Data from: Generalized spatial mark-resight models with an application to grizzly bears
1. The high cost associated with capture-recapture studies presents a major challenge when monitoring and managing wildlife populations. Recently-developed spatial mark-resight (SMR) models were proposed as a cost-effective alternative because they only require a single marking event. However, exist...
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fttriple:oai:gotriple.eu:50|dedup_wf_001::c48edc4b7945d108c2342dbf57a3d333 2023-05-15T18:42:15+02:00 Data from: Generalized spatial mark-resight models with an application to grizzly bears Whittington, Jesse Hebblewhite, Mark Chandler, Richard B. 2018-05-25 https://doi.org/10.5061/dryad.fn4nf undefined unknown http://dx.doi.org/10.5061/dryad.fn4nf https://dx.doi.org/10.5061/dryad.fn4nf lic_creative-commons oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:97561 10.5061/dryad.fn4nf oai:easy.dans.knaw.nl:easy-dataset:97561 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 re3data_____::r3d100000044 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 Life sciences medicine and health care population density point process model spatial capture-recapture hierarchical model telemetry camera trap Banff National Park Alberta Canada geo envir Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2018 fttriple https://doi.org/10.5061/dryad.fn4nf 2023-01-22T16:53:11Z 1. The high cost associated with capture-recapture studies presents a major challenge when monitoring and managing wildlife populations. Recently-developed spatial mark-resight (SMR) models were proposed as a cost-effective alternative because they only require a single marking event. However, existing SMR models ignore the marking process and make the tenuous assumption that marked and unmarked populations have the same encounter probabilities. This assumption will be violated in most situations because the marking process results in different spatial distributions of marked and unmarked animals. 2. We developed a generalized SMR model that includes sub-models for the marking and resighting processes, thereby relaxing the assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities. 3. Our simulation study demonstrated that conventional SMR models produce biased density estimates with low credible interval coverage when marked and unmarked animals had differing spatial distributions. In contrast, generalized SMR models produced unbiased density estimates with correct credible interval coverage in all scenarios. 4. We applied our SMR model to grizzly bear (Ursus arctos) data where the marking process occurred along a transportation route through Banff and Yoho National Parks, Canada. Twenty-two grizzly bears were trapped, fitted with radio-collars, and then detected along with unmarked bears on 214 remote cameras. Closed population density estimates (posterior median + 1 SD) averaged from 2012 to 2014 were much lower for conventional SMR models (7.4 + 1.0 bears per 1,000 km2) than for generalized SMR models (12.4 + 1.5). When compared to previous DNA-based estimates, conventional SMR estimates erroneously suggested a 51% decline in density. Conversely, generalized SMR estimates were similar to previous estimates, indicating that the grizzly bear population was relatively stable. 5. Synthesis and application. Conventional SMR models that ignore the marking ... Dataset Ursus arctos Unknown Canada |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
unknown |
topic |
Life sciences medicine and health care population density point process model spatial capture-recapture hierarchical model telemetry camera trap Banff National Park Alberta Canada geo envir |
spellingShingle |
Life sciences medicine and health care population density point process model spatial capture-recapture hierarchical model telemetry camera trap Banff National Park Alberta Canada geo envir Whittington, Jesse Hebblewhite, Mark Chandler, Richard B. Data from: Generalized spatial mark-resight models with an application to grizzly bears |
topic_facet |
Life sciences medicine and health care population density point process model spatial capture-recapture hierarchical model telemetry camera trap Banff National Park Alberta Canada geo envir |
description |
1. The high cost associated with capture-recapture studies presents a major challenge when monitoring and managing wildlife populations. Recently-developed spatial mark-resight (SMR) models were proposed as a cost-effective alternative because they only require a single marking event. However, existing SMR models ignore the marking process and make the tenuous assumption that marked and unmarked populations have the same encounter probabilities. This assumption will be violated in most situations because the marking process results in different spatial distributions of marked and unmarked animals. 2. We developed a generalized SMR model that includes sub-models for the marking and resighting processes, thereby relaxing the assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities. 3. Our simulation study demonstrated that conventional SMR models produce biased density estimates with low credible interval coverage when marked and unmarked animals had differing spatial distributions. In contrast, generalized SMR models produced unbiased density estimates with correct credible interval coverage in all scenarios. 4. We applied our SMR model to grizzly bear (Ursus arctos) data where the marking process occurred along a transportation route through Banff and Yoho National Parks, Canada. Twenty-two grizzly bears were trapped, fitted with radio-collars, and then detected along with unmarked bears on 214 remote cameras. Closed population density estimates (posterior median + 1 SD) averaged from 2012 to 2014 were much lower for conventional SMR models (7.4 + 1.0 bears per 1,000 km2) than for generalized SMR models (12.4 + 1.5). When compared to previous DNA-based estimates, conventional SMR estimates erroneously suggested a 51% decline in density. Conversely, generalized SMR estimates were similar to previous estimates, indicating that the grizzly bear population was relatively stable. 5. Synthesis and application. Conventional SMR models that ignore the marking ... |
format |
Dataset |
author |
Whittington, Jesse Hebblewhite, Mark Chandler, Richard B. |
author_facet |
Whittington, Jesse Hebblewhite, Mark Chandler, Richard B. |
author_sort |
Whittington, Jesse |
title |
Data from: Generalized spatial mark-resight models with an application to grizzly bears |
title_short |
Data from: Generalized spatial mark-resight models with an application to grizzly bears |
title_full |
Data from: Generalized spatial mark-resight models with an application to grizzly bears |
title_fullStr |
Data from: Generalized spatial mark-resight models with an application to grizzly bears |
title_full_unstemmed |
Data from: Generalized spatial mark-resight models with an application to grizzly bears |
title_sort |
data from: generalized spatial mark-resight models with an application to grizzly bears |
publishDate |
2018 |
url |
https://doi.org/10.5061/dryad.fn4nf |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Ursus arctos |
genre_facet |
Ursus arctos |
op_source |
oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:97561 10.5061/dryad.fn4nf oai:easy.dans.knaw.nl:easy-dataset:97561 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 re3data_____::r3d100000044 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 |
op_relation |
http://dx.doi.org/10.5061/dryad.fn4nf https://dx.doi.org/10.5061/dryad.fn4nf |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.5061/dryad.fn4nf |
_version_ |
1766231883491835904 |