Disentangling data discrepancies with integrated population models
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1002/ecy.2714 http://hdl.handle.net/11449/187663 |
Resumo: | A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963–2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types. |
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Disentangling data discrepancies with integrated population modelsAmerican Woodcockannual cycleband-recoverydata integrationdata integration for population models Special Featureharvestsinging-ground surveyA common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963–2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.Department of Integrative Biology Michigan State University, 288 Farm Lane RM 203National Audubon Society, 225 Varick Street, 7th FloorEcology Evolutionary Biology and Behavior Program Michigan State UniversityDepartment of Biological Sciences Kent State University, 249 Cunningham HallInstitute of Biosciences São Paulo State University (Unesp)Department of Fisheries Wildlife & Conservation Biology University of Minnesota, 2003 Upper Buford Circle, Suite 135Institute of Biosciences São Paulo State University (Unesp)Michigan State UniversityNational Audubon SocietyKent State UniversityUniversidade Estadual Paulista (Unesp)University of MinnesotaSaunders, Sarah P.Farr, Matthew T.Wright, Alexander D.Bahlai, Christie A.Ribeiro, Jose W. [UNESP]Rossman, SamSussman, Allison L.Arnold, Todd W.Zipkin, Elise F.2019-10-06T15:43:24Z2019-10-06T15:43:24Z2019-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1002/ecy.2714Ecology, v. 100, n. 6, 2019.0012-9658http://hdl.handle.net/11449/18766310.1002/ecy.27142-s2.0-85065673832Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEcologyinfo:eu-repo/semantics/openAccess2021-10-23T02:05:55Zoai:repositorio.unesp.br:11449/187663Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:50:33.252939Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Disentangling data discrepancies with integrated population models |
title |
Disentangling data discrepancies with integrated population models |
spellingShingle |
Disentangling data discrepancies with integrated population models Saunders, Sarah P. American Woodcock annual cycle band-recovery data integration data integration for population models Special Feature harvest singing-ground survey |
title_short |
Disentangling data discrepancies with integrated population models |
title_full |
Disentangling data discrepancies with integrated population models |
title_fullStr |
Disentangling data discrepancies with integrated population models |
title_full_unstemmed |
Disentangling data discrepancies with integrated population models |
title_sort |
Disentangling data discrepancies with integrated population models |
author |
Saunders, Sarah P. |
author_facet |
Saunders, Sarah P. Farr, Matthew T. Wright, Alexander D. Bahlai, Christie A. Ribeiro, Jose W. [UNESP] Rossman, Sam Sussman, Allison L. Arnold, Todd W. Zipkin, Elise F. |
author_role |
author |
author2 |
Farr, Matthew T. Wright, Alexander D. Bahlai, Christie A. Ribeiro, Jose W. [UNESP] Rossman, Sam Sussman, Allison L. Arnold, Todd W. Zipkin, Elise F. |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Michigan State University National Audubon Society Kent State University Universidade Estadual Paulista (Unesp) University of Minnesota |
dc.contributor.author.fl_str_mv |
Saunders, Sarah P. Farr, Matthew T. Wright, Alexander D. Bahlai, Christie A. Ribeiro, Jose W. [UNESP] Rossman, Sam Sussman, Allison L. Arnold, Todd W. Zipkin, Elise F. |
dc.subject.por.fl_str_mv |
American Woodcock annual cycle band-recovery data integration data integration for population models Special Feature harvest singing-ground survey |
topic |
American Woodcock annual cycle band-recovery data integration data integration for population models Special Feature harvest singing-ground survey |
description |
A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963–2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T15:43:24Z 2019-10-06T15:43:24Z 2019-06-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1002/ecy.2714 Ecology, v. 100, n. 6, 2019. 0012-9658 http://hdl.handle.net/11449/187663 10.1002/ecy.2714 2-s2.0-85065673832 |
url |
http://dx.doi.org/10.1002/ecy.2714 http://hdl.handle.net/11449/187663 |
identifier_str_mv |
Ecology, v. 100, n. 6, 2019. 0012-9658 10.1002/ecy.2714 2-s2.0-85065673832 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ecology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128989254909952 |