Improving prediction of rare species' distribution from community data.

ABSTRACT: Species distribution models (SDMs) have been increasingly used to predict the geographic distribution of a wide range of organisms; however, relatively fewer research efforts have concentrated on rare species despite their critical roles in biological conservation. The present study tested whether community data may improve modelling rare species by sharing information among common and rare ones. We chose six SDMs that treat community data in different ways, including two traditional single-species models (random forest and artificial neural network) and four joint species distribution models that incorporate species associations implicitly (multivariate random forest and multi-response artificial neural network) or explicitly (hierarchical modelling of species communities and generalized

SUBMITTER: Zhang C 

PROVIDER: S-EPMC7376031 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature