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

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/convolutional-neural-networks-improve-species-distribution-modelling-by/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Benjamin Deneu,Maximilien Servajean,Pierre Bonnet,Christophe Botella,François Munoz,Alexis Joly |
| Citations | 116 |
| DOI | 10.1371/journal.pcbi.1008856 |
| Fields | Environmental Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008856&type=printable |
| OpenAlex ID | https://openalex.org/W3155261023 |
| PMID | 33872302 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Pierre Bonnet](https://scholariq.org/researchers/pierre-bonnet/)

## Paper primary topic

- [Species Distribution and Climate Change](https://scholariq.org/topics/species-distribution-and-climate-change/)

## Paper topics

- [Species Distribution and Climate Change](https://scholariq.org/topics/species-distribution-and-climate-change/)
- [Ecology and Vegetation Dynamics Studies](https://scholariq.org/topics/ecology-and-vegetation-dynamics-studies/)
- [Remote Sensing in Agriculture](https://scholariq.org/topics/remote-sensing-in-agriculture/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.