Quantifying forest carbon dynamics as a function of tree species composition and management under projected climate RACHEL J. SWANTESON-FRANZ, DANIEL J. KROFCHECK, AND MATTHEW D. HURTEAU
Department of Biology, University of New Mexico, MSC03 2020, Albuquerque, New Mexico 87131 USA Citation: Swanteson-Franz, R. J., D. J. Krofcheck, and M. D. Hurteau. 2018. Quantifying forest carbon dynamics as a function of tree species composition and management under projected climate. Ecosphere 9(4):e02191. 10.1002/ecs2.2191
Abstract. Uncertainty remains about whether current rates of forest carbon uptake will be maintained with on-going climate change and increasing rates of disturbance. The potential exists for climate and disturbance to exceed the physiological tolerances of certain tree species and push forest ecosystems to a point where they become C sources. Thus, a diversity of tree species with a range of physiological tolerances could provide adaptive capacity and potentially sustain a C sink despite adverse abiotic influences. The fire-prone pine forests of the southeastern USA have been impacted by a combination of land use and fire exclusion, which has altered the demographics and composition of these historically diverse forests. We sought to quantify how prescribed fire and planting of climate-resilient tree species would alter forest carbon dynamics under projected climate change at Fort Benning, Georgia. This landscape is comprised of a diversity of forest types with a range of land-use histories and is heavily managed to meet military training objectives and federally listed species habitat requirements. We used a simulation approach to determine species-specific growth responses to projected climate and develop two management scenarios: no-management and prescribed fire coupled with planting. We ran landscape simulations of these two management scenarios under climate projections from ten global climate models to quantify how active management would alter forest carbon dynamics as a function of changing climate and wildfire. We found that the prescribed fire and plant scenario increased total ecosystem carbon (TEC) over our no-management scenario by over 20 Mg C/m2 by late century. Despite the differences in TEC, differences in net ecosystem exchange were not realized over the entire simulation. The primary drivers of TEC differences were sustained carbon uptake and lower carbon loss to wildfire in the prescribed fire and plant scenario. Our results demonstrate that under projected climate, managing to reduce the impacts of fire and planting climate-adapted species can increase the mitigation potential of these forests. Key words: adaptation; carbon; climate change; fire; forest management; mitigation; southern pine. Received 8 March 2018; accepted 12 March 2018. Corresponding Editor: Debra P. C. Peters. Copyright: © 2018 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. E-mail:
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INTRODUCTION
factors, such as land-use change, disturbance, and climatic variability, all influence forest C dynamics (Pan et al. 2011). Yet, forest response to these factors is realized at the local-scale through biotic interactions that result from demographics and physiological differences among tree species (Barford et al. 2001, Samuelson et al. 2012). Thus, quantifying how species composition influences
Forests have the potential to be managed for net negative carbon (C) emissions, making them central in efforts to regulate climate (Canadell and Raupach 2008). However, there is a hierarchy of factors that influence forest C dynamics. At the regional scale, human and environmental ❖ www.esajournals.org
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be moderated by climate (Dangal et al. 2014). In the southeastern USA, inter-annual precipitation variability can influence tree growth by a factor of three, which is in part driven by species-specific responses to precipitation variability (Hanson et al. 2001). Given that precipitation projections under climate change have a high level of uncertainty in this region, understanding how the tree species diverse forests of the southeastern USA will respond to this future variability is central to projecting future forest C dynamics (Wear and Greis 2002). Forests of the southeastern USA span a range of conditions, from fire-maintained, low-density longleaf pine-dominated forests to fire-suppressed, broadleaved-dominated forests. Historically, these systems were routinely affected by surface fire, and deviations from that disturbance regime tend to introduce susceptibility to ecosystem type change in these frequent fire-adapted systems (Gilliam and Platt 1999, Addington et al. 2015a). This compounds the influence of past land use and land-use change on forest composition across the region, which has been considerable (Oswalt et al. 2012). For the lands maintained in forest cover, widespread conversion to monoculture pine plantations has reduced tree species diversity across much of the landscape (Glitzenstein et al. 1995, Landers et al. 1995). Yet, the diversity of tree species capable of growing in this region suggests a high potential for functional redundancy if mixed-species forests are restored (Waldrop et al. 1992). Given on-going climate change and the uncertainty associated with projected precipitation in this region, we sought to determine whether active forest management that selected for species with the highest rates of C uptake could markedly increase forest C storage at Fort Benning, Georgia, USA. This landscape is composed of a mix of pine-dominated and mixed pinebroadleaved forest types and has a history of active forest management, including the use of prescribed fire to restore longleaf pine (Pinus palustris) forests. Given that pine species are commonly planted for biomass production because of their faster growth rates, we hypothesized that the application of prescribed fire combined with conifer planting would yield greater C uptake and storage than an alternative that did not include management. Further, we hypothesized
local-level response to changing climate and disturbance is central to quantifying the future mitigation potential of forests. In terms of C dynamics, physiological differences between co-occurring species can provide a level of functional redundancy within ecosystems. Increased warming, which is projected to continue, coupled with projections for increasing inter-annual precipitation variability can lead to a more water-limited environment (Allen et al. 2010, Knutti and Sedl acek 2012). At the extreme end, hotter droughts have already led to widespread tree mortality (Williams et al. 2013), which has been shown to vary by tree size and species (Elliott and Swank 1994, Mueller et al. 2005). Species-specific variability in drought tolerance, root system extent, response to CO2 enrichment, among others, is important in determining forest responses to future drought and heat stress (Breshears and Allen 2002, Breshears et al. 2005, Bonan 2008, Martin et al. 2015). Diversity in these traits can lead to lower mortality rates and compensatory growth, allowing for C uptake to be sustained even as climate negatively affects some component species (Klos et al. 2009, Hurteau et al. 2014). Forest species composition, structure, and tree age distribution can be altered by both natural and anthropogenic disturbances. Globally, many common forest disturbances are directly altered by climate and rising temperatures are projected to increase the frequency and size of several types of disturbance, including wildfire (Siedl et al. 2017). While the implications of climate-driven increases in natural disturbance on forest C dynamics are significant, anthropogenic forest disturbance has the largest impact on forest C globally (Houghton 1999, Rhemtulla et al. 2009, Zhao et al. 2010, 2013). Forest management, a form of anthropogenic disturbance, is used to meet a variety of objectives, including lessening the potential impacts of natural disturbance events (Marshall et al. 2008). Therefore, understanding how on-going management can influence forest C uptake is important for sustaining this ecosystem service under on-going climate change (Thom and Seidl 2016). In heavily managed forests, like those of the eastern USA, recovery from anthropogenic disturbance, such as land-use change and harvest, is an important driver of forest C uptake that can ❖ www.esajournals.org
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polyphemus), that rely on a longleaf pine-dominated forest (Baskaran et al. 2006, USAIC 2006, Addington et al. 2015a, Martin et al. 2015). Habitat restoration for these federally listed species has included longleaf planting, prescribed fire, and thinning of hardwoods and loblolly pine. Longleaf pine is a shade-intolerant species and sustaining longleaf pine-dominated forests requires regular burning. Historically, longleaf pine systems had fire return intervals of 2–4 yr, making prescribed fires and managed natural fire ignitions essential to longleaf establishment and persistence (Lemon 1949, Kirkman et al. 2004).
that the actively managed landscape would yield lower wildfire emissions because regular prescribed burning would reduce the connectivity of surface and canopy biomass by limiting the total biomass of resprouting species. We used a landscape simulation model and climate projections from ten global climate models to test these hypotheses.
METHODS Study location description Fort Benning is a military installation that covers 73,533 ha and is located near the GeorgiaAlabama border in the Southeastern Plains ecoregion (USAIC 2006; Fig. 1). With latitude of 32°N and elevation varying from 58 to 226 m, this location is categorized as being part of the Cfa (mild temperate–fully humid–hot sum€ppen climate classifications mer) region under Ko (http://hanschen.org/koppen/; USAIC 2006). The mean monthly maximum temperature of 33.3°C occurs during July, and the mean monthly minimum temperature of 2.8°C occurs during January (NCEI 2017). Mean annual precipitation is 1295 mm (USAIC 2006). Fort Benning has low topographic variation; conifer and broadleaved tree species occupy the well-drained upland areas whereas wetland tree species occupy the poorly drained lowland areas. In this study, we focused on the upland forests because they are actively managed to meet multiple objectives. Forest types across these upland sites range from longleaf pine-dominated forests, to mixed pine forests that include Pinus palustris as well as shortleaf (Pinus echinata) and loblolly pine (Pinus taeda), to mixed pine-broadleaved forests that include Quercus falcata, Quercus marilandica, Quercus laevis, and Carya tomentosa (Addington et al. 2015a, Martin et al. 2015). The land-use history of agriculture and logging has heavily influenced forest structure at Fort Benning (USAIC 2006). Due to logging interests throughout much of the 20th century, loblolly pine was extensively planted because it was the fastest growing conifer species (USAIC 2006). However, this practice began to conflict with the management of federally listed threatened and endangered species at the installation, specifically the red-cockaded woodpecker (RCW; Picoides borealis) and gopher tortoise (Gopherus ❖ www.esajournals.org
Simulation model framework and parameterization In order to quantify the effects of management and climate on C dynamics in the forests of Fort Benning, we utilized the LANDIS-II (v6.2) landscape forest succession model. LANDIS-II uses climate, disturbance, and physiological parameters to model landscape level effects on forest succession (Scheller and Mladenoff 2004). In the model, species are represented by age cohorts of biomass, and species composition is determined by individual species’ responses to disturbance, climate, and interspecific interactions, which are a function of their individual life history and physiological parameters. Model parameterization requires development of an initial communities layer and division of the study area into ecoregions of similar abiotic characteristics. For this study, we used the initial communities layer, ecoregions layer, and species level parameter values developed by Martin et al. (2015). The initial communities layer was developed using inventory data from Fort Benning’s natural resource department, and the ecoregions were defined using NRCS SSURGO data (NRCS 2013). These data layers and the parameterization were validated against an independent inventory data set (Martin et al. 2015). We used three extensions of the core LANDISII model to simulate carbon dynamics, wildfire, and forest management. The Net Ecosystem Carbon & Nitrogen (NECN) succession extension integrates the CENTURY soil model (Metherell et al. 1993, Parton et al. 1993) and the biomass succession model (Scheller and Mladenoff 2004) to simulate above and belowground pools and 3
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Fig. 1. Map of Fort Benning forest types and geographic location.
fluxes of carbon and nitrogen (Scheller et al. 2011a, b). The Dynamic Fire & Fuels System simulates wildfires and the influence of fire events on the distribution of biomass. This extension requires user-defined fire weather and fire size distributions, ignition frequencies, and spread parameters (Sturtevant et al. 2009). We used a combination of parameter values from Martin et al. (2015) and Addington et al. (2015b) to parameterize the model such that the number of wildfires and area burned approximated recent fire events at Fort Benning (Appendix S1: Table S1). The Biomass Harvest extension is capable of simulating partial cohort disturbance and allows for the simulation of user-developed management prescriptions (Gustafson et al. 2000). ❖ www.esajournals.org
Climate scenarios We used 1/8° bias-corrected and downscaled projected climate data from the Coupled Model Intercomparison Project phase 5 (CMIP5, Reclamation 2013). We selected model projections that used the representative concentration pathway 8.5 (RCP 8.5). Because model agreement is limited for the southeastern USA (Kunkel et al. 2013), we selected ten models to capture the range of projected precipitation variability (Mote et al. 2011). We ranked the projections from 41 climate models based on cumulative precipitation over the 21st century and chose the models that corresponded to the maximum, median, minimum, and first and third quartile of cumulative precipitation values. The model with the 4
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chose to plant all three species, allowing us to maintain some level of functional redundancy in the system. Because the model represents trees as age cohorts of biomass, when planting is initiated, a one-year-old cohort of the planted species is established. The biomass that the cohort achieves following planting is a function of the light environment within a given grid cell as determined by established cohorts and competition for resources with the planted cohorts of the other two species. Resource limitations in a particular grid cell may preclude the survival of the planted cohort. Additional mortality of newly planted cohorts occurs during the subsequent prescribed fire that is simulated five years following planting. The no-management scenario, which did not include any active management, allowed for regeneration of conifer and broadleaved species at rates only influenced by dispersal and competition. Both management scenarios included wildfire.
maximum precipitation projection (GFDLCM3.1) had cumulative precipitation over the century that was 29% higher than the model with the minimum precipitation projection (ISPLCM5A-MR). In addition to these five, we arbitrarily selected an additional five models (Appendix S1: Table S2). The climate generator in the NECN succession extension uses the daily sequence of climate data from each projection to generate monthly climate parameter values for simulating photosynthesis and respiration.
Management scenarios To quantify species-specific response to projected climate and inform the development of our planting strategy, we ran single-species simulations for the ten most common species by biomass at Fort Benning using projected climate data from the models that had the minimum, median, and maximum cumulative precipitation. The initial communities for each single-cell simulation included ten cohorts ranging from 10 to 100 yr of age for the species of interest to determine the response of a mixed-age stand. The single-cell simulations were run for 100 yr, in the absence of wildfire or management. The C storage capacity of the conifers greatly outperformed that of the hardwoods under all precipitation scenarios (Appendix S1: Fig. S1). Pinus palustris had the largest stock of total ecosystem carbon (TEC) by 2100, ending with nearly triple the average TEC of the broadleaved species (Appendix S1: Fig. S1). Based on the single-species results, we developed two management scenarios, no-management and prescribed burning combined with conifer planting, to test the potential for active management to increase C uptake and storage. The prescribed burning and conifer planting scenario simulated a five-year fire return interval, such that 20% of the landscape was burned annually. Prior research in longleaf pine forest suggests little change in C between prescribed fire intervals ranging from 1 to 5 yr (GonzalezBenecke et al. 2015). Following prescribed burning, we simulated planting of the three conifer species in each grid cell to accelerate carbon uptake based on the results of our single-species simulations. Planting occurred after each prescribed fire. Since all of the conifers performed similarly in our single-species simulations, we ❖ www.esajournals.org
Simulations and data analysis We ran 10 replicate 100-yr simulations (2000– 2099) of each management scenario under each of the ten climate projections using a 1-yr timestep. We created ensembles of the model output for each management scenario, such that each management scenario included 10,000 simulation years of data. In order to quantify how the management scenarios, wildfire, and climate interacted to influence carbon dynamics, we evaluated TEC, net ecosystem exchange (NEE), cumulative wildfire emissions, and the coefficient of variation in fire severity. To account for within landscape variability as a function of ecoregion characteristics, we calculated area-weighted sums for TEC, NEE, and cumulative wildfire emissions for each simulation year. We used the area-weighted sums of TEC and NEE from all replicate simulations to calculate means by management scenario. To calculate the net ecosystem carbon balance (NECB) of the two management scenarios, we subtracted wildfire and prescribed fire emissions from NEE. To quantify the cumulative wildfire emissions, NEE, and NECB by management scenario, we summed each output of interest by year for each replicate simulation and then calculated means using all replicate simulations for each of the ten climate scenarios. We used Python for analyzing 5
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model output and generating figures (Python Software Foundation, version 2.7., http://www. python.org).
early in the simulation period, but the effect of the prescribed fire and plant scenario on both wildfire losses and NEE reduced the rate of decline in mean NECB that is common as a forest ages (slope: no-management 0.01, prescribed burn and plant 0.006; Figs. 4, 5). The mean NECB values for both management scenarios remained carbon sinks over the 100-yr period. However, the range of NECB values indicates the potential for the combined effects of wildfire and climate to shift the system to a C source periodically (Fig. 4).
RESULTS As we hypothesized, late-century TEC under the prescribed burn and plant scenario surpassed the no-management scenario by over 20 Mg C/ m2, which was driven primarily by differences in aboveground C (Fig. 2). We had hypothesized that the prescribed burn and plant scenario would also have a faster rate of carbon uptake, yet we found little difference in NEE between the two scenarios for the first half of the simulation period (Fig. 3A). This result is driven by the fact that the prescribed fires are primarily impacting broadleaved species, which have a lower rate of carbon uptake than the pine species. However, over the latter half of the simulation period, the prescribed fire and plant scenario maintained a higher rate of uptake and a narrower range across climate projections due to the transition toward a more pine-dominated overstory (Fig. 3A). When we accounted for C losses from both wildfire and prescribed fire, cumulative NECB was higher for the no-management scenario (Fig. 3B). Losses from prescribed fire depressed NECB relative to no-management
DISCUSSION Forest management activities typically incur some level of near-term C reduction, but can yield a larger total C stock over the long-term because reducing resource competition can increase tree growth (Wiechmann et al. 2015, Hurteau et al. 2016). However, this C outcome is often system dependent and management activities that remove tree biomass can lead to long-term C reductions (Campbell et al. 2012, Martin et al. 2015, Laflower et al. 2016). The long-term C outcome is largely influenced by the productivity of the system, rates of disturbance, and the influence of management activities in mitigating disturbance-driven C loss. In
Fig. 2. The means (lines) and complete range of simulated total ecosystem carbon (A) and aboveground carbon (B) for the no-management and prescribed fire and plant scenarios. Means were calculated using the 10 replicate simulations from each of the 10 climate projections for the two management scenarios. The ranges represent the simulated outputs from all ten climate projections.
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Fig. 3. The means (lines) and complete range of simulated cumulative net ecosystem exchange (NEE, A) and net ecosystem carbon balance (NECB, B) for the no-management and prescribed fire and plant scenarios. Both NEE and NECB are from a plant perspective, where positive values are a sink. Means were calculated using the 10 replicate simulations from each of the 10 climate projections for the two management scenarios. The ranges represent the simulated outputs from all ten climate projections.
the fire-prone forests of Fort Benning, we had hypothesized that actively reducing competition from broadleaved species through prescribed burning, coupled with planting faster-growing conifer species, would yield an increase in TEC compared to no-management. Our simulation results supported this hypothesis, and TEC in
our prescribed fire simulations was similar to empirical results from a chronosequence of firemaintained longleaf pine stands sampled across the southeastern USA (Samuelson et al. 2017, Fig. 2). Our finding that TEC increased as a result of active management was contrary to the results of
Fig. 4. The means (lines) and complete range of simulated net ecosystem carbon balance (NECB) over time for the no-management (A) and prescribed fire and plant (B) scenarios. Net ecosystem carbon balance is from a plant perspective, where positive values are a sink. Means are a three-year moving average, and ranges represent the simulated outputs from all ten climate projections.
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no-management scenario was approximately 15 Mg C/m2 lower by late century than the control scenario in Martin et al. (2015) and our prescribed fire and plant scenario late-century mean TEC was approximately 30 Mg C/m2 higher than their prescribed burn results. Simulating wildfire created an opportunity for C loss from the nomanagement scenario, which is ever-present on a military installation that includes ignition sources from training (Addington et al. 2015b). Further, by following prescribed fire with conifer planting, this scenario altered the distribution of biomass among species to favor the species that are better able to sustain growth when precipitation is more variable (Fig. 2; Appendix S1: Fig. S1). Like any simulation results, ours should be viewed in the context of our model assumptions and our scenario development. The fire simulations we utilized were from fire weather distributions developed using meteorological data from 51 yr (1962–2012). Given the increase in temperature and greater inter-annual precipitation variability projected for this region, fire weather could become more conducive to extreme fire behavior. If future fire weather increases the risk of larger high-severity fires, this could reduce the efficacy of our prescribed fire and plant scenario. Management activities that provide a higher level of resistance against high-severity wildfire often require mechanical thinning to increase the canopy base height and this additional biomass removal further reduces TEC (Krofcheck et al. 2017). Quantifying the potential for reduced treatment efficacy will require additional empirical data that capture the interaction between forest structure and fire behavior under extreme fire weather conditions in southeastern U.S. forests. An additional source of uncertainty in our results comes from the projected climate scenarios. While we followed best practices by including projections from 10 climate models (Mote et al. 2011), extreme climatic events that are most influential for forest growth and mortality are heavily influenced by fine-scale processes that are not well represented in global climate models (Diffenbaugh et al. 2005). Extreme climatic events, such as hotter droughts, have been linked to widespread tree mortality, and projections for the southeastern USA include increased frequency of extreme heat and precipitation events
Fig. 5. The means (lines) and complete range of simulated cumulative wildfire emissions for the no-management and prescribed fire and plant scenarios. Means were calculated using the 10 replicate simulations from each of the 10 climate projections for the two management scenarios. The ranges represent the simulated outputs from all ten climate projections.
Martin et al. (2015), who simulated forest C dynamics at Fort Benning using the same simulation approach. Martin et al. (2015) found that the no-management control scenario yielded a higher carbon stock than that of either their prescribed fire or combined prescribed fire and thin scenarios. Similar results have been found in other studies where the C density of the fire-suppressed forest is high and any management intervention causes a decrease in the C stock that can persist for decades (Hurteau et al. 2011, Krofcheck et al. 2017). Similar to Martin et al. (2015), initial differences in NEE between our two management scenarios were minimal. However, the prescribed fire and plant scenario maintained a higher rate of cumulative NEE increase and there was less variability in the range from the different climate projections (Fig. 3). In both studies, the limited differences in NEE early in the simulation were due to some form of compensatory growth and competitive release following fire that allowed for increased C sequestration to offset any C loss due to prescribed fire. The primary drivers of the disparate results in TEC between our research and that of Martin et al. (2015) were our inclusion of wildfire, projected climate, and the addition of planting following prescribed fire. Mean TEC in our ❖ www.esajournals.org
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(Diffenbaugh et al. 2005). Growth and mortality rates due to increasing drought severity vary as a function of species group, forest composition, and forest structure in the southeastern USA (Klos et al. 2009). There is evidence that management activities, such as fertilization, can increase water use efficiency during drought (Samuelson et al. 2018). However, better accounting for species-specific responses to extreme climatic events will require additional physiological research to identify drought-induced mortality thresholds for southeastern U.S. tree species. While the focus of our simulation experiment was on carbon uptake and storage for climate change mitigation, forest ecosystems provide a broader suite of ecosystem services. Seeking to maximize any single ecosystem service can limit the provision of other services. At Fort Benning, longleaf pine restoration began in response to the listing of the RCW under the Endangered Species Act. The RCW recovery plan details specific longleaf pine forest structural attributes that are required to meet nesting and roosting habitat requirements (USFWS 2003). Prescribed fire alone, as simulated in our study, may be insufficient to achieve the forest structural attributes required to meet the recovery plan targets, as has been found in other research (Martin et al. 2015). Accelerating the development of these structural attributes may require mechanically thinning the forest, which would further reduce TEC relative to our prescribed burn and plant scenario. Furthermore, a RCW focused treatment would limit planting to longleaf pine, since this tree species is susceptible to a fungus that facilitates cavity construction by RCW. From a climate change mitigation perspective, single-species planting could reduce functional redundancy in the system and come with costs in terms of climate adaptation. Going forward, evaluating the tradeoffs associated with the different management objectives will require better understanding of how the effects of management activities influence the adaptive capacity of the forest under projected climate change.
late century. Our work demonstrates that the C outcomes are heavily influenced by the interaction of forest management activities and wildfire, a disturbance that may become more influential if changing climate leads to increasingly extreme fire weather. Further, our results demonstrate that by altering the distribution of biomass by species, we have the potential to leverage species-specific physiological differences and build climate change adaptive capacity into these systems.
ACKNOWLEDGMENTS Funding for this research was provided by the US Department of Defense’s Strategic Environmental Research and Development Program (SERDP, project RC-2118).
LITERATURE CITED Addington, R. N., B. O. Knapp, G. G. Sorrell, M. L. Elmore, G. G. Wang, and J. L. Walker. 2015a. Factors affecting broadleaf woody vegetation in upland pine forests managed for longleaf pine restoration. Forest Ecology and Management 354:130–138. Addington, R. N., S. J. Hudson, J. K. Hiers, M. D. Hurteau, T. F. Hutcherson, G. Matusick, and J. M. Parker. 2015b. Relationships among wildfire, prescribed fire, and drought in a fire-prone landscape in the south-eastern United States. International Journal of Wildland Fire 24:778–783. Allen, C. D., et al. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660–684. Barford, C. C., S. C. Wofsy, M. L. Goulden, J. W. Munger, E. H. Pyle, S. P. Urbanksi, L. Hutyra, S. R. Saleska, D. Fitzjarrald, and K. Moore. 2001. Factors controlling long- and short-term sequestration of atmospheric CO2 in a mid-latitude forest. Science 294:1688–1691. Baskaran, L. M., V. H. Dale, R. A. Efroymson, and W. Birkhead. 2006. Habitat modeling within a regional context: an example using gopher tortoise. American Midland Naturalist 155:335–351. Bonan, G. B. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:1444–1449. Breshears, D., and C. Allen. 2002. The importance of rapid, disturbance-induced losses in Carbon management and sequestration. Global Ecology & Biogeography 11:1–5.
CONCLUSION This study has shown that there is potential for forests of the southeastern USA to remain a C sink and store a substantial amount of C through ❖ www.esajournals.org
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SWANTESON-FRANZ ET AL. wildfire-prone southwestern ponderosa pine forests. Ecological Applications 26:383–391. Hurteau, M. D., T. A. Robards, D. Stevens, D. Saah, M. North, and G. W. Koch. 2014. Modeling climate and fuel reduction impacts on the mixed conifer forest carbon stocks in the Sierra Nevada, California. Forest Ecology and Management 315:30–42. Hurteau, M. D., M. T. Stoddard, and P. Z. Fule. 2011. The carbon costs of mitigating high-severity wildfire in southwestern ponderosa pine. Global Change Biology 17:1516–1521. Kirkman, L. K., K. L. Coffey, R. J. Mitchell, and E. B. Moser. 2004. Ground cover recovery patterns and life-history traits: implications for restoration obstacles and opportunities in a species-rich savanna. Journal of Ecology 92:409–421. Klos, R. J., G. G. Wang, W. L. Bauerle, and J. R. Rieck. 2009. Drought impact on forest growth and mortality in the southeast USA: an analysis using Forest Health and Monitoring data. Ecological Applications 19:699–708. Knutti, R., and J. Sedl acek. 2012. Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change 3:369–373. Krofcheck, D. J., M. D. Hurteau, R. M. Scheller, and E. L. Loudermilk. 2017. Restoring surface fire stabilizes forest carbon under extreme fire weather in the Sierra Nevada. Ecosphere 8:1–18. Kunkel, K. E., et al. 2013. Regional climate trends and scenarios for the U.S. National Climate Assessment. Part 2. Climate of the Southeast U.S., NOAA Technical Report NESDIS 142-2. Laflower, D. M., M. D. Hurteau, G. W. Koch, M. P. North, and B. A. Hungate. 2016. Climate-driven changes in forest succession and the influence of management on forest carbon dynamics in the Puget Lowlands of Washington State, USA. Forest Ecology and Management 362:194–204. Landers, J. L., D. H. Vanlear, and W. D. Boyer. 1995. The longleaf pine forests of the southeast—requiem or renaissance. Journal of Forestry 93:39–44. Lemon, P. C. 1949. Successional responses of herbs in the long-leaf-slash pine forest alter fire. Ecology 30:135–145. Marshall, D. J., M. Wimberly, P. Bettinger, and J. Stanturf. 2008. Synthesis of knowledge of hazardous fuels management in loblolly pine forests. General Technical Report SRS-110. U.S. Department of Agriculture Forest Service, Southern Research Station, Asheville, North Carolina, USA. Martin, K. L., M. D. Hurteau, B. A. Hungate, G. W. Koch, and M. P. North. 2015. Carbon tradeoffs of restoration and provision of endangered species habitat in a fire-maintained forest. Ecosystems 18:76–88.
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SWANTESON-FRANZ ET AL. C. Lewis. 2017. Ecosystem carbon density and allocation across a chronosequence of longleaf pine forests. Ecological Applications 27:244–259. Samuelson, L. J., T. A. Stokes, and K. H. Johnsen. 2012. Ecophysiological comparison of 50-year-old longleaf pine, slash pine and loblolly pine. Forest Ecology and Management 274:108–115. Scheller, R. M., D. Hua, P. V. Bolstad, R. A. Birdsey, and D. J. Mladenoff. 2011a. The effects of forest harvest intensity in combination with wind disturbance on carbon dynamics in Lake States Mesic Forests. Ecological Modelling 222:144–253. Scheller, R. M., S. Van Tuyl, K. L. Clark, J. Hom, and I. La Puma. 2011b. Carbon sequestration in the New Jersey pine barrens under different scenarios of fire management. Ecosystems 14:987–1004. Scheller, R. M., and D. J. Mladenoff. 2004. A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecological Modelling 180:211–229. Siedl, R., et al. 2017. Forest disturbances under climate change. Nature Climate Change 7:395–402. Sturtevant, B. R., R. M. Scheller, B. R. Miranda, D. Shinneman, and A. Syphard. 2009. Simulating dynamic and mixed-severity fire regimes: a process-based fire extension for LANDIS-II. Ecological Modelling 220:3380–3393. Thom, D., and R. Seidl. 2016. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biological Reviews 91:760–781. USAIC [US Army Infantry Center]. 2006. Integrated natural resources management plan, Fort Benning army installation, 2006. Incremental revision. Directorate of Public Works, Environmental Management Division, Fort Benning, Georgia, USA. USFWS [United States Fish and Wildlife Service]. 2003. Recovery plan for the red-cockaded woodpecker (Picoides borealis): second revision. United States Fish and Wildlife Service, Atlanta, Georgia, USA. Waldrop, T. A., D. L. White, and S. M. Jones. 1992. Fire regimes for pine-grassland communities in the southeastern United States. Forest Ecology and Management 47:195–210. Wear, D. N., and J. G. Greis. 2002. Southern forest resource assessment: summary of findings. Journal of Forestry 100:6–14. Wiechmann, M. L., M. D. Hurteau, M. P. North, G. W. Koch, and L. Jerabkova. 2015. The carbon balance of reducing wildfire risk and restoring process: an analysis of 10-year post-treatment carbon dynamics in a mixed-conifer forest. Climatic Change 132:709–719. Williams, A. P., et al. 2013. Temperature as a potent driver of regional forest drought stress
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SWANTESON-FRANZ ET AL. Fort Benning. Environmental Science & Technology 44:992–997. Zhao, D., S. Liu, J. Oeding, and S. Zhao. 2013. Forest cutting and impacts on carbon in the eastern United States. Scientific Reports 3:1–7.
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