Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

ABSTRACT: Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the

SUBMITTER: Adjei KP 

PROVIDER: S-EPMC10918728 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature