Unpacking the \"black box\": improving ecological interpretation of regression based models

AimMany tree species distribution models use black-box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximising predictive accuracy. In this study, we outline an interpretative modelling framework to gain better ecological insights while mapping abundance patterns of six North American species.
LocationContinental United States and Canada
MethodsWe develop an innovative procedure using regression trees by stabilising variance and mapping dominant rules which we term ‘optimized regression tree bagging for interpretation and mapping’ (ORTBIM). We apply this technique to understand ecological features influencing the abundance patterns of three eastern (Pinus strobus, Acer saccharum, and Quercus montana), and three western (Picea engelmannii, Pinus ponderosa, and Pseudotsuga menziesii) tree species in North America. For these species, we assess and map the dominant climate-terrain interactions that partly determine abundance patterns in t..., The data is from the Forest Inventory Analysis of the USDA Forest Service. We also use data from the AdaptWest climate and USGS elevational data., R - https://cran.r-project.org/Â