Range of variability Concepts

Instructor: K. McGarigal

Assigned Reading: Landres et al. (1999)

Objective: Provide an overview of range of variability (ROV) concepts and their application inlandscape planning and management. Highlight the use of historic range of variability (HRV) inestablishing context for the current landscape condition and its potential role in guidingspecification of desired future landscape condition.

Topics covered:1. Origins of range of variability (ROV) concepts2. Premises for ROV concepts3. ROV concepts4. Methods for describing ROV5. Planning and management applications6. Challenges in the use and interpretation of natural variability

Comments: Lots of material taken from Landres et al. (1999).

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1. Origins of Range of Variability Concepts

1.1. From equilibrium to dynamic views

The changing state of ecological systems has long been of interest to ecologists. However, itwasn’t until the late 1970's that concept of spatial and temporal variability in ecological systemsstarted to emerge as the dominant ecological paradigm. Indeed, the 1980s saw the rise of whatsome have called the new paradigm in ecology and what is referred to as the “dynamic view” (or“flux of nature”). Before, the focus of community ecology was characterized by two things inparticular: (1) an equilibrium view, where “equilibrium” (as used here) refers to the constancy inthe species composition of a community (while disturbance and succession clearly altercommunities, they are subordinate in the equilibrium view to the major theme of nature, which isthe relatively unchanging climax community) and (2) the belief that ecosystems were self-contained, that their trajectories were determined solely by internal interactions or, put anotherway, that you could understand the dynamics of an ecosystem without ever looking outside itsboundaries.

Since the late 1970s, ecologists have increasingly focused on community and ecosystemdynamics rather than static endpoints such as the climax. Disturbance and response todisturbance are now recognized as natural processes that lay at the core of ecosystem dynamics,

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a concept that plant ecologists have variously expressed as “patch dynamics”, the “shiftinglandscape mosaic” and the “mosaic cycle.” This is also known as the “nonequilibrium” or“dynamic” view, reflecting the increasing recognition that disturbances and other factorsmitigate against ecological communities attaining a lasting equilibrium in species composition.

This “dynamic view” has had a tremendous impact on land management. It is now universallyaccepted that ecosystems and landscapes are dynamic; that disturbance and successionalprocesses influence the range of variability in ecosystem structure, composition, and function,and many scientists and managers believe that the “range of variability” (ROV) can provide auseful framework for managing ecosystems/landscapes. Natural resource managers have becomeespecially interested in ROV as human activity is increasingly seen as the causal agent of large-scale environmental changes, including departure of the ecosystem/landscape from its “natural”range of variability.

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1.2. From species to ecosystem management

Management use of ROV concepts began in earnest out of a search for a legally defensiblestrategy for maintaining biological diversity and sustaining the viability of threatened andendangered species, pursuant to the requirements of laws such as the National ForestManagement Act of 1976 and Endangered Species Act of 1973. However, the ROV concept hasgradually evolved and expanded into a general “coarse filter” strategy for sustaining ecologicalintegrity and as a benchmark for evaluating the impacts of human activities on ecosystems andlandscapes. This ecosystem management approach required a baseline or reference, andhistorical ecology appeared to be the best reference. Currently, ROV concepts guide and/orconstrain most current land management activities on National Forests and other public lands.

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1.3. First attempts

The first attempts to incorporaterange of variability conceptsfocused on designing “target stand”conditions from historicalevidence; specifically, prescribingstand treatments to recreatehistorical stand structures. Thisinvolved selected an appropriatestand structure, designing asilvicultural or prescribed burn(Rx) treatment, and implementingthe treatment to create acontemporary stand that emulatesthe structure of the historical condition.

Not surprisingly, the “target stand”approach encountered manyproblems: 1) it was the wrong scalefor effective management, sincerange of variability is notparticularly meaningful at the standscale because a single stand, evenunder historical referenceconditions, can exist in multipleconditions; 2) it selected the standcondition mostly arbitrarily, sinceno one stand condition could beassociated with the historicreference condition; 3) it wasdifficult and inappropriate to treatthe entire landscape with one treatment, since heterogeneity in the physical landscape (e.g.,landforms) often requires modifications to treatments and range of variability dictates that arange of conditions be maintained; and 4) it did not recognize the inherent variability of thesystem, which is ironic given the stated goal of ROV.

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Eventually, it was realized thatincorporating ecological variabilityinto management is key tomaintaining ecosystem integritybecause it: 1) ensures optimalbiodiversity, 2) recognizes the rolesof disturbance, 3) widens the optionsfor management, and 4) maintainsresilience.

Finally, in 1999 a collection ofpapers that serve as the foundation ofthe range of variability concept waspublished in a special section ofEcological Applications. However,none of the papers dealt withapplications of the concept and manypapers have been written since. Theconcept is still in a state of flux as ithas encountered many problems inimplementation. In particular, somehave question whether the concept isstill viable with rapid climatechange?

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2. Premises for range of variability concepts

Landres et al. (1999) identified the following premises for the use of ROV concepts (see Landreset al. 1999 for references).

1. Ecosystems are naturally dynamic and native species have adapted to disturbance-drivenfluctuations in their habitats. Therefore, the potential for survival of any given species maydiminish if temporal and spatial patterns of species’ habitats shift outside their natural rangeof variation. In other words, contemporary anthropogenic change may diminish the viabilityof many species adapted to past or historical conditions and processes.

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2. Approximating historical conditions provides a coarse-filter management strategy that islikely to sustain the viability of diverse species, even those for which we know little about.Similarly, because of limited understanding about ecosystems, approximating past conditionsoffers one of the best means for predicting and reducing impacts to present-day ecosystems.

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3. Managing within the constraints of site variability and history is easier, requires fewerexternal subsidies, and is more cost effective than trying to achieve management goals thatare outside the bounds of the system.

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4. Natural variability is a useful reference for evaluating the influence of anthropogenic changein ecological systems, including lakes, commodity production lands, and protected areas suchas wilderness.

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5. Analysis of an ecological system at different sites and over long time frames provides thecontext that hierarchy theory suggests is important in understanding the driving variables,constraints, and behavior of a system at local and shorter time scales. Such analysis yieldsessential understanding about the dynamic ecological processes that drive both spatial andtemporal variation in ecological systems, as well as the influence of this variation onevolution and biological diversity.

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6. Similar to the classic driving variables of moisture and temperature, disturbances such as fireand insect outbreaks have a strong and lasting influence on species, communities, andecosystems, and have been called a "key structuring process" at midscales, i.e., the scale offorest stands.

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7. Spatial heterogeneity per se is an important component of ecological systems. Reducingspatial variability typically results in declining biological diversity, increased vulnerability toinsects, pathogens, or other disturbances, and decreased resiliency to subsequentdisturbances.

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3. Defining range of variability concepts

Natural Range of Variability

Landres and colleagues define natural variability as “the ecological conditions, and the spatialand temporal variation in these conditions, that are relatively unaffected by people, within aperiod of time and geographical area appropriate to an expressed goal.” Note, from a landscapeecological perspective, the ecological conditions we are principally concerned with are thosedealing with landscape structure and function.

A variety of alternative phrases have been used by ecologists and managers to describe the samething, including: range of natural variability, natural range of variability, and historic range ofvariability. A lack of precision and clarity in the terms "natural," "range," and "historical" hasgenerated considerable debate over the appropriate time period and spatial extent used indefining and evaluating "natural variation". Much of this debate has centered on whether impactsof native Americans are considered natural or not, and on defining a point in time whenecological systems were relatively unaffected by people, usually considered the time beforeEuro-American settlement.

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Historic Range of Variability (HRV) and Future Range of Variability (FRV)

Although the notion of "natural" in ecological systems is equivocal (Sprugel 1991), it is mostcommon to define an historical reference period during which human activities had relativelyminor effects on overall landscape structure and function, and use this as a benchmark forcomparison with contemporary or potential future conditions. Key to this approach is the abilityto define a meaningful historical reference period (discussed in more detail below). A additionalprerequisite to this management strategy is knowledge of the range of variation in key landscapeattributes during the historic reference period, hereafter referred to as the historic range ofvariability (HRV). Ultimately, quantitative understanding of HRV is essential if we are to knowwhether recent human activities have caused landscapes to move outside their HRV.

Future Range of Variability is similar to HRV but focused on the future instead of the past. FRVfocuses on the estimated range of some ecological condition or process that may occur in thefuture, and is deemed especially relevant when the effects of anthropogenic stressors (e.g.,climate change, invasives, development) are high and thus likely to move the system away fromits HRV.

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Social Range of Variability (SRV)

The Social Range of Variability (SRV) is defined as the range of an ecological condition thatsociety finds acceptable at a given time, perhaps expressed as a distribution of public acceptance.Duncan et al. (2007) developed the idea of SRV to reflect social desires at any time and describehow that range interacts with the potential range of ecological conditions to create the HRVwhen looking at the past and the FRV when looking at the future.

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4. Methods for Describing Range of Variability

There is no universally accepted method for describing range of variability in landscape structuregiven the diversity of goals, data and analytical tools available, but the following interrelatedsteps are generally considered essential ingredients.

Step 1. Define the geographic area (landscape extent)

Choosing the appropriate geographic extent is of paramount concern when describing the ROVin landscape structure, especially if the description is to be quantitative. All quantitativemeasures of landscape structure vary with landscape definition -- especially spatial scale.Therefore, any quantitative assessment of ROV will ultimately be constrained by the spatialscale of the landscape. In general, as the spatial extent of the landscape increases, temporalvariability in landscape structure decreases – because larger landscapes are better able toincorporate and subsume the changes induced by disturbances than smaller landscapes. Giventhis, the choice of landscape extent will play a pivotal role in the quantitative measurement ofROV.

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In addition, the ROV concept is probably only relevant at intermediate landscape extents. Atrelatively small extents, the variation in landscape structure may be too large to be meaningful.Consider a small watershed subject to natural catastrophic disturbance events (e.g., wildfire) thatexceed the size of the watershed. The landscape structure is likely to vary from one extremecondition (e.g., 0% of the landscape in mature forest cover) to the other (e.g., 100% in matureforest cover) over time, rendering the “range” of variability to be meaningless. Conversely,consider a region two or three orders of magnitude larger than the largest disturbance events. Inthis case, the changes over time may be too small to register as significant and the landscape maybe in perfect steady-state equilibrium. In this case, ROV is meaningless. Ideally, the landscapeshould represent a logical ecological unit in which the ecological patterns and processes aretightly coupled, resulting in measurable and ecologically meaningful fluctuations in landscapestructure over time.

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One of the reasons that relatively large areas may exhibit a relatively small and stable range ofvariability is due to asynchrony in fluctuations in state variables across the broad landscape. Forexample, one area may experience a disturbance while another is recovering from a pastdisturbance. The net result may be a shifting mosaic of landscape conditions in which the overallvariability is dampened.

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Step 2. Define the reference period of time

A crucial part of describing the natural or historical range of variability is selecting the referencetime period used to characterize system dynamics. There is no single, widely applicable optimalperiod, and relevance is lost if too long a time period is used, because conditions such as climateand species composition may have changed drastically. Similarly, the ROV concept isinappropriate if too short a time period is used, because the landscape may not have had enoughtime to cycle through one or more rotations of the characteristic disturbance regime. Otherconsiderations in selecting the reference time period include the presence of exotic species,known climate changes, human influences, and record length and quality. Morgan et al. (1994)suggested that natural variability be assessed over relatively consistent climatic, edaphic,topographic, and biogeographic conditions. For the Interior Columbia Basin EcosystemManagement Project, for example, Hann et al. (1997) used the last 2000 yr as the appropriatetemporal depth, based on studies showing the vegetation in this area was in relative equilibriumwith the macroclimate and native Americans during that time.

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In our work on the San Juan National Forest in southwestern Colorado, we chose the period fromabout 1300 to the late 1800s, representing the period from Ancestral Puebloan abandonment toEuroAmerican settlement, as the reference or benchmark. This period is often referred to as theperiod of indigenous settlement, in contrast to the period of EuroAmerican settlement that beganin the mid to late 1800s (Romme et al. 2003). The period of several centuries prior to 1900represents 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 nowhave greatly altered many Rocky Mountain landscapes -- through fire suppression, grazing,road-building, timber cutting, recreation, and other activities. It cannot be emphasized toostrongly that the chosen reference period was not a time of stasis, climatically, ecologically, orculturally. For example, the “Little Ice Age” occurred during this time, and there were smallshifts in the position of the upper timberline and in the elevational breadth of the forest zone onthe middle slopes of the mountains. Local human inhabitants obtained horses and newtechnology and were affected by disease and displacement of other tribes brought about byEuropean colonization farther to the east. Nevertheless, compared with some other periods inhistory, the period from about 1300 to the late 1800s was a time of relatively consistentenvironmental and cultural conditions in the region, and a time for which we have a reasonableamount of specific information to enable us to model the system.

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Step 3. Define the state variables or system descriptors

Another important part of describing ROV is selecting the state variable or variables to use incharacterizing the system. While there are myriad possible ecological indicator variables, from alandscape ecological perspective, state variables are typically measures of landscape structure,representing either landscape composition or landscape configuration. The choice of landscapemetrics, however, is not a trivial one, as it requires a deep understanding of the metrics and theirlimitations. In general, the selected state variables should be: 1) measurable (readily quantified),2) representative (i.e., represent many ecological characteristics), and 3) appropriate (i.e., reflectthe goal of the analysis and be appropriate given the scope and limitations of the available data).

In most applications, the analysis is limited to measures of landscape composition, which tend tobe less sensitive to landscape definition issues associated with spatial resolution. The mostcommon application involves a categorical classification of vegetation into discrete communitiestypes and seral stages or stand condition classes. In this case, the percentage of the landscape ineach land cover class (combination of community type and seral stage) or the percentage of aparticular community type in each seral stage or condition class serve as state variables fordescribing the composition of the landscape at any point in time.

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Step 4. Define the approach for measuring the range of variability in the state variables.

Lastly, a method for quantifying the range of variability in the selected state variables for thedesignated time period and geographic area must be determined. There are a number ofcomponents to the approach that must be considered.

1. Empirical or Model.–A fundamental decision is whether to rely solely on empirical datacollected from field studies or to employ the use of a computer model to characterize ROV.There are significant tradeoffs with both approaches. The empirical approach might involve achronological sequence involving multiple records over time of an attribute in one place, or aspatial sequence involving multiple records of an attribute across similar places in space at onepoint in time. An empirical approach based solely on field data suffers in most cases fromseverely limited data availability. In addition, while field studies can provide information oncertain parameters of the historical disturbance regime (e.g., disturbance return intervals), theyare almost certainly unable to quantify aspects of landscape structure that are of paramountinterest to the characterization of HRV (e.g., the seral-stage distribution).

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The alternative modeling approach can 1) ensure spatial consistency, 2) include spatial effects,3) derive long time series, 4) expand spatially and temporally inconsistent data, 5) integratemultiple processes, and 6) include other factors (e.g., land use, exotics).

Perhaps the ideal approach is a combination, whereby field data are used to parameterize andhelp verify a simulation model. This is the approach most commonly used and boils down tochoosing a particular model.

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2. Statistical measure.–There are numerous statistical measures available for characterizing ROVin a state variable. Common metrics include mean, median, percentile, range, standard deviation(SD), coefficient of variation (CV), skewness, frequency, spatial arrangement, and size andshape distributions. Different attributes of interest require different descriptors. HRV has beendefined as: 1) the absolute range of a variable, 2) the range of means of a variable overconsecutive time steps across the reference period (an approach that ignores extremes), and 3)the overall mean for the reference period. The first approach, in theory, calls for continuousmeasurement of the variable of interest over time and demands a level of detail beyond mosthistorical reconstructions and is therefore generally only applicable with computer simulationapproaches. The range when used alone it may not be appropriate, because rare, extreme eventsdefine these bounds. Percentiles may be a more robust measure of ROV as they do not involveany underlying assumptions about the distribution (such as SD CV) and fully describe theempirical distribution. The second approach breaks the reference period into time steps (e.g., 10years) and estimates the average for the variable of interest for each time period and thensummarizes the range in these averages. This approach can miss the extremes but is lessdemanding in the details needed than the first approach. The third approach uses the overallmean for the reference period. This approach does not specifically attempt to estimate a range.Deviations from HRV in this case would be based on distance from the mean. Note, measures ofcentral tendency such as the mean and median are inappropriate descriptors of ROV when usedalone as they do not explicitly express variability – a fundamental aspect of the ROV concept.

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3. Tabular or graphical summary.–ROV can be expressed in either tabular or graphical form.Tabular results are often useful when multiple statistical measures are used and/or when multiplestate variables exist; i.e., when there are lots of metrics to report, and for comparative purposes;for example, when comparing HRV to FRV. Graphical summaries, however, are generally mucheasier to interpret than tabular summaries. Trajectory plots depicting the change in a statevariable over time is extremely effective in depicting the unaltered ROV. Statistical measuressuch as the mean and percentiles can be superimposed on the plot to provide additionalinformation.

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4. Univariate or multivariate summary.–ROV can be expressed separately for each state variable(i.e., univariate summary) or multivariate statistical techniques can be used to summarize ROV.The most common approach is to simply describe the ROV for each state variable separately.However, for approaches involving multiple, interrelated landscape structure metrics,multivariate approaches such as ordination can provide an effective means of summarizing theredundancy among metrics and extracting the major, independent landscape structure gradients.

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5. Planning and management applications

5.1. Understanding and evaluating change

ROV is a useful tool for understanding and evaluating change. Hypotheses about the drivers andmechanisms of ecosystem change can be developed and tested with spatial and temporal data.This understanding is helpful for predicting how ecosystems will change, even in response tonovel structures and processes, and nonnative species. Until we fully know how ecosystemsfunction, the past is one of the best means for understanding and predicting impacts to ecologicalconditions. ROV is an especially useful tool for communicating the concept that landscapes aredynamic to the public.

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5.2. Determining Current Departure

One of the principal applications of HRV is to provide a benchmark for evaluating the conditionof the current landscape; to determine whether the current landscape structure is “outside” itsHRV. Current departure is defined as “the degree of deviation of the current landscape from itshistoric range of variability, as defined.” Different approaches exist for examining currentdeparture. One approach is the Fire Regime Condition Class (FRCC) determination. FRCC is acategorical classification of the degree to which the current fire regime and composition andstructure of a vegetation community deviates from its historic range of variability (HRV) under adesignated reference period (FRCC website). The FRCC approach is being implemented by anInteragency and TNC (The Nature Conservancy) working group chartered and managed by theInteragency Fuels Committee and as a component under the Rocky Mountain Research Station(RMRS) Fire Effects Unit in association with the Fire Monitoring and Inventory system(FIREMON) and in parallel with the RMRS Rapid Assessment and LANDFIRE projects. FRCChas been an integral component of the development of a cohesive strategy for the restoration offire-adapted ecosystems and for the National Fire Plan within the Forest Service and USDI landmanagement agencies. FRCC has become a driving force behind current land managementactivities and is widely being used as the primary basis for identifying and prioritizing areas forecological restoration, including the reduction of wildfire risks associated with hazardous fuels.

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We developed an alternative approach for application on the San Juan National Forest insouthwester Colorado, which has subsequently been modified an adopted by the LANDFIREproject. Our approach, described in detail below, modified (and supplemented) the establishedFRCC approach in several important ways.

(1) Our approach was based on a spatially-explicit model (RMLANDS) of disturbance andsuccession that provides a much more realistic depiction of how disturbance processesoperate in this landscape than is possible with a nonspatial approach. In particular, our model simulates the initiation and spread of disturbance across the landscape in which thespread process interacts with landscape structure (e.g., terrain, vegetation-fuels, winddirection) to create disturbance patterns. The result is that landscape context (i.e., theconditions surrounding a burning cell, for example) has an important influence ondisturbance processes and ultimately has a strong influence on vegetation patterns anddynamics. This complexity cannot be captured in a nonspatial model.

(2) Our approach explicitly incorporates multiple disturbance processes. The establishedapproach emphasizes the role of wildfire. In contrast, we model the effects of severaldominant disturbance processes including wildfire in addition to several importantinsect/disease agents that interact to affect vegetation patterns and dynamics. Ultimately, weassess departure as the degree to which current vegetation patterns deviate from the historic

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range of variation in vegetation patterns, where wildfire is one of several processes drivingthose changes.

(3) Our approach explicitly incorporates the simulated range of variation in each parameter(see below) into the departure index, instead of calculating departure based on the mean. Theestablished approach does assume variability about the mean, but it assumes that the varianceis both symmetrical about the mean and the same for all parameters, an assumption that ishighly unlikely to be met in real landscapes. In contrast, we use the actual simulateddistribution of values and thus make no assumptions about symmetry and constant variance.Instead, we use percentiles of the simulated distribution to quantify the range of variability ineach parameter. The use of percentiles provides a standardize measure of variation thataccounts for differences in distribution shape and extent.

(4) Our approach generates a continuously-scaled departure index (ranging from 0-100),instead of a categorical index consisting of three classes. The continuous index preserves thefull amount of information and does not require the specification of arbitrary thresholdvalues for class assignment.

(5) Our approach adopts a multivariate perspective, instead of the bivariate one used in theestablished approach that assigns condition class on the basis of two variables: (1)vegetation-fuels departure, and (2) fire regime (frequency-severity) departure. First of all, wedecided not to evaluate fire regime departure due to the difficulties in clearly establishing thecurrent fire regime. Specifically, as there has been only one recent fire in the study area ofsignificant size (i.e., the Missionary Ridge fire, 2002, 20,093 ha) in which fire severitydetermination was made (a procedure also fraught with numerous methodological problems),we did not feel confident in establishing fire severity levels across cover types. Second, theestablished approach for evaluating vegetation-fuels departure involves comparing thecurrent seral-stage distribution for each cover type (i.e., percent of cover type in each seralstage) to the mean seral-stage distribution under the natural or historic reference period(obtained via a nonspatial model). Thus, vegetation-fuels departure is based on landscapecomposition (i.e., the abundance of each seral stage) only, without consideration of landscapeconfiguration (i.e., the spatial pattern, distribution or arrangement of seral stages). Ourapproach focuses exclusively on vegetation-fuels departure, but considers both compositionand configuration (evaluated using several different landscape metrics) as components of thedeparture index (as described below).

(6) Our approach directly addresses the issues of scale (i.e., landscape extent) and context(i.e., landscape location) in the evaluation of departure. While these issues are consideredimportant under the established approach, and some guidance is offered for selecting the“right” scale, there is nothing in the procedure that allows for direct examination of theseeffects. Our HRV departure analysis was done at several different spatial scales and fordifferent landscape contexts at each scale, as described in the previous section.

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Our approach involves evaluating departure separately for each cover type and for the aggregatelandscape as a whole, as follows:

Cover Type Departure:

Our procedure for determining departure for each cover type involves the following steps:First, we determined the HRV distribution in seral-stage distribution for each cover type. Weused RMLANDS and the simulations described elsewhere to simulate vegetation dynamicsunder the historical reference period. For each cover type, we quantified the range of variabilityin seral-stage distribution pooled across simulation runs, after excluding the first 100 yearequilibration period of each run (i.e., 5 runs x 70 timesteps = 350 snapshots). The seral-stagedistribution for a single snapshot was represented as the percentage of the cover type in eachseral stage (i.e., stand condition). For each seral stage, we summarized its range of variation bycomputing the 0th, 5th, 25th, 50th, 75th, 95th and 100th percentiles of the distribution of observedvalues. The 0th percentile represented the lowest observed percentage of the cover type in thecorresponding seral stage. In other words, over the course of the simulations, the relativeabundance of the corresponding seral stage was never less than the 0th percentile level. Similarly,the 25th percentile represented the percentage in that seral stage below which the landscape fell25% of the time, and so on for the other percentiles.

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Second, we determined the seral-stage distribution for the current landscape. For our purposes,the “current” condition refers to the landscape in 2003 after the Missionary Ridge fire. Wesimply computed the percentage of each cover type in each seral stage directly from the initial(input) grids (i.e., timestep 0 in the simulation) and then converted these percentages topercentiles of the HRV distribution by determining what percentage of the HRV distribution was$equal to the value for the corresponding seral stage. Thus, we converted the actual percentagein each seral stage to a percentile of the corresponding HRV distribution. If the actual percentagevalue was # the value of the 0th percentile of the HRV distribution, we assigned it the 0th

percentile (since all HRV values were greater than this value). Similarly, if the actual percentagevalue was $ the value of the 100th percentile of the HRV distribution, we assigned it the 100th

percentile.

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Third, we compared the current seral-stage distribution to the simulated HRV distribution todetermine the degree of departure. Specifically, we compared the current condition percentile tothe percentiles of the HRV distribution. It is important to note that we compared percentiles notthe actual percentages of area in each seral stage. This was done to standardize the measure ofdeparture as discussed above. We computed the magnitude of departure as follows. For eachcover type and seral stage, if the current condition percentile was between 25-75th percentile, wetreated it as well within the HRV distribution, and it was considered no further (i.e., it did notcontribute to the departure index, or, equivalently, it was given a weight of 0 in the departureindex). If the current condition percentile was <25th percentile, we computed the absolute valueof the difference (i.e., the number of percentiles <25). Similarly, if the current conditionpercentile was >75th percentile, we computed the absolute value of the difference (i.e., thenumber of percentiles >75). For example, if the current condition was 10 (percentiles), itreceived a score of 15 (25-10); if the current condition was 95, it received a score of 20 (95-75).Thus, each seral stage received a departure score that ranged between 0-25 based on the numberof percentiles it deviated outside the 25-75th percentile range.

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Fourth, for each cover type we computed the mean departure score across seral stages. Themean departure score was used to account for the varying number of seral stages among covertypes and to provide an overall measure of departure for each cover type in which equal weightwas given to all seral stages. Thus, each cover type received a departure score between 0-25 thatindicated its average degree of departure from the HRV distribution.

Fifth, we rescaled the mean departure score to range between 0-100 (by dividing by 25, themaximum score, and multiplying by 100) to create the final Seral-Stage Departure Index, wherea 0 indicates no departure from the 25-75th percentile of variation and a 100 indicates completedeparture (i.e., current landscape is outside the HRV). While this rescaling was not necessary, itallows for adjustments to the 25-75th percentile thresholds (for considering something as“departure”) without changing the range and interpretation of the final index. Thus, regardless ofwhether the 25-75 range or any other user-specified range is considered as “within the HRV”,the 0-100 interpretation of the final index remains the same.

Sixth, we repeated the process above for several class configuration metrics. Each classconfiguration metric represents a different aspect of the spatial character, distribution orarrangement of patches of the corresponding class, where the class represents a unique standcondition or seral stage associated with a particular cover type. Briefly, each class-levelconfiguration metric was evaluated for the degree of departure of the current landscape from the

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simulated HRV distribution. Thus, each cover type received a departure index (range 0-100) foreach metric, derived by averaging the scores across the seral stage classes. For each cover typewe computed the mean departure score across metrics to create the final Class ConfigurationDeparture Index, similar to the process for seral-stage departure.

Lastly, we combined the seral-stage and configuration departure indices into an overall CoverType Departure Index by taking the average of the two component indices. Note, the simplemean is not the only way to combine the component indices. The final index could just as easilybe computed as from the geometric mean, weighted arithmetic mean, or maximum componentindex, depending on the emphasis sought.

Landscape Structure Departure:

Our procedure for determining departure for full landscape involved the following steps:

First, we determined the HRV distribution in the areal extent of each unique combination ofcover type and seral-stage (i.e., stand condition) based on the RMLANDS simulations describedabove. Each unique cover type and seral stage was treated as a unique patch type (or class) inthis analysis, and the mosaic of resulting patches comprised the landscape mosaic in eachsnapshot (timestep) of the simulation. For each class (patch type), we summarized its range ofvariation by computing the 0th, 5th, 25th, 50th, 75th, 95th and 100th percentiles of the distribution ofobserved values in the same manner as described above.

Second, we determined the areal extent of patch type for the current landscape. We simplycomputed the percentage of each patch type directly from the initial (input) grids (i.e., timestep 0in the simulation) and then converted these percentages to percentiles of the HRV distribution bydetermining what percentage of the HRV distribution was # to the value for the correspondingpatch type. Thus, weconverted the actualpercentage in each patchtype to a percentile of thecorresponding HRVdistribution in the samemanner as describedabove.

Third, we compared thecurrent landscapecomposition to thesimulated HRVdistribution to determinethe degree of departure.Specifically, wecompared the current

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condition percentile to the percentiles of the HRV distribution for each patch type in the samemanner as described above.

Fourth, we computed the mean departure score across patch types. The resulting score rangedbetween 0-25 and indicated the average degree of departure from the HRV distribution.

Fifth, we rescaled the mean departure score to range between 0-100 (by dividing by 25, themaximum score, and multiplying by 100) to create the final Landscape Composition DepartureIndex, where a 0 indicates no departure from the 25-75th percentile of variation and a 100indicates complete departure (i.e., current landscape is outside the HRV).

Sixth, we repeated the process above for several landscape configuration metrics. Briefly, eachlandscape metric was evaluated for the degree of departure of the current landscape from thesimulated HRV distribution. Thus, the landscape received a departure index (range 0-100) foreach metric. We computed the mean departure score across metrics to create the final LandscapeConfiguration Departure Index, similar to the process for seral-stage departure.

Lastly, we combined the landscape composition and configuration departure indices into anoverall Landscape Structure Departure Index by taking the average of the two componentindices. Note, the simplemean is not the only wayto combine the componentindices. The final indexcould just as easily becomputed as from thegeometric mean, weightedarithmetic mean, ormaximum componentindex, depending on theemphasis sought.

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5.3. Specifying desired future condition

An understanding of HRV and current departure can provide a basis for forest managementpolicies that seek to mimic natural disturbance patterns in our logging, grazing, and otheractivities involving commodity production from public forest lands; that is, in establishing adesired future condition or future range of variability.

It should be emphasized, however, that the use of HRV and current departure does not suggestthat it should be our goal in management to recreate all of the ecological conditions anddynamics of the historical reference period. Complete achievement of such a goal would beimpossible in most cases, given the climatic, cultural, and ecological changes that have occurredin the last century. It also would be unacceptable socially, economically, and politically in mostcases. Nor does it suggest that the reference period was preferable in all ways to today’slandscape.

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Regardless, current agency implementation of ecosystem management relies heavily on the HRVand current departure concepts in defining target conditions for the full array of managed lands,from timber harvest areas to wilderness. Natural variability is also useful as a reference forsetting general management goals. Comparing current conditions, desired future conditions (anexpression of ecosystem conditions preferred by stakeholders and managers), and naturalvariability clarifies management direction. Maintaining situations where current and desiredconditions are within natural variability, or restoring current conditions to that state, are just twoof the many possible situations managers face. Desired future conditions may or may not beequivalent to either natural variability or current conditions. When they are not, desiredconditions may need to be reevaluated. The actions needed to move current conditions to desiredconditions, and the external subsidies required to maintain those desired conditions, need to beevaluated for their ecological and socioeconomic acceptability.

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6. Challenges in the Use and Interpretation of Natural Variability

Landres and colleagues identified three major barriers and/or challenges to the use of ROVconcepts.

6.1. Is natural variability relevant?

The primary criticisms against the use of natural variability include the following:

1. Native and contemporary people have so altered natural systems that there are no pristinenatural areas left on our planet, making information derived from the past difficult tointerpret or irrelevant.

2. Each point in time and space is unique, and dominant climate patterns are continuallychanging, therefore a description of past patterns and processes is largely irrelevant today orin the future.

3. Management goals based on natural variability seek to recreate past environments and thenmaintain those environments in a static condition.

The use of HRV does not require pristine conditions during the reference period. In general,understanding past conditions and the natural processes that influenced those conditions,regardless of the level of human impact, yields insight into why and how current conditionsdeveloped, and what changes might be expected in the future. Nevertheless, the use of HRV isperhaps most applicable when the human impact during the reference period was ecologicallyminor. In addition, the use of these concepts is not necessarily an attempt to simply mimic orrecreate the processes that occurred on a site long ago, or to return managed landscapes to asingle and unchanging past condition. Rather, it is an attempt to improve understanding about theecological context of an area and the landscape-scale effects of disturbance. This understandingmay then be used to make existing and future conditions more relevant and variable, and therebyecologically sustainable.

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6.2. Is there sufficient understanding about natural variability?

For many areas, there is insufficient and/or inadequate data for generating a reliablereconstruction of the historical landscape conditions, including the following specificdeficiencies:

1. Site-specific data are lacking for most areas, requiring extrapolation from other areas and agreat deal of expert opinion to fill the knowledge gaps.

2. There is insufficient temporal depth of data for most areas, precluding a reconstruction ofhistorical conditions for a sufficiently long reference period, and the estimates derived frompaleoreconstructions become more uncertain further back in time for several reasons, makingthe analysis of long-term trends difficult.

3. The spatial configuration and severity of disturbances are not usually identified withconfidence from historical data, resulting in a general lack of information about the spatialvariation of past conditions.

Our understanding of spatial and temporal dynamics in ecological systems will never becomplete. Consequently, in applying natural variability concepts, multiple sources ofinformation are needed, ranging from site-specific data and simulation models, to expertopinions and judgements. These disparate types of information allow for the forming and testingof hypotheses about how natural variability concepts can best be applied to managing ecologicalsystems. Ultimately, HRV results must be interpreted within the scope and limitations of theinformation sources.

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6.3. How are natural variability concepts used in managing dynamic systems?

Management systems are not geared for managing moving targets or coping with the uncertaintyand surprise that are inherent and fundamental aspects of ecological systems. Consequently,managing dynamic systems will allows be difficult and fraught with surprises. Adaptivemanagement has been proposed as the best strategy for managing systems with a high degree ofuncertainty.

Even in those cases where there is sufficient ecological understanding of how to manage theprocesses that drive dynamic systems, there may be insufficient social or political will tomaintain or restore these processes.

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