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Landscape Ecolapplication of linked gap models at scales >10 2 hahas been limited due to high computationaldemands and the intensive input data required(Mladenoff 2004). The calculation of disturbanceeffects on individual trees is inherently complex(Hely et al. 2003) and spatial interactions withinlinked-gap models are typically limited to finescaleprocesses, including fire, seed dispersal, andcompetition (Miller and Urban 1999; Easterlinget al. 2001).Nevertheless, gap models have played a criticalrole forming the links between ecosystem processrates and dynamic communities. In addition, gapmodels have become an important source of inputdata for broader-scale FLSMs (He et al. 1998;Urban et al. 1999; Acevedo et al. 2001; Schelleret al. 2005).Group 5. Including spatial interactions; staticcommunities; excluding ecosystem processesWithin this model group, community types aresometimes extraordinarily broad with the landscapedivided only into forested (with forest age)and non-forested types. Such models commonlyserve as ‘null’ models for exploring interactionsamong landscape processes and a small number ofstate variables. Such null models have been usedextensively to simulate fires, including the effectsof landscape connectivity on fire spread (Turneret al. 1989), fires and fuel moisture (Turner et al.1995; Finney 1998), or time since last fire (Bakeret al. 1991; Baker 1992, 1993; Peterson 2002).Null models have also been used to simulatespecies dispersal and rate of spread (Hart andGardner 1997) and the effects of harvesting onlandscape pattern (Wallin et al. 1994; Gustafsonand Crow 1998).Other models within this group focus on thetransitions between community types due todisturbances (generally fire and logging) (Klenneret al. 2000). Successional stages (and any associatedecosystem properties) and transition probabilitiesamong stages are calculated a priori.Using relatively simple community types andlikely transitions has enabled rapid model deploymentbased on surveys of expert knowledge(Klenner et al. 2000).Models in Group 5 benefit from the emphasison spatial interactions. Limiting the array ofpossible interactions (with species, with ecosystemprocesses) has yielded important insights onthe interactions between disturbances and landscapepattern or between disturbances and thedistribution of different community types.Group 6. Including spatial interactions; staticcommunities; including ecosystem processesSeveral spatially interactive models simulate ecosystemprocesses and have static communities.Again, static communities dictate certain assumptions.For example, MC1, which operates withoutspatial interactions at continental scales, has beenapplied to a smaller landscape (1250 ha) throughthe inclusion of a fire spread module (Bacheletet al. 2000). The application of MC1 to a grassland-forestecotone assumed that all propaguleswere available throughout the landscape (Bacheletet al. 2000). Similarly, SEM-LAND simulatesforest growth and fire spread (Li 2000). Notably,SEM-LAND assumes that the climate is stableand forest type never changes, although age andbiomass accumulation are dynamic (Li 2000).These models can address local or regionalscale questions (
Landscape Ecoldisturbance, and landscape patterns (Roberts1996a). Later models extended species age categoriesto grids that allowed spatial interactionsbeyond the first-order neighborhood (Mladenoffet al. 1996; Urban et al. 1999). For example,LANDIS includes many types of spatially interactiveprocesses, each with its own neighborhoodstructure (He and Mladenoff 1999; Gustafsonet al. 2000; Sturtevant et al. 2004).However, the emphasis on individual speciesbehavior can introduce significant uncertainty.For example, mean and maximum seed dispersaldistances for many tree species remains unresolved(Clark 1998; Higgins et al. 2003). Assemblingthe necessary species data can be timeconsuming and may require significant estimation.All of these models generate estimates ofspecies age distributions across a forested landscape.Species age distribution data provide anopportunity to link to many other processescorrelated with age, including reproduction andmortality, harvesting, and the development ofuneven-aged stand characteristics (Scheller et al.2005). Significantly, they provide estimates oflandscape-scale demographics that are necessaryfor identifying local extinction risk (Syphard et al.2006; Scheller et al. 2005).Group 8. Including spatial interactions; dynamiccommunities; including ecosystem processesFLSMs that include spatial interactions, dynamiccommunities, and ecosystem processes are amore recent development. At a relatively smallextent (typically 10 4 ha) often represent ecosystem processesusing relatively simple growth, mortality, anddecay functions (Keane et al. 1996; Scheller andMladenoff 2004). By necessity, such modelsexclude many finer-scale interactions, such asshading caused by neighboring cells. These modelsbenefit from direct and indirect links torelatively simple ecosystem process models, suchas PnET-II (Aber and Federer 1992; Scheller andMladenoff 2004) or FOREST-BGC (Running andGower 1991; Keane et al. 1996) and simultaneouslyinclude broad-scale spatial interactionsand dynamic communities.DiscussionStrategies for deploying FLSMsIncreases in model availability and usabilityprovide an opportunity to open up the modelingprocess to more forest ecologists and managers.However, a friendly interface cannot overcomethe inherent limitations found in every model.Modeling requires many choices between extentand resolution, precision and generality, accuracyand meaningful prediction, parameterization andvalidation (Levins 1966; Baker and Mladenoff1999; Mladenoff 2004).Our classification can serve as a guide insofaras it can help elucidate what kind of model will beappropriate for a researcher or manager. Eachresearch question will require that differentweights be applied to our three criteria and theclassification system can be customized into aunique decision tree based on an individualranking of the three criteria. Although eachmodel group is diverse, there are commonassumptions within each group that will limithypothesis testing or the scope of the projections123
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