Testing Chronosequences through Dynamic Approaches: Time and Site Effects on Tropical Dry Forest Succession

Chronosequences, commonly used to assess succession, have been questioned because of their failure to project successional trajectories. Here, we develop a simple analytical approach combining both chronosequence and dynamic data to test the power of age of abandonment and site factors to explain and predict succession. The approach proceeds by first fitting statistical models relating age to attribute values (the chronosequence model) and their observed changes (the dynamic model) to test explanatory power. Predictive power is then tested by bootstrapping the chronosequence model to derive confidence intervals for expected changes and comparing them with the dynamic model. Finally, residuals from both models are tested against site factors. The procedure was applied to six attributes (basal area, plant density, mean plant height, species richness, evenness, and composition) of the woody community (plants >1 cm dbh within 0.1‐ha plots) in nine abandoned cattle pastures (0–12 yr) and three old growth tropical dry forests monitored over 6 yr. Age explained 60–97 percent of the variance in community attributes and only 32–57 percent in observed changes. It significantly overestimated basal area and mean height, while species richness and composition were highly predicted. Besides age, management history also explained successional dynamics. Our results suggest age is not necessarily a reliable predictor of short‐term successional dynamics, and explanatory power is not indicative of predictive power. Because of this low reliability, caution is needed when applying chronosequences to evaluate ecosystem services' recovery. The analytical approach developed here contributes to a better exploration of those possible limitations.