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Predicting population trends using citizen science data: do subsampling methods produce reliable estimates for mammals?

Lookup NU author(s): Dr Graham Smith



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2018, The Author(s). Accurate assessment of population trends is invaluable in wildlife management, particularly for identifying species which are of conservation concern, and consequently, reliable cost-effective methods for their determination are highly desirable. In a recent publication (Eur J Wildl Res 62:407–413, 2016), the authors apply a subsampling method, used in several studies to quantify population trends from citizen science data for butterflies, birds, and plants, to assess the status of West European hedgehogs (Erinaceus europaeus) in England. Whilst the findings may be in agreement with expert opinion, we argue that this type of approach does not adequately account for spatial bias common in mammal data and that without further evaluation it is unclear whether the result is reliable or simply coincidental. To explore this concern, we apply the method across a range of terrestrial mammal species and compare the resulting trends to other published studies. Our findings show that the method fails to reproduce the accepted qualitative trends for the majority of species. Furthermore, comparison of trends based on data obtained from different sources produced conflicting predictions suggesting that the method is indeed vulnerable to survey bias. We therefore conclude that at present, without additional modification to address survey bias, this is not a reliable method for predicting population trends for mammals. However, more generally, this raises questions about the validity of subsampling methods based on citizen science data, and we would urge future studies to exercise caution by performing analysis across a suite of species including those with known trends for validation.

Publication metadata

Author(s): Callcutt K, Croft S, Smith GC

Publication type: Article

Publication status: Published

Journal: European Journal of Wildlife Research

Year: 2018

Volume: 64

Online publication date: 30/04/2018

Acceptance date: 20/04/2018

Date deposited: 31/05/2018

ISSN (print): 1612-4642

ISSN (electronic): 1439-0574

Publisher: Springer Verlag


DOI: 10.1007/s10344-018-1189-7


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