Forest Ecology and Management 310 (2013) 796–811
Contents lists available at ScienceDirect
Forest Ecology and Management
journal homepage: www.elsevier.com/locate/foreco
Temporal carbon dynamics of forests in Washington, US: Implications
for ecological theory and carbon management
Crystal L. Raymond a,⇑, Donald McKenzie b
a
b
School of Forest Resources, University of Washington, Box 352100, Seattle, WA 98195-2100, USA
Pacific Wildland Fire Sciences Lab, Pacific Northwest Research Station, USDA Forest Service, 400 N. 34th St., Suite 201, Seattle, WA 98103, USA
a r t i c l e
i n f o
Article history:
Received 12 April 2013
Received in revised form 12 September
2013
Accepted 13 September 2013
Available online 13 October 2013
Keywords:
Carbon
Biomass
Net primary productivity
Forest management
Pacific Northwest
a b s t r a c t
We quantified carbon (C) dynamics of forests in Washington, US using theoretical models of C dynamics
as a function of forest age. We fit empirical models to chronosequences of forest inventory data at two
scales: a coarse-scale ecosystem classification (ecosections) and forest types (potential vegetation) within
ecosections. We hypothesized that analysis at the finer scale of forest types would reduce variability,
yielding better fitting models. We fit models for three temporal dynamics: accumulation of live biomass,
accumulation of dead biomass, and net primary productivity (NPP). We compared fitted model parameters among ecosections and among forest types to determine differences in potential C storage and
uptake.
Models of live biomass C accumulation and NPP fit the data better at the scale of forest types, suggesting this finer scale is important for reducing variability. Model fit for dead biomass C accumulation
depended more on the region than on the scale of analysis. Dead biomass C was highly variable and a
relationship with forest age was found only in some forest types of the eastern Cascades and Okanogan
Highlands. Indicators of C storage potential differed between forest types and differences were consistent
with expectations based on spatial variability in climate. Across the study area, maximum live biomass C
varied from 6.5 to 38.6 kg C m2 and the range of ages at which 90% of maximum is reached varied from
57 to 838 years. Maximum NPP varied from 0.37 to 0.94 kg C m2 yr1 and the age of maximum NPP varied from 65 to 543 yrs. Forests with the greatest C storage potential are wet forests of the western Cascades. Forests with the greatest potential NPP are 65–100-year-old mesic western redcedar-western
hemlock forests and riparian forests, although limited data suggest maximum NPP of coastal sitka spruce
forests may be even greater. The observed relationship between the ages at which maximum NPP and
maximum live biomass are reached for a given forest type suggests that there is a trade-off between managing for maximum live biomass (storage) vs. NPP (uptake) in some forest types but an optimal age for C
management in others. The empirical models of C dynamics in this study can be used to quantify the
effects of age-class distributions on C storage and NPP for large areas composed of different forest types.
Also, the models can be used to test the effects of current or future natural and anthropogenic disturbance
regimes on C sequestration, providing an alternative to biogeochemical process models and stand-scale
methods.
Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction
Evidence of climate change caused by anthropogenic emissions
of greenhouse gases, including carbon dioxide (CO2), has increased
interest in quantifying spatial and temporal patterns of forest
carbon (C) dynamics. This information can be used to alter forest
management to retain current C stocks or store more C in forests.
Most research on C dynamics in forest ecosystems has focused
⇑ Corresponding author. Present address: City of Seattle, Seattle City Light, 700
Fifth Avenue, Suite 3200, Seattle, WA 98124, USA. Tel.: +1 2063861620.
E-mail address: crystal.raymond@seattle.gov (C.L. Raymond).
0378-1127/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.foreco.2013.09.026
on quantifying the contribution of these systems to the terrestrial
C sink (Goodale et al., 2002; Pacala et al., 2001) and projecting how
this sink will change over time with changes in climate and land
use (Bachelet et al., 2001; Hurtt et al., 2002). Research on the potential of forest ecosystems to take up CO2 and store C can help
determine to what extent forest management can enhance C storage (Birdsey et al., 2006). Although C storage is only one of many
factors considered in forest management, its importance is increasing as an ecosystem service with local-to-global significance.
Ground-based inventories and remotely sensed data quantify
the current magnitude and spatial distribution of C stocks in
forests at regional (Hicke et al., 2007), national (Heath et al.,
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
2011), and continental scales (Goodale et al., 2002; SOCCR, 2007).
A limitation of these studies is that they represent single points in
time and must be updated with additional inventory or remotely
sensed data as disturbances affect forest C by releasing C to the
atmosphere, redistributing C within ecosystems, and altering the
age-class distribution over large areas (Kurz et al., 2008). Changes
in C stocks over time, or fluxes, can be simulated with mechanistic
process models, but these models require an understanding of how
C stocks and fluxes change with disturbances and time since
disturbance.
Empirical models of C stocks and fluxes as a function of forest
age quantify the temporal changes in C dynamics following disturbance. These empirical models have multiple uses for understanding, modeling, and managing changes in C with forest succession,
disturbance, and management. They can be used to project the effects of natural disturbances on forest C and post-disturbance C
trajectories (Raymond and McKenzie, 2012). They have been used
directly in landscape simulation models (Smithwick et al., 2007),
indirectly to validate mechanistic process models (Rogers et al.,
2011), or to manage return intervals of natural or anthropogenic
disturbances to meet C storage objectives (Euskirchen et al., 2002).
Patterns of C stocks and fluxes with succession are documented
in the theory of ecosystem ecology, but empirical data are needed
to identify parameters in the theoretical models. Live biomass
accumulates rapidly in young forests, the rate of accumulation declines after peak production, and live biomass reaches a maximum
in mature forests (Odum, 1969). Net primary productivity (NPP,
the uptake of C by ecosystems) increases rapidly in young forests,
reaches a peak near the time of maximum leaf area (i.e. canopy closure), and then declines in old forests (Kira and Shidei, 1967; Gower et al., 1996; Ryan et al., 2004). Temporal patterns of dead
biomass have been studied in forests of the Pacific Northwest
(PNW) because of the relatively high proportion of biomass stored
in dead pools (Smithwick et al., 2002). Immediately after a standinitiating disturbance, young forests have high levels of dead biomass (i.e. legacy biomass). Dead biomass declines as this legacy
biomass decomposes, and then increases again with tree mortality
in old forests. This combination of legacy biomass and increasing
mortality creates, in theory, a U-shaped pattern of dead biomass
accumulation during succession.
These theoretical patterns of C dynamics have been quantified
in forest ecosystems but typically at the coarse spatial scales of
biomes (Pregitzer and Euskirchen, 2004) or regions (Hudiburg
et al., 2009), or at the scale of individual forest types but for only
a few sites (e.g. Law et al., 2003). Patterns based on the theory of
live biomass accumulation with forest age have been quantified
in tropical (Ryan et al., 2004), boreal (Bond-Lamberty et al.,
2004), and temperate forests (Hudiburg et al., 2009; Masek and
Collatz, 2006; Van Tuyl et al., 2005). Similarly, the temporal pattern
of peak and decline in NPP has been quantified at the scale of forest
biomes (Pregitzer and Euskirchen, 2004). The U-shaped pattern of
dead biomass with succession has been observed in temperate
coniferous forests throughout the western US (Agee and Huff,
1987; Janisch and Harmon, 2002; Kashian et al., 2013; Romme,
1982).
Theoretical patterns of C dynamics during succession are rarely
quantified at the scale of forest types within regions and biomes, a
scale that is most useful for evaluating effects of disturbance intervals and for forest management. Thus the objective of this study
was to quantify patterns of C dynamics as a function of forest
age at two scales: the coarse scale of ecological regions (ecosections, Bailey, 1995) and the finer scale of forest types within ecosections. We quantified three temporal patterns (accumulation of
live and dead biomass C and NPP) by fitting empirical models,
the forms of which are based on ecological theory, to forest
chronosequences using data from the USDA Forest Service Forest
797
Inventory and Analysis (FIA) program. We hypothesized that theoretical patterns would be detectable at both scales, but that the finer scale of forest types might reduce variability in coarse-scale
models attributed to differences in climate and species composition, thereby yielding better fitting models. We fit models for
ecosections and forest types for an 8.3 million hectare forested
region of Washington US, an area for which these patterns have
not been previously quantified at either scale. This area serves as
a useful domain because of the high diversity of forest types over
a relatively small geographic area. Large gradients in climate and
elevation give rise to high diversity of species assemblages, disturbance regimes, and thus potential productivity and C storage. We
used FIA data because they provides a semi-systematic sample of
forest conditions throughout the US, covering a wide variety of
forest types with different drivers of productivity and C storage.
Furthermore, the large FIA dataset provides many sites per forest
type, capturing a representative range of conditions within a single
forest type.
Our second objective was to quantify how key indicators of C
storage potential and the timing of C storage and uptake differ
among ecosections and among forest types. We compared parameters of the empirical models for differences in five indicators of C
storage potential: (1) maximum live biomass, (2) stand age at
which 90% of maximum is reached, (3) maximum NPP, (4) stand
age of maximum NPP, and (5) maximum dead biomass. We expanded on previous research that quantified relationships between
climatic zones and maximum biomass and NPP (Gholz, 1982;
Smithwick et al., 2002) in two ways. First, we used a much larger
dataset that includes more sites that capture the range of conditions within a forest type. Second, we quantified differences in
maximum biomass accumulation and NPP, but also in their timing,
among ecosections and forest types, and compared these empirical
patterns to ecological theory.
Steady-state theories of ecological succession as described
above have been criticized because they account for only the effects of autogenic development on biomass and productivity without considering the effects of exogenous disturbances (Bormann
and Likens, 1979). In this study, we use this theory as a basis for
quantifying autogenic development of C stocks and fluxes, but this
does not imply that disturbances are unimportant in these ecosystems. The empirical models quantified in this study can be combined with natural and anthropogenic disturbance intervals to
quantify effects of disturbances on C dynamics, thus capturing
the influence of autogenic development and exogenous disturbances on C stocks and fluxes (e.g. Raymond and McKenzie,
2012). Quantifying these models at the scale of forest types is particularly useful because this is the scale at which disturbance regimes are typically quantified.
2. Methods
2.1. Study area
The study area covers 8.3 million hectares in Washington US
comprising four primarily forested ecosections (Fig. 1) (Bailey,
1995). Ecosections represent sub-regional aggregations of
biophysical controls, such as climate and topography, on ecosystem processes. Climate is highly variable across the domain, especially precipitation, because of the orographic effect of the
Olympic and Cascade Ranges (Daly et al., 1994). The Coast Range
ecosection has a maritime climate with high annual precipitation
and moderate winter and summer temperatures. Mean annual
precipitation can exceed 600 cm in west-facing valleys of the
Olympic Mountains. The Western Cascades also has a maritime
climate with high annual precipitation, but the ecosection is
influenced less by the Pacific Ocean and has a larger elevational
798
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
Fig. 1. The study area is four forested ecosections in Washington (Coast Range, Western Cascades, Eastern Cascades, and Okanogan Highlands) and the environmental site
potentials (ESPs) within them. Approximately 90% of the area is classified as forest. Areas in brown are outside of the study area. ESPs are the vegetation that could be
supported on a site based on the biophysical environment in the absence of disturbance (Holsinger et al., 2006). NPHM is North Pacific Hypermaritime, NPM is North Pacific
Maritime, NP is North Pacific, EC is Eastern Cascades, NRM is Northern Rocky Mountain, and RM is Rocky Mountain.
gradient, and thus more seasonal variability in temperature. The
crest of the Cascade Range divides the Western and Eastern Cascade ecosections. Climate of the Eastern Cascades and Okanogan
Highlands is transitional from maritime to continental with less
annual precipitation, warmer summers, and colder winters. In
all ecosections, most precipitation falls between November and
May (Daly et al., 1994) and at higher elevations much of the annual precipitation falls as snow. Dominant soil orders are andisols
in areas of volcanic parent material, spodsols in high-elevation
cool humid forests, and inceptisols in forests of the Okanogan
Highlands ecosection (Table 1).
The domain of the finer-scale analysis was the area of the four
ecosections, but the resolution was a 90m grid of forest types defined
by potential vegetation types (Fig. 1). We used a potential vegetation
classification, rather than dominant species, because dominant
species change during succession and our objective was to quantify
trends in C dynamics with succession. We used the environmental
site potential (ESP) classification of the LANDFIRE project for a
spatially explicit classification of potential vegetation (available at
http://www.landfire.gov/products_national.php). ESPs are the
species assemblages that can be supported on a site based on its
biophysical environment and absence of disturbance. Holsinger
et al. (2006) modeled ESPs using empirical data on vegetation
composition and spatial data on biophysical gradients, including
topography, climate, soil, and ecophysiological parameters. The
names of ESPs typically reflect three factors: regional climate, environmental or topographic setting, and dominant plant association
(Comer et al., 2003).
799
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
Table 1
Area and climate for 15 environmental site potentials (ESPs) within four forested ecosections in Washington, US.
Environmental site potential
Area (ha)
North Pacific Maritime and Hypermaritime (NPHM-M)
NPHM sitka spruce
NPHM western redcedar-western hemlock
NPM mesic-wet Douglas-fir-western hemlock
NPM dry-mesic Douglas-fir-western hemlock
North Pacific (NP)
NP mountain hemlock-subalpine parkland
NP mesic western hemlock-silver fir
NP riparian forest
NP dry-mesic silver fir-western hemlockDouglas-fir
Eastern Cascades Rocky Mountain (EC-NRM)
EC mesic mixed-conifer forest
NRM dry-mesic montane mixed conifer forest
NRM subalpine woodland and parkland
NRM mesic montane mixed conifer forest
NRM ponderosa pine woodland and savanna
RM subalpine spruce-fir
RM montane riparian forests
Dominant soil
order
Mean annual precip.
(cm)
472,300
386,372
728,891
332,955
Andisols
Andisols
Andisols
Spodosols
275
272
229
222
2.4
6.6
5.8
8.6
18.3
19.5
20.3
21.6
505,777
917,408
291,257
303,096
Spodosols
Spodosols
Andisols
Andisols
249
261
215
217
9.8
8.9
10.1
8.6
18.6
19.3
22.6
18.9
Andisols
Inceptisols
Spodosols
Inceptisols
Andisols
Inceptisols
Andisols
98
56
137
94
46
102
57
8.2
10.1
14.8
9.0
9.7
13.5
8.6
21.9
22.9
19.5
20.5
23.0
20.3
21.6
606,973
2,174,724
139,156
273,475
267,716
379,176
113,591
Mean min. Jan. temp.
(°C)
Mean max. July temp.
(°C)
Note: Climate data are from the PRISM climate mapping system and are climatology normals from 1971 to 2000 (Daly et al., 1994). The mean is calculated for all 800 m cells
that fall within the ESP area.
Relative to the ecosection-scale classification, the ESP classification distinguishes between high- (cold) and low- (warm) elevation
forests and riparian forests within the four ecosections (Table 1)
and between wetter and drier forests of the Eastern Cascades and
Okanogan Highlands. At the scale of ESPs, the influence of tree species attributes (e.g. maximum size and lifespan) on potential biomass and productivity can be detected. We grouped ESPs into 15
forested ESPs that covered the greatest area. Four ESPs that covered
only a small portion of the study area were grouped with ESPs that
had the same regional climate classification and either the same
environmental and topographic setting or the same plant association. The ‘‘other forest’’ type includes woodlands and forest types
that cover less than 1% of the study area. We resampled the 30m raster to 90-m cells using the majority resampling criterion to
make the resolution of the ESP raster consistent with the resolution of inventory plots. To group plots by ESP, plot coordinates
were intersected with the ESP spatial data layer.
2.2. Inventory data
We used the forest inventory data collected as part of the US
Forest Service (USFS) Pacific Northwest Region Current Vegetation
Survey (CVS) and FIA Program. The CVS inventory was conducted
on USFS ownership and the FIA inventory was conducted on
private, state, and some other federal ownerships (excluding USDI
National Park Service). We used data from CVS plots that were
inventoried between 1993 and 2000 and FIA plots that were inventoried between 1989 and 1991. CVS plots are 1-ha plots on a grid
with 5.5-km spacing in areas designated as wilderness and
2.7-km spacing in all other areas. FIA plots are 0.4 ha plots on a
grid with 5.5-km spacing in eastern Washington and 3.9 km
spacing in western Washington.
The PNW Integrated Database (IDB) version 2.0 combines data
from the FIA and CVS inventories into a common set of variables
and formats (Waddell and Hiserote, 2005a). We selected 4126
plots from the 5576 plots available in the study area based on
two criteria: both trees and understory vegetation were sampled
and the plot had a single condition. Plots with multiple conditions
have distinctly different areas of forest attributes (e.g. forest type,
stand density, or stand age), so we excluded plots with multiple
conditions because they cannot be classified as a single age class
or forest type. A similar study using FIA data from Oregon and
northern California found that excluding plots with multiple conditions did not affect regional temporal C dynamics (Hudiburg et al.,
2009). Fewer plots were available with sufficient data to calculate
dead biomass because coarse woody debris (CWD) was sampled
only in the CVS inventory of western Washington, not the CVS
inventory in eastern Washington or the FIA inventory. The analysis
of dead biomass included 3154 plots in which both CWD and
standing dead trees were sampled.
2.3. Estimating live biomass carbon pools
Total aboveground biomass C (kg C m2) of tree wood components for each plot was estimated as the sum of stems, branches,
and bark. Unless otherwise stated, we converted biomass to C
using a conversion factor of 0.5 C content (Janisch and Harmon,
2002). The PNW-IDB includes calculated tree-level biomass of
stems, bark, and branches, but we used calculated variables for
only stem biomass. We did not use values for bark and branch biomass, because we used more species- and ecosection-specific
equations for estimating these components. In the PNW-IDB, stem
biomass of tress with DBH >2.5 cm (kg, dry weight) was calculated
from stem volume (m3) (calculated with species-specific equations
based on DBH and height) and species-specific values for wood
density (kg m3) (Waddell and Hiserote, 2005b). We estimated
biomass of branches and bark with species- and ecosection-specific
allometric equations that predict biomass from DBH or DBH and
height (Gholz et al., 1979; Standish et al., 1985; Means et al.,
1994; Jenkins et al., 2004) with occasional substitutions if a species- or ecosection-specific equation was not available. Trees with
DBH <2.5 cm were tallied by species, so we calculated total aboveground biomass of these trees using an allometric equation for
conifer species with heights <4.5 m (Brown, 1978). We expanded
C per tree to C per ha based on the trees per ha represented by each
tree in the sample (i.e. the expansion factor) (Waddell and Hiserote, 2005a).
The PNW-IDB did not include foliar biomass of trees, so we
estimated foliar biomass at the tree-level using a three-step
process based on DBH, sapwood area, leaf area per tree, and
800
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
species-specific values of specific leaf area (Smithwick et al.,
2002; Raymond and McKenzie, in press). First, we calculated sapwood area as a function of DBH using species-specific regression
equations. Second, we calculated leaf area per tree (m2) using
species-specific ratios of leaf area (m2) to sapwood area (cm2).
Third, we converted leaf area to biomass (kg) using speciesspecific values of specific leaf area (cm2 g1). We converted foliar
biomass to C using a conversion factor of 0.45 (Campbell et al.,
2004) and expanded foliar biomass per tree to per hectare using
the expansion factors.
Belowground tree components were not measured or calculated
in the PNW-IDB, so we estimated biomass of coarse roots
(>10 mm) at the tree level using allometric equations that predict
root biomass from DBH or DBH and height. Few allometric equations for root biomass are available, so we used equations for only
four species: Douglas-fir (Pseudotsuga menziesii) (Gholz et al.,
1979), ponderosa pine (Pinus ponderosa) (Omdal et al., 2001), western redcedar (Thuja plicata) (Feller, 1992), and red alder (Alnus rubra) (Gholz et al., 1979). We used genus-level or family-level
substitutions for all other species and adjusted biomass estimates
using species-specific values for wood density (Campbell et al.,
2004; Van Tuyl et al., 2005). We estimated biomass of fine roots
at the plot-level using an equation that predicts fine root biomass
from leaf area index (LAI) (Van Tuyl et al., 2005). We calculated LAI
(m2 m2) of each plot as the total leaf area of all trees in the plot
divided by plot area.
We estimated biomass of understory vegetation using allometric equations that predict biomass from percentage cover or percentage cover and height. The PNW-IDB includes percentage
cover and height of understory vegetation by species and life
form (i.e. herb, graminoid, or shrub). We estimated biomass of
herbs and graminoids using a single allometric equation developed from a combination of several understory species in Douglas-fir forests of central Washington (Olson and Martin, 1981).
We estimated biomass of shrubs using several species-specific
equations (Ohmann et al., 1981; Olson and Martin, 1981; Smith
and Brand, 1983; Alaback, 1986; Means et al., 1994). Many species substitutions were necessary, but whenever possible, we
substituted species from the same genus or family. We applied
an equation to each species and averaged understory biomass
to the plot-level.
2.4. Estimating dead biomass carbon pools
We estimated total dead biomass C (kg C m2) for each plot as
the sum of biomass of coarse woody debris (CWD) and standing
dead tree components (stem, bark, branches, and coarse roots).
We used calculated fields in the PNW-IDB for biomass of CWD
and dead tree stems (Waddell, 2002; Waddell and Hiserote,
2005a). CWD (>7.6 cm in diameter for CVS plots, and >12.5 cm in
diameter for FIA plots) was sampled using the line intercept method. For CWD and standing dead trees, biomass was estimated from
volume and species-specific wood density and reduced according
to decay class to account for the loss of weight and density with
decomposition (Waddell, 2002). Biomass was converted to C using
conversion factors of 0.521 for softwoods, 0.491 for hardwoods,
and 0.506 for unknown species (Waddell and Hiserote, 2005a).
Carbon per log was expanded to C per area based on the line intersect sampling design and C per tree stem was expanded to area
using expansion factors (Waddell, 2002; Waddell and Hiserote,
2005a). The PNW-IDB did not include calculated fields for biomass
of bark, branches, or coarse roots of standing dead trees. We estimated these biomass components using the same species- and region-specific equations that we used for live trees, but we reduced
the biomass of each component based on the decay class of the tree
(Waddell and Hiserote, 2005a).
2.5. Estimating net primary productivity
Net primary productivity (NPP) (kg C m2 yr1) includes two
parts: (1) production associated with increasing plant dimensions
and (2) production associated with annual replacement of plant
tissues (turnover). We estimated NPP for each ecosystem component as the part of productivity that drives most production for
that component. Thus, we estimated NPP of tree wood components
(stems, bark, branches, and coarse roots) as the productivity associated with increasing tree dimensions and assumed turnover of
branches and bark to be minimal. In contrast, we estimated NPP
of fine roots, foliage, and understory vegetation as the productivity
associated with turnover rates.
We estimated NPP of tree wood components at the tree level as
the difference between current and previous biomass (Grier and
Logan, 1977). For plots that were inventoried only once, we calculated previous biomass using a back calculation of biomass
10 years before the inventory year using measurements of the
10-year radial growth increment and divided by 10 for annual
NPP (Van Tuyl et al., 2005). For plots that were inventoried twice,
we estimated annual NPP as the difference between biomass of the
current and previous inventories divided by number of years between inventories. We estimated NPP of foliage at the tree level
as a fraction of current foliar biomass (Van Tuyl et al., 2005; Hudiburg et al., 2009). To calculate annual NPP we divided current foliar
biomass by species- and ecosection-specific values for leaf retention time (T. Hudiburg, personal communication), the average
number of years that a species retains foliage. We estimated production of fine roots at the stand level by multiplying total fine root
biomass by published values of fine root turnover rates, the proportion of fine root biomass that is replaced annually (Santantonio
and Hermann, 1985).
Production of understory biomass was difficult to estimate given the limited data on understory vegetation that were collected
in the inventory. We assumed that production of herbaceous vegetation and graminoids was 50% of current biomass and did not include an estimate for production of shrubs.
2.6. Calculating stand age
The PNW-IDB did not include disturbance history, but time
since the last stand-replacing disturbance for each plot can be
approximated with stand age. Inventory data include tree ages
from increment cores for some or all trees in a plot. We calculated
stand age as the average age of the oldest 10% of trees in a plot. For
plots for which the oldest 10% was fewer than three trees, we calculated stand age as the mean age of all cored trees in the plot. We
aggregated plots into age bins to account for the inherent uncertainty in calculating stand ages from tree ages and to allow for replicates within each age bin for use in the statistical analysis. We
binned stand ages into 10-year bins for ages <300 years and 20year bins for ages >300 years. For plots in the Western Cascades
and Coast Range ecosections, all stand ages >600 years were
grouped into the 600-year age bin. Only a few plots exceeded
400 years in the Eastern Cascades and Okanogan Highlands ecosections, so these plots were grouped into the 400-year bin.2.6 Statistical analysis
We fit non-linear regression models for the three C dynamics
(live biomass C accumulation, dead biomass C accumulation, and
NPP) as a function of stand age at two scales: the coarse scale of
the four ecosections and the finer scale of the 15 ESPs (57 regression models). For non-linear models, it is useful to select equation
forms and parameters with ecological relevance, as well as statistical significance. We selected appropriate forms for the non-linear
equations for each dynamic from the literature. The equation forms
that we used are grounded in ecological theory, enabling direct
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
inferences about differences in C storage potential and the age of
maximum C storage and uptake among ecosystems.
We used a Chapman–Richards function (Janisch and Harmon,
2002) to model accumulation of live biomass (kg C m2) as a function of stand age:
Lage ¼ Lmax ð1 ekL ageÞcL
ð1Þ
where Lage is live biomass at any stand age (age), Lmax is the maximum asymptotic live biomass reached in late succession, kL is an
empirically derived rate constant, and cL controls the position of
the curve between zero and the asymptote. Lmax, kL, cL are all fitted
parameters. Live biomass at any age is a fraction of the asymptotic
maximum, Lmax, which can be interpreted as the maximum potential storage of live biomass C for the ecosystem. The time required
for an ecosystem to approach this maximum is controlled by kL
and cL. Although kL is an empirically derived rate of accumulation,
it is difficult to interpret ecologically and compare between ecosystems because its value depends on Lmax and cL (Yang et al., 2005).
Therefore, we calculated a time parameter, age90max (years), from
the fitted models, which is the age at which 90% of Lmax is reached.
age90max is an ecologically meaningful parameter for comparing the
years required for an ecosystem to approach its theoretical maximum live biomass storage.
We used the sum of two equations to model the U-shaped pattern for the accumulation of dead biomass (kg C m2) as function of
stand age: (1) an exponential decay function (for decay of legacy
biomass) and (2) a Chapman–Richards function (for accumulation
of dead biomass from mortality in the new stand) (Janisch and Harmon, 2002). The equation is:
Dage ¼ D0 ðekD1 age Þ þ Dmax ð1 ekD2 age Þ
cD
ð2Þ
where Dage is dead biomass at any stand age (age), D0 is the initial
legacy biomass, kD1 is an empirically derived rate constant for
decomposition of legacy biomass, Dmax is the asymptotic maximum
dead biomass, kD2 is an empirically derived rate constant for the
accumulation of new dead biomass, and cD controls the shape of
the accumulation portion of the function. All parameters are fitted
parameters. D0 (kg C m2) is interpreted as the dead biomass
remaining after a stand-replacing disturbance and kD1 is the rate
at which it decays. The parameters of the second part of the equation are interpreted the same as for accumulation of live biomass;
Dmax (kg C m2) is the maximum asymptotic storage of dead
biomass.
We used a peak function to model NPP (kg C m2 yr1) as a
function of stand age (Janisch and Harmon, 2002; Hudiburg
et al., 2009):
2
NPPage ¼ NPP max ef0:5½lnðage=agemax Þ=kN g
ð3Þ
where NPPage is the NPP at any stand age (age), NPPmax is the maximum NPP reached in early succession, kN is the rate at which the
stand reaches NPPmax, and agemax is the age at which NPP begins
to decline from the maximum. NPPmax, agemax, and kN are all fitted
parameters. This function represents the theory that NPP increases
rapidly in young stands, reaches a peak in mid-succession, and declines in mature stands. NPPmax is interpreted as the maximum
potential rate at which the ecosystem removes C from the atmosphere. We fit this peak function to total NPP, and separately for
the wood component of aboveground NPP (wood ANPP) because
of the higher uncertainty in estimating non-wood components of
NPP (roots, foliage, and understory), and because the original theory
of NPP as a function of stand age was developed for wood ANPP
(Gower et al., 1996).
We used the nls function in the R statistical environment (R
development Core Team, 2008) to fit all non-linear regression
models. The nls function uses a Gauss–Newton algorithm (Bates
801
and Watts, 1988), and requires initial estimates of the fitted
parameters. We estimated initial parameter values by fitting
approximate curves to scatter plots and using parameter estimates
from the literature.
To evaluate if each C dynamic model fit the data of each ecosection and ESP, we used an F-test for lack of model fit (Neter et al.,
1996). The lack of fit F-statistic is calculated by dividing the lack
of fit mean square by the pure error mean square. Unlike more traditional hypothesis tests, the null hypothesis for an F-test for lack
of fit is that the specified regression function is appropriate for the
data and the alternative hypothesis is that the specified regression
function is not appropriate for the data. Therefore, a model does not
have a significant lack of fit (i.e. the alternative hypothesis is rejected in favor of the null hypothesis) for high p values.
To test for differences in model parameters among ecosections
and among ESPs for each of the three C dynamic model, we borrowed from the principles of analysis of covariance for linear models (ANCOVA, Pineiro and Bates, 2000) to perform a similar analysis
for non-linear least squares. We created a design matrix that included dummy-variable coding for the explanatory variables that
are factors, then tested those contrasts using nls. All parameters require initial estimates when fitting non-linear models, including
parameters for differences between factors (i.e. ecosections and
ESPs). Therefore, we fit models separately for each factor to determine starting estimates for differences between parameters and
used these parameter estimates to test for differences among factors. Rather than using a single ANCOVA model with an unwieldy
number (15) of factor levels, we simplified comparisons by grouping the 15 ESPs into three groups by geoclimatic region and compared model parameters within these groups: North Pacific
Hypermaritime and Maritime (NPHM-M), North Pacific (NP), and
Eastern Cascades and Northern Rocky Mountains (EC-NRM). This
analysis enabled comparisons of C uptake and storage potential
(maximum NPP and biomass) and rate (age at which maximums
are reached) among ESPs.
3. Results
3.1. Ecosection-scale carbon models
At the scale of ecosections, live biomass C as a function of forest age was highly variable. Although a solution for the Chapman–Richards function for accumulation of live biomass during
succession (Eq. (1)) could be fit to the data, the models had a significant lack of fit for all four ecosections. Similarly, dead biomass
C as a function of forest age was highly variable at the scale of
ecosections. The theoretical U-shaped function for accumulation
of dead biomass C during succession (Eq. (2)) could not be fit to
the data for the Coast Range or the Western Cascades. For these
two ecosections, a linear model of increasing dead biomass C with
forest age fit the data. In contrast, a solution for the U-shaped
function could be fit in the Eastern Cascades and Okanogan Highlands ecosections and the lack of fit test for these two models was
not significant. Legacy dead biomass C (D0) was 3.85 (0.59) kg
C m2 and 1.17(2.34) kg C m2 for the Eastern Cascades and
Okanogan Highlands respectively. Maximum dead biomass C
(Dmax) was 4.43 (1.00) kg C m2 and 3.46 (0.79) kg C m2 for the
Eastern Cascades and Okanogan Highlands. Although these
parameters were not significantly different between these two
ecosections, they suggest that the Eastern Cascades has greater
potential to store C in dead biomass than the Okanogan
Highlands.
Similar to biomass, NPP was highly variable among ecosections.
The peak model for NPP as a function of forest age had a significant
lack of fit for the Coast Range and Western Cascades, but not for the
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C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
Eastern Cascades and Okanogan Highlands. However, maximum
potential NPP (NPPmax) and the forest age at which this maximum
is reached (agemax) were not significantly different between the
two ecosections. Maximum NPP was 0.55 (0.02) kg C m2 yr1
and 0.53 (0.01) kg C m2 yr1 for the Eastern Cascades and Okanogan Highlands respectively. The age of NPPmax was 384 (118) years
and 215 (50) years for the Eastern Cascades and Okanogan Highlands respectively.
3.2. ESP-scale carbon model: live biomass C accumulation
At the ESP scale, variability was less than the ecosection scale;
this scale of analysis yielded better fitting models for most ESPs,
particularly for live biomass C and NPP. At the scale of ESPs, dead
biomass C remained highly variable and the U-shaped function of
dead biomass C accumulation with forest age could not be detected
(i.e. a solution for the model could not be found) for most ESPs. The
Chapman-Richards function (Eq. (1)) for accumulation of live biomass C with forest age generally fit the data better at the ESP-scale,
with the exception of the NPHM-M group of ESPs (Fig. 2). Dry-mesic Douglas-fir-western hemlock was the only ESP in this group
that did not have a significant lack of model fit (Table 2). In contrast, all four of the models in the NP group (Fig. 3) and six of
the seven models in the EC-NRM group (Fig. 4) did not have a significant lack of fit (Table 2).
The high variability in C storage potential in live biomass among
ESPs was evident in the full range of values for maximum live biomass C (Lmax) and the age at which this maximum was reached
(age90max). Among all ESPs, Lmax varied from a low of 6.5 kg C m2
in the NRM subalpine woodland and parkland to a high of 38.6
kg C m2 in the NP mesic western hemlock-silver fir (Table 2).
The age at which 90% of this maximum was reached also varied
greatly among ESPs from a young age of 57 years in NP riparian forests to an old age of 838 years in the NP mountain hemlock-subalpine parkland (Table 2).
Comparisons of the parameters among ESPs within the groups
indicated significant differences for some models. ESPs in the NP
group had a wide range of values for both Lmax and age90max, and
these parameters differed between the ESPs in this group (Table 2).
The highest Lmax was in the mesic western hemlock-silver fir and
the mountain hemlock-subalpine parkland ESPs (38.6 and 38.1 kg
C m2, respectively) (Fig 3). Dry-mesic silver fir-western hemlock
had an intermediate Lmax (29.2 kg C m2) and the riparian forest
had the lowest Lmax (15.6 kg C m2). The mesic western hemlocksilver fir and the mountain hemlock-subalpine parkland ESPs
reached 90% of Lmax at much older ages (697 and 838 years) compared to the dry-mesic silver fir-western hemlock-Douglas-fir
(294 yrs) and the riparian forests (57 years).
Unlike ESPs in the NP group, ESPs in the EC-NRM group had a
narrower range of parameter values for Lmax and age90max and the
parameters were not significantly different among most ESPs in
this group (Table 2). EC mesic mixed-conifer had the highest Lmax
(18.3 kg C m2) and was the only ESP for which Lmax differed significantly. For all other ESPs in this group, Lmax was significantly lower, between 6.5 kg C m2 and 12.4 kg C m2 (Table 2). The lowest
Lmax was in the subalpine woodland and parkland and the highest
was in the montane mixed-conifer. Values for age90max were also
more similar among ESPs in the EC-NRM group than among ESPs
in the NP group (Table 2). Three ESPs in the EC-NRM group (mesic
montane mixed-conifer, ponderosa pine woodland and savanna,
and riparian forests) reached 90% of Lmax at younger ages (61–
156 yrs) compared to the other ESPs in this group, which required
230–340 yrs to accumulate 90% of Lmax.
3.3. ESP-scale carbon model: dead biomass carbon accumulation
Unlike the model for live biomass C accumulation, the U-shaped
function for dead biomass C accumulation (Eq. (2)) generally did
not fit the data better at the ESP scale than it did at the ecosection
scale. As with the ecosection scale, dead biomass at the ESP scale
was highly variable and had only a weak relationship with forest
age. Eq. (2) could be fit to the data for only three ESPs, dry-mesic
silver fir-western hemlock-Douglas-fir, dry-mesic montane mixed
conifer, and spruce-fir forest and woodland (Table 3). Linear models did not have a significant lack of fit for seven of the ESPs for
which Eq. (2) could not be fit. Generally, the model parameters
for both Eq. (2) and the linear models did not differ significantly
Fig. 2. Live biomass C as a function of stand age for four environmental site potentials (ESPs) in the North Pacific Hypermartime – Maritime (NPHM-M) group. Stand ages
were binned into 10-year age classes for ages <300 years and 20-year age classes for ages >300 years. All ages >600 years were grouped into a single age bin. Points are mean
live biomass C for the age bins and error bars are one standard deviation. Age-class means are shown for simplicity; models were fit to original data not means.
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C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
Table 2
Coefficients of models of live biomass C accumulation as a function of forest age for environmental site potentials (ESPs).
Coefficients (Standard errors)
n
Lmax
North Pacific Maritime And Hypermaritime (NPHM-M)
NPHM sitka spruce
NPHM western red cedar-western hemlock
NPM mesic-wet Douglas-fir-western hemlock
NPM dry-mesic Douglas-fir-western hemlock*
261
247
457
215
25.7
29.3
28.3
24.7
(1.2)
(1.1)
(0.9)
(0.9)
0.040
0.013
0.029
0.029
(0.007)
(0.003)
(0.004)
(0.006)
3.2
1.1
2.2
2.4
(0.9)
(0.2)
(0.4)
(0.8)
86
187
106
109
North Pacific (NP)
NP mountain hemlock-subalpine parkland*
NP mesic western hemlock-silver fir*
NP riparian forest*
NP dry-mesic silver fir-western hemlock-Douglas-fir*
137
515
121
188
38.1
38.6
15.6
29.2
(9.8)ac
(5.0)a
(1.2)b
(1.9)c
0.003
0.003
0.069
0.009
(0.002)a
(0.001)a
(0.039)b
(0.003)b
1.3
0.8
5.3
1.4
(0.4)a
(0.1)a
(4.5)a
(0.4)a
838
697
57
294
Eastern Cascades Rocky Mountain (EC-NRM)
EC mesic mixed-conifer*
NRM dry-mesic montane mixed conifer
NRM subalpine woodland and parkland*
NRM mesic montane mixed conifer*
NRM ponderosa pine woodland*
RM spruce-fir forest and woodland*
RM montane riparian forest*
339
1113
47
121
52
201
30
18.3 (1.6)a
12.4 (1.4)
6.5 (3.8)b
12.3 (0.5)b
9.7 (4.1)b
12.2 (1.5)b
10.1 (1.7)b
0.007
0.006
0.013
0.094
0.019
0.009
0.025
(0.003)a
(0.002)
(0.024)a
(0.037)b
(0.024)b
(0.006)a
(0.024)ab
0.9 (0.2)a
0.8 (0.1)
2.1 (4.2)a
64.7 (120.3)a
2.0 (2.5)a
1.0 (0.5)a
3.4 (5.4)a
312
341
232
68
156
256
140
kL
cL
age90max
Note: Letter superscripts indicate differences between model coefficients within groups of ESPs (NPHM-M, NP, EC-NRM) based on the ANCOVA (p < 0.10). Only models for
which the lack of fit test was not significant were compared. To calculate age90max Eq. (1) is solved for 90% of Lmax.
*
Lack of fit test was not significant suggesting the nonlinear model is appropriate (p < 0.10).
Fig. 3. Live biomass C as a function of stand age for four environmental site potentials (ESPs) in the North Pacific (NP) group. Stand ages were binned into 10-year age classes
for ages <300 years and 20-year age classes for ages >300 years. All ages >600 years were grouped into a single bin. Points are mean live biomass C for the bins and error bars
are one standard deviation. Age-class means are shown for simplicity; models were fit to original data not means.
among ESPs within any group because of the high variability in
dead biomass (Table 3).
3.4. ESP-scale carbon model: net primary productivity
Similar to the model of accumulation of live biomass C with forest age, the peak model of NPP as a function of forest age (Eq. (3))
fit the data better at the scale of ESPs than it did at the scale of ecosections. Models for twelve of the 15 ESPs did not have a significant
lack of fit (Figs. 5–7). Maximum NPP (NPPmax) and the age of maximum were highly variable among ESPs (Table 4). The NPPmax
parameter for all ESPs varied form a low of 0.37 kg C m2 yr1 in
the NRM subalpine parkland to a high of 0.94 kg C m2 yr1 in
HM sitka sprue. The agemax parameter varied from 65 years to
543 years. Only one of the four models in the NPHM-M group did
not have significant lack of fit (Table 4). In contrast, all four of
the models in the NP group and all seven of the models in the
EC-NRM group did not have a significant lack of fit (Table 4).
For ESPs in the NP group, NPPmax varied from 0.59 kg C m2 yr1
to 0.80 kg C m2 yr1 (Fig. 6) but the only ESP for which NPPmax differed significantly was the riparian forest, which had significantly
higher NPPmax (Table 4). The range of values for agemax was larger
than NPPmax and parameters differed significantly between ESPs
in this group (Table 4). The riparian forest had the lowest value
for agemax parameter at 65 years and the mountain hemlock-subalpine parkland had the highest value for agemax at 543 years.
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C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
Fig. 4. Live biomass C as a function of stand age for seven environmental site potentials (ESPs) in the Eastern Cascades Rocky Mountain (EC-NRM) group. Stand ages were
binned into 10-year age classes for ages <300 years and 20-year age classes for ages >300 years. All ages >400 years were grouped into a single bin. Points are mean live
biomass C for the bins and error bars are one standard deviation. Age-class means are shown for simplicity; models were fit to original data not means.
Table 3
Coefficients of models of dead biomass C accumulation as a function of forest age for environmental site potentials (ESPs).
Coefficients (Standard errors)
North Pacific Maritime and Hypermaritime (NPHM-M)
Dead biomass (kg C m2) (linear)
NPHM western redcedar – western hemlock*
NPM mesic-wet Douglas-fir-western hemlock*
NPM dry-mesic Douglas-fir-western hemlock*
n
96
140
152
b0
3.0 (0.7)a
3.9 (0.5)b
2.5 (0.5)a
b1
0.019 (0.003)a
0.011 (0.003)b
0.011 (0.002)b
North Pacific (NP)
Dead biomass (kg C m2) (linear)
NP mesic western hemlock -silver fir*
n
439
b0
2.64
b1
0.010 (0.001)
Dead biomass (kg C m2) (non-linear)
NP mountain hemlock-subalpine parkland
NP dry-mesic silver fir-western hemlock-Douglas-fir*
n
133
176
D0
3.9 (2.0)a
4.7 (1.6)a
kD1
0.009 (0.011)a
0.034 (0.024)a
Eastern Cascades Rocky Mountain (EC-NRM)
Dead biomass (kg C m2) (linear)
EC mesic mixed-conifer*
NRM subalpine woodland and parkland*
NRM mesic montane mixed-conifer*
n
333
47
118
b0
1.41 (0.27)a
0.29 (0.39)a
1.17 (0.46)a
b1
0.008 (0.001)a
0.007 (0.003)a
0.013 (0.004)a
Dead biomass (kg C m2) (non-linear)
NRM dry-mesic montane mixed conifer*
RM spruce-fir forest and woodland*
n
1105
203
D0
5.1 (1.2)a
8.1 (4.7)a
kD1
0.067 (0.021)a
0.084 (0.077)a
Dmax
5.6 (2.6)a
8.9 (2.6)a
kD2
0.006 (0.007)a
0.005 (0.004)a
cD
4.0 (9.4)a
1.5 (1.1)a
Dmax
3.1 (0.8)a
3.9 (0.7)a
kD2
0.007 (0.006)a
0.011 (0.011)a
cD
1.3 (0.7)a
1.1 (1.2)a
Note: Letter superscripts indicate differences between model coefficients within groups of ESPs (NPHM-M, NP, EC-NRM) based on the ANCOVA (p < 0.10). Only models for
which the lack of fit test was not significant were compared.
*
Lack of fit test was not significant suggesting the nonlinear model is appropriate (p < 0.10).
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
805
Fig. 5. Aboveground wood net primary productivity (wood ANPP, dashed line) and total NPP (solid line) as a function of stand age for four environmental site potentials in the
North Pacific Hypermartime – Maritime (NPHM-M) group. Points are mean total NPP for the age bins and error bars are one standard deviation. Stand ages were binned into
10-year age classes for ages <300 years and 20-year age classes for ages >300 years. All ages >600 years were grouped into a single bin. Age-class means are shown for
simplicity; models were fit to original data not means.
Fig. 6. Aboveground wood net primary productivity (wood ANPP, dashed line) and total NPP (solid line) as a function of stand age for four environmental site potentials in the
North Pacific (NP) group. Points are mean total NPP for the age bins and error bars are one standard deviation. Stand ages were binned into 10-year age classes for ages
<300 years and 20-year age classes for ages >300 years. All ages >600 years were grouped into a single bin. Age-class means are shown for simplicity; models were fit to
original data not means.
The EC-NRM group had the largest range of values for the
NPPmax parameter compared to the other two groups and many
of the parameters of the ESP-scale models differed significantly
(Table 4). Mesic montane mixed-conifer had the highest NPPmax
(0.74 kg C m2 yr1) and the subalpine woodland and parkland
ESP had the lowest NPPmax (0.37 C m2 yr1). Despite the large
range and significant differences in NPPmax, the range of values
for agemax was smaller (148–297 yrs) and agemax did not differ significantly between ESPs in the EC-NRM group (Table 4).
Models of wood ANPP followed a similar pattern to those of total NPP (Figs. 5–7), but with lower agemax for most ESPs. For ESPs in
the NPHM-M group, agemax for wood ANPP was lower by 20–
30 years (20–40%) with most ESPs in this group reaching NPPmax
in about 50 years. For ESPs in the NP group, agemax was lower by
14–77 years (14–52%). For ESPs in the EC-NRM group, agemax was
lower by 4–150 years (5–56%), but agemax for wood ANPP was high
for ESPs in this group, and two ESPs had a higher agemax for wood
ANPP than for NPP. For ESPs in this group that had a lower agemax
for wood ANPP, the range was 108–195 years. NPP of fine roots
showed little relationship with stand age in most ESPs, but some
ESPs had an initial increase in fine root NPP in young forests and
reached an asymptote at older ages. NPP of understory decreased
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C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
Fig. 7. Aboveground wood net primary productivity (wood ANPP, dashed line) and total NPP (solid line) as a function of stand age for seven environmental site potentials in
the Eastern Cascades Rocky Mountain (EC-NRM) group. Points are mean total NPP for the bins and error bars are one standard deviation. Stand ages were binned into 10-year
age classes for ages <300 years and 20-year age classes for ages >300 years. All ages >600 years were grouped into a single bin. Age-class means are shown for simplicity;
models were fit to original data not means.
with stand age in all ESPs. For most ESPs, NPP of foliage followed a
similar pattern to that of total NPP and wood ANPP, a rapid
increase in young forests to a peak, followed by a decline at older
ages.
4. Discussion
4.1. The importance of scale in quantifying forest carbon dynamics
Models of live biomass accumulation and NPP with forest age
generally fit the data better at the finer scale of ESPs than at the
coarser scale of ecosections, suggesting that variability in species
composition, disturbance regimes, and climatic controls is too high
at the scale of ecosections to capture age-based C dynamics.
Temporal patterns of C dynamics are better captured at the scale
of forest types within ecosections, which reduces the variability
in biomass and NPP driven by variability among ESPs in climate,
elevation, disturbance regimes, and species composition (i.e.
species-specific differences in productivity, potential mass, and
longevity). The significantly different values among ESPs for NPPmax
and agemax provide further evidence that the finer-scale ESP classification more effectively captures the variability in C uptake potential across the forested region, particularly in the Eastern Cascades
and Okanogan Highlands where differences were greatest. Relative
to live biomass and NPP, model fit of the U-shaped pattern of dead
biomass C accumulation depended more on the ecosection and forest type and less on the scale of analysis.
Model fit did not improve at the ESP-scale for three of four ESPs
in the NPHM-M group (primarily in the Coast Range or low elevations of the Western Cascades, Fig. 2), likely because of limited
inventory data for some forest types and age classes in this region.
The inventories did not include the National Park Service ownership, which is approximately 40% of the area in the Coast Range
ecosection and includes much of the area in older age classes. Thus
stand ages >350 years were underrepresented in these ESPs. The
limited representation of older age classes might also explain the
relatively low values for Lmax in the Coast Range and NPHM-M
group and the lack of fit for NPP models, which appear to overestimate the late-succession decline in NPP.
4.2. Observed patterns compared to succession theory
For all ESPs with high Lmax, the shapes of the live biomass models suggest that although the rate of live biomass C accumulation
slows with succession, it remains substantial in mature forests
(Figs. 2–4). The age at which 90% of Lmax was reached was
>300 years for these ESPs. Furthermore, the shapes of the models
and old values of age90max suggest that an asymptote may not exist
in these forests. This result contradicts similar studies in the PNW
that have proposed that live biomass accumulation stabilizes in
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C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
Table 4
Coefficients of models of net primary productivity (kg C m2 yr1) as a function of forest age for environmental site potentials (ESPs).
Coefficients (Standard errors)
n
NPPmax
agemax
kN
North Pacific Maritime and Hypermaritime (NPHM-M)
NPHM sitka spruce
NPHM western redcedar-western hemlock*
NPM mesic-wet Douglas-fir-western hemlock
NPM dry–mesic Douglas-fir-western hemlock
261
247
457
215
0.94
0.86
0.92
0.75
(0.02)
(0.02)
(0.02)
(0.02)
85
103
74
81
(5)
(6)
(3)
(5)
1.34
1.49
1.39
1.50
(0.06)
(0.05)
(0.05)
(0.09)
North Pacific (NP)
NP mountain hemlock-subalpine parkland*
NP mesic western hemlock-silver fir*
NP riparian forest*
NP dry-mesic silver fir-western hemlock-Douglas-fir*
137
515
121
188
0.65
0.60
0.80
0.59
(0.03)a
(0.01)a
(0.03)b
(0.02)a
543(243)a
149 (12)a
65 (7)b
145 (14)a
2.13
2.01
1.43
1.73
(0.43)ab
(0.13)a
(0.15)b
(0.14)ab
339
1113
47
121
52
201
30
0.59
0.48
0.37
0.74
0.53
0.55
0.56
(0.01)a
(0.01)b
(0.07)b
(0.05)c
(0.06)abd
(0.02)d
(0.06)ad
185 (39)a
268 (74)a
297 (466)a
240 (179)a
198 (136)a
271 (113)a
148 (31)a
2.13
2.58
2.07
2.46
1.86
2.29
1.07
(0.33)a
(0.33)a
(1.42)ab
(0.84)a
(0.64)ab
(0.57)a
(0.31)b
Eastern Cascades Rocky Mountain (EC-NRM)
EC mesic mixed-conifer*
NRM dry-mesic montane mixed conifer*
NRM subalpine woodland and parkland*
NRM mesic montane mixed conifer*
NRM ponderosa pine woodland*
RM spruce-fir forest and woodland*
RM montane riparian forest*
Note: Letter superscripts indicate differences between model coefficients within groups of ESPs (NPHM-M, NP, EC-NRM) based on the ANCOVA (p < 0.10). Only models for
which the lack of fit test was not significant were compared.
*
Lack of fit test was not significant suggesting the nonlinear model is appropriate (p < 0.10).
mature stands (e.g. Janisch and Harmon, 2002) because of increasing mortality or decreasing productivity (Ryan et al., 1997). The
continued accumulation of live biomass may be attributed to the
higher rates of NPP observed in older stands or low rates of mortality in these ESPs.
The lack of fit for the U-shaped model of dead biomass accumulation in the Coast Range, Western Cascades, and associated
ESPs suggests that dead biomass C accumulation in forests of
the western PNW is less related to forest age than are other C
dynamics (Spies et al., 1988; Nonaka et al., 2007; Hudiburg
et al., 2009). Other factors are likely more important drivers of
temporal patterns of dead biomass, such as disturbance type, frequency, and severity (Nonaka et al., 2007), and this information is
needed to explain temporal patterns of dead biomass C accumulation. The U-shaped pattern is more evident in even-aged forests
that initiated from stand-replacing fire (Agee and Huff, 1987;
Spies et al., 1988; Kashian et al., 2013), but even in these forests,
little of the variability in dead biomass can be explained by age
because of the variability introduced by pre-fire stand conditions
and on-going mortality (Kashian et al., 2013). The linear models
of dead biomass C accumulation observed in some ESPs are likely
because timber harvests, rather than fire, were the dominant
stand-initiating disturbance in western Washington in the last
century. Thus legacy dead biomass was not detectable in young
stands (Wimberly, 2002). The U-shaped model was more evident
in forests of the Eastern Cascades and Okanogan Highlands where
more stands likely initiated after fire. Similarly, Hudiburg et al.
(2009) found that the U-shaped pattern of dead biomass accumulation was difficult to detect and that the model fit better in the
eastern Cascades than in the western Cascades or Coast Range.
ESP-specific models of NPP in the NPHM-M group showed a
peak at young ages followed by a large decline in older forests,
but most other ESPs showed a smaller decline in NPP in older forests relative to the declines observed in previous studies (Ryan
et al., 1997; Pregitzer and Euskirchen, 2004). These smaller declines were evident for both total NPP and wood ANPP, although
the peak occurred at younger ages for wood ANPP. A lack of decline
was especially evident for forests in EC-NRM group, in which NPP
did not begin to decline for 150–300 years (108–195 years for
wood ANPP) and after the peak, NPP still remained high in older
forests. This lack of a large decline in NPP in older forests probably
can be attributed to the inability of the stand-age theory to quantify one value for age in uneven-aged forests with frequent disturbances that are not stand-replacing. In these forests, NPP remains
high in older forests because trees continually regenerate following
low-severity disturbances. Further evidence of this is the higher
variability in NPP observed for forest ages near and after NPPmax,
especially in ESPs in which NPP did not decline greatly in older
forests.
Differences in quantifying forest age might explain differences
in observed temporal patterns of NPP relative to theory and results
from flux tower sites or from forest chronosequences that use fewer stands but where the year of the stand-initiating disturbance is
known (e.g. Kashian et al., 2013). Similar to other recent studies
(e.g. Van Tuyl et al., 2005), we used forest chronosequences that
are based on a large number of stands, but the year of the standinitiating disturbance is not known precisely for the sites. As an
alternative, forest ages are estimated based on the distribution of
measured tree ages in each stand, which is likely a different value
for stand age than time-since-disturbance (Bradford et al., 2008).
This method of calculating forest age represents time-since-disturbance less effectively in stands with a few old trees (>400 years)
that are legacies from the stand-replacing disturbance because
the ages of these trees skew the stand age. This method also causes
stands with bimodal distributions of tree ages to be defined by the
mean, which overestimates the age of young stands and underestimates the age of old stands (Bradford et al., 2008). Regardless
of the methods, quantifying stand age does not account for lowseverity disturbances, which contribute to the observed variability
in biomass and NPP, particularly in stand ages of 100–300 years in
the Eastern Cascades and Okanogan Highlands. Thus the empirical
models for these forest types represent temporal C trajectories for
more dynamical systems than were envisioned by the original
theory.
4.3. Differences in Indicators of C storage potential and productivity
among ESPs
Differences among ESPs in indicators of the potential to store
C (i.e. maximum potential biomass and the time required to
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approach maximum) were generally consistent with differences in
climate and species composition, with a few notable exceptions.
For ESPs in the NP and NPHM-M, Lmax was greatest in ESPs with
higher mean annual precipitation, providing supporting evidence
that live biomass in this region is positively correlated with water
balance (Gholz, 1982). The riparian forest had the lowest Lmax
among ESPs in the NP and NPHM groups, which can be attributed
dominance of deciduous species, which have lower potential mass
than conifer species in this region. The high Lmax in the mountain
hemlock-subalpine parkland and the lack of significant differences
between that ESP and mesic western hemlock-silver fir (lower elevation) were unexpected given the relatively colder temperatures
and shorter growing season in the mountain hemlock-subalpine
parkland. In subalpine forests of the PNW, biomass accumulation
and productivity are negatively correlated with winter temperatures and positively correlated with summer temperatures (Gholz,
1982; Graumlich et al., 1989). Mountain hemlock and subalpine fir
also have smaller potential mass and shorter lifespans than coniferous species in low-elevation ESPs. The relatively high Lmax in the
mountain hemlock-subalpine parkland may be because of error
associated with the biophysical modeling of this ESP. Species composition of plots within this ESP included 50% (by stem density) Pacific silver fir and western hemlock, in addition to subalpine fir and
mountain hemlock. Furthermore, the low-elevation area of the ESP,
which includes more western hemlock and Pacific silver fir, is better represented in the inventory by a higher density of plots.
In the EC-NRM group, maximum live biomass C was lowest in
the dry (ponderosa pine woodland and savanna) and cold (subalpine woodland and parkland) extremes of the range, suggesting
that both water and temperature limit C accumulation in forests
of central and eastern Washington. EC mesic mixed-conifer, which
had the highest Lmax of the EC-NRM group, also had the warmest
minimum January temperature and moderate precipitation. The
lack of significant differences in Lmax between the other ESPs in this
group suggests that climatic controls on C accumulation may be
similar across the region (Bradford et al., 2008).
Parameters of the ESP-scale models of NPP indicate a consistent
pattern with variation in climate, providing supporting evidence
that NPP in the forested region of Washington is positively related
to mean annual precipitation and minimum winter temperature
(Gholz, 1982). ESPs with higher precipitation and minimum January temperature had higher NPPmax that was reached at younger
ages. In contrast, ESPs with either lower annual precipitation or
colder minimum January temperature had lower NPPmax that was
reached at older ages. Although NPPmax was generally lower in
the EC-NRM group than in the other groups, differences in NPPmax
and agemax among ESPs showed similar patterns with climate to
those observed for the study area as a whole. The two ESPs with
the warmest minimum January temperature and moderate precipitation had the highest NPPmax. In contrast, ESPs with the coldest
minimum January temperature (subalpine woodland and parkland) and the lowest mean annual precipitation (ponderosa pine
woodland and savanna and dry-mesic montane mixed conifer)
had the lowest NPPmax.
Our results indicate that the range of maximum live biomass C
of forests in Washington is similar to the range for temperate forests as whole. For temperate forests globally, Pregitzer and Euskirchen (2004) found that live biomass in the oldest age class (121–
200 years) had an interquartile range of 10–45 kg C m2 (median
of 18 kg C m2). In our study, ESPs in the NPHM-M and NP group
had greater maximum live biomass than the median for temperate
forests. In contrast, maximum live biomass values for ESPs in the
EC-NRM group were less than the median for temperate forests,
and some were less than the lower quartile. Pregitzer and Euskirchen (2004) found that maximum NPP for temperate forests had
an interquartile range of 0.6–1.1 kg C m2 yr1 (median of
0.80 kg C m2 yr1). The ESPs in the NPHM-M and NP group had
maximum NPP similar to the median of temperate forests. In contrast, ESPs in the EC-NRM group had values of maximum NPP less
than the lower quartile.
4.4. Uncertainty in estimates of biomass and net primary productivity
There are several sources of uncertainty in estimating C pools
and fluxes from FIA data, although it is difficult to quantify the relative contribution of each source. Sources of uncertainty in this
study are limitations associated with allometric equations for estimating biomass, which are extrapolation errors (i.e. using equations with predictor variables outside the range of data on which
the equation was developed) and substitution errors (i.e. using
equations for different species or geographic regions). These
sources of uncertainty affect some forest types and biomass pools
more than others. Species most affected by substitution uncertainty are those not historically used for timber production – species that grow at high elevations and hardwoods – because fewer
equations are available for these species. Biomass estimates for
these species are also more susceptible to errors associated with
extrapolation because allometric equations developed for non-timber species are typically based on narrower ranges of diameter and
height. Most allometry for tree biomass has been developed for
species in the Western and Eastern Cascade ecosections, thus
uncertainty associated with geographic substitutions is greater
for the Okanogan Highlands and Coast Range.
Estimates of aboveground biomass C pools are more certain
than estimates of belowground biomass C pools because of the lack
of allometric equations for belowground biomass. Despite this
uncertainty, excluding estimates of belowground biomass would
greatly affect results because belowground biomass can be as
much as 20% of total live biomass. Therefore, we included estimates of belowground biomass and minimized uncertainty in
two ways. First, we used allometric equations for four species,
rather than a single species (e.g. Van Tuyl et al., 2005), because
the use of equations developed for different species may reduce
the uncertainty associated with species and geographic substitutions. Different species can have different root structures that
may be poorly captured by allometry of a single species (Kimmins,
1997). Second, we adjusted biomass estimates with species-specific values for wood density, reducing error associated with substitutions (Van Tuyl et al., 2005).
Estimates of understory biomass are more variable than estimates of tree biomass. We made the best estimate of understory
biomass possible given the limitations of the data and allometric
equations for estimating biomass of understory species. Understory biomass is typically only 1–3% of total live biomass in forest
ecosystems, so uncertainty in estimates of this pool is unlikely to
affect the overall models of live biomass accumulation with forest
age.
Another source of uncertainty in estimating biomass pools is
the limited availability of some biomass components in the periodic FIA and CVS inventories. The estimate of dead biomass C in
this study does not include C stored in stumps, woody debris
<7.6 cm for CVS plots and <12.5 cm for FIA plots, and the soil organic layer because these pools were not sampled in the periodic
inventory. The objective of our study was to quantify age-based
patterns of C dynamics, not to account for total biomass in these
forest ecosystems, thus including these components in the estimate of the dead biomass C would affect the magnitude of estimates, but is unlikely to affect the temporal patterns of biomass
during succession.
We reduced uncertainty in estimating NPP from inventory data
by estimating NPP for each ecosystem component based on the
process that drives most production for that component. This
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
method may underestimate NPP, however, because it does not account for all processes that contribute to NPP. For the tree wood
component, calculating NPP using a back-calculation of previous
biomass does not account for production by trees that are dead
at the time of the inventory but were alive during the period over
which NPP is estimated. For the foliar biomass component, estimating NPP using leaf turnover rates assumes that foliar biomass
has reached a steady state and does not account for production
associated with increasing crown dimensions in young trees. NPP
of fine roots is the most uncertain component of NPP because it
is highly variable spatially and temporally, yet it can be 10–60%
of annual NPP in forest ecosystems (Kimmins, 1997). We estimated
NPP of fine roots using a method that takes advantage of the relationship between fine root NPP and water availability, and thus is
based on ecological theory. NPP of fine roots (absolute basis) is
greater in moisture-limited environments because plants allocate
more C to roots to acquire more water (Santantonio and Hermann,
1985). Certainty in estimates of NPP of understory biomass was
limited by the data, which did not include measurements of radial
growth increment of woody shrubs. Therefore, we could not use
the back-calculation method for calculating NPP. Our assumptions
for calculating understory NPP may over-estimate NPP of herbaceous vegetation and underestimate shrub NPP, but production
of understory vegetation is generally only 1–3% of total NPP.
4.5. Data needs for improved models of age-based forest C dynamics
The methods used in this study to quantify age-based patterns
of C dynamics could be applied to any forested region for which the
minimum data requirements are available: forest inventory data,
allometric equations for biomass, and spatial data layers of potential vegetation. These data are available for the US, allowing the
methods to be easily applied nationally. We used publicly available
USFS forest inventory data and spatial data layers with continuous
coverage across the US (available at http://www.landfire.gov/products_national.php). Although more species-specific allometric
equations are available for the PNW than for other forested regions, biomass estimates can be made using a national database
of allometric equations for general species groups in North America (Jenkins et al., 2004).
Recent changes in FIA sampling protocols will make FIA data
even more useful for quantifying temporal C dynamics in forested
809
ecosystems. Additional ownerships, including USDI National Park
Service lands, are included in the more recent inventories, which
will fill some age-class and forest-type gaps. The recent annual
FIA data include measurements of fine woody debris (FWD), forest
floor organic material, understory vegetation, and soil (Woodall
et al., 2010), allowing for the quantification of additional ecosystem C pools. Additional data on fine woody debris would improve
models of dead biomass accumulation because fine woody debris
can be abundant in young stands after disturbance and may contribute to the U-shaped pattern with stand age (Agee and Huff,
1987). More complete and consistent sampling of understory vegetation will improve estimates of biomass and productivity. Measurements of the basal diameter of shrubs would improve
estimates of shrub biomass and productivity because many species-specific allometric for shrub species require basal diameter
as a predictor variable. As more plots in the FIA program are remeasured, NPP can be better estimated based on changes in biomass between inventory years, rather than back calculations.
Development of more allometric equations for estimating biomass of non-timber species and belowground tree components
would improve estimates of forest C stocks and fluxes. Allometric
equations for high-elevation species will be especially important
for quantifying changes in C stocks and fluxes as climate changes
because high-elevation forests are expected to be more sensitive
to climate change (McKenzie et al., 2001). Despite its potentially
large contribution to total biomass, biomass of tree roots is often
not included in forest C accounting because of the limited data
and allometric equations for estimating this component. Furthermore, additional information on factors that control resource allocation between aboveground and belowground C is need to better
quantify belowground C stocks (e.g. Litton et al., 2004).
4.6. Implications for ecological modeling and forest management
The ESP-specific models of age-based C dynamics quantified in
this study can be used to assess the effects of succession, disturbance, and landscape age-class distributions on C storage for large
geographic areas composed of different forest types. This approach
has been used previously for hypothetical landscapes, disturbance
regimes, and age-class distributions (Euskirchen et al., 2002; Kashian et al., 2006; Smithwick et al., 2007), but the empirical models of
quantified in this study can be used to estimate the C budget for
Fig. 8. The relationship between the age of maximum NPP and the age at which 90% of maximum live biomass C accumulates for 15 environmental site potentials (ESPs). All
points below the one to one line represent a tradeoff between managing for maximum NPP (uptake) and maximum live biomass C storage. Points near the one to one line
indicate a potential optimum age for forest C management that balances C uptake and storage.
810
C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811
the forested area of Washington when combined with current or
potential future disturbance regimes and associated age-class distributions. This approach can be used as an empirical alternative to
biogeochemical process models, or the age-based models can be
used to calibrate process models. Biogeochemical process models
are useful for simulating C dynamics over large geographical areas
and accounting for direct effects of climate on C dynamics (e.g.
Bachelet et al., 2001), but they have some notable limitations. Process models typically have a limited representation of disturbance
effects on forest age and C dynamics. Taxonomic resolution is limited to plant functional types that do not account for the influence
of species attributes on C dynamics or disturbances. Furthermore,
process models require many assumptions and parameters, and
can be highly sensitive to parameter choices, which are often subjective because true parameters are unknown (Lenihan et al., 2008;
Rogers et al., 2011).
Management of forests for C uptake and storage can be informed by data on maximum potential biomass and productivity
by forest type and ages at which these maxima are reached. This
information can be used to identify forest types with the greatest
potential to store additional C and to establish baselines against
which the additionality through forest management can be assessed. Carbon storage is only one of many objectives that forest
managers might consider (McKinley et al., 2011), but this information can help optimize multiple management objectives. Carbon
storage and uptake potential and the timing of maximum C storage
and uptake varied widely among forest types in our study. Forest
ecosystems with the greatest potential for long-term C storage in
live and dead biomass are moist forests of the western Cascades
(mesic western hemlock-Pacific silver fir-Douglas-fir forests),
although more data are needed to quantify C storage potential in
older sitka spruce and western redcedar forests. Forest ecosystems
with the greatest C uptake are 65–100-year-old mesic western
redcedar/western hemlock forests and riparian forests. The data
suggest maximum C uptake might be higher in 75–85-year-old sitka spruce and wet Douglas-fir-western hemlock forests, but models for these ESPs were not statistically significant.
The ESP-specific models of C dynamics suggest a trade-off between managing forests for maximum C uptake vs. maximum C
stocks in some forest types and an optimum age for C management
in other forest types (Fig. 8). In the NP group of ESPs, the age of
maximum NPP (i.e. C uptake) was reached several decades, and
for some ESPs, centuries, before 90% of maximum live C storage.
Therefore, managing for maximum live biomass C storage in these
ESPs would require a substantially different age-class distribution
than managing for maximum NPP. In contrast, most ESPs in the
EC-NRM group reached maximum NPP and 90% of live biomass C
storage at similar ages, suggesting an optimum rotation age for forest C management might exist in these ESPs. Some ESPs in this
group (e.g. NRM ponderosa pine woodland and savannah) show
an optimum age of approximately 200 years and other ESPs (e.g.
RM subalpine spruce-fir) have an optimum age of approximately
300 years. This relationship between the age of maximum NPP
and 90% of live biomass C suggests a potential optimum of
100 years for ESPs in the NPHM-M group, but this should be interpreted cautiously given the poor fit of models in this group and the
lack of data from older forests. This relationship considers only NPP
and the storage of C in live biomass; C storage in dead biomass continues beyond the age of maximum live biomass and is an important C storage pool in ecosystems with high biomass and slow rates
of decomposition.
The empirical models of C dynamics quantified in our study can
be used to inform management of C stocks and fluxes for large geographical areas, rather than managing C at the scale of individual
forest stands. These models can be combined with spatial data
on forest age to quantify C stocks and NPP over large areas
(Hudiburg et al., 2009). They can validate remotely sensed data
on biomass and NPP or augment remotely sensed data with
estimates of surface and belowground ecosystem C pools, which
are difficult to estimate with remote sensing. Empirical models of
C dynamics can also inform optimal intervals for natural or
anthropogenic disturbances to meet C management objectives
and the potential gains in C storage that can be achieved by
increasing intervals (e.g. Euskirchen et al., 2002; Smithwick et al.,
2007). Similarly, these age-based models can be combined with
projected changes in disturbance intervals to estimate changes in
C stocks and NPP as a function of the age-class distribution that
would be expected under a new disturbance regime (e.g. Raymond
and McKenzie, 2012).
Acknowledgements
This publication was partially supported by the Joint Institute
for the Study of the Atmosphere and Ocean (JISAO) under NOAA
Cooperative Agreement No. NA17RJ1232 and NA10OAR4320148.
Additional funding came from the USDA Forest Service Pacific
Northwest Research Station and the USGS Global Change Research
Program. This publication is a product of the Western Mountain
Initiative. James K Agee, David L Peterson, Sean Healey, and Maureen Kennedy provided helpful comments on an early draft of this
manuscript. Robert Norheim created figure 1.
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