Predictor complexity and feature selection affect Maxent model transferability : Evidence from global freshwater invasive species

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

*Corresponding author for this work

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

15 Citations (Scopus)

Abstract

Aim: Ecological niche models (ENMs) are widely used to address urgent real-world problems such as climate change effects or invasive species; however, the generality of models when projected through space and/or time, that is transferability, remains a key challenge. Here, we explored the effects of complex predictors and feature selection on ENM transferability in a widely employed algorithm, Maxent, using five globally invasive freshwater species as case studies. Location: Global. Methods: We modelled the global distributions of five notorious freshwater invasive species (African sharptooth catfish Clarias gariepinus, Mozambique tilapia Oreochromis mossambicus, American bullfrog Lithobates catesbeianus, red swamp crayfish Procambarus clarkii, and Australian redclaw crayfish Cherax quadricarinatus), using three predictor datasets of varying complexities derived from two commonly used climatic data sources (WorldClim and IPCC) and three methods of model tuning that differentially incorporated feature selection. Spatially explicit transferability assessments were then conducted using a suite of evaluation metrics previously used to quantify Maxent model performance. Results: We show that in the absence of detailed biological knowledge of focal species, simpler predictor datasets produce models that are more accurate than those calibrated using comprehensive “bioclimatic” datasets. Additionally, we find that tuning models for both optimal regularization parameters as well as feature-class combinations led to the greatest increases in transferability and geographic niche conservatism. Results indicate a tenuous link between model transferability and Akaike's information criterion corrected for small sample sizes (AICc), suggesting that the indiscriminate use of AICc as an estimate of model parsimony may lead to erratic model performance. Main conclusions: Our findings demonstrate that methodological considerations can drastically affect the reliability of spatial and possibly temporal projections, which has severe implications when ENMs are used to infer species’ niches, and quantify ecological or evolutionary change across impacted landscapes.

Original languageEnglish
Pages (from-to)497-511
Number of pages15
JournalDiversity and Distributions
Volume27
Issue number3
DOIs
Publication statusPublished - Mar 2021
Externally publishedYes

Bibliographical note

Funding Information:
We thank Dan Warren, Rudolf Meier, Ryan Chisholm and Chong Kwek Yan for valuable insights and comments on early drafts of the manuscript. 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). BW Low was supported by additional funding from the Lee Kong Chian Natural History Museum, National University of Singapore. Y Zeng acknowledges support from the National Research Foundation (NRF) Singapore under its Commonwealth Research Fellowship Grant (Grant Number NRF‐CSC‐ICFC2017‐05).

Publisher Copyright:
© 2020 The Authors. Diversity and Distributions published by John Wiley & Sons Ltd.

Keywords

  • aquatic invasions
  • bioclimatic variables
  • ecological niche model
  • feature selection
  • Maxent
  • model tuning
  • niche conservatism
  • predictor selection
  • species distribution model
  • transferability

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