UBC Faculty Research and Publications

Ecosystem carbon dioxide fluxes after disturbance in forests of North America Davis, K. J.; Clark, K. L.; Chen, J.; Black, T. Andrew; Barr, J. G.; Brown, M.; Bracho, R.; Barr, Alan G.; Amiro, B. D.; Margolis, H. A.; Kolb, T. E.; Goulden, M. L.; Law, Beverly E.; Lavigne, M. B.; Engel, V.; Dore, S.; Goldstein, A. H.; Fuentes, Jose D.; Desai, A. R.; Xiao, J.; Starr, G.; Randerson, J. T.; Noormets, A.; Montes-Helu, M.; Mission, L.; McCaughey, J. H.; Martin, T. 2010-10-31

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Ecosystem carbon dioxide fluxes after disturbance in forestsof North AmericaB. D. Amiro,1A. G. Barr,2J. G. Barr,3T. A. Black,4R. Bracho,5M. Brown,4J. Chen,6K. L. Clark,7K. J. Davis,8A. R. Desai,9S. Dore,10V. Engel,3J. D. Fuentes,8A. H. Goldstein,11M. L. Goulden,12T. E. Kolb,10M. B. Lavigne,13B. E. Law,14H. A. Margolis,15T. Martin,5J. H. McCaughey,16L. Misson,11,17M. Montes‐Helu,10A. Noormets,18J. T. Randerson,12G. Starr,19and J. Xiao20Received 15 April 2010; revised 9 June 2010; accepted 18 June 2010; published 27 October 2010.[1] Disturbances are important for renewal of North American forests. Here wesummarize more than 180 site years of eddy covariance measurements of carbon dioxideflux made at forest chronosequences in North America. The disturbances included stand‐replacing fire (Alaska, Arizona, Manitoba, and Saskatchewan) and harvest (BritishColumbia, Florida, New Brunswick, Oregon, Quebec, Saskatchewan, and Wisconsin)events, insect infestations (gypsy moth, forest tent caterpillar, and mountain pine beetle),Hurricane Wilma, and silvicultural thinning (Arizona, California, and New Brunswick).Net ecosystem production (NEP) showed a carbon loss from all ecosystems following astand‐replacing disturbance, becoming a carbon sink by 20 years for all ecosystems and by10 years for most. Maximum carbon losses following disturbance (g C m−2y−1) rangedfrom 1270 in Florida to 200 in boreal ecosystems. Similarly, for forests less than 100 yearsold, maximum uptake (g C m−2y−1) was 1180 in Florida mangroves and 210 in borealecosystems. More temperate forests had intermediate fluxes. Boreal ecosystems wererelatively time invariant after 20 years, whereas western ecosystems tended to increase incarbon gain over time. This was driven mostly by gross photosynthetic production (GPP)because total ecosystem respiration (ER) and heterotrophic respiration were relativelyinvariant with age. GPP/ER was as low as 0.2 immediately following stand‐replacingdisturbance reaching a constant value of 1.2 after 20 years. NEP following insectdefoliations and silvicultural thinning showed lesser changes than stand‐replacing events,with decreases in the year of disturbance followed by rapid recovery. NEP decreased in amangrove ecosystem following Hurricane Wilma because of a decrease in GPP and anincrease in ER.Citation: Amiro, B. D., et al. (2010), Ecosystem carbon dioxide fluxes after disturbance in forests of North America,J. Geophys. Res., 115, G00K02, doi:10.1029/2010JG001390.1Department of Soil Science, University of Manitoba, Winnipeg,Manitoba, Canada.2Environment Canada, Saskatoon, Saskatchewan, Canada.3Everglades National Park, Homestead, Florida, USA.4Faculty of Land and Food Systems, University of British Columbia,Vancouver, British Columbia, Canada.5School of Forest Resources and Conservation, University of Florida,Gainesville, Florida, USA.6Department of Earth, Ecological, and Environmental Sciences,University of Toledo, Toledo, Ohio, USA.7USDA Forest Service, New Lisbon, New Jersey, USA.8Department of Meteorology, Pennsylvania State University,University Park, Pennsylvania, USA.9Department of Atmospheric and Oceanic Sciences, University ofWisconsin‐Madison, Madison, Wisconsin, USA.10College of Engineering, Forestry, and Natural Sciences, NorthernArizona University, Flagstaff, Arizona, USA.11Department of Environmental Science, Policy, and Management,University of California, Berkeley, California, USA.12Department of Earth System Science, University of California, Irvine,California, USA.13Canadian Forest Service, Fredericton, New Brunswick, Canada.14College of Forestry, Oregon State University, Corvallis, Oregon,USA.15Faculty of Forestry and Geomatics, Université Laval, Quebec, Quebec,Canada.16Department of Geography, Queen’s University at Kingston,Kingston, Ontario, Canada.17Deceased 5 March 2010.18Department of Forestry and Environmental Resources, NorthCarolina State University at Raleigh, Raleigh, North Carolina, USA.19Department of Biological Sciences, University of Alabama,Tuscaloosa, Alabama, USA.20Complex Systems Research Center, University of New Hampshire,Durham, New Hampshire, USA.Copyright 2010 by the American Geophysical Union.0148‐0227/10/2010JG001390JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, G00K02, doi:10.1029/2010JG001390, 2010G00K02 1of131. Introduction[2] Disturbances are an important feature of North Amer-ican forests, renewing forest stands or changing the vegeta-tion dynamics following less severe disturbances. Wildlandfires burn an average of about 40,000 km2in Canada and theU.S. annually [Stocks et al., 2003; U.S. National InteragencyForestFireCentre,http://www.nifc.gov/fire_info/fires_acres.htm, accessed 25 March 2010] with large interannual vari-ability. About 50,000 km2of forested land is harvestedannually in Canada and the U.S., with about half of this asclear‐cutharvest[KurzandApps,1999;Birdseyetal.,2006].Moderate to severe insect infestations are highly variableamong years but can affect a greater area than either fireor harvesting [Kurz and Apps, 1999; Birdsey et al., 2006].Hurricanes and tornados are estimated to affect about17,000 km2annually in the U.S. [Dale et al., 2001] andthere are many additional less severe storms that causewindthrow in forests. These disturbances have a large effecton the carbon (C) balance of North American forests. Forexample, analyses for Canadian forests showed clearly thatinsects and fire have caused a net forest C loss that fluctuatesannually, but has a lasting legacy [Kurz and Apps, 1999;Kurz et al., 2008a]. Disease and storms also affect theC balance, but their impacts are often difficult to quantifybecause they result in decreased growth without initial cat-astrophic removals of C. The net C effects of disturbanceshave been modeled extensively with some of the modeloutputs being compared to direct eddy covariance mea-surements of C flux over daily to annual scales [e.g., Grantet al., 2007]. These direct determinations of net ecosystemexchange (NEE) of carbon dioxide (CO2) have helped usunderstand the processes controlling C exchange at indi-vidual sites following fire [e.g., Goulden et al., 2006; Welpet al., 2007; Dore et al., 2008; Mkhabela et al., 2009],harvesting [e.g., Chen et al., 2002; Schwarz et al., 2004;Misson et al., 2005; Giasson et al., 2006; Noormets et al.,2007; Krishnan et al.,2009;Zha et al., 2009], insect infesta-tions [Cook et al., 2008; Clark et al., 2010; Brown et al.,2010], and storms [Barr et al., 2010].[3] In North America, there has been collaboration amongresearchers making eddy covariance measurements [e.g.,Baldocchi et al., 2001] through the AmeriFlux and Cana-dian Carbon Program (Fluxnet‐Canada Research Network)networks. In particular, the Canadian network had a dis-turbance focus [Margolis et al., 2006]. The North AmericanCarbon Program (NACP) [Wofsy and Harriss, 2002] pro-vides a framework for bringing some of these measurementstogether to investigate some general relationships betweendisturbance and forest C exchange across North America.Eddy covariance flux towers have not yet been establishedalong disturbance chronosequences in Mexico. Our goal inthe present paper is to synthesize the measurements for lo-cations where tower clusters have been used to measureNEE of forest chronosequences after different types ofdisturbances.[4] Oneoptiontoinvestigatetheeffects ofdisturbanceistomeasure an ecosystem for several years prior to the distur-bance, and then follow with long‐term continuous observa-tions to understand the ecosystem response and recoveryfollowing the disturbance. Such experiments are not alwayspossible, especially considering the very long times neededto measure ecosystem development over decades or centu-ries. Hence investigators often study sites of different ages inparallel to infer the status of an ecosystem as a function ofage (time since disturbance). This chronosequence approachis a practical design to acquire age‐related data in a shortperiod of time. This is important when employing eddycovariance because we do not have an archive of compar-ative measurements taken over several decades to study theeffects of forest development on the net CO2flux. However,there are now some relatively long‐term eddy covariancesequences that have run well over a decade at mature forestsites [Barr et al., 2007; Dunn et al., 2007; Urbanski et al.,2007].[5] A conceptual trajectory of C flux following a distur-bance, based on vegetation development [Odum, 1969] anddynamics of decomposition, is that the predisturbance eco-system is fixing C at some rate, and then there is animmediate C loss as a result of the disturbance, with sub-sequent recovery over some period [e.g., Kashian et al.,2006]. This loss is caused by death of photosynthesizingvegetation, biomass combustion in the case of fire, harvestremovals, or insect herbivory. In cases where insects killtrees (e.g., bark beetles) or a storm causes tree defoliationand mortality, there is usually minimal initial export of Cfrom the ecosystem. However, following most disturbances,changes to the ecosystem have the potential to alter NEE.This could be driven by changes to heterotrophic respirationwhen forest C pools change, coupled with decreased pho-tosynthesis because of less leaf area. The ecosystem canchange quite quickly as new growth follows the disturbance[Chen et al., 2002; Gough et al., 2007]. These general-izations also depend on the severity of the disturbance, withstand‐replacing disturbances having different trajectoriesthan less severe partial disturbances (e.g., stand thinning). Afundamental difference between harvesting and fire is thatharvesting removes the coarse woody material, whereas fireremoves the fine materials (forest floor and fine fuels). Sucha difference likely impacts the postdisturbance respiration.Fire may also mineralize nutrients more rapidly, makingthem available for vegetation uptake. Insect infestationsusually do not totally replace forest stands although there areexceptions in single‐species forests [e.g., Kurz et al.,2008b]. Similarly, some storms can be catastrophic caus-ing high immediate losses of C [Lindroth et al., 2009].Typically, shorter disturbance intervals result in lesser Cstocks [Gough et al., 2008].[6] Here, we synthesize eddy covariance tower‐basedCO2flux data from forest disturbance chronosequencestudies across Canada and the U.S. We have included eco-systems affected by fire, harvesting, and major insect andstorm (hurricane) events. There are many more chronose-quence studies that have measured C fluxes using chambers[e.g., Bond‐Lamberty et al., 2003; Irvine et al., 2007] orbiometric sampling [e.g., Bond‐Lamberty et al., 2004;Campbell et al., 2004, 2009; Gough et al., 2008]. However,our synthesis of only tower‐based eddy covariance dataallows us to compare whole ecosystem fluxes using similarmethodologies and footprints. Further, we have onlyincluded studies where annual Net Ecosystem Production(NEP) has been measured. This excludes many studieswhere data are only available from short (usually summer)field campaigns or partial years. We have used data fromAMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K022of13forested sites that are less than 100 years old to concentrateon younger ecosystems. We recognize that this period is lessthan the disturbance cycle in some areas (e.g., the boreal firecycle tends to be slightly longer than this on average) so thatwe are not integrating to estimate the net C budget for a fulldisturbance cycle. Also, there have been several previoussyntheses that have concentrated on the C dynamics of old‐growth forests [Carey et al., 2001; Luyssaert et al., 2008].We have not included the C removals during the disturbanceevent, such as biomass combustion, harvest removals, orinsect migration, and only measure the postdisturbanceeffects. In this paper we focus on the following broadhypotheses to reconcile the responses of a broad range offorests to disturbance using the existing eddy covariancetower flux data: (1) Disturbances decrease NEP because ofdecreased Gross Primary Production (GPP) with little effecton Ecosystem Respiration (ER); (2) NEP recovers similarlyfollowing stand‐replacing fire and harvesting (null hypoth-esis); (3) Non‐stand‐replacing disturbances such as insects,storms, and silvicultural thinning have very short‐termeffects on NEP.2. Methods2.1. Site Descriptions2.1.1. Fire Chronosequences[7] There are four major fire chronosequences included inthis synthesis (Table 1). Three are located in the borealforest in Alaska, Saskatchewan and Manitoba. The Alaskaand Manitoba sites represent more northerly parts of theboreal forest dominated by black spruce (Picea mariana),whereas the Saskatchewan sites are at the southern fringeand have components of black spruce, jack pine (Pinusbanksiana) and trembling aspen (Populus tremuloides). TheManitoba and Saskatchewan locations correspond to thenorthern and southern study areas, respectively, and wereinvestigated during the BOREAS experiment [Sellers et al.,1997]. However, the fire chronosequences were establishedin the late 1990s following the completion of BOREAS. Atboth of these chronosequences, long‐term flux towers atmature sites anchor the longer‐term flux record [Dunn et al.,2007; Kljun et al., 2006]. At all the boreal fire chron-osequences, different forest stand ages were selected in areasonably close geographic area, typically within 100 km.However, despite attempts to match site conditions, some ofthe variability among sites within a chronosequence will becaused by local environmental factors such as differences insoil type and hydrology. The fire chronosequence in Arizonacaptured the effects of a stand‐replacing wildfire in a high‐elevation semiarid ecosystem dominated by ponderosa pine(Pinus ponderosa).2.1.2. Harvest Chronosequences[8] Most of our harvest chronosequences were standreplacing, involving harvest with most of the larger treesremoved and perhaps some protective vegetation left forregeneration [e.g., Giasson et al., 2006]. However, slashmay also have been left at the site, depending on localharvesting practices (e.g., New Brunswick site). In theboreal forest, harvest chronosequences were measured inSaskatchewan and Quebec. In both cases, mature (control)forests were fire generated because harvesting is a relativelynew activity. The Saskatchewan site represents a southernboreal condition with jack pine, whereas the Quebec site is anorthern boreal black spruce site. These sites also differ bylongitudinal gradient with the more eastern Quebec sitereceiving greater annual precipitation.[9] In temperate forests, we have harvest chronosequencesfrom several sites in the Wisconsin‐Michigan northernhardwoods forest area. These sites are part of the largerChEAS experiment [Chen et al., 2008]. Essentially allstands less than 100 years old in this area regenerated fol-lowing harvesting. There are several different forest standtypes, ranging from jack pine to aspen and maple‐basswood‐ash (Acer‐Tilia‐Fraxinus spp.) deciduous species mixes.There are two temperate forest chronosequences on thewest coast, located on Vancouver Island and in Oregon,representing Douglas fir (Pseudotsuga menzesii) and pon-derosa pine forests, respectively. The clear‐cut site in NewBrunswick is a balsam fir (Abies balsamea) forest. TheFlorida chronosequence is a fast‐growing slash pine (Pinuselliottii var. elliottii) plantation managed on a rotation of20–25 years.[10] Thinning treatments were conducted in a balsam firforest in New Brunswick and ponderosa pine forests inArizona and California. Measurements were made prior tothinning and then followed after the treatment. These thin-nings are part of local forest management and representtreatments that are employed routinely. In Arizona, the slashfrom thinning was piled and burned the first year followingthinning; this was undertaken to reduce forest fuels and therisk of an intense fire.2.1.3. Insect Chronosequences[11] There are three insect chronosequences where infesta-tions have been sufficiently severe to cause major defolia-tion or tree death. The mountain pine beetle (Dendroctonusponderosae) site in British Columbia was established spe-cifically to measure the effects of the beetle, which has beenkilling large areas of lodgepole pine (Pinus contorta) forest[Kurz et al., 2008b]. The affected areas have almost 100%tree mortality, but there is new growth by early successionalspecies. The site in New Jersey experienced a severe defo-liation by gypsy moth (Lymantria dispar) in 2007. Severalforest types were measured that represented various mix-tures of pine (Pinus rigida, P. echinata) and oak (Quercusvelutina, Q. prinus, Q. alba, Q. marlandica, Q. ilicifolia).The Willow Creek site in Wisconsin, although established aspart of a harvest chronosequence, experienced a severedefoliation by forest tent caterpillar (Malacosoma disstria)in 2001. This allowed an opportunity to study the effects ofdefoliation of hardwoods (Tilia americana, Fraxinus penn-sylvanica, Quercus rubra), although there was a secondgrowth of leaves following defoliation (i.e., most trees werenot killed).2.1.4. Storm Chronosequence[12] Although severe storms take a toll on North Americanforests through tree windthrow, there have been few oppor-tunities to measure the effects using flux towers. A study ina mangrove ecosystem in southwest Everglades NationalPark, Florida quantified the impact of Hurricane Wilma(October 2005) on mangrove forest NEE. Species includedred (Rhizophora mangle), black (Avicennia germinans), andwhite (Laguncularia racemosa) mangroves. The storm wassevere enough to defoliate the forest crown and destroyedabout 30% of the mangrove trees. Flux tower instrumentsAMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K023of13Table1.DescriptionofSitesaChronosequenceLocation(Latitude,Longitude)VegetationTypebYearofMostRecentDisturbanceYearsMeasuredAnnualMeanAirTemperature(°C)AnnualTotalPrecipitation(mm)ReferenceforDataSiteNameUsedinReferenceFireChronosequencesAlaska63.9N,145.7Wgroundvegetation19991999,2002−1.9300Randersonetal.[2006];Welpetal.[2006,2007]Bn363.9N,145.4Wbs,ta,willow19872002–2004Bn263.9N,145.7Wbs19202002–2004Bn1Saskatchewan53.9N,106.1Wjp,bs,ta19982001–20060.4470Amiroetal.[2006];Mkhabelaetal.[2009]F9854.3N,105.9Wjp,ta19892002–2005F8954.5N,105.8Wjp19772004–2006F7753.9N,104.7Wjp19192000–2007Kljunetal.[2006]OJPManitoba56.6N,98.2Wta,willow,bs20032004–2005−3.2520Gouldenetal.[2006];McMillanetal.[2008]NSA200356.6N,98.2Wta,willow,bs19982003–2005NSA199855.9N,99.0Wta,willow,bs19892003–2005NSA198955.8N,98.4Wta,willow,bs19812002–2005NSA198155.8N,98.4Wbs,jp,ta19642002–2005NSA196455.8N,98.5Wbs19302002–2004NSA1930Arizona35.1N,111.8Wpp19962006–20087.9580Doreetal.[2010]Fwf35.1N,111.8Wpp19192006–2008Doreetal.[2008,2010]FufHarvestChronosequencesSaskatchewan53.9N,104.6Wgroundvegetation,jp20022004–20050.4470Kljunetal.[2006];Mkhabelaetal.[2009];Zhaetal.[2009]HJP0253.9N,104.7Wjp19942004–2005HJP9453.9N,104.6Wjp19752004–2005HJP7553.9N,104.7Wjp19192000–2007OJPQuebec49.3N,74.0Wgroundvegetation,bs2000harvest,2003scarified2002–20080960Giassonetal.[2006];Bergeronetal.[2008]HBS0049.8N,74.6Wbs19752008H.A.Margolis,personalcommunication,2009HBS7549.7N,74.3Wbs19152004–2008Bergeronetal.[2007,2008]EOBSVancouverIsland49.9N,125.2Wdf20002001–20088.61450Humphreysetal.[2006];Krishnanetal.[2009]HDF0049.5N,124.8Wdf19882002–2008HDF8849.4N,125.3df19491998–2008DF49NewBrunswick46.5N,67.1Wbf20042005–20083.41190M.B.Lavigne,personalcommunication,2009NL‐CC46.5N,67.1Wbf1975,thinnedin1991and20052004–2008NL‐CTAMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K024of13Table1.(continued)ChronosequenceLocation(Latitude,Longitude)VegetationTypebYearofMostRecentDisturbanceYearsMeasuredAnnualMeanAirTemperature(°C)AnnualTotalPrecipitation(mm)ReferenceforDataSiteNameUsedinReferenceWisconsin45.9N,90.1Wta,hardwoods200120075.2765A.R.Desai,personalcommunication,2009RileyCreek45.8N,90.1Whardwoods19302000–2006Cooketal.[2004,2008]WillowCreek45.5N,84.7Whardwoods19201999–2003Goughetal.[2008]UMBS46.6N,91.1Wrp19392002–2003Noormetsetal.[2007,2009]MRP46.6N,91.1Whardwoods19992002YHW46.6N,91.1Wjp,rp19952002YRP46.6N,91.1Whardwoods19922003IHW46.6N,91.1Wrp19822003IRP41.6N,83.8Woak19472004–20059.2840Noormetsetal.[2008]TOLOregon44.4N,121.5Wpp19842004–20057.6460Lawetal.[2003];Schwarzetal.[2004];Thomasetal.[2009]Me344.4N,121.6Wpp19782001–20027.6600Me544.5N,121.6Wpp19122002–20087.1600Me2Florida29.8N,82.2Wsp1972,19981996–200720.61230Clarketal.[2004];Binfordetal.[2006]SP229.8N,82.2Wsp19901999–2005SP3Arizona35.4N,111.8Wpp2006thinned2006–20087.9580Doreetal.[2010]FMFCalifornia38.9N,120.6Wpp1990,2000thinned1999–200211.91290Missonetal.[2005]BlodgettInsectChronosequencesNewJerseyGypsyMoth39.9N,74.6Woak,pine1914,insects20072005–200812.31155Clarketal.[2010]SLT39.8N,74.4Wpine,oak1995,insects2007–20082005–2008CEDBritishColumbiaMountainPineBeetle54.5N,122.7Wlp20032007–20082.3655Brownetal.[2010]MPB0355.1N,122.8Wlp20062007–2008MPB06WisconsinForestTentCaterpillar45.8N,90.1Whardwoods1930,insects20012000–20065.2765Cooketal.[2008]WillowCreekHurricaneFlorida25.4N,81.1Wmangroves1992,hurricane20052004,2007–200923.81390Barretal.[2010]SharkRiveraOnlysiteslessthan100yearsoldwereusedinthisanalysis.Annualmeanairtemperatureandtotalprecipitation(roundedtonearest5mm)dataare30yearnormalstakenfromthenearestmeteorologicalstation.bSpeciesareasfollows:jp,Pinusbanksiana;bs,Piceamariana;ta,Populustremuloides;pp,Pinusponderosa;df,Pseudotsugamenzesii;sp,Pinuselliottii;rp,Pinusresinosa;lp,Pinuscontorta.AMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K025of13and data acquisition were also destroyed by the storm.Researchers reestablished the study field site in October2006. Hence, measurements here are a true sequence withpredisturbance and postdisturbance measurements.2.2. Field Measurements and Data Processing[13] The details of each individual field measurement andannual data processing protocol are given in the referenceslisted in Table 1. Note that for this synthesis, each investi-gator supplied their best estimates of their annual totals of Cfluxes for each site. This makes these measurements con-sistent with previous publications from each site. NEE wascalculatedevery30minusingthecovarianceofafast‐responseinfrared gas analyzer for CO2and a three‐dimensional sonicanemometer for wind velocity. The gas analyzers wereeither closed‐path or open‐path systems. Although therewas some variability among researchers, typical data pro-cessing included corrections for nonzero mean vertical windvelocity, tubing losses (closed‐path analyzers) and densityeffects (especially for open‐path analyzers). Known pro-blems with sensor heating of open‐path analyzers [Burbaet al., 2008] were either corrected, or were compensatedby excluding cold weather data [Welp et al., 2007] or fillingcold‐weather periods with modeled respiration [Mkhabelaet al., 2009].[14] NEP was calculated as the gap‐filled annual sum ofNEE, defined so that a downward flux is positive (i.e.,terrestrial ecosystem gain is positive). ER (positive flux isupward) was calculated based on nighttime NEE measure-ments combined with regression and modeling. GPP(downward flux is positive) was calculated as the sum ofNEP and ER, sometimes combined with light‐responsemodeling to fill gaps [e.g., Moffat et al., 2007].[15] In a multiple‐site synthesis, variability among indi-vidual field techniques, annual data processing, and algo-rithms for GPP and ER contribute additional uncertainty[Desai et al., 2008]. We appreciate the general need for agreater degree of homogeneity in postprocessing flux data,however the site investigators are contributors to either theAmeriFlux or Canadian Carbon Program networks, andsome standardization has been achieved through these col-laborations. Annual NEP is likely to be estimated withinabout ± 25 g C m−2y−1based on uncertainty in gap‐fillingtechniques [Moffat et al., 2007]. However, there is someadditional uncertainty caused by random error of about±20gCm−2y−1[Richardson and Hollinger, 2005]. Typ-ically, we would expect annual NEP to be estimated to betterthan ± 50 g C m−2y−1at most sites, with better estimates atcertain sites. Baldocchi [2008] gives a good review of thepotential issues that affect flux tower measurements.3. Results and Discussion3.1. Fire Chronosequences[16] NEP, GPP and ER showed similar trends with timesince disturbance for all of the four fire chronosequences(Figure 1). All sites that are less than 10 years of age were Csources. The three boreal chronosequences (Saskatchewan,Manitoba, Alaska) became net C sinks after about 10 years.During this period of positive NEP, there was substantialinterannual variability at any given site, often ranging about100 g C m−2y−1. This magnitude of interannual variabilityseemed to be similar at any given location, even when therewere only three site years. Considering this variability, theNEP response with time for sites older than 10 years of agewas relatively invariant with time. The Saskatchewanchronosequence at about 70 years of age included data fromthe southern old jack pine site, which tended to have lowerNEP than at the nearby old aspen site [Kljun et al., 2006],and likely does not fully represent the successional endpointof the young postfire forests. At about 30 years of age, theSaskatchewan chronosequence showed three site years withconsecutive negative NEP. This site had visibly decayingcoarse woody debris lying on the surface contributing to ER.The Arizona site at about 10 years of age had similar NEPto the slightly younger boreal sites. Trees had still notregenerated at the Arizona site, and a sparse grassland hadsucceeded (seasonal maximum leaf area index = 0.6) with alarge amount of coarse woody debris present [Dore et al.,2008]. We do not have intermediate‐aged forest data forArizona, so the comparative trajectory is not clear eventhough the older Arizona site (about 90 years of age) had asimilarpositiveNEPtotheborealsites.However,thepostfireFigure 1. Annual (a) NEP, (b) GPP, and (c) ER for the firechronosequences.AMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K026of13vegetation dynamics are very different at the Arizona sitecompared to the boreal sites, and it could take many years toreach positive NEP following fire at the Arizona site.[17] Separation of NEP into the GPP and ER componentshelps to identify the relative driving forces. GPP clearlyincreased with stand agefor thefirst 20–30 years (Figure 1b).The Saskatchewan sites had the highest GPP in the 15–30 year age range. These sites had a warmer climate than theManitoba or Alaska sites and had a fast‐growing deciduouscomponent of trembling aspen. After 30 years, the threeboreal chronosequences tended to show similar GPP. TheArizona site had similar GPP to the boreal sites at about10 years, but greater GPP at about 90 years. A possibleexplanation is that the Arizona chronosequence was moremoisture limited for vegetation establishment following firebut the older stand benefited from the warmer climate,compared to the boreal sites.[18] Ecosystem respiration had a less well‐defined tra-jectory with age than either NEP or GPP, although it isslightly lower at very young ages (Figure 1c). With theexception of the Saskatchewan sites at 15 and 30 years, andthe 90 year old Arizona site, ER did not vary much with age.The high ER at the Arizona site and at some Saskatchewansites is consistent with the high GPP at these sites. However,the negative NEP at the 30 year old site indicates that het-erotrophic respiration is an important factor, likely becauseof decaying coarse woody debris. It is important to note thatthe Saskatchewan sites at 15 and 30 years used open‐pathgas analyzers that have known problems with sensor heating[Burba et al., 2008]. Use of these analyzers without heatingcorrections will underestimate ER. In this study, data col-lected when temperatures were below 0°C were excludedand replaced using respiration estimated as a function of soiltemperature. Although this increases the uncertainty in ER,the high ER was mostly caused by summertime losses atthese sites, likely enhanced by decomposition of coarsewoody debris [Mkhabela et al., 2009].3.2. Harvest Chronosequences[19] NEP trajectories following harvesting showed similartemporal changes to those after fire, although there wasmore variability caused by location (Figure 2a). In particular,sites in warmer climates had higher NEP, and the Floridatrajectory clearly showed faster recovery following harvest.The greatest C loss in the early years following harvestingwas also at warmer sites, which caused a greater dyna-mism in the C flux than at colder sites. The boreal sites(Saskatchewan, Quebec) had reduced amplitude throughoutthe trajectory. The scale of Figure 2a used to accommodateall ecosystems diminishes the relative interannual variabilityof the boreal sites. This comparison showed greater absoluteinterannual variability in C flux for warmer sites than coldersites in North America. However, the overall interannualvariability effect needs to be integrated spatially over allNorth American forests to evaluate its importance.[20] Similarly to NEP, GPP showed clear ecosystem dif-ferences among geographic locations (Figure 2b). The muchlarger fluxes for Florida, Vancouver Island and Oregondwarfed the boreal trajectories. However, for all chron-osequences, a clear GPP recovery occurred within about thefirst 20 years following harvest. The boreal, Wisconsin, andFlorida GPP values were relatively flat after 20 years,whereas the west coast (Vancouver Island, Oregon) eco-systems showed an increase over time. This western forestincrease is consistent with continued C accumulationsobserved in biometric and inventory data [Law et al., 2004;Hudiburg et al., 2009]. Lower photosynthetic assimilationfluxes were also documented by Buchmann and Schulze[1999] for forest stands less than 20 years of age. As inthe case of fire, ER had a relatively flat trajectory, with somereduction in ER in the very first years following harvest atsome sites (Figure 2c).3.3. Respiration Components for Fireand Harvest Chronosequences[21] The large differences in C fluxes among geographicregions are largely climate driven. Process models can helpexplain these differences, but there is no simple climatescaling variable such as normalization by annual tempera-ture alone when we consider the chronosequence. Forexample, at very young (<4 years old) harvested sites, therewere similar values of NEP and GPP for sites with differentannual temperatures, although ER was much better sepa-Figure 2. Annual (a) NEP, (b) GPP, and (c) ER for theharvest chronosequences.AMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K027of13rated (Figure 2, Table 1). However, we would expect Callocation to scale in some fundamental ways. For example,the ratio of GPP to ER clearly showed low values at earlyyears following disturbance (Figure 3) for both postfireand postharvest sites. As discussed for NEP, GPP/ERbecame greater than unity (i.e., NEP > 0) by 20 years forall chronosequences and by 10 years for most. Moreimportantly, GPP/ER did not vary much with age followingthis initial increase for sites generated by either harvest orfire, and had an asymptote of 1.23. This is consistent with aglobal analysis of flux data where most of the forested siteswere mature, which gave an average ratio of GEP/ER = 1.2[Law et al., 2002].[22] ER can be separated into autotrophic (Ra) andheterotrophic (Rh) respiration components. Although weusually do not have independent measures for each of these,it is instructive to examine the chronosequence data byassuming some relationship between Raand GPP. We rec-ognize that the Ra/GPP ratio can vary [DeLucia et al., 2007;Piao et al., 2010], especially among our large range ofecosystem types and climates. However, a range of studiesestimate Rato be about 0.55·GPP [e.g., Landsberg andWaring, 1997; Jassal et al., 2007], which would yieldGPP/ER = 1.82 if Rh= 0 (horizontal line in Figure 3). Allpoints were below this maximum because Rh> 0, andthe relative effect of Rhwas clearly greatest in the first10–20 years following disturbance (i.e., Rais low). WhenGPP = ER (i.e., NEP = 0), Rh= 0.45·ER. At the lowestmeasured value of GPP/ER of about 0.2 in Figure 3, Rhwasabout 90% of ER. This corresponded to the sites where GPPwas low immediately following harvest (Figure 2b), withmost of this GPP likely being from ground vegetationcontributions before new trees were established.[23] We can carry the respiration analysis further, againassuming a constant fraction of Rato GPP (0.55), recog-nizing that this may change with forest age and species[DeLucia et al., 2007]. In Figure 4, we plot the derived Rhasa function of age. Here we have normalized the Rhfor eachsite by the mean value for all sites in the local chronose-quence (e.g., each Saskatchewan fire site year Rhis dividedby the mean value of all Saskatchewan fire site years) toallow comparison on the same scale. Although there weresome higher Rhvalues at young sites, there was no signif-icant regressional relationship with age (y = 0.98 + 0.0004 x;r2= 0.003). Splitting the sites into groups of > 10 and≤ 10 years of age also showed no statistical difference(Student’s t test P = 0.5). Hence, it appears that Rhis largelyinvariant with age for the full data set, even though there arelikely trends in some of the individual chronosequences.This is evident for some of the youngest harvested sitesand for the 30 year old Saskatchewan fire site, which hadhigh Rh.[24] The data support the hypothesis that disturbancesdecrease NEP mostly because of decreased GPP. Fire andharvesting slightly decreased ER soon after the disturbance,but ER then either increased over time or did not vary.Figure 3 also supports the hypothesis that NEP recovery issimilar following stand‐replacing fire and harvesting, atleast in a broad sense. However, there are differences at anygiven location, as has been demonstrated for individualchronosequences [Mkhabela et al., 2009].3.4. Insects, Storms, and Thinning[25] The studies of insects, storms, and thinning followeda true sequence of years that encompass the disturbance at agiven site. The strength of these measurements is that thereis no spatial variability when a true chronosequence is fol-lowed. However, interannual variability caused by climatefluctuations needs to be assessed using a control site [e.g.,Dore et al., 2010]. Although the time period (i.e., age effect)was shorter, the continuous measurements demonstrated thenature of the disturbance effect. NEP following gypsy mothand forest tent caterpillar infestations showed a decrease inthe year of infestation, with a clear recovery in subsequentFigure 3. The ratio of annual GPP/ER with stand age forfire and harvest sites. The horizontal line at GPP/ER =1.82 corresponds to a constant autotrophic respirationvalue of 0.55·GPP and no heterotrophic respiration. Thedashed line is the regression for all data points where y =1.23 (1 − e−0.224x), r2= 0.60, and n = 162.Figure 4. Estimated heterotrophic respiration with standage. We assume that heterotrophic respiration = ER −0.55·GPP. Heterotrophic respiration values for each siteare normalized by the mean value for the local chronose-quence for comparison on the same scale. There is no trendwith age (r2= 0.003).AMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K028of13years (Figure 5a). The gypsy moth‐affected sites wereslightly more complicated because of 2 years of infestationin the case of one site, and the use of prescribed fire in somestands. The oak‐pine site was not defoliated in 2005 and2006, but was completely defoliated in 2007. The pitchpine‐scrub oak site was not defoliated in 2005 and 2006,and understory oaks and some shrubs were defoliated in2007. In Figure 5a, we have included data from the oak‐pineand pitch pine‐scrub oak stands [Clark et al., 2010]. Wehave not included the data in the year following infestationat the pitch pine‐scrub oak site, which experienced pre-scribed fire in March 2008 and could have complicated theresponse due to insect disturbance.[26] The mountain pine beetle sequence included twosites: one site had data for the first 2 years followinginfestation, whereas the other was measured 4 and 5 yearsafter. For all four site years, NEP was slightly negativebecoming neutral by year five, whereas we would expectthese 80 to 100 year old lodgepole pine stands to have beennet C sinks (there is no pre‐effect measurement site). Duringthe year of gypsy moth and forest tent caterpillar infesta-tions, both GPP (Figure 5b) and ER (Figure 5c) decreased.GPP increased following the infestation year, however ERcontinued at a lesser rate than it was prior to the forest tentcaterpillar infestation. We recognize that differences canalso be caused by interannual variability in climate, which isdifficult to assess with a single sequence.[27] The effect of Hurricane Wilma in Florida is shownwhere a complete year (2004) and a partial year (2005, withNovember and December gapfilled) were measured beforethe October 2005 event [Barr et al., 2010], and then mea-surements resumed in 2007. A decrease in NEP was mostlyattributed to an increase in ER, presumably because of morewoody debris available for decomposition. Also, an increasein soil temperature of 1 to 3°C resulting from increasedradiation penetration to the surface may have contributed tohigher heterotrophic respiration following the storm. Thehigh NEP values at this Florida mangrove site were drivenby a high GPP without a correspondingly high ER. This waslikely caused by a net lateral efflux of particulate and dis-solved organic C that was not available for local respiration.In addition, a portion of the respired CO2was exported withthe outgoing tide as dissolved inorganic C and released tothe atmosphere outside of the tower flux footprint.[28] The thinned site in Arizona showed a decrease inNEP during the treatment year, recovering in the followingyear. ER at this site increased through the 3 years of mea-surement and GPP decreased in the year of thinning andthen increased in the following year. The California site,which was also ponderosa pine, showed a similar NEP andGPP trajectory to the Arizona site, being carbon neutral inthe thinning year. ER increased 2 years after the thinningtreatment. This site was quite young, having been planted in1990. The New Brunswick site had some thinning con-ducted 14 years prior to the commercial thinning. Followingthe commercial thinning, NEP, GPP and ER all decreased.The short period of record is insufficient to know the longer‐term effect. More measurements are needed on the effects offorest operation treatments, especially over longer periods[e.g., Gough et al., 2008]. In summary, the results on theeffects of insects and thinning support the hypothesis thatthe C impact is relatively short‐term, with the greatestdecrease in the year of disturbance. However, the hurricaneeffects appear to be longer lasting.3.5. Implications for Modelingand Further Measurements[29] The observed fluxes exhibited a trajectory charac-terized initially by C losses occurring immediately after thedisturbance, followed by a recovery phase of positive NEP.However, the speed of the recovery was important, and alltrajectories showed a positive NEP at 20 years followingdisturbance and most were positive at 10 years. Once theyoung forests became net C sinks, NEP quickly reached afairly stable value that remained relatively constant withage, at least for the 100 year limit of the trajectories pre-sented here. The exceptions to this trend were the west coastsites (Vancouver Island, Oregon) which tended to have anincrease in NEP over the period of our data sets. This wascaused by steadily increasing GPP with relatively littleFigure 5. Annual (a) NEP, (b) GPP, and (c) ER for insects,storms,andthinning.Thezeroyearistheyearofdisturbance.AMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K029of13change in ER. The relatively time‐invariant trajectories ofNEP in boreal, southern pine and deciduous forests after20 years were caused by little change in either ER or GPP.For many chronosequences, leaf area index did not changemuch for forests greater than 20 years of age [e.g., McMillanet al., 2008]. The GPP/ER stayed approximately constant atbetween 1 and 1.5 once NEP > 0 for both fire and harvestchronosequences (Figure 3). Heterotrophic respiration wasinvariant with age (Figure 4), suggesting that this processcan be modeled as close to being constant for a chronose-quence at a given geographical location, similar to thatreported by Law et al. [2003] and Luyssaert et al. [2008].[30] Integration of the NEP trajectories over time gives thenet C sequestration. When considering the effects of dis-turbance, this integration should be over the period betweendisturbance events. However, there was sufficient inter-annual variability and differences among sites to make asimple integration of any of the NEP figures difficult. Forexample, a regression of NEP versus age for the boreal firechronosequences (Figure 1a) yielded a best fit of NEP =−99.5 + 42.27·ln(age) with r2= 0.34. We also included threesite years at about 160 years of age at the Manitoba site inthis regression. Removing the three negative NEP points atabout 30 years (the Saskatchewan F77 site) only improvedthe regression slightly to r2= 0.42. Using this regression ofall the boreal fire sites, the NEP crossover time to a positivevalue was 10.5 years. However, the potential to have asecondary period of negative NEP caused by woody debrisdecomposition was evident and should be considered inprocess models. This is because postfire decay requires thewoody material to be wet and support microbial decom-posers, typically through contact with the ground, whichlags the disturbance event. Such a lag may not occur withwhole‐tree harvest when the finer slash materials left onthe postharvest site would have the opportunity to startdecomposing without lag. It was difficult to gauge thisprocess in the flux trajectories, but the boreal fire and harvestsites did not appear much different in the very early years,and the most negative NEP was seen at the warmer sites inFlorida, New Brunswick, Vancouver Island and Wisconsin(Figure 2a). However, a closer comparison of harvest andfire on an expanded scale show some differences whenclosely located sites were compared. For example, the fire‐generated sites had higher GPP and ER than harvest‐generated sites in Saskatchewan [Mkhabela et al., 2009],whereas in Arizona, the thinned sites had higher GPP andER than the postfire sites [Dore et al., 2010].[31] In this synthesis, we examined all the disturbancedata on a common scale. This approach helped to showsome large generalities, but it is clear that more completetrajectories would help for any given location. The Floridaharvest sites were likely the most complete because of theircontinuity and the relatively quick vegetation growth fol-lowing a disturbance. The Manitoba sites were the mostcomplete fire chronosequence, but given the relatively longfire cycle (typically over 100 years) [Stocks et al., 2003],missing ages still pose questions about whether the trajec-tories are relatively smooth. Measurements at more sites in agiven geographic area would help account for spatial vari-ability. However, measurements at the Wisconsin group ofsites showed that interannual variability at a given site is ofthe same magnitude as intersite variability [Noormets et al.,2009]. For recently disturbed sites where NEP and GPPrecovery was rapid (e.g., boreal fire), both interannual andspatial variability were relatively less important during thisearly period compared to later in the trajectory, at least on a100 year timescale. Although we need to consider the lengthof each disturbance cycle, these results suggest that goodmodeling of the period beyond about 10 years is importantto get the net rotational C balance because of greater vari-ability during this period. The first 10–20 years is also veryimportant where NEP recovery is slower, such as theArizona fire or Vancouver Island harvest sites, or where therotational period is quite short (e.g., Florida harvest).[32] We were fortunate to have some data on the effects ofinsects and storms, but clearly these were insufficient tounderstand fully the impacts on C exchange. The continuoussequence data at these sites, and the thinned sites, helped tobracket the nature of the impacts. However, we wouldexpect some of the effects to have a temporal lag. Forexample, changes in GPP in a given year could affect ER inthe following year because of changes to leaf‐litter inputs.This is further complicated by potentially different weatherconditions in subsequent years. Despite this complication, ashort‐term decrease in NEP was observed in the year ofdefoliation (Figure 5a). For insect infestations causing treedeath, such as for the mountain pine beetle sites, new veg-etation growth appears to be compensating for decomposi-tion of killed trees with the ecosystem being C neutral about5 years following the attack [Brown et al., 2010]. However,as in the case of fire, it is likely that there will be a secondperiod of enhanced ER when these dead trees fall over anddecompose more quickly. Such processes could be docu-mented by selectively measuring annual fluxes in standswhere this event has happened.[33] The thinning sites in Arizona, California and NewBrunswick illustrate the potential to investigate C dynamicsasaresultofsilviculturalpractices.Giventhattherearemanypossible ecosystem management options, it is likely thatfuture eddy covariance measurements need to be employedin a diverse range of practices and sites to capture the rangeof responses [e.g., Vesala et al., 2005]. Although silvicultureusually focuses on tree growth performance or fuel reduc-tion, the ability to determine annual NEP for different forestmanagement options would help establish net C benefitsranging from years to decades. The effect of HurricaneWilma reduced NEP for at least 4 years following the event,and continued data are needed to evaluate the recoverydynamics of severe storms.4. Conclusions and Recommendations[34] Eddy covariance studies following forest disturbanceillustrate that C flux trajectories are consistent with a con-ceptual model of net loss in early years, followed by C gainas the new forest becomes established. The data showed thatrecovery to a net C sink is relatively rapid in most ecosys-tems investigated, usually occurring within 20 years. Thepostfire Arizona site is an exception because of successionto grassland without tree establishment. We had fewermeasurements following less severe ecosystem changes,such as insect defoliation or forest silvicultural thinning, andthese effects need to be assessed more fully and compared tointerannual variability caused by climate. We also recognizeAMIRO ET AL.: CARBON FLUXES AFTER FOREST DISTURBANCE G00K02G00K0210 of 13that our chronosequence measurements are essentially a“snapshot” of recent CO2fluxes, with older forests havingdeveloped under past conditions that had lower ambientCO2concentrations and a different climate.[35] Although the boreal fire and some of the harvestchronosequence sites have been decommissioned, there is aneed to learn more about developmental dynamics, such asthe decomposition of coarse woody debris following dis-turbance. It is possible that this is a short‐term respirationflux that occurs in a pulse of a few years in favorable cli-matic conditions, or could be a more continuous process thataffects the net flux for several decades as in the semiaridponderosa pine region [Sun et al., 2004]. We also have poorspatialrepresentation,especiallyinnonborealfire‐dominatedecosystems. For example, there are many ecosystems whereneedleleaf evergreen forests regenerate directly after firewithout a broadleaf deciduous successional component. Wecould then expect a more delayed NEP recovery. Alterna-tively, tree generation can be greatly delayed or absent, as inthe Arizona fire chronosequence (Figure 1a). Understoryvegetation has shown a compensatory effect on NEP withthe thinning of overstory trees [Campbell et al., 2009], soecosystem studies should include understory measurementsto quantify the trajectory of response of ecosystem compo-nents after disturbance. Postharvest trajectories only have asmall spatial sample. Even in a single geographical area,there are many possible trajectories. For example, the datafrom Wisconsin showed considerable spatial variability attimes less than 20 years, even though we had few points(Figure 2a). Continuity of data at these sites and addition ofnearby sites would help address the issue of spatial andtemporal variability.[36] Research opportunities provided by major dis-turbances, such as Hurricane Wilma, provide valuable datafor tracking recovery of ecosystems, as changes in poolsand fluxes after such events are difficult to model. Moreobservations in disturbed systems are essential for calibrat-ing models that are used to map C balances across regions.At sites that have been shut down, there is an opportunity torevisit these sites again in the future to fill in gaps in thetrajectories. The difficulty is that we will likely miss sur-prises, either caused by vegetation dynamics (e.g., changesin species mix), climate/weather, or other forcings (storms,insects, disease). An example is the forest tent caterpillarevent in Wisconsin [Cook et al., 2008]. Even with a goodmodel, it would be difficult to plan the best period to revisitsites, or to determine how long we would need to makemeasurements. Given site logistics, a campaign of at least5 years would likely be needed at a given site. Hence,revisiting sites will likely be opportunistic, and new dis-turbances (e.g., storms) [Chambers et al., 2007] will alsocreate additional opportunities. In addition to revisiting sites,there are many additional scientific questions that need to beanswered. In particular, those tied to a management decisionwould be a high priority. This involves spending resourcesfor a silvicultural practice, insect and disease control, orsuppression or addition of fire. Many of these managementoptions are linked to values other than C, but C is becomingan increasingly valued commodity, and needs to be con-sidered. In addition, experiments should be conducted foreach disturbance type to investigate the impacts of climate.This could be done with paired chronosequences withsimilar disturbances under different climates, which wouldprovide a partial analog for future climate change and allowus to identify differences in disturbance recovery caused byclimate. Whole ecosystem studies of the C consequences ofthese management options using eddy covariance will con-tinue to provide valuable information to assess C sourcesand sinks in a biosphere undergoing rapid changes resultingfrom climatic variations and disturbances.[37] Acknowledgments. Data collected at each site were supportedby a host of collaborators as outlined in papers describing each site. TheAmeriFlux network was supported by the U.S. Department of Energy(e.g., B.E. 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