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Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area

文献类型：期刊论文

作者Zhang, Zhixin2,3,4,5; Zhou, Jinxin6; Molinos, Jorge Garcia7; Mammola, Stefano8,9,10; Bede-Fazekas, Akos11,12; Feng, Xiao13; Kitazawa, Daisuke6; Assis, Jorge1; Qiu, Tianlong14; Lin, Qiang2,4,15
刊名MARINE LIFE SCIENCE & TECHNOLOGY
出版日期2024-05-13
页码14
关键词Bayesian approach Climate change Habitat suitability Physiological knowledge Species distribution model
ISSN号2096-6490
DOI10.1007/s42995-024-00226-0
通讯作者Zhang, Zhixin(zxzhang@scsio.ac.cn) ; Lin, Qiang(linqiang@scsio.ac.cn)
英文摘要Correlative species distribution models (SDMs) are important tools to estimate species' geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species' physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a na & iuml;ve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models' sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with na & iuml;ve models, the physiologically informed models successfully captured the negative influence of high temperature on A. japonicus and were less sensitive to the choice of calibration area. The na & iuml;ve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available.
WOS关键词CLIMATE-CHANGE ; ABSENCE DATA ; OCEAN ; RANGE ; COMMUNITIES ; IMPACTS ; QUALITY
资助项目National Key R&D Program of China[2022YFC3102403] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42030204] ; Science and Technology Planning Project of Guangdong Province, China[2023B1212060047] ; South China Sea Institute of Oceanology of the Chinese Academy of Sciences[SCSIO202208] ; JST SICORP, Japan[JPMJSC20E5] ; FCT-Foundation for Science and Technology[UIDB/04326/2020] ; FCT-Foundation for Science and Technology[UIDP/04326/2020] ; FCT-Foundation for Science and Technology[LA/P/0101/2020] ; FCT-Foundation for Science and Technology[PTDC/BIA-CBI/6515/2020] ; FCT-Foundation for Science and Technology[2022.00861.CEECIND] ; National Multidisciplinary Laboratory for Climate Change[RRF-2.3.1-21-2022-00014] ; National Multidisciplinary Laboratory for Climate Change[NKFIH-471-3/2021]
WOS研究方向Marine & Freshwater Biology
语种英语
WOS记录号WOS:001220924200001
出版者SPRINGERNATURE
源URL[http://ir.qdio.ac.cn/handle/337002/185763]  
专题海洋研究所_实验海洋生物学重点实验室
通讯作者Zhang, Zhixin; Lin, Qiang
作者单位1.Univ Algarve, Ctr Marine Sci, Campus Gambelas, P-8005139 Faro, Portugal
2.Chinese Acad Sci, South China Sea Inst Oceanol, CAS Key Lab Trop Marine Bioresources & Ecol, Guangzhou 510301, Peoples R China
3.Chinese Acad Sci, South China Sea Inst Oceanol, Guangdong Prov Key Lab Appl Marine Biol, Guangzhou 510301, Peoples R China
4.South China Sea Inst Oceanol, Marine Biodivers & Ecol Evolut Res Ctr, Guangzhou 510301, Peoples R China
5.South China Sea Inst Oceanol, Global Ocean & Climate Res Ctr, Guangzhou 510301, Peoples R China
6.Univ Tokyo, Inst Ind Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778574, Japan
7.Hokkaido Univ, Arctic Res Ctr, Sapporo, Hokkaido 0010021, Japan
8.Univ Helsinki, Finnish Museum Nat Hist, Helsinki, Finland
9.Water Res Inst IRSA, Natl Res Council Italy CNR, Mol Ecol Grp MEG, I-28922 Verbania, Italy
10.Natl Biodivers Future Ctr NBFC, Palermo, Italy
推荐引用方式
GB/T 7714		 Zhang, Zhixin,Zhou, Jinxin,Molinos, Jorge Garcia,et al. Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area[J]. MARINE LIFE SCIENCE & TECHNOLOGY,2024:14.
APA Zhang, Zhixin.,Zhou, Jinxin.,Molinos, Jorge Garcia.,Mammola, Stefano.,Bede-Fazekas, Akos.,...&Lin, Qiang.(2024).Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area.MARINE LIFE SCIENCE & TECHNOLOGY,14.
MLA Zhang, Zhixin,et al."Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area".MARINE LIFE SCIENCE & TECHNOLOGY (2024):14.

入库方式： OAI收割

来源：海洋研究所

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