Rethinking tipping points in spatial ecosystems

TL;DR

This paper explores how spatial processes and pattern formation in ecosystems can influence the occurrence of tipping points, suggesting that spatial dynamics may help ecosystems avoid critical transitions under environmental stress.

Contribution

It provides a synthesis of mechanisms by which spatial processes affect ecosystem tipping points, emphasizing the role of pattern formation in different ecosystems.

Findings

Self-organized Turing patterns can help drylands evade tipping.

Environmental heterogeneity-driven patterns are key to evasion in humid savannas.

Spatial dynamics are linked to ecological interactions affecting resilience.

Abstract

The theory of alternative stable states and tipping points has garnered substantial attention in the last several decades. It predicts potential critical transitions from one ecosystem state to a completely different state under increasing environmental stress. However, typically, ecosystem models that predict tipping do not resolve space explicitly. Ecosystems being inherently spatial, it is important to understand the effects of spatial processes. In fact, it has been argued that spatial dynamics can actually help ecosystems evade tipping. Here, using a dryland and a savanna-forest model as example systems, we provide a synthesis of several mechanisms by which spatial processes can change our predictions of tipping in ecosystems. We show that self-organized Turing patterns can emerge in drylands that help evade tipping, but that (non-Turing) patterns driven by environmental heterogeneity are key to evasion of tipping in humid savannas. Since the ecological interactions driving the dynamics of these ecosystems differ from each other, we suggest that tipping evasion mechanisms in ecosystems may be connected to the key ecological interactions in a system. This highlights the need for further research into the link between the two in order to formulate better strategies to make ecosystems resilient to global change.