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Removal modelling in ecology

View ORCID ProfileOscar Rodriguez de Rivera, Rachel McCrea
doi: https://doi.org/10.1101/2020.02.20.957357
Oscar Rodriguez de Rivera
1Statistical Ecology @ Kent, National Centre for Statistical Ecology. School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7FS, UK
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  • For correspondence: O.Ortega@kent.ac.uk
Rachel McCrea
1Statistical Ecology @ Kent, National Centre for Statistical Ecology. School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7FS, UK
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Abstract

Removal models were proposed over 80 years ago as a tool to estimate unknown population size. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important feature of the data and we discuss the shift in the required assumption for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique.

Author summary Since the introduction of the removal in 1939, the method has being extensively used by ecologists to estimate population size. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Here, we introduce the method and how it has been applied and how it has evolved over time. Our study provides a literature review of the methods and applications followed by a review of available software. We conclude with a discussion about the opportunities of this model in the future.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 20, 2020.
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Removal modelling in ecology
Oscar Rodriguez de Rivera, Rachel McCrea
bioRxiv 2020.02.20.957357; doi: https://doi.org/10.1101/2020.02.20.957357
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Removal modelling in ecology
Oscar Rodriguez de Rivera, Rachel McCrea
bioRxiv 2020.02.20.957357; doi: https://doi.org/10.1101/2020.02.20.957357

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