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ffgc-05-740869 November 2, 2022 Time: 14:24 # 1
TYPE Original Research
PUBLISHED 08 November 2022
DOI 10.3389/ffgc.2022.740869
OPEN ACCESS
EDITED BY
Mark Andrew Adams,
Swinburne University of Technology,
Australia
REVIEWED BY
Mathias Neumann,
University of Natural Resources
and Life Sciences Vienna, Austria
Manfred J. Lexer,
University of Natural Resources
and Life Sciences Vienna, Austria
*CORRESPONDENCE
Charles J. Maxwell
charlesjmaxwell@gmail.com
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This article was submitted to
Forest Management,
a section of the journal
Frontiers in Forests and Global Change
RECEIVED 13 July 2021
ACCEPTED 10 October 2022
PUBLISHED 08 November 2022
CITATION
Maxwell CJ, Scheller RM, Wilson KN
and Manley PN (2022) Assessing the
effectiveness of landscape-scale
forest adaptation actions to improve
resilience under projected climate
change.
Front. For. Glob. Change 5:740869.
doi: 10.3389/ffgc.2022.740869
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© 2022 Maxwell, Scheller, Wilson and
Manley. This is an open-access article
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reproduction is permitted which does
not comply with these terms.
Assessing the effectiveness of
landscape-scale forest
adaptation actions to improve
resilience under projected
climate change
Charles J. Maxwell1,2*, Robert M. Scheller2, Kristen N. Wilson3
and Patricia N. Manley4
1Institute for Natural Resources, Oregon State University (OSU), Corvallis, OR, United States,
2Department of Forestry and Environmental Resources, North Carolina State University (NCSU),
Raleigh, NC, United States, 3The Nature Conservancy, San Francisco, CA, United States, 4Pacific
Southwest Research Station, Placerville, CA, United States
Climate change will increase disturbance pressures on forested ecosystems
worldwide. In many areas, longer, hotter summers will lead to more wildfire
and more insect activity which will substantially increase overall forest
mortality. Forest treatments reduce tree density and fuel loads, which in
turn reduces fire and insect severity, but implementation has been limited
compared to the area needing treatment. Ensuring that forests remain near
their reference conditions will require a significant increase in the pace and
scale of forest management. In order to assess what pace and scale may
be required for a landscape at risk, we simulated forest and disturbance
dynamics for the central Sierra Nevada, USA. Our modeling framework
included forest growth and succession, wildfire, insect mortality and locally
relevant management actions. Our simulations accounted for climate change
(five unique global change models on a business-as-usual emissions pathway)
and a wide range of plausible forest management scenarios (six total, ranging
from less than 1% of area receiving management treatments per year to 6% per
year). The climate projections we considered all led to an increasing climatic
water deficit, which in turn led to widespread insect caused mortality across
the landscape. The level of insect mortality limited the amount of carbon
stored and sequestered while leading to significant composition changes,
however, only one climate change projection resulted in increased fire over
contemporary conditions. While increased pace and scale of treatments led
to offsets in fire related tree mortality, managing toward historic reference
conditions was not sufficient to reduce insect-caused forest mortality. As
such, new management intensities and other adaptation actions may be
necessary to maintain forest resilience under an uncertain future climate.
KEYWORDS
forest ecology, climate change, wildfire, disturbance return interval, insect mortality
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Introduction
Forests reflect the disturbances that have shaped them:
fires, droughts, insect outbreaks, and harvesting all shape forest
composition and structure (White and Jentsch,2001). But under
climate change, disturbance regimes will shift, and current
forest conditions may no longer be in a “safe-operating space”
and can change rapidly (Johnstone et al.,2016;Serra-Diaz
et al.,2018). Across California climate change is projected
to increase drought (Diffenbaugh et al.,2015;Crockett and
Westerling,2018), forest fire activity (Westerling,2016), and
insect outbreaks (Fettig et al.,2019). With such disturbance
pressures, shifts in forest composition and structure will have
long-term consequences for the types and levels of ecosystem
services provided by future forests.
The 2012–2015 drought in California led to a mass tree
mortality event in the southern Sierra Nevada caused by direct
water stress (via C storage, hydrologic cavitation, or both;
Sevanto et al.,2014), insect attacks on drought stressed trees,
and increased fire activity. Historically, pre-European settlement
forests across the Sierras were of lower density and experienced
a short fire return interval (FRI) marked by frequent fires (every
11–16 years), but low to moderate fire severity (Hessburg et al.,
2019). A history of fire suppression and timber harvesting over
the past century resulted in a denser forest as shade tolerant
species filled in, leading to anomalously high fuel loadings and
larger, more severe fire patterns (Keeley and Syphard,2019).
While current forests are at or near a self-thinning phase due
to the forest densification, pre-European settlement forests were
likely sufficiently sparse that trees experienced little resource
competition (North et al.,2022). Forest densification amplified
the mass tree mortality event by increasing competition for soil
water leading to increased insect outbreak severity (Safford and
Stevens,2017). Forest treatments reduce stand density and fuel
loads and increase spatial and structural heterogeneity, which
increases the capacity of the forest to resist mortality events
(Restaino et al.,2019;Knapp et al.,2020) and can further
enhance the resilience of the forest by allowing it to recover after
a fire to pre-suppression conditions (Coop et al.,2020).
Because of budget limitations and competing objectives,
there has been limited opportunity to implement widespread
thinning and prescribed fire treatments despite the compelling
need. As such, managers will need to rely on wildfires to reduce
fuels and stand density (North et al.,2012). It is, however,
unknown whether such treatments can sufficiently tame wildfire
to effectively contribute to the objectives of a lower severity
fire regime, improved drought and insect mortality resistance,
and resilience in response to disturbance. Previous studies
have suggested that early and aggressive, large-scale thinning
treatments can reduce fire severity and increase carbon storage
across the whole Sierra Nevada range (Liang et al.,2018).
Accelerated forest treatments on 14% of the Sierra Nevada
ecoregion per decade reduced the risk of tree mortality from
fire and held more carbon (Liang et al.,2018). Within the Lake
Tahoe Basin (a subset of the larger Sierra Nevada), Loudermilk
et al. (2017) found that strategically placed fuel treatments in
high ignition areas covering less than 7% of the forested area
can reduce wildfire risk, increased the fire resiliency of forest,
and benefitted carbon storage. However, Scheller et al. (2018)
found that fuel management practices in Lake Tahoe based on
Loudermilk et al. (2017) would not reduce landscape-scale forest
mortality from beetle outbreaks.
We hypothesized that mimicking the historic fire-return
interval, by matching it with the combined frequency of
natural disturbances (wildfire) and management (i.e., thinning
and prescribed fire), will maintain forest resilience despite a
changing climate. We measured this resilience by tracking
several metrics through time: (1) tree-mortality due to wildfire,
(2) tree mortality due to insect outbreaks, (3) carbon storage
and sequestration, and (4) avoidance of forest conversion to
shrub. To test our hypothesis, we deployed a forest landscape
simulation model, LANDIS-II, to test a broad range of
management scenarios under climate change. LANDIS-II is
able to simulate multiple processes within forests, including
forest growth and succession, wildfire, insects, management,
and climate change. This allowed us to test a variety of
management scenarios ranging in intensity and methodology.
We specifically focused on scenarios that encapsulated a range
of proposed actions for returning the landscape to the historic
disturbance interval. Our scenarios allowed us to parse the
contributions of specific and plausible management actions and
therefore to evaluate their relative performance at maintaining
forest resilience over the 21st century. Those management
scenarios were paired with climate change projections selected
to highlight a broad range of potential future climate conditions.
Materials and methods
Study area
The study area which represents the area covered by the
Tahoe Central Sierra Initiative (TCSI), covers 978,381 hectares
(2,416,000 acres) of the central Sierra Nevada and has over
3000 m of topographic relief (Figure 1). The primary forest type
is Sierra mixed conifer, which includes such species as ponderosa
pine (Pinus ponderosa), Douglas-fir (Pseudotsuga menziesii),
incense-cedar (Calocedrus decurrens), and white fir (Abies
concolor) at mid-elevations. The types range from low elevation
oak woodlands (Quercus spp.) to mixed conifer to high elevation
montane conifers (Abies magnifica,Pinus albicaulis,Pinus
monticola). The climate is generally Mediterranean, Koppen
climate classification of Csa to Dsa, with warm, dry summers
and cool, wet winters. The region receives approximately
1300 mm of precipitation a year, mostly as snow. Annual
mean maximum temperatures are about 17◦C and minimums
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FIGURE 1
Management zones (A), historic fire return intervals (B) lumped by climate class and used to set scenario treatment amounts; and slopes (C) all
influenced treatment prescriptions. Study area location within the USA (D). Forested areas that are within 400 m of urban areas are called
wildland urban interface (WUI) Defense areas, and those within 2000 m are WUI Threat areas. Forests outside of WUI areas in public ownership
that were not legislatively restricted in some way are considered general forests, while roadless and wilderness areas are not allowed to have
roads built within them and wilderness areas cannot have any mechanized equipment within them.
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are about 3◦C. The region was largely spared from the insect
outbreaks that contributed to the mass mortality event in the
southern Sierras, but it still has seen large areas affected by insect
outbreaks (USFS Aerial Detection Survey).
The predominant landowner in this region is the Federal
Government, with approximately 687,967 ha of National Forest
System lands, 41% of which is within 2.4 km of houses or
other buildings (i.e., the wildland urban interface, or WUI).
There are four National Forests with lands in the study area:
Tahoe, Eldorado, Lake Tahoe Basin Management Unit, and
Plumas. Private ownership, either non-industrial forestland or
industrial forestland, covers 143,549 ha, 11% of which is within
the WUI. The remainder (146,865 ha) is either developed land
or water bodies.
Model description
LANDIS-II simulates forests as tree or shrub species-age
cohorts within a grid of interacting cells, allowing spatial
interactions among processes (e.g., management, growth and
succession, and disturbance) over many decades and across
large landscapes. Individual cohorts compete for resources
(e.g., soil moisture, nitrogen, and growing space) among the
different species-age cohorts within each cell, and within
LANDIS-II, disturbances and succession interact via the
species-age cohorts. For example, cohorts killed by wildfire
are not subsequently available to serve as hosts for insects; if
management substantially reduces insect-susceptible cohorts,
the likelihood of outbreaks is subsequently reduced; prescribed
fires reduce ladder fuels that co-determine fire severity; etc.
As such, the landscape is an evolving spatial representation
of forest demographics, capturing regeneration and mortality
that respond dynamically to climate change-driven disturbance
events. We modeled from 2020 to 2100 using future climate
projections with at least five replicates of each climate
projection to capture stochastic variation in disturbances.
To isolate the effect of climate projections, we modeled one
management scenario under historical climate by resampling
random weather years from 1990 to 2019. We divided the
landscape into a 180-m (3.24 ha) grid. All model parameters,
and the model and extension versions used, are available
on github at: https://github.com/LANDIS-II- Foundation/
Project-Tahoe-Central-Sierra-2019. This repository will also be
archived on Zenodo.
Succession and carbon dynamics
Forest succession and carbon dynamics were simulated
using the Net Ecosystem Carbon and Nitrogen (NECN)
succession extension (v6.5) (Scheller et al.,2011a). NECN
simulates both above and belowground processes, tracking C
and N through multiple live and dead pools, as well as tree
growth (as a function of age, climate, and competition for
available water and N) and landscape carbon sequestration
[as net ecosystem carbon balance (NECB)–a function of
growth, decomposition, and disturbance]. Soil, wood, and litter
decomposition are based on the CENTURY soil model (Parton
et al.,1988;Scheller et al.,2011a). Daily weather inputs of
precipitation and temperature drive forest growth, regeneration
and decay, with each species responding uniquely to those
inputs. Seeding is based on several factors: (1) cohorts can
only create seeds when they have reached sexual maturity; (2)
dispersal distance follows a double-exponential distribution.
Regeneration specifically is bounded by growing season and
must fall within a range of growing degree days above 5◦C,
and have sufficient water, see Supplementary Appendix 1;
Supplementary Table 1 for the full list of species parameters.
Shrub groups based on functional type groupings (N-fixing and
resprouting versus seeding) were included within the model in
order to serve as a competitor to tree species during post-fire
regeneration (Serra-Diaz et al.,2018).
Net Ecosystem Carbon and Nitrogen inputs and parameters
were based on a suite of forest inventory, satellite data,
and literature sources (see section “ Model Calibration and
Evaluation,” and Supplementary Appendix 1). Soil data, such
as soil depth, field capacity, and percent clay among others,
were from a gridded SSURGO product of California (Soil Survey
Staff,2017). Duff, litter, and deadwood layers were derived from
interpolated FIA data (Wilson et al.,2013). Initial communities
were derived from FIA plots that were interpolated using a
series of algorithms and LANDSAT imagery with total forest
cover aligning with the LANDSAT derived Dewitz and U. S.
Geological Survey (2021). We simulated 39 tree and shrub
species; species traits were derived from literature sources [Liang
et al.,2017, USFS Silvics Manual (Burns and Honkala,1990),
USFS Fire Effects Information System (FEIS)].
Wildfire disturbances
Wildfire was simulated as a function of ignition (human
or lightning), fuels, topography, and fire weather using the
SCRPPLE extension (v.2.2) (Scheller et al.,2019). Ignitions
themselves are stochastic within the model but use a
probability surface based on prior wildfire ignition data to
distribute the ignitions across the study area and rely on
the calculated Canadian Fire Weather Index (FWI) from
the input climate to determine the general timing pattern
(e.g., the number of ignitions trend upwards during summer
months) which is calculated by a poisson regression with
FWI as an independent variable. Human caused ignition
probability surfaces were derived from Short (2021) wildfire
occurrence data (Supplementary Appendix 1;Supplementary
Figure 4). Lightning caused ignition probability surfaces were
derived from 15-year lightning strike records (Supplementary
Appendix 1;Supplementary Figure 5).
Within the model, increasing fire intensity is based three
conditions: (1) if the amount of fine fuels [litter, duff, and small
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down woody debris (<7.6 cm)] within a cell crosses a set
threshold, (2) if the amount of ladder fuels within a cell crosses
a set threshold, and (3) if a neighboring cell is experiencing high
intensity fire. For high intensity fire to occur, two of those three
conditions need to be met, and for moderate intensity fire, just
one of those conditions need to be met. Because fire severity
is dependent on fine and ladder fuel accumulation, it therefore
reflects events on the landscape including management like
prescribed fire (reducing fine fuels) or thinning (reducing ladder
fuels), prior fire events (reducing fine and ladder fuels), and
insect outbreaks (increasing fine fuels). To translate fire intensity
into fire severity, as measured by tree mortality, we used data
from the Cansler et al. (2020) Fire and Tree mortality database
to calculate what percent of a species-age cohort would die from
a certain fire intensity.
Bark beetle disturbances
Four of the most prevalent insects to conifer trees in the
Sierra Nevada were simulated using a modified version of the
BDA extension (v2.1) (Sturtevant et al.,2004): fir engraver
(Scolytus ventralis), Jeffrey pine beetle (Dendroctonus jeffreyi),
mountain pine beetle (Dendroctonus ponderosae), and western
pine beetle (Dendroctonus brevicomis). Insect outbreaks were
simulated as a function of drought stress, as measured by
climatic water deficit, and warm winter temperatures. The
probability of bark beetle outbreak spread and outbreak severity
reflect the neighborhood density of hosts, which is calculated
from the amount of biomass of a specific host in a given cell
(Sturtevant et al.,2004). Changes in levels of the host species
biomass—whether from past occurrences of wildfires or forest
management—would reduce spread and intensity.
Outbreak thresholds (CWD and minimum winter
temperatures as averaged across the landscape) were derived
from the presence of outbreaks greater than 400 ha (>1000
acres) in the USFS Aerial Detection Survey (ADS) dataset
for 1992–2017. This was done to try to match the temporal
occurrence of outbreaks; however, this resulted in an overall
accuracy of 54% of outbreak years (i.e., where outbreak and
non-outbreak years coincided) but still underestimated the
total frequency of outbreak incidence in the ADS dataset.
This underestimate is likely the result of only capturing the
potential climate signal and specific host presence rather than
insect ecology. Tree susceptibility to insects was based on age
values taken from the literature (Supplementary Appendix 1;
Supplementary Table 1) and then was further adjusted to
match landscape-scale field data using Fettig et al.’s (2019) plots
in the Stanislaus National Forest, which are located immediately
to the south of the study area.
Forest management scenarios
Forest management scenarios and treatment prescriptions
were developed based on expert-opinion from National Forest
silviculturalists and managers, along with input from private
timber industry, US Forest Service scientists, academics, and
The Nature Conservancy ecologists. We developed six scenarios
to forecast how increasing the scale of forest management and
greater use of prescribed fire would improve resilience outcomes
under climate change.
To implement our six scenarios, we assigned target
disturbance return intervals (DRI), the time in years between a
management action (e.g., thinning or prescribed fire), wildfire,
or insect outbreak, to every cell (Figure 1). The target DRI
was a 10–20 year range centered on mean historic fire
return intervals (van Wagtendonk et al.,2018) associated
with climate zones (climate classes as defined by Jeronimo
et al.,2019) (Table 1 and Figure 1E). Harvest rates across
the landscape were then based on the frequency necessary to
maintain that respective return interval target (Supplementary
Appendix 2;Supplementary Table 3). We delineated seven
management zones and two slope classes to reflect land
ownership, administrative restrictions, and slope limitations on
forest prescriptions (Figure 1). We defined the WUI Defense
and Threat zones as 402 m (<0.25 miles) and 2,012 m
(<1.25 miles) from development, respectively. Developed areas
represented more than two dwelling units per 40 hectares,
or commercial, industrial, institutional, transportation, or golf
course land use types using the ICLUS v.2.1 dataset (HadGEM2-
ES RCP8.5 SSP2 2020, U.S.EPA,2017). Forest treatment on
public forest lands outside the Defense and Threat zones
(i.e., General Forest) was triggered by a stand meeting two
conditions: (1) a minimum time since disturbance threshold
based on the most recent forest treatment or disturbance (mean
of the DRI range); and (2) a minimum biomass threshold (which
was based on the conversion of the minimum mean basal area
from contemporary reference forest structure associated with a
climate class) (Table 1). These conditions were set to push stands
toward their historic fire return interval while also ensuring
sufficient time for a stand to recover from disturbance before
any management activity took place.
Our six scenarios were derived from the variable application
of four treatment prescriptions: clearcutting, mechanical
thinning in mature stands, mechanical thinning in young
stands, and hand-thinning (Supplementary Appendix 2;
Supplementary Table 4). Mechanical thinning treatments
encompass a range of possible activities depending on tree
size, but can include harvesting, mastication, and drumming
amongst other methods. Hand thinning treatments are crew
and chainsaw based, and while able to work on a wider
range of slopes, cannot remove as large of trees or as much
material as mechanical thinning methods. Except for clear
cutting on private lands, 14 of the 39 tree species were
thinned at a range of intensities spanning a range of diameter
classes based on existing forest practices (Table 2). These
thinnings targeted all diameter classes of more shade-tolerant
and less fire-intolerant species: white fir, red fir, incense cedar,
juniper, Douglas-fir, and mountain hemlock. Pines were also
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TABLE 1 Climatic zones, their historic fire return intervals, the targeted disturbance return intervals (DRIs) and the thresholds for conducting
treatment, as simulated.
Climate class Historic fire
return interval
Target DRI
range (median)
Treatment thresholds
DRI
(years)
Basal area
(m2/ha)
Biomass
(g/m2)
Very hot low montane Short 5–15 (10) 10 35 9373
Warm dry low montane Short 5–15 (10) 10 35 9373
Warm mesic low montane Short 5–15 (10) 10 35 9373
Warm mesic mid montane Short 5–15 (10) 10 35 9373
Xeric mid montane Short 5–15 (10) 10 35 9373
Foothill-low montane transition Short medium 10–20 (15) 15 25 7636
Hot low montane Short medium 10–20 (15) 15 25 7636
Cool dry mid montane Short medium 10–20 (15) 15 25 7636
Foothill valleys Medium 20–40 (30) 30 26 7809
Cool mesic high montane Medium 20–40 (30) 30 26 7809
Xeric high montane Medium 20–40 (30) 30 26 7809
Cool dry high montane Medium long 30–50 (40) 40 30 8503
Cold dry high montane Medium long 30–50 (40) 40 30 8503
High sierra Long 40–60 (50) 50
Biomass values are the equivalents used to represent basal area per hectare (BAPH) threshold values (mean plus one standard deviation of contemporary reference basal area) in the
simulation model based on the follow equation: = (0.0021*BAPH)ˆ2 + 39.312*BAPH + 3330.2. Treatment targets for the High sierra region were not established given the small area
represented by this region (0.2% of the study area).
targeted, but only up to 76.2 cm (30 in) in diameter, with
greater removal of lodgepole pine, whitebark pine, and western
white pine and lesser treatment of Jeffrey pine, sugar pine,
ponderosa pine, Washoe pine, and gray pine (Supplementary
Appendix 2;Supplementary Table 4). Mechanical thinning of
young stands, or pre-commercial thinning, and hand thinning
removed understory trees up to 25.4 cm (10 in) diameter
of all species. We did not simulate salvage logging following
wildfire or insect outbreaks. We set the model to have two
thresholds for treatment - time since significant disturbance set
at a minimum value of based on FRI, and stand condition, based
on exceeding the upper end of the desired range of biomass. We
designed treatments to remove sufficient material by age class to
bring the stand into desired condition. The end result of each
management action was unique to each stand and each stand
entry based on the dynamic nature of the modeling.
Across all six scenarios, the treatment interval and
prescription were held constant for three management zones:
Private Industrial (every 25 years), Private Non-industrial
(every 40 years), and Defense (matched to the DRI). The
Defense zone is the zone where fires often start and is
a priority for reducing wildfire risk to people and infrastructure.
There was no prescribed fire in these three zones, and
clearcuts only occurred on Private Industrial land. On National
Forest lands with slopes >30% all prescriptions were hand
thinning or prescribed fire. There was no treatment in the
Wilderness zone.
The treatment goal for Scenario 1 for private land and the
Defense zone was 9,300 ha (23,000 acres) per year. In Scenario
2, which is equivalent to the “business as usual” management
scenario, we added treatments in the Threat zone [16,600 ha
(41,000 acres) per year]. The treatment area in Scenario 2 is
designed to be close to the average annual forest treatment
by public and private land managers in the study area. In
2018, the treatment in the area was 15,469 ha (38,226 acres)
per year based on the U.S. Forest Service FACTs database
and CALFIRE treatment database for private land. In Scenario
3, we added treatment in the General Forest, Roadless, and
Wilderness management zones [32,780 ha (81,000 acres) per
year]. In Scenario 3, the General Forest and Roadless zones
received 5 and 20% of the total treatment was prescribed fire,
respectively. The percent of mechanical thinning treatment
varied across scenarios depending on the balance with the
percent of prescribed fire for Scenarios 3–6, with the total adding
up to 100% for both <30% and >30% slopes (Table 3). In
Scenario 4, the Threat zone treatment interval was lowered to
match the historic FRI and prescribed fire represented 20%
of the total treatment for all slopes [39,250 ha (97,000 acres)
per year]. In Scenario 5 and Scenario 6, we set the treatment
interval equal to the historic FRI for the General Forest and
Roadless management zones [51,400 ha (127,000 acres) per
year, Supplementary Appendix 2]. The total treatment goal in
Scenarios 5 and 6 was the same, however, scenario 6 included
more prescribed fire in the General Forest and Roadless zones,
30% of the total treatment for both zones. The treatment goal
for Scenarios 1 through 6 was to treat 1–6% of the forested
landscape per year, with Scenarios 5 and 6 matching the historic
fire return interval, or 11–63% per decade.
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TABLE 2 Parameters for major tree species that were modeled, beetle susceptibility of those species, and forest treatments that remove cohorts within the listed specific age range.
Species Life-span
(years)
Age of sexual
maturity (years)
Fire tolerance
(1–5)
Shade
tolerance (1–5)
Beetle susceptible
(common name)
Clear cut Mech. thin,
young stand
Mech. thin,
mature stand
Hand thin
Abies concolor 450 35 3 4 Fir engraver All All All 1–70
Abies magnifica 500 40 4 4 Fir engraver All All All 1–71
Pinus jeffreyi 500 25 5 2 Jeffrey pine beetle All 1–68 1–140 1–68
Pinus lambertiana 550 20 5 3 Mountain pine beetle All 1–57 1–125 1–64
Pinus contorta 270 7 2 1 Mountain pine beetle All 1–68 1–200 1–88
Pinus monticola 550 18 4 3 Mountain pine beetle All 1–81 1–200 1–88
Pinus albicaulis 900 30 2 3 Mountain pine beetle All 1–79 1–200 1–87
Pinus ponderosa 600 10 5 1 Western pine beetle All 1–59 1–125 1–68
Pinus washoensis 600 10 5 1 Western pine beetle All 1–59 1–125 1–60
Pseudotsuga menziesii 650 15 3 3 No All All All 1–56
Calocedrus decurrens 500 30 5 3 No All All All 1–78
Tsuga mertensiana 800 20 1 5 No All All All 1–71
Pinus sabiniana 300 20 3 1 No All 1–50 1–125 1–64
Climate projections
We simulated the six management scenarios under five
climate futures from the Coupled Model Intercomparison
Project Phase 5. We selected five climate models based on
the recommended subset from California’s Fourth Climate
Change Assessment, plus one additional model (Pierce et al.,
2018): HadGEM2-ES, CNRM-CM5, CanESM2, MIROC5, plus
GFDL-ESM2. These models were identified by the California
Department of Water Resources as some of the best models to
use for water resource planning in California (Department of
Water Resources [DWR],2015). Only the relative concentration
pathway (RCP) 8.5 projections were chosen for each climate
model, given that RCP 8.5 best represents current and expected
near term emissions levels (Schwalm et al.,2020). These five
models ranged from a 5% increase in annual precipitation
relative to the 2000–2009 decade by end of century (MIROC5)
to a >50% increase by the end of century (CNRM-
CM5). Maximum daily temperature increased from >20%
(CNRM-CM5) to >30% (GFDL-ESM2) (see Supplementary
Appendix 1 for more details). All projections were downscaled
using the MACA methodology to a 4 km grid (Abatzoglou and
Brown,2012) available through the USGS Geo Data Portal,1
before being averaged across ten ecoregions that were delineated
by elevation to improve computational tractability.
Model calibration and evaluation
We calibrated the LANDIS-II model using 1990–2019
gridMET historical climate data (Abatzoglou,2013). We were
able to recreate similar levels of forest growth, net ecosystem
exchange, fire size and beetle mortality compared to other
sources. We first compared the initial forest biomass conditions
that served as inputs into the model to other landscape scale
biomass imputation estimates such as TREE MAP (Riley et al.,
2021). The modeled average biomass (mean 218 Mg/ha, sd
91 Mg/ha) was 33–71 Mg/ha higher but fell within a standard
deviation of other estimates (see Supplementary Appendix 2;
Supplementary Figure 1).
Forest growth was calibrated based on a 15-year average
(2000–2015) of the MODIS 17A3 annual net primary
productivity (NPP) product and the calculated net ecosystem
exchange (NEE) measured by an eddy-covariance flux tower
located within the study area. Modeled NPP was 535 g C m−2
(sd 145 g C m−2), which is comparable to the MODIS value of
506 g C m−2(sd 145 g C m−2), see Supplementary Appendix 2;
Supplementary Figure 3 (Running and Mu,2015). Modeled
NEE for the flux tower location was 60 (sd 140) g C m−2
between 2015 and 2019, similar to the measured value of 66 (sd
72) g C m−2at the tower (ORNL DAAC,2018;Kimball et al.,
2021).
1https://cida.usgs.gov/gdp/
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TABLE 3 Percent of different treatment prescriptions by scenario, management zone, and slope.
Scenario Management zone Percent of treatment, slopes <30% Percent of treatment, slopes >30%
Clear cut Mechanical thin,
mature stand
Mechanical thin,
young stand
Hand thin Rx fire Mechanical thin,
young stand
Hand thin Rx fire
1–6 Private non-industrial 100% 100%
1–6 Private industrial 100% 100%
1–6 Defense (<400 m from urban) 100% 100%
2, 3 Threat (<2000 m from
urban)
70% 30% 100%
4–6 Threat (<2000 m from
urban)
50% 30% 20% 80% 20%
3–5 General forest
(non-restricted publicly
owned forest >2000 m
from urban)
65% 30% 5% 95% 5%
6 General forest
(non-restricted publicly
owned forest >2000 m
from urban)
40% 30% 30% 70% 30%
3–5 Roadless (restricted
publicly owned forest)
80% 20% 80% 20%
6 Roadless (restricted
publicly owned forest)
70% 30% 70% 30%
Scenarios 1–6 all contained the same treatment in three management zones in gray.
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Fire size was calibrated using historical fire perimeter
data from the Monitoring Trends in Burn Severity (MTBS)
(Eidenshink et al.,2007) and the California Department of
Fire Resource Assessment Program’s (FRAP) fire data. The
average annual fire area based on five replicate model runs
was 3,857 ha/year (sd 6,484 ha/year, maximum: 40,223 ha/year)
while the mean annual area burned based on MTBS and FRAP
was 3,375 ha (sd 7,722 ha, maximum: 40,105 ha, Supplementary
Appendix 2;Supplementary Figures 6,7). We calibrated
average annual fire severity to the average annual thematic fire
severity data from MTBS. The percentage of area that burned at
high severity was 12% in the model compared to 16% in MTBS.
From 2010 to 2020, the percentage of high severity fire increased,
accounting for up to 35% of fire area, and the model matched
this increase, 30% of fire area was high severity over the past
decade.
To calibrate insect mortality, it was necessary to find
vegetation inputs that predated the recent mass mortality
event that occurred in California due to the extreme drought
conditions of 2012–2015 so as to avoid the forest compositional
and structural changes that resulted from that event. As such,
we used the initial species-age vegetation data from Syphard
et al. (2011) from the nearby Stanislaus National Forest, which
is immediately to the south of the study area. We then calibrated
insect mortality to the observed mortality rates in Fettig et al.
(2019) (Supplementary Appendix 2;Supplementary Table 2).
Under these conditions the modeled mortality was higher than
Fettig et al. (2019) findings for ponderosa pine mortality (range
63–99% modeled vs. 45–85% observed), but lower for white fir
(range 0–25% modeled vs. 15–50% observed), and sugar pine
(0–39% modeled vs. 20–70% observed).
Results
While the management scenarios were designed to integrate
management activities with wildfire to achieve a disturbance
return interval that matched the historic fire return interval,
the target historic interval was not achieved (Figure 2). These
differences are due in part to restrictions on management
activities. Scenarios that used high levels of thinning (like
Scenario 5) were further restricted by other disturbance
processes (mainly insects) that were dispersed across the
landscape resulting in a decline in the amount of area harvested
or thinned through time as those stands were not eligible to
be treated. Between the first decade, 2020–2030, and the last
decade, 2090–2100, the area treated by management declined by
60% for scenario one and 40% for scenario six (Supplementary
Appendix 2;Supplementary Figure 9). The forest treatment
goal for scenarios one through six was 11–63% of the landscape
per decade, while the actual treatment modeled was 4–59%.
Moreover, as the amount of wildfire increased over time,
especially around model year 2050, the treatment area (thinning
and prescribed fire) declined because individual cells were
already disturbed and within their historic disturbance return
interval and therefore not eligible to be treated.
Future forest change will depend on the climatic stressors
going forward. Our climate projections indicate that the
climatic water deficit will increase, driven mainly by increases
in temperature. However, in four out of the five future
climate projections selected, despite those increases in
temperature, there was also a moderate to substantial increase
in projected precipitation, which lowers the likelihood of
extreme fire weather conditions (Supplementary Appendix 1;
Supplementary Figures 1–4). Recent droughts (2003–2004,
2012–2015, 2020+) are exceptional and are not recreated in
any of our climate projections, with the MIROC5 projection
coming closest in the 2050s. While projected climatic water
deficit increased into the future, this did not necessarily
translate to increases in projected fire weather due to timing
of the precipitation. As a result, area burned did not exceed
recent trends until the end of the century under the non-
MIROC5 projections; but extreme fire events (events greater
than 100,000 ha), which is only slightly larger than a fire that
happened in the study area in the fall of 2021 (Caldor Fire
at 90,000 ha) had a chance of occurring with the MIROC5
projection. Regardless of climate projection, and even for
historic climate, insect outbreaks were spatially pervasive
and the leading source of forest mortality (Figure 3 and
Supplementary Appendix 1;Supplementary Figure 6).
Only the MIROC5 future climate projection had increases in
fire mortality beyond the levels generated from the historic
contemporary climate and projected resampled contemporary
climate. Repeating historic climate into the future produced
more variability in tree mortality and source of mortality given
the random resampling of the climate data.
Because of the rapid onset of outbreaks, regardless of
treatments conducted prior to the period simulated, simulated
forest management had limited effect on reducing insect caused
forest mortality (Figure 3). While insect mortality rapidly
increases flammable fuels (duff, litter, and dead wood), the
limited frequency of wildfire meant that large patches of high
severity fires were rare except under the MIROC5 projection
(Supplementary Appendix 1;Supplementary Figure 7). The
primary effect of higher intensity management scenarios
(Scenarios 3–6, beyond the business-as-usual Scenario 2) was
lowering the cumulative mortality by end of century caused by
wildfires compared to Scenarios 1 and 2 for only the MIROC5
climate projection due to the overall higher levels of area
burned in that projection (Figure 4) and those differences
were statistically significant (Supplementary Appendix 1;
Supplementary Table 2).
Simulated forest composition change was substantial and
rapid due to disturbance and climate change. Most of the
insects we modeled have various species of the genus Pinus
as their preferred host, and because of the high mortality
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FIGURE 2
The frequency of disturbance by management or wildfire (of any severity) over the 2020–2100 time period by scenario averaged over all
replicates compared to the reference fire return interval. The disturbance return interval is the number of modeled years (80 years total), divided
by the number of disturbances plus one. Only Scenario 6 approaches recreating the reference fire return interval. White areas in the reference
map are not available for forest treatment or water bodies.
rate from these insects, other tree species from within the
Sierran mixed conifer forest type (Douglas-fir, incense-cedar)
that were already extant in those cells became the dominant tree
species in outbreak-prone areas (Figure 5 and Supplementary
Appendix 1;Supplementary Figure 8). At higher elevations,
tree species such as mountain hemlock and red fir declined due
to reduced regeneration as they were no longer in their ideal
climate envelope by the end of the century (Supplementary
Appendix 1;Supplementary Figure 9) but this was balanced by
upslope movement of the mixed conifer type by end of century
(Supplementary Appendix 1;Supplementary Figure 10). Tree
cover was generally maintained as a result—only a small
percentage of cells in the modeled forested landscape converted
to non-forested (shrub dominated)—and mainly in areas that
had experienced high severity fire rather than insect mortality
(Figure 5). For historic climate projected into the future, there
was a 2% increase in non-forested area after 30 years (from
2% of the landscape to 4%). For the non-MIROC5 future
climate projections, there was a 4–6% increase, depending on
management scenario by end-of-century. And for the MIROC5
climate projection, there was an 8% increase of non-forested
area.
In spite of the compositional shift of forest cover, the
total amount of carbon in the forest remained relatively stable
across the duration of our simulations, except for a modest
decline under the MIROC5 projection (Figures 6,7). With
prescribed fire being the central treatment in Scenario 6,
and prescribed fire removing more down and dead material,
Scenario 6 maintained less total C overall compared to the other
management scenarios. Nevertheless, the landscape switched
from being a moderate carbon sink in 2020 to becoming a
carbon source by year 2050, regardless of management scenario.
Discussion
We hypothesized that mimicking the historic fire-return
interval by combining natural disturbances (wildfire) and
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FIGURE 3
Cumulative forest mortality by disturbance type in Mg Biomass. Management includes harvesting, thinning, and prescribed fire.
FIGURE 4
Cumulative fire mortality, in megagrams of biomass, in model years 2050 and 2090. Error bars represent ±1 standard deviation among the
model replicates.
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FIGURE 5
Percent cover for the dominant forest types by management scenario using all replicates averaged across all climate projections.
FIGURE 6
Carbon: Net Ecosystem Exchange averaged across model replicates by climate projection and management scenario.
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FIGURE 7
Total Carbon in Mg ha-1for all scenarios averaged across climate projection (A) and for all climates averaged across management scenarios (B).
management (i.e., thinning and prescribed fire) will maintain
forest resilience despite a changing climate. We assessed our
hypothesis by tracking: (1) tree mortality due to wildfire, (2)
tree mortality caused by insect outbreaks, (3) carbon storage and
sequestration, and (4) the maintenance of tree cover.
Our simulations show that tree mortality due to wildfire can
be reduced by management, particularly under more extreme
weather conditions, as expressed by MIROC5. Under this
climate future, Scenarios 3 and 4 reduced tree mortality and
this reduction exceeded the mortality generated by management
itself, while Scenario 5 was a more even substitution of mortality
agents and Scenario 6 had a net increase in mortality due to
higher mortality associated with prescribed fires over thinning.
The other climate scenarios generally did not show a strong
effect of management. Our MIROC5 results are consistent
with prior research that suggested that fuels management
would become more effective as with climate change due to
more frequent intersections between wildfire and treated areas
(Syphard et al.,2011;Loudermilk et al.,2014).
Our simulations further suggest that insect mortality was
not substantially affected by management as was simulated.
Regardless of management scenario, and with little variation
among climate futures, our simulations indicate that insect
mortality will become the dominant disturbance agent over the
next century. While these treatments were in line with current
levels of removal, treatments specifically designed to mitigate
wildfire hazard may not confer drought or insect resistance.
Modeling the impact of more aggressive treatments to reduce
insect mortality by thinning the overstory, up to 75% rather
than the ∼20% currently in the model (Fettig et al.,2007), and
intentionally creating structural forest diversity over large areas
(Fettig et al.,2012) with greater biomass removed per hectare
could provide insights into how management may affect insect
mortality.
Carbon storage in this landscape will be limited by the
frequency, severity, and type of future disturbances as well
as the potential interactions among disturbances. While forest
management activities can offset the potential C losses from
wildfire (Liang et al.,2017), the high levels of projected insect
mortality are not offset by the simulated management activities
(Figure 3). Unlike fire, where C is released through combustion,
the dead materials resulting from insect outbreaks remain in the
forest until they decompose, though it is at risk of combustion
at a later point. The timing of fire after the insect disturbance
is important. Meigs et al. (2016) found that fire mortality
eventually declined due to reductions in the live vegetation that
was susceptible to fire, though this effect was most pronounced
several years after fire, while Hart et al. (2015) found negligible
links between insect mortality and fire area. This process of
insect mortality offsetting future fire mortality might explain
why C stocks were able to increase even under the MIROC5
projection through 2040. Later in our simulations, as the effects
of climate change intensify in the latter half of the century, C
stocks declined as disturbances increased (Figure 6).
Finally, our hypothesis that forest management would
increase the retention of tree cover was not confirmed. Although
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there has been concern over the possible loss of forest to
shrub or grassland due to large high severity fire events (Serra-
Diaz et al.,2018;Coop et al.,2020), we found only a limited
area of conversion from forest to shrub (Figure 5). This has
been noted in certain environments due to large patches of
high severity fire and the climatic conditions post-fire that
limit successful establishment (Harvey et al.,2016;Coop et al.,
2020). Such conversions were not found in our results at
scale (Figure 5), mainly because of the limited increase in
proportion of high severity fire within the model. Although
our wildfire sub-model simulated large, high-severity fires under
the MIROC5 projection, in aggregate they were not a large
driver of forest conversion. Because insect outbreaks are the
dominant mortality agent into the future but do not result
in 100% mortality, the remaining understory allowed for the
maintenance of forest cover and depending on the dominant
species and presence of remaining adult trees after a wildfire or
insect outbreak, such as yellow pines in the Sierra Nevada, the
potential for a drought induced forest conversion to shrubland
(Young et al.,2019) may be limited.
Climate change will drive direct and indirect substantial
changes to forest composition and structure. It may not be
possible to maintain the forest as we know it today (Schoennagel
et al.,2017). Fettig et al. (2019) found in the southern Sierras
that, after the insect outbreaks in response to the 2012–
2015 drought, there was a shift in the understory competition
toward more shade tolerant species in mixed conifer stands,
similar to our findings of a transition from mixed conifer to
increasing dominance by Douglas-fir at low to mid-elevations
(Figure 5). This trend also reflects historical trends based on
climate envelope modeling: from 1930–1996 a 240,800-hectare
area within the study area increased in cover of Douglas-
fir, hardwoods, and grassland, while upper elevation conifers
decreased (Thorne et al.,2008); and from 2001 to 2018, Douglas-
fir saw an increase in population expansion within the Sierra
Nevada (Stanke et al.,2021). In looking at potential future
climate refugia, Thorne et al. (2020) found that 27% of Douglas-
fir forest in Sierra Nevada was within a climate refugia compared
to just 2% of red fir. This decline in red fir and other montane
species is reflected in our findings (Figure 5) and moving
upslope to occupy that space were primarily Douglas-fir and
other components of the Sierra mixed conifer forest type.
Study limitations
There may be an interaction between insect mortality and
future wildfire that is not sufficiently understood (or represented
within our forecasting framework) given the recent occurrence
of the mass insect outbreaks. Because of the high levels of
mortality and the large pulse of fine and coarse materials onto
the forest floor, this may result in landscape-scale fire severity
that vastly exceeds the historical record. With large enough
fuel loadings over a large enough area, it is possible to develop
mass fires, which result in fires that have complicated spread
patterns and extreme growth that are not predictable with
existing Rothermel based fire models (Stephens et al.,2018).
Given the high levels of variation in the past three decades
in fire weather for the region and its relative stability under
the future climate projections (Supplementary Appendix 1;
Supplementary Figure 3), there is a question of whether these
downscaled future projections adequately reflect the rate that
climate is changing in California. California is currently in
a megadrought and in the past 20 years has been the driest
it has been since 800 CE, with the drought likely persisting
beyond 2021 (Williams et al.,2022). Only the MIROC projection
contained drought events (at mid-century) that are close to
the ongoing drought conditions in the state, and so model
results with that particular projection are going to be the most
informative to present landscape management.
In addition to potential limitations of climate projections,
calibrating the wildfire model to the TCSI region and the
1990–2018 period likely did not capture recent fire trends
in the State of California from 2019 to 2021 and so may
underestimate annual area burned. Ultimately, novel and
uncertain future climatic conditions make it challenging
to predict future forests and conditions (Scheller,2018).
As such it is important for forest managers to implement
treatments that maintain and enhance tree species richness
over as wide a range as possible, including moving
species outside existing or even historical distributions
where appropriate, as well as conserving old trees and
spatial heterogeneity as a hedge against such uncertainty
(Knapp et al.,2020).
Recommendations for improving
forest management
Our results suggest that the management scenarios we
modeled that go beyond the business-as-usual annual treatment
area could reduce tree mortality from fire if they reached across
the entire landscape beyond the WUI and highlight the potential
for beetle-caused mortality in the future. As such, management
activity on the landscape may need to be far more intensive
than what was modeled here based on the historic fire interval
in order to build resilience (North et al.,2022). The range of
management we tested has only a minor effect relative to the
indirect effects of climate on insect outbreaks. Variation among
climate projections obfuscated whatever tangible differences
may result from the management actions tested. The Sierra
Nevada is a highly stochastic system (Scheller et al.,2011b) with
large mega-fires resulting from unpredictable combinations
of fuel moisture, topography, and wind speeds. Although
management may be highly effective locally and in the near-
term, at the landscape-scale and at long durations, these positive
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Maxwell et al. 10.3389/ffgc.2022.740869
outcomes may be obfuscated by the long-term trajectories
generated by climate change.
Finally, our results suggest that forest carbon storage and
forest cover could be resilient despite climate change. The spatial
reallocation of forest types is one of the primary climate adaptive
capacities of forests and future management activities should
consider facilitating this transformation through reforestation
practices that promote more climate tolerant species in greater
diversity in order to maintain long-term functioning (Millar
et al.,2007;Folke et al.,2010;McWethy et al.,2019). Such
adaptation actions are expected be more effective at mid- to
high-elevations where species were already moving upslope;
however, at low elevations, a trend toward less diverse forests
seems likely due to pressures exerted by bark beetles. In the
instances that occurred, at low and mid-elevations, conversion
to shrub and chaparral types may be unavoidable for areas that
eventually do experience high severity fire.
Data availability statement
The datasets presented in this study can be found in
online repositories. The names of the repository/repositories
and accession number(s) can be found below: https://github.c
om/LANDIS-II-Foundation/Project-Tahoe-Central-Sierra-201
9and archived through zenodo at: doi: 10.5281/zenodo.7199459.
Author contributions
RS designed the model. CM implemented the simulations
and took the lead in writing the manuscript. CM and KW
analyzed the data. All authors provided critical feedback and
helped shape the research, analysis, and manuscript.
Funding
This research was funded by a grant from an anonymous
foundation.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be
found online at: https://www.frontiersin.org/articles/10.3389/
ffgc.2022.740869/full#supplementary-material
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