Historic Range of Variability in Landscape Structure

and Wildlife Habitat


San Juan National Forest



Executive Summary



January 2005



Principal Investigators:

            

Kevin McGarigal

Department of Environmental Conservation, University of Massachusetts, Amherst, MA01003


William H. Romme

Department of Forest, Rangeland, and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523


Technical Assistants:


David Goodwin, Erik Haugsjaa, Eduard Ene, and Brad Compton

Department of Environmental Conservation, University of Massachusetts, Amherst, MA01003


Dan Kashian

Department of Forest, Rangeland, and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523



Purpose


      We developed a suite of computer models (RMLANDS, FRAGSTATS, HABIT@) that simulate and quantify changes in vegetation patterns and wildlife habitat under a range of natural and anthropogenic disturbance regimes. This report documents the results of the first phase of this modeling effort to characterize the pre-1900 range of variability (hereafter referred to as the “historic range of variability” [HRV]) in landscape structure and wildlife habitat on the San Juan National Forest (SJNF) in southwestern Colorado. Specifically, our results were intended to: (1) improve our general understanding of landscape dynamics; (2) use as a reference or benchmark for evaluating the state of the current landscape (i.e., to determine to the degree of “departure” from HRV conditions); and (3) use as a reference or benchmark for comparison with alternative management scenarios in the next phase of this study. This report is intended to complement the detailed, but largely qualitative, landscape condition analysis completed for the South Central Highlands Section, southwestern Colorado and northwestern New Mexico (Romme et al. 2003).


Justification


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

 

          The need for a quantitative description of the historic range of variability in landscape structure and wildlife habitat to use as a reference or benchmark for comparison with contemporary or potential future conditions.

 

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

 

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


Scope and Limitations


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

 

          Our analyses were designed to simulate vegetation dynamics for the period from about 1300 to the late 1800s, representing the period from Ancestral Puebloan abandonment to EuroAmerican settlement (i.e., the period of indigenous settlement). This period represents a time 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 have greatly altered many Rocky Mountain landscapes. Moreover, it was a time of relatively consistent (though not static) environmental and cultural conditions in the region, and a time for which we have a reasonable amount of specific information to enable us to model the system.

 

          Our approach relied on the use of computer models, and while it is important to recognize the many advantages of models, it is critical to understand that models are abstract and simplified representations 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, we utilized local empirical data; however, we also drew on relevant scientific studies often from other geographic areas and relied heavily on expert opinion. Thus, our results should not be viewed as definitive, but rather as an informed estimate of the HRV based on our current scientific understanding.

 

          This report focuses on upland vegetation types, largely for pragmatic reasons. Vegetation patterns 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 of upland vegetation. We note, however, that the patterns and ecological processes of surrounding upland vegetation have profound influences on aquatic ecosystems; thus, our results 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 disturbances tend to be localized in time or space and have far less impact on vegetation patterns over broad spatial and temporal scales than do fire and insects.


Methods


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


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


      FRAGSTATS is a spatial pattern analysis program that computes a large number of landscape metrics that quantify specific spatial characteristics of categorical map patterns represented at a particular scale. We used FRAGSTATS to quantify the composition (i.e., the percentage of the landscape in each of 57 distinct and dynamic patch types defined by unique combinations of cover type and stand condition), and configuration (i.e., the spatial character and arrangement, 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 at each 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 simulated HRV in landscape structure to determine the degree of “departure” of the current landscape.


      HABIT@ is a multi-scale, spatially-explicit GIS-based system for modeling wildlife habitat based on grids representing environmental variables. HABIT@ allows for complex spatial relationships in which the habitat capability value at each cell is dependent not only on the local resources available at that cell, but on resources and/or their configuration in a species-specific neighborhood, on impediments to movement, and on the density of roads or development in the neighborhood. We used HABIT@ to model habitat capability for four selected wildlife indicator species (pine marten, three-toed woodpecker, olive-sided flycatcher, and elk) selected on the basis of differences in life history and habitat associations. Specifically, we quantified habitat capability for each species from the vegetation mosaic produced by RMLANDS at each timestep and then summarized the range of variation in habitat capability over the 800-year simulation. In addition, we compared the habitat capability of the current landscape to the simulated HRV in habitat 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 range of variation in landscape structure and wildlife habitat to determine the degree of “departure” of the current landscape. For our purposes, the “current” condition refers to the landscape in 2003 after the Missionary Ridge fire. Specifically, we modified the approach for Fire Regime Condition Class (FRCC) determination to one better suited to our modeling environment, and possessing other distinct advantages over FRCC. Briefly, our approach is based on a spatially-explicit model of disturbance and succession (instead of a nonspatial model), incorporates multiple disturbance processes (not just fire), explicitly incorporates the measured range of variation in each metric (instead of using the mean), results in a continuously-scaled departure index (instead of a 3-class categorization of departure level), adopts a truly multivariate perspective on vegetation departure by incorporating multiple composition and configuration metrics (instead of a bivariate summary), and allows for an explicit 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) and context (i.e., geographic location) by examining the relative variability and the degree of similarity 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 empirical data, Romme et al. (2003) concluded that the median fire return interval varied dramatically across the landscape along an elevational gradient in relation to fuels and moisture conditions and varied considerably within a cover type, creating a complex vegetation mosaic at the landscape scale:

 

          Our simulations largely confirmed these observations and provided a detailed quantitative summary of the wildfire disturbance regime (Table-rotation).

 

          We also noted the distinct variability in return intervals among locations within a single cover type (e.g., Figure-return), highlighting the importance that landscape context has on fire regimes and demonstrating that spatial and temporal variability is the trademark of these disturbance regimes. This variability within any single vegetation type underscores the idea that no single statistic, such as mean fire interval (MFI), is adequate to characterize historical fire regimes, and that the widely used MFI actually may be quite misleading if taken literally.

 

          Perhaps the single greatest insight gained from our simulations with regards to wildfire stems from the shear magnitude of wildfire disturbance that is required to produce the return intervals that are widely accepted for the reference period. On average, once every two decades, >10% of the area (~80,000 ha) was burned, and roughly once every 120 years, >20% (~160,000 ha) was burned (Figure-recurrence), inclusive of both high- and low-mortality affected areas. This is a tremendous amount of burning and perhaps a magnitude of burning that is poorly appreciated by land managers and the general public based on public reactions to recent “large” fires in the west.


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

 

          The overall rotation periods for insect/disease disturbances were generally much longer than wildfire; spruce budworm had the shortest rotation period of any insect/disease agent at 103 years (Table-rotation), followed by spruce beetle at 273 years (Table-rotation), pine beetle at 306 years (Table-rotation), pinyon decline at 466 years (Table-rotation) and Douglas-fir beetle at almost 1,200 years (Table-rotation). Taken individually, with the exception of spruce budworm, insect/disease disturbances had much less overall impact on the landscape than wildfire; however, taken collectively, insects/diseases clearly impacted more area per unit time than wildfire.

 

          The ecological impacts of insects/diseases on vegetation patterns were notably different than wildfire. With the exception of spruce beetle outbreaks, all other insect/disease outbreaks resulted in proportionately very little stand replacement. Most insect/disease disturbances were low mortality and either promoted successional advancement of younger stands or acted to maintain older stands in old-growth, shifting mosaic condition. Hence, with exceptions, wildfire was principally responsible for maintaining the coarse-grained mosaic of successional stages across the landscape, whereas insect/disease agents were responsible for creating much of the fine-scale heterogeneity in vegetation patterns.


Vegetation Patterns & Dynamics


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

 

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

 

          Although the vegetation mosaic was not in a steady-state equilibrium, the mosaic was generally 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 bounds about a stable mean (e.g., Figure-equilibrium). This behavior is essential to our objective of describing the range of variability in landscape structure, because the concept of a “range of variability” implies that the range is stable.

 

          Most metrics achieved a stable, bounded equilibrium within a 100-300 year period - although it took twice that length of time to verify that the range of variation was in fact stable. Not surprisingly, the period required for equilibration in landscape composition varied somewhat among cover types. In general, cover types that experienced shorter disturbance return intervals and/or faster rates of succession equilibrated in the shortest period (e.g., Figure-mts condition).

 

          The range of variability in landscape structure cannot be expressed in a single metric - at least not effectively - because the metrics associated with different aspects of landscape composition and configuration exhibit varying degrees and patterns of dynamism. Two general types of landscape metrics are: (1) landscape composition metrics, which refer to non-spatial features associated with the variety and abundance of patch types within the landscape; and (2) landscape configuration metrics, which refer to the spatial character and arrangement, position, or orientation of patches within the class or landscape. Landscape composition metrics exhibited five-times greater dynamism (on average) overall than landscape configuration metrics (Table-hrv-combined). Thus, while the composition of the vegetation mosaic fluctuated dramatically over time, the spatial pattern of the mosaic was relatively stable. The variability in configuration was principally associated with changes in the size and continuity of the large patches in the landscape. This suggests that large, severe disturbance events, those that occurred relatively infrequently but that substantially altered the seral-stage distribution and created a coarse-grained mosaic of vegetation patches, were disproportionately important in regulating the dynamism in landscape structure. In contrast, the relatively frequent small disturbances had little impact on overall landscape pattern or change through time.


Wildlife Habitat Patterns & Dynamics


      We characterized the baseline range of variability in habitat capability for a suite of species representing a diversity of habitat requirements. It is important to note that we did not simulate wildlife populations per se. Rather, we simulated habitat conditions, and thereby implied potential population distributions and dynamics as a function of habitat conditions. Given the direct link between vegetation patterns and wildlife habitat, the habitat analysis did not reveal any 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 somewhat among species (Table-LC index). We initially predicted that habitat characteristics for generalist species (represented by elk in this study) would exhibit the least variation over time, and that habitat for specialist species (pine marten, three-toed woodpecker, and olive-sided flycatcher in this study) would fluctuate more widely over time. Our results generally confirmed these predications, although to our surprise the pine marten exhibited the least variability in habitat capability, apparently due to the maintenance of large patches of interior forest in the higher elevations.

 

          Not surprisingly, indicator species exhibited unique spatial and temporal patterns of variability 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-disturbance environments).


HRV Departure


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

 

          The current landscape structure appears to deviate substantially from the simulated HRV (Table-hrv-summary-combined), although the level of “departure” varies spatially across the forest in relation to differences among cover types (Figure-hrv-departure-map). Many characteristics appear far outside that range of variability. Indeed, one-third of the landscape composition metrics (16/48) and most of the landscape configuration metrics (15/19) are completely outside their HRV’s (i.e., 100% departure index)(Table-hrv-combined).

 

          In general, the current landscape has fewer, larger, more extensive and less isolated patches with less edge habitat than existed under the simulated HRV. The larger patches tend to be geometrically less complex and contain proportionately more core area than existed under the simulated HRV. Overall, the current landscape is more contagious and less structurally diverse than ever existed under the simulated HRV. This can be interpreted as a more homogenous landscape, where the lack of any extensive disturbance during the past 100 years has led to large, mostly late-seral patches, with low contrast due to the paucity of younger seral stages. This landscape condition appears to be largely a legacy of the last century of land management practices, in particular fire exclusion. However, the generally benign climate of the twentieth century also was a significant reason for the lack of large, stand-replacing disturbances, either by fire or spruce beetle.

 

          The patterns of departure were generally similar at the class level for each of the cover types with reliable data on current conditions (i.e., forest types). In particular, the current high-elevation landscape is dominated by large patches of late-seral conifer forest and an almost total absence of early-seral forest due to the lack of extensive disturbance. The story is similar for pure aspen forests. Aspen-dominated forest, which includes the early- and mid-seral stages of the mixed conifer-aspen forest types, exhibits a notable deviation from this general pattern. While the current landscape has less aspen in the early- and mid-seral stages, and more in the late-seral stage - similar to the other high-elevation forest types - the coarse spatial configuration of aspen-dominated forest appears to be generally within the simulated range of variation. The low-elevation forests, especially ponderosa pine, also contain an overabundance of stands in the late-seral stages, but the most notable departure is the complete absence of stands in the fire-maintained open canopy condition.

 

          Due to the above vegetation conditions and patterns, the current landscape appears to deviate substantially from the HRV in susceptibility to at least four of the simulated insects/pathogens disturbances (Table-hrv-susceptibility). The current landscape appears especially vulnerable to pine beetle, spruce budworm and spruce beetle outbreaks.

 

          Given the direct link between vegetation patterns and wildlife habitat, it is not surprising that the wildlife indicator species we analyzed also exhibit substantial departure from their simulated HRVs (Table-LC index). Of the species considered, the pine marten is the principal beneficiary of the current landscape departure. Extensive late-seral conifer forest in the higher elevations is likely providing ideal habitat conditions for this species. Three-toed woodpeckers also benefit from these conditions, even though this species is better adapted to exploit post-disturbance environments. The three-toed woodpecker is the only species not exhibiting significant departure. Elk and olive-sided flycatchers are both disadvantaged in the current landscape. Both species benefit from edges between early- and late-seral vegetation patches. The paucity of disturbances over the past century has left the current landscape rather deprived of edge habitat and has reduced the overall interspersion and juxtaposition of the vegetation mosaic, with negative consequences on habitat capability for these two indicator species.

 

          Managers need to be cognizant of two important considerations when interpreting these HRV departure results. First, although it is clear that the current landscape structure is not within the 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 was probably not comparably represented in the Forest Service map of current vegetation. We took precautions to safeguard against reaching spurious conclusions in this regard. First, we eliminated the finest heterogeneity by rescaling the vegetation maps to a 0.5-ha minimum mapping unit and then evaluated HRV departure on these rescaled vegetation maps in addition to the original high-resolution maps. Second, in our interpretation of HRV departure, we emphasized several area-weighted landscape metrics that are insensitive to variations affecting very small patches. Nevertheless, several landscape configuration metrics sensitive to fine-grained heterogeneity were incorporated into the overall configuration departure index. Consequently, while we feel confident in concluding that the current landscape structure is well outside the modeled range of variability, it is important to be aware that our reported HRV departure indices, except for the seral-stage departure index and landscape composition departure index, are probably biased high (i.e., inflated).

 

          Second, any conclusions regarding HRV departure depend on an accurate mapping of stand conditions in the current landscape. In particular, we lack reliable age and stand condition data for most non-forested types (e.g., mountain shrublands, mesic sagebrush, pinyon-juniper woodlands). 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-stage distributions estimated by local experts. In either case, we are not confident that our current condition estimates are accurate. Hence, until more complete data on current stand age and condition are available, the HRV departure results for these cover types must be viewed with extreme caution.

 

          Our simulations indicate that returning the landscape structure to a condition that falls within the simulated HRV would likely be a difficult and long-term undertaking if it were deemed desirable. We deduced this from the time it took the current landscape to equilibrate to the reference-period disturbance regime. We can infer that if management activities were designed to emulate natural disturbance processes, then it would take a length of time equal to the equilibration period to return the landscape to its HRV. In our simulations, most landscape 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 of the ecological conditions and dynamics of the reference period. Complete achievement of such a goal would be impossible, given the climatic, cultural, and ecological changes that have occurred in the last century. Moreover, the extent and intensity of disturbance required to emulate the natural disturbance regime would be unacceptable socially, economically, and politically. 


Effects of Scale and Context


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

 

          We examined landscape structure dynamics at two spatial resolutions: (1) 0.0625-ha (25 m cell size) minimum mapping unit, and (2) 0.5-ha minimum mapping unit. The results were largely insensitive to spatial resolution. It was apparent that small patches had a trivial impact on most configuration metrics and virtually no impact on the metrics selected for interpretation (i.e., area-weighted metrics). This is not to say that the fine-grained patterns of heterogeneity are not important ecologically, only that at the scale of the large landscape extents we 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 and describe a stable range of variation in landscape structure. In general, a minimum of 100-300 years is needed to capture the full range of variation in most metrics, and twice that long to confirm that the range is stable. Thus, a management strategy designed to emulate the natural disturbance regime would take 100-300 years to see the landscape fluctuate through its full range of conditions. This is a humbling thought given that most professional careers last no more than 30 years - a blip on the scale of these landscape dynamics - and that most policies are geared toward 10- to 20-year planning horizons.

 

          When we examined progressively smaller spatial units of the entire simulated landscape (Figure-map), temporal variability increased – as would be expected, and there was an apparent threshold in the relationship between landscape extent and temporal variability (Figure-scale-land). Specifically, the magnitude of variability in landscape structure increased only modestly as the landscape extent decreased from the forest scale (847,638 ha) to the district scale (average = 282,546 ha), but increased dramatically as the landscape extent decreased to the watershed scale (average = 38,469 ha). We interpret this to mean that at the district extent (and larger), the landscape is large enough to fully incorporate the disturbance regime and exhibit stable dynamical behavior. We conclude that under the simulated disturbance regime, characterizing HRV is best done at the district or forest scale.

 

          Each of the sub-landscapes we examined was somewhat unique in its absolute range of variability in landscape structure, demonstrating that no two landscapes in this mountainous region are identical; each has more or less unique characteristics of topography, vegetation, etc. that affect its dynamical behavior. Most of these differences can be attributed to differences in landscape composition (Table-areal coverage). The challenge to managers is in deciding whether to give explicit recognition to the these differences when establishing management direction, or to subsume these difference at the forest level on pragmatic grounds. We believe that it is probably sufficient to characterize HRV at the forest scale for purposes of general communication, but that it would be wise if possible to use the district-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 relatively invariant to scale and context. Thus, all of the sub-landscapes we examined appear to be roughly equally outside their simulated HRV.


Concluding Remarks


      In closing, it is important to remember that our simulation study was intended to complement the detailed landscape condition analysis completed for the South Central Highlands Section of southwestern Colorado and northwestern New Mexico (Romme et al. 2003). Our study provides a detailed quantitative analysis of the simulated vegetation dynamics under the historic reference period that complements the detailed, but qualitative, landscape condition assessment of the previous report. Overall, our findings are in complete qualitative agreement with the previous assessment. In addition to enhancing our general understanding of landscape dynamics, our HRV results are of paramount use as a reference or benchmark for comparison with alternative future land 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 Analysis for the South Central Highlands Section, southwestern Colorado & Northern New Mexico. Draft Final 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 revised concept of landscape equilibrium: disturbance and stability on scaled landscapes. Landscape Ecology 8(3): 213-227.