Landscape dynamic models

Landscape models are spatially explicit models aiming at projecting a landscape (structure, function, composition) over time. They can include spatial interactions, community dynamics or/and ecosystem processes. Landscape models are typically used to simulate different management or global change scenarios. Two broad classes of examples are gap/ landscape models (e.g. LANDIS, ForCLIM) and dynamic vegetation models (e.g. IBIS, LPJ).

LANDIS

Overview: LANDIS (Mladenoff et al. 1996; He & Mladenoff 1999) is an effort to design a landscape model that balances the integration of ecological processes across different spatial and temporal scales to be able to simulate large areas over long time spans, and within current computational capability. The purpose of the model is to simulate species-level forest dynamics in combination with fire, windthrow, and harvesting, with adequae mechanistic realism for a range of spatial scales. LANDIS is a spatially explicit, stochastic, raster-based model, based on an object-oriented modeling approach. Each cell is a spatial object tracking (1) the presence or absence of 10-yr age cohorts of individual species, (2) fuel regimes based on their accumulation and decomposition characteristics; (3) mean fire/wind return interval, (4) the time since last fire/wind disturbance, and (5) species establishment ability in particular environments.

URL: http://web.missouri.edu/~umcsnrlandis/, http://web.missouri.edu/~umcsnrlandis/umcsnrlandispro/Home.htm

Applications: Akçakaya et al (2005)

Extensions: FIN-LANDIS (Pennanen & Kuuluvainen 2002). Improvement of LANDIS (Schumacher et al. 2004).

FATE (Functional Attributes in Terrestrial Ecosystems)

Overview: FATE (Moore & Noble 1990) is a general model of vegetation dynamics, which is based on the performance of individual plants in a stand. It predicts vegetation dynamics at a qualitative level and from simple parameters describing life history traits; these include maturation time, lifespan, resprouting and germination ability after fire, seed ability to colonize a new site, seed dormancy and shade tolerance. The model is deterministic and simulates cohorts of plants that pass through a series of discrete stages: seeds, seedlings, immature and mature (adult) plants. The model estimates the performance of the different species from the response of he different stages to decreasing light levels caused by the presence of neighbours. The model runs at annual time steps, and the outputs are qualitative descriptions of the abundance of each stage, measured on a scale of absent, low, medium and high. Although FATE is not spatially explicit originally, there are raster-based versions of the FATE model (Cousins et al. 2003, Pausas 2006).

Applications: Pausas (1999), Pausas (2006).

LaMoS (Landscape Modelling Shell)

Overview: LaMoS (Lavorel et al 2000, Cousins et al . 2003) is based on the concept of plant functional type (PFT), defined as a group of species with similar responses to and/or effects on their environment. It assumes that abiotic conditions are suitable for each PFT in the area where they are studied. LaMoS is a spatially and temporally explicit model that accounts for basic vegetation dynamic processes resulting from the interactions between plant functional types (or species), habitat conditions, disturbances and spatial patterns (Cousins et al . 2003). Within LaMoS, the landscape is described as a raster grid, in which landscape dynamics is modelled by three interacting modules. First, a succession model drives within-pixel yearly successional dynamics. We used a modification of the FATE model (Moore & Noble 1990), which determines the abundance of competing PFTs represented as age cohorts based on a simple set of traits relating to plant life history, tolerance to light interaction at different life stages, and recruitment (Cousins et al . 2003). Second, a dispersal model distributes seeds across the landscape. Third, a disturbance model establishes the potential disturbed fraction of abundance (output from the succession model) for each PFT and affects each PFT specifically, leading to death, resprouting or no effect on the different defined age classes. We used a uniform mowing disturbance within fields still in use under various land-use scenarios.

Applications: Cousins et al . (2003), Grigulis et al. (2005), Albert et al (2008).

FORCLIM

Overview: FORCLIM (Bugmann 1996) is designed as a modular model, i.e., it is composed of several independent submodels, which are assembled through defined interfaces to form a complete forest gap model. This approach bears several advantages. The structure of the model becomes clear- er, the couplings between submodels are explicit, and it is easy to exchange one submodel without affecting the others. FORCLIM consists of three submodels:

• • FoRCLIM-E (Environment): This submodel provides time-dependent abiotic variables. It generates monthly weather data and uses them to calculate bioclimatic output variables. It does not depend on any of the other submodels and acts as an input model. Its output variables are summarized to annual indices.

  • FORCLIM-P (Plants): The plant submodel calculates establishment, growth, and mortality of trees on a forest patch. It requires bioclimatic variables and nitrogen availability as input and calculates litter pro- duction as an output. FORCLIM-P is formulated as a discrete-time model with an annual time step.

  • FORCLIM-S (Soil): The soil submodel tracks the decay of plant litter and humus in the soil as a function of bioclimatic variables. It is a discrete-time model with an annual time step and calculates the amount of nitrogen available for plant growth.

Applications: McRae et al (2008)

Bibliography

• • Akçakaya, H.R., Franklin, J., Syphard, A.D. & Stephenson, J.R. (2005) Viability of Bell’S Sage Sparrow (Amphispiza Belli Ssp. Belli): Altered Fire Regimes. Ecological Applications, 15, 521-531.

  • Bugmann, H. (1996) A Simplified Forest Model to Study Species Composition Along Climate Gradients. Ecology, 77, 2055-2074.

  • Cousins, S., Lavorel, S. & Davies, I. (2003) Modelling the effects of landscape pattern and grazing regimes on the persistence of plant species with high conservation value in grasslands in south-eastern. Landscape Ecology, 18, 315-332.

  • Grigulis, K., Lavorel, S., Davies, I. D., Dossantos, A., Lloret, F. & Vilà, M. (2005). Landscape-scale positive feedbacks between fire and expansion of the large tussock grass, Ampelodesmos mauritanica in Catalan shrublands. Global Change Biology 11, 1042-1053.

  • He, H. & Mladenoff, D.J. (1999) Spatially explicit and stochastic simulation of forest-landscape fire disturbance and succession. Ecology, 80, 81-99.

  • Lavorel, S., Davies, I. and Noble, I. (2000). LAMOS: a Landscape modelling shell. In: Proceeding of the Landscape Fire Modeling Workshop, pp. 25–28. Hawkes, B.C. and Flannigan, M.D. (eds), Victoria, British Columbia, November 15–16.

  • McRae, B. H., Schumaker, N. H., McKane, R. B., Busing, R. T., Solomon, A. M. & Burdick, C. A. (2008) A multi-model framework for simulating wildlife population response to land-use and climate change Ecological Modelling, 219, 77-91.

  • Moore, A.D. & Noble, I.R. (1990). An individualistic model of vegetation stand dynamics. Journal of Environmental Management 31, 61-81.

  • Pausas, J.G. (1999). Response of plant functional types to changes in the fire regime in Mediterranean ecosystems: A simulation approach. Journal of Vegetation Science 10, 17-722.

  • Pausas, J.G. (2006). Simulating Mediterranean landscape pattern and vegetation dynamics under different fire regimes. Plant Ecology 187, 249-259.

  • Pennanen, J., & T. Kuuluvainen (2002). A spatial simulation approach to natural forest landscape dynamics in boreal Fennoscandia. Forest Ecology and Management 164:157-175.

  • Schumacher, S., H. Bugmann, & D. Mladenoff. (2004). Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecological Modelling 180:175-194.