FORECAST (model)

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FORECAST is a management-oriented, stand-level, forest-growth and ecosystem-dynamics model. The model was designed to accommodate a wide variety of silvicultural and harvesting systems and natural disturbance events (e.g., fire, wind, insect epidemics) in order to compare and contrast their effect on forest productivity, stand dynamics, and a series of biophysical indicators of non-timber values.

Contents

Model description

Projection of stand growth and ecosystem dynamics is based upon a representation of the rates of key ecological processes regulating the availability of, and competition for, light and nutrient resources (a representation of moisture effects on soil processes, plant physiology and growth, and the consequences of moisture competition is being added). The rates of these processes are calculated from a combination of historical bioassay data (such as biomass accumulation in plant components and changes in stand density over time) and measures of certain ecosystem variables (including decomposition rates, photosynthetic saturation curves, and plant tissue nutrient concentrations) by relating ‘biologically active’ biomass components (foliage and small roots) to calculated values of nutrient uptake, the capture of light energy, and net primary production. Using this ‘internal calibration’ or hybrid approach, the model generates a suite of growth properties for each tree and understory plant species that is to be represented in a subsequent simulation. These growth properties are used to model growth as a function of resource availability and competition. They include (but are not limited to): (1) photosynthetic efficiency per unit foliage biomass and its nitrogen content based on relationships between foliage nitrogen, simulated self-shading, and net primary productivity after accounting for litterfall and mortality; (2) nutrient uptake requirements based on rates of biomass accumulation and literature- or field-based measures of nutrient concentrations in different biomass components on sites of different nutritional quality (i.e. fertility); (3) light-related measures of tree and branch mortality derived from stand density and live canopy height input data in combination with simulated vertical light profiles. Light levels at which mortality of branches and individual trees occur are estimated for each species. [1] Many of FORECAST's calculations are made at the stand level, but the model includes a sub-model which disaggregates stand-level productivity into the growth of individual stems with user-supplied information on stem size distributions at different stand ages. Top height and DBH are calculated for each stem and used in a taper function to calculate total and individual gross and merchantable volumes. Snags and logs are created in the model from natural stand self-thinning (mainly due to light competition) and from different types of user-defined disturbance events such as insect/disease-induced mortality, windthrow, non-commercial thinning and stand harvesting. Snag fall rates and log-decomposition are simulated using species-specific and tree-size-specific decay parameters derived from the literature, expert opinion, or field measurements. [1]

The process of model application

FORECAST has four stages in its application: 1) data assembly and input verification, 2) establishing the ecosystem condition for the beginning of a simulation run (by simulating the known or assumed history of the site), 3) defining a management and/or natural disturbance regime, and 4) simulating this regime and analyzing model output. The first two stages represent model calibration. Calibration data are assembled that describe the accumulation of biomass (above and below-ground components) in trees and minor vegetation for three chronosequences of stands, each one developed under relatively homogeneous site conditions, representing three different nutritional site qualities. Tree biomass and stand self-thinning rate data are often generated from the height, DBH and stand density output of traditional growth and yield models in conjunction with species-specific component biomass allometric equations. To calibrate the nutritional aspects of the model, data describing the concentration of nutrients in the various biomass components are required. FORECAST also requires data on the degree of shading produced by different quantities of foliage and the photosynthetic response of foliage to different light levels (photosynthetic light saturation curves for either average foliage or separately for sun and shade adapted foliage). A comparable but simpler set of data for minor vegetation must be provided if the user wishes to represent this ecosystem component. Lastly, data describing the rates of decomposition of various litter types and soil organic matter are required for the model to simulate nutrient cycling. Simulation of soil leaching losses and certain measures of soil nutrient availability require input data that define cation- and anion-exchange capacity data for organic matter and mineral soil, and sorption-desorption processes. The second aspect of calibration requires running the model in “set-up” mode to establish initial site conditions. The detailed representation of many different litter types and soil organic matter conditions makes it impractical to measure initial litter and soil pools and conditions directly in the field; consequently, the model is used to generate starting conditions. [2]

Complexity of the model

As an ecosystem level model FORECAST offers the user the ability to represent a high degree of complexity in vegetation (multiple species and different life forms), plant community structure (canopy layering as a simple even-age single canopy layer or a complex multi-age, multi canopy) and population, community and ecosystem processes. However, the model can be simplified to any desired level of complexity that matches the user's interests, specific application and data availability. In its simplest form it can be run as a single age cohort, plant monoculture, light competition model. At the other extreme the model can be used to simulate succession and disturbance responses in a complex multi species, multi age cohort ecosystem-level application with population, community and ecosystem processes represented with light, nutrient and moisture effects and their interactions, and the possibility to examine potential climate change effects.

Model extensions and linkages

FORECAST has been extended to a spatially explicit landscape local level (LLEMS), [3] a spatially explicit individual tree model FORCEE, and to an interactive 3-D visualization (CALP Forester), FORECAST has been linked to a variety of landscape-level models such as ATLAS and DYNA-PLAN. [4] The model has been used as the foundation for two educational applications (FORTOON and POSSIBLE FOREST FUTURES) [5]

Model evaluation

FORECAST has been validated against field data for a range of growth and yield and structural variables in: coastal Western Hemlock zone in British Columbia, [6] coastal Douglas-fir forests, [2] [7] and interior mixedwood forests in British Columbia [6] [8]

History of model application

FORECAST model has been applied to a variety of forest types: mixed Douglas-fir and paper birch forest, [9] mixed aspen and white spruce forest, [10] [11] Chinese-fir plantations, [12] coastal Douglas-fir forest. [2]

Related Research Articles

Ecosystem Community of living organisms together with the nonliving components of their environment

An ecosystem consists of all the organisms and the physical environment with which they interact. These biotic and abiotic components are linked together through nutrient cycles and energy flows. Energy enters the system through photosynthesis and is incorporated into plant tissue. By feeding on plants and on one another, animals play an important role in the movement of matter and energy through the system. They also influence the quantity of plant and microbial biomass present. By breaking down dead organic matter, decomposers release carbon back to the atmosphere and facilitate nutrient cycling by converting nutrients stored in dead biomass back to a form that can be readily used by plants and microbes.

Energy flow (ecology) Flow of energy through food chains in ecological energetics

Energy flow is the flow of energy through living things within an ecosystem. All living organisms can be organized into producers and consumers, and those producers and consumers can further be organized into a food chain. Each of the levels within the food chain is a trophic level. In order to more efficiently show the quantity of organisms at each trophic level, these food chains are then organized into trophic pyramids. The arrows in the food chain show that the energy flow is unidirectional, with the head of an arrow indicating the direction of energy flow; energy is lost as heat at each step along the way.

Ecological yield is the harvestable population growth of an ecosystem. It is most commonly measured in forestry: sustainable forestry is defined as that which does not harvest more wood in a year than has grown in that year, within a given patch of forest.

Old-growth forest Forest that has attained great age without significant disturbance

An old-growth forest – also termed primary forest, virgin forest, late seral forest or primeval forest – is a forest that has attained great age without significant disturbance and thereby exhibits unique ecological features and might be classified as a climax community. The Food and Agriculture Organization of the United Nations defines primary forests as naturally regenerated forests of native tree species where there are no clearly visible indications of human activity and the ecological processes are not significantly disturbed. More than one-third of the world’s forests are primary forests. Old-growth features include diverse tree-related structures that provide diverse wildlife habitat that increases the biodiversity of the forested ecosystem. Virgin forests are old-growth forests that have never been logged. The concept of diverse tree structure includes multi-layered canopies and canopy gaps, greatly varying tree heights and diameters, and diverse tree species and classes and sizes of woody debris.

Soil food web

The soil food web is the community of organisms living all or part of their lives in the soil. It describes a complex living system in the soil and how it interacts with the environment, plants, and animals.

Ecosystem ecology Study of living and non-living components of ecosystems and their interactions

Ecosystem ecology is the integrated study of living (biotic) and non-living (abiotic) components of ecosystems and their interactions within an ecosystem framework. This science examines how ecosystems work and relates this to their components such as chemicals, bedrock, soil, plants, and animals.

Forest dynamics describes the underlying physical and biological forces that shape and change a forest ecosystem. The continuous state of change in forests can be summarized with two basic elements: disturbance and succession.

Forest ecology Study of interactions between the biota and environment in forets

Forest ecology is the scientific study of the interrelated patterns, processes, flora, fauna and ecosystems in forests. The management of forests is known as forestry, silviculture, and forest management. A forest ecosystem is a natural woodland unit consisting of all plants, animals, and micro-organisms in that area functioning together with all of the non-living physical (abiotic) factors of the environment.

<i>Celastrus orbiculatus</i> Species of plant

Celastrus orbiculatus is a woody vine of the family Celastraceae. It is commonly called Oriental bittersweet, as well as Chinese bittersweet, Asian bittersweet, round-leaved bittersweet, and Asiatic bittersweet. It is native to China, where it is the most widely distributed Celastrus species, and to Japan and Korea. It was introduced into North America in 1879, and is considered to be an invasive species in eastern North America. It closely resembles the native North American species, Celastrus scandens, with which it will readily hybridize.

Disturbance (ecology) Temporary change in environmental conditions that causes a pronounced change in an ecosystem

In ecology, a disturbance is a temporary change in environmental conditions that causes a pronounced change in an ecosystem. Disturbances often act quickly and with great effect, to alter the physical structure or arrangement of biotic and abiotic elements. A disturbance can also occur over a long period of time and can impact the biodiversity within an ecosystem.

Soil respiration

Soil respiration refers to the production of carbon dioxide when soil organisms respire. This includes respiration of plant roots, the rhizosphere, microbes and fauna.

Forest dieback Stand of trees losing health and dying

Forest dieback is a condition in trees or woody plants in which peripheral parts are killed, either by pathogens, parasites or conditions like acid rain, drought, and more. These episodes can have disastrous consequences such as reduced resiliency of the ecosystem, disappearing important symbiotic relationships and thresholds. Some tipping points for major climate change forecast in the next century are directly related to forest diebacks.

Agent-based models have many applications in biology, primarily due to the characteristics of the modeling method. Agent-based modeling is a rule-based, computational modeling methodology that focuses on rules and interactions among the individual components or the agents of the system. The goal of this modeling method is to generate populations of the system components of interest and simulate their interactions in a virtual world. Agent-based models start with rules for behavior and seek to reconstruct, through computational instantiation of those behavioral rules, the observed patterns of behavior. Several of the characteristics of agent-based models important to biological studies include:

  1. Modular structure: The behavior of an agent-based model is defined by the rules of its agents. Existing agent rules can be modified or new agents can be added without having to modify the entire model.
  2. Emergent properties: Through the use of the individual agents that interact locally with rules of behavior, agent-based models result in a synergy that leads to a higher level whole with much more intricate behavior than those of each individual agent.
  3. Abstraction: Either by excluding non-essential details or when details are not available, agent-based models can be constructed in the absence of complete knowledge of the system under study. This allows the model to be as simple and verifiable as possible.
  4. Stochasticity: Biological systems exhibit behavior that appears to be random. The probability of a particular behavior can be determined for a system as a whole and then be translated into rules for the individual agents.
Gap dynamics

Gap dynamics refers to the pattern of plant growth that occurs following the creation of a forest gap, a local area of natural disturbance that results in an opening in the canopy of a forest. Gap dynamics are a typical characteristic of both temperate and tropical forests and have a wide variety of causes and effects on forest life.

Deforestation in British Columbia

The deforestation in British Columbia has occurred at a heavy rate during periods of the past, but with new sustainable efforts and programs the rate of deforestation is decreasing in the province. In British Columbia, forests cover over 55 million hectares, which is 57.9% of British Columbia's 95 million hectares of land. The forests are mainly composed of coniferous trees, such as pines, spruces and firs.

Mycorrhizal network Underground hyphal networks that connect individual plants together

Mycorrhizal networks are underground hyphal networks created by mycorrhizal fungi that connect individual plants together and transfer water, carbon, nitrogen, and other nutrients and minerals.

Mycorrhizal fungi and soil carbon storage

Soil carbon storage is an important function of terrestrial ecosystems. Soil contains more carbon than plants and the atmosphere combined. Understanding what maintains the soil carbon pool is important to understand the current distribution of carbon on Earth, and how it will respond to environmental change. While much research has been done on how plants, free-living microbial decomposers, and soil minerals affect this pool of carbon, it is recently coming to light that mycorrhizal fungi—symbiotic fungi that associate with roots of almost all living plants—may play an important role in maintaining this pool as well. Measurements of plant carbon allocation to mycorrhizal fungi have been estimated to be 5 to 20% of total plant carbon uptake, and in some ecosystems the biomass of mycorrhizal fungi can be comparable to the biomass of fine roots. Recent research has shown that mycorrhizal fungi hold 50 to 70 percent of the total carbon stored in leaf litter and soil on forested islands in Sweden. Turnover of mycorrhizal biomass into the soil carbon pool is thought to be rapid and has been shown in some ecosystems to be the dominant pathway by which living carbon enters the soil carbon pool.

Biomass partitioning is the process by which plants divide their energy among their leaves, stems, roots, and reproductive parts. These four main components of the plant have important morphological roles: leaves take in CO2 and energy from the sun to create carbon compounds, stems grow above competitors to reach sunlight, roots absorb water and mineral nutrients from the soil while anchoring the plant, and reproductive parts facilitate the continuation of species. Plants partition biomass in response to limits or excesses in resources like sunlight, carbon dioxide, mineral nutrients, and water and growth is regulated by a constant balance between the partitioning of biomass between plant parts. An equilibrium between root and shoot growth occurs because roots need carbon compounds from photosynthesis in the shoot and shoots need nitrogen absorbed from the soil by roots. Allocation of biomass is put towards the limit to growth; a limit below ground will focus biomass to the roots and a limit above ground will favor more growth in the shoot.

Forest growth models are mathematical or computer models to project the future state and yields of forest stands or forest trees, over a time scale of from a few years to many decades.

FORMIND

FORMIND is an individual based forest gap model that is able to simulate the growth of species-rich forests. It was developed in the late 1990s to simulate forest dynamics of tropical forests.

References

  1. 1 2 Kimmins, J.P.; D. Mailly; b. Seely (20 October 1999). "Modelling forest ecosystem net primary production: the hybrid simulation approach used in FORECAST". Ecological Modelling. Elsevier Science B.V. 122 (3): 195–224. doi:10.1016/S0304-3800(99)00138-6.
  2. 1 2 3 Blanco, J.A.; Seely, B.; Welham, C.; Kimmins, J.P.; Seebacher, T.M. (1 October 2008). "Testing the performance of a forest ecosystem model (FORECAST) against 29 years of field data in a Pseudotsuga menziesii plantation". Canadian Journal of Forest Research. NRC Research Press. 37 (10): 1808–1820. doi:10.1139/x07-041.
  3. ↑ "Archived copy" (PDF). Archived from the original (PDF) on 2010-11-23. Retrieved 2010-12-02.{{cite web}}: CS1 maint: archived copy as title (link)
  4. ↑ "The Models: Summary of Model Linkages and Integration for K2". Kamloops Future Forest Strategy II. Archived from the original on 2010-11-14. Retrieved December 2, 2010.
  5. ↑ "Archived copy" (PDF). Archived from the original (PDF) on 2010-11-23. Retrieved 2010-12-02.{{cite web}}: CS1 maint: archived copy as title (link)
  6. 1 2 Gerzon, Michael (2005). Modelling the Recovery of Old-Growth Attributes in Coastal Western Hemlock Forests Following Management and Natural Disturbances (PDF) (M.S. thesis). University of British Columbia. Retrieved December 2, 2010.
  7. ↑ Boldor, Marius Ioan (2007). A Field and Simulation Study of the Initiation Phase in Douglas-Fir Plantations (PDF) (M.S. thesis). University of British Columbia. Retrieved December 2, 2010.
  8. ↑ Seely, B.; Hawkins C.; Blanco J.A.; Welham C.; Kimmins J.P. (August 2009). "Evaluation of an ecosystem-based approach to mixedwood modelling". Forest Growth and Timber Quality: Crown Models and Simulation Methods for Sustainable Forest Management. Portland, OR: United States Forest Service (General Technical Report PNW-GTR-791). pp. 205–210. CiteSeerX   10.1.1.150.4159 .
  9. ↑ Sachs, D. (1996). Simulation of the growth of mixed stands of Douglas-fir and paper birch using the FORECAST model. Silviculture of temperate and boreal broadleaf conifer mixtures (eds P.G. Comeau & K.D. Thomas), pp. 152. BC Ministry of Forests, Victoria, BC, Canada.
  10. ↑ Welham, C., B. Seely and J.P. Kimmins. 2002. The utility of the two-pass harvesting system: an analysis using the ecosystem simulation model FORECAST. Can. J. For. Res. 32:1071-1079.
  11. ↑ Seely, B.; Welham, C.; Kimmins, H. (15 September 2002). "Carbon sequestration in a boreal forest ecosystem: results from the ecosystem simulation model, FORECAST". Forest Ecology and Management. Elsevier Science B.V. 169 (1–2): 123–135. doi:10.1016/S0378-1127(02)00303-1.
  12. ↑ Bi J., Blanco J.A., Kimmins J.P., Ding Y., Seely B., Welham C. 2007. Yield decline in Chinese Fir plantations: A simulation investigation with implications for model complexity. Can. J. For. Res. 37: 1615-1630.
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