Ecology and economics in the science of anthropogenic biosphere change

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Ecology and economics in the science of anthropogenic biosphere change

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Charles Perrings1 , Ann Kinzig Arizona State University, Tempe, AZ, United States of America author: e-mail address: [email protected]

1 Corresponding

CONTENTS 1 Introduction ...................................................................................... 2 The Dynamics of Coupled Hierarchical Systems ........................................... 3 Carrying Capacity and Assimilative Capacity ............................................... 4 Resilience and Stability ........................................................................ 5 Biodiversity and the Portfolio of Natural Assets............................................ 6 The Value of Ecosystem Functions ........................................................... 7 Concluding Remarks ............................................................................ References............................................................................................

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1 INTRODUCTION In this paper, we examine the consequences of a forty-year experiment in interdisciplinary collaboration between ecologists and economists. It is an experiment that has brought researchers from both disciplines together to understand anthropogenic impacts on the biosphere. The effort has sometimes been stimulated by economists, and sometimes by ecologists. Along the way it has spawned an array of new journals and professional societies, as well as new teaching and research programs. As an experiment, it has provoked more than its fair share of controversy. While it has united some ecologists and economists in a common cause, it has also led to rifts in each of the constituent disciplines. In what follows we consider the impact of this experiment on the science of anthropogenic biodiversity change. This is not the only long-term effort to realize the potential gains from interdisciplinary collaboration. In announcing a new program of research designed to uncover the ‘rules of life’, the National Science Foundation recently noted that deep integration across disciplines has already put us “on the cusp of solving one of the greatest Handbook of Environmental Economics, Volume 4, ISSN 1574-0099, https://doi.org/10.1016/bs.hesenv.2018.03.002 Copyright © 2018 Elsevier B.V. All rights reserved.

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challenges in understanding the living world – namely, predicting how the set of observable characteristics (phenotype) arises from the genetic makeup of the individual in concert with environmental factors acting at diverse spatial and temporal scales” (National Science Foundation, 2017). Increasing collaboration across the biological sciences, computer and information sciences, engineering, geosciences, mathematical and physical sciences, and social, behavioral, and economic sciences has already deepened understanding of the emergent properties of living systems. Anthropogenic biosphere change expresses the emergent properties of living systems in a world in which environmental factors reflect the effects of economic growth and development, and in turn enable or constrain that growth. The experiment we report has yielded a number of striking benefits for both disciplines. It has strengthened our understanding of the impact of anthropogenic biodiversity change on the dynamics of hierarchical ecological systems. Appreciation of the biological factors determining the carrying and assimilative capacity of the environment has provided a different perspective on the substitutability of produced and natural capital. Research on the linkages between biodiversity and ecosystem function has offered a proper scientific basis for the valuation of non-marketed environmental goods and services. Perhaps most important, work on the resilience of ecological systems has deepened our understanding of the stability and sustainability of economic states and processes. In what follows we take the areas in which the experiment has affected the science of anthropogenic biodiversity change and ask how interdisciplinary collaboration has altered our approach to the problem. We consider the spatial and temporal scale at which the problem is addressed, the level of abstraction sought (the biophysical processes included or excluded from models of the coupled system), the data used to calibrate and validate those models, and the range of interventions considered. We do not offer an exhaustive review of the literature. It spans too many fields, and too wide an array of problems for this to be feasible. Instead we identify cases where the experiment has led to a change in approach that has given real traction on the problem. Einstein famously said that a model should be as simple as possible but no simpler. In some cases, traction has been gained by adding ecological detail – taking account of processes that have traditionally been neglected or treated parametrically in economic models. In other cases it has come by redefining the spatial and temporal scale of the problem. Anthropogenic environmental change involves stocks whose dynamics play out at very different temporal scales. It also involves processes that have effects at very different spatial scales. More importantly, progress has come by developing an understanding of the ways in which the emergent properties of the coupled system reflect interactions between the organisms involved and the environment, broadly interpreted, in which they exist.

2 The Dynamics of Coupled Hierarchical Systems

2 THE DYNAMICS OF COUPLED HIERARCHICAL SYSTEMS One of the earliest impacts of work at the intersection of ecology and economics was the realization that the economy and the natural world co-evolve (Norgaard, 1984). An obvious current example of this is anthropogenic climate change. The short-term climatic impacts of anthropogenic carbon emissions modify the climatic effects of the much longer-term Milankovitch cycles, in turn stimulating change in the economic system. But the same interdependence between economic and natural systems plays out at many different spatial and temporal scales. One of the key lessons from ecology is that the dynamics of natural systems involve a combination of slow large-scale and fast small-scale processes. A good example is Holling’s work on boreal forests (Holling, 1973, 1988), which showed how the dynamics of the system involve cycles ranging from the scale of the leaf over a period of days to the scale of the forest over a period of years. Hierarchical systems of this kind are nested at different spatial and temporal scales. Small fast-moving systems are embedded in and constrained by large slow-moving systems, although there also occur junctures at which smaller systems are able to disrupt larger systems. In ecology, this has prompted analyses that focus on interactions between biotic and abiotic processes at different scales (O’Neill et al., 1989; Levin, 1992; Allen and Starr, 2017). While economists have a concept of stocks that remain invariant over some period of time (the short-run), and have some notion of renewal processes in ‘business’ and ‘product’ cycles, they have not typically modeled economic processes in the same way. But if the economic and ecological components of a coupled system both consist of a structure of subsystems, each operating at distinct spatial and temporal scales, then the analysis of the coupled system needs to address the interactions between them. Gunderson and Holling referred to such a system as a ‘panarchy’, arguing that it should be understood through interactions between cycles at different scales (Gunderson and Holling, 2002). There are now many examples of studies of the dynamic interactions between the economic and ecological components of coupled systems (Brown and Roughgarden, 1995; Finnoff and Tschirhart, 2003; Batabyal, 2005; Eichner and Pethig, 2005; Tilman et al., 2005; Eichner and Tschirhart, 2007). From an economic perspective, what matters is that not all interactions are factored into peoples’ decisions. Consider the role of spatial structure. In ecological systems, a landscape typically contains a number of populations whose interactions determine the dynamics of the general system. Those interactions are constrained by topography, hydrology, vegetation and so on. In managed landscapes, interactions between populations of different species may be further constrained by a structure of barriers in the form of roads, railways, or fences, and by efforts to promote some species and to suppress others. Decisions that alter the dynamics of one species in one time and one place can have unintended consequences for other species at other times and other places. In the Greater Yellowstone Area (GYA) of the USA, for example, interactions among cattle, bison, and elk are dominated by elk migration, which is constrained by landscape structure, habitat connectivity, and land use. Contact between elk, bison, and cattle is a source of concern because it is associated with the transmission

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of brucellosis. The establishment of elk feedgrounds to reduce localized contact between cattle and elk in the short-run has had the perverse effect of increasing elk densities and intra-specific competition for resources, leading to greater dispersal of elk in search of forage, and so more widespread contact between elk and cattle, and the greater spread of brucellosis in the longer-run (National Academies of Sciences Engineering and Medicine, 2017). In economic analyses of renewable natural resource systems, it has become common to model spatial interactions (Sanchirico and Wilen, 1999, 2001; Fenichel et al., 2010; Horan et al., 2011) and spatial externalities (Anselin, 2003) explicitly. The temporal structure of coupled systems is also increasingly recognized to be important. Models of renewable natural resource extraction assume that the dynamics of the social system ‘contain’ the dynamics of the exploited population. In other words, the decision-maker operates on a time scale (over a horizon) that exceeds the renewal period of the exploited population. For infinite-horizon problems this is trivially true. For finite horizon problems, it is a matter of choice. If the renewal period of the resource is greater than the decision-maker’s time horizon the resource is defined to be exhaustible, and its dynamics of little consequence. The insight coming from the study of hierarchical ecological systems is that localized short-term decisions affecting the dynamics of small fast-moving systems may have consequences for the time-behavior of large slow-moving systems. For example, the fast dynamics of many pests and pathogens can have significant consequences for the slower dynamics of human populations. Epidemics that involve the explosive growth of infectious agents may affect the longer-term demographics of the host population. HIV in Africa is a well-known example (Johnson and Dorrington, 2006), but there are numerous other historical examples of human societies where demographics have been transformed by epidemics (Diamond, 1997). The development of the field of economic-epidemiology is stimulated by the insight that human behavioral responses to disease risk may change both the course of the disease and the demographic and socio-economic factors that affect future disease risk (Delfino and Simmons, 2000; Fenichel et al., 2011; Perrings et al., 2014). Within environmental and resource economics more generally, the longer-term consequences of present decisions are frequently modeled as intertemporal externalities. In addition to the long term impacts of current investment decisions on future stocks of natural and produced assets (Solow, 1974; Hartwick, 1977, 1978), environmental and resource economists recognize the need to model the intertemporal externalities of current production and consumption decisions (McKitrick, 2011). This may be driven by concerns over the sustainability of current decisions (Baumgärtner and Quaas, 2010; van den Bergh, 2010), but it also reflects the long-standing recognition that many environmental processes play out on timescales that exceed the time horizon of private decision-makers (John and Pecchenino, 1994; John et al., 1995). What we have learned from the ecology of large-scale systems like boreal forests is that processes involving interactions between cycles of differing periodicity mean that interventions that change one stock affecting one cycle can have future consequences that affect many stocks over many cycles (Ludwig et al., 1978).

3 Carrying Capacity and Assimilative Capacity

What this element of the experiment has done is to push resource economists to move beyond the single stock models that characterized the initial development of bioeconomics as a field. The basic principles involved, elaborated by Colin Clark in the 1970s, remain intact (Clark, 1973, 1976, 1979; Clark et al., 1979). Decision makers are assumed to optimize an objective function that includes the benefits of exploiting some natural resource, subject to the dynamics of that resource. The state variables of the problem are available stocks of assets (including environmental stocks), and the control variables are feasible management actions. It is recognized that feedbacks within the system alter the dynamics of natural stocks, and that this has implications for their management. What has been added to this is the recognition that there are always multiple stocks involved, that feedbacks may induce changes that play out on quite different time scales, and that it is not sufficient to bundle all these effects into single parameters – such as carrying capacity. There is now a substantial body of papers that capture at least some of the complexity of coupled resource systems (see Schlüter et al., 2012 for a review). Examples include Bulte and Damania (2003), Bulte and Horan (2003), Brock and Xepapadeas (2004), Perrings and Walker (2004), Polasky et al. (2004), Polasky et al. (2005), Quaas et al. (2007), Horan et al. (2011, 2017). Most use an extended bio-economic model, in which economic agents optimize an objective function using controls that affect the dynamics of one or more species in a supporting ecosystem. Interactions between the economy and its environment depend on the ‘connectedness’ and so the dynamic structure of the joint system. Components that are unconnected over one time horizon may be highly connected over another, and a level of connectedness that is insignificant at one scale of activity may be highly significant at another (Perrings, 1987).

3 CARRYING CAPACITY AND ASSIMILATIVE CAPACITY A second area where the experiment has changed the science of anthropogenic biosphere change relates to the environmental limits to growth. Since Thomas Malthus first suggested that human populations would always be constrained by productivity growth rates, there has been a succession of attempts by natural scientists to warn of the consequences of exceeding the carrying capacity of the natural environment (Ehrlich and Holdren, 1971; Meadows et al., 1972; Rockström et al., 2009; Ripple et al., 2017). This has also led to the identification of measures of carrying capacity at the largest scale (Rees, 1996; Wackernagel and Rees, 1998). Economists have always reacted instinctively and dismissively to the assumption that the world would stand still while supplies of natural resources run out. However, a better understanding of the ecologists’ concept of assimilative or carrying capacity, and the degree to which environmental constraints are reflected in the decisions of households and firms, has yielded a better understanding of the environmental limits to economic growth.

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In the canonical bioeconomic renewable resource model, the carrying capacity that defines the natural equilibrium in a logistic equation does constrain the growth potential of an economy dependent on that resource. However, it is now recognized that carrying capacity may vary both with the way resources are used (technology) and with environmental conditions. Whether or not carrying capacity is treated as endogenous to the economic problem depends on the time horizon over which decisions are made, and on market conditions and property rights. If the time horizon is long enough, if the environmental constraints within which production decisions are made are reflected in resource prices, and if those constraints are sensitive to technology, then the carrying capacity of the natural system may be a choice variable. The enhancement of carrying capacity has in fact become an objective in environmental problems such as tourism (Brown et al., 1997; Liu and Borthwick, 2011). On the other hand, if environmental constraints are not reflected in resource prices – as when resources are open access – carrying capacity will be treated as exogenous to the problem. Activity levels will then be selected subject to a given carrying capacity. In ecological communities, carrying capacity for particular species is a function of trophic structure. It varies with the conditions that affect the relative abundance of species at different trophic levels. Classically, a Lotka–Volterra system of equations for predator prey interactions implies the dynamic interdependence in carrying capacity for both predator and prey. While biologists have criticized the Lotka– Volterra model for oversimplifying predator–prey interactions, work in statistical physics has shown that when spatial degrees of freedom and stochastic fluctuations are included, the results differ substantially from the deterministic mean-field model. Instead of singularities associated with particular population cycles, they find stable nodes (involving localized clusters of predators and abundant prey) and foci (involving complex spatio-temporal predator–prey patterns that oscillate irregularly in time) (Mobilia et al., 2007). The carrying capacity and stability of exploited predator prey systems remains a very active area of research (Wang et al., 2015; Ganguli et al., 2017). Capture fisheries – which originally motivated Lotka’s work – are good examples of resource systems where much effort has gone into the identification of time-varying limits on harvest, and the design of mechanisms, such as ITQs (individual transferable quotas), to implement those limits (Beddington et al., 2007). Many resource models in which the carrying capacity of the resource would previously have been given as a biological datum now model it as a function both of environmental conditions and policy interventions. The best-known examples relate to the exploitation of shallow lakes that may exist in either a eutrophic or oligotrophic state depending on the level of nutrient loading (Scheffer, 1997; Carpenter et al., 1999; Mäler et al., 2003; Peterson et al., 2003; Janssen et al., 2014), but the approach has been applied to other systems (Stringham et al., 2003; Perrings and Walker, 2004; Quaas et al., 2007). It is easy to see how this is related to the sustainability of activities that exploit the natural environment. Activities that remain within the carrying or assimilative capac-

4 Resilience and Stability

ity of the environment are sustainable. It is also easy to see that the sustainability of activities is always going to depend on context. Activities that are sustainable in one set of environmental, institutional, or technological conditions may not be sustainable under another set of conditions. The sustainability of harvest of a wild species at some level requires that level to be on the sustained yield curve, but the sustained yield curve itself depends on the environmental, institutional, or technological conditions that determine the topological structure of the system. The observation that some ecosystems are characterized by multiple stable states has become an important determinant of both the science and management of such systems (Ives and Carpenter, 2007). While empirical evidence for regime shifts in purely natural ecosystems is limited (Capon et al., 2015), there is substantial evidence for anthropogenically induced regime shifts – involving trophic cascades often at large scales (Österblom et al., 2007; Moellmann et al., 2009).

4 RESILIENCE AND STABILITY The third area in which the experiment has changed both science and management is closely related to the question of carrying capacity. The carrying capacity of any natural resource system is a function of the stable equilibria of that system. Ecologists typically analyze the stability of equilibrium using three different concepts: resistance, persistence, and resilience (Walker and Meyers, 2004). Resistance is a measure of the capacity to resist change, and is therefore a measure of local stability. Persistence, by contrast, is a measure of the capacity of the system in some state to endure, and so is a measure of the global stability of the equilibrium corresponding to that state. The third category, resilience is interpreted in two different ways. The first is a measure of local stability – the speed of return to equilibrium following perturbation (Pimm, 1984). The second is a measure of the size of a disturbance needed to dislodge a system from its stability domain, or the size of the stability domain corresponding to some attractor (Holling, 1973). More generally, it is the conditional probability that a system will flip into another stability domain given (a) its current state and (b) the disturbance regime. For systems that can exist in multiple stable states, the response to perturbations from any equilibrium give a sense of strength of local stability of that equilibrium, but not the capacity of the system to remain in that state. That capacity is given by the size of the perturbation needed to move the system to a different stability domain. Holling resilience is a measure of the shock needed to move the system from one attractor to another. If the system is away from equilibrium, an equivalent measure is the size of disturbance in any direction that is sufficient to dislodge the system to a different stability domain. For a system close to an unstable equilibrium (an unstable manifold of the system) the perturbation needed to dislodge it might be very small. To take the simplest case, consider a critically depensatory growth curve of the kind given in Fig. 1. If the system is at the stable equilibrium such that the population is at carrying capacity, X = K, its resilience with respect to perturbation in X is

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FIGURE 1 System resilience with and without anthropogenic disturbance.

the distance between carrying capacity and the critical minimum population size, M, K − M. If the population is reduced by harvest to that corresponding to the maximum sustainable yield, MSY, its resilience is reduced to MSY − M. In this simplest of cases the stable equilibria of the system are at carrying capacity and the origin – i.e. the death of all individuals in the population. In many other cases the system may exist in multiple stable states, the movement between stable states being a function of environmental conditions. Shallow lakes subject to nutrient loading, for example, are typically observed in one of two states: oligotrophic and eutrophic. Both states are locally stable only, so convergence on a eutrophic state does not preclude reversion to an oligotrophic state. The dynamics involved in the movement between states may, however, be quite complex. Reversion from a eutrophic to an oligotrophic state may, for example, be subject to hysteresis. That is, it may require nutrient loadings far below the level that induced the flip in the first place (Carpenter et al., 1999). And if the system includes negative feedbacks (e.g. the growth of vegetation in the eutrophic state reduces phosphorous loading), the same general phenomenon can lead to fast-slow cycling between states (Fig. 2). There are by now a large number of case studies using this approach (Walker et al., 1999, Perrings and Stern, 2000; van de Koppel and Rietkerk, 2004; Walker et al., 2004; Adger et al., 2005; Hughes et al., 2007). The focus of most such studies is the resilience of the system in particular states – whether desirable or not. From an economic perspective this maps into traditional concerns about the existence and stability of system equilibria. However, by treating resilience as endogenous it adds an extra dimension: the challenge of managing the local and global stability of systems, and adaptively responding to loss of local stability. Since the resilience of systems can be enhanced or eroded, systems can be engineered to absorb larger shocks without changing in fundamental ways, or can be made vulnerable to increasingly minor perturbations. A system that is destined to exist in a number of states can be made resilient across states by ensuring that it retains the components needed for renewal

4 Resilience and Stability

FIGURE 2 Negative feedbacks can induce cycling. (A) If vegetation has no negative effect on the phosphorus content in the lake, the system has alternative equilibria. (B) If vegetation has a negative effect and if the nutrient nullcline intersects the unstable part of the catastrophe fold, and the nutrient equilibrium sets slowly, there can be slow–fast cycles. (C) The system has alternative equilibria over a range of phosphorus loadings (F1 and F2 are fold bifurcations). (D) The system has cycles over a range of phosphorus loadings (H1 and H2 are Hopf bifurcations). (E) The effects of increasing and decreasing P loading on a system with alternative states. (F) The effect of P loading on a cyclic system. Source: van Nes, E.H., Rip, W.J., Scheffer, M., 2007, A theory for cyclic shifts between alternative states in shallow lakes, Ecosystems 10, 17.

and reorganization (Gunderson and Holling, 2002). The work on shallow lakes, for example, has highlighted the importance of the interactive effects of processes that work at different spatial and temporal scales. It is now understood that system stability and sustainability depends most heavily on the slowly changing variables of the

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system: i.e. state variables with slow turnover rates or stochastic processes with long return times (Scheffer, 1997; Mäler et al., 2003). An important implication of this approach is that system stability depends on particular sets of stocks – the infrastructure of the system. From an ecological perspective, these stocks (the ‘slow variables’ of the system) would include, for example, reservoirs of soil nutrients or the variety of genotypes and species (Folke et al., 2004; Folke, 2006). Most aggregate economic models capture such stocks only indirectly in total factor productivity – the part of output not explained by the factors explicitly listed in the aggregate production function. Economists have known for some time that summarizing all of the effects of institutions, markets, and the biophysical and social environment in total factor productivity is an unsatisfactory way to deal with a wide range of things, each of which may affect system performance in different ways (Prescott, 1998). If total factor productivity is constant it is understood that the rate at which wellbeing changes is determined by the rate at which each of the capital stocks (evaluated at its shadow price) changes. It is also understood that an economic program is sustainable if and only if aggregate net investment is positive, and aggregate wealth is non-declining. Moreover, wellbeing can increase over time if and only if the Lindahl criterion is satisfied, i.e. if consumption is less than the difference between output and depreciation of assets (Dasgupta and Mäler, 2000; Dasgupta, 2001). We return to the implications of this for wealth accounting later. Here we note only that if total factor productivity is not constant, as would be expected in an evolving system, then the sustainability of an economic program, and the resilience of the system, both imply conditions on the stocks embedded in total factor productivity.

5 BIODIVERSITY AND THE PORTFOLIO OF NATURAL ASSETS A fourth area in which deep interdisciplinary integration has altered the science of anthropogenic biosphere change relates to the way that biodiversity is conceptualized. Ecologists typically use four main measures of biodiversity. The first, alpha diversity, is a measure of the taxonomic diversity (the species richness) within a particular community or ecosystem. The most common indices of alpha diversity are due to Shannon (1948) and Simpson (1949), both of which measure a combination of the number of species present, and the abundance of each species. Other measures of alpha diversity exist, but like these focus on some combination of richness and abundance (Magurran, 2004). The second, beta diversity, is a measure of the difference in species diversity between ecosystems or along environmental gradients. More particularly, it measures the number of taxa that are unique to each of the ecosystems being compared. For example, if there are two ecosystems, the index of beta diversity developed by Sørensen (1948) takes the form: β = 2c/ (s1 + s2 ), where si is species richness in the ith community and c is the number of species common to both communities. Like the Simpson’s index, it takes a value of 0 when there is no species overlap between the communities, and a value of 1 when exactly the same species

5 Biodiversity and the Portfolio of Natural Assets

are found in both communities. The third, gamma diversity, is a measure of taxonomic diversity across all systems being evaluated. For the case of two systems it is γ = s1 + s2 − c, i.e. a count of the number of distinct species across all systems (Whittaker, 1972). The fourth, omega or phylogenetic diversity, is a measure of the taxonomic difference between species. A number of indices have been proposed for phylogenetic diversity (Schweiger et al., 2008), most falling into one of two classes: one using a minimum spanning path approach, the other using a pairwise distance approach. The pairwise distance approach most  familiar to economists is due to Solow et al. (1993) and Weitzman (1992), DD = i di min where di min is the nearest neighbor distance of species i to all other species. All indices explicitly or implicitly weight species in some way. The measure of gamma diversity given above implicitly weights all species at unity. Every species counts as much as every other species. Measures of alpha diversity weight each species by relative abundance, while measures of omega diversity weight species by their phylogenetic distance from other species. Where collaboration across disciplines has the largest impact on both science and management is in understanding the weights attaching to distinct species. Consider the gamma diversity of a region comprising both managed (agricultural) and wild (protected) land. If all species are weighted at unity, the gamma diversity is simply the union of the set of taxonomically distinct species in each land type. But land managers in each area might be expected to have very different perspectives. In both cases, the desired gamma diversity of the region would be the sum of taxonomically distinct species weighted by the value attached to each. Agricultural land managers would positively weight crops and other species supporting crop production. They would negatively weight crop competitors, pests, and pathogens. In the same way protected area managers would positively weight protected wild species, and would negatively weight competitors, pests, and pathogens including introduced species. One of the most significant results of the experiment is the deepening of the understanding of weighting systems across both disciplines (Perrings, 2014). Within ecology, the most telling evidence for this is the findings of the Millennium Ecosystem Assessment (MA). The MA analyzed biodiversity change in terms of the effects it had on four types of ecosystem service: provisioning services, cultural services, regulating services and supporting services (Millennium Ecosystem Assessment, 2005). The provisioning services correspond to the ‘goods’ in ‘goods and services’ obtained from renewable natural resource systems. They comprise foods, fuels, fibers, freshwater, pharmaceutical products and the like. The cultural services correspond to the ‘services’ element. They comprise the non-consumptive benefits yielded by ecosystems, including recreation, tourism, as well as the amenity, aesthetic, religious, spiritual and totemic value of systems. The remaining services identify the ecological functions that underpin and regulate the production of goods and services. They include processes such as photosynthesis, nutrient cycling and soil formation, and the processes that regulate air and water quality, soil erosion, disease transmission or natural hazards. The notion that biodiversity change might be analyzed in terms of the impact it has on the production of goods and services marked a

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sea change in ecology. While it created tensions within the discipline, it also fundamentally altered the way ecologists think about biodiversity and the role it plays in ecosystems. Increasingly, biodiversity change was analyzed less in terms of species richness and the taxonomic distinctness of species, and more in terms of ecosystem process and function. The ecological literature on the relationship between biodiversity, traits, and ecological functioning has led to the evolution of more appropriate and applicable measures of biodiversity than traditionally employed, focusing on functional diversity (Loreau et al., 2002; Díaz and Cabido, 2001; Naeem, 2002; Petchey and Gaston, 2002; Petchey et al., 2009). The literature has also explored the mechanisms involved in the relationship between biodiversity and stability (Naeem et al., 2009). It has, for example, been shown that the taxonomic differences between species is less relevant to the functioning of ecosystems than their functional traits (Solan et al., 2004; Bunker et al., 2005; McIntyre et al., 2007; Bracken et al., 2008). The biodiversity needed to assure the supply of freshwater, for example, differs from the biodiversity needed to assure the supply of timber, but in both cases is determined by the traits associated with those two functions. The main implication of this is that how much diversity is needed within functional groups depends primarily on the range of environmental conditions expected to occur. The greater the expected variation in environmental conditions, the greater will be the required diversity in functional groups (Elmqvist et al., 2003). Within economics, this has direct parallels in the risk-spreading function of asset portfolios. Called the insurance effect in ecology (Loreau et al., 2003), it postulates that if environmental conditions vary, some species in a community may be expected to perform well when others are performing badly. It has been shown that the diversity of functional groups reduces variability in system functioning (Tilman et al., 2001, 2005; Griffin et al., 2009) through the niche differentiation effect (Tilman et al., 1996). In particular, niche differentiation should lead to the emergence of species specialized in terms of environmental as well as geophysical conditions. Portfolio effects depend on the correlation between responses to some environmental change. So in the case of the gamma diversity described above, if a functional group consisted of just two species, s1 and s2 , associated with yields y1 = y1 (s1 ) and y2 = y2 (s2 ), then the expected yield of the portfolio would be: E (y) = i ρi E (yi ), i = 1, 2 in which ρi is the share of total biomass accounted for by the ith species. The variance in yield would be: σp2 = ρ12 σ12 + ρ22 σ22 + 2ρ12 ρ22 σ1 σ2 r12 in which σi is the standard deviation of the yield associated with si , and rij is the correlation coefficient between yields from species si and sj . If the correlation coefficient is positive, both species respond to environmental perturbations in similar ways. If the correlation coefficient is negative, the species respond in opposite ways (see, for example, Doak et al., 1998; Tilman et al., 1998; Lhomme and Winkel, 2002). Negative correlation coefficients unambiguously enhance stability, but the portfolio effect can operate even if the responses to perturbations of different species are positively correlated. Yachi and Loreau, for example, showed that unless species’ responses to environmental perturbations were perfectly correlated (rij = 1) increasing the number of species in

6 The Value of Ecosystem Functions

a system would at once increase average productivity and reduce temporal variance in productivity (Yachi and Loreau, 1999). The portfolio effect in ecological systems is closely related to the notion of redundancy: that species may be functionally redundant (their deletion would have little effect on ecosystem functioning) in some conditions. It implies that the contribution of individual species to ecosystem functioning is dependent on both environmental conditions and the degree to which species are substitutes or complements in the performance of some function. If there is some functional overlap between species as a result of fluctuating environmental conditions, the redundancy of particular species in particular environmental conditions (Naeem, 1998; Walker et al., 1999; Wohl et al., 2004) is evidence of a classic portfolio effect. While the treatment of biodiversity as a portfolio choice problem is motivated by reference to ideas of functional redundancy and insurance in ecology, it applies wherever the value of species varies with environmental conditions. This may occur because different species perform more or less effectively in different conditions – i.e. where value derives from function. But it may also occur because different species are more or less vulnerable in different conditions – i.e. where value derives from survival probability. Regardless of the source of value at risk, a major benefit of the experiment is that the power of the theory of portfolio choice, developed in financial economics, can be brought to the task of managing biodiversity change (Perrings, 2014). The approach is now common in agroecosystems where crop mixes are selected to balance risks (Di Falco and Chavas, 2007), but is less common in wild lands. Many protected wild lands are organized around the conservation of charismatic species or landscapes, with relatively little consideration being given to the balance between species. The set of all protected areas does, however, look much more like a portfolio choice problem.

6 THE VALUE OF ECOSYSTEM FUNCTIONS The final area in which the long experiment has changed the nature of both science and management is closely related to the weighting problem. Deep interdisciplinary integration has had a profound effect on the valuation of ecosystem services, and hence the valuation of environmental changes that alter the flow of ecosystem services. Building on the efforts of market researchers to uncover willingness to pay for new products, environmental economists have developed increasingly refined methods to estimate willingness to pay for non-marketed environmental resources. A wide range of methods now exists to measure preferences over non-marketed biotic and abiotic resources. As long as respondents have full information about the benefits they obtain from the resource, these methods yield reasonable estimates of willingness to pay to acquire (or willingness to accept compensation for the loss of) the resource. The information requirement does, however, limit the range of resources to which these methods can be applied.

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What collaboration across the disciplines has done is to enable the same methods to be used to derive demand for components of the biosphere that are unknown by (and unknowable to) respondents. Scientific understanding of the ways in which the processes of the natural environment support the production of ecosystem services makes it possible to derive demand for those processes. Prior to the MA, a number of studies had drawn attention to the relation between the benefits people derive from nature and broader environmental changes in terrestrial (Daily, 1997; Daily et al., 1997), marine (Duarte, 2000) and agricultural ecosystems (Björklund et al., 1999). There were also attempts to generate broad-brush estimates of the value of such changes (Costanza et al., 1997; Bolund and Hunhammar, 1999; Norberg, 1999; Woodward and Wui, 2001). After the MA the focus of research on the value of ecosystem change has been on the relationship between the goods and services that people consume directly, and the ecological functions and processes that underpin those goods and services. In the case of both provisioning and cultural services the approach is relatively straightforward, involving the specification of ecological production functions that connect the biophysical environment to the production of goods and services. From an economic perspective the core methodology involved follows Mäler (1974), a contribution as fundamental, in its way, as Hotelling (1931) or Gordon (1954). Mäler established the axiomatic foundations for all revealed preference approaches to the valuation of ecosystem services, and demonstrated the conditions in which ecosystems derive value from their role in supporting a stream of services as well as the conditions in which ecosystem services would be zero-valued. Nice applications include Allen and Loomis (2006), which addresses the problem of species that support some totemic species valued for cultural reasons. The value of species at lower trophic levels is derived from the results of surveys of willingness to pay for the conservation of species at higher trophic levels. The prey species in this case are ‘intermediate inputs’ in the production of the valued predator species. In addition to understanding whether environmental assets are complements or substitutes, the approach has allowed identification of ‘critical points’ in the supply of individual services – frequently thresholds, or boundaries between different potential states of the system. This has provided a route to estimating the value of the buffering functions of nature, the regulating services. Within the MA, the regulating services were defined to include: air quality regulation; climate regulation at multiple scales (changes in land cover affect both temperature and precipitation at a local scale, while changes in carbon sequestration or greenhouse gas emissions have significant effects at a global scale); regulation of hydrological flows including runoff, flooding, and aquifer recharge through changes in land cover; erosion control; water purification and waste treatment services including the capacity to assimilate and detoxify soil and subsoil compounds; disease regulation; pest regulation; and natural hazard regulation (covering a wide range of buffering functions, particularly in coastal ecosystems where mangroves and coral reefs can reduce the damage caused by hurricanes and storm surges) (Millennium Ecosystem Assessment, 2005).

6 The Value of Ecosystem Functions

The common feature of the regulating services is that they affect the variability in the supply of provisioning or cultural services, either by changing the consequence of environmental variability (including extreme events), or by changing the level of environmental variability. That is, the regulating services affect the variance and higher moments of the distribution of provisioning and cultural services. The approach is well illustrated by Brock and Xepapadeas (2003), which relates the degree of functional diversity in an ecosystem to the capacity of that system to deliver services over a range of environmental conditions. For example, the loss of functional diversity among pest predators reduces the effectiveness of pest predation, and so reduces the value of the ecosystem. Other good illustrations of the approach include efforts to estimate the role of mangroves in buffering coastal storm damage (Barbier, 2007, 2008). Barbier estimates the willingness to pay for the effect of a change in wetland area on expected damages from coastal storm events. That is, the value of a change in ecosystem state that reduces risk (the probability and severity of damage) is measured by the reduction in that risk. There is a clear connection between the valuation of regulating services and the resilience and stability of coupled systems (Scheffer et al., 2000; Walker et al., 2004; Kinzig et al., 2006; Scheffer et al., 2009). Integration of ecological models of the linkages between biodiversity and the security of ecosystem services (Reich et al., 2004; Hooper et al., 2005; Cardinale et al., 2012) and economic models of the linkages between biodiversity and risk (Di Falco and Chavas, 2007; Baumgärtner and Strunz, 2014) has the potential to signal the importance of stocks that affect future capacity to negotiate environmental fluctuations. While most ecological work on climate change focuses on the threat it poses to biodiversity, there is increasing interest in the role of biodiversity in supporting adaptation to a more variable climate (Thompson et al., 2009; Mercer and Perales, 2010; Frison et al., 2011; Dempewolf et al., 2014; Khoury et al., 2014). There is also a clear connection between the value of the MA regulating services and the value of biodiversity as a portfolio of natural assets. As in any portfolio, the combination of species that is ‘best’ depends on how decision-makers balance certain against uncertain gains. The value of the biodiversity portfolio then depends on the covariances in the responses of different species to changes in environmental conditions. We have already noted that economists have established the conditions on asset values for wellbeing to be non-declining. These build on conditions established for the sustainable exploitation of exhaustible natural resources by Hotelling (1931), Solow (1974), and Hartwick (1977, 1978, 1990). Significant progress has been made in the development of wealth accounts that test for these conditions in real economies. The most effective measure to date is the net change in the value of a country’s capital stocks, where that includes produced, human and at least some stocks of natural capital measure of change in wealth (adjusted net savings) (Pearce and Atkinson, 1993; Pearce et al., 1996; Hamilton and Clemens, 1999; Ferreira et al., 2008). A necessary and sufficient condition for wealth to be increasing over time is that adjusted net savings be positive (Hamilton and Hartwick, 2005; World Bank, 2011; Arrow et al., 2012; UNU-IHDP and UNEP, 2014). Other measures are in development (Fenichel

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et al., 2016). All show that social well-being increases over an interval if and only if net investment in an appropriate measure of inclusive wealth is positive. As of now this does not include the value of the biodiversity portfolio, which remains buried in residuals such as total factor productivity or the World Bank’s ‘intangible capital’. What can be done, however, is to keep some track of the performance of the portfolio in terms of the covariances in responses to common environmental shocks.

7 CONCLUDING REMARKS The most profound effect of deep interdisciplinary integration between economics and ecology has been the widespread adoption of the ecological concept of resilience (sensu Holling) in both the science and management of biosphere change. By focusing on the capacity of coupled systems to continue to function over an evolving range of environmental conditions, it provides both a foundation for sustainability science and a way to test the environmental consequences of demographic, technological, institutional and economic changes in human societies. An understanding of the capacity of systems subject to perturbation to return to an original state within an economically meaningful time frame provides a test of sustainability and the irreversibility of change. A loss of resilience to a system in some state implies a change in the range of socio-economic or environmental conditions over which the system can maintain the flow of services. It is economically interesting if the value of the system varies across states. An economic program is not sustainable if it is not resilient. It is not resilient if it induces the economy to flip from a desirable to an undesirable state, and if that change is either irreversible or only slowly reversible – noting that many examples of irreversible changes cited in the literature are not irreversible in any strict sense, but denote variables that are slow relative to the time horizon of the decision-maker (Pindyck, 2000; Perrings, 2006; Perrings and Brock, 2009). The challenge this poses for management is that there may be few signals of impending changes in the state of coupled systems. The dynamics of the system may be revealed only through the response of the state variables to the controls. Moreover, the closer a system is to the boundaries of the stability domain, the greater the risk that shock will result in irreversible or only slowly reversible loss. This complicates the use of economic instruments to protect against changes of state, or to induce restoration of an initial state (Brock et al., 2002). There is certainly considerable interest in the identification of leading indicators of impending state changes. It is argued that the dynamics of systems approaching state changes have generic properties. Critical thresholds correspond to bifurcations, beyond which positive feedbacks push the system into contrasting state. There are number of potential precursors to such bifurcations in both model and real systems (Scheffer et al., 2009). The most reliable advance warning of regime shifts lies in the high-frequency signal in the spectral density of a time-series (Contamin and Ellison, 2009). Candidates include rising variance in state variables (Carpenter and Brock, 2006), slowing rates of return (Carpenter et al., 2011), a combination of spatial variance and spatial skewness (Gut-

References

tal and Jayaprakash, 2009), and flickering – where the system enters a bistable region between two attractors before a bifurcation (Carpenter et al., 2008). There are, however, many systems in which there are no advance indicators of impending change (Hastings and Wysham, 2010). There are also systems in which advance indicators give insufficient time to avert impending change. A study of advance signals of impending change in lake systems, for example, found that when the monitored variable was directly and causally linked to the regime shift, detection was quick enough to avert the change. But when it was only indirectly linked to the regime shift, detection came too late to head off the change (Carpenter et al., 2014). For regime shifts that are driven by human behavior, this is a very interesting finding. If variation in the spectral density of a price series signals impending changes in behavior, price interventions may head off the change. If variation in the spectral density of a price series reflects changes in behavior driven by other factors the information it gives may be too late. We do not yet have good advance indicators of impending changes in the stability of many managed or impacted ecosystems. Deep interdisciplinary integration across the ecological and economic sciences has, however, provided an understanding both of the stability properties of such systems, and the potential drivers of change. It has therefore given us places to look for advance indicators of impending change – both in the natural environment and in the human behaviors that are implicated in anthropogenic environmental change. More importantly, it has given us criteria to evaluate both the predictive power of those indicators, and the scope they offer for informing timely action.

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