Geology topics

Angela K. Fuller

Publications and source records attributed to Angela K. Fuller.

At least 19 recordsLinked to original sources

USGS2026

Decision-centered systems for the future of wildlife conservation

The future of wildlife conservation will be determined by how well we decide, not simply by how much we know. Advances in quantitative ecology, sensor networks, and artificial intelligence have enhanced the field's ability to estimate population states and predict ecological dynamics. Yet, these technological and analytical advancements have not translated into proportional improvements in wildlife conservation decision making. The prevailing science-centered paradigm, which prioritizes research production as the primary route to better management, was appropriate when limited ecological understanding was the primary constraint on decision making. Today, wildlife conservation challenges are more often restricted by inadequate decision-making processes rather than by insufficient information. This paper defines a decision-centered system as an institutional and organizational arrangement in which the generation, integration, and application of scientific knowledge are explicitly structured around the decisions required for wildlife conservation. Decision systems evaluate success not only through scientific rigor, but through the quality of decision-making processes and outcomes they support. Artificial intelligence (AI) is likely to deepen this mismatch by rapidly expanding analytical capacity while leaving the underlying challenges of decision making unresolved. As AI data synthesis, modeling, and estimation become increasingly automated, the comparative advantage of conservation professionals is likely to shift toward tasks requiring human judgment (e.g., eliciting stakeholder values, weighting competing management objectives). The conceptual basis for this shift already exists in structured decision making, adaptive management, and similar frameworks. The remaining barriers to implementation are both institutional and organizational. The rules, norms, and incentive structures governing what counts as good conservation work, including what gets funded, published, and rewarded, will benefit from shifting alongside the agencies, universities, and professional societies that train and employ wildlife professionals. Conditions for this transition include redesigning incentives, training, and governance so that organizations are prepared not only to generate knowledge, but to consistently translate it into effective action.

The Wildlife Society Bulletin↗

USGS2026

Bayesian belief network model to predict human-wildlife conflict in protected areas

Human-wildlife conflict (HWC) poses a pervasive global challenge, affecting livelihoods and threatening biodiversity. To better anticipate and mitigate HWC risk, we developed a large-scale predictive model using a Bayesian Belief Network (BBN). We surveyed 1,011 park rangers across 135 terrestrial protected areas in three Andean countries, documenting recent HWC incidents involving wildlife persecution or killing, livestock depredation, crop damage, or threats to human safety and property. We identified key drivers of HWC risk, including governance, wildlife acceptance, participation, and habitat quality. A sensitivity analysis revealed that enhancing governance and improving wildlife acceptance could reduce HWC risk by > 85%. The BBN model demonstrated scalability, effectively identifying strategies to reduce HWC risk at multiple scales, from individual protected areas to national networks. Our findings highlight the importance of strengthening governance, increasing wildlife acceptance, and enhancing community participation in conservation efforts. BBNs provide a flexible, cost-effective, and data-driven tool to guide protected areas and wildlife managers in monitoring, anticipating, and making informed decisions to mitigate conflict and promote coexistence.

Scientific Reports↗

USGS2026

Exploring management options for moose at their southern range limits considering growing disease risk

1. Populations of cold-adapted species are increasingly vulnerable along their low-latitude range limits due to shifting environmental conditions, biotic interactions, and anthropogenic pressures. Managing these populations is particularly challenging because of complex ecological dynamics, conflicting stakeholder interests, and decision-making under uncertainty. 2. We explored population growth (λ) of moose ( Alces alces ) under different hypothetical management scenarios, simulating combinations of five hypothetical harvest levels with three levels of disease impact. 3. Facing current disease, the population could support multiple levels of conservative harvest. Under elevated levels of disease-caused mortality, projections indicated declines for all harvest scenarios. We projected increases across all harvest scenarios when overlap with white-tailed deer ( Odocoileus virginianus ) was hypothetically reduced and disease mortality in moose minimized. We investigated whether uncertainty in moose demographic parameters altered population trajectory, and found changes in adult fecundity and calf survival could alter harvest decisions when λ<1. 4. Practical implication . While moose population trajectories may remain stable under current conditions, management of white-tailed deer that reduces moose exposure to lethal parasites may provide the greatest utility in sustaining moose in New York given the potential for increased disease. Continued monitoring of population size, growth, and disease prevalence would inform sustainable moose harvest levels that balance social and ecological management considerations.

New York↗

USGS2026

Leopard occupancy and habitat use in the multi-use Chitwan-Annapurna Landscape, Nepal

We estimated leopard ( Panthera pardus fusca ) occupancy in a multi-use region within Nepal’s Chitwan-Annapurna Landscape to evaluate leopard habitat use and inform conservation planning in areas where most of the species’ habitat occurs outside protected areas. In 2021, sign surveys were conducted along 1277 km of transects distributed among 145 grid cells of 7×7 km within a 7105 km² study area, where 226 leopard signs (pugmarks and scats) were documented. We used an occupancy modeling framework to evaluate the influence of environmental and anthropogenic factors on leopard habitat use. We found that leopard occupancy (ψ = 0.73 ± 0.17 CI) was strongly and positively associated with areas used by wild prey such as red muntjac ( Muntiacus muntjak ), rhesus macaques ( Macaca mulatta ), chital ( Axis axis ), and wild boars ( Sus scrofa ). Our results provide evidence that large carnivores like leopards can persist in human-dominated landscapes when native prey remains abundant, underscoring the need for community-based conservation that sustains both prey and predator populations beyond protected areas. By estimating leopard occupancy outside of protected areas, the research establishes a baseline for developing management strategies to ensure the continued existence of leopards in Nepal's multi-use landscapes.

Chitwan-Annapurna Landscape↗

USGS2025

Moose survival and habitat‐associated risk of endoparasites

Parasite-induced morbidity and mortality can alter the trajectories of incidental host populations. Yet, parasites rarely act in isolation and may be one of a multitude of biotic and abiotic stressors that collectively shape mortality risk in vertebrate populations. We quantified sources of mortality in a low-density population of moose ( Alces alces ) in New York State and investigated factors including parasite infection, nutritional limitation, and thermal stress influencing mortality risk in calf moose. We observed high rates of annual survival (0.81–0.92) in adult ( n = 25) and calf ( n = 27) moose monitored 2015–2018 and 2022–2024, respectively. The majority of cause-specific mortality was attributed to disease induced by giant liver fluke ( Fascioloides magna ; 75% in adults, 67% in calves). Calf mortality risk increased by 72% for every unit increase in giant liver fluke infection risk, measured as cumulative monthly proportion of wetlands used by moose, and decreased by 16% with each additional unit of nutritional energy available. The combination of flukes, coinfecting parasites, and available nutritional energy is important to calf survival in this population, highlighting the importance of managing multiple stressors for species conservation, although the effects are hard to disentangle given the high rates of survival observed. Identifying causes of mortality and mechanisms underlying increased mortality risk contributes to the continued conservation of moose in fluctuating populations and highlights the importance of managing parasite-induced disease.

New York↗

USGS2025

DeepFaune New England: A species classification model for trail camera images in northeastern North America

The DeepFaune New England model classifies wildlife species in trail camera images, identifying 24 taxa from northeastern North America with high (97%) accuracy. The model was adapted from the DeepFaune model for identifying European wildlife, demonstrating the practicality of transfer learning across continents. The majority of training data is openly licensed, and the model itself is open source, enabling easy integration into camera trapping workflows. The open source software is available at ( https://code.usgs.gov/vtcfwru/deepfaune-new-england ), and has been further integrated into the PyTorch-Wildlife framework.

New England↗

USGS2025

Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes

1. Current and accurate information on wildlife populations is integral to successful biodiversity management and conservation globally. Nevertheless, many monitoring programs fail in their attempts to accurately monitor populations of interest due to interlinked issues including insufficient sample sizes, inappropriate duration, lack of reproducibility, and lack of clearly stated objectives. These common pitfalls could be avoided through the elicitation of explicit monitoring objectives and the a priori use of simulations to inform minimum sampling design requirements to meet said objectives. 2. Here, we provide a blueprint for using spatially explicit power analyses to inform the design and implementation of multi-species monitoring programs on landscape-scales. As a demonstration, we used spatially explicit simulations to devise a suitable sampling regime to meet clearly specified monitoring objectives in New York State: to use annual occupancy-based monitoring to be able to detect 25% and 50% changes in abundance of populations over five- and ten- year periods for all species of management interest in New York State, USA. We focused our simulation efforts on three challenging focal species (black bear, Ursus americanus, bobcat, Lynx rufus, and American marten, Martes americana) that differ notably in their morphology, life histories, space use, detection probability, habitat suitability, and population sizes/trajectories, and thus provide extremes in the challenges presented when it comes to sampling appropriately to detect changes in abundance. 3. Our simulations demonstrate variable context dependent trade-offs in sampling designs (i.e. number of sites [J] and number of sampling occasions [K]), and identify necessary minimum detection probabilities that must be attained to achieve statistical power to detect changes of varying magnitudes in populations of varying sizes in the three focal species. The simulations also highlight that monitoring population increases is likely beyond the reach of occupancy-based monitoring programs for wide-ranging or locally abundant species. 4. Synthesis and applications : We combine the results from the single-species simulations to produce a multi-species sampling design that meets the specified objectives for all three species. While the case study is centered on developing a multi-species sampling regime for New York State, it provides a reproducible step-by-step framework using established methods for wildlife managers and other practitioners to inform their own context- and objective- specific multi-species occupancy-based monitoring programs.

New York↗

USGS2025

Parasite‐mediated competition limits dominant cervid competitor

Species interactions structure ecological communities through direct and indirect pathways with ecosystem-wide implications. Despite mounting interest in the importance of indirect interactions, empirical evidence remains limited. Here, we demonstrate the critical role of parasite-mediated competition in driving community outcomes in a multi-species system of conservation and management concern. We leveraged 2 years of detection/non-detection data of moose ( Alces alces ) and white-tailed deer ( Odocoileus virginianus ) and parasite loads in faecal samples within a hierarchical abundance-mediated interaction model to test hypotheses regarding interactions between these cervids and their shared parasites ( Parelaphostrongylus tenuis , Fascioloides magna ). We demonstrate that moose occupancy was limited by parasite-mediated competition, with no evidence of population-level effects of direct competitive interactions between moose and white-tailed deer. Such evidence of the importance of indirect interactions and resulting community outcomes is critical for species conservation and managing range contractions due to increasing pressures from habitat loss, disease and climate change.

New York↗

USGS2025

Highly pathogenic avian influenza virus exposure and infection in free-ranging bobcats (Lynx rufus) in New York, USA

Highly pathogenic H5N1 influenza A virus (HP H5N1) cases in wild mammals have been increasing globally. Carnivora has been the most affected mammal order; however, the extent of morbidity and mortality in carnivores exposed to HP H5N1 remains undefined. We assessed the presence of antibodies to H5 and N1 in the sera of 16 live-captured bobcats ( Lynx rufus ) in New York State, USA; reported on postcapture outcomes of HP H5N1-exposed bobcats; and reported on a case of HP H5N1 infection in one bobcat. In 2024, 4/16 bobcats (25%) tested positive for antibodies to H5 and N1. Two bobcats were confirmed alive as of November 2024 according to GPS data; the other two were alive as of June 2024, when their GPS collars stopped communicating. Another bobcat, which was negative for HP H5N1 antibodies at capture, died from HP H5N1 infection within 5 wks of capture. Our results provide evidence of bobcats both surviving and succumbing to HP H5N1 infection and highlight the importance of focused health studies paired with monitoring data to better understand exposure, infection, and outcomes for novel pathogens and species.

New York↗

USGS2024

Effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly

Prevention and early detection of invasive species are championed as the most cost-effective and efficient strategies for reducing or preventing negative impacts on ecosystems. Spotted lanternfly (SLF), Lycorma delicatula , is a recently introduced invasive insect whose range in the United States has been expanding rapidly since it was first discovered in Pennsylvania in 2014. Feeding by this planthopper can cause severe impacts on agricultural production, particularly grapes ( Vitis spp.). Human visual surveys are the most common search method employed for detection but can be ineffective due to the insect's cryptic egg masses and low density during early stages of infestation. Therefore, finding alternative early detection methods has become a priority for agencies tasked with addressing SLF management. This study experimentally tested whether trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. We surveyed transects in 20 vineyards and their adjacent wooded areas in Pennsylvania and New Jersey, USA, and used a multiscale occupancy model to estimate detection probability achieved by human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. Occupancy probability of SLF was higher on vines within vineyards than in forests, and occupancy declined with increasing distance from forests, which is informative for future search efforts. Detection probability of SLF was lower at forested sites but was higher at high infestation sites. Detection dogs had a lower detection probability than humans in the vineyards, but the detection probability of dogs was >3× greater than that of humans in forested sites. Our study suggests that detection dogs are more effective than human visual searches as an early detection method for SLF in forested areas, and utilizing detector dogs could strengthen SLF early detection efforts. This study demonstrates the potential applicability of using canine-assisted search strategies combined with occupancy models to enhance the surveillance and prevention of other difficult-to-detect invasive species.

New Jersey, Pennsylvania↗

USGS2024

Abundance-mediated species interactions

Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes ( Canis latrans ), fishers ( Pekania pennanti ), and American marten ( Martes americana ) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.

Ecology↗

USGS2024

Integrating presence-only and detection/non-detection data to estimate distributions and expected abundance of difficult-to-monitor species on a landscape-scale

Estimating species distribution and abundance is foundational to effective management and conservation. Using an integrated species distribution model that combines presence-only data from various sources with detection/non-detection data from structured surveys, we estimated the distribution and expected abundance of three difficult-to-monitor mammals of management concern across New York State, namely, coyotes ( Canis latrans ), bobcats ( Lynx rufus ) and black bears ( Ursus americanus ). Three distinct landscape-scale camera trap surveys provided detection/non-detection data over 9 years between 2013 and 2021, and we augmented those data with incidental records of our focal species from public repositories. We used an inhomogeneous Poisson point process to construct an integrated model that fit both data types simultaneously. We demonstrate a simple application of spatial point density of all species records in the accessed public databases to inform the thinning process to account for unknown spatial sampling in the presence-only data, often referred to as the ‘magic covariate’. Using this approach, we examine habitat associations and provide spatially explicit estimates in expected abundance across the entirety of New York State for all three focal species. As expected, coyotes were the most widely distributed and abundant species, with a strong positive association with agricultural land uses. Bobcats exhibited low expected abundance throughout the state and showed positive associations with deciduous forest and forest edge, and a negative association with road density. Finally, we observed considerable spatial variation in abundance of black bears with expected abundance increasing in association with various forest cover and composition covariates and decreasing with crop cover. We present insights into habitat associations and spatial variation in abundance, and provide management implications for each of the species of interest. Synthesis and applications . Our integrated modelling method allows for managers to use citizen sightings combined with detection/non-detection surveys to estimate robust indices of abundance for both high- and low-density, and wide-spread versus patchily distributed species. Through comparison with previous studies, we highlight how broad-scale programmes, such as the statewide efforts to estimate species distributions undertaken here, can benefit substantively from integrated models that leverage additional data (here, incidental records) from a larger region of space, and thus capture more landscape heterogeneity than is plausible within formalized surveys alone.

New York↗

USGS2024

Intraguild interactions and abiotic conditions mediate occupancy of mammalian carnivores: Co-occurrence of coyotes–fishers–martens

The widespread eradication of large carnivores and subsequent expansion of top mesopredators has the potential to impact species and community interactions with ecosystem-wide implications. An example of these trophic dynamics is the widespread establishment of coyotes following extirpation of wolves and mountain lions in eastern North America. Here, we examined occupancy of three carnivores in northern New York considering both environmental/habitat factors and interspecific interactions. We estimated the co-occurrence of coyotes, fishers, and martens from a landscape-scale winter camera trap survey repeated annually for three years. Martens occurred independently of both coyotes and fishers, while fishers and coyotes displayed positive intraguild interactions that were constant across the landscape. Both marten and fisher first-order occupancy were driven by a combination of biotic and abiotic factors, with both species displaying positive associations with forest cover but antithetical responses to average snow depth. The integral and antithetical role of snow depth in driving the occurrence of martens (positive) and fishers (negative) in the landscape indicates that future climatic warming could reduce the availability of current spatial refuges for martens created by severe winter conditions. Climate-driven alterations to established competitive interactions and co-existence patterns between marten and fisher have critical implications for the species survival and conservation. We provide correlational evidence consistent with the potential for positive top–down effects of dominant mesocarnivores on subordinate species, with fisher occupancy increasing conditional on the presence of coyotes across the landscape. These findings align with the hypothesis that under certain conditions, coyotes may facilitate certain subordinate carnivores. The evidence produced here is consistent with hypotheses on the dynamic nature of trophic niches. We demonstrate the need to consider the interplay between climate, habitat, and interspecific interactions to understand wildlife occupancy patterns and inform wildlife management in a rapidly changing world.

New York↗

USGS2024

Using global remote camera data of a solitary species complex to evaluate the drivers of group formation

The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of “social” and “solitary” inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae , Order Carnivora ) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species’ context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation.

PNAS↗

USGS2024

Landscape-scale population trends in the occurrence and abundance of wildlife populations using long term camera-trapping data

Accurate estimation and monitoring of wildlife population trends is foundational to evidence-based conservation. Here, we use hierarchical modelling to estimate population trends for six species of management interest (coyotes; red foxes , white-tailed deer , gray foxes; eastern wild turkey , and bobcats) while accounting for observation error from a long-term camera trap survey conducted across the State of New York. We were able to detect population level trends in occurrence and abundance and produce spatially explicit predictions for all six species using a combination of single-species occupancy models and Royle-Nichols models. Coyote (mean λ = 1.22, 95 % CI = 0.85–1.82) and red fox (mean λ = 1.17, 95 % CI = 0.95–1.46) populations were widely distributed with stable populations across the sampling period from 2014 to 2021. White-tailed deer populations were highly abundant and displayed an increasing population trend (mean λ = 1.85, 95 % CI = 1.54–2.10). Eastern wild turkey occupancy remained low across the state despite displaying a slight increase in occupancy over the sampling period (mean ψ = 0.16, 95 % CI = 0.07–0.25). Gray fox occupancy was also low (mean ψ = 0.22, 95 % CI = 0.12–0.29), consistent with growing concerns over the species across North America. Despite recent recoveries elsewhere, bobcat populations in New York State displayed very low occupancy (mean ψ = 0.07, 95 % CI = 0.02–0.12), highlighting the necessity of monitoring to inform conservation action. We provide empirically supported management implications for each species and demonstrate the efficacy of long-term camera trapping to provide robust evidence on population trends while accounting for imperfect detections, over scales meaningful to species management and conservation.

Biological Conservation↗

USGS2023

Pardus in the press: Drivers of leopard (Panthera pardus fusca) attack occurrence on humans in Nepal

The negative impact of large carnivore presence in human-dominated landscapes manifests as livestock depredation and in extreme cases as attacks on humans. In the case of conflict with leopards in Nepal, attacks resulting in human fatality have become more frequent over time, thus creating an urgent socio-ecological and management issue. We estimated the occurrence of leopard attacks in Nepal from human-leopard conflict cases reported in the media. We used occupancy models to analyse data collected from online news reports on incidents of leopard attacks on humans to explore drivers of leopard attacks on a landscape scale. Our results suggest that the probability of occurrence of leopard attack is associated with human population density, terrain ruggedness and livestock density. The human population density effect may be indicative of a density-dependent relationship, where attacks are more likely in areas where an increased abundance of humans increases encounter rates with leopards. The positive effect of livestock density suggests that livestock may be drawing leopards into human settlements, and consequently increasing the likelihood of attacks on humans. Terrain ruggedness might be offering ideal conditions to facilitate attacks on humans, for example remoteness and high amounts of cover to launch ambush attacks. We provide inference and insights into key determinants of leopard attacks on humans on a landscape scale. These insights can be used to guide future research, inform mitigation measures to reduce leopard attacks and foster a better understanding of the interaction between people and leopards. This study demonstrates the applicability and novelty of using a hierarchical modelling framework applied to freely and publicly available media reports to inform the applied management of human-wildlife conflict at a national scale.

People and Nature↗

USGS2023

Landscape-scale drivers of tayra abundance in the Ecuadorian Andes

Habitat conversion to agriculture and overexploitation of wildlife are the two largest drivers of biodiversity loss globally. Biodiversity loss is especially prevalent in areas undergoing rapid economic development at the expense of natural land cover as is the case across much of South America. Despite expected declines in wildlife populations associated with ongoing large-scale land conversions, for many species we lack sufficient data on key threats and drivers of abundance in order to inform appropriate management and conservation. Collecting data to estimate critical state variables such as abundance can be expensive, logistically challenging, and even implausible on spatial scales relevant to species management, especially for carnivores which are elusive and difficult to monitor. Here, we use detection-non-detection data collected using a structured camera trap survey repeated over two years in the Ecuadorian Andes to produce insights into the habitat associations and potential threats faced by the tayra, a medium-sized carnivore that despite being perceived to be relatively common in South America, remains largely understudied. We use hierarchical modelling to estimate an index of abundance for the tayra while accounting for imperfect detection. We demonstrate the tayra to be a lowland habitat generalist, with conversion of land to agriculture potentially benefitting this species in the short term, with increasing proportion of core agricultural land being associated with higher indices of abundance. However, we highlight that this state could potentially serve as an ecological trap in the long term. We provide evidence for negative impacts of human population density on tayra abundance. We hypothesize this relationship could be underpinned by conflict and subsequent persecution by humans, which is likely to be exacerbated in agricultural landscapes. These findings suggest that like many carnivores, the tayra may be able to adapt and even thrive in human modified landscapes given societal acceptance, thus, conservation strategies for the tayra that focus on fostering co-existence between humans and this medium-sized carnivore may contribute to its future.

Biodiversity and Conservation↗

USGS2023

Perception and trust influence acceptance for black bears more than bear density or conflicts

Introduction: To sustain black bear (Ursus americanus) populations, wildlife managers should understand the coupled socio-ecological systems that influence acceptance capacity for bears. Method: In a study area encompassing a portion of New York State, we spatially matched datasets from three sources: human-bear conflict reports between 2006 and 2018, estimates of local bear density in 2017–2018, and responses to a 2018 property owner survey (n=1,772). We used structural equation modeling to test hypothesized relationships between local human-bear conflict, local bear density, and psychological variables. Results: The final model explained 57% of the variance in acceptance. The effect of bear population density on acceptance capacity for bears was relatively small and was mediated by a third variable: perception of proximity to the effects of human-bear interactions. The variables that exerted a direct effect on acceptance were perception of bear-related benefits, perception of bear-related risks, perceived proximity to effects of human-bear interactions, and being a hunter. Perception of bear-related benefits had a greater effect on acceptance than perception of bear-related risks. Perceived proximity to effects of human-bear interactions was affected by local bear density, but also was affected by social trust. Increased social trust had nearly the same effect on perceived proximity as decreased bear density. Social trust had the greatest indirect effect on acceptance of any variable in the model. Discussion: Findings suggest wildlife agencies could maintain public acceptance for bears through an integrated approach that combines actions to address bear-related perceptions and social trust along with active management of bear populations.

New York↗