Abstract
As population-level patterns of interest in forests emerge from individual vital rates, modelling forest dynamics requires making the link between the scales at which data are collected (individual stems) and the scales at which questions are asked (e.g. populations and communities). Structured population models (e.g. integral projection models (IPMs)) are useful tools for linking vital rates to population dynamics. However, the application of such models to forest trees remains challenging owing to features of tree life cycles, such as slow growth, long lifespan and lack of data on crucial ontogenic stages. We developed a survival model that accounts for size-dependent mortality and a growth model that characterizes individual heterogeneity. We integrated vital rate models into two types of population model; an analytically tractable form of IPM and an individual-based model (IBM) that is applied with stochastic simulations. We calculated longevities, passage times to, and occupancy time in, different life cycle stages, important metrics for understanding how demographic rates translate into patterns of forest turnover and carbon residence times. Here, we illustrate the methods for three tropical forest species with varying life-forms. Population dynamics from IPMs and IBMs matched a 34 year time series of data (albeit a snapshot of the life cycle for canopy trees) and highlight differences in life-history strategies between species. Specifically, the greater variation in growth rates within the two canopy species suggests an ability to respond to available resources, which in turn manifests as faster passage times and greater occupancy times in larger size classes. The framework presented here offers a novel and accessible approach to modelling the population dynamics of forest trees.
Highlights
Forests host the majority of terrestrial biodiversity and regulate global climate, making the need to understand forest dynamics across spatial, temporal and biological scales greater than ever [1 –4]
Both integral projection models (IPMs) and individual-based model (IBM) take into account size-dependent survival and temporal correlations in growth rates, enabling us to capture the subset of the population that survive to adulthood
With longer time series of data we expect the median passage time to a given size to increase, i.e. the right-hand side of the distribution to be revealed. Their impact on population dynamics, is a critical step in advancing our understanding of how forests will respond to global change
Summary
Forests host the majority of terrestrial biodiversity and regulate global climate, making the need to understand forest dynamics across spatial, temporal and biological scales greater than ever [1 –4]. Over long time-spans average growth will trend towards the mean; a poor reflection of how trees typically grow—usually consistently slowly with rare instances of release from growth suppression resulting in sustained periods of fast growth [31] To address this problem, we adopt the method presented by Caswell [34], adjusting the age-by-stage matrix model for a growth-by-size IPM that describes the transitions of individuals between growth distributions. Both IPMs and IBMs take into account size-dependent survival and temporal correlations in growth rates, enabling us to capture the subset of the population that survive to adulthood The parametrization of these models from demographic data, combined with straightforward analytic tractability, offers the potential for spatial and temporal extrapolation, for example, inferring forest dynamics across landscapes [41], or exploring evolutionary strategies through the analysis of equilibrium dynamics [42]
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