The regulation of a spatially heterogeneous externality: Tradable groundwater permits to protect streams

Groundwater pumping can reduce the flow of surface water in nearby streams. In the United States, recent awareness of this externality has led to intra- and inter-state conflict and rapidly-changing water management policies and institutions. Although the marginal damage of groundwater use on stream flows depends crucially on the location of pumping relative to streams, current regulations are generally uniform over space. We use a population data set of irrigation wells in the Nebraska portion of the Republican River Basin to analyze whether adopting spatially differentiated groundwater pumping regulations leads to significant reductions in farmer abatement costs and costs from damage to streams. We find that regulators can generate most of the potential savings in total social costs without accounting for spatial heterogeneity. However, if regulators need to increase the protection of streams significantly from current levels, spatially differentiated policies will yield sizable cost savings.     

Estimating equations for separable spatial-temporal binary data

For binary data with correlation across space and over time, the literature concerning the estimation of fixed effects in marginal models is limited. In this paper, we model the marginal probability of binary responses in terms of parameters of interest by a logistic function. An estimating equation based on the quasi-likelihood concept is developed to estimate parameters. Under separable correlation models, we show that the quasi-likelihood estimate is asymptotically optimal. A series of simulations is conducted to evaluate how the efficiency varies with the regression coefficients. We also compare the relative efficiency with another estimating equation by simulations. The proposed method is applied to an ecological study of forest decline to test independence of two spatial-temporal binary outcomes.     

Regression equations for estimating throughfall nutrient fluxes using wet deposition data and their applicability for simulating the soil acid–base status using the dynamic forest soil–atmosphere model SAFE

Throughfall may contribute large amounts of nutrients to forest soils via the leaching of accumulated dry particulates on the canopy, and by altering incoming precipitation, it may have some control on the acid–base status of the soil. Unfortunately, information about throughfall in forests is sparse and thus, scientists must deal with this gap in knowledge before conducting regional applications of dynamic soil acidification models. The first objective of this paper was to test the possibility of developing regression equations that could allow modellers to estimate throughfall nutrient fluxes using wet deposition nutrient fluxes as input data. The second objective was to test the relative importance of this simplification on regional applications of the dynamic soil–atmosphere model Soil Acidification in Forested Ecosystems (SAFE) using one published application of this model as the base case. Annual throughfall nutrient fluxes were estimated successfully from annual wet deposition fluxes for individual ions. The success of these relationships were however inversely proportional to the intensity at which an ion was involved in exchange reactions: models generally performed better with more conservative ions. The simulation of the soil acid–base status with SAFE suggested that it was appropriate to use the throughfall estimates yielded using the regression equations. Also, testing of the SAFE output using different regression equations in throughfall showed that, in the case of base cations, the key for modelling the soil acid–base status was to produce accurate throughfall estimates of Ca and Mg, and that K had marginal effects. However, a small bias in solution pH was introduced as the balance between alkalinity and acidity in the different categories of deposition appeared to be diverging from the base case (measured) values. The use of our approach at other sites may indicate if there is a systematic bias or not in the regressions. Yet, results suggest that the regression equations are appropriate for the purpose of modelling the soil acid–base status at the scale of the landscape because it assures that the same set of assumptions in throughfall are used for each application.     

A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: understanding the variability within cities

In many older US cities, lead (Pb) contamination of residential soil is widespread; however, contamination is not uniform. Empirically based, spatially explicit models can assist city agencies in addressing this important public health concern by identifying areas predicted to exceed public health targets for soil Pb contamination. Sampling of 61 residential properties in Baltimore City using field portable X-ray fluorescence revealed that 53 % had soil Pb that exceeded the USEPA reportable limit of 400 ppm. These data were used as the input to three different spatially explicit models: a traditional general linear model (GLM), and two machine learning techniques: classification and regression trees (CART) and Random Forests (RF). The GLM revealed that housing age, distance to road, distance to building, and the interactions between variables explained 38 % of the variation in the data. The CART model confirmed the importance of these variables, with housing age, distance to building, and distance to major road networks determining the terminal nodes of the CART model. Using the same three predictor variables, the RF model explained 42 % of the variation in the data. The overall accuracy, which is a measure of agreement between the model and an independent dataset, was 90 % for the GLM, 83 % for the CART model, and 72 % for the RF model. A range of spatially explicit models that can be adapted to changing soil Pb guidelines allows managers to select the most appropriate model based on public health targets.     

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.     

Space-time modeling for the Missouri Turkey Hunting Survey

The Missouri Turkey Hunting Survey (MTHS) is a postseason mail survey conducted by the Missouri Department of Conservation. The 1996 MTHS provides information concerning the number of turkeys harvested by hunters on each day and the total number of trips made to the counties by these hunters on each day of the hunting season. The success rates are then found from this information. Small sample sizes produce large standard errors for the estimates at the county level. We use a Bayesian hierarchical generalized linear model to estimate daily hunting success rates at the county level. The model includes an autoregressive process for the days of the hunting season and spatially correlated random geographic effects. The computations are performed using Gibbs sampling and adaptive rejection sampling techniques. Results show that there are significant spatial corelations between counties and correlations between days of the hunting season. The estimates are close to the frequency estimates at the state level and much more stable at the county level.     

Population growth in snow geese: a modeling approach integrating demographic and survey information

There are few analytic tools available to formally integrate information coming from population surveys and demographic studies. The Kalman filter is a procedure that facilitates such integration. Based on a state-space model, we can obtain a likelihood function for the survey data using a Kalman filter, which we may then combine with a likelihood for the demographic data. In this paper, we used this combined approach to analyze the population dynamics of a hunted species, the Greater Snow Goose (Chen caerulescens atlantica), and to examine the extent to which it can improve previous demographic population models. The state equation of the state-space model was a matrix population model with fecundity and regression parameters relating adult survival and harvest rate estimated in a previous capture-recapture study. The observation equation combined the output from this model with estimates from an annual spring photographic survey of the population. The maximum likelihood estimates of the regression parameters from the combined analysis differed little from the values of the original capture-recapture analysis, though their precision improved. The model output was found to be insensitive to a wide range of coefficient of variation (CV) in fecundity parameters. We found a close match between the surveyed and smoothed population size estimates generated by the Kalman filter over an 18-year period, and the estimated CV of the survey (0.078-0.150) was quite compatible with its assumed value (approximately 0.10). When we used the updated parameter values to predict future population size, the model underestimated the surveyed population size by 18% over a three-year period. However, this could be explained by a concurrent change in the survey method. We conclude that the Kalman filter is a promising approach to forecast population change because it incorporates survey information in a formal way compared with ad hoc approaches that either neglect this information or require some parameter or model tuning.     

Combining multistate capture-recapture data with tag recoveries to estimate demographic parameters

Matrix population models that allow an animal to occupy more than one state over time are important tools for population and evolutionary ecologists. Definition of state can vary, including location for metapopulation models and breeding state for life history models. For populations whose members can be marked and subsequently reencountered, multistate mark-recapture models are available to estimate the survival and transition probabilities needed to construct population models. Multistate models have proved extremely useful in this context, but they often require a substantial amount of data and restrict estimation of transition probabilities to those areas or states subjected to formal sampling effort. At the same time, for many species, there are considerable tag recovery data provided by the public that could be modeled in order to increase precision and to extend inference to a greater number of areas or states. Here we present a statistical model for combining multistate capture-recapture data (e.g., from a breeding ground study) with multistate tag recovery data (e.g., from wintering grounds). We use this method to analyze data from a study of Canada Geese (Branta canadensis) in the Atlantic Flyway of North America. Our analysis produced marginal improvement in precision, due to relatively few recoveries, but we demonstrate how precision could be further improved with increases in the probability that a retrieved tag is reported.     

Composite likelihood approach to the regression analysis of spatial multivariate ordinal data and spatial compositional data with exact zero values

In many environmental and ecological studies, it is of interest to model compositional data. One approach is to consider positive random vectors that are subject to a unit-sum constraint. In landscape ecological studies, it is common that compositional data are also sampled in space with some elements of the composition absent at certain sampling sites. In this paper, we first propose a practical spatial multivariate ordered probit model for multivariate ordinal data, where the response variables can be viewed as the discretized non-negative compositions without the unit-sum constraint. We then propose a novel two-stage spatial mixture Dirichlet regression model. The first stage models the spatial dependence and the presence of exact zero values, and the second stage models all the non-zero compositional data. A maximum composite likelihood approach is developed for parameter estimation and inference in both the spatial multivariate ordered probit model and the two-stage spatial mixture Dirichlet regression model. The standard errors of the parameter estimates are computed by an estimate of the Godambe information matrix. A simulation study is conducted to evaluate the performance of the proposed models and methods. A land cover data example in landscape ecology further illustrates that accounting for spatial dependence can improve the accuracy in the prediction of presence/absence of different land covers as well as the magnitude of land cover compositions.     

Rent dissipation in a limited-access common-pool resource: Experimental evidence

This paper examines group behavior in an experimental environment designed to parallel the conditions specified in noncooperative models of limited-access common-pool resources. Using experimental methods, we investigate the strength of theoretical models which predict that users of such resources will appropriate units at a rate at which the marginal returns from appropriation are greater than the marginal appropriation costs. Our results confirm the prediction of suboptimal accrual of rents and offer evidence on the effects of increasing investment capital available to appropriators.     

Poaching Risks in Community‐Based Natural Resource Management

Poaching can disrupt wildlife‐management efforts in community‐based natural resource management systems. Monitoring, estimating, and acquiring data on poaching is difficult. We used local‐stakeholder knowledge and poaching records to rank and map the risk of poaching incidents in 2 areas where natural resources are managed by community members in Caprivi, Namibia. We mapped local stakeholder perceptions of the risk of poaching, risk of wildlife damage to livelihoods, and wildlife distribution and compared these maps with spatially explicit records of poaching events. Recorded poaching events and stakeholder perceptions of where poaching occurred were not spatially correlated. However, the locations of documented poaching events were spatially correlated with areas that stakeholders perceived wildlife as a threat to their livelihoods. This result suggests poaching occurred in response to wildlife damage occurred. Local stakeholders thought that wildlife populations were at high risk of being poached and that poaching occurred where there was abundant wildlife. These findings suggest stakeholders were concerned about wildlife resources in their community and indicate a need for integrated and continued monitoring of poaching activities and further interventions at the wildlife‐agricultural interface. Involving stakeholders in the assessment of poaching risks promotes their participation in local conservation efforts, a central tenet of community‐based management. We considered stakeholders poaching informants, rather than suspects, and our technique was spatially explicit. Different strategies to reduce poaching are likely needed in different areas. For example, interventions that reduce human‐wildlife conflict may be required in residential areas, and increased and targeted patrolling may be required in more remote areas. Stakeholder‐generated maps of human‐wildlife interactions may be a valuable enforcement and intervention support tool. Riesgos de Cacería Furtiva en el Manejo de Recursos Naturales Basado en Comunidades     

A meta‐analysis of studies on attitudes toward bears and wolves across Europe 1976–2012

The ranges of wolves (Canis lupus) and bears (Ursus arctos) across Europe have expanded recently, and it is important to assess public attitudes toward this expansion because responses toward these species vary widely. General attitudes toward an object are good predictors of broad behavioral patterns; thus, attitudes toward wolves and bears can be used as indicators to assess the social foundation for future conservation efforts. However, most attitude surveys toward bears and wolves are limited in scope, both temporally and spatially, and provide only a snapshot of attitudes. To extend the results of individual surveys over a much larger temporal and geographical range so as to identify transnational patterns and changes in attitudes toward bears and wolves over time, we conducted a meta‐analysis. Our analysis included 105 quantitative surveys conducted in 24 countries from 1976 to 2012. Across Europe, people's attitudes were more positive toward bears than wolves. Attitudes toward bears became more positive over time, but attitudes toward wolves seemed to become less favorable the longer people coexisted with them. Younger and more educated people had more positive attitudes toward wolves and bears than people who had experienced damage from these species, and farmers and hunters had less positive attitudes toward wolves than the general public. For bears attitudes among social groups did not differ. To inform conservation of large carnivores, we recommend that standardized longitudinal surveys be established to monitor changes in attitudes over time relative to carnivore population development. Our results emphasize the need for interdisciplinary research in this field and more advanced explanatory models capable of capturing individual and societal responses to changes in large carnivore policy and management.     

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social–ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.     

Compositional analysis of overdispersed counts using generalized estimating equations

Multivariate abundance data are commonly collected in ecology, and used to explore questions of “community composition”—how relative abundance of different taxa changes with environmental conditions. In this paper, we propose a log-linear marginal modeling approach for analyzing such compositional count data, via generalized estimating equations. This method exploits the multiplicative nature of log-linear models for counts, by reparameterizing models that describe marginal effects on mean abundance. This allows partitioning into “main effects” and compositional effects, which is appealing for interpretation. We apply the proposed approach to reanalyze compositional counts of benthic invertebrates from Delaware Bay, and data of invertebrate communities inhabiting Acacia plants in eastern Australia. In both cases we resort to a resampling approach to make inferences about regression parameters, because the number of clusters was not large compared to cluster size.     

Pollution control and optimal taxation: A static analysis

This article attempts to contribute to the analysis of identifying an optimum tax for the generation of pollutants when both anti-bads and bads are included in the utility function. Bads and anti-bads are introduced via a technological tradeoff relationship with substitution allowed. It is shown that in some instances in order to know the optimum tax on polluters it may not be important to know the marginal damage function. A brief analysis is undertaken of the tradeoffs between private actions to reduce the effect of pollutants and collective provisions for pollution control. The analysis further demonstrates that under certain circumstances the appropriate tax on polluters can be calculated from observed defensive behavior on the part of receptors.     

Mapping water chemical variables with spatially correlated errors

One of the most important considerations in many environmental studies is need to allow for correlations among the variables. Monitoring and analyzing relationships between chemical environmental parameters using spatial correlation based regression modelling is the main motivation of this applied study. For this purpose, some noticeable environmental parameters of data sets obtained from two lakes have been considered and the concentrations of chemical variables such as cadmium and nitrate have been appraised by a regression-based geostatistical methodology. The modelling procedure consists of two stages. In the first stage, spatial variables are analyzed via multi-linear regression and some relationships are provided. Next, by using the spatial auto-correlations of the residuals, a type of regression-based kriging procedure is applied. The capacity of the model for appraising the water chemical variables is also tested and performance comparisons with ordinary kriging are conducted. Finally, the applications showed that analyzing water chemical variables with spatially correlated errors is a convenient and applicable approach for assessing the environmental systems.     

Resources from another place and time: responses to pulses in a spatially subsidized system

As the theoretical bases for the dynamics of spatially subsidized communities emerge, ecologists question whether spatially subsidized communities exhibit similar structure or dynamics to communities that receive strongly pulsed resources. In both cases, communities may be structured by responses to resources that are potentially absent at any given point in time (pulsed communities) or space (subsidized communities), even if pulsed resources are part of the in situ productivity of the system or the subsidies arrive as a relatively constant input from a nearby system. The potential for significant spatial or temporal resource limitation, therefore, may be a key factor influencing in similar ways the persistence of populations, the structure and dynamics of communities, and the evolution of specific life history traits. In most complex systems, however, multiple resources may arrive for various trophic entities at various points in time and from various points in space, and thus it may be difficult to separate or compare the dynamics of spatially subsidized and pulsed systems. In this paper, we explore the effects of interactions between pulses and subsidies in plant and animal populations and communities on highly pulsed and variably subsidized islands in the Gulf of California. While many of the plant and animal communities on the unsubsidized islands in this system respond to pulses of rain in classic ways, responses to these rain pulses on islands subsidized by seabird guano or other marine resources are quite different and variable, and depend on a combination of life history characteristics, physiology, competitive interactions, and trophic relationships. These variable responses to rain pulses then translate into large differences in dynamics and community structure of subsidized vs. unsubsidized islands. Indeed, most systems experience both temporal pulses and spatial subsidies. When considered in tandem, complementary or synergistic effects of the multiple, temporally and spatially variable resources may emerge that help explain complex food web structure and dynamics.     

Modeling leaf dispersal in mixed hardwood forests using a ballistic approach

In mixed-species stands, modeling leaf litter dispersal is important to predict the physical and chemical characteristics of the forest floor, which plays a major role in nutrient cycling and in plant population dynamics. In this study, a spatially explicit model of leaf litterfall was developed and compared with two other models. These three models were calibrated for a mixed forest of oak and beech using litterfall data from mapped forest plots. All models assumed that an allometric equation described individual leaf litter production, but they strongly differed in the modeling of the probability density of leaf shedding with distance from source trees. Two models used a negative exponential function to account for leaf dispersal with distance, and this function was allowed to vary according to wind direction in one of them. In contrast, our approach was based on a simple ballistic equation considering release height, wind speed, wind direction, and leaf fall velocity; the distributions of wind speeds and wind directions were modeled according to a Weibull and a Von Mises distribution, respectively. Using an independent validation data set, all three models provided predictions well correlated to measurements (r > 0.83); however, the two models with a direction-dependent component were slightly more accurate. In addition, parameter estimates of the ballistic model were in close agreement with a foliar litter production equation derived from the literature for beech and with wind characteristics measured during leaf litterfall for both species. Because of its mechanistic background, such a spatially explicit model might be incorporated as a litterfall module in larger models (nutrient cycling, plant population dynamics) or used to determine the manner in which patch size in mixed-species stands influences litter mixture.     

The importance of spatial autocorrelation,extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.     

New hypothesis helps explain elasmobranch "outburst" on Georges Bank in the 1980s.

Regime shifts are a feature of many ecosystems. During the last 40 years, intensive commercial exploitation and environmental changes have driven substantial shifts in ecosystem structure and function in the northwest Atlantic. In the Georges Bank-southern New England region, commercially important species have declined, and the ecosystem shifted to one dominated by economically undesirable species such as skates and dogfish. Aggregated abundance indices indicate a large increase of small and medium-sized elasmobranchs in the early 1980s following the decline of many commercial species. It has been hypothesized that ecological interactions such as competition and predation within the Georges Bank region were responsible for and are maintaining the "elasmobranch outburst" at the heart of the observed ecosystem shift. We offer an alternative hypothesis invoking population connectivity among winter skate populations such that the observed abundance increase is a result of migratory dynamics, perhaps with the Scotian Shelf (i.e., it is an open population). Here we critically evaluate the survey data for winter skate, the species principally responsible for the increase in total skate abundance during the 1980s on Georges Bank, to assess support for both hypotheses. We show that time series from different surveys within the Georges Bank region exhibit low coherence, indicating that a widespread population increase was not consistently shown by all surveys. Further, we argue that observed length-frequency data for Georges Bank indicate biologically unrealistic population fluctuations if the population is closed. Neither finding supports the elasmobranch outburst hypothesis. In contrast, survey time series for Georges Bank and the Scotian Shelf are negatively correlated, in support of the population connectivity hypothesis. Further, we argue that understanding the mechanisms of ecosystem state changes and population connectivity are needed to make inferences about both the causes and appropriate management responses to large-scale system change.