1. Engineering resilience, a forest’s ability to maintain its properties in the event of disturbance, comprises two components: resistance and recovery. In human-dominated landscapes, forest resilience depends mostly on recovery. Forest recovery largely depends on autogenic regulation, which entails a negative feedback loop between rates of change of forest state variables and state variables themselves. Hence community dynamics changes in response to deviations from forest equilibrium state. Based on the premise that autogenic regulation is a key aspect of the recovery process, here we tested the hypothesis that combined old-growth forest (OGF) and secondary forest (SF) dynamics should show autogenic regulation in state variables, and thus convergence towards OGF-based reference points, indicating forest resilience.

2. We integrated dynamic data for OGF (11-year monitoring) and SF (16-year monitoring) to analyse three key state variables (basal area, tree density, species richness), their annual rates of change, and their underlying demographic processes (recruitment, growth, mortality). We examined autogenic regulation through generalized linear mixed-effects models (GLMMs) to quantify functional relationships between rates of change of state variables (and underlying demographic processes), and their respective state variables.

3. State variables in OGF decreased moderately over time, against our prediction of OGF constancy. In turn, the three state variables analysed showed negative relationships with their respective rates of change, which allows the return of SF to OGF values after disturbance. In all cases, recruitment decreased with increasing values in state variables, while mortality increased.

4. The observed negative relationships between state variables, their rates of change and their underlying demographic processes support our hypothesis of integrated OGF and SF dynamics showing autogenic regulation for state variables. Competition seems to be a major driver of autogenic regulation given its dependence on a resource availability that declines as forest structure develops.

5. Synthesis. Based on a straightforward and comprehensive approach to quantify the extent to which tropical forest dynamics is self-regulated, this study highlights the role of autogenic regulation in tropical dry forest as a basic component of its resilience. This approach is potentially valuable for a generalised assessment of engineering resilience of forests worldwide.

Data comes from 28 tropical dry forest plots of secondary forest (SF) and old-growth forest (OGF). SF plots were established in 2003 and OGF plots were established in 2008. Within these plots, woody stems meeting inclusion criteria (see Supplementary Material Figure S1) were tagged and identified to species. Diameter at breast height and survival were recorded annually for all tagged individuals. Plant diameter, survival and taxonomical identity were used to estimate basal area, tree density and species richness (state variables of the study) at plot level for all plots on a yearly basis.

We used survival information to describe the ongoing demographic process (recruitment, growth, mortality) per individual and annual period. Further, for each individual and period, we computed the difference in state variable values. Then, we estimated the contribution of each demographic process towards each of the three state variables by grouping individuals going through the same demographic process in each year, and then adding their changes in state variable values.

This database contains information on state variable values, annual changes, and contributions per demographic process on a plot-year resolution.

For some plot-year datapoints, tree density's sum of recruitment and mortality do not add up to the net change of the state variable. This is because we used a stratified sampling: trees shifting from a size category to another change their scaling factor, although they do not undergo recruitment or mortality. Therefore, their tree density value might differ from one year to the next one. These differences are negligible and were not accounted for in the analysis.