Name: Gap model, patch model, cellular automata forest model
Key references: (Shugart 1984, Bugmann 2001)
- Simple, no interactions: JABOWA v1 (Botkin et al. 1972)
- With interactions: ZELIG (Urban et al. 1990)
- Vertical structure of light environment: FORSKA (Prentice and Leemans 1990, Prentice et al. 1993)
- Physiological-based gap models: PROMOD (Battaglia and Sands 1997).
Description: Forest gap models simulate dynamics at the scale of a forest gap or patch which is horizontally homogeneous and commonly sized at least the area of a large tree canopy. Gaps in the tree canopy following mortality of large trees are important for succession within forests by allowing light to the forest floor, stimulating growth of smaller stems. Through ensemble runs gap models can encompass forest heterogeneity, with a range of patches representing successional stages from bare ground to climax forest.
Gap models can either be independent, or interact for example through shading or dispersal (including long-range dispersal). Within a patch the light environment may be modelled through ‘flat top’ leaf arrangements or with asymmetric competition for light. Gap model dynamics may be driven by simple birth, growth and death demographic processes at annual or longer time scales, or may be physiologically based processes at timescales of a day or less. Gap models have been built and parameterised in temperate, boreal and tropical forests. They may include a number of natural forest processes (e.g. seed dispersal and fire) and forest management practices. Gap models are typically used for understanding successional dynamics, predicting responses to disturbance, predicting carbon dynamics, predicting forest yields
Prerequisite skills: Gap models can be computationally demanding. A basic knowledge of programming is usually required, though many gap models have existing software or code that can be downloaded.
Strengths: Very flexible conceptual framework for forest modelling. Less computationally demanding than spatially explicit individual based models (e.g. SORTIE). Gap models are usually used to represent successional dynamics and other long-term forest processes. Easy to incorporate different processes, including rare and stochastic disturbance events. Allometric data are readily incorporated into a gap model.
Limitations: Computational predictions typically produced using large number of model ensembles, masking outputs occasionally difficult to interpret. Validation is difficult because models are typically run over decadal time-scales. Initialisation can be difficult, and model may be sensitive to initial conditions of (e.g. species distributions or stem size distributions). Gap models are not necessarily suitable for small scale processes, as model inputs are tied to the scale of a forest gap. Process detail and therefore predictive ability for a given scenario is highly variable between models.
Data requirements: Demographic data are usually required. Good mortality and recruitment rates can be particularly hard to produce, and available forest inventory data do not always match model requirements. Alternatively a good representation of the forest light environment and plant physiological processes can be used. Physiological models in particular have very high numbers of parameters and are difficult to constrain.
Resources: A number of gap models have code that may be downloaded.
Validation: Validation difficult because models are typically run over decadal time-scales. Tree growth is usually validated with multiple return forest inventory, chronosequence data and long-term plot-based experiments.
Similar methods: Individual based models, forest cohort models
Battaglia, M., & Sands, P. (1997). Modelling site productivity of Eucalyptus globulus in response to climatic and site factors. Functional Plant Biology,24(6), 831-850.
Botkin, D. B., Janak, J. F., & Wallis, J. R. (1972). Some ecological consequences of a computer model of forest growth. The Journal of Ecology, 849-872.
Bugmann, H. (2001). A review of forest gap models. Climatic Change, 51(3-4), 259-305.
Prentice, I. C., & Leemans, R. (1990). Pattern and process and the dynamics of forest structure: a simulation approach. The Journal of Ecology, 340-355.
Prentice, C. I., Sykes, M. T., & Cramer, W. (1993). A simulation model for the transient effects of climate change on forest landscapes. Ecological modelling, 65(1), 51-70.
Shugart, H. H. (1984). A theory of forest dynamics. The ecological implications of forest succession models. Springer-Verlag.
Urban, D. L. (1990). A versatile model to simulate forest pattern: a user’s guide to ZELIG version 1.0. University of Virginia, Charlottesville, VA.