A new statistical approach for identifying rare species under imperfect detection

Belmont Osuna, J. , Miller, C. , Scott, M. and Wilkie, C. (2022) A new statistical approach for identifying rare species under imperfect detection. Diversity and Distributions, 28(5), pp. 882-893. (doi: 10.1111/ddi.13495)

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Aim: Species rarity is often used as a measure to assess the risk of extinction of species, and thus, different methods have been developed to describe the composition of rare species in biological communities. These methods usually depend on species attributes that are not always available and very often ignore imperfect species detection. In this work, we developed a new method to characterize species rarity in a community when species are detected imperfectly. Our modelling framework is based on Bayesian occupancy models to estimate species distributions under imperfect detection using presence-nondetection data. Innovation: We propose a finite mixture occupancy model to identify rare species based on their occupancy and class-membership probabilities. Here, we explored a two-class finite mixture model to distinguish between rare and common species classes and presented the general modelling framework for a problem with more than two classes. By using simulations, we were able to compare our model results under different scenarios obtaining a high-classification performance across all of them. Additionally, we applied our model to a data set of Odonata occurrence records that were partially observed due to imperfect detection and quantified the proportion of rare species on a national scale across waterbodies in the United Kingdom. Main conclusions: Nowadays, biodiversity conservation involves monitoring programmes that target multiple species within a community where individual species responses may vary widely. This high variability makes the task of identifying the ecological processes that drive distributions of rare species difficult. Thus, our method represents a new approach to characterize the composition of a community in terms of species rarity while correcting for detectability bias. Our modelling framework also suggests lines of research and future developments for the understanding of how species rarity can be measured in a wide range of scenarios.

Item Type:Articles
Additional Information:This project was possible thanks to NERC funding (grant NE/N005740/1). JB was supported by CONACyT scholarship (494334)
Glasgow Author(s) Enlighten ID:Miller, Professor Claire and Wilkie, Dr Craig and Belmont Osuna, Mr Jafet and Scott, Professor Marian
Authors: Belmont Osuna, J., Miller, C., Scott, M., and Wilkie, C.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Mathematics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Diversity and Distributions
ISSN (Online):1472-4642
Published Online:17 February 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Diversity and Distributions 28(5): 882-893
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172180Hydroscape:connectivity x stressor interactions in freshwater habitats.Claire MillerNatural Environment Research Council (NERC)NE/N005740/1M&S - Statistics