Introduction

Understanding how the historical environment and tree functional traits impact tropical forest stress responses is important for predicting climate change feedbacks. The impacts of water availability on tree performance are determined by vascular and other tree functional traits. The long-term pattern of soil water and water table depth—the hydrological regime—influences this functional composition, with more varied regimes likely supporting more functional trait variation. The coupling between traits and long-term hydrological regimes, however, and how this coupling influences forest functions like production under rapidly shifting regimes, remain little-understood.

Objectives

We develop a theory that first links long-term tropical forest hydrological regimes with tree trait composition, specifically of traits that influence water-related stress tolerance. Traits are then linked to forest functional responses over hydrological conditions to predict the impacts of regime shifts. We apply this framework to the question of Amazon forest variation over long-term water regimes, from frequent drought to waterlogging, to understand the full spectrum of forest responses to climate change.

Methods

We develop a flexible quantitative framework that assumes (i) that a community functional response—we focus on canopy production responding to soil water—is directly coupled to the frequency of environmental conditions (regime), however, with (ii) limitations to function imposed by physiologically stressful conditions such as soil water excess (anoxia), or water deficits. We simulate scenarios for community production responses to hydrological regime shifts, and compare with Amazon forest case studies.

Results

The addition of drought and waterlogging stress limitations offset community functional response curves from hydrological regime curves; this introduces nonlinear responses to regime shifts that can be both positive and negative depending on the degree of shift. For example, moderate drying in a very waterlogged regime decreases the stress limitation of function, predicting enhanced production, agreeing with growing Amazon field evidence. However, further drying would produce strong production losses. Our theory makes symmetrical predictions when very dry regimes become wetter. Ecological and biogeographical factors controlling the coupling of the long-term regimes and trait-determined functional responses strongly influence climate impacts.

Implications

Large waterlogged shallow water table depth forest regions may provide a source of Amazon resilience to moderate climate change. However, our findings also point to a high degree of nonlinearity, including the potential for forest collapse. Growing remote and field network data provide opportunities to fit, refine, and evaluate this theory, offering new predictive insight into the roles of environmental regimes, functional traits, and their variations, in forest climate responses.