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Lookup NU author(s): Dr Gavin StewartORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 The Authors. Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America. Biodiversity monitoring usually involves drawing inferences about some variable of interest across a defined landscape from observations made at a sample of locations within that landscape. If the variable of interest differs between sampled and nonsampled locations, and no mitigating action is taken, then the sample is unrepresentative and inferences drawn from it will be biased. It is possible to adjust unrepresentative samples so that they more closely resemble the wider landscape in terms of “auxiliary variables.” A good auxiliary variable is a common cause of sample inclusion and the variable of interest, and if it explains an appreciable portion of the variance in both, then inferences drawn from the adjusted sample will be closer to the truth. We applied six types of survey sample adjustment—subsampling, quasirandomization, poststratification, superpopulation modeling, a “doubly robust” procedure, and multilevel regression and poststratification—to a simple two-part biodiversity monitoring problem. The first part was to estimate the mean occupancy of the plant Calluna vulgaris in Great Britain in two time periods (1987–1999 and 2010–2019); the second was to estimate the difference between the two (i.e., the trend). We estimated the means and trend using large, but (originally) unrepresentative, samples from a citizen science dataset. Compared with the unadjusted estimates, the means and trends estimated using most adjustment methods were more accurate, although standard uncertainty intervals generally did not cover the true values. Completely unbiased inference is not possible from an unrepresentative sample without knowing and having data on all relevant auxiliary variables. Adjustments can reduce the bias if auxiliary variables are available and selected carefully, but the potential for residual bias should be acknowledged and reported.
Author(s): Boyd RJ, Stewart GB, Pescott OL
Publication type: Article
Publication status: Published
Journal: Ecology
Year: 2024
Volume: 105
Issue: 2
Print publication date: 01/02/2024
Online publication date: 13/12/2023
Acceptance date: 20/10/2023
Date deposited: 22/01/2024
ISSN (print): 0012-9658
ISSN (electronic): 1939-9170
Publisher: Ecological Society of America
URL: https://doi.org/10.1002/ecy.4214
DOI: 10.1002/ecy.4214
Data Access Statement: The data and an R Markdown document containing all code to reproduce our analysis are available on Zenodo in Boyd (2023) at https://doi.org/10.5281/zenodo.10029669.
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