Chiaverini, Luca and Macdonald, David W. and Hearn, Andrew J. and Kaszta, Zaneta and Ash, Eric and Bothwell, Helen M. and Can, Ozgun Emre and Channa, Phan and Clements, Gopalasamy Reuben * and Haidir, Iding Achmad and Kyaw, Pyae Phyoe and Moore, Jonathan H. and Rasphone, Akchousanh and Tan, Cedric Kai Wei and Cushman, Samuel A. (2023) Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids. Ecological Informatics, 75. ISSN 1878-0512
Full text not available from this repository.Abstract
Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). The former is a parametric regression model providing functional models with direct interpretability. The latter is a machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown to outperform parametric algorithms. Other approaches have been developed to produce robust SDMs, like training data bootstrapping and spatial scale optimisation. Using felid presence-absence data from three study regions in Southeast Asia (mainland, Borneo and Sumatra), we tested the performances of SDMs by implementing four modelling frameworks: GLM and RF with bootstrapped and non-bootstrapped training data. With Mantel and ANOVA tests we explored how the four combinations of algorithms and bootstrapping influenced SDMs and their predictive performances. Additionally, we tested how scale-optimisation responded to species' size, taxonomic associations (species and genus), study area and algorithm. We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. Additionally, algorithm followed by study area and species, were the main factors driving differences in the spatial scales identified. SDMs trained with GLM showed higher predictive performance, however, ANOVA tests revealed that algorithm had significant effect only in explaining the variance observed in sensitivity and specificity and, when interacting with bootstrapping, in Percent Correctly Classified (PCC). Bootstrapping significantly explained the variance in specificity, PCC and True Skills Statistics (TSS). Our results suggest that there are systematic differences in the scales identified and in the predictions produced by GLM vs. RF, but that neither approach was consistently better than the other. The divergent predictions and inconsistent predictive abilities suggest that analysts should not assume machine learning is inherently superior and should test multiple methods. Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | bootstrapping; generalised linear model; machine learning; multi-scale; random forest; species distribution modelling |
| Subjects: | Q Science > Q Science (General) Q Science > QH Natural history S Agriculture > SD Forestry |
| Divisions: | Others > Non Sunway Academics Sunway University > School of Medical and Life Sciences [formerly School of Healthcare and Medical Sciences until 2020] > Dept. Biological Sciences Sunway University > Sunway Business School [formerly Sunway University Business School until 2023] > Jeffrey Sachs Centre on Sustainable Development |
| Depositing User: | Ms Yong Yee Chan |
| Date Deposited: | 02 Jul 2024 02:06 |
| Last Modified: | 02 Jul 2024 02:06 |
| URI: | http://eprints.sunway.edu.my/id/eprint/2717 |
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