related to the quality, diversity, management, and analysis of available data. Reconciling different concepts for classifying ecosystems and their dynamics and agreeing on goals for STM development efforts were common challenges in expanded collaborations. Time and funding constraints and recruitment/retention of participants were additional obstacles. Because many natural resource professionals are unfamiliar with ESDs and STMs, key concepts must be taught to all participants and reinforced with additional teaching throughout the process. Similarly, when working with nontechnical stakeholders, care must be taken to define key terms in a clear and accessible manner and to provide an introduction to STM concepts and applications. Although some professionals express skepticism about the accessibility of STMs to nonprofessionals (Knapp et al. 2011b ), we have found that most people readily grasp these concepts, especially once they are engaged in the process of model development.

The keys to successful participatory STM development are similar to those for any participatory natural resource management effort (Wondolleck and Yaffee 2000 ; Daniels and Walker 2001 ). First, involve the right people at the right time. Make sure that the needed expertise is present, particularly experienced specialists in soils and rangeland ecology, but also hydrology, fire, wildlife, geographic information systems, and cultural resources. When integrating local knowledge is an important objective, seek diversity and depth of experience in local knowledge holders. Community referrals are often an effective way to identify knowledgeable residents (Knapp and Fernandez-Gimenez 2009 ; Knapp et al. 2010 ).

Second, it is important to maintain clear and open communication, a willingness to learn from others, and focus on mutually beneficial outcomes. In multiagency collaborations, conflicts can arise over the differing mandates and procedures of different agencies. When multiple stakeholders are involved, careful facilitation is required to balance power dynamics and ensure that the contributions of all participants are respected. Clear ground rules should be established regarding the criteria for including states and transitions and how potentially conflicting views of ecosystem dynamics will be handled and represented in the model. In multi-stakeholder STM workshops, the level of agreement among participants about each state and transition can be explicitly documented and used to identify uncertainties to test through targeted field sampling or adaptive management experiments (Knapp et al. 2011a ; Kachergis et al. 2013 ). This leads to more efficient use of limited field sampling resources.

Third, support from management within participating agencies is critical. If administrators do not value collaboration and support their staff in participating in such efforts, it is very difficult to sustain the level of participation and commitment needed for success. Fourth, many participants reported that joint field visits were key to successful collaborative STM development. Discussing conditions observed in specific areas can help resolve misunderstandings and elicit new sources of information. Fifth, because many of the challenges identified relate to data collection, management, and analysis, it is important to discuss and agree upon responsibilities and protocols for these activities up front. Often the university or research partners in STM collaborations take the lead on data analysis. However, we strongly encourage groups to invite broad participation in data analysis and especially in data interpretation. We also recommend formal data sharing and use agreements to facilitate information sharing and protect confidentiality where needed.

Reported participant experiences suggest that collaboration is a good investment that increases the efficiency of STM development. It requires significant human, financial, and time resources, but yields both tangible and intangible benefits that participants perceive to increase the quality, credibility, and utility of STMs.

6.2 Structured Decision-Making via State and Transition Models

In this section, we ask: can STMs be used in a more systematic way to prioritize management objectives and to efficiently allocate management funds? Below we discuss why managers may benefit from integrating STMs into a structured decision-making process, and developing STMs such that they enable quantitative predictions of management outcomes .

Ecosystem management decisions are invariably complex. There may be a lack of understanding about the processes underlying a specific problem. Alternatively, there may be multiple and potentially competing objectives for management, which may not be readily apparent, but which should be determined before developing the model. For instance, when faced with an imperative to both manage for a certain plant community and protect a threatened species, it may be that the habitat for that species does not correspond to the desired vegetation state. In addition, it may be that an objective to minimize costs is at odds with the funds required to restore a community to the desired state. Stakeholders will not value all of these objectives in the same way, but it is the role of the decision-maker to evaluate these trade-offs. Last, there may be multiple potential alternative management strategies, but high uncertainty and disagreement about ecosystem responses to management. For the decision-maker, choosing the best course of action to help achieve the specified objectives can be extremely difficult (Runge 2011 ; Gregory et al. 2012 ).

Many of these problems can be addressed by using a systematic approach to the decision-making process. The term “structured decision-making” broadly refers to a framework that incorporates a logical sequence of steps to help decision-maker s (1) define their decision context; (2) identify measurable objectives; (3) formulate alternative management strategies; (4) explore the consequences of those alternatives in relation to the specified objectives; and, if necessary (5) make trade-offs among objectives (Gregory et al. 2012 ). The framework utilizes a broad suite of decision-analysis tools that can aid transparent and logical decision-making (Addison et al. 2013 ). Despite the multitude of tools and methods that may be applied, the basic premise is a framework that is driven by the objectives, or values, of those involved in the decision-making process (Keeney 1996 ; Runge 2011 ).

STMs are typically developed as conceptual models, informed by expert knowledge and existing data. Such models may quantify the characteristics of states but lack a quantification of transition probabilities given particular values of controlling variables and management actions (i.e., they are qualitative or semiquantitative STMs). Within the structured decision-making framework (Fig. 9.4 ), a qualitative STM can be used to clarify the decision context among stakeholders, the desired direction of change and key attributes of interest (objectives), and the different management interventions that might be employed to achieve this change (alternatives). In addition, qualitative STMs could be used to begin exploring the consequences of the alternatives in relation to the objectives. As a decision-support tool, a qualitative STM is often all that is required to guide a good management decision within the structured decision-making process. For instance, an STM (based on Bestelmeyer et al. 2010 ) can be used to identify the interventions required to achieve the ecological conditions for a reference state (Bunchgrass savannah; Fig. 9.4 ). In this instance there is one objective (the reference state), and clearly defined interventions. However, recognized uncertainty about the effects of climate change may result in different models of cause-and-effect, uncertainty about the most effective interventions, or even uncertainty about whether the goal state is attainable. In cases where there are numerous alternatives to choose from, multiple and competing objectives, conflicting values among stakeholders, differing stories of cause-and-effect, or “critical uncertainty” (i.e., uncertainty that bears on key decisions), decision-making based on quantitative STMs can help select the best decision.

The structured decision-making framework adapted from Wintle et al. ( 2011 ). A conceptual STM (adapted from Bestelmeyer et al. 2010 ) is commonly used to frame the problem, whereas the quantitative version of the STM (structured here as a Bayesian network) is useful to identify, explore and resolve critical uncertainty

Quantitative (or process-based) models are useful for identifying and exploring the uncertainties that impact management decisions (Duncan and Wintle 2008 ; Rumpff et al. 2011 ). A process-based model represents the current state of knowledge and assumptions about the dynamics of the system, and allows predictions to be made about the efficacy of the different management strategies in relation to the objectives of interest. For instance, in Fig. 9.4 , the assumptions behind the STM have been quantified and converted into a probabilistic model of cause-and-effect (a Bayesian network). Probabilistic transition estimates now include uncertainty about the efficacy of management interventions under various climatic scenarios.

A management decision will often involve multiple objectives, with no one management strategy that maximizes all objectives. For example, there may be a trade-off between achieving the reference state and maximizing agricultural productivity . The quantitative model should first be expanded to enable predictions for both objectives. The predictions can then be combined with value judgments (or preferences) that specify which objective should benefit over the other, given the range of possible outcomes (Gregory et al. 2012 ). The true value of an alternative management strategy is a combination of the consequences (including uncertainty), and the weight or value attributed to the objectives (step 5, Fig. 9.4 ). At this point, the decision may be obvious, or uncertainty may be obscuring the preferred management strategy.

Uncertainty is inevitable, but decision-makers should pay particular attention to resolving critical uncertainties, as this can result in modified and potentially more effective management decisions. Monitoring is used to resolve this uncertainty, by iteratively updating the knowledge within the process-based model (step 6, Fig. 9.4 ). This is known as adaptive management , which is a form of structured decision-making, required when decisions are recurrent and hampered by critical uncertainty (Runge 2011 ). Thus, adaptive management requires extra steps in the structured decision-making framework to provide a plan for motivating, designing, and interpreting the results of monitoring.

Although the development of quantitative state-and-transition models has increased (Bashari et al. 2009 ; Nicholson and Flores 2011 ; Rumpff et al. 2011 ), to date their application in a management context is rare. Thus, it can be concluded that STMs have yet to reach their full potential as decision-support tools for the implementation of natural resource management and the evaluation of its outcomes. Both quantitative and qualitative models can be used to capture our current understanding about system dynamics, and to identify and explore uncertainty surrounding the response to management (Rumpff et al. 2011 ; Runge 2011 ). The choice of decision support tool should be dictated by the availability and form of knowledge, whether qualitative or quantitative predictions are required to make a decision, and whether quantitative skills are accessible given the timeframe available for decision-making.

Whether the model is quantitative or qualitative, structured decision-making can help to provide a systematic and transparent framework for identifying objectives, collate existing knowledge, explore the consequences of management alternatives and identify and evaluate uncertainty. The value of qualitative STMs to help frame and guide vegetation management decisions in rangelands is not in question. Rather, managers and researchers should acknowledge the complexities of their particular problem context, and assess whether structured decision-making approaches are useful.

6.3 Mapping State-and-Transition Model Information

Managers currently represent ecosystem variations across a wide range of scales for various uses. In rangelands, potential natural vegetation is mapped via land unit classifications such as habitat types (Jensen et al. 2001 ), range units and range sites (Kunst et al. 2006 ), and ecological sites (Bestelmeyer et al. 2003 ). More recently, attention has focused on the delineation of the current states of a set of land units based on its STM (Steele et al. 2012 ). The product is called a “state map ” that can make the information within STMs spatially explicit for its use in management.

STMs are typically linked to land units that define the spatial extent to which information in STMs should be extrapolated.Soil survey is often used to map land units bearing distinct STMs (such as ecological sites), particularly in the USA. Hence, STMs can be linked to maps of soil types or landforms. Soil maps thus provide a template for mapping ecological states across multiple STMs. One constraint in linking soil maps to STMs is that any errors in existing soil spatial data are transferred to the state map. Many soil maps in rangelands consist of “soil map units” that represent multiple soil types either due to a limitation of mapping scale or landscape heterogeneity (Duniway et al. 2010 ). In some cases, soil types are similar and grouped to the same ecological site; however, soil types with contrasting properties combined within the same soil map unit may belong to different ecological sites and STMs. In the USA, it has been a priority to resolve these discrepancies in order to improve the utility of STMs (Steele et al. 2012 ).

Ecological sites and states can be mapped simultaneously using environmental variables, such as from remote sensing products (Browning and Steele 2013 ; Hernandez and Ramsey 2013 ). One benefit of utilizing remotely sensed data to characterize ecological sites and states is the ability to produce scalable information that can be tailored to particular needs (Kunst et al. 2006 ). For example, West et al. ( 2005 ) outlined a strategy for producing a hierarchical map of ecological units for 4.5 million hectares area in western Utah based on a variety of data sources. The finest level was a “vegetation stand” that is similar to ecological states represented in STMs.

Mapping of ecological states can be difficult in rangelands because spectral data from conventional sources, such as MODIS or LANDSAT satellites , is often not of sufficient resolution or quality to distinguish states. Blanco et al. ( 2014 ) integrated hyperspectral and multi-spectral remote sensing data to identify ecological sites in rangelands of Argentina. This approach could be extended to map states. Steele et al. ( 2012 ) outlined a framework for mapping ecological sites and states in rangelands of southern New Mexico using a combination of soil survey spatial data combined with image interpretation of aerial photography to manually delineate ecological site and state polygons (i.e., line maps).

Digital soil mapping (DS M) is an emerging technique that can improve estimates of soil property and ecological state information at fine spatial scales in rangelands by predicting the properties of pixels of varying resolution (e.g., to 5 m) (Levi and Rasmussen 2014 ; Nauman et al. 2014 ). Although DSM has not yet been applied to state mapping, it could fill a much needed gap by increasing automation, using a greater range of data sources, and allowing for rapid updating of state maps when new data become available.Data-driven classification algorithms can greatly reduce the time needed to produce state maps because they provide a means of grouping pixels into similar units, thereby reducing the burden of hand digitizing (Laliberte 2007 ; MacMillan et al. 2007 ). DSM approaches can also be scaled up or down to meet desired management objectives, which is currently difficult to do with polygon-based maps. In turn, DSM could be used to identify vegetation responses to soil properties that may improve STMs (Browning and Duniway 2011 ).

State mapping can extend the utility of STMs for management. In landscapes with a mix of ecological sites and states, state mapping distills information across multiple STMs into a simpler classification scheme that can be used for communication among stakeholders and to develop action plans (Fig. 9.5 ). For example, a state map was used in the southwestern USA in planning for brush control treatments to identify areas that were (1) near a desired reference condition where no treatment was needed, (2) areas that had experienced soil erosion where treatment would likely not produce increases in perennial grass cover, and (3) areas where treatment would be most likely to produce desired changes. In a similar way, state mapping can be used to plan for land use changes, such as by prioritizing development away from desirable reference states (Stoms et al. 2013 ). State mapping could also be used to visualize or model spatial interactions in a landscape, such as where increases in grass cover would have the greatest impact on water retention within a watershed.

An example of a product based on an ecological state map for a single ecological site type. The map illustrates interpretations of an STM according to brush management treatment options (courtesy of Eldon Ayers)

STMs evolved from the recognition that vegetation change was more complex than could be accounted for by succession alone, and could occur along numerous pathways, be discontinuous, and result in multiple stable states in the same environment. Conceptualizing vegetation as discrete states also provides a useful platform for tailoring management actions to the properties and possibilities associated with each state. For rangeland managers, the value of STMs resides both in their flexibility for organizing information and in their ability to foster a general understanding about how rangelands function.

Progress toward developing rangeland STMs at a global level has been uneven due to several factors, including limitations of data and fiscal and personnel resources. As strategies to overcome these limitations are developed, the ultimate success of STMs as management tools will require careful attention to several topics. First, there should be a clear understanding of the characteristics of alternative states, including a reference state where such a concept is meaningful. Field sampling, synthesis of experimental results and long-term vegetation records, and participatory approaches are important resources for defining states. State characterization should ideally represent information on a variety of ecosystem services. In most cases, this will require coordinated sampling efforts to link variations in plant community states to empirical or model-based evaluations of habitat quality, soil carbon storage potential, and value for livestock, for example.

Second, STMs should attempt to distinguish transient dynamics from state transitions. Evidence-based approaches necessitate clear statements not only about drivers of transition but also about the controlling variables and processes constraining recovery and timelines for ecosystem change. STMs should feature logical and testable statements about how states will respond to management, such that STMs can support experimentation, quantitative models, and eventual revision. Even where data are scarce, local knowledge can be framed as testable propositions. Predictions regarding the effects of climate change on ecosystems may best be addressed at a regional scale, but information on the impact of past extreme events can be highlighted. Strategies to manage alternative states, such as through novel uses of states invaded by woody plants, may help with climate adaptation over the longer term.

Third, STM development programs should consider how to make information available, useful, and believable to users. Participatory approaches can promote understanding and acceptance of STMs. There should be a clear link between STMs and specific management actions, which can facilitate the inclusion of STMs into collaborative adaptive management programs supported by local communities, nongovernmental organizations, or governmental agencies (Fig. 9.6 ). Regional or landscape collaborative groups can develop STMs and identify ecosystem services of interest from different states. The linkage of STMs to maps of ecological states can facilitate management application and testing. Hypotheses for management responses can be developed for specific land units (Fig. 9.5 ) and structured decision-making approaches can be used for cases when multiple management options are possible, trade-offs make decisions difficult, and the preferred decision is unclear or controversial. Tests of hypotheses via monitoring can be used to either revise the STM or make minor management adjustments.

A schematic of how STMs can be used in collaborative adaptive management, adapted from Bestelmeyer and Briske ( 2012 )

In order to facilitate their use in collaborative adaptive management, STMs should be presented and used in a variety of ways, including simple extension materials, formal hypotheses for ecological research and tests of management efficacy, rangeland evaluation criteria, maps, or Bayesian models. Policymakers, technical assistance personnel, regulators, scientists, land managers, and stakeholders should be working from the same general understanding of how a rangeland ecosystem functions, even if those parties differ in their preferred states or ecosystem services. STMs should link understanding across different organizational levels as a basis for collaborative adaptive management. Our hope is that the recommendations presented here will promote development of STMs that are indispensable for the management of global rangelands.

Primary authors are A. Ash and J. Brown.

Primary authors are D. Lopez and R. Peinetti.

Primary authors are J. Brown and P. Shaver.

Primary authors are B. Densambuu and B. Bestelmeyer.

Primary authors are M. Fernandez-Gimenez and J. Johanson.

Primary author is L. Rumpff.

Primary author is M. Levi.

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Acknowledgements

We thank David Briske, Ian Watson, and an anonymous reviewer for comments that substantially improved this chapter. B.T.B. was supported by appropriated funds to the USDA Agricultural Research Service and by the Jornada Basin LTER program (DEB-1235828). M.E.F.-G. was supported by Colorado Agricultural Experiment Station Project COL00698A and NRCS Conservation Innovation Grant Agreement No. 69-3A75-12-213.

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Bestelmeyer, B.T. et al. (2017). State and Transition Models: Theory, Applications, and Challenges. In: Briske, D. (eds) Rangeland Systems. Springer Series on Environmental Management. Springer, Cham. https://doi.org/10.1007/978-3-319-46709-2_9

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1 Introduction: Transformations of the State

Evelyne Huber is Distinguished Professor of Political Science and Chair of the Department of Political Science at the University of North Carolina at Chapel Hill, USA; 2005 and 2013 Fellow at the Hanse-Wissenschaftskolleg Institute for Advanced Study (HWK) in Northwest Germany in cooperation with the Collabo¬rative Research Center on Transformations of the State (TranState, 2003–2014) and the Bremen International Graduate School of Social Sciences (BIGSSS, 2007 ff.) and its forerunner (GSSS, 2001–2007).

Matthew Lange is Associate Professor of Sociology in the Department of Sociology at McGill University, Montreal, Canada.

Stephan Leibfried is Professor of Social and Public Policy in the Department of Political Science, Director of the Collabo¬rative Research Center on Transformations of the State (TranState, 2003–2014), Codirector of the Division “Institutions and History of the Welfare State” of the Center for Social Policy Research (ZeS), and faculty member of the Bremen International Graduate School of Social Sciences (BIGSSS, 2007 ff.), all at the University of Bremen, Germany; also Research Professor at Jacobs University Bremen.

Jonah D. Levy is Professor of Political Science at the University of California, Berkeley.

Frank Nullmeier is Professor of Political Science at the University of Bremen.

John D. Stephens is the Gerhard E. Lenski, Jr. Distinguished Professor of Political Science and Sociology and Director of the Center for European Studies at the University of North Carolina, Chapel Hill.

• Published: 02 September 2014
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This introduction sets the stage for the Handbook’s presentation of the latest social science knowledge about the state and its transformations. It develops an analytical framework that helps identify and grasp the diversity of state transformations. Transformations of the state differ in their causes and driving forces, the elements of the state that are affected, and the extent and intensity of change. Our analytical framework includes categories for the analysis of the determinants of state transformation, the dimensions of state transformations, and the intensity and extent of state transformations.

The state remains the most important political unit of the modern world. In the most recent phase of globalization ( Rieger and Leibfried 2003 : 18 ff.; Osterhammel 2005b ; Maier 2014 ) the role and position of the state has changed, but after a short intermezzo in which nothing less than the “end of the state” was frequently proclaimed, the social sciences have reached consensus about the ongoing centrality of states. This Handbook focuses on state transformations. Transformations are fundamental changes of the state. We take into consideration the entire period from the emergence of the nation state in Europe to the present, but we concentrate on state transformations over the past five decades. This Handbook presents the latest social science knowledge about the state and its transformations along with issues for further research. Transformations of the state are considered for all regions of the world, for countries in economically advanced and less developed regions, for young states and those which can look back at a long tradition of state development, for democratic states and authoritarian regimes, for countries with (previously) socialist economic systems and the states where the idea of liberal market economies originated, for states with a colonial past and their erstwhile colonial masters. It is challenging and ambitious to examine such a wide range of states and their transformations, even in an extensive Handbook profiting from the participation of a large number of leading experts.

Before we present the history of state theories and extant research approaches to the study of statehood by Levy et al. in Chapter 2 , this introduction develops an analytical framework that helps identify and grasp the diversity of state transformations. Our analytical framework includes categories for the analysis of the

determinants of state transformation (Section 1 );

dimensions of state transformations (Section 2 ); and

intensity and extent of state transformations (Section 3 ).

The chapter concludes with an outline of the organization of the Handbook (Section 4 ).

1 The Determinants of State Transformation

State transformations are of the utmost importance. Although they are hardly at the center of the state-centered literature, there are a variety of works that consider state transformations in different ways. Some focus on the origins of states and trace how states transformed over time to reach their “modern” forms. Others, mostly in the developed states, focus on particular types of state change, such as the expansion and contraction of welfare states, transformations in state economic activities, in their political structures, and in the public sphere, and also on state decentralization or internationalization; a whole series of transformation studies has pointed the way. 1 More recently, a number of broader analyses of state transformation investigate how to build states after they have broken down, and others analyze how certain factors—most notably, globalization—potentially have enormous effects on states. Together, these subliteratures point to a variety of factors, some more influential than others, that promote major state transformations. In accordance with the main lines of discussion, we categorize these factors into three groups: international factors, domestic factors, and preexisting state structures.

International Determinants of State Transformation

State power depends on the domination and pacification of a territory, its capacity for warfare, and the centralizing force of a higher level of bureaucratic administration. But these attributes are constitutive for states solely to the extent that they are recognized by other state powers. Thus, only the formation of an international sphere in which the related political units perceive each other as formally equal and legitimate made the enforcement of the state as the basic model of political unity and order possible. For the development of states, the international sphere therefore has constitutive significance.

In the traditional view, the international system is not pre-structured politically. Rather, it is conceived as an initially anarchic interplay of states, despite the emergence of international law. A monopoly of legitimate coercion exists only within the territorial states. In the relationships among states, the outbreak of violence is a permanent threat. In this respect, war initially moves into the center of an explanation of state transformations. Charles Tilly’s work kick-started this area of study and left a lasting influence by emphasizing the utmost importance of international war (cf. now Hui 2005 ; Boucoyannis 2006 ). In his famous words, “[w]ar made the state, and the state made war” (1975: 42). His classic argument is that international warfare pushed state elites in Western Europe to reform the state in ways that improved their success at war. To avoid conquest by international competitors, state elites needed to implement major reforms to improve revenue collection and the capacity to organize mass warfare.

Tilly also recognizes that international warfare made great demands on citizens and—over an extended period of time—forced the state to make concessions to their citizens in return for paying taxes and serving in the military. He therefore links international warfare to the development of representative government and the welfare state. Peter Flora and Arnold Heidenheimer (1981) also point to international warfare as a determinant of welfare state expansion. They differ from Tilly, however, because they pay less attention to the international environment creating a need for certain types of reform, although they discuss this. Instead, they focus on how international conflict created domestic consensus and how this consensus made the implementation of broad-based new policy possible, thereby initiating a dramatic expansion in the British welfare state during World War II. In particular, they claim that the war helped mend differences between Conservative, Labour, and Liberal parties, something exemplified by Churchill’s coalition government between 1940 and 1945. Dan Slater (2010) makes a similar argument in his analysis of state-building in Asia, finding that international threats made state expansion possible by improving consensus among competing political elites and that this consensus proved vital to the successful implementation of major political reforms.

Colonization and imperial expansion, often resulting in “state death” ( Fazal 2007 ), were other forms of a violent penetration of foreign territories. States conquer regions and impose their rule on foreign lands and peoples. Such foreign imposition has been extremely common throughout much of the world over the past millennium, most notably through overseas colonialism ( Osterhammel 2005a ). The resulting colonial states sometimes built a state apparatus from scratch and sometimes radically modified preexisting states but always followed basic elements of the state model developed in Western Europe ( Lange 2009 ). The formation of administrative structures in the former colonies and the processes of decolonization were supported by factors that might previously have been described as “domestic,” intra-imperial factors, but which, with decolonization, turned into international factors in the twentieth century. Today, new forms of asymmetric warfare—typically conflicts between states and insurgents, or guerillas, who resort to unconventional warfare—may lead to the disintegration of the territory of these states and can affect surrounding states as well ( Münkler 2005 ; Kaldor 2012 ). It is true that violence—as in civil wars—may arise from domestic factors, but states and other international actors play a prominent role in most forms of violent conflict.

At the other, more peaceful extreme, the international environment can disseminate models of the state, or of major parts of it, by offering an example. Thomas Ertman (1997) notes that the dissemination of organizational models during the Early Modern period had major effects on the shape and capacities of Western European states. Outside Europe, several countries that successfully avoided colonialism copied Western states in an effort to reform their states to better defend themselves against colonial conquest, with Japan and Thailand being notable examples. Along these same lines, global institutionalists claim that global economic, political, and cultural institutions presently push the Western model of the state throughout the world, thereby promoting state isomorphism ( Krücken and Drori 2009 ; on the limits, see Scott 2009 ).

The international arena affects states or stateless territories in two very different ways: first, by coercion, imposition, and war ; and, second, as a source of policy models adopted by the states in a learning process. But we should not ignore types of international influence lying in between these extreme cases of coercion and persuasion. Often these state models or blueprints for policies have been adapted following an international trend or a fashion. In these cases, emulation is the mechanism that gives international developments influence at the level of states. The literature on diffusion and convergence ( Simmons et al. 2008 ; Obinger et al. 2013 ) recognizes a fourth mechanism besides learning, emulation, and coercion: competition . It is the economic pressure to maintain or improve international competitiveness that guides states in their learning behavior. Changing older beliefs about appropriate economic strategies and learning processes may be part of this type of diffusion, but the adaptation of international concepts is primarily fostered by economic incentives and pressure.

Major transformations of the international economy notably include the state-induced internationalization of capital markets, the growth of foreign direct investment, the emergence of global production chains, the growth of international trade, and the increase in international labor migration ( Eichengreen 2008 ; Panitch and Gindin 2012 ). Under these conditions, states have to adequately integrate their economies into the world market and ensure the advantages of a strong domestic market. In order to understand external pressures on states, we have to understand changes in the international economy and system of states over the past half century, and we have to take into account that pressures from the international system are essentially different for states in different positions in the world economy and in the system of states. Immanuel Wallerstein (1984 , 2011 ) conceptualized a rather rigidly structured world economic system. Without subscribing to his view of severely limited mobility in that system, we need to recognize the powerful systemic pressures operating on states.

Today’s world is no longer the anarchic world of the early development phase of the international system. The number of international organizations has been growing since the nineteenth century. After World War II, a fundamental restructuring of the international system took place with the transfer of competences and authority to the newly created United Nations (UN). Although the UN does not have the means to implement a monopoly of coercion, this worldwide organization institutionalized formally, but not de facto , the obligation to refrain from the threat or use of force against the territorial integrity or political independence of any state, other than in cases of self-defense. The legal prohibition at least shifted the balance of proof for a legitimate intervention to the aggressor.

The political and economical importance of International Financial Institutions (IFIs) such as the World Bank and the International Monetary Fund (IMF), both in the phase of the Cold War and the subsequent period of the Washington Consensus, is also considerable. The policies of many countries have been temporarily controlled by these organizations.

Instead of a purely international world determined by states, a transnational sphere including states, international organizations (IOs), non-governmental organizations (NGOs), transnational organizations (TNOs), 2 and supranational organizations such as the European Union (EU) has emerged as a form of distributed governance, usually referred to as “global governance.” It would certainly be wrong, however, to conclude that these developments have ushered in the end of the state or a fundamental loss of state agency. States remain prominent as the key players of international politics, and hence power relations among states are still worth studying (Mann 1968–2013: vol. 4, 8–9). Significant shifts of equilibria have certainly occurred: for instance, the end of the Cold War, the emergence of the BRICs, 3 and especially the economic rise of China to a top political and economic position, entail a transition from a bipolar world to a more complex global constellation of actors.

The impact of the international system can be supportive of the development of state capacities, as it was in the cases of South Korea and Taiwan in the 1950s and 1960s. The geopolitical situation provided these countries with major incentives to strengthen their economic and security capacities. Moreover, the growing US and European economies provided the markets for their rapidly increasing low-wage manufacturing exports, thus consolidating these developmental states. In contrast, a negative impact became highly visible in the case of the debt crisis in Latin America in the 1980s. The international financial institutions and US agencies exerted very strong pressures for state-shrinking and for a reduction of all kinds of state functions, and these pressures succeeded in many countries. Finally, the international system can be relevant as a simple survival function for states, providing them with legal recognition and some foreign aid to ward off challengers, without effectively expanding their state capacities.

The Domestic Determinants of State Transformation

Many explanations of state transformations in the established democracies focus on domestic factors. Previously highlighted factors include the expansion of the education sector, the strength of trade unions and left-wing political parties, and the role of national levels of industrialization, mass mobilization, and democratization. For instance, the growth of the welfare state may be explained as the outcome of a specific level of socio-economic development or of a specific power balance between labor and capital. This one-sided analytical focus on domestic factors has now been proven inadequate, even in the study of the most industrialized countries. In the post-World War II phase, international political and economic trends were also crucially important as explanatory factors. Today, the expansion of the EU has a major impact on—and occasionally even forces—national policies ( Cowles et al. 2001 ; Wallace et al. 2015 ).

Still, explanations of state transformations have to be grounded in a detailed analysis of domestic factors. The erstwhile focus on domestic structures in comparative politics scholarship may have reflected a problematic methodological nationalism. Yet replacing this approach with an equally one-sided focus on international diffusion processes and compliance with international norms would also be wrong. In order to be effective and meaningful, international influences must be built into and resonate with national political institutions and power relations, and hence domestic factors remain key variables in the explanation of state transformations.

The political forces that trigger state transformations or stabilize existing state structures and the resources at their disposal certainly depend to a large extent on the level of economic development , as suggested by the many variants of modernization theory. Early or late industrialization, the size of the agrarian sector and of domestic markets, the greater or lesser endowment with natural resources, and nationally specific economic structures are all relevant factors that influence both the nature of state action at the national level as well as state transformations. The same is true for skill levels and the endowment with human capital, the expansion of the education sector, or the transition from Fordist to service economies. Besides economic development, the availability of raw materials and human resources is a further domestic determinant. Similarly, a number of works link state-building to the construction of an effective system of taxation, as such taxes were necessary for sustainable state expansion ( Bräutigam et al. 2008 ).

More recently, however, a number of scholars suggest that the availability of certain types of resources—natural resources—has negative effects on states ( Gawrich et al. 2011 ). Some draw on Tilly’s classic argument and suggest that the availability of natural resources limits states’ needs to develop effective taxation systems. Others focus on lootable natural resources and find that they create opposition to states, thereby promoting anti-state violence and even state breakdown. Going beyond economic resources, one can certainly argue that states governing societies with stronger human resources have an advantage in adapting to the requirements of competition in the knowledge economy.

In an effort to identify the causal pathways of state change, it is necessary to take the formation of social groups, the organization and mobilization of these groups, and related cleavages into account ( Kriesi et al. 2008 ). Social structures, and especially socio-economic classes , represent the key intervening variable between economic development and political conflicts. These class structures vary considerably among world regions and countries, in line with their level of economic development. The size of the agrarian sector and the distribution of land are as important in this respect as the size of the informal sector and the timing and scope of the transition to service economies, resulting in more or less deindustrialization. And the size of a middle class with post-secondary education is as important as the dominance of big landowners, industrial entrepreneurs, or financial interests on the side of capital. Hence the analysis of social structures cannot draw on one-size-fits-all class typologies, and an exclusive focus on the working class is inappropriate for the analysis of many countries in the Global South, due to their high shares of informal workers.

Rather than the strength of individual classes, however, one has to consider class constellations, or the distribution of power within complex social structures. The class structure of a given society is an important explanatory factor for the development of a democratic state and its level of democratization, and likewise for transitions from moderately democratic to authoritarian regimes. The literature on welfare state expansion offers similar findings, as the strength of labor creates strong domestic pressure pushing state elites to build welfare states or replacing state elites resisting this impulse with new democratically elected representatives of working and middle class interests ( Huber and Stephens 2001 ). More broadly, Barrington Moore Jr.’s (1966) classic analysis notes that different class-based power configurations promoted different trajectories of state development, ranging from communist to fascist, to democratic. Specifically, bourgeois-dominated economic development generated pressures for democratization because it strengthened the organizational capacity of working and middle classes and weakened large landowners dependent on cheap labor, who were the key enemies of democracy ( Rueschemeyer et al. 1992 ).

Such a power-configuration view need not focus exclusively on class. Power can be based on a variety of factors, and other cleavages—such as ethnicity, religion, race or gender —are in some cases greatly influential. The basic mechanism here is that power is organized along different lines and that the constellation of power shapes state transformations. Religious cleavages influence the development of party systems and modify the effects of class structures ( Kersbergen and Manow 2009 ). A further major divide is between ethnically homogenous polities and multi-ethnic, multi-cultural, or multi-religious polities ( Stepan et al. 2011 ). When combined with socio-economic conflicts, ethnic conflicts may lead to secession and the formation of new states, civil wars, or even failed states. Alternatively, they may lead to reforms designed to accommodate the demands of ethno-nationalist movements for more power and resources, such as federalism, decentralization, and devolution. Race discrimination may have a strong and long-standing effect on the stalled democratization of a class segregationist state (United States) and on a caste-segregationist type of political system (South Africa). The differential integration of the sexes into rural and urban-industrial production regimes as well as changes in family structures in the wake of modernization processes are additional socio-structural factors whose importance is growing.

However, social structures do not necessarily translate into specific state structures, either. The interests of classes, religious, and ethnic groups are shaped by the discourses of the general public or the more limited public spheres of these groups themselves; they are socially constructed and therefore depend on communication processes that unfold within civil society . Variations in the freedom of expression, in the development of mass media, and in terms of greater or lesser opportunities for social groups to organize as parties, interest groups, and other types of associations strongly influence the chances of successful political mobilization. In authoritarian regimes or illiberal democracies, the organization and mobilization of particular classes, and especially of underprivileged groups, is frequently repressed. Yet as soon as basic political rights are granted and democratic procedures are introduced, political forces that support the maintenance of democracy and further democratization are strengthened; the democratization of a state may thus become a self-reinforcing process.

Yet even a broadly educated civil society is no more than an enabling domestic factor for state transformations. Whether civil society enables the organization of interests in parties and large associations , such as employers’ associations and unions, may be more crucial ( Huber and Stephens 2012 ). The rise of Christian democratic parties in Europe and Latin America ( Kalyvas 1996 ; Mainwaring and Scully 2003 ; Kaiser 2007 ) has structured the opportunities for state transformation quite differently than did two-party systems with a social democratic and a conservative-liberal party. Hence an explanation of state transformations in democracies, and even in authoritarian regimes dominated by a single party, must consider parties and party systems as key domestic factors. For instance, where social democratic parties have a strong hold on government power, whether alone or in coalitions, the welfare state is likely to expand more than it would elsewhere ( Huber and Stephens 2001 ; Hemerijck 2013 ).

The coalition and cooperation potential of parties—itself determined by the class bases of party systems and the structures of civil society—and hence greater or lesser opportunities of political elite consensus create quite different types of political systems, as exemplified by the differences between majoritarian and consensus democracies ( Lijphart 1999 ). These patterns of cooperation and conflict also matter in authoritarian regimes, especially in electoral autocracies, and in state formation processes: it is important to acknowledge the strength of competing political elites and factors that either cause one to gain power and influence over another or promote greater cooperation between formerly competing elites. Growing consensus among formerly competing power blocs promoted relatively rapid and extensive state change in Botswana, Malaysia, and elsewhere ( Lange 2009 ; Slater 2010 ). This finding coincides with Matthew Lange and Dietrich Rueschemeyer’s (2005) more general claims that rapid state-building depends first and foremost on some minimum degree of elite consensus.

A further group of domestic conditions that has received considerable attention is state-society relations . Sometimes the study of this bundle of explanatory factors is viewed as a distinct explanatory perspective ( Migdal 2001 ; see also Migdal 2004 ). This perspective looks at how relations between state and societal actors affect the state and its transformations. Analyses using this perspective find that state transformations depend on resources, information, and manpower and that active relations with societal actors are valuable sources of all three. Close relationships between the state and civil society permit the use of societal resources for the formation of states. For example, Robert Putnam et al. (1993) famously found that robust civil society was vital to the success of decentralizing reforms in parts of Italy, offering evidence that it allowed the state to engage the public in the actual implementation of the reforms, improved the quality of leadership, and helped hold officials accountable. Similarly, a large literature on developmental states finds that state economic reforms depend greatly on state ties with societal actors, as the latter do much of the legwork and are an important source of information and knowledge ( Evans 1995 ).

Yet strong ties between state and society may also take the form of clientelism . Favored by specific electoral rules, parties in clientelist systems use the state apparatus, including public corporations, to supply party members and voters with employment and public contracts ( Kitschelt and Wilkinson 2007 ). These parties and the state apparatus develop an arrangement that no longer guarantees state autonomy but rather advances the interests of parties, churches, and specific classes. Besides this partisan-economic use of the state and its resources, the instrumentalization or capturing of parts of the state apparatus by associations also exists: with the growth of the state apparatus and the differentiation of bureaucracies, individual segments of the state apparatus become directly linked to the interests of specific groups. The orientation of parties towards clientelistic politics and the capacity of associations, the rich, or individual corporations to capture state bureaucracies are key explanatory factors of state transformations in the state-society relations category ( Hacker and Pierson 2010 ; Culpepper 2011 ).

The engagement of society in state reforms, in turn, depends greatly on the legitimacy of the state and its leaders. According to Max Weber (1978 [1921/1922] : 211–301, esp. 211–216), legitimacy allows authorities to dominate others without having to resort to physical violence, and this ability to get citizens to follow state commands affects the possibility and type of state reforms. The willingness to respect state norms may vary: Philip Gorski’s (2003) analysis of state origins in Europe, for example, finds that Calvinism promoted discipline and that disciplined populations facilitated state-building because the populace accepted and willingly followed state authority. A society’s distinct patterns of legitimation and its political ideologies may also facilitate state transformations if they move state institutions closer to widely accepted standards of legitimacy, and they may hinder change if they move in the opposite direction. The propensity to protest or to act independently beyond the state and its support programs may be more pronounced in some countries than in others, resulting in a greater distance between civil society and the state, or even strong skepticism with regard to the state. However, state transformations may not be influenced only by relations between state and society, but also by the state and its own structure.

The State Determinants of State Transformation

It may appear counterintuitive to consider the state itself as a determinant of its own transformations. In early debates on state transformations the state was not perceived as an agent. Yet as a mere vector of societal forces, the state cannot have the autonomy required to be plausibly considered an independent explanatory factor. However, the state is more than the outcome of social forces. It is an active and effective mover of its own transformations ( Leibfried and Zürn 2005 ; Levy 2006 ). The international relations literature conceptualizes the distinction between international and domestic factors as one that excludes any third category. By contrast, we consider states effecting their own transformations as a distinct category of domestic factors—or, maybe, a distinct category of its own. Extant comparative literature rather distinguishes between social developments, social structures, civil society, political actors, and movements as one set of domestic factors, and established institutional arrangements as another set. Hence, socio-structural, socio-economic, and actor-related forces are considered in isolation from the effects of core state institutions. Under the title of “domestic determinants,” we discuss the societal part, that is, the social forces within the boundaries of the (nation) state that act on state transformations. “State determinants” are those in which the states themselves act as agents and structures that prepare and adopt their own transformation. The state’s influence on its own development, the inertia of established institutional pathways, and the impulses of change or stability that stem from institutions and constitutions are thus considered separately.

The relative importance of societal determinants is thus viewed as co-determined by the established state structure . The potential of a society to influence the state is in turn influenced by the openness of the political system , which is higher in democracies than in autocracies. The expansion of democratic procedures—elections, referenda, and other channels of participation—is also crucial for the extent to which the voice of minorities and underprivileged members of a society is heard. And conversely, state autonomy vis-à-vis civil society is greater in authoritarian regimes than in democratic states. In addition, traditions of political habits and orientations, which are reflected in political thought, can be distinguished into more state-centered and more society-centered cultures ( Dyson 1980 ). These cultures make for weaker or stronger, more or less permeable boundary lines between state and society and thus facilitate (legitimate) or hinder (delegitimate) the crossing of these borders.

The state structure determines not only the openness to societal influences but also the greater or lesser political difficulty of achieving state transformations. If the constitutional structure contains many veto points —such as in presidentialism and bicameralism, or by judicial review and popular referenda—transformation is made difficult ( Tsebelis 2002 ). In systems with many veto points, opponents of change have multiple entry points to the political process to defend the status quo, and the very availability of these entry points encourages pro-status quo forces to mobilize ( Immergut 1992 ). This applies in extremis to a quasi-state organization like the EU, which, at its core, relies on the unanimity principle ( Scharpf 2010 ). The more clearly state and society are separated from each other and mutually impermeable, the more likely are state transformations triggered by the state apparatus itself. In an actor-related perspective, then, bureaucratic elites reform the state apparatus, as for example in the Meiji Revolution in Japan.

Such initiatives may be grounded in some understanding of the public interest, as by G.W.F. Hegel (cf. Avineri 1972 ), or reflect the interests of the administrative apparatus. In the field of social policy research, the introduction of social insurance under Bismarck provides the prototypical example for explanations of state change that focus on the political power of bureaucratic elites ( Heclo 1974 ). However, in the context of such explanations, one has to examine whether there is an autonomous bureaucratic caste or a group linked to other social classes that acts in accordance with clientelistic politics. And even in the case of relative administrative autonomy, one has to clarify why administrators were strong enough to impose their will in the face of their country’s class constellation and partisan dynamics.

Besides this actor-centered view of the state as determinant of state transformations, there are structuralist models of explanation, which link to the role of institutional and policy legacies. The literature points to two basic ways through which pre-existing state characteristics shape future state transformations: first, the characteristics of states dictate trajectories of state change; and, second, the characteristics of states mediate the impact of domestic and international factors on state transformations.

The first basic way.

State transformation appears to happen commonly in a path-dependent fashion, and this is a way in which the characteristics of states can shape their subsequent transformations. Path dependence suggests that dramatic transformations during critical junctures are commonly followed by extended periods of re-production and relatively long-lasting stability ( Pierson 2011 ). Path dependence does not deny that states transform but claims that these changes build on the pre-existing structure in ways that do not radically alter them.

A variety of mechanisms potentially promote path-dependent state transformations. Once states are present, for example, radical reforms are much more costly than more minor reforms that only modify the given state structure, thereby creating a first , cost-based mechanism of state re-production. Path-dependent state transformation can also be reinforced by a second mechanism: powerful interests frequently have a stake in the status quo and exert their power to prevent change. In the case of a federal system of government, this structure empowers regional authorities who, in turn, frequently use their power to oppose and obstruct any centralizing reforms (see Obinger et al. 2005 ). A third general mechanism is cognitive. It occurs when actors have cognitive blinders that impair their ability to seriously consider alternatives, thereby locking themselves into the status quo. So, if a federal government causes people to perceive it as the natural state structure, it helps to create cognitive frameworks that perpetuate the structure. Finally, there is a fourth mechanism: path dependence can be reinforced through norms and values. This can occur when individuals perceive the status quo as superior and therefore act in ways that perpetuate it. Thus, if an educational system socializes students to believe that a federal system of government is the most adequate form of rule, values can promote the maintenance of a federal system.

The second basic way.

State structures also determine state transformations through mediating the impact of domestic and international factors. The very characteristics of states shape how state actors deal with pressures for change. As such, states with certain traits might transform in ways different from states with other traits. Tilly’s claims about warfare and state-building offer one example. Miguel Centeno (2002) applies Tilly’s theory to Latin America and finds that conflict only promoted state-building when states already had a relatively high level of political authority (see also Centeno and Ferraro 2013 : chs. 1–3). Similarly, Deborah Boucoyannis (2010) analyzes state-building in Europe but offers evidence that relatively high levels of state capacity were a necessary precondition for war-making to lead to state-making. When state capacity was limited, warfare served essentially as an added stress that contributed more to state breakdown than to state-building. This argument suggests that state transformation is characterized by virtuous and vicious circles, with the pre-existing characteristics dictating whether state transformations follow one or the other pattern.

Many of the important forces shaping state transformations are located at the intersection of international and domestic politics. Along these lines, Paul Hirst and Grahame Thompson claim that states are now more important than ever because they must link local, national, international, and transnational institutions in order to create a coherent system of governance. “The nation state,” they write, “is central to this process of ‘suturing’: the policies and practices of states in distributing power upwards to the international level and downwards to subnational agencies are the ties that will hold the system of governance together” (1999: 270).

This centrality of the state, in turn, highlights the important roles states play in mediating the transformative effects of domestic and international factors. All states face some of the same challenges, for example, environmental change, security threats from non-state actors, and competition in world markets. But all states face them from different positions in the world economy and in the system of states, with different organizational capacities and entrenched worldviews. These positions in turn heavily influence the ways in which states adapt.

An Integrative Perspective

In order to understand the complex reality of state transformations, we need to transcend disciplinary and sub-field boundaries, adopting a more integrative approach that recognizes the importance of both domestic and international influences, as well as the ways in which states affect their own transformations. Such an integrative perspective regarding the explanation of state transformations requires a combination of the three groups of factors in a type of analysis that focuses on “constellations” ( Leibfried and Zürn 2005 ; similarly now Le Galès 2014 ).

It is undoubtedly important to clarify the relative strength, the dominance, or the irrelevance of individual factors. There are cases in which the international sphere has no influence or class interests are dominant enough to trump even long-standing state structures. Yet cases in which the dominance of individual factors is conspicuous are infrequent. Usually all three bundles of factors have to be considered to explain state change.

If so, however, it is insufficient to simply assign weights to these factors. Rather, one has to ask how exactly the factors are interrelated, why international influences are in some cases registered, welcomed, and even integrated into state structures while such influences are fought in other cases, and why domestic factors permit only very limited transformations of state structures. All of this requires a form of analysis that considers the interrelationships of these factors and their mutual positioning. It also requires the analysis to be focused on constellations. Given that there are multiple paths to the same type of state transformation (equifinality) as well as interaction effects that may exist between factors, such an analysis of constellations appears more realistic than alternative, presumably more parsimonious approaches. There are different ways to achieve such an analysis. Yet it is appropriate to conceptualize the interaction of factors and the change of political constellations as an expression of power relations ( Huber and Stephens 2012 ).

Thematic and disciplinary considerations also suggest a more integrative perspective. Much of the contemporary literature on state transformations is ghettoized by sub-field—or even by policy—or by substantive area of interest. International relations scholars tend to focus on international pressures that are challenging states, whereas scholars of comparative politics emphasize cross-national variation in response to common external challenges. Those interested in political economy generally analyze changes in economic and social policy-making, while those drawn to interstate relations examine the functioning of supranational institutions and their shaping via diplomatic and military affairs. In a similar spirit, we seek to transcend the divide between “high politics” and “low politics.” Position in the world economy and economic as well as human resources fundamentally shape the capacities of states as geopolitical or military actors. It is no coincidence that most of the countries spearheading the “responsibility to protect” against mass atrocity crimes are affluent democracies—claiming that sovereignty is not simply a right but a responsibility. That said, interactions among states, from regional integration agreements to the extreme of war, can reshape the distribution of economic resources. The creation of the European Common Market in 1957 and its development helped West European countries prosper. This large market then became a lure to countries on the European periphery—the Southern periphery in the 1980s, the Eastern periphery in the 1990s—and a means for bolstering their fledgling democracies ( Wallace et al. 2015 ).

The trajectory of the Common Market demonstrates the reciprocal influence between “low politics” (e.g. market integration) and “high politics” (e.g. the transformation of authoritarian and militarized countries on the European periphery into peaceful democracies). In this instance, the interplay between “high politics” and “low politics” traced a virtuous circle. Vicious circles are equally possible. The cumulating present troubles of the EU in its Eurozone offer an example ( Genschel and Jachtenfuchs 2013 ). Currency integration may have appeared to be primarily about “low politics,” but it is affecting the “high politics” of democracy in Greece, Cyprus, Ireland, Spain, Portugal, and Italy. Each battle won on the fiscal stabilization front seems to be a battle lost on the domestic democratic front. Moreover, these battles are fueling a major decline in support for European integration among citizens in both donor and recipient countries. Thus, the “low politics” of currency integration appears to be undermining the “high politics” of European integration. Ultimately, then, an understanding of state transformations requires a focus on the interaction between the economic and geopolitical spheres. An analysis that captures “high politics” and “low politics” in their interdependence, that transcends (sub-)disciplinary boundaries where required, and that conceptualizes the determinants of state change as constellations opens up an integrative view of state transformations. Not every individual chapter should be expected to live up to this integrative perspective, but taken together the analyses offered by this Handbook trace the contours of such an integrative perspective.

2 The Dimensions of State Transformation

By state transformations we mean fundamental shifts in the scope of state activities, bureaucratic capacities, purposes, instruments, and structures of authority. The literature makes various suggestions for the specification of dimensions of state transformations ( Zürn and Leibfried 2005 ; 4   Hay et al. 2006 ), but there is no established toolkit for the analysis of states, state transformations, and their basic elements or dimensions. We consider five dimensions of state transformation as a heuristic. State change may be confined to one of these dimensions. Other forms of state transformation may cover several or even all dimensions, and opposite trends in different dimensions may occur at the same time.

Scope of State Intervention

The development of state capacities and the relationship between the state and civil society or the market economy is at the center of nearly all studies of state transformation. Which tasks and areas of responsibility are performed by the state, and which are left to the market and society? Does the state secure these forms of social self-organization or does it regulate, limit, and control particular societal developments in the name of a politically constructed common good? Is the state capable of performing the tasks it takes on? Does it have the bureaucratic capacity to do so (see later)?

Key concepts for the characterization of types of state transformation relate to the scope of state responsibilities or to the scope of its intervention. The spectrum of state activities has ranged from the liberal minimal state to the totalitarian state. In communist countries, state intervention reached its pinnacle during these countries’ totalitarian phases—Stalinist USSR, Maoist China, Kimist North Korea—and remained at a high level even in moderate authoritarian phases. State control over the economy, temporarily even including the complete abolition of markets, required an extension of state power that the bureaucratic apparatus ultimately could not provide. Added to this was the political and ideological control of all social processes, which required the transformation of the state into an instrument of repression, a surveillance state, and an ideological center. Since the market orientation of China after 1978 and the demise of socialism in 1990/1991, government control of the economy via a planned economy has become extremely rare.

The fall of communism ushered in worldwide limitations in the aspirations of the state to control the economy. However, differences between states in controlling and regulating the market economy remain significant. For the advanced capitalist societies, state intervention into the market economy and the varieties of capitalism play a central role ( Hall and Soskice 2001 ; Hancké et al. 2007 ; Fioretos 2011 ). To what extent does the state act as an owner, producer, and employer, or merely as a regulator ( Pierson 2004 )? To what extent are state-owned enterprises part of the economy, and what is the size of the public sector ( Schuster et al. 2013 ; Schmitt 2013 )? Yet a full retreat of the state does not take place even where strong neoliberal and pro-market policies are preferred. In these cases the state is assigned the task of generating, managing, and stabilizing private markets ( Gingrich 2011 ). 5

The dimension of state intervention does not only refer to the relationship between the state and the economy. Other spheres of society may be subject to state control and regulation. Some societal spheres are traditionally viewed as core fields of public responsibility. What Michael Mann (1984) has called the “infrastructural power” of the state is apposite here, since it deals with a shift in emphasis from “despotic power” to an encompassing penetration of all of society by friendly state power. 6 Are education and health defined as a public duty or not? The state changes as its infrastructural power is extended across society, though “despotic power” does not cease to exist. Nevertheless, the adoption of public responsibility for entire sectors may include the assignment of important tasks to social forces for implementing state policies so that they can remain important players in these fields, for example, private schools and hospitals, churches and charities. This leads to specific public-private mixes, corporatist-like arrangements including the state and churches or charities in the field of social services, or quasi-markets ( Le Grand 2006 ).

The scope of state responsibility and intervention also refers to the relationship between the state and the public sphere. In authoritarian regimes, state control of the public is a core element of the strategy of domination. In liberal and democratic regimes, the creation of and support for a free public as well as responsiveness towards the results of public deliberation are cornerstones of a principled limitation of state intervention. However, states may attempt to establish a centralized production of ideology backed up by repressive measures, or they may constitutionally bind themselves to the cultural and normative resources of a specific social actor, such as a religious organization or social movements. Consequently, the state then obtains forms of a Christian or Islamic state, or a state bound by a specific secular ideology, rather than an open and therefore ideologically neutral state.

It is well known that the scope of state intervention can increase as well as decline. Partly in response to concerns about governability and partly as a means of pursuing a neoliberal agenda, the Thatcher administration in Britain strengthened the central state by breaking the power of key interest groups, most notably the unions; abolishing certain local authorities; and beating back European integration ( Gamble 1994 ). Law and order, or policing, is another area where British authorities expanded state responsibilities, a development that has been echoed in many other countries, including the US, as citizens and politicians demand protection against crime, terrorism, and illegal immigration. Ironically, those who have sought to spread the neoliberal model beyond the Anglo-American heartland have often lost sight of the fundamental importance of state power in forging and upholding a neoliberal order. By calling for a generalized rollback of the state, as opposed to a redeployment of the state on behalf of pro-competitive objectives, neoliberal enthusiasts have often precipitated or aggravated economic crises, thereby undermining public support for neoliberalism itself ( Stiglitz 2007 ).

Much of the discussion about the erosion of successful state intervention has focused on the role of international developments, such as globalization, that can narrow the scope for certain practices like Keynesian fine tuning or selective industrial policy—although these developments may create opportunities for new exercises of state power. Internal developments can also erode successful state intervention. Beginning in the late 1960s, state authorities in the core Organisation for Economic Co-operation and Development (OECD) countries were confronted by growing protests of students and new social movements along with an increasingly mobilized labor movement. A wave of worker strikes and factory occupations, starting with France’s near revolution in 1968 and culminating in Britain’s so-called “winter of discontent” in 1978/79 ( Jenkins 1988 ), fueled the sense that democratic states were becoming “ungovernable” ( Crozier et al. 1975 ). States can also fail in more piecemeal ways. In countries on the European periphery such as Greece or Cyprus, states appear to have lost much of their capacity for intervening in economic governance, raising sufficient revenues to fund critical missions, and providing public services in a cost-effective manner. This loss raises several important theoretical questions. One is whether countries such as Greece or Cyprus can extract sufficient concessions from international lenders, bond markets, and IFIs, as well as undertake sufficient internal reforms to regain their economic governance capacity ( Bermeo and Pontusson 2012 ; Schäfer and Streeck 2013 ).

Bureaucratic Capacities

The efficiency and effectiveness of states depend fundamentally on their organizational resources and bureaucratic capacities. States may extend their areas of responsibility without having the ability to perform new tasks. Only where bureaucratic capacity is high can an expansion of state intervention result in higher state capacity. An overload of the state or “state failures” may result from a gap between areas of intervention and bureaucratic capacity. Bureaucratic capacities in turn significantly depend on the success of the state in creating a bureaucratic organization close to the Weberian ideal type. Nowadays, a functioning bureaucracy also needs to adapt business-oriented and managerial procedures ( Pollitt and Bouckaert 2011 ).

Bureaucratic capacities can both exceed and fall short of this ideal type of efficient hierarchical organization (for the latter on the US cf. Jacobs and King 2009 ). Bureaucracies may transform into a surveillance apparatus or run idle while raising the control capacity to the maximum. Bureaucratic capacity may also be too weak. In these cases, nepotism and corruption instead of legal rules characterize administrative life, or lobbying and the capture of agencies by specific social groups undermine the administrative obligation to follow the results of democratic decision-making. Moreover, the size of the administration and its degree of fragmentation may be crucial. A weak bureaucracy is not in a position to enforce the administrative penetration of diverse areas of society. Bureaucratic capacity also depends on a state’s greater or lesser centralization and on whether or not local governments are subordinated to the central or federal government. 7 With the rise of international and supranational organizations, state bureaucracies are also required to deal with these new administrative units of different sizes and weights. Under such conditions, bureaucratic capacity is high when interdependencies between the various levels of authority and the interplay between administrative agencies can be managed effectively. In this context, states act as integral elements of a system of multilevel governance ( Hooghe and Marks 2001 ).

In the countries of the Global South, the onsite presence of international organizations plays a key role not least because of these organizations’ role as financiers ( Risse 2011 ). In the affluent democracies, the fact that international organizations and the EU delegate the implementation of their decisions to the national bureaucracies is more important. International organizations and the EU have not established their own bureaucratic agencies at the country level and therefore do not possess enforcement power. However, in crisis situations, administrative oversight of national bureaucracies by international organizations such as the IMF, the European Central Bank (ECB), and the EU—often termed the Troika—may occur even in advanced industrialized countries, as could be seen in the wake of the 2008 financial crisis and the Troika’s role in the management of that crisis. With new transnational administrative agencies, new actors come into play. Shifts in the territorial organization of the state—whether from below through decentralization and federalization or from above via the pooling of sovereignty in international and supranational organizations such as the EU—have multiplied the number of stakeholders involved in decision-making while giving the stakeholders different weights. The proliferation of domestic interest groups and transnational NGOs has further fragmented the decision-making arenas. Rather than issuing orders, states participate in international institutions and negotiate agreements with a broad range of actors—be they public, private, domestic, international, or transnational.

Undoubtedly, the emergence of strongly formalized and extensive multilevel politics, as exemplified by the EU, changes the overall capacity of the state. State capacities could be reduced, as an important strand of the literature points out. But this assessment might be premature. Even in the case of explicit shifts of competence, the rise of international and supranational organizations does not necessarily lead to a decline in state or bureaucratic capacities. What may look like a loss of sovereignty for individual member states may turn out to be a gain of sovereignty and autonomy for the EU as a whole. Moreover, the calculation of gains and losses for member states is by no means straightforward, as these states gain—collectively exercised—leverage in the world market or world politics by pooling their sovereignty. The gain in political authority for the EU may thus even translate into increased capacities for individual member states. Thus, the state is strengthened by its integration into multilevel politics. Such calculations are reflected in the ongoing Europe-wide struggle over whether to grant the EU some power to tax or issue bonds or whether to let the EU run large-scale Keynesian spending programs that are no longer very effective if conducted solely at the national level. Due to the new ways of wielding influence at the international level, the state receives its own new possibilities for action.

Goals and Purposes

Transformations of state areas of intervention and bureaucratic capacities are often preceded or accompanied by changes in the objectives of government action. But transformations of state purposes may also occur independently of these other transformations. States may use their expanding state capacities to stimulate growth or to initiate sustainable development, to reduce poverty, or to enhance the surveillance and ideological manipulation of the population. State capacities may thus serve diverse purposes. Hence, change in the purposes or goals of state action represents a third dimension of the transformations of the state. However, state capacities are often not neutral with regard to goals: some objectives require the removal or reduction of state capacity; infrastructural power of states in education makes it impossible to give economic elites complete control over the socialization of future generations.

It is not unusual for state authorities to change fundamental objectives or identify new goals, whether for intellectual, economic, geostrategic, or political reasons. For example, during the initial stages of industrial development, state authorities tend to focus on economic growth, often to the exclusion of all other considerations, but over time, social protection generally moves onto the agenda, followed by environmental protection. Industrialization both generates demands for stricter environmental rules and provides the economic resources that make the costs of environmental protection affordable. Recent developments in China suggest that even authoritarian regimes can feel pressure to attenuate the environmental costs of industrialization, although democracies tend to be more responsive to citizen concerns. Much the same trajectory can be described in the case of social protection. Industrialization destroys traditional family-based modes of social protection and creates a number of risks for industrial workers—including ill health, workplace accidents, unemployment, and the inability to earn an income due to old age or disability. At the same time, it generates new actors, primarily unions, that push for new forms of social safety nets provided or at least guaranteed by the state, along with the fiscal resources to build these safety nets and other aspects of the welfare state. As in the case of environmental protection, social protection tends to emerge more rapidly and comprehensively under democratic regimes ( Castles et al. 2010 ).

Non-economic processes can likewise lead to the identification of new goals. Changing social norms about such issues as the place of women and the functioning of families have fueled new agendas—in this instance, the promotion of gender equality through legal rights for women, public support for childcare, workplace reforms, and measures to enhance female political representation. Geopolitical shifts and shocks are another potential source of new state objectives. By lifting the threat of Soviet retaliation, the end of the Cold War opened opportunities for military intervention to defend human rights in troubled states, labeled the so-called “responsibility to protect,” or, in some cases, to depose unsavory regimes altogether. The end of the Cold War also expanded the possibilities for promoting democracy via both military and non-military means. Some of these goals changes have led authors to conceive of new state ideal-types, such as “competition state” ( Cerny 2006 ), “trading state” ( Rosecrance 1986 )—now transfigured into a “virtual state” ( Rosecrance 1996 , 1999 )—, “social security state” ( Nullmeier/Rüb 1993 ), 8 “women-friendly state” ( Hernes 1987 ), and “national security state” ( Stuart 2012 ), although these concepts have not entered common usage.

Instruments and Policy Tools

A fourth form of state transformation concerns the instruments or policy tools deployed by state authorities. Governments are constantly looking for new ways to pursue their objectives more effectively or at lower cost. One example of instrument change is the movement from so-called “passive” to “active” labor market policies: instead of seeking to prevent poverty through “passive” transfer payments that pay people to remain outside the labor force, state authorities are increasingly pursuing “active” labor market policies that emphasize employment as the most effective means of escaping poverty ( Huo 2009 ). The objective remains poverty prevention, but the policy tool becomes job promotion, as opposed to transfer payments. This shift in policy instruments may entail a combination of reduced state intervention in some areas (lower unemployment or disability benefits) and increased intervention in others (heightened surveillance and discipline of the unemployed, education and training services, job placement assistance, subsidies and tax benefits for those taking jobs, and expanded child care for working parents)—that is, of policy-dismantling ( Bauer et al. 2012 ) and policy-building.

The adoption of new policy tools, such as labor market activation, often occurs through international diffusion or borrowing spearheaded by policy experts ( Orenstein 2008 ; Obinger et al. 2013 ). In other cases, though, new instruments are devised in response to crises or the failure of old instruments ( Gourevitch 1986 ). The 9/11 attacks prompted authorities in the US and elsewhere to rethink the tools used to ensure national security and to move beyond the projection of conventional military power against established states. Another failure, the financial meltdown of 2008, is leading governments to devise new regulations to try to safeguard financial stability ( Admati and Hellwig 2013 ; more broadly on the crisis: Eichengreen and Pak 2012 ; Gourevitch 2013 ). In all of these instances, the basic goals of government policy have remained essentially the same, but the instruments deployed in the pursuit of these goals appear to be changing in significant ways. Sometimes these instrumental changes are perceived to be so encompassing and massive as to constitute a new kind of state, be it the “enabling state” ( Gilbert and Gilbert 1989 ), the “social investment state” ( Giddens 1998 ), the “regulatory state” ( Majone 1994 ), or, now understood in an instrumental sense, the “national security state” ( Stuart 2012 ), which, in an earlier incarnation, was called the “police state” ( Wise 1976 ).

Structures of Authority and Political Communities

A fifth type of state transformation concerns structures of authority and political communities. Of course, the most far-reaching changes affecting political representation and the structural character of the whole political system have been transitions to and away from democracy in many parts of the world, most notably Latin America and the former communist countries.

In the twentieth century, a remarkable development took place. After a slow rise and proliferation of democracies at the beginning of the century, the process of democratization stopped from the 1920s to the end of World War II. In the second half of the century, the number of democracies increased to about 120, more than 60 percent of all independent states, accompanied by transitions from democracy to authoritarian regimes in a few countries. Yet the economic attractiveness of authoritarian regimes such as Singapore and China and the rise of an electoral authoritarianism signal that the process of global democratization might be reversible. Furthermore, the rapid spread of democracy ( Acemoğlu and Robinson 2006 ; Tilly 2007 ; Norris 2012 ) led to strong differences among democracies. The literature differentiates between “hybrid,” “illiberal,” and “liberal” democracies besides more traditional dichotomies such as “consensus democracies” versus “majoritarian democracies” ( Lijphart 1999 ; Kriesi et al. 2013 ).

Yet even among the established OECD democracies, access to political power is changing. One such change is the incorporation of subordinate groups such as peasants and workers, which occurred historically through the extension of the suffrage and the forging of corporatist concertation. This process continues even today, as can be seen in the establishment of quotas for female representation on electoral lists or the right for immigrants to vote in local elections ( McAdam et al. 2001 ; Tilly and Tarrow 2007 ). Constitutional reforms are another vehicle for modifying access to political power. Authorities in many countries have altered electoral laws or expanded the use of referenda, often in the name of making governments more responsive to the will of the people. Going in the opposite direction, the extension of judicial review and the delegation of key responsibilities to unelected bodies, such as central banks, have been motivated by the desire to remove certain kinds of decisions from the political arena. The modern states are predominantly nation states. They build their unity and their understanding as nation states on cultural, ethnic, political, or historical bonds. In addition, there are several countries that perceive themselves as multinational units and have constructed other concepts of the unity of a political community. These different modes of establishing political unity are exposed to changes. Political identities based on categories of “the people” or “the nation” may be triggers for secession, mergers, or the redrawing of state boundaries. In most cases, the colonial heritage is a burden for any strategy of fostering national identities ( Lange 2009 ). But in other cases, the definition of political communities may be under pressure. Political identities may shift, for example, from formerly national identities to a European or double identity ( Risse 2010 ), without precluding processes of re-nationalization at any time.

3 Variation in State Transformations

State transformations vary in their extent and intensity . The terms “reform” and “revolution” are traditionally used to denote more or less far-reaching state transformations. Many particularly important concepts for the analysis of state transformations originate in the study of revolutions . Given their pronounced event character, the formation and transformation of states in the wake of revolutionary—and typically violent—acts may be viewed as the most conspicuous form of change. Yet the point that state structures may even survive revolutions was made as early as 1856 by Alexis de Tocqueville in his classic The Old Regime and the Revolution (1998) . Hence, while revolutions usually trigger state transformations, even the scope of revolutionary change may be limited. Elements of continuity, such as persisting bureaucratic or state capacities, must therefore not be neglected in the study of revolutionary change.

Political regime change is located between reform and revolution on an intensity scale of state transformations. A revolution may bring about no more than political regime change, or it may completely overthrow a country’s social and political structures. In the long run, regime change—defined as the set of political transformations that usher in democratic regimes instead of authoritarian ones or vice versa—may revolutionize the entire state apparatus and its relationship with civil society. Thus, the study of regime change must take the dimensions and extent of state transformations into consideration.

The utility of concepts such as revolution and regime change for the analysis of state transformations is limited, however. What is required is an analytical toolkit that enables researchers to disaggregate the relevant dimensions and to examine which kinds of change—if any—have occurred in each of them. The term “institutional change” denotes a rather more narrow type of state transformations than regime change. Examples include the reorganization of administrative or government structures, such as a shift of responsibilities between the three branches of government; transformations of this kind affect the intervention capacity or the power relationships of different political elites and social classes. Given the breadth of the term “institution,” this category of transformations covers a wide array of phenomena, and hence the intensity of state transformations cannot be inferred from a simple enumeration of institutional reforms, which may represent no more than irrelevant adaptations of formal institutional rules on one end of the spectrum or massive transformations of the entire state apparatus on the other ( Ostrom 2005 ; Mahoney and Thelen 2010 ).

The present volume nevertheless gives a lot of weight to gradual, limited transformations of state capacities , that is, transformations of the reform-type in the areas of economic, trade, financial, and budgetary policy. Such changes are not limited to a single policy area, but may encompass whole sectors, such as the totality of economic policy towards world markets. The transition from closed to open economies, or from import substitution industrialization (ISI) to neoliberalism is an example. This transformation reduced the state’s responsibilities in economic management and assigned a larger role to the market, and it reduced the state’s capacities to direct economic outcomes. The transformations of state capacities that encompass more than one policy field and also touch upon a country’s economic integration into the world market represent a type of gradual change that may well have a strong impact on social structures and the distribution of wealth. A gradual shift of state capacities may even have more far-reaching effects than wholesale institutional transformations of the state apparatus or a political regime change. Hence the analysis of state transformations must pay the requisite attention to gradual transformations. There is indeed a burgeoning literature on gradual change, although its focus has so far been on gradual institutional change ( Mahoney and Thelen 2010 ). Yet there are equally important shifts at the level of one or more policy fields that, while leaving institutional arrangements intact, nevertheless transform the role of the state in society. For the OECD countries, these shifts will receive the greatest attention in the present volume.

Of course, gradual shifts may in the long run usher in a situation in which even minor events trigger—relatively non-violent—revolutionary processes and radical state transformations. The combination of a reduced ability to deliver growth and long-simmering citizen dissatisfaction with corruption and political repression led to the implosion of communist states in Eastern Europe and the former Soviet Union in the late 1980s and early 1990s. The most sweeping state transformations are arguably those of the former communist countries. Here, a large number of countries experienced dramatic changes across multiple dimensions almost simultaneously. The transition from closed to open economies went along with the transition from planned to market economies and often from authoritarianism to democracy. In addition, in the former Soviet Union, Eastern Europe, and the former Yugoslavia, the exit from communism coincided with the redefinition of a number of national boundaries against a backdrop of longstanding repression of ethnic and religious divisions—in some cases, through violent means. Thus, state borders, regimes, responsibilities, and capacities all changed radically and virtually simultaneously. Equally dramatic are the cases of so-called “failed states,” such as Somalia or Mali, that is, previously functioning regimes, be they democratic or authoritarian, that have lost their fundamental capacity to monopolize organized force and thus have lost their capacities in all other areas, from economic management to social policy.

The cases of partial or limited state transformation are not necessarily less significant or theoretically less revealing. For example, China has experienced fundamental economic change, while the political regime has remained largely intact, with the Chinese Communist Party continuing to monopolize power. The Chinese case poses the fundamental theoretical question of whether the relaxing of the state’s grasp in the economic arena must lead necessarily to a similar set of changes in the political arena. Is China an unstable halfway house, ripe for revolution—or, at least, democratic transition—or is it a stable alternative, a durable, non-democratic pathway to prosperity?

Substantial state transformations may occur in a single country or in several countries at once. The most significant state transformations tend to extend beyond a single country, redefining the regimes, responsibilities, or capacities, of a number of states. Again, the transition from ISI to the open economies occurred in virtually all Latin American countries, albeit in slightly different ways and at different speeds. Another example is the process of democratization that has transformed the regimes in states located in all parts of the world in recent decades. Therefore the extent of state transformations is crucially important. Which world regions are affected by which transformations? Are there ubiquitous trends or opposite developments in some regions? Is it thus possible to make general inferences about state transformations across countries and regions? Certain state theories and their propositions about transformation processes refer implicitly to a specific region such as the OECD, Europe, or affluent democracies, while ignoring developments in other parts of the world and are thus over-generalizing.

Theoretical propositions that explicitly refer to the development of the state in all world regions may be found in the context of modernization theories. They posit that waves of social and economic modernization are linked to similar development patterns at the level of state capacities and state organization, and hence that state development follows in the wake of economic modernization. As a result, an essentially universal model of state development—with some nuances—is suggested. By contrast, other theories assume that late development and certain forms of integration into the world market result in specific state forms, and hence that early birds both require and tend to establish different state capacities than latecomers. Hence states presumably do not experience the same phases of one and the same model of development, differing only in the timing of development stages. Rather, from the very beginning of modernization and world market integration, states are pushed onto specific trajectories of state development. Different paths instead of mere phase shifts thus characterize state transformations across world regions.

As these examples highlight, there is also regional variation in state transformations. This is hardly surprising, as different regions face different domestic environments, hold different positions within the global system, and have different state institutions and institutional histories. Because no two states are alike in this regard, all will necessarily transform differently. At the same time, common global factors are likely to promote general trends in state transformations across countries. In between these two extremes, states within particular regions are likely to have relatively similar domestic environments, have a similar position within the global system, and have similar states. In general, core OECD countries are relatively wealthy and democratic, have large and effective states, and have a privileged position within the international system. The states in former communist countries, though, are less wealthy and democratic and face particular state challenges resulting from the breakdown of the communist bloc and the introduction of capitalist economies. And many states in the Global South face their own challenges resulting from colonial legacies, ineffective states, relative poverty, and limited geopolitical power. The recognition of regional variation is therefore vital to any broad analysis of state transformations.

4 Organization of the Book

The study of state transformations should not restrict itself to the proclamation of new state types like “neo-Schumpeterian workfare state,” “postmodern state,” or “social investment state.” Nor should it be grounded in assumptions about either a “crisis of the state” or the meta-stability of the state in a globalized world. Such formulas may be useful at best as a starting point for further research. Yet we require detailed empirical research to get a hold on the diversity of state transformations without sacrificing insights into the plurality of—possibly even contradictory—developments to classification exercises.

This Handbook aims at a differentiated inventory of state transformations in all parts of the world over the last decades. This is possible only through the cooperation of a large number of researchers. The determinants of state transformations (Section 1 of this chapter), the categories of state dimensions (Section 2 ), the extent and intensity of state transformations and their variation (Section 3 ) discussed earlier help us to identify the relevant research issues. The individual chapters give priority to selected categories in line with their specific subject matter. To ensure a high degree of differentiation and sensitivity to the developments in individual countries or groups of countries, we classify the world of states into three large groups of countries: post-communist countries, Global South, and affluent democracies. Our Handbook provides an overview of the state transformations in all three groups and with reference to a large number of policy fields.

Our Handbook is organized into five Parts, encompassing 44 chapters, along with two introductory chapters (Huber et al., Chapter 1 ; Levy et al., Chapter 2 ), a self-evaluative chapter (Levy, Chapter 9 ), and a concluding chapter (Huber et al., Chapter 44 ). The first two Parts of the book are primarily conceptual and historical in nature. Part I (3–9) analyzes theories and concepts of states plus the emergence of states over time, and Part II (10–18) deals with the state’s embeddedness in the international environment. The last three Parts focus on three regionally differentiated groups of states in the contemporary period, with Part III (19–30) exploring the affluent democracies, Part IV (31–35) the former communist world, and Part V (36–43) the developing—or non-developing—countries of the Global South.

The Emergence of Modern States

Part I begins with a chapter (John Hall, Chapter 3 ) that examines the main theories and concepts of the state. Among the issues considered are: classical and contemporary attempts to define the state, the characteristics of the modern state that distinguish it from older political orders, and the sources of variation in state development. The ensuing chapters explore the historical emergence of states first in Europe (Manow and Ziblatt, Chapter 4 ), then in settler regions (Kelly and Mahoney, Chapter 5 ), and finally in post-colonial contexts (Lange, Chapter 6 ). These chapters pose the question of why states were established and why they took different forms in different places. The remaining chapters in Part I (7–9) examine the major social scientific attempts to theorize how states function and how they evolve (vom Hau, Chapter 7 ), including a critical perspective on the limited nature of the modern state (Risse, Chapter 8 ) 9 and a self-evaluative, comparative—across time, policy, and place—perspective on the analyses of state transformations in the Handbook itself (Levy, Chapter 9 ).

Internationalization and the State

Part II of the book is devoted to the relationship between the state and the international environment, and we have been supported by Michael Zürn and Nicole Deitelhoff in editing it. It begins with an overview of international models of the state (Zürn and Deitelhoff, Chapter 10 ), then probes the various ways in which states are shaped and transformed by the international system (10–18).

Individual chapters look at “crucial types” and examine the tensions between formal sovereign equality and the de facto inequality of states (Viola, Snidal, and Zürn, Chapter 11 ), the competition among states in the international economy (Genschel and Seelkopf, Chapter 12 ), and the changing division of labor between the state and international organizations as well as non-state actors (Hanrieder and Zangl, Chapter 13 ). Special attention is paid to the EU (Shakel, Hooghe, and Marks, Chapter 14 ), which is arguably the most developed instance of “supranationalization” (in European parlance) or “pooled state sovereignty” (in North American parlance), approaching the status of a quasi-federal entity. The more general issue of the evolving relationship between transnational institutions and the state is also explored (Mattli, Chapter 15 ).

Part II concludes with three chapters on crucial contemporary challenges that require states to coordinate their actions with other states: civil wars and terrorism (Daase, Chapter 16 ), the world financial crisis (Helleiner, Chapter 17 ), and environmental risks (Dingwerth and Jörgens, Chapter 18 ).

Contemporary Transformations of the Established OECD Democracies

Part III (19–30) examines the main transformations among the established OECD democracies. Building on a grand overview (Levy et al., Chapter 19 ), it looks first at the evolution of the state in the three commonly identified crucial subtypes of political economy—statist (Levy, Chapter 20 ), corporatist (Huo and Stephens, Chapter 21 ), and liberal (Peter Hall, Chapter 22 )—and then considers the transformation of states surrounding the transition from closed ISI to open economies (Schwartz and Etchemendy, Chapter 23 ). These chapters cover macro-economic management and key forms of state economic intervention, including public ownership, industrial promotion, regulation, trade policy, industrial relations, and social policy. The chapter on the transition away from ISI also serves as a bridge to similar transitions beyond the affluent democracies.

The remainder of Part III is devoted to an examination of some crucial issues concerning changes in state responsibilities and capacities in particular areas. Chapters first address the welfare state (Obinger and Starke, Chapter 24 ), gender relations (O’Connor, Chapter 25 ), and the transformation from the positive to the regulatory state (Holzinger and Schmidt, Chapter 26 ). They are followed by chapters on immigration (Bauböck, Chapter 27 ), plurinational states (Keating, Chapter 28 ), national security (Busch, Chapter 29 ), and the democratic state (Nullmeier et al., Chapter 30 ). All these chapters show that states are evolving in diverse ways in response to common international and domestic changes.

State Transformations in the Former Communist World

Part IV (31–35) analyzes state transformations among the former communist countries, where states long held the greatest power, cumulating economic and political control. With the collapse of the communist ideal, state authorities have sought to build market economies. This has been a wrenching, contested process. In many cases, the transition from plan to market has been accompanied by a transition from authoritarianism to democracy, a transition that has proven no less wrenching and contested. A striking feature of the post-communist transitions is their tremendous diversity. Out of the uniformity of the communist crucible has emerged quite a variety of political and economic outcomes.

The first chapter (Grzymała-Busse and Luong, Chapter 31 ) of Part IV provides an overview of the different post-communist state outcomes and their causes, while the ensuing four chapters (32–35) discuss major types of outcomes in more detail. Eastern European countries (Vachudova, Chapter 32 ) illustrate the different pathways away from communism and the central role of the EU in that process. Other countries, once a part of the former Soviet Union (Luong, Chapter 33 ), are analyzed with regards to the significance that vast natural resources have for political and economic transformations. The Russian case (Taylor, Chapter 34 ) itself is treated as emblematic of the wider trend of resurgent authoritarianism. Finally, China (Tsai, Chapter 35 ) suggests that it may be possible for a communist regime to become more flexible, responsive, and open to experimentation, thereby allowing for the transition to a dynamic market economy, while retaining an authoritarian, single-party political structure.

State Transformations in the Global South

Part V (36–43) explores the evolution of the state among the less developed countries of the Global South. The first chapter (Lange, Chapter 36 ) presents a general overview of states in Africa, Asia, Latin America, and the Middle East, tracing the basic characteristics of the states in each region and differentiating them from states in the OECD and post-communist countries. In order to explain the distinctive characteristics of states in the Global South, this chapter points to such factors as colonial origins, late development, resource endowments, and social and ethnic cleavages.

The structure of Part V parallels that of Part III on the core OECD states in two ways. First, it provides a set of chapters that are devoted to transformations of crucial ideal types of states—in this case, developmental (Evans and Heller, Chapter 37 ), rentier (Waldner and Smith, Chapter 38 ), predatory (Reno, Chapter 39 ), and failed (Chojnacki and Menzel, Chapter 40 ). Second, Part V probes changes in state responsibilities and capacities in some crucial areas, including ethno-nationalism (Lange and Schlichte, Chapter 41 ), democratization (Pop-Eleches and Robertson, Chapter 42 ), and social protection (Huber and Niedzwiecki, Chapter 43 ).

The parallel structures of Parts III and V highlight how different the challenges faced by states in the Global South are from those faced by the core states of the OECD, and they also show how much more limited the capacities of states in the developing world are for meeting these challenges. Cross-regional comparisons also cast light on the interaction between the state and the international system. In particular, such comparisons point to the tensions between formal sovereign equality and the de facto subordinate position of the states of the Global South. Relatedly, they draw attention to the disproportionate impact of international actors and organizations on critical outcomes in the developing world, such as regime change, ethnic conflict, human development, and social protection.

5 Conclusion

Following Part V , we summarize the main findings of the volume in a conclusion (Huber et al., Chapter 44 ). It points to the ubiquity of state transformations both historically and in the present era. It argues that states are transforming, gaining new responsibilities and exercising power in new ways, as opposed to simply retreating or defending established domains of influence. Finally, it shows how the character of state transformations varies across the main regions analyzed in the Handbook—the established OECD democracies, the post-communist countries, and the Global South—and that the economic and political gulf separating the Global North and the Global South will most likely not only persist but widen.

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When we inquire into the formation of new nation states, federal or quasi-federal sub-units that one might dub “segment-states” are regularly the foundation for achieving a breakaway ( Roeder 2007 ; for the reverse process cf. Fazal 2007 ). Independence appears as an “administrative upgrade” of an existing “segment-state” ( Roeder 2007 : 10).

For Frank Nullmeier and Friedbert Rüb, the “social security state” is a state that pursues the overarching goal of ensuring the standard of living against certain standard risks of life, including old age, sickness, and unemployment. They contrast this state form with a current trend towards a “securitizing state” ( Sicherungsstaat ), i.e. securitizing the welfare state; for them this state form denotes a developed welfare state that no longer focuses primarily on outcomes, but rather shrinks its focus to its own internal consistency and affordability. On the same level as the “social security state” lies the French “state of providence” ( l’état providence ), except that this variant accents equity and solidarity in general ( Ewald 1986 ).

This contribution stands for a radically alternative approach to state analysis anchored mainly in international relations. It is pursued in depth at Berlin’s Collaborative Research Center on “Governance in Areas of Limited Statehood.” The contours of this approach are outlined in Thomas Risse (2011) . The Berlin approach is contrasted with the one pursued in this Handbook—and in the Bremen Collaborative Research Center TranState—in Marianne Beisheim et al. (2011) .

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Key difference between transition state stabilization and ground state destabilization: increasing atomic charge densities before or during enzyme–substrate binding †

First published on 21st June 2022

The origin of the enormous catalytic power of enzymes has been extensively studied through experimental and computational approaches. Although precise mechanisms are still subject to much debate, enzymes are thought to catalyze reactions by stabilizing transition states (TSs) or destabilizing ground states (GSs). By exploring the catalysis of various types of enzyme–substrate noncovalent interactions, we found that catalysis by TS stabilization and the catalysis by GS destabilization share common features by reducing the free energy barriers (Δ G ‡ s) of reactions, but are different in attaining the requirement for Δ G ‡ reduction. Irrespective of whether enzymes catalyze reactions by TS stabilization or GS destabilization, they reduce Δ G ‡ s by enhancing the charge densities of catalytic atoms that experience a reduction in charge density between GSs and TSs. Notably, in TS stabilization, the charge density of catalytic atoms is enhanced prior to enzyme–substrate binding; whereas in GS destabilization, the charge density of catalytic atoms is enhanced during the enzyme–substrate binding. Results show that TS stabilization and GS destabilization are not contradictory to each other and are consistent in reducing the Δ G ‡ s of reactions. The full mechanism of enzyme catalysis includes the mechanism of reducing Δ G ‡ and the mechanism of enhancing atomic charge densities. Our findings may help resolve the debate between TS stabilization and GS destabilization and assist our understanding of catalysis and the design of artificial enzymes.

1. Introduction

By contrast, many studies propose that enzymes can catalyze reactions through destabilizing the GSs of reactions. 23–25 Ruben, et al. reported that, in the KSI-catalyzed isomerization of 5-AND, desolvation of the COO − group of Asp40 partially catalyzes the reaction via GS destabilization (Fig. S1 † ). 24 By comparing the binding affinity of ground state analogues for KSI with a wild-type anionic Asp general base or with uncharged Asn and Ala in the general base position, this study showed that desolvation of the wild-type anionic Asp general base decreased the binding affinity of ground state analogues and thus destabilized the reaction's GS. In this example, the reduction in binding free energy caused by the desolvation of the wild-type anionic Asp general base was estimated using an experimental approach. Although the binding affinity of ground state analogues is different from the binding affinity of ground state substrates, this estimation is sufficient to demonstrate GS destabilization. Whether an enzyme–substrate interaction increases or decreases the binding free energy of the substrate for the enzyme can be determined without accurate quantification. It is well known that moving charged atoms from water to the nonpolar active sites of enzymes is unfavorable in free energy and the desolvation of charged atoms definitely destabilizes the GSs of enzymatic reactions. The catalysis by the desolvation of charged atoms, which has been reported in many studies, 26–28 supports the GS destabilization mechanism.

Even though the TS stabilization and GS destabilization mechanisms are both supported by experimental and computational studies, whether enzymes catalyze reactions via TS stabilization or via GS destabilization is still under intense debate. 15,29–32 Some studies are not generally supportive of GS destabilization as a catalytic mechanism because the GSs of the enzymatic reactions are more stable than the GSs of the corresponding reference reactions. 15 Other studies support the GS destabilization mechanism based on the observation that the destabilization of specific atoms can reduce the Δ G ‡ s of reactions. 24 The studies denying GS destabilization and the studies supporting GS destabilization are similar in defining GS destabilization based on the effects that enzyme–substrate interactions have on the binding affinity of GS substrates for enzymes. However, the studies denying the GS destabilization mechanism tend to consider all enzyme–substrate interactions in an enzymatic reaction; while the studies supporting the GS destabilization mechanism focus merely on specific enzyme–substrate interactions. Thus, the interactions supporting the GS destabilization mechanism are different from the interactions denying the GS destabilization mechanism, which is an important reason for the debate. In addition, the catalysis of specific enzyme–substrate electrostatic interactions via TS stabilization does not imply that other noncovalent interactions ( e.g. nonpolar–polar interactions, electrostatic stress) cannot catalyze reactions. It is thus possible that TS stabilization and GS destabilization are not opposite to each other. However, it is not easy to resolve the debate by using experimental and traditional computational approaches because the debate cannot be resolved without considering the facts supporting both TS stabilization and GS destabilization simultaneously. To better understand how these contrasting mechanisms catalyze reactions, we investigated similarities and differences that are evident between the catalysis via TS stabilization versus GS destabilization. We found that TS stabilization and GS destabilization utilize the same molecular mechanism when lowering the Δ G ‡ s of reactions, but differ in achieving the requirement for Δ G ‡ reduction. These findings may contribute significantly to our mechanistic understanding of how TS stabilization and GS destabilization augment the power of enzymatic reactions.

2. Results and discussion

2.1. effects of noncovalent interactions on the charge density of polar atoms.

In reactions involving electron-movement, there are substrate atoms accepting or donating electrons during the reactions. If the substrate atoms have a larger charge density in the TS versus GS, the atoms that form electrostatic interactions with the substrate atoms will have a lower charge density in the TS versus GS. As shown in Fig. 2B , if a negatively charged substrate atom S accepts electrons, S has a larger negative charge density and forms a stronger electrostatic interaction with the positively charged environmental atom H in the TS versus GS. As a result, the positive charge density of H decreases from the GS to the TS.

2.2. Enzyme–substrate noncovalent interactions that modulate Δ G ‡ s

In the binding process, all enzyme–substrate interactions affect the binding free energy of 5-AND. Among these interactions, the H-bond interactions between the substrate oxygen atom and the polar H-atoms of Tyr16 and Asp103 stabilize the GS to a minor extent (note: free energy contributed by the formation of the H-bonds is partially cancelled by the breaking of the H-bonds of the atoms with water, see Fig. S5 † ). The interaction between the nonpolar substrate α-H and the negatively charged oxygen atom of Asp40 destabilizes the GS largely. The other enzyme–substrate interactions are mainly hydrophobic, resulting from the exclusion of water from the nonpolar surfaces of 5-AND and in the KSI active site. Because of the large nonpolar surface area of 5-AND and the active site of KSI, the hydrophobic interactions stabilize the GS largely (Fig. S6 † ). However, the exclusion of water from nonpolar surfaces occurs merely in the binding process, and thus, the hydrophobic interactions have little effect on the Δ G ‡ of the reaction. Only the interactions of the substrate atoms experiencing a charge density alteration, which includes the substrate oxygen atom and the α-H, change during the reaction process and affect the Δ G ‡ of the reaction, as demonstrated in previous studies. 33,39 Thus, we focus our investigation on the interactions of substrate atoms that experience a charge alteration to evaluate their role in Δ G ‡ reduction. For the sake of completion, we also consider the roles of the interactions of substrate atoms without charge density alterations.

2.3. Comparison of reaction coordinate diagrams for TS stabilization and GS destabilization

2.4. similarity between ts stabilization and gs destabilization in δ g ‡ reduction, 2.5. generality of the common δ g ‡ reduction mechanism.

For R⋯E to affect ΔΔ G ‡ RE , R must have a charge density alteration. The charge density of R either becomes higher or lower from the GS to TS. If R has a higher charge density in the TS than in the GS ( H R‡ > H R ), H E must be larger than H W (E must have a larger charge density than W) for catalysis to occur (ΔΔ G ‡ RE <0). Because R ‡ has a higher charge density than R, the charge densities of E and W are lower in the TS than in the GS ( Fig. 2B ). Therefore, if R has a higher charge density in the TS versus GS, E has a reduction in charge density and must have a larger charge density than W so that the interaction R⋯E contributes to the catalytic power.

If R has a lower charge density in the TS versus GS ( H R‡ < H R ), H E must be less than H W for catalysis to occur. H E is less than H W in the following cases: (1) E has a lower charge density than W ( H W > H E ≥ 0) and (2) E has a similar charge to R, resulting in unfavourable electrostatic repulsion ( H E < 0). In both cases, R and R ‡ have weaker favourable electrostatic attraction or stronger unfavourable electrostatic repulsion with E than with W. As a result, both R and R ‡ have a higher charge density in the enzymatic reaction than in the reference reaction. Thus, if the charge density of R ‡ is lower than R, the latter must have a larger charge density than the corresponding substrate atom in the solution reaction so that the enzyme–substrate interaction contributes to the catalytic power.

Therefore, irrespective of whether an enzyme–substrate interaction catalyzes a reaction via a TS stabilization or GS destabilization mechanism, either the substrate or enzyme-based atom of the interaction has a reduction in charge density. This respective atom must have a higher charge density than the corresponding atom in the reference reaction for catalysis to be favourable. For a general reaction in which electrons move from one substrate atom (R 1 ) to another (R 2 ), the negatively charged atom in the electron-donating center has a reduction in charge density and the positively charged atom in the electron-accepting center has a reduction in charge density. The Δ G ‡ reduction mechanism is summarized in Fig. 5 . In the electron-donating center, either R 1 or E 1 (the enzyme-based atom interacting with R 1 ) is negative and has a larger negative charge density than the corresponding atom in the reference reaction. In the electron-accepting center, either R 2 or E 2 (the enzyme-based atom interacting with R 2 ) is positive and has a larger positive charge density than the corresponding atom in the reference reaction.

2.6. Consistency between the Δ G ‡ reduction mechanism versus the charge alteration mechanism

2.7. difference between ts stabilization versus gs destabilization in the binding process.

Fig. 6A shows the interaction change of Cl − during the binding process of the halogenase-catalyzed halide alkylation. Cl − has a favourable interaction with water before binding and this interaction is eliminated after binding. This desolvation event enhances the charge density of Cl − . Without desolvation, Cl − in the GS of the enzymatic reaction would not have a larger charge density than the Cl − in the GS of the reference reaction and catalysis would not be favourable. Thus, GS destabilization appears to enhance the charge density of catalytic atoms that have a reduction in charge density. Fig. 6B shows the interaction change of the hydrogen atom of Tyr16 during the binding process of 5-AND to KSI. The electrostatic interaction of the hydrogen atom becomes stronger and the charge density of the hydrogen atom decreases during the binding process. Even so, the hydrogen atom in the GS still has a higher charge density than the corresponding atom in the reference solution reaction. Thus, for interactions that catalyse reactions by TS stabilization, the atoms involving a reduction in charge density must have a larger charge density preceding enzyme–substrate binding than the corresponding atoms in the reference reactions. Even though the charge density of the catalytic atoms becomes lower following binding, these atoms still have a higher charge density than the corresponding atoms in the reference reaction.

An apparent fundamental difference between catalysis by TS stabilization versus that by GS destabilization is the timing of enhancing charge densities of the catalytic atoms experiencing a reduction in charge density. For TS stabilization, the charge density of catalytic atoms is enhanced before enzyme–substrate binding. For example, in the KSI-catalyzed isomerization of 5-AND, the hydrogen atoms from Tyr16 and Asp103 already have higher positive charge densities than the hydrogen atoms of water before the enzyme–substrate binding occurs, which is caused by the electron withdrawing groups bound to the hydrogen atoms (Fig. S7 † ). For GS destabilization, the charge density of catalytic atoms is totally or partially enhanced during enzyme–substrate binding.

The charge density of the Cl − in the halogenase-catalyzed halide alkylation is totally enhanced during enzyme–substrate binding. While the charge density of the oxygen atom of Asp40 in the KSI-catalysed isomerization of 5-AND is partially enhanced during enzyme–substrate binding because the oxygen atom has fewer electrostatic interactions than the corresponding atom in the reference reaction before the enzyme–substrate binding. In the haloperoxidase-catalyzed halogenation of organic compounds performed in 1-propanol (Fig. S8 † ), 40 the halide has weaker electrostatic interactions with 1-propanol than the halide in water. The charge density of the halide is partially enhanced before the enzyme–substrate binding and partially enhanced during the enzyme–substrate binding process. No matter how the charge density of a catalytic atom is enhanced, the catalytic power contributed by the catalytic atom depends on the relative charge density of the atom in the GS of the enzymatic reaction versus the corresponding atom in the reference reaction, and has little relationship with the charge density of the atom before the enzyme–substrate binding.

2.8. Relationship between TS stabilization of the enzymatic reaction and GS destabilization of enzyme–substrate interactions

2.9. ts stabilization versus gs destabilization.

In the present study, by comparing similarities in diverse enzymatic catalysis, we contribute a new enzymatic concept that addresses the origin of catalytic power and identifies a common mechanism that is shared by TS stabilization and GS destabilization. Importantly, this common Δ G ‡ reduction mechanism is supported by a theoretical approach that applies to diverse examples of catalyses mediated by TS stabilization and GS destabilization. The mechanism of enhancing atomic charge densities can elucidate why some interactions catalyse reactions via TS stabilization and other interactions catalyse reactions via GS destabilization. The full mechanism of enzyme catalysis includes the mechanism of reducing Δ G ‡ and the mechanism of enhancing atomic charge densities. This full mechanism represents a significant advance that adds favourably to the TS stabilization versus GS destabilization debate. Although the methods for determining TS stabilization and GS destabilization are different in literature, this full mechanism has no relationship with the methods.

It is worthwhile emphasizing that this study focused exclusively on enzymatic catalysis contributed by the noncovalent interactions of the substrate atoms involving a charge density alteration because the controversy between TS stabilization and GS destabilization is mainly centered on such reactions. The Δ G ‡ reduction mechanism developed in this study is targeted for the reaction processes of enzymatic reactions, in which chemical bonds form and break. If the substrate of an enzymatic reaction must undergo a large conformation change prior to the reaction process, the reaction can be catalyzed by facilitating the conformation change in the enzyme–substrate binding process. For example, in the polycyclization of polyisoprene catalyzed by terpene cyclase (Fig. S9 † ), 43 the polyisoprene in its most stable linear conformation cannot undergo polycyclization because the atoms for forming chemical bonds are far from each other. The linear conformation must change to a much less stable productive precyclic conformation so that chemical bonds can form. Terpene cyclase catalyzes the polycyclization of polyisoprene by facilitating the conformation changes. The release of ordered water molecules in the enzyme active site, which is favorable in entropy, provides energy for the conformation change and plays a dominant role in the catalysis.

3. Conclusions

4.1 methods for calculating h-bonding capabilities, 4.2 experimental water to chloroform partition coefficients, 4.3 method for calculating the change in surface accessible surface area (δsasa) during the binding process of 5-and to ksi, 4.4 method for exploring the similarity between ts stabilization and gs destabilization in δ g ‡ reduction, 4.5 method for exploring the difference between ts stabilization versus gs destabilization in the binding process, data availability, author contributions, conflicts of interest, acknowledgements, notes and references.

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Article contents

Demographic transition in india: insights into population growth, composition, and its major drivers.

• Usha Ram Usha Ram International Institute for Population Sciences, Department of Public Health and Mortality Studies
•  and  Faujdar Ram Faujdar Ram Population Council of India and International Institute for Population Sciences
• https://doi.org/10.1093/acrefore/9780190632366.013.223
• Published online: 26 April 2021

Globally, countries have followed demographic transition theory and transitioned from high levels of fertility and mortality to lower levels. These changes have resulted in the improved health and well-being of people in the form of extended longevity and considerable improvements in survival at all ages, specifically among children and through lower fertility, which empowers women. India, the second most populous country after China, covers 2.4% of the global surface area and holds 18% of the world’s population. The United Nations 2019 medium variant population estimates revealed that India would surpass China in the year 2030 and would maintain the first rank after 2030. The population of India would peak at 1.65 billion in 2061 and would begin to decline thereafter and reach 1.44 billion in the year 2100. Thus, India’s experience will pose significant challenges for the global community, which has expressed its concern about India’s rising population size and persistent higher fertility and mortality levels. India is a country of wide socioeconomic and demographic diversity across its states. The four large states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan accounted for 37% of the country’s total population in 2011 and continue to exhibit above replacement fertility (that is, the total fertility rate, TFR, of greater than 2.1 children per woman) and higher mortality levels and thus have great potential for future population growth. For example, nationally, the life expectancy at birth in India is below 70 years (lagging by more than 3 years when compared to the world average), but the states of Uttar Pradesh and Rajasthan have an average life expectancy of around 65–66 years.

The spatial distribution of India’s population would have a more significant influence on its future political and economic scenario. The population growth rate in Kerala may turn negative around 2036, in Andhra Pradesh (including the newly created state of Telangana) around 2041, and in Karnataka and Tamil Nadu around 2046. Conversely, Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan would have 764 million people in 2061 (45% of the national total) by the time India’s population reaches around 1.65 billion. Nationally, the total fertility rate declined from about 6.5 in early 1960 to 2.3 children per woman in 2016, a result of the massive efforts to improve comprehensive maternal and child health programs and nationwide implementation of the national health mission with a greater focus on social determinants of health. However, childhood mortality rates continue to be unacceptably high in Uttar Pradesh, Bihar, Rajasthan, and Madhya Pradesh (for every 1,000 live births, 43 to 55 children die in these states before celebrating their 5th birthday). Intertwined programmatic interventions that focus on female education and child survival are essential to yield desired fertility and mortality in several states that have experienced higher levels. These changes would be crucial for India to stabilize its population before reaching 1.65 billion. India’s demographic journey through the path of the classical demographic transition suggests that India is very close to achieving replacement fertility.

• demographic transition
• contraception
• family planning
• life expectancy
• child mortality

India is one of the oldest civilizations and has a vibrant cultural heritage coupled with remarkable diversity. The Mughals ruled the country from 1526 to 1761 , and were mainly located north of Vindhyanchal. India was a British colony from 1612 until 1947 , when the country attained its independence and became a sovereign nation. The British occupied all of present-day India after defeating Tipu Sultan in Mysuru and Marathas in Maharashtra. The British East India Company governed India and controlled trade throughout the region, except for Goa, which the Portuguese controlled in 1510–1961 , and Pondicherry, which the French controlled in 1673–1693 and again in 1699–1962 .

India has conducted a regular decadal census since 1881 that measures population size and composition as well as decadal growth at the national and subnational levels (including states, districts, and tehsils). At the dawn of Indian independence, there were about 345 million Indians. The year 1951 witnessed the first census of an independent India, recording a total population of 361 million and a moderate annual exponential growth rate of 1.25% during 1941–1951 . From a population growth perspective, the year 1951 became a turning point because it indicated a population explosion since it multiplied threefold by 2001 .

According to a United Nations (UN, 2019 ) report, India constituted 17.7% of the total world population, and was second only to China, whose share was 18.5%. The same estimates revealed that India would not only surpass China in the year 2030 with its share of 17.6% (and China’s would decrease to 17.1%) but it would also maintain the first rank after 2030 . The report further indicated that Africa’s share would rise to 25.6% in 2050 and 39.4% in 2100 . In contrast, the percentage share of Asia would decline from 59.5% in 2020 to 43.4% in 2100 . By 2100 , India would attain the first rank as far as the share of a single country is concerned. Nonetheless, its relative share would decline to 16.8% in 2050 and 13.3% in 2100 . It is thus essential to examine the dynamics of population growth, its potential, and future drivers of population growth of India.

The rapid population growth caused by a comparatively quick decline in mortality and persisting higher fertility levels has been the cause of concern in most developing counties, including India. The 1961 census of India revealed an annual exponential national growth rate close to 2% during 1951–1961 . The concerns were raised about the population growth and its rising size, both nationally and globally. The demographics of India—population size, growth rate, fertility, mortality, and so on—continue to occupy significant space discussions concerning its impact on various global health and developmental indicators. Alarmed at burgeoning numbers, and a view to accelerating a rapid decline in fertility levels, many developing countries, especially in Southeast Asia, launched official family planning programs in the mid-1960s. In the 1970s and 1980s, most witnessed a strong commitment by leaders to reduce fertility levels. As a result, they experienced one of the fastest transitions in levels of fertility (Pathak & Ram, 1981 ; Srinivasan & Pathak, 1981 ). Although India launched an official family planning program in the early 1950s, the real inputs for the program were recorded from the 1960s, when the program became method-mixed and target oriented. Post-independence, upon the advice of several researchers (Chitre, 1964 ; Gopalaswamy, 1962 ; Laxmi, 1964 ), the Indian government implemented its official family planning program in 1952 that promoted sterilization on a large scale. This was considered as the most cost-effective and impactful approach by the government given resource constraints. However, Agarwala ( 1964 ) disagreed with this and criticized the program. Recently, Srinivasan ( 2006 ) also opined that the continuous focus on sterilization (female) has dominated the Indian national family planning program. In the mid-1960s, government expanded the basket of methods for the clients and included IUD into the program. This, nonetheless failed due to several side effects on the users (Pujari et al., 1967 ).

The well-known linguistic, economic, and social-cultural diversity of India and its century-old demographic diversity across geographies have expanded, especially since independence. Several states in India, including Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu in the southern region, have moved much faster in achieving the national goal of the replacement fertility. The onset of fertility transition in these southern states occurred when the social and development indicators such as female literacy rates, per capita income, mortality and so on were rather poorer. At the same time, Hindi-speaking states in the northern region, including Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh, continue to experience high levels of fertility as well as mortality. Nationally, fertility levels in India have fallen, and by 2000 Indian women were having an average of about 3.3 children. A significant portion of this decline came from the states in the southern region, where female literacy rates were higher, and women enjoyed greater autonomy than the women in the rest of India. While the southern states of Kerala and Tamil Nadu attained replacement-level fertility long ago, the giant northern states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan continue to reproduce at a prodigious rate (Krishnamoorthy, 1997 ; Rajan, 1994 ; Seal & Talwar, 1994 ). It is important to note that the prevailing social and economic conditions in the southern states at the time of onset of fertility transitions varied considerably. The doctrine of demographic-transition theory advocates indicates that a rise in per capita income, industrialization, and urbanization subsequently leads to reduced levels of fertility and mortality in populations. However, this did not happen in Kerala. Fertility and mortality levels in Kerala were not accompanied by the concurrent improvements in the levels of per capita income, industrialization, and urbanization (Zachariah, 1983 ).

Until the end of the 20th century , family welfare programs and policies in India focused on lowering fertility rates because the authorities visualized that the persisting higher fertility rates would further add to the built-in growth momentum of its population age composition. The UN’s ( 1987 ) population projections revealed that the population momentum alone would add substantially to growing numbers in India. Visaria and Visaria ( 1994 ) warned that the ultimate population size of India would be enormous if the country failed to put a brake on the fertility rate and achieved the replacement levels before 2016 . It would thus be useful to elaborate on the demographic transition in India and identify gaps to provide future directions for the program to enable positive changes in matters of population growth, thereby improving the lives and well-being of its people. The national scenario masks the diversity across states. Thus, achieving the goals may be less feasible without any understanding of the issues at the subnational level. This article documents the demographic transition of India at the national and subnational levels and examines various drivers of the transition.

The data for the present research come from several sources. The world population for the past and future years comes from the UN’s ( 2019 ) World Population Prospects . The time-series data for India on population size, growth rates, and age distribution at the national and state levels come from Indian government censuses conducted between 1881 and 2011 . The Government of India’s National Commission on Population (NCP, 2019 ) projections provides the numbers for the period 2021–2036 . The indicators of fertility (total fertility rates) and mortality (infant mortality rate, under-5 mortality rate, and life expectancy at birth come) are from various rounds of the Indian government’s Sample Registration System (SRS). The data for multiple years is available in the annual statistical reports published by the Registrar General of India ( 2020 ). The information on contraceptive use and marriage comes from the National Family Health Surveys (International Institute for Population Sciences [IIPS], 1993 ; IIPS & ICF, 2017 ; IIPS & ORC-Macro, 2000 , 2007 ). Figures and tables presented throughout the article give detailed data from these sources.

Demographic Transition Theory: A Brief Description

The demographers Warren Thompson ( 1929 ) and Adolphe Landry circa 1934 (Landry, 1987 ), described the classical demography/population transition. However, Frank W. Notestein ( 1945 ), an American demographer proposed a precise framework and presented a systematic formulation of the theory in its real sense According to the demographic transition theory, most countries will go through a process of population change from the stage of high birth and death rates (pretransition stage 1) to the last stage of lowest birth and death rates (stage 4). In other words, countries move from the lowest pretransition stage 1 (sometimes negative growth rate) to the highest growth rate (stages 2 and 3) before reaching stage 4, when the growth rate is extremely low (occasionally negative) and the country has attained below-replacement fertility. According to the theory, the demographic transition of a nation can be described with the help of the growth rates if the country has regular censuses over a reasonably long period. In his critical exploration of the demographic transition, Kirk ( 1996 ) stated that

the timing of the decline in countries with Non-European tradition conformed to the forecast by the original authors of the theory, without exception, fall in mortality preceded the decline in the levels of fertility . . . In general, the transition period was shorter in Non-European countries than the countries inhabited by Europeans. (p. 383)

Further, the non-European countries are transitioning with a lower level of socioeconomic development (Cleland & Wilson, 1987 ).

Several researchers (Kaa, 1987 , 2002 ; Lesthaeghe, 2011 , 2014 ; Lesthaeghe & Surkyn, 2004 ) have referred to a second demographic transition (SDT). The SDT is a period of continued fertility decline much below-replacement fertility. The most critical factors related to this continued decline are increase in nonmarriage, individual autonomy, self-actualization, rising symmetry in sex roles, advancing female education, and economic independence of women (for details, see Lesthaeghe, 2014 ). Nevertheless, the postulate of SDT based on the experiences of European countries may not hold in developing countries (Cleland, 2001 ; Dyson, 2010 ). The SDT, nevertheless, is much more challenging than the original demographic transition because the countries face declining population sizes, shrinking working population, and graying population. To an extent, replacement migration could help these nations overcome these emerging challenges. Coleman ( 2006 ), using the emergence of migrants as the dominant community in some geographies compared to the natives, advocated the concept of the third demographic transition (TDT), which emphasizes the drastic change in population composition. However, the idea of TDT could be a reflection of the adjustment for the shrinking labor force that arises out of SDT, and it does not fit into the purview of demographic transition theory per se.

This section discusses changes in population size, growth and its age-sex composition over time to understand India’s population transition. This is followed by a detailed exploration of the crucial factors that led to population transition. For this, we have considered four major drivers of population change that include fertility, mortality, family planning and changes in marriage pattern. Changes in fertility levels have been studied using total fertility rate. The changes in mortality have been studied using three indicators of infant mortality, under-5 mortality and expectation of life at birth. The changes in contraceptive use is examined with the help of contraceptive prevalence rate. Finally, changes in marriage pattern is examined with the help of percentage of women aged 20–24 years who were married before reaching age 18 years and women aged 30–34 years who remained single.

Population Size, Growth, and Age Structure

The UN ( 2019 ) estimated a total of 7,795 million people globally in 2020 . They suggested that this number would surpass 10 billion by the turn of the 21st century (Table 1 ). In 2020 , about 60% of the people live in Asia and a little over 17% live in Africa. By 2100 , Asia would be home to 43% of the global people and Africa to 39%. The share of European countries is estimated to reduce from 9.6% in 2020 to less than 6% in 2100 . While a similar pattern is predicted for the countries in Latin America and the Caribbean and the North American regions, the share of Oceania remains unchanged. China’s population, was about 19% of the global population in 2020 , would reduce to less than 10% by 2100 . In India the share would decrease from less than 18% in 2020 to slightly over 13% in 2100 .

Table 1. Population Size and Share of the Population of World Regions, China, and India, 2020–2100

Source: UN ( 2019 ).

The indirect estimates of crude birth and death rates for India are for the period 1901–1961 . After 1971 , the SRS, which was established in the late 1960s, started to provide the crude birth rate (CBR) and crude death rate (CDR) for India and bigger states annually. The most recent SRS estimates are available for the year 2017 . At the beginning of the 20th century , India had very high levels of crude birth and death rates (48 births/deaths per 1,000 persons; Figure 1 ), which persisted until 2021 . The death rates started to decline around 1930 and reached 16 deaths per 1,000 persons in 1971 . The CBR, too, began to fall at a much slower pace. While the CBR was 36 births per 1,000 persons in the early 1970s, the CDR was 16 deaths per 1,000 persons. This declining trend continues, and the gap between the two rates is narrowing over time. The CBR was 20 per 1,000 persons in 2017 as compared to the CDR of 6 per 1,000 persons.

Figure 1. Crude birth rate (CBR) and crude death rate (CDR) for India, 1901–2017.

At the beginning of the 20th century , India had 238 million people. The results of the first census of the new millennium revealed that India had crossed the one billion mark by the end of the 20th century as the 2001 census enumerated a total of 1,029 million Indians (Table 2 ). The country annually added 16.1 million people in the 1980s and 18.2 million in the 1990s. While the world population increased threefold (from 2 to 6 billion) during the last century, it grew five times in India. The 15th census of India conducted in 2011 enumerated a total of 1,210 million Indians. The population of India grew with a decadal growth rate of about 17.5% during 2001–2011 , resulting in an annual exponential growth rate of 1.62% (a decline from 1.96% observed during 1991–2001 ). Despite a substantial reduction in the growth rate during 2001–2011 , India added nearly 181 million people. The UN’s 2019 projections indicated a similar addition during 2011–2021 , before the country experienced a drastic decline in the subsequent decades.

Figure 2. Estimated and observed exponential annual population growth rate (%) during 1901–2011 and 2021–2101, respectively, for India.

Indian annual population growth peaked at 2.22% during 1961–1971 (Table 1 and Figure 2 ) and stayed around 2% for the next four decades until 2001 . This period may be referred to as the second stage (population explosion stage) of demographic transition for India, during which the country added approximately 590 million people. Between 2001 and 2011 India experienced a substantial decline in its population growth rate (from 1.95% in 1991–2001 to 1.62% in 2001–2011 ). The UN’s 2019 assessment suggested that as far as the population size as concerned, India would surpass China in the next 7–8 years and would continue to increase until the year 2061 when its population size would reach 1,650 million. India may experience a decline in its total population after 2061 and count 1,444 million people in the year 2101 . Thus, India would add another 440 million people to its 2011 population size before achieving stabilization. In other words, India is likely to enter the fourth stage (near-zero growth rate) in the next 50 years or so. For India, the third stage of the demographic transition may fall between 2011 and 2051 . The momentum inbuilt in the age structure of the population would mostly lead to its growth.

Table 2. Population Size, Intercensal Change (Absolute and Percentage), and Exponential Annual Growth Rate, India, 1901–2001

Source : Registrar General of India ( n.d.-a ); Population estimated from UN ( 2019 ).

Examination of the current growth rate in specific states of India, especially for the larger Indian states (in terms of population size), helps to locate growth potentials. Table 3 gives population size for 2001 and 2011 , the two recent censuses of India, absolute change and state share in the total national change during 2001–2011 , and the exponential population growth rate observed during 2001–2011 for 20 large states of India. The four states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan deserve particular attention. With a population increase of 33.6 million, Uttar Pradesh contributed the most significant growth to the total national change of 182.2 million during 2001–2011 , followed by Bihar at 21.1 million and Maharashtra at 15.5 million. Kerala recorded the lowest annual exponential growth rate of 0.48%, followed by Andhra Pradesh (1.04%), Punjab (1.30%), and Odisha (1.31%). Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh together added 446 million (43%) of the total national addition and each state had an annual growth rate of 2% or more. These states are likely to make significant contributions to Indian population growth in the future because the fertility and mortality rates in these states are comparatively high and the decline in these rates has been much slower than that of other states. The most recent projections of the Government of India (NCP, 2019 ) indicated that by the year 2036 there would be a total of 596 million Indians, and half of them would come from these four states.

Table 3. Population Size, Intercensal Change (Absolute and Percentage), and Exponential Annual Growth Rate for Selected States of India, 2001–2011

a Sum of states may not match to India due to rounding of the numbers.

b Undivided including Telangana.

Table 4 gives a future population scenario in the 13 large states of India subdivided into three groups based on the attainment of the replacement level of fertility. These 13 states together cover nearly 80% of the national total. Group 1 consists of four states—Rajasthan, Uttar Pradesh, Bihar, and Madhya Pradesh—that have yet to attain replacement fertility. Group 2 and Group 3 consist of the states that have recently reached replacement fertility and a long time ago, respectively. The four large states in Group 1 have enormous potential for growth, and during 2026–2036 their combined growth rate is projected to be close to 1% (0.83%). Bihar is an outlier even within this group, with a growth rate of 1.16% annually. Group 2 states would have a growth rate of around 0.37% and Group 3 of about 0.20%. These findings indicate that a major part of India’s population growth potential lies in the four states of Group 1.

Table 4. Population Size and Year of the Attainment of Replacement Fertility in 13 Large States of India Stratified by Level of Total Fertility Rate, 2011–2036

a 2011 population data from the census of India.

b Projected population for the period 2016–2036 is from NCP ( 2019 ).

c Undivided including Uttarakhand.

d Undivided including Jharkhand.

e Undivided including Chattisgarh.

Source : NCP ( 2019 ).

Population Age-Sex Composition

The population age-sex composition of a country narrates historical experiences, including wars, epidemics, famines, and so on. Population age distribution and the female to male ratio are indicative of fertility and mortality levels and the social status of the women in the populations. Along with the demographic transition in India described earlier, there has been an inevitable change in the age-sex structure—that is, the decline in mortality followed by fertility has resulted in changes to the population’s age structure. Several studies have debated and discussed the role of these changes in economic growth. Sex composition (population sex ratio overall and, more important, at birth) reflects the status of women in the society. Globally, the population sex ratio (males per 1,000 females) is favorable to the female gender. An overall sex ratio of 1,030–1,050 females per 1,000 males is standard under the natural conditions. The situation is slightly different in India.

Table 5 gives the sex ratio overall and for children younger than 5 years of age for India for a period of 120 years ( 1881–2011 ) along with the absolute change in them. For India, the overall sex ratio was close to normal until around 1931 . It started to rise gradually in favor of males after that. The 1991 census of India revealed a higher overall sex ratio nationally: 1,078 males per 1,000 females. However, the scenario is different for the child sex ratio. Female children marginally outnumbered male children until 1941 as the sex ratio was in favor of the female children (960–995 male children per 1,000 female children below age 5). However, the scenario reversed when the 1951 census results were declared as the child sex ratio turned in favor of male children (1,008 male children per 1,000 female children) and has deepened over the years with the widening female-male children gap. The child sex ratio in India increased from 1,022 in 1981 to 1,047 in 1991 and further to 1,071 in 2001 and 1,082 in 2011 male children per 1,000 female children. Nationally, during the periods 1981–1991 and 1991–2001 , the child sex ratio increased astonishingly by 25 and 24 units, respectively. The distorted child sex ratio in India as well as in neighboring countries in the region has been a matter of concern and point of debate and investigations among policy makers and researchers. Many have cited widespread gender-based discrimination (neglect) in the form of son preference, lower autonomy to the women, and so on as the leading cause of this distortion. These practices result in sex-selective abortions and gender-specific mortality differentials (Bongaarts, 2013 ; Bongaarts & Guilmoto, 2015 ; Guilmoto et al., 2018 ; Jha et al., 2011 ; Kashyap, 2019 ; Ram & Ram, 2018 ).

Table 5. Sex Ratio (Males per 1,000 Females) of the Total Population and Children Younger Than 5 Years of Age, India, 1881–2011

Notes: The sex ratio for the years 1881 and 1891 was calculated using data from Mukherji ( 1976 ). The sex ratio for children younger than 5 years of age was calculated using data from a C-series in the respective census of India.

Source : Registrar General of India ( n.d.-b ).

A few studies have estimated a decrease in girls due to the practice of sex-selective abortions in India and found that these practices are not universal across geographies. Instead, they vary considerably in subregions of India (Jha et al., 2011 ; Ram & Ram, 2018 ). Table 6 presents the sex ratio for selected states in India for the period 1991–2011 and the change in it. Regardless of the year, Kerala is the only state that has an overall sex ratio lower than 1,000 (i.e., females exceeding the male population). In addition, the male-female gap has widened over the past two decades by almost 43 units. Punjab and Haryana have the most skewed overall sex ratio, varying between 1,117 and 1,162 males per 1,000 females. The overall sex ratio has been in favor of males in the remaining states. However, the gaps in sex ratio seemingly have bridged over time. While the decline was sharp in the states of Uttar Pradesh, West Bengal, and Assam, it has remained mostly similar in Madhya Pradesh and Maharashtra. Similar to the overall sex ratio, Haryana and Punjab had a highly skewed child sex ratio, varying between 1,128 and 1,144, respectively, in 1991 and 1,190 and 1,169 in 2011 . In 2011 , Gujarat (1,110), Rajasthan (1,120), and Maharashtra (1,117) also showed a child sex ratio skewed in favor of male children. Other states also showed a considerable deficit of female children. Haryana topped the list as the child sex ratio increased by 62 units in favor of males during 1991–2011 . The corresponding increase was by 59 units in Maharashtra, 50 units in Rajasthan, 44 units in Gujarat, 42 units in Madhya Pradesh, and 30–39 units in Andhra Pradesh, Bihar, Odisha, and Uttar Pradesh. Kerala was the only state where the child sex ratio improved in favor of female children by 16 units between the 1991 and 2011 censuses.

Table 6. Sex Ratio (Males per 1,000 Females) of the Total Population and Children Younger Than 5 Years of Age for India and Selected States, 1991–2011

Note: Sex ratio from respective censuses of India (Table C-6 of 1991 and C-14 of 2001 and 2011).

a Undivided including Telangana.

Almost half of the districts in the country in 2011 had a deficit of girl children. The practice of neglect of the female child resulting in sex-selective abortion and excess female mortality is universal (Guilmoto et al., 2018 ; Ram & Ram, 2018 ). A more recent analysis for India by Kashyap ( 2019 ) indicated the dominance of prenatal factors (sex-selective abortion) compared to excess female mortality (postnatal factor). Table 7 presents the sex ratio at birth (SRB) for India and selected states. The data suggest that the SRB is favorable to male children for India nationally and subnationally. Punjab and Haryana, followed by Rajasthan, Uttar Pradesh, Gujarat, and Bihar, had a highly disturbing SRB in 1999 . For every 100 female births, Punjab and Haryana recorded 125 to 126 male births each, the other states recorded 112 to 118 male births. The male-female imbalance at birth has continued over time, although with a sign toward bridging the gaps. At the national level, the SRB has mostly remained unchanged at 112 male children for every 100 female children. Nonetheless, the imbalance has widened in Andhra Pradesh, Assam, and Haryana, suggesting that the efforts to address this have failed to yield desirable results. The study by Jha et al. ( 2011 ) demonstrated that the practices are more prevalent among affluent and educated people.

Table 7. Sex Ratio at Birth (Male Births Per 1,000 Female Births) and Absolute Change in Sex Ratio at Birth in India and Selected States, 1999–2016

a Undivided including Telangana for the years 1999, 2004, 2009, and 2013.

b Undivided including Jharkhand for the year 1999.

c Undivided including Chhattisgarh for the year 1999.

d Undivided including Uttarakhand for the years 1999, 2004, and 2009.

Source: Sex ratio from the annual statistical report of the Sample Registration System of India.

Table 8 presents age distribution by sex and dependency ratios (child, old age, and overall) for the period 1981–2011 (census of India) and 2036 for India (NCP, 2019 ). Figures 3A and 3B present age-sex population pyramids. The results in Table 8 suggest a visible change in the age structure over the decades. Nationally, the share of children below age 15 in the total population declined to from about 40% in 1981 to 31% in 2011 . The NCP ( 2019 ) projections indicated that the share would decrease to 20% by 2036 . The percentage of people aged 60 years and older increased to 9% in 2011 and is estimated to reach 15% in 2036 (over 227 million). The changes in the dependency ratios for children and older people also confirm a transition in the age structure. While the child dependency ratio in India declined from 73% in 1981 and to 51% in 2011 , the dependency ratio for older people increased marginally from 12% to 14%. The official population projections suggest that in 2036 the child dependency ratio would further decline to 30% and the dependency ratio for older people would increase to 23% nationally. In 2001 , India had about 587 million people in the working ages, between 15 and 59 years. Those aged 15–34 years accounted for nearly 60% (349 million). The number of people in the working ages of 15–59 years and 15–34 years increased to 733 million and 425 million, respectively, in the year 2011 . Population projections suggest that in 2036 , while the number of people of working age would increase to almost 989 million, young labor would reach 464 million. Such changes would impact future economic development and would call on the government to initiate innovative strategies to take care of the older population. Besides, a sharp rise in the labor force demands generation of more employment.

Table 8. Share of the Male and Female Population Out of the Total Population by Age Groups and Dependency Ratios (for Children, Older People, and Overall), India, 1981–2011 and 2036

a Population is taken from the censuses of India 1981, 1991, 2001, and 2011.

b Projected population for 2036 is from NCP ( 2019 ).

c Dependency ratio from author calculations. The child dependency ratio is defined as the number of children below 15 years of age per 100 persons in the working ages of 15–59 years. The old-age dependency ratio is defined as the number of persons aged 60 years or older per 100 persons in the working ages of 15–59 years. The overall dependency ratio is defined as the number of children below 15 years of age and persons aged 60 years or older per 100 persons in the working ages of 15–59 years.

Figure 3A. Age-sex population pyramids of India, 1991.

Figure 3B. Age-sex population pyramids of India, 2036.

Major Drivers of Population Growth

Three drivers impact the population growth rate and are responsible for demographic transition: fertility, mortality, and international migration. Generally speaking, international migration has a limited role, as its volume is small. Thus, it is mainly the changes in fertility and mortality levels in a population that lead to demographic transition. This section discusses fertility and mortality transition in India and specific programmatic interventions responsible for the change in the fertility and mortality levels. India lacks good quality civil registration data on births and deaths (Ram et al., 2020 ; Yadav & Ram, 2019 ). Until the early 1970s, the estimated fertility and mortality for India and its states came from indirect methods that used census data stratified by age and sex. In the early 1970s, the Registrar General of India launched an annual nationwide system of collecting data on fertility and mortality (known as the sample registration system; SRS), which provides invaluable data for India and its states, especially for the bigger states. For the most part, the present research used fertility and mortality data from the SRS.

Figure 4 presents the total fertility rate (TFR) for India spanning over nearly 150 years (Ram et al., 1995 ). The TFR gives the number of children a woman would have at the end of the reproductive period, assuming that she experiences the prevailing age patterns of fertility. The data suggests that the TFR in India virtually remained unchanged at around 6.3 children per woman from 1871–1881 until 1951–1961 (standard deviation = 0.27). There has been little fluctuation in the TFR, which is mainly attributed to the variations in the quality of age-sex data in different censuses (Mukherji, 1976 ). Coale’s ( 1986 ) proposition of survival strategy postulates that a TFR of less than six for the expectation of life at birth (e o o ) of 20–25 years could lead to a zero or negative population growth. Thus, under a high mortality regime, maintaining a TFR of 6 and above was an excellent strategy to ensure moderate positive population growth. The decline in the TFR during the period 1896–1901 might have been the result of the famines of 1896–1997 and 1899–1901 , which were among the worst ever experienced in history and affected substantial sections of the population (Dyson, 1991 ).

The fertility transition in India most likely began during the late 1960s. Since the inception of fertility transition, the TFR in India declined by 19% to about 1.1 fewer children per woman during the first decade ( 1966–1971 to 1976–1981 ). The 1960s witnessed a substantial change in the family planning program in India, which became target-oriented and included the introduction of intrauterine devices to the official program in 1965 . The initial inherent demand for family planning and a persistently higher level of fertility may have been the reason for a relatively faster fertility decline during the first decade following the onset of the demographic transition. In the next decade ( 1976–1981 to 1986–1991 ), although the decrease in fertility continued, its pace slowed down. The decline in TFR slowed down notably in the subsequent decade of 1976–1981 to 1986–1991 when the reduction was only about 15%. The coercive approach adopted during the emergency period ( 1975–1977 ) was mainly responsible for this reduction in several states, more specifically in the larger Hindi-speaking states of Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh. This in turn accelerated the decline in TFR. Between 1986–1991 and 1996–2001 , the TFR declined by 19% (from about 4 children to 3.2 children per woman). During 1996–2001 , the TFR in India declined by about 14%. The mid-1990s saw a paradigm shift in the national family planning program as the country revamped the program from a target-oriented to target-free regime. This paradigm shift resulted in an initial decline/stagnation in the family planning performance in the country.

Figure 4. Total fertility rate, India, 1871–2018.

Nationally, the TFR almost halved in the 30 years between 1986 and 2016 from 4.2 to 2.3 children per woman (Table 9 ). Many states in India showed a similar trend. Rural India also experienced a decline in the TFR from 4.5 in 1986 to 2.5 in 2016 . However, urban India had already achieved replacement fertility in 2006 . Of the states included in this analysis, eight states have already attained replacement or below-replacement fertility. The lagging states are Bihar Madhya Pradesh, Rajasthan, and Uttar Pradesh, where TFR continues to be close to 3 children per woman. As noted, these are the states that are or could be center for India population growth in the coming years. The urban areas in several states attained replacement or below-replacement fertility in 2016 : the urban areas had a TFR of as low as 1.3 children per woman in West Bengal, 1.4 in Odisha, 1.5 in Andhra Pradesh, and 1.6 in Karnataka and Tamil Nadu. Further, the rural areas of Andhra Pradesh, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu, and West Bengal had a TFR that varied between 1.7 and 1.9 children per woman in 2016 .

Table 9. Total Fertility Rate for Combined, Rural, and Urban Areas and the Ratio of Rural to Urban Rate for India and Selected States, 1986–2016

a Undivided including Telangana for the years 1986, 1996, and 2006.

b Undivided including Jharkhand for the years 1986 and 1996.

c Undivided including Chhattisgarh for the years 1986 and 1996.

d Undivided including Uttarakhand for the years 1986, 1996, and 2006.

Source: Total fertility rate from the annual statistical report of the Sample Registration System of India.

Improved child survival and concurrent expansion of female education have led to fertility decline in developing countries like India (Davis, 1963 ; Dyson, 2010 ). We have already discussed geographic diversity in the TFR and transition. In Table 10 , we present the levels of TFR by education for India and selected states. In 1992–1993 , the TFR for India was 4.3 per woman for women who had completed fewer than 5 years of schooling (including nonliterate) compared to 3.3 for those who had 10 or more years of schooling; a difference of one child. By 2015–2016 , the TFR declined to 2.9 per woman and 1.8 for the respective groups. Over time there is no convergence in the level of fertility in lower and higher education groups as TFR declined by 45.5% among those who had 10 or more years of schooling compared to 32.6% among those who had fewer than 5 years of schooling. Nationally, around 22% of women aged 15–49 had completed 10 or more years of schooling in 1992–1993 . The share of these women increased to about 60% in 2015–2016 . Although TFR is higher for less educated people in India, their share in total women aged 15–49 has been reducing rapidly due to the expansion of education. The rise in education has a significant impact on delay in age at marriage.

A similar trend is observed at the state level as well. In 2015–2016 , with the exception of Bihar (TFR = 2.3), women who had 10 or more years of schooling had reached the replacement level of fertility. The lowest being in Punjab (TFR = 1.4) and the highest in Uttar Pradesh (TFR = 2.0). Women with 5–9 years of schooling in many states except Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh either reached replacement or below-replacement level fertility or are very close to achieving it. The four larger states (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh) have lower child survival and limited outreach of female education. In 2015–2016 , Kerala had 95% of women aged 15–49 with 10 or more years of schooling, which was 44% in Bihar (including Jharkhand), 46% in Rajasthan, 52% in Madhya Pradesh (including Chhattisgarh), and 53% in Uttar Pradesh (including Uttarakhand).

Table 10. Total Fertility Rate by the Educational Status of the Women, India and Selected States, 1992–2016

a Undivided including Telangana (1992–1993, 1998–1999, and 2005–2006).

b Undivided including Jharkhand (1992–1993 and 1998–1999).

c Undivided including Chhattisgarh (1992–1993 and 1998–1999).

d Undivided including Uttarakhand (1992–1003 and 1998–1999).

Source : International Institute for Population Sciences ( 1993 ); International Institute for Population Sciences & ICF ( 2017 ); International Institute for Population Sciences & ORC-Macro ( 2000 ); International Institute for Population Sciences & ORC-Macro ( 2007 ).

The mortality data has information on three key indicators: infant mortality rate (IMR), under-5 mortality rate (U5MR), and expectation of life at birth (LEB; e o o ). The data comes from the SRS for India and covers about 25 years ( 1990–2016 ). The year 1990 is chosen as a base since it benchmarks the Millennium Development Goals (MDG) base year, and the year 2016 benchmarks the base year of the recently declared Sustainable Development Goals (SDGs). The MDG goal for U5MR for India was to attain a U5MR of 42 deaths of children aged below 5 years per 1,000 live births by the year 2015 . The corresponding goal for the IMR was 37 infant deaths per 1,000 live births. Under the SDG, the goals are 21 and 15, respectively, for the year 2030 .

At the beginning of the 20th century , in India, a newborn baby had an average life expectancy of 21–23 years (Davis, 1951 ; Mukherji, 1976 ). The SRS life table available for the period 2013–2017 revealed that a newborn baby in India would live an average of more than 69 years, which is considerably lower than in other countries globally and in the South Asian region. Nonetheless, this is a significant improvement from just about 20 years to close to 70 years, and an essential aspect of this improvement relates to IMR. At the national level, the IMR was 80 infant deaths per 1,000 live births in 1990 , which declined to 68 in 2000 (12 points in 10 years; see Table 11 ). The first decade of the 21st century unfolded a significant decline in the IMR for India— 47 infant deaths per 1,000 live births in 2010 and 34 per 1,000 in 2016 . Mortality decline in India and its states may have been due to improvements in access to health services and also an incremental increase in access to improved drinking water and sanitation. Similar to the global evidence (Fink et al., 2011 ), the National Family Health Survey (NFHS) data for 1992–1993 and 2015–2016 revealed a quantum jump in access to sanitation facilities (IIPS, 1993 ; IIPS & ICF, 2017 ).

The acceleration, especially after 2005 , may be due to the Janani Suraksha Yojana program implemented under the National Health Mission (erstwhile known as the National Rural Health Mission). The program provided a cash incentive of Rs. 1400 to women who delivered their babies in a health facility (Stephen et al., 2010 ). However, compliance varies considerably across India’s states. In the year 1990 , Kerala had the lowest IMR (17 infant deaths per 1,000 live births), whereas it was higher in Odisha (122), followed by Madhya Pradesh (111) and Uttar Pradesh (99). By 2016 , IMR declined significantly in all states. While Kerala continued to occupy the first place with the lowest IMR, Madhya Pradesh replaced Odisha with an IMR of 47 deaths per 1,000 live births. The states, on the whole, have succeeded in reducing the IMR; however, the usual lagging states of Assam, Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh, and Odisha continue to have higher IMRs.

Table 11. Infant Mortality Rate and Percentage Change in the Rate in India and Selected States, 1990–2016

a Undivided including Telangana for the years 1990, 1995, 2005, and 2010

b Undivided including Jharkhand for the years 1990 and 1995.

c Undivided including Chhattisgarh for the years 1990 and 1995.

d Undivided including Uttarakhand for the years 1990 and 1995.

Source: Infant mortality rates from the annual statistical report of the Sample Registration System of India.

Table 12 presents the gender-specific U5MRs for India and states for 1990–2016 . An average of 114 children per 1,000 live births died in India in 1990 before celebrating their 5th birthday, which declined to 39 in 2016 ; a two-thirds decline in 26 years. During the same period, the U5MR declined from 119 to 37 for male children and from 132 to 41 for female children. Similar to IMR, the U5MR fell relatively faster in the last 16 years in India ( 2000–2016 ) when compared with the corresponding change during 1990–2000 . Once again, there are vast differences across states of India in U5MR as well; the lagging states continue to have significantly higher levels of childhood mortality. In 2016 Kerala had the lowest U5MR (11), and the Madhya Pradesh had the highest (55), followed by Assam (52) and Odisha (50). The improvement in child survival in India brought a sense of security for the families to go for smaller families and contributed to the lowering of the TFR. An important point to note here is that regardless of the period studied, the U5MR in India has exceeded for female children compared to the male children. Surprisingly, most states have revealed a gender gap in childhood mortality. A study by Ram et al. ( 2013 , 2014 ) documented wide disparities in the levels of under-5 mortalities in districts of India.

Table 12. Gender-Specific Under-5 Mortality Rate and Percentage Change, India and Selected States, 1990–2016

a Undivided including Telangana for the years 1990, 1995, 2005, and 2010.

b Undivided including Jharkhand for the years 1990, 1995, 2005, and 2010.

c Undivided including Chhattisgarh for the years 1990, 1995, 2005, and 2010.

d Undivided including Uttarakhand for the years 1990, 1995, 2005, and 2010.

Source: Author calculations based on data from SRS Based Life Tables for 1988–1992, 1993–1997, 1998–2002, and 2003–2007. Data for 2015 and 2016 from the annual statistical report of the Sample Registration System of India for the respective years.

We now examine levels of life expectancy at birth (LEB). Table 13 presents the relevant data for India and its states for both sexes combined as well as separately. The LEB for India was nearly 49 years during 1970–1975 , which increased to about 58 years in 1986–1990 , an increase of 9 years in 16 years resulting in an annual improvement of approximately 0.6 years. By 1996–2000 , the LEB in India increased to 62 years and further to 69 years in 2013–2017 . Up until the 1980s, nationally, Indian males lived longer than the Indian females (Ram & Ram, 1997 ). Data on gender-specific LEB since 1993 indicates that in India, females now live longer than males, and the gap was by 2 years in 2013–2017 . The gender gap indeed widened in the mid-1990s when male LEB was at 60.4 years and females at 61.8 years. But at the same time, gender gaps in mortality have also widened for adolescents (to the female disadvantage), an anomaly indicating the downside of using only LEB for exploring gender disparity.

Table 13. Gender-Specific Life Expectancy at Birth and Changes in the Life Expectancy, India and Selected States, 1986–2017

$$ authors calculation using SRS gender-specific life tables.

b Undivided including Jharkhand for 1986–1990 and 1996–2000.

c Undivided including Chhattisgarh for 1986–1990 and 1996–2000.

d Undivided including Uttarakhand for 1986–1990, 1996–2000, and 2006–2010

Source: From Life tables of the Sample Registration System (SRS) of India.

Family Planning and Unmet Need

India acquired the status of being the first nation globally to launch an official family planning program in 1952 . However, the real push to the program came through in the 1960s when the program adopted a target-specific approach. The federal authorities in India assigned targets to the states, which were allocated to districts and further to the individual health workers at the lowest level of service provision. These targets became extremely volatile over the years, and the authorities announced disincentives and incentives to the users and the service providers based on performance (Pachauri, 2014 ). This period was accompanied by the emergency period ( 1975–1977 ) in India, when the program became extremely coercive. This act of the government damaged the program to a great extent and impacted the northern Hindi-speaking belt where fertility levels were higher. Although the success in fertility reduction in India is not comparable to that of other Asian countries, its achievements are by no means modest. In the initial phase, the program success was mostly monitored and evaluated using service statistics with the help of the number of acceptors and births averted as a result of family planning acceptance. Family planning surveys conducted in the 1970s and 1980s (ORG, 1972 , 1982 , 1990 ) complemented monitoring and evaluating efforts. After 1990 , India launched nationwide surveys (see IIPS, 1993 ; IIPS & ICF, 2017 ; IIPS & ORC-Macro 2000 , 2007 ). Tables 14 , 15 , and 16 give selected family planning indicators for India.

There has been a continuous rise in the percentage of married women using modern contraception in India. For example, just over 10% of married Indian women in 1970 used modern contraception (ORG, 1972 ). This percentage increased to 42.8% in 1998–1999 and to 48.5% in 2005–2006 (Table 14 ). India’s contraceptive prevalence rates (CPRs) are presented for the period between 1992–1993 to 2015–2016 in Table 13 . At the national level, overall CPR has increased from a little over 36% in the early 1990s to close to 48% in 2015–2016 , which translates to an increase of 12 units over the 23 years (an annual increase of 1.4%). The 2017 NFHS indicated that modern method CPR had marginally decreased from 48.5% in 2005–2006 to 47.8% in 2015–2016 (IIPS & ICF, 2017 ; IIPS & ORC-Macro, 2007 ). The decline in CPR of the modern method is substantial in many states, including Bihar, Gujarat, Karnataka, Kerala, Madhya Pradesh, and Tamil Nadu. This has raised debates among policy makers and researchers because these states have concurrently exhibited a significant decline in TFR levels. There is some research evidence that has indicated doubt about the estimated CPR for the period 2015–2016 . A study by Jayachandran and Stover ( 2018 ) expressed concern over the quality of contraceptive data collected in the 2017 NFHS. The modern limiting method CPR showed an increase of five units (from 31% to a little over 36%) and there was a twofold rise in the modern spacing method CPR (from about 6% to over 11%) during the same period. Interestingly, CPR for traditional methods also increased, from 4% to almost 6% (IIPS & ICF, 2017 ).

The levels of CPR, as well as the pace of change in it, varied considerably across Indian states included in the analysis. Generally, the states in the southern and western regions revealed higher levels of CPR compared to those in the northern and eastern regions of India. While the CPR rose over time, Gujarat and Kerala had a marginal decline in the overall CPR. Assam, Odisha, and West Bengal (all three in the eastern region) and Uttar Pradesh in the northern part had higher CPR of the traditional method (abstinence and withdrawal/rhythm) compared to the remaining states. While the CPR for traditional methods declined in Assam and West Bengal, it increased from 1%–2% in 1992–1993 to over 12%–14% in 2015–2016 in Odisha and Uttar Pradesh. The use of traditional methods is higher among women who live in urban areas, who were more educated and resided in economically better-off households. The patterns of CPR are somewhat similar for the modern limiting and spacing methods across states, as seen for all methods combined. Nonetheless, a few states, such as Assam, Haryana, Odisha, Uttar Pradesh, and West Bengal, have shown a tremendous rise in the CPR for modern spacing methods.

Table 14. Contraceptive Prevalence Rate for Modem Limiting, Modern Spacing Methods and Traditional Methods of Family Planning and Percentage Change in Them, India and Selected States, 1992–2016

a Undivided including Telangana (1992–1993).

c Undivided including Chhattisgarh (1992–1993).

d Undivided including Uttarakhand (1992–1993 and 1998–1999).

Tables 15 and 16 provide data on the future demand for family planning as assessed using the information on unmet need for family planning over 25 years. Nationally, the unmet need for family planning declined by nearly 37% in two and a half decades; the unmet need of almost 20% in 1992–1993 to about 13% in 2015–2016 (Table 15 ). The unmet need for modern spacing methods had halved in the country from nearly 12% to 6% during the same period. However, the unmet need for family planning seemingly has remained unchanged since 2010 , as the decline was by only one percentage point (from 14% to 13% for all methods and from 6.1% to 5.6% for spacing methods). Gujarat and Kerala were the only states where the total unmet need for family planning increased over time. In the remaining states, the change in the total unmet need has followed the national pattern. While the total unmet need remained nearly unchanged in Haryana, Karnataka, Madhya Pradesh, Maharashtra, and Tamil Nadu, it increased only marginally in Andhra Pradesh, Assam, and Wes Bengal. The unmet need doubled in Gujarat and increased substantially in Kerala.

In contrast, the unmet need declined in Bihar, Odisha, Rajasthan, and Uttar Pradesh during the same period. In case of unmet need for spacing methods, the data indicated substantial decline over the period for all states except Kerala, where unmet need for spacing methods rose from 6% to 8% in the last decade. A on-going investigation of NFHS data by Ram et al. ( in press ) showed that unmet need increased mainly due to the rise in the unmet need among the nonusers.

Table 15. Total Unmet Need for Family Planning, Unmet Need for Spacing, and Percentage Change, India and Selected States, 1992–2016

There are 46 million married women aged 15–49 in India who have expressed an unmet need for modern contraception, of whom 14 million prefer limiting methods and 18 million prefer spacing methods. The remaining 14 million couples, who used traditional methods, are considered to have an unmet need for modern methods of contraception in the NFHS for 2015–2016 (IIPS & ICF, 2017 ). It is important to note that all of the nonusers having unmet need will not convert into the users for various reasons as unmet need is highly unlikely to attain a zero value. The current unmet need of 18.7% may best reduce to 4%–5%, as observed in some states (as well as other countries in the neighborhood). In other words, 35 million couples actually can be converted to users. Nonuse of contraception could be due to sterility (primary and secondary), which varies considerably across India’s states, especially after age 30 (Ram, 2010 ). In other words, the potential pool of available users will include fewer people, around 28–30 million. Table 16 presents the share of current users and couples with unmet needs in the states of India in the national totals. The 14 states included consist of 88% of all users in India, and nearly 84% of the couples with unmet need belonged to these 14 states. Almost 47% of the couples with unmet need come from Bihar (13%), Madhya Pradesh (5%), Rajasthan (7%), and Uttar Pradesh (21%). This share is likely to rise because the demand for contraception in other states has almost reached a saturation point. The geographic allocation of unmet need creates a challenging situation because program strength and social development in these states are inadequate and of poor quality.

Table 16. State Share of the Users of Modern Methods of Family Planning and State Share of Couples Having Total Unmet Need for Family Planning (Limiting and Spacing Combined) in the National Totals, 1992–2016

A very dark side of Indian culture has been the practice of child marriage, which was rampant in the 20th century . The Hindu scripture advocated marriage for a girl before puberty (onset of menstruation). However, girls who married early remained in the parental home until “Gauna” (Kapadia, 1966 ), which was generally performed at the age when the girl attains physical maturity (onset of menstruation). The Sarda Act enacted in 1929 , followed by the Child Marriage Restraint Act of 1978 in India, defined the minimum legal age for marriage as 18 years for girls and 21 years for boys. Early marriage has a multidimensional effect on the lives of the females in India throughout their life course, from deprivation of education, skill development, health care access, and so on. At the macro level, the marriage pattern of a population has a significant effect on fertility and mortality (especially child mortality) levels. Marriage is one of the proximate determinants of fertility besides family planning use. The female age at marriage in India is rising, but rather slowly. The singulate mean age at marriage in India was 15.9 years in 1961 , which increased to 18.3 years in 1981 and 20.8 years in 2011 , an increase of about five years in five decades. In the 1990s, nearly half of the women aged 20–24 in India were married before age 18 years. This percentage reduced to about 45% in 2005–2006 .

The institution of marriage in India almost remained universal. Close to 97% of the Indian women aged 30–34 years in 2011 were married (Table 17 ). The percentage of these women varied marginally across states. Only two states (Kerala and Odisha) had 5% of the women aged 30–34 years who were single. The percentage of single women aged 30–34 years was 4% in Karnataka and West Bengal. Data from the 2015–2016 survey indicated that about one-quarter of women aged 20–24 years were married before they were 18 years (in absolute terms, 14.5 million women married below age 18). There is a great deal of variation across the states. Around 42% of women aged 20–24 years were married before age 18 in West Bengal, followed by 40% in Bihar, 31–33% in Rajasthan, Madhya Pradesh and Andhra Pradesh, and 23–26% in Gujarat, Karnataka, and Maharashtra.

Table 17. Percentage of Women Ages 20–24 Married Before Age 18 and Percentage of Single Women Ages 30–34, India and Selected States, 2015–2016

Source: Authors’ calculation based on data from NCP ( 2019 ) and IIPS and ICF ( 2017 ). Percent of single women data from Census of India, 2011.

Concluding Remarks

Although India holds a national treasure in its decadal censuses that have been continuously reported since 1881 , the country has failed to develop and strengthen its civil registration system for births and deaths. A significant constraint faced by Indian policy makers is a lack of data with regard to its socioeconomic and demographic scenario, including fertility and mortality. This shortcoming became apparent in several policies and programs that lacked evidence-based decisions to improve the health and well-being of the population. These experiences motivated the authorities in India, and nearly two decades after the country attained independence, the Government of India initiated the sample registration system SRS in an effort to replace the civil registration system and fill the data void. In the early 1990s, the government’s focus on health and well-being led to the publication of the first National Family Health Survey in 2017 . The data from these surveys has helped policy makers and researchers to gain insight into the demographic changes in India, nationally and subnationally.

India is the second-most populous country in the world. The international community has expressed concerns about the rising population size and high growth rate in India, which has received unprecedented attention in almost all platforms. Between 1961 and 2001 , India’s population grew at an average rate of about 2%, and the size of the population in absolute terms exceeded one billion in 2001 . During 2001–2011 , the population growth slowed down substantially. However, India continued to add an average of 18 million people annually to its already large base, leading to a total national population of 1.21 billion in 2011 . An assessment by the UN ( 2019 ) indicated that India’s population would peak at 1.65 billion in 2061 and would begin to decline after that and reach 1.44 billion in the year 2100 . The four large states in India (Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan) continue to reveal high levels of fertility and mortality (especially during early childhood), and have great potential for future population growth. The spatial distribution of India’s population will have a significant influence on its future political and economic scenario. Kerala state may experience a negative population growth rate around 2036 . The undivided Andhra Pradesh (including the newly created state of Telangana) may experience the same around 2041 and Karnataka and Tamil Nadu around 2046 . Four states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan would have 764 million people in 2061 (45% of the national total) by the time India’s population reaches around 1.65 billion (Verma, 2018 ).

Changes in fertility and mortality are the two most important demographic factors contributing to population growth in India. The total fertility rate (TFR) in India declined from about 6.5 children per woman in the early 1960s to 2.3 children per woman in 2016 (a reduction of 4.2 children per woman in fewer than six decades). India is concerned about relatively high TFR in Bihar (3.3 children per woman), Uttar Pradesh (3.1 children per woman), Madhya Pradesh (2.8 children per woman), and Rajasthan (2.7 children per woman). The states have exhibited a higher unmet need for contraception and a weak public health-care delivery system. Childhood mortality in India has declined substantially, especially after the 1990s (114 in 1990 to 39 children deaths per 1,000 live births in 2016 ). This remarkable improvement is the result of massive efforts to improve comprehensive maternal and child health programs and nationwide implementation of the national health mission. The latter focused attention on improving the maternal and child health indicators in the country. Despite this, childhood mortality continues to be unacceptably high in Uttar Pradesh (47 children deaths per 1,000 live births), Bihar (43 children deaths per 1,000 live births), Rajasthan (45 children deaths per 1,000 live births), and Madhya Pradesh (55 children deaths per 1,000 live births). Besides, more considerable attention to improving access to public health-care services would promote contraception use immensely by way of reducing unmet needs and, in turn, reduce child mortality.

Figure 5. Future prospects of the demographic transition for India, 1950–2100.

A great deal of scientific evidence suggests that the intertwined programmatic interventions focusing on female education and child survival are essential. Such efforts, notably in the four large states of Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan, would go a long way to reduce unmet need for contraception and enhance contraception use giving a big push to reducing fertility in the future. This would be crucial for India to stabilize its population before reaching 1.65 billion. India’s demographic journey through the path of the classical demographic transition suggests that the country is very close to achieving replacement fertility. Figure 5 outlines the future path of India’s transition according to the UN’s ( 2019 ) assessment. Although India may achieve replacement level fertility very soon (around 2023 ), the population will continue to grow until 2060 due to population momentum. Only after this, India may experience a negative growth rate; that is, the crude death rate will exceed the crude birth rate.

Further Reading

• Caldwell, J. (1980). Mass education as a determinant of the timing of fertility decline. Population and Development Review , 6 (2), 225–255.
• Cleland, J. , & Rodriguez, G. (1988). The effect of parental education on marital fertility in developing countries. Population Studies , 42 , 419–442.
• Cochrane, S. H. (1979). Fertility and education: What do we really know? Johns Hopkins University Press.
• Dreze, J. , & Murthi, M. (2001). Fertility, education, and development: Evidence from India. Population and Development Review , 27 (1), 33–63.
• Lutz, W. , & Kebede, E. (2016). Education and health: Redrawing the Preston curve. Population and Development Review , 44 (2), 343–361.
• Pamuk, E. R. , Fuchs, R. , & Lutz, W. (2011). Comparing relative effects of education and economic resources on infant mortality in developing countries. Population and Development Review , 37 (4), 637–664.
• Preston, S. H. (1975). The changing relation between mortality and level of economic development . Population Studies , 29 (2), 231–248.
• Preston, S. H. (1980). Causes and consequences of mortality declines in less developed countries during the twentieth century. In R. A. Easterlin (Ed.), Population and economic change in developing countries (pp. 289–360). University of Chicago Press.
• Samir, K. C. , Wurzer, M. , Speringer, M. , & Lutz, W. (2018). Future population and human capital in heterogeneous India . Proceedings of the National Academy of Sciences , 115 (33), 8328–8333.
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State Transition Analysis: A Rule-Based Intrusion Detection Approach

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This paper presents a new approach to representing and detecting computer penetrations in real-time. The approach, called state transistion analysis, models penetrations as a series of state changes that lead from an initial secure state to a target compromised state. State transition diagrams, the graphical representation of penetrations, identify precisely the requirements for the compromise of a penetration and present only the critial events that must occur for the successful completion of the penetration. State transition diagrams are written to correspond to the states of an actual computer system, and these diagrams form the basis of a rule-based expert system for detecting penetrations, called the state transition analysis tool (STAT). The design and implementation of a UNIX-specific prototype of this expert system, called USTAT, is also presented. This prototype provides a further illustration of the overall design and functionality of this intrusion detection approach. Lastly STAT is compared to the functionality of comparable intrusion detection tools.

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Laser Excitation of the Th-229 Nucleus

J. tiedau, m. v. okhapkin, k. zhang, j. thielking, g. zitzer, e. peik, f. schaden, t. pronebner, i. morawetz, l. toscani de col, f. schneider, a. leitner, m. pressler, g. a. kazakov, k. beeks, t. sikorsky, and t. schumm, phys. rev. lett. 132 , 182501 – published 29 april 2024, see viewpoint: shedding light on the thorium-229 nuclear clock isomer.

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The 8.4 eV nuclear isomer state in Th-229 is resonantly excited in Th-doped CaF 2 crystals using a tabletop tunable laser system. A resonance fluorescence signal is observed in two crystals with different Th-229 dopant concentrations, while it is absent in a control experiment using Th-232. The nuclear resonance for the Th 4 + ions in Th: CaF 2 is measured at the wavelength 148.3821(5) nm, frequency 2020.409(7) THz, and the fluorescence lifetime in the crystal is 630(15) s, corresponding to an isomer half-life of 1740(50) s for a nucleus isolated in vacuum. These results pave the way toward Th-229 nuclear laser spectroscopy and realizing optical nuclear clocks.

• Received 5 February 2024
• Revised 12 March 2024
• Accepted 14 March 2024

DOI: https://doi.org/10.1103/PhysRevLett.132.182501

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Shedding Light on the Thorium-229 Nuclear Clock Isomer

Published 29 april 2024.

Researchers use a laser to excite and precisely measure a long-sought exotic nuclear state, paving the way for precise timekeeping and ultrasensitive quantum sensing.

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Authors & Affiliations

• Physikalisch-Technische Bundesanstalt, 38116 Braunschweig, Germany
• Vienna Center for Quantum Science and Technology, Atominstitut, TU Wien, 1020 Vienna, Austria
• * These authors contributed equally to this letter.
• † [email protected]
• ‡ [email protected]

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Vol. 132, Iss. 18 — 3 May 2024

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Excitation scheme (a) and experimental apparatus (b) for VUV laser spectroscopy of the isomeric state in Th-doped crystals. The VUV source consists of two cw lasers with the frequencies ν 1 , ν 2 , two pulsed dye amplifiers that introduce frequency shifts ( δ B 1 , δ B 2 ) due to Brillouin mirrors, a THG stage, and a xenon gas cell. The scanning is provided by tuning of the difference frequency Ω 2 via ν 2 . The spectroscopy vacuum chamber contains the Th-doped crystal mounted on a cold finger, signal collection optics, and a PMT. (c) False color CCD camera image of the crystal during VUV laser excitation and a schematic representation of the crystal structure of Th 4 + ions doped into the CaF 2 lattice with 2 F − for interstitial charge compensation.

(a) VUV fluorescence signals from the Th-229-doped X2 crystal, recorded in frequency scans from higher to lower frequency (squares) and lower to higher frequency (dots). The measurement time between frequency steps is shorter than the isomer decay time (see Fig.  3 ), which leads to an asymmetry in the resonance curves. (b) The resonance asymmetry is removed, together with the radioluminescence background, from the plots (a) in postprocessing. (c) Result of a control experiment with the Th-232-doped V12 crystal.

Th-229 fluorescence decay curve after resonant excitation (blue trace) for 500 s measured with crystal X2 at a temperature of 150 K. Gray trace: result of a control experiment with 200 GHz off-resonant excitation, showing no long-lived photoluminescence. Inset: fluorescence decay times for crystal temperatures between 108 and 168 K. No changes in the decay time are observed within the measurement uncertainty. An overall decay time constant τ = 630 ( 15 )     s is observed.

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Martin Walschburger Hurtado Studying Stone Man Disease through Research Collaboration between Kent State Stark and Walsh University

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For the past seven years, Dr. Dinah Qutob of Kent State University at Stark and Dr. Adam Underwood from Walsh University have partnered to guide Stark County undergraduate students pursuing careers in biology, instilling in them a comprehensive understanding of how biological networks transition between health and disease states. Currently, they are collaborating through the Undergraduate Research Program to study the molecular mechanisms underlying rare diseases. Their recent work involves student research on the impact of specific proteins in cancer, as well as a genetic mutation that causes stone man disease.

Qutob and Underwood teach and mentor groups of up to 10 Kent State and Walsh students each year, providing them with essential skills needed for post-graduate programs in biomedical specializations such as neurobiology, immunology, molecular pharmacology and therapeutics. The students contribute as co-authors on peer-reviewed scientific papers and give presentations at regional and national conferences to showcase the impact of their work. The faculty pair recently applied for the National Institute of Health Research Enhancement Award (R15) which, if granted, will provide continued funding of the program. The NIH is the largest funder of medical research in the world.

The duo mentors a diverse student population and also prioritizes inclusivity with research to make a positive impact on the global community.

“It’s truly inspiring to witness the mutual support among our students. Our more senior, experienced students step up to mentor and train incoming students,” Qutob noted. “We have a diverse representation of undergraduates from various backgrounds, all united in their dedication to hard work and teamwork.”

One standout student in the program is Martin Walschburger Hurtado who graduated with a Bachelor of Science degree in organismal biology last December and aspires to earn a doctorate degree in mycology, the study of fungi.

Martin focused his research on the ACVR1 gene that is implicated in the pathogenesis of Fibrodysplasia Ossificans Progressiva (FOP). FOP, more commonly known as stone man disease, is the result of a rare genetic mutation within the ACVR1 gene that causes soft tissues such as muscle, tendons, cartilage, ligaments and connective tissue to undergo abnormal ossification and form more bone tissue. This disease has serious and potentially life-threatening complications, such as the progressive fusion of joints, and seems to occur most often in people from the United States. While there are currently no known treatments, fully understanding FOP is crucial to eventually developing effective treatments.

The research on this genetic mutation is still in the beginning stage, yet the team already effectively cloned the mutant and native forms of the ACVR1 gene into an expression vector, and successfully transfected eukaryotic cell lines. This helps researchers better understand the mutation’s biological implications in the development of life-altering complications and, hopefully, lead them to developing possible preventative measures.

Martin’s experience in the research program provided numerous lessons for him. “It shows that, while things are hard and even complicated, working, enduring and persevering through them allows you to accomplish things beyond what you thought you were capable of,” he reflected. “It’s fun and if you enjoy a challenge, I encourage people to go into it and experience it. It’s nice to have faculty that supports you and that you can work with, because that’s the greatest way to get undergraduate research under your belt.”

This undergraduate research project is also attracting attention from others in the scientific community, including two scientists from the Federal University of Minas Gerais in Belo Horizonte, Brazil: Dr. Helen Lima del Puerto, an expert in SOX research, and Dr. Almir Martins, specializing in ACVR1 research. They are currently collaborating with the program and guiding participants by sharing their experiences with scientific inquiry and research.

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