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.

  • Source
    • "Biophysical process models, on the other hand, predict stock (e.g., biomass) and fluxes (e.g., aboveground net primary production) of plant functional types by incorporating leaf photosynthesis, carbohydrate allocation, and nutrient and water cycling (Sitch et al. 2003, Morin et al. 2008, Medvigy et al. 2009, Tang et al. 2010, Hickler et al. 2012). Despite the recognized importance of succession and harvest, both niche and process models usually use coarse spatial resolutions (e.g., 10–20 km) in regional scale predictions and consequently succession and disturbances (e.g., harvest and fire) are either simplified or ignored (Neilson et al. 2005, Purves and Pacala 2008, Iverson et al. 2011, McMahon et al. 2011). Therefore, we still lack an understanding of the relative importance of succession, disturbance, and climate change in determining future forest composition changes. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Most temperate forests in U.S. are recovering from heavy exploitation and are in intermediate successional stages where partial tree harvest is the primary disturbance. Changes in regional forest composition in response to climate change are often predicted for plant functional types using biophysical process models. These models usually simplify the simulation of succession and harvest and may not consider important species-specific demographic processes driving forests changes. We determined the relative importance of succession, harvest, and climate change to forest composition changes in a 125-million ha area of the Central Hardwood Forest Region of U.S. We used a forest landscape modeling approach to project changes in density and basal area of 23 tree species due to succession, harvest, and four climate scenarios from 2000 to 2300. On average, succession, harvest, and climate change explained 78, 17, and 1% of the variation in species importance values (IV) at 2050, respectively, but their contribution changed to 46, 26, and 20% by 2300. Climate change led to substantial increases in the importance of red maple and southern species (e.g., yellow-poplar) and decreases in northern species (e.g., sugar maple) and most of widely distributed species (e.g., white oak). Harvest interacted with climate change and accelerated changes in some species (e.g., increasing southern red oak and decreasing American beech) while ameliorated the changes for others (e.g., increasing red maple and decreasing white ash). Succession was the primary driver of forest composition change over the next 300 years. The effects of harvest on composition were more important than climate change in the short term but climate change became more important than harvest in the long term. Our results show that it is important to model species-specific responses when predicting changes in forest composition and structure in response to succession, harvest, and climate change.
    Full-text · Article · Dec 2015 · Ecosphere
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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.
    Full-text · Article · Nov 2015 · Geoscientific Model Development
  • Source
    • "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). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Global environmental change necessitates increased predictive capacity; for forests, recent advances in technology provide the response to this challenge. "Next-generation" remote-sensing instruments can measure forest biogeochemistry and structural change, and individual-based models can predict the fates of vast numbers of simulated trees, all growing and competing according to their ecological attributes in altered environments across large areas. Application of these models at continental scales is now feasible using current computing power. The results obtained from individual-based models are testable against remotely sensed data, and so can be used to predict changes in forests at plot, landscape, and regional scales. This model-data comparison allows the detailed prediction, observation, and testing of forest ecosystem changes at very large scales and under novel environmental conditions, a capability that is greatly needed in this time of potentially massive ecological change.
    Full-text · Article · Nov 2015 · Frontiers in Ecology and the Environment
Show more