Recent Methodological Solutions to Identifying Scales of Effect in Multi-scale Modeling

Abstract

This review summarizes the characteristics of contemporary multi-scale studies (i.e., those that quantify ecological relationships over multiple spatial extents), outlines limitations associated with analyses typically used to model multi-scale ecological relationships, and highlights recent methodological progress overcoming these limits. The majority of recent studies investigating scales of effect in ecological relationships were conducted within a simplified model selection, information theoretic framework that suffers under the specters of collinearity, imperfect detection, and limited candidate scale space. The few studies that deviate from this framework offer greater flexibility in identifying or directly estimating relevant scales of effect by allowing predictors to have independent scales of effect, simultaneously evaluated, and explicitly consider the unique issue of collinearity in multi-scale studies, with extensions to hierarchical models that account for imperfect detection. Accurate ecological inference requires that the scales of ecological processes, observations, and analyses align. Wider adoption of emerging analytical methods for identifying important scales of effect, though not traditional, and in some cases more computationally intensive, will broaden the scope for understanding the ecological factors operating on organisms at different scales. By estimating full models (i.e., not single-predictor models), which are not scale constrained (i.e., each predictor can have its own scale of effect), and that disentangle ecological processes from observational processes when relevant will provide a comprehensive base of estimates from which to explore underlying mechanisms governing scale dependence in ecology.