Harvest disturbance has substantial impacts on forest carbon (C) fluxes and
stocks. The quantification of these effects is essential for the better
understanding of forest C dynamics and informing forest management in the
context of global change. We used a process-based forest ecosystem model,
PnET-CN, to evaluate how, and by what mechanisms, clear-cuts alter ecosystem
C fluxes, aboveground C stocks (AGC), and leaf area index (LAI) in northern
temperate forests. We compared C fluxes and stocks predicted by the model
and observed at two chronosequences of eddy covariance flux sites for
deciduous broadleaf forests (DBF) and evergreen needleleaf forests (ENF) in
the Upper Midwest region of northern Wisconsin and Michigan, USA. The
average normalized root mean square error (NRMSE) and the Willmott index of
agreement (
Disturbance has been widely recognized as a key factor influencing ecosystem
structure and function at decadal to century scales (Magnani et al., 2007;
Williams et al., 2012; Kasischke et al., 2013). Harvesting is an important
anthropogenic disturbance shaping North American forest landscapes.
Approximately 61 000 km
Harvests transfer living biomass C to harvested wood C and litter C, resulting in successional changes in C fluxes and stocks. Leaf biomass increases rapidly in secondary succession and then typically stabilizes at a certain level that is determined by light, water, nutrient availability, and forest type (Sprugel, 1985). Gross primary productivity (GPP) thus increases gradually over time, reaches its maximum in middle age, and declines slightly thereafter in response to nutrient limitations and aging (Odum, 1969; Chapin et al., 2002; Tang et al., 2014). The successional change in plant respiration (i.e., autotrophic respiration) after stand-replacing harvesting is similar to that of GPP, although the C use efficiency, the ratio of net primary productivity (NPP) to GPP (or NPP / GPP), generally declines with forest age (DeLucia et al., 2007). As a result of these patterns, along with increases in C loss through woody litterfall, living tree biomass C gradually increases following a typical logistic growth curve (Odum, 1969; Sprugel, 1985).
Heterotrophic respiration following stand-replacing harvesting can be stimulated at the beginning of stand development because the removal of trees alters the environmental conditions (e.g., soil temperature, moisture, and nutrients) and possibly leads to an increase in litter quantity depending on harvest types (e.g., stem-only harvesting). Heterotrophic respiration is expected to gradually decrease thereafter because the regrowing forest reduces net radiation, water, and nutrient availability to the soil (Chapin et al., 2002), and the amount of decomposable soil organic matter from the prior forest and harvest residue (e.g., litter, coarse woody debris, and soil organic C) also gradually decreases. Over time, however, heterotrophic respiration could be enhanced because of accumulation of woody debris and litter with stand development. This theorized successional trajectory in ecosystem respiration (ER; the sum of autotrophic and heterotrophic respiration) can also be influenced by harvest types and forest composition. Unlike GPP or NPP, quantifying the trajectory of heterotrophic respiration (and consequently total ecosystem respiration) with age is not as straightforward (Amiro et al., 2010). Observational studies have shown that forest ecosystems generally become C sources (i.e., negative net ecosystem productivity, NEP) immediately following stand-replacing harvests, approach the maximum NEP as they mature, and then experience a gradual decline in NEP thereafter (e.g., Law et al., 2003; Gough et al., 2007; Goulden et al., 2011), following the trajectories hypothesized by Chapin et al. (2002).
The changes in C fluxes and stocks after harvesting have been examined in
many forest ecosystems using ecological measurements (e.g., eddy covariance
or EC observations) from chronosequences using a space-for-time substitution
approach (e.g., Gough et al., 2007; Goulden et al., 2011). The trajectory
and amplitude of C fluxes and stocks vary with forest ecosystem types
(Amiro et al., 2010). For example, Noormets et al. (2007) reported that a
young red pine (
Although using the chronosequence approach to evaluate the changes of ecological processes with age after disturbances is attractive, this approach is often limited by the lack of biological and climatic data (Yanai et al., 2003; Bond-Lamberty et al., 2006) and full representation of stand development stages. Process-based ecosystem models provide a means of quantifying the effects of disturbances on C dynamics under changing climate over various spatial and temporal scales. Ecosystem models have been used to assess the effects of clear-cuts and climate change on forest C dynamics at the stand/ecosystem (e.g., Bond-Lamberty et al., 2006; Grant et al., 2009; Wang et al., 2012b) or regional scales (Desai et al., 2007; Dangal et al., 2014). Moreover, ecosystem models can also be used to assess forest C dynamics under various scenarios of climate change and harvesting regimes (e.g., Albani et al., 2006; Peckham et al., 2012) since these models have been developed based on physiological, biogeochemical, and ecological theories. However, few studies have used ecosystem models to examine the changes of C fluxes and stocks with stand regrowth after stand-replacing disturbances for forest chronosequences.
The objectives of this study were to evaluate the ability of an ecosystem model to capture the trajectories of forest C dynamics after stand-replacing harvests for two northern temperate plant functional types (PFTs: deciduous broadleaf forests, DBF; evergreen needleleaf forests, ENF), to examine which processes influence successional trajectories in these ecosystems and to test the role of PFT on the successional trajectory of C fluxes. We applied a process-based forest ecosystem model, PnET-CN (Aber et al., 1997; Ollinger et al., 2002), for simulating the effects of clear-cut on forest C dynamics, and evaluated the simulated C fluxes and stocks for both PFTs using in situ measurements (e.g., EC observations and aboveground biomass C, AGC). We hypothesized that (1) both DBF and ENF will have similar successional patterns in C fluxes (GPP, ER, and NEP) and aboveground biomass C stocks after stand-replacing harvests, but (2) DBF will recover faster than ENF from a net C source to a net C sink and lose a smaller amount of C (negative NEP) following a stand-replacing harvest.
Our study sites consist of eight EC sites in the Upper Midwest region of
northern Wisconsin and Michigan (Chen et al., 2008; Table 1). The study area is
characterized by a humid-continental climate with hot
summers and cold winters. The mean annual temperature is 4.4
Our sites consist of four DBF sites (YHW, IHW, WIC, and UMBS) and four ENF
sites (YRP, YJP, IRP, and MRP). The four DBF sites range from 3 to 86 years
in age and constitute a chronosequence. Dominant tree species are bigtooth
aspen (
The PnET-CN model is a process-based forest ecosystem model designed to
simulate C, nitrogen (N), and water dynamics at daily to monthly time steps.
PnET-CN is driven by climate variables (temperature, precipitation, and
photosynthetically active radiation; PAR), site variables and atmospheric
properties (soil moisture, disturbance history, wet and dry N deposition,
and atmospheric CO
A characteristic feature of PnET-CN is its use of generalized leaf trait
relationships to simulate potential photosynthesis in a multilayered canopy
(Aber and Federer, 1992). Actual photosynthesis is then constrained
by air temperature, vapor pressure deficit, and soil water availability for
simulating actual GPP. The effects of elevated CO
Site characteristics for two chronosequences of deciduous broadleaf forests (DBF) and evergreen needleleaf forests (ENF) in the Upper Midwest region of Wisconsin and Michigan, USA.
PnET-CN includes a complete N cycle, and simulates N mineralization and
nitrification, plant N uptake, allocation, and leaching losses. N deposition
is added to corresponding soil N pools (NH
The model also simulates key hydrological processes including rainfall interception, evaporation, transpiration, surface runoff, and drainage at each time step. Rainfall interception is treated as a constant fraction of precipitation. Transpiration is estimated based on water use efficiency and plant demand via photosynthesis. Surface runoff is calculated as a constant fraction of the difference between precipitation and evaporation. Drainage is estimated when potential soil water exceeds soil water holding capacity.
Prescribed disturbance events can be simulated in the model through four parameters: disturbance year, disturbance intensity, biomass removal fraction (live and dead), and the loss rate of soil organic matter. In this study, when stand-replacing disturbance events occur, a uniform PFT was assumed to be regenerated on-site. For the first year after clear-cuts, a minimum LAI of 0.1 was assumed to regulate maximum potential foliage mass that controls leaf production. The photosynthetic production is transported to plant non-structural C pool where C could be allocated to leaves, stems, and roots. There is, therefore, no need for initialization (e.g., stand density) after disturbances in the model. More details about the model structure and processes have been described elsewhere (Aber et al., 1997; Ollinger et al., 2002).
The model inputs include air temperature, precipitation, PAR, wet and dry N
deposition, atmospheric CO
Annual rates of wet and dry N deposition were obtained from the United
States Environmental Protection Agency (EPA;
For each site, we prescribed the disturbance events using the site disturbance history (Table 1). For each stand-replacing harvest, stand mortality was assumed to be 100 %. The merchantable wood removal (biomass removal out of the ecosystem) fraction was assumed to be 0.8 in this study. The soil removal fraction was assumed to be 0, given that the content of soil organic C might not be considerably affected by harvesting (Johnson and Curtis, 2001; Yanai et al., 2003). We also conducted a sensitivity analysis on these assumptions as described below in Sect. 2.4.
Simulated (lines) and observed (symbols) monthly carbon fluxes: GPP, ER, and NEP for the deciduous broadleaf chronosequence sites from 1999–2007.
PnET-CN has been parameterized and tested for temperate DBF (Aber et al.,
1997; Ollinger et al., 2002; Peters et al., 2013), temperate ENF (Aber et
al., 1997; Peters et al., 2013), and mixed forests (Aber
et al., 1997) for forest productivity, net N mineralization, and foliar N
concentrations. The parameter values used in this study are given in
Table S1 in the Supplement. To apply the model to the transient simulation period
(1860–2010), a 200-year spin-up run was conducted to ensure that the
equilibrium (
To examine the effects of stand-replacing harvests on C dynamics, we
conducted all simulations using the site disturbance history (Table 1),
vegetation parameters (Table S1), climate, N deposition, and
atmospheric CO
Simulated (lines) and observed (symbols) monthly carbon fluxes: GPP, ER, and NEP for the evergreen needleleaf chronosequence study sites from 2002 to 2005.
PnET-CN model performance in monthly carbon fluxes (GPP: gross primary productivity; ER: ecosystem respiration; NEP: net ecosystem productivity), leaf area index (LAI), and aboveground carbon stock (AGC) for the two chronosequences.
The sensitivity of ecosystem C dynamics to changes in harvesting practices
during the secondary succession was assessed using sensitivity analysis. The
model was run at WIC and MRP for 100 years after scenario harvests in 1910
using the same climate data sequence. Sensitivity scenarios involved
applying the stand mortality (80 and 60 %, compare to 100 % in the
model test) and soil organic matter loss (20 and 40 %, compare to 0 in
the model test) to assess the effects of different harvest intensity and
soil organic matter loss on C dynamics. We also tested the model sensitivity
to CO
Simulated C fluxes were generally consistent with EC derived C fluxes for
both DBF and ENF sites (Figs. 1 and 2). The NRMSEs between simulated and
tower fluxes (GPP, ER, and NEP) were between 10 and 21 % (Table 2).
The Willmott index of agreement between simulated and tower C fluxes for
both PFTs ranged from 0.91 to 0.94 with the exception of NEP (
Comparisons of simulated and observed
The simulated and observed stand characteristics (LAI and AGC) showed good agreement (Table 2 and Fig. 3). The model slightly underestimated LAI for the young forest sites, and overestimated LAI for the mature forest sites. Generally, the model overestimated AGC for the mature forest sites. The NRMSE was 28 for AGC and 31 % for LAI. The Willmott index of agreement was 0.95 and 0.96 for AGC and LAI, respectively. Overall, the model evaluation metrics indicated that the model performed better in the DBF sites than in the ENF sites.
Simulated trajectories of GPP, ER, and NEP for each site based on the site disturbance history (Table 1). The time series started from the earliest major disturbance for each site.
PnET-CN generally captured the changes of C fluxes following the clear-cuts
for each chronosequence site (Fig. 4). The predicted annual GPP generally
increased with time since disturbance and approached peak values
(1200–1500 g C m
Predicted annual ER was initially as high as 860–1030 and 710–860 g C m
As expected, the ratio of annual GPP to annual ER (GPP : ER) simulated by
PnET-CN was low during the early years after clear-cutting for both DBF and
ENF (Fig. 5). Within
Simulated trajectories of the annual GPP
The model predicted negative NEP (C source) for the first 6 and 17 years
after stand-replacing harvests for the DBF and the ENF, respectively (Fig. 4). The simulated peak annual net C loss occurred in the first or second
year after clear-cutting. The average C loss was 530–710 g C m
LAI fully recovered within 10–15 years after disturbance for the DBF sites
and within 40 years of age for the ENF sites (Fig. 6). The recovery of LAI
led to the gradual increase in GPP and the subsequent increase in AGC (Fig. 7). In general, AGC recovered much more slowly than C fluxes and LAI. The
changes of simulated AGC followed the logistic growth curve with slow
accumulation in the early years, fast accumulation in middle age, and
slow accumulation afterwards. The predicted LAI and AGC generally fell
within the range of observed values across two chronosequences (Figs. 3, 6,
7). For mature forests (> 60 years of age) in 2010, the DBF sites
generally stored more C in aboveground biomass than the ENF sites (10–12
vs. 8.5 kg C m
Harvest intensity had little effect on long-term C dynamics for both PFTs,
but it had sizeable effects during early succession (Fig. 8). Increasing
harvest intensity delayed GPP (Fig. 8a and f) and LAI (Fig. 8d and i)
rises and led to a lower reduction in ER (Fig. 8b and g), resulting in later-rising NEP (Fig. 8c and g). High harvest intensity (e.g., 100 % removal
of living trees) also directly reduced living tree AGC (Fig. 8e and i). By
reducing the harvest intensity parameter to 80 and 60 % from 100 % used
in the original model, average annual NEP over 100 years for DBF decreased
by 104 and 88 g C m
PnET-CN generally simulated the expected post-harvest trajectories in C
fluxes (GPP, ER, and NEP) and stock (LAI and AGC). Our simulations showed that LAI first increased rapidly and then stabilized
during the following development stages, because the model estimates foliage
growth through the parameter of maximum relative growth rate (Table S1) with
the restriction of current foliage biomass and resource availability. This
modeled response is consistent with the previous finding that foliage
biomass increased rapidly after disturbance and then stabilized (Sprugel,
1985). Our chronosequence-based results are generally consistent with
previous results. For example, Goulden et al. (2011) observed that LAI along a
chronosequence of boreal forest stands increased rapidly from 0.3 m
The simulated successional change in annual GPP for both PFTs generally followed the trajectory hypothesized by Odum (1969). However, despite a slight decrease in GPP hypothesized in Odum's trajectory, our simulations show a relatively flat GPP in mature forests (Figs. 4 and 10). In the model, GPP tracks LAI in the absence of significant changes in light, water, or nutrient stress. As LAI stabilizes in mature forests, GPP also stabilizes. Our results are consistent with previous studies showing relatively flat patterns in GPP after 20 years following harvests in temperate pine forests in Florida (Clark et al., 2004), northern temperate DBF in Wisconsin (Desai et al., 2008), and boreal jack pine forests in Saskatchewan (Zha et al., 2009).
Simulated trajectories of LAI for each site based on the site disturbance history (Table 1). The time series started from the earliest major disturbance for each site. Symbols represent measured LAI.
Furthermore, Humphreys et al. (2006) reported continuous increases of GPP with increasing forest age for temperate rainforests using three different stands at different stages of development (2, 14, and 53 years of age) following clear-cuts in British Columbia, Canada. However, northern temperate ENF showed a small difference in GPP between young and mature sites (Noormets et al., 2007; Desai et al., 2008). Desai et al. (2005) found that a nearby old-growth mixed forest had slightly lower GPP and significantly higher ER than nearby DBF sites. Site-to-site variations in species and soil fertility could result in variations in the successional trajectory of GPP after clear-cuts such that the observed trajectories may deviate from hypothesized or modeled trajectories. But the model was unable to simulate high GPP rates estimated by the EC technique in mature forests regardless of vegetation type, suggesting that there is room for improvement in model simulation of secondary succession. In addition, our chronosequences lack old-growth sites and do not encompass the full range of forest development stages, which limits the representativeness of the C flux and stock trajectories derived from chronosequence studies based on EC or other ecological observations (e.g., Clark et al., 2004; Humphreys et al., 2006; Noormets et al., 2007).
Simulated trajectories of aboveground biomass carbon (AGC) for each site based on the site disturbance history (Table 1). The time series started from the earliest major disturbance for each site. Symbols represent estimated AGC.
We found that annual ER for secondary temperate forests declined slightly in
the first ten years because of low autotrophic respiration at first after
the removal of trees. Amiro et al. (2010) reported that ER was reduced in
the very first year following harvests for a number of EC flux sites over
North America. Previous field studies showed that ER following clear-cuts
increased with forest age (e.g., Humphreys et al., 2006; Zha et al.,
2009), partly supporting our results that ER slightly increased after the
short decline period (10–25 years of age) in northern temperate forests
until the stands reached maturity. Martin and Bolstad (2005) showed
that chamber-based soil respiration in DBF of northern Wisconsin ranged from
857–1143 g C m
The trajectory of our simulated GPP : ER ratio is similar to the curve derived
by Amiro et al. (2010) using EC observations and forest age from fire and
harvest chronosequences across North America (GPP : ER
Sensitivity of carbon fluxes (GPP, gross primary production; ER,
ecosystem respiration; NEP, net ecosystem production) and stand
characteristics (LAI: leaf area index; AGC: aboveground carbon stock) to
changes in harvest intensity (reduced by 0.2 and 0.4 compared to 1 for
assumed clear-cuts used in the model tests) for
Our simulated successional dynamics of NEP following clear-cuts generally supported the trajectories of Chapin et al. (2002), but for different reasons. The hypothesized trajectories show declining GPP and relatively flat ER with time. Our simulated decline in NEP resulted from relatively flat GPP and growing ER with stand development (Figs. 4 and 8). This has been observed for northern temperate hardwood chronosequence sites (Desai et al., 2008), northern temperate pine forests (Peichl et al., 2010), and boreal DBF forests (e.g., Goulden et al., 2011). A recent North American Carbon Program (NACP) synthesis study showed similar changes in NEP after either stand-replacing fire or harvest based on EC chronosequence measurements across North America (Amiro et al., 2010).
Chapin et al. (2002) hypothesized that heterotrophic respiration is
initially high following disturbance, declines in early succession, rises in
middle succession, and declines thereafter, while NPP reaches a peak in
middle age and declines in old stands. The simulated successional
trajectories in heterotrophic respiration were supported by hypothesized
pattern change, although our simulated NPP did not decline in mature stands
(Fig. S1). Previous studies also support our simulated trajectory
in heterotrophic respiration. For example, Pregitzer and Euskirchen (2004) reported that heterotrophic respiration was high (mean
value of 970 g C m
Although our model underestimated NEP and GPP for both the DBF and ENF sites
in the Upper Midwest region (Figs. 1 and 2), our predicted NEP was comparable
to estimates from other studies in similar regions. For example, our
predicted maximum NEP for the ENF sites (567–602 g C m
We found that the simulated AGC during forest regrowth gradually increased following the typical logistic growth curve (Sprugel, 1985). In the model, low NPP in the early stages results in slow AGC accumulation. Once the amount of NPP approximately equals annual dead biomass C that is largely controlled by the wood turnover rate, the trajectory of AGC reaches a plateau. Previous chronosequence studies showed that AGC increased with increasing age (e.g., Peichl and Arain, 2006; Goulden et al., 2011; Powers et al., 2012). Powers et al. (2012) reported that AGC increased rapidly with age in young red pine stands across a chronosequence in northern Minnesota, USA. However, the representativeness and generalization of these findings were limited by the small number of young stands (Powers et al., 2012).
Sensitivity analysis shows that more intensive harvests could have larger and longer impacts on successional trajectories of C dynamics in early succession for both DBF and ENF. Fewer EC-based studies have investigated the effects of harvest intensity on forest C fluxes (e.g., GPP, ER, and NEP) because of the high establishment cost of EC systems. Nevertheless, some modeling studies have provided insights into how forest C fluxes and stocks are affected by harvest intensity. Our findings are supported by previous modeling studies. For example, a recent modeling study of temperate forests reported that more intensive harvests increased the recovery time of NPP for ENF and DBF in Minnesota and Wisconsin, USA (Peters et al., 2013). In the boreal forests of central Canada, less intensive harvest and longer rotation length might increase total C sink up to 40 % (Peng et al., 2002), although recent studies indicate that longer rotation length could not necessarily increase C sequestration under changing climate conditions (Wang et al., 2012b; Wang et al., 2013). A recent synthesis study on the effects of partial cutting on forest carbon stocks found that partial cutting has no significant effects on litter C and soil organic C, although more intensive cutting can reduce AGC more (Zhou et al., 2013b). This synthesis study is not able to determine the recovery duration due to the lack of long-term observations. If harvesting operations largely reduce soil organic matter, C fluxes (e.g., GPP, NPP, ER, and NEP) and living AGC are reduced for both PFTs. Consistent with this, Peters et al. (2013) showed that simulated NPP could not recover to pre-harvesting levels due to greater removal of soil organic matter. Therefore, our model results suggest management practices should aim to decrease soil disturbance caused by harvest operations.
We found that DBF may reach a peak in LAI and GPP faster than ENF after clear-cutting, showing clear differences in pattern of ecosystem development between the DBF and ENF sites. More rapid recovery of LAI and GPP for DBF sites lead to sooner recovery of NEP and AGC regardless of harvest intensity, supporting our second hypothesis. The foliage-related parameters such as FolRelGroMax and AmaxB mainly govern the differences in successional trajectories between the two PFTs (Table S1). DBF is assumed to have more productive foliage than ENF, and more photosynthetic production then can lead to more foliage production. With this positive feedback in the model, GPP, NEP, and AGC of the DBF sites recover more rapidly than those of the ENF sites. Our findings are consistent with the chronosequence studies showing that the temperate DBF in northern Michigan rapidly became a net C sink after 6 years following disturbances (Gough et al., 2007) and that ENF stands in northern Wisconsin became net C sinks within 10–15 years after harvesting (Noormets et al., 2007). Through the analysis of the Forest Inventory and Analysis (FIA) data, Williams et al. (2012) suggested that faster growth in AGC at high productivity sites caused higher C fluxes and stocks. Our findings are also consistent with a recent modeling study suggesting that temperate DBF switches to positive NEP faster than temperate ENF after clear-cuts, and DBF has a higher peak in NEP compared to ENF (Peckham et al., 2012). A modeling study conducted in boreal forests also reported that low productive boreal ENF needed 1–3 more years to attain a positive NEP than boreal DBF after clear-cuts in Saskatchewan, Canada (Wang et al., 2012b). These observed and modeled successional changes further indicate that DBF tend to have higher photosynthetic capacity than ENF in the early stage of stand development following stand-replacing harvests.
The sensitivity analysis suggests that more productive forests could be more
strongly affected by greater soil removal fractions, as soil removal reduces
soil organic matter thereby resulting in relatively low N mineralization in
the model. Peters et al (2013) showed that NPP was more strongly reduced for
aspen than for jack pine in their simulations. However, productive lodgepole
pine (
PnET-CN can explicitly simulate the effects of disturbance, pollution, and climate change on forest C dynamics (e.g., Ollinger et al., 2002; Pan et al., 2009; Peters et al., 2013). Despite the capability of the model, the model has some limitations in simulating harvesting effects, and accurate representation of the trajectories of C fluxes and stocks following harvests still remains a challenge.
The performance of the model to simulate forest regrowth after harvests is limited by the absence of population and community dynamics associated with regeneration. Most process-based models such as PnET-CN and TEM (Raich et al., 1991) have been developed primarily to simulate C balance for mature forests over past decades (Landsberg, 2003), resulting in no provision for simulating regeneration such as shrub component and species-specific successional dynamics in these models. Changes in forest composition (e.g., evergreen and deciduous tree species and understory shrubs) along the course of succession are not fully considered by most ecosystem models. PnET-CN does not simulate shrubs and herbs that likely dominate stands in the early successional stage after stand-replacing harvests. The model thus is not able to simulate the particularly high GPP and ER in the young forests where forest canopy has not yet fully recovered.
Understory layer is also an important component for mature forest ecosystems in terms of C fluxes and stocks. Misson et al. (2007) reported that understory can contribute 11 % (range, 0–39 %) of GPP at 10 sites across a wide range of forest types and climates. PnET-CN slightly overestimated overstory LAI for the mature DBF sites and reasonably predicted foliar N concentration compared to satellite-based estimates. The lack of understory layer in the model is possibly responsible for the underestimation of GPP for mature DBF sites. Species competition and cohort methods that have been employed in other models such as ED (Medvigy et al., 2009) and LPG-Guess (Smith et al., 2001) could be used to improve the regeneration and understory components of PnET-CN in the future.
Parameter values used in the model were generally derived from specific measurements for a given stand development stage, particularly mature forests, although the parameter values likely differ with stand development. For example, the canopy light attenuation constant coefficient is typically measured in mature forests (e.g., Ryu et al., 2008), although the coefficient is known to change with canopy cover (Brantley and Young, 2007). The use of the canopy light attenuation coefficient measured in mature forest for whole forest life simulations could slow down stand development due to the underestimation of photosynthesis in young forests. Understanding the relationship between such parameters and state variables (e.g., LAI) is thus one of the challenges to simulate the effects of stand-replacing harvests on forest C dynamics.
Changing climate conditions can also affect the values of some parameters. For example, wood turnover rate (%, tree mortality in terms of biomass losses), to which wood living biomass C and soil organic C are sensitive, could be altered by extreme weather conditions including droughts (Allen et al., 2010; Wang et al., 2012a). Most process-based models are not able to simulate the mechanistic processes associated with tree mortality under changing climate conditions (McDowell, 2011; Wang et al., 2012a), although there is growing interest in the mechanistic modeling of forest mortality (e.g., McDowell et al., 2013; Powell et al., 2013). Recent studies have revealed that climate and disturbances govern forest C dynamics (Magnani et al., 2007; Bond-Lamberty et al., 2013). Future modeling efforts can benefit from improved understanding of the effects of climate change on parameter values that are assumed to be constant in the model.
Harvest methods depend on forest types, management needs, and species to be regenerated. For example, selective harvesting or the shelterwood system is typically used for hardwoods in Wisconsin (Wisconsin Department of Natural Resources, 2011). Stand-replacing harvesting was assumed for both DBF and ENF chronosequence sites due to the lack of harvesting information and the types of clearing applied to the sites studied. The sensitivity analysis conducted in this study suggests that harvest intensity affects C dynamics in early succession after harvesting. Observations in residuals and post stands after each operation type (e.g., pre-commercial thinning and selective harvesting) are needed to parameterize process-based models for better mechanistic understanding of the harvest effects on forest C dynamics.
The PnET-CN model was generally able to simulate the effects of stand-replacing harvests on forest C dynamics (C fluxes and AGC) for two northern temperate forest chronosequences. The predicted dynamics in NEP and AGC following clear-cuts generally follow the hypothesized trajectories, although our simulations show that the decline in NEP was due to relatively stable GPP and gradually increasing ER. Our study also shows that DBF recovered faster (11 years) from net C sources to net sinks and accumulated more C in AGC than ENF. Northern temperate ENF is more vulnerable to stand-replacing harvests than northern temperate DBF. Future research is needed to better understand how respiration components (e.g., ecosystem and soil respiration) and production components (e.g., overstory and understory) change with forest age and their determinants. Modeling the combined effects of climate change and forest management will benefit from the incorporation of forest population dynamics (e.g., regeneration and mortality), relationships between age-related model parameters and state variables (e.g., LAI), and silvicultural system into the model. With these improvements, process-based ecosystem models can better simulate regional C balance associated with disturbance regime under changing climate.
This study was supported by the National Science Foundation (NSF) through MacroSystems Biology (award no. 1065777) and by the National Aeronautics and Space Administration (NASA) through Terrestrial Ecology Program (award no. NNX11AB88G) and Carbon Cycle Science Program (award no. NNX14AJ18G). We thank Lucie Lepine, Zaixing Zhou, Andrew Ouimette, and Alexandra Thorn for helpful discussions. We also thank the anonymous reviewers and Peter Curtis for their constructive comments on the manuscript. Edited by: S. Liu