Random subset feature selection for ecological niche models of wildfire activity in Western North America

Variable selection in ecological niche modelling can influence model projections to a degree comparable to variations in future climate scenarios. Consequently, it is important to select feature (variable) subsets for optimizing model performance and characterizing variability. We utilize a novel random subset feature selection algorithm (RSFSA) for niche modelling to select an ensemble of optimally sized feature subsets of limited correlation (|r| < 0.7) from 90 climatic, topographic and anthropogenic indices, generating wildfire activity models for western North America with higher performance. Monitoring Trends in Burn Severity and LANDFIRE wildfire data were used to develop thousands of MaxEnt, GLM and Glmnet models. The RSFSA-selected models performed better than random models, having higher accuracy (Area Under the Curve statistic; AUC), lower complexity (corrected Akaike Information Criterion; AICc), and, in some cases, lower overfitting (AUCdiff). The RSFSA-selected MaxEnt quadratic/hinge (β-regularization 2) feature models generally had higher AUC and lower AICc, outperforming other niche model parameterizations and methods. Feature subset ensembles of RSFSA-selected 15-variable MaxEnt quadratic/hinge models were used to characterize variability in projected areas of large wildfires for three burn severities under current, 2050, and 2070 climate scenarios. Expert screening of variables before RSFSA did not improve model performance. Widespread contemporary wildfire deficits and projected regional changes in wildfires highlight the need to manage fuel loads and restore natural fire regimes. The RSFSA is valuable for optimizing niche model performance and generating feature subset ensembles to characterize model variability across niche models of various feature subset sizes, modelling methods, and climate scenarios.