On the trade-off between accuracy and spatial resolution when estimating species occupancy from geographically biased samples

Boyd, Robin J. ORCID: https://orcid.org/0000-0002-7973-9865; Bowler, Diana E. ORCID: https://orcid.org/0000-0002-7775-1668; Isaac, Nick J.B. ORCID: https://orcid.org/0000-0002-4869-8052; Pescott, Oliver L. ORCID: https://orcid.org/0000-0002-0685-8046. 2024 On the trade-off between accuracy and spatial resolution when estimating species occupancy from geographically biased samples. Ecological Modelling, 493 (July), 110739. 7, pp. https://doi.org/10.1016/j.ecolmodel.2024.110739

Before downloading, please read NORA policies.

Preview

Text N537403JA.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview

Abstract/Summary

Species occupancy is often defined as the proportion of areal units (sites) in a landscape that the focal species occupies, but it is usually estimated from the subset of sites that have been sampled. Assuming no measurement error, we show that three quantities–the degree of sampling bias (in terms of site selection), the proportion of sites that have been sampled and the variability of true occupancy across sites–determine the extent to which a sample-based estimate of occupancy differs from its true value across the wider landscape. That these are the only three quantities (measurement error notwithstanding) to affect the accuracy of estimates of species occupancy is the fundamental insight of the “Meng equation”, an algebraic re-expression of statistical error. We use simulations to show how each of the three quantities vary with the spatial resolution of the analysis and that absolute estimation error is lower at coarser resolutions. Absolute error scales similarly with resolution regardless of the size and clustering of the virtual species’ distribution. Finely resolved estimates of species occupancy have the potential to be more useful than coarse ones, but this potential is only realised if the estimates are at least reasonably accurate. Consequently, wherever there is the potential for sampling bias, there is a trade-off between spatial resolution and accuracy, and the Meng equation provides a theoretical framework in which analysts can consider the balance between the two. An obvious next step is to consider the implications of the Meng equation for estimating a time trend in species occupancy, where it is the confounding of error and true change that is of most interest.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.ecolmodel.2024.110739
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-)
ISSN:	0304-3800
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	sampling bias, spatial grain, representativeness, biodiversity monitoring, spatial pattern
NORA Subject Terms:	Ecology and Environment Mathematics Data and Information
Related URLs:	Other
Date made live:	08 May 2024 08:43 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537403

Actions (login required)

View Item

Document Downloads

More statistics for this item...