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At least 757 records · Page 42Linked to original sources
USGS2026

Breeding shorebird surveys in the Arctic National Wildlife Refuge, Alaska, suggest population declines over two decades for most species

Shorebird populations are declining globally but it generally remains unclear how those declines translate to changes at the regional scale. We conducted the first longitudinal surveys of breeding shorebirds in Alaska under the Program for Regional and International Shorebird Monitoring (PRISM), resurveying the Coastal Plain (1002 Area) of the Arctic National Wildlife Refuge (NWR) in 2019 and 2022 to compare with initial surveys conducted in 2002 and 2004. Our goals were to (1) estimate contemporary population sizes of breeding shorebirds across this 6,249 km 2 area, and (2) assess population trends for the species detected in both survey periods. We estimated population sizes for 16 species, with a combined total of 135,178 (95% CI: 113,532–156,824) in 2019 and 2022—a decline of approximately 17% (90% CI: –34% to + 3%) from 2002 and 2004 when the same survey methods were used. Four species showed a statistically significant decrease (α = 0.10): Calidris alpina arcticola (Dunlin), Limnodromus scolopaceus (Long-billed Dowitcher), Phalaropus lobatus (Red-necked Phalarope), and P. fulicarius (Red Phalarope). Only C. melanotos (Pectoral Sandpiper) showed a significant increase. Overall, 5 of 10 species—and all species combined—had a > 90% probability of decline. Population changes for the polygamous species (i.e., Phalaropus sp. and C. melanotos ), which show irruptive breeding and low breeding site fidelity, may reflect temporary immigration or emigration driven by annual environmental variation, rather than true population change. Nevertheless, the overall pattern of declines aligns with migration surveys outside the Arctic. These findings highlight the vulnerability of Arctic-breeding shorebirds to threats throughout their annual cycles and underscore the potential for sustained long-term monitoring in this rapidly changing region to inform effective, flyway-scale conservation strategies across the Western Hemisphere.

Alaska↗
USGS2024

Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs

An important part of infectious disease management is predicting factors that influence disease outbreaks, such as R , the number of secondary infections arising from an infected individual. Estimating R is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated R for Bacillus anthracis infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations and simulation models, we show that R is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within 2 years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction ( R < 1) to explosive outbreaks ( R > 10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host–pathogen interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.

Etosha National Park↗
USGS2025

A system dynamics model to understand the integrated ecological and human dimension aspects of wildlife health and disease management

Chronic wasting disease (CWD) presents an ongoing challenge for the management of deer populations and sustaining harvest opportunities across North America. Existing disease models often fail to fully capture the complex interplay between disease dynamics, host ecology, and socio-economic factors. We developed a comprehensive system dynamics (SD) model that integrates demographic, epidemiological, ecological, and socio-economic processes within a single model to more fully characterize the complex network of causal feedbacks throughout the system. The model was calibrated using a Bayesian approach that incorporates prior knowledge to generate biologically interpretable outcomes, even with sparse data. For estimating the joint posterior distribution of model parameters, we leveraged time series of deer abundance, harvest composition, genetic profiles, CWD surveillance, and hunter demographics and behavior. Model outputs reproduced key system behaviors, including observed CWD prevalence trends, deer population dynamics, and hunter license purchasing patterns. Model predictions were most sensitive to parameters governing initial deer population size and recruitment. While model predictions generally aligned with observed data, discrepancies in early CWD detection and overestimation of the reactivation of long-inactive hunters reflect data limitations and modeling challenges. Key results suggest that indirect transmission is necessary to explain observed prevalence, that transmission is moderately density-dependent, and that observed population-level genetic shifts driven by CWD may play a role in transmission and progression. The SD modelling approach enabled estimation of difficult-to-measure parameters and identified potential leverage points for management—such as prioritizing increasing participation in antlerless harvest of existing hunters over the recruitment of new hunters. This integrated modeling approach offers a flexible foundation for adaptive wildlife disease management and emphasizes the value of unifying biological and human dimension processes to better inform effective, evidence-based policy.

BioRxiv↗
USGS2017

Safari Science: Assessing the reliability of citizen science data for wildlife surveys

Protected areas are the cornerstone of global conservation, yet financial support for basic monitoring infrastructure is lacking in 60% of them. Citizen science holds potential to address these shortcomings in wildlife monitoring, particularly for resource-limited conservation initiatives in developing countries – if we can account for the reliability of data produced by volunteer citizen scientists (VCS). This study tests the reliability of VCS data vs. data produced by trained ecologists, presenting a hierarchical framework for integrating diverse datasets to assess extra variability from VCS data. Our results show that while VCS data are likely to be overdispersed for our system, the overdispersion varies widely by species. We contend that citizen science methods, within the context of East African drylands, may be more appropriate for species with large body sizes, which are relatively rare, or those that form small herds. VCS perceptions of the charisma of a species may also influence their enthusiasm for recording it. Tailored programme design (such as incentives for VCS) may mitigate the biases in citizen science data and improve overall participation. However, the cost of designing and implementing high-quality citizen science programmes may be prohibitive for the small protected areas that would most benefit from these approaches. Synthesis and applications . As citizen science methods continue to gain momentum, it is critical that managers remain cautious in their implementation of these programmes while working to ensure methods match data purpose. Context-specific tests of citizen science data quality can improve programme implementation, and separate data models should be used when volunteer citizen scientists' variability differs from trained ecologists' data. Partnerships across protected areas and between protected areas and other conservation institutions could help to cover the costs of citizen science programme design and implementation.

Journal of Applied Ecology↗
USGS2018

Decision making for mitigating wildlife diseases: From theory to practice for an emerging fungal pathogen of amphibians

Conservation science can be most effective in its decision‐support role when seeking answers to clearly formulated questions of direct management relevance. Emerging wildlife diseases, a driver of global biodiversity loss, illustrate the challenges of performing this role: in spite of considerable research, successful disease mitigation is uncommon. Decision analysis is increasingly advocated to guide mitigation planning, but its application remains rare. Using an integral projection model, we explored potential mitigation actions for avoiding population declines and the ongoing spatial spread of the fungus Batrachochytrium salamandrivorans ( Bsal ). This fungus has recently caused severe amphibian declines in north‐western Europe and currently threatens Palearctic salamander diversity. Available evidence suggests that a Bsal outbreak in a fire salamander ( Salamandra salamandra ) population will lead to its rapid extirpation. Treatments such as antifungals or probiotics would need to effectively interrupt transmission (reduce probability of infection by nearly 90%) in order to reduce the risk of host extirpation and successfully eradicate the pathogen. Improving the survival of infected hosts is most likely to be detrimental as it increases the potential for pathogen transmission and spread. Active removal of a large proportion of the host population has some potential to locally eradicate Bsal and interrupt its spread, depending on the presence of Bsal reservoirs and on the host's spatial dynamics, which should therefore represent research priorities. Synthesis and applications . Mitigation of Batrachochytrium salamandrivorans epidemics in susceptible host species is highly challenging, requiring effective interruption of transmission and radical removal of host individuals. More generally, our study illustrates the advantages of framing conservation science directly in the management decision context, rather than adapting to it a posteriori .

Journal of Applied Ecology↗
USGS2014

Disease dynamics during wildlife translocations: disruptions to the host population and potential consequences for transmission in desert tortoise contact networks

Wildlife managers consider animal translocation a means of increasing the viability of a local population. However, augmentation may disrupt existing resident disease dynamics and initiate an outbreak that would effectively offset any advantages the translocation may have achieved. This paper examines fundamental concepts of disease ecology and identifies the conditions that will increase the likelihood of a disease outbreak following translocation. We highlight the importance of susceptibility to infection, population size and population connectivity &ndash; a characteristic likely affected by translocation but not often considered in risk assessments &ndash; in estimating outbreak risk due to translocation. We then explore these features in a species of conservation concern often translocated in the presence of infectious disease, the Mojave Desert tortoise, and use data from experimental tortoise translocations to detect changes in population connectivity that may influence pathogen transmission. Preliminary analyses comparing contact networks inferred from spatial data at control and translocation plots and infection simulation results through these networks suggest increased outbreak risk following translocation due to dispersal-driven changes in contact frequency and network structure. We outline future research goals to test these concepts and aid managers in designing effective risk assessment and intervention strategies that will improve translocation success.

Animal Conservation↗
USGS2021

Survey design optimization for monitoring wildlife communities in areas managed for federally endangered species

In wildlife communities composed of federally endangered species, there are often several species of conservation concern that have not yet warranted federally mandated protection. These species often need continued monitoring to inform the direction of future management. While recovering endangered species is an important conservation goal, practitioners are challenged by balancing federally mandated protocols with actions that promote non-listed priority species. Practitioners need an understanding of how focused, single-species management actions may affect non-listed priority species, but developing a monitoring protocol that can detect such effects with limited resources is a challenge. Here we use constrained optimization as a path to identifying a sampling scheme that overcomes these logistical challenges and then illustrate its potential in the Sandhills region of North Carolina, USA. Using empirical results from multi-year avian community monitoring, we parameterized simulations to understand how varying the number of sampling locations and site visits affected the optimal monitoring protocol across three different avian community composition scenarios: a community with (1) 10 percent, (2) 25 percent, or (3) 50 percent non-listed priority species. We found the greatest rate of change in precision of community-level metrics such as species richness by increasing sampling replicates when surveying up to 50 sites. Importantly, this trend was apparent across all three community scenarios, indicating relatively predictable changes in uncertainty regardless of community composition. In contrast, increasing the sampling frequency did not consistently reduce uncertainty in species-level parameters such as occupancy probability. Concerningly, we saw the greatest variation when communities were comprised of 50 percent non-listed species suggesting increasingly complex monitoring protocols may be required if the number of non-listed priority species continues to increase. Practitioners could consider reducing detection error of priority species through increasing sampling frequency, as this can strongly affect optimization study designs.

North Carolina↗
USGS2018

A spatial approach to combatting wildlife crime

Poaching can have devastating impacts on animal and plant numbers, and in many countries has reached crisis levels, with illegal hunters employing increasingly sophisticated techniques. Here, we show how geographic profiling – a mathematical technique originally developed in criminology and recently applied to animal foraging and epidemiology – can be adapted for use in investigations of wildlife crime, using data from an eight-year study in Savé Valley Conservancy, Zimbabwe that in total includes more than 10,000 incidents of illegal hunting and the deaths of 6,454 wild animals. Using a subset of these data for which the illegal hunters’ identities are known, we show that the model can successfully identify the illegal hunters’ home villages using the spatial locations of hunting incidences (for example, snares) as input, and show how this can be improved by manipulating the probability surface inside the Conservancy to reflect the fact that – although the illegal hunters mostly live outside the Conservancy, the majority of hunting occurs inside (in criminology, ‘commuter crime’). The results of this analysis – combined with rigorous simulations – show for the first time how geographic profiling can be combined with GIS data and applied to situations with more complex spatial patterns – for example, where landscape heterogeneity means that some parts of the study area are unsuitable (e.g. aquatic areas for terrestrial animals, or vice versa), or where landscape permeability differs (for example, forest bats tending not to fly over open areas). More broadly, these results show how geographic profiling can be used to target anti-poaching interventions more effectively and more efficiently, with important implications for the development of management strategies and conservation plans in a range of conservation scenarios.

Conservation Biology↗
USGS2017

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Ecology Letters↗
USGS2009

Adult survival, apparent lamb survival, and body condition of desert bighorn sheep in relation to habitat and precipitation on the Kofa National Wildlife Refuge, Arizona

The decline of desert bighorn sheep on the Kofa National Wildlife Refuge (KNWR) beginning in 2003 stimulated efforts to determine the factors limiting survival and recruitment. We 1) determined pregnancy rates, body fat, and estimated survival rates of adults and lambs; 2) investigated the relationship between precipitation, forage conditions, previous year’s reproductive success, and adult body condition; 3) assessed the relative influence of body condition of adult females, precipitation, and forage characteristics on apparent survival of lambs; and 4) determined the prevalence of disease. To assess the influence of potential limiting factors on female desert bighorn sheep on the KNWR, we modeled percent body fat of adult females as a function of previous year’s reproductive effort, age class, and forage conditions (i.e., seasonal NDVI and seasonal precipitation). In addition, we assessed the relative influence of the body condition of adult females, precipitation, and forage conditions (NDVI) on length of time a lamb was observed at heel. Adult female survival was high in both 2009 (0.90 [SE = 0.05]) and 2010 (0.96 [SE = 0.03]). Apparent lamb survival to 6 months of age was 0.23 (SE = 0.05) during 2009-2010 and 0.21 (SE = 0.05) during 2010-2011 lambing seasons. Mean body fat for adult females was 12.03% (SE = 0.479) in 2009-2010 and 11.11% (SE= 0.486) in 2010-2011 and was not significantly different between years. Pregnancy rate was 100% in 2009 and 97.5% in 2010. Models containing the previous year’s reproductive effort, spring NDVI and previous year’s reproductive effort and spring precipitation best approximated data on percent body fat in adult females in 2009-2010. In 2010-2011, the two highest-ranking models included the previous year’s reproductive effort and winter NDVI and previous year’s reproductive effort, and winter and spring NDVI. None of the models assessing the influence of maternal body fat, precipitation, or forage conditions were particularly useful for predicting apparent lamb survival. The high pregnancy rates and body fat levels in excess of 11% do not indicate that this population of desert bighorn was nutritionally stressed during our study and are thus likely not contributing to the low lamb survival estimates we observed. However, body condition data during the population decline is not available and whether this population was nutritionally limited during the initial population decline remains unknown. The prevalence of disease in the Kofa herd may be a limiting factor; however, due to a lack of disease monitoring during the population decline it is uncertain if disease contributed to the decline. Further research is needed to fully understand the complex interaction of disease in this population at the individual and population level and determine to what extent disease predisposes individuals to predation or other causes of mortality.

Arizona↗
USGS1980

DDT contamination at Wheeler National Wildlife Refuge

Disposal of industrial waste resulted in massive DDT contamination at Wheeler National Wildlife Refuge, Alabama. Nearly a decade after the cessation of DDT manufacturing at the facility responsible, concentrations of DDT residues in the local fauna are still high enough to suggest avian reproductive impairment and mortality. Populations of fish-eating birds are low, endangered species are being exposed, and muscle lipids of game birds contain up to 6900 parts of DDT (isomers and metabolites) per million.

Alabama↗
USGS2020

Threats posed by the Fungal Kingdom to humans, wildlife, and agriculture

The Fungal Kingdom includes at least six million eukaryotic species and is remarkable with respect to its profound impact on global health, biodiversity, ecology, agriculture, manufacturing, and biomedical research. Approximately 625 fungal species have been reported to infect vertebrates, 200 of which can be human-associated, either as commensals and members of our microbiome or as pathogens that cause infectious diseases. These organisms pose a growing threat to human health with the global increase in the incidence of invasive fungal infections, prevalence of fungal allergy, and the evolution of fungal pathogens resistant to some or all current classes of antifungals. More broadly, there has been an unprecedented and worldwide emergence of fungal pathogens impacting animal and plant biodiversity. Approximately 8,000 species of fungi and Oomycetes are associated with plant disease. Indeed, across agriculture, such fungal diseases of plants include new devastating epidemics of trees and jeopardize food security worldwide by causing epidemics in staple and commodity crops that feed billions. Further, ingestion of mycotoxins contributes to ill health and causes cancer. Coordinated international research efforts, enhanced technology translation, and greater policy outreach by scientists are needed to more fully understand the biology and drivers that underlie the emergence of fungal diseases and to mitigate against their impacts. Here, we focus on poignant examples of emerging fungal threats in each of three areas: human health, wildlife biodiversity, and food security.

mBio↗
USGS1998

Relationship between deer mouse population parameters and dieldrin contamination in the Rocky Mountain Arsenal National Wildlife Refuge

A small-mammal capture-recapture study was conducted in the Rocky Mountain Arsenal National Wildlife Refuge to quantify the effects of soil contamination with dieldrin on demographic parameters of deer mouse (Peromyscus maniculatus) populations. Increased dieldrin concentrations were significantly associated with larger deer mouse populations, although the size of populations on contaminated sites decreased during the study. The most parsimonious model for estimating survival rates was one in which survival was a decreasing function of dieldrin concentration. A significantly higher proportion of female deer mice in the populations residing on the more highly contaminated sites exhibited signs of reproductive activity. Development of genetic resistance in P. maniculatus to chronic chemical exposure is suggested as a possible mechanism responsible for the species' observed dominance and relatively high densities on contaminated sites. Under the additional stress of unfavorable environmental conditions, however, these populations may suffer disproportionately greater mortality. The design and analytical methods presented offer a rigorous statistical approach to assessing the effects of environmental contamination on small mammals at the population level.

Canadian Journal of Zoology↗
USGS1998

Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for humans and wildlife

An expert meeting was organized by the World Health Organization (WHO) and held in Stockholm on 15-18 June 1997. The objective of this meeting was to derive consensus toxic equivalency factors (TEFs) for polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and dioxinlike polychlorinated biphenyls (PCBs) for both human, fish, and wildlife risk assessment. Based on existing literature data, TEFs were (re)evaluated and either revised (mammals) or established (fish and birds). A few mammalian WHO-TEFs were revised, including 1,2,3,7,8-pentachlorinated DD, octachlorinated DD, octachlorinated DF, and PCB 77. These mammalian TEFs are also considered applicable for humans and wild mammalian species. Furthermore, it was concluded that there was insufficient in vivo evidence to continue the use of TEFs for some di-ortho PCBs, as suggested earlier by Ahlborg et al. [Chemosphere 28:1049-1067 (1994)]. In addition, TEFs for fish and birds were determined. The WHO working group attempted to harmonize TEFs across different taxa to the extent possible. However, total synchronization of TEFs was not feasible, as there were orders of a magnitude difference in TEFs between taxa for some compounds. In this respect, the absent or very low response of fish to mono-ortho PCBs is most noticeable compared to mammals and birds. Uncertainties that could compromise the TEF concept were also reviewed, including nonadditive interactions, differences in shape of the dose-response curve, and species responsiveness. In spite of these uncertainties, it was concluded that the TEF concept is still the most plausible and feasible approach for risk assessment of halogenated aromatic hydrocarbons with dioxinlike properties.

Environmental Health Perspectives↗
USGS2004

Tertiary thrust systems and fluid flow beneath the Beaufort coastal plain (1002 area), Arctic National Wildlife Refuge, Alaska, U.S.A.

Beneath the Arctic coastal plain (commonly referred to as "the 1002 area") in the Arctic National Wildlife Refuge, northeastern Alaska, United States, seismic reflection data show that the northernmost and youngest part of the Brookian orogen is preserved as a Paleogene to Neogene system of blind and buried thrust-related structures. These structures involve Proterozoic to Miocene (and younger?) rocks that contain several potential petroleum reservoir facies. Thermal maturity data indicate that the deformed rocks are mature to overmature with respect to hydrocarbon generation. Oil seeps and stains in outcrops and shows in nearby wells indicate that oil has migrated through the region; geochemical studies have identified three potential petroleum systems. Hydrocarbons that were generated from Mesozoic source rocks in the deformed belt were apparently expelled and migrated northward in the Paleogene, before much of the deformation in this part of the orogen. It is also possible that Neogene petroleum, which was generated in Tertiary rocks offshore in the Arctic Ocean, migrated southward into Neogene structural traps at the thrust front. However, the hydrocarbon resource potential of this largely unexplored region of Alaska's North Slope remains poorly known. In the western part of the 1002 area, the dominant style of thin-skinned thrusting is that of a passive-roof duplex, bounded below by a detachment (floor thrust) near the base of Lower Cretaceous and younger foreland basin deposits and bounded above by a north-dipping roof thrust near the base of the Eocene. East-west-trending, basement-involved thrusts produced the Sadlerochit Mountains to the south, and buried, basement-involved thrusts are also present north of the Sadlerochit Mountains, where they appear to feed displacement into the thin-skinned system. Locally, late basement-involved thrusts postdate the thin-skinned thrusting. Both the basement-involved thrusts and the thin-skinned passive-roof duplex were principally active in the Miocene. In the eastern part of the 1002 area, a northward-younging pattern of thin-skinned deformation is apparent. Converging patterns of Paleocene reflectors on the north flank of the Sabbath syncline indicate that the Aichilik high and the Sabbath syncline formed as a passive-roof duplex and piggyback basin, respectively, just behind the Paleocene deformation front. During the Eocene and possibly the Oligocene, thin-skinned thrusting advanced northward over the present location of the Niguanak high. A passive-roof duplex occupied the frontal part of this system. The Kingak and Hue shales exposed above the Niguanak high were transported into their present structural position during the Eocene to Oligocene motion on the long thrust ramps above the present south flank of the Niguanak high. Broad, basement-cored subsurface domes (Niguanak high and Aurora dome) formed near the deformation front in the Oligocene, deforming the overlying thin-skinned structures and feeding a new increment of displacement into thin-skinned structures directly to the north. Deformation continued through the Miocene above a detachment in the basement. Offshore seismicity and Holocene shortening documented by previous workers may indicate that contractional deformation continues to the present day.

Alaska↗
USGS2014

Using auxiliary information to improve wildlife disease surveillance when infected animals are not detected: A Bayesian approach

There are numerous situations in which it is important to determine whether a particular disease of interest is present in a free-ranging wildlife population. However adequate disease surveillance can be labor-intensive and expensive and thus there is substantial motivation to conduct it as efficiently as possible. Surveillance is often based on the assumption of a simple random sample, but this can almost always be improved upon if there is auxiliary information available about disease risk factors. We present a Bayesian approach to disease surveillance when auxiliary risk information is available which will usually allow for substantial improvements over simple random sampling. Others have employed risk weights in surveillance, but this can result in overly optimistic statements regarding freedom from disease due to not accounting for the uncertainty in the auxiliary information; our approach remedies this. We compare our Bayesian approach to a published example of risk weights applied to chronic wasting disease in deer in Colorado, and we also present calculations to examine when uncertainty in the auxiliary information has a serious impact on the risk weights approach. Our approach allows “apples-to-apples” comparisons of surveillance efficiencies between units where heterogeneous samples were collected

PLoS ONE↗
USGS2015

Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publicly available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Wyoming↗