Unpacking the ‘black box’: Improving ecological interpretation of regression‐based models

Abstract

AbstractAimMany tree species distribution models use black‐box machine learning techniques that often neglect interpretative aspects and instead focus mainly on maximizing 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 stabilizing 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 the eastern and western regions. In the process, we examine the role of varying responses and scales and explore finer‐scale species climate–terrain niches and non‐linear relationships.ResultsOur study emphasizes the prominent role of elevation and heat–moisture variables in the west and the greater importance of seasonal precipitation and seasonal temperature in the east. The abundance patterns under future climate (SSP5‐8.5) show climate–terrain habitats shifting northward and westward into Canada and Alaska for the eastern species, and predominantly north‐westward for the western species.ConclusionOur interpretative modelling framework can be used to gain a more comprehensive understanding of the abundance patterns across the full species range, formulate better predictive models and facilitate improved management practices under climate change.