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Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning
The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or re...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062518/ https://www.ncbi.nlm.nih.gov/pubmed/35519811 http://dx.doi.org/10.3389/fpls.2022.839407 |
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author | Andermann, Tobias Antonelli, Alexandre Barrett, Russell L. Silvestro, Daniele |
author_facet | Andermann, Tobias Antonelli, Alexandre Barrett, Russell L. Silvestro, Daniele |
author_sort | Andermann, Tobias |
collection | PubMed |
description | The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments. |
format | Online Article Text |
id | pubmed-9062518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90625182022-05-04 Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning Andermann, Tobias Antonelli, Alexandre Barrett, Russell L. Silvestro, Daniele Front Plant Sci Plant Science The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments. Frontiers Media S.A. 2022-04-19 /pmc/articles/PMC9062518/ /pubmed/35519811 http://dx.doi.org/10.3389/fpls.2022.839407 Text en Copyright © 2022 Andermann, Antonelli, Barrett and Silvestro. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Andermann, Tobias Antonelli, Alexandre Barrett, Russell L. Silvestro, Daniele Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning |
title | Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning |
title_full | Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning |
title_fullStr | Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning |
title_full_unstemmed | Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning |
title_short | Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning |
title_sort | estimating alpha, beta, and gamma diversity through deep learning |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062518/ https://www.ncbi.nlm.nih.gov/pubmed/35519811 http://dx.doi.org/10.3389/fpls.2022.839407 |
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