Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment.

ABSTRACT: Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted "punctual models"). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of speci

SUBMITTER: Deneu B 

PROVIDER: S-EPMC8084334 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature