Inter-population variability in movement parameters: practical implications for population density estimation
Motion-sensitive cameras are popular as non-invasive monitoring tools, and several methods have been developed to estimate population densities from camera data. These methods frequently rely on auxiliary movement data including the distance traveled by an individual in a day and the proportion of t...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/353552 |
| Acceso en línea: | http://hdl.handle.net/10261/353552 |
| Access Level: | acceso abierto |
| Palabra clave: | Abundance Activity Camera trap Unmarked Day range Random encounter model Ecology Non‐invasive |
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| dc.title.none.fl_str_mv |
Inter-population variability in movement parameters: practical implications for population density estimation |
| title |
Inter-population variability in movement parameters: practical implications for population density estimation |
| spellingShingle |
Inter-population variability in movement parameters: practical implications for population density estimation Palencia, Pablo Abundance Activity Camera trap Unmarked Day range Random encounter model Ecology Non‐invasive |
| title_short |
Inter-population variability in movement parameters: practical implications for population density estimation |
| title_full |
Inter-population variability in movement parameters: practical implications for population density estimation |
| title_fullStr |
Inter-population variability in movement parameters: practical implications for population density estimation |
| title_full_unstemmed |
Inter-population variability in movement parameters: practical implications for population density estimation |
| title_sort |
Inter-population variability in movement parameters: practical implications for population density estimation |
| dc.creator.none.fl_str_mv |
Palencia, Pablo Acevedo, Pelayo Hofmeester, Tim R. Sereno-Cadierno, Jorge Vicente, Joaquín |
| author |
Palencia, Pablo |
| author_facet |
Palencia, Pablo Acevedo, Pelayo Hofmeester, Tim R. Sereno-Cadierno, Jorge Vicente, Joaquín |
| author_role |
author |
| author2 |
Acevedo, Pelayo Hofmeester, Tim R. Sereno-Cadierno, Jorge Vicente, Joaquín |
| author2_role |
author author author author |
| dc.contributor.none.fl_str_mv |
Ministerio de Economía y Competitividad (España) Ministerio de Ciencia e Innovación (España) Ministerio de Ciencia, Innovación y Universidades (España) Junta de Comunidades de Castilla-La Mancha Agencia Estatal de Investigación (España) European Commission Swedish Environmental Protection Agency Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
Abundance Activity Camera trap Unmarked Day range Random encounter model Ecology Non‐invasive |
| topic |
Abundance Activity Camera trap Unmarked Day range Random encounter model Ecology Non‐invasive |
| description |
Motion-sensitive cameras are popular as non-invasive monitoring tools, and several methods have been developed to estimate population densities from camera data. These methods frequently rely on auxiliary movement data including the distance traveled by an individual in a day and the proportion of the day that an animal spends moving when individual recognition is not possible. The estimation of these movement parameters is time-consuming, which could limit the applicability of cameras to estimate population density. To investigate the relevance of measuring movement parameters for the target population, we monitored 54 wildlife populations of red deer (Cervus elaphus), fallow deer (Dama dama), roe deer (Capreolus capreolus), wild boar (Sus scrofa), and red fox (Vulpes vulpes) in different seasons through Europe with cameras. We estimated 91-day ranges and activity levels. We fitted mixed models for day range and activity level as response variables to assess if the inter-population variability in movement was explained by a set of a priori relevant geographical, environmental, biological, and management predictors. We then explored the bias in density estimates obtained in 25 independent populations when using predicted movement data. There was high intra-species variation in day range and activity level among species and populations. Only species explained a small proportion of this variability; other predictor variables did not. We observed bias in densities when predicting the day range and activity for independent populations. Considering the intra-species variability in movement parameters and the consequent unacceptable bias in density estimates, we recommend that monitoring and conservation programs estimate movement parameters for the target population and survey populations from camera data for more accurate density estimates. While this increases the handling time needed to estimate densities, it is worth the cost because of the reliability of camera-based methodologies to estimate needed movement parameters. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2024 2024 |
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info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Publisher's version info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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http://hdl.handle.net/10261/353552 |
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http://hdl.handle.net/10261/353552 |
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Inglés |
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Inglés |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Wiley-VCH |
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Wiley-VCH |
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reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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Inter-population variability in movement parameters: practical implications for population density estimationPalencia, PabloAcevedo, PelayoHofmeester, Tim R.Sereno-Cadierno, JorgeVicente, JoaquínAbundanceActivityCamera trapUnmarkedDay rangeRandom encounter modelEcologyNon‐invasiveMotion-sensitive cameras are popular as non-invasive monitoring tools, and several methods have been developed to estimate population densities from camera data. These methods frequently rely on auxiliary movement data including the distance traveled by an individual in a day and the proportion of the day that an animal spends moving when individual recognition is not possible. The estimation of these movement parameters is time-consuming, which could limit the applicability of cameras to estimate population density. To investigate the relevance of measuring movement parameters for the target population, we monitored 54 wildlife populations of red deer (Cervus elaphus), fallow deer (Dama dama), roe deer (Capreolus capreolus), wild boar (Sus scrofa), and red fox (Vulpes vulpes) in different seasons through Europe with cameras. We estimated 91-day ranges and activity levels. We fitted mixed models for day range and activity level as response variables to assess if the inter-population variability in movement was explained by a set of a priori relevant geographical, environmental, biological, and management predictors. We then explored the bias in density estimates obtained in 25 independent populations when using predicted movement data. There was high intra-species variation in day range and activity level among species and populations. Only species explained a small proportion of this variability; other predictor variables did not. We observed bias in densities when predicting the day range and activity for independent populations. Considering the intra-species variability in movement parameters and the consequent unacceptable bias in density estimates, we recommend that monitoring and conservation programs estimate movement parameters for the target population and survey populations from camera data for more accurate density estimates. While this increases the handling time needed to estimate densities, it is worth the cost because of the reliability of camera-based methodologies to estimate needed movement parameters.P. Palencia received support from the MINECO-UCLM through an FPU grant (FPU16/00039) and a mobility grant (EST19/00481). This work was partly funded by the HAWIPO project MICINN (PID2019-111699RB-I00) and CAMEAR project JCCM (SBPLY/21/180501/000193), both co-funded by the European Union. The data collection from the Swedish populations was made possible by grants from the Swedish Environmental Protection Agency (Scandcam NV-00695-17, Beyond Moose NV-01337-15/NV-03047-16), Kempestiftelserna (grant JCK-1514), Västerbotten county's Älgvårdsfonden (grant 218-9314-15), and the Swedish Association for Hunting and Wildlife Management (Svenska Jägareförbundet, grant 5855/2015). T. Hofmeester received support from the Swedish EPA (NV-2020-00088).Peer reviewedWiley-VCHMinisterio de Economía y Competitividad (España)Ministerio de Ciencia e Innovación (España)Ministerio de Ciencia, Innovación y Universidades (España)Junta de Comunidades de Castilla-La ManchaAgencia Estatal de Investigación (España)European CommissionSwedish Environmental Protection AgencyConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/353552reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111699RB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.1002/jwmg.22473https://doi.org/10.1002/jwmg.22473Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3535522026-05-22T06:33:51Z |
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15,81155 |