|Title||Successional dynamics in Neotropical forests are as uncertain as they are predictable|
|Author(s)||Norden, Natalia; Angarita, H.A.; Bongers, Frans; Martínez-Ramos, Miguel; Cerda, I.G. De la; Breugel, Michiel Van; Lebrija-Trejos, Edwin; Meave, J.A.; Vandermeer, John; Williamson, G.B.; Finegan, Bryan; Mesquita, Rita; Chazdon, R.L.|
|Source||Proceedings of the National Academy of Sciences of the United States of America 112 (2015)26. - ISSN 0027-8424 - p. 8013 - 8018.|
Forest Ecology and Forest Management
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Dynamical models - Predictability - Succession - Tropical secondary forests - Uncertainty|
Although forest succession has traditionally been approached as a deterministic process, successional trajectories of vegetation change vary widely, even among nearby stands with similar environmental conditions and disturbance histories. Here, we provide the first attempt, to our knowledge, to quantify predictability and uncertainty during succession based on the most extensive long-term datasets ever assembled for Neotropical forests. We develop a novel approach that integrates deterministic and stochastic components into different candidate models describing the dynamical interactions among three widely used and interrelated forest attributes - stem density, basal area, and species density. Within each of the seven study sites, successional trajectories were highly idiosyncratic, even when controlling for prior land use, environment, and initial conditions in these attributes. Plot factors were far more important than stand age in explaining successional trajectories. For each site, the best-fit model was able to capture the complete set of time series in certain attributes only when both the deterministic and stochastic components were set to similar magnitudes. Surprisingly, predictability of stem density, basal area, and species density did not show consistent trends across attributes, study sites, or land use history, and was independent of plot size and time series length. The model developed here represents the best approach, to date, for characterizing autogenic successional dynamics and demonstrates the low predictability of successional trajectories. These high levels of uncertainty suggest that the impacts of allogenic factors on rates of change during tropical forest succession are far more pervasive than previously thought, challenging the way ecologists view and investigate forest regeneration.