Detecting Temporal Trends in Species Assemblages with Bootstrapping Procedures and Hierarchical Models

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Detecting Temporal Trends in Species Assemblages with Bootstrapping Procedures and Hierarchical Models

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Title: Detecting Temporal Trends in Species Assemblages with Bootstrapping Procedures and Hierarchical Models
Author: Gotelli, Nicholas; Dorazio, Robert; Ellison, Aaron M.

Note: Order does not necessarily reflect citation order of authors.

Citation: Gotelli, Nicholas J., Robert M. Dorazio, Aaron M. Ellison, and Gary D. Grossman. 2010 Detecting temporal trends in species assemblages with bootstrapping procedures and hierarchical models. Philosophical transactions of the Royal Society of London: series B, biological sciences 356(1558): 3621-3631.
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Abstract: Quantifying patterns of temporal trends in species assemblages is an important analytical challenge in community ecology. We describe methods of analysis that can be applied to a matrix of counts of individuals that is organized by species (rows) and time-ordered sampling periods (columns). We first developed a bootstrapping procedure to test the null hypothesis of random sampling from a stationary species abundance distribution with temporally varying sampling probabilities. This procedure can be modified to account for undetected species. We next developed a hierarchical model to estimate species-specific trends in abundance while accounting for species-specific probabilities of detection. 51 We analyzed two long-term data sets on stream fishes and grassland insects to demonstrate these methods. For both assemblages, the bootstrap test indicated that temporal trends in abundance were more heterogeneous than expected under the null model. We used the hierarchical model to estimate trends in abundance and identified sets of species in each assemblage that were steadily increasing, decreasing, or remaining constant in abundance over more than a decade of standardized annual surveys. Our methods of analysis are broadly applicable to other ecological data sets, and they represent an advance over most existing procedures, which do not incorporate effects of incomplete sampling and imperfect detection.
Published Version: doi:10.1098/rstb.2010.0262
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4677617

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  • FAS Scholarly Articles [7219]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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