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Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area
Zhang, Z.; Zhou, J.; García Molinos, J.; Mammola, S.; Bede-Fazekas, Á.; Feng, X.; Kitazawa, D.; Assis, J.; Qiu, T.; Lin, Q. (2024). Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area. Marine Life Science & Technology 6(2): 349-362. https://dx.doi.org/10.1007/s42995-024-00226-0
In: Marine Life Science & Technology. Springer Nature: London. ISSN 2096-6490; e-ISSN 2662-1746, meer
Peer reviewed article  
Beschikbaar in  Auteurs 
Trefwoorden
    Climate change
    Marien/Kust
Author keywords
    Bayesian approach  · Habitat suitability · Physiological knowledge · Species distribution model
Auteurs  Top 
  • Zhang, Z.
  • Zhou, J.
  • García Molinos, J.
  • Mammola, S.
  • Bede-Fazekas, Á.
  • Feng, X.
  • Kitazawa, D.
  • Assis, J.
  • Qiu, T.
  • Lin, Q., meer
Abstract
    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 naïve 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 naïve 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 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 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.
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