Xiao, Bingqing and Yuan, Songxi and Bede-Fazekas, Ákos and Zhou, Jinxin and Song, Xingyu and Lin, Qiang and Cui, Lei and Zhang, Zhixin (2025) Improving Distribution Prediction by Integrating Expert Range Maps and Opportunistic Occurrences: Evidence From Japanese Sea Cucumber. ECOLOGY AND EVOLUTION, 15 (7). No.-e71747. ISSN 2045-7758
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2025.07.BX-SY-ABF-JZ-XS-QL-LC-ZZtengeriuborkarange-informedEcolEvol10oldalen.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
In an era of biodiversity crisis, it is critical to perform biodiversity assessments to better inform conservation strategies. In this regard, species distribution models (SDMs) represent a widely used tool for biodiversity assessment. Despite their popularity, the accuracy of SDM predictions has long been criticized because we have incomplete or biased information on species distribution. To overcome this limitation, researchers have proposed improving predictions of SDMs by integrating different types of distribution data, but this idea has rarely been explored in the marine realm. In this study, we explored the idea of data integration using the Japanese sea cucumber, whose distribution is known to be restricted by freshwater discharge of the Yangtze River. We first fitted SDMs for this species based on opportunistic occurrence records via four modeling algorithms, then built two types of ensemble models using stacked generalization: an ensemble model that solely used four model predictions and an expert‐informed ensemble model that further accounted for distance to the IUCN expert range map. Our results showed that integrating an expert range map into the opportunistic occurrence model improved distribution prediction by avoiding overprediction in the south of the dispersal barrier for this species. Our study highlights the benefits of integrating expert range maps into opportunistic occurrence SDMs, which improve the reliability of species' spatial distributions.
| Item Type: | Article |
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| Additional Information: | State Key Laboratory of Tropical Oceanography, Guangdong Provincial Key Laboratory of Applied Marine Biology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China University of Chinese Academy of Sciences, Beijing, China HUN-REN Centre for Ecological Research, Institute of Ecology and Botany, Vácrátót, Hungary ELTE Eötvös Loránd University, Institute of Geography and Earth Sciences, Department of Environmental and Landscape Geography, Budapest, Hungary Institute of Industrial Science, The University of Tokyo, Chiba, Japan Nansha Marine Ecological and Environmental Research Station, Chinese Academy of Sciences, Sansha, China Key Laboratory of Eutrophication and Red Tide Prevention of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou, China Export Date: 30 July 2025; Cited By: 0; Correspondence Address: Z. Zhang; State Key Laboratory of Tropical Oceanography, Guangdong Provincial Key Laboratory of Applied Marine Biology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China; email: zxzhang@scsio.ac.cn; L. Cui; Key Laboratory of Eutrophication and Red Tide Prevention of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou, China; email: leicui@jnu.edu.cn |
| Uncontrolled Keywords: | data integration , expert range map , opportunistic occurrence , species distribution model , stacked generalization |
| Subjects: | G Geography. Anthropology. Recreation / földrajz, antropológia, kikapcsolódás > G Geography (General) / Földrajz általában Q Science / természettudomány > QH Natural history / természetrajz > QH540 Ecology / ökológia |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 04 Sep 2025 06:43 |
| Last Modified: | 04 Sep 2025 06:43 |
| URI: | https://real.mtak.hu/id/eprint/223448 |
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