Solving sampling bias problems in presence–absence or presence-only species data using zero-inflated models

Nolan, Victoria; Gilbert, Francis; Reader, Tom

Abstract

Aim: Large databases of species records such as those generated through citizen science projects, archives or museum collections are being used with increasing frequency in species distribution modelling (SDM) for conservation and land management. Despite the broad spatial and temporal coverage of the data, its application is often limited by the issue of sampling bias and consequently, zero inflation; there are more zeros (which are potentially ‘false absences’) in the data than expected. Here, we demonstrate how pooling species presence data into a ‘pseudo-abundance’ count can allow identification and removal of sampling bias through the use of zero-inflated (ZI) models, and thus solve a common SDM problem. Location: All locations. Taxon: All taxa. Methods: We present the results of a series of simulations based on hypothetical ecological scenarios of data collection using random and non-random sampling strategies. Our simulations assume that the locations of occurrence records are known at a high spatial resolution, but that the absence of occurrence records may reflect under-sampling. To simulate pooling of presence–absence or presence-only data, we count occurrence records at intermediate and coarse spatial resolutions, and use ZI models to predict the counts (species abundance per grid cell) from environmental layers. Results: ZI models can successfully identify predictors of bias in species data and produce abundance prediction maps that are free from that bias. This phenomenon holds across multiple spatial scales, thereby presenting an advantage over presence-only SDM methods such as binomial GLMs or MaxEnt, where information about species density is lost, and model performance declines at coarser scales. Main Conclusions: Our results highlight the value of converting presence–absence or presence-only species data to ‘pseudo-abundance’ and using ZI models to address the problem of sampling bias. This method has huge potential for ecological researchers when using large species datasets for research and conservation.

Citation

Nolan, V., Gilbert, F., & Reader, T. (2022). Solving sampling bias problems in presence–absence or presence-only species data using zero-inflated models. Journal of Biogeography, 49(1), 215-232. https://doi.org/10.1111/jbi.14268

• Journal Article Type: Article
• Acceptance Date: Aug 27, 2021
• Online Publication Date: Nov 29, 2021
• Publication Date: Jan 1, 2022
• Deposit Date: Dec 14, 2021
• Publicly Available Date: Nov 30, 2022
• Journal: Journal of Biogeography
• Print ISSN: 0305-0270
• Electronic ISSN: 1365-2699
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 1
• Pages: 215-232
• DOI: https://doi.org/10.1111/jbi.14268
• Keywords: Ecology; Ecology, Evolution, Behavior and Systematics
• Public URL: https://nottingham-repository.worktribe.com/output/6843478
• Publisher URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/jbi.14268
• Additional Information: This is the peer reviewed version of the following article: Nolan, V., Gilbert, F., & Reader, T. (2022). Solving sampling bias problems in presence–absence or presence-only species data using zero-inflated models. Journal of Biogeography, 49, 215– 232. , which has been published in final form at https://doi.org/10.1111/jbi.14268 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.