Ecosystem management via interacting models of political and ecological processes

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Animal Biodiversity and Conservation 27.1 (2004) 231 Ecosystem mana gement via interacting models of political and ecological processes T. C. Haas Haas, T. C., 2004. Ecosystem management via interacting models of political and ecological processes. Animal Biodiversity and Conservation, 27.1: 231–245. Abstract Ecosystem management via interacting models of political and ecological processes.— The decision to implement environmental protection options is a political one. Political realities may cause a country to not heed the most persuasive scientific analysis of an ecosystem’s future health. A predictive understanding of the political processes that result in ecosystem management decisions may help guide ecosystem management policymaking. To this end, this article develops a stochastic, temporal model of how political processes influence and are influenced by ecosystem processes. This model is realized in a system of interacting influence diagrams that model the decision making of a country’s political bodies. These decisions interact with a model of the ecosystem enclosed by the country. As an example, a model for Cheetah (Acinonyx jubatus) management in Kenya is constructed and fitted to decision and ecological data. Key words: Social systems, Ecological systems, Influence diagrams. Resumen Gestión de ecosistemas mediante modelos interactivos de procesos políticos y ecológicos.— La decisión de implementar opciones de protección medioambiental es de carácter político. Las realidades políticas de un país pueden permitir ignorar los análisis científicos más rotundos acerca de la futura salud de un ecosistema. Una comprensión predictiva de los procesos políticos que conducen a la toma de decisiones sobre la gestión de los ecosistemas puede contribuir a orientar las políticas relativas a dichas áreas. Con este objetivo, el presente artículo desarrolla un modelo estocástico temporal acerca de cómo los procesos políticos influyen y son influidos por los procesos de los ecosistemas. Dicho modelo se ha estructurado a partir de un sistema de diagramas de influencia interactivos que configuran la toma de decisiones de las instituciones políticas de un país. Dichas decisiones interactúan con un modelo del ecosistema presente en el país. Así, a modo de ejemplo, se elabora un modelo para la gestión del guepardo (Acinonyx jubatus) en Kenia, ajustándose a los datos ecológicos y de toma de decisiones. Palabras clave: Sistemas sociales, Sistemas ecológicos, Diagramas de influencia. Timothy C. Haas, School of Business Administration, Univ. of Wisconsin at Milwaukee, P. O. Box 742, Milwaukee, WI 53201, U.S.A. ISSN: 1578–665X © 2004 Museu de Ciències Naturals 232 I nt roduc t ion Ultimately, the decision to implement ecosystem protection policies is a political one. Currently, the majority of ecosystem management research is concerned with ecological and/or physical processes. A management option that is suggested by examining the output of these models and/or data analyses may not be implemented unless the option addresses the goals of each involved social group (hereafter, group). For example, Francis & Regier (1995) describe efforts to sustain the Great Lakes ecosystem. These authors identify the following major barriers to the sustainable management of this ecosystem: 1. Social science research and model building is restricted by research funding decisions to "safe" projects – typically the economic benefits of Great Lakes resource utilization. These authors see a strong need for social science research to understand the goals and restrictions that drive the many groups that advise, regulate, pollute, and advocate for the Great Lakes ecosystem. 2. Great Lakes physical and biological science is University department compartmentalized and hence ecosystem models that integrate limnological and terrestrial subsystems are under–developed. 3. Because of (2), science–based management policies are lacking in their reliability and hence are either ignored, corrupted or, at best have limited impact during the political process of negotiating treaties between Canada and the U.S. for the regulation of pollution, fishing, and recreation on the Great Lakes. As a step towards meeting these needs, an Ecosystem Management System (EMS) is described herein that links political processes and goals to ecosystem processes and ecosystem health goals. This system is used to identify first the set of ecosystem management policies that have a realistic chance of being accepted by all involved groups, and then, within this set, those policies that are most beneficial to the ecosystem. Haas (2001) gives one way of defining the main components, workings, and delivery of an EMS. The central component of this EMS is a quantitative, stochastic, and causal model of the ecosystem being managed (hereafter, the EMS model). The other components are (2) links to data streams, (3) freely–available software for performing all ecosystem management computations and displays, and (4) a web–based archive and delivery system for items 1–3. This article focuses on ecosystem management in developing countries. One of the first questions then, is what theoretical framework should be used to model political groups in developing countries? The "new institutionalists" (see Gibson 1999, pp. 9– 14, 163, 169–171; Brewer & De Leon, 1983; Lindblom, 1980) draw on political economy theory to stress the following: (a) decision makers are pursuing their own personal goals, e.g. increasing their influence and protecting their job; and (b) decision makers work to modify institutions to help Haas them achieve these goals. This view of the policy making process is particularly relevant for studying wildlife management in developing countries: as Gibson (1999, pp. 9–10) states "New institutionalists provide tools useful to the study of African wildlife policy by placing individuals, their preferences, and institutions at the center of analysis. They begin with the assumption that individuals are rational, self–interested actors who attempt to secure the outcome they most prefer. Yet, as these actors search for gains in a highly uncertain world, their strategic interactions may generate suboptimal outcomes for society as a whole. Thus, rational individuals can take actions that lead to irrational social outcomes." New institutionalism is not limited to explaining ecosystem management policymaking in developing countries. Healy & Ascher (1995) document the effect that individual actor goal seeking behavior had on how analytical ecosystem health models were used to manage national forests in the United States during the 1970s, ’80s, and ’90s. Another paradigm for political decision making is the descriptive model (see Vertzberger, 1990). This approach emphasizes that humans can only reach decisions based on their internal, perceived models of other actors in the decision making situation. These internal models may in fact be inaccurate portrayals of the capabilities and intentions of these other actors. The ecosystem model component of the EMS described in Haas (2001) can be extended to synthesize these two policy making paradigms. In Haas (2001), the ecosystem model is expressed as an influence diagram (ID) (see the online tutorial, Haas (2003b) for an introduction to IDs). To incorporate the interaction between groups and the ecosystem, a set of IDs are constructed, one for each group, and one for the ecosystem. Then, optimal decisions computed by each of these group IDs through time are allowed to interact with the solution history of the ecosystem ID. The model that emerges from the interactions of the group IDs and the ecosystem ID is called an interacting influence diagrams (IntIDs) model. In this model, each group makes decisions that they perceive will further their individual goals. Each of these groups however, has a perceived, possibly inaccurate internal model of the ecosystem and the other groups. The IntIDs model approach then, synthesizes insights from political economics (groups acting to maximize their own utility functions) and descriptive decision making theory (groups using —possibly— distorted internal models of other groups to reach decisions). By choosing from a pre–determined repertoire of options, each group implements the option that maximizes a multiobjective (multiple goals) utility function. This is accomplished by having each group’s ID contain a decision option node representing the different actions that the group can take. Each group has an overall goal satisfaction node (hereafter, a utility node) that is influenced by the group’s goals. Each group implements an op- 233 Animal Biodiversity and Conservation 27.1 (2004) tion that maximizes the expected value of its utility node. This is called evaluating the ID (see Nilsson & Lauritzen, 2000). A schematic of the architecture of an IntIDs model is given in figure 1. As new types of actions are observed, the fixed repertoire of actions is periodically enlarged and the EMS model is re–fitted to the entire set of actions observations —see the Parameter Estimation with CA section, below. Ecosystem management emerges as each group implements management actions that best satisfy its goals conditional on the actions of the other groups and the ecosystem’s status. Conditional on these implemented management options, the marginal distributions of all ecosystem status variables are updated. By simulating these between–group and group–to–ecosystem interactions many years into the future, predictions of future ecosystem status can be computed. Ecosystem status, state, or health (hereafter status) is a multidimensional concept and has been defined differently depending on those ecosystem characteristics of most interest to the analyst. This article focuses on the status of an endangered species within an ecosystem. One way to quantify this characteristic is with the number of animals of such a species in the ecosystem at a particular spatio–temporal point. Kelly & Durant (2000) identify the cheetah in East Africa as an endangered species. The example given below of cheetah survivability in Kenya contains count variables for cheetah, and cheetah prey (herbivores having a biomass less than 35 kg). Future extensions of this model will have variables for individual cheetah prey herbivores such as Thomson’s gazelle. An output of the EMS model is the probability distribution on each of these counts by region and time point. These distributions are used to compute a typical measure of species survivability: the probability of extinction (POE) defined as the chance of a non–sustainable cheetah count at a specified spatial location and future point in time. The uses and benefits of an EMS that combines both political and ecological processes are: (1) a (possibly empty) set of ecosystem management policies can be found that are both politically acceptable and effective at protecting the ecosystem; (2) the most likely sequence of future management activities can be identified so that more plausible predictions of future ecosystem status can be computed such as extinction probabilities; and (3) international audiences can predict which countries have any chance of reaching ecosystem status goals such as averting the extinction of an endangered species. Because this modeling effort draws on several disciplines, the goals that are driving the model’s development need to be clearly stated. They are (in order of priority): 1. Usability: develop a model that, because of its predictive and construct validity, contributes to the ecosystem management debate by delivering reliable insight into how groups reach ecosystem man- President ID Ecosystem ID Rural residents ID Environmental Protection Agency ID Legislature ID Action Message Bulletin Board Pastoralists ID Ranchers ID Fig. 1. Schematic of the IntIDs model of interacting political and ecological processes. Fig. 1. Diagrama esquemático del modelo de IntIDs (diagramas de influencia interactivos) para los procesos interactivos de carácter ecológico y político. agement decisions, what strategies are effective in influencing these decisions, and how ecosystems respond to management actions. 2. Clarity and accessibility: develop a model that can be exercised and understood by as wide a range of users as possible. Such users will minimally need to be literate and have either direct access to the EMS website or access to a printed copy of the EMS report. For the cheetah viability example below, all groups except rural residents and pastoralists meet these minimum requirements. A major challenge will be to bring the contents of the EMS report to groups that are illiterate and/or lack web access. One idea is to deliver the EMS report to any literate members of such groups, e.g. schoolteachers. There is a tension between predictive and construct validity in that the development of a model rich enough in structure to represent theories of group decision making and ecosystem dynamics can easily become overparameterized which in–turn can reduce its predictive performance. The approach taken here is to develop as simple a model as is faithful to theories of group decision making and ecosystem dynamics —followed by a fit of this model to data so as to maximize its predictive performance. Specifically, success in the model building effort presented herein will be measured along the following two dimensions: (1) the model’s one– Haas 234 step–ahead prediction error rate wherein at every step, the model is refitted with all available data up to that step; and (2) the degree to which the model’s internal structure (variables and inter–variable relationships) agrees with theories of group behavior and ecology/population dynamics theories. The first dimension measures predictive validity and the second, construct validity. One step ahead predictive validity is seen as essential to establishing the reliability of the EMS model. Such predictive validity is however, not without its challenges. For example, it is possible that once an EMS model becomes known to the groups it is modeling, these groups may alter their behavior in response to model predictions. This Heisenberg Principle effect would invalidate model predictions. The influence of groups attempting to game EMS model predictions could be represented in the EMS model by adding another group called "the modelers." This group would post EMS model predictions to the bulletin board at every time step for all other groups to read. Once evidence on how model predictions are gamed is observed, such gaming behavior could be included in the group submodels of the EMS model and one step ahead prediction error rates computed as described in the Results section, below. Of course a second level of gaming is possible wherein groups attempt to manipulate an EMS model that includes groups attempting to game EMS model predictions. This second level of a Heisenberg Principle effect would be difficult to correct for and no solution is offered at this time. This article proceeds as follows. The Materials and methods section gives the architecture of a group ID. The Results section applies this framework to the management of cheetah in Kenya and describes how the model can be statistically fitted to observations on political and ecosystem variables represented in the model. Conclusions are drawn in the Discussion section along with brief comparisons with related efforts. M a t e ria ls a nd m e t hods Group ID architecture Overview A group’s ID is partitioned into subsets of connected nodes called the Situation, and Scenario subIDs (see figure 2 in Haas 2003a, Section 2). The Situation subID is the group’s internal representation of the state of the decision situation and contains Situation state nodes. Conditional on what decision option is chosen, the Scenario subID is the group’s internal representation of what the future situation (the Scenario) will be like after a proposed option is implemented. See Haas (1992, 2003a) for the cognitive theory that supports this decision making model architecture. Actors, actions, and the time node A decision option will hereafter be referred to as an action. Groups interact with each other and the ecosystem by executing actions. The decision making group, referred to as the DM_group receives an input action that is executed by an actor referred to as the input–action–actor group or InAc_group. The subject of this action is the input–action–subject group or InS_group (which may or may not be the DM_group). The DM_group implements an output action whose subject is the target group or T_group. Actions are either verbal (message) or physical events that include all inter– and intra–country interactions. Each ID is a dynamic model and therefore has a deterministic root node Time. Time starts at t0 and increments discretely through t1, ., tT in steps of t. Ecosystem status perceptions nodes Quantities that represent ecosystem status can be input nodes to a group ID. These nodes influence a node that represents how sensitive the group is to the value of the corresponding ecosystem status node. The idea is that a group is affected by the ecosystem but is only conscious of it through filtered, perceptual functions of the underlying ecosystem status nodes. For example, a group ID is sensitive to the presence of a land animal such as the cheetah through the animal’s density (number per hectare). This sensitivity is modeled by having the animal’s density node influence a perceived animal prevalence node that takes on the values none, few, and many. An ecosystem status node is stochastic due to it being a component of a stochastic ecosystem model. Sources of noise within this model include climate, unmodeled or mismodeled ecological functions, and inaccurate specification of model parameters. Even if an ecosystem status node was deterministic, how values on equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30]. The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step [31], raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps [31]) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee [29], or a slightly open conformation as found in Jimenez-Useche et al. [30]. Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to source of circulating FGF-21. The lack of association between circulating and muscle-expressed FGF-21 also suggests that muscle FGF-21 primarily works in a local manner regulating glucose metabolism in the muscle and/or signals to the adipose tissue in close contact to the muscle. Our study has some limitations. The number of subjects is small and some correlations could have been significant with greater statistical power. Another aspect is that protein levels of FGF-21 were not determined in the muscles extracts, consequently we cannot be sure the increase in FGF-21 mRNA is followed by increased protein expression. In conclusion, we show that FGF-21 mRNA is increased in skeletal muscle in HIV patients and that FGF-21 mRNA in muscle correlates to whole-body (primarily reflecting muscle) insulin resistance. These findings add to the evidence that FGF-21 is a myokine and that muscle FGF-21 might primarily work in an autocrine manner. Acknowledgments We thank the subjects for their participation in this study. 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