Forest Ecology and Management - Home | US Forest …Temporal carbon dynamics of forests in Washington, US: Implications for ecological theory and carbon management Crystal L. Raymonda,

Forest Ecology and Management 310 (2013) 796–811

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Forest Ecology and Management

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Temporal carbon dynamics of forests in Washington, US: Implicationsfor ecological theory and carbon management

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⇑ Corresponding author. Present address: City of Seattle, Seattle City Light, 700Fifth Avenue, Suite 3200, Seattle, WA 98124, USA. Tel.: +1 2063861620.

E-mail address: [email protected] (C.L. Raymond).

Crystal L. Raymond a,⇑, Donald McKenzie b

a School of Forest Resources, University of Washington, Box 352100, Seattle, WA 98195-2100, USAb 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 a b s t r a c t

Article history:Received 12 April 2013Received in revised form 12 September2013Accepted 13 September 2013Available online 13 October 2013

Keywords:CarbonBiomassNet primary productivityForest managementPacific Northwest

We quantified carbon (C) dynamics of forests in Washington, US using theoretical models of C dynamicsas a function of forest age. We fit empirical models to chronosequences of forest inventory data at twoscales: a coarse-scale ecosystem classification (ecosections) and forest types (potential vegetation) withinecosections. 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 parame-ters among ecosections and among forest types to determine differences in potential C storage anduptake.

Models of live biomass C accumulation and NPP fit the data better at the scale of forest types, suggest-ing this finer scale is important for reducing variability. Model fit for dead biomass C accumulationdepended more on the region than on the scale of analysis. Dead biomass C was highly variable and arelationship with forest age was found only in some forest types of the eastern Cascades and OkanoganHighlands. Indicators of C storage potential differed between forest types and differences were consistentwith expectations based on spatial variability in climate. Across the study area, maximum live biomass Cvaried from 6.5 to 38.6 kg C m�2 and the range of ages at which 90% of maximum is reached varied from57 to 838 years. Maximum NPP varied from 0.37 to 0.94 kg C m�2 yr�1 and the age of maximum NPP var-ied from 65 to 543 yrs. Forests with the greatest C storage potential are wet forests of the western Cas-cades. Forests with the greatest potential NPP are 65–100-year-old mesic western redcedar-westernhemlock forests and riparian forests, although limited data suggest maximum NPP of coastal sitka spruceforests may be even greater. The observed relationship between the ages at which maximum NPP andmaximum live biomass are reached for a given forest type suggests that there is a trade-off between man-aging for maximum live biomass (storage) vs. NPP (uptake) in some forest types but an optimal age for Cmanagement in others. The empirical models of C dynamics in this study can be used to quantify theeffects 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 disturbanceregimes on C sequestration, providing an alternative to biogeochemical process models and stand-scalemethods.

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1. Introduction

Evidence of climate change caused by anthropogenic emissionsof greenhouse gases, including carbon dioxide (CO2), has increasedinterest in quantifying spatial and temporal patterns of forestcarbon (C) dynamics. This information can be used to alter forestmanagement to retain current C stocks or store more C in forests.Most research on C dynamics in forest ecosystems has focused

on quantifying the contribution of these systems to the terrestrialC sink (Goodale et al., 2002; Pacala et al., 2001) and projecting howthis sink will change over time with changes in climate and landuse (Bachelet et al., 2001; Hurtt et al., 2002). Research on the po-tential of forest ecosystems to take up CO2 and store C can helpdetermine to what extent forest management can enhance C stor-age (Birdsey et al., 2006). Although C storage is only one of manyfactors considered in forest management, its importance is increas-ing as an ecosystem service with local-to-global significance.

Ground-based inventories and remotely sensed data quantifythe current magnitude and spatial distribution of C stocks inforests at regional (Hicke et al., 2007), national (Heath et al.,

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2011), and continental scales (Goodale et al., 2002; SOCCR, 2007).A limitation of these studies is that they represent single points intime and must be updated with additional inventory or remotelysensed data as disturbances affect forest C by releasing C to theatmosphere, redistributing C within ecosystems, and altering theage-class distribution over large areas (Kurz et al., 2008). Changesin C stocks over time, or fluxes, can be simulated with mechanisticprocess models, but these models require an understanding of howC stocks and fluxes change with disturbances and time sincedisturbance.

Empirical models of C stocks and fluxes as a function of forestage quantify the temporal changes in C dynamics following distur-bance. These empirical models have multiple uses for understand-ing, modeling, and managing changes in C with forest succession,disturbance, and management. They can be used to project the ef-fects of natural disturbances on forest C and post-disturbance Ctrajectories (Raymond and McKenzie, 2012). They have been useddirectly 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 anthropogenicdisturbances to meet C storage objectives (Euskirchen et al., 2002).

Patterns of C stocks and fluxes with succession are documentedin the theory of ecosystem ecology, but empirical data are neededto identify parameters in the theoretical models. Live biomassaccumulates rapidly in young forests, the rate of accumulation de-clines after peak production, and live biomass reaches a maximumin 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 clo-sure), and then declines in old forests (Kira and Shidei, 1967; Gow-er et al., 1996; Ryan et al., 2004). Temporal patterns of deadbiomass have been studied in forests of the Pacific Northwest(PNW) because of the relatively high proportion of biomass storedin dead pools (Smithwick et al., 2002). Immediately after a stand-initiating disturbance, young forests have high levels of dead bio-mass (i.e. legacy biomass). Dead biomass declines as this legacybiomass decomposes, and then increases again with tree mortalityin old forests. This combination of legacy biomass and increasingmortality creates, in theory, a U-shaped pattern of dead biomassaccumulation during succession.

These theoretical patterns of C dynamics have been quantifiedin forest ecosystems but typically at the coarse spatial scales ofbiomes (Pregitzer and Euskirchen, 2004) or regions (Hudiburget al., 2009), or at the scale of individual forest types but for onlya few sites (e.g. Law et al., 2003). Patterns based on the theory oflive biomass accumulation with forest age have been quantifiedin tropical (Ryan et al., 2004), boreal (Bond-Lamberty et al.,2004), and temperate forests (Hudiburg et al., 2009; Masek andCollatz, 2006; Van Tuyl et al., 2005). Similarly, the temporal patternof peak and decline in NPP has been quantified at the scale of forestbiomes (Pregitzer and Euskirchen, 2004). The U-shaped pattern ofdead biomass with succession has been observed in temperateconiferous 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 rarelyquantified at the scale of forest types within regions and biomes, ascale that is most useful for evaluating effects of disturbance inter-vals and for forest management. Thus the objective of this studywas to quantify patterns of C dynamics as a function of forestage at two scales: the coarse scale of ecological regions (ecosec-tions, Bailey, 1995) and the finer scale of forest types within eco-sections. We quantified three temporal patterns (accumulation oflive and dead biomass C and NPP) by fitting empirical models,the forms of which are based on ecological theory, to forestchronosequences using data from the USDA Forest Service Forest

Inventory and Analysis (FIA) program. We hypothesized that theo-retical patterns would be detectable at both scales, but that the fi-ner scale of forest types might reduce variability in coarse-scalemodels attributed to differences in climate and species composi-tion, thereby yielding better fitting models. We fit models forecosections and forest types for an 8.3 million hectare forestedregion of Washington US, an area for which these patterns havenot been previously quantified at either scale. This area serves asa useful domain because of the high diversity of forest types overa relatively small geographic area. Large gradients in climate andelevation give rise to high diversity of species assemblages, distur-bance regimes, and thus potential productivity and C storage. Weused FIA data because they provides a semi-systematic sample offorest conditions throughout the US, covering a wide variety offorest types with different drivers of productivity and C storage.Furthermore, the large FIA dataset provides many sites per foresttype, capturing a representative range of conditions within a singleforest type.

Our second objective was to quantify how key indicators of Cstorage potential and the timing of C storage and uptake differamong ecosections and among forest types. We compared param-eters of the empirical models for differences in five indicators of Cstorage potential: (1) maximum live biomass, (2) stand age atwhich 90% of maximum is reached, (3) maximum NPP, (4) standage of maximum NPP, and (5) maximum dead biomass. We ex-panded on previous research that quantified relationships betweenclimatic zones and maximum biomass and NPP (Gholz, 1982;Smithwick et al., 2002) in two ways. First, we used a much largerdataset that includes more sites that capture the range of condi-tions within a forest type. Second, we quantified differences inmaximum biomass accumulation and NPP, but also in their timing,among ecosections and forest types, and compared these empiricalpatterns to ecological theory.

Steady-state theories of ecological succession as describedabove have been criticized because they account for only the ef-fects of autogenic development on biomass and productivity with-out considering the effects of exogenous disturbances (Bormannand Likens, 1979). In this study, we use this theory as a basis forquantifying autogenic development of C stocks and fluxes, but thisdoes not imply that disturbances are unimportant in these ecosys-tems. The empirical models quantified in this study can be com-bined with natural and anthropogenic disturbance intervals toquantify effects of disturbances on C dynamics, thus capturingthe influence of autogenic development and exogenous distur-bances on C stocks and fluxes (e.g. Raymond and McKenzie,2012). Quantifying these models at the scale of forest types is par-ticularly useful because this is the scale at which disturbance re-gimes are typically quantified.

2. Methods

2.1. Study area

The study area covers 8.3 million hectares in Washington UScomprising four primarily forested ecosections (Fig. 1) (Bailey,1995). Ecosections represent sub-regional aggregations ofbiophysical controls, such as climate and topography, on ecosys-tem processes. Climate is highly variable across the domain, espe-cially precipitation, because of the orographic effect of theOlympic and Cascade Ranges (Daly et al., 1994). The Coast Rangeecosection has a maritime climate with high annual precipitationand moderate winter and summer temperatures. Mean annualprecipitation can exceed 600 cm in west-facing valleys of theOlympic Mountains. The Western Cascades also has a maritimeclimate with high annual precipitation, but the ecosection isinfluenced less by the Pacific Ocean and has a larger elevational

Fig. 1. The study area is four forested ecosections in Washington (Coast Range, Western Cascades, Eastern Cascades, and Okanogan Highlands) and the environmental sitepotentials (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 besupported 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 PacificMaritime, NP is North Pacific, EC is Eastern Cascades, NRM is Northern Rocky Mountain, and RM is Rocky Mountain.

798 C.L. Raymond, D. McKenzie / Forest Ecology and Management 310 (2013) 796–811

gradient, and thus more seasonal variability in temperature. Thecrest of the Cascade Range divides the Western and Eastern Cas-cade ecosections. Climate of the Eastern Cascades and OkanoganHighlands is transitional from maritime to continental with lessannual precipitation, warmer summers, and colder winters. Inall ecosections, most precipitation falls between November andMay (Daly et al., 1994) and at higher elevations much of the an-nual precipitation falls as snow. Dominant soil orders are andisolsin areas of volcanic parent material, spodsols in high-elevationcool humid forests, and inceptisols in forests of the OkanoganHighlands ecosection (Table 1).

The domain of the finer-scale analysis was the area of the fourecosections, but the resolution was a 90m grid of forest types definedby potential vegetation types (Fig. 1). We used a potential vegetation

classification, rather than dominant species, because dominantspecies change during succession and our objective was to quantifytrends in C dynamics with succession. We used the environmentalsite potential (ESP) classification of the LANDFIRE project for aspatially explicit classification of potential vegetation (available athttp://www.landfire.gov/products_national.php). ESPs are thespecies assemblages that can be supported on a site based on itsbiophysical environment and absence of disturbance. Holsingeret al. (2006) modeled ESPs using empirical data on vegetationcomposition and spatial data on biophysical gradients, includingtopography, climate, soil, and ecophysiological parameters. Thenames of ESPs typically reflect three factors: regional climate, envi-ronmental or topographic setting, and dominant plant association(Comer et al., 2003).

http://www.landfire.gov/products_national.php

Table 1Area and climate for 15 environmental site potentials (ESPs) within four forested ecosections in Washington, US.

Environmental site potential Area (ha) Dominant soilorder

Mean annual precip.(cm)

Mean min. Jan. temp.(�C)

Mean max. July temp.(�C)

North Pacific Maritime and Hypermaritime (NPHM-M)NPHM sitka spruce 472,300 Andisols 275 �2.4 18.3NPHM western redcedar-western hemlock 386,372 Andisols 272 �6.6 19.5NPM mesic-wet Douglas-fir-western hemlock 728,891 Andisols 229 �5.8 20.3NPM dry-mesic Douglas-fir-western hemlock 332,955 Spodosols 222 �8.6 21.6

North Pacific (NP)NP mountain hemlock-subalpine parkland 505,777 Spodosols 249 �9.8 18.6NP mesic western hemlock-silver fir 917,408 Spodosols 261 �8.9 19.3NP riparian forest 291,257 Andisols 215 �10.1 22.6NP dry-mesic silver fir-western hemlock-

Douglas-fir303,096 Andisols 217 �8.6 18.9

Eastern Cascades Rocky Mountain (EC-NRM)EC mesic mixed-conifer forest 606,973 Andisols 98 �8.2 21.9NRM dry-mesic montane mixed conifer forest 2,174,724 Inceptisols 56 �10.1 22.9NRM subalpine woodland and parkland 139,156 Spodosols 137 �14.8 19.5NRM mesic montane mixed conifer forest 273,475 Inceptisols 94 �9.0 20.5NRM ponderosa pine woodland and savanna 267,716 Andisols 46 �9.7 23.0RM subalpine spruce-fir 379,176 Inceptisols 102 �13.5 20.3RM montane riparian forests 113,591 Andisols 57 �8.6 21.6

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 cellsthat fall within the ESP area.


Relative to the ecosection-scale classification, the ESP classifica-tion distinguishes between high- (cold) and low- (warm) elevationforests and riparian forests within the four ecosections (Table 1)and between wetter and drier forests of the Eastern Cascades andOkanogan Highlands. At the scale of ESPs, the influence of tree spe-cies attributes (e.g. maximum size and lifespan) on potential bio-mass and productivity can be detected. We grouped ESPs into 15forested ESPs that covered the greatest area. Four ESPs that coveredonly a small portion of the study area were grouped with ESPs thathad the same regional climate classification and either the sameenvironmental and topographic setting or the same plant associa-tion. The ‘‘other forest’’ type includes woodlands and forest typesthat cover less than 1% of the study area. We resampled the 30-m raster to 90-m cells using the majority resampling criterion tomake the resolution of the ESP raster consistent with the resolu-tion of inventory plots. To group plots by ESP, plot coordinateswere intersected with the ESP spatial data layer.

2.2. Inventory data

We used the forest inventory data collected as part of the USForest Service (USFS) Pacific Northwest Region Current VegetationSurvey (CVS) and FIA Program. The CVS inventory was conductedon USFS ownership and the FIA inventory was conducted onprivate, state, and some other federal ownerships (excluding USDINational Park Service). We used data from CVS plots that wereinventoried between 1993 and 2000 and FIA plots that were inven-toried between 1989 and 1991. CVS plots are 1-ha plots on a gridwith 5.5-km spacing in areas designated as wilderness and2.7-km spacing in all other areas. FIA plots are 0.4 ha plots on agrid with 5.5-km spacing in eastern Washington and 3.9 kmspacing in western Washington.

The PNW Integrated Database (IDB) version 2.0 combines datafrom the FIA and CVS inventories into a common set of variablesand formats (Waddell and Hiserote, 2005a). We selected 4126plots from the 5576 plots available in the study area based ontwo criteria: both trees and understory vegetation were sampledand the plot had a single condition. Plots with multiple conditionshave 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 classor forest type. A similar study using FIA data from Oregon andnorthern California found that excluding plots with multiple condi-tions did not affect regional temporal C dynamics (Hudiburg et al.,2009). Fewer plots were available with sufficient data to calculatedead biomass because coarse woody debris (CWD) was sampledonly in the CVS inventory of western Washington, not the CVSinventory in eastern Washington or the FIA inventory. The analysisof dead biomass included 3154 plots in which both CWD andstanding dead trees were sampled.

2.3. Estimating live biomass carbon pools

Total aboveground biomass C (kg C m�2) of tree wood compo-nents for each plot was estimated as the sum of stems, branches,and bark. Unless otherwise stated, we converted biomass to Cusing a conversion factor of 0.5 C content (Janisch and Harmon,2002). The PNW-IDB includes calculated tree-level biomass ofstems, bark, and branches, but we used calculated variables foronly stem biomass. We did not use values for bark and branch bio-mass, because we used more species- and ecosection-specificequations for estimating these components. In the PNW-IDB, stembiomass of tress with DBH >2.5 cm (kg, dry weight) was calculatedfrom stem volume (m3) (calculated with species-specific equationsbased on DBH and height) and species-specific values for wooddensity (kg m�3) (Waddell and Hiserote, 2005b). We estimatedbiomass of branches and bark with species- and ecosection-specificallometric equations that predict biomass from DBH or DBH andheight (Gholz et al., 1979; Standish et al., 1985; Means et al.,1994; Jenkins et al., 2004) with occasional substitutions if a spe-cies- or ecosection-specific equation was not available. Trees withDBH <2.5 cm were tallied by species, so we calculated total above-ground biomass of these trees using an allometric equation forconifer species with heights <4.5 m (Brown, 1978). We expandedC per tree to C per ha based on the trees per ha represented by eachtree in the sample (i.e. the expansion factor) (Waddell and Hise-rote, 2005a).

The PNW-IDB did not include foliar biomass of trees, so weestimated foliar biomass at the tree-level using a three-stepprocess based on DBH, sapwood area, leaf area per tree, and


species-specific values of specific leaf area (Smithwick et al.,2002; Raymond and McKenzie, in press). First, we calculated sap-wood area as a function of DBH using species-specific regressionequations. Second, we calculated leaf area per tree (m2) usingspecies-specific ratios of leaf area (m2) to sapwood area (cm2).Third, we converted leaf area to biomass (kg) using species-specific values of specific leaf area (cm2 g�1). We converted foliarbiomass to C using a conversion factor of 0.45 (Campbell et al.,2004) and expanded foliar biomass per tree to per hectare usingthe expansion factors.

Belowground tree components were not measured or calculatedin the PNW-IDB, so we estimated biomass of coarse roots(>10 mm) at the tree level using allometric equations that predictroot biomass from DBH or DBH and height. Few allometric equa-tions for root biomass are available, so we used equations for onlyfour species: Douglas-fir (Pseudotsuga menziesii) (Gholz et al.,1979), ponderosa pine (Pinus ponderosa) (Omdal et al., 2001), wes-tern redcedar (Thuja plicata) (Feller, 1992), and red alder (Alnus ru-bra) (Gholz et al., 1979). We used genus-level or family-levelsubstitutions for all other species and adjusted biomass estimatesusing species-specific values for wood density (Campbell et al.,2004; Van Tuyl et al., 2005). We estimated biomass of fine rootsat the plot-level using an equation that predicts fine root biomassfrom leaf area index (LAI) (Van Tuyl et al., 2005). We calculated LAI(m2 m�2) of each plot as the total leaf area of all trees in the plotdivided by plot area.

We estimated biomass of understory vegetation using allome-tric equations that predict biomass from percentage cover or per-centage cover and height. The PNW-IDB includes percentagecover and height of understory vegetation by species and lifeform (i.e. herb, graminoid, or shrub). We estimated biomass ofherbs and graminoids using a single allometric equation devel-oped from a combination of several understory species in Doug-las-fir forests of central Washington (Olson and Martin, 1981).We estimated biomass of shrubs using several species-specificequations (Ohmann et al., 1981; Olson and Martin, 1981; Smithand Brand, 1983; Alaback, 1986; Means et al., 1994). Many spe-cies substitutions were necessary, but whenever possible, wesubstituted species from the same genus or family. We appliedan equation to each species and averaged understory biomassto the plot-level.

2.4. Estimating dead biomass carbon pools

We estimated total dead biomass C (kg C m�2) for each plot asthe sum of biomass of coarse woody debris (CWD) and standingdead tree components (stem, bark, branches, and coarse roots).We used calculated fields in the PNW-IDB for biomass of CWDand dead tree stems (Waddell, 2002; Waddell and Hiserote,2005a). CWD (>7.6 cm in diameter for CVS plots, and >12.5 cm indiameter for FIA plots) was sampled using the line intercept meth-od. For CWD and standing dead trees, biomass was estimated fromvolume and species-specific wood density and reduced accordingto decay class to account for the loss of weight and density withdecomposition (Waddell, 2002). Biomass was converted to C usingconversion 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 inter-sect sampling design and C per tree stem was expanded to areausing expansion factors (Waddell, 2002; Waddell and Hiserote,2005a). The PNW-IDB did not include calculated fields for biomassof bark, branches, or coarse roots of standing dead trees. We esti-mated these biomass components using the same species- and re-gion-specific equations that we used for live trees, but we reducedthe 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 m�2 yr�1) includes twoparts: (1) production associated with increasing plant dimensionsand (2) production associated with annual replacement of planttissues (turnover). We estimated NPP for each ecosystem compo-nent as the part of productivity that drives most production forthat component. Thus, we estimated NPP of tree wood components(stems, bark, branches, and coarse roots) as the productivity asso-ciated with increasing tree dimensions and assumed turnover ofbranches and bark to be minimal. In contrast, we estimated NPPof fine roots, foliage, and understory vegetation as the productivityassociated with turnover rates.

We estimated NPP of tree wood components at the tree level asthe difference between current and previous biomass (Grier andLogan, 1977). For plots that were inventoried only once, we calcu-lated previous biomass using a back calculation of biomass10 years before the inventory year using measurements of the10-year radial growth increment and divided by 10 for annualNPP (Van Tuyl et al., 2005). For plots that were inventoried twice,we estimated annual NPP as the difference between biomass of thecurrent and previous inventories divided by number of years be-tween inventories. We estimated NPP of foliage at the tree levelas a fraction of current foliar biomass (Van Tuyl et al., 2005; Hudi-burg et al., 2009). To calculate annual NPP we divided current foliarbiomass by species- and ecosection-specific values for leaf reten-tion time (T. Hudiburg, personal communication), the averagenumber of years that a species retains foliage. We estimated pro-duction of fine roots at the stand level by multiplying total fine rootbiomass by published values of fine root turnover rates, the pro-portion of fine root biomass that is replaced annually (Santantonioand Hermann, 1985).

Production of understory biomass was difficult to estimate gi-ven the limited data on understory vegetation that were collectedin the inventory. We assumed that production of herbaceous veg-etation and graminoids was 50% of current biomass and did not in-clude an estimate for production of shrubs.

2.6. Calculating stand age

The PNW-IDB did not include disturbance history, but timesince the last stand-replacing disturbance for each plot can beapproximated with stand age. Inventory data include tree agesfrom increment cores for some or all trees in a plot. We calculatedstand age as the average age of the oldest 10% of trees in a plot. Forplots for which the oldest 10% was fewer than three trees, we cal-culated stand age as the mean age of all cored trees in the plot. Weaggregated plots into age bins to account for the inherent uncer-tainty in calculating stand ages from tree ages and to allow for rep-licates within each age bin for use in the statistical analysis. Webinned stand ages into 10-year bins for ages <300 years and 20-year bins for ages >300 years. For plots in the Western Cascadesand Coast Range ecosections, all stand ages >600 years weregrouped into the 600-year age bin. Only a few plots exceeded400 years in the Eastern Cascades and Okanogan Highlands ecosec-tions, so these plots were grouped into the 400-year bin.2.6 Statis-tical analysis

We fit non-linear regression models for the three C dynamics(live biomass C accumulation, dead biomass C accumulation, andNPP) as a function of stand age at two scales: the coarse scale ofthe four ecosections and the finer scale of the 15 ESPs (57 regres-sion models). For non-linear models, it is useful to select equationforms and parameters with ecological relevance, as well as statis-tical significance. We selected appropriate forms for the non-linearequations for each dynamic from the literature. The equation formsthat we used are grounded in ecological theory, enabling direct


inferences about differences in C storage potential and the age ofmaximum C storage and uptake among ecosystems.

We used a Chapman–Richards function (Janisch and Harmon,2002) to model accumulation of live biomass (kg C m�2) as a func-tion of stand age:

Lage ¼ Lmaxð1� e�kLageÞcL ð1Þ

where Lage is live biomass at any stand age (age), Lmax is the maxi-mum asymptotic live biomass reached in late succession, kL is anempirically derived rate constant, and cL controls the position ofthe curve between zero and the asymptote. Lmax, kL, cL are all fittedparameters. Live biomass at any age is a fraction of the asymptoticmaximum, Lmax, which can be interpreted as the maximum poten-tial storage of live biomass C for the ecosystem. The time requiredfor 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 ecosys-tems because its value depends on Lmax and cL (Yang et al., 2005).Therefore, we calculated a time parameter, age90max (years), fromthe fitted models, which is the age at which 90% of Lmax is reached.age90max is an ecologically meaningful parameter for comparing theyears required for an ecosystem to approach its theoretical maxi-mum live biomass storage.

We used the sum of two equations to model the U-shaped pat-tern for the accumulation of dead biomass (kg C m�2) as function ofstand age: (1) an exponential decay function (for decay of legacybiomass) and (2) a Chapman–Richards function (for accumulationof dead biomass from mortality in the new stand) (Janisch and Har-mon, 2002). The equation is:

Dage ¼ D0ðe�kD1ageÞ þ Dmaxð1� e�kD2ageÞcD ð2Þ

where Dage is dead biomass at any stand age (age), D0 is the initiallegacy biomass, kD1 is an empirically derived rate constant fordecomposition of legacy biomass, Dmax is the asymptotic maximumdead biomass, kD2 is an empirically derived rate constant for theaccumulation of new dead biomass, and cD controls the shape ofthe accumulation portion of the function. All parameters are fittedparameters. D0 (kg C m�2) is interpreted as the dead biomassremaining after a stand-replacing disturbance and kD1 is the rateat which it decays. The parameters of the second part of the equa-tion are interpreted the same as for accumulation of live biomass;Dmax (kg C m�2) is the maximum asymptotic storage of deadbiomass.

We used a peak function to model NPP (kg C m�2 yr�1) as afunction of stand age (Janisch and Harmon, 2002; Hudiburget al., 2009):

NPPage ¼ NPPmax � ef�0:5½lnðage=agemaxÞ=kN�2g ð3Þ

where NPPage is the NPP at any stand age (age), NPPmax is the max-imum NPP reached in early succession, kN is the rate at which thestand reaches NPPmax, and agemax is the age at which NPP beginsto decline from the maximum. NPPmax, agemax, and kN are all fittedparameters. This function represents the theory that NPP increasesrapidly in young stands, reaches a peak in mid-succession, and de-clines in mature stands. NPPmax is interpreted as the maximumpotential rate at which the ecosystem removes C from the atmo-sphere. We fit this peak function to total NPP, and separately forthe wood component of aboveground NPP (wood ANPP) becauseof the higher uncertainty in estimating non-wood components ofNPP (roots, foliage, and understory), and because the original theoryof 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 (Rdevelopment Core Team, 2008) to fit all non-linear regressionmodels. The nls function uses a Gauss–Newton algorithm (Bates

and Watts, 1988), and requires initial estimates of the fittedparameters. We estimated initial parameter values by fittingapproximate curves to scatter plots and using parameter estimatesfrom the literature.

To evaluate if each C dynamic model fit the data of each ecosec-tion 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 lackof fit mean square by the pure error mean square. Unlike more tra-ditional hypothesis tests, the null hypothesis for an F-test for lackof fit is that the specified regression function is appropriate for thedata and the alternative hypothesis is that the specified regressionfunction is not appropriate for the data. Therefore, a model does nothave a significant lack of fit (i.e. the alternative hypothesis is re-jected in favor of the null hypothesis) for high p values.

To test for differences in model parameters among ecosectionsand among ESPs for each of the three C dynamic model, we bor-rowed from the principles of analysis of covariance for linear mod-els (ANCOVA, Pineiro and Bates, 2000) to perform a similar analysisfor non-linear least squares. We created a design matrix that in-cluded dummy-variable coding for the explanatory variables thatare factors, then tested those contrasts using nls. All parameters re-quire initial estimates when fitting non-linear models, includingparameters for differences between factors (i.e. ecosections andESPs). Therefore, we fit models separately for each factor to deter-mine starting estimates for differences between parameters andused these parameter estimates to test for differences among fac-tors. Rather than using a single ANCOVA model with an unwieldynumber (15) of factor levels, we simplified comparisons by group-ing the 15 ESPs into three groups by geoclimatic region and com-pared model parameters within these groups: North PacificHypermaritime and Maritime (NPHM-M), North Pacific (NP), andEastern Cascades and Northern Rocky Mountains (EC-NRM). Thisanalysis enabled comparisons of C uptake and storage potential(maximum NPP and biomass) and rate (age at which maximumsare reached) among ESPs.

3. Results

3.1. Ecosection-scale carbon models

At the scale of ecosections, live biomass C as a function of for-est age was highly variable. Although a solution for the Chap-man–Richards function for accumulation of live biomass duringsuccession (Eq. (1)) could be fit to the data, the models had a sig-nificant lack of fit for all four ecosections. Similarly, dead biomassC as a function of forest age was highly variable at the scale ofecosections. The theoretical U-shaped function for accumulationof dead biomass C during succession (Eq. (2)) could not be fit tothe data for the Coast Range or the Western Cascades. For thesetwo ecosections, a linear model of increasing dead biomass C withforest age fit the data. In contrast, a solution for the U-shapedfunction could be fit in the Eastern Cascades and Okanogan High-lands ecosections and the lack of fit test for these two models wasnot significant. Legacy dead biomass C (D0) was 3.85 (0.59) kgC m�2 and 1.17(2.34) kg C m�2 for the Eastern Cascades andOkanogan Highlands respectively. Maximum dead biomass C(Dmax) was 4.43 (1.00) kg C m�2 and 3.46 (0.79) kg C m�2 for theEastern Cascades and Okanogan Highlands. Although theseparameters were not significantly different between these twoecosections, they suggest that the Eastern Cascades has greaterpotential to store C in dead biomass than the OkanoganHighlands.

Similar to biomass, NPP was highly variable among ecosections.The peak model for NPP as a function of forest age had a significantlack of fit for the Coast Range and Western Cascades, but not for the


Eastern Cascades and Okanogan Highlands. However, maximumpotential NPP (NPPmax) and the forest age at which this maximumis reached (agemax) were not significantly different between thetwo ecosections. Maximum NPP was 0.55 (0.02) kg C m�2 yr�1

and 0.53 (0.01) kg C m�2 yr�1 for the Eastern Cascades and Okano-gan Highlands respectively. The age of NPPmax was 384 (118) yearsand 215 (50) years for the Eastern Cascades and Okanogan High-lands 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, deadbiomass C remained highly variable and the U-shaped function ofdead biomass C accumulation with forest age could not be detected(i.e. a solution for the model could not be found) for most ESPs. TheChapman-Richards function (Eq. (1)) for accumulation of live bio-mass 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-me-sic Douglas-fir-western hemlock was the only ESP in this groupthat did not have a significant lack of model fit (Table 2). In con-trast, all four of the models in the NP group (Fig. 3) and six ofthe seven models in the EC-NRM group (Fig. 4) did not have a sig-nificant lack of fit (Table 2).

The high variability in C storage potential in live biomass amongESPs was evident in the full range of values for maximum live bio-mass 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 m�2

in the NRM subalpine woodland and parkland to a high of 38.6kg C m�2 in the NP mesic western hemlock-silver fir (Table 2).The age at which 90% of this maximum was reached also variedgreatly among ESPs from a young age of 57 years in NP riparian for-ests to an old age of 838 years in the NP mountain hemlock-subal-pine parkland (Table 2).

Comparisons of the parameters among ESPs within the groupsindicated significant differences for some models. ESPs in the NPgroup had a wide range of values for both Lmax and age90max, andthese parameters differed between the ESPs in this group (Table 2).

Fig. 2. Live biomass C as a function of stand age for four environmental site potentialswere binned into 10-year age classes for ages <300 years and 20-year age classes for ageslive biomass C for the age bins and error bars are one standard deviation. Age-class me

The highest Lmax was in the mesic western hemlock-silver fir andthe mountain hemlock-subalpine parkland ESPs (38.6 and 38.1 kgC m�2, respectively) (Fig 3). Dry-mesic silver fir-western hemlockhad an intermediate Lmax (29.2 kg C m�2) and the riparian foresthad the lowest Lmax (15.6 kg C m�2). The mesic western hemlock-silver fir and the mountain hemlock-subalpine parkland ESPsreached 90% of Lmax at much older ages (697 and 838 years) com-pared 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 anarrower range of parameter values for Lmax and age90max and theparameters were not significantly different among most ESPs inthis group (Table 2). EC mesic mixed-conifer had the highest Lmax

(18.3 kg C m�2) and was the only ESP for which Lmax differed signif-icantly. For all other ESPs in this group, Lmax was significantly low-er, between 6.5 kg C m�2 and 12.4 kg C m�2 (Table 2). The lowestLmax was in the subalpine woodland and parkland and the highestwas in the montane mixed-conifer. Values for age90max were alsomore similar among ESPs in the EC-NRM group than among ESPsin the NP group (Table 2). Three ESPs in the EC-NRM group (mesicmontane 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 required230–340 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-shapedfunction for dead biomass C accumulation (Eq. (2)) generally didnot fit the data better at the ESP scale than it did at the ecosectionscale. As with the ecosection scale, dead biomass at the ESP scalewas highly variable and had only a weak relationship with forestage. Eq. (2) could be fit to the data for only three ESPs, dry-mesicsilver fir-western hemlock-Douglas-fir, dry-mesic montane mixedconifer, and spruce-fir forest and woodland (Table 3). Linear mod-els did not have a significant lack of fit for seven of the ESPs forwhich Eq. (2) could not be fit. Generally, the model parametersfor both Eq. (2) and the linear models did not differ significantly

(ESPs) in the North Pacific Hypermartime – Maritime (NPHM-M) group. Stand ages>300 years. All ages >600 years were grouped into a single age bin. Points are mean

ans are shown for simplicity; models were fit to original data not means.

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 classesfor 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 barsare one standard deviation. Age-class means are shown for simplicity; models were fit to original data not means.

Table 2Coefficients of models of live biomass C accumulation as a function of forest age for environmental site potentials (ESPs).

Coefficients (Standard errors)

n Lmax kL cL age90max

North Pacific Maritime And Hypermaritime (NPHM-M)NPHM sitka spruce 261 25.7 (1.2) 0.040 (0.007) 3.2 (0.9) 86NPHM western red cedar-western hemlock 247 29.3 (1.1) 0.013 (0.003) 1.1 (0.2) 187NPM mesic-wet Douglas-fir-western hemlock 457 28.3 (0.9) 0.029 (0.004) 2.2 (0.4) 106NPM dry-mesic Douglas-fir-western hemlock* 215 24.7 (0.9) 0.029 (0.006) 2.4 (0.8) 109

North Pacific (NP)NP mountain hemlock-subalpine parkland* 137 38.1 (9.8)ac 0.003 (0.002)a 1.3 (0.4)a 838NP mesic western hemlock-silver fir* 515 38.6 (5.0)a 0.003 (0.001)a 0.8 (0.1)a 697NP riparian forest* 121 15.6 (1.2)b 0.069 (0.039)b 5.3 (4.5)a 57NP dry-mesic silver fir-western hemlock-Douglas-fir* 188 29.2 (1.9)c 0.009 (0.003)b 1.4 (0.4)a 294

Eastern Cascades Rocky Mountain (EC-NRM)EC mesic mixed-conifer* 339 18.3 (1.6)a 0.007 (0.003)a 0.9 (0.2)a 312NRM dry-mesic montane mixed conifer 1113 12.4 (1.4) 0.006 (0.002) 0.8 (0.1) 341NRM subalpine woodland and parkland* 47 6.5 (3.8)b 0.013 (0.024)a 2.1 (4.2)a 232NRM mesic montane mixed conifer* 121 12.3 (0.5)b 0.094 (0.037)b 64.7 (120.3)a 68NRM ponderosa pine woodland* 52 9.7 (4.1)b 0.019 (0.024)b 2.0 (2.5)a 156RM spruce-fir forest and woodland* 201 12.2 (1.5)b 0.009 (0.006)a 1.0 (0.5)a 256RM montane riparian forest* 30 10.1 (1.7)b 0.025 (0.024)ab 3.4 (5.4)a 140

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 forwhich 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).


among ESPs within any group because of the high variability indead biomass (Table 3).

3.4. ESP-scale carbon model: net primary productivity

Similar to the model of accumulation of live biomass C with for-est 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 eco-sections. Models for twelve of the 15 ESPs did not have a significantlack of fit (Figs. 5–7). Maximum NPP (NPPmax) and the age of max-imum were highly variable among ESPs (Table 4). The NPPmax

parameter for all ESPs varied form a low of 0.37 kg C m�2 yr�1 inthe NRM subalpine parkland to a high of 0.94 kg C m�2 yr�1 in

HM sitka sprue. The agemax parameter varied from 65 years to543 years. Only one of the four models in the NPHM-M group didnot have significant lack of fit (Table 4). In contrast, all four ofthe models in the NP group and all seven of the models in theEC-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 m�2 yr�1

to 0.80 kg C m�2 yr�1 (Fig. 6) but the only ESP for which NPPmax dif-fered significantly was the riparian forest, which had significantlyhigher NPPmax (Table 4). The range of values for agemax was largerthan NPPmax and parameters differed significantly between ESPsin this group (Table 4). The riparian forest had the lowest valuefor agemax parameter at 65 years and the mountain hemlock-subal-pine parkland had the highest value for agemax at 543 years.

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 werebinned 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 livebiomass 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 3Coefficients of models of dead biomass C accumulation as a function of forest age for environmental site potentials (ESPs).


North Pacific Maritime and Hypermaritime (NPHM-M)Dead biomass (kg C m�2) (linear) n b0 b1

NPHM western redcedar – western hemlock* 96 3.0 (0.7)a 0.019 (0.003)a

NPM mesic-wet Douglas-fir-western hemlock* 140 3.9 (0.5)b 0.011 (0.003)b

NPM dry-mesic Douglas-fir-western hemlock* 152 2.5 (0.5)a 0.011 (0.002)b

North Pacific (NP)Dead biomass (kg C m�2) (linear) n b0 b1

NP mesic western hemlock -silver fir* 439 2.64 0.010 (0.001)

Dead biomass (kg C m�2) (non-linear) n D0 kD1 Dmax kD2 cD

NP mountain hemlock-subalpine parkland 133 3.9 (2.0)a 0.009 (0.011)a 5.6 (2.6)a 0.006 (0.007)a 4.0 (9.4)a

NP dry-mesic silver fir-western hemlock-Douglas-fir* 176 4.7 (1.6)a 0.034 (0.024)a 8.9 (2.6)a 0.005 (0.004)a 1.5 (1.1)a

Eastern Cascades Rocky Mountain (EC-NRM)Dead biomass (kg C m�2) (linear) n b0 b1

EC mesic mixed-conifer* 333 1.41 (0.27)a 0.008 (0.001)a

NRM subalpine woodland and parkland* 47 0.29 (0.39)a 0.007 (0.003)a

NRM mesic montane mixed-conifer* 118 1.17 (0.46)a 0.013 (0.004)a

Dead biomass (kg C m�2) (non-linear) n D0 kD1 Dmax kD2 cD

NRM dry-mesic montane mixed conifer* 1105 5.1 (1.2)a 0.067 (0.021)a 3.1 (0.8)a 0.007 (0.006)a 1.3 (0.7)a

RM spruce-fir forest and woodland* 203 8.1 (4.7)a 0.084 (0.077)a 3.9 (0.7)a 0.011 (0.011)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 forwhich 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).


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 theNorth 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 into10-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 forsimplicity; 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 theNorth 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 tooriginal data not means.


The EC-NRM group had the largest range of values for theNPPmax parameter compared to the other two groups and manyof the parameters of the ESP-scale models differed significantly(Table 4). Mesic montane mixed-conifer had the highest NPPmax

(0.74 kg C m�2 yr�1) and the subalpine woodland and parklandESP had the lowest NPPmax (0.37 C m�2 yr�1). Despite the largerange and significant differences in NPPmax, the range of valuesfor agemax was smaller (148–297 yrs) and agemax did not differ sig-nificantly between ESPs in the EC-NRM group (Table 4).

Models of wood ANPP followed a similar pattern to those of to-tal 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 by14–77 years (14–52%). For ESPs in the EC-NRM group, agemax waslower by 4–150 years (5–56%), but agemax for wood ANPP was highfor ESPs in this group, and two ESPs had a higher agemax for woodANPP 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 rootsshowed little relationship with stand age in most ESPs, but someESPs had an initial increase in fine root NPP in young forests andreached an asymptote at older ages. NPP of understory decreased

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 inthe 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-yearage 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 asimilar pattern to that of total NPP and wood ANPP, a rapidincrease in young forests to a peak, followed by a decline at olderages.

4. Discussion

4.1. The importance of scale in quantifying forest carbon dynamics

Models of live biomass accumulation and NPP with forest agegenerally fit the data better at the finer scale of ESPs than at thecoarser scale of ecosections, suggesting that variability in speciescomposition, disturbance regimes, and climatic controls is too highat the scale of ecosections to capture age-based C dynamics.Temporal patterns of C dynamics are better captured at the scaleof forest types within ecosections, which reduces the variabilityin 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, andlongevity). The significantly different values among ESPs for NPPmax

and agemax provide further evidence that the finer-scale ESP classi-fication more effectively captures the variability in C uptake poten-tial across the forested region, particularly in the Eastern Cascadesand Okanogan Highlands where differences were greatest. Relativeto live biomass and NPP, model fit of the U-shaped pattern of dead

biomass C accumulation depended more on the ecosection and for-est type and less on the scale of analysis.

Model fit did not improve at the ESP-scale for three of four ESPsin the NPHM-M group (primarily in the Coast Range or low eleva-tions of the Western Cascades, Fig. 2), likely because of limitedinventory data for some forest types and age classes in this region.The inventories did not include the National Park Service owner-ship, which is approximately 40% of the area in the Coast Rangeecosection and includes much of the area in older age classes. Thusstand ages >350 years were underrepresented in these ESPs. Thelimited representation of older age classes might also explain therelatively low values for Lmax in the Coast Range and NPHM-Mgroup and the lack of fit for NPP models, which appear to overes-timate 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 mod-els suggest that although the rate of live biomass C accumulationslows 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 modelsand old values of age90max suggest that an asymptote may not existin these forests. This result contradicts similar studies in the PNWthat have proposed that live biomass accumulation stabilizes in

Table 4Coefficients of models of net primary productivity (kg C m�2 yr�1) as a function of forest age for environmental site potentials (ESPs).


n NPPmax agemax kN

North Pacific Maritime and Hypermaritime (NPHM-M)NPHM sitka spruce 261 0.94 (0.02) 85 (5) 1.34 (0.06)NPHM western redcedar-western hemlock* 247 0.86 (0.02) 103 (6) 1.49 (0.05)NPM mesic-wet Douglas-fir-western hemlock 457 0.92 (0.02) 74 (3) 1.39 (0.05)NPM dry–mesic Douglas-fir-western hemlock 215 0.75 (0.02) 81 (5) 1.50 (0.09)

North Pacific (NP)NP mountain hemlock-subalpine parkland* 137 0.65 (0.03)a 543(243)a 2.13 (0.43)ab

NP mesic western hemlock-silver fir* 515 0.60 (0.01)a 149 (12)a 2.01 (0.13)a

NP riparian forest* 121 0.80 (0.03)b 65 (7)b 1.43 (0.15)b

NP dry-mesic silver fir-western hemlock-Douglas-fir* 188 0.59 (0.02)a 145 (14)a 1.73 (0.14)ab

Eastern Cascades Rocky Mountain (EC-NRM)EC mesic mixed-conifer* 339 0.59 (0.01)a 185 (39)a 2.13 (0.33)a

NRM dry-mesic montane mixed conifer* 1113 0.48 (0.01)b 268 (74)a 2.58 (0.33)a

NRM subalpine woodland and parkland* 47 0.37 (0.07)b 297 (466)a 2.07 (1.42)ab

NRM mesic montane mixed conifer* 121 0.74 (0.05)c 240 (179)a 2.46 (0.84)a

NRM ponderosa pine woodland* 52 0.53 (0.06)abd 198 (136)a 1.86 (0.64)ab

RM spruce-fir forest and woodland* 201 0.55 (0.02)d 271 (113)a 2.29 (0.57)a

RM montane riparian forest* 30 0.56 (0.06)ad 148 (31)a 1.07 (0.31)b

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 forwhich 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 increas-ing mortality or decreasing productivity (Ryan et al., 1997). Thecontinued accumulation of live biomass may be attributed to thehigher rates of NPP observed in older stands or low rates of mortal-ity in these ESPs.

The lack of fit for the U-shaped model of dead biomass accu-mulation in the Coast Range, Western Cascades, and associatedESPs suggests that dead biomass C accumulation in forests ofthe western PNW is less related to forest age than are other Cdynamics (Spies et al., 1988; Nonaka et al., 2007; Hudiburget al., 2009). Other factors are likely more important drivers oftemporal patterns of dead biomass, such as disturbance type, fre-quency, and severity (Nonaka et al., 2007), and this information isneeded to explain temporal patterns of dead biomass C accumu-lation. The U-shaped pattern is more evident in even-aged foreststhat 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 agebecause of the variability introduced by pre-fire stand conditionsand on-going mortality (Kashian et al., 2013). The linear modelsof dead biomass C accumulation observed in some ESPs are likelybecause timber harvests, rather than fire, were the dominantstand-initiating disturbance in western Washington in the lastcentury. Thus legacy dead biomass was not detectable in youngstands (Wimberly, 2002). The U-shaped model was more evidentin forests of the Eastern Cascades and Okanogan Highlands wheremore stands likely initiated after fire. Similarly, Hudiburg et al.(2009) found that the U-shaped pattern of dead biomass accumu-lation was difficult to detect and that the model fit better in theeastern Cascades than in the western Cascades or Coast Range.

ESP-specific models of NPP in the NPHM-M group showed apeak at young ages followed by a large decline in older forests,but most other ESPs showed a smaller decline in NPP in older for-ests relative to the declines observed in previous studies (Ryanet al., 1997; Pregitzer and Euskirchen, 2004). These smaller de-clines were evident for both total NPP and wood ANPP, althoughthe peak occurred at younger ages for wood ANPP. A lack of declinewas especially evident for forests in EC-NRM group, in which NPPdid not begin to decline for 150–300 years (108–195 years forwood ANPP) and after the peak, NPP still remained high in older

forests. This lack of a large decline in NPP in older forests probablycan be attributed to the inability of the stand-age theory to quan-tify one value for age in uneven-aged forests with frequent distur-bances that are not stand-replacing. In these forests, NPP remainshigh in older forests because trees continually regenerate followinglow-severity disturbances. Further evidence of this is the highervariability in NPP observed for forest ages near and after NPPmax,especially in ESPs in which NPP did not decline greatly in olderforests.

Differences in quantifying forest age might explain differencesin observed temporal patterns of NPP relative to theory and resultsfrom flux tower sites or from forest chronosequences that use few-er stands but where the year of the stand-initiating disturbance isknown (e.g. Kashian et al., 2013). Similar to other recent studies(e.g. Van Tuyl et al., 2005), we used forest chronosequences thatare based on a large number of stands, but the year of the stand-initiating disturbance is not known precisely for the sites. As analternative, forest ages are estimated based on the distribution ofmeasured tree ages in each stand, which is likely a different valuefor stand age than time-since-disturbance (Bradford et al., 2008).This method of calculating forest age represents time-since-distur-bance less effectively in stands with a few old trees (>400 years)that are legacies from the stand-replacing disturbance becausethe ages of these trees skew the stand age. This method also causesstands with bimodal distributions of tree ages to be defined by themean, which overestimates the age of young stands and underes-timates the age of old stands (Bradford et al., 2008). Regardlessof the methods, quantifying stand age does not account for low-severity disturbances, which contribute to the observed variabilityin biomass and NPP, particularly in stand ages of 100–300 years inthe Eastern Cascades and Okanogan Highlands. Thus the empiricalmodels for these forest types represent temporal C trajectories formore dynamical systems than were envisioned by the originaltheory.

4.3. Differences in Indicators of C storage potential and productivityamong ESPs

Differences among ESPs in indicators of the potential to storeC (i.e. maximum potential biomass and the time required to


approach maximum) were generally consistent with differences inclimate and species composition, with a few notable exceptions.For ESPs in the NP and NPHM-M, Lmax was greatest in ESPs withhigher mean annual precipitation, providing supporting evidencethat live biomass in this region is positively correlated with waterbalance (Gholz, 1982). The riparian forest had the lowest Lmax

among ESPs in the NP and NPHM groups, which can be attributeddominance of deciduous species, which have lower potential massthan conifer species in this region. The high Lmax in the mountainhemlock-subalpine parkland and the lack of significant differencesbetween that ESP and mesic western hemlock-silver fir (lower ele-vation) were unexpected given the relatively colder temperaturesand shorter growing season in the mountain hemlock-subalpineparkland. In subalpine forests of the PNW, biomass accumulationand productivity are negatively correlated with winter tempera-tures and positively correlated with summer temperatures (Gholz,1982; Graumlich et al., 1989). Mountain hemlock and subalpine firalso have smaller potential mass and shorter lifespans than conif-erous species in low-elevation ESPs. The relatively high Lmax in themountain hemlock-subalpine parkland may be because of errorassociated with the biophysical modeling of this ESP. Species com-position of plots within this ESP included 50% (by stem density) Pa-cific silver fir and western hemlock, in addition to subalpine fir andmountain hemlock. Furthermore, the low-elevation area of the ESP,which includes more western hemlock and Pacific silver fir, is bet-ter represented in the inventory by a higher density of plots.

In the EC-NRM group, maximum live biomass C was lowest inthe dry (ponderosa pine woodland and savanna) and cold (subal-pine woodland and parkland) extremes of the range, suggestingthat both water and temperature limit C accumulation in forestsof central and eastern Washington. EC mesic mixed-conifer, whichhad the highest Lmax of the EC-NRM group, also had the warmestminimum January temperature and moderate precipitation. Thelack of significant differences in Lmax between the other ESPs in thisgroup suggests that climatic controls on C accumulation may besimilar across the region (Bradford et al., 2008).

Parameters of the ESP-scale models of NPP indicate a consistentpattern with variation in climate, providing supporting evidencethat NPP in the forested region of Washington is positively relatedto mean annual precipitation and minimum winter temperature(Gholz, 1982). ESPs with higher precipitation and minimum Janu-ary temperature had higher NPPmax that was reached at youngerages. In contrast, ESPs with either lower annual precipitation orcolder minimum January temperature had lower NPPmax that wasreached at older ages. Although NPPmax was generally lower inthe EC-NRM group than in the other groups, differences in NPPmax

and agemax among ESPs showed similar patterns with climate tothose observed for the study area as a whole. The two ESPs withthe warmest minimum January temperature and moderate precip-itation had the highest NPPmax. In contrast, ESPs with the coldestminimum January temperature (subalpine woodland and park-land) and the lowest mean annual precipitation (ponderosa pinewoodland and savanna and dry-mesic montane mixed conifer)had the lowest NPPmax.

Our results indicate that the range of maximum live biomass Cof forests in Washington is similar to the range for temperate for-ests as whole. For temperate forests globally, Pregitzer and Euskir-chen (2004) found that live biomass in the oldest age class (121–200 years) had an interquartile range of 10–45 kg C m�2 (medianof 18 kg C m�2). In our study, ESPs in the NPHM-M and NP grouphad greater maximum live biomass than the median for temperateforests. In contrast, maximum live biomass values for ESPs in theEC-NRM group were less than the median for temperate forests,and some were less than the lower quartile. Pregitzer and Euskir-chen (2004) found that maximum NPP for temperate forests hadan interquartile range of 0.6–1.1 kg C m�2 yr�1 (median of

0.80 kg C m�2 yr�1). The ESPs in the NPHM-M and NP group hadmaximum NPP similar to the median of temperate forests. In con-trast, ESPs in the EC-NRM group had values of maximum NPP lessthan the lower quartile.

4.4. Uncertainty in estimates of biomass and net primary productivity

There are several sources of uncertainty in estimating C poolsand fluxes from FIA data, although it is difficult to quantify the rel-ative contribution of each source. Sources of uncertainty in thisstudy are limitations associated with allometric equations for esti-mating biomass, which are extrapolation errors (i.e. using equa-tions with predictor variables outside the range of data on whichthe equation was developed) and substitution errors (i.e. usingequations for different species or geographic regions). Thesesources of uncertainty affect some forest types and biomass poolsmore than others. Species most affected by substitution uncer-tainty are those not historically used for timber production – spe-cies that grow at high elevations and hardwoods – because fewerequations are available for these species. Biomass estimates forthese species are also more susceptible to errors associated withextrapolation because allometric equations developed for non-tim-ber species are typically based on narrower ranges of diameter andheight. Most allometry for tree biomass has been developed forspecies in the Western and Eastern Cascade ecosections, thusuncertainty associated with geographic substitutions is greaterfor the Okanogan Highlands and Coast Range.

Estimates of aboveground biomass C pools are more certainthan estimates of belowground biomass C pools because of the lackof allometric equations for belowground biomass. Despite thisuncertainty, excluding estimates of belowground biomass wouldgreatly affect results because belowground biomass can be asmuch as 20% of total live biomass. Therefore, we included esti-mates of belowground biomass and minimized uncertainty intwo ways. First, we used allometric equations for four species,rather than a single species (e.g. Van Tuyl et al., 2005), becausethe use of equations developed for different species may reducethe uncertainty associated with species and geographic substitu-tions. Different species can have different root structures thatmay be poorly captured by allometry of a single species (Kimmins,1997). Second, we adjusted biomass estimates with species-spe-cific values for wood density, reducing error associated with sub-stitutions (Van Tuyl et al., 2005).

Estimates of understory biomass are more variable than esti-mates of tree biomass. We made the best estimate of understorybiomass possible given the limitations of the data and allometricequations for estimating biomass of understory species. Under-story biomass is typically only 1–3% of total live biomass in forestecosystems, so uncertainty in estimates of this pool is unlikely toaffect the overall models of live biomass accumulation with forestage.

Another source of uncertainty in estimating biomass pools isthe limited availability of some biomass components in the peri-odic FIA and CVS inventories. The estimate of dead biomass C inthis 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 or-ganic layer because these pools were not sampled in the periodicinventory. The objective of our study was to quantify age-basedpatterns of C dynamics, not to account for total biomass in theseforest ecosystems, thus including these components in the esti-mate of the dead biomass C would affect the magnitude of esti-mates, but is unlikely to affect the temporal patterns of biomassduring succession.

We reduced uncertainty in estimating NPP from inventory databy estimating NPP for each ecosystem component based on theprocess that drives most production for that component. This


method may underestimate NPP, however, because it does not ac-count for all processes that contribute to NPP. For the tree woodcomponent, calculating NPP using a back-calculation of previousbiomass does not account for production by trees that are deadat the time of the inventory but were alive during the period overwhich NPP is estimated. For the foliar biomass component, esti-mating NPP using leaf turnover rates assumes that foliar biomasshas reached a steady state and does not account for productionassociated with increasing crown dimensions in young trees. NPPof fine roots is the most uncertain component of NPP because itis highly variable spatially and temporally, yet it can be 10–60%of annual NPP in forest ecosystems (Kimmins, 1997). We estimatedNPP of fine roots using a method that takes advantage of the rela-tionship between fine root NPP and water availability, and thus isbased on ecological theory. NPP of fine roots (absolute basis) isgreater in moisture-limited environments because plants allocatemore C to roots to acquire more water (Santantonio and Hermann,1985). Certainty in estimates of NPP of understory biomass waslimited by the data, which did not include measurements of radialgrowth increment of woody shrubs. Therefore, we could not usethe back-calculation method for calculating NPP. Our assumptionsfor calculating understory NPP may over-estimate NPP of herba-ceous vegetation and underestimate shrub NPP, but productionof 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 patternsof C dynamics could be applied to any forested region for which theminimum data requirements are available: forest inventory data,allometric equations for biomass, and spatial data layers of poten-tial vegetation. These data are available for the US, allowing themethods to be easily applied nationally. We used publicly availableUSFS forest inventory data and spatial data layers with continuouscoverage across the US (available at http://www.landfire.gov/prod-ucts_national.php). Although more species-specific allometricequations are available for the PNW than for other forested re-gions, biomass estimates can be made using a national databaseof allometric equations for general species groups in North Amer-ica (Jenkins et al., 2004).

Recent changes in FIA sampling protocols will make FIA dataeven more useful for quantifying temporal C dynamics in forested

Fig. 8. The relationship between the age of maximum NPP and the age at which 90% of mpoints below the one to one line represent a tradeoff between managing for maximumindicate a potential optimum age for forest C management that balances C uptake and

ecosystems. Additional ownerships, including USDI National ParkService lands, are included in the more recent inventories, whichwill fill some age-class and forest-type gaps. The recent annualFIA data include measurements of fine woody debris (FWD), forestfloor organic material, understory vegetation, and soil (Woodallet al., 2010), allowing for the quantification of additional ecosys-tem C pools. Additional data on fine woody debris would improvemodels of dead biomass accumulation because fine woody debriscan be abundant in young stands after disturbance and may con-tribute to the U-shaped pattern with stand age (Agee and Huff,1987). More complete and consistent sampling of understory veg-etation will improve estimates of biomass and productivity. Mea-surements of the basal diameter of shrubs would improveestimates of shrub biomass and productivity because many spe-cies-specific allometric for shrub species require basal diameteras a predictor variable. As more plots in the FIA program are re-measured, NPP can be better estimated based on changes in bio-mass between inventory years, rather than back calculations.

Development of more allometric equations for estimating bio-mass of non-timber species and belowground tree componentswould improve estimates of forest C stocks and fluxes. Allometricequations for high-elevation species will be especially importantfor quantifying changes in C stocks and fluxes as climate changesbecause high-elevation forests are expected to be more sensitiveto climate change (McKenzie et al., 2001). Despite its potentiallylarge contribution to total biomass, biomass of tree roots is oftennot included in forest C accounting because of the limited dataand allometric equations for estimating this component. Further-more, additional information on factors that control resource allo-cation between aboveground and belowground C is need to betterquantify 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 inthis study can be used to assess the effects of succession, distur-bance, and landscape age-class distributions on C storage for largegeographic areas composed of different forest types. This approachhas been used previously for hypothetical landscapes, disturbanceregimes, and age-class distributions (Euskirchen et al., 2002; Kash-ian et al., 2006; Smithwick et al., 2007), but the empirical models ofquantified in this study can be used to estimate the C budget for

aximum live biomass C accumulates for 15 environmental site potentials (ESPs). AllNPP (uptake) and maximum live biomass C storage. Points near the one to one linestorage.




the forested area of Washington when combined with current orpotential future disturbance regimes and associated age-class dis-tributions. This approach can be used as an empirical alternative tobiogeochemical process models, or the age-based models can beused to calibrate process models. Biogeochemical process modelsare useful for simulating C dynamics over large geographical areasand accounting for direct effects of climate on C dynamics (e.g.Bachelet et al., 2001), but they have some notable limitations. Pro-cess models typically have a limited representation of disturbanceeffects on forest age and C dynamics. Taxonomic resolution is lim-ited to plant functional types that do not account for the influenceof species attributes on C dynamics or disturbances. Furthermore,process models require many assumptions and parameters, andcan be highly sensitive to parameter choices, which are often sub-jective because true parameters are unknown (Lenihan et al., 2008;Rogers et al., 2011).

Management of forests for C uptake and storage can be in-formed by data on maximum potential biomass and productivityby forest type and ages at which these maxima are reached. Thisinformation can be used to identify forest types with the greatestpotential to store additional C and to establish baselines againstwhich the additionality through forest management can be as-sessed. Carbon storage is only one of many objectives that forestmanagers might consider (McKinley et al., 2011), but this informa-tion can help optimize multiple management objectives. Carbonstorage and uptake potential and the timing of maximum C storageand uptake varied widely among forest types in our study. Forestecosystems with the greatest potential for long-term C storage inlive 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 inolder sitka spruce and western redcedar forests. Forest ecosystemswith the greatest C uptake are 65–100-year-old mesic westernredcedar/western hemlock forests and riparian forests. The datasuggest maximum C uptake might be higher in 75–85-year-old sit-ka spruce and wet Douglas-fir-western hemlock forests, but mod-els for these ESPs were not statistically significant.

The ESP-specific models of C dynamics suggest a trade-off be-tween managing forests for maximum C uptake vs. maximum Cstocks in some forest types and an optimum age for C managementin other forest types (Fig. 8). In the NP group of ESPs, the age ofmaximum NPP (i.e. C uptake) was reached several decades, andfor some ESPs, centuries, before 90% of maximum live C storage.Therefore, managing for maximum live biomass C storage in theseESPs would require a substantially different age-class distributionthan managing for maximum NPP. In contrast, most ESPs in theEC-NRM group reached maximum NPP and 90% of live biomass Cstorage at similar ages, suggesting an optimum rotation age for for-est C management might exist in these ESPs. Some ESPs in thisgroup (e.g. NRM ponderosa pine woodland and savannah) showan optimum age of approximately 200 years and other ESPs (e.g.RM subalpine spruce-fir) have an optimum age of approximately300 years. This relationship between the age of maximum NPPand 90% of live biomass C suggests a potential optimum of100 years for ESPs in the NPHM-M group, but this should be inter-preted cautiously given the poor fit of models in this group and thelack of data from older forests. This relationship considers only NPPand the storage of C in live biomass; C storage in dead biomass con-tinues beyond the age of maximum live biomass and is an impor-tant C storage pool in ecosystems with high biomass and slow ratesof decomposition.

The empirical models of C dynamics quantified in our study canbe used to inform management of C stocks and fluxes for large geo-graphical areas, rather than managing C at the scale of individualforest stands. These models can be combined with spatial dataon forest age to quantify C stocks and NPP over large areas

(Hudiburg et al., 2009). They can validate remotely sensed dataon biomass and NPP or augment remotely sensed data withestimates of surface and belowground ecosystem C pools, whichare difficult to estimate with remote sensing. Empirical models ofC dynamics can also inform optimal intervals for natural oranthropogenic disturbances to meet C management objectivesand the potential gains in C storage that can be achieved byincreasing intervals (e.g. Euskirchen et al., 2002; Smithwick et al.,2007). Similarly, these age-based models can be combined withprojected changes in disturbance intervals to estimate changes inC stocks and NPP as a function of the age-class distribution thatwould be expected under a new disturbance regime (e.g. Raymondand McKenzie, 2012).

Acknowledgements

This publication was partially supported by the Joint Institutefor the Study of the Atmosphere and Ocean (JISAO) under NOAACooperative Agreement No. NA17RJ1232 and NA10OAR4320148.Additional funding came from the USDA Forest Service PacificNorthwest Research Station and the USGS Global Change ResearchProgram. This publication is a product of the Western MountainInitiative. James K Agee, David L Peterson, Sean Healey, and Mau-reen Kennedy provided helpful comments on an early draft of thismanuscript. Robert Norheim created figure 1.

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Forest Ecology and Management - Home | US Forest …Temporal carbon dynamics of forests in Washington, US: Implications for ecological theory and carbon management Crystal L. Raymonda,