Jeliazkov, A, Gavish, Y, Marsh, CJ et al. (6 more authors) (2022) Sampling and modelling rare species: conceptual guidelines for the neglected majority. Global Change Biology, 28 (12). pp. 3754-3777. ISSN 1354-1013
Abstract
Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data is available, predictions are usually spatially biased toward locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness, and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance to the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and for spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve prediction of rare species distribution depending on rarity type and available data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is the peer reviewed version of the following article: Jeliazkov, A., Gavish, Y., Marsh, C.J., Geschke, J., Brummitt, N., Rocchini, D., Haase, P., Kunin, W.E. and Henle, K. (2022), Sampling and modelling rare species: conceptual guidelines for the neglected majority. Global Change Biology. Accepted Author Manuscript. , which has been published in final form at https://doi.org/10.1111/gcb.16114. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. |
Keywords: | bias, detectability, distribution change, methods, occupancy, rare species, sampling, spatial data, species distribution modelling, survey |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Feb 2022 12:20 |
Last Modified: | 31 Jan 2023 01:13 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/gcb.16114 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183753 |