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A multistate dynamic site occupancy model for spatially aggregated sessile communities

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/70650

Title: A multistate dynamic site occupancy model for spatially aggregated sessile communities
Authors: Fukaya, Keiichi Browse this author
Royle, J. Andrew Browse this author
Okuda, Takehiro Browse this author
Nakaoka, Masahiro Browse this author →KAKEN DB
Noda, Takashi Browse this author →KAKEN DB
Keywords: classification error
community dynamics
hierarchical models
intertidal
kernel smoothing
site occupancy models
spatial correlation
transition probability
Issue Date: 1-Jun-2018
Publisher: John Wiley & Sons
Journal Title: Methods in Ecology and Evolution
Volume: 8
Issue: 6
Start Page: 757
End Page: 767
Publisher DOI: 10.1111/2041-210X.12690
Abstract: 1. Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e. a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations. 2. We developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error. 3. By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real data set of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account. 4. Results suggest the importance of accounting for resampling error and local community structure for developing management plans that are based on Markovian models. Our approach provides a solution to this problem that is applicable to broad sessile communities. It can even accommodate an anisotropic spatial correlation of species composition, and may also serve as a basis for inferring complex nonlinear ecological dynamics.
Rights: This is the peer reviewed version of the following article: http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12690/full, which has been published in final form at 10.1111/2041-210X.12690. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Type: article (author version)
URI: http://hdl.handle.net/2115/70650
Appears in Collections:環境科学院・地球環境科学研究院 (Graduate School of Environmental Science / Faculty of Environmental Earth Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 野田 隆史

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