Capturing juvenile tree dynamics from count data using Approximate Bayesian Computation. Issue 3 (1st December 2019)

• Record Type: Journal Article
• Authors: Lines, E. R.; Zavala, M. A.; Ruiz‐Benito, P.; Coomes, D. A.
• Is Part Of: Ecography. Volume 43:Issue 3(2020)
• Page Start: 406
• Page End: 418
• Publication Date: 2019-12-01
• Subjects: Approximate Bayesian Computation -- forest inventory -- juvenile dynamics -- predictive modelling -- recruitment; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.04824
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : The juvenile life stage is a crucial determinant of forest dynamics and a first indicator of changes to species' ranges under climate change. However, paucity of detailed re‐measurement data of seedlings, saplings and small trees means that their demography is not well understood at large scales, and rarely represented in forest models in detail. In this study we quantify the effects of climate and density dependence on recruitment and juvenile growth and mortality rates of thirteen species measured in the Spanish Forest Inventory. Single‐census sapling count data is used to constrain demographic parameters of a simple forest juvenile dynamics model based on the perfect plasticity approximation model (PPA) within a likelihood‐free parameterisation method, Approximate Bayesian Computation. Our results highlight marked differences between species, and the important role of climate and stand structure, in controlling juvenile dynamics. Recruitment had a hump‐shaped relationship with conspecific density, and for most species conspecific competition had a stronger negative effect than heterospecific competition. Mediterranean species showed on average higher mortality and lower growth rates than temperate species, and in low density stands recruitment and mortality rates were positively correlated. Under climate change our model predicted declines in recruitment rates for almost all species. Reliable predictive models of forest dynamics should include realistic representation of critical early life‐stage processes and our approach demonstrates that existing coarse count data can be used to parameterise such models. Approximate Bayesian Computation may have wide application in many fields of ecology to unlock information about past processes from single survey observations.