气候变化领域集成服务门户

Aim Climate and disturbance alter forest dynamics, from individual trees to biomes and from years to millennia, leaving legacies that vary with local, meso- and macroscales. Motivated by recent insights in temperate forests, we argue that temporal and spatial extents equivalent to that of the underlying drivers are necessary to characterize forest dynamics across scales. We focus specifically on characterizing mesoscale forest dynamics because they bridge fine-scale (local) processes and the continental scale (macrosystems) in ways that are highly relevant for climate change science and ecosystem management. We revisit ecological concepts related to spatial and temporal scales and discuss approaches to gain a better understanding of climate-forest dynamics across scales. Location Eastern USA. Time period Last century to present. Major taxa studied Temperate broadleaf forests. Methods We review regional literature of past tree mortality studies associated with climate to identify mesoscale climate-driven disturbance events. Using a dynamic vegetation model, we then simulate how these forests respond to a typical climate-driven disturbance. Results By identifying compound disturbance events from both a literature review and simulation modelling, we find that synchronous patterns of drought-driven mortality at mesoscales have been overlooked within these forests. Main conclusions As ecologists, land managers and policy-makers consider the intertwined drivers of climate and disturbance, a focus on spatio-temporal scales equivalent to those of the drivers will provide insight into long-term forest change, such as drought impacts. Spatially extensive studies should also have a long temporal scale to provide insight into pathways for forest change, evaluate predictions from dynamic forest models and inform development of global vegetation models. We recommend integrating data collected from spatially well-replicated networks (e.g., archaeological, historical or palaeoecological data), consisting of centuries-long, high-resolution records, with models to characterize better the mesoscale response of forests to climate change in the past and in the future.