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Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated

Advances in multi‐species monitoring have prompted an increase in the use of multi‐species occupancy analyses to assess patterns of co‐occurrence among species, even when data were collected at scales likely violating the assumption that sites were closed to changes in the occupancy state for the ta...

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Autor principal: Lonsinger, Robert C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273567/
https://www.ncbi.nlm.nih.gov/pubmed/35845361
http://dx.doi.org/10.1002/ece3.9104
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author Lonsinger, Robert C.
author_facet Lonsinger, Robert C.
author_sort Lonsinger, Robert C.
collection PubMed
description Advances in multi‐species monitoring have prompted an increase in the use of multi‐species occupancy analyses to assess patterns of co‐occurrence among species, even when data were collected at scales likely violating the assumption that sites were closed to changes in the occupancy state for the target species. Violating the closure assumption may lead to erroneous conclusions related to patterns of co‐occurrence among species. Occurrence for two hypothetical species was simulated under patterns of avoidance, aggregation, or independence, when the closure assumption was either met or not. Simulated populations were sampled at two levels (N = 250 or 100 sites) and two scales of temporal resolution for surveys. Sample data were analyzed with conditional two‐species occupancy models, and performance was assessed based on the proportion of simulations recovering the true pattern of co‐occurrence. Estimates of occupancy were unbiased when closure was met, but biased when closure violations occurred; bias increased when sample size was small and encounter histories were collapsed to a large‐scale temporal resolution. When closure was met and patterns of avoidance and aggregation were simulated, conditional two‐species models tended to correctly find support for non‐independence, and estimated species interaction factors (SIF) aligned with predicted values. By contrast, when closure was violated, models tended to incorrectly infer a pattern of independence and power to detect simulated patterns of avoidance or aggregation that decreased with smaller sample size. Results suggest that when the closure assumption is violated, co‐occurrence models often fail to detect underlying patterns of avoidance or aggregation, and incorrectly identify a pattern of independence among species, which could have negative consequences for our understanding of species interactions and conservation efforts. Thus, when closure is violated, inferred patterns of independence from multi‐species occupancy should be interpreted cautiously, and evidence of avoidance or aggregation is likely a conservative estimate of true pattern or interaction.
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spelling pubmed-92735672022-07-15 Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated Lonsinger, Robert C. Ecol Evol Research Articles Advances in multi‐species monitoring have prompted an increase in the use of multi‐species occupancy analyses to assess patterns of co‐occurrence among species, even when data were collected at scales likely violating the assumption that sites were closed to changes in the occupancy state for the target species. Violating the closure assumption may lead to erroneous conclusions related to patterns of co‐occurrence among species. Occurrence for two hypothetical species was simulated under patterns of avoidance, aggregation, or independence, when the closure assumption was either met or not. Simulated populations were sampled at two levels (N = 250 or 100 sites) and two scales of temporal resolution for surveys. Sample data were analyzed with conditional two‐species occupancy models, and performance was assessed based on the proportion of simulations recovering the true pattern of co‐occurrence. Estimates of occupancy were unbiased when closure was met, but biased when closure violations occurred; bias increased when sample size was small and encounter histories were collapsed to a large‐scale temporal resolution. When closure was met and patterns of avoidance and aggregation were simulated, conditional two‐species models tended to correctly find support for non‐independence, and estimated species interaction factors (SIF) aligned with predicted values. By contrast, when closure was violated, models tended to incorrectly infer a pattern of independence and power to detect simulated patterns of avoidance or aggregation that decreased with smaller sample size. Results suggest that when the closure assumption is violated, co‐occurrence models often fail to detect underlying patterns of avoidance or aggregation, and incorrectly identify a pattern of independence among species, which could have negative consequences for our understanding of species interactions and conservation efforts. Thus, when closure is violated, inferred patterns of independence from multi‐species occupancy should be interpreted cautiously, and evidence of avoidance or aggregation is likely a conservative estimate of true pattern or interaction. John Wiley and Sons Inc. 2022-07-11 /pmc/articles/PMC9273567/ /pubmed/35845361 http://dx.doi.org/10.1002/ece3.9104 Text en Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Lonsinger, Robert C.
Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated
title Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated
title_full Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated
title_fullStr Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated
title_full_unstemmed Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated
title_short Co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated
title_sort co‐occurrence models fail to infer underlying patterns of avoidance and aggregation when closure is violated
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273567/
https://www.ncbi.nlm.nih.gov/pubmed/35845361
http://dx.doi.org/10.1002/ece3.9104
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