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Predictive ability of a process-based versus a correlative species distribution model

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00005644
URN to cite this document: urn:nbn:de:bvb:703-epub-5644-3

Title data

Higgins, Steven I. ; Larcombe, Matthew J. ; Beeton, Nicholas J. ; Conradi, Timo ; Nottebrock, Henning:
Predictive ability of a process-based versus a correlative species distribution model.
In: Ecology and Evolution. Vol. 10 (2020) Issue 20 . - pp. 11043-11054.
ISSN 2045-7758
DOI der Verlagsversion: https://doi.org/10.1002/ece3.6712

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Project information

Project title:
Project's official titleProject's id
EMSAfrica01LL1801A
Open Access PublizierenNo information

Project financing: Bundesministerium für Bildung und Forschung

Abstract

Species distribution modeling is a widely used tool in many branches of ecology and evolution. Evaluations of the transferability of species distribution models—their ability to predict the distribution of species in independent data domains—are, however, rare. In this study, we contrast the transferability of a process-based and a correlative species distribution model. Our case study uses 664 Australian eucalypt and acacia species. We estimate models for these species using data from their native Australia and then assess whether these models can predict the adventive range of these species. We find that the correlative model—MaxEnt—has a superior ability to describe the data in the training data domain (Australia) and that the process-based model—TTR-SDM—has a superior ability to predict the distribution of the study species outside of Australia. The implication of this analysis, that process-based models may be more appropriate than correlative models when making projections outside of the domain of the training data, needs to be tested in other case studies.

Further data

Item Type: Article in a journal
Keywords: ecological niche model; extrapolation; invasive species; MaxEnt; mechanistic models; model transferability; TTR-SDM
DDC Subjects: 500 Science > 580 Plants (Botany)
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Plant Ecology > Chair Plant Ecology - Univ.-Prof. Dr. Steven Ian Higgins
Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Plant Ecology
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-5644-3
Date Deposited: 31 May 2021 08:22
Last Modified: 31 May 2021 08:25
URI: https://epub.uni-bayreuth.de/id/eprint/5644

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