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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    • "All plants have access to the same water pool, as described in Supplement A. Traditional DGVMs (Sitch et al., 2003; Woodward et al., 2004) prescribe only one single average individual of each PFT without the use of the cohort concept; thus, the ED approach represents a compromise in representation of forest dynamics between these two approaches. Other " cohortized " forest models exist in the literature, notably, GAP- PARD (Scherstjanoi et al., 2013, 2014), TREEMIG (Lischke et al., 2006; Zurbriggen et al., 2014; Nabel et al., 2014), the PPA model (Purves et al., 2008; Lichstein and Pacala, 2011; Weng et al., 2015) and later versions of the LPJ-GUESS model (e.g., Hickler et al., 2008; Pappas et al., 2015), but few studies (if any) have looked into the comparative merits and drawbacks of these different approaches. "
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    ABSTRACT: We describe an implementation of the Ecosystem Demography (ED) concept in the Community Land Model. The structure of CLM(ED) and the physiological and structural modifications applied to the CLM are presented. A major motivation of this development is to allow the prediction of biome boundaries directly from plant physiological traits via their competitive interactions. Here we investigate the performance of the model for an example biome boundary in eastern North America. We explore the sensitivity of the predicted biome boundaries and ecosystem properties to the variation of leaf properties using the parameter space defined by the GLOPNET global leaf trait database. Furthermore, we investigate the impact of four sequential alterations to the structural assumptions in the model governing the relative carbon economy of deciduous and evergreen plants. The default assumption is that the costs and benefits of deciduous vs. evergreen leaf strategies, in terms of carbon assimilation and expenditure, can reproduce the geographical structure of biome boundaries and ecosystem functioning. We find some support for this assumption, but only under particular combinations of model traits and structural assumptions. Many questions remain regarding the preferred methods for deployment of plant trait information in land surface models. In some cases, plant traits might best be closely linked to each other, but we also find support for direct linkages to environmental conditions. We advocate intensified study of the costs and benefits of plant life history strategies in different environments and the increased use of parametric and structural ensembles in the development and analysis of complex vegetation models.
    Geoscientific Model Development 11/2015; 8:3593-3619. DOI:10.5194/gmd-8-3593-2015 · 3.65 Impact Factor
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    • "These inaccuracies are a result of the underlying assumptions in these models and indicate the necessity of additional insights from other models run at different scales. Purves and Pacala (2008) highlighted the need for improvement in this realm and suggested the use of models based on the dynamics of individual trees (ie IBMs). These IBMs – or more general agent-based models – simulate change through the interacting individual trees that compose the system in question (Figure 1). "

    Frontiers in Ecology and the Environment 11/2015; 13(9):503-511. DOI:10.1890/140327 · 7.44 Impact Factor
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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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