Geology Reports⌕ Search

USGS · 70262055

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

Abstract

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.

Explore related subjects

90° N90° S · 180° W ← longitude → 180° E
Source-reported bounding extent: 40.760363749644455° to 43.50144007856014° latitude; -79.76953018691533° to -73.41562767810787° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

William F. Siemer, T. Bruce Lauber, Richard C. Stedman, Jeremy E. Hurst, Catherine C. Sun, Angela K. Fuller, Nicholas A. Hollingshead, Jerrold L. Belant, Kenneth Kellner. 2023-02-09. Perception and trust influence acceptance for black bears more than bear density or conflicts. https://doi.org/10.3389/fcosc.2023.1041393

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Mountain lion prey-selection in a community comprised of native and introduced ungulate species

Species introductions are a hallmark of human influence on ecosystems worldwide. In communities where native and introduced fauna coexist, predators may select prey based on availability or vulnerability. Free-roaming feral horses ( Equus caballus ) are a recent addition to North America and have become widespread, abundant, and in some areas serve as major prey for mountain lions ( Puma concolor ), a native carnivore. The proliferation of horses as well as potential effects of mountain lion predation on native ungulates create ecological and social management challenges; our goal was to assess mountain lion prey selection in a system composed of native mule deer ( Odocoileus hemionus ) and introduced feral horses. Our objectives were to: (1) describe seasonal patterns of prey use within individual mountain lion home ranges; (2) quantify prey use relative to availability; and (3) determine seasonal kill rates on the two primary ungulates in the study area. Fieldwork occurred in southeastern Nevada, 2021–2022. We identified prey use by investigating cache sites from 6 GPS-collared mountain lions. Prey availability and relative abundance were estimated with 49 camera traps distributed across the 3,158-km² study area. During the growing season, weekly kill rates averaged 0.22 deer and 0.55 horses, and in winter, 1.43 deer and 0.33 horses. Thus, lions killed horses at 2.54 times the rate of deer in the growing season but deer at 4.33 times the rate of horses in winter. Model selection indicated mule deer density and prey age class were the strongest predictors of prey species. Lions selected adult deer over fawns, and foals over adult horses, suggesting choices may be driven by optimal prey size rather than species. Mountain lions prey on foals in areas with relatively high mule deer densities, suggesting deer presence may increase foal vulnerability. We hypothesize that lions select foals when available, potentially reducing pressure on mule deer and/or supporting larger lion populations than would occur without this additional prey source. Although individual-level data on mountain lions are challenging to obtain, such information could help managers identify individuals for removal or conservation, improving predator-management outcomes in mixed-prey systems.

Nevada↗

Effects of mountain lion predation on reducing feral horse population growth rates: Panacea or pipedream?

A goal of applied ecology is to evaluate how demographic rates contribute to population growth and how demography might be manipulated to achieve management objectives. In western North America, many feral horse ( Equus caballus ) populations occupying public lands are protected by federal law and managed for site-specific numerical targets. However, feral horses can exhibit population growth rates exceeding 20% per yr, which can lead to overpopulation, and therefore conflict with other permitted land uses. In response, some stakeholder groups have advocated for natural solutions to the problem of rapid growth and overabundance. Mountain lion ( Puma concolor ) predation has been hypothesized to have suppressive effects on horse population growth rates under some conditions. Here, we evaluated the degree to which this phenomenon might reduce feral horse growth rates using elasticity analysis, scenario analysis with simulations, and an empirical state-space model for a horse population in southeastern Nevada subject to chronic predation. Age-specific elasticities revealed that annual population growth rates (λ) were more sensitive to perturbations in foal and yearling survival rates than for older age-classes. This finding, in conjunction with empirical measures indicating that foals comprised approximately 60% of horses killed by mountain lions, suggests that predation may have greater potential to reduce horse population growth rates than previously recognized. Scenario analysis predicted that horse populations could decrease to target levels within 10 years if predation reduced: (1) annual foal survival by 80% each year, or (2) annual survival rates of foals, yearlings, and 2-year olds by >60%. The state-space model indicated that the heavily predated Nevada horse population experienced positive population growth during 2022 and 2023 (λ = 1.07, both years). Using information on known predation-caused mortalities from 2020–2021, our model predicted that population growth rate in the absence of predation (‘predicted λ’) would have been approximately twice as high (predicted λ 2020 = 1.14; predicted λ 2021 = 1.14 in 2021). Taken together, our results suggest that predation is unlikely to singularly induce stable or negative population growth of feral horses. That said, our findings suggest two aspects of predation that may benefit resource managers. First, chronic predation may reduce λ, thereby increasing the time required for populations to either recover from declines, or exceed management objectives; and second, predation may be most effective in achieving management objectives for horse populations when combined with other interventions, such as removals or fertility control treatments.

Nevada↗

Captivity may broadly impede bison ecological function: Space, season, and management

American bison ( Bos bison ) were spared from numerical extinction but considered to be ecologically extinct, partially due to spatial restrictions in their former range. A key component of the ecological function of bison is their ability to adjust their spatial behaviors at multiple scales to match the seasonal distributions and availability of resources. To understand how management influences the amount and seasonality of space bison use at various scales, we estimated home range using autocorrelated kernel density estimators (95% utilization distribution) and foraging patch size by the maximum variance in first-passage time at four temporal scales (annual, growing, nongrowing, and focal seasons) for two free-ranging and three captive bison herds. The seasonal differences in space-use within management regimes and study areas were treated as nonparametric related measures while differences amongst management regimes and study areas were treated as nonparametric independent measures. Seasonal variations in space-use were detected in both management regimes, but only free-range bison depicted this pattern at the larger home range scale (p < 0.001). Free-range bison had significantly larger home ranges across seasons compared to captive bison (p < 0.001), but captive bison had larger foraging patch sizes during the growing season than free-range bison. Study areas with captive bison did not differ in their amount of space-use at any scale or season (p > 0.05). The strongest differences were apparent among study herds in annual home range size (p < 0.001) and growing season foraging patch size (p < 0.001). Our results demonstrate that captivity likely prevents bison from using their available landscape seasonally at broad scales, which could have ecological consequences for this historically nomadic herbivore. Although sociopolitical realities prevent bison restoration at a continental scale, increasing the amount of space available to bison where possible has the potential to replicate previous ecological processes. Further research can evaluate the influence of local environmental conditions on seasonality of bison space-use and can assess effects of scale on the ecological impacts of bison.

Colorado, Montana, Texas, Utah↗