Van Gunst, K.J. 2012. Forest Mortality in Lake Tahoe Basin from ...

University of Nevada, RenoForest Mortality in Lake Tahoe Basin from 1985-2010: Influences of Forest Type,Stand Density, Topography and ClimateA thesis submitted in partial fulfillment of the requirements for the degree of Master ofScience in Natural Resources and Environmental SciencebyKristin Jane Van GunstDr. Peter J. Weisberg/Thesis AdvisorDecember, 2012

Copyright by Kristin Jane Van Gunst 2012All Rights Reserved

iTHESIS ABSTRACTWidespread and synchronous outbreaks of forest mortality during the 1990s andcontinuing to present day have drastically altered millions of hectares of forestlands fromMexico to Alaska (Bentz 2009). Tree mortality is a complex process with a variety ofboth exogenous and endogenous factors influencing the extent, pattern, and severity oftree mortality (Franklin et al. 1987; Holdenreider et al. 2004; Powers et al; Raffa et al.2008; Simard et al 2012). Although the vast majority of mortality is mediated by nativebark beetles (Coleoptera: Scolytinae), more proximal exogenous factors such as drought,and endogenous factors such as stand density and environmental setting have also beenimplicated (Allen et al. 2010). This study utilizes remote sensing imagery from the 1985-2010 Landsat Thematic Mapper archive to explore relationships among forest types,climate, stand density, and environmental gradients and mortality in the Lake TahoeBasin in the central Sierra Nevada.In coniferous forests of the western U.S. and Europe, increased stand densityresulting from forest management and land use practices is widely hypothesized toincrease forest-wide mortality levels (Dobbertin et al. 2007; Guarín and Taylor 2005;Millar et al. 2012; Maloney and Rizzo 2002; Maloney et al. 2011). In my first chapter, Iexamine how the relationship between mortality and stand density has differed amongfive forest types and wet and dry climatic periods. This part of the project uses annualmaps of forest mortality (Plates 1-2) and an index of stocking level, a proxy for standdensity, derived from classification of remotely sensed imagery. Logistic regression isthen used to evaluate how a 20% increase in stocking level affects probability ofmortality. Our analysis showed that the strength of density dependence for influencing

iimortality is variable: all forest types exhibited periods when density-dependent mortalitywas positive (increased density increases risk of mortality), negative (increased densitydecreases risk of mortality) or when density was not associated with mortality. However,positive density-dependent mortality was found to a greater degree in Jeffrey pine andlodgepole pine-dominated forests and during climatic periods characterized by drought.In middle- and upper-elevation forests, density-dependent mortality was more variable byclimate with negative density-dependent mortality apparent over much of the time period.In my second chapter, I explore 1) Variations in mortality levels by forest type andclimatic period, and 2) How the probability of mortality is associated with elevation andincident solar radiation. These two topographical variables are important in governingproductivity, resource availability, and temperature and climate regimes in thetopographically-complex LTB (Urban et al. 2000). We used generalized linear modelsand regression tree analysis to elucidate relationships between mortality levels, foresttype and climatic period, and logistic regression to quantify association between mortalityrisk and environmental gradients. We discovered that mortality is not consistentlyassociated with drought. Mortality levels were higher for all forest types during an earlycold and dry period, but not during a later warm and dry period. Although forestmortality was episodic for upper-elevation forests, lower-and mid-elevation forestsexhibited more stable mortality levels. Over the majority of the time period, we foundthat mortality risk was greater on more north-facing slopes. The relationship betweenmortality and elevation differed among forest types. In lower-elevation forests and duringthe first dry period, lower elevations were associated with decreased mortality risk. For

iiimiddle and upper-elevation forests, higher elevations were associated with greatermortality risk.Results highlight the need for further study into the relative role of species traits,stand characteristics, insect herbivory, and climate for influencing forest mortality at bothstand and landscape scales. Variable response to both climate and stand density meansthat scientists should be cautious about generalizing these effects to all western forests.Thinning treatments should not be expected to improve forest health in all forest types.Consistent relationships of forest mortality with forest type and environmental setting canbe used to guide landscape-level management of forest health.LITERATURE CITEDAllen, Craig D., Alison K. Macalady , Haroun Chenchouni, Dominique Bachelet, NateMcDowell,Michel Vennetier, Thomas Kitzberger, Andreas Rigling , David D.Breshears, E.H. (Ted) Hogg, Patrick Gonzalez , Rod Fensham, Zhen Zhang, JorgeCastro, Natalia Demidova, Jong-Hwan Lim, Gillian Allard, Steven W. Running,Akkin Semerci, and Neil Cobb. 2010. A global overview of drought and heatinducedtree mortality reveals emerging climate change risks for forests. ForestEcology and Management 259 (4): 660–684.Bentz Barbara J. ed. 2009. Bark Beetle Outbreaks in Western North America: Causes andConsequences. University of Utah Press.Bentz, Barbara, Jacques Régnière, Christopher J. Fettig, E. Matthew Hansen, Jane L.Hayes, Jeffrey A. Hicke, Rick G. Kelsey, Jose F. Negrón, and Steven J. Seybold.

iv2010. Climate change and bark beetles of the western United States and Canada:Direct and indirect effects. Bioscience 60(8): 602-613.Dobbertin, Matthias, Beat Wermelinger, Christof Bigler, Matthias Bürgi, MatthiasCarron, Beat Forster, Urs Gimmi, and Andreas Rigling. 2007. Linking increasingdrought stress to Scots pine mortality and bark beetle infestations. The ScientificWorld 7(S1): 231–239.Franklin, Jerry F., H.H. Shugart, and M.E. Harmon. 1987. Tree death as an ecologicalprocess. Bioscience 27: 259–288.Guarín, Alejandro and Alan H. Taylor. 2005. Drought triggered tree mortality in mixedconifer forests in Yosemite National Park, California, USA. Forest Ecology andManagement 218: 229–244.Holdenrieder, Ottmar, Marco Pautasso, Peter J. Weisberg, and David Lonsdale. 2004.Tree diseases and landscape processes: the challenge of landscape pathology.Trends in Ecology and Evolution 19(8): 446-452.Maloney, Patricia E. and David M. Rizzo. 2002. Pathogens and insects in a pristine forestecosystem: the Sierra San Pedro Martir, Baja, Mexico. Canadian Journal of ForestResearch 32: 448-457.Maloney, Patricia E., Detlev R. Vogler , Andrew J. Eckert, Camille E. Jensen, and DavidB. Neale. 2011. Population biology of sugar pine (Pinus lambertiana Dougl.) withreference to historical disturbances in the Lake Tahoe Basin: Implications forrestoration. Forest Ecology and Management 262: 770–779.Millar, Constance I., Robert D. Westfall, Diane L. Delany, Matthew J. Bokach, Alan L.Flint, and Lorraine E. Flint. 2012. Forest mortality in high-elevation whitebark

vpine (Pinus albicaulis) forests of eastern California, USA; influence ofenvironmental context, bark beetles, climatic water deficit, and warming.Canadian Journal of Forest Research 42: 749–765.Powers, Jennifer Sarah, Phillip Sollins, Mark E. Harmon and Julia A. Jones. 1999. Plantpestinteractions in time and space: A Douglas-fir bark beetle outbreak as a casestudy. Landscape Ecology 14: 105–120.Raffa, Kenneth F., Brian H. Aukema, Barbara J. Bentz, Allan L. Carroll, Jeffrey A.Hicke, Monica G. Turner, and William H. Romme. 2008. Cross-scale drivers ofnatural disturbances prone to anthropogenic amplification: the dynamics of barkbeetle eruptions. BioScience 58(6): 501-517.Simard, Martin, Erinn N. Powell, Kenneth F. Raffa, and Monica G. Turner. 2012. Whatexplains landscape patterns of tree mortality caused by bark beetle outbreaks inGreater Yellowstone? Global Ecology and Biogeography 21: 556–567.Urban, Dean L., Carol Miller, Patrick Halpin, and Nathan L. Stephenson. 2000. Forestgradient response in Sierran landscapes: the physical template. LandscapeEcology 15: 603–620.

viI dedicate this thesis to my family:Marilyn, Roger, Sara & Andrea Van Gunst

viiACKNOWLEDGMENTS: Primary financial support for this project was provided bythe Lake Tahoe License Plate Program (Project No. LTLP 08-06). I thank my advisor Dr.Peter Weisberg for his guidance and support throughout this project. As a researchassistant in the Great Basin Landscape Ecology Lab, my understanding of remotesensing, data analysis, and forest ecology has progressed greatly and I thank Peter for hisrole in fostering such a comprehensive graduate school education. This project has alsobenefited from input and guidance from my committee, Dr. Bob Nowak and Dr. ScottBassett. I thank them for their insights and suggestions in improving and strengtheningthis project. I thank members of the Great Basin Landscape Ecology Lab, especiallySarah Karam and Jian Yang for advice on all things R, statistics, and remote sensing. Iespecially thank Yuanchao Fan for his guidance on remote sensing, processing Landsatimagery, and collecting field measurements: this project would have been exponentiallymore difficult without him. I thank Roland Shaw and Elizabeth Harrison at the NevadaDivision of Lands for their comments during the progression of this project and BrentOblinger of the USFS Forest Health Program in deciphering the where and when of forestmortality in the Lake Tahoe Basin. I also thank Kurt Fesenmyer, Steve Hanser, andMatthias Leu: their support and guidance in mentoring a new scientist is unsurpassed.Lastly, I thank friends and family far and wide, who have provided countless hours ofsupport and comic relief.

viii1986-87 1994-952002-03 2009-10Plate 1: Remote sensing derived maps of annual (Fall Year One: Fall Year Two) canopydieback and healthy pixels in coniferous forests of the Lake Tahoe Basin. High mortalityyears, 1986-1987 and 1994-1995, occurred during reported drought periods. Lower levelsof mortality occurred in a later drought period (2002-2003) with 2009-2010 indicative ofmore average mortality levels in addition to patches of tree mortality within densityreduction treatments.

ixPlate 2. Photograph of mountain pine beetle damage in High Meadows area in lodgepolepine forests in southern region of LTB. Figure on right shows 2006-2009 canopymortality derived from remote sensing imagery classification.

xTABLE OF CONTENTSAbstract ............................................................................................................................... iLiterature Cited ................................................................................................................ iiiDedication ......................................................................................................................... viAcknowledgments ............................................................................................................ viiPlate 1: Remote sensing derived maps of annual (Fall Year One-Fall Year Two) canopydieback and healthy pixels in coniferous forests of the Lake Tahoe Basin. High mortalityyears, 1986-1987 and 1994-1995, occurred around a reported drought period. Lowerlevels of mortality occurred in a later drought period (2002-2003) with 2009-2010indicative of more average mortality levels in addition to patches of tree mortality withindensity reduction treatments……………………………………………………………viiiPlate 2. Photograph of mountain pine beetle damage in High Meadows area in lodgepolepine forests in southern region of LTB. Figure on right shows 2006-2009 canopymortality derived from remote sensing imagery classification…………………………...ixList of Tables .................................................................................................................. xiiiList of Figures ................................................................................................................. xivChapter 1 – Are Denser Forests Less Healthy: A Remote Sensing Analysis of Density-Dependent Forest Mortality .................................................................................................1Abstract ...............................................................................................................................2Introduction .........................................................................................................................2Methods .............................................................................................................................10Study Area ..................................................................................................................10

xiForest Types and Ecoregions.......................................................................................12Image Calibration and Normalization .........................................................................13Leaf Area Index and Stocking Inde ..............................................................................14Forest Mortality ...........................................................................................................15Mortality Validation.....................................................................................................18Climate .........................................................................................................................19Data Analysis ...............................................................................................................21Results ...............................................................................................................................22Interaction of Climatic Period and Forest Mortality ........................................................22Influences on the Density Dependence of Forest Mortality .....................................…..…23A. By Forest Type ……………………………………………………………23B. By Climatic Period………………………………………………….....24C. Forest Type and Climatic Period…………………………...…….....25Discussion .........................................................................................................................26Management Implications ............................................................................................30Literature Cited .................................................................................................................32Appendix A. 95 th Percentile of LAI values in 17 forest groups .......................................72Appendix B. Relationships among modeled snow deficits, Snow Water Equivalent(SWE) from California Cooperative Snow Surveys, and Palmer Drought Severity Index(PDSI) over the study period……………...…………………………………………..…74Appendix C. Final models in 5 forest types in 25-year study period…............................76

xiiChapter 2 – Spatial and Temporal Patterns of Forest Mortality in Lake Tahoe Basin,USA: Influence of Climate and Environment ....................................................................80Abstract .............................................................................................................................81Introduction .......................................................................................................................82Topographic influences on forest mortality……………………...………....82Phytophagous insect population dynamics and forest mortality...................85Climate and temporal variation in forest mortality y...................................86Study Objectives............................................................................................87Methods .............................................................................................................................88Study Area...................................................................................................88Forest Types and Ecoregions......................................................................89Image Processing and Mortality Classification………..............................89Climate……………………………………........………..............................90Environmental Variables.............................................................................91Data Analysis...............................................................................................91Results ...............................................................................................................................92Forest Mortality across Drought and Wet Periods......................................92Ecoregional Influences on Forest Mortality ................................................93Topographic effects on forest mortality: Solar Radiation andElevation........................................................................................................94Discussion .........................................................................................................................95Conclusion ......................................................................................................................103Literature Cited ...............................................................................................................104

xiiiAPPENDIX A. 25 years of annual mortality (Fall Year One – Fall Year Two) from 1985-2010 in the LTB. Mortality maps are derived by subtracting year one NDWI from yeartwo NDWI to create continuous maps of dNDWI showing forested areas in varyingstages of health, from canopy dieback to vegetation recovery to growth ……………...134Thesis Summary...............................................................................................................137LIST OF TABLESTable 1-1. Description of LTB forest types defined using the USFS CalVeg vegetationclassification (USDA Forest Service. 1981. CALVEG: A Classification of CaliforniaVegetation)……………………………………………………………………..………...49TABLE 1-2. Host-specific native bark beetles of LTB forests and impact of successfulattack on canopy foliage…………………………………………………………………50TABLE 1-3a,b. Validation of remote sensing mortality classification with overallclassification accuracy (oca) and kappa statistics for two values of dNDWI,corresponding to a 5% and 10% loss of green canopy per pixel. The kappa statistic is ameasure of agreement between predicted and actual values in a category………………51TABLE 1-4 Annual mortality summary statistics by LTB forest type and climatic period(d1: 1987-1995, d2: 1999-2006, w1: 1985-1987, w2: 1995-1999, w3: 2006-2010)…….52TABLE 1-5. Summary of odds ratios for the stocking-level predictor variable fromannual models of mortality by forest type over the 25-year time series (SD=StandardDeviation). Odds ratios are derived from annual mortality models in each forest type and

xivshow the change in the probability of mortality for every 0.2 increase in stockinglevel……………………………………………………………………………...……….53TABLE 1-6. Summary of odds ratios for the stocking-level predictor variable fromannual models of mortality in each climate period occurring in the 25-year time series(SD=Standard Deviation)……………………………………………………………..…54TABLE 2-1. Relationships between current and lagged year modeled snow deficits, asratio of current year snow to 30-year norm (1980-2009) by forest type. Derivation of lagsis shown below, using 1985 as an example year. Current year is defined as most currentwintertime precipitation. Positive relationships indicate increased moisture is associatedwith increased mortality. Negative relationships indicate decreased moisture associatedwith increased mortality………………………………………………………………...118TABLE 2-2. Linear regression analysis results for Annual Mortality ~ Climatic Period ineach of five forest types (JP, LP, MF, RF, WF)with significance at the p < 0.05 level………………………………………………….120LIST OF FIGURESFIGURE 1-1. Location of Lake Tahoe Basin (LTB) study area…………………………55FIGURE 1-2. Lake Tahoe Basin (LTB) forest types per the USFS CalVeg Classification(USDA Forest Service. 1981. CALVEG: A Classification of California Vegetation )….56FIGURE 1-3a, b. LAI Validation. Relationship between field-measured leaf area index(LAI) measured with LICOR LAI-2000 Plant Canopy Analyzer in 30 plots in the LTBand LAI modeled from normalized difference vegetation index (NDVI), derived fromremote sensing imagery (a). Modeling efficiency of observed vs. modeled LAI. Modeling

xvefficiency measures agreement between observed and simulated values against the line ofperfect fit (blue) rather than the regression line (b, Mayer and Butler 1993)……...…….57FIGURE 1-4. Values of the single year normalized difference vegetation index (NDWI)across 254, 30m x 30m pixels digitized in areas ranging from severe and extensivemortality across the pixel (High) to pixels without evident canopy damage (None).Results of the ANOVA with follow-up Bonferroni comparison are significant, validatingthe sensitivity of the single-year NDWI to LTB mortality (F=33.27, p < 0.006)….……58FIGURE 1-5. Relationship between percent green canopy per plot as measured in 24,60m x 60m pixels and the single year normalized difference vegetation index (NDWI)value derived from Landsat TM imagery. Percent green canopy is derived from percentgreen canopy x average percent green canopy per tree……………………………….…59FIGURE 1-6. Pictorial sequence of remote sensing processing used to derive annualmortality maps. Level 1 terrain corrected Landsat TM 5 satellite image is acquired,resized to study area, and normalized to reference imagery (a).Year one (ex.2009) andyear two (ex. 2010) normalized images are transformed into single-year NDWI maps (b1,2009: b2, 2010). Continuous differenced NDWI (dNDWI) map (c) is derived bysubtracting 2009 NDWI (Year 1) from 2010 NDWI (Year 2). Map of continuous dNDWIis classified into maps of healthy and canopy dieback pixels using validated thresholds(dNDWI < -.053)………………………………………….……………………….……60FIGURE 1-7. Derivation of five climatic periods throughout the 1985-2009 study period,with 1980-1984 for reference. Single-year snow deficits are derived from the currentyear/30-year norm precipitation as snow variable from the ClimateWNA dataset (Wanget al. 2012). Four-year lagged deficits (average of current year deficit + 3 previous years)

xvireplicate historical accounts of dry and wet periods in the LTB. Climatic periods:w1=1985-1987 (August 1985-July 1986, August1986-July 1987, 2-year duration);d1=1987-1995, 8-year duration: w2= 1995-1999, 4-year duration; d2= 1999-2006, 7-year duration; w3: 2006-2010, 4-year duration.……………………………….………...61FIGURE 1-8. Mortality levels and pattern by forest type over the 25-year time series,with smooth local estimator (lowess) curve……………………………………………...62FIGURE 1-9. Mortality levels by forest type and climatic period (w1: 1985-1987, d1:1987-1995, w2: 1995-1999, d2: 1999-2006, w3: 2006-2010)…………………………...63FIGURE 1-10. Direction and magnitude of density dependent mortality as odds ratios forevery absolute 20% increase in stocking level by forest type over the 25-year timeinterval. Confidence intervals at the 95 th percentile are shown. Odds ratios symbolized by“x” show years in which the stocking variable was not included in final model, due tooutcome of likelihood ratio test. Odds ratios symbolized by “0” show years in which thestocking variable was included in final model……………………………………….….64FIGURE 1-11. Box plot of odds ratios of density-dependent mortality across 25 annualmodels derived for each forest type. Odds ratios illustrate the change in the probability ofmortality for every 0.2 (20%) increase in stocking level……………………………..…65FIGURE 1-12. Number of models where; 1) Stocking level alone or in combination withenvironmental variables appeared in final, most parsimonious model, 2) Environmentalvariables only appeared in final, most parsimonious model or, 3) Mortality was notexplained by stand structure or environmental variables……………………………...…66FIGURE 1-13. Type of density dependent mortality exhibited over percent of 25-yeartime series by direction. Positive DDM: increased density increases probability of

xviimortality; Negative DDM: decreased density increases risk of mortality; and NoAssociation: no association between density and mortality…………………………......67FIGURE 1-14. Ratio of positive to negative density-dependent mortality over five foresttypes. Positive density-dependent mortality indicates an increase in the probability ofmortality with increased density. Negative density-dependent mortality indicates adecrease in the probability of mortality with increased density………………..……..…68FIGURE 1-15. Type of density dependent mortality exhibited over percent of eachclimatic period in all five climatic periods by direction. Positive DDM: increased densityincreases probability of mortality; Negative DDM: decreased density increases risk ofmortality; and No Association: no association between density and mortality………….69FIGURE 1-16. Average odds ratio for every 0.2 (20%) increase in stocking level byclimatic period and forest type. Error bars reflect standard errors……………………....70FIGURE 1-17. Relative frequency of negative (N) or positive (P) density-dependentmortality found in each forest type in each of four climatic periods (d1, d2, w2, w3)…..71FIGURE 2-1a-d. Ecoregions (a) in the LTB with ecoregional differences in (b) averageannual precipitation (in); (c) average maximum temperature ( o F) ; (d) average minimumtemperature ( o F), derived from PRISM 1971-2000 norms.. ………………………..…121FIGURE 2-2a,b. Average winter minimum temperature (a) and number of frost free days(b) in five climatic periods of study time series……………………………………..….122FIGURE 2-3. Solar radiation (WH/m 2 ) calculated for August 1, 2010. Solar radiationcombines slope and aspect to measure both direct and diffuse radiation over a 30m x 30mpixel. Higher values are associated with south-facing and lower values with north-facingaspects…………………………………………………………………………………..123

xviiiFIGURE 2-4. Elevation (m) derived from a 30m Digital Elevation Model……………124FIGURE 2-5. Boxplots of annual mortality by forest types in five climatic periods ofstudy [d1:1987-1995, d2:1999-2006, w1:1985-1987, w2: 1995-1999, w3:2006-20010]………………………………………………………………………….…...…125FIGURE 2-6. Regression tree of predicted mortality levels by forest type and climaticperiod. Regression trees are comprised of a series of binary splits created throughrecursive partitioning with splitting criterion based on overall deviance reduction.Predicted values of annual mortality are shown at the terminus of each node for thatgroup. For example, the predicted annual mortality value for JP, MF, and WF duringeither d2 or w3 is 9.69……………………………………………………………….….126FIGURE 2-7. Effect plots for Annual Mortality ~ Climatic Period (ClimPd: d1, d2, w1,w2, w3) in each of the five forest types…………………………………………...……127FIGURE 2-8. Effect plots for regression analysis of Annual Mortality ~ EcoRegion *Climatic Period in JP, MF, and RF forests. Red lines indicate the upper and lower 95%confidence intervals around regression coefficients……………………………………128FIGURE 2-9a-c. Levels of JP(a), MF (b), and RF(c) forest mortality by ecoregion (eh, ej,ek, el, et) over the 25 year time period…………………………………………………129FIGURE 2-10. Effect of a 1,000 WH/m 2 increase in solar radiation on mortality for fiveforest types across 25-year time period as odds ratios with 95% confidence intervals.N=odds ratios with confidence intervals that overlap 1 (CI shown) or years when solarradiation did not appear in final model (CI not shown). U= odds ratios with confidenceintervals that do not overlap 1. Results from annual models occuring during dry periodsare in orange. Results from annual models occuring in dry periods are in green. Odds

xixratios greater than one indicate increase in probability of mortality associated withincreases in south-facing slopes. Odds ratios less than one indicate probability ofmortality associated with north-facing slopes…………………………………………..130FIGURE 2-11. Location of increased risk of mortality by aspect derived from solarradiation for five forest types across 25-year time period (Not Significant: confidenceinterval of regression coefficient and resulting odds ratio overlapped 0 or 1; NoAssociation: no association between probability of mortality and elevation)…………131FIGURE 2-12. Effect of a 500m increase in elevation on mortality for five forest typesacross 25-year time period as odds ratios with 95% confidence intervals. N=odds ratioswith confidence intervals that overlap 1 (CI shown) or years when elevation did notappear in final model (CI not shown). U= odds ratios with confidence intervals that donot overlap 1. Results from annual models occuring during dry periods are in orange.Results from annual models occuring in dry periods are in green. Odds ratios greater thanone indicate increase in probability of mortality associated with increases in elevation.Odds ratios less than one indicate probability of mortality associated with decreases inelevation………………………………………………………………………………132FIGURE 2-13. Location of increased risk of mortality by elevation for five forest typesacross 25-year time period (Upper: Upper elevation; Lower: lower elevation; No Effect:no association between probability of mortality and elevation; Not Significant:confidence interval of regression coefficient and resulting odds ratio overlapped0 or 1)…………………………………………………………......................................133

1Are Denser Forests Less Healthy: A Remote Sensing Analysis of Density-DependentForest MortalityKristin Jane Van Gunst ,a,c , Peter J. Weisberg a,ba Department of Natural Resources and Environmental Science, University of NevadaReno, 1664 N. Virginia Street, Reno, NV, 89557.b Program in Ecology, Evolution and Conservation Biology, University of Nevada Reno,Mail Stop 314, Reno, NV, 89557.cCorresponding Author: Phone: 775.784.4020, Fax: 775.784.4583 Email:kvangunst@cabnr.unr.eduEmail. P.Weisberg: pweisberg@cabnr.unr.edu

2ABSTRACTIncreased stand density is commonly cited as a predisposing factor in forest mortality,although evidence over larger landscapes and longer timeframes is lacking. Weinvestigated how the relationship between stand-level mortality and stand density variesby forest type and climatic period in the mixed conifer forests of the Lake Tahoe Basin(LTB) in the central Sierra Nevada. Our analyses used Landsat TM data from 1985-2010to derive annual mortality maps and an annual stocking index. We found that the strengthof density-dependent mortality was variable in the LTB, with no forest showing aubiquitous relationship between mortality and density. However, increases in densitywere weakly associated with increased probability of mortality during major dry periodsand in lower-elevation and pine-dominated forests. In middle- and upper-elevationforests, increased density was associated with increased mortality risk in dry periods,with this effect reversed in wet periods. Years when particular forest types experiencedthe greatest mortality levels did not correspond to years when density-dependentmortality was greatest, suggesting that agents of epidemic mortality may act in a densityindependentmanner.INTRODUCTIONWidespread increases in forest mortality have become prevalent over much of thewestern United States, with mortality rates doubling every 17-29 years in some areas (vanMantgem et al. 2009). Recent increases in tree mortality have been evidenced inwhitebark pine (Pinus albicaulis) in the Greater Yellowstone Ecosystem (Logan et al.2010) and the Sierra Nevada (Millar et al. 2012); limber pine (Pinus flexilis) and mixedconifer stands of the Sierra Nevada (Egan et al. 2011; Ferrell et al. 1994; Guarín and

3Taylor 2005; Maloney et al. 2011; Millar et al. 2007, 2012; van Mantgem andStephenson 2007; van Mantgem et al. 2009); ponderosa pine (Pinus ponderosa), mixedconifer, and pinyon-juniper woodlands of the Southwestern US (Breshears et al. 2005;Floyd et al. 2009; Negrón et al. 2009; Ganey and Vojta 2011); subalpine and middleelevationforests of Utah and Colorado (DeRose and Long 2012; Hebertson and Jenkins2008), lodgepole pine forests of British Columbia, Canada (Aukema et al. 2006); spruceforests of Alaska (Berg et al. 2006), and temperate coniferous forests of Europe (Allen etal. 2010 and references therein; Dobbertin et al. 2005, 2007). These dieback events havebeen caused by a diversity of host-specific mortality agents including mountain pinebeetle (MPB, Dendroctonus ponderosae Hopkins) in lodgepole pine (Pinus contorta),sugar pine (Pinus lambertiana), and whitebark pine stands; spruce beetle (Dendroctonusrufipennis Kirby) in Englemann spruce stands; pinyon ips (Ips confusus Leconte) inpinyon-juniper woodlands, and several insect species including fir engraver (Scolytusventralis) and Jeffrey pine beetle (Dendroctonus jeffreyi) in mixed conifer forests (Eganet al 2011; Ferrell et al. 1994; Ferrell 1996). Forest dieback has been linked to climatewarming and drought (Bentz et al. 2010; Berg et al. 2006; Guarín and Taylor 2005;Hebertsen and Jenkins 2008; Millar et al. 2012; van Mantgem and Stephenson 2007), butalso attributed to denser forests associated with fire exclusion (Dobbertin et al. 2005;Egan et al.2011; Ferrell et al. 1994; Galiano et al. 2010; Guarín and Taylor 2005;Maloney et al. 2011; Millar et al. 2012; Negrón et al. 2009).Forest mortality can be either patchy or widespread, occurring at chronic backgroundlevels with small groups of trees affected, or at outbreak levels with widespread tree dieoffacross much of the landscape. Causes of forest mortality and the conditions that give

4rise to outbreaks of widespread mortality are complex and hard to predict (Bentz et al.2010; Berg et al. 2006; Dukes et al. 2009; Franklin et al. 1987 but see Auclair 2006). Inconiferous forests, mortality is mediated by a diverse array of host-specific pests andpathogens that interact with edaphic and climatic factors, as well as individual treecharacteristics, to suppress growth or cause tree mortality (Castello et al.1995). Manion(1991) framed reductions in tree vigor often leading to mortality as a result of threefactors: “predisposing factors” due to long-term stress, “inciting factors” of shortduration, and “contributing factors” that ultimately lead to mortality if a tree cannotrecover from the inciting stress. While a plethora of both endogenous and exogenousfactors combine to influence tree mortality, competition among trees in dense foreststands is often considered a predisposing factor leading to episodes of widespread forestmortality (Guarín and Taylor 2005; Millar et al. 2012; Maloney and Rizzo 2002;Maloney et al. 2011; Raumann and Cablk 2008).The primary role of competition in governing forest dynamics during early standdevelopment is widely recognized, but less well understood is the intensity and impact ofcompetitive forces in structuring mature forests (Das et al. 2011; Franklin et al 2002; Peetand Christensen 1987; Reineke 1933; Vygodskaya et al. 2002; Yoda et al. 1963). Inmature forests, the “zone of influence” of competition is typically restricted to a smallarea proximate to the plant, with abiotic stressors more important in driving tree mortality(Franklin et al. 2002; Peet and Christensen 1987). Background mortality rates in matureand old-growth forests are hypothesized to be low, with elevated levels caused by largescaleepisodic factors, such as bark beetle outbreaks (Franklin et al. 2002; Peet andChristensen 1987).

5Across the western United States, increased stand density resulting from over acentury of fire exclusion is often considered a significant cause of heightened forestmortality. The strength of density-dependent mortality can be positive, where increases instand density are associated with increased probability of mortality or negative, wheredecreases in density area associated with increased probability of mortality. The linkbetween mortality and density is based on the Optimal Defense Theory in whichsustained inter-tree competition for shared critical resources limits the ability of trees togenerate the energy-intensive chemical and physical structures necessary to survive barkbeetle attacks (Raffa and Berryman 1983; Showalter and Filip 1993; Stamp 2003).However, a competing theory of plant defense, the Growth-Differentiation-Balancehypothesis, states that when growth is slowed by environmental factors, the resource poolavailable to allocate to secondary defense increases (Coley et al. 1985; Loomis 1932,1953; Herms and Mattson 1992; Stamp 2003). Although a comprehensive understandingabout the impacts of plant stress on mortality may be lacking (Stamp 2003), many studieshave shown that decreased tree vigor increases risk of successful bark beetle attack.Although some have shown that declining growth rate separates surviving trees fromdead trees (Bigler et al. 2007; Das et al. 2007; Linares et al. 2009, 2010; Pedersen 1998;Suarez et al. 2004), others have not found that distinction (Kane and Kolb 2010; Powerset al. 1999; Reid and Robb 1999). Whether vigorous or weakened trees are preferred notonly depends on the bark beetle species, with some such as MPB preferentially attackinghealthy trees, but also on mortality induced during non-epidemic or epidemic outbreakswhen mass attacks can overwhelm tree defenses of even healthy trees (Logan et al. 1998;Powers et al. 1999; Raffa and Berryman 1983; Wallner 1987). In addition, manufacture

6of compounds and structures necessary to mount plant defense systems is likely moreproximal to understanding why some trees die and others do not (Kane and Kolb 2010).Increased stand density can also directly influence the transmission of pests andpathogens by altering landscape connectivity. In Western forests, a majority of mortalityis mediated by bark beetles (Dendroctonus, Ips, Scolytus) which attack during the lateSpring to early Fall and, in the case of a successful attack, excavate egg galleries in theinner bark from which new beetles emerge in the next Spring (Christensen et al. 1987;Ferrell 1996). Successful attack by bark beetle in one tree in a crowded stand can increaseprobability of attack to proximate conspecifics, due to increased numbers of bark beetlesin the stand as well as proximity to infected trees (Christensen et al. 1987; Das et al.2008; Raffa and Berryman 1983). Proximate and touching canopies and tree root systemscan facilitate the spread of damage agents such as dwarf mistletoe (Arceuthobium spp.)and root rots which decrease photosynthetic capacity (Aukema et al. 2010; Cruickshanket al. 1997; Maloney and Rizzo 2002). Infection by these damage agents may furtherweaken trees, predisposing them to mortality due to bark beetle attack (Ferrell 1996;Kenaley et al. 2008, but see Maloney and Rizzo 2002). Increased forest homogeneity,decreased structural complexity, decreased species diversity, and increased stand densityresulting from fire exclusion can lead to more extensive mortality by increasing supplyand continuity of food resources for host-specific bark beetles (Moritz et al. 2010; Perryet al. 2011).Several studies, however, have pointed out the limited role that stand density can playin forest mortality, with recent forest declines attributed more to the effects of increasingmoisture stress due to prolonged drought, spread and proliferation of native bark beetle

7populations, edaphic conditions, and topographic gradients (Bentz et al. 2010; Das et al.2011; Ganey and Vojta 2011; Lines et al. 2010; Nelson et al. 2007; Sánchez-Martínezand Wagner 2002; van Mantgem and Stephenson 2007; van Mantgem et al. 2009).Drought-induced mortality has been implicated as a primary factor in many widespreadtree die-offs both globally and across the western United States (Allen et al. 2010;Breshears et al. 2005; McDowell et al. 2008; Millar et al. 2011; van Mantgem andStephenson 2007). The role of drought versus elevated insect herbivory on tree mortalitycan be confounded, however, as warming temperatures can also directly influence barkbeetle populations (Bentz et al. 2010). Numerous damaged and standing dead treescaused by windstorms or heavy snow loads have also been shown to lead to widespreadforest mortality (Christiansen et al.1987). Local conditions defined by environmental andedaphic gradients can also influence mortality risk (Allen et al. 2010; Nelson et al. 2007)and confound importance of stand density. Forest structure is tied to the physicaltemplate, and failure to account for the effects of topography on mortality can stronglyoverestimate the role of density. He and Duncan (2000) found that accounting forelevation rendered spurious their initial finding of strong density-dependent mortalitybetween Douglas fir (Pseudotsuga menziesii) and western hemlock (Tsuga heterophylla).Finally, the epidemic or non-epidemic nature of forest mortality can obscure primarydrivers. During epidemic periods, increased populations of bark beetles capable ofoverwhelming tree defenses through sustained mass attacks may overshadow theimportance of tree vigor and stand-level characteristics (Nelson et al. 2006). Differencesin the importance of tree vigor and spatial aggregation may help explain why hazardratings based on stand characteristics have little predictive power when applied to

8landscapes (Logan et al. 1998; Nelson et al. 2006). Logan et al (1998) posit that currentinterest in bark beetle epidemics has biased our understanding of the role of standstructure in augmenting mortality risk: with more self-focusing damage agents on thelandscape, the role of spatial proximity and landscape configurations may become moreimportant than stand characteristics (Holdenreider et al. 2004; Logan et al. 1998;MacQuarrie and Cooke 2011).The Lake Tahoe Basin (LTB) in the central Sierra Nevada provides an ideal studyarea for exploring the relative role of stand density for influencing forest mortalitypatterns. The LTB is characterized by steep elevational gradients, facilitating study ofvarying forest types and influences of environmental gradients on forest mortality.During the study period, the LTB experienced two drought cycles and three wet periodswith bark beetle populations at both epidemic and non-epidemic levels. Extensive forestdecline occurred during the late 1980s to the mid-1990s. By 1991, Elliot-Fisk (1996)estimated 300 million board feet of timber in the Tahoe Basin was dead or dying. Usingremotely sensed imagery, Macomber and Woodcock (1994) quantified mortality in theconifer and red fir zones of the LTB at a 15% loss of timber volume between 1988 and1992. In addition, as elsewhere in the western United States, the LTB experienced majorincreases in stand density following 19 th Century timber harvests and subsequent fireexclusion (Barbour et al. 2002; Taylor 2004).Changing climate regimes, post-settlement logging and fire suppression policiesinstituted in the early 1900s have altered post-settlement (mid 1800s) forest structure andcomposition, with lower elevation post-settlement forests more dense and dominated byshade-tolerant white fir compared to pre-settlement forests (Barbour et al. 2002; Millar

9and Woolfenden 1999; North et al. 2005; Taylor 2004). In Carson Range forests, Taylor(2004) estimates a five-fold increase in tree density and two-fold increase in basal areabetween pre and post-settlement Jeffrey pine-white fir forests. Post-settlement lodgepolepine and red fir (Abies magnifica)-western white pine (Pinus monticola) forests have alsoincreased in density, with species composition in red fir-western white pine forestsincreasingly comprised of lodgepole pine (Taylor 2004).Increases in forest mortality rates, projected climatic changes, and concerns aboutforest health and wildfire severity have led forest management agencies in the LTB, aselsewhere in the western United States, to plan and implement forest-wide densityreductions. The objectives of such density reduction treatments are to mitigate fire riskand restore forest health (Fettig et al. 2007). Although fuel reduction treatments havedemonstrated effectiveness in reducing fire intensity during subsequent wildfires in theSierra Nevada (Graham et al. 1999; Safford et al. 2009), the effectiveness of suchtreatments for improving long-term forest health is not well demonstrated (Bradley andTueller 2001; Fettig et al. 2007, 2008, 2012; Schwilk et al. 2006).Although increased density is often implicated in studies of forest mortality, fewstudies have directly addressed the question: how are forest density and forest mortalityrelated? Remote sensing analysis of Landsat Thematic Mapper (TM) archival imagery inthe LTB provides insight into how stand structure affects mortality at the wholewatershedlevel over decadal time scales. The overall objective of this study was toexamine how density dependent mortality has varied with forest type and for wet and dryperiods over the past 25 years in the Lake Tahoe Basin, which includes forests that arerepresentative of pine and mixed-conifer types throughout the western United States. We

11(Sierra Nevada) of the LTB with minor rainfall amounts during the summer and fallmonths. Average annual precipitation is approximately 79cm.January and February are the coldest months with average minimum temperaturesjust above 19F (-7.22 o C) (Tahoe,CA Western Regional Climate Center (WRCC)http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca8758 ). July and August are the warmestmonths with average maximum temperatures just above 77F (25 o C). The eastern side ofthe LTB, formed by the Carson Range, is much drier. December-March snowfall stillaccounts for the majority of the precipitation inputs with an average monthly snowfall of46.23cm (Glenbrook, NV WRCC http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?nv3205 ).Average annual precipitation is approximately 45cm.LTB forests have been affected by land use practices from both pre- and post-EuroAmerican settlement periods (SNEP 1996). Fire, due to both Native American andlightning ignitions, was common in the Sierra Nevada during the pre-settlement period(SNEP 1996). The LTB experienced extensive deforestation starting in 1860 to providetimber for mines of the Comstock lode, with approximately two-thirds of LTB forests cutby 1930 (SNEP 1996). After the Comstock Era, sheep grazing was widespread withmillions of sheep trailed from California to the Great Basin from 1865 through the 1890s.In the early to mid 1900s, fire suppression policies were instituted by forest managementagencies across the West, including the Sierras and the LTB (SNEP 1996).In many lower and middle-elevation forests of the western United States, frequent fireregulated stand density and favored dominance by fire-tolerant, shade-intolerant pines(Taylor 2004). Dendrochronological reconstructions for the Lake Tahoe Basin of theSierra Nevada indicate that historic mixed-severity surface fire occurred with a mean fire

12rotation interval of 11.4 years (Taylor 2004) in the Jeffrey pine-white fir forest type. Inupper elevation red fir forests, the fire free interval was much greater. Scholl and Taylor(2006) estimated a mean point fire interval of 76 years for old-growth stands on the eastside of the LTB, with fires characterized as having been of low to moderate severity.Lodgepole pine in the Sierra Nevada typically does not produce serotinous cones, likelydid not depend on crown fire for regeneration, and probably experienced crown andsurface fire only infrequently (Parker 1986; Taylor 2004).Forest Types and EcoregionsWe examined forest mortality patterns for five forest types and five ecoregions acrossthe Tahoe Basin using the 2009 USFS CalVeg vegetation classification (Figure 1-2;USDA Forest Service. 1981. CALVEG: A Classification of California Vegetation.CalVegZone3. EVeg Existing Vegetation Tiles 17B, 21A, and 21B). Forest types wereidentified according to dominant forest composition as: Jeffrey Pine Alliance (JP)(Jeffrey Pine or Eastside Pine), Red Fir (RF), Lodgepole Pine (LP), White Fir (WF), andthe Mixed Conifer Alliance (MF) (Table 1-1). We further divided each forest type by oneof five ecoregions defined by the CalVeg dataset to define 15 unique forest type xecoregion areas. We selected only those pixels identified by the coniferous cover typewith the dominant vegetation as WF, RF, EP (East-side pine, incorporated into the JPforest type), JP, LP, and MF. We removed all non-forested areas designated as urban inthe CalVeg dataset, except for certain misclassified areas that were not near urbandevelopment. We buffered all major roads in the LTB by 120 meters (60 meters bothsides of the road) and removed those pixels from the analysis. Pixels located in historicfires (California Department of Forestry and Fire Protection) and in fuel reduction

13treatment areas were also excluded. Pixels dominated by tall (>2m) chaparral cover wereomitted as these contributed to an overestimation of tree leaf area index. Areas formerlydominated by chaparral but classified as tree-dominated pixels in 2009 may have beenincluded in our analysis. However, using high-resolution imagery from the ArcServer(ArcGIS 10, ESRI), we removed all pixels in which chaparral has dominated in recentyears. Other dominant shrub and herbaceous vegetation had diminished photosyntheticactivity by the time of image collection in late September to mid-October, and so did notbias leaf area index estimations. We removed all meadows, edges of small water bodies,areas dominated by aspen (Populus tremuloides), riparian vegetation and developed areassurrounding campgrounds, ski areas, or residential areas.Image Calibration and NormalizationWe used Fall Landsat Thematic Mapper (TM) imagery from 1985 – 2010, availablefrom the USGS Glovis site (http://glovis.usgs.gov/). All pixels used in our analyses werecloud- and snow-free. We subset all images to a bounding rectangle that included theLTB and performed an absolute radiometric correction of errors associated withatmospheric transmission and reflectance between sensor and earth surface for theSeptember 2010 reference image. Images from other years (1985 – 2009) werenormalized to the reference imagery using the IRMAD method (Canty and Nielsen2008). The IRMAD method uses canonical correlation to isolate “true change” due tovegetation change rather than cloud cover or satellite discrepancies between the referenceand the target imagery. This process allowed images from different years, with differentatmospheric conditions, to be truly comparable. Pixels within Lake Tahoe were notincluded in the normalization process.

14Leaf Area Index and Stocking IndexAll images were transformed to Normalized Difference Vegetation Index (NDVI), aratio of red to near-infrared bands which contains information about the health and extentof green vegetation in a pixel (Huete et al. 1997). NDVI is calculated using[1]where NIR=Near-Infrared wavelength, Landsat TM band 4 and VIS=Visible (Red)wavelength, Landsat TM band 3. We used regression modeling against field-measureddata to further transform NDVI into leaf area index (LAI) based on the nonlinear modelof Baret and Guyot (1991), which accounts for NDVI saturation when LAI increases tohigh values. This model uses the following equation:[2] –where VI equals NDVI, VI g equals NDVI value of bare soil, VI∞ equals asymptoticvalue of NDVI when LAI tends toward infinity, and K VI equals the extinction coefficientcontrolling slope of relationship between NDVI and LAI.For model calibration, we used the NDVI and LAI values from 30 field-sampled plotssampled with a LI-COR 2000 Plant Canopy Analyzer (LI-COR, Lincoln, Nebraska). andfound the NDVI value of 0.257 at bare soil (VI g ) in 5 areas in the study area almostcompletely dominated by bare soil and solved for the remaining two parameters: theasymptotic value of NDVI at 0.760 (VI∞ ) and the extinction rate between NDVI andLAI of 0.345 (K VI ). In many coniferous systems, the asymptotic value of NDVI isbetween 0.6 - 0.8 (White et al. 1997).

15We evaluated the accuracy of modeled LAI results using both regression analysis(Figure 1-3a) and the modeling efficiency (EF) statistic (Figure 1-3b, Mayer and Butler1993). The EF statistic is similar to the common R 2 statistic, but compares fit betweenpredicted and observed values against the line of true fit, with values close to oneindicating a perfect fit. Results indicate that modeled LAI is strongly proportional tofield-measured LAI, but is overestimated. However, the overestimation is notproblematic for the stocking index calculation (described below) because this indexnormalizes LAI in that it is used in both numerator and denominator.We used modeled leaf area index to derive an estimate of stocking level (“stockingindex”) where higher values indicate greater conversion of available resources into foliarbiomass. Stocking index was developed from estimated LAI as the ratio of LAI at agiven pixel in a given year, to the 95 th percentile of LAI for the ecoregion encompassingthat pixel (Appendix A). Thus, values for the stocking index ranged between 0 and 1,with values above the 95 th percentile truncated at 1. A stocking index near 1 indicates thatthe leaf area index of a forest is near the maximum encountered for a given ecoregion,which represents a particular combination of forest composition, soil type, andmicroclimate.Forest MortalityTo estimate reductions in canopy vigor, we used the Normalized Difference WetnessIndex (NDWI) and the change in NDWI values (differenced NDWI, dNDWI) betweentwo consecutive years (Gao 1996). NDWI is calculated using[3]

16where NIR=Near-Infrared wavelength, Landsat TM band 4 and SWIR=ShortwaveInfrared wavelength, Landsat TM band 5. NDWI is less sensitive to atmosphericscattering and more sensitive to water absorption, rendering the index better suited todetection of coniferous forest mortality than other vegetation indices, such as NDVI orthe Tasseled Cap Indices (Gao 1996; Hunt and Rock 1989; Jin and Sader 2005;Vogelmann 1990; Vogelmann et al. 2009; Wilson and Sader 2002). Index strength incapturing gradations of foliage health was measured against 254 pixels in 80 polygons ofvarying mortality levels, delineated within USFS aerial survey damage polygons for 2008that were reconstructed using 2009 high-resolution imagery (Figure1-4; USDA ForestService-Forest Health Monitoring, 2008 Damage Polygons). Trees in high mortality plotswere defined by USFS as having over 75% of crown as red/brown/gray needles with treeswith fading crown foliage covering over 75% of the digitized polygon. Areas with lowmortality had less than 25% of fading trees covering the polygon, and medium mortalitypolygons had from 25-50% of the polygon covered by trees with 25-50% of fadingfoliage. Two way-ANOVA with a follow-up Bonferroni multiple comparison test wasused to compare NDWI values with the USFS forest mortality polygons. NDWI valueswere significantly different among high, medium and low, and no mortality pixels (p

17field data. In early fall of 2010, we measured canopy cover using the moosehorn canopymeasurement tool and recorded percent green canopy cover for trees over 5cm DBHacross 24, 60m x 60m plots with varying species composition, density, and amount ofmortality. Average percent green canopy per plot was regressed against the single-year2010 NDWI (Figure1-5). We used linear regression to estimate percent green canopycover per pixel, differenced NDWI among consecutive years to obtain dNDWI, and thenconsidered three possible thresholds of canopy mortality to convert continuous annualmortality maps, based on dNDWI, into binary maps of “healthy” and “unhealthy” pixels.The three dNDWI thresholds considered corresponded to an absolute loss of 5, 10, or20% of green canopy per year. Initial sensitivity analyses revealed that the 20% thresholdwas too strict, but that the 5% loss (corresponding dNDWI= -0.0503) and 10% loss(corresponding dNDWI= -0.106) thresholds exhibited reasonable behavior. Simard et al.(2012) used a similar approach to transform remote-sensing derived mortality maps tobeetle-killed and undisturbed pixels using a threshold of 10% beetle-killed basal area. Insummary, annual mortality maps were derived in the following sequence (Figure 1-6): 1)Image acquisition and processing (a), 2) Transformation of image pairs into single-yearNDWI (b1 and b2), 3) Calculation of differenced NDWI between image pairs in forestedareas of LTB (c), and 4) Transformation of continuous dNDWI into binary classificationof healthy v canopy dieback pixels (d).Mortality processes in tree crowns are defined by progressive changes in spectralindices as canopy foliage dies, with needles turning from green to red to gray andeventually dropping from the tree (Ahern 1988; Dennison et al. 2010; Wulder et al.2006). Pixel NDWI may decline further following the initial onset of mortality due to

18spatial spread of mortality agents among proximate conspecifics, and due to changes inspectral reflectance as needles become chlorotic and fall from tree.Therefore we used only the first incidence of mortality within a four-year period (asdefined by the dNDWI threshold), in a previously-healthy pixel. We used the temporaltrajectories of canopy fade by major bark beetle types within the Basin (Table 1-2) to seta mortality period at four years to allow for the longest sequence of bark beetle attackexpressed as canopy foliage change. Once a pixel reached the dNDWI threshold of atleast five percent loss of green canopy, that pixel was considered “available” formodeling, provided it had at least 6% green canopy.Using thresholded values and the four year window, we derived annual mortality foreach forest type as a quantification of the amount of new mortality seen on the landscapein each year. For the years 1986-1988, we excluded years that had not experiencedmortality in the preceding 1-3 years. Thus, “mortality” as used in this study equates to theonset of a significant canopy dieback event occurring within a 30m x 30m pixel.Mortality ValidationWe validated the five and ten percent loss of green canopy thresholds in 776 pixels,using 2010 high resolution imagery to select areas where gray and red canopies wereeither present (“mortality” pixels, n=277) or absent (“healthy”, n=499). We used the2010-2009 dNDWI values for the healthy pixels and the 4-year minimum of 2007-2006,2008-2007, 2009-2008, or 2010-2009 dNDWI for the mortality pixels. The majority ofmortality pixels were digitized in the High Meadows mountain pine beetle outbreak inthe southern portion of the LTB (Plate 2). Nearly all pixels were a mix of both red and

19gray canopies, a result of the continued presence and spread of MPB damage from about2005-2009 (USDA Forest Service Report #SS09-03).Agreement (kappa statistic or Κ) between our Landsat classification and USFSmortality mapping was greater at the lower threshold of green canopy loss (5% loss:Κ=0.78, 10% loss: Κ=0.56) (Table 1-3a,b). The 10% loss threshold was sensitive tooutbreak mortality but was unable to consistently detect more incremental decreases incanopy vigor. We set the mortality threshold at a loss of at least 5% of green canopy peryear as most sensitive to the distinction between “ healthy” and “mortality” pixels with ahigher overall classification accuracy (5% loss: 89.69%, 10% loss: 81.69%) and higherkappa statistic.ClimateOver the 25-year time series we quantified how relationships between stand structureand mortality changed by forest type and climatic period, using five climatic periodsbased on historical snow levels and winter minimum temperatures (ClimateWNA dataset;Wang et al. 2012). ClimateWNA downscales PRISM (Parameter-elevation Regressionson Independent Slopes Model; Daly et al. 2002) data to derive estimates for seasonalclimatic values based on elevation and geographical location. Because winter snowaccounts for over 80% of precipitation inputs to the LTB, we used snow departure fromnorm over a four-year window to identify years with significant moisture deficits. Wealso used winter minimum temperatures to account for relative differences in snowmeltand also because winter temperatures are known to regulate the distribution and lifecycles of bark beetles, key mortality agents in the Tahoe Basin (Logan et al. 1998).

20We sampled approximately 9,000 points from the ClimateWNA dataset, located on auniform grid with spacing of 300 m. From this data set we derived yearly averages forsnow and temperature, annual departure from norm, as well as a four year movingaverage (current year + 3 previous) for the 1980-2009 period. We correlatedClimateWNA-modeled snow levels with snow water equivalent collected by theCalifornia Cooperative Snow Surveys for the Ward Creek (WR2) and Glenbrook (GL2)areas (Appendix B) to ensure that modeled snow levels agreed with on-the-groundmeasurements. We then identified five climatic periods based on historical accounts ofdry and wet years and periods in the LTB and surrounding areas as well as lagged snowdeficits (Egan et al. 2011; Ferrell et al. 1994; Guarín and Taylor 2005; Millar et al. 2012;Nevada Department of Water Resources, SNEP 1996; USDA Forest Service ReportSS09-12). Because reported dry periods differed in beginning and end dates dependingon location and study timeline, we tested the relationship between mortality and varyinglags of snow deficit to establish beginning and ending dates of the five climatic periods.Here, we accounted not only for antecedent moisture conditions but also the impact ofanomalous wet years that occurred during overall dry periods and dry years that occurredduring overall wet periods. In the Sierra Nevada and elsewhere, tree mortality is not oftencorrelated with single year precipitation deficits and overall tree mortality levels areaffected dissimilarly by differences in drought timing, duration, and magnitude (Bigler etal. 2007; Guarín and Taylor 2005; Millar et al. 2007, 2010). Based on historicalprecipitation and temperature records, Truckee River chronology, historical accounts ofdrought periods in the LTB, and lagged snow deficits derived from the ClimateWNA, we

21established five climatic periods during the 1985-2010 study series (Figure 1-7) ofvarying duration (w1=2years, d1=8years, w2=4years, d2=7years, w3=4years).Data AnalysisWe used the Rare Events Logistic Regression (ReLogit) method to developgeneralized linear models of mortality probability as a function of environmental andstand structure predictors (King and Zeng 2001). As normal logistic regression cansharply underestimate the probability of occurrence for events that are intrinsically rare,the Relogit method adds a pseudo-Bayesian prior to the logistic regression model andadjusts parameter estimates based on the probability of a rare event’s occurrence in thepopulation. This leads to narrower standard errors for each parameter and thus a moreconservative estimate of model error. In our analysis, we found very slight differencesbetween parameter estimates derived in a typical generalized linear model and thoseresulting from the ReLogit algorithm.Logistic regression using the Relogit method was used to model the probability of amortality event within a pixel as a statistical function of forest density and environmentalinfluences. For each forest type in each year, we sampled 20% of pixels that had notexperienced mortality in the preceding four years. For JP, MF, and RF forests that areseparated into five ecoregions, we sampled 20% of pixels from each ecoregion.We used a two-part regression modeling approach to test whether stand structure issignificantly associated with forest health. Mortality at the 5% threshold was statisticallymodeled as a function of environmental variables (ecoregion, elevation and solarradiation) to create a “covariate model” of best fit. Likelihood ratio tests were then usedto evaluate whether inclusion of the stocking variable, in turn, improved the regression

22model beyond the covariate model. The final model was chosen based on highestexplanatory power and model parsimony (Appendix C). Results are interpreted as oddsratios, derived from parameter coefficients as effect sizes for every 0.2 (20%) increase instocking level. Odds ratios greater than 1 indicate a positive relationship betweenpredictor and response and less than 1, a negative relationship. In models where thestocking variable was not included in the final model, the odds ratio for the stockingvariable implicitly equals 1, showing no relationship between mortality and stocking. Therelative frequency of models with positive or negative odds ratios was used to describedifferences among forest types with respect to density-dependent influences on forestmortality. Similarly, the frequency of models with positive or negative odds ratios acrosseach wet and dry period was further used to describe the variable effects of climate ondensity dependent mortality.RESULTSInteraction of Climatic Period and Forest MortalityForest mortality has varied by year and forest type throughout the study period, butwas generally greatest prior to 1995 which spanned the first wet period and first drought(Table 1-4; Figure 1-8; Figure 1-9). Upper-elevation forests (RF and LP) showed highmagnitude spikes of mortality at the end of drought periods, following successive yearsof drought, or following years of abundant snowfall, with low mortality in interveningyears. These upper-elevation forest types also experienced the greatest mean levels offorest mortality overall. JP and MF showed more stable mortality levels, with slightlygreater mortality during the first dry period. WF forests were intermediate, with majormortality events occurring at the end of prolonged drought periods. Mortality levels were

23far greater during the first drought period (1987 – 1994) than during the second droughtperiod of record (1999 – 2005) (Figure 1-9).Influences on the Density Dependence of Forest MortalityA. By Forest TypeThe strength of density dependent mortality was variable in magnitude and directionamong forest types, years, and climatic periods (Figure 1-10). For all forest types, thestrongest positive relationship between stocking level and forest mortality (positiveDDM) occurred between 1993 and 1994, when the risk of mortality was increased by 1.7-2.8 times for every 20% increase in stocking level. The strongest negative relationshipbetween stocking level and forest mortality (negative DDM), consistent among foresttypes, occurred in 1985. Negative DDM was exhibited to a greater degree from 1995-1999 with positive DDM more frequent during the 1987-1994 and 2000-2009 periods.Over the time series, JP had the highest average density-dependent mortality with anodds ratio of 1.16 (95% CI = 1.05 to 1.27), followed by LP, with an average odds ratio of1.10 (95% CI = 1.01 to 1.19). Average odds ratios for MF, RF, and WF forests weremuch lower at 1.06 (95% CI = 0.92 to 1.20), 1.05 (95% CI = 0.96 to 1.14), and 1.03(95% CI = 0.87 to 1.19) respectively (Table 1-5; Figure 1-11). Relative to other foresttypes, JP and LP forests exhibited negative density-dependence least frequently andpositive density-dependence most frequently at 44% and 40% of the time seriesrespectively (Figure 1-12; Figure 1-13). Mortality in RF forests exhibited positive densitydependence over 40% of the time series, but exhibited negative density dependence over36% of the time series. In MF forests, positive DDM occurred half as frequently asnegative DDM, which occurred over 40% of the time period.

24Over at least half of the time period, WF and JP models showed no relationshipbetween density and mortality. Mortality and density were not associated in 48% of LPmodels. In MF and RF forests, density and mortality were associated over a greatermajority of the time series, at 36% and 24% respectively. Relative frequency of positiveto negative DDM over the time period was highest for JP (11), LP (3.3), and RF (1.1)(Figure 1-14). Ratio of positive to negative density-dependence was less than one and ofsimilar magnitude for WF (0.57) and MF (0.6), indicating that these forests experiencednegative DDM more frequently than positive DDM over the time series.B. By Climatic PeriodClimate period influenced the strength and direction of average density-dependentmortality, with both drought periods exhibiting positive DDM and the first two wetperiods exhibiting negative DDM (Table 1-6). Average strength of DDM was highestoverall during the first drought period at 1.20 (95% CI = 1.06 to 1.34), followed by thesecond drought at 1.08 (95% CI = 1.02 to 1.14), and the third wet period at 1.03 (95% CI= 0.96 to 1.11). Average DDM was lowest in the first wet period (0.89, 95% CI = 0.82 to1.11) and the second wet period at 0.98 (95% CI = 0.89 to 1.07).In wet periods, the frequency of negative DDM equaled or exceeded that of positiveDDM (Figure 1-15). During the first wet period, negative DDM was found in 60% of allmodels with lack of DDM in the remaining models. In the later two wet periods, negativeDDM was evidenced in 40% and 30% respectively of all models. During both dryperiods, positive DDM accounted for 40% of the time period and mortality was notassociated with density over roughly 45% of each dry period. Negative DDM was

25slightly more frequent during the second dry period compared to the earlier droughtperiod.Model results showing no relationship between density and mortality occurred mostfrequently in dry periods. Models showing no relationship between mortality and densityaccounted for approximately 47% and 45% of d1 and d2, respectively. In the first andthird wet periods, mortality was not associated with density in over 40% of all modelsoccurring during the climate period. In the second wet period, mortality was notassociated with density in over 35% of all models.C. Forest Type and Climatic PeriodAverage strength of DDM varied by forest type and climatic period, with positiveDDM evident for JP and LP in both wet and dry periods and DDM more variable for MF,RF, and WF (Figure 1-16). During the first wet period, all forests exhibited averageDDM between 0.85 and 0.96. During the first dry period, all forests exhibited positiveDDM, with highest average DDM for JP and MF and lowest for LP. In the second andthird wet period, average DDM was between 0.93 and 0.98 for MF and RF. During thesewet periods, average DDM for JP and LP was approximately 1.1 in the second wetperiod, and declined to approximately 1.06 in the third wet period. For WF, averageDDM was positive in the third wet period with an average odds ratio of approximately1.13 and between 0.83 and 0.91 in the second wet period and second dry periodrespectively.In the first dry period, all forest types exhibited positive DDM to a much greaterdegree than negative DDM (Figure 1-17). This separation is magnified in JP and RFforests. Negative and positive DDM were of more similar frequency in LP, MF, and WF

26forests. In the second dry period, both JP and RF maintained D1 disparities betweenpositive and negative DDM, with positive relationships accounting for a greaterpercentage of each time period. However, in MF and WF forests negative DDM is eitherequal (MF) or of greater (WF) frequency than positive DDM.JP and LP forests maintained highest or near-highest positive DDM over all climaticperiods. For MF and RF forests, however, negative DDM is more frequent than positiveDDM during each wet period. WF behaves as other forest types during the first drought,but then exhibits a different pattern than the other forest types, with positive DDMdominant in the second wet period and negative DDM dominant in the second dry periodand the following wet period.DISCUSSIONWe found evidence for both positive and negative density-dependent mortality acrossall forests of the LTB. No forest type showed a persistent, unidirectional relationshipbetween density and mortality over the entire time series and no forest type showed arelationship between density and mortality in every year of the 25-year time period.Rather, relationships between mortality and density were variable over the 25-year timeperiod in ways that were somewhat consistent for each forest type.Positive density dependent mortality suggests competition as a causal mechanism,although other explanations are possible. For example, denser stands may facilitatespread of pathogens and insect herbivores (Das et al. 2008), or may simply indicate areasof previously high rates of establishment on currently unfavorable microsites(Greenwood and Weisberg 2008). However, if competition is to be inferred from positivedensity dependent mortality, our results show that competition has been a relatively weak

27but persistent factor for forest mortality in the LTB, with its importance heightened indrier forest types and during drought periods. Our results parallel those of other studiesthat have inferred a limited role of competition for leading to increased forest mortality inmature and old-growth forests (Das et al. 2011; Floyd et al. 2009; Ganey and Vojta2011; Lines et al. 2010; van Mantgem and Stephenson 2007; van Mantgem et al. 2009;Vygodskaya et al. 2002).Our study agrees with others finding that extended dry periods and increased moisturestress are more proximal causes of elevated mortality than are stand densities (Das et al.2012; Floyd et al. 2009; Lines et al. 2010; van Mantgem and Stephenson 2007; vanMantgem et al. 2009), with one important caveat. While upper-elevation RF and LPforests experienced high mortality levels during dry periods, we surmise that this forestdecline was augmented by the preceding wet and warm period during the early 1980s.High numbers of damaged trees, relicts of prolonged winter storms in the early 1980’smay have provided abundant resources and substrate for epidemic bark beetlepopulations (Christiansen et al. 1987). In the interior West, MPB outbreaks often followextremely wet periods, with elevated temperatures important in regulating MPBabundance, distribution, and epidemics (Bentz et al. 2010; Logan et al. 1998). In addition,while extended drought often reduces tree vigor, it does not necessarily predispose treesto sustained and successful attack as many beetles select high-quality food resources(Christiansen et al. 1987; Logan et al. 1998).Variations in the strength of density dependent mortality during non-epidemic orepidemic periods have been posited in many studies and the preponderance of forestmortality studies during or following epidemics may have biased our understanding of

28the role of stand density (Logan et al. 1998). In the Sierra Nevada and elsewhere, studiesshowing strong, positive density-dependence have been conducted during periods ofepidemic forest decline (Ferrell et al. 1994, Egan et al. 2011), with little support for suchrelationships during non-epidemic periods (Das et al. 2011; Sanchez-Martinez andWagner 2002; van Mantgem and Stephenson 2007; van Mantgem et al. 2009, but seeFloyd et al. 2009). As mortality becomes more widespread and spatially aggregated,forest structure at the stand level may become less important. The issue becomes one ofscale: mortality during non-epidemic periods is expressed at smaller scales and driven byprocesses that operate at that scale. Mortality during epidemic periods is more of alandscape-scale phenomenon (Aukema et al. 2006; Holdenreider et al. 2004; Levin 1992;Nelson et al 2006). In our study, the strength of DDM was generally highest for all foresttypes during a period of reported insect outbreak in the LTB. In this case, strength ofdensity dependent mortality at the stand level was imposed by a larger-scale constraint atthe landscape level (beetle population clusters) with low magnitude variations imposedby the characteristics of species assemblages and forest types (Levin 1992).During non-epidemic bark beetle conditions that were more typical of the yearsfollowing 1994, distinctions between strength of DDM in forest types and climates aremore evident. In the mesic and lower-stress environments of WF and MF, negative DDMdominated or was extremely weak. In more stressful environments at upper and lowerelevations, increased resource scarcity for shared and limited resources may have fosteredincreased competition in less diverse (JP and LP) or more clumped (RF) forests. Das et al(2008) found similar distinctions in the relative strength of DDM between fir and pine.

29For sugar pine, mortality risk increased with increasing density of conspecifics but forwhite fir, mortality risk decreased with increasing density of conspecifics.In JP forests, DDM during the second drought period was similar to subsequent andpreceding wet periods. For LP and RF forests, however, positive DDM during the seconddry period might be more representative of environmental conditions conducive togrowth in subalpine communities: warm and dry weather (Millar et al. 2004). Acceleratedgrowth rates and increased recruitment in existing clumped RF forests and in gaps in LPstands may have fostered increased competition for space and nutrients.While weak but significant and persistent density-dependent mortality was found inour study, we do not find support for the widely held assumption that increased densitydependentmortality drives more severe and widespread mortality. For example, althoughpositive density dependence is evident in JP forests, forest mortality levels for this typeare the most stable and of lowest magnitude of all forest types over the time period. Ourresults further suggest that findings developed at the stand level may not translate directlyto understanding forest mortality patterns at the landscape level.Our findings suggest that climate may play a greater role than forest structure forinfluencing forest mortality patterns in the central Sierra Nevada. Further, the effect offorest structure on forest mortality may be closely linked to climate variability. For red firand mixed fir forests in particular, the direction of DDM is linked to climate, with dryperiods characterized by positive DDM and wet periods characterized by negative DDM.These results are in accord with numerous studies of plant-plant interspecific interactionsthat have found facilitation to be most important in environments of high abiotic stress,and competition most important in moderate conditions where stress is low (e.g. Bertness

30and Callaway 1994; Callaway 1998). Dense and clumped stands at upper elevations canameliorate harsh environmental extremes, such as high winds, heavy snowloads, andprolonged extreme minimum temperatures (James et al. 1994; Lingua et al. 2008;Tranquillini 1979). In our study, density dependence switched from a positive to negativeeffect during wet periods characterized by increasing stress from heavy snow loads.Stand density may play an important facilitative role in favoring establishment,recruitment, and survival during such wet winters.Management ImplicationsOur results provide guidance for forest managers seeking to use density reductiontreatments to improve forest health in the Sierra Nevada. First, stand density reductionsmay not consistently lead to forest health benefits in middle and upper-elevation forests.Red fir and mixed fir stands with greater densities experienced less mortality, especiallyduring wet periods. In such forests, any beneficial impacts of thinning treatments to foresthealth are likely to be short-lived, with reversal in subsequent wet periods. Second,density reduction treatments may improve forest health for low-elevation forests whereincreased tree density was associated with increased forest mortality. However, suchforests did not experience widespread mortality over the period of record. For Jeffreypine forests, where positive DDM is most frequent of all forest types, annual forestmortality levels were lowest and most stable, suggesting limited forest-wide impacts ofstand-level risk. Positive relationships between forest density and subsequent forestmortality were apparent in less than half of years, even for the Jeffrey pine forests. Forestmanagers of the Lake Tahoe Basin should expect the forest health benefits of thinning

31treatments to be variable, inconsistent, limited to periods of non-epidemic mortality, andto be greatest for forests dominated by shade-intolerant pine species.

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49Table 1-1. Description of LTB forest types defined using the USFS CalVeg vegetationclassification (USDA Forest Service. 1981. CALVEG: A Classification of CaliforniaVegetation).Forest Type Species Composition Elevation Number of Number ofRange(m) Ecoregions PixelsJeffrey PineAlliance (JP)Lodgepole (LP)Mixed Conifer –Fir Alliance(MF)Red Fir Alliance(RF)White FirAlliance (WF)Jeffrey Pine (Pinus Jeffreyi Balf.),Ponderosa Pine (Pinus ponderosae Dougl.ex. Loud.), White Fir (Abies concolor A.Murray), Washoe Pine (Pinus washoensis)1890-2746 5 32,024Lodgepole pine (Pinus contorta var. 1897-3031 1 25,449murrayana Grev.& Balf.)White Fir, Jeffrey Pine, Lodgepole Pine, 1889-2717 5 142,878Sugar Pine (Pinus lambertiana A.Murray), Incense Cedar (Calocedrusdecurrens)Lower Elevations: White FirUpper Elevations: Red Fir (Abiesmagnifica A. Murr.)Red Fir, White Fir, Western White Pine 1987-2878 5 82,521(Pinus monticola Dougl.), Lodgepole Pine,isolated Mountain Hemlock (Tsugamertensiana)Pure white fir in cool, moist, shady 1900-2638 1 14,522environments

50TABLE 1-2. Host-specific native bark beetles of LTB forests and impact of successfulattack on canopy foliage.Mountain PineBeetle(Dendroctonusponderosae)Common Bark Beetles of Sierra Nevada ForestsCanopy FadeSpectral SignatureCanopy Fade TemporalSignatureFlightSeasonGreen –Yellow- Generally 8-10June toReddish/Brown- months after attack. In AugustBrownunusually dry years,Progresses upward foliage fade may beginor downward within a fewthroughout crown weeksHostLP,PP,SP,WWPNo. Trees KilledWestern PineBeetle(Dendroctonusbrevicomis)Pale green –Yellow-Red-Reddish/BrownEarly Season Attack:yellow to red in sameseason,Late Season Attack: yellowto red by Spring offollowing seasonPP Low population #:Single tree, smallgroupsHigh pop. #:groups of 10-20treesRedTurpentineBeetle(Dendroctonusvalens)Jeffrey PineBeetle(Dendroctonusjeffreyi)Green –Yellow-Reddish/Brown-BrownFades evenlythroughout crownYellow to red in yearfollowing infestationSpring toearly FallJune toOctoberJP,LP,PP, SPJPTree mortalitytends to occur ingroups,ranging from 10 to50 to severalhundredFir Engraver(Scolytusventralis)Pine Engraver(Ips pini)CaliforniaFive-SpinnedIps (Ipsparaconfusus)Yellow to redGreen –Lime green- Yellow-Reddish/Brown-BrownGreen –Lime green- Yellow-Reddish/Brown-BrownYellow to red in yearfollowing infestation.USFS: Can kill upperportion of crown or entiretree in a single summer.Begins fade within fewmonths after attack.Depending on tree speciesand weather, may fade bylate summer/early fall orSpring of following season.June toSeptemberRF,WFJP,LP, PPJP,LP,PP, SPUsually

51TABLE 1-3a,b. Validation of remote sensing mortality classification with overallclassification accuracy (oca) and kappa statistics for two values of dNDWI,corresponding to a 5% and 10% loss of green canopy per pixel. The kappa statistic is ameasure of agreement between predicted and actual values in a category.a. Classification matrix of healthy v mortality pixels classified at the >=5% loss of greencanopy threshold (dNDWI= -0.053)Predicted (5%)Actual(5%)healthymortalityhealthy mortality N458(91.78%) 41 (8.22%) 49939 238(14.08%) (85.92%) 277oca=89.69%, kappa=.78b. Classification matrix of healthy v mortality pixels classified at the >=10% loss of greencanopy threshold (dNDWI= -0.106)Predicted (10%)healthymortalityNActual(10%)healthy 498(99.8%) 1 (.2%) 499mortality 139 (50.18%)138(49.82%) 277oca=81.69%, kappa=.56

52TABLE 1-4. Annual mortality summary statistics by LTB forest type and climatic period(d1: 1987-1995, d2: 1999-2006, w1: 1985-1987, w2: 1995-1999, w3: 2006-2010).Forest TypeMean SD Min Max nJP 12.73 5.00 2.65 21.13 25LP 15.88 9.60 5.56 44.02 25MF 12.61 6.19 1.92 24.41 25RF 17.39 9.41 3.98 42.88 25WF 12.74 7.73 2.52 40.96 25Climatic Periodd1 17.60 7.53 6.64 42.88 40d2 10.71 5.65 2.01 30.87 35w1 18.87 13.67 5.25 44.02 10w2 14.87 6.64 6.60 32.52 20w3 10.96 5.85 1.92 25.13 20

53TABLE 1-5. Summary of odds-ratios for the stocking-level predictor variable fromannual models of mortality by forest type over the 25-year time series (SD=StandardDeviation). Odds ratios are derived from annual mortality models in each forest type andshow the change in the probability of mortality for every 0.2 increase in stocking level.Forest Type Mean SD Min Max nJP 1.16 0.28 0.92 2.23 25LP 1.10 0.23 0.64 1.77 25MF 1.06 0.36 0.76 2.62 25RF 1.05 0.23 0.71 1.92 25WF 1.03 0.42 0.60 2.70 25

54TABLE 1-6. Summary of odds-ratios for the stocking-level predictor variable fromannual models of mortality in each climate period occurring in the 25-year time series(SD=Standard Deviation).Climatic Mean SD Min Max nPeriodd1 1.20 0.46 0.60 2.70 40d2 1.08 0.19 0.66 1.53 35w1 0.89 0.11 0.71 1.00 10w2 0.98 0.21 0.64 1.51 20w3 1.03 0.19 0.67 1.65 20

FIGURE 1-1. Location of Lake Tahoe Basin (LTB) study area.55

56FIGURE 1-2. Lake Tahoe Basin (LTB) forest types per the USFS CalVeg Classification(USDA Forest Service. 1981. CALVEG: A Classification of California Vegetation ).

57a.b.FIGURE 1-3a, b. LAI Validation. Relationship between field-measured leaf area index(LAI) measured with LICOR LAI-2000 Plant Canopy Analyzer in 30 plots in the LTBand LAI modeled from normalized difference vegetation index (NDVI), derived fromremote sensing imagery (a). Modeling efficiency of observed vs. modeled LAI. Modelingefficiency measures agreement between observed and simulated values against the line ofperfect fit (blue) rather than the regression line (b, Mayer and Butler 1993).

58FIGURE 1-4. Values of the single year normalized difference vegetation index (NDWI)across 254, 30m x 30m pixels digitized in areas ranging from severe and extensivemortality across the pixel (High) to pixels without evident canopy damage (None).Results of the ANOVA with follow-up Bonferroni comparison are significant, validatingthe sensitivity of the single-year NDWI to LTB mortality (F=33.27, p < 0.006).

60b1.b2.d. c.FIGURE 1-6. Pictorial sequence of remote sensing processing used to derive annualmortality maps. Level 1 terrain corrected Landsat TM 5 satellite image is acquired,resized to study area, and normalized to reference imagery (a).Year one (ex.2009) andyear two (ex. 2010) normalized images are transformed into single-year NDWI maps (b1,2009: b2, 2010). Continuous differenced NDWI (dNDWI) map (c) is derived bysubtracting 2009 NDWI (Year 1) from 2010 NDWI (Year 2). Map of continuous dNDWIis classified into maps of healthy and canopy dieback pixels using validated thresholds(dNDWI

Average Winter MinimumTemperature ( o C)Snow: Depature from 30 Year Norm610-2-41980 1985 1990 1995 2000 2005w1 d1 w2 d2 w31.51.41.31.2SnowDeficit-6-8-10-121.110.90.80.70.6AverageWinterMinimumTemp.FIGURE 1-7. Derivation of five climatic periods throughout the 1985-2009 study period,with 1980-1984 for reference. Single-year snow deficits are derived from the currentyear/30-year norm precipitation as snow variable from the ClimateWNA dataset (Wanget al. 2012). Four-year lagged deficits (average of current year deficit + 3 previous years)replicate historical accounts of dry and wet periods in the LTB. Average winter minimumtemperature is current-year temperature. Climatic periods: w1=1985-1987 (August 1985-July 1986, August1986-July 1987, 2-year duration); d1=1987-1995, 8-year duration:w2= 1995-1999, 4-year duration; d2= 1999-2006, 7-year duration; w3: 2006-2010, 4-year duration.

62FIGURE 1-8. Mortality levels and pattern by forest type over the 25-year time series(with smooth local estimator (lowess) curve).

63FIGURE 1-9. Mortality levels by forest type and climatic period (w1: 1985-1987, d1:1987-1995, w2: 1995-1999, d2: 1999-2006, w3: 2006-2010).

64FIGURE 1-10. Direction and magnitude of density dependent mortality as odds ratios forevery absolute 20% increase in stocking level by forest type over the 25-year timeinterval. Confidence intervals at the 95 th percentile are shown. Odds ratios symbolized by“x” show years in which stocking variable was not included in final model, due tooutcome of likelihood ratio test. Odds ratios symbolized by “0” show years in which thestocking variable was included in final model.

65FIGURE 1-11. Box plot of odds ratios of density-dependent mortality across 25 annualmodels derived for each forest type. Odds ratios illustrate the change in the probability ofmortality for every 0.2 (20%) increase in stocking level.

66FIGURE 1-12. Number of models where; 1) Stocking level alone or in combination withenvironmental variables appeared in final, most parsimonious model, 2) Environmentalvariables only appeared in final, most parsimonious model or, 3) Mortality was notexplained by stand structure or environmental variables.

Percent of 25-Year Time Period671009080706050403020100JP LP MF RF WFForest TypePositiveDDMNoAssociationNegativeDDMFIGURE 1-13. Type of density dependent mortality exhibited over percent of 25-yeartime series by direction. Positive DDM: increased density increases probability ofmortality; Negative DDM: decreased density increases risk of mortality, and NoAssociation: no association between density and mortality.

Ratio: Positive DDM:Negative DDM6811109876543210JP LP MF RF WFForestTypeFIGURE 1-14. Ratio of positive to negative density-dependent mortality over five foresttypes. Positive density-dependent mortality indicates an increase in the probability ofmortality with increased density. Negative density-dependent mortality indicates adecrease in the probability of mortality with increased density.

Percent of 25-Year Time Period691009080706050403020100d1 d2 w1 w2 w3Climatic PeriodPositive DDMNoAssociationNegative DDMFIGURE 1-15. Type of density dependent mortality exhibited over percent of eachclimatic period in all five climatic periods by direction. Positive DDM: increased densityincreases probability of mortality; Negative DDM: decreased density increases risk ofmortality, and No Association: no association between density and mortality.

70FIGURE 1-16. Average odds ratio for every 0.2 (20%) increase in stocking level byclimatic period and forest type. Error bars reflect standard errors.

71FIGURE 1-17. Relative frequency of negative (N) or positive (P) density-dependentmortality found in each forest type in each of four climatic periods (d1, d2, w2, w3).

72APPENDIX A. 95 th Percentile of Leaf Area Index ( LAI) values in 17 forest groups (Five Forest Types (LP, MF, JP, RF, WF)with MF, JP, and RF forests further separated by five ecoregions (EH, EJ, EK, EL, ET) per the 2009 update to the USFS CalVegvegetation classification. Year: Value of LAI at 95 th percentile by year.ForestType EcoRegion Group 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997LP LP LP 3.31 3.42 3.15 3.50 3.44 3.48 3.50 3.25 3.46 3.07 4.16 3.19 3.16MF EH MFEH 6.30 6.32 5.93 5.81 5.29 6.14 5.22 5.21 4.96 3.89 5.35 4.63 4.88MF EJ MFEJ 5.04 5.02 4.63 4.66 4.41 4.83 4.49 4.52 4.36 3.51 4.82 4.36 4.47MF EK MFEK 4.06 4.26 4.06 4.38 4.11 4.08 4.04 3.79 4.14 3.43 4.56 3.97 4.03MF El MFEl 5.53 5.68 5.37 5.46 5.03 5.43 4.61 4.55 4.64 3.87 5.06 4.77 4.81MF ET MFET 4.06 4.17 3.91 3.98 3.76 3.70 3.40 3.23 3.13 2.97 3.55 3.37 3.49JP EH PEH 4.84 5.00 4.59 4.39 4.05 4.34 3.95 4.03 3.90 4.03 4.39 3.65 3.98JP EJ PEJ 4.25 4.58 4.18 4.48 4.11 4.41 4.54 4.12 4.55 3.35 5.15 3.96 4.11JP EK PEK 4.53 4.79 4.46 4.71 4.40 4.18 4.04 3.89 4.14 3.67 4.19 4.16 4.21JP EL PEL 4.64 4.59 4.51 4.46 4.18 4.08 3.91 3.83 3.90 3.46 4.19 3.86 3.88JP ET PET 3.69 3.62 3.33 3.21 3.31 3.23 2.98 2.86 2.69 2.87 2.97 3.03 2.90RF EH RFEH 3.52 3.48 3.32 3.32 3.53 3.66 3.61 3.51 3.49 2.97 3.83 3.25 3.33RF EJ RFEJ 4.16 3.88 3.76 4.10 4.06 4.28 4.22 4.29 3.93 3.37 4.39 4.08 3.95RF EK RFEK 3.32 3.23 2.99 3.38 3.36 3.35 3.38 3.24 3.29 3.08 3.66 3.22 3.16RF EL RFEL 4.34 4.26 3.97 4.27 4.28 4.38 3.95 3.67 3.85 3.35 4.58 3.89 3.90RF ET RFET 3.52 3.55 3.15 3.38 3.42 3.26 3.19 3.08 2.84 2.94 3.28 3.04 3.01WF WF WF 5.49 5.46 5.14 5.27 4.99 5.60 5.21 5.01 5.08 3.96 5.47 4.98 5.00

ForestType EcoRegion Group 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010LP LP LP 3.12 3.33 3.21 3.30 3.65 3.46 3.48 3.12 3.40 3.23 3.48 3.37 3.56MF EH MFEH 4.77 5.16 5.13 4.95 5.21 5.20 5.32 4.96 5.11 4.92 5.03 4.74 5.32MF EJ MFEJ 4.32 4.71 4.50 4.35 4.71 4.58 4.63 4.49 4.70 4.46 4.28 4.33 4.79MF EK MFEK 3.93 4.16 4.20 4.27 4.37 4.20 4.39 3.90 4.19 4.04 4.35 4.08 4.38MF El MFEl 4.60 4.98 4.78 4.72 4.85 4.88 5.14 4.60 4.84 4.46 4.69 4.55 4.96MF ET MFET 3.45 3.60 3.48 3.55 3.54 3.49 3.65 3.42 3.68 3.52 3.59 3.46 3.68JP EH PEH 4.01 4.13 4.14 4.04 4.30 4.16 4.25 3.83 4.05 4.14 4.21 3.87 4.28JP EJ PEJ 4.11 4.50 4.22 4.46 4.67 4.67 5.06 4.46 4.65 4.32 4.86 4.59 4.56JP EK PEK 4.11 4.41 4.30 4.55 4.50 4.22 4.66 4.00 4.47 4.32 4.46 4.16 4.38JP EL PEL 3.87 4.05 3.98 3.94 3.94 3.98 4.02 3.91 4.07 4.06 3.98 3.99 4.19JP ET PET 3.03 3.05 2.82 2.90 2.82 2.89 2.91 2.96 3.09 2.97 2.91 2.79 2.99RF EH RFEH 3.25 3.52 3.31 3.30 3.54 3.47 3.47 3.42 3.42 3.28 3.53 3.35 3.61RF EJ RFEJ 3.86 4.29 4.05 3.99 4.30 4.08 4.26 4.24 4.30 4.32 4.35 4.16 4.57RF EK RFEK 3.12 3.33 3.18 3.30 3.33 3.30 3.32 3.22 3.29 3.11 3.28 3.22 3.36RF EL RFEL 3.73 4.13 3.93 3.93 4.03 4.06 4.25 4.00 3.95 3.76 3.88 3.84 4.15RF ET RFET 3.11 3.22 3.07 3.01 3.08 3.02 3.15 3.10 3.18 2.99 3.09 2.98 3.17WF WF WF 4.88 5.50 4.99 4.98 5.21 5.25 5.35 5.18 5.44 4.91 4.86 4.59 5.2973

198019821984198619881990199219941996199820002002200420062008Departure from 30-yr Norm (as ratio)74APPENDIX B. Relationships among modeled snow deficits, Snow Water Equivalent (SWE)from California Cooperative Snow Surveys, and Palmer Drought Severity Index (PDSI) over thestudy period. Appendix C.1: Snow deficit is 4-year averaged snow deficit (Current Year/30 YearNorm (1980-2009)) derived from the ClimateWNA dataset. Appendix C.2 Monthly April PDSIfor California Region 3 compared against Snow Survey SWE and Snow Deficits.1.61.51.41.31.21.110.90.80.70.6April SnowWaterEquivalent: 4-Year AverageSnow Deficit: 4-Year AverageAppendix C.1

198019821984198619881990199219941996199820002002200420062008Snow Departure (Current Year/30-Year Norm)PDSI751.7101.51.31.10.90.70.586420-2-4-6April PDSI4-YearAveragedSnow Deficit4-Yr AverageSWE: CAApril 1 SnowSurveysAppendix C.2

76APPENDIX C. Final models in five forest types in 25-year study period. LRT =Likelihood Ratio TestForestType Year Model LRT ModelTypePr(>Chisq)JP 1985 Mortality~Group+sr+stk 2.98E-03 environment + stkLP 1985 Mortality~elevation+sr 3.01E-01 environmentMF 1985 Mortality~Group+sr+stk

77ForestType Year Model LRT ModelTypePr(>Chisq)LP 1992 Mortality~1 7.45E-01 nullMF 1992 Mortality~Group+elevation+sr+stk 1.47E-02 environment + stkRF 1992 Mortality~Group+sr+stk 1.04E-04 environment + stkWF 1992 Mortality~sr 3.24E-01 environmentJP 1993 Mortality~Group+sr+elevation+stk

78ForestType Year Model LRT ModelTypePr(>Chisq)LP 2000 Mortality~sr+stk 3.23E-05 environment + stkMF 2000 Mortality~Group+elevation+sr+stk 3.93E-03 environment + stkRF 2000 Mortality~Group+elevation+stk 6.64E-11 environment + stkWF 2000 Mortality~elevation+sr 1.69E-01 environmentJP 2001 Mortality~Group+stk 7.82E-05 environment + stkLP 2001 Mortality~stk 4.85E-03 stkMF 2001 Mortality~Group+elevation+sr 1.68E-01 environmentRF 2001 Mortality~Group+sr+stk 6.23E-08 environment + stkWF 2001 Mortality~sr 2.47E-01 environmentJP 2002 Mortality~Group+elevation+sr 6.37E-01 environmentLP 2002 Mortality~elevation+sr

79ForestType Year Model LRT ModelTypePr(>Chisq)WF 2007 Mortality~elevation+sr+stk 1.62E-03 environment + stkJP 2008 Mortality~Group+elevation 5.41E-01 environmentLP 2008 Mortality~elevation+stk 9.86E-03 environment + stkMF 2008 Mortality~Group+sr 5.34E-02 environmentRF 2008 Mortality~Group+stk 1.65E-03 environment + stkWF 2008 Mortality~elevation 6.87E-01 environmentJP 2009 Mortality~Group+elevation+stk 1.40E-02 environment + stkLP 2009 Mortality~sr 7.34E-01 environmentMF 2009 Mortality~Group+sr+stk 2.13E-02 environment + stkRF 2009 Mortality~Group+sr+stk 7.67E-04 environment + stkWF 2009 Mortality~sr+stk 1.96E-02 environment + stk

80Chapter 2 - Spatial and Temporal Patterns of Forest Mortality in Lake TahoeBasin, USA: Influence of Climate and Environment.Kristin Jane Van Gunst a , Peter J. Weisberg a,b .a Department of Natural Resources and Environmental Science, University of NevadaReno, 1664 N. Virginia Street, Reno, NV, 89557.b Program in Ecology, Evolution and Conservation Biology, University of Nevada Reno,Mail Stop 314, Reno, NV, 89557.cCorresponding Author: Phone: 775.784.4020, Fax: 775.784.4583 Email:kvangunst@cabnr.unr.eduEmail. P. Weisberg: pweisberg@cabnr.unr.edu.

81ABSTRACTGlobal increases in forest mortality have been widely attributed to changing climate andaltered disturbance regimes, yet there is limited understanding of how forest mortalitytrends and spatial patterns vary with forest type, climate and topographic variability.Using the Landsat TM archival database, we investigated how patterns of forest mortalitychanged annually over five forest types and five climatic periods from 1985-2010 in theconiferous forests of the Lake Tahoe Basin, in the central Sierra Nevada. In addition, wequantified the relationships between mortality and environmental gradients defined byelevation and solar radiation. Our results show that mortality levels and patterns aredissimilar across forest types and climatic periods, suggesting that increases in forestmortality are driven more by unique sequences of extreme events rather than variabilityin normative conditions. Throughout the time series for almost all forests, mortality riskwas greater on north-facing slopes. Relationships between mortality and elevation arepredictable for lower-elevation forests, with decreases in elevation consistently associatedwith greater mortality risk. For middle- and upper-elevation forest types, increasedmortality risk is associated with increases in elevation during wet periods and decreasesin elevation during dry periods. Results of our study corroborate those of other studies inxeric forests that have found increased mortality risk on north-facing slopes and withdecreasing elevation during droughty periods. In the Lake Tahoe Basin, widespreadforest mortality has occurred during both wet and dry periods but with differentcharacteristic patterns.

83mountainous regions of snow-dominated hydrology, lower elevation forests are subject toearlier draw-down of snowpack runoff inputs to soil moisture, experiencing a seasonaldrought lasting from mid-Summer to early-Fall depending on previous winter snowpack.Increased droughty conditions have been correlated with increased mortality, likely dueto moisture stress that predisposes trees to successful beetle attack (Allen et al.2010).Studies of both short-term and long-term increases in coniferous forest mortalityassociated with drought conditions have found increasing mortality at lower elevations(Ferrell et al. 1994; Millar et al. 2007, 2012; Negrón et al. 2009; van Mantgem andStephenson 2007) although others have found no relationship between elevation andmortality (Ganey and Vojta 2011).Upper-most elevations receive highest snowfall inputs and experience a shortenedgrowing season bracketed by low minimum temperatures not conducive to tree growth.Studies of treeline dynamics have illustrated the role of climate in fostering differentialgrowth and mortality patterns. Establishment above treeline or into subalpine meadows isgreatest during warm and dry periods; during cold and wet periods mortality levels oftenincrease (Lloyd and Graumlich 1997; Millar et al. 2004; Villalba et al. 1994). Lloyd andGraumlich (1997) also found that mortality of subalpine conifers increased withincreasing elevation and posited that, although mortality severity is unlikely to be greatestat upper elevations, higher transpiration rates at upper elevations or increasedvulnerability of trees established in marginal sites might lead to mortality due to waterstress. Villalba et al. (1994) also showed that upper-elevation xeric habitats can beaffected by moisture stress.

84Many studies have attributed elevated levels of forest mortality on south-facingslopes or drier sites due to increased moisture stress (Dobbertin et al. 2005; Oberhuber2001; Vilà-Cabrera et al. 2011; Worrall et al. 2008). However, other studies haveobserved the opposite relationship where increased mortality was associated with northfacingslopes (Guarín and Taylor 2005; Millar et al. 2012). Differences between northfacingand south-facing slopes exert a major influence on soil moisture. South-facing,gentle slopes experience heightened and prolonged solar radiation while north-facingslopes are more mesic and characterized by lower heatloads. Stands on gentle, northfacingslopes are often more productive and of higher tree density while stands onsouthern aspects are more sparse. Studies of the influence of aspect or solar radiation onmortality susceptibility engender two hypotheses: 1)Mortality is elevated on southernslopes because soil moisture is reduced below critical levels during dry periods, and2)Mortality is elevated on north-facing slopes during dry periods because trees havelower drought tolerance compared to trees on south-facing slopes.Studies that have associated increased mortality with north-facing slopes suggest thatgreater drought tolerance on south-facing slopes might mitigate mortality risk (Kubiskeand Abrams 1994). In a study of 19 tree species in xeric (xeric barrens), mesic (valleyfloor), and “wet-mesic” (floodplain) ecosystems, Kubiske and Abrams (1994)found thatspecies in mesic and wet-mesic sites were most affected by decreasing soil moisture andsuggested this was due to lack of drought tolerance mechanisms. Many studies havefound that more drought-tolerant trees or trees in more moisture-stressed areas haveincreased resistance to xylem cavitation, thereby maintaining water potentials during soilmoisture scarcity (Alder et al. 1996; Hacke et al. 2001; Maherali and DeLucia 2000;

85McDowell et al. 2008; Rice et al. 2004). Maherali (1999) suggest that increased xylemefficiency is largely phenotypic and a plastic response to environmental conditions. Inblack cottonwood (Populus trichocarpa), Sparks and Black (1999) found that trees inmore mesic sites showed poor stomatal control in maintaining internal water balance,lowered cavitation resistance, and higher stem mortality, likely due to a trade-off betweencavitation resistance and maximizing plant conductance.Phytophagous insect population dynamics and forest mortalityThe environmental setting and tree species composition of a forest stand, oftensummarized as “forest type”, influence population processes of phytophagous insectssuch as bark beetles. Bark beetles are the primary mortality agents in coniferous forests,exhibiting both non-epidemic populations with tree-kills confined to small and patchilydistributed groups of trees, and epidemic populations, causing wide-spread mortalityacross entire forests (Bentz et al. 2010; Christansen et al. 1987). Beetles arepoikilotherms, with developmental processes and overwintering survival tied totemperature (Bentz et al. 2010; Logan and Powell 2001). For example, fir engraverbeetles (Scolytus ventralis), key herbivores of red fir (Abies magnifica) and white fir(Abies concolor), can produce one generation per year at lower elevations but require 2-3years to produce one generation at upper elevations (Ferrell and Hall 1975). In lowerelevationforests, insects are generally more abundant, with increasingly harsh conditionsand cold temperatures at upper elevations regulating population size and activity (Loganand Powell 2001; Millar et al. 2007). Shifts in generation duration associated withwarmer temperatures have been found for spruce beetle (Dendroctonus rufipennis)populations in Alaska, Utah, Colorado and mountain pine beetle (MPB, Dendroctonus

86ponderosae) in higher-elevation forests (Bentz and Schen-Langenheim 2007; Hansen etal. 2001; Werner and Hosten 1985). Warmer temperatures and earlier and prolongedwarmer temperatures lead to increased bark beetle overwinter survival, earlier andprolonged flying seasons, and increased numbers of life cycles per year. Such conditionslikely augment abundance and increase exposure to beetle attacks at lower- and middleelevationforests, which may overwhelm tree defense systems (Christensen et al. 1987).Upper elevation forests may have reduced populations of bark beetles and many fungalpathogens because of cold temperatures, widely-spaced trees, rocky substrates, and lowrelative humidity (Millar et al. 2007). However, mechanical damage sustained fromheavy snowpack and high winds during a prolonged winter period can provide food andshelter resources for bark beetles. Winter conditions not only augment increased spatialrisk of death or damage from neighboring tree-falls but increased densities of dead anddamaged trees can lead to increases in the abundance of bark beetle populations,especially during warm periods (Bouget and Duelli 2004; Gilbert et al. 2005; Peltonen1999; Schroeder and Lindelöw 2002).Climate and temporal variation in forest mortalityStand characteristics combine in unique ways to influence differential levels andtiming of mortality across forested landscapes. Further regulation of mortality is imposedby top-down control from climatic variation and bottom-up control as altered standstructure, species and age class composition, and forest spatial pattern from forestresponse to past historic climates or disturbance events (DeRose and Long 2007;Vygodskaya et al. 2002) Not all drought events are associated with forest healthproblems. Yet few studies have explored how and why differences between drought

87periods in the same forest may result in differing levels of forest mortality. Many recentstudies of forest mortality have centered on large-scale and severe mortality outbreaks,with much focus on the link between persistent drought and forest mortality (Allen et al.2010; Breshears et al. 2005; Dobbertin et al. 2007; Floyd et al. 2009; Ganey and Vojta2011; Guarín and Taylor 2005; Hebertson and Jenkins 2008; Millar et al. 2012; vanMantgem et al. 2009). While a strong association between drought and mortality seemsreasonable, few studies have more comprehensively addressed variations in mortalitylevels over multiple dry and wet periods.In a study of three tree species in Florida, subjected to differing timing andmagnitude of drought and flood events, Miao et al. (2009) found that plantgrowth/mortality responded in unique ways to disturbance events. The sequence ofdrought and flood events more strongly influenced plant mortality response than didspecies-specific life history traits. Plant mortality was heightened in droughts followingflood, but not necessarily in floods following drought. Auclair (1993) found this samepattern when studying tree mortality in the Pacific Rim. Canopy dieback in dry periodswas correlated not just with the occurrence of dry periods, but with extreme climaticfluctuation between wet and dry periods.Study ObjectivesAvailability of a continuous, 25-year remote sensing image archive from the Landsatsatellite allowed us to quantify and statistically model forest mortality patterns across a623-km 2 watershed, the Lake Tahoe Basin (LTB), spanning the border of California andNevada, USA. We studied how the magnitude and spatial distribution of forest mortalitychanged over environmental gradients and over wet and dry periods that occurred during

88the 1985-2010 timeframe. Our study goals were: 1) To quantify how mortality levelsvaried by forest type and climatic period, 2) To quantify how mortality levels variedaccording to elevation and solar radiation in individual forests, and 3) To quantify howassociations among topography and mortality levels varied among forest types andclimatic periods.METHODSStudy AreaLake Tahoe Basin (LTB) is in the central Sierra Nevada, comprised of approximately62,300 hectares (ha) of forestland ranging from 1880 meters (m) to over 3000 m inelevation (Figure 1-1). The LTB has a typical Mediterranean climate characterized bycold, wet winters and warm, dry summers. Most precipitation in the LTB arrives assnowfall from December-March with small rainfall inputs during the summer and fallmonths. The Sierra Nevada range, which forms the western side of the LTB, exert a largerain-shadow effect on the eastern side (Carson Range) of the LTB, resulting in decreasedannual precipitation and more regional xeric conditions. Snow amounts decrease along awest-east gradient in the LTB, with lower snow input in the Carson Range. Soils at thelowest elevations, e.g. alluvial flats and flood plains, in the LTB are primarily derivedfrom igneous rocks (mostly granodiorite) with some soils derived from igneous extrusiverocks (mostly andesitic lahar) from the watershed above. Middle and upper-elevationmountains in the LTB are formed by colluvium from either granodiorite or volcanicmaterials. Glaciation and glacial deposition strongly influenced the LTB as well, withglacial outwash leaving large quantities of sediment at the lower elevations (USDANRCS 2007).

89Forest Types and EcoregionsWe examined mortality patterns in five forest types across the LTB. Forest types weredefined using the 2009 USFS CalVeg vegetation classification (USDA Forest Service.1981. CALVEG; CalVeg Zone3: EVEG (Existing Vegetation) Tiles 17B, 21A, and21B). Forest types were identified according to dominant forest composition as: Pines(JP) (Jeffrey Pine or Eastside Pine), Red Fir (RF), Lodgepole Pine (LP), White Fir (WF),and the Mixed Conifer Alliance (MF) (Table 1-2). Because of their widespreaddistribution around the LTB, JP, MF, and RF forests were further separated into fiveecoregions defined by the CalVeg classification (Figure 2-1).We removed all urban areas, including urban forests, as well as all meadows, edges ofsmall water bodies, areas dominated by aspen (Populus tremuloides), riparian vegetation,chaparral fields, restoration projects and historical fires. Developed areas surroundingcampgrounds, ski areas, or residential areas were also removed. We buffered all majorroads in the LTB by 120 meters (60 meters both sides of the road) and removed thoseareas from the analysis.Image Processing and Mortality ClassificationWe used fall Landsat Thematic Mapper (TM) imagery from 1985 – 2010 availablefrom the USGS Glovis site (see Chapter One for further detail) and normalized all imagesto the September 2010 image using the IRMAD change detection algorithims (Canty andNielsen 2008). To estimate reductions in canopy vigor, we used the NormalizedDifference Wetness Index (NDWI) and the change in NDWI (Differenced NDWI,dNDWI) values between two subsequent years (Gao 1996). NDWI uses Landsat spectralbands 4 and 5, which are sensitive to leaf water content, and is more sensitive to detection

90of coniferous forest mortality than other vegetation indices, such as NDVI (Gao 1996;Vogelmann et al. 2009). To estimate annual mortality, we used dNDWI to quantifysignificant deterioration of tree canopy that exceeded pre-defined thresholds calibratedwith field data. Mortality classification and validation procedures are further explained inChapter One.ClimateWe quantified how relationships between stand structure and mortality changedby forest type and climatic period within our 25-year time series (see Chapter One forfurther description). We designated five climatic periods based on historical accounts ofclimatic variation in the LTB and proximate areas as well as historical hydrologicaldatasets, such as the California Cooperative Snow Survey Program. To verify dates fromthese accounts, we used the “annual precipitation as snow” (PAS) variable derived fromthe ClimateWNA dataset to determine annual snow deficits as departure from the 30-yearnorm (Wang et al. 2012). We then tested accuracy of historical accounts against variouslagged modeled snow deficits (from 2-5 years) as well as annual winter minimumtemperature to ensure correct representation of climatic periods. We found the four yearaveraged snow deficit most accurately portrayed historical accounts and established fiveclimatic periods during the 1985-2010 study series of varying duration (w1=2years,d1=8years, w2=4years, d2=7years, w3=4years) (Figure 1-3). We additionally used thenumber of frost free days and winter minimum temperature in our analyses of variabilityacross the five climatic periods (Figure 2-2a,b).

91Environmental VariablesIn addition to ecoregional variables, we used solar radiation (WH/m 2 , Figure 2-3).and elevation (m, Figure 2-4) from a 30-meter digital elevation model (DEM) inArcMap10 (ESRI). Solar radiation was calculated using the Spatial Analyst feature inArcMap and uses aspect and slope derived from a 30m DEM to derive a measure of totalclear-sky radiation, both diffuse and direct, over a single-day period (Fu and Rich 1999).We used the total radiation received per pixel over the length of daylight on August 1 asrepresentative of the influences of diffuse and direct sunlight on soil moisture duringsummer conditions.Data AnalysisWe used the ReLogit (Rare Events Logistic Regression) method to test forrelationships between mortality and environmental and stand structure predictors (Kingand Zeng 2001). Logistic regression was used to model the probability of a mortalityevent within a pixel as a statistical function of forest density and environmentalinfluences, with final model selection based on highest explanatory power and modelparsimony. Results are interpreted as odds ratios, derived from parameter coefficients forevery 500m increase in elevation or 1000 WH/m 2 increase in solar radiation. Odds ratiosgreater than 1 indicate a positive relationship between predictor and response and oddsratios less than 1 indicate a negative relationship. Odds ratios that equal 1 show norelationship between predictor and response variable.To examine differences in the levels of mortality among forest types across the timeperiod, we regressed annual mortality for an individual forest type by climatic period. Toexamine differences in the levels of mortality among ecoregions across the time period,

92we regressed annual mortality for an individual forest type with the interaction ofecoregion and climatic period. Results are interpreted as percent of ecoregionexperiencing canopy dieback.To examine differences in mortality levels among forest types and climatic periods,we used a regression tree with forest type and climatic period as the categoricaldependent predictor variables with annual mortality as the response variable. Regressiontrees use binary recursive partitioning to split the dataset into meaningful groups of thepredictor variables that are most similar in terms of the value of the response variable(Breiman et al. 1984; De'ath and Fabricius 2000). We did not prune or snip the tree assplits in the initial tree were reasonable and of acceptable parsimony. Because antecedentmoisture conditions have been important in tree mortality in other studies in the westernU.S., we examined relationships between annual mortality and differing lag periods ofthe snow deficit variable (Table 1-2; Bigler et al. 2007; Guarín and Taylor 2005; Millar etal. 2012). We investigated relationships with lagged snow deficits ranging from 0-5years, where lag 0 equals most recent winter snowfall.RESULTSForest Mortality Levels across Drought and Wet PeriodsUsing the mortality classification process described in Chapter One, we derivedannual mortality levels for each forest type that reflect the amount of new mortality seenon the landscape in each year (Appendix A, Figure 2-5). Regression results showed twodistinct periods of differing forest mortality that were consistent across forest types: anearly to mid-period from 1985 to1995 (w1, d1) characterized by heightened mortality,and a later period from 2000 to 2009 (w2, d2, w3) characterized by lower mortality levels

93(Figure 2-6). Forest mortality magnitude and temporal trend also varied by forest type,with upper-elevation forests (LP and RF) exhibiting higher mortality levels than lowerandmiddle-elevation JP, MF, and WF forests. JP and MF showed higher mortality levelsduring the first drought than for preceding or subsequent wet periods. For LP forest,mortality levels were highest in W1 and W2 and lower in the first drought (Figure 2-7;Table 2-2). In LP and RF forests, mortality was characterized by years of heightenedmortality with intervening years characterized by lower mortality levels. In JP, MF, andWF forests mortality was less episodic, with annual mortality levels that were similarover all climatic periods. All forests showed increased mortality in the first droughtperiod compared to the second. Although mortality was stable at low levels over the timeperiod, WF forests experienced approximately three years (1988, 1994, and 2008) ofepisodic high mortality similar to LP and RF forests. During the first and second wetperiod, mortality in these forests was elevated with respect to mortality incurred duringthe second drought and third wet period.Ecoregional Influences on Forest MortalityRegression analysis of ecoregion x climatic period interactions did not revealsignificant differences among annual mortality levels in ecoregions of similar forests, butshowed dissimilar mortality patterns among ecoregions in some forest types (Figure 2-8;Figure 2-9). In JP and MF forests, all ecoregions were characterized by much highermortality levels during the first drought, with lower mortality levels throughout the rest ofthe time series. In RF forests, mortality levels were greatest in west side ecoregions andleast in the upper-elevation southern ecoregion during the first wet period. East-side redfir forests did not respond similarly with slight mortality increases seen during the first

94drought and subsequent and preceding wet periods. Over all climatic periods andecoregions, mortality levels were typically highest in RF ecoregions and lower for JP andMF ecoregions. In the east-side Carson Range forests and upper-elevation southernecoregions, JP forests showed lowest mortality rates over nearly all climatic periods.Topographic effects on forest mortality: Solar Radiation and ElevationAll forests exhibited negative relationships between solar radiation and mortality riskover at least 30% of the 25-year time period, with positive relationships accounting foronly 0-9% of the time period with the exception of WF forests (Figure 2-10; Figure 2-11). In WF forests, south-facing aspects were positively associated with mortality morefrequently than for other forest types, with this effect heightened in the last wet period ofthe time series. Mortality risk in JP forests shows the greatest insensitivity to sitevariations in solar radiation followed by LP, RF, WF, and MF forests. In JP, LP, and MFforests, at least 1-2 models showed increased mortality risk on southern or steeperaspects. In MF forests, when solar radiation was associated with mortality risk, morenorth-facing, gentle aspects were always predisposed to mortality.Throughout the time series, relationships among mortality and elevation were mostvariable in direction for LP and RF forests and less so for JP, MF, and WF forests (Figure2-12). In LP and RF forests, positive relationships between mortality and elevation weremore frequent in the second half of the time series, and generally negative in the first partof the time series. In MF and WF forests, mortality was generally positively associatedwith elevation. When associated with elevation, the probability of mortality increasedwith increasing elevation for upper-elevation RF and LP forests during all three wetperiods.

95In JP forests, decreases in elevation increased mortality risk over 60% of the timeperiod and decreased mortality risk less than 10% of the time (Figure 2-13). In MFforests increases in elevation increased mortality risk over 35% of the time series, themost of any forest type, and decreased mortality risk around 11% of the time period. InRF and WF forests, increases in elevation increased mortality risk over approximately20% of the time period, and decreased mortality risk over approximately 22% and 9%respectively. For LP forests, increases in elevation increased mortality risk over 20% ofthe time period, and decreased mortality risk in only 15% of the time period. Elevationdid not show a relationship with mortality in at least 30% of the time period for all foresttypes, with the greatest lack of effect shown for WF over approximately 70% of the timeperiod. WF, however, showed the strongest magnitude spikes where increased elevationincreased the risk of mortality by 7 to 8 times for every 500 m increase in elevation. RF,WF, LP, and MF showed high magnitude positive relationships between elevation andmortality in the first two years of the time period, with smaller magnitude effects seenprimarily in the first and second dry periods.DISCUSSIONIn our study, mortality response was variable and influenced by forest type andclimatic period. While recent studies on forest mortality suggest that extensive dry ordrought-like periods almost always create widespread forest mortality events (reviewedin Allen et al. 2010), we found evidence to the contrary. The disparity between mortalitylevels in the first drought and the second drought is surprising, considering that the laterdrought period, where forest mortality was relatively limited, was characterized byelevated temperatures as well as low precipitation amounts.

96Results parallel those of Millar et al (2007) who observed elevated levels of limberpine mortality (Pinus flexilis) in the 1987-1992 drought but not the subsequent 1999-2004drought, for the eastern escarpment of the central-southern Sierra Nevada. The authorshypothesized that earlier drought-induced mortality promoted short-term increases inforest health and forest resilience. Support for this hypothesis is found in our study in theubiquitous decrease in mortality observed in the latter drought period (d2) compared tomore widespread mortality associated with the first earlier period. Early, intense droughtin the early part of this time series may have selectively reduced populations of lessdrought-tolerant tree individuals or species, adjusting forest composition tolerance suchthat the forest experienced less mortality during the later droughts of the early 2000s.The timing of high-mortality events in the LTB confirms that droughts, as defined bya duration of precipitation deficits, do not always impact forests in the same way, and thatsequence matters (sensu Miao et al. 2009). Miao et al. (2009) measured forest mortalityin two disturbance sequences determined by either 4-month transitions between floodfollowed by intermediate conditions and then drought, or by 4-month transitions betweendrought followed by intermediate conditions and then flood. In our study, fluctuationsbetween abnormally high snowloads and drought proved analogous to the drought-wetdroughtsequence of Miao et al. (2009). In the LTB, increases in tree mortality are likelydue to the abrupt transition between two climatic extremes as well as the impact of thefirst disturbance on the outcome of the subsequent disturbance.In the search for predictive relationships between tree growth rates and mortality,many researchers have found that variable growth is of equal or greater importance thanslow growth in separating live and dead trees (Ogle et al. 2000; Suarez et al. 2004) and

97that abrupt declines in growth are key indicators of eventual mortality (Das et al. 2007;Pedersen 1998). Active tree growth, fostered during periods of abundant moisture andwarm temperatures, such as those that occurred in early-mid 1980s, may have decreasedrelative availability of carbon needed for manufacture of secondary defense compounds.Limited and variable supplies of water and nutrients engender different resourcepartitioning and allocation strategies within plants that impact trade-offs between growthand defense against herbivores and pathogens (Herms and Mattson 1992; Stamp 2003).A landscape with ample numbers of vigorous trees, as required by MPB and likely otherDendroctonus beetles, with lowered carbon stores in addition to temperature-mediatedbark beetle increases and refugia for bark beetles in snow-damaged trees may have set thestage for outbreak mortality during the first drought. For trees in which moisture stressoccurs on a seasonal and annual basis, it may be that climate variability and sharpfluctuations in moisture stress influence the magnitude of forest mortality more than theduration of drought-like conditions.Mortality patterns were similar across ecoregions in all forest types, indicating thatmore fine-scale, regional conditions, as defined by the ecoregion variable, may havelimited influence on overall forest health. Most distinct mortality patterns amongecoregions occurred in RF forest types, where forests in the southern and eastern (CarsonRange) portions of the LTB had lower mortality levels than those of western and northernRF forests. This distinction could be due to lower snow loads on the eastern side of theLTB, increased drought tolerance of these forests, or less insect herbivory. However,Ferrell et al. (1994) reported on widespread fir engraver activity in the upper-elevationCarson Range and JP forests in the Carson Range experienced heightened mortality in the

98first wet and dry period. Similar patterns of forest mortality across all ecoregions couldbe due to the widespread nature of the mid 1980s-mid 1990s beetle epidemic and thesubsequent non-epidemic nature of beetle populations. The conceptual model suggestedby our results includes top-down influences (including climate, bark beetle populations,and weather) driving overall forest mortality patterns, with local variations due to edaphicand microclimate conditions important in regulating mortality levels, particularly duringnon-epidemic periods.We find that increasingly productive systems often experience highest mortality, butfor different reasons. In contrast to more xeric Carson Range forests, RF forests on thewest side of the LTB experienced heightened mortality levels likely due to extremewinter storms and resulting increases in bark beetle populations. In JP forests, moremesic areas may experience increased pressure from damage agents. For MF forests,percent mortality in more mesic west-side ecoregions (EH and EJ) remains stable duringthe second and third wet period when mortality declines in eastern and southern stands.The positive relationship between net primary productivity (NPP) and forest mortalityhas been identified at global scales, though common drivers remain elusive (Franklin etal. 1987; Stephenson et al. 2011). Mortality patterns in the Lake Tahoe Basin suggest thatthis relationship may hold at finer spatial scales associated with topographic variation.In all forests and across the time period, mortality risk is generally greater on northfacingaspects and steeper slopes, similar to other studies of mortality in the SierraNevada (Guarín and Taylor 2005; Millar et al. 2012). This near ubiquitous relationshipcould be indicative of the increase in moisture stress and the limited capability of standson north-facing slopes to adapt to increased moisture stress during droughty periods (d1

99and d2) and increased temperatures (w2 and w3) (Maherali and DeLucia 2000; Zwiazekand Blake 1989).The relationship between aspect and mortality risk can also be a result of bark beetlepreferences. Similar to elevation, aspect moderates both over-winter temperatures andgeneration period, with warmer slopes associated with outbreak conditions in MPB(Safranyik et al. 1974; Safranyik et al. 1989). In their study of environmental conditionsassociated with MPB damage in lodgepole pine forests, Nelson et al. (2007) found thatsouthern and western aspects were tied to heightened beetle activity. In our study, it maybe that increased stand density on north-facing slopes promotes warmer winterconditions, as wind speeds are decreased and ambient temperatures in the stand aremoderated. In addition, many of the winter storms in the Sierra Nevada arrive from awesterly-southwesterly direction, with severity likely heightened on sparsely forested,southern slopes (Dettinger et al. 2004).Increased pathogen pressure from root pathogens (Armillaria root disease (Armillariaspp.), Annosus root disease (Heterobasidion annosum), laminated root rot (Phellinusweirii), black stain root disease (Leptographium wageneri) may also help explainelevated mortality risk on more north-facing slopes. A concurrent exploration ofmortality drivers in the LTB found elevated levels of tree root disease in stands on morenorth-facing aspects (I. Munck & R. Nowak, pers. comm., University of Nevada, Reno).Villalba and Veblen (1998) found increased incidence of root disease in more mesic areasof their study site and increased mortality from laminated root rot and Annosus rootdisease in true firs (Goheen and Goheen 1989; Nelson and Sturrock 1993; Slaughter andParmeter 1989), common in more mesic sites with deeper soil (North et al. 2005).

100Our study results parallel those of other studies of mortality in coniferous forests thatfound increased mortality at lower elevations in the western U.S. during drought periodscharacterized by outbreak populations, both in the western United States (Ferrell et al.1994; Ferrell 1996; van Mantgem and Stephenson 2007; Negrón et al 2009; Millar et al.2007; Millar et al. 2012) and in European temperate forests (Schutt and Cowling 1985).However, our findings also show that the relationship between mortality and elevation isvariable by forest type, with less support for a common relationship between mortalityand elevation in either time or place.Increasing average winter minimum temperatures and number of frost free days wereevidenced after the first drought with similar increases in climatic water deficit found inother studies of forest mortality in the Sierra Nevada (Millar et al. 2012; van Mantgemand Stephenson 2007). Increases in temperature in addition to lower precipitation duringthe second drought may have combined to cause higher moisture stress for both JP forestsas has been found in other studies of forest mortality (Allen et al. 2010) and for relativelyxeric areas within upper-elevation RF, LP and MF forests in California (Lloyd andGraumlich 2007). However, beetle activity is also associated with elevation, due to linksbetween temperature and terrain, with elevation critical to host selection for mountainpine beetle (Safranyik and Carroll 2006). Reduced generation time is necessary forelevated numbers of beetles across the landscape and Amman (1973) and Safranyik andCarroll (2006) showed that limitations on a 1-year life cycle are linearly related toelevation and latitude. In a study of MPB hot-spots across lodgepole pine forests inBritish Columbia, Canada, Nelson et al. (2007) showed that MPB infestations were

101concentrated at lower elevations, and increased from northern to southern portions of thestudy area.Increased mortality risk at lower elevations in JP forests could be due to increasedbeetle populations and earlier emergence from infected trees resulting in a prolongedflight season. In almost all wet years in upper-elevation forests, mortality probability wasgreater at upper elevations, likely from mechanical damage and mortality on more recentestablishment on marginal sites during preceding dry periods (Lloyd and Graumlich2007; Millar et al. 2004; Minnich 1984; Villalba and Veblen 1998). Natural thinningincurred during the first drought may have increased mortality risk to sparse upperelevation stands, where stands of greater density were observed to experience lowermortality risk (see Chapter One). The same could apply for MF and WF forests duringthe latter part of the time series. Increased moisture stress due to higher temperaturescompounded by increased susceptibility in stands that were formerly sheltered frompersistent moisture stress may be more proximal explanations (Kubiske and Abrams1994; Maherali et al. 2004; Zwiazek and Blake 1989).Inter-annual variability of forest mortality in the LTB has been most similar for lowerand middle-elevation forests Jeffrey pine and mixed-fir forests, which differed fromupper-elevation lodgeple pine and red fir forests. Elevated mortality in JP and MF forestsoccurred during the 1987-1994 drought when insect outbreaks, primarily fir engraver andJeffrey pine beetle, were widespread across the LTB (Egan et al. 2012; Elliot-Fisk 1996;Ferrell et al.1994; Ferrell 1996; SNEP 1996). For JP forests in the east-side CarsonRange, our study shows that 1985-1986 mortality levels increased before the onset of thefirst drought. Either regional drought began earlier or mortality was dominated by spread

102of fir engraver mortality that was extensive in upper-elevation forests (Ferrell et. al1994). Our results agree with other studies that have found diminished response inmortality levels of pine-dominated forests to drought-like conditions (Bigler et al. 2007;Ganey and Vojta 2011). Hurteau et al. (2007), working in the southern Sierra Nevada,documented increased climatic sensitivity in white fir compared to sugar pine and foundlittle support for a relationship between annual growth rate and climate for Jeffrey pine.Lower elevation forest types may experience less drought-related mortality due tostrong drought-tolerant characteristics of pine dominants. In the LTB, JP forests aredominated by larger Jeffrey pine and sugar pine, both species that are characterized byhigh drought tolerance, likely tied to better stomatal control of water loss and cavitationresistance as has been shown for ponderosa pine in more xeric environments (Maheraliand DeLucia 2000). In lower-elevation forests which often undergo prolonged seasonaldrought, extended duration of drought in addition to widespread insect abundance may benecessary to elevate mortality levelsUpper-elevation forests, however, experienced a more elevated and episodic forestmortality pattern. In RF and LP forests, individual years experienced severe mortalitylevels up to 4 times higher than that of intervening stable years. Episodic mortalityoccurred throughout the time series and seemed a response to two divergentenvironmental conditions: wet periods characterized by heavy snowfall or drought. Thehighest mortality levels for both forest types were observed in 1995 and 1996, possiblydue to damage from heavy snowfall followed by extreme drought. Of the two dominantspecies in affected forests, LP exhibited greater response to environmental stress duringwet years and relatively little response to stress during dry years. These differential

103responses may be due to the greater drought-tolerance of lodgepole pine (DeClerck et al.2005).CONCLUSIONResults of this study evidence the importance of both endogenous and exogenousfactors and their interaction in shaping heterogeneous forest mortality patterns acrossclimatic periods and forest types. Mortality, rather than being an additive combination ofspecies traits, environmental position, and climate, is a non-linear response to factorsoperating at multiple scales. Needed are more mechanistic models that provide theconceptual, cross-scale linkage among detailed ecophysiological studies, forest pathologysurveys of stand-level patterns and broad-scale remote sensing studies. Forest mortalitypatterns are inherently multi-causal and unresolvable through observational studies alone.However, observational studies at sufficiently broad scales of space and time are usefulfor generating alternative hypotheses that can be addressed at finer scales. For example,are apparent effects of environmental gradients on forest mortality ecophysiological innature, or due to indirect effects mediated by bark beetle population dynamics? Furtherstudy into separating causative mechanisms in forest mortality is needed.

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118TABLE 2-1. Relationships between current and lagged year modeled snow deficits [asratio of current year snow/30-year norm (1980-2009) by forest type. Derivation of lags isshown below, using 1985 as an example year. Current year is defined a most currentwintertime precipitation. Positive relationships indicate increased moisture is associatedwith increased mortality. Negative relationships indicate decreased moisture associatedwith increased mortality.Derivation of Lag Timelines (example=1985)Lag Snow Accumulation Timeline Snow Deficit Variable YearCurrent Aug 1984-July 1985 1985Lag 1 Aug1983-July1984 1984Lag 2 Aug 1982-July1983 1983Lag3 Aug 1981-July 1982 1982Lag 4 Aug1980 - July1981 1981Lag 5 Aug1979-July1980 1980Regression coefficients from Annual Mortality ~ Lagged Snow Deficitsin Five Forest TypesForestType Coefficients Standard Error t-value Pr(>|t| SignificanceJP (Intercept) 4.788 5.873 0.815 0.426Current -4.160 2.431 -1.711 0.104Lag1 6.164 2.603 2.368 0.029 *Lag2 5.651 2.107 2.682 0.015 *Lag3 -0.461 2.001 -0.231 0.820Lag4 -0.043 1.929 -0.022 0.983Lag5 1.064 1.963 0.542 0.594

119ForestType Coefficients Standard Error t-value Pr(>|t| SignificanceLP (Intercept) -11.857 10.806 -1.097 0.287Current 7.058 4.474 1.578 0.132Lag1 10.463 4.790 2.184 0.042 *Lag2 4.742 3.877 1.223 0.237Lag3 0.291 3.681 0.079 0.938Lag4 10.542 3.548 2.971 0.008 **Lag5 -4.159 3.611 -1.152 0.264MF (Intercept) 12.444 9.577 1.299 0.210Current 0.293 3.965 0.074 0.942Lag1 0.051 4.246 0.012 0.991Lag2 2.063 3.436 0.600 0.556Lag3 -1.791 3.263 -0.549 0.590Lag4 -2.915 3.145 -0.927 0.366Lag5 2.578 3.200 0.805 0.431RF (Intercept) -5.087 11.577 -0.439 0.666Current 3.234 4.793 0.675 0.508Lag1 13.059 5.132 2.545 0.020 *Lag2 1.764 4.154 0.425 0.676Lag3 1.045 3.944 0.265 0.794Lag4 7.748 3.802 2.038 0.057 .Lag5 -3.392 3.869 -0.877 0.392WF (Intercept) 6.209 11.738 0.529 0.603Current 5.275 4.860 1.086 0.292Lag1 1.618 5.203 0.311 0.759Lag2 -0.337 4.212 -0.080 0.937Lag3 -4.052 3.999 -1.013 0.324Lag4 3.005 3.855 0.780 0.446Lag5 1.570 3.922 0.400 0.694

120TABLE 2-2. Linear regression analysis results for Annual Mortality ~ Climatic Period ineach of five forest types (JP, LP, MF, RF, WF) with significance at the p < 0.05 level.ForestTypeClimaticPeriodStandardErrort-valuePr(>|t| SignificanceJP d1(intercept) 15.490 1.696 9.136 0.000 ***JP d2 -6.008 2.590 -2.320 0.031 *JP w1 -1.189 3.791 -0.314 0.757JP w2 -2.408 2.937 -0.820 0.422JP w3 -4.168 2.734 -1.524 0.143LP d1(intercept) 16.336 3.162 5.166 0.000 ***LP d2 -4.324 4.830 -0.895 0.381LP w1 11.090 7.071 1.569 0.132LP w2 5.103 5.477 0.932 0.363LP w3 -5.600 5.099 -1.098 0.285MF d1(intercept) 18.903 1.547 12.221 0.000 ***MF d2 -10.365 2.363 -4.387 0.000 ***MF w1 -7.180 3.459 -2.076 0.051 .MF w2 -6.235 2.679 -2.327 0.031 *MF w3 -11.167 2.494 -4.477 0.000 ***RF d1(intercept) 19.883 3.206 6.202 0.000 ***RF d2 -6.478 4.897 -1.323 0.201RF w1 9.059 7.168 1.264 0.221RF w2 -2.548 5.552 -0.459 0.651RF w3 -6.253 5.169 -1.210 0.241WF d1(intercept) 17.370 2.709 6.413 0.000 ***WF d2 -7.119 4.137 -1.721 0.101WF w1 -5.434 6.057 -0.897 0.380WF w2 -7.558 4.691 -1.611 0.123WF w3 -6.398 4.367 -1.465 0.158

121a.b.c.d.FIGURE 2-1. Ecoregions (a) in the LTB with ecoregional differences in (b) averageannual precipitation (in); (c) average maximum temperature ( o F) ; (d) average minimumtemperature ( o F), derived from PRISM 1971-2000 norms.

122abFIGURE 2-2a,b. Average winter minimum temperature (a) and number of frost free days(b) in five climatic periods of study time series

123FIGURE 2-3. Solar radiation (WH/m 2 ) calculated for August 1, 2010. Solar radiationcombines slope and aspect to measure both direct and diffuse radiation over a 30m x 30mpixel. Higher values are associated with south-facing and lower values with north-facingaspects.

FIGURE 2-4. Elevation (m) derived from a 30m Digital Elevation Model.124

125FIGURE 2-5. Boxplots of annual mortality by forest types in five climatic periods ofstudy [d1:1987-1995, d2:1999-2006, w1:1985-1987, w2: 1995-1999, w3:2006-20010].

126FIGURE 2-6. Regression tree of predicted mortality levels by forest type and climaticperiod. Regression trees are comprised of a series of binary splits created throughrecursive partitioning with splitting criterion based on overall deviance reduction.Predicted values of annual mortality are shown at the terminus of each node for thatgroup. For example, the predicted annual mortality value for JP, MF, and WF duringeither d2 or w3 is 9.69.

127FIGURE 2-7. Effect plots for Annual Mortality ~ Climatic Period (ClimPd: d1, d2, w1,w2, w3) in each of the five forest types.

128Forest Type Multiple R 2 Adjusted R 2 F DF P-valueJP 0.272 0.097 1.554 24 0.068MF 0.258 0.0799 1.448 24 0.105RF 0.185 -0.010 0.948 24 0.539FIGURE 2-8. Effect plots for regression analysis of Annual Mortality ~ EcoRegion *Climatic Period in JP, MF, and RF forests. Red lines indicate the upper and lower 95 th %confidence intervals around regression coefficients.

129a: JPb: MFc: RFFIGURE 2-9a-c. Levels and patterns of JP(a), MF (b), and RF(c) forest mortality byecoregion (eh, ej, ek, el, et) over the 25 year time period.

130FIGURE 2-10. Effect of a 1,000 WH/m 2 increase in solar radiation on mortality for fiveforest types across 25-year time period as odds ratios with 95% confidence intervals.N=odds ratios with confidence intervals that overlap 1 (CI shown) or years when solarradiation did not appear in final model (CI not shown). U= odds ratios with confidenceintervals that do not overlap 1. Results from annual models occuring during dry periodsare in orange. Results from annual models occuring in dry periods are in green. Oddsratios greater than one indicate increase in probability of mortality associated withincreases in south-facing slopes. Odds ratios less than one indicate probability ofmortality associated with north-facing slopes.

131FIGURE 2-11. Location of increased risk of mortality by aspect derived from solarradiation for five forest types across 25-year time period (Not Significant: confidenceinterval of regression coefficient and resulting odds ratio overlapped 0 or 1; NoAssociation: no association between probability of mortality and elevation).

132FIGURE 2-12. Effect of a 500m increase in elevation on mortality for five forest typesacross 25-year time period as odds ratios with 95% confidence intervals. N=odds ratioswith confidence intervals that overlap 1 (CI shown) or years when elevation did notappear in final model (CI not shown). U= odds ratios with confidence intervals that donot overlap 1. Results from annual models occuring during dry periods are in orange.Results from annual models occuring in wet periods are in green. Odds ratios over oneindicate increase in probability of mortality associated with increases in elevation. Oddsratios less than one indicate probability of mortality associated with decreases inelevation.

133FIGURE 2-13. Location of increased risk of mortality by elevation for five forest typesacross 25-year time period (Upper: Upper elevation, Lower: Lower elevation, No Effect:no association between probability of mortality and elevation, Not Significant:confidence interval of regression coefficient and resulting odds ratio overlapped 0 or 1).

134APPENDIX A. 25 years of annual mortality (Fall Year One – Fall Year Two) from 1985-2010 in the LTB. Mortality maps are derived by subtracting year one NDWI from yeartwo NDWI to create continuous maps of dNDWI showing forested areas in varyingstages of health, from canopy dieback to vegetation recovery.1986-871985-86 1987-88 1988-891989-901990-91 1991-92 1992-93

1351993-94 1994-95 1995-96 1996-971997-981998-991999-20002000-01

1362001-02 2002-03 2003-04 2004-052005-06 2006-07 2007-08 2008-092009-10

137THESIS SUMMARYForest mortality is an issue of global concern, with outbreaks of bark beetle-mediatedmortality more severe, widespread, and synchronized than evidenced previously. Whilethe association between increasing moisture stress, bark beetle dynamics, and forestmortality has been found in numerous studies, it is widely reported that increased standdensity, due to land-use and land-management policies, is as a predisposing factor inforest mortality. Global increases in forest mortality have also been widely attributed tochanging climate and altered disturbance regimes, yet there is limited understanding ofhow forest mortality trends and spatial patterns vary with forest type, climate andtopographic variability. Though the influence of density and environmental variables onforest mortality during single, and often epidemic, episodes of mortality has beenelucidated (though with conflicting results), a more comprehensive understandingencompassing various climatic periods and forest types is lacking.We investigated how the relationship between stand-level mortality and stand densityhas varied over five forest types and five climatic periods in the mixed conifer forests ofthe Lake Tahoe Basin (LTB) in the central Sierra Nevada. Our analyses used LandsatTM data from 1985-2010 to derive annual mortality maps and an annual stocking index.We found that the strength of density-dependent mortality was variable in the LTB, withno forest showing a ubiquitous relationship between mortality and density. However, inmore droughty climatic periods and in pine-dominated forests, increases in density wereweakly associated with increased probability of mortality. In middle- and upper-elevationforests, the association between mortality and density was variable. Increased density

138was associated with increased mortality risk in dry periods, with this relationshipreversed in wet periods.Using annual mortality maps derived from remote sensing analysis, we quantifiedthe relationships between mortality and environmental gradients defined by elevation andsolar radiation. Our results suggest that increases in forest mortality are driven more byunique sequences of extreme climatic events, rather than associated solely with droughtperiods. Mortality is highest in upper-elevation forests and specifically when droughtfollows periods of heavy and prolonged snowfall. Pine-dominated forests were lesssensitive to drought. Throughout the time series for almost all forests, mortality risk wasgreater on north-facing slopes. Relationships between mortality and elevation werepredictable for lower-elevation forests: with decreases in elevation were consistentlyassociated with elevated mortality risk. For middle-and upper elevation forest types,increased mortality risk is associated with increases in elevation during wet periods anddecreases in elevation during dry periods.Our results provide boundary conditions for the widely-accepted hypothesis thatdensity-dependent mortality leads to heightened mortality levels. Patterns revealed in ouranalysis between mortality and environmental gradients illustrate that mortality, ratherthan an additive combination of species traits, environmental position, and climate, is anon-linear response to factors operating at multiple scales. More cross-scale studieslinking ecophysiological mechanisms of tree death, forest pathology, and landscapedynamics of mortality spread are needed to improve our understanding of the relative roleof endogenous and exogenous factors for influencing variable mortality patterns acrossthe landscape.