Large Carbon Release Legacy from Bark Beetle Outbreaks across Western United
States
Bardan Ghimire12†, Christopher A. Williams1, G. James Collatz3, Melanie Vanderhoof1, John
Rogan1, Dominik Kulakowski1, Jeffrey G. Masek3
1
Graduate School of Geography, Clark University, Worcester, MA 01610, USA
2
Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
3
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771,
USA
†
Corresponding author: (Tel: +1-510-495-8141, bghimire@lbl.gov)
Supporting information
This supporting information has three sections describing the derivation of characteristic
post-disturbance carbon flux trajectories, quantification of recent beetle-induced mortality, and
quantification of carbon consequences of recent outbreaks.
Data S1 Derivation of characteristic post-disturbance carbon flux trajectories
Parameterization of partial disturbance, instead of stand-replacing disturbance as in our
prior work, is treated as follows. We obtained characteristic trajectories for 1680 different
severity levels (designated to capture the variability in aerial detection survey (ADS) derived
mortality) with beetle-induced mortality of aboveground biomass equal to the midpoints of the
ranges starting at 0 kg C m-2 and ending at 42 kg C m-2 in steps of 0.025 kg C m-2 with
simulations truncated at the maximum biomass in forests that hold less than 42 kg C m-2. A
portion of each live pool (i.e. leaf, fine root, aboveground wood and belowground wood) is
transferred to its respective downstream dead carbon pool. For example, during the year of bark
beetle outbreak reported in the ADS data, foliage is transferred to surface litter pool,
aboveground wood to snag pool, and belowground wood and fine root to soil pool. The portions
killed are based on the percent mortality imposed for each severity level derived from the ratio of
biomass at the midpoint of each severity class to the pre-disturbed aboveground biomass values
(corresponding to the mean age of given forest type). Live pools are then subsequently allowed
to recover from disturbance with associated forest type, region and productivity class specific
regrowth consistent with forest inventory and analysis (FIA) data.
This study uses a bark beetle disturbance geospatial product, age cohort specific biomass
trajectories, and an ecosystem carbon cycle model to determine characteristic carbon flux
trajectories. An important element that builds on our past work (Williams et al., 2012) is that
bark beetle disturbances are treated as partial mortality events depending on the severity of beetle
infestation. The amount of biomass killed influences the shape of the carbon flux trajectories
though all exhibit a general structure of transient reduction in net primary productivity and
transient increase in heterotrophic respiration due to additional beetle-killed biomass. The
transient reduction in net primary productivity is assumed to occur over a period of eight years
with lower severity bark beetles starting from higher productivity and a more gentle recovery
reflecting the productivity of younger trees unaffected by bark beetles, and vice-versa for higher
severity bark beetle outbreaks. We assumed a quick recovery period of eight years because
recovery from bark beetles outbreaks is usually quicker than other disturbance types (e.g., fires)
because bark beetles mainly target older trees, leaving behind younger trees (Edburg et al.,
2012). Trajectories reported here are consistent with the typical patterns of disturbance recovery
reported in the literature (Bond-Lamberty et al., 2004, Gough et al., 2007, Goulden et al., 2011,
Law et al., 2004, Litvak et al., 2003, Noormets et al., 2007, Pregitzer & Euskirchen, 2004) but
introduce important detail about variation by forest type, disturbance severity, and region.
Generally, higher severity bark beetle infestations are associated with larger initial reductions in
net primary productivity (NPP), transient increases in heterotrophic respiration, larger peaks in
net ecosystem productivity (NEP), and a longer time to change from carbon source to sink.
These processes occur due to the immediate post-disturbance decomposition and release of
beetle-killed biomass, combined with post-disturbance vegetation regrowth. The carbon flux
dependence on age differs from many other approaches that rely mainly on biome type and
climate to characterize carbon fluxes.
Data S2 Quantifying recent beetle-induced mortality
We rely on the United States Forest Service (USFS) ADS dataset that characterizes the
area of outbreaks in recent years and records the corresponding mortality of trees. The year of
outbreak reported in the ADS data corresponds to trees fading or needles/leaves changing in
color (also called the red stage of attack) when the trees get stressed due to bark beetle attack and
the needles/leaves turn yellow/red. Trees usually die and needles fall few years (usually 2-5
years) subsequent to the red stage. As a result, the decline in net primary productivity (i.e.,
photosynthesis) starts occurring one year earlier (i.e. during the green stage when the bark beetle
attack initiates) and much of the carbon transfers associated with bark beetle-killed plant parts
occurs over multiple years (usually 2-5 years). However, in our study the decline in net primary
productivity, and all the carbon transfers are performed at the year of bark beetle detection in the
ADS data and this does not have major impacts on our results as the trends and patterns in the
carbon balance at a regional scale are unaffected.
There are uncertainties inherent in the ADS data which were confirmed and
quantitatively assessed by our own field based observations and also by Meddens and others
(2012). Aerial observers map bark beetle infested areas by identifying dead trees with attached
needles over large areas. Observers record data at a fast rate (~20 seconds per km) (Johnson &
Wittwer, 2008). As a result, the aerial observers can provide only a general estimate of mortality
over the landscape rather than map and identify each individual bark beetle affected tree. As such
there are three major sources of uncertainty in our use of the ADS data that can influence our
results: 1) area flown, 2) bark beetle infested area, and 3) number of trees killed. Firstly, the area
flown does not cover total forest area and is targeted towards areas known to be bark beetle
affected. Area flown also varies from year to year, such that bark beetle infested locations could
be missed from the sample. Uncertainty due to flown area is minimized by considering the most
recent data record from 2000 to 2009, when most of the forest area is sampled, with some
regions having complete wall-to-wall flown area coverage. Secondly, in contrast to the
underestimation of area flown, the bark beetle infested area drawn by the aerial observers are
commonly larger than the actual outbreak area because the infested area is reported as affected
area which includes both live and dead trees as opposed to mortality area (i.e. the crown area of
dead trees) (Meddens et al., 2012). However, the overestimation of bark beetle infested area does
not have major implications for our study because this leads to a corresponding underestimation
of number of trees (biomass) killed per infested area, cancelling out the uncertainties in
overestimation of bark beetle affected area. Additionally, since the purpose of this study is to
determine the regional carbon balance, we are interested in getting the number of trees killed
across the whole landscape as accurate as possible, rather than the number of trees killed per
infested area. In many instances, the observers aerially determine the number of trees killed
directly rather than the number of trees killed per infested area. In cases where the number of
trees killed per infested area is observed aerially, the number of trees killed per infested area
when multiplied by the infested area would remove the infested area bias from the estimate of
number of trees killed. Thirdly, after accounting for the overestimation of bark beetle infested
area, the number of trees killed is still underestimated by ADS due to the fast rate of identifying
new damage by the aerial observers and also because the trees in the understorey and codominant canopy that are not visible from the air at that particular view angle. Accuracy
assessment performed by ADS personnel using field based plots shows that the actual estimates
of number of trees killed ranged from a factor of 3 to 10 times more than that recorded by ADS
data. Uncertainties due to the number of trees killed are quantified in this study by providing
two additional scenarios where biomass killed is multiplied by factors of 3 and 10 (upper limit),
in addition to reporting the scenario without any biomass correction (lower limit). These biomass
multipliers are also consistent with those reported in Meddens and others (2012) and our own
field-based analysis conducted at the Colorado’s Front Range.
In our own field based observations, the damage severity (beetle killed trees per ha) was
compared between the USFS region 2 ADS data and field data (Table SI1). As USFS ADS data
documents only the “new” damage of each year, the number of trees damaged per acre for each
field plot location was summed across all available surveys (1994-2011). Field data was
collected predominately in Rocky Mountain National Park, Arapaho National Forest and
Roosevelt National Forest in Colorado from June 18 to July 20, 2012, within 108 plots each of
900 m2 area (97,200 m2 total). Plots were targeted to be representative of 1) the surrounding 500
x 500 m area, 2) a severity spectrum (from healthy to severe) of bark beetle damage, and 3) a
spectrum of attack age (green attack, red attack and gray attack stages). Our results show that the
biomass multipliers can range from around 6 to 16 depending on the stand density (Table SI1).
The distribution of stand ages categorized into areal bins of 20 years in different forest
service regions expressed as percent of area of stand age class within a particular forest type is
shown in Fig. SI1. The areal distribution of stand age classes demonstrates variability across
forest types and forest service regions. However, in general, the majority of the area within each
forest type corresponds to a stand age of 200 or less. The area weighted average forest ages are
young with mean ages clearly less than 100 years. Forest area varies by forest type and forest
service regions in western United States forest (Table SI2). The total forest area for the six forest
types combined is highest in RMN (32.85 Mha) followed by RMS (24.95 Mha), PNW (23.83
Mha) and PSW (2.61 Mha). Across all regions, Douglas-fir comprises the largest total area of
37.38 Mha followed by ponderosa pine (22.21 Mha) whereas western white pine has the smallest
total area of 0.22 Mha followed by whitebark pine (1.94 Mha).
The average area infested by bark beetles per year averaged from 2000 to 2009, and the
associated mortality rates varies by forest types and regions are presented in Table SI2. Summed
across all regions, lodgepole pine mountain pine beetle (MPB) affects the largest area per year of
940000 ha followed by Douglas-fir beetle (120000 ha yr-1), ponderosa pine MPB (80000 ha yr-1),
whitebark pine MBP (70000 ha yr-1), Engelmann spruce beetle (68000 ha yr-1) and western white
pine MPB (5000 ha yr-1). Summed across all forest types, RMN had the largest infested area per
year of 560000 ha followed by RMS (490000 ha yr-1), PNW (240000 ha yr-1), and PSW (10000
ha yr-1). The highest areal mortality of 14 % yr-1 is observed for PNW western white pine MPB,
followed by PNW whitebark pine MBP (8 % yr-1), RMS lodgepole pine MPB (7% yr-1), PNW
lodgepole pine MPB (6% yr-1), RMN lodgepole pine MPB (6 % yr-1), and RMS whitebark pine
MPB (5 % yr-1). These rates of disturbance are large compared to the typical 1% to 2% per year
commonly reported for the conterminous US (Huang et al., 2010) but we again note that the
ADS data likely overestimates the area disturbed but correspondingly underestimates beetle
damage per unit area in those areas that are in fact infested.
If we adopt the rate of beetle-induced biomass mortality recorded in the ADS we find that
most of the bark beetle disturbed forest areas correspond to the smallest above-ground biomass
mortality class of 0-0.5 kg C m-2 (Fig. SI2). However it is important to keep in mind that these
are believed to be underestimated by 3 to 10 fold if not more and we adjust estimates
correspondingly in this work. Variations in average above-ground biomass affected per year by
bark beetles from 2000 to 2009 exist across forest types and regions (Table SI2). Aggregated
across all forest types, RMN had the greatest above-ground biomass mortality of 255 Gg C yr-1,
followed by RMS (243 Gg C yr-1), PNW (179 Gg C yr-1), and PSW (9 Gg C yr-1). Combined
across all regions, lodgepole pine MPB killed the most above-ground biomass of 510 Gg C yr-1,
followed by Engelmann spruce beetle (67 Gg C yr-1) and Douglas-fir beetle (46 Gg C yr-1)
whereas western white pine MPB killed only 3 Gg C yr-1 during this period.
Data S3 Quantifying carbon consequences of recent outbreaks
Regional carbon fluxes are estimated for the ADS-mapped bark beetle outbreaks of
recent decades by applying the characteristic carbon flux trajectories to areas of outbreak. The
assessment involved two NEP estimates, a reference estimate using the characteristic trajectory
of NEP in the absence of disturbance, and a severity-specific NEP trajectory that includes effects
of beetle infestation. The difference is used to represent the impact of the beetle disturbance.
The reference control represents the background effect of an age structure legacy from past
disturbance that would have persisted in the absence of bark beetle disturbance also assuming
that forests remain undisturbed by other factors.
The insect-killed biomass per unit area as derived for each insect outbreak polygon from
ADS and FIA data is reclassified to match the 1680 severity classes described in the previous
section. Each severity classes’ unique carbon flux trajectory generated by Carnegie-AmesStanford Approach (CASA) model is applied to the area of the corresponding outbreak polygon
to obtain the regional carbon fluxes. Corresponding regional carbon fluxes are estimated starting
from an initial year, equaling the starting year of the insect dataset, to the final year, equaling the
ending year of the insect dataset for each bark beetle event as follows. For the reference case
NEP (Nref) is obtained for each bark beetle outbreak polygon recorded to have been impacted in a
particular year (yrb). We estimate Nref for each year (i) from the initial (yri) to the final year (yrf)
utilizing the NEP value from the characteristic pre-disturbance NEP trajectory (fpre(.))
corresponding to years before (tpre) or after (tpost) the year of beetle disturbance (yrb).
𝑓𝑝𝑟𝑒 (𝑡𝑝𝑟𝑒 ) , 𝑡𝑝𝑟𝑒 = 𝑎𝑔𝑒 − (𝑦𝑟𝑏 − 𝑖) , 𝑖 < 𝑦𝑟𝑏
𝑁𝑟𝑒𝑓 = {
𝑓𝑝𝑟𝑒 (𝑡𝑝𝑜𝑠𝑡 ), 𝑡𝑝𝑜𝑠𝑡 = 𝑎𝑔𝑒 + (𝑖 − 𝑦𝑟𝑏 ), 𝑖 ≥ 𝑦𝑟𝑏
(s1)
where age is the FIA derived mean age specific to forest types and regions. For the case with
beetle impacts, NEP (Nb) is obtained similarly, only using the NEP value from the characteristic
post-disturbance NEP trajectory for the specific severity class (fpost(.)).
𝑓𝑝𝑟𝑒 (𝑡𝑝𝑟𝑒 ) , 𝑡𝑝𝑟𝑒 = 𝑎𝑔𝑒 − (𝑦𝑟𝑏 − 𝑖) , 𝑖 < 𝑦𝑟𝑏
𝑁𝑏 = {
𝑓𝑝𝑜𝑠𝑡 (𝑡𝑝𝑜𝑠𝑡 ), 𝑡𝑝𝑜𝑠𝑡 = 𝑖 − 𝑦𝑟𝑏
, 𝑖 ≥ 𝑦𝑟𝑏
(s2)
Beetle-induced heterotrophic respiration (Rhb) is obtained for each bark beetle outbreak
polygon infested at a given year (yrb) for each year (i) from the initial year (yri) to the final year
(yrf) utilizing the heterotrophic respiration value from the bark beetle induced characteristic postdisturbance heterotrophic respiration trajectory (fresp(.)) corresponding to year since disturbance
(tpost), if the disturbance had occurred (i ≥ yrb). If the disturbance had not occurred (i < yrb),
beetle induced heterotrophic respiration (Rhb) equals zero. The mathematical representation for
determining beetle induced heterotrophic respiration (Rhb) is:
𝑅ℎ𝑏 = {
0
,
𝑖 < 𝑦𝑟𝑏
𝑓𝑟𝑒𝑠𝑝 (𝑡𝑝𝑜𝑠𝑡 ), 𝑡𝑝𝑜𝑠𝑡 = 𝑖 − 𝑦𝑟𝑏 , 𝑖 ≥ 𝑦𝑟𝑏
(s3)
Because outbreak polygons can span a heterogeneous mix of high and low productivity
class sites (and forest types), carbon fluxes are estimated for both these classes, and postaggregated to the polygon-integrated flux by weighting with the county specific fractions of high
and low productivity classes. Carbon fluxes are then further aggregated across all bark beetle
outbreak events considered in the study region.
References
Adler RF, Huffman GJ, Chang A et al. (2003) The version-2 global precipitation climatology
project (GPCP) monthly precipitation analysis (1979-present). Journal of
Hydrometeorology, 4, 1147-1167.
Bond-Lamberty B, Wang C, Gower ST (2004) Net primary production and net ecosystem
production of a boreal black spruce wildfire chronosequence. Global Change Biology, 10,
473-487.
Edburg SL, Hicke JA, Brooks PD et al. (2012) Cascading impacts of bark beetle-caused tree
mortality on coupled biogeophysical and biogeochemical processes. Frontiers in Ecology
and the Environment, 10, 416-424.
Gough CM, Vogel CS, Harrold KH, George K, Curtis PS (2007) The legacy of harvest and fire
on ecosystem carbon storage in a north temperate forest. Global Change Biology, 13,
1935-1949.
Goulden ML, Mcmillan AMS, Winston GC, Rocha AV, Manies KL, Harden JW, BondLamberty BP (2011) Patterns of NPP, GPP, respiration, and NEP during boreal forest
succession. Global Change Biology, 17, 855-871.
Hansen J, Ruedy R, Glascoe J, Sato M (1999) GISS analysis of surface temperature change.
Journal of Geophysical Research, 104, 30997–31022.
Huang C, Goward SN, Masek JG, Thomas N, Zhu Z, Vogelmann JE (2010) An automated
approach for reconstructing recent forest disturbance history using dense Landsat time
series stacks. Remote Sensing of Environment, 114, 183-198.
Johnson EW, Wittwer D (2008) Aerial detection surveys in the United States. Australian
Forestry, 71, 212-215.
Law BE, Turner D, Campbell J, Sun OJ, Van Tuyl S, Ritts WD, Cohen WB (2004) Disturbance
and climate effects on carbon stocks and fluxes across Western Oregon USA. Global
Change Biology, 10, 1429-1444.
Leemans R, Cramer WP (1991) The IIASA Database for Mean Monthly Values of Temperature,
Precipitation, and Cloudiness on a Global Terrestrial Grid. pp 60, Laxenburg, Austria,
International Institute for Applied Systems Analysis.
Litvak M, Miller S, Wofsy SC, Goulden M (2003) Effect of stand age on whole ecosystem CO2
exchange in the Canadian boreal forest. Journal of Geophysical Research, 108, DOI:
10.1029/2001JD000854.
Meddens AJH, Hicke JA, Ferguson CA (2012) Spatial and temporal patterns of observed bark
beetle-caused tree mortality in British Columbia and western US. Ecological
Applications, 22, 1876-1891.
Noormets A, Chen J, Crow TR (2007) Age-dependent changes in ecosystem carbon fluxes in
managed forests in northern Wisconsin, USA. Ecosystems, 10, 187-203.
Pregitzer KS, Euskirchen ES (2004) Carbon cycling and storage in world forests: biome patterns
related to forest age. Global Change Biology, 10, 2052-2077.
Ruefenacht B, Finco MV, Nelson MD et al. (2008) Conterminous U.S. and Alaska forest type
mapping using forest inventory and analysis data. Photogrammetric Engineering and
Remote Sensing, 74, 1379-1388.
Williams CA, Collatz GJ, Masek J, Goward SN (2012) Carbon consequences of forest
disturbance and recovery across the conterminous United States. Global Biogeochemical
Cycles, 26, DOI: 10.1029/2010GB003947.
Table S1. A comparison of beetle mortality quantified by field-based plot data (plot size = 900
m2) and USFS ADS (1994-2011).
Comparison Parameter
Number of plots
Stand density (trees ha-1)
% of plots with MPB damage not documented by ADS.
% of healthy1 plots incorrectly documented by ADS as damaged
Mean number of annual ADS surveys per MPB damaged plot
Mean polygon area intersecting with damaged plots (ha)
Multiplier (field TPA / ADS TPA)2
Multiplier (field TPA / ADS TPA)
1
Where "healthy" is defined as no MPB damage observed.
2
Lodgepole
pine stands
55
1729.5±132.8
8.70%
88.89%
3.00±0.31
475.6±53.2
32.05±8.455
15.83±3.763
Ponderosa
pine stands
53
727.3±61.1
0.00%
37.50%
2.11±0.16
957.2±157.4
5.92±0.61
5.92±0.61
Where Trees Per Acre (TPA) is # of MPB killed trees per acre, field TPA is derived from 2012
field data, and ADS TPA is derived from summing 1994 through 2011 annual surveys.
3
Excludes plots in which beetle damage was field documented but not documented by any ADS
survey.
Table S2. Average aboveground biomass killed and area infested by bark beetles per year from 2000 to 2009, forest area, and
mortality for different forest service regions and forest types.
Region
Forest type
PNW
PNW
PNW
PNW
PNW
PNW
PSW
PSW
PSW
PSW
PSW
PSW
RMN
RMN
RMN
RMN
RMN
RMN
RMS
RMS
RMS
RMS
RMS
RMS
DF
PP
WWP
ES
LP
WBP
DF
PP
WWP
ES
LP
WBP
DF
PP
WWP
ES
LP
WBP
DF
PP
WWP
ES
LP
WBP
Aboveground biomass
per year (Mg C yr-1) –
stand age > 20 yrs
16269
8763
1907
32548
113625
5428
1
90
357
6706
1368
12589
10843
410
94
217015
14409
17555
8450
13
34101
172316
10906
Aboveground biomass
per year (Mg C yr-1) –
old stands
28459
11414
2070
205947
130701
5461
3
119
357
7635
1374
19147
12069
497
839
275829
16549
22964
8896
13
275718
189388
12787
Area per year
(ha yr-1)
Forest Area Mortality
(ha)
(% of forest area)
34146
31819
2735
9881
149250
11109
7
150
401
9612
541
42192
22929
1744
278
450744
37622
48416
26916
30
57734
332149
22046
15912672
5127956
19375
251855
2382192
132582
587959
975683
118440
832132
92749
16176068
5304383
78084
1986207
8052803
1257081
4707676
10798029
2738
4270578
4714191
457948
0.21
0.62
14.11
3.92
6.27
8.38
0.001
0.02
0.34
1.16
0.58
0.26
0.43
2.23
0.01
5.60
2.99
1.03
0.25
1.10
1.35
7.05
4.81
Aboveground biomass and area refer to bark beetle affected aboveground biomass and area, respectively.
Region abbreviations: PNW is Pacific Northwest, PSW is Pacific Southwest, RMN is Rocky Mountain North and RMS is Rocky
Mountain South.
Forest type abbreviations: DF is Douglas-fir, PP is ponderosa pine, WWP is western white pine, ES is Engelmann spruce, LP is
lodgepole pine and WBP is whitebark pine.
Table S3. Cumulative regional NEP (Gg C) reductions from 2000 to 2009 due to bark beetle infestations for combinations of forest
types, regions, stand ages and biomass multipliers.
Region
Stand
Age
Biomass
Multiplier
Douglas-fir
Beetle
Engelmann
Spruce Beetle
PNW
PNW
PNW
PNW
PNW
PNW
PSW
PSW
PSW
PSW
PSW
PSW
RMN
RMN
RMN
RMN
RMN
RMN
RMS
RMS
RMS
RMS
RMS
RMS
>20
>20
>20
Old
Old
Old
>20
>20
>20
Old
Old
Old
>20
>20
>20
Old
Old
Old
>20
>20
>20
Old
Old
Old
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
1628
1681
1870
1646
1737
2056
1
1
1
1
1
1
1056
1101
1262
1068
1137
1378
707
761
955
716
786
1043
276
384
702
564
1001
1429
0
0
0
0
0
0
4
5
6
4
7
15
912
1033
1449
1339
2190
3901
MPB
(Lodgepole
Pine)
3160
3621
5163
3191
3728
5402
143
164
236
144
168
250
5939
6735
9507
6039
7071
10543
3600
4197
6278
3638
4306
6594
MPB
(Ponderosa
Pine)
486
517
622
490
531
662
1
2
3
1
2
3
131
164
273
134
170
292
587
619
732
588
622
741
MPB
(Whitebark
Pine)
92
108
165
92
108
165
8
12
24
8
12
24
206
252
412
209
263
444
120
156
272
123
164
300
MPB
(Western
White Pine)
124
132
161
124
133
165
1
2
5
1
2
5
60
62
71
60
63
74
0
0
1
0
0
1
Data values are rounded to the nearest Gg (109 g) of carbon.
Region abbreviations: PNW is Pacific Northwest, PSW is Pacific Southwest, RMN is Rocky Mountain North and RMS is Rocky
Mountain South.
MPB refers to Mountain Pine beetle.
Table S4. Future carbon flux (NEP in Gg C y-1) legacies in years 2009 and 2039 of historical bark beetle infestations from 2000 to
2009 for combinations of regions, stand ages and biomass multipliers.
Region
PNW
PNW
PNW
PNW
PNW
PNW
PSW
PSW
PSW
PSW
PSW
PSW
RMN
RMN
RMN
RMN
RMN
RMN
RMS
RMS
RMS
RMS
RMS
RMS
Stand Age
>20
>20
>20
Old
Old
Old
>20
>20
>20
Old
Old
Old
>20
>20
>20
Old
Old
Old
>20
>20
>20
Old
Old
Old
Biomass
Multiplier
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
None
3
10
Control NEP
2009
408
408
408
408
408
408
-46
-46
-46
-46
-46
-46
492
492
492
492
492
492
-114
-114
-114
-114
-114
-114
2039
597
597
597
597
597
597
5
5
5
5
5
5
1196
1196
1196
1195
1195
1195
785
785
785
785
785
785
Post-bark Beetle NEP
2009
-873
-991
-1381
-938
-1148
-1578
-93
-101
-129
-93
-102
-132
-1752
-2119
-3393
-1794
-2260
-3832
-1788
-1972
-2607
-1841
-2132
-3052
2039
639
617
547
618
571
483
3
2
-3
3
1
-4
1260
1224
1099
1255
1209
1055
771
733
601
745
663
445
Decomposition – Bark
Beetle Killed Biomass
2009
2039
55
27
159
79
502
250
114
58
299
149
689
352
4
1
11
3
33
10
4
1
12
4
35
12
140
39
409
114
1346
374
171
48
514
145
1671
470
81
33
235
98
772
322
135
68
394
194
1184
539
Douglas-fir
Ponderosa Pine
Western White Pine
Engelmann Spruce
Lodgepole Pine
Whitebark Pine
a) PNW
60
50
40
30
20
10
0
10 30 50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390 410 430 450 470 490 510
b) PSW
60
50
40
30
20
Percent of Area
10
0
10 30 50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390 410 430 450 470 490 510
c) RMN
60
50
40
30
20
10
0
10 30 50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390 410 430 450 470 490 510
d) RMS
60
50
40
30
20
10
0
10 30 50 70 90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390 410 430 450 470 490 510
Stand Age Class (Years)
Figure S1. Areal distribution of stand age classes for (a) Pacific Northwest (PNW), (b) Pacific
Southwest (PSW), (c) Rocky Mountain North (RMN), and (d) Rocky Mountain South (RMS)
regions of the western US and for dominant forest types impacted by bark beetles. Results for
each forest type are expressed as a percent of total area for that forest type.
Percent of Area
Douglas-fir
Engelmann Spruce
Ponderosa Pine
Lodgepole Pine
100
a) PNW 100
50
50
0
Western White Pine
Whitebark Pine
b) PSW
0
0-0.5
0.5-1.0
1.0-1.5
>1.5
0-0.5
100
c) RMN 100
50
50
0
0.5-1.0
1.0-1.5
>1.5
d) RMS
0
0-0.5
0.5-1.0
1.0-1.5
>1.5
0-0.5
0.5-1.0
1.0-1.5
>1.5
-2
Biomass Class (kg C m )
Figure S2. Percent of total beetle-disturbed area (spatially summed and then temporally averaged
from 2000 to 2009) by forest type for four above-ground biomass mortality classes affected by
bark beetles in different forest service regions.
Figure S3. Future carbon flux legacies of historical bark beetle infestations from 2000 to 2009
aggregated across six forest types for different combinations of regions and biomass multipliers
assuming that beetles attack stands of age larger than 20 years using control without disturbance.
Figure S4. Same as in Figure SI3 but assuming that beetles attack only old forest stands defined
as stands with stand age larger than 120 and 80 years, for Douglas fir beetle and MPB attacks
respectively, and diameter larger than 16 inches for spruce beetles attacks using control without
disturbance.
Figure S5. Characteristic NEP trajectories for different bark beetle severity levels across forest
types and forest service regions with complete dip in NEP.