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How ideas from ecological capture-recapture models may inform multiple systems estimation analyses

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Worthington_2020_CD_ideas_ecological_CC.pdf (211.9Kb)
Date
07/12/2020
Author
Worthington, Hannah
McCrea, Rachel
King, Ruth
Vincent, Kyle
Keywords
Behavioral effects
Integrated modeling
Multi-state modeling
Temporal data
QH301 Biology
QA Mathematics
T-NDAS
Metadata
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Abstract
Abundance estimation, for both human and animal populations, informs policy decisions and population management. Capture-recapture and multiple sources data share a common structure; the population can be partially enumerated and individuals are identifiable. Consequently, the analytical methods were developed simultaneously. However, whilst ecological models have been developed to describe highly complex, biologically realistic scenarios, for example modeling population changes through time and combining different forms of data, multiple systems estimation has changed comparatively less so. In this paper we provide a brief description of the historical development of ecological and epidemiological capture-recapture and discuss the associated underlying differences that have led to model divergence. We identify three key areas where ecological modeling methods may inform and improve multiple systems estimation.
Citation
Worthington , H , McCrea , R , King , R & Vincent , K 2020 , ' How ideas from ecological capture-recapture models may inform multiple systems estimation analyses ' , Crime and Delinquency , vol. OnlineFirst . https://doi.org/10.1177/0011128720974319
Publication
Crime and Delinquency
Status
Peer reviewed
DOI
https://doi.org/10.1177/0011128720974319
ISSN
0011-1287
Type
Journal article
Rights
Copyright © The Author(s) 2020. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Collections
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/21194

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