Net ecosystem production across successional time in a North American eastern temperate forest chronosequence

Abstract:

Forest ecosystems play a major role in the global carbon cycle, holding up to 60% of all carbon stored in plant biomass and soil. Due primarily to direct human-induced disturbances such as clearing for agriculture or lumber harvest, as well as indirect, climate induced disturbances such as severe wildfire and windfall, forests in middle North America have experienced disturbances of varying intensities during the last 200 years, and now exist at a wide variety of ages. It has been previously theorized that across successional time, forests will initially sequester large amounts of carbon (through relatively high NEP values), and gradually reach a steady state in their later, old growth years in which they will become net carbon neutral. Studies have also demonstrated that severe disturbances can depress a forest’s ability to reach its full ecological functioning and NEP potential. However, there is currently limited data that explores NEP changes over a multi-successional stage timespan, as well as limited data explaining how disturbance severity may interact with time to alter trajectories of carbon sequestration. Using two forest chronosequences, both over 100 years old and each established with a different degree of disturbance severity, we measured woody growth, fine root turnover, leaf litter, coarse woody debris decomposition, heterotrophic soil respiration, and belowground woody biomass to characterize the century-long recovery of NEP. We modeled the relationship between soil heterotrophic respiration and temperature, soil nutrients and moisture, and tracked changes across successional time in these forest systems. We expect to find that older, late successional forests will sequester more carbon than previously anticipated, and also that more severely disturbed forests will sequester less carbon than those that experienced lower severity disturbance. Our findings provide new mechanistic insights that can be integrated into predictive models to forecast long-term carbon cycling changes in forests. Our results also will help inform which forest age structure supports the highest NEP values, supporting carbon management and policy.