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Moreover, since expert assessments are inherently subjective and prone to biases, expert‐driven SDMs should calibrate their assessments. We propose a method to tackle these challenges by extending the hierarchical Bayesian integrated species distribution modeling framework to expert informed species distribution modeling. We treated map‐like expert assessments as data and integrated them with calibration data on species recordings. Our integrated SDM has model components to estimate experts' reliability and to adjust for potential biases in their assessments. After integrated inference, we used the model to make predictions over a study area. We tested our approach with an extensive simulation study and a real world case study comprising ten expert assessments and survey data on pikeperch larvae from a coastal area of the Gulf of Finland. Expert assessments significantly improved species distribution predictions compared to predictions conditioned on survey data only. They also improved parameter inference, thus strengthening the ecological interpretation of the results. The skill of the experts, and biases in their assessments, varied considerably in the case study though, emphasizing the importance of formal expert calibration provided by our model. Our results show that expert elicitation can be an efficient tool for improving species distribution model predictions. Our approach is especially useful for applications where any type of species data are expensive to collect but local species experts can easily be reached.</jats:p>"],"publicationDate":"2026-02-06","publisher":"Wiley","embargoEndDate":"2022-06-01","sources":["Crossref","Methodology"],"formats":["12","application/pdf"],"contributors":["Department of Mathematics and Statistics","Environmental and Ecological Statistics Group","Faculty of Science","Department of Organismal and Evolutionary Biology","Research Centre for Ecological Change","Faculty Common Matters (Faculty of Biology and Environmental 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