Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife
Abstract Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-03-01
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| Series: | Ecosphere |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ecs2.3046 |
| _version_ | 1811198934983376896 |
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| author | Todd E. Katzner Melissa A. Braham Tara J. Conkling Jay E. Diffendorfer Adam E. Duerr Scott R. Loss David M. Nelson Hannah B. Vander Zanden Julie L. Yee |
| author_facet | Todd E. Katzner Melissa A. Braham Tara J. Conkling Jay E. Diffendorfer Adam E. Duerr Scott R. Loss David M. Nelson Hannah B. Vander Zanden Julie L. Yee |
| author_sort | Todd E. Katzner |
| collection | DOAJ |
| description | Abstract Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable. |
| first_indexed | 2024-04-12T01:39:36Z |
| format | Article |
| id | doaj.art-6bb098c866dd4a69805d8427ffaf4ad7 |
| institution | Directory Open Access Journal |
| issn | 2150-8925 |
| language | English |
| last_indexed | 2024-04-12T01:39:36Z |
| publishDate | 2020-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj.art-6bb098c866dd4a69805d8427ffaf4ad72022-12-22T03:53:14ZengWileyEcosphere2150-89252020-03-01113n/an/a10.1002/ecs2.3046Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlifeTodd E. Katzner0Melissa A. Braham1Tara J. Conkling2Jay E. Diffendorfer3Adam E. Duerr4Scott R. Loss5David M. Nelson6Hannah B. Vander Zanden7Julie L. Yee8U.S. Geological Survey Forest and Rangeland Ecosystem Science Center Boise Idaho USADivision of Geology and Geography West Virginia University Morgantown West Virginia USAU.S. Geological Survey Forest and Rangeland Ecosystem Science Center Boise Idaho USAU.S. Geological Survey Geosciences and Environmental Change Science Center Denver Colorado USABloom Research Los Angeles California USADepartment of Natural Resource Ecology & Management Oklahoma State University Stillwater Oklahoma USAAppalachian Laboratory University of Maryland Center for Environmental Science Frostburg Maryland USADepartment of Biology University of Florida Gainesville Florida USAU.S. Geological Survey Western Ecological Research Center Santa Cruz California USAAbstract Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.https://doi.org/10.1002/ecs2.3046anthropogenic stressorsbatsbirdsdemographic impactsintegrated population modelrenewable energy |
| spellingShingle | Todd E. Katzner Melissa A. Braham Tara J. Conkling Jay E. Diffendorfer Adam E. Duerr Scott R. Loss David M. Nelson Hannah B. Vander Zanden Julie L. Yee Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife Ecosphere anthropogenic stressors bats birds demographic impacts integrated population model renewable energy |
| title | Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife |
| title_full | Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife |
| title_fullStr | Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife |
| title_full_unstemmed | Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife |
| title_short | Assessing population‐level consequences of anthropogenic stressors for terrestrial wildlife |
| title_sort | assessing population level consequences of anthropogenic stressors for terrestrial wildlife |
| topic | anthropogenic stressors bats birds demographic impacts integrated population model renewable energy |
| url | https://doi.org/10.1002/ecs2.3046 |
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