Assessing Risk to Birds from Industrial Wind Energy Development via Paired Resource Selection Models

When wildlife habitat overlaps with industrial development animals may be harmed. Because wildlife and people select resources to maximize biological fitness and economic return, respectively, we estimated risk, the probability of eagles encountering and being affected by turbines, by overlaying models of resource selection for each entity. This conceptual framework can be applied across multiple spatial scales to understand and mitigate impacts of industry on wildlife. We estimated risk to Golden Eagles (Aquila chrysaetos) from wind energy development in 3 topographically distinct regions of the central Appalachian Mountains of Pennsylvania (United States) based on models of resource selection of wind facilities (n = 43) and of northbound migrating eagles (n = 30). Risk to eagles from wind energy was greatest in the Ridge and Valley region; all 24 eagles that passed through that region used the highest risk landscapes at least once during low altitude flight. In contrast, only half of the birds that entered the Allegheny Plateau region used highest risk landscapes and none did in the Allegheny Mountains. Likewise, in the Allegheny Mountains, the majority of wind turbines (56%) were situated in poor eagle habitat; thus, risk to eagles is lower there than in the Ridge and Valley, where only 1% of turbines are in poor eagle habitat. Risk within individual facilities was extremely variable; on average, facilities had 11% (SD 23; range = 0–100%) of turbines in highest risk landscapes and 26% (SD 30; range = 0–85%) of turbines in the lowest risk landscapes. Our results provide a mechanism for relocating high‐risk turbines, and they show the feasibility of this novel and highly adaptable framework for managing risk of harm to wildlife from industrial development. Evaluación del Riesgo para las Aves por el Desarrollo de Energía Eólica Industrial Mediante Modelos de Selección de Recursos Pareados.     

Modeling resource selection using polytomous logistic regression and kernel density estimates

Wildlife resource selection studies typically compare used to available resources; selection or avoidance occurs when use is disproportionately greater or less than availability. Comparing used to available resources is problematic because results are often greatly influenced by what is considered available to the animal. Moreover, placing relocation points within resource units is often difficult due to radiotelemetry and mapping errors. Given these problems, we suggest that an animal’s resource use be summarized at the scale of the home range (i.e., the spatial distribution of all point locations of an animal) rather than by individual points that are considered used or available. To account for differences in use-intensity throughout an animal’s home range, we model resource selection using kernel density estimates and polytomous logistic regression. We present a case study of elk (Cervus elaphus) resource selection in South Dakota to illustrate the procedure. There are several advantages of our proposed approach. First, resource availability goes undefined by the investigator, which is a difficult and often arbitrary decision. Instead, the technique compares the intensity of animal use throughout the home range. This technique also avoids problems with classifying locations rigidly as used or unused. Second, location coordinates do not need to be placed within mapped resource units, which is problematic given mapping and telemetry error. Finally, resource use is considered at an appropriate scale for management because most wildlife resource decisions are made at the level of the patch. Despite the advantages of this use-intensity procedure, future research should address spatial autocorrelation and develop spatial models for ordered categorical variables.     

A proposed methodology for assessing the quality of wildlife habitat

Measurement of the quality and quantity of wildlife habitat is a necessity in planning and analyzing water resource projects. A methodology that provides a means of examining the following major components of habitat quality is proposed: (1) the quantity of land uses, (2) the degree of interspersion of land use, aand (3) the stage of land management and vegetative types. The model can be calibrated to a specific region by experienced wildlife biologists and applied by field technicians using aerial photographs and field evaluation of randomly located points. Biologists familiar with a region develop transformation curves relating variables that represent the above three major components to standardizing factors that range from 0 to 1; these transformation curves reflect the sensitivity of wildlife populations to determinants of habitat quality. The methodology also provides the means for biologists to weigh each components as to its relative importance to the wildlife group or species. A weighted geometric mean of the components provides an indication of the overall quality of the habitat. Changes in the habitat components that may occur because of alternative project actions can be used to evaluate the effectiveness of the alternatives. The change in the index of habitat quality will indicate the need for mitigation in water resource projects. The system then can be used to identify areas for mitigation and the effects of mitigating measures.     

Dealing with the Clandestine Nature of Wildlife‐Trade Market Surveys

Abstract: Illegal international trade in wildlife (excluding fisheries and timber) has been valued at more than US$20 billion. A more precise figure has not been determined in part because of the clandestine nature of the trade, and for this same reason even regional and local levels of wildlife trade are difficult to assess. The application of recent developments in wildlife field‐survey methods (e.g., occupancy) now allows for a more‐accurate estimation of wildlife trade occurrence, including its hidden components at a variety of scales (e.g., regional, local) and periods (e.g., single season, 1 year, multiple years). Occupancy models have been applied in wildlife field studies to address the problem of false absences when conducting presence–absence surveys. Occupancy surveys differ from traditional presence–absence surveys because they incorporate repeat surveys, allowing for the likelihood of detecting a species (the probability of detection) to be estimated explicitly (in contrast to traditional surveys that often incorrectly treat this probability as close to one to allow for estimation of presence). Occupancy methods can be applied to a variety of wildlife‐trade surveys, including, for example, single‐species availability, links between two illegally traded species (i.e., co‐occurrence), and disease occurrence in live trade. In addition, free user‐friendly software (i.e., PRESENCE) allows even nonstatisticians to adequately address this issue. I simulated a hypothetical wildlife‐trade market survey that resulted in an apparent 20% decline in naïve occupancy (proportion of surveyed towns engaged in the trade) over 2 years, but when I accounted for change in probability of detection over the years the difference in occupancy was not statistically significant. As more sophisticated methods, such as occupancy, are applied to wildlife‐trade market surveys, results will be more robust and defensible and therefore, theoretically, more powerful when presented to conservation policy and decision makers.     

The role of agent-based models in wildlife ecology and management

Conservation planning of critical habitats for wildlife species at risk is a priority topic that requires the knowledge of how animals select and use their habitat, and how they respond to future developmental changes in their environment. This paper explores the role of a habitat-modeling methodological approach, agent-based modeling, which we advocate as a promising approach for ecological research. Agent-based models (ABMs) are capable of simultaneously distinguishing animal densities from habitat quality, can explicitly represent the environment and its dynamism, can accommodate spatial patterns of inter- and intra-species mechanisms, and can explore feedbacks and adaptations inherent in these systems. ABMs comprise autonomous, individual entities; each with dynamic, adaptive behaviors and heterogeneous characteristics that interact with each other and with their environment. These interactions result in emergent outcomes that can be used to quantitatively examine critical habitats from the individual- to population-level. ABMs can also explore how wildlife will respond to potential changes in environmental conditions, since they can readily incorporate adaptive animal-movement ecology in a changing landscape. This paper describes the necessary elements of an ABM developed specifically for understanding wildlife habitat selection, reviews the current empirical literature on ABMs in wildlife ecology and management, and evaluates the current and future roles these ABMs can play, specifically with regards to scenario planning of designated critical habitats.     

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     

Human–wildlife coexistence in a changing world

Human–wildlife conflict (HWC) is a key topic in conservation and agricultural research. Decision makers need evidence-based information to design sustainable management plans and policy instruments. However, providing objective decision support can be challenging because realities and perceptions of human–wildlife interactions vary widely between and within rural, urban, and peri-urban areas. Land users who incur costs through wildlife argue that wildlife-related losses should be compensated and that prevention should be subsidized. Supporters of human–wildlife coexistence policies, such as urban-dwelling people, may not face threats to their livelihoods from wildlife. Such spatial heterogeneity in the cost and benefits of living with wildlife is germane in most contemporary societies. This Special Section features contributions on wildlife-induced damages that range from human perspectives (land use, psychology, governance, local attitudes and perceptions, costs and benefits, and HWC and coexistence theory) to ecological perspectives (animal behavior). Building on current literature and articles in this section, we developed a conceptual model to help frame HWC and coexistence dimensions. The framework can be used to determine damage prevention implementation levels and approaches to HWC resolution. Our synthesis revealed that inter- and transdisciplinary approaches and multilevel governance approaches can help stakeholders and institutions implement sustainable management strategies that promote human–wildlife coexistence.     

Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Crop and livestock depredation by wildlife is a primary driver of human–wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08–0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human–wildlife interactions.     

An evaluation of three statistical methods used to model resource selection

The performance of statistical methods for modeling resource selection by animals is difficult to evaluate with field data because true selection patterns are unknown. Simulated data based on a known probability distribution, though, can be used to evaluate statistical methods. Models should estimate true selection patterns if they are to be useful in analyzing and interpreting field data. We used simulation techniques to evaluate the effectiveness of three statistical methods used in modeling resource selection. We generated 25 use locations per animal and included 10, 20, 40, or 80 animals in samples of use locations. To simulate species of different mobility, we generated use locations at four levels according to a known probability distribution across DeSoto National Wildlife Refuge (DNWR) in eastern Nebraska and western Iowa, USA. We either generated 5 random locations per use location or 10,000 random locations (total) within 4 predetermined areas around use locations to determine how the definition of availability and the number of random locations affected results. We analyzed simulated data using discrete choice, logistic-regression, and a maximum entropy method (Maxent). We used a simple linear regression of estimated and known probability distributions and area under receiver operating characteristic curves (AUC) to evaluate the performance of each method. Each statistical method was affected differently by number of animals and random locations used in analyses, level at which selection of resources occurred, and area considered available. Discrete-choice modeling resulted in precise and accurate estimates of the true probability distribution when the area in which use locations were generated was ≥ the area defined to be available. Logistic-regression models were unbiased and precise when the area in which use locations were generated and the area defined to be available were the same size; the fit of these models improved with increased numbers of random locations. Maxent resulted in unbiased and precise estimates of the known probability distribution when the area in which use locations were generated was small (home-range level) and the area defined to be available was large (study area). Based on AUC analyses, all models estimated the selection distribution better than random chance. Results from AUC analyses, however, often contradicted results of the linear regression method used to evaluate model performance. Discrete-choice modeling was best able to estimate the known selection distribution in our study area regardless of sample size or number of random locations used in the analyses, but we recommend further studies using simulated data over different landscapes and different resource metrics to confirm our results. Our study offers an approach and guidance for others interested in assessing the utility of techniques for modeling resource selection in their study area.     

Resolving structural uncertainty in natural resources management using POMDP approaches

In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems.     

Forks in the Road: Choices in Procedures for Designing Wildland Linkages

Abstract:  Models are commonly used to identify lands that will best maintain the ability of wildlife to move between wildland blocks through matrix lands after the remaining matrix has become incompatible with wildlife movement. We offer a roadmap of 16 choices and assumptions that arise in designing linkages to facilitate movement or gene flow of focal species between 2 or more predefined wildland blocks. We recommend designing linkages to serve multiple (rather than one) focal species likely to serve as a collective umbrella for all native species and ecological processes, explicitly acknowledging untested assumptions, and using uncertainty analysis to illustrate potential effects of model uncertainty. Such uncertainty is best displayed to stakeholders as maps of modeled linkages under different assumptions. We also recommend modeling corridor dwellers (species that require more than one generation to move their genes between wildland blocks) differently from passage species (for which an individual can move between wildland blocks within a few weeks). We identify a problem, which we call the subjective translation problem, that arises because the analyst must subjectively decide how to translate measurements of resource selection into resistance. This problem can be overcome by estimating resistance from observations of animal movement, genetic distances, or interpatch movements. There is room for substantial improvement in the procedures used to design linkages robust to climate change and in tools that allow stakeholders to compare an optimal linkage design to alternative designs that minimize costs or achieve other conservation goals.     

Tracking a half century of media reporting on gray wolves

Natural resource and wildlife managers must balance the disparate priorities of a diversity of stakeholders. To manage these priorities, a firm understanding of topics salient to the public is needed. The media often report on issues of importance to the public; therefore, these reports may be a useful measure of public interest. However, efficient methods for distinguishing diverse topics related to a wildlife management issue reported in the media and changes in the salience of those topics have been lacking. We used latent Dirichlet allocation, a Bayesian mixture model, to quantitatively assess the salience of topics surrounding the gray wolf (Canis lupus), which was reintroduced to Idaho (U.S.A.) in 1995. We analyzed articles published from 1960 to 2015 in an Idaho newspaper. We identified 6 distinct topics associated with gray wolves: policy, hunting, biological status, implementation of management, recovery, and human-wolf conflict. The salience of topics pre- and postreintroduction of wolves (1995) and pre- and postdelisting of wolves from the U.S. Endangered Species Act (2009) differed significantly, underscoring that these events were turning points in how issues were being publicly discussed and framed. Articles written by the local reporters were more likely to report on topics regarding conflict between humans and wolves, whereas articles sourced from a national outlet reported more on topics pertaining to wolf policy and biological status. In the context of managing a contentious, far-ranging, and long-lived wildlife species, our methods can help guide the location and timing of a suite of management strategies (e.g., media relation plans and stakeholder engagement) that promote human-wildlife coexistence across different landscapes.     

Evaluating and managing wildlife impacts of climate change under uncertainty

Wildlife managers face the daunting task of managing wildlife in light of uncertainty about the nature and extent of future climate change and variability and its potential adverse impacts on wildlife. A conceptual framework is developed for managing wildlife under such uncertainty. The framework uses fuzzy logic to test hypotheses about the extent of the wildlife impacts of past climate change and variability, and fuzzy multiple attribute evaluation to determine best compensatory management actions for adaptively managing the potential adverse impacts of future climate change and variability on wildlife. A compensatory management action is one that can offset some of the potential adverse impacts of climate change and variability on wildlife. Implementation of the proposed framework requires wildlife managers to: (1) select climate impact states, hypotheses about climate impact states, possible management actions for alleviating adverse wildlife impacts of climate change and variability, and future climate change scenarios; (2) choose biological attributes or indicators of species integrity; (3) adjust those attributes for changes in non-climatic variables; (4) define linguistic variables and associated triangular fuzzy numbers for rating both the acceptability of biological conditions under alternative management actions and the relative importance of biological attributes; (5) select minimum or maximum acceptable levels of the attributes and reliability levels for chance constraints on the biological attributes; and (6) define fuzzy sets on the extent of species integrity and biological conditions and select a fuzzy relation between species integrity and biological conditions. A constructed example is used to illustrate a hypothetical application of the framework by a wildlife management team. An overall best compensatory management action across all climate change scenarios is determined using the minimax regret criterion, which is appropriate when the management team cannot assign or is unwilling to assign probabilities to the future climate change scenarios. Application of the framework can be simplified and expedited by incorporating it in a web-based, interactive, decision support tool.     

Emerging prion disease drives host selection in a wildlife population

Infectious diseases are increasingly recognized as an important force driving population dynamics, conservation biology, and natural selection in wildlife populations. Infectious agents have been implicated in the decline of small or endangered populations and may act to constrain population size, distribution, growth rates, or migration patterns. Further, diseases may provide selective pressures that shape the genetic diversity of populations or species. Thus, understanding disease dynamics and selective pressures from pathogens is crucial to understanding population processes, managing wildlife diseases, and conserving biological diversity. There is ample evidence that variation in the prion protein gene (PRNP) impacts host susceptibility to prion diseases. Still, little is known about how genetic differences might influence natural selection within wildlife populations. Here we link genetic variation with differential susceptibility of white-tailed deer to chronic wasting disease (CWD), with implications for fitness and disease-driven genetic selection. We developed a single nucleotide polymorphism (SNP) assay to efficiently genotype deer at the locus of interest (in the 96th codon of the PRNP gene). Then, using a Bayesian modeling approach, we found that the more susceptible genotype had over four times greater risk of CWD infection; and, once infected, deer with the resistant genotype survived 49% longer (8.25 more months). We used these epidemiological parameters in a multi-stage population matrix model to evaluate relative fitness based on genotype-specific population growth rates. The differences in disease infection and mortality rates allowed genetically resistant deer to achieve higher population growth and obtain a long-term fitness advantage, which translated into a selection coefficient of over 1% favoring the CWD-resistant genotype. This selective pressure suggests that the resistant allele could become dominant in the population within an evolutionarily short time frame. Our work provides a rare example of a quantifiable disease-driven selection process in a wildlife population, demonstrating the potential for infectious diseases to alter host populations. This will have direct bearing on the epidemiology, dynamics, and future trends in CWD transmission and spread. Understanding genotype-specific epidemiology will improve predictive models and inform management strategies for CWD-affected cervid populations.     

Exploring nationality and social identity to explain attitudes toward conservation actions in the United States and Australia

Understanding human attitudes toward wildlife management is critical to implementing effective conservation action and policy. Understanding the factors that shape public attitudes toward different wildlife management actions is limited, however, which can result in unpredictable public responses to interventions. We drew on comparisons between residents of 2 countries on separate continents to explore differences in attitudes toward wildlife management and determine factors important in shaping these attitudes. We surveyed representative publics via market research panels in Australia (n = 881 respondents) and the United States (n = 1287). We applied a social-identity approach and demography to identify factors that explained variance between responses about wildlife management. We compared responses between countries overall and within subgroups of respondents who strongly identified as environmentalists, animal rights activists, wildlife conservation advocates, and farmers. We created aggregate scores for the management-related response items per respondent and used regression analyses to identify the relative importance of country, identity, age, and gender in explaining variance between responses. These factors accounted for 15.3% of variance among responses. Australians overall were generally more accepting of lethal wildlife management actions than U.S. respondents. Differences in national attitudes reflected differences between United States and Australian wildlife management and policy, highlighting the importance of understanding social attitudes in shaping conservation policy. Identifying as a farmer followed by identifying as an animal rights activist most shaped attitudes toward wildlife management. Identity-related conflicts could be initiated or exacerbated by conservation interventions that fail to consider identity-related processes.     

Community Conservation and the Future of Africa's Wildlife

Abstract: The term community-based conservation (CBC) refers to wildlife conservation efforts that involve rural people as an integral part of a wildlife conservation policy. The key elements of such programs are that local communities participate in resource planning and management and that they gain economically from wildlife utilization. In part, CBC is seen as an alternative to the more exclusionary protectionist policies of the past, which often alienated rural people from conservation efforts. The new approach acts to make rural people a constituency for wildlife and therefore active backers of wildlife protection. Africans, however, are struggling with severe social and economic problems such as poverty, long-standing economic stagnation, rapid population growth, and environmental deterioration. Because of the pressures that Africans face in making a living, the application of CBC may not occur as readily or as successfully as its advocates would hope. It may also be that the approach is being oversold. I use brief case studies from Madagascar, Ethiopia, Zimbabwe, and Swaziland to highlight the possible conflicts between rural people's economic needs and the implementation of community conservation. In addition, the CBC literature treats the role of protection vaguely, as it does the question of what might happen if CBC fails to achieve wildlife conservation goals. Community-based conservation is an obvious advance over past practices because of its inclusive philosophy, but if rural people accept CBC because of its economic benefits, they may reject it at some point in the future if a better economic alternative is presented. Thus, CBC programs can work to produce a better relationship between wildlife and people, but only a vast improvement in the lives of rural Africans will ultimately produce a more secure future for the continent's wildlife.     

Wildlife and coal development: An overview

This paper briefly reviews the changes which have occurred in the past three hundred years in the public's attitudes toward our Nation's natural resources, particularly the wildlife resources. It discusses how these changing attitudes have contributed to today's surface coal mine reclamation laws which require that the disturbed land be restored to a level of productivity equal to or greater than that which occurred before mining. This paper stresses the need to manage each reclamation site on an ecosystem basis, recognizing the interrelationships of all the resource components. This paper emphasizes both the fundamental importance of the holistic approach to mine site reclamation and the responsibility that the wildlife biologist has to see to it that wildlife habitat considerations are incorporated into each phase of the reclamation plan. Brief management considerations are given to three unique reclamation habitat types; riparian, agriculture and forest.     

Modelling the responses of wildlife to human disturbance: An evaluation of alternative management scenarios for black-crowned night-herons

The impact of anthropogenic disturbance on wildlife is increasing becoming a source of concern as the popularity of outdoor recreation rises. There is now more pressure on site managers to simultaneously ensure the continued persistence of wildlife and provide recreational opportunities. Using ‘Simulation of Disturbance Activities’, a model designed to investigate the impact of recreational disturbance on wildlife, we demonstrate how a simulation modelling approach can effectively inform such management decisions. As an example, we explored the implications of various design and management options for a proposed recreational area containing a historic breeding bird colony. By manipulating the proximity, orientation and intensity of recreation, we were able to evaluate the impact of recreational activities on the behaviour of black-crowned night-heron nestlings (Nycticorax nycticorax). Using a classification and regression tree (CART) procedure to analyse simulation output, we explored the dynamics of multiple strategies in concert. Our analysis revealed that there are inherent advantages in implementing multiple strategies as opposed to any single strategy. Nestlings were not disturbed by recreation when bird-watching facility placement (proximity and orientation) and type were considered in combination. In comparison, proximity alone only led to a <10% reduction in disturbance. Thus we demonstrate how simulation models based on customised empirical data can bridge the gap between field studies and active management, enabling users to test novel management scenarios that are otherwise logistically difficult. Furthermore, such models potentially have broad application in understanding human-wildlife interactions (e.g. exploring the implications of roads on wildlife, probability of bird strikes around airports, etc.). They therefore represent a valuable decision-making tool in the ecological design of urban infrastructures.     

Nonparametric spatial regression of survival probability: visualization of population sinks in Eurasian woodcock

Both evolutionary ecologists and wildlife managers make inference based on how fitness and demography vary in space. Spatial variation in survival can be difficult to assess in the wild because (1) multisite study designs are not well suited to populations that are continuously distributed across a large area and (2) available statistical models accounting for detectability less than 1.0 do not easily cope with geographical coordinates. Here we use penalized splines within a Bayesian state-space modeling framework to estimate and visualize survival probability in two dimensions. The approach is flexible in that no parametric form for the relationship between survival and coordinates need be specified a priori. To illustrate our method, we study a game species, the Eurasian Woodcock Scolopax rusticola, based on band recovery data (5000 individuals) collected over a > 50 000-km2 area in west-central France with contrasted habitats and hunting pressures. We find that spatial variation in survival probability matches an index of hunting pressure and creates a mosaic of population sources and sinks. Such analyses could provide guidance concerning the spatial management of hunting intensity or could be used to identify pathways of spatial variation in fitness, for example, to study adaptation to changing landscape and climate.     

Generalized functional responses for species distributions

Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.