Modeling forest disturbance and recovery in secondary subtropical dry forests of Puerto Rico

Abstract:

Because of human pressures, the need to understand and predict the long-term dynamics of subtropical dry forests is urgent. Through modifications to the ZELIG vegetation demographic model, including the development of species- and site-specific parameters and internal modifications, the capability to predict forest change within the Guanica State Forest in Puerto Rico can now be accomplished. One objective was to test the capability of this new model (i.e. ZELIG-TROP) to predict successional patterns of secondary forests across a gradient of abandoned fields currently being reclaimed as forests. Model simulations found that abandoned fields that are on degraded lands have a delayed response to fully recover and reach a mature forest status during the simulated time period; 200 years. The forest recovery trends matched predictions published in other studies, such that attributes involving early resource acquisition (i.e. canopy height, canopy coverage, density) were the fastest to recover, but attributes used for structural development (i.e. biomass, basal area) were relatively slow in recovery. Biomass and basal area, two attributes that tend to increase during later successional stages, are significantly lower during the first 80–100 years of recovery compared to a mature forest, suggesting that the time scale of resilience in subtropical dry forests needs to be partially redefined. A second objective was to investigate the long and short-term effects of increasing hurricane disturbances on vegetation structure and dynamics, due to hurricanes playing an important role in maintaining dry forest structure in Puerto Rico. Hurricane disturbance simulations within ZELIG-TROP predicted that increasing hurricane intensity (i.e. up to 100% increase) did not lead to a large shift in long-term AGB or NPP. However, increased hurricane frequency did lead to a 5-40% decrease in AGB, and 32-50% increase in NPP, depending on the treatment. In addition, the modeling approach used here was able to track changes in litterfall, coarse woody debris, and other forest carbon components under various hurricane regimes, a critical step for understanding the future state of subtropical dry forests.