Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears
A fundamental challenge to estimating population size with mark–recapture methods is heterogeneous capture probabilities and subsequent bias of population estimates. Confronting this problem usually requires substantial sampling effort that can be difficult to achieve for some species, such as carni...
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ftunivnebraskali:oai:digitalcommons.unl.edu:natlpark-1049 2023-11-12T04:27:45+01:00 Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears Boulanger, John Kendall, Katherine C. Stetz, Jeffrey B. Roon, David A. Waits, Lisette P. Paetkau, David 2008-01-01T08:00:00Z application/pdf https://digitalcommons.unl.edu/natlpark/50 https://digitalcommons.unl.edu/context/natlpark/article/1049/viewcontent/Kendall_EA_2008_MULTIPLE_DATA_SOURCES_IMPROVE.pdf unknown DigitalCommons@University of Nebraska - Lincoln https://digitalcommons.unl.edu/natlpark/50 https://digitalcommons.unl.edu/context/natlpark/article/1049/viewcontent/Kendall_EA_2008_MULTIPLE_DATA_SOURCES_IMPROVE.pdf U.S. National Park Service Publications and Papers bear rub trees DNA Glacier National Park Montana USA grizzly bears hair sampling mark–recapture mixture models noninvasive genetic sampling population estimation program MARK Ursus arctos text 2008 ftunivnebraskali 2023-10-30T11:00:49Z A fundamental challenge to estimating population size with mark–recapture methods is heterogeneous capture probabilities and subsequent bias of population estimates. Confronting this problem usually requires substantial sampling effort that can be difficult to achieve for some species, such as carnivores. We developed a methodology that uses two data sources to deal with heterogeneity and applied this to DNA mark–recapture data from grizzly bears (Ursus arctos). We improved population estimates by incorporating additional DNA ‘‘captures’’ of grizzly bears obtained by collecting hair from unbaited bear rub trees concurrently with baited, grid-based, hair snag sampling. We consider a Lincoln-Petersen estimator with hair snag captures as the initial session and rub tree captures as the recapture session and develop an estimator in program MARK that treats hair snag and rub tree samples as successive sessions. Using empirical data from a large-scale project in the greater Glacier National Park, Montana, USA, area and simulation modeling we evaluate these methods and compare the results to hair-snag-only estimates. Empirical results indicate that, compared with hair-snag-only data, the joint hair-snag–rub-tree methods produce similar but more precise estimates if capture and recapture rates are reasonably high for both methods. Simulation results suggest that estimators are potentially affected by correlation of capture probabilities between sample types in the presence of heterogeneity. Overall, closed population Huggins-Pledger estimators showed the highest precision and were most robust to sparse data, heterogeneity, and capture probability correlation among sampling types. Results also indicate that these estimators can be used when a segment of the population has zero capture probability for one of the methods. We propose that this general methodology may be useful for other species in which mark–recapture data are available from multiple sources. Text Ursus arctos University of Nebraska-Lincoln: DigitalCommons@UNL Petersen ENVELOPE(-101.250,-101.250,-71.917,-71.917) Snag ENVELOPE(-140.371,-140.371,62.399,62.399) Huggins ENVELOPE(162.483,162.483,-78.283,-78.283) |
institution |
Open Polar |
collection |
University of Nebraska-Lincoln: DigitalCommons@UNL |
op_collection_id |
ftunivnebraskali |
language |
unknown |
topic |
bear rub trees DNA Glacier National Park Montana USA grizzly bears hair sampling mark–recapture mixture models noninvasive genetic sampling population estimation program MARK Ursus arctos |
spellingShingle |
bear rub trees DNA Glacier National Park Montana USA grizzly bears hair sampling mark–recapture mixture models noninvasive genetic sampling population estimation program MARK Ursus arctos Boulanger, John Kendall, Katherine C. Stetz, Jeffrey B. Roon, David A. Waits, Lisette P. Paetkau, David Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears |
topic_facet |
bear rub trees DNA Glacier National Park Montana USA grizzly bears hair sampling mark–recapture mixture models noninvasive genetic sampling population estimation program MARK Ursus arctos |
description |
A fundamental challenge to estimating population size with mark–recapture methods is heterogeneous capture probabilities and subsequent bias of population estimates. Confronting this problem usually requires substantial sampling effort that can be difficult to achieve for some species, such as carnivores. We developed a methodology that uses two data sources to deal with heterogeneity and applied this to DNA mark–recapture data from grizzly bears (Ursus arctos). We improved population estimates by incorporating additional DNA ‘‘captures’’ of grizzly bears obtained by collecting hair from unbaited bear rub trees concurrently with baited, grid-based, hair snag sampling. We consider a Lincoln-Petersen estimator with hair snag captures as the initial session and rub tree captures as the recapture session and develop an estimator in program MARK that treats hair snag and rub tree samples as successive sessions. Using empirical data from a large-scale project in the greater Glacier National Park, Montana, USA, area and simulation modeling we evaluate these methods and compare the results to hair-snag-only estimates. Empirical results indicate that, compared with hair-snag-only data, the joint hair-snag–rub-tree methods produce similar but more precise estimates if capture and recapture rates are reasonably high for both methods. Simulation results suggest that estimators are potentially affected by correlation of capture probabilities between sample types in the presence of heterogeneity. Overall, closed population Huggins-Pledger estimators showed the highest precision and were most robust to sparse data, heterogeneity, and capture probability correlation among sampling types. Results also indicate that these estimators can be used when a segment of the population has zero capture probability for one of the methods. We propose that this general methodology may be useful for other species in which mark–recapture data are available from multiple sources. |
format |
Text |
author |
Boulanger, John Kendall, Katherine C. Stetz, Jeffrey B. Roon, David A. Waits, Lisette P. Paetkau, David |
author_facet |
Boulanger, John Kendall, Katherine C. Stetz, Jeffrey B. Roon, David A. Waits, Lisette P. Paetkau, David |
author_sort |
Boulanger, John |
title |
Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears |
title_short |
Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears |
title_full |
Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears |
title_fullStr |
Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears |
title_full_unstemmed |
Multiple Data Sources Improve DNA-Based Mark–Recapture Population Estimates of Grizzly Bears |
title_sort |
multiple data sources improve dna-based mark–recapture population estimates of grizzly bears |
publisher |
DigitalCommons@University of Nebraska - Lincoln |
publishDate |
2008 |
url |
https://digitalcommons.unl.edu/natlpark/50 https://digitalcommons.unl.edu/context/natlpark/article/1049/viewcontent/Kendall_EA_2008_MULTIPLE_DATA_SOURCES_IMPROVE.pdf |
long_lat |
ENVELOPE(-101.250,-101.250,-71.917,-71.917) ENVELOPE(-140.371,-140.371,62.399,62.399) ENVELOPE(162.483,162.483,-78.283,-78.283) |
geographic |
Petersen Snag Huggins |
geographic_facet |
Petersen Snag Huggins |
genre |
Ursus arctos |
genre_facet |
Ursus arctos |
op_source |
U.S. National Park Service Publications and Papers |
op_relation |
https://digitalcommons.unl.edu/natlpark/50 https://digitalcommons.unl.edu/context/natlpark/article/1049/viewcontent/Kendall_EA_2008_MULTIPLE_DATA_SOURCES_IMPROVE.pdf |
_version_ |
1782341247268028416 |