Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.
Name
fpls-13-839407.pdf
Description
visibility:open
Size
16.95 MB
Format
Adobe PDF
Checksum (CRC64NVME)
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Resource type
Journal article
Date published
April 19, 2022
Abstract
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.
Creator (organisation)
Andermann, Tobias
Antonelli, Alexandre
Barrett, Russell L.
Silvestro, Daniele
Project(s)
Priority 1: Ecosystem Stewardship
Funder
| Funder name | Awards |
Vetenskapsrådet, Sweden | 2019-05191 - 2019-04739 |
Stiftelsen för Strategisk Forskning, Sweden | FFL15-0196 |
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Switzerland | PCEFP3_187012 |
Royal Botanic Gardens, Kew, United Kingdom | |
Journal title
Frontiers in Plant Science
Volume
13
Article number
839407
Publisher
Frontiers Media SA
Place of publication
Lausanne, Switzerland
eISSN
1664-462X
Date accepted
March 24, 2022
Official URL
Rights statement
In Copyright
Additional information
IF = 5.753 (2021-2022)