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Title: Imperfect observations in ecological studies
Authors: Shimadzu, Hideyasu
Foster, Scott D.
Darnell, Ross
Keywords: Compound distributions
Detection probability
Ecological modelling
Marine surveys
Species distribution models (SDMs)
Issue Date: 2016
Publisher: © The Author. Published by Springer
Citation: SHIMADZU, H., FOSTER, S.D. and DARNELL, R., 2016. Imperfect observations in ecological studies. Environmental and Ecological Statistics, 23 (3), pp. 337-358.
Abstract: © 2016 The Author(s) Every ecological data set is the result of sampling the biota at sampling locations. Such samples are rarely a census of the biota at the sampling locations and so will inherently contain biases. It is crucial to account for the bias induced by sampling if valid inference on biodiversity quantities is to be drawn from the observed data. The literature on accounting for sampling effects is large, but most are dedicated to the specific type of inference required, the type of analysis performed and the type of survey undertaken. There is no general and systematic approach to sampling. Here, we explore the unification of modelling approaches to account for sampling. We focus on individuals in ecological communities as the fundamental sampling element, and show that methods for accounting for sampling at the species level can be equated to individual sampling effects. Particular emphasis is given to the case where the probability of observing an individual, when it is present at the site sampled, is less than one. We call these situations ‘imperfect observations’. The proposed framework is easily implemented in standard software packages. We highlight some practical benefits of this formal framework: the ability of predicting the true number of individuals using an expectation that conditions on the observed data, and designing appropriate survey plans accounting for uncertainty due to sampling. The principles and methods are illustrated with marine survey data from tropical northern Australia.
Description: This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Version: Published
DOI: 10.1007/s10651-016-0342-2
URI: https://dspace.lboro.ac.uk/2134/21304
Publisher Link: http://dx.doi.org/10.1007/s10651-016-0342-2
ISSN: 1352-8505
Appears in Collections:Published Articles (Maths)

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