Climate‐informed models benefit hindcasting but present challenges when forecasting species–habitat associations

• Published in: Ecography (Copenhagen) Vol. 2022; no. 10
• Main Authors: Barnes, Cheryl L., Essington, Timothy E., Pirtle, Jodi L., Rooper, Christopher N., Laman, Edward A., Holsman, Kirstin K., Aydin, Kerim Y., Thorson, James T.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.10.2022; John Wiley & Sons, Inc
• Subjects: Biomass; Case studies; Climate change; Climate models; Climate prediction; Datasets; Dynamic models; Environment models; Environmental conditions; Fisheries management; forecast skill; Forecasting; Gadus chalcogrammus; generalized additive model; Generalized linear models; Geographical distribution; hindcast performance; Hindcasting; Parameter estimation; Performance evaluation; Population; retrospective skill testing; Species; species distribution model; Static models; Time series; Bering Sea

Although species distribution models (SDMs) are commonly used to hindcast fine‐scale population metrics, there remains a paucity of information about how well these models predict future responses to climate. Many conventional SDMs rely on spatially‐explicit but time‐invariant conditions to quantify...