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

Abstract

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 as the proportion of sampled sites in which the species has been observed. Assuming perfect detection (i.e. no false absences), 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 the truth. That these are the only three quantities 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 actual estimation error is lower at coarser resolutions. Although finely resolved estimates of species occupancy have the potential to be more useful than coarse ones, 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.

Is It Time for a New Paradigm in Biodiversity Monitoring? Lessons From Opinion Polling

“…I think these arguments are sound and apply to biodiversity monitoring. In terms of the new paradigm itself, I needed no convincing, having already found the Meng equation to be useful in the context of biodiversity monitoring (Boyd, Bowler, et al, 2023;. I strongly recommend ecologists read Bailey's excellent article and book on the topic (Bailey, 2023, in press) and would be interested to hear his thoughts on the broader applicability of his new paradigm.…”