UNDERSTANDING ECOLOGICAL CHANGE ACROSS LARGE SPATIAL, TEMPORAL, AND TAXONOMIC SCALES: INTEGRATING DATA AND METHODS IN LIGHT OF THEORY

11 April 2019

Rapacciuolo, Giovanni; Blois, Jessica

The difficulty of integrating multiple theories, data, and methods has slowed progress towards making unified inferences of ecological change generalizable across large spatial, temporal and taxonomic scales. However, recent progress towards a theoretical synthesis now provides a guiding framework for organizing and integrating all primary data and methods for spatiotemporal assemblage-level inference in ecology. In this paper, we describe how recent theoretical developments can provide an organizing paradigm for linking advances in data collection and methodological frameworks across disparate ecological sub-disciplines and across large spatial and temporal scales. First, we summarize the set of fundamental processes that determine change in multispecies assemblages across spatial and temporal scales by reviewing recent theoretical syntheses of community ecology. Second, we review recent advances in data and methods across the main sub-disciplines concerned with ecological inference across large spatial, temporal and taxonomic scales, and organize them based on the primary fundamental processes they include, rather than the spatiotemporal scale of their inferences. Finally, we highlight how iteratively focusing on only one fundamental process at a time, but combining all relevant spatiotemporal data and methods, may reduce the conceptual challenges to integration among ecological sub-disciplines. Moreover, we discuss a number of avenues for decreasing the practical barriers to integration among data and methods. We aim to reconcile the recent convergence of decades of thinking in community ecology and macroecology theory with the rapid progress in spatiotemporal approaches for assemblage-level inference, at a time where a robust understanding of spatiotemporal change in ecological assemblages is more crucial than ever to conserve biodiversity.

Doi
10.1111/ecog.04616