BGBiogeosciencesBGBiogeosciences1726-4189Copernicus GmbHGöttingen, Germany10.5194/bg-11-6417-2014Technical Note: Linking climate change and downed woody debris
decomposition across forests of the eastern United StatesRussellM. B.russellm@umn.eduhttps://orcid.org/0000-0002-7044-9650WoodallC. W.D'AmatoA. W.FraverS.BradfordJ. B.Department of Forest Resources, University of Minnesota,
St. Paul, Minnesota, USAUSDA Forest Service, Northern Research Station, St. Paul,
Minnesota, USASchool of Forest Resources, University of Maine, Orono,
Maine, USAUS Geological Survey, Southwest Biological Science Center,
Flagstaff, Arizona, USAM. B. Russell (russellm@umn.edu)26November201411226417642527April201413June201421October201427October2014This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://www.biogeosciences.net/11/6417/2014/bg-11-6417-2014.htmlThe full text article is available as a PDF file from https://www.biogeosciences.net/11/6417/2014/bg-11-6417-2014.pdf
Forest ecosystems play a critical role in mitigating greenhouse gas
emissions. Forest carbon (C) is stored through photosynthesis and released
via decomposition and combustion. Relative to C fixation in biomass, much
less is known about C depletion through decomposition of woody debris,
particularly under a changing climate. It is assumed that the increased
temperatures and longer growing seasons associated with projected climate
change will increase the decomposition rates (i.e., more rapid C cycling) of
downed woody debris (DWD); however, the magnitude of this increase has not
been previously addressed. Using DWD measurements collected from a national
forest inventory of the eastern United States, we show that the residence
time of DWD may decrease (i.e., more rapid decomposition) by as much as
13 % over the next 200 years, depending on various future climate change
scenarios and forest types. Although existing dynamic global vegetation
models account for the decomposition process, they typically do not include
the effect of a changing climate on DWD decomposition rates. We expect that
an increased understanding of decomposition rates, as presented in this
current work, will be needed to adequately quantify the fate of woody
detritus in future forests. Furthermore, we hope these results will lead to
improved models that incorporate climate change scenarios for depicting
future dead wood dynamics in addition to a traditional emphasis on live-tree
demographics.
Introduction
Live and dead trees are thought to contain ∼ 60 % of forest C in
mature forests, while soil and litter pools contribute the remaining
∼ 40 % (Ryan et al., 2010). In particular, downed woody debris
(DWD) is an important component of overall C stocks, accounting for
approximately 20 % of total C in old-growth (Harmon et al., 1990) and
secondary (Bradford et al., 2009) forests. Whether measured in terms of decay
rates, C flux, and/or residence time (TRES; the number of years
until a DWD piece loses structural integrity and transitions to another
ecosystem pool), previous investigations have quantified DWD C depletion in
many regions and forest types (Fraver et al., 2013; Mackensen et al., 2003;
Radtke et al., 2009; Russell et al., 2014). The potential for altered
decomposition is well recognized for C stored in soil (Conant et al., 2011;
Davidson and Janssens, 2006; Giardina and Ryan, 2000) and litter (Brovkin et
al., 2012; Prescott, 2010); however, the impact of changing environmental
conditions on DWD debris dynamics remains largely unexplored. While it has
been shown that fungal colonization and termite biomass account for nearly
three-quarters of the variability in wood decomposition on the local scale
(Bradford et al., 2014), the factors driving variability on regional scales
remain largely unexplored, particularly under changing environmental
conditions. It is essential to understand and quantify these temporal
patterns as DWD represents not only a substantial C pool but also facilitates
tree regeneration, is a determinant of fire behavior, and serves as a vital
wildlife habitat whose dynamics may be altered under future climates.
Although DWD decomposition is included in a variety of ecosystem simulation
models primarily through their relationship with temperature (e.g.,
Kirschbaum, 1999; White et al., 2000), the degree to which DWD decomposition
may be altered under potential global-change scenarios remains to be
quantified and incorporated into projections of long-term forest C dynamics.
One unknown is the influence that projected future climates may have on DWD
decomposition rates. The importance of temperature and moisture as drivers
of DWD decomposition is well established (Edmonds et al., 1986),
underscoring the potential for climate change to alter the future dynamics
of this critical ecosystem component. Increased rates of decomposition will
likely reduce the duration that woody debris is available for dead wood-dependent organisms (Mazziotta et al., 2014). Woody-detritus
decomposition rates depend not only on climate but also on DWD size and
condition, the decomposer community, and geographic locale (Bradford et al.,
2014; Brovkin et al., 2012; Fraver et al., 2013; Radtke et al., 2009;
Russell et al., 2013; Russell et al., 2014). Specifically, the eastern
United States has experienced increased mean annual temperatures (MAT)
throughout much of the region over the past century, with the exception of
the southeast, which is characterized by both cooling winters and warmer
summers (Zhu et al., 2012). woody debris decomposition rates should be
sensitive to these changes in MAT (Brovkin et al., 2012), recognizing that
local-scale factors additionally contribute to determining wood decay
patterns (Bradford et al., 2014). To accurately represent DWD dynamics in
ecosystem processes, models should be sensitive to transient responses, such
as changes in disturbance regimes, when depicting the nonlinear patterns
inherent in DWD decomposition (Harmon et al., 2011a).
Our objective was to link current and future climate information with models
representing woody debris decomposition to quantify potential future changes
in temporal DWD dynamics in forests of the eastern US. Specific objectives
were to (1) compare differences in DWD residence time assuming a static
versus dynamic climate throughout the duration of decomposition and (2)
forecast ecosystem-level C flux for DWD using the static- and dynamic-climate
scenarios.
MethodsStudy area
The geographic scope investigated here ranged eastward from the US state of
Minnesota to Maine in the north and Louisiana and Georgia in the south
(latitude range from 29.56 to 48.74∘ N; longitude range from 67.06 to
96.71∘ W). Data from the eastern US are employed because
remeasurement data have been collected in the region which have informed DWD
modeling efforts (e.g., Russell et al., 2013), whereas remeasurement data are
not yet available for western US states. Forest types across this region
varied in terms of species assemblage, forest productivity potential, and
climate. More than 75 forest types were identified by the US Department of
Agriculture Forest Service's Forest Inventory and Analysis (FIA) program
across the study area, which represented 14 broader forest type groups
(Woudenberg et al., 2010).
Data
Data used to simulate the decomposition of woody debris were obtained from a
DWD inventory conducted in 2001 on 516 FIA plots across the eastern US
(Russell et al., 2014). Each plot consisted of four 7.32 m fixed radius
subplots for a total plot area of approximately 0.07 ha, where tree and site
attributes were measured. Downed woody pieces were defined as DWD in forested
conditions if they had a diameter greater than 7.62 cm along a length of at least
0.91 m. All plots displayed a minimum of at least one DWD piece that met
this definition. Individual DWD pieces were sampled using a line-intercept
sampling method (Van Wagner, 1968) on 18.0 m horizontal distance transects
radiating from each FIA subplot center at azimuths of 30, 150, or 270
degrees. Only two transects from the three azimuths were sampled within each
subplot, depending on spatial arrangement (30∘ and 150∘ for
north and southeast subplots; 150∘ and 270∘ for center and
southwest subplots); these transects totaled 143.6 m for an entire inventory plot. Data
collected on each DWD piece included small-end and large-end diameters, decay
class (DC), length (LEN), species, and piece location (i.e., plot, subplot,
and transect number; horizontal distance along a sampling transect). In the
field, DC was assigned to each DWD piece using a five-class system, with 1
being least and 5 being most decayed. Piece LEN was defined as the total
length of the log in meters. In total, 4384 DWD pieces were collected from 32
conifer and 87 hardwood species as part of the inventory.
Climate information was obtained by specifying the latitude, longitude, and
elevation of each FIA plot location to a spline surface model developed from
climate station data across forests of North America (Table 1; Fig. 1)
(Rehfeldt, 2006; US Forest Service, 2014). Downscaled predictions from 17
Coupled Model Intercomparison Project (CMIP5) model outputs were used to assess differences in DWD decomposition
rates resulting from future climate predictions: Representative Concentration Pathway (RCP) 4.5, RCP 6.0, and RCP
8.5 (Intergovernmental Panel on Climate Change, IPCC, 2013). Downscaled
General Circulation Model (GCM)
data were obtained from the Moscow (Idaho) Forestry Sciences Laboratory
(http://forest.moscowfsl.wsu.edu/climate); they were produced by
adapting spline surfaces from present-climate data to GCM predictions
(Rehfeldt, 2006).
Violin plots of precipitation and degree day trends for
study plots by geographic region across the eastern US for the climate
normal period (1961–1990) and projected climates using an ensemble of 17
GCMs for CMIP5 models and an RCP 6.0 scenario.
Current climate conditions for 516 plot locations using the US
Forest Service Moscow Laboratory climate model
(http://forest.moscowfsl.wsu.edu/climate/) for determining differences
in downed woody debris decomposition dynamics across the eastern US.
VariableDefinitionUnitsMeanSDMinMaxDD5Annual degree days>5∘C2667.6915.2406.05669.0MAPMean annual precipitationmm869.8360.2219.03282.0MATMean annual temperature∘C9.24.3-0.320.6MTCMMean temperature in the coldest month∘C-5.36.6-18.012.7MTWMMean temperature in the warmest month∘C22.53.29.328.9Analyses
Decay class transition models were used to project the mass loss of DWD
(Russell et al., 2013). Here, DC transition was defined as the probability
that a DWD piece would remain in the same DC or advance to subsequent DCs at
repeated inventories. These DC transitions were estimated as a function of
climate (as measured in the number of degree days greater than 5 ∘C
[DD5] observed in the FIA plot), LEN, and DC (Russell et al., 2013). Given
the relationship between log size and woody debris decomposition (Cornwell et
al., 2009; Janisch et al., 2005; Mackensen et al., 2003), LEN was used as a
predictor in the DC transition model. We assumed a conic-paraboloid form
(Fraver et al., 2007) to estimate the initial volume (Vol) of each DWD
piece. Initial density (ID; kg m-3) for an individual species m (Harmon et al., 2008) and the appropriate DC reduction factor (DCRF) for
DWD of a given species group n in a DC k (Harmon et al., 2011b) was
obtained to estimate losses in wood density. To accurately represent DWD mass
loss, a volume reduction factor (VRF) was subsequently applied to account
for structural reductions in DWD Vol as decay progresses. We applied a
VRF of 1, 1, 1, 0.800, and 0.412 for DC 1, 2, 3, 4, and 5 pieces, respectively
(Fraver et al., 2013). Hence, DWD mass was calculated as:
Mass=IDm×DCRFkn×Vol×VRFk,
where all variables are as previously defined.
A Monte Carlo simulation framework was used to estimate DWD Mass in 5-year
intervals using the DC transitionequations (Russell et al., 2013; Russell
et al., 2014). For the simulations, 1000 runs were carried out for 200
years to introduce uncertainty in estimating DC changes. This method
involved simulating the DWD pieces by first assuming they were non-decayed,
then drawing a random number from a uniform distribution and comparing it to
the cumulative 5-year probability predicted using the DC transition
model. Downed woody debris DC transitions were estimated by predicting the
cumulative probabilities of pieces advancing in decay using a cumulative
link mixed model. Cumulative link models (CLMs) are a type of ordinal
regression model in which response variables are considered categorical or
ordered (Agresti, 2007). The variables DD5, LEN, and initial DC were used
to indicate decomposition potential across the eastern US and thus estimate
DWD DC transitions (Russell et al., 2014):
logγikj=θk-β1DD5-β2LEN-uForTypej+ε,
where θk is the intercept term for DC k (i.e., DC 1,
DC 2, DC 3, DC 4, or DC 5), γ is the cumulative probability for DWD
piece i moving through each of the successive k decay classes within each
ForType j, βi are the parameters estimated for conifer and
hardwood species separately, and ε is the random residual term.
The random effect u was specified to represent forest-type-specific effects
on the DC transition process. The finding that LEN was a more effective
predictor of decomposition than log diameter in these DC transition models is
consistent with other studies that suggested a lack of a consistent
relationship between log diameter and woody debris decomposition (e.g.,
Harmon et al., 1987; Radtke et al., 2009).
Predictions were accomplished by applying the DWD DC transition equations
(Russell et al., 2013) to the data described above using the simulation
framework. For each of the 4384 DWD pieces, a 1000-run Monte Carlo
simulation was performed up to 200 years.
We assumed DWD would decay according to one of two scenarios: (1) a fixed
(i.e., static) climate throughout the timespan of DWD decomposition or (2)
a dynamic climate throughout DWD decomposition depending on the future
climate predicted at each FIA plot location.
DWD decomposition scenariosBaseline
For a baseline scenario, a static climate was assumed throughout the timespan
of DWD decomposition. Hence, the independent variable DD5 used to represent
climate regime in the DWD DC equation was held fixed and assumed to be the
30-year (1961–1990) normal, depending on its location within the region.
To compare relative differences in DWD decomposition, pieces were separated
into species group (i.e., conifer and hardwood species) and geographic region
(i.e., north and south; Supplement Table S1). Smaller sample sizes for
some species (e.g., < 10 DWD pieces) constrained us to analyze
relative differences according to the general species group.
Simulating the DWD pieces allowed us to approximate the number of years in
which the proportion of biomass remaining attained any specified proportion.
Residence time (TRES) was calculated as the number of years in
which the mean proportion of biomass remaining fell within one standard error
of the mean for a decay class 5 log (Russell et al., 2014). From a biological
perspective, TRES might be used as a surrogate for the number of
years until a DWD piece loses all structural integrity and transitions to
another population (i.e., another C pool). At this point, the DWD piece may
be incorporated into the soil organic horizon and thus no longer meets the
criteria for being inventoried as DWD within the FIA protocol (exclusive of
combustion or harvest removal).
Baseline estimates of downed woody debris residence times, assuming
a static-climate scenario.
Species groupRegionnResidence time (years) MeanSDConifersNorth164887.413.0South49049.97.5HardwoodsNorth158180.016.4South66551.611.0
Baseline estimates assume a static-climate scenario throughout the duration
of decomposition, assumed to be the 30-year (1961–1990) normal, depending on the number of degree days (DD5) >5∘C for each plot
location; n is the number of observations.
Future climate
For a changing-climate scenario, a dynamic climate was assumed to occur
throughout DWD decomposition. Current CMIP5 models (Taylor et al., 2012) as
described in the fifth assessment report (AR5) of the IPCC (2013) were
obtained using three scenarios (RCP 4.5, RCP 6.0, RCP 8.5; US Forest Service,
2014). An ensemble of 17 AR5 model predictions was used for each RCP scenario
(Supplement Table S2). Given that the DC transition equation operated on a
5-year interval, while climate information was provided for the
30-year normal (1961–1990) and years 2030, 2060, and 2090, values for the
DD5 variable were assumed to transition linearly between 2001 and 2030, 2030
and 2060, 2060 and 2090, and post-2090 (if TRES was not yet
reached by the year 2090). Within the simulation, a dynamic DD5 variable
resulted in different values for TRES and C flux when compared to
the baseline scenario.
Projected changes in temperature (i.e., DD5) were more apparent at these
sites compared to variables representing moisture, such as mean annual
precipitation (MAP). Comparing the 30-year normal with the projected 2090
climate, DD5 would increase on average by 39.1 % (SD = 10.8 %), while
MAP is projected to increase by only 7.2 cm or 7.1 % (SD = 2.8 %).
Regionally, increases in the percent difference in current versus projected
DD5 would range from as low as 29.3 % (SD = 5.4 %) in the southeast
to as high as 51.2 % (4.5 %) in the Northern Lake States (Fig. 1).
Hence, in the absence of local-scale factors to use as a surrogate for
decomposition (e.g., Bradford et al., 2014), temperature
differences under future-climate scenarios may be employed to
explain DWD flux across the eastern US at least in part.
Decreases in downed woody debris (DWD) residence time
compared to baseline scenario. Mean values by species group and region in
eastern US forests for a baseline current-climate scenario and assuming
changes in future climate (based on three RCP scenarios from an ensemble of
17 CMIP5 models). Error bars indicate one standard deviation.
Projected downed woody debris carbon flux initialized using the
most recent inventory (2007–2011) in eastern US forests for a baseline
current-climate scenario and assuming changes in future climate (based on
three RCP scenarios from an ensemble of 17 CMIP5 models and not accounting
for future DWD inputs). Error bars indicate ± one standard error.
Conifer forests include loblolly–shortleaf pine, longleaf–slash pine,
spruce–fir, white–red–jack pine, and other softwood forest type groups.
Hardwood forests include aspen–birch, elm–ash–cottonwood, maple–beech–birch,
oak–hickory, and other hardwood forest type groups.
Distribution of estimated decreases in downed woody debris residence
time (years) by piece size, species group, and region for eastern US forests
for a baseline current-climate scenario and assuming changes in future
climate for up to 200 years (based on an RCP 6.0 scenario from an ensemble of
17 CMIP5 models).
Species groupRegionLengthQuantile (years) Min255075MaxConifersNorthShort-9.8-7.4-4.9-4.0-0.9Med-11.4-9.9-9.4-8.6-1.2Long-24.5-14.2-12.0-10.6-1.9SouthShort-2.8-2.4-2.0-1.8-0.7Med-3.0-2.4-2.1-1.8-0.8Long-5.2-2.7-2.1-1.9-0.9HardwoodsNorthShort-8.8-7.1-6.6-6.0-1.4Med-12.2-7.9-7.2-6.5-3.0Long-24.6-12.8-10.0-8.0-6.3SouthShort-7.1-3.3-2.8-2.4-1.0Med-8.7-4.1-3.1-2.6-1.6Long-10.7-7.2-5.8-3.2-12.0C flux
To scale our estimates of TRES changes for DWD pieces, we
forecasted an ecosystem-level DWD C flux. This was accomplished by projecting
current DWD stocks inventoried from 2007–2011 (hereafter termed “year
2010”) by the FIA program in 29 eastern US states (Woodall et al., 2013).
These data were collected in a similar manner to the 2001 data, with the
primary difference being that DWD was sampled along three 7.32 m transects
at each of four subplots, totaling 87.8 m for a complete FIA plot (Woodall
and Monleon, 2008).
Current DWD C stocks were first estimated by multiplying plot-level biomass
values by a C concentration constant of 0.5 (Mg ha -1), followed by a simulation
of DWD pieces. Carbon stocks in the DWD pool were then estimated in 5-year
time steps from 2010 onward. Assuming no inputs into the DWD pools over a
100-year span, C flux was defined as the amount of C lost for each 5-year
span (Mg ha -1/5 year). If the estimate of TRES for a given species
was exceeded by the number of simulation years, then it was assumed that the
piece had completely decomposed (i.e., biomass was set equal to 0). Means
for C flux were summarized by general forest type group (i.e., conifer and
hardwood) following multiple simulation runs.
Results
Baseline estimates of TRES ranged from 49.9 ± 7.5 years
(mean ± SD) for conifer species in the southern US to 87.4 ± 13.0
years for conifer species in the northern regions (Table 2). For all RCP
scenarios, TRES was predicted to decrease for all species groups
and regions compared to the baseline scenario (see Table 3 for RCP 6.0).
Decreases in TRES were generally less than 10 years for southern
species, while northern species displayed greater decreases. The decrease in
TRES for smaller DWD pieces was generally less than 10 years.
However, in some cases the decrease in TRES exceeded 20 years for
larger DWD (> 20 m in length) pieces located in the north
(Table 3).
We estimated that the mean decrease in TRES was greatest for
northern hardwood species. When averaged across all climate models, the
maximum mean decrease for this group was 10.3 ± 3.5 years, or a
decrease of 13 %. Decreases in TRES were lowest for both
southern conifer species, where a 6 % decrease was found, followed by
northern conifer and southern hardwood species (10 %; Fig. 2).
Carbon flux was initially greater for RCP scenarios compared to the baseline
scenario (Fig. 3). For conifer forest types during the first 5 years, flux
ranged from -0.23 ± 0.05 Mg C ha -1 when considering an RCP 6.0 scenario
to -0.26 ± 0.05 Mg C ha -1 considering an RCP 8.5 scenario. Similarly,
flux ranged from -0.50 ± 0.10 Mg C ha -1 when considering the baseline
scenario to -0.56 ± 0.08 Mg C ha -1 for an RCP 8.0 scenario in hardwood
forest types during the first 5 years. Carbon flux generally tended to
decrease more rapidly throughout the duration of the simulation (e.g., from
2015 to 2095) for RCP scenarios when compared to that of the static baseline
climate assumption.
Discussion
Our study suggests that increased decomposition rates as resulting from
future climate changes will decrease DWD residence times and increase
initial C emissions from decaying logs. These findings have direct
implications for modeling C dynamics from DWD under future global-change
scenarios and suggest that future forest management and conservation
activities may need to proactively manage for DWD to maintain contemporary
levels. Given the range in climate and total number of species, the eastern
US was an appropriate region to explore changes in DWD dynamics under future
projected climates.
The findings of a shorter residence time for northern hardwoods as opposed to
conifers, assuming a baseline scenario, were expected, given our general
understanding of species differences in wood decay (Cornwell et al., 2009).
The observation of the largest percent difference in residence time change
when comparing the RCP 6.0 scenario with that of the baseline for northern
hardwoods (13 %) may be due to greater projected increases in DD5 for the
northern compared to southern regions (Fig. 1). The length of DWD pieces will
likely further influence DWD residence time if one is interested in a
particular species of a general size class (Russell et al., 2014).
Future work merging our results with ecosystem models representing tree
growth and mortality in conjunction with DWD dynamics could allow for an
array of C flux and stock projections (Mazziotta et al., 2014). Moreover,
the long-recognized ecological importance of DWD argues for increased
empirical and modeling studies that account for the impacts of climate
change on this critical component of forest ecosystem functioning (Krajick,
2001; Stokland et al., 2012). The results highlight the need for detailed
inventories of DWD so that the stocking in various pools can be assessed
with a more accurate quantification of decomposition pathways. Future
investigations of DWD decomposition rates should focus on employing
climate-related parameters in addition to assessments of local-scale
factors (e.g., fungal colonization; Bradford et al., 2014) to examine the
response of DWD to potential interactions between altered disturbances and
changing climate conditions. Determining how to better incorporate
site-specific factors within ecosystem simulations will encourage modelers
to investigate the role of local-scale factors in addition to climate for
representing DWD decomposition.
We note that these simulations did not account for future DWD inputs – we
quantified decomposition trajectories of current DWD C stocks under
alternative climate scenarios to characterize temperature effects on DWD
dynamics independent of other processes. Particularly when examining C flux,
incorporating the contribution of live-tree C simultaneously with DWD
dynamics will better depict the total-ecosystem-C response to changes in
climate. Such an approach was recently highlighted by Mazziotta et al.
(2014) through their use of a gap-based forest simulation model to forecast
changing DWD populations. Given that model parameters for decomposition are
largely dependent on temperature in dynamic global vegetation (Cramer et
al., 2001), process (Kirschbaum, 1999; Kirschbaum and Paul, 2002), and
empirical models that represent DWD decomposition (Crookston et al., 2010;
Rebain et al., 2010), there is a need to examine the influence of changing
temperatures on woody debris dynamics. A key modeling development would be
the incorporation of key forest disturbances common to a region (e.g.,
windstorms, insect and disease outbreaks) in a stochastic framework, given
the linkage with inputs into the standing and DWD pools.
Despite not including C inputs to the DWD pool in this study, emerging
research from the same study area suggests that climate change may increase
the rate of forest development (i.e., turnover; Zhu et al., 2014). The
potentially increased rates of stand development appear to align with our
study's projections of increased detrital C emission and hence elevated DWD
turnover. The combination of these two results suggests that the residence
time of C in the major forest ecosystem pools of live and dead biomass will
decrease. Although the effect of decreased residence times on the overall
sink strength of forest ecosystems will be dependent on future biomass
production rates (e.g., longer growing seasons, droughts, and/or CO2
enrichment), it does suggest that managers will have less time to consider
management options (Malmsheimer et al., 2008) as forest biomass becomes more
transitory. Moreover, given the critical role of DWD as a habitat for a myriad
of dead wood-dependent organisms, these future dynamics need to be
considered in species vulnerability assessments and action plans,
particularly for species requiring habitat elements as refugia during
drought and temperature extremes (Amaranthus et al., 1989). Such future
dynamics argue for an increasing emphasis on the deliberate retention and
creation of DWD habitats in managed landscapes to compensate for accelerated
rates of depletion associated with future climate conditions.
The Supplement related to this article is available online at doi:10.5194/bg-11-6417-2014-supplement.
Acknowledgements
This work was supported by a joint venture agreement established between the
US Forest Service, Northern Research Station, and the University of
Minnesota, Department of Forest Resources. Additional funding was available from the US Department of Interior Northeast Climate Science Center. We thank Ben Bond-Lamberty,
Sabina Burrascano, Christopher Schwalm, John Stanovick, and an anonymous
reviewer for comments that helped to improve this work.
Edited by: Y. Kuzyakov
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