LAB EXERCISE #5 – Landscape Dynamics: Alternative Scenarios€¦ · simulate disturbance and succession processes affecting the structure and dynamics of Rocky Mountain landscapes

LAB EXERCISE #5 – Landscape Dynamics: Alternative Scenarios

Instructors: K. McGarigal

Overview: In this exercise, students will use the results of RMLands to quantify the dynamics inlandscape structure for a sample landscape under several alternative management scenariosrepresenting a factorial of climate scenarios, level of fire suppression and intensity of vegetationmanagement. Students will gain a practical understanding of scenario analysis as an aid to forestplanning and gain an appreciation for the challenges and limitations associated with theinterpretation of results. Specifically, students will learn how to conduct scenario analysis to comparethe potential impacts of alternative management strategies aimed at controlling fire andinsect/pathogen disturbance regimes.

Objectives

• To develop a practical understanding of the RMLands model framework, as an example of aLandscape Disturbance-Succession Model (LDSM).

• To gain practical understanding of how to use simulation modeling to evaluate alternative landmanagement scenarios for strategic assessment and planning.

• To gain practical understanding of the interactions among climate, fire suppression andvegetation management and the implications for ecosystem management.

• To gain practical experience conducting scenario analysis.

Model Background

In the lectures associated with this lab exercise, we gained an overall familiarity with HRV concepts,described the use of landscape disturbance-succession models (LDSMs) to quantify HRV, anddiscussed some of the practical challenges in using HRV to inform land management. In this lab, weare going to use the results of the Rocky Mountain Landscape Simulator (RMLands) to quantify therange of variability in landscape structure for a sample landscape in the Bitterroot Mountains ofwestern Montana. For practical reasons it is beyond the scope of this lab to describe the details ofRMLands and its parameterization for the case study landscape. Instead, a brief conceptual overviewof RMLands will have to suffice. In addition, because we are using an LDSM to estimate the “rangeof variability” for the study landscape, we are going to refer to it as the “simulated range ofvariability” or SRV to make it explicit that the result is from a simulation.

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Conceptual Overview

RMLands is a grid-based, spatially-explicit, stochastic landscape simulation model designed tosimulate disturbance and succession processes affecting the structure and dynamics of RockyMountain landscapes. RMLands simulates two key processes: succession and disturbance. Theseprocesses are fully specified by the user (i.e., via model parameterization) and are implementedsequentially within 10-year time steps for a user-specified period of time. Succession occurs at thebeginning of each time step in the simulation and represents the gradual growth and/ordevelopment of vegetative communities over time. Succession is implemented using a stochasticstate-based transition approach in which vegetation cover types transition probabilistically betweendiscrete states (conditions). Transition pathways and rates of transition between states are defineduniquely for each cover type and are conditional on several attributes of a vegetation patch. Thesepatches, as defined for succession, represent spatially contiguous cells having the same cell attributes(e.g., identical disturbance history and age). Most cover types progress through a series of standconditions (states) over time as a result of successional processes (albeit at different rates due to thestochastic nature of succession). In some cases, these transitions are affected by the occurrence ofcertain disturbances (e.g., low-severity fire) or are regulated by management (e.g., silviculture). Othercover types (e.g., meadows, barren, water) are treated as having a single, static condition and are notaffected over time by the interplay of disturbance and succession.

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Model Characteristics

RMLands is stand-alone program written entirely in Visual C++ for use in a Microsoft WindowsOperating System environment. RMLands expects input grids in Arc Grid (ESRI) format andrequires libraries from either ArcGIS or ArcView Spatial Analyst. RMLands was designed to makefull use of required Forest Service data, supported by systems such as NRIS, INFRA, and FACTS.Importantly, it does not require detailed stand inventory data. RMLands can be classified as a hybridstatistical/probabilistic model with the following distinguishing characteristics:

• Grid-based.--RMLands utilizes a grid-based data model in which the landscape isrepresented in a regular grid latticestructure. The grid structure allows forefficient and powerful spatial processing.Each grid cell (pixel), representing a fixedgeographic area, possesses a number ofecological attributes (e.g., cover type, age).Attributes possess multiple states (i.e.,unique values), many of which changeover time in response to succession anddisturbance.

• Spatially explicit.--Consistent with the grid structure, RMLands is a spatially-explicit model; gridcells are geographically explicit and topological relationships are important in all processes (e.g.,disturbance initiation and spread).

• Process-based.–RMLands simulates two key processes: disturbance and succession. Disturbanceprocesses include a variety of both natural and anthropogenic disturbances implemented in acommon fashion. Succession is based on a discrete state transition model for each cover type.

• Stochastic.--RMLands is a stochastic model; that is, there is an element of chance (oruncertainty) associated with the outcome of each process. For example, each cell has aprobability of initiation for each disturbance process that is contingent on several cell attributes.A probability less than 1 means that there is only a chance of a disturbance initiating. Thus,given the same cell attributes, some cells will initiate while others will not. There is a stochasticelement to nearly all processes in RMLands.

• Spatial scale.--The grid can be defined at any spatial resolution, although current applicationsutilize a relatively high resolution (25-30 m cell size) grid that allows for detailed representationof landscape patterns. In addition, while RMLands does not limit the extent of the landscape, itis most applicable to landscapes between 10,000's ha to over 1 million ha.

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• Temporal scale.–RMLands currently operates on a 10-yr time step and is most applicable tosimulating landscape dynamics over 100's to 1000's of years.

GIS Database

RMLands is designed to work in concert with an ArcGIS grid database, although the relationshipconsists solely of input/output. On the inputside, RMLands requires at a minimum 9separate Arc grids; 19 additional optional inputgrids can be used as well depending on theapplication. On the output side, RMLandsproduces a large number of optional grids,including 62 specific grids plus an unlimitednumber of additional grids based on user-specified reclassifications and/or rescalings ofthe cover-condition grid. A completedescription of these grids is included in aseparate document (see...\exercises\rmlands\parameters\RMLands_spatial_data.pdf).

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Succession

RMLands simulates succession using a simple state-based transition approach in which discretevegetation states are defined for each cover type. Succession involves the probabilistic transitionfrom one state to another over time and it occurs at the beginning of each time step in response togradual growth and development of vegetation over time. Transition probabilities are typically basedon the age of the stand (i.e., the time since thelast stand-replacing event), but they can bebased on any number of parameters such asthe abiotic setting or disturbance history. Eachcover type has a separate transition model thatuniquely defines its successional stages.

In contrast to disturbance, which isimplemented at the cell level, succession isentirely patch based. Specifically, each cellbelongs to a temporary patch defined ascontiguous (touching based on the 8-neighborrule) cells sharing the same values for each ofthe attributes used to define successionprobabilities. For example, if in a particular cover type transition model, age, time since low-mortality fire and aspect are all used to define transition probabilities, then contiguous cells with thesame values for these three attributes will be treated as a patch and undergo succession together.Note, successional patches are not static; they change over time in response to disturbance events,which can act both to break up single patches into several new patches and to coalesce severalpatches into a single patch by changing the disturbance history at the cell level. This patch-basedapproach for succession is necessary to avoid the salt-and-pepper effect of cell-based successiongiven that succession is implemented as a stochastic process.

Disturbance Processes

RMLands simulates a variety of natural and anthropogenic disturbances. Natural disturbances aresimulated with a generic disturbance process that can be parameterized for a wide variety ofdisturbance agents, includeing wildfire, a varietyof insects/pathogens (pinyon decline [pinyonips beetle and black stain root rot], pine beetlecomplex, Douglas-fir beetle, spruce beetle, andspruce budworm), and drought. Each naturaldisturbance process is implemented separately,but effects and is effected by other disturbanceprocesses operating concurrently to producechanges in landscape conditions. For example,the occurrence of beetle-killed trees derivedfrom the spruce beetle disturbance process canaffect the local probability of ignition andspread of wildfire.

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Each natural disturbance is modeled as astochastic process; that is, there is an elementof chance (or uncertainty) associated with theinitiation, spread, and ecological effects of thedisturbance. The disturbance algorithm iscommon among all natural disturbanceprocesses, however, it is parameterizeddifferently for each disturbance agent, andconsists of the following key components:

# Climate.–The climate plays a significantrole in determining the temporal andspatial characteristics of the disturbanceregime. Climate is specified as a globalparameter that optionally can effectinitiation, spread, and mortality of alldisturbances within a time step. Climate canbe specified as constant with a user-specified level of temporal variability, atrend over time (with variability), or as auser-defined trajectory - perhaps reflectingthe climate conditions during a specificreference period (e.g., as indexed using thePalmer Drought Severity Index).

# Initiation.–Disturbance events are initiatedat the cell level. Each cell has a probabilityof initiation in each time step that is afunction of its susceptibility to disturbanceand, optionally, its proximity to otherdisturbance events or landscape features(e.g., roads). Susceptibility to wildfire, forexample, is a function of cover type, standcondition, time since last fire, time since lastinsect outbreak, elevation, aspect, slope, androad proximity - factors that influence fuelmass and moisture and risk of human-caused ignition.

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# Spread.–Once initiated, the disturbance spreads to adjacent cells in a probabilistic fashion. Eachcell has a probability of spread that is a function of its susceptibility to disturbance (as above),which is modified by its relative position (e.g., relative elevation or wind direction) and theinfluence of potential barriers (e.g., roadsand streams). In addition, there is anoptional provision for the ‘spotting’ ofdisturbances during spread so thatdisturbances are not constrained tocontiguous spread only.

# Termination.–The spread is terminatedbased on a user-specified disturbance sizedistribution intended to reflect variableweather conditions associated with thedisturbance event that may cause adisturbance to terminate despite otherwisefavorable fuel conditions.

# Mortality.–Following spread, each cell isevaluated to determine the magnitude ofecological effect (i.e., severity) of thedisturbance. Each cell can exhibit high orlow mortality of the dominant plants. Highmortality occurs when all or nearly all(>75%) of the dominant plant individualsare killed. Cells are aggregated intovegetation patches for purposes ofdetermining mortality response, wherepatches are defined as spatially contiguouscells having the same cell attributes (e.g.,identical disturbance history and age).

# Transition.–Following mortalitydetermination, each mortality vegetationpatch is evaluated for potential immediatetransition to a new stand condition (state).Transition pathways and rates of transitionbetween states are defined uniquely foreach cover type and are conditional onseveral attributes at the patch level. Note,these disturbance-induced transitions aredifferentiated from the successionaltransitions that occur at the beginning ofeach time step in response to gradualgrowth and development of vegetationover time.

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RMLands simulates a variety of vegetation treatments. Treatments are implemented via managementregimes defined by the user. Management regimes are uniquely specified within management zones,or user-defined geographic units (e.g., urban-wildland interface verus interior). Management zonesare further divided into one or more management types based on cover type. Each cover type canbe treated separately or it can be combined with other cover types to form aggregate managementtypes. Each management type is then given aunique management regime, which consistsof one or more treatment types andassociated spatial and temporal constraints.Specifically, each management regime isdefined by the following components:

# Management zones.–Management zonesare simply optional user-definedgeographic units (e.g., urban-wildlandinterface verus interior) within which tospecify unique management regimes. Thegeographic units can represent anythingand there can be an unlimited number ofzones.

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# Management types.–Management zonescan be subdivided into managementtypes based on aggregations of covertypes. By default each cover type withina management zone is consideredseparately and optionally assigned atreatment regime. However, cover typescan be aggregated into managementtypes if they are to receive the sametreatment regime and you want toessentially ignore the cover typeboundaries during treatment unit layout.

# Treatment types and allocation.–Treatment intensity within amanagement regime is controlled bytreatment area; i.e., within each timestep treatments are implementedsubject to the availability of suitablearea and a maximum treatment areaconstraint. In addition, treatmentintensity is also optionally subject torestriction based on user-specifiedwatershed constraints. Specifically, oncea watershed exceeds a specifieddisturbance threshold, all furthertreatments can be prohibited in thatwatershed. The watershed disturbancethreshold is defined in terms of clearcutequivalence and is designed to reflectthe impact of disturbances (both naturaland anthropogenic) on water resources.Each combination of cover type anddisturbance type is given a clearcut-equivalent coefficient and recoverytrajectory over time. Treatmentsavailable for inclusion in a managementregime include a variety of silviculturalsystems associated with commercialtimber harvest and fuels treatment.Treatments included in a managementregime are implemented according to anallocation scheme in which a specifiedproportion of the total treatment area isallocated to each treatment type.

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Treatment types currently include the following (images not included):

! clearcut – single entry, regeneration cut removing >90% of the canopy and returning thestand to early seral.

! shelterwood – three-stage shelterwood; first entry is prep cut (no change in vegetation state);second entry is seed cut in which the bulk of the overstory is removed (change to open-canopy state) and regeneration is initiated; third entry is final cut in which the remainingoverstory is removed and the state transitions to early seral. The user-specified treatmentinterval determines the number of years between entries.

! group cut – similar to clearcut, except implemented in randomly-sized patches within a user-specified percentage of the treatment unit (i.e., treatment intensity) and harvested over timeaccording to the user-specified treatment intensity. For example, if the treatment period is100 years and the treatment interval is 20 years and the treatment intensity is 10%, then 10%of the unit will get harvested in randomly sized patches every 20 years over a period of 100years.

! thinning – single-tree selection designed to maintain an uneven-age stand structure andpromote continuous regeneration. Thinning involves both overstory and understoryremoval. A user-specified percentage of the treatment unit is thinned at the specifiedtreatment interval over the treatment period.

! mastication – mechanical mastication of understory vegetation (small diameter stems),primarily for the purpose of compacting and distributing the understory fuels to reduce thelikelihood of severe fire, within a user-specified percentage of the treatment unit (i.e.,treatment intensity). This has no affect on the overstory of woodlands and forests (i.e., itdoes not change the vegetation state from closed to open canopy), but it can be used toremove shrubland vegetation.

! prescribed burning – prescribed fire designed to be predominantly non-lethal surface fire toreduce fine fuels, but allowing for some lethal surface or crown fire in small patchesdesigned to open up the stand.

! matrix thin and group cut – combined group cuts within a user-specified percentage of thetreatment unit (i.e., treatment intensity) and overstory thinning of the rest of the unit.

! thin and burn – combined overstory thinning within a user-specified percentage of thetreatment unit (i.e., treatment intensity) followed by prescribed fire over the entire unitaimed at reducing fuels and maintaining an open-canopy condition over time.

! masticate and burn – combined understory mastication of small diameter material within auser-specified proportion of the unit (i.e., treatment intensity) followed by prescribed fireover the entire unit.

! hand cut, pile and burn – combined hand cutting and piling of small diameter material within auser-specified percentage of the unit (i.e., treatment intensity) followed by prescribed fire

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over the entire unit.

! thin and masticate – combined understory mastication of small diameter material within a user-specified percentage of the unit (i.e., treatment intensity) and overstory thinning of the entireunit.

! matrix thin, group cut and burn – combined group cuts within a user-specified percentage of theunit (i.e., treatment intensity) and overstory thinning of the rest of the unit, followed byprescribed fire over the entire unit.

! thin, masticate and burn – combined mastication of small diameter material within a user-specified percentage of the unit (i.e., treatment intensity) and overstory thinning of the entireunit, followed by prescribed fire over the entire unit.

! thin, hand cut, pile and burn – combined hand cutting and piling of small diameter materialwithin a user-specified percentage of the unit (i.e., treatment intensity) and overstorythinning of the entire unit, followed by prescribed fire over the entire unit.

# Static spatial constraints andpriorities.–For each treatment type in amanagement regime, static spatialconstraints limit where treatment isallowed. Spatial constraints can bedefined on the basis of several factors,including ownership, timber suitability,roadless areas, riparian buffer zones, firemanagement zones, road proximity, andslope. Each of these factors alone or incombination can restrict the potentialtreatment area. In addition, the initiationof treatment units within the potentialtreatment area can be prioritized basedon these factors. These spatialconstraints and priorities are static; thatis, they do not change over the course ofthe simulation.

# Dynamic suitability constraints.–Foreach treatment type in a managementregime, dynamic constraints limit wheretreatment is allowed in any particulartime step based on vegetationcharacteristics that change over time.Dynamic constraints can be defined onthe basis of stand condition class (i.e.,seral stage), stand age, or any other age-related attribute (e.g., age since last low

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mortality fire). These constraints aredynamic because they vary spatially over thecourse of the simulation.

The static and dynamic constraints andpriorities layers are combined in eachtimestep using a geometric mean todetermine the actual constraints andpriorities within each timestep. In thismanner, the static constraints and prioritieshave an overarching and constant effect onthe constraints and priorities and thedynamic constraints and priorities modifythem based on the current condition of thevegetation. The combined constraints andpriorities determine the probability oftreatment initiation and spread of thetreatment units as they are created from thepoints of initiation.

Importantly, the combined constraints andpriorities layer changes over time due tochanging vegetation conditions, and it differsamong treatment types within the sametimestep depending on the user-specifications.

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# Treatment units.–Treatment units arederived stochastically within eachmanagement zone and management typeaccording to the targeted treated area andallocation among treatment types, subjectto the constraints and priorities above,and the following parameters:

! Initiation and spread – individual unitsare created from a point of initiationwithin the correspondingmanagement zone and managementtype according to the correspondingconstraints and priorities, and thenthe unit spreads outward from thepoint of initiation based on the “resistance” conferred by the constraints and priorities layersuch that the unit will tend to grow through areas of higher priority and where constraintsrepresent absolute barriers to spread. There is also an optional “boundaries” layer that canimpose additional constraints on spread. The unit stops “growing” when there is no furthersuitable area to spread into (i.e., a constraint or boundary) or a user-specified maximum unitsize is achieved. The final unit must also exceed a user-specified minimum unit size as well.

! Minimum canopy cover constraint – this isan optional user-specified minimumcanopy cover constraint that must bemaintained or the unit is notimplemented. Specifically, thepotential unit is created and treatedto determine the resulting averagecanopy cover, and if this falls belowthe average canopy cover as specifiedin a user-defined minimum canopycover layer, then the unit is discarded(i.e., not treated).

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! Unit dispersion – this is the manner inwhich treatment units should bespatially dispersed across thelandscape. There are four options: (1)random - units are randomlydistributed, (2) aggregated withinwatersheds - units are randomlydistributed within user-specifiedwatersheds, (3) aggregated withincompartments - units are randomlydistributed within user-specifiedcompartments, and (4), aggregated bydistance – units are aggregated withina user-specified distance of the firstunit.

! Unit buffers and fallow periods – this is anoptional spatial buffer zone (inmeters) around a treatment unit inwhich no other treatments are allowedfor the duration of the designatedfallow period. The fallow period isonly meaningful if a buffer zone isdesignated.

! Treatment regime – each treatment typewithin a management zone andmanagement type is implementedaccording to a treatment regime (orprescription) that includes a treatmentperiod, treatment interval, andtreatment intensity. The treatmentperiod is the period (in years) underwhich the treatment unit stays under“management” and remains inviolateby other treatments. At the end of thetreatment period the unit is “released”and the cells are free to be selectedand incorporated into a new unit. Thetreatment interval is the interval (inyears) between individual treatments(cutting or burning) or stand entriesfor all treatment types that involveperiodic entries (see above). Lastly,treatment intensity is the percentage of the treatment unit to be treated with one of thetreatments included in the treatment type (see above). For example, for group cut itrepresents the percentage of the unit to be included in group cuts.

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! Vegetation transitions –the last step ofthe treatment unit implementation isthe vegetation transitions. Based onuser-specified disturbance transitionrules, each cell potentially undergoesa state transition (i.e., change to adifferent seral stage).

In summary, the vegetation treatmentmodule in RMLands is quite complex, whichprovides great flexibility for specifyingmanagement scenarios. A single managementscenario involves specifying one or moremanagement zones (i.e., geographic units),which are further subdivided into one ormore management types based on aggregations of cover types. Within each management type, atotal treatment intensity (i.e., maximum total treatment area) is specified and allocated among one ormore treatment types. A treatment regime is specified for each treatment type. The treatment regimehas many components, but includes specifying static spatial constraints and priorities (e.g., based ontimberland suitability, road proximity, ownership, etc.) dynamic suitability constraints (e.g., based onseral stage, age and disturbance history), constraints on treatment unit size, dispersion (i.e., random,aggregated or dispersed), adjacency (i.e., buffer width and fallow period), and temporal attributesassociated with rotation period and treatment interval.

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Detailed Instructions

Step 1. Establish the objective of the analysis

The first step (as always) is to establish the objective of the analysis. Our overall objective is toevaluate the impact of alternative land management strategies on landscape dynamics and, morespecifically, to assess the interaction of climate, fire suppression and vegetation management. Here,we will focus on the following specific question:

How do climate, fire suppression and vegetation management potentially interact to effect landscape dynamics, andwhat are the potential ecological and socio-economic consequences of these interactions?

Step 2. Define the digital landscape

The next step is to define the landscape. First, let’s get familiar with the sample landscape and theassociated GIS data. The sample landscape is the 47,058 ha Prospect Creek watershed in theBitterroot Mountains of western Montana. The landscape is mostly public land managed by the LoloNational Forest, although there is some private land holdings in the lower elevation valleys. Thislandscape supports a wide range of environmental gradients producing a forest diverse in vegetationand disturbance processes, and is part of a much larger case study landscape encompassing theBitterroot Mountains.

Open up in GoogleEarth the project file “...\exercises\scenarios\scenarios.kml”

Take some time to survey the study area in GoogleEarth.

Next, let’s define the digital landscape. To meet the objective above (and for purposes of this labexercise), we defined the landscape as follows:

• We selected a single sample landscape from the broader case study landscape in the BitterrootMountains based on the following criteria: 1) landscape extent large enough to incorporatemeaningful landscape dynamics given the scale of the major disturbance processes, yet smallenough to be computationally efficient for lab use, 2) a heterogeneous mixture of land usepractices, including developed lands with a wildland-urban interface, a mixture of public andprivate lands dominated by the former, and an adequate road network to facilitate futurevegetation treatments, and 3) a logical ecological unit, in this case, a watershed, meeting theabove criteria.

• We classified the sample landscape into land cover classes based on the LANDFIRE project.Specifically, land cover classes represent unique biophysical settings (BpS) or potentialvegetation types (PVT). See “...\exercises\scenarios\Z10_BpS_Model_Descriptions.pdf” forthe descriptions of each BpS class. The only significant change we made to this classificationscheme was to combine three separate BpS classes corresponding to “riparian” settings into asingle “riparian” class. Note, it is beyond the scope of this exercise to describe how these BpS’sclasses were derived, but full documentation is available at the LANDFIRE website. Note, notall BpS classes found in the case study landscape are present in the sample landscape. See“...\exercises\scenarios\composition.xls” for the land cover composition of Prospect Creek

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basin in comparison to the case study landscape.

• As noted above, the spatial scale of the sample landscape was established in part to meetcomputational efficiencies for the lab, thus it is smaller than we might otherwise prefer. Thespatial grain (or resolution) of the landscape was set at 30 m, consistent with the spatialresolution of the data sources used in the LANDFIRE project. The spatial extent of thelandscape was based on the hydrological watershed of Prospect Creek, a tributary of Clark ForkRiver; however, for simulation purposes we included a 2-km wide buffer zone around the basin,bringing the total extent of the simulation landscape to 69,293 ha.

A wealth of GIS data is available for this landscape.

Open up in ArcMap the project file “...\exercises\scenarios\scenarios.mxd”

Take some time to review each of layer for the purpose of familiarizing yourself with the landscape– the instructor will guide you through this process. Most of these layers are used by RMLands inthe simulation. See the document entitled “...\exercises\scenarios\ RMLands_spatial_data.pdf” fora detailed description of each layer.

The single most important layer is the cover grid, since it establishes a static vegetation templatewithin which vegetation seral stages change over time in response to disturbance and successionprocesses. This is a land cover GRID at 30 m spatial resolution classified into biophysical settings(BpS) from a variety of remotely sensed data sources, including LANDSAT images, terrain variablesand meteorological variables. Note, this data layer is based principally on the BpS layer produced bythe LANDFIRE project, although it has been modified slightly here to also include currentdevelopment (e.g., agriculture and urban), large roads (i.e., highways), and large rivers. In addition,three separate BpS classes corresponding to “riparian” settings have been combined into a single“riparian” class. See the document entitled“...\exercises\scenarios\Z10_BpS_Model_Descriptions.pdf” for the descriptions of each BpS class.

Questions to ponder:2.1 What are the tradeoffs and/or limitations imposed by the chosen landscape definition? Is the thematic content and

resolution of the landscape appropriate for the stated objective? How would you modify the thematic content andresolution and why? Is the spatial extent and resolution appropriate for the stated objective? How would youmodify the spatial scale of the analysis to better meet the stated objective and why?

Step 3. Design the simulation experiment (i.e., alternative scenarios)

The next step is to set up an experimental design to evaluate the interaction among climate, firesuppression and vegetation management. While there are numerous possibilities, for the purpose ofthis exercise we established an incomplete 2x2x2 factorial of climate, fire suppression and vegetationmanagement, as follows:

Climate Scenario Fire SuppressionVegetation Management

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No Treatment Vegetation Treatments

Historic No hc-letburn-notreat(session.id=5)

none

Yes hc-suppress-notreat(session.id=6)

none

Future No fc-letburn-notreat(session.id=7)

none

Yes fc-suppress-notreat(session.id=8)

fc-suppress-vegtreat(session.id=1)

We created these scenarios (models) in advance based on the criteria below; unfortunately, thedetailed model parameterization for each scenario can only be examined by having RMLandsinstalled on your computer and loading the models into RMLands (see...\exercises\scenarios\models\model.name from the table above), which is likely to be beyond thescope of this exercise (but see the instructor if you want to try to install the software).

Climate Scenarios

We created two climate scenarios representing a contrast between “historic” and “potential future”climate conditions, as follows:

Historic Climate.–In this scenario, we parameterized the climate modifier in RMLands for thevarious disturbance processes as follows:

• Wildfire – based on the historic record as represented by the mean Palmer DroughtSeverity Index, averaged over 5 sample locations in the vicinity of the case studylandscape for each 10-year interval for the period 1600-1900 (note, this sequence wasrepeated to create the 1,000 year time series needed for the simulation).

• Pine beetle – based on the historic record as represented by the cumulative threshold PalmerDrought Severity Index, averaged over 5 sample locations in the vicinity of the casestudy landscape for each 10-year interval for the period 1600-1900, as above. Note, thecumulative threshold PDSI is based on the maximum cumulative consecutive years ofdrought within each 10-year interval, but is thresholded so that timesteps with an index< 1 are set to 0, preventing any disturbances from occurring. This results in periodic orepisodic outbreaks (or epidemics) against a background of endemic levels of disturbance.

Future Climate.–In this scenario, we parameterized the climate modifier as above except that themean climate value was increased by 10% (from 1 to 1.1). Note, there are many possiblealternative future climate scenarios. This particular scenario represents the case in which thefrequency and severity of drought conditions conducive to burning and bark beetle outbreaks isincreased by 10%.

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Fire Suppression

We created two fire suppression scenarios representing a contrast between a “let burn” policy andan “aggressive fire suppression” policy, as follows:

Let burn.–In this scenario, we employed a “let burn” policy and simulated no fire suppression.In this case, no suppression means that the frequency, size and severity of wildfires is based onthe HRV disturbance regime.

Suppress.–In this scenario, we employed an aggressive fire suppression policy. Unfortunately,the best way to emulate fire suppression effects is not entirely clear; there are many possibilities.For the purpose of this exercise, we assumed that fire suppression per se does not change thefrequency of ignitions or the severity of fires, although indirectly it will likely increase both overthe long term if the vegetation becomes more susceptible with age. Instead, we assumed that firesuppression directly effects the probability of fire spread, and thus directly influences thedistribution of fire sizes. To emulate this effect, we modified the size distribution of fires in thespread parameters for wildfire as follows:

Size (ha)Percent of Fires

Letburn Suppress

1 76.25 92.25

10 12.00 4.00

100 6.00 2.00

1,000 3.00 1.00

10,000 1.50 0.50

100,000 0.75 0.25

Note, the changes here are designed to emulate a fire suppression policy that is reasonably butnot perfectly effective in preventing the spread of fires. Consequently, while the distribution isconsiderably compressed to the left (more smaller fires), large fires (including the maximum firesize) are still simulated under the suppression scenario, but with a much reduced probability.One could certainly imagine other possible scenarios.

Vegetation Management

We created two vegetation management scenarios representing a contrast between “no treatment”and “aggressive vegetation treatment”, as follows:

No treatment.–In this scenario, we employed a “do nothing” policy and simulated no activevegetation management.

Vegetation treatment.–In this scenario, we employed an aggressive vegetation management

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policy. There are myriad options for vegetation treatments in RMLands; however, in the contextof this lab it is impractical to consider more than one vegetation treatment scenario. Thus, weattempted to emulate the current National Forest management focus on: (1) fuels reduction inthe wildland-urban interface (WUI) and (2) salvage of timber following large-scale disturbanceevents. Other current management objectives, such as ecosystem restoration and timber standimprovement, were considered as secondary objectives and were addressed only indirectly as by-products of the vegetation treatments aimed at the primary objectives.

The parameterization of the vegetation treatments is quite complex and requires considerableunderstanding of RMLands. Rather than try to describe the detailed parameterization, asummary of the important distinctions are give below:

(1) Wildland-urban interface (WUI) zone [MZ-1 in the output]:

Objective.–WUI treatments were designed primarily to reduce fuels, and thus reduce the riskof high severity fire and improve the likelihood of effective fire suppression, and secondarilyto restore ecosystem structure and function to something akin to the historic referencecondition.

Spatial constraints and priorities.–Treatments were excluded from private lands, unsuitabletimberland (as designated), riparian zones, and roadless areas. All other lands wereconsidered eligible for treatments.

Intensity.–The goal was to treat all eligible lands with the WUI, which amounts toapproximately 15,000 ha. Recognizing that even under the best circumstances, it is highlyunlikely or even undesirable to get 100% of the eligible land under treatment, we insteadtargeted 12,000 ha, with a target of 3,000 ha of land treated per decade on a 40-yeartreatment interval. Thus, the goal was to get up to 12,000 ha of land treated every 40 years.However, there are numerous factors conspiring against meeting this target. For example, ifwe target closed-canopy forest conditions for treatment (which we did), it is likely thatfollowing extensive low-mortality wildfire (which by and large converts closed-canopy forestto open-canopy forest), there may considerably less area suitable for treatment or in need oftreatment – no need to treat lands that mother nature treats for us. In addition, if weconstrain treatments to sufficiently large contiguous areas of eligible lands containingsuitable forest conditions for logistical and economic reasons, there will be locations andtimes when patches of eligible forest are simply too small and too scattered for efficienttreatment. Thus, the targeted “maximum treatment area per timestep” should not beinterpreted as a hard target to be met at all costs, but rather as a flexible target that variesdepending on the vegetation conditions and which is probably never met due to numerousother constraints.

Treatment regime.–Two silvicultural treatments were employed: (1) “restoration” treatments,which involves the combination of individual tree removal (i.e., basal area reduction) andprescribed underburning (i.e., low mortality fire); and (2) “individual tree selection”, whichinvolves individual tree removal without under burning. Treatment units were distributed inaggregated fashion in units of 4-200 ha.

Lab. 5 Pg. 20

(2) Non-wildland-urban interface (non-WUI) zone [MZ-2 in the ouput]:

Objective.–Non-WUI treatments were designed primarily to salvage timber following majorwildfires and insect outbreaks.

Spatial constraints and priorities.–Treatments were excluded from private lands, unsuitabletimberland (as designated), riparian zones, and roadless areas. All other lands wereconsidered eligible for treatments.

Intensity.–Given spatial constraints above, approximately 17,000 ha of land are eligible fortreatments in the non-WUI zone. The goal was to salvage up to a maximum of 2,000 ha inany decade experiencing extensive wildfires and/or insect outbreaks.

Treatment regime.–The basic silvicultural treatments included a combination of “clearcut”and “individual tree selection” in RMLands. Both treatments were single entry treatmentswithout follow-up. Treatment units were distributed in aggregated fashion in units of 4-40 hafor clearcut and 10-200 ha for individual tree selection.

Questions to ponder:3.1 Given your current understanding of RMLands, and Landscape Disturbance-Succession Models in general, what

would be an alternative strategy for simulating climate change that is significantly different than the oneimplemented in this experiment?

3.2 Similarly, what would be an alternative strategy for simulating fire suppression that is significantly different thanthe one implemented in this experiment?

3.3 Similarly, what would be an alternative strategy for simulating vegetation treatments that is significantly differentthan the one implemented in this experiment?

Step 4. Run the simulations and quantify landscape structure

The next step is to run the simulations and quantify the structure of the simulated landscapetrajectories. Given the limitation of this lab, it is not feasible or that important for you to learn howto parameterize and run RMLands. Moreover, it is time-consuming and somewhat tedious to runthese eight simulations and analyze the output using Fragstast. Thus, we have already done this foryou in advance, as follows:

• RMLands simulations.–Each scenario was run for 2,000 years. The cover-condition (covcond)output grids plus a suite of grids associated with wildfires and pine beetles were saved in aseparate folder for each scenario (...\exercises\scenarios\results\scenario).

• FRAGSTATS analysis.–The cover-condition (covcond) grids output from each scenario wereanalyzed with FRAGSTATS. For the purposes of this exercise, we chose a broad suite ofmetrics encompassing a variety aspects of landscape pattern, including both structural andfunctional metrics at the class and landscape level. For the functional metrics, we applied theedgedepth and edge contrast weights constructed based on expert opinion. All the files used tocomplete the FRAGSTATS analysis are included in ...\exercises\scenarios\fragstats. At the classlevel, we enabled the classes corresponding to two cover types: (1) mixed-conifer forest-ponderosa pine-Douglas-fir and (2) mesic-wet spruce-fir forest and woodland. The

Lab. 5 Pg. 21

FRAGSTATS output files have been saved and are included in ...\exercises\scenarios\results.The landscape and class metrics are included in fragout.land and fragout.class, respectively.

Questions to ponder:4.1 Given your current understanding of RMLands and the disturbance scenarios implemented in this experiment,

what is your prediction for the relative effect of future climate on landscape structure? Specifically, which aspects oflandscape structure will be effected and how will they be effected?

4.2 Similarly, what is your prediction for the effect of fire suppression on landscape structure? Specifically, whichaspects of landscape structure will be effected and how will they be effected?

4.3 Similarly, what is your prediction for the relative effect of your vegetation treatment scenario on landscapestructure? Specifically, which aspects of landscape structure will be effected and how will they be effected?

Step 5. Examine the results

The final step is to examine the results. There are numerous ways to examine the results, graphicallyand statistically, as follows.

5.1 View simulation grids

The first thing we can do is visually examine the simulation output grids. This can be a challengingand time-consuming task due to the many different output grids available. Here, we will focus onexamining only a small subset of the spatial data layers produced by the simulation.

(1) Perhaps the easiest way to view the simulation is as a slide show. Here, we will view a slide showof the simulation based on the cover-condition, wildfire mortality, and mountain pine beetlemortality grids in which each slide represents a 10-year time step. To view a slide show of any ofthese output grids for any of the scenarios, simply navigate to the desired folder containing images(e.g,. ...\exercises\scenarios\results\movies\fc-letburn-notreat\covcond\ and right click on the firstimage and choose “preview”. With any luck, it will open with Windows Picture and Fax Viewer andyou can advance through the slides at your own pace:

(2) A second more tedious option is to view the output grids themselves in ArcMap. While notnecessary, if you want to view the grids in this way, open up the ArcMap project(...\exercises\scenarios\scenarios.mxd) and load any of the stored output grids in the correspondingresults folders.

5.2 Summarize results using R

The second thing we can do is examine the results statistically. For this purpose, we will use the Rlanguage and statistical computing environment (note, it is assumed that you have pre-installed theR software). Note, detailed instruction on the use of R is beyond the scope of this exercise, so theinstructor will guide you through the following steps.

Open the R interface: Start ( Programs ( R ( R 2.4.0 (or higher)

For the series of R functions used in this exercise, you can execute these functions from the script provided. To open the provided R script, do the following:

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From the R console:Select ‘open script’ from the ‘file’ drop-down menu: File ( Open script

Navigate to and open the following file:...\exercises\scenarios\scripts\rmlstats.calls.R

To execute a line of the script, do either of the following:• With the cursor on the desired line, enter control R from the keyboard• With the cursor on the desired line, right click mouse and select ‘run line or selection’

Now let’s look at the results of the simulation. To begin, we need to load the RMLands R statslibrary (rmlstats.R):

source('.../exercises/scenarios/scripts/rmlstats.R')

Important: Note the use of forward slashes ‘/’ instead of the customary backslash.

The rmlstats.R library includes a number of scripts that can be used to create pre-formatted tablesand graphs. For this exercise we will focus on a several different tables and graphs useful forcomparing scenarios. Each table and/or graph is produced by calling one of the pre-existing rmlstatsfunctions, as described below.

In using the rmlstats functions below, note the following:

• Most of the functions contain the same set of arguments, e.g., session=, start.step=,stop.step=, and var=, so once you get used to using these arguments to modify the query, itis basically the same for all functions.

• All of the functions contain a session= or scenario= argument that allows you to specifysingle or multiple scenarios. In all cases, the single scenario query produces a different tableand/or plot than the multiple scenario query. In general, it is useful to first produce a singlescenario query in order to familiarize yourself with the nature of the output and thenproduce a multiple scenario query to compare scenarios. Ultimately, the multiple scenarioqueries will be most useful for this exercise.

• The multiple scenario queries can be done using all of the existing scenarios (dy default) orany specific set of scenarios. Given that we have 5 scenarios, the plots may be too clutteredto visualize effectively. It may be more useful to focus on specific comparisons. Forexample, to examine the effect of climate on the simulated range of variability (SRV) inlandscape structure, it may be useful to compare scenarios that differ only in climate, such as5 vs 7 or 6 vs 8.

• Most of the functions have an outfile= argument, which is a logical; i.e., if outfile=TRUE,then output files will be generated and saved to disk using a default naming convention. Thedefault is always outfile=FALSE.

1. Area disturbed summary.–Examine the tabular and graphical summary of the total area

Lab. 5 Pg. 23

disturbed by timestep for each natural disturbance process and scenario, as follows:

darea('.../exercises/scenarios/results/',session=5,start.step=1)

A separate table and graph is produced for each disturbance process. • To advance through the disturbance processes, simply hit the return key (you may have to

click the cursor in the console window before hitting enter).• To save the graph as a bitmap for use in a presentation, right click on the graph and select

“copy as bitmap” and then paste directly into your presentation.

To change the first timestep to be displayed, change the start.step= argument.

To change the scenario to be displayed, change the session number using the table in step 3.

Alternatively, it may be more effective to compare particular scenarios in single plot, byspecifying a vector of session ids, as follows:

darea('.../exercises/scenarios/results/',session=c(7,8),start.step=1)

Or you can omit the session= argument altogether and all the unique session id’s in thedarea.csv file will be compared.

darea('.../exercises/scenarios/results/',start.step=1)

Note, when multiple sessions are specified, this function produces a clustered bar chart, witheach cluster representing a different scenario (session id) and the bars representing the meanarea disturbed per timestep (by default) for low, high and any mortality disturbances.

To view the ‘minimum’, ‘maximum’, or ‘median’ area disturbed per timestep, instead of thedefault ‘mean’, specify the var= argument, as follows:

darea('.../exercises/scenarios/results/', start.step=1,var=’median’)

2. Treated area summary.–Examine the tabular and graphical summary of the total area treated pertimestep for each management type and management zone, as follows:

tarea('.../exercises/scenarios/results/',session=1,start.step=1)

Note a couple of things. First, the treated area represents the acreage actually treated during atimestep, not the total area under management at any point in time. Second, the tarea.csv tablethat this function calls only contains records for the vegetation treatment scenarios (session 1),so any calls to the function with other sessions will produce an error. Lastly, the codes reportedin the output correspond to the codes used to parameterize the model, as follows:

MZ1 = Wildland-urban interface (WUI) zone• MZ1-15 = includes individual tree selection treatment of the cool-moist forest cover

types (e.g., western hemlock-western red cedar, dry-mesic spruce-fir forest and

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woodland, and mesic-wet spruce-fir forest and woodland) within the WUI.• MZ1-11 = includes restoration treatment of the warm-dry forest cover types (e.g., mixed

conifer forest-ponderosa pine-Douglas fir) combined within the WUI.MZ2 = Non WUI zone

• MZ2-11 = includes clearcut salvage treatments and individual tree selection salvagetreatments in all forest cover types combined outside the WUI.

To change the first timestep to be displayed, change the start.step= argument.

3. Rotation period summary.–Examine the tabular summary of the observed rotation period bycover type for each disturbance process and scenario. Only the cover types eligible for aparticular disturbance (as defined in the model) are displayed. Rotation period is defined as thenumber years required to disturbed an area equal in size to the area under consideration (e.g.,extent of a particular cover type) and it is equivalent to the overall point-specific mean returninterval (MRI) for the area under consideration.

rotation('.../exercises/scenarios/results/',session=5,start.step=1,step.length=10)

A separate table is produced for each disturbance process with the following summary: • Total area (ha) for each eligible cover type and for all eligible cover types combined.• Rotation period (years) for low mortality, high mortality and any mortality disturbance for

each eligible cover type and for all eligible cover types combined.

To change the first timestep to be displayed, change the start.step argument.


Alternatively, it may be more effective to compare all of the scenarios in single table and savethe output to a csv file, as follows:

rotation('.../exercises/scenarios/results/',start.step=1,step.length,outfile=TRUE)

Note, when multiple sessions are included, this function produces a table in which the columnsrepresent the different scenarios.

The default table compares the rotation period for ‘any mortality’ disturbances (any.mort). Tocompare ‘low’ or ‘high’ mortality rotation periods, specify the var= argument as follows:

rotation('.../exercises/scenarios/results/',start.step=1,step.length=10,var='low.mort',outfile=TRUE)

Although though you were not responsible for calibrating the model (this was done in advanceby the instructors), it is useful to examine how well the simulation results agree with certainexpectations or targets for key parameters. Although there is no consensus on the best way tocalibrate LDSMs, for our purpose, it is most useful to compare the observed disturbancerotation periods with the user-specified target rotation periods, as follows:

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rotation('.../exercises/scenarios/results/',session=5,start.step=20,step.length=10)

Note, here we arbitrarily changed the start.step to 20 (200 years) to account for the modelequilibration period.

Compare the simulation results with the following wildfire targets specified by expert teams aspart of the Landfire project. Again, it would be prudent to pay attention to only those covertypes with substantial area in the simulation landscape. Note, the values in the table below aremean wildfire return intervals or rotation periods in years.

Cover type mri.low.mort mri.high.mort mri.any.mort

Riparian 133 200 80

Mixed Conifer Forest-Ponderosa Pine-Douglas fir 23 300 21

Mixed Conifer Forest-Larch 50 200 40

Mixed Conifer Forest-Grand Fir 100 220 69

Subalpine Woodland and Parkland 270 400 161

Western Hemlock-Western Red-cedar Forest 133 200 80

Western Hemlock-Western Red-cedar: Cedar Groves 2365 385 334

Dry-Mesic Spruce-Fir Forest and Woodland 400 200 133

Mesic-Wet Spruce-Fir Forest and Woodland 3420 180 172

Montane-Foothill Deciduous Shrubland 400 100 80

Columbia Plateau Low Sagebrush Steppe 143 250 91

Montane Sagebrush Steppe 400 100 80

Lower Montane-Foothill-Valley Grassland na 17 17

Subalpine-Upper Montane Grassland na 150 150

Conifer Swamp na 400 400

Montane Douglas-fir Forest and Woodland 54 75 31

4. Cover-condition (seral stage) range of variability and departure summary.–Examine the tabularsummary of the simulated range of variability (SRV) in cover-condition and the departure of thecurrent condition from the SRV, as follows:

covcond('.../exercises/scenarios/results/',session=5,start.step=1,cover.names=c('Mesic-Wet Spruce-Fir Forest and Woodland','Mixed Conifer Forest-PonderosaPine-Douglas fir'))

Two summary tables are produced, as follows:

Cover-condition class statistics.–This table provides a summary of the simulated range ofvariability in the distribution of area among condition classes (i.e., seral stages) for each covertype and the departure of the current landscape from the simulated range of variability for eachcondition class, and has the following fields:• covcond.id = unique numeric code for each cover-condition class (corresponds to grid)• cover.type = cover type name (from the cover.csv table)

Lab. 5 Pg. 26

• condition.class = condition class name (from the condition.csv table)• SRV0% – SRV100% = simulated range of variability in percent of the cover type comprised

of the corresponding condition class based on percentiles of the simulated distribution,where SRV0% = the 0 percentile or the minimum simulated value observed, SRV25% =th

the 25 percentile of the simulated distribution, and so on.th

• SRV.cv = simulated range of variability coefficient of variation. This is a measure of relativevariability computed by taking the difference between the 95 and 5 percentiles andth th

dividing by the median (50 percentile), and multiplying by 100 to convert to a percentage.th

This measure is analogous to a standard coefficient of variation which is computed by takingthe standard deviation divided by the mean.

• current.%cover = current landscape condition regarding the percent of the cover typecomprised of the corresponding condition class.

• current.%SRV = current landscape departure from the simulated range of variation, definedhere as simply the percent of the simulated distribution less than the current landscape value.

Cover type SRV departure.–This table provides a summary of the computed cover type departureindex (CTDI). This index represents the overall departure of a cover type from the simulatedrange of variability in the distribution of area among condition classes. This table has thefollowing fields:• cover.type = cover type name (from the cover.csv table)• total area = total area (ha) of cover type• SRV departure = cover type departure index, ranging from 0 (no departure) to 100 (outside

the simulated range of variability)

To change the first timestep to be displayed, change the start.step argument.



covcond('.../exercises/scenarios/results/',start.step=1,cover.names=c('Mesic-WetSpruce-Fir Forest and Woodland','Mixed Conifer Forest-Ponderosa Pine-Douglasfir'),outfile=TRUE)

Note, when multiple sessions are included, this function produces a table in which the columnsrepresent the different scenarios.

The default table compares the seral stage distribution for the ‘median’ simulated range ofvariability (srv50%). To compare other SRV quantiles (srv0%, srv5%, srv25%, srv75%, srv95%,srv100%) or the SRV coefficient of variation (srv.cv), specify the var= argument, as follows:

covcond('.../exercises/scenarios/results/',start.step=1,cover.names=c('Mesic-WetSpruce-Fir Forest and Woodland','Mixed Conifer Forest-Ponderosa Pine-Douglasfir'),var='srv.cv',outfile=TRUE)

5. Cover-condition (seral-stage) distribution trajectory.–Examine the trajectory of change in the

Lab. 5 Pg. 27

seral-distribution, as follows:

covcond.plot('.../exercises/scaling/results/',session=5,start.step=1,step.length=10,cover.names=c('Mesic-Wet Spruce-Fir Forest and Woodland','Mixed ConiferForest-Ponderosa Pine-Douglas fir'))

A separate plot is produced for each cover type, depicting its seral stage distribution over time.Specifically, for each timestep, the proportion of area in each condition (seral stage) class isdepicted as a separate color in a 100% stacked bar chart.

Change the start.step to 0 in order to visualize the equilibration period (and current departure) inthe seral stage distribution.


Alternatively, it may be more effective to compare all of the scenarios in single plot, as follows:

covcond.plot('.../exercises/scenarios/results/',start.step=1,session=5,start.step=1,step.length=10,cover.names=c('Mesic-Wet Spruce-Fir Forest and Woodland','MixedConifer Forest-Ponderosa Pine-Douglas fir'))

Note, when multiple sessions are included, this function produces a clustered bar chart, witheach cluster representing a different scenario (session id) and the bars representing the SRV 50%(by default). Specifically, the default plot compares the seral stage distribution for the ‘median’simulated range of variability (srv50%). To compare other SRV quantiles (srv0%, srv5%,srv25%, srv75%, srv95%, srv100%) or the SRV coefficient of variation (srv.cv), specify thevar= argument, as follows:

covcond.plot('.../exercises/scenarios/results/',start.step=1,step.length=10,cover.names=c('Mesic-Wet Spruce-Fir Forest and Woodland','Mixed ConiferForest-Ponderosa Pine-Douglas fir'),var='srv.cv')

6. Landscape metric range of variability and current departure.–Examine the tabular summary ofthe SRV in landscape pattern and current departure based on the FRAGSTATS landscape-levelmetrics, as follows:

fragland(path='c:/work/landeco-umass/exercises/scenarios/results/', infile='fragout.land',LID.path='D:\\landeco\\exercises\\scenarios\\results\\', scenarios='hc-letburn-notreat',start.step=1,outfile=TRUE)

Note that LID.path is the path listed in the FRAGSTATS output file in the LID column but onlyup to but including the scenario folder. In addition, note the change in the argument name from‘sessions’ to ‘scenarios’ and that scenarios are referenced by their name not their session IDnumber (due to differences in the RMLands and FRAGSTATS output).

Note, the structure of this table is very similar to that of the covcond table (see descriptionabove), the only difference being that instead of unique cover-condition classes (rows), we have

Lab. 5 Pg. 28

landscape metrics. The landscape departure index (LDI) is computed in the same manner as the covertype departure index (CTDI) and represents the average departure among landscape metrics.

To change the scenario to be displayed, change the scenario name using the table in step 3.


fragland(path='c:/work/landeco-umass/exercises/scenarios/results/', infile='fragout.land',LID.path='D:\\landeco\\exercises\\scenarios\\results\\', start.step=1,outfile=TRUE)

Note, when multiple scenarios are specified, this function produces a table in which the columnsrepresent the different scenarios. Specifically, the table compares the ‘median’ simulated range ofvariability (srv50%) in each landscape metric. To compare other SRV quantiles (srv0%, srv5%,srv25%, srv75%, srv95%, srv100%) or the SRV coefficient of variation (srv.cv), specify thevar= argument as follows:

fragland(path='c:/work/landeco-umass/exercises/scenarios/results/', infile='fragout.land',LID.path='D:\\landeco\\exercises\\scenarios\\results\\', start.step=1,var='srv.cv',outfile=TRUE)

7. Landscape metric trajectory.–Examine the trajectory of change in each landscape metric, asfollows:

fragland.plot(path='c:/work/landeco-umass/exercises/scenarios/results/', infile='fragout.land',LID.path='D:\\landeco\\exercises\\scenarios\\results\\', scenarios='hc-letburn-notreat',start.step=1,step.length=10)

This function produces a figure for each landscape metric depicting its simulated trajectory ofchange over time. In addition, the value of the metric for the current landscape and the 5 , 50 ,th th

and 95 percentiles of the simulated distribution are plotted as horizontal lines.th

Change the start.step to 0 in order to visualize the equilibration period (and current departure) ineach landscape metric.

To change the scenario to be displayed, change the scenarios name using the table in step 3.


fragland.plot(path='c:/work/landeco-umass/exercises/scenarios/results/', infile='fragout.land',LID.path='D:\\landeco\\exercises\\scenarios\\results\\', start.step=1,step.length=10,cex.legend=1)

By omitting the scenarios= argument altogether all the unique scenarios in the fragout.land filewill be compared. Or you can view particular scenarios by specifying a vector of scenario names.

Lab. 5 Pg. 29

8. Landscape metric probability density functions.–Examine the probability density functions foreach landscape metric, as follows:

fragland.pdf.plot(path='c:/work/landeco-umass/exercises/scenarios/results/',infile='fragout.land',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',scenarios='hc-letburn-notreat',start.step=1)

This function produces a figure for each landscape metric depicting its probability density forthe period designated. In addition, the value of the metric for the current landscape and the 5 ,th

50 , and 95 percentiles of the simulated distribution are plotted as vertical lines.th th



fragland.pdf.plot(path='c:/work/landeco-umass/exercises/scenarios/results/',infile='fragout.land',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',start.step=1,cex.legend=1)


9. Class metric range of variability and current departure.–Examine the tabular summary of theSRV in landscape pattern an departure based on the FRAGSTATS class-level metrics, asfollows:

fragclass(path='c:/work/landeco-umass/exercises/scenarios/results/',inland='fragout.land',inclass='fragout.class',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',scenarios='hc-letburn-notreat', gridname='covcond',start.step=1,classes=c('MW_SF:LATE_OP','MCF_PPDF:LATE_OP'),metrics=c('PLAND','ED','AREA_AM','GYRATE_AM','SHAPE_AM','CORE_AM', 'DCORE_AM','CAI_AM','CWED','TECI','CLUMPY','IJI','AI'),outfile=TRUE)

This function produces a separate table for each unique class (cover-condition class) in theFRAGSTATS class output file; otherwise, each table is exactly like the one for the landscapemetrics. The classes= argument allows you to specify which classes you want to computestatistics for and the metrics= argument allows to specify which metrics to display. Leaving theseargument off altogether of setting them to NULL will produces statistics for all classes and allmetrics.



Lab. 5 Pg. 30

fragclass(path='c:/work/landeco-umass/exercises/scenarios/results/',inland='fragout.land',inclass='fragout.class',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',gridname='covcond',start.step=1,classes=c('MW_SF:LATE_OP','MCF_PPDF:LATE_OP'),Metrics=c('PLAND','ED','AREA_AM','GYRATE_AM','SHAPE_AM','CORE_AM', 'DCORE_AM','CAI_AM','CWED','TECI','CLUMPY','IJI','AI'),outfile=TRUE)

To compare other SRV quantiles (srv0%, srv5%, srv25%, srv75%, srv95%, srv100%) or theSRV coefficient of variation (srv.cv), specify the var= argument, as follows:

fragclass(path='c:/work/landeco-umass/exercises/scenarios/results/',inland='fragout.land',inclass='fragout.class',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',gridname='covcond',start.step=1,classes=c('MW_SF:LATE_OP','MCF_PPDF:LATE_OP'),Metrics=c('PLAND','ED','AREA_AM','GYRATE_AM','SHAPE_AM','CORE_AM', 'DCORE_AM','CAI_AM','CWED','TECI','CLUMPY','IJI','AI'),var=’srv.cv’,outfile=TRUE)

10. Class metric trajectory.–Examine the trajectory of change in each class-level metric, as follows:

fragclass.plot(path='c:/work/landeco-umass/exercises/scenarios/results/',inland='fragout.land',inclass='fragout.class',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',scenarios='hc-letburn-notreat', gridname='covcond',start.step=1,step.length=10,classes=c('MW_SF:LATE_OP','MCF_PPDF:LATE_OP'),metrics=c('PLAND','ED','AREA_AM','GYRATE_AM','SHAPE_AM','CORE_AM', 'DCORE_AM','CAI_AM','CWED','TECI','CLUMPY','IJI','AI'))

Note, this function produces figures similar to fragland.plot, but in this case a separate figure isproduced for each combination of cover-condition class and class metric.



fragclass.plot(path='c:/work/landeco-umass/exercises/scenarios/results/',inland='fragout.land',inclass='fragout.class',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',gridname='covcond',start.step=1,step.length=10,classes=c('MW_SF:LATE_OP','MCF_PPDF:LATE_OP'),metrics=c('PLAND','ED','AREA_AM','GYRATE_AM','SHAPE_AM','CORE_AM', 'DCORE_AM','CAI_AM','CWED','TECI','CLUMPY','IJI','AI'),cex.legend=1)

Lab. 5 Pg. 31

By omitting the scenarios= argument altogether all the unique scenarios in the fragout.class filewill be compared. Or you can view particular scenarios by specifying a vector of scenario names.

11. Class metric probability density functions.–Examine the probability density functions for eachclass metric, as follows:

fragclass.pdf.plot(path='c:/work/landeco-umass/exercises/scenarios/results/',inland='fragout.land',inclass='fragout.class',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',scenarios='hc-letburn-notreat',gridname='covcond',start.step=1,classes=c('MW_SF:LATE_OP','MCF_PPDF:LATE_OP'),metrics=c('PLAND','ED','AREA_AM','GYRATE_AM','SHAPE_AM','CORE_AM',

'DCORE_AM','CAI_AM','CWED','TECI','CLUMPY','IJI','AI'))

This function produces a figure for each cover-condition class and class metric depicting itsprobability density for the period designated. In addition, the value of the metric for the currentlandscape and the 5 , 50 , and 95 percentiles of the simulated distribution are plotted asth th th

vertical lines.



fragclass.pdf.plot(path='c:/work/landeco-umass/exercises/scenarios/results/',inland='fragout.land',inclass='fragout.class',LID.path='D:\\landeco\\exercises\\scenarios\\results\\',gridname='covcond',start.step=1,classes=c('MW_SF:LATE_OP','MCF_PPDF:LATE_OP'),metrics=c('PLAND','ED','AREA_AM','GYRATE_AM','SHAPE_AM','CORE_AM',

'DCORE_AM','CAI_AM','CWED','TECI','CLUMPY','IJI','AI'),cex.legend=1)


Lab. 5 Pg. 32

Assignment

As a team, complete the exercise above and discuss the findings. Each team will be assigned toanswer one of the following sets of questions in an oral report. Note, be sure to read the questionscarefully and answer all components. Also, be sure to support each of your conclusions using theresults examined above; a single example will suffice in most cases.

IMPORTANT, in the questions below, as appropriate, focus on the following two cover types(biophysical settings): mesic-wet spruce-fir forest and woodland (MW_SF) and mixed conifer forest-ponderosa pine-Douglas fir (MCF_PPDF) and on the late-development, open-canopy seral stage(LATE_OP).

Report #1:1. Is there a detectable model equilibration period in landscape composition, based on cover type seral-stage

distributions, and landscape structure, based on the Fragstats landscape-level metrics, and what is your bestestimate of the length of the period (in years)?

2. Does the model equilibration period differ among cover types? If so, why do you think some cover types equilibratefaster than others?

3. Consider the possibility of the model not equilibrating, at least over some time frame (e.g., 1,000 years). Actually,equilibration is essentially guaranteed in a stationary model over a long enough timeframe, despite the high level ofstochasticity, but consider nonequilibrium dynamics nonetheless. If the model does not equilibrate over a giventimeframe, what are the implications for quantifying the range of variability in landscape structure? What mightbe the causes of nonequilibrium dynamics?

4. Does the existence of a model equilibration period provide an indication of current departure? In other words, doesthe very existence of the equilibration period indicate that the current landscape is outside its historic range ofvariability (HRV)? Or could it reflect other properties of the simulation and, if so, which?

5. With regards to model calibration, how well do the simulation results agree with the user-specified targets? Arethere any notable differences and, if so, what might be the cause of the discrepancies? What other results besides thesimulated disturbance rotation periods might you look at to calibrate the model, and why? Discuss at least one.

Report #2:1. What was the relative effect of fire suppression and vegetation management on the simulated disturbance regime?

Specifically, what was the magnitude of change in the amount of mountain pine beetle and wildfire disturbanceunder each scenario and did suppression or vegetation management have the greater effect? Hint: examine thedarea() and/or rotation() results.

2. What was the relative effect of fire suppression and vegetation management on the simulated seral-stagedistribution of mesic-wet spruce-fir forest and woodland (MW_SF) and mixed conifer forest-ponderosa pine-Douglas fir (MCF_PPDF) and did suppression or vegetation management have the greater effect? Hint: examinethe covcond() and/or covcond.plot() results and consider both the median seral-stage distribution (srv50%) and thevariability over time (srv.cv) for these two cover types.

3. What was the relative effect of fire suppression and vegetation management on the simulated landscape structuredynamics and did suppression or vegetation management have the greater effect? Hint: examine the fragland() andfragclass() results and consider both the median landscape structure (srv50%) and the variability over time (srv.cv)for one or more landscape metrics. Note, you don’t need to discuss each metric and class; instead, pick one (or afew) metrics and a class that appears representative of the patterns (and that you can interpret) and discuss it asan example.

4. In the context of ecosystem management, describe the ecological and/or management implications of the results.

Lab. 5 Pg. 33

There are no sideboards on this question. Here, you need to identify one major ecological or management concernand discuss the implications of the results for that concern.

5. Identify and describe the major challenges and/or limitations in quantifying and interpreting these simulationresults.

Report #3:1. What was the relative effect of climate change and vegetation management on the simulated disturbance regime?

Specifically, what was the magnitude of change in the amount of mountain pine beetle and wildfire disturbanceunder each scenario and did climate change or vegetation management have the greater effect? Hint: examine thedarea() and/or rotation() results.

2. What was the relative effect of climate change and vegetation management on the simulated seral-stage distributionof mesic-wet spruce-fir forest and woodland (MW_SF) and mixed conifer forest-ponderosa pine-Douglas fir(MCF_PPDF) and did climate change or vegetation management have the greater effect? Hint: examine thecovcond() and/or covcond.plot() results and consider both the median seral-stage distribution (srv50%) and thevariability over time (srv.cv) for these two cover types.

3. What was the relative effect of climate change and vegetation management on the simulated landscape structuredynamics and did climate change or vegetation management have the greater effect? Hint: examine the fragland()and fragclass() results and consider both the median landscape structure (srv50%) and the variability over time(srv.cv) for one or more landscape metrics. Note, you don’t need to discuss each metric and class; instead, pick one(or a few) metric and a class that appears representative of the patterns (and that you can interpret) and discuss itas an example.

4. In the context of ecosystem management, describe the ecological and/or management implications of the results.There are no sideboards on this question. Here, you need to identify one major ecological or management concernand discuss the implications of the results for that concern. As an example, you might discuss what kind ofwildlife species, in terms of life history characteristics and habitat associations, would benefit most and, conversely,be adversely impacted most under each scenario

5. Identify and describe the major challenges and/or limitations in quantifying and interpreting these simulationresults.

Lab. 5 Pg. 34

Documents

LAB EXERCISE #5 – Landscape Dynamics: Alternative Scenarios€¦ · simulate disturbance and succession processes affecting the structure and dynamics of Rocky Mountain landscapes