Dataset Information

Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint.

ABSTRACT: Anthropogenic landscape change (i.e., disturbance) is recognized as an important factor in the decline and extirpation of wildlife populations. Understanding and monitoring the relationship between wildlife distribution and disturbance is necessary for effective conservation planning. Many studies consider disturbance as a covariate explaining wildlife behavior. However, we propose that there are several advantages to considering the spatial relationship between disturbance and wildlife directly using utilization distributions (UDs), including objective assessment of the spatially explicit overlap between wildlife and disturbance, and the ability to track trends in this relationship over time. Here, we examined how central mountain woodland caribou (Rangifer tarandus caribou) distribution changed over time in relation to (i) anthropogenic disturbance, baseline range (defined using telemetry data from 1998 to 2005), and alpine habitat; and (ii) interannual climate variation (North Pacific Index; NPI). We developed seasonal UDs for caribou in west-central Alberta and east-central British Columbia, Canada, monitored with GPS collars between 1998 and 2013. We mapped the cumulative annual density of disturbance features within caribou range and used indices of overlap to determine the spatial relationship and trend between caribou UDs, anthropogenic disturbance, baseline range, alpine habitat, and the NPI. Anthropogenic disturbance increased over time, but the overlap between caribou UDs and disturbance did not. Caribou use of alpine habitat during spring, fall, and late winter increased over time, concurrent with a decrease in use of baseline range. Overlap between caribou UDs and disturbance increased during spring and fall following relatively cold, snowy winters (high NPI), but overall, climate did not explain changes in caribou distribution over time. We provide evidence supporting the hypothesis that caribou populations adjust their spatial distribution in relation to anthropogenic landscape change. Our findings could have implications for population persistence if distributional shifts result in greater use of alpine habitat during winter. Monitoring long-term changes in the distribution of populations is a valuable component of conservation planning for species at risk in disturbed landscapes.

SUBMITTER: MacNearney D

PROVIDER: S-EPMC5058522 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint.

MacNearney Doug D Pigeon Karine K Stenhouse Gordon G Nijland Wiebe W Coops Nicholas C NC Finnegan Laura L

Ecology and evolution 20160818 18

Anthropogenic landscape change (i.e., disturbance) is recognized as an important factor in the decline and extirpation of wildlife populations. Understanding and monitoring the relationship between wildlife distribution and disturbance is necessary for effective conservation planning. Many studies consider disturbance as a covariate explaining wildlife behavior. However, we propose that there are several advantages to considering the spatial relationship between disturbance and wildlife directly ...[more]

PMID: 27777724

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Project description:Woodland caribou (Rangifer tarandus caribou) are threatened in Canada, with population and distribution declines evident in most regions of the country. Causes of declines are linked to landscape change from forest fires and human development, notably forestry oil and gas activities, which result in caribou habitat loss and affect ecosystem food webs. The Federal Species at Risk Act requires effective protection and restoration of caribou habitat, with actions to increase caribou survival. These requirements call for effective monitoring of caribou population trends to gauge success. Many woodland caribou populations are nearly impossible to count using traditional aerial survey methods, but demographic-based monitoring approaches can be used to estimate population trends based on population modeling of vital rates from marked animals. Monitoring programs have used a well-known simple population model (the Recruitment-Mortality [R/M] equation) to estimate demographic rates for woodland caribou, but have faced challenges in managing large data streams and providing transparency in the demographic estimation process. We present a stand-alone statistical software application using open-source software to permit efficient, transparent, and replicable demographic estimation for woodland caribou populations. We developed an easy-to-use, interactive web-based application for the R/M population model that uses a Bayesian estimation approach and provides the user flexibility in choice of prior distributions and other output features. We illustrate the web-application to the A la Pêche Southern Mountain (Central Group) woodland caribou population in west-central Alberta, Canada, during 1998-2017. Our estimates of population demographics are consistent with previous research on this population and highlight the utility of the application in assessing caribou population responses to species recovery actions. We provide example data, computer code, the web-based application package, and a user manual to guide installation and use. We also review underlying assumptions and challenges of population monitoring in this case study. We expect our software will contribute to efficient monitoring of woodland caribou and help in the assessment of recovery actions for this species. © 2019 The Authors. Wildlife Society Bulletin Published by Wiley Periodicals, Inc.

Project description:Land use and land cover (LULC) change is one anthropogenic disturbance linked to infectious disease emergence. Current research has focused largely on wildlife and vector-borne zoonotic diseases, neglecting to investigate landscape disturbance and environmental bacterial infections. One example is Buruli ulcer (BU) disease, a necrotizing skin disease caused by the environmental pathogen Mycobacterium ulcerans (MU). Empirical and anecdotal observations have linked BU incidence to landscape disturbance, but potential relationships have not been quantified as they relate to land cover configurations.A landscape ecological approach utilizing Bayesian hierarchical models with spatial random effects was used to test study hypotheses that land cover configurations indicative of anthropogenic disturbance were related to Buruli ulcer (BU) disease in southern Benin, and that a spatial structure existed for drivers of BU case distribution in the region. A final objective was to generate a continuous, risk map across the study region. Results suggested that villages surrounded by naturally shaped, or undisturbed rather than disturbed, wetland patches at a distance within 1200 m were at a higher risk for BU, and study outcomes supported the hypothesis that a spatial structure exists for the drivers behind BU risk in the region. The risk surface corresponded to known BU endemicity in Benin and identified moderate risk areas within the boundary of Togo.This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence. Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

Dataset Information

Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint.

Publications

Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint.

Similar Datasets

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