Novel methods to select environmental variables in MaxEnt : A case study using invasive crayfish

Yiwen ZENG, Bi Wei LOW, Darren C.J. YEO*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

76 Citations (Scopus)

Abstract

The popularity of MaxEnt in species distribution modeling has been driven by several factors including its high degree of accuracy, and flexibility to tailor efforts to species-specific situations. Although many recent studies have identified the importance of adjusting mathematical transformation (feature class) and regularization of coefficient values, collectively known as tuning, few studies have addressed the need to customize the variables used in species distribution modeling, and use unselected variable sets. This study presents two novel methods to select for environmental variables in MaxEnt. The first involves selecting from a priori determined environmental variable sets (pre-selected based on ecological or biological knowledge), and the second utilizes a reiterative process of model formation and stepwise removal of least contributing variables. Both methods were tested on eight known species of invasive crayfish, with results reinforcing the need for species-specific environmental variable sets. While the reiterative process generally performs better than the a priori selected variables, selection of method can be based on information availability. These techniques appear to outperform the current practice of utilizing unselected variable sets and is especially important considering the increasing application of species distribution modeling (across spatial and temporal barriers) in conservation and management efforts whereby inaccurate predictions might have adverse effects.

Original languageEnglish
Pages (from-to)5-13
Number of pages9
JournalEcological Modelling
Volume341
DOIs
Publication statusPublished - 10 Dec 2016
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to thank two anonymous reviewers, Luis Roman Carrasco, Siti Zarina Zainul Rahim and Chong Kwek Yan for their valuable input towards improving this paper. We would also like to thank Shawn Tan for his advice and help in writing and editing the bash scripts. This study was supported by an AcRF Tier 1 grant from the Singapore Ministry of Education (National University of Singapore Grant Number R-154-000-633-112 ) and the Ah Meng Memorial Conservation Fund ( National University of Singapore Grant Number R-154-000-617-720 ).

Publisher Copyright:
© 2016 Elsevier B.V.

Keywords

  • A priori
  • Distribution
  • Ecological niche
  • Species distribution model
  • Stepwise removal
  • Tuning

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