Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data
While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect de...
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Online Access: | https://doi.org/10.5061/dryad.g9s1d |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::3e6782b86ba7dfe62cbc9c8f5126b41e 2023-05-15T15:50:14+02:00 Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data Louvrier, Julie Duchamp, Christophe Lauret, Valentin Marboutin, Eric Cubaynes, Sarah Choquet, Rémi Miquel, Christian Gimenez, Olivier 2017-01-01 https://doi.org/10.5061/dryad.g9s1d undefined unknown Dryad Digital Repository https://dx.doi.org/10.5061/dryad.g9s1d http://dx.doi.org/10.5061/dryad.g9s1d lic_creative-commons 10.5061/dryad.g9s1d oai:easy.dans.knaw.nl:easy-dataset:97757 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:97757 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 re3data_____::r3d100000044 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 Occupancy model large carnivores opportunistic data France 1994 - 2016 Canis lupus Life sciences medicine and health care envir geo Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2017 fttriple https://doi.org/10.5061/dryad.g9s1d 2023-01-22T16:53:26Z While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves (Canis lupus) that have been recolonizing France since the early 90’s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500m and positively influenced by the number of observed occupied sites at short and longdistances , forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decisionmakers to target sites where large carnivores are likely to occur and mitigate conflicts. Historic of detections of wolves per site per yearThe rows of this matrix are the 3547 sites and the 23 columns are the 23 years from 1994 to 2016. For each line a 0 means that no ... Dataset Canis lupus Unknown |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
unknown |
topic |
Occupancy model large carnivores opportunistic data France 1994 - 2016 Canis lupus Life sciences medicine and health care envir geo |
spellingShingle |
Occupancy model large carnivores opportunistic data France 1994 - 2016 Canis lupus Life sciences medicine and health care envir geo Louvrier, Julie Duchamp, Christophe Lauret, Valentin Marboutin, Eric Cubaynes, Sarah Choquet, Rémi Miquel, Christian Gimenez, Olivier Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data |
topic_facet |
Occupancy model large carnivores opportunistic data France 1994 - 2016 Canis lupus Life sciences medicine and health care envir geo |
description |
While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves (Canis lupus) that have been recolonizing France since the early 90’s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500m and positively influenced by the number of observed occupied sites at short and longdistances , forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decisionmakers to target sites where large carnivores are likely to occur and mitigate conflicts. Historic of detections of wolves per site per yearThe rows of this matrix are the 3547 sites and the 23 columns are the 23 years from 1994 to 2016. For each line a 0 means that no ... |
format |
Dataset |
author |
Louvrier, Julie Duchamp, Christophe Lauret, Valentin Marboutin, Eric Cubaynes, Sarah Choquet, Rémi Miquel, Christian Gimenez, Olivier |
author_facet |
Louvrier, Julie Duchamp, Christophe Lauret, Valentin Marboutin, Eric Cubaynes, Sarah Choquet, Rémi Miquel, Christian Gimenez, Olivier |
author_sort |
Louvrier, Julie |
title |
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data |
title_short |
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data |
title_full |
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data |
title_fullStr |
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data |
title_full_unstemmed |
Data from: Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data |
title_sort |
data from: mapping and explaining wolf recolonization in france using dynamic occupancy models and opportunistic data |
publisher |
Dryad Digital Repository |
publishDate |
2017 |
url |
https://doi.org/10.5061/dryad.g9s1d |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
10.5061/dryad.g9s1d oai:easy.dans.knaw.nl:easy-dataset:97757 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:97757 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 re3data_____::r3d100000044 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 |
op_relation |
https://dx.doi.org/10.5061/dryad.g9s1d http://dx.doi.org/10.5061/dryad.g9s1d |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.5061/dryad.g9s1d |
_version_ |
1766385217087471616 |