Disequilibrium vegetation dynamics under future climate change

Abstract

Premise of the study: Near-future climate changes are likely to elicit major vegetation changes. Disequilibrium dynamics, which occur when vegetation comes out of equilibrium with climate, are potentially a key facet of these. Understanding these dynamics is crucial for making accurate predictions, informing conservation planning, and understanding likely changes in ecosystem function on time scales relevant to society. However, many predictive studies have instead focused on equilibrium end-points with little consideration of the transient trajectories.

Methods: We review what we should expect in terms of disequilibrium vegetation dynamics over the next 50-200 yr, covering a broad range of research fields including paleoecology, macroecology, landscape ecology, vegetation science, plant ecology, invasion biology, global change biology, and ecosystem ecology.

Key results: The expected climate changes are likely to induce marked vegetation disequilibrium with climate at both leading and trailing edges, with leading-edge disequilibrium dynamics due to lags in migration at continental to landscape scales, in local population build-up and succession, in local evolutionary responses, and in ecosystem development, and trailing-edge disequilibrium dynamics involving delayed local extinctions and slow losses of ecosystem structural components. Interactions with habitat loss and invasive pests and pathogens are likely to further contribute to disequilibrium dynamics. Predictive modeling and climate-change experiments are increasingly representing disequilibrium dynamics, but with scope for improvement.

Conclusions: The likely pervasiveness and complexity of vegetation disequilibrium is a major challenge for forecasting ecological dynamics and, combined with the high ecological importance of vegetation, also constitutes a major challenge for future nature conservation.

Keywords: climate change; dispersal limitation; global change; plant migration; plant population dynamics; predictive modeling; range shift; succession; time lags; transient dynamics.