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

doi:10.1007/s42995-024-00226-0

IOCAS-IR > 实验海洋生物学重点实验室

	Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area
	Zhang, Zhixin 1,2,3,4; Zhou, Jinxin 5; Molinos, Jorge Garcia 6; Mammola, Stefano 7,8,9; Bede-Fazekas, Akos 10,11; Feng, Xiao 12; Kitazawa, Daisuke 5; Assis, Jorge 13; Qiu, Tianlong14 ; Lin, Qiang 1,3,15
	2024-05-13
发表期刊	MARINE LIFE SCIENCE & TECHNOLOGY
ISSN	2096-6490
页码	14
通讯作者	Zhang, Zhixin([email protected]) ; Lin, Qiang([email protected])
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.
关键词	Bayesian approach Climate change Habitat suitability Physiological knowledge Species distribution model
DOI	10.1007/s42995-024-00226-0
收录类别	SCI
语种	英语
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
WOS类目	Marine & Freshwater Biology
WOS记录号	WOS:001220924200001
出版者	SPRINGERNATURE
WOS关键词	CLIMATE-CHANGE ; ABSENCE DATA ; OCEAN ; RANGE ; COMMUNITIES ; IMPACTS ; QUALITY
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/185764
专题	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.Chinese Acad Sci, South China Sea Inst Oceanol, CAS Key Lab Trop Marine Bioresources & Ecol, Guangzhou 510301, Peoples R China 2.Chinese Acad Sci, South China Sea Inst Oceanol, Guangdong Prov Key Lab Appl Marine Biol, Guangzhou 510301, Peoples R China 3.South China Sea Inst Oceanol, Marine Biodivers & Ecol Evolut Res Ctr, Guangzhou 510301, Peoples R China 4.South China Sea Inst Oceanol, Global Ocean & Climate Res Ctr, Guangzhou 510301, Peoples R China 5.Univ Tokyo, Inst Ind Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778574, Japan 6.Hokkaido Univ, Arctic Res Ctr, Sapporo, Hokkaido 0010021, Japan 7.Univ Helsinki, Finnish Museum Nat Hist, Helsinki, Finland 8.Water Res Inst IRSA, Natl Res Council Italy CNR, Mol Ecol Grp MEG, I-28922 Verbania, Italy 9.Natl Biodivers Future Ctr NBFC, Palermo, Italy 10.HUN REN Ctr Ecol Res, Inst Ecol & Bot, Vacratot, Hungary 11.Eotvos Lorand Univ, Dept Environm & Landscape Geog, Budapest, Hungary 12.Univ N Carolina, Dept Biol, Chapel Hill, NC 27599 USA 13.Univ Algarve, Ctr Marine Sci, Campus Gambelas, P-8005139 Faro, Portugal 14.Chinese Acad Sci, CAS Key Lab Expt Marine Biol, Inst Oceanol, Qingdao 266071, Peoples R China 15.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
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.