IUCNN – Deep learning approaches to approximate species' extinction risk-Reference-Cited by-同舟云学术

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IUCNN – Deep learning approaches to approximate species' extinction risk

Published:2021-12-16 Issue:2 Volume:28 Page:227-241
ISSN:1366-9516
Container-title:Diversity and Distributions
language:en
Short-container-title:Diversity and Distributions

Author:

Zizka Alexander¹²^ORCID,Andermann Tobias³⁴^ORCID,Silvestro Daniele⁵⁶^ORCID

Affiliation:

1. German Center for Integrative Biodiversity Research Halle‐Jena‐Leipzig (iDiv)University of Leipzig Leipzig Germany

2. Department of Biology Philipps‐University Marburg Marburg Germany

3. Department of Biological and Environmental Sciences University of Gothenburg Göteborg Sweden

4. Gothenburg Global Biodiversity Centre Göteborg Sweden

5. Department of Biology University of Fribourg Fribourg Switzerland

6. Swiss Institute of Bioinformatics Lausanne Switzerland

Funder

Deutsche Forschungsgemeinschaft

Vetenskapsrådet

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Wiley

Subject

Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.13450

Reference81 articles.

1. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models

2. iucn_sim : a new program to simulate future extinctions based on IUCN threat status

3. bRacatus : A method to estimate the accuracy and biogeographical status of georeferenced biological data

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