1Department of Natural Resources Conservation, University of Massachusetts, Amherst,MA01003; 2Department of Forest, Rangeland, and Watershed Stewardship, Colorado StateUniversity, Fort Collins, CO 80523

Historic Range of Variability in Landscape Structureand Wildlife Habitat

Uncompahgre Plateau Landscape

Executive Summary

January 2005

Kevin McGarigal and William H. Romme1

Purpose

We developed a suite of computer models (RMLANDS, FRAGSTATS, HABIT@) thatsimulate and quantify changes in vegetation patterns and wildlife habitat under a range of naturaland anthropogenic disturbance regimes. This report documents the results of the first phase ofthis modeling effort to characterize the pre-1900 range of variability (hereafter referred to as the“historic range of variability” [HRV]) in landscapestructure and wildlife habitat on the UncompahgrePlateau Landscape (UPL) in southwestern Colorado(Figure). Specifically, our results were intended to:(1) improve our general understanding of landscapedynamics; (2) use as a reference or benchmark forevaluating the state of the current landscape (i.e., todetermine to the degree of “departure” from HRVconditions); and (3) use as a reference or benchmarkfor comparison with alternative managementscenarios in the next phase of this study. This report isintended to complement the detailed, but largelyqualitative, landscape condition analysis completedfor the South Central Highlands Section, southwesternColorado and northwestern New Mexico (Romme etal. 2003).

Justification

Our study was motivated by the need to provide a better quantitative understanding oflandscape dynamics and was stimulated by a few basic information needs:

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• The need for a quantitative description of the historic range of variability in landscapestructure and wildlife habitat to use as a reference or benchmark for comparison withcontemporary or potential future conditions.

• The need for a quantitative examination of the relationship between landscape dynamics andthe scale (landscape extent) and context (geographic location) of the landscape.

• The need for better understanding of just how dynamic these systems are, with therecognition that the temporal as well as spatial structure of habitats is important to ecologicalintegrity and landscape function (e.g., population persistence).

Scope and Limitations

Our results and conclusions must be interpreted within the scope and limitations of thisstudy. The most important considerations are as follows:

• Our analyses were designed to simulate vegetation dynamics for the period from about 1300to the late 1800s, representing the period from Ancestral Puebloan abandonment toEuroAmerican settlement (i.e., the period of indigenous settlement). This period represents atime when broad-scale climatic conditions were generally similar to those of today, but Euro-American settlers had not yet introduced the sweeping ecological changes that now havegreatly altered many Rocky Mountain landscapes. Moreover, it was a time of relativelyconsistent (though not static) environmental and cultural conditions in the region, and a timefor which we have a reasonable amount of specific information to enable us to model thesystem.

• Our approach relied on the use of computer models, and while it is important to recognize themany advantages of models, it is critical to understand that models are abstract and simplifiedrepresentations of reality. Thus, our results should not be interpreted as “golden”. Rather,they should be used to help identify the most influential factors driving landscape change,identify critical empirical information needs, identify interesting system behavior (e.g.,thresholds), identify the limits of our understanding, and help us to explore “what if”scenarios.

• Models are only as good as the data used to parameterized them. To the extent possible, weutilized local empirical data; however, we also drew on relevant scientific studies often fromother geographic areas and relied heavily on expert opinion. Thus, our results should not beviewed as definitive, but rather as an informed estimate of the HRV based on our currentscientific understanding. Of particular concern is our estimate of mean fire return intervals inthe pinyon-juniper woodlands and semi-desert communities. Recent data suggest that themean fire return interval in pinyon-juniper woodlands during the reference period may havebeen quite a bit longer than we simulated. This has widespread ramifications because alonger return interval in the pinyon-juniper woodlands, which represents the dominant covertype on the UPL, would have a dramatic influence on return intervals in adjacent cover types.In particular, the realized return intervals would be longer in the higher-elevation forested

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cover types and the net result would be a significant shift in the seral stage and agedistributions towards the later/older stages.

• This report focuses on upland vegetation types, largely for pragmatic reasons. Vegetationpatterns and dynamics of riparian and aquatic vegetation are more complex, more variable,and more difficult to model in a straightforward fashion than are patterns and dynamics ofupland vegetation. We note, however, that the patterns and ecological processes ofsurrounding upland vegetation have profound influences on aquatic ecosystems; thus, ourresults for terrestrial vegetation provide a partial basis for future assessments of aquatic HRV.

• This report focuses on the effects of two major natural disturbances: fire and insects/diseases.Other kinds of natural disturbances also occur, but the impacts of these other disturbancestend to be localized in time or space and have far less impact on vegetation patterns overbroad spatial and temporal scales than do fire and insects.

Methods

We developed and used three different computer models in conjunction to simulate landscapechanges and quantify the dynamics in landscape structure and wildlife habitat under the referenceperiod disturbance regime, as follows.

RMLANDS is a grid-based, spatially-explicit, stochastic landscape simulation modeldesigned to simulate disturbance and succession processes affecting the structure and dynamicsof Rocky Mountain landscapes. RMLANDS simulates two key processes: succession anddisturbance, implemented sequentially within 10-year time steps for a user-specified period oftime. Succession is implemented using a stochastic state-based transition approach in whichvegetation cover types transition probabilistically between discrete states (conditions). Naturaldisturbances include wildfire and a variety of insects/pathogens (pinyon decline [pinyon ipsbeetle and black stain root rot], mountain pine beetle, Douglas-fir beetle, spruce beetle, andspruce budworm). Each natural disturbance is modeled as a stochastic process involving theinitiation, spread, and ecological effects of disturbances as affected by climate. We usedRMLANDS to simulate vegetation patterns over an 800-year period under the reference perioddisturbance regime.

FRAGSTATS is a spatial pattern analysis program that computes a large number oflandscape metrics that quantify specific spatial characteristics of categorical map patternsrepresented at a particular scale. We used FRAGSTATS to quantify the composition (i.e., thepercentage of the landscape in each of 63 distinct and dynamic patch types defined by uniquecombinations of cover type and stand condition), and configuration (i.e., the spatial character andarrangement, position, or orientation of patches) of the landscape using a suite of metrics.Specifically, we quantified the structure of the vegetation mosaic produced by RMLANDS ateach timestep and then summarized the range of variation in landscape structure over the 800-year simulation. In addition, we compared the structure of the current landscape to the simulatedHRV in landscape structure to determine the degree of “departure” of the current landscape.

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HABIT@ is a multi-scale, spatially-explicit GIS-based system for modeling wildlife habitatbased on grids representing environmental variables. HABIT@ allows for complex spatialrelationships in which the habitat capability value at each cell is dependent not only on the localresources available at that cell, but on resources and/or their configuration in a species-specificneighborhood, on impediments to movement, and on the density of roads or development in theneighborhood. We used HABIT@ to model habitat capability for four selected wildlife indicatorspecies (pine marten, three-toed woodpecker, olive-sided flycatcher, and elk) selected on thebasis of differences in life history and habitat associations. Specifically, we quantified habitatcapability for each species from the vegetation mosaic produced by RMLANDS at each timestepand then summarized the range of variation in habitat capability over the 800-year simulation. Inaddition, we compared the habitat capability of the current landscape to the simulated HRV inhabitat for each species to determine the degree of “departure” of the current landscape.

HRV Departure.–As noted above, we compared the current landscape to the simulated rangeof variation in landscape structure and wildlife habitat to determine the degree of “departure” ofthe current landscape - although our departure estimates were limited to forested cover types dueto lack of available current condition data for non-forested types. For our purposes, the “current”condition refers to the landscape in 2003 after the Burn Canyon fire. Specifically, we modifiedthe approach for Fire Regime Condition Class (FRCC) determination to one better suited to ourmodeling environment, and possessing other distinct advantages over FRCC. Briefly, ourapproach is based on a spatially-explicit model of disturbance and succession (instead of anonspatial model), incorporates multiple disturbance processes (not just fire), explicitlyincorporates the measured range of variation in each metric (instead of using the mean), results ina continuously-scaled departure index (instead of a 3-class categorization of departure level),adopts a truly multivariate perspective on vegetation departure by incorporating multiplecomposition and configuration metrics (instead of a bivariate summary), and allows for anexplicit assessment of the effects of scale on departure.

Scale and Context.--We quantified the dynamics in landscape structure (using FRAGSTATS)and wildlife habitat capability (using HABIT@) in relation to scale (i.e., landscape extent) andcontext (i.e., geographic location) by examining the relative variability and the degree ofsimilarity in the observed range of values among several sub-landscapes.

Results and Conclusions

Disturbance Processes & Dynamics

Wildfire.–Based on a number of fire history studies and relatively extensive local empiricaldata, Romme et al. (2003) concluded that the median fire return interval varied dramaticallyacross the landscape along an elevational gradient in relation to fuels and moisture conditionsand varied considerably within a cover type, creating a complex vegetation mosaic at thelandscape scale:

• Our simulations largely confirmed these observations and provided a detailed quantitative

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summary of the wildfire disturbance regime (Table). The differences between our findingsand those recently reported for pinyon-juniper woodlands largely reflect differences betweenthe data we used to parameterize the model and more recent data not available at the time ofour experiments. Otherwise, the differences can be attributed to biases associated with theapproaches used to estimate return intervals in each study, and these biases are important tounderstand in order to properly interpret our results.

• We also noted the distinct variability in return intervals among locations within a singlecover type (e.g., Figure), highlighting the importance that landscape context has on fireregimes and demonstratingthat spatial and temporalvariability is the trademarkof these disturbanceregimes. This variabilitywithin any singlevegetation typeunderscores the idea thatno single statistic, such asmean fire interval (MFI),is adequate to characterizehistorical fire regimes, andthat the widely used MFIactually may be quitemisleading if takenliterally.

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• Perhaps the single greatest insight gained from our simulations with regards to wildfire stemsfrom the shear magnitude of wildfire disturbance that is required to produce the returnintervals that are widely accepted for the reference period. On average, once every twodecades, >10% of the area (~66,000 ha) was burned, and roughly once every 120 years,>20% (~132,000 ha) wasburned (Figure), inclusive ofboth high- and low-mortalityaffected areas. This is atremendous amount ofburning and perhaps amagnitude of burning that ispoorly appreciated by landmanagers and the generalpublic based on publicreactions to recent “large”fires in the west. As noteabove, it is quite possible thatwe simulated too much fire,especially in the low and midelevations, but even if wewere to double the firerotation period for thelandscape as whole, it wouldstill result in a tremendousmagnitude of wildfire.

Insects & Disease.-In contrast to wildfire, there was comparatively little empirical data oninsect/disease disturbance regimes for the reference period and almost no local data.Consequently, we were forced to draw heavily on contemporary observations of outbreaks fromthroughout the Rocky Mountain region in combination with local and regional expert opinion:

• The overall rotation periods for insect/disease disturbances were generally much longer thanwildfire; spruce budworm had the shortest rotation period of any insect/disease agent at 104years, followed by pinyon decline at 197 years, pine beetle at 222 years, spruce beetle at 323years and Douglas-fir beetle at almost 1,247 years. Taken individually, with the exception ofspruce budworm, insect/disease disturbances had much less overall impact on the landscapethan wildfire; however, taken collectively, insects/diseases clearly impacted more area perunit time than wildfire.

• The ecological impacts of insects/diseases on vegetation patterns were notably different thanwildfire. With the exception of spruce beetle outbreaks, all other insect/disease outbreaksresulted in proportionately very little stand replacement. Most insect/disease disturbanceswere low mortality and either promoted successional advancement of younger stands or actedto maintain older stands in old-growth, shifting mosaic condition. Hence, with exceptions,

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wildfire was principally responsible for maintaining the coarse-grained mosaic ofsuccessional stages across the landscape, whereas insect/disease agents were responsible forcreating much of the fine-scale heterogeneity in vegetation patterns.

Vegetation Patterns & Dynamics

It is widely accepted that during the reference period natural disturbance processes operatedto maintain a complex vegetation mosaic of successional stages and cover types. It is less clearwhether this vegetation mosaic was stable in structure (composition and configuration) or thedegree to which it varied over time. With our simulations, we sought to quantify the range ofvariability in landscape structure during the reference period to help ascertain the degree ofdynamism in landscape structure and to provide a benchmark for comparison with alternativefuture land management scenarios. To this end, our simulations produced important findings:

• The vegetation mosaic was remarkably variable in structure over time. While the landscapecould be characterized as a shifting mosaic of successional stages and cover types, it was nota steady-state shifting mosaic (sensu Bormann and Likens 1979). In other words, thecomposition of the mosaic was not constant.

• Although the vegetation mosaic was not in a steady-state equilibrium, the mosaic wasgenerally in dynamic (or bounded) equilibrium (sensu Turner et al. 1993). That is to say,while the structure of the landscape varied over time, it generally fluctuated within boundsabout a stable mean (e.g.,Figure). This behavior isessential to our objectiveof describing the range ofvariability in landscapestructure, because theconcept of a “range ofvariability” implies thatthe range is stable.

• Most metrics achieved astable, boundedequilibrium within a 100-300 year period - althoughit took twice that length oftime to verify that therange of variation was infact stable. Notsurprisingly, the periodrequired for equilibration in landscape composition varied somewhat among cover types. Ingeneral, cover types that experienced shorter disturbance return intervals and/or faster rates ofsuccession equilibrated in the shortest period (e.g., Figure, above).

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• The range of variability in landscape structure cannot be expressed in a single metric - at leastnot effectively - because the metrics associated with different aspects of landscapecomposition and configuration exhibit varying degrees and patterns of dynamism. Twogeneral types of landscape metrics are: (1) landscape composition metrics, which refer tonon-spatial features associated with the variety and abundance of patch types within thelandscape; and (2) landscape configuration metrics, which refer to the spatial character andarrangement, position, or orientation of patches within the class or landscape. Landscapecomposition metrics, represented here by the percentage of the landscape comprised of eachof 63 dynamic patch types (i.e., unique combinations of cover type and seral stage), exhibitedfive-times greater dynamism (on average) overall than landscape configuration metrics,represented here by 19 different metrics. Thus, while the composition of the vegetationmosaic fluctuated dramatically over time, the spatial pattern of the mosaic was relativelystable. The variability in configuration was principally associated with changes in the sizeand continuity of the large patches in the landscape. This suggests that large, severedisturbance events, those that occurred relatively infrequently but that substantially alteredthe seral-stage distribution and created a coarse-grained mosaic of vegetation patches, weredisproportionately important in regulating the dynamism in landscape structure. In contrast,the relatively frequent small disturbances had little impact on overall landscape pattern orchange through time.

Wildlife Habitat Patterns & Dynamics

We characterized the baseline range of variability in habitat capability for a suite of speciesrepresenting a diversity of habitat requirements. It is important to note that we did not simulatewildlife populations per se. Rather, we simulated habitat conditions, and thereby impliedpotential population distributions and dynamics as a function of habitat conditions. Given thedirect link between vegetation patterns and wildlife habitat, the habitat analysis did not revealany insights that could not have been gained by careful consideration of the vegetation results.Nevertheless, there were a few noteworthy findings:

• We demonstrated that habitat capability varies over time and space for all species –something that has long been recognized intuitively, but rarely quantified.

• Not surprisingly, the magnitude and pattern of variation in habitat capability differed amongspecies (Table, page 9). We predicted that habitat characteristics for generalist species(represented by elk in this study) would exhibit the least variation over time, and that habitatfor specialist species (pine marten, three-toed woodpecker, and olive-sided flycatcher in thisstudy) would fluctuate more widely over time. To our surprise, one of the specialists, theolive-sided flycatcher, exhibited the least variation in habitat capability over time. This waslikely due to the consistent supply of high-contrast edges (the preferred habitat) borderingpermanent openings such as meadows, barren areas, and lakes and ponds that acted like abuffer against major fluctuations in habitat. Not surprisingly, the three-toed woodpeckerexhibited the greatest variation in habitat capability over time - almost twice that observed forpine marten and elk. The dramatic fluctuations reflected the periodic pulses of high qualityhabitat following large-scale disturbance events in the high-elevation conifer forests.

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• Not surprisingly, indicator species exhibited unique spatial and temporal patterns ofvariability in capable habitat that reflected differences in life history (e.g., home range size)and habitat affinities (e.g., preferences for edges, forest interiors, or post-disturbanceenvironments).

HRV Departure

One of the principal purposes of gaining a better quantitative understanding of the historicreference period is to know whether recent human activities have caused landscapes to moveoutside their historic range of variability. To this end, we modified the approach for FRCCdetermination (see methods above) and made the following key findings.

• The current landscape structure appears to deviate substantially from the simulated HRV,although the level of “departure” varies spatially across the forest in relation to differencesamong cover types(Table, pg 10). Many characteristics appear far outside that range ofvariability. Indeed, the majority of both landscape composition metrics (21/40) and landscapeconfiguration metrics (10/19) are completely outside their HRV’s (i.e., 100% departureindex).

• In general, the current landscape has fewer, larger, more extensive and less isolated patcheswith less edge habitat than existed under the simulated HRV. The larger patches tend to begeometrically less complex and contain proportionately more core area than existed under thesimulated HRV. Overall, the current landscape is more contagious and less structurallydiverse than existed under the simulated HRV. This can be interpreted as a morehomogenous landscape, where the lack of any extensive disturbance during the past 100

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years has led to large, mostly late-seral patches, with low contrast due to the paucity ofyounger seral stages. This landscape condition appears to be largely a legacy of the lastcentury of land management practices, in particular fire exclusion. However, the generallybenign climate of the twentieth century also was a significant reason for the lack of large,stand-replacing disturbances, either by fire or spruce beetle.

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• The patterns of departure were generally similar at the class level for each of the forestedcover types with reliable data on current conditions, although there were some noteworthyvariations. In particular, overall the high-elevation forest types exhibited much less departurein terms of both composition and configuration than the low-elevation forest types,suggesting that the disturbance regime in high-elevation forests has not been altered bytwentieth century land use practices to the same extent as low-elevation forests. In addition,while the current seral-stage distribution of high-elevation spruce-fir forests appears to begenerally within the simulated HRV, the mid- and low-elevation forest types, includingaspen, mixed-conifer, and ponderosa pine types, appear to contain an overabundance ofstands in the mid-seral stages (i.e., stem exclusion) compared to the simulated HRV.Moreover, these mid-seral stands appear to be larger, more extensive, geometrically morecomplex and less isolated than was typically observed under the simulated HRV. The mostnotable departure, however, is the complete absence of stands in the fire-maintained opencanopy condition in the low-elevation ponderosa pine and warm dry mixed-conifer forests.

• Despite the apparent departure of the current landscape structure from the simulated HRV,the current landscape does not appear to deviate substantially from the HRV in susceptibilityto at least four out of five of the simulated insects/pathogens disturbances (Table). However,the magnitude of departure varies dramatically across the forest with relatively large areasexhibiting high susceptibility to each of the disturbance agents.

• Given the direct link between vegetation patterns and wildlife habitat, it is not surprising thatthe wildlife indicator species we analyzed also exhibited substantial departure from theirsimulated HRVs (see Table, pg 9). All of the species we considered are doing poorly in thecurrent landscape compared to the simulated HRV. The pine marten is doing the least poorlyin the current landscape. The moderately extensive late-seral conifer forest in the higherelevations is likely providing some ideal habitat for this species, although the total amount ofideal habitat is apparently somewhat less than was realized on average under the simulatedHRV. The three-toed woodpecker, a species better adapted to exploit post-disturbanceenvironments, is doing slightly worse than pine marten owing to the paucity of recent largedisturbances. Elk and olive-sided flycatchers are both disadvantaged the most in the currentlandscape. Both species benefit from edges between early- and late-seral vegetation patches.

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The paucity of disturbances over the past century has left the current landscape ratherdeprived of edge habitat and has reduced the overall interspersion and juxtaposition of thevegetation mosaic, with negative consequences on habitat capability for these two indicatorspecies.

• Managers need to be cognizant of two important considerations when interpreting these HRVdeparture results. First, although it is clear that the current landscape structure is not withinthe modeled range of variability, the magnitude of departure is less clear due to inconsistencies in the spatial resolution of the initial cover type map. Specifically, the fine-grained heterogeneity in vegetation created by the disturbance processes in RMLANDS wasprobably not comparably represented in the Forest Service map of current vegetation. Wetook precautions to safeguard against reaching spurious conclusions in this regard. First, weeliminated the finest heterogeneity by rescaling the vegetation maps to a 0.5-ha minimummapping unit and then evaluated HRV departure on these rescaled vegetation maps inaddition to the original high-resolution maps. Second, in our interpretation of HRV departure,we emphasized several area-weighted landscape metrics that are insensitive to variationsaffecting very small patches. Nevertheless, several landscape configuration metrics sensitiveto fine-grained heterogeneity were incorporated into the overall configuration departureindex. Consequently, while we feel confident in concluding that the current landscapestructure is outside the modeled range of variability, it is important to be aware that ourreported HRV departure indices, except for the seral-stage departure index and landscapecomposition departure index, are probably biased high (i.e., inflated).

• Second, any conclusions regarding HRV departure depend on an accurate mapping of standconditions in the current landscape. In particular, we lack reliable age and stand conditiondata for most non-forested types (e.g., mountain shrublands, mesic sagebrush, pinyon-juniperwoodlands). Consequently, our initial assignment of stands to condition classes (seral stages)was based on interpolation from sparse data or on a random assignment based on seral-stagedistributions estimated by local experts. In either case, we are not confident that our currentcondition estimates are accurate, therefore we did not report departure estimates for thesecover types.

• Our simulations indicate that returning the landscape structure to a condition that falls withinthe simulated HRV would likely be a difficult and long-term undertaking if it were deemeddesirable. We deduced this from the time it took the current landscape to equilibrate to thereference-period disturbance regime. We can infer that if management activities weredesigned to emulate natural disturbance processes, then it would take a length of time equalto the equilibration period to return the landscape to its HRV. In our simulations, mostlandscape structure metrics equilibrated within 100-300 years. It must be emphasized,however, that this does not imply that it should be our goal in management to recreate all ofthe ecological conditions and dynamics of the reference period. Complete achievement ofsuch a goal would be impossible, given the climatic, cultural, and ecological changes thathave occurred in the last century. Moreover, the extent and intensity of disturbance requiredto emulate the natural disturbance regime would be unacceptable socially, economically, andpolitically.

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Effects of Scale and Context

The pattern detected in any ecological mosaic is a function of scale, and we cannot detectpatterns or changes in patterns beyond the extent or below the resolution of the grain. In thisregard, our simulations produced several important findings:

• We examined landscape structure dynamics at two spatial resolutions: (1) 0.0625-ha (25 mcell size) minimum-mapping unit, and (2) 0.5-ha minimum-mapping unit. The results werelargely insensitive to spatial resolution. It was apparent that small patches had a trivial impacton most configuration metrics and virtually no impact on the metrics selected forinterpretation (i.e., area-weighted metrics). This is not to say that the fine-grained patterns ofheterogeneity are not important ecologically, only that at the scale of the landscape extentswe examined (10s-100s of thousands of hectares), the quantitative importance of the fine-grained patterns was dwarfed by the coarse-grained patterns created by the larger patches.

• We established the temporal extent of our simulations based on our desire to capture anddescribe a stable range of variation in landscape structure. In general, a minimum of 100-300years is needed to capture the full range of variation in most metrics, and twice that long toconfirm that the range is stable. Thus, a management strategy designed to emulate the naturaldisturbance regime would take 100-300 years to see the landscape fluctuate through its fullrange of conditions. This is a humbling thought given that most professional careers last nomore than 30 years - a blip on the scale of these landscape dynamics - and that most policiesare geared toward 10- to 20-year planning horizons.

• When we examined progressively smaller spatial units of the entire simulated landscape(Figure, right), temporal variability increased – as would be expected, and there was anapparent threshold in the relationshipbetween landscape extent and temporalvariability (Figure, pg 14). Specifically,the magnitude of variability in landscapecomposition increased only modestly as thelandscape extent decreased from the forestscale (659,246 ha) to the quadrant scale(average = 164,812 ha), but increaseddramatically as the landscape extentdecreased to the watershed scale (average =37,929 ha). We interpret this to mean thatat the quadrant extent (and larger), thelandscape is large enough to fullyincorporate the disturbance regime andexhibit stable dynamical behavior. Weconclude that under the simulateddisturbance regime, characterizing HRV isbest done at the quadrant or forest scale.

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• Each of the sub-landscapeswe examined was somewhatunique in its absolute rangeof variability in landscapestructure, demonstrating thatno two landscapes in thismountainous region areidentical; each has more orless unique characteristics oftopography, vegetation, etc.that affect its dynamicalbehavior. Most of thesedifferences can be attributedto differences in landscapecomposition (Table). Thechallenge to managers is indeciding whether to giveexplicit recognition to thethese differences whenestablishing management direction, or to subsume these difference at the forest level onpragmatic grounds. We believe that it is probably sufficient to characterize HRV at the forestscale for purposes of general communication, but that it would be wise if possible to use thedistrict-specific HRV results when setting management targets.

• Despite the importance of landscape extent and context on the measured range of variability,the degree of departure of the current landscape from the simulated HRV was relativelyinvariant to scale and context. Thus, all of the sub-landscapes we examined appear to beroughly equally outside their simulated HRV.

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Concluding Remarks

In closing, it is important to remember that our simulation study was intended to complementthe detailed landscape condition analysis completed for the South Central Highlands Section ofsouthwestern Colorado and northwestern New Mexico (Romme et al. 2003). Our study providesa detailed quantitative analysis of the simulated vegetation dynamics under the historic referenceperiod that complements the detailed, but qualitative, landscape condition assessment of theprevious report. Overall, our findings are in complete qualitative agreement with the previousassessment. In addition to enhancing our general understanding of landscape dynamics, our HRVresults are of paramount use as a reference or benchmark for comparison with alternative futureland management scenarios - the focus of the next phase of this project.

Literature Cited

Bormann, F.H., and G.E. Likens. 1979. Pattern and process in a forested ecosystem. Springer-Verlag, New York.

Romme, W. H., M. L. Floyd, D. Hanna, and J. S. Redders. 2003. Landscape Condition Analysisfor the South Central Highlands Section, southwestern Colorado & Northern New Mexico. DraftFinal Report to the U.S. Forest Service, Rocky Mountain Region, Lakewood, Colorado.

Turner, M.G., W.H. Romme, R.H. Gardner, R.V. O'Neil, and T.K. Kratz. 1993. A revisedconcept of landscape equilibrium: disturbance and stability on scaled landscapes. LandscapeEcology 8(3): 213-227.