Static modeling of dynamic recreation behavior: Implications for prediction and welfare estimation

This paper examines the consequences of using a static model of recreation trip-taking behavior when the underlying decision problem is dynamic. Specifically we examine the implications for trip forecasting and welfare estimation using a panel dataset of Lake Michigan salmon anglers for the 1996 and 1997 fishing seasons. We derive and estimate both a structural dynamic model using Bellman's equation, and a reduced-form static model with trip probability expressions mimicking those of the dynamic model. We illustrate an inherent identification problem in the reduced-form model that creates biased welfare estimates, and we discuss the general implications of this for the interpretation of preference parameters in static models. We then use both models to simulate trip taking behavior and show that although their in-sample trip forecasts are similar, their welfare estimates and out-of-sample forecasts are quite different.     

Congestion and participation in outdoor recreation: A household production function approach

This paper examines the effects of congestion on recreational behavior within a household production model of consumer behavior. We assume that congestion affects the household's ability to produce constant quality recreational service flows and derive a reduced-form model for participation decisions in remote and developed camping. Empirical estimates of the effects of a congestion measure on the conditional probability of participation as well as on the level of participation are estimated for each activity by the type of trip using information from the 1972 National Recreation Survey. The findings suggest that congestion was most likely to affect the decision to participate and not the level once that decision had been made. While differences in these effects were observed across the activities studied, it is not clear how they should be interpreted since our congestion measure was a proxy variable likely to perform better for remote camping than developed camping.     

Designing Transactions without Framing Effects in Iterative Question Formats

Several studies document that iterative question formats used in contingent valuation studies produce anomalies in respondent behavior that appear to threaten the validity of welfare estimates. By decomposing iterative question formats into their ascending and descending sequences, we show that these anomalies occur only in ascending sequences. We describe the conditions under which comparable patterns of behavior are likely to be found in other iterative question formats that begin with a discrete willingness-to-pay question. We then develop a unified explanation of these anomalies using our model of framing based on prospect theory. We provide the first head-to-head test of rival explanations of these behavioral patterns by developing refutable hypotheses for the strategic behavior, yea-saying, anchoring, and cost-expectations models. Finally, based on our own statistically robust model, we show how these anomalies can be eliminated without loss of the statistical efficiency of most iterative question formats.     

Dynamic adjustment to land taxation policy: Neutrality and welfare implications

In this paper we use a dynamic three sector model to examine the neutrality and welfare effects of land income taxes. We find that (1) taxes that are neutral in long run equilibrium need not be neutral in the short run; (2) short run neutrality depends upon the tax treatment of development costs and losses; and (3) many of the neutrality results hold under both static and rational expectations assumptions. We also find that, even without externality assumptions, nonneutrality in the short run does not necessarily entail a welfare cost and may be welfare-enhancing when agents have less-than-perfect foresight.     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

Use of ambiguous detections to improve estimates from species distribution models

As large carnivores recover throughout Europe, their distribution needs to be studied to determine their conservation status and assess the potential for human-carnivore conflicts. However, efficient monitoring of many large carnivore species is challenging due to their rarity, elusive behavior, and large home ranges. Their monitoring can include opportunistic sightings from citizens in addition to designed surveys. Two types of detection errors may occur in such monitoring schemes: false negatives and false positives. False-negative detections can be accounted for in species distribution models (SDMs) that deal with imperfect detection. False-positive detections, due to species misidentification, have rarely been accounted for in SDMs. Generally, researchers use ad hoc data-filtering methods to discard ambiguous observations prior to analysis. These practices may discard valuable ecological information on the distribution of a species. We investigated the costs and benefits of including data types that may include false positives rather than discarding them for SDMs of large carnivores. We used a dynamic occupancy model that simultaneously accounts for false negatives and positives to jointly analyze data that included both unambiguous detections and ambiguous detections. We used simulations to compare the performances of our model with a model fitted on unambiguous data only. We tested the 2 models in 4 scenarios in which parameters that control false-positive detections and true detections varied. We applied our model to data from the monitoring of the Eurasian lynx (Lynx lynx) in the European Alps. The addition of ambiguous detections increased the precision of parameter estimates. For the Eurasian lynx, incorporating ambiguous detections produced more precise estimates of the ecological parameters and revealed additional occupied sites in areas where the species is likely expanding. Overall, we found that ambiguous data should be considered when studying the distribution of large carnivores through the use of dynamic occupancy models that account for misidentification.     

Stochastic bycatch, informational asymmetry, and discarding

The existing literature (i) examines bycatch and discard behavior in a static framework and (ii) treats bycatch as a deterministic process uniform across vessels. Using a dynamic representative agent model in a two-stock resource, this paper explores strategic interactions between a social planner and two groups of harvesters, one of which imposes a stochastic “technological externality” (bycatch) on the other. In addition to limitations on entry and the number of trips taken in each industry, three bycatch control instruments are compared to the unconstrained case: taxes, trip limits, and value-based quotas. Implementation and enforcement costs aside, taxes dominate both types of quota, and value limits outperform trip limits by eliminating one type of discarding. In simulations, relative performance depends upon variance in the bycatch process, differences in the ex vessel prices of stocks, relative efficiency of the harvester types, and fixed costs on the trip and industry margins.     

Negotiating a voluntary agreement when firms self-regulate

Does self-regulation improve social welfare? We develop a policy game featuring a regulator and a firm that can unilaterally commit to better environmental or social behavior in order to preempt future public policy efforts. We show that the answer depends on the set of policy instruments available to the regulator. Self-regulation improves welfare if the regulator can only use mandatory regulation, but it reduces welfare when the regulator opts for a voluntary agreement. This suggests that self-regulation and voluntary agreements are not good complements from a welfare point of view. We derive policy implications, and extend the basic model in several dimensions.     

Optimal pollution permit endowments in markets with endogenous emissions

In a permit market with endogenous emissions, both firms and citizens purchase permits. Presented here are static and dynamic models of pollution permit markets with endogenous emissions. The optimal permit endowments are characterized when the regulator faces uncertainty about damages and uncertainty about the severity of the citizens’ collective action problem. Due to the possibility of learning over time, the regulator issues a larger number of permits in the first period of the dynamic model than in the static model.     

Identifying key species in ecosystems with stochastic sensitivity analysis

The development of approaches to estimate the vulnerability of biological communities and ecosystems to extirpations and reductions of species is a central challenge of conservation biology. One key aim of this challenge is to develop quantitative approaches to estimate and rank interaction strengths and keystoneness of species and functional groups, i.e. to quantify the relative importance of species. Network analysis can be a powerful tool for this because certain structural aspects of ecological networks are good indicators of the mechanisms that maintain co-evolved, biotic interactions. A static view of ecological networks would lead us to focus research on highly-central species in food webs (topological key players in ecosystems). There are a variety of centrality indices, developed for several types of ecological networks (e.g. for weighted and un-weighted webs). However, truly understanding extinction and its community-wide effects requires the use of dynamic models. Deterministic dynamic models are feasible when population sizes are sufficiently large to minimize noise in the overall system. In models with small population sizes, stochasticity can be modelled explicitly. We present a stochastic simulation-based ecosystem model for identification of “dynamic key species” in situations where stochastic models are appropriate. To demonstrate this approach, we simulated ecosystem dynamics and performed sensitivity analysis using data from the Prince William Sound, Alaska ecosystem model. We then compare these results to those of purely topological analyses and deterministic dynamic (Ecosim) studies. We present the relationships between various topological and dynamic indices and discuss their biological relevance. The trophic group with the largest effect on others is nearshore demersals, the species mostly sensitive to others is halibut, and the group of both considerable effect on and sensitivity to others is juvenile herring. The most important trophic groups in our dynamical simulations appear to have intermediate trophic levels.     

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.     

Imperfectly optimal animals

Summary We consider models of behavior that apply to two different problems: when a predator should leave a foraging site and how a female should choose the best available male. In each case we derive rules for an optimal solution to the problem. We also derive models based on very simple, plausible rules of behavior that we suspect animals may actually use. Although the expected payoffs from optimality models always exceed the expected payoffs from our simpler behavioral models, under certain conditions the difference is not large. When good foraging sites last but a short time and when females' mobility in their habitat is limited, the results of simple models and optimal models are very close indeed.Because of the difficulty of distinguishing between the results of each type of model and because natural selection will presumably provide a best mix of solutions to a range of problems rather than a best solution to any one problem, we suggest that behavioral ecologists expend more effort on simple, plausible models of animal behavior. Such models provide ready-made testable hypotheses about the animal's approximation to optimality and about the actual mechanisms of behavior.     

Willingness to Pay for Quality Improvements: Should Revealed and Stated Preference Data Be Combined?

In this article we propose theoretically consistent welfare measurement of use and nonuse values for an improvement in environmental quality with revealed and stated preference data. An analytical model based on the comparative static analysis of the variation function that describes the relationship between recreation demand and dichotomous choice contingent valuation models is estimated. Our results show that revealed and stated data should not be combined under the same assumed preference structure unless the two decisions imply the same change in behavior induced by the quality change. In addition, our results indicate scope effects in willingness to pay measures estimated with stated preference data.     

A Bayesian state-space formulation of dynamic occupancy models

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.     

Modelling of matter cycle in a mesotrophic bog ecosystem II. Dynamic model and ecological succession

Based upon the the results of static environ analysis of the organic matter cycle through the ecosystem, a number of dynamic models are developed, in this second part, for the matter and nitrogen cycles. Probable outcomes of ecological succession are obtained within the framework of the dynamic model without using the ergodic hypothesis that is implicitly adopted in ecological studies. The dynamics of both stocks in the ecosytem components and flows among them can be observed within the model in order to compare some turnover characteristics of the succession series with those of the ecological one.     

Choosing a Management Strategy: Two Structured Decision-Making Methods for Evaluating the Predictions of Stochastic Simulation Models

We describe two structured decision-making methods—one using a hierarchy of goals and a second using ranking on the sum of weighted criteria—that may be useful for many practical conservation problems, particularly when advisory groups evaluate the output of simulation models. We illustrate both methods by applying them to the problem of choosing a management strategy to address the "mobbing" problem in endangered Hawaiian monk seals. Both methods require estimates of the probabilities of various outcomes, such as a population size of more than 400 seals after 20 years under a specific management regime. We used a simulation model of a small monk seal population to generate these probabilities. Both methods provide an explicit, well-documented, and reproducible decision process that helps justify the decision. Furthermore, they are easy for those untrained in decision analysis to understand and use, they focus discussion on management objectives, they facilitate an examination of trade-offs in the light of multiple and sometimes conflicting objectives, they are suitable for use in workshops, and, at least in our example, they lead to management recommendations that are not highly sensitive to minor changes in probability estimates or other factors.     

Emissions cap or emissions tax? A multi-sector business cycle analysis

We develop a multi-sector business cycle model to analyze stochastic implications of reducing CO₂ emissions with carbon permits or with carbon taxes in the presence of multiple sources of macroeconomic uncertainty. The model is calibrated to reflect the U.S. experience. As in previous studies, using a single-sector version of our model, we find that the cap regime generates lower volatility of real variables than the tax regime, but the latter may be preferable from the welfare perspective. Still, our multi-sector analysis points to the importance of the origin of the shocks in the ranking of the two instruments and to the desirability of going beyond a single-sector analysis in evaluating their merits. We find no significant difference between the cap and the tax regimes when shocks come from non-energy sectors. In contrast, the cap has lower volatility but higher welfare costs than the tax for the shocks to energy production.     

Reprint of: Linking conservation and welfare: A theoretical model with application to Nepal

Theoretical articles linking conservation and welfare find a negative relationship between these two variables while empirical studies show that land protection may be positively related to welfare. Several authors attribute this empirical result to the development of ecotourism in protected areas. We thus argue that the gap between the theory and existing empirical results is partly explained by the fact that most theoretical models do not account for a productive activity on protected land. Therefore, we develop a theoretical model in which conservation allows developing an alternative sector and show that the relationship between conservation and welfare is U-shaped. We test this theoretical prediction using Nepalese data and find that conservation combined with ecotourism is indeed positively related to local welfare.     

Comparing model averaging with other model selection strategies for benchmark dose estimation

Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further, when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides an alternative method for risk managers to estimate risk while incorporating model uncertainty.