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Range-Wide Threats to a Foundation Tree Species fromDisturbance InteractionsAuthor(s): Whalen W. Dillon , Ross K. Meentemeyer and John B. VoglerRichard C. Cobb , Margaret R. Metz and David M. RizzoSource: Madroño, 60(2):139-150. 2013.Published By: California Botanical SocietyDOI: http://dx.doi.org/10.3120/0024-9637-60.2.139URL: http://www.bioone.org/doi/full/10.3120/0024-9637-60.2.139

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RANGE-WIDE THREATS TO A FOUNDATION TREE SPECIES FROMDISTURBANCE INTERACTIONS

WHALEN W. DILLON, ROSS K. MEENTEMEYER, AND JOHN B. VOGLER

Center for Earth Observation and Department of Forestry and Environmental Resources,North Carolina State University, 5106 Jordan Hall, Raleigh, NC 27695

[email protected]

RICHARD C. COBB, MARGARET R. METZ, AND DAVID M. RIZZO

Department of Plant Pathology, One Shields Ave, University of California,Davis, CA 95616

ABSTRACT

The geographic range of tanoak, Notholithocarpus densiflorus (Hook. & Arn.) Manos, Cannon & S.H. Oh (Fagaceae), encompasses tremendous physiographic variability, diverse plant communities, andcomplex disturbance regimes (e.g., development, timber harvest, and wildfire) that now also includeserious threats posed by the invasive forest pathogen Phytophthora ramorum S. Werres, A.W.A.M. deCock. Knowing where these disturbance factors interact is critical for developing comprehensivestrategies for conserving the abundance, structure, and function of at-risk tanoak communities. In thisstudy, we present a rule-based spatial model of the range-wide threat to tanoak populations from fourdisturbance factors that were parameterized to encode their additive effects and two-way interactions.Within a GIS, we mapped threats posed by silvicultural activities; disease caused by P. ramorum;human development; and altered fire regimes across the geographic range of tanoak, and weintegrated spatially coinciding disturbances to quantify and map the additive and interacting threatsto tanoak. We classified the majority of tanoak’s range at low risk (3.7 million ha) from disturbanceinteractions, with smaller areas at intermediate (222,795 ha), and high (10,905 ha) risk. Elevated risklevels resulted from the interaction of disease and silviculture factors over small extents in northernCalifornia and southwest Oregon that included parts of Hoopa and Yurok tribal lands. Our resultsillustrate tanoak populations at risk from these interacting disturbances based on one set ofhypothesized relationships. The model can be extended to other species affected by these factors, usedas a guide for future research, and is a point of departure for developing a comprehensiveunderstanding of threats to tanoak populations. Identifying the geographic location of disturbanceinteractions and risks to foundation species such as tanoak is critical for prioritizing and targetingconservation treatments with limited resources.

Key Words: Decision support system, fire, forest ecosystems, foundation species, landscapeepidemiology, Notholithocarpus densiflorus, sudden oak death, tree disease.

Ecological disturbance regimes play an integralrole in ecosystem dynamics by altering resourceavailability, modifying ecosystem structures, andcreating new landscape spatial patterns (e.g.,Mou et al. 1993; Spies et al. 1994; Turner et al.2003). Increasing global connectivity, populationgrowth, and climate change are rapidly alteringdisturbance regimes, resulting in the emergence ofnovel disturbance interactions with pronouncedimpacts to socio-ecological systems from local toglobal scales (Turner 2010). Interacting distur-bance regimes that alter the abundance andstructure of foundation species effectively disruptthe fundamental ecosystem processes that theysupport and stabilize, such as clean water,decomposition, and carbon sequestration (Cha-pin et al. 1997; Ellison et al. 2005), which are vitalfor maintaining the physical, social, and econom-ic health of human populations (Costanza et al.1997). Recognition of the relationships andimpacts of disturbance interactions by local,regional, and global stakeholders is necessary to

manage for the resiliency of foundation speciesand their functions (Folke et al. 2004). Researchexplicitly addressing the effects of interactingdisturbances in forest ecosystems has recentlyincreased (e.g., Bebi et al. 2003; Buma andWessman 2011; Metz et al. 2011), but we stillunderstand relatively little about their impacts.The landscape heterogeneity and spatial extentover which forest disturbances occur challengesthe development of ecosystem conservationstrategies, presenting a pressing need to developtools that engage and guide stakeholders inachieving conservation objectives across thesebroad areas.

Accessibility to and familiarity with geographicinformation system (GIS) technologies (e.g.,GPS-enabled smartphones and online mapping)has become more widespread in recent years,increasing the potential to effectively bringstakeholders together in addressing complexconservation management problems. Spatiallyexplicit models developed using GIS can be

MADRONO, Vol. 60, No. 2, pp. 139–150, 2013

utilized as spatial decision support systems toidentify the geographic location and potentialseverity of disturbance events, providing anessential tool for threat assessment and manage-ment. This adaptable framework can be appliedto identify at-risk populations when the modelsare built using known biological relationshipsand sound ecological theory, even when knowl-edge of the precise relationships is scant. Forexample, Andersen et al. (2004) developed a riskmodel of threats to biodiversity across a large,heterogeneous landscape by examining relativerisk of land use factors on several resident taxa,and Meentemeyer et al. (2004) similarly mappedthe risk of establishment and spread of a forestpathogen (Phytophthora ramorum S. Werres,A.W.A.M. de Cock) across California. Mappingthe threat to the abundance and structure offoundation species from disturbance interactionshelps guide stakeholders in developing andimplementing effective conservation strategiesthat protect vital ecosystem services.

Tanoak, Notholithocarpus densiflorus (Hook. &Arn.) Manos, Cannon & S. H. Oh (Fagaceae), isa foundation species threatened with functionalextinction by multiple interacting disturbancesthroughout large portions of its range. This treeis, a dominant component of the ecosystems itinhabits (Cobb et al. this volume), has uniqueecological characteristics in coastal Californiaforests (Bergemann and Garbelotto 2006; Wrightand Dodd this volume), and as the lonerepresentative of its genus it is a significantcontributor to regional as well as global biodi-versity. This shade-tolerant tree can form multi-storied forest canopies with other dominantoverstory species, providing important standstructure for wildlife, such as the spotted owl(Strix occidentalis; LaHaye et al. 1997; Northet al. 1999). Tanoak acorns are a traditionallyimportant nutrition source for Native Americans,and the trees were a principal source of bark-extracted tannins until the advent of chemicaltanning compounds in the 19509s (Bowcutt 2011).Since the collapse of the tanoak bark market,perspective on this species has shifted from animportant forest commodity to an impediment toproduction of more valuable timber species, suchas redwood, Sequoia sempervirens (Lamb. ex D.Don) Endl., and Douglas-fir, Pseudotsuga men-ziesii (Mirb.) Franco. Since the 19509s, mostapplied forest research on tanoak has focused ontechniques to reduce competition with timberspecies, primarily through herbicide applicationsto reduce tanoak prevalence and biomass inforests managed for timber production (Harring-ton and Tappeiner 2009; Bowcutt 2011). In thelast ten years, Phytophthora ramorum, and theresulting disease sudden oak death, has emergedas a major cause of tanoak mortality in centralcoastal California, and increasingly in northern

coastal California and southwest Oregon (Rizzoet al. 2005; Meentemeyer et al. 2008; Vaclavıket al. 2010). Phytophthora ramorum has a largearray of hosts, but susceptibility, impacts ofinfection on host health, and the competency ofhosts to transmit infection varies dramaticallyover the 137 regulated native and exotic hostspecies (APHIS 2012).

Silviculture, disease, development, and alteredfire regimes are arguably the major disturbancesthreatening the abundance, function, and persis-tence of tanoak throughout its range. Silvicultur-al practices have explicitly suppressed tanoak topromote the growth of conifer species; humanpopulation expansion has resulted in conversionof forested land to development; and fire regimeshave been altered from historic baselines (Hav-lina et al. 2010). In addition to these disturbances,tanoak is being severely impacted by P. ramorumon local to regional scales (Meentemeyer et al.2008; Meentemeyer et al. 2011; Cobb et al.2012b). Tanoak readily supports sporulation ofP. ramorum and can be rapidly killed followinginfection by this pathogen (Hansen et al. 2008).Several other common native species also supportsporulation, but do not die following infection,most notably California bay laurel, Umbellulariacalifornica (Hook. & Arn.) Nutt. (DiLeo et al.2009). Thus, the distribution of bay laurel andtanoak strongly influences the risk of pathogenestablishment and disease emergence (Meente-meyer et al. 2004; Davidson et al. 2008; Cobb etal. 2010). The geographic variation and extent ofeach of these disturbances present major chal-lenges to the conservation of tanoak ecosystems.

We present a spatially explicit model toquantify and map the area threatened by thesefour disturbances and their interactions acrossthe geographic range of tanoak. This hypothesis-based modeling approach is readily integratedwith adaptive management strategies. Thus, asknowledge of the system grows, the parameters ofour model can be adjusted in accordance withevolving goals and the efficacy of treatments.

GEOGRAPHIC RANGE

The geographic range of tanoak stretchescontiguously along the Pacific coast from afour-county area in southwestern Oregon in thenorth, to Monterey County, CA, in the south,with disjunct populations occurring in the SierraNevada foothills to the east. Our analysisexcluded an isolated population occurring nearSanta Barbara, CA (Tappeiner et al. 1990),because the data we used for tanoak abundanceand area estimates (Lamsal et al. 2011) wereincomplete in this region. Tanoak’s geographicrange possesses tremendous physiographic vari-ability and complex disturbance regimes, and isbroadly characterized by a Mediterranean-type

140 MADRONO [Vol. 60

climate with cool, wet winters and warm, drysummers. Tanoak occurs from sea level toroughly 2190 m, with greater abundance inforests on the leeward side of the Coast Range(Lamsal et al. 2011).

MODEL DEVELOPMENT

We developed a rule-based spatial model toquantify and map the relative threat to tanoakpopulations from four disturbance factors occur-ring throughout tanoak’s geographic range. Thisheuristic approach is akin to a mental shortcut,where empirically undefined relationships can behypothesized and examined. In order to param-eterize the relative threat to tanoak from eachdisturbance factor, we classified the threat level ofeach factor at a particular location into an integerranking system from zero to three, with zerorepresenting no threat and three representing highthreat. At each location, we assigned each factora low (1), intermediate (2), or high (3) threatranking by breaking the range of values greaterthan zero into three equal intervals. We alsoassigned a weight value to each ranked distur-bance factor indicating its relative importance as athreat to tanoak (Table 1). We based these weightvalues on our interpretations of published re-search and expert knowledge evaluating theimpacts of each disturbance to tanoak. They canbe altered within the modeling framework toexplore other hypotheses of the impacts to tanoakfrom these disturbances. We calculated multipli-cative two-way disturbance interactions for eachlocation, assigning an additional exponential termto weight the interaction that we hypothesizedas representing the greatest threat to tanoak(Table 1). For each location, we then selectedthe highest-valued interaction for inclusion in the

final threat calculation to visualize and highlightareas with the most at-risk populations for the setof hypotheses (i.e., weights) being examined.

The equation used to calculate potential threatto tanoak from these disturbance factors is thesum of the products of each factor’s rank andimportance weight, plus the highest valuedinteraction at each mapped location (grid cell):

Pj~Xn

i

WiRijz½W ai Ra

ij|W bi Rb

ij�y ð1Þ

where P is the calculated risk for a grid cell inthe model output, Wi is the weight of the ithdisturbance factor, Rij is the rank value of the ithfactor at location j, and y is the weight assignedto the interaction of two factors. The interactionweight (y) is determined by which pair of

weighted factors, W ai Ra

ij and W bi Rb

ij, occur

together at a given location (Table 1). Thesuperscripts of these parameters (a and b) ensurethat a factor is never multiplied by itself. Wedeveloped maps of each disturbance factor andoverlaid them with a tanoak abundance surface(Lamsal et al. 2011). We then applied theinteraction model (Eq. 1) in a GIS environmentto generate a map of at-risk tanoak populationsfrom these interacting disturbances across thegeographic range. We classified the model outputinto a 1–3 threat-level ranking by breaking thecalculated values at equal intervals. Similar to theindividual factors, we qualitatively labeled thethreat levels as ‘Low’ (1), ‘Intermediate’ (2), and‘High’ (3). The lack of zero values in the finalmap output demonstrates that at least one factorwas present at every estimated tanoak location.

QUANTIFYING NUMBER OF TANOAK AT RISK

We quantified the area and number of tanoaktrees in each threat category by intersecting theoutput of equation 1 with maps of tanoak densityproduced by Lamsal et al. (2011). These datawere further organized by county to aid toregional decision making. We then multipliedthe county-level tanoak areas (hectares) in eachthreat level by the average number of tanoak perhectare in each county derived from dataprovided by Lamsal et al. (2011). This producedthe estimated number of tanoak in each threatcategory for each county in the study system. Wereport on a limited number of areas in this paper,with more detailed county summaries availablefrom the authors.

DISTURBANCE FACTORS

Silviculture

Beginning in the early to mid-20th century,silviculture in Oregon and California strongly

TABLE 1. THE WEIGHTS ASSIGNED TO INDIVIDUAL

DISTURBANCE FACTORS AND THE INTERACTIONS IN THE

INITIAL MODEL BASED ON OUR INTERPRETATIONS OF

RELEVANT LITERATURE AND EXPERT OBSERVATIONS.Each disturbance factor was first standardized to a 0–3ranking so that assigned weights used in the modelingcalculations reflect their relative importance as a threatto tanoak.

Disturbance Weights

silviculture 30disease 25development 10fire 5

Interaction Interaction weight

disease x fire 2disease x silviculture 2disease x development 1fire x development 1development x silviculture 1fire x silviculture 1

2013] DILLON ET AL.: RANGE-WIDE THREATS 141

favored softwood conifer species at the expenseof hardwood species, especially tanoak (Bowcutt2011). Most notable are broad-scale applicationsof herbicide to reduce tanoak competition withtimber species in these forests. Tanoak vigorouslysprouts following cutting and can reduce thegrowth of planted or naturally regeneratingconifers (Harrington and Tappeiner 1997; Lor-imer et al. 2009). Herbicide applications areeffective in reducing tanoak cover and increasingthe growth and dominance of coniferous timberspecies (Tappeiner et al. 1987; Harrington andTappeiner 2009). When applied as a broadcastspray from aircraft, or at very high efficiencies byground crews, it is reasonable to expect thesepractices would result in functional extinction oftanoak at local scales. For these reasons, thesilviculture risk factor received a weight of 30, thehighest weight (Table 1).

We developed the silviculture risk factor layerusing Forest Inventory and Analysis (FIA)(USDA 2008) data for plots in California andOregon where tanoak was reported (n 5 565).FIA surveys record evidence of silviculturaltreatments that affect areas of one acre or more;however, they do not specify herbicide applica-tion. We assumed the following about these data:1. silviculture activities included suppression ofundesirable species, i.e., tanoak, 2. a greaternumber of treatments is equivalent to greaterthreat to tanoak populations, and 3. recenttreatments were more efficient and effective,whereas older treatments may have been over-come by recolonization from tanoak in adjacentstands (Tappeiner and McDonald 1984). We usedall recorded treatment types with the exception of‘‘Firewood or local use cut,’’ which we interpret-ed as unlikely to target tanoak (or any species)for removal or suppression. We ranked locationsaccording to the number of recorded treatmentsweighted by the timing of those treatments. Weused 20-year intervals to capture increasedactivity surrounding timber harvests as well asrelated treatments during intervening years. Thisprocess assigned the highest risk from silvicultureto locations where treatments were both persis-tent and more recent. We used these scores tocreate a map of silviculture intensity with valuesranging from 0–10, which we reclassified into 0–3rankings by splitting non-zero values at equalintervals.

Disease

Phytophthora ramorum is an unprecedentedpathogen in terms of its capacity to impact theabundance, structure, and function of tanoakcommunities. The mortality rate of P. ramoruminfected trees (especially tanoak but also Quercusspecies) increases with tree size (Ramage et al.2011; Cobb et al. 2012b), leading to rapid declines

in tanoak biomass, dominance, and ecologicalfunction. We assigned the disease factor a weightof 25, slightly lower than silviculture because itsimpact on tanoak is slower, more heterogeneous,and highly dependent on other landscape andvegetation characteristics (Haas et al. 2011). Forthe disease disturbance factor, we used twopreviously developed maps detailing the risk ofP. ramorum establishment and spread for Oregon(Vaclavık et al. 2010) and California (Meente-meyer et al. 2004). These studies used heuristicmodels incorporating host indices and climatefactors derived from known infestations tocharacterize disease risk throughout each land-scape. We created a mosaic of these independentrisk layers with values ranging from 0–100, andreclassified this map along equal intervals into 0–3 rankings.

Development

Development impacts tanoak abundance,function, and persistence through conversion offorests to developed landscapes characterized bymixtures of impervious surfaces, soil, and vege-tation (including forest remnants, planted lawns,shrubs, and trees). In addition to this directimpact, development also increases human activ-ity in extant wildland areas. We interpreted thedirect threat to tanoak from development to berelatively less than from disease and silviculturefactors, but greater than from fire and assignedit a weight of 10 (Table 1). To estimate tanoakarea at risk from development, we produceda development density layer for the geographicrange using 2006 data from the National LandCover Database (NLCD) (Fry et al. 2011).

The NLCD classification system breaks itslevel 1 ‘‘Developed’’ class into four sub-categoriesbased on a ratio of human-made impervioussurfaces to vegetation present within 30 m 3 30 mpixels as mapped from Landsat EnhancedThematic Mapper (ETM+) imagery. Thus, asthe relative proportion of impervious surfacesincreases, the development ‘‘intensity’’ increasesfrom a ‘‘developed low intensity’’ to ‘‘developedhigh intensity’’ category. We reclassified theNLCD low to high development intensity cate-gories into our ranking system so that one, two,and three represented low, moderate, and highrespectively, with the assumption that higherdevelopment intensity presents a greater threat totanoak. We reclassified all remaining undevel-oped NLCD classes to zero. We resampled thislayer to 100 m 3 100 m cells to match thesmallest grain size of other spatial data beingutilized. We then generated a developmentdensity surface by summing all rank values (0–3) within a 500 m radius (i.e., 1 km 3 1 kmrectangular neighborhood) of each grid celllocation. This process effectively spreads some


development risk into adjacent undevelopedareas. We then reclassified the resulting develop-ment density map (values ranging from 0–75)into our common 0–3 ranking system using equalintervals.

Fire

Tanoak is a species well adapted to survive andrecover from fire. Mature trees have thick, fireresistant bark, and it vigorously regenerates frombasal sprouts following mortality of the aboveground portion of the tree (Tappeiner et al. 1990).These characteristics enable tanoak to persist andoften thrive in a wide range of fire regimes,including areas of low fire frequency (Hunter1997), albeit with varying functional roles. Wehypothesized that fire regimes altered fromhistoric baselines as well as increases in potentialfire severity from higher fuel loads result ingreater threat to tanoak populations. Thus, wedeveloped the fire disturbance factor to incorpo-rate departure from historic fire regimes andpotential fire severity. We represented departurefrom historic fire regimes using Fire Regimeand Condition Class (FRCC) layers created forOregon (USDA 2010) and California (CDF2003). FRCC is an interagency, standardizedtool for determining the degree of departure fromreference condition vegetation, fuels, and distur-bance regimes (Havlina et al. 2010). FRCC layersconsist of ranked categories that quantify thedifference between current vegetation conditionsand fire frequency from historic reference condi-tions. Three ordinal categories rank the degree ofdeparture from reference conditions in additionto a ‘not applicable’ category based on land covertype. We represented potential fire severity usingfuel risk layers developed for Oregon (ODF 2006)and California (CDF 2005). The fuel risk layerswere similarly categorized with three ordinalrankings of fuel risk and one indicating ‘notapplicable’ due to land cover type. We recodedthese existing categories of the fire regimedeparture and fuel risk layers to the 0–3 rankingscheme. We then summed the two layers andreclassified the resulting values (ranging from 0–6) to the 0–3 rankings. Given tanoak adaptationsto fire and its occurrence and persistence under avariety of fire regimes, we assigned the fire factorthe lowest weight of 5 (Table 1).

DISTURBANCE INTERACTIONS

We calculated two-way multiplicative interac-tions for all possible disturbance pairs at a givenlocation weighted by the assigned exponent (y,Eq. 1). In locations where more than two factorsoverlapped, we selected only the highest-rankinginteraction for calculating the threat value at thatlocation. Using this approach, we assumed that

one set of disturbance interactions is the dominantthreat to tanoak at a given location for the set ofhypotheses (i.e., factor and interaction weights)being examined. Below, we describe our reasoningbehind the weights assigned to each interactionparameter in our initial model (Table 1).

Disease x Fire

We assigned the disease-fire interaction aweight of two, reflecting our hypothesis that thethreat to tanoak increases where these factorscoincide. Importantly, P. ramorum has beenshown to decrease average tanoak size (Daviset al. 2010; Cobb et al. 2012b), and tree size isclosely associated with the likelihood of post-firetree survival (Hengst and Dawson 1994; Kobziaret al. 2006). This disease also increases fuel loads(Valachovic et al. 2011; Cobb et al. 2012a), whichare associated with increased soil damage follow-ing fire (Metz et al. 2011). Thus, we hypothesizethat tanoak in disease-impacted areas are moresusceptible to fire-caused mortality, and thatdead material from disease would increase fireseverity (particularly ground fire), further im-pacting tanoak recovery. Notably, slower orreduced tanoak recovery would decrease sourcesof P. ramorum inoculum.

Disease x Silviculture

We assigned the interaction of disease andsilviculture factors a weight of two. This hypoth-esis is supported by research demonstrating theeffectiveness of herbicide applications in causingtanoak mortality and reducing tanoak domi-nance (Tappeiner et al. 1987; Harrington andTappeiner 2009), and the broad patterns oftanoak mortality (Meentemeyer et al. 2008) andreduced average tree size in disease impactedforests (Cobb et al. 2012b). In stands where thesedisturbances coincide, they have the potential topermanently remove large, mature tanoak trees.More broadly, actions such as salvage harvestingcan increase the decline of dominant tree speciesalready impacted by landscape level outbreaks ofinsects or pathogens (Kizlinski et al. 2002; Fosterand Orwig 2006; Freinkel 2007). Tanoak gener-ally has little or a restricted specialty marketvalue, and so salvage harvesting is unlikely tooccur for this species. However, herbicide use hasa similar effect on tanoak populations except thatit may be more efficient in reducing tanoakbiomass (Tappeiner and McDonald 1984). For-estry practices may also decrease local tanoakpopulations to levels where P. ramorum is unableto invade stands, but these stands would likely bedevoid of ecological functions unique to tanoak(Cobb et al. 2012b; Wright and Dodd thisvolume).


Development x Fire

This term aims to characterize the threat totanoak from increased fire frequency related todevelopment density. Syphard et al. (2007) showedthat fire incidence is greatest at the wildland-urbaninterface; however, we did not have explicitevidence for this relationship within the geographicrange of tanoak. It is also likely that these fireswould be aggressively combated resulting insmaller fires with shorter burn times compared tomore remote areas. Thus, we assigned the interac-tion of these factors a weight of one (Table 1).

Development x Disease

This term represents risk to tanoak based on therelationship between higher development densityand increasing likelihood of disease introductionevents. While development alone has no directphysical impact in this case, Cushman andMeentemeyer (2008) showed an increased proba-bility of P. ramorum occurrence in forests nearhigher population densities, suggesting that roads,larger populations, urban and suburban land-scaping, and heavier use of wildland recreationareas provide additional spatial pathways forpathogen movement and introductions. However,we did not interpret this relationship as producinga significant impact relative to other interactionsand so assigned it a weight of one.

Development x Silviculture

Silviculture and development require infra-structure (e.g., roads) for transportation andaccessibility. This infrastructure enables furthersilviculture, recreation, and development activi-ties. We hypothesized that this interaction wouldnominally increase the threat to tanoak and soassigned it a weight of one.

Fire x Silviculture

While silviculture reduces average tree size andtherefore predisposes individual stems to fire-caused mortality, we also hypothesize that itreduces the risk of wildfire ignition and mayreduce potential severity. Additionally, tanoakadaptations producing robust sprouting andgrowth following fire or timber harvest allow foran increased likelihood of tanoak persistence whenthese factors interact, though most mature treesmay be removed. According to these postulations,we assigned this interaction a weight of one.

SENSITIVITY ANALYSIS

We tested model sensitivity to interactionparameters by varying interaction weights in aseries of model runs. We ran the model with all

interaction weights set to zero, which producedthreat values for only the additive part of themodel. While this sums the weighted rank valuesoccurring at each location it does not provideinsight into local factor interactions. We then ranthe model with all the interaction weights set toone (equally weighted) and used multiple itera-tions to examine how results changed when eachinteraction term was assigned a weight of twowhile holding all other factors at one. These testsproduced no zero values, indicating that at leastone factor was present at each location in ourmap of tanoak distribution (Lamsal et al. 2011).Using equal intervals, we reclassified the resulting

FIG. 1. The spatial distribution of (a) tanoak and (b)threats from weighted interacting disturbances (seeTable 1) across tanoak’s geographic range. Tanoakrange adapted from Tappeiner (1990). Tanoak popula-tions facing elevated threats were concentrated inHumboldt and Mendocino Counties, and partiallylocated on Hoopa and Yurok tribal lands.


range of values from each model run into low (1),intermediate (2), and high (3) threat levels.

RESULTS

Risk from Disturbance Interactions

Across its geographic range tanoak predomi-nantly faces low to intermediate threats fromdisturbance interactions, with smaller areas athigh risk. The weights listed in Table 1 resulted ina threat map (Fig. 1) with 10,905 hectares (,1%)of estimated tanoak area at high risk, 222,795 ha(5.6% of estimated tanoak area) at intermediaterisk, and over 3.7 million ha (94% of estimatedtanoak area) at low risk from disturbanceinteractions. Elevated fire and silviculture riskfactors overlap in the Sierra Nevada, whereasdisease risk was low throughout much of thisregion (Fig. 2a, b, d). The influence of develop-ment on disturbance interaction risk was observed

primarily in the San Francisco Bay Area, where itcoincided with low values for other disturbancefactors (Fig. 2c). Tanoak faces intermediate and/or high threats from disturbance interactions in 20of the 30 counties within its geographic range.Using this model formulation, areas classified asintermediate and high threat occurred predomi-nantly where disease and silviculture factorsoverlapped (Figs. 1 and 2a, 2b). The intermediateand high categories covered 233,700 ha containingan estimated 134.4 million tanoak, with 108.9million (81%) of these trees located in Humboldtand Mendocino counties, CA. Some of theelevated threat categories are located in areaswhere tanoak has high cultural importance,including on Hoopa and Yurok tribal lands(Fig. 1).

Sensitivity analysis of the interaction weightsdemonstrated general robustness of the totalamount of tanoak area classified into low andintermediate threat levels, with these two catego-ries accounting for 91% to 99% of the totaltanoak range across model runs with differentinteraction weights. With interaction weights allheld at zero (the sum of weighted factors only),nine percent of tanoak area was at high risk, 73%at intermediate risk, and 18% at low risk. With allinteraction weights set to one (the sum of theweighted factors plus the highest valued interac-tion at each location), six percent of tanoak areawas at high risk, 24% at intermediate risk, and70% at low risk. Results from these analyses,respectively, highlight areas where overlappingdisturbance factors accumulate and where higherweighted factors coincide. In sensitivity analyseswith each interaction weight increased to twowhile holding others constant at one, we foundthe disease-silviculture interaction produced thesame result as the weighting scheme of the initialmodel (Table 1). The fire-silviculture interactionresulted in values similar to those produced byour initial model parameters: 6419 ha (,1%) athigh risk, 222,790 ha (5.6%) at intermediate risk,and 3.7 million ha (94%) at low risk. Mostsignificantly, the disease-fire interaction was mostsensitive to changes in the interaction weightparameter and resulted in five percent of tanoakarea at high risk, 31% at intermediate risk, and64% at low risk. Development impacts on riskwere generally small and consistently resulted in.99% of tanoak area in the low threat category.Of the three disturbance interactions that includ-ed development, the disease-development inter-action resulted in the greatest area in intermediate(9990 ha) and high (1593 ha) threat levels.

DISCUSSION

Mapping the geographic distribution of distur-bance factors that threaten foundation species isessential for understanding and managing popu-

FIG. 2. Four disturbance factors overlaid on estimatedtanoak area. Each map shows the classified threat levelto tanoak from each disturbance factor across tanoak’sgeographic range, ranked zero to three (‘none’, ‘low’,‘intermediate’, and ‘high’, respectively).


lation and ecosystem impacts (Holdenreider et al.2004; Ellison et al. 2005). Since many landscapesare influenced by multiple disturbances, spatiallyexplicit tools identifying areas at risk fromdisturbance interactions are critical to conserva-tion of threatened populations. These tools canbe used for prioritizing limited resources forefficient and effective conservation of at-riskspecies.

Since European settlement, harvesting of ta-noak bark, and the subsequent increasing appli-cation of herbicides by industrial forestry inter-ests to favor more marketable conifer species(Bowcutt 2011), undoubtedly altered the struc-ture and function of tanoak forests. Coincidingwith these processes was an increasing humanpopulation resulting in development of forest andwildlands, and alteration of fire regimes to favorconifer species that were valued over tanoak inpost-1950 timber markets. Remarkably, tanoakhas shown substantial resilience under theseadverse conditions, but the introduction of P.ramorum into tanoak ecosystems presents a newand significant threat to this species. Althoughdiseases can increase extinction risk (Smith et al.2006), it is unlikely that tanoak could beeliminated by this pathogen alone. Speciesextinction most often occurs when multiplestressors coincide to reduce at-risk populationsto unsustainable levels (de Castro and Bolker2005; Smith et al. 2006). Analogously, distur-bance interactions, especially novel ones such asthose resulting from impacts of an emergentpathogen like P. ramorum (e.g., Metz et al. 2011),may increase the likelihood of stand-level tanoakextirpation. Thus, the functional extinction oftanoak due to the removal of all or most largetrees over broad areas may be more likely tooccur where disturbances interact (cf., Americanchestnut blight, Paillet 2002; jarrah dieback,Podger 1972).

Cobb et al. (2012b, this volume) indicated thatsignificantly more P. ramorum-caused tanoakmortality is likely to occur over the comingdecades. This is largely due to the epidemiologicalrole of tanoak in driving pathogen spread anddisease emergence, and the high abundance oftanoak in climates favorable to P. ramorum(Meentemeyer et al. 2011). Annual variability oftemperature and precipitation significantly im-pact the likelihood of pathogen establishmentand spread (Rizzo et al. 2005; Davidson et al.2011), which was reflected in the observeddifference in risk of disease establishment be-tween coastal and inland landscapes (Fig. 2b;Meentemeyer et al. 2004; Vaclavık et al. 2010).

Global to regional climatic changes are fore-casted to influence fire incidence, and changes infire frequency and intensity could affect tanoakresilience. Moritz et al. (2012) projected signifi-cant increases of fire frequency in the near future

across much of the globe, including the west coastof North America. A significant portion oftanoak’s geographic range coincided with inter-mediate and high threat categories for the firefactor in our analysis, emphasizing the threat totanoak from altered fire regimes and increasedfuel loads (Fig. 2d). The forecasted changes tofire regimes throughout tanoak’s geographicrange would increase the probability of interac-tions with other disturbances and consequentlythe threat to tanoak populations.

With this model, we identified locations whereinteracting disturbances have the potential tothreaten tanoak populations. Knowing the geo-graphic variation of disturbance interactions isfundamental for developing and implementingmanagement strategies that are landscape appro-priate. Managers and researchers can oftenidentify the dominant disturbance influencing alandscape, but these events do not occur in avacuum and individual disturbances can shapethe nature and intensity of other events (Turner2010). This model provides the capacity toidentify, target, and test management treatmentsin the context of multiple disturbances and theirinteractions. For example, areas at high risk frominteractions of silviculture and disease factorsin Mendocino and Humboldt counties could bereduced through judicious use of ‘‘slow-the-spread’’ actions such as proactive thinning ofsmaller tanoak and California bay laurel. Mean-while, maintaining tanoak contribution to eco-system function in the face of silviculture could beaccomplished by retaining large tanoak in standsmanaged for timber. Tanoak in the SierraNevada is primarily threatened by fire andsilviculture interactions, again suggesting thatretention of large tanoak in stands managed fortimber would be appropriate to enhance habitatas well as maintain tanoak resilience to fire.

Using models to guide decision-making re-quires recognition of model assumptions andlimitations, principally that results (in this casemapped disturbance interaction threats to tan-oak) are often sensitive to the values of the inputparameters. The weighting of disturbances andthe interactions using the initial model parame-ters (Table 1) resulted in a map where theinteraction of silviculture and disease factorsproduced intermediate and high threat levels totanoak over a relatively small portion of tanoak’sgeographic range (Fig. 1). This essentially showsthat high intensity silviculture and disease factorsare concentrated in a few smaller areas. Also,these two factors could potentially be the mostaddressable by management action and minoralteration to forestry practices in these areas. Oursensitivity analysis demonstrated that the modelis robust with respect to parameter values fordisease and fire interactions. Further, thesefactors resulted in the greatest total tanoak area


at intermediate and high threat levels (.1.4million ha, or 36%). This is indicative of the roleof tanoak as a host of P. ramorum as well as thepotential alteration to fire regimes in tanoakecosystems following the establishment of P.ramorum. We emphasize that the results of ourmodel represent threats based on hypothesizedrelationships among these disturbances. Fieldmeasurements are necessary to validate theseexpected outcomes and provide appropriatemodel updates for further predictions, such asactual measurements of tanoak mortality fromeach factor across a wide range of environments.

Apposite model interpretation is especiallyimportant when results are used to informmanagement actions, because misconception ofeither inputs or outcomes could lead to decisionsthat are contrary to stakeholder objectives.Through careful analysis, diverse managementgoals may be accomplished by applying moreeffective, or ‘‘designer’’ treatments to areas withdistinct threats. For example, forests at low riskand currently unaffected by disturbances may bemost appropriate for the establishment of refugetanoak populations. Areas facing intermediatethreat levels that also border regions with higherthreat levels (Fig. 1) may be ideal for treatmentsthat slow pathogen invasion into adjacent stands.The effective threat from interacting disturbancesis temporally implicit, and the actual impacts totanoak are dependent on the state of a stand, aswell as the order and timing of disturbance events(Lorimer et al. 2009; Turner 2010). Therefore, thetiming of treatments is an essential consideration.For example, fuel load reduction activities couldalso address disease risk by reducing densitiesof bay laurel and tanoak, but these treatmentswould be most beneficial when applied betweenJuly and the onset of winter rains to avoidintroduction or spread of the P. ramorumpathogen (Davidson et al. 2008, 2011). Alsoregarding disease, management efficacy decreaseswith time since P. ramorum arrival (Filipe et al.2012), highlighting the importance of treatmentsthat may prevent establishment as well as rapidresponses to new invasions in order to mitigateimpacts (Meentemeyer et al. 2012). These actionscan provide time and space needed to implementfurther treatments that reduce the cost of disease(and potential interactions with other distur-bances) to local communities (Kovacs et al.2011; Cobb et al. this volume). Lastly, policychanges favoring retention of high value tanoakhabitat, especially in locations at high risk fromdisturbance interactions could be effective atreducing the rate and extent of tanoak populationdecline as well as maintaining biodiversity andecosystem function.

Although the maps of areas at-risk fromdisturbance interactions are static, the databasesused to produce them are typically dynamic as

new data is acquired and analyzed over time. Asnew discoveries are made and our knowledgeof disturbance interactions and their impactsevolves, model parameters can be updated andresults tested in order to maintain reliability ofrecommendations. Through integration into anadaptive management framework, updates canbe quickly applied, enabling new strategies tobe developed and implemented in a timely andeffective manner. The model framework may alsobe similarly applied to examine the spatialvariation of threats to other species fromdisturbance interactions.

CONCLUSIONS

With increasingly limited resources it is impor-tant to rapidly identify target areas wheremanagement actions will have the greatest chanceof achieving objectives. We propose that thismodel of threats to tanoak from interactingdisturbances could be used as part of an adaptivemanagement plan to bring stakeholders togetherin prioritizing and achieving conservation of theabundance, structure, and function of tanoaktrees and ecosystems. Tanoak is by all accounts aresilient species, persisting and sometimes thriv-ing under a variety of pressures. By applyingknowledge and tools currently available, thisresiliency can be enhanced, tanoak mortality maybe reduced, and the vital services provided bytanoak ecosystems can be conserved for thehealth and prosperity of current and futuregenerations.

ACKNOWLEDGMENTS

We are grateful to Monica Dorning and fouranonymous reviewers for helpful comments on previousversions of this manuscript. This research was support-ed by a grant from the National Science Foundation(EF-0622770) as part of the joint NSF-NIH Ecology ofInfectious Diseases program, the Gordon and BettyMoore Foundation, and the USDA Forest Service,Pacific Southwest Research Station.

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