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Imperfect observations in ecological studies

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journal contribution
posted on 20.05.2016, 12:25 by Hideyasu ShimadzuHideyasu Shimadzu, Scott D. Foster, Ross Darnell
© 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.

History

School

  • Science

Department

  • Mathematical Sciences

Published in

Environmental and Ecological Statistics

Pages

1 - 22

Citation

SHIMADZU, H., FOSTER, S.D. and DARNELL, R., 2016. Imperfect observations in ecological studies. Environmental and Ecological Statistics, 23 (3), pp. 337-358.

Publisher

© The Author. Published by Springer

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Publication date

2016

Notes

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/

ISSN

1352-8505

eISSN

1573-3009

Language

en