Biodiversity Information Science and Standards : Conference Abstract
Conference Abstract
Species Distribution Modelling Using Deep Learning
expand article infoRutger Aldo Vos‡,§, Mark Rademaker|,, Laurens Hogeweg‡,
‡ Naturalis Biodiversity Center, Leiden, Netherlands
§ Leiden University, Leiden, Netherlands
| Wageningen University and Research, Wageningen, Netherlands
¶ COSMONiO, Groningen, Netherlands
Open Access


Species distribution modelling, or ecological niche modelling, is a collection of techniques for the construction of correlative models based on the combination of species occurrences and GIS data. Using such models, a variety of research questions in biodiversity science can be investigated, among which are the assessment of habitat suitability around the globe (e.g. in the case of invasive species), the response of species to alternative climatic regimes (e.g. by forecasting climate change scenarios, or by hindcasting into palaeoclimates), and the overlap of species in niche space. The algorithms used for the construction of such models include maximum entropy, neural networks, and random forests. Recent advances both in computing power and in algorithm development raise the possibility that deep learning techniques will provide valuable additions to these existing approaches. Here, we present our recent findings in the development of workflows to apply deep learning to species distribution modelling, and discuss the prospects for the large-scale application of deep learning in web service infrastructures to analyze the growing corpus of species occurrence data in biodiversity information facilities.


species distribution modelling, ecological niche modelling, deep learning, machine learning, biodiversity informatics, GIS, species occurrences

Presenting author

Rutger Vos

Presented at

Biodiversity_Next 2019

Hosting institution

Naturalis Biodiversity Center

Ethics and security


Conflicts of interest

None indicated