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A geostatistical state-space model of animal densities for stream networks
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2018
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Source: Ecological Applications, 28(7), 1782-1796
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Journal Title:Ecological Applications
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Description:Population dynamics are often correlated in space and time due to correlations in envi- ronmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses ofpopulations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty underesti-mated. We developed a novel statistical method to account for spatiotemporal correlations within den-dritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated.We found that increasing the number of years surveyed substantially improved the model accuracywhen estimating spatial and temporal correlation coefficients, especially from 10 to 15 yr. Increasing the number of survey sites within the network improved the performance of the nonspatial model but
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Source:Ecological Applications, 28(7), 1782-1796
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ISSN:1051-0761
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Rights Information:CC0 Public Domain
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Rights Statement:This article is a U.S. Government work and is in the public domain in the USA.
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