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

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Marine Life Science & Technology 2024年第2期6卷 349-362页

作者： Zhixin Zhang Jinxin Zhou Jorge García Molinos Stefano Mammola Ákos Bede‑Fazekas Xiao Feng Daisuke Kitazawa Jorge Assis Tianlong Qiu Qiang Lin CAS Key Laboratory of Tropical Marine Bio-resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Guangdong Provincial Key Laboratory of Applied Marine Biology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Marine Biodiversity and Ecological Evolution Research Center South China Sea Institute of OceanologyGuangzhou 510301China Global Ocean and Climate Research Center South China Sea Institute of OceanologyGuangzhou 510301China Institute of Industrial Science The University of Tokyo5-1-5 KashiwanohaKashiwaChiba 277-8574Japan Arctic Research Center Hokkaido UniversitySapporoHokkaido 001-0021Japan Finnish Museum of Natural History University of HelsinkiHelsinkiFinland Molecular Ecology Group(MEG) Water Research Institute(IRSA)National Research Council of Italy(CNR)28922 Verbania PallanzaItaly National Biodiversity Future Center(NBFC) PalermoItaly Institute of Ecology and Botany HUN-REN Centre for Ecological ResearchVácrátótHungary Department of Environmental and Landscape Geography ELTE Eötvös Loránd UniversityBudapestHungary Department of Biology University of North CarolinaChapel HillNC 27599USA Centre of Marine Sciences University of AlgarveCampus de GambelasFaroPortugal CAS Key Laboratory of Experimental Marine Biology Institute of OceanologyChinese Academy of SciencesQingdao 266071China University of Chinese Academy of Sciences Beijing 100049China

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 *** can be biased when occurrences do not fully represent the environmental requirement of a *** tested to what extent species’physiological knowledge might influence SDM *** on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia,we compiled a comprehensive dataset of occurrence *** then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs:a naïve model that solely depends on environmental correlates,and a physiologically informed model that further incorporates physiological information as *** further tested the models’sensitivity to calibration area choices by fitting them with different buffered areas around known *** with naïve models,the physiologically informed models successfully captured the negative influence of high temperature on *** and were less sensitive to the choice of calibration *** naïve 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 *** findings highlight benefits from incorporating physiological information into correlative SDMs,namely mitigating the uncertainties associated with the choice of calibration *** these promising features,we encourage future SDM studies to consider species physi-ological information where available.

关键词： Bayesian approach Climate change Habitat suitability physiological knowledge Species distribution model

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