Spatial Clustering of Citizen Science Data Improves Downstream Species Distribution Models

作     者：Ahmed, Nahian Roth, Mark Hallman, Tyler A. Robinson, W. Douglas Hutchinson, Rebecca A. 

作者机构：School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States Department of Fisheries Wildlife and Conservation Sciences Oregon State University CorvallisOR97331 United States School of Natural Sciences Bangor University BangorLL57 2DG United Kingdom 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

摘      要：Citizen science biodiversity data present great opportunities for ecology and conservation across vast spatial and temporal scales. However, the opportunistic nature of these data lacks the sampling structure required by modeling methodologies that address a pervasive challenge in ecological data collection: imperfect detection, i.e., the likelihood of under-observing species on field surveys. Occupancy modeling is an example of an approach that accounts for imperfect detection by explicitly modeling the observation process separately from the biological process of habitat selection. This produces species distribution models that speak to the pattern of the species on a landscape after accounting for imperfect detection in the data, rather than the pattern of species observations corrupted by errors. To achieve this benefit, occupancy models require multiple surveys of a site across which the site’s status (i.e., occupied or not) is assumed constant. Since citizen science data are not collected under the required repeated-visit protocol, observations may be grouped into sites post hoc. Existing approaches for constructing sites discard some observations and/or consider only geographic distance and not environmental similarity. In this study, we compare ten approaches for site construction in terms of their impact on downstream species distribution models for 31 bird species in Oregon, using observations recorded in the eBird database. We find that occupancy models built on sites constructed by spatial clustering algorithms perform better than existing alternatives. Copyright © 2024, The Authors. All rights reserved.