About this Research Topic
Climate change increases the frequency and intensity of climate extremes, such as droughts, heatwaves, deep frosts, hurricanes, floods, wildfires, and so on. These extreme events reshape forest structure and function in diverse ways and over multiple timescales. For instance, eco-physiological processes (e.g., photosynthesis) can immediately respond to sudden temperature and precipitation changes. Instantaneous response functions such as this are essential in ecosystem modeling. Many other ecological processes (e.g., primary productivity, nutrient availability, soil structure, species composition, and forest succession) instead take time to manifest, displaying profound and potentially irreversible outcomes. Such outcomes may not be noticeable or measurable until several seasons or years after the extreme events occur (e.g., increased tree mortality in the years after a severe drought). These phenomena are often viewed as "legacy effects", a series of long-term consequences caused by previous disturbing events or the system's internal pressure. Although the significance of legacy effects has been widely recognized conceptually, in practice legacy effects are harder to delineate, and their representation in ecosystem models is categorical rather than continuous. The same is true for the methodology used to identify and track legacy effects triggered by climate extremes or management activities. The knowledge gaps on such legacy effects result in uncertainty in model simulations and predictions.
Quantifying legacy effects is even more challenging than instantaneous responses. One reason is that legacy effects can be stochastic with a lower probability of occurrences. Identifying legacy effects and related processes from confounding fast factors requires more research resources and longer timeframes. Fortunately, the rapidly increasing long-term ecological and carbon flux databases (e.g., LTER, FLUXNET, NEON, etc.) expand available remote sensing products and data analytical tools (e.g., machine learnings), and improvements in models provide unprecedented opportunities to understand and quantify legacy effects. For example, decadal ecosystem carbon flux data can help identify possible legacy effects after climate extremes. Existing process-based models may be a digital laboratory for comparing tree growth or demography between "normal" and "extreme" scenarios. Long-term remote sensing images are an excellent source for tracking legacy effects in space and time. Field observations on forest recovery pave the way to understand the mechanisms behind various legacy effects.
This Research Topic invites original research, meta-data analysis, modeling studies, and review papers to better understand legacy effects triggered by climate extremes and their underlying mechanisms. We encourage all studies from organism to ecosystem scales, such as tree growth, demography, species compositions, community dynamics, carbon and water fluxes, primary productivity, among others. Interdisciplinary studies combining tree rings, field observations, flux towers, remote sensing, historical data, and modeling approaches are highly welcome and contributions to novel methodologies to detect legacy effects and their application.
Keywords: Legacy Effects, Climate Change, Extreme Events, Forest Response, Forest Recovery
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