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Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations

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Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations

Content Provider	Springer Nature Link
Author	Yamaura, Yuichi Kéry, Marc Andrew Royle, J.
Copyright Year	2016
Abstract	Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance $$(\bar{\lambda }$$ : 0.1, 0.5, 1, 5), detection probability $$(\bar{p}$$ : 0.1, 0.2, 0.5), and number of sampling sites (n $_{ site }$: 10, 20, 40) and visits (n $_{ visit }$: 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables $$(\bar{\lambda }$$ , $$\bar{p}$$ , n $_{ site }$, n $_{ visit }$) increased. Detection probability $$\bar{p}$$ was most important for the estimates of mean abundance, while $$\bar{\lambda }$$ was most influential for covariate effect and species richness estimates. For all parameters, increasing n $_{ site }$ was more beneficial than increasing n $_{ visit }$. Minimal conditions for obtaining adequate performance of community abundance models were n $_{ site }$ ≥ 20, $$\bar{p}$$ ≥ 0.2, and $$\bar{\lambda }$$ ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
Starting Page	289
Ending Page	305
Page Count	17
File Format	PDF
ISSN	09123814
Journal	Ecological Research
Volume Number	31
Issue Number	3
e-ISSN	14401703
Language	English
Publisher	Springer Japan
Publisher Date	2016-03-10
Publisher Place	Tokyo
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	β (beta) diversity Count data Data augmentation False negative Species richness Ecology Plant Sciences Zoology Evolutionary Biology Behavioral Sciences Forestry
Content Type	Text
Resource Type	Article
Subject	Ecology, Evolution, Behavior and Systematics

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Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations

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Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations

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Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations

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Study of biological communities subject to imperfect detection: bias and precision of community N-mixture abundance models in small-sample situations