Abstract
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.
Highlights
Ecological niche modelling, is a collection of techniques for the construction of correlative models based on the combination of species occurrences and GIS data
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
We present our recent findings in the development of workflows to apply deep learning to species distribution modelling, and discuss the prospects for the largescale application of deep learning in web service infrastructures to analyze the growing corpus of species occurrence data in biodiversity information facilities
Summary
Rutger Aldo Vos‡,§, Mark Rademaker|,‡, Laurens Hogeweg‡,¶ Corresponding author: Rutger Aldo Vos (rutgeraldo@gmail.com) Received: 17 Jul 2019 | Published: 30 Jul 2019 Citation: Vos RA, Rademaker M, Hogeweg L (2019) Species Distribution Modelling Using Deep Learning.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.