Drought Stress and Bark Beetle Outbreaks in the
Future Forest: Extending an Existing Model to Inform
Climate Change Adaptation1
Final Report
November 2014
SNPLMA (Southern Nevada Public Lands Management Act) project (P086)
PI: Robert M. Schellera
Co-PIs: E. Louise Loudermilka,b, Matthew D. Hurteauc, Peter J. Weisbergd,
Agency Collaborator: Carl Skinnere
Project Researchers: Alec M. Kretchuna,f, Soumaya Belmecherig
Final Report developed by E.L. Loudermilk and A.M. Kretchun
aPortland
State University
Environmental Science and Management Department
Portland State University
PO Box 751, Portland, OR 97207 USA
bUSDA
Forest Service, Center for Forest Disturbance Science
Southern Research Station
320 Green St., Athens, GA, 30606 USA
cPennsylvania
State University
Department of Ecosystem Science and Management
306 Forest Resources Building, University Park, PA, 16802
dUniversity
of Nevada, Reno
Department of Natural Resources and Environmental Science
1664 N. Virginia St., Mail Stop 186, Reno, Nevada 89557 USA
eUSDA
Forest Service
Pacific Southwest Research Station
3644 Avtech Parkway, Redding, CA 96002 USA
fUSDA
Forest Service
Pacific Northwest Research Station
1220 SW 3rd Ave, Portland, OR 97204USA
gPennsylvania
State University
Department of Meteorology
503 Walker Building, University Park, PA, 16802
1
Citation: Loudermilk, E. Louise; Kretchun, Alec M.; Scheller, Robert M.; Hurteau, Matthew D.; Weisberg, Peter J.,
Skinner, Carl; Belmecheri, Soumaya. 2014. Drought stress and bark beetle outbreaks in the future forest: Extending
an existing model to inform climate change adaptation. Final Report for Southern Nevada Public Lands Management
Act project P086, Pacific Southwest Research Station, Tahoe Center for Environmental Studies, Incline Village, NV.
Table of Contents
Key Findings................................................................................................................................................... 4
Introduction .................................................................................................................................................. 5
Study objectives ............................................................................................................................................ 7
Methods ........................................................................................................................................................ 8
Study Area ................................................................................................................................................. 8
Model parameterization and calibration ................................................................................................... 8
Landscape forest composition............................................................................................................... 9
Climate change data .............................................................................................................................. 9
Modeling C ............................................................................................................................................ 9
Modeling wildfires ............................................................................................................................... 10
Modeling forest fuel treatments ......................................................................................................... 10
Simulating bark beetles with BDA extension ....................................................................................... 11
ANPP data & scaling ................................................................................................................................ 12
Tree-core data: field data collection &scaling ..................................................................................... 12
Landscape simulations& analysis ........................................................................................................ 12
Experimental design for ANPP analysis ............................................................................................... 13
Tree Ring and Model Estimate Comparison ........................................................................................ 13
Long-term simulations of multiple disturbances ..................................................................................... 14
Results ......................................................................................................................................................... 15
ANPP Scaling analysis .............................................................................................................................. 15
Tree ring ANPP estimates .................................................................................................................... 15
LANDIS-II Bark beetle modeling ............................................................................................................... 15
C trajectories ....................................................................................................................................... 16
Species composition shifts .................................................................................................................. 16
Likelihood of continued C sequestration ............................................................................................. 17
Fire behavior ....................................................................................................................................... 17
Bark beetle outbreak dynamics ........................................................................................................... 18
Discussion .................................................................................................................................................... 19
ANPP scaling analysis............................................................................................................................... 19
LANDIS-II bark beetle & fuel treatment modeling ................................................................................... 20
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Bark Beetles and C ............................................................................................................................... 20
Ecological Mechanisms........................................................................................................................ 20
Climate Change Implications ............................................................................................................... 21
Uncertainties ....................................................................................................................................... 21
Management Implications ................................................................................................................... 21
Policy Implications ............................................................................................................................... 21
Acknowledgements ..................................................................................................................................... 22
Figures ......................................................................................................................................................... 23
References................................................................................................................................................... 38
APPENDIX .................................................................................................................................................... 43
3
KEY FINDINGS
In the final report of the first phase of the project (P049, Loudermilk et al. 2012), we presented key
findings associated with simulated interactions among future climate, forest growth, C sequestration,
wildfires (including changing ignition patterns), and fuel treatments in the Lake Tahoe Basin (LTB). Here,
we present more in-depth findings concerning effects from bark beetles and drought, future
effectiveness of fuel treatments, as well as results from the ANPP scaling analysis.
Simulated interactions with bark beetles, climate, and fuel treatments
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Under baseline climate, bark beetles do not significantly reduce total ecosystem carbon over the
simulated century beyond the reductions cause by fuel treatments
Projected increases in bark beetle activity in the A2 climate (high emissions scenario) generally
increased C emissions due to heightened tree mortality in the latter half of the century compared
to simulations assuming a contemporary climate. In addition, these outbreaks caused greater
uncertainty in forest composition and carbon dynamics.
Bark beetle outbreaks and wildfire activity did not overlap significantly in our simulations,
regardless of climate scenario applied. This is in large part due to wildfires generally occurring
near the WUI, and BB outbreaks occurring basin-wide, including more remote and high elevation
areas.
In the A2 climate, fuel treatments remained effective at reducing wildfire activity, but were not
particularly effective at reducing bark beetle outbreaks, due to the great magnitude of bark
beetle outbreaks and their widespread distribution throughout the basin.
A Log Odds Ratio analysis on Net Ecosystem C Balance indicated that bark beetles significantly
increased the probability of the landscape becoming a C source after mid-century with climate
change.
The effects of bark beetles and fuel treatments had compensatory effects on species interactions,
where, e.g., fire tolerant or less targeted species (by beetles or management) regenerated in
areas affected by these disturbances. Under high emissions, this compensatory capacity was
reduced in the latter half of the century, when effects from bark beetles were severe and forest
recovery lagged behind outbreak frequency and intensity.
The beetle outbreak area varied by species, with the fir engraver causing the most damage. The
damage potential of the fir engraver was reduced when fir populations declined from significant
beetle kill under high emissions.
Under high emissions climate, fir engraver outbreaks were 35% greater in area when fuel
treatments were not simulated, indicating that simulated fuel treatments have the capacity to
reduce outbreak size of fir engraver.
ANPP scaling analysis

Two significant drought periods occurred during the historical study period of 1987-2006; these
droughts caused reductions in median ANPP when estimated by both increment coring and
LANDIS-II, particularly in the sites with the highest potential productivity.

Moisture sensitivity and bark beetles both caused reductions in aboveground productivity,
moisture sensitivity via slowed growth and bark beetles via mortality, particularly in older larger
tree cohorts.
LANDIS-II captured the underlying dynamics of tree growth well. Scenarios that included bark
beetle outbreaks and moderate moisture sensitivity matched the increment core productivity
estimates best.
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4
INTRODUCTION
Lake Tahoe Basin (LTB) managers are faced with managing a forest disturbed by multiple and interacting
forces (climate, insects, drought, wildfire) that are highly dynamic across space and time. Climate change
is of particular concern because of the overarching impacts on Basin wide processes, such as forest
productivity(Battles et al. 2008), resilience(Millar et al. 2007), carbon (C) sequestration potential (Hurteau
and Brooks 2011), fire and insect risk(Coops and Waring 2011), amongst others (e.g., water quality).
Throughout the western U.S., increasing temperature and decreasing precipitation have been identified
as the root cause of increasing tree mortality(van Mantgem et al. 2009). Climate projections for California
however, suggest little change in annual total precipitation until late-century, although temperatures are
expected to continue increasing(Cayan et al. 2008). Increasing temperature amplifies drought stress in
plants because of the direct effects on evapotranspiration rates(Seager et al. 2007). In the dominant
Sierran mixed-conifer forest, future climate is projected to decrease productivity and increase mortality
(Battles et al. 2008). Large-scale droughts are already occurring in some systems. In southwestern piñonjuniper woodlands, prolonged water stress has increased susceptibility to other disturbance agents and
large mortality events(Breshears et al. 2009). A five-fold increase in drought related mortality is expected
to occur in southwestern U.S. forests by the end of the century(Adams et al. 2009).
Across the U.S., more than 70 million acres of forested land are at risk of mortality from 26 different
insects and diseases(US Department of Agriculture 2004). Bark beetles (e.g., Dendroctonus spp., Ips spp.,
Scolytus spp.) in particular, have been an especially important factor contributing to tree mortality in the
U.S.(Fettig et al. 2007). The link between bark beetle outbreaks and drought is significant (Guarín and
Taylor 2005, Dobbertin et al. 2007). This is especially true in areas that have been fire suppressed and
have high tree densities; the stress of resource competition from surrounding trees coupled with warm
temperatures creates a suitable environment for beetle infestation(Parker et al. 2006), notwithstanding
improved conditions for longer or multiple insect life cycles and dispersal capacity(Hicke et al. 2006).
Beetle outbreaks are expected to increase in the near future due to prolonged dry seasons and increased
drought stress caused by climate change. It is projected that bark beetle infestation risk will increase by
2.5-5 times in western forests by the end of the 21stcentury (Gan 2004, Hicke et al. 2006). The coniferous
forests of the western U.S. have already experienced unprecedented levels (> 20 million ha annually,
including pathogens (Dale et al. 2001)) of bark beetle infestations driven by the compounding factors of
fire suppression, high tree density, increasing temperatures, and extended drought(Hicke et al. 2006,
Fettig et al. 2007). This is also evident in the LTB where an extended drought period (1986-1992) caused
landscape-level drought and bark beetle driven mortality patterns, damaging >260,000 m2of tree basal
area (Macomber and Woodcock 1994). During this drought period, thirty percent of the conifers in the
LTB were killed by bark beetles, with concentrated mortality rates as high as 80% in some areas. The
intersection with the Wildland Urban Interface increased fire risk (i.e., higher dead fuel loads) and
reduced the forest’s aesthetic quality (Macomber and Woodcock 1994, Donaldson and Seybold 1998a, b).
5
The excessive fuel accumulation resulting from nearly a century of fire suppression(Stephens and Ruth
2005)and several decades of climate warming(Westerling et al. 2006) has also caused increasingly greater
area burned in U.S. National Forests. The amount of U.S. forest lands affected by wildfires and
phytophagous insects are similar and often overlap in area(Hicke et al. 2006). The overlap is due to the
feedbacks associated with tree stress and fuel loading. Even if trees survive a fire, they are stressed and
become more susceptible to beetle infestation.
An area not recently burned may still experience bark beetle attack (e.g., from drought stress) and will
subsequently increase dead fuel loading in outbreak areas(Parker et al. 2006). Where prescribed burning
is applied and fire intensities are lowered, trees may be fire-stressed and may therefore become prone to
subsequent bark beetle invasion. Specific to the LTB, (Bradley and Tueller 2001)found that bole charring,
duff consumption, and crown scorch were significant predictors of beetle attack after a prescribed burn
and over 24% of the trees were infested in prescribed burn plots. These feedbacks between bark beetle
outbreaks and wildland fire are, however, dependent on disturbance intensity, extent, timing, and
location, as well as tree physiological response to each disturbance.
With increasing temperatures in the west, these multifaceted tree stressors weaken individual tree
resistance and reduce overall forest resilience to drought, bark beetles, and wildfires. As a consequence,
LTB forests may be more vulnerable to rapid change than previously assumed, with the potential for a
climate regime shift to non-analogue vegetation(Hurteau and Brooks 2011). For example, if a Jeffrey pine
stand experiences drought, the resulting mortality and stress may trigger subsequent insect outbreaks
and/or provide more fuel for wildfire. If a large, contiguous area is similarly affected, Jeffrey pine seed
rain will diminish and, if the drought continues, germination and establishment will be reduced. The result
would be a large area dominated by chaparral and shrubs with substantially lower embodied C and
reduced ability to continue to uptake C. Furthermore, if conditions conducive to subsequent fire occur
more frequently, establishment of tree regeneration may be further limited because of the increase in
fire-induced mortality(McGinnis et al. 2010). Forest resilience can however, be maintained and even
enhanced through active management that reduces resource competition and that limits insect mortality
and the risk of high-intensity wildfire(Fettig et al. 2007).
Although climate, drought, and insects are external factors that LTB managers cannot directly control, it is
nonetheless important to understand the inherent processes and projected effects on the forest. This
knowledge provides the foundation for developing strategic adaptive planning, especially related to
invasive field applications, such as forest thinning. In addition to reducing fire risk, forest thinning has the
potential to reduce drought stress and bark beetle susceptibility(Fettig et al. 2007). Research and
extension(Donaldson and Seybold 1998b) has suggested ‘integrated pest management’ techniques to
mitigate bark beetle infestation, including thinning and sanitation methods. These techniques are geared
towards maintaining the health of un-infested trees and the removal of infested ones. Mitigation
strategies, such as fuel treatments, are a necessity in western U.S. forests, where a course of ‘no action’ is
not without consequence. The clear relationship of tree density and drought stress to bark beetle
6
susceptibility (as well as fire risk) drives the management motivation to implement extensive fuel
treatments in the LTB(Fettig et al. 2007).
Effective forest management requires a better understanding of a) the potential for amplified drought
and beetle induced mortality, b) the interactions among climate-driven processes (wildfire, drought,
beetle outbreaks, forest succession), c) the effects of current and future management activities (i.e.,
forest treatments) on mitigating the severity of drought and insect mortality, d) additional management
alternatives for maintaining forest resilience, while remaining logistically flexible given the large
uncertainty about the future of the LTB. A landscape-scale approach is critical to addressing these issues –
the LTB is a tightly linked system and projections beyond the next decade require an integrated,
landscape-scale approach. Furthermore, understanding the long-term effectiveness of various thinning
strategies is most feasibly done using a modeling approach, given the difficulty of designing and
maintaining long-term field experiments that incorporate multiple disturbance types and inherent
feedbacks among disturbances, succession, forest insects and pathogens, and climate change.
This project used an existing modeling framework (developed for P049, Loudermilk et al. 2012) of forest
landscape processes (wildfires, climate change, succession, and fuel treatments) to include effects from
drought and bark beetle outbreaks. More specifically, for P049, we evaluated climate change effects on
net ecosystem C balance (Loudermilk et al. 2013), as well as a suite of fuel treatment scenarios to assess
treatment effectiveness for reducing wildfire C emissions and maximizing total landscape C
storage(Loudermilk et al. 2014). For this project (P086), we integrated the effects of drought and insects
into our estimates of C dynamics and tree community response and feedbacks over the next 100 years, b)
examined model accuracy of forest productivity (i.e., ANPP) with empirical estimates, and c) examined
fuel treatment effectiveness for mitigating the effects from the interacting disturbances, i.e., climate
(including drought), wildfires, and bark beetle outbreaks.
STUDY OBJECTIVES
Our primary objectives were to evaluate climate change effects associated with drought stress (reduced
forest productivity), bark beetle outbreaks, and management mitigation options across the forested
landscape of the Lake Tahoe Basin, CA and NV, USA (Fig. 1, 2). In addition, we compared in situ estimates
of scaled ANPP from tree-ring data with model outputs across a coincident 20-year period (1987-2006) to
climate to investigate model accuracy of forest productivity, as influenced by moisture sensitivity and
bark beetle outbreaks. This research was an extension of our previous SNPLMA project [P049,(Loudermilk
et al. 2012)] which assessed the impacts of climate change on wildfire and above and belowground forest
C dynamics (Loudermilk et al. 2013), and the effectiveness of fuel treatment options for reducing fire
emissions and maintaining forest C (Loudermilk et al. 2014). This research leveraged the spatial data,
model parameterization, and analysis for P049, while incorporating drought-impact growth estimates
from site-level data collected from another SNPLMA project (P029, Hurteau et al. 2014). A conceptual
diagram of interacting disturbances and simulation approach are in Fig. 1.
7
METHODS
STUDY AREA
Our study area comprised approximately 85,000 ha of forested land in the Lake Tahoe Basin (Loudermilk
et al. 2013, Fig. 2). The climate is Mediterranean with a summer drought period. The basin-like
topography and elevation range (ca. 1897 to 3320 m) control local temperature and precipitation
patterns. The majority of precipitation falls as snow between October and May and snowpack persists
into the summer, dependent on elevation. The western portion of the Basin has a higher water balance
and greater productivity than the northern and eastern portions of the Basin. Mean annual precipitation
on the west shore at Tahoe City, CA is 80 cm and mean annual snowfall is 483 cm. Mean annual
precipitation on the east shore at Glenbrook, NV is 46 cm and mean annual snowfall is 236 cm. At Tahoe
City, average January high temperature is 6 °C and the low is -6 °C. Summers are mild with an average
high temperature of 26 °C in August and a low of 6 °C (Western Regional Climate Center 2012). Soils are
classified as shallow Entisols or Inceptisols and the more developed soils are Alfisols. The substrate is
mainly granite with ancient volcanic bedrock lining the north shore(Rogers 1974).
Tree species distribution in the LTB is controlled by elevation and precipitation (Barbour 2002). The lower
montane zone in the west Basin is primarily a mixed conifer forest consisting of up to six co-dominant
species including white and red fir (Abies concolor, A. magnifica), incense cedar (Calocedrus decurrens),
and Jeffrey, sugar, and lodgepole pine (Pinus Jeffreyi, P. lamertiana, P. contorta). The east side montane
zone is dominated by Jeffrey pine, red fir, and/or white fir. The subalpine zone consists of whitebark pine
(P. ablicaulis), western white pine (P. monticola), and mountain hemlock (Tsuga mertensiana).
Almost 70% of the lower montane zone in the LTB was clearcut during the Comstock logging era
beginning around 1870 and continuing through the turn of the century. Timber harvested from the LTB
was used for shoring up mineshafts, creating timber flumes, and extending rail lines to nearby Virginia
City, NV. The timber harvest and subsequent fire suppression throughout the last century has led to a
shift in the age and size distribution of the forest from a characteristic old-growth canopy, with an open
mid-story, to a more dense forest of younger age-cohorts (<120 years old) and more closed mid-story
(Barbour et al. 2002, Nagel and Taylor 2005). This shift has allowed more surface and ladder forest fuels
to accumulate and has increased fire risk(Beaty and Taylor 2008). In addition, shade tolerant trees like
white fir and incense cedar have increased disproportionately over fire-adapted species like Jeffrey and
sugar pine (Nagel and Taylor 2005).
MODEL PARAMETERIZATION AND CALIBRATION
This project (P086) is a continuation of the previous modeling efforts of project P049 (Management
Options for Reducing Wildfire Risk and Maximizing C Storage under Future Climate Changes, Ignition
Patterns, and Forest Treatments). As such, extensive details on model parameterization, calibration, and
validation are elsewhere (Loudermilk et al. 2013, 2014), including the original project’s final report
(Loudermilk et al. 2012). As such, details on initial forest composition, climate change data (including
downscaling), C dynamics, wildfires, and fuel treatments are written in brief. For the methods in this final
report, we extend these modeling efforts by providing full details on modeling bark beetles, as well as
8
examining model drought sensitivity and beetle outbreaks with empirical data (tree-cores) on forest
growth.
We used the Landscape Disturbance and Succession model LANDIS- II (v.6.0). The LANDIS-II model has
been used throughout the U.S. (Scheller et al. 2011a, Scheller et al. 2011b), b) and elsewhere (Cantarello
et al. 2011, Steenberg et al. 2011)for landscape climate change research (Scheller and Mladenoff 2008,
Xu et al. 2009), as well as and feedbacks associated with wildfire (Sturtevant et al. 2009), fuel treatments
(Syphard et al. 2011, Loudermilk et al. 2014), and insect outbreaks (Kretchun et al. 2014). LANDIS- II offers
the flexibility to integrate various ecosystem processes and disturbances that interact across large spatial
extents and long time periods, ideal for projecting forest succession and responses to climatic variability.
Input parameters for LANDIS-II modeling for the LTB are found in the Appendix.
LANDSCAPE FOREST COMPOSITION
To characterize the initial forest communities across the landscape, we utilized a database and a map of
age-cohorts of trees and shrubs developed for the LTB based on the Fuel Characteristic Classification
System and the existing vegetation map (CALVEG) from the GIS Clearinghouse of the Pacific Southwest
Region (Ottmar and Safford 2011). The resulting forest community map was coupled with Forest
Inventory Analysis data from the Basin and nearby Sierra Nevada forests to provide ground estimates of
species composition and age distribution by forest type (e.g., mixed conifer), similar to (Syphard et al.
2011). This map was refined to account for the largest wildfires from years 2002 to 2010.
CLIMATE CHANGE DATA
Study site climate projections were processed using existing downscaled Geophysical Fluid Dynamics
Laboratory (GFDL) General Circulation Models (GCM) of high (A2) and moderate(B1) global CO2 emissions
scenarios of climate up to year 2100, specific to the LTB(Coats et al. 2010). These downscaled daily
precipitation and temperature values (12 km ) were further processed using PRISM data (Parameterelevation Regressions on Independent Slopes Model, http://www.prism.oregonstate.edu/, PRISM 30-year
Normals (1971–2000), 800 m ) to provide area-weighted averages across each fire region and
ecoregion. For this project (P086), we only used the high (A2) emissions scenario of future climate. For
the Century and Dynamic Fire extensions of LANDIS-II (discussed below), the climate variables were
updated every 5 years (e.g., 2020–2024), and influenced monthly growth, decomposition, establishment
ability as well as seasonal fire weather, respectively, within each 5-year-time step.
2
2
MODELING C
Ecosystem C dynamics were modeled using the LANDIS-II Century Succession extension (Scheller et al.
2011a), which is based on the original CENTURY soil model (Parton et al. 1983). This extension (hereon
called ‘Century’) integrates aboveground processes of stand dynamics with C and nitrogen cycling as well
as decomposition and accumulation of soil C. Pool flows and interactions with climate (e.g., effects from
temperature and changes in soil moisture), as well as further parameter descriptions and examples of
calibration procedures for Century, are found elsewhere(Scheller et al. 2011a, Scheller et al.
2011b)including the Century User’s Guide (http://www.landis-ii.org/exts/Century-succession). Six target
model outputs were chosen to calibrate and validate Century parameters based on available literature
and expert opinion. These include aboveground net primary productivity (ANPP), NPP, net ecosystem
9
production (NEP), aboveground live biomass, soil organic C (SOC), and soil inorganic nitrogen (Mineral
N). Full details on Century parameterization, calibration, and validation are found elsewhere (Loudermilk
et al. 2012, 2013, 2014).
MODELING WILDFIRES
The Dynamic Fire and Fuels extension (hereon called ‘Dynamic Fire’) simulates fire behavior and fire
effects based on a parameterized fire regime and forest fuel types as well as input fire weather and
topography (Sturtevant et al., 2009). The LTB was divided into three fire regions, representing distinct fire
regime characteristics, and associated with varying elevation, climate, and ignition density estimations.
Each fire region had unique characteristics associated with ignitions and fire weather that influenced fire
ignition and spread. Details on fire region classification and parameters, as well as calibration based on
local wildfire data are found elsewhere (Loudermilk et al. 2012, 2013, 2014).
MODELING FOREST FUEL TREATMENTS
Fuel treatment (i.e., forest thinning) prescriptions and scenarios were developed using an expertknowledge approach similar to Syphard et al. (2011) and (Collins et al. 2010), where agency personnel at
the federal, state, and local level provided information on fuel treatment implementation and tactics,
including treatment efficacy. From these communications, we developed fuel treatment strategies that
represented their current and anticipated management activities in the LTB at the stand to landscape
level. Full details on fuel treatment prescriptions, fuel treatment areas and stand selection approach, as
well as fuel treatment scenarios are in Loudermilk et al. (2012, 2014).
Fuel treatments were simulated using the Leaf Biomass Harvest extension (v. 2.0.1) of LANDIS-II, that has
been successfully used in other fuel treatment (Scheller et al., 2011b; Syphard et al., 2011) and forest
harvesting studies (Scheller et al., 2011a). The LeafBiomass Harvest extension was designed specifically to
link with Century to simulate removal of aboveground live leaf and woody biomass of designated species
age-cohorts within selected areas and with Dynamic Fire to simulate post-treatment effects on
firebehavior and subsequent fire effects. Similar to Syphard et al. (2011), we used this extension to
simulate forest thinning from below (i.e., fuel treatments), where much of the older cohort biomass was
left intact.
Simulated fuel treatments represented the basic prescriptions deployed in the LTB including hand and
mechanical thinning of understory and mid-story trees up to specified diameter limits. The light thinning
prescription (Syphard et al., 2011) was designed to represent hand-thinning of understory and mid-story
trees up to 14 in. (35.6 cm) in diameter. The moderate thinning prescription (Syphard et al., 2011) was
designed to represent a more intense mechanical thinning of understory and mid-story trees up to 30 in.
(76.2 cm) in diameter. Fuel treatments were simulated within three designated treatment areas (Marlow
et al., 2007): the defensible space, defense zone, and extended wildland urban interface (WUI). These
treatment areas were further divided into treatment stands, representing an area that was completely
treated if chosen based on selection criteria and treatment interval. Stands were selected for treatment
based on estimated ‘‘fire hazard’’. The ‘‘fire hazard’’ stand selection method (found in v. 2.1.1 of the Base
Harvest extension) was created based on a management approach of stand selection that assesses forest
and fuel characteristics that describe a stand’s latent wildfire risk. Although multiple fuel treatment
10
scenarios were developed under the original project (P049), we only used one fuel treatment scenario for
this project; A ‘Continued Intensity’ scenario was designed to apply fuel treatments on a designated 15
rotation period continuously through time through all three management areas.
SIMULATING BARK BEETLES WITH BDA EXTENSION
Bark beetle outbreaks were simulated using the Biological Disturbance Agent (BDA) extension for LANDISII (Sturtevant et al. 2004). A conceptual diagram representing the interactions between climate, the
Century extension, and the BDA extension for modeling bark beetles effects on C dynamics are in Fig. 3.
This extension simulates tree mortality resulting from outbreaks of insects and disease that are significant
enough to influence forest succession, fire disturbance, or harvest disturbance at the landscape scale.
Outbreaks are probabilistic at the site level, where the probability of a cell being disturbed is based on the
available hosts at that site. Individual host species are ranked as one of four possible levels: primary,
secondary, minor, and non-host. These four categories are described as species and age categories. For
example, in the LTB, the Jeffrey pine beetle (Dendroctonus jeffreyi) is an obligate pest of Jeffrey pine,
though it prefers older cohorts (>60 years, primary host) much more than younger cohorts (<20 years old,
minor host). These host categorizations help determine ‘site vulnerability’ of a given site(Sturtevant et al.
2004). The severity of outbreaks that occurs is a direct function of site vulnerability, classified as one of
three categories: light, moderate, and severe. ‘Light’ outbreaks kill all vulnerable cohorts; ‘moderate’
outbreaks kill all tolerant and vulnerable cohorts; and ‘severe’ outbreaks kill resistant, tolerant, and
vulnerable cohorts. Outbreaks are synchronous, and the user can bound the severity by defining a
minimum and maximum possible outbreak level. For this study, we were matching a historical outbreak
considered to be severe compared to others in recent history; therefore the outbreak level within the
BDA was restricted to only the ‘severe’ level. The BDA extension reduces site and landscape ANPP
through mortality of affected cohorts rather than direct reductions in cohort growth rates.
Three bark beetle species were modeled: the Jeffrey Pine Beetle (Dendroctonus jefrreyi, JPB), the
Mountain Pine beetle (Dendroctonus ponderosae, MPB), and the Fir engraver beetle (Scolytus ventralis,
FE). Although there are other beetles active in the area (e.g. Red turpentine beetle (Dendrocotnus
valens)), these three beetles are responsible for the vast majority of the recorded damage in the Basin
and there is very little overlap in host species between the three. Empirical data from the literature and
expert opinion was used to determine host species and ages most preferred by each of the three
modeled beetle species (Kretchun et al. in review Ecosystems). JPB and FE are limited in their primary host
selection (Jeffrey pine and red/white fir respectively), whereas MPB is much more of a generalist,
impacting a variety of pine species across the basin. Beetle dispersal is modeled within BDA, defined at an
annual rate (m yr-1). This is unique for each species, and reflects the physiological differences in each
species’ ability to spread across a landscape.
A widespread outbreak of bark beetles occurred in the region, concurrent with a severe drought that
began in 1988. USFS Aerial Detection Survey (ADS) maps of the basin indicated >15,000 ha of damaged
area. These maps attribute damage to specific agents, meaning that area affected by beetles could be
identified on an annual basis. Maximum damage captured by these maps for each beetle species was:
mountain pine beetle (933.01 ha), Jeffrey pine beetle (3,126.2 ha), and Fir engraver beetle (11,726.30
11
ha). From the ADS flyover maps and expert opinion (Bulaon, personal communication), total acreage
affected by Fir engraver beetle required some correction for two reasons. Fir engraver beetles are
typically less aggressive (lower mortality percentage amongst affected stands) than species of genus
Dendroctonus and they are also more restricted to areas dominated by their hosts. This means that areas
identified by the flyover maps may be overstating the total area affected by these beetles. To correct for
this possible overestimation, stand dominance by tree species was determined using biomass estimates
within a 5 ha moving window across modeled sites. Stands that contained >75% red and white fir were
within an identified outbreak zone were determined to have likely been impacted, which could then be
totaled to calculate total area affected by fir engraver beetle. This correction factor was not applied to
area affected by the two species of Dendroctonus beetles, as it is believed the flyover maps area
reasonable estimates of damage. Therefore, area affected by each beetle species was calibrated to
mountain pine beetle (933.01 ha), Jeffrey pine beetle (3,126.2 ha), and fir engraver (8,794.5 ha). Total
area affected in the peak outbreak year of 1991 within the model was 10,417 ha, compared to remotely
sensed estimates of 15,783 ha. Outbreaks were simulated from 1990-1998, matching flyover maps and
other records. The outbreaks were deterministic in length, lasting for 8 years.
ANPP DATA & SCALING
TREE-CORE DATA: FIELD DATA COLLECTION &SCALING
Field data were collected at two to four plots in each of 21 creek drainages ranging from 1900-2200m
elevation (Fig. 4). Forest structural attributes were measured using a nested design in which all trees >
5cm dbh were measured in a 1/50th ha subplot, all trees > 50 cm dbh were measured in a 1/10th ha
subplot, and all trees >80 cm dbh were measured in a 1/5th ha plot. Within each plot two to three
individual trees were selected for coring from the five smallest and five largest individuals (Hurteau et al.
2014).
Annual ring width was measured to the nearest 0.001 mm using Windendro and error prone cores were
re-measured using a Unislide TA measuring system (Velmex, Bloomfield, NY). Raw tree-ring widths were
then used to calculate the radius of each tree to account for cores that missed pith. The inferred radius
was then used to estimate diameter at breast height (DBH) for each tree at each annual
increment. Annual DBH values were then used to estimate annual biomass production using allometric
equations from(Jenkins et al. 2004).
To scale tree-level estimates of annual biomass production to the plot level, individuals measured at DBH,
but not cored, were matched with cored individuals of the same species and similar diameter from the
nearest plot. Annual growth measurements from the cored individuals and genus-specific allometric
equations were then used to estimate annual biomass production for the trees that were not cored. Plot
level estimates of annual ANPP were then scaled to the hectare level using the appropriate scaling factor
(e.g., a tree > 50cm DBH represents 50 trees ha-1) from the nested plot design.
LANDSCAPE SIMULATIONS& ANALYSIS
We selected the period from 1987-2006 because it had the highest number of available tree core samples
and it captured two of the most significant droughts of the latter 20 th century which were accompanied
12
by large bark beetle outbreaks. Over the study period, maximum summer temperature ranged from 14
to 18.5⁰C, and minimum January temperature ranged from 0.8 to -4.2⁰C. The two drought periods
occurred from 1988 to1992 and 2001 to 2002, with the earlier of these two droughts generally being
regarded as more severe. The drought of 2001 was characterized by below average wintertime
(December-February) precipitation and slightly above average temperatures, whereas the drought of
1988 was characterized primarily by extreme below average wintertime precipitation (Fig. 5).
We chose the Palmer Drought Severity Index (PDSI) (Palmer 1965) to identify distinct drought periods for
the years of study. PDSI is a supply-demand model of soil moisture, which has been widely applied as a
general measure of drought(Zhao et al. 1986, Nicault et al. 2008). Values above zero indicate above
average moisture; values below zero indicate a drier period. More extreme values signify increased levels
of wetness or drought, respectively. The two drought periods on record for LTB for our study period are
reflected in the low PDSI (-2.08 and -2.2 respectively) values for 1988 and 2002 (Fig. 5).
EXPERIMENTAL DESIGN FOR ANPP ANALYSIS
Using LANDIS-II, we ran 20-year simulations with historical climate from 1987-2006. Monthly
temperature and precipitation values for 1986-2006 were from the PRISM dataset(Daly et al. 1997) for
the LTB. There were no records or physical evidence in the field of any fire (wildfire or prescribed burning)
or forest thinning within the study sites where tree-cores were collected. Although there may have been
some forest thinning operations and wildfires that occurred in the LTB during that timeframe, they were
not simulated to be congruent with disturbances evident at the field study sites. Therefore, our fullfactorial design included two levels of conifer drought sensitivity (moderate and high sensitivity) and two
levels of bark beetle outbreak (with and without beetles). Because the moisture sensitivity parameters
are not empirically derived values, the two levels of moisture sensitivity were determined by
incrementally increasing the DroughtB parameter by the minimum amount anticipated to have an effect
(0.1). The scenarios were as follows: low moisture sensitivity with no beetles (LowM-NoBB), low moisture
sensitivity with beetles (LowM-BB), high moisture sensitivity with no beetles (HiM-NoBB), and high
moisture sensitivity with beetles (HiM-BB). ANPP values from these four model scenarios were then
compared with empirical ANPP values.
TREE RING AND MODEL ESTIMATE COMPARISON
In order to make meaningful comparisons of model and tree-ring estimates of ANPP throughout the study
period, we bootstrapped both the empirical and modeled ANPP estimates using 500 draws from each
population to calculate the median and 95% confidence intervals. To visualize the relative densities of
ANPP between empirical and modeled data, kernel density estimates were constructed for each year of
the 20-year study period. Kernel density estimation is a non-parametric approach to estimate a
probability density function of a random variable; in this case, ANPP (Gagolewski 2013). Within these
plots, each year is represented by density distributions for empirical and modeled ANPP. Increasing yvalues (i.e. ‘peaks’ in the distributions) correspond to greater frequency of ANPP. For instance, if the
highest point, aka ‘peak’, of a distribution lay at 200 g C m 2 on the a-axis, that is the most frequent ANPP
value found across the study area. All statistical and graphical analyses were done using the R statistical
software package (R Core Team 2013).
13
LONG-TERM SIMULATIONS OF MULTIPLE DISTURBANCES
We used a scenario approach to assess the multiple climate-disturbance interactions and their influence
on long-term forest change and ecosystem C dynamics. Each scenario was simulated for 100 years (20102110). The landscape was simulated at a 1 ha spatial resolution, and each scenario was replicated five
times to account for the stochastic variation (due to climate, wildfire, insects, treatments, and
regeneration) among replicates. These scenarios of climate and disturbance were run in an iterative
process of incorporating bark beetles and fuel treatments:




High emissions climate and base climate (no change in climate) were simulated without effects
from bark beetles or fuel treatments
Bark beetles were simulated using the base and high emissions climate
Fuel treatments were simulated using the base and high emissions climate
Bark beetles and fuel treatments were simulated together using the base and high emissions
climate
14
RESULTS
Here we present results on the ANPP scaling analysis of 20 year (1987-2006) tree-core data and
coincident LANDIS-II model runs, as well as LANDIS-II projection modeling of climate and bark beetle
impacts over the next 100 years (2010-2110).
ANPP SCALING ANALYSIS
TREE RING ANPP ESTIMATES
Median ANPP derived from tree-cores was generally below 200 g C m-2 (Fig. 6), with increasing variance
over the 20-year study period. Over the simulation period, median ANPP values generally increased; from
118.5 g C m2 in 1987 to 207.1 g C m2 in 2006, with temporal fluctuations throughout the 20 year period.
Following two drought events median ANPP dropped from 118.5 g C m2 to 93.26 g C m2 (years 19871988) and from 229.4 g C m2 to 150.15 g C m2 (years 2000-2001). Changes in median ANPP values closely
followed changes in PDSI and total winter precipitation (Fig. 5).
Simulations that included bark beetle presence (LowM-BB and HiM-BB) experienced declines in both
aboveground biomass and total ecosystem C. Aboveground biomass was reduced by 9.7 ± 0.09% on
average for the two scenarios and total ecosystem C (all C pools combined including live, detrital, and
soil) was reduced by 8.59 ± 0.085%.
Simulated median ANPP values for the moderate drought sensitivity with beetles (LowM-BB) scenario and
high drought sensitivity (Hi) scenario fell within the bootstrapped confidence intervals of the empirical
data within 16 of the 20 years (Fig. 6). The LowM-BB and HiM-noBB median ANPP values showed a
consistently closer match than the LowM-noBB and HiM-BB scenarios to the empirical values in a majority
of years. Agreement between all model scenarios and the empirical data was lowest in the early
simulation years (1988-1993). A sharp decline in tree ring and all modeled median ANPP values occurred
in 2001 (Fig. 6; 229.4 g C/m2 to 150.2 g C/m2 in tree ring scenario), which corresponded with a similarly
steep drop in PDSI (Fig. 5; -0.07 to -1.75). Confidence intervals for the empirical data were much larger
than those of any of the model scenarios, likely because of the discrepancy in sample size between the
empirical (n=41) and modeled data (n≈31,000 grid cells).
Kernel density distributions illustrated similar temporal ANPP values among all model scenarios and
empirical data (Fig. 7). In years of severe drought (1988, 2001), ANPP values in the empirical data rarely
exceeded 400 g C m2; yet during years with more favorable growing conditions (more precipitation, less
beetle kill), ANPP values above 400 g C m2 were more common.
LANDIS-II BARK BEETLE MODELING
We estimated PDSI for a suite of 6 high-emission GCMs and found similar patterns in all, though
projections were highly variable, as expected. Values below the zero line indicate a dry period, with more
severe droughts indicated by more negative numbers. Regular dry and wet periods were commonly
projected by all GCMs. Generally speaking, average PDSI (indicated by the smoothed black spline in Fig. 8)
is expected to remain steady until about mid-century. From ~2050 on, projected PDSI shows a slow,
15
steady decline. The GCM chosen for our simulations, the GFDL A2 scenario, was among the most extreme
with regards to the increasing frequency and persistence of drought conditions, as defined by PDSI. Using
this GCM, there are several notable dips in PDSI towards the end of the century.
C TRAJECTORIES
In the baseline climate, simulated total ecosystem C, including above and below-ground live C, detrital C,
and soil C, was reduced with fuel treatments and bark beetles (Fig. 9), with bark beetles creating more
uncertainty (more variability between simulation replicates). This had similar effects within management
areas and simulated total ecosystem C was strongly affected by the combined effects of climate change
and bark beetles (Fig. 10). Total ecosystem C increased over the 100 year simulations regardless of
climate or disturbance scenario. Bark beetles (with wildfire and harvesting) and without climate change
had only a minor effect on total C. Total C was generally lower under high emissions climate, whether
bark beetles were included or not. However, when bark beetles were included under high emissions
climate, the persistent drought conditions created the conditions necessary for large and repeated bark
beetle outbreaks and total C was reduced by ~25% after 2070 .
SPECIES COMPOSITION SHIFTS
Baseline climate
The effects of climate, fuel treatments, and bark beetles differed according to species, particularly with
regards to the two species of primary management concern, Jeffrey pine and white fir (Fig.11, 12). The
effect of bark beetles on the competitive balance between Jeffrey pine and white pine, indicates that
without climate change, bark beetles would only have very small effects. As white fir is reduced due to
thinning and prescribed fire, Jeffrey pine increases, a compensatory mechanism that maintains forest
productivity (Loudermilk et al. 2014). The primary impact of bark beetle outbreaks on Jeffrey pine under
baseline climate is an increase in variability of aboveground biomass.
Though the competitive dynamics between Jeffrey pine and white fir are not noticeably changed by bark
beetle outbreak under baseline climate, bark beetle outbreaks do have an effect on white fir
biomass. This is true when focusing on individual management units (Fig. 11, 12), between which fuel
treatment prescriptions are highly variable, as well as landscape averages (Fig. 13). The primary impact of
bark beetles on white fir under baseline climate is a reduction of aboveground biomass via outbreak
related mortality. This is true in all management units, but the reductions are proportional to overall
white fir biomass. For instance, the largest white fir biomass reductions are seen in the Defense zone and
extended WUI, the two management units with the most white fir.
High Emissions climate
Under high emissions climate, the impacts of bark beetles and fuel treatments on the two species of
primary management focus, white fir and Jeffrey pine, are more pronounced and variable than under
baseline climate. When looking at landscape averages, Jeffrey pine shows increases in biomass with fuel
treatments (Fig. 14). These gains are severely reduced by bark beetle outbreaks in the second half of the
simulated century, as outbreaks become more frequent and severe. Variability is also significantly
increased. This same pattern is borne out when focusing on individual management units (Fig. 15, 16). As
biomass of Jeffrey pine increases through thinning and prescribed fire, the reductions in biomass caused
16
by bark beetles, particularly the Jeffrey pine beetle, become larger as well (Fig. 15). When fuel treatments
are implemented however, there is still an upward trajectory of Jeffrey pine biomass after beetle
outbreaks suggesting an ability to recover after beetle-caused losses.
White fir showed similar patterns to those of Jeffrey pine - under high emissions climate, bark beetle
outbreak decreased aboveground biomass in all management units as well as across the entire landscape
(Fig. 16). Unlike Jeffrey pine however, bark beetle outbreak accelerated the white fir biomass losses
caused by the implementation of fuel treatments. Outbreaks, primarily from the fir engraver beetle,
caused significant biomass reductions with an attendant increase in variability. There was less evidence of
post-outbreak recovery in white fir suggesting a greater uncertainty in C stability and continued
sequestration in white fir.
LIKELIHOOD OF CONTINUED C SEQUESTRATION
The probability of the LTB forests becoming net C sources to the atmosphere was sensitive to bark
beetles and climate change. A Log Odds Ratio (LOR) above zero indicates there is a higher likelihood of
the landscape switching from a net C sink to a net C source in any given year during that particular
quarter century when comparing two scenarios (Fig. 17). Both climate scenarios indicate the odds of
switching from a C sink to a C source are higher with bark beetles, although the uncertainty and temporal
patterns differ between the two. Under baseline emissions, there is some temporal change in the
likelihood of a sink/source switch. The LOR for NECB indicates that bark beetles significantly increase the
probability of the landscape becoming a C source after mid-century with climate change. However, bark
beetles increase the probability in the first quarter century under baseline climate, in part due to the
higher PDSI under our chosen climate projection.
FIRE BEHAVIOR
In order to estimate the effect of bark beetles as compared to climate change on area burned, we
conducted an ANOVA of total area burned (ha) across all years. Neither insects nor climate change were
significantly correlated with total area burned (p = 0.19 and 0.12, respectively) (see also Loudermilk et al.
2013).
Mean area burned in response to fuel treatment and bark beetle outbreaks was also analyzed by year,
climate scenario, and management unit. Patterns of area burned were similar under both baseline and
high emissions climate scenarios (see also Loudermilk et al 2014). Both the defensible space and defense
zone, the two units with the most active fire management programs due to their proximity to the
wildland urban interface, burned around 200 ha yr-1 on average with no fuel treatment (Fig. 18, 19).
When fuel treatments were simulated, average hectares burned dropped significantly, sometimes into
the single digits per year. This pattern did not significantly change with the scenarios that included bark
beetle outbreaks. This is true of years with and without active outbreaks on the landscape.
Fire and bark beetles generally did not spatially overlap often. At its maximum across all years and
replicates, fire only occurred on 8% of sites that had experienced a bark beetle outbreak in the prior 15
years.
17
BARK BEETLE OUTBREAK DYNAMICS
The area of defoliation varied by bark beetle species with fir engraver defoliating the largest area (Fig. 20).
The differing area of outbreaks by beetle was largely a result of the prevalence of that beetle’s preferred
hosts. Jeffrey pine beetle outbreaks affected the second most cumulative area, followed by mountain
pine beetle with the least area affected. This followed our calibration and the general pattern held across
all model scenarios.
Fir engraver outbreaks were 35% higher when fuel treatments were not simulated (data not shown),
indicating that simulated fuel treatments have the capacity to reduce outbreak size of fir engraver.
18
DISCUSSION
ANPP SCALING ANALYSIS
Productivity is influenced by a number of biotic and abiotic factors in the conifer forests of the Sierra
Nevada. Available soil moisture drives tree growth and ANPP(Dolanc et al. 2013), notwithstanding the
effects from natural disturbances (bark beetle outbreaks, wildfire), as well as land-use legacies (past
clear-cutting) and management (forest thinning for fuels reduction). In our study, we estimated ANPP
from tree-ring data and scaled these tree-level data to a per unit area basis. These validation data were
similar to simulation runs of ANPP, illustrating comparable distributions of ANPP within and between
years in response to drought and insect attack.
While empirical estimates of ANPP are useful for quantifying inter-annual variability as a function of both
biotic and abiotic factors, isolating the contribution of individual factors that can occur simultaneously
presents a challenge. Using model simulation to isolate the effects of different influential factors on ANPP
can improve our understanding of the mechanisms driving a particular empirical response. The two
scenarios that most closely approximated the empirical ANPP data were the ‘LowM-BB’ and ‘HiM-noBB’
scenarios (Fig. 6). Although both scenarios fell within the empirically-derived ANPP confidence intervals, a
fundamental difference in approach and inherent feedbacks exists between the two – climate alone
drives ANPP patterns in the ‘HiM-noBB’ scenario, whereas climate and beetle-induced mortality explained
these patterns in ‘LowM-BB’. Tree mortality was the primary effect of simulated bark beetle outbreaks,
removing cohorts of susceptible tree species and reducing ANPP. This mortality is captured within the
LowM-BB scenario, where median ANPP declines are seen in years of high bark beetle activity (e.g. 1993;
Fig. 6). However, the HiM-noBB scenario ignores bark beetle mortality, explaining intra-annual ANPP
patterns only through physiological responses to climate. The more conservative drought sensitivity and
bark beetle scenario (LowM-BB scenario) was a better representation of the coupled processes affecting
forest productivity during this timeframe because of the clear biological link of drought and beetle
attack(Guarín and Taylor 2005, Hebertson and Jenkins 2008, Creeden et al. 2014)and the prevalence of
bark beetles in the LTB(Bradley and Tueller 2001, Walker et al. 2007, Egan et al. 2010). Given the
occurrence of beetle outbreaks, excluding bark beetles and driving the decline in ANPP with higher
drought sensitivity (e.g. HiM-noBB) less accurately represents realistic feedbacks in the system; a dynamic
that is especially important for long-term simulations of climate-forest dynamics, where drought-induced
bark beetle outbreaks are common.
Focusing more specifically on individual bark beetle dynamics within our model scenarios, the Fir
engraver beetle affected the largest area across the LTB, resulting in widespread mortality of their
preferred hosts, white and red fir. This is consistent with remotely sensed data from the study period
(USDA Forest Service Pacific Southwest Region 2013). The large reductions in ANPP (Fig 5; -79.2 g C m-2 in
year 2001) highlight the sensitivity of these fir-dominated communities to drought conditions. This also
highlights the additive impacts of disturbance and climate on fir dominated areas – many of the plots with
the highest potential ANPP are highly stocked white fir stands, which are highly productive and prolific
seeders, but are most sensitive to drought conditions compared to most other species in the LTB(Hurteau
et al. 2007, Earles et al. 2014).
19
Although we explored two processes that influence ANPP at multiple scales, many critical processes were
excluded by design or necessity. Wildfires and forest thinning were not included in our simulations
because there was no evidence of recent fire or thinning practices within the stands selected for tree
coring. As in much of the western US, the hydrology of the LTB is snowpack driven; melting snow is
responsible for gradually recharging soil moisture in the spring and summer. As such, there is an inherent
lag between snowfall and soil water availability. Although Century simulates spring snowmelt, the
underlying hydrology model within Century does not fully capture long-term snowmelt dynamics into the
summer months. Previous comparisons of snow hydrology models have revealed that representing
snowpack dynamics within forested systems is particularly challenging; for example, there can be major
differences in model performance between open and closed sites(Rutter et al. 2009). Though these issues
persist, forests play an essential role in snow retention and therefore forest landscape models such as
LANDIS-II would benefit from the inclusion of a prolonged seasonal snow melt.
LANDIS-II BARK BEETLE & FUEL TREATMENT MODELING
BARK BEETLES AND C
Bark beetles have the capacity to substantially reduce ecosystem C storage capacity and can cause NECB
to become negative (a C source) for any given year (Bright et al. 2012, Hicke et al. 2012a). Our simulations
suggest that the probability of negative NECB will substantially increase due to climate change,
particularly after 2050. (Kurz et al. 2008)similarly linked mountain pine beetle outbreaks in British
Columbia to negative NECB, although their projections were near term (out to 2020) and did not include
an explicit link between climate and insect outbreaks. Although our simulations were limited to the LTB,
the implications for the interior western US are that current trends of increasing insect-caused forest
mortality(Weed et al. 2013)will accelerate, particularly after mid-century when PDSI is forecast to become
increasingly negative(Coats et al. 2013).
ECOLOGICAL MECHANISMS
The proximal mechanism by which bark beetles reduce NECB is a reduction of the compensatory growth
(sensu (Connell et al. 1984) of the system. As compared to fuels management or low-severity fire in
which the decline of one dominant species, (i.e., white fir) is compensated by release and growth of
another (i.e., Jeffrey pine) our climate change and bark beetle simulations suggest substantially reduced
projected growth of both. This is in part due to the confluence of multiple bark beetle species, both
generalists and specialists, as simulated. Although other extant trees species and/or shrubs would be
expected to occupy the newly available growing space, none has the fecundity or growth necessary to
maintain extant C stocks. Earles et al. (2014) also suggest that fir dominated stands subjected to drought
and wildfire would exhibit greater C instability. Bark beetle susceptibility increases after stands burn by
prescription(Bradley and Tueller 2001), however, our simulations suggest that such effects are and will
continue to be minor at landscape-scales due to current and foreseeable levels of fire suppression and
minimal prescribed burning efforts. Bark beetle damage effects on subsequent fuel conditions may also
be a consideration, but again the landscape effects are likely minor because of active fire suppression and
disparate location and timing between wildfires and bark beetle outbreaks (Hicke et al. 2012b).
20
CLIMATE CHANGE IMPLICATIONS
Over many decades, the spatial distribution of major insects are expected to shift with climate change as
their preferred host distributions shift (H. Aukema et al. 2008, Bentz et al. 2010). In the near term, there
may be a substantial time lag during which the hosts may be particularly susceptible due to drought
stress (Bentz et al. 2010)and an expansion of niche-availability for insects(Ward and Masters 2007). For
instance, MPB, will likely experience range expansion due to climatic warming and flexibility in life history
strategies, but could become limited to higher elevations in some locations (Hicke et al. 2006, Bentz et al.
2010). Over time mortality caused by insects with high host specificity would be expected to decline
because of reduced host density (Negrón and Popp 2004, H. Aukema et al. 2008). Although this effect
was not apparent overall in our 100 year simulations, this was evident in our simulations, where
increased white fir mortality from wildfires in the high emissions climate and targeting of white fir for
treatment in both climate scenarios removed large portions of the insect host, causing a decline in area
affected by that host (Fig. 20).
UNCERTAINTIES
Our simulation approach integrated many factors critical to projecting insect outbreaks and landscape C
dynamics over the coming decades, including climate and host availability. There are, however, subtle
mechanisms that were not explicitly incorporated and contribute to uncertainty in our results. For
example, large variation in MPB outbreaks suggest additional factors were not accounted for by climate
(Creeden et al. 2014). These may include local variation in host damage caused by thinning or prescribed
fires (Bradley and Tueller 2001) or wind damage or aspect (Guarín and Taylor 2005). Nor did we evaluate
the full life-cycle of each beetle population (e.g., overwintering mortality) and assumed that PDSI was a
sufficient proxy for the spatiotemporal scales of interest.
MANAGEMENT IMPLICATIONS
Managing for fuels may substantially reduce outbreak area by reducing host density and altering forest
structure. Conversely, climate change and insect outbreaks may reduce the need for fuels management
by eliminating a recurring source of ladder fuels, namely white fir. Similarly, areas of ponderosa pine
plantations in the Modoc National forest which were thinned prior to a MPB outbreak showed
significantly lower mortality rates than untreated areas (Fettig et al. 2007, Egan et al. 2010). Work in
other parts of the western US has also shown reductions in beetle-caused mortality in conifer forests as a
result of thinning treatments, though outbreaks that reach ‘epidemic’ levels may nullify the benefits of
such preventative measures (Fettig et al. 2007).
POLICY IMPLICATIONS
Developing strategic approaches to managing in these future conditions is essential. Field applications
used in the basin, primarily forest thinning, are intended to reduce fire risk but may also have the
potential to reduce drought stress and potential beetle-related mortality. This may be particularly
important in a changing climate, where higher temperatures may exacerbate these conditions (Coats et
al. 2010, Loudermilk et al. 2013). Management strategies to reduce outbreaks need to be preemptive,
whereby forests are thinned to create a more resilient forest structure (Egan et al. 2010). Beetles would
21
be forced to travel farther between trees and are less likely to find suitable hosts because the remaining
trees are often less drought stressed.
ACKNOWLEDGEMENTS
We would like to thank all of the Lake Tahoe Basin agency personnel as well as others from across the
region who participated in our final project workshop in May 2014. Here, we discussed results and
implications for beetle management in the Basin and across the Sierra Nevada forests. We especially
thank Dave Fournier and the Lake Tahoe Basin Management Unit (USDA Forest Service) for hosting the
workshop and providing invaluable feedback both at the workshop and throughout the project. We
thank Beverly Bulaon (USDA Forest Service) for datasets and insight to bark beetle outbreaks and
management as well as for presenting at the workshop. We graciously thank all of the 15 participants in
the workshop. Everyone was very engaged, where we all discussed project results, management
implications, future research directions, and potential collaborations. We would especially like to thank
Tiffany Van Huysen of the USDA Forest Service Pacific Southwest Research Station for her support,
administration of the grant, and participation in the project.
This research was supported using funds provided by the Bureau of Land Management through the sale
of public lands as authorized by the Southern Nevada Public Land Management Act, and was funded in
part through grant 10 DG-11272170-038 from the USDA Forest Service Pacific Southwest Research
Station. Writing on this report was funded in part by the Strategic Environmental Research and
Development Program, project RC-2243. We thank Portland State University, Pennsylvania State
University, and University of Nevada, Reno for their administrative support. The research team of the
Dynamic Ecosystems and Landscapes Lab, Portland State University was especially important for the
success of this project. We thank the Forestry Sciences Laboratory, Southern Research Station, USDA
Forest Service, Athens, GA, for their support. The views in this report are those of the authors and do not
necessary reflect those of the USDA Forest Service or Bureau of Land Management.
22
FIGURES
Figure 1: Conceptual diagram of interaction between climate, forest disturbances, and forest carbon.
Figure 2: Map of the study area for the LANDIS-II project in the Lake Tahoe Basin. Forest communities are
divided into ecoregions that were used in the LANDIS-II simulations: Eastside forest and woodland
(34,346 ha), upper montane (21,854 ha), subalpine (6,292 ha), montane shrubland (3,541 ha), and
riparian areas (3,017 ha).
23
Figure 3: Conceptual diagram of LANDIS-II components, particularly related to the interactions between
climate, the century extension, and the BDA extension for modeling bark beetles effects on C dynamics in
the LTB.
24
Figure 4: Study area for ANPP scaling analysis. Dark red dots indicate sites at which tree core samples
were taken; the orange area represents the modeling extent used in the LANDIS-II simulations; the grey
area is the entirety of the Lake Tahoe Basin.
25
Figure 5: Palmer Drought Severity Index (PDSI), total winter (December-February) precipitation, and tree
core-derived aboveground net primary productivity values for the Lake Tahoe Basin. The solid line within
the ANPP figure shows the median, while the shaded ribbon is constructed from the 90 th and 10th
percentile of the data respectively.
Figure 6: Comparison of empirical ANPP data with 2 LANDIS-II model scenarios. The ‘LowM-BB’ scenario
includes low moisture sensitivity and bark beetle outbreaks, ‘HiM-NoBB’ represents high moisture
sensitivity and no bark beetle outbreaks. Each line represents the median ANPP, shaded areas around
each median line represent bootstrapped 95% confidence intervals, 500 draws each.
26
Figure 7: Kernel density distributions of ANPP values for empirical and 4 modeled scenarios. Peaks in the
distributions correspond to more frequent ANPP values in any given year.
27
Bark Beetle Climate Change Analysis
Figure 8: Projected PDSI for 6 different GCM high emissions scenarios. The red line indicates the GFDL A2
scenario that was used to simulate climate change within our LANDIS-II simulations
Figure 9: Mean carbon across the Lake Tahoe Basin landscape (g C m2) for baseline (low emissions)
climate. The blue envelope represents total variation among five replicates for scenarios with no thinning
and no bark beetles; cyan represents thinning and no bark beetles; yellow represents no thinning with
bark beetles; red represents thinning with bark beetles.
28
Figure 10: Mean carbon across the Lake Tahoe Basin landscape (g C m 2) for baseline (low emissions)
climate. The blue envelope represents total variation among five replicates for scenarios with no thinning
and no bark beetles; cyan represents thinning and no bark beetles; yellow represents no thinning with
bark beetles; red represents thinning with bark beetles.
29
Baseline Climate
Figure 11: Aboveground biomass of Jeffrey pine under baseline climate for the 4 distinct management
units across the Lake Tahoe Basin. WUI stands for wildland-urban interface.
30
Baseline Climate
Figure 12: Aboveground biomass of white fir under baseline climate for the 4 distinct management units
across the Lake Tahoe Basin. WUI stands for wildland-urban interface.
Figure 13: Mean aboveground biomass for the entire Lake Tahoe Basin under baseline climate for the two
most prominent species of management concern (Jeffrey pine and white fir).
31
High Emissions Climate
Figure 14: Mean aboveground biomass for the entire Lake Tahoe Basin under baseline climate for the two
most prominent species of management concern (Jeffrey pine and white fir).
Figure 15: Aboveground biomass of Jeffrey pine under high emissions climate for the 4 distinct
management units across the Lake Tahoe Basin. WUI stands for wildland-urban interface.
32
High Emissions Climate
Figure 16: Aboveground biomass of white fir under high emissions climate for the 4 distinct management
units across the Lake Tahoe Basin. WUI stands for wildland-urban interface.
33
Figure 17: Log Odds ratio for NECB. Values above zero indicate the likelihood of the landscape switching
from a net carbon sink to a net carbon source in any given year during that particular quarter century
when comparing two scenarios. The LOR in Figure 17 compares scenarios with and without bark beetles;
the solid line represents baseline climate, while the dotted line represents high emissions climate. Error
bars within the figure represent the 95% confidence interval. Baseline climate is in the solid line while
high emissions is in the dashed line. Both climate scenarios indicate the odds of switching from a C sink to
a C source are higher with bark beetles and fuel treatments (FT), although the uncertainty and temporal
patterns differ between the two. There also some evidence in the baseline of fuel treatments decreasing
the likelihood of the landscape becoming a C source. Dotted line represents high emissions scenario, solid
line no emissions
34
Figure 18: Mean area burned by management area under the baseline climate scenario.
35
Figure 19: Mean area burned by management area under the high emissions climate scenario.
36
Figure 20: Average hectares affected by individual bark beetle species during outbreak years. The
patterns differ between fir engraver (whose preferred and only hosts are red and white fir) and jeffrey
pine beetle (preferred host: Jeffrey pine)/mountain pine beetle (preferred host: generalist pines).
37
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42
APPENDIX
Tree species and functional group attributes used in LANDIS-II modeling of the Lake Tahoe Basin, CA, NV, USA.
Species or functional group
Longevity
(yrs.)
Age of Sexual
Maturity (yrs.)
Shade
tolerance
(1-5)
Fire
tolerance
(1-5)
Effective
seeding
distance (m)
Maximum
seeding
distance
(m)
Vegetative
Reproduction
probability
Minimum
resprouting
age
Maximum
resprouting
age
Post-fire
regeneration
Pinus jeffreyi
500
25
2
5
50
300
0
0
0
none
Pinus lambertiana
550
20
3
5
30
400
0
0
0
none
Calocedrus decurrens
500
30
4
5
30
2000
0
0
0
none
Abies concolor
450
35
4
3
30
500
0
0
0
none
Abies magnifica
500
40
3
4
30
500
0
0
0
none
Pinus contorta
250
7
1
2
30
300
0
0
0
none
Pinus monticola
550
18
3
4
30
800
0
0
0
none
Tsuga mertensiana
800
20
5
1
30
800
0.0005
100
800
none
Pinus albicaulis
900
30
3
2
30
5000
0.0001
100
900
none
Populus tremuloides
Non N-fixing resprouting
shrubs
Non N-fixing obligate seeding
shrubs
175
15
1
2
30
1000
0.9
1
175
resprout
80
5
2
1
30
550
0.85
5
70
resprout
80
5
2
1
30
1000
0
0
0
none
N-fixing resprouting shrubs
N-fixing obligate seeding
shrubs
80
5
1
1
30
500
0.75
5
70
resprout
80
5
1
1
30
800
0
0
0
none
43
Physiological parameters for 11 tree species and 4 shrub functional groups found in the Lake Tahoe Basin, CA, NV, USA. GDD: Growing Degree
Days
Species or functional group
Functional
type
Nitrogen
tolerance
GDD
min
GDD
max
Min.
Jan.
Temp.
(C)
Max
Drought
Leaf
longevity
(yrs.)
Leaf
lignin
(%)
Fine
root
lignin
(%)
Wood
lignin
(%)
Coarse
root
lignin
(%)
Leaf
CN
ratio
Fine
root
C:N
ratio
Wood
C:N
ratio
Coarse
root
C:N
ratio
Litter
C:N
ratio
Pinus jeffreyi
1
N
555
2149
-5
0.94
6.0
0.28
0.2
0.25
0.25
48
48
250
167
100
Pinus lambertiana
1
N
815
2866
-5
0.90
2.5
0.17
0.2
0.25
0.25
53
53
278
185
100
Calocedrus decurrens
1
N
837
2938
18
0.99
4.0
0.1
0.2
0.25
0.25
48
48
500
333
100
Abies concolor
1
N
540
2670
-10
0.93
8.0
0.17
0.2
0.25
0.25
30
30
333
222
100
Abies magnifica
1
N
483
1144
-18
0.87
8.0
0.17
0.2
0.25
0.25
30
30
250
167
100
Pinus contorta
1
N
276
993
-18
0.87
3.5
0.25
0.2
0.25
0.25
48
48
500
333
100
Pinus monticola
1
N
155
1016
-18
0.82
7.0
0.31
0.2
0.25
0.25
37
37
500
333
100
Tsuga mertensiana
1
N
235
894
-18
0.80
4.5
0.24
0.2
0.25
0.25
80
80
333
222
100
Pinus albicaulis
1
N
230
950
-18
0.90
5.5
0.27
0.2
0.25
0.25
80
80
333
222
100
Populus tremuloides
2
N
600
3000
-10
0.82
1.0
0.18
0.2
0.25
0.25
62
62
333
222
100
Non N-fixing resprouting shrubs
Non N-fixing obligate seeding
shrubs
3
N
400
4000
-10
0.99
1.5
0.25
0.2
0.25
0.25
56
56
333
222
100
3
N
400
4000
-10
0.97
1.5
0.25
0.2
0.25
0.25
59
59
333
222
100
N-fixing resprouting shrubs
3
Y
400
4000
-10
0.97
1.5
0.25
0.2
0.25
0.25
20
28
333
222
50
N-fixing obligate seeding shrubs
3
Y
400
4000
-10
0.99
1.5
0.25
0.2
0.25
0.25
20
30
333
222
50
44
Functional type parameters for the species list in Table above.
Name
Functional
type index
PPDF1
mean
PPDF2
max
PPDF1
shape 1
PPDF1
shape 2
NPP
leaf
(%)
BTOLAI
KLAI
MAXLAI
PPRPTS2
PPRPTS3
Wood
decay
rate
Wood
mortality
(%/mo.)
Age
mortality
shape
Leaf Drop
Month
Conifers
1
23.0
40.0
0.05
6.0
0.2
0.002
5000
10
0.2
0.1
1.0
0.002
10.0
9.0
Hardwoods
2
23.0
35.0
0.05
7.0
0.3
0.002
5000
20
0.2
0.1
1.0
0.002
10.0
9.0
Shrubs
3
23.0
32.0
0.05
10.0
0.3
0.002
500
5
0.2
0.1
1.0
0.002
10.0
9.0
Ecoregion fixed parameters for the Lake Tahoe Basin, CA, NV, USA.
Ecoregion
Soil
depth
(cm)
% Clay
fraction
% Sand
fraction
Field
capacity
Wilting
point
Storm
flow
fraction
Base
flow
fraction
Drainage
Atmospheric
N (slope)
Atmospheric
N (intercept)
Latitude
Decay
rate
Surface
soil
Decay
rate
SOM1
Decay
rate
SOM2
Decay
rate
SOM3
Denitrification
Eastside Forest & Woodland
100
0.070
0.714
0.109
0.055
0.4
0.4
0.691
0.08
0.005
39.02
0.4
1
0.02
0.0002
0.5
Upper Montane
100
0.050
0.786
0.090
0.046
0.4
0.4
0.815
0.08
0.005
39.02
0.4
1
0.02
0.0002
0.5
Subalpine
100
0.061
0.744
0.097
0.051
0.4
0.4
0.735
0.08
0.005
39.02
0.4
1
0.02
0.0002
0.5
Montane shrubland
100
0.078
0.718
0.090
0.060
0.4
0.4
0.826
0.08
0.005
39.02
0.4
1
0.02
0.0002
0.5
Riparian areas
100
0.068
0.704
0.123
0.053
0.4
0.4
0.523
0.08
0.005
39.02
0.4
1
0.02
0.0002
0.5
45
Initial values of Carbon and Nitrogen in various soil organic pools.
Ecoregion
SOM1 C (surface)
SOM1 N (surface)
SOM1 C (soil)
SOM1 N (soil)
SOM2 C
SOM2 N
SOM3 C
SOM3 N
Mineral N
Eastside Forest & Woodland
75.0
3.0
100.0
10.0
3000.0
50.0
300.0
15.0
3.0
Upper Montane
75.0
3.0
100.0
10.0
3000.0
50.0
300.0
15.0
3.0
Subalpine
75.0
3.0
100.0
10.0
3000.0
50.0
300.0
15.0
3.0
Montane shrubland
75.0
3.0
100.0
10.0
3000.0
50.0
300.0
15.0
3.0
Riparian areas
75.0
3.0
100.0
10.0
3000.0
SOM1: Soil Organic Matter fast pool; SOM2: Soil Organic Matter fast pool; SOM3: Soil Organic Matter passive pool
50.0
300.0
15.0
3.0
Fire regime parameter inputs for the Dynamic Fire extension used for the Lake Tahoe Basin, CA, NV.
FMC: Foliar moisture content.
Fire Region
Region
size
(ha)
sigma
Maximum
Size or
Duration
Spring
FMC Low
Spring
FMC
High
Spring High
Proportion
Summer
FMC Low
Summer
FMC
High
Summer
High
Proportion
mu
Lower-elevation
9,603
5.2
0.32
4500
135
175
0.05
85
100
0.89
Mid-elevation
Higher-elevation
28,777
5.3
0.32
4500
135
175
0.05
85
100
0.89
31,194
5.2
0.32
4500
135
175
0.05
85
100
0.89
46
Bark beetle species, their host tree species, and associated minor, moderate, and major host ages as input in the BDA extension used for the Lake
Tahoe Basin, CA, NV.
Bark beetle species
Host tree species
Minor host
age
Moderate
host age
Major host
age
Mountain pine beetle (Dendroctonus ponderosae)
Pinus albicaulis
20
60
80
Pinus lambertiana
20
60
80
Pinus contorta
20
60
80
Pinus monticola
20
60
80
Calocedrus decurrens
20
60
80
Jeffrey pine beetle (Dendroctonus jeffreyi)
Pinus jeffreyi
15
25
40
Fir engraver beetle (Scolytus ventralis)
Abies concolor
15
30
60
Abies magnifica
15
30
60
47