Dataset Information

Inferring forest fate from demographic data: from vital rates to population dynamic models.

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.

SUBMITTER: Needham J

PROVIDER: S-EPMC5879618 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

Inferring forest fate from demographic data: from vital rates to population dynamic models.

Needham Jessica J Merow Cory C Chang-Yang Chia-Hao CH Caswell Hal H McMahon Sean M SM

Proceedings. Biological sciences 20180301 1874

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 owin ...[more]

PMID: 29514966

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Project description:Frequently, vital rates are driven by directional, long-term environmental changes. Many of these are of great importance, such as land degradation, climate change, and succession. Traditional demographic methods assume a constant or stationary environment, and thus are inappropriate to analyze populations subject to these changes. They also require repeat surveys of the individuals as change unfolds. Methods for reconstructing such lengthy processes are needed. We present a model that, based on a time series of population size structures and densities, reconstructs the impact of directional environmental changes on vital rates. The model uses integral projection models and maximum likelihood to identify the rates that best reconstructs the time series. The procedure was validated with artificial and real data. The former involved simulated species with widely different demographic behaviors. The latter used a chronosequence of populations of an endangered cactus subject to increasing anthropogenic disturbance. In our simulations, the vital rates and their change were always reconstructed accurately. Nevertheless, the model frequently produced alternative results. The use of coarse knowledge of the species' biology (whether vital rates increase or decrease with size or their plausible values) allowed the correct rates to be identified with a 90% success rate. With real data, the model correctly reconstructed the effects of disturbance on vital rates. These effects were previously known from two populations for which demographic data were available. Our procedure seems robust, as the data violated several of the model's assumptions. Thus, time series of size structures and densities contain the necessary information to reconstruct changing vital rates. However, additional biological knowledge may be required to provide reliable results. Because time series of size structures and densities are available for many species or can be rapidly generated, our model can contribute to understand populations that face highly pressing environmental problems.

Dataset Information

Inferring forest fate from demographic data: from vital rates to population dynamic models.

Publications

Inferring forest fate from demographic data: from vital rates to population dynamic models.

Similar Datasets

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