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Factors Controlling Long- and Short-Term Sequestration of Atmospheric CO <sub>2</sub> in a Mid-latitude Forest

Factors Controlling Long- and Short-Term Sequestration of Atmospheric CO 2 in a... UC Irvine Faculty Publications Title Factors Controlling Long- and Short-Term Sequestration of Atmospheric CO2 in a Mid- latitude Forest Permalink https://escholarship.org/uc/item/81c6n0nf Journal Science, 294(5547) ISSN 00368075 10959203 Authors Barford, C. C. WOFSY, S.C. GOULDEN, M.L. et al. Publication Date 2001-11-01 DOI 10.1126/science.1062962 License https://creativecommons.org/licenses/by/3.0/ 4.0 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Factors Controlling Long- and Short-Term Sequestration of Atmospheric CO in a Mid-latitude Forest Carol C. Barford et al. Science 294, 1688 (2001); DOI: 10.1126/science.1062962 This copy is for your personal, non-commercial use only. If you wish to distribute this article to others, you can order high-quality copies for your colleagues, clients, or customers by clicking here. 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The following resources related to this article are available online at www.sciencemag.org (this information is current as of August 7, 2013 ): Updated information and services, including high-resolution figures, can be found in the online version of this article at: http://www.sciencemag.org/content/294/5547/1688.full.html Supporting Online Material can be found at: http://www.sciencemag.org/content/suppl/2001/11/26/294.5547.1688.DC1.html This article cites 30 articles, 10 of which can be accessed free: http://www.sciencemag.org/content/294/5547/1688.full.html#ref-list-1 This article has been cited by 235 article(s) on the ISI Web of Science This article has been cited by 16 articles hosted by HighWire Press; see: http://www.sciencemag.org/content/294/5547/1688.full.html#related-urls This article appears in the following subject collections: Atmospheric Science http://www.sciencemag.org/cgi/collection/atmos Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2001 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS. Downloaded from www.sciencemag.org on August 7, 2013 R EPORTS 36. E. Beniash, W. Traub, A. Veis, S. Weiner, J. Struct. Biol. 41. Web Þgures 1 and 2 are available on Science Online from DOE (grant no. DE-FG02-00ER45810/A001), NSF (grant no. DMR9996253), and Air Force OfÞce of 132, 212 (2000). at www.sciencemag.org/cgi/content/full/294/5547/ ScientiÞc ResearchÐMulti University Research Initia- 1684/DC1. 37. J. R. Harris, Ed., Electron Microscopy in Biology, A tive (grant no. F49620-00-1-0283/P01). We would Practical Approach (Oxford Univ. Press, New York, 42. L. Addadi, S. Weiner, Proc. Natl. Acad. Sci. U.S.A. 82, like to thank B. Rabatic for assistance with MALDI- 4110 (1985). 1991), p. 32 and p. 206. TOF MS, the EPIC center for use of the Hitachi H8100 43. G. Falini, S. Albeck, S. Weiner, L. Addadi, Science 271, 38. S. Krimm, J. Bandekar, Adv. Protein Chem. 38, 181 TEM, the Keck Biophysics Facility for use of the TEM 67 (1996). (1986). cryo holder, and the Analytical Sciences Laboratory 39. W. K. Surewicz, H. H. Mantsch, D. Chapman, Bio- 44. S. Weiner, W. Traub, FASEB J. 6, 879 (1992). for use of the NMR and FTIR instruments. chemistry 32, 389 (1993). 45. W. J. Landis, K. J. Hodgens, J. Arena, M. J. Song, B. F. McEwen, Microsc. Res. Tech. 33, 192 (1996). 40. W. A. Petka, J. L. Harden, K. P. McGrath, D. Wirtz, D. A. Tirrell, Science 281, 389 (1998). 46. We are grateful for the generous Þnancial support 6 June 2001; accepted 28 October 2001 72.2W) measured by using eddy-covariance Factors Controlling Long- and techniques (14–16 ). These data are compared with 8 years of biometric measurements of Short-Term Sequestration of species-specific changes in C storage in live and dead wood, showing where and how the forest is storing C. We determine the C budget Atmospheric CO in a and responses to environmental forcing, includ- ing diel variations, weather patterns (14 ), phe- Mid-latitude Forest nology, and interannual climate variability (15). Eddy fluxes may underreport respiration at 1 1 2 Carol C. Barford, * Steven C. Wofsy, † Michael L. Goulden, night in calm winds (17 ), and methods for 1 1 J. William Munger, Elizabeth Hammond Pyle, removing this bias (18) remain controversial. 1 1 1 Shawn P. Urbanski, Lucy Hutyra, Scott R. Saleska, Here we address possible errors in eddy-covari- 3 3 David Fitzjarrald, Kathleen Moore ance data using the biometric data and combine the observations to define the causes of C se- Net uptake of carbon dioxide (CO ) measured by eddy covariance in a 60- to questration and its variation on time scales from 80-year-old forest averaged 2.0 6 0.4 megagrams of carbon per hectare per hourly to decadal. year during 1993 to 2000, with interannual variations exceeding 50%. Biometry Eddy-covariance data extend from 28 Octo- indicated storage of 1.6 6 0.4 megagrams of carbon per hectare per year over ber 1991 to 27 October 2000, with valid data for 8 years, 60% in live biomass and the balance in coarse woody debris and soils, 46,500 of 79,000 hours. Gaps occurred for cal- conÞrming eddy-covariance results. Weather and seasonal climate (e.g., vari- ibration, data transfer, intense precipitation, ations in growing-season length or cloudiness) regulated seasonal and inter- maintenance, equipment failure, and weak ver- ) (Fig. 1). Eco- annual ßuctuations of carbon uptake. Legacies of prior disturbance and man- tical exchange (u* , 20 cm s agement, especially stand age and composition, controlled carbon uptake on system respiration (R) was observed directly at the decadal time scale, implying that eastern forests could be managed for night and extrapolated for daytime on the basis sequestration of carbon. of day-night changes in soil temperature (18). Gross ecosystem exchange (GEE) was comput- The terrestrial biosphere has sequestered signif- (12) or N deposition (13). These factors must be ed from (NEE 2 R). The 9-year mean annual 21 21 since 1980, year , is similar to icant amounts of atmospheric CO understood in order to predict growth rates of NEE, 22.0 Mg C ha with major contributions from northern mid- atmospheric CO and to develop strategies for observations at other forested sites in the north- latitude forests (1–3). The sink has varied inter- restraining future increases. eastern United States (19, 20). Annual sums annually by a factor of 2 or more, correlating We report here rates of net ecosystem ex- of NEE at this site are insensitive to u* within for 9 years in a northern with global-scale climate variations (4–6 ), and change (NEE) of CO the limits established for valid data (Fig. 1). may have increased in the 1990s (3). The mag- hardwood forest (Harvard Forest, 42.5N, Biometric observations of tree growth and nitude of uptake attributed to mid-latitude for- ests is controversial, however, partly due to Fig. 1. Mean annual C uptake from eddy- sharp disagreement between atmospheric in- covariance data ver- verse models and forest inventories (7 ). The sus the hourly-mean cause of net C uptake is also uncertain, with value, U*, adopted recent studies variously citing land-use change for the minimum fric- (8, 9), fire suppression (10), longer growing tion velocity u*[[ (2 1/2 seasons (11), and fertilization by elevated CO momentum ßux) ] threshold for valid data. Flux values for periods Division of Engineering and Applied Science and De- with u* , U* are Þlled partment of Earth and Planetary Science, Harvard by interpolation (18). University, Cambridge, MA 02138, USA. Department of Earth System Science, University of California, Dashed lines show the Irvine, CA 92697, USA. Atmospheric Sciences Re- range of acceptable search Center, University at Albany, State University values for annual NEE of New York, Albany, NY 12203, USA. (2.02 6 0.15 Mg C 21 21 ha year )onthe *Present address: Center for Sustainability and the basis of the criteria es- Global Environment, Institute for Environmental 0 5 10 15 20 25 30 tablished in (18). Inclu- -1 Studies, University of Wisconsin, Madison, WI 53706, U* threshold (cm s ) sion of ßux data with USA. 21 21 †To whom correspondence should be addressed. E- u* , 17 cm s results in underestimation of R. Thresholds of u* . 30 cm s leave insufÞcient ßux mail: steven_wofsy@harvard.edu data for meaningful annual sums. 1688 23 NOVEMBER 2001 VOL 294 SCIENCE www.sciencemag.org -1 NEE (MgC ha ,9-yr mean) -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 Downloaded from www.sciencemag.org on August 7, 2013 R EPORTS accumulation of coarse woody debris (CWD) tics of the ecosystem. Agriculture at the site was northern hardwoods (27 ). The rate of AGWI 21 21 year ) ( Table 1) were initiated in 1993 (21–24 ) to measure over- abandoned in the 19th century, and by the 1930s (mean rate of 1.4 Mg C ha all CO sequestration and to provide more de- a stand of “old field” white pine was established. varied little from year to year (Fig. 2) (28). tailed information about C cycling at the site. A hurricane in 1938 and subsequent salvage Significant C also accumulated in CWD ( Table Table 1 shows the mean C budget from biomet- removed 70% of the crown area (25) and dis- 1), although less than in live trees, as expected ric data. The average total rate of C sequestra- turbed the soil, allowing establishment of a for a maturing forest (9). NEE will likely decline 21 21 tion, 1.6 6 0.4 Mg C ha year , agrees well hardwood stand dominated by northern red oak as the stand matures, and the rate of net C with the cumulative sum of eddy fluxes, pro- (Quercus rubra L.). The present stand has 100 storage in CWD should also diminish (9). viding independent confirmation of the C bud- MgCha above ground, which is ;80% of Tree growth rates are relatively slow at get from eddy covariance at this site ( Table 2 mean wood C in mature hardwood stands (9, Harvard Forest (29), possibly due to N limi- and Fig. 1). 26 ). Aboveground woody increment (AGWI) tation in soils (30) resulting from pre–20th Carbon sequestration on the decadal time dominated C uptake during 1993 to 2000, ac- century N export in crops and fuel wood. scale was driven by historical land-use and dis- counting for 70% of 8-year mean ecosystem net Nitrogen limitation may also constrain the fertilization (31) at Harvard turbance, which determine critical characteris- uptake (biometric), a typical proportion for potential for CO Forest. Deposition of anthropogenic N over 2000 past decades may have helped restore fertili- 1998 1999 ty, and thus contributed to C storage, but annual N deposition is modest, only ;12% of annual N mineralization (32). - NEE Completely different processes govern AGWI NEE on shorter time scales, as shown by eddy-covariance data. Hourly and daily vari- ations in NEE result from prompt ecosystem responses to ambient sunlight and tempera- ture (14, 15). Monthly and seasonal anoma- lies reflect primarily weather and climate variations (15). For example, low net uptake in 1998 ( Table 2 and Fig. 2) was caused in part by reduced photosynthesis due to low temperatures and excess cloudiness in early summer (33). Net uptake was high in 1995 (15) because ecosystem respiration was de- pressed by dry surface soil in summer (34 ). Seasonal climatic anomalies modify decom- position rates of fine organic matter, such as N J MM J S N J MM J S N J MM J S leaf litter, fine roots, and twigs. The resulting effects on NEE can emerge as variations on Fig. 2. Cumulative ecosystem net uptake (21 3 NEE) and AGWI for years with detailed dendrometry. annual time scales, aliasing climatic variations. For example, winter anomalies in R (relative to 9-year monthly mean R) were positively corre- Table 1. Carbon budget for Harvard Forest from biometry, and NEE (mean of 1993 to 2000, Mg C ha 21 lated with R anomalies in the next growing year ). Numbers in parentheses give the 95% conÞdence intervals. Belowground ßuxes were inferred as 20% of aboveground values (27). CWD respiration was based on 6% mass loss per year (40) from the season (Fig. 3, left panel), indicating that winter estimated stock of CWD (39). Mortality uncertainty was not included in error propagation because net weather (e.g., snow cover) significantly influ- C storage due to mortality is zero (tree death transfers C from live to dead pools, giving equal and enced rates of decomposition over many opposite contributions to AGWI and CWD). Change in soil C is based on the residence time of Cin months (35). Anomalies in winter NEE showed Harvard Forest soils, measured by Gaudinski et al.(44). Table 2. Annual CO exchange (summed from 28 Component Totals October of the previous year to 27 October of the nominal year). Negative values indicate CO ßux D Live biomass from the atmosphere to the ecosystem (i.e., A. Aboveground storage). 1. Growth (AGWI) 1.4 (6 0.2) 2. Mortality 20.6 (6 0.6) Annual exchange B. Belowground (estimated) 21 21 (MgCha year ) 1. Growth 0.3 Year 2. Mortality 20.1 NEE GEE R Subtotal 1.0 (60.2) D Dead wood (CWD) 1992 22.0 211.4 9.5 A. Mortality 1993 21.9 213.3 11.4 1. Aboveground 0.6 (6 0.6) 1994 22.0 212.3 10.3 2. Belowground 0.1 1995 22.5 212.3 9.9 B. Respiration 20.3 (6 0.3)* 1996 22.0 213.2 11.3 Subtotal 0.4 (60.3)* 1997 22.1 213.9 11.8 D Soil (net) 0.2 (60.1) 1998 21.2 212.1 10.9 Comparison of budgets 1999 22.3 213.9 11.6 S Carbon budget (NEP) 1.6 (6 0.4) 2000 22.1 214.3 12.2 S NEE [3 (21)] 2.0 (60.4) Mean 22.0 213.0 11.0 *See (39). www.sciencemag.org SCIENCE VOL 294 23 NOVEMBER 2001 1689 Cumulative C Uptake (Mg C/ha) -3 -2 -1 0 1 2 3 Downloaded from www.sciencemag.org on August 7, 2013 1.0 0.5 0.0 -0.5 -1.0 R EPORTS positive lagged correlations with early spring, are needed to reduce uncertainty in trends of the environmental factors mediating interannual when NEE ; R, but a negative association with mortality and CWD stocks. Reconciliation of a changes, the age structure, species composition, NEE in late summer (Fig. 3, right panel). High biometric budget with NEE in a single year is and health of forest ecosystems are subject to rates of decomposition in winter appear to stim- evidently subject to large errors, and several direct human intervention, indicating that long- ulate anomalously strong gross uptake in the years are required to determine mean rates of C term rates of C sequestration can be deliberately following summer, possibly by increasing the sequestration using either biometry or eddy manipulated (43) through forest management. availability of inorganic nutrients. Turnover covariance. References and Notes times of leaf litter and other fine organic matter Short-term variations of NEE at Harvard 1. P. P. Tans, I. Y. Fung, T. Takahashi, Science 247, 1431 are a year or more, allowing seasonal climate Forest reflect prompt responses of the forest (1990). anomalies to induce annual and interannual to environmental influences. Interannual 2. R. F. Keeling, S. C. Piper, M. Heimann, Nature 381, 218 (1996). variations in C fluxes (36 ). variations reflect effects of weather and cli- 3. M. Battle et al., Science 287, 2467 (2000). Growth rates, like respiration, depend mate on ecosystem characteristics such as 4. Climate Change 2001: The ScientiÞc Basis, Contribu- partly on C fixed in previous years (37 ). tree mortality, autotrophic and heterotrophic tion of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Radial tree growth in deciduous trees begins respiration, pool sizes of labile detritus, Change (Cambridge Univ. Press, Cambridge, 2001). by production of springwood in early May, length of the growing season, and available 5. J. T. Randerson, C. B. Field, I. Y. Fung, P. P. Tans, up to 2 weeks before the daily average NEE light. Because seasonal and annual climatic Geophys. Res. Lett. 26, 2765 (1999). becomes negative and before new leaves start anomalies are often coherent over large spa- 6. B. H. Braswell, D. S. Schimel, E. Linder, B. Moore III, Science 278, 870 (1997). to export carbohydrate (Fig. 2) (37, 38). This tial scales (5, 6 ), the processes described here 7. S. W. Pacala et al., Science 292, 2316 (2001). springwood necessarily derives from stored are important in mediating observed interan- 8. J. P. Caspersen et al., Science 290, 1148 (2000). carbohydrate and is affected by prior growing nual variations of the rate of increase of 9. S. L. Brown, P. E. Schroeder, Ecol. Appl. 9, 968 (1999). 10. R. A. Houghton, J. L. Hackler, K. T. Lawrence, Science conditions. global atmospheric CO . 285, 574 (1999). Biometric C budgets should not be expected Rates of long-term C sequestration at Har- 11. R. B. Myneni et al., Nature 396, 698 (1997). to reconcile with NEE in a single year due to vard Forest change much more slowly, because 12. D. Schimel et al., Science 287, 2004 (2000). 13. E. A. Holland et al., J. Geophys. Res. 102, 15849 annual shifts in C fluxes. For example, AGWI they are driven by ecosystem properties that (1997). composed 100% of NEE in 1998 (Fig. 2), as evolve slowly, i.e., stand composition, biomass 14. S. C. Wofsy et al., Science 260, 1314 (1993). compared with ;70% for the long-term mean, and mortality, soil fertility, and CWD pool size. 15. M. L. Goulden et al., Science 271, 1576 (1996). 16. Data are available at http://www.as.harvard.edu/ indicating a transient budget imbalance given The large areas occupied by mid-succession data/data.html. expected mortality, belowground growth, and forests (30 to 100 years old) have been cited as 17. X. Lee, Agric. For. Meteorol. 97, 65 (1999). so forth. Episodic tree mortality (0.4, 1.0, and the major factor in present terrestrial uptake of 18. M. L. Goulden et al., Global Change Biol. 2, 169 21 21 year aboveground in 1998 to (1996). 0.3 Mg C ha C(41, 42). This work provides support for the 19. H. P. Schmid et al., Agric. For. Meteorol. 103, 355 2000, respectively) (39, 40) also contributed to view that historical legacies are a dominant (2000). annual budget imbalances. More observations factor in C sequestration for these lands. Unlike 20. X. Lee, J. D. Fuentes, R. M. Staebler, H. H. Neumann, J. Geophys. Res. 104D, 15975 (1999). 21. The biometric study measured net ecosystem produc- tion (NEP) by making sequential inventories of pools of R NEE Jan Jan C with relatively long turnover times (i.e., wood, dead Feb Feb wood, and soil; Þne roots and litter stocks were not inventoried, but leaf litter fall was measured). NEP is equivalent to 21 3 NEE, and to net primary production (NPP) minus heterotrophic respiration. In July 1993 we measured diameter at breast height (DBH) of all trees . 10 cm DBH in 40 300-m plots, randomly located within 100-m segments of eight 500-m transects extending northwest and southwest (the dominant wind directions, four transects along each direction) from the eddy-covariance tower. Live trees from the original sample plus trees grown into the 10-cm DBH size class (824 trees) were remeasured and Þtted with steel dendrometer bands in April 1998. In 1998, 1999, and 2000, tree circumference was mea- sured weekly in the growing season and at three other times per year. Woody biomass was calculated by using DBH and allometric equations (22). Aboveground wood increment (AGWI) was the annual increase in woody biomass of live trees; tree mortality (M) was deter- mined separately at the end of each year [i.e., change in live, aboveground woody biomass (DAGWB) 5 AGWI 2 M]. One hundred and Þfty trees were Þtted with a second band in the spring of 2000 to determine corrections for settling, applied to 1998 AGWI. DBH was also rechecked with tapes in October 2000. Coarse woody debris (CWD, dead wood . 7.5-cm diameter) was surveyed in 27 of 40 plots. CWD biomass was calculated by using measured volumes (23) and Feb May Aug Nov Feb Feb May Aug Nov Feb densities from a study of northern hardwood CWD Fig. 3. Correlations of anomalies in NEE and R. Eddy-covariance data were block-averaged into at similar latitude and elevation (24). Leaf litter was collected weekly during September to Novem- monthly intervals, and anomalies were computed relative to the 9-year monthly averages. ber from three 0.13-m traps per plot, sorted by CoefÞcients (r) of correlations between the anomalies of R in winter ( January and February) and genus, dried, and weighed. Dry biomass was as- anomalies of R in subsequent months (x axis) are shown in the left panel. Correlations between sumed to be 50% C in live wood, CWD, and leaf winter and subsequent anomalies in NEE are shown in the right panel. Note that during November litter. through February, GEE '0, and thus NEE ' R. The set of correlation coefÞcients observed here is 22. L. M. Tritton, J. W. Hornbeck, U.S. Department of signiÞcant at the 95% conÞdence interval: assuming a null hypothesis in which anomalies at lags , Agriculture Forest Service General Technical Report 3 months are autocorrelated, the probability of observing this pattern of correlations at lags $ 3 NE-69 (1982). months with ?r?. 0.5 is , 0.05 for both R and NEE (33). 23. M. E. Harmon, J. Sexton, Publication No. 20, U.S. LTER 1690 23 NOVEMBER 2001 VOL 294 SCIENCE www.sciencemag.org correlation coefficient -1.0 -0.5 0.0 0.5 1.0 Downloaded from www.sciencemag.org on August 7, 2013 R EPORTS Network OfÞce (University of Washington, Seattle, WA, 1996). A Near-Earth Asteroid 24. G. G. McGee, D. J. Leopold, R. D. Nyland, Ecol. Appl. 9, 1316 (1999). 25. D. R. Foster et al., BioScience 47, 437 (1997). Population Estimate from the 26. J.C. Jenkins, R.A. Birdsey, Y. Pan, Ecol. Appl. 11, 1174 (2001). 27. R. H. Whittaker, F. H. Bormann, G. E. Likens, T. G. LINEAR Survey Siccama, Ecol. Monogr. 44, 233 (1974). 28. In 1998 to 2000, AGWI was 1.1, 1.2, and 1.4 Mg C Joseph Scott Stuart 21 21 ha year , respectively. 29. I. L. Sander, in R. M. Burns, B. H. Honkala, Eds., Silvics of North America (Agriculture Handbook 654, Forest I estimate the size and shape of the near-Earth asteroid (NEA) population using Service/U.S. Department of Agriculture, Washington, survey data from the Lincoln Near-Earth Asteroid Research (LINEAR) project, cov- DC, 1990). ering 375,000 square degrees of sky and including more than 1300 NEA detections. 30. J. D. Aber et al., BioScience 48, 921 (1998). 31. P. S. Curtis, X. Z. Wang, Oecologia 113, 299 (1998). A simulation of detection probabilities for different values of orbital parameters 32. J. W. Munger et al., J. Geophys. Res. 103 (D7), 8355 and sizes combined with the detection statistics in a Bayesian framework (1998). provides a correction for observational bias and yields the NEA population 33. Supplementary Web material is available on Science distribution as a function of absolute magnitude, semi-major axis, eccentricity, Online at www.sciencemag.org/cgi/content/full/294/ 5547/1688/DC1. and inclination. The NEA population is more highly inclined than previously 34. K. E. Savage, E. A. Davidson, Global Biogeochem. estimated, and the total number of kilometer-sized NEAs is 1227 (1s). Cycles 15, 337 (2001). 35. It is unlikely that variation in leaf litter fall contrib- Attempts to estimate the number of NEAs (1) uted signiÞcantly to variation in heterotrophic respi- the nightly brightness threshold is more diffi- ration, because annual litter fall in our study was have always been hampered by selection biases cult. Because of LINEAR’s short integration quite consistent (e.g., 1.30 and 1.37 Mg C ha in inherent to all observations as well as by small times (7 ) and large pixels (2.2 by 2.2 arcsec- 1998 and 1999, respectively). detection sample sizes. Bottke et al. (2, 3) ad- onds), NEAs move less than the size of a pixel. 36. See www.lternet.edu/hfr/symposium/symp01/ symp01abs.html#davidson dressed this problem by using theoretical orbital Asteroids and stars are all point sources, thus 37. T. T. Kozlowski, Bot. Rev. 58, 107 (1992). dynamical constraints in combination with 138 they can be treated with the same photometric 38. The daily average NEE at Harvard Forest normally detections from the SPACEWATCH program model. The 50% detectability threshold is estab- becomes negative in late May (16). This roughly coincides with the date that new leaves begin to to constrain the size and shape of the NEA lished using the signal-to-noise ratios of 200 to export C to the trees (.50% leaf expansion) (37). population. Rabinowitz et al. (4 ) estimated the 300 cataloged solar-type stars in each field. The See Harvard Forest phenology data at www.lternet. NEA population using 45 detections from the limiting magnitude for each night is then set by edu/hfr/data/hf003/hf003.html. 39. Annual variation in tree mortality did not contribute NEAT program. Here, I use the order-of-mag- averaging these detectability thresholds. Uncer- directly to uncertainty in the biometric C budget (see nitude larger detection sample size of the LIN- tainty in the overall bias of the limiting magni- Table 1), but did add uncertainty to the estimate of the EAR project (5) to estimate the size and shape tude calculation contributes to the error esti- mean CWD respiration rate. To Þnd this rate, we began with the current (year 2000) measured stock of of the NEA population constrained solely by mate in the derived number of NEAs. An aboveground CWD (7.5 Mg C ha ; composed of 5.5 Mg observational data. An estimate of the number estimate of this error is added in quadrature 21 21 Cha standing snags and 2.0 Mg C ha logs). We then of NEAs as a function of absolute magnitude, with the formal statistical errors described calculated the aboveground CWD present midway through the study, assuming constant tree mortality which is related to the size of the asteroid, is of below to obtain the final error value for the (mean mortality for 1993 to 2000 5 0.64 Mg C ha critical importance in assessing the collision number of NEAs and the error envelopes for year aboveground). We assumed dead woody roots 5 hazard for Earth. The distribution of the orbital the distributions. 20% of aboveground CWD (27). We then calculated 6% parameters of the NEAs is important for under- To determine which NEAs were detected on annual loss of C (40) from the total time-averaged CWD pool. The conÞdence interval for CWD respiration (and standing processes of solar system formation any given night, the nightly telescope logs are thus for DCWD) reßects only the statistical uncertainty and dynamics and for evaluating the collision combined with definitive identifications provid- in the CWD pool size. There was no statistical basis for hazard. ed by the International Astronomical Union’s estimating the uncertainty associated with our choice of 6% annual respiration of CWD, and therefore we omit- In 3 years of operation, the LINEAR project Minor Planet Center (MPC). LINEAR reports ted it from the overall budget. Thus, it is possible that searched almost 500,000 square degrees (6)of all of its observations to the MPC, including the conÞdence interval about the estimate of NEP sky on nearly 600 nights, discovering 657 new those that have motions characteristic of main- should be slightly larger. However, we believe that the central estimate is conservative because the majority of NEAs and over 110,000 new main-belt aster- belt asteroids, and provides intentional coverage standing snags in the CWD pool argues against rapid oids. On many of the nights, however, the overlap after a few nights or during the follow- CWD decomposition. weather was sufficiently variable that it was ing month. This follow-up allows NEAs with 40. D. P. Turner, G. J. Koerper, M. E.Harmon, J. J. Lee, Ecol. Appl. 5, 421 (1995). difficult to characterize the limiting magnitude motions initially mimicking main-belt asteroids 41. R. A. Birdsey, A. J. Plantinga, L. S. Heath, For. Ecol. of the search. Selecting only the nights with to be identified, so that the number of detections Manage. 58, 33 (1993). stable atmospheric transparency leaves 412 not identified as NEAs is low, on the order of 42. P. E. Kauppi, K. Mielika ¬inen, K. Kuusela, Science 256, 70 (1992). nights, covers more than 375,000 square de- 1% of the number of NEA detections. Errors in 43. R. N. Sampson, D. Hair, Eds., Forests and Global grees of sky, and includes 1343 detections of which main-belt asteroids or false detections are Change, vol. 2, Forest Management Opportunities for 606 different near-Earth asteroids (Fig. 1). erroneously labeled as NEAs are low because Mitigating Carbon Emissions (American Forests, To understand the selection biases of the all NEA detections are verified on multiple Washington DC, 1996). 44. J. B. Gaudinski, S. E. Trumbore, E. A. Davidson, S. LINEAR system, one must know where the nights, and usually by multiple observers, before Zheng, Biogeochemistry 51, 33 (2000). telescope searched each night, the nightly orbits are issued by the MPC. 45. We thank J. Budney, B. Daube, A. Bright, K. Bagstad, F. brightness threshold for detecting an NEA, and To determine correction factors for observa- Frizzell, S. Heath, and D. Patterson for technical assist- ance. This work was supported by grants from the U.S. the identities of all NEAs detected. The nightly tional bias in the LINEAR search, I accounted Department of Energy (DE-FG02-95ER62002, NIGEC observing logs provide the search locations and for the time-correlated nature of the asteroid DE-FC03-90ER61010), National Science Foundation areas to within a few arcseconds. Determining search space. I divided the orbital parameter (ATM-99-81782, DEB-008-0592, BSR-88-11764), Na- tional Aeronautics and Space Administration (NAGW- space (a-e-i-H) into 49,200 bins (8). In each bin, 3082), and Harvard University (Division of Engineering I generated 144,000 asteroid orbits (9). Each of Massachusetts Institute of Technology Lincoln Labo- and Applied Science and the Harvard Forest). these 144,000 test particles is propagated ratory, 244 Wood Street, Room S4-571, Lexington, 30 May 2001; accepted 23 October 2001 MA 02421, USA. E-mail: stuart@ll.mit.edu through the time covered by the search and www.sciencemag.org SCIENCE VOL 294 23 NOVEMBER 2001 1691 Downloaded from www.sciencemag.org on August 7, 2013 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Science Unpaywall

Factors Controlling Long- and Short-Term Sequestration of Atmospheric CO <sub>2</sub> in a Mid-latitude Forest

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UC Irvine Faculty Publications Title Factors Controlling Long- and Short-Term Sequestration of Atmospheric CO2 in a Mid- latitude Forest Permalink https://escholarship.org/uc/item/81c6n0nf Journal Science, 294(5547) ISSN 00368075 10959203 Authors Barford, C. C. WOFSY, S.C. GOULDEN, M.L. et al. Publication Date 2001-11-01 DOI 10.1126/science.1062962 License https://creativecommons.org/licenses/by/3.0/ 4.0 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Factors Controlling Long- and Short-Term Sequestration of Atmospheric CO in a Mid-latitude Forest Carol C. Barford et al. Science 294, 1688 (2001); DOI: 10.1126/science.1062962 This copy is for your personal, non-commercial use only. If you wish to distribute this article to others, you can order high-quality copies for your colleagues, clients, or customers by clicking here. 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The following resources related to this article are available online at www.sciencemag.org (this information is current as of August 7, 2013 ): Updated information and services, including high-resolution figures, can be found in the online version of this article at: http://www.sciencemag.org/content/294/5547/1688.full.html Supporting Online Material can be found at: http://www.sciencemag.org/content/suppl/2001/11/26/294.5547.1688.DC1.html This article cites 30 articles, 10 of which can be accessed free: http://www.sciencemag.org/content/294/5547/1688.full.html#ref-list-1 This article has been cited by 235 article(s) on the ISI Web of Science This article has been cited by 16 articles hosted by HighWire Press; see: http://www.sciencemag.org/content/294/5547/1688.full.html#related-urls This article appears in the following subject collections: Atmospheric Science http://www.sciencemag.org/cgi/collection/atmos Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2001 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS. Downloaded from www.sciencemag.org on August 7, 2013 R EPORTS 36. E. Beniash, W. Traub, A. Veis, S. Weiner, J. Struct. Biol. 41. Web Þgures 1 and 2 are available on Science Online from DOE (grant no. DE-FG02-00ER45810/A001), NSF (grant no. DMR9996253), and Air Force OfÞce of 132, 212 (2000). at www.sciencemag.org/cgi/content/full/294/5547/ ScientiÞc ResearchÐMulti University Research Initia- 1684/DC1. 37. J. R. Harris, Ed., Electron Microscopy in Biology, A tive (grant no. F49620-00-1-0283/P01). We would Practical Approach (Oxford Univ. Press, New York, 42. L. Addadi, S. Weiner, Proc. Natl. Acad. Sci. U.S.A. 82, like to thank B. Rabatic for assistance with MALDI- 4110 (1985). 1991), p. 32 and p. 206. TOF MS, the EPIC center for use of the Hitachi H8100 43. G. Falini, S. Albeck, S. Weiner, L. Addadi, Science 271, 38. S. Krimm, J. Bandekar, Adv. Protein Chem. 38, 181 TEM, the Keck Biophysics Facility for use of the TEM 67 (1996). (1986). cryo holder, and the Analytical Sciences Laboratory 39. W. K. Surewicz, H. H. Mantsch, D. Chapman, Bio- 44. S. Weiner, W. Traub, FASEB J. 6, 879 (1992). for use of the NMR and FTIR instruments. chemistry 32, 389 (1993). 45. W. J. Landis, K. J. Hodgens, J. Arena, M. J. Song, B. F. McEwen, Microsc. Res. Tech. 33, 192 (1996). 40. W. A. Petka, J. L. Harden, K. P. McGrath, D. Wirtz, D. A. Tirrell, Science 281, 389 (1998). 46. We are grateful for the generous Þnancial support 6 June 2001; accepted 28 October 2001 72.2W) measured by using eddy-covariance Factors Controlling Long- and techniques (14–16 ). These data are compared with 8 years of biometric measurements of Short-Term Sequestration of species-specific changes in C storage in live and dead wood, showing where and how the forest is storing C. We determine the C budget Atmospheric CO in a and responses to environmental forcing, includ- ing diel variations, weather patterns (14 ), phe- Mid-latitude Forest nology, and interannual climate variability (15). Eddy fluxes may underreport respiration at 1 1 2 Carol C. Barford, * Steven C. Wofsy, † Michael L. Goulden, night in calm winds (17 ), and methods for 1 1 J. William Munger, Elizabeth Hammond Pyle, removing this bias (18) remain controversial. 1 1 1 Shawn P. Urbanski, Lucy Hutyra, Scott R. Saleska, Here we address possible errors in eddy-covari- 3 3 David Fitzjarrald, Kathleen Moore ance data using the biometric data and combine the observations to define the causes of C se- Net uptake of carbon dioxide (CO ) measured by eddy covariance in a 60- to questration and its variation on time scales from 80-year-old forest averaged 2.0 6 0.4 megagrams of carbon per hectare per hourly to decadal. year during 1993 to 2000, with interannual variations exceeding 50%. Biometry Eddy-covariance data extend from 28 Octo- indicated storage of 1.6 6 0.4 megagrams of carbon per hectare per year over ber 1991 to 27 October 2000, with valid data for 8 years, 60% in live biomass and the balance in coarse woody debris and soils, 46,500 of 79,000 hours. Gaps occurred for cal- conÞrming eddy-covariance results. Weather and seasonal climate (e.g., vari- ibration, data transfer, intense precipitation, ations in growing-season length or cloudiness) regulated seasonal and inter- maintenance, equipment failure, and weak ver- ) (Fig. 1). Eco- annual ßuctuations of carbon uptake. Legacies of prior disturbance and man- tical exchange (u* , 20 cm s agement, especially stand age and composition, controlled carbon uptake on system respiration (R) was observed directly at the decadal time scale, implying that eastern forests could be managed for night and extrapolated for daytime on the basis sequestration of carbon. of day-night changes in soil temperature (18). Gross ecosystem exchange (GEE) was comput- The terrestrial biosphere has sequestered signif- (12) or N deposition (13). These factors must be ed from (NEE 2 R). The 9-year mean annual 21 21 since 1980, year , is similar to icant amounts of atmospheric CO understood in order to predict growth rates of NEE, 22.0 Mg C ha with major contributions from northern mid- atmospheric CO and to develop strategies for observations at other forested sites in the north- latitude forests (1–3). The sink has varied inter- restraining future increases. eastern United States (19, 20). Annual sums annually by a factor of 2 or more, correlating We report here rates of net ecosystem ex- of NEE at this site are insensitive to u* within for 9 years in a northern with global-scale climate variations (4–6 ), and change (NEE) of CO the limits established for valid data (Fig. 1). may have increased in the 1990s (3). The mag- hardwood forest (Harvard Forest, 42.5N, Biometric observations of tree growth and nitude of uptake attributed to mid-latitude for- ests is controversial, however, partly due to Fig. 1. Mean annual C uptake from eddy- sharp disagreement between atmospheric in- covariance data ver- verse models and forest inventories (7 ). The sus the hourly-mean cause of net C uptake is also uncertain, with value, U*, adopted recent studies variously citing land-use change for the minimum fric- (8, 9), fire suppression (10), longer growing tion velocity u*[[ (2 1/2 seasons (11), and fertilization by elevated CO momentum ßux) ] threshold for valid data. Flux values for periods Division of Engineering and Applied Science and De- with u* , U* are Þlled partment of Earth and Planetary Science, Harvard by interpolation (18). University, Cambridge, MA 02138, USA. Department of Earth System Science, University of California, Dashed lines show the Irvine, CA 92697, USA. Atmospheric Sciences Re- range of acceptable search Center, University at Albany, State University values for annual NEE of New York, Albany, NY 12203, USA. (2.02 6 0.15 Mg C 21 21 ha year )onthe *Present address: Center for Sustainability and the basis of the criteria es- Global Environment, Institute for Environmental 0 5 10 15 20 25 30 tablished in (18). Inclu- -1 Studies, University of Wisconsin, Madison, WI 53706, U* threshold (cm s ) sion of ßux data with USA. 21 21 †To whom correspondence should be addressed. E- u* , 17 cm s results in underestimation of R. Thresholds of u* . 30 cm s leave insufÞcient ßux mail: steven_wofsy@harvard.edu data for meaningful annual sums. 1688 23 NOVEMBER 2001 VOL 294 SCIENCE www.sciencemag.org -1 NEE (MgC ha ,9-yr mean) -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 Downloaded from www.sciencemag.org on August 7, 2013 R EPORTS accumulation of coarse woody debris (CWD) tics of the ecosystem. Agriculture at the site was northern hardwoods (27 ). The rate of AGWI 21 21 year ) ( Table 1) were initiated in 1993 (21–24 ) to measure over- abandoned in the 19th century, and by the 1930s (mean rate of 1.4 Mg C ha all CO sequestration and to provide more de- a stand of “old field” white pine was established. varied little from year to year (Fig. 2) (28). tailed information about C cycling at the site. A hurricane in 1938 and subsequent salvage Significant C also accumulated in CWD ( Table Table 1 shows the mean C budget from biomet- removed 70% of the crown area (25) and dis- 1), although less than in live trees, as expected ric data. The average total rate of C sequestra- turbed the soil, allowing establishment of a for a maturing forest (9). NEE will likely decline 21 21 tion, 1.6 6 0.4 Mg C ha year , agrees well hardwood stand dominated by northern red oak as the stand matures, and the rate of net C with the cumulative sum of eddy fluxes, pro- (Quercus rubra L.). The present stand has 100 storage in CWD should also diminish (9). viding independent confirmation of the C bud- MgCha above ground, which is ;80% of Tree growth rates are relatively slow at get from eddy covariance at this site ( Table 2 mean wood C in mature hardwood stands (9, Harvard Forest (29), possibly due to N limi- and Fig. 1). 26 ). Aboveground woody increment (AGWI) tation in soils (30) resulting from pre–20th Carbon sequestration on the decadal time dominated C uptake during 1993 to 2000, ac- century N export in crops and fuel wood. scale was driven by historical land-use and dis- counting for 70% of 8-year mean ecosystem net Nitrogen limitation may also constrain the fertilization (31) at Harvard turbance, which determine critical characteris- uptake (biometric), a typical proportion for potential for CO Forest. Deposition of anthropogenic N over 2000 past decades may have helped restore fertili- 1998 1999 ty, and thus contributed to C storage, but annual N deposition is modest, only ;12% of annual N mineralization (32). - NEE Completely different processes govern AGWI NEE on shorter time scales, as shown by eddy-covariance data. Hourly and daily vari- ations in NEE result from prompt ecosystem responses to ambient sunlight and tempera- ture (14, 15). Monthly and seasonal anoma- lies reflect primarily weather and climate variations (15). For example, low net uptake in 1998 ( Table 2 and Fig. 2) was caused in part by reduced photosynthesis due to low temperatures and excess cloudiness in early summer (33). Net uptake was high in 1995 (15) because ecosystem respiration was de- pressed by dry surface soil in summer (34 ). Seasonal climatic anomalies modify decom- position rates of fine organic matter, such as N J MM J S N J MM J S N J MM J S leaf litter, fine roots, and twigs. The resulting effects on NEE can emerge as variations on Fig. 2. Cumulative ecosystem net uptake (21 3 NEE) and AGWI for years with detailed dendrometry. annual time scales, aliasing climatic variations. For example, winter anomalies in R (relative to 9-year monthly mean R) were positively corre- Table 1. Carbon budget for Harvard Forest from biometry, and NEE (mean of 1993 to 2000, Mg C ha 21 lated with R anomalies in the next growing year ). Numbers in parentheses give the 95% conÞdence intervals. Belowground ßuxes were inferred as 20% of aboveground values (27). CWD respiration was based on 6% mass loss per year (40) from the season (Fig. 3, left panel), indicating that winter estimated stock of CWD (39). Mortality uncertainty was not included in error propagation because net weather (e.g., snow cover) significantly influ- C storage due to mortality is zero (tree death transfers C from live to dead pools, giving equal and enced rates of decomposition over many opposite contributions to AGWI and CWD). Change in soil C is based on the residence time of Cin months (35). Anomalies in winter NEE showed Harvard Forest soils, measured by Gaudinski et al.(44). Table 2. Annual CO exchange (summed from 28 Component Totals October of the previous year to 27 October of the nominal year). Negative values indicate CO ßux D Live biomass from the atmosphere to the ecosystem (i.e., A. Aboveground storage). 1. Growth (AGWI) 1.4 (6 0.2) 2. Mortality 20.6 (6 0.6) Annual exchange B. Belowground (estimated) 21 21 (MgCha year ) 1. Growth 0.3 Year 2. Mortality 20.1 NEE GEE R Subtotal 1.0 (60.2) D Dead wood (CWD) 1992 22.0 211.4 9.5 A. Mortality 1993 21.9 213.3 11.4 1. Aboveground 0.6 (6 0.6) 1994 22.0 212.3 10.3 2. Belowground 0.1 1995 22.5 212.3 9.9 B. Respiration 20.3 (6 0.3)* 1996 22.0 213.2 11.3 Subtotal 0.4 (60.3)* 1997 22.1 213.9 11.8 D Soil (net) 0.2 (60.1) 1998 21.2 212.1 10.9 Comparison of budgets 1999 22.3 213.9 11.6 S Carbon budget (NEP) 1.6 (6 0.4) 2000 22.1 214.3 12.2 S NEE [3 (21)] 2.0 (60.4) Mean 22.0 213.0 11.0 *See (39). www.sciencemag.org SCIENCE VOL 294 23 NOVEMBER 2001 1689 Cumulative C Uptake (Mg C/ha) -3 -2 -1 0 1 2 3 Downloaded from www.sciencemag.org on August 7, 2013 1.0 0.5 0.0 -0.5 -1.0 R EPORTS positive lagged correlations with early spring, are needed to reduce uncertainty in trends of the environmental factors mediating interannual when NEE ; R, but a negative association with mortality and CWD stocks. Reconciliation of a changes, the age structure, species composition, NEE in late summer (Fig. 3, right panel). High biometric budget with NEE in a single year is and health of forest ecosystems are subject to rates of decomposition in winter appear to stim- evidently subject to large errors, and several direct human intervention, indicating that long- ulate anomalously strong gross uptake in the years are required to determine mean rates of C term rates of C sequestration can be deliberately following summer, possibly by increasing the sequestration using either biometry or eddy manipulated (43) through forest management. availability of inorganic nutrients. Turnover covariance. References and Notes times of leaf litter and other fine organic matter Short-term variations of NEE at Harvard 1. P. P. Tans, I. Y. Fung, T. Takahashi, Science 247, 1431 are a year or more, allowing seasonal climate Forest reflect prompt responses of the forest (1990). anomalies to induce annual and interannual to environmental influences. Interannual 2. R. F. Keeling, S. C. Piper, M. Heimann, Nature 381, 218 (1996). variations in C fluxes (36 ). variations reflect effects of weather and cli- 3. M. Battle et al., Science 287, 2467 (2000). Growth rates, like respiration, depend mate on ecosystem characteristics such as 4. Climate Change 2001: The ScientiÞc Basis, Contribu- partly on C fixed in previous years (37 ). tree mortality, autotrophic and heterotrophic tion of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Radial tree growth in deciduous trees begins respiration, pool sizes of labile detritus, Change (Cambridge Univ. Press, Cambridge, 2001). by production of springwood in early May, length of the growing season, and available 5. J. T. Randerson, C. B. Field, I. Y. Fung, P. P. Tans, up to 2 weeks before the daily average NEE light. Because seasonal and annual climatic Geophys. Res. Lett. 26, 2765 (1999). becomes negative and before new leaves start anomalies are often coherent over large spa- 6. B. H. Braswell, D. S. Schimel, E. Linder, B. Moore III, Science 278, 870 (1997). to export carbohydrate (Fig. 2) (37, 38). This tial scales (5, 6 ), the processes described here 7. S. W. Pacala et al., Science 292, 2316 (2001). springwood necessarily derives from stored are important in mediating observed interan- 8. J. P. Caspersen et al., Science 290, 1148 (2000). carbohydrate and is affected by prior growing nual variations of the rate of increase of 9. S. L. Brown, P. E. Schroeder, Ecol. Appl. 9, 968 (1999). 10. R. A. Houghton, J. L. Hackler, K. T. Lawrence, Science conditions. global atmospheric CO . 285, 574 (1999). Biometric C budgets should not be expected Rates of long-term C sequestration at Har- 11. R. B. Myneni et al., Nature 396, 698 (1997). to reconcile with NEE in a single year due to vard Forest change much more slowly, because 12. D. Schimel et al., Science 287, 2004 (2000). 13. E. A. Holland et al., J. Geophys. Res. 102, 15849 annual shifts in C fluxes. For example, AGWI they are driven by ecosystem properties that (1997). composed 100% of NEE in 1998 (Fig. 2), as evolve slowly, i.e., stand composition, biomass 14. S. C. Wofsy et al., Science 260, 1314 (1993). compared with ;70% for the long-term mean, and mortality, soil fertility, and CWD pool size. 15. M. L. Goulden et al., Science 271, 1576 (1996). 16. Data are available at http://www.as.harvard.edu/ indicating a transient budget imbalance given The large areas occupied by mid-succession data/data.html. expected mortality, belowground growth, and forests (30 to 100 years old) have been cited as 17. X. Lee, Agric. For. Meteorol. 97, 65 (1999). so forth. Episodic tree mortality (0.4, 1.0, and the major factor in present terrestrial uptake of 18. M. L. Goulden et al., Global Change Biol. 2, 169 21 21 year aboveground in 1998 to (1996). 0.3 Mg C ha C(41, 42). This work provides support for the 19. H. P. Schmid et al., Agric. For. Meteorol. 103, 355 2000, respectively) (39, 40) also contributed to view that historical legacies are a dominant (2000). annual budget imbalances. More observations factor in C sequestration for these lands. Unlike 20. X. Lee, J. D. Fuentes, R. M. Staebler, H. H. Neumann, J. Geophys. Res. 104D, 15975 (1999). 21. The biometric study measured net ecosystem produc- tion (NEP) by making sequential inventories of pools of R NEE Jan Jan C with relatively long turnover times (i.e., wood, dead Feb Feb wood, and soil; Þne roots and litter stocks were not inventoried, but leaf litter fall was measured). NEP is equivalent to 21 3 NEE, and to net primary production (NPP) minus heterotrophic respiration. In July 1993 we measured diameter at breast height (DBH) of all trees . 10 cm DBH in 40 300-m plots, randomly located within 100-m segments of eight 500-m transects extending northwest and southwest (the dominant wind directions, four transects along each direction) from the eddy-covariance tower. Live trees from the original sample plus trees grown into the 10-cm DBH size class (824 trees) were remeasured and Þtted with steel dendrometer bands in April 1998. In 1998, 1999, and 2000, tree circumference was mea- sured weekly in the growing season and at three other times per year. Woody biomass was calculated by using DBH and allometric equations (22). Aboveground wood increment (AGWI) was the annual increase in woody biomass of live trees; tree mortality (M) was deter- mined separately at the end of each year [i.e., change in live, aboveground woody biomass (DAGWB) 5 AGWI 2 M]. One hundred and Þfty trees were Þtted with a second band in the spring of 2000 to determine corrections for settling, applied to 1998 AGWI. DBH was also rechecked with tapes in October 2000. Coarse woody debris (CWD, dead wood . 7.5-cm diameter) was surveyed in 27 of 40 plots. CWD biomass was calculated by using measured volumes (23) and Feb May Aug Nov Feb Feb May Aug Nov Feb densities from a study of northern hardwood CWD Fig. 3. Correlations of anomalies in NEE and R. Eddy-covariance data were block-averaged into at similar latitude and elevation (24). Leaf litter was collected weekly during September to Novem- monthly intervals, and anomalies were computed relative to the 9-year monthly averages. ber from three 0.13-m traps per plot, sorted by CoefÞcients (r) of correlations between the anomalies of R in winter ( January and February) and genus, dried, and weighed. Dry biomass was as- anomalies of R in subsequent months (x axis) are shown in the left panel. Correlations between sumed to be 50% C in live wood, CWD, and leaf winter and subsequent anomalies in NEE are shown in the right panel. Note that during November litter. through February, GEE '0, and thus NEE ' R. The set of correlation coefÞcients observed here is 22. L. M. Tritton, J. W. Hornbeck, U.S. Department of signiÞcant at the 95% conÞdence interval: assuming a null hypothesis in which anomalies at lags , Agriculture Forest Service General Technical Report 3 months are autocorrelated, the probability of observing this pattern of correlations at lags $ 3 NE-69 (1982). months with ?r?. 0.5 is , 0.05 for both R and NEE (33). 23. M. E. Harmon, J. Sexton, Publication No. 20, U.S. LTER 1690 23 NOVEMBER 2001 VOL 294 SCIENCE www.sciencemag.org correlation coefficient -1.0 -0.5 0.0 0.5 1.0 Downloaded from www.sciencemag.org on August 7, 2013 R EPORTS Network OfÞce (University of Washington, Seattle, WA, 1996). A Near-Earth Asteroid 24. G. G. McGee, D. J. Leopold, R. D. Nyland, Ecol. Appl. 9, 1316 (1999). 25. D. R. Foster et al., BioScience 47, 437 (1997). Population Estimate from the 26. J.C. Jenkins, R.A. Birdsey, Y. Pan, Ecol. Appl. 11, 1174 (2001). 27. R. H. Whittaker, F. H. Bormann, G. E. Likens, T. G. LINEAR Survey Siccama, Ecol. Monogr. 44, 233 (1974). 28. In 1998 to 2000, AGWI was 1.1, 1.2, and 1.4 Mg C Joseph Scott Stuart 21 21 ha year , respectively. 29. I. L. Sander, in R. M. Burns, B. H. Honkala, Eds., Silvics of North America (Agriculture Handbook 654, Forest I estimate the size and shape of the near-Earth asteroid (NEA) population using Service/U.S. Department of Agriculture, Washington, survey data from the Lincoln Near-Earth Asteroid Research (LINEAR) project, cov- DC, 1990). ering 375,000 square degrees of sky and including more than 1300 NEA detections. 30. J. D. Aber et al., BioScience 48, 921 (1998). 31. P. S. Curtis, X. Z. Wang, Oecologia 113, 299 (1998). A simulation of detection probabilities for different values of orbital parameters 32. J. W. Munger et al., J. Geophys. Res. 103 (D7), 8355 and sizes combined with the detection statistics in a Bayesian framework (1998). provides a correction for observational bias and yields the NEA population 33. Supplementary Web material is available on Science distribution as a function of absolute magnitude, semi-major axis, eccentricity, Online at www.sciencemag.org/cgi/content/full/294/ 5547/1688/DC1. and inclination. The NEA population is more highly inclined than previously 34. K. E. Savage, E. A. Davidson, Global Biogeochem. estimated, and the total number of kilometer-sized NEAs is 1227 (1s). Cycles 15, 337 (2001). 35. It is unlikely that variation in leaf litter fall contrib- Attempts to estimate the number of NEAs (1) uted signiÞcantly to variation in heterotrophic respi- the nightly brightness threshold is more diffi- ration, because annual litter fall in our study was have always been hampered by selection biases cult. Because of LINEAR’s short integration quite consistent (e.g., 1.30 and 1.37 Mg C ha in inherent to all observations as well as by small times (7 ) and large pixels (2.2 by 2.2 arcsec- 1998 and 1999, respectively). detection sample sizes. Bottke et al. (2, 3) ad- onds), NEAs move less than the size of a pixel. 36. See www.lternet.edu/hfr/symposium/symp01/ symp01abs.html#davidson dressed this problem by using theoretical orbital Asteroids and stars are all point sources, thus 37. T. T. Kozlowski, Bot. Rev. 58, 107 (1992). dynamical constraints in combination with 138 they can be treated with the same photometric 38. The daily average NEE at Harvard Forest normally detections from the SPACEWATCH program model. The 50% detectability threshold is estab- becomes negative in late May (16). This roughly coincides with the date that new leaves begin to to constrain the size and shape of the NEA lished using the signal-to-noise ratios of 200 to export C to the trees (.50% leaf expansion) (37). population. Rabinowitz et al. (4 ) estimated the 300 cataloged solar-type stars in each field. The See Harvard Forest phenology data at www.lternet. NEA population using 45 detections from the limiting magnitude for each night is then set by edu/hfr/data/hf003/hf003.html. 39. Annual variation in tree mortality did not contribute NEAT program. Here, I use the order-of-mag- averaging these detectability thresholds. Uncer- directly to uncertainty in the biometric C budget (see nitude larger detection sample size of the LIN- tainty in the overall bias of the limiting magni- Table 1), but did add uncertainty to the estimate of the EAR project (5) to estimate the size and shape tude calculation contributes to the error esti- mean CWD respiration rate. To Þnd this rate, we began with the current (year 2000) measured stock of of the NEA population constrained solely by mate in the derived number of NEAs. An aboveground CWD (7.5 Mg C ha ; composed of 5.5 Mg observational data. An estimate of the number estimate of this error is added in quadrature 21 21 Cha standing snags and 2.0 Mg C ha logs). We then of NEAs as a function of absolute magnitude, with the formal statistical errors described calculated the aboveground CWD present midway through the study, assuming constant tree mortality which is related to the size of the asteroid, is of below to obtain the final error value for the (mean mortality for 1993 to 2000 5 0.64 Mg C ha critical importance in assessing the collision number of NEAs and the error envelopes for year aboveground). We assumed dead woody roots 5 hazard for Earth. The distribution of the orbital the distributions. 20% of aboveground CWD (27). We then calculated 6% parameters of the NEAs is important for under- To determine which NEAs were detected on annual loss of C (40) from the total time-averaged CWD pool. The conÞdence interval for CWD respiration (and standing processes of solar system formation any given night, the nightly telescope logs are thus for DCWD) reßects only the statistical uncertainty and dynamics and for evaluating the collision combined with definitive identifications provid- in the CWD pool size. There was no statistical basis for hazard. ed by the International Astronomical Union’s estimating the uncertainty associated with our choice of 6% annual respiration of CWD, and therefore we omit- In 3 years of operation, the LINEAR project Minor Planet Center (MPC). LINEAR reports ted it from the overall budget. Thus, it is possible that searched almost 500,000 square degrees (6)of all of its observations to the MPC, including the conÞdence interval about the estimate of NEP sky on nearly 600 nights, discovering 657 new those that have motions characteristic of main- should be slightly larger. However, we believe that the central estimate is conservative because the majority of NEAs and over 110,000 new main-belt aster- belt asteroids, and provides intentional coverage standing snags in the CWD pool argues against rapid oids. On many of the nights, however, the overlap after a few nights or during the follow- CWD decomposition. weather was sufficiently variable that it was ing month. This follow-up allows NEAs with 40. D. P. Turner, G. J. Koerper, M. E.Harmon, J. J. Lee, Ecol. Appl. 5, 421 (1995). difficult to characterize the limiting magnitude motions initially mimicking main-belt asteroids 41. R. A. Birdsey, A. J. Plantinga, L. S. Heath, For. Ecol. of the search. Selecting only the nights with to be identified, so that the number of detections Manage. 58, 33 (1993). stable atmospheric transparency leaves 412 not identified as NEAs is low, on the order of 42. P. E. Kauppi, K. Mielika ¬inen, K. Kuusela, Science 256, 70 (1992). nights, covers more than 375,000 square de- 1% of the number of NEA detections. Errors in 43. R. N. Sampson, D. Hair, Eds., Forests and Global grees of sky, and includes 1343 detections of which main-belt asteroids or false detections are Change, vol. 2, Forest Management Opportunities for 606 different near-Earth asteroids (Fig. 1). erroneously labeled as NEAs are low because Mitigating Carbon Emissions (American Forests, To understand the selection biases of the all NEA detections are verified on multiple Washington DC, 1996). 44. J. B. Gaudinski, S. E. Trumbore, E. A. Davidson, S. LINEAR system, one must know where the nights, and usually by multiple observers, before Zheng, Biogeochemistry 51, 33 (2000). telescope searched each night, the nightly orbits are issued by the MPC. 45. We thank J. Budney, B. Daube, A. Bright, K. Bagstad, F. brightness threshold for detecting an NEA, and To determine correction factors for observa- Frizzell, S. Heath, and D. Patterson for technical assist- ance. This work was supported by grants from the U.S. the identities of all NEAs detected. The nightly tional bias in the LINEAR search, I accounted Department of Energy (DE-FG02-95ER62002, NIGEC observing logs provide the search locations and for the time-correlated nature of the asteroid DE-FC03-90ER61010), National Science Foundation areas to within a few arcseconds. Determining search space. I divided the orbital parameter (ATM-99-81782, DEB-008-0592, BSR-88-11764), Na- tional Aeronautics and Space Administration (NAGW- space (a-e-i-H) into 49,200 bins (8). In each bin, 3082), and Harvard University (Division of Engineering I generated 144,000 asteroid orbits (9). Each of Massachusetts Institute of Technology Lincoln Labo- and Applied Science and the Harvard Forest). these 144,000 test particles is propagated ratory, 244 Wood Street, Room S4-571, Lexington, 30 May 2001; accepted 23 October 2001 MA 02421, USA. E-mail: stuart@ll.mit.edu through the time covered by the search and www.sciencemag.org SCIENCE VOL 294 23 NOVEMBER 2001 1691 Downloaded from www.sciencemag.org on August 7, 2013

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