Predictive Models of Forest Dynamics

Computational Ecology and Environmental Science Group, Microsoft Research, Cambridge, UK.
Science (Impact Factor: 33.61). 07/2008; 320(5882):1452-3. DOI: 10.1126/science.1155359
Source: PubMed


Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

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    • "To increase the confidence in their models, modelers compare model predictions to independent knowledge or observations. For example, in forest dynamics models, it is common to compare the predictions of tree community composition to potential natural vegetation types (Bugmann, 1996; Lexer and Honninger, 2001; Botkin, 1993) or old growth forest plots (Pacala et al., 1996; Ruger et al., 2007), or to use historical records of forest inventories over several decades to compare the evolution of predicted and observed capital (e.g., basal area) (Wehrli et al., 2005; Wehrli et al., 2007) and/or distributions of trees in diameter classes (Seidl et al., 2005; Didion et al., 2009; Wehrli et al., 2005; Purves et al., 2008). "
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    ABSTRACT: Ecological models are increasingly used as decision-making tools and their reliability is becoming a key issue. At the same time, the sophistication of techniques for model development and analysis has given rise to a relative compartmentalization of model building and evaluation tasks. Several guidelines invite ecological modelers to follow an organized sequence of development and analysis steps and have coined the term " evaludation " for this process. The objective of this paper is to assess the feasibility and the value of a structured evaludation process, based on the working example of the Samsara2 model, a spatially explicit individual-based forest dynamics model. We implemented the six steps of model design, process level calibration, qualitative evaluation, quantitative evaluation, global sensitivity analysis, and partial recalibration using approximate Bayesian computing. We then evaluated how the evaludation process revealed model strengths and weaknesses, specified the model's conditions of use, clarified how the model works, and provided insights into forest ecosystem functioning. Finally, the efficiency/cost ratio of the process and future improvements are discussed.
    Ecological Modelling 10/2015; 314:1-14. DOI:10.1016/j.ecolmodel.2015.06.039 · 2.32 Impact Factor
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    • "the result of drift, speciation and immigration, and not the result of competitive asymmetries between the species in the local community. The neutrality assumption is the most debated assumption of the Neutral Theory of Biodiversity (McGill et al., 2006; Purves and Pacala, 2008; Turnbull et al., 2008; Gotelli et al., 2009). Most importantly, the neutrality assumption refutes the idea of the unique correspondence between a species and its niche (interpreted here as the set of conditions and requirements for a species to survive (Hutchinson, 1958), although the exact meaning of the niche concept is unclear (Chase and Leibold, 2003; McInerny and Etienne, 2012)). "
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    ABSTRACT: Over the past decade, the neutral theory of biodiversity has stirred up community assembly theory considerably by suggesting that stochasticity in the form of ecological drift is an important factor determining community composition and community turnover. The neutral theory assumes that all species within a community are functionally equivalent (the neutrality assumption), and therefore applies best to communities of trophically similar species. Evidently, trophically similar species may still differ in dispersal ability, and therefore may not be completely functionally equivalent. Here we present a new sampling formula that takes into account the partitioning of a community into two guilds that differ in immigration rate. We show that, using this sampling formula, we can accurately detect a subdivision into guilds from species abundance distributions, given ecological data about dispersal ability. We apply our sampling formula to tropical tree data from Barro Colorado Island, Panama. Tropical trees are divided depending on their dispersal mode, where biotically dispersed trees are grouped as one guild, and abiotically dispersed trees represent another guild. We find that breaking neutrality by adding guild structure to the neutral model significantly improves the fit to data and provides a better understanding of community assembly on BCI. Our findings are thus an important step towards an integration of neutral and niche theory. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Journal of Theoretical Biology 03/2015; 374. DOI:10.1016/j.jtbi.2015.03.018 · 2.12 Impact Factor
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    • "In addition, LANDMOD is scaled up from a gap model to accelerate the computation efficiency (Garman, 2004), where growth and mortality functions and bioclimatic values are fitted by meta-modeling to model gap simulations (Urban et al., 1999). Studies have shown that simulated forest change is highly sensitive to the formulation of site-scale processes (Araújo and Luoto, 2007; Dawson et al., 2011; Elkin et al., 2012; McMahon et al., 2011; Purves and Pacala, 2008; Tylianakis et al., 2008). For example, different formulations of tree growth rates at the sitescale lead to different FLMs simulation results when more detailed response variables are considered, such as species compositional changes associated with elevation. "
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    ABSTRACT: Forest landscape models (FLMs) are important tools for testing basic ecological theory and for exploring forest changes at landscape and regional scales. However, the ability of these models to accurately predict changes in tree species’ distributions and their spatial pattern may be significantly affected by the formulation of site-scale processes that simulate gap-level succession including seedling establishment, tree growth, competition, and mortality. Thus, the objective of this study is to evaluate the effects of site-scale processes on landscape-scale predictions of tree species’ distributions and spatial patterns.
    Ecological Modelling 03/2015; 300(24):89-101. DOI:10.1016/j.ecolmodel.2015.01.007 · 2.32 Impact Factor
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