Modeling Carbon Stocks in a Secondary Tropical Dry Forest in the Yucatan Peninsula, Mexico

The carbon balance of secondary dry tropical forests of Mexico’s Yucatan Peninsula is sensitive to human and natural disturbances and climate change. The spatially explicit process model Forest-DeNitrification-DeComposition (DNDC) was used to estimate forest carbon dynamics in this region, including the effects of disturbance on carbon stocks. Model evaluation using observations from 276 sample plots in a tropical dry forest in the Yucatan Peninsula indicated that Forest-DNDC can be used to simulate carbon stocks for this forest with good model performance efficiency. The simulated spatial variability in carbon stocks was large, ranging from 5 to 115 Mg carbon (C) ha⁻¹, with a mean of 56.6 Mg C ha⁻¹. Carbon stocks in the forest were largely influenced by human disturbances between 1985 and 2010. Based on a comparison of the simulations with and without disturbances, carbon storage in the year 2012 with disturbance was 3.2 Mg C ha⁻¹, lower on average than without disturbance. The difference over the whole study area was 154.7 Gg C, or an 8.5 % decrease. There were substantial differences in carbon stocks simulated at individual sample plots, compared to spatially modeled outputs (200 m²plots vs. polygon simulation units) at some locations due to differences in vegetation class, stand age, and soil conditions at different resolutions. However, the difference in the regional mean of carbon stocks between plot-level simulation and spatial output was small. Soil CO₂and N₂O fluxes varied spatially; both fluxes increased with increasing precipitation, and soil CO₂also increased with an increase in biomass. The modeled spatial variability in CH₄uptake by soils was small, and the flux was not correlated with precipitation. The net ecosystem exchange (NEE) and net primary production (NPP) were nonlinearly correlated with stand age. Similar to the carbon stock simulations, different resolutions resulted in some differences in NEE and NPP, but the spatial means were similar.