Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-6bf8c574d5-zc66z Total loading time: 0 Render date: 2025-03-04T00:36:32.375Z Has data issue: false hasContentIssue false

Chapter Fourteen - Detecting and projecting changes in forest biomass from plot data

Published online by Cambridge University Press:  05 June 2014

By
Helene C. Muller-Landau ,
Matteo Detto ,
Ryan A. Chisholm ,
Stephen P. Hubbell  and
Richard Condit
Edited by
David A. Coomes ,
David F. R. P. Burslem  and
William D. Simonson
Helene C. Muller-Landau
Affiliation:
Smithsonian Tropical Research Institute
Matteo Detto
Affiliation:
Smithsonian Tropical Research Institute
Ryan A. Chisholm
Affiliation:
Smithsonian Tropical Research Institute
Stephen P. Hubbell
Affiliation:
Smithsonian Tropical Research Institute
Richard Condit
Affiliation:
Smithsonian Tropical Research Institute
David A. Coomes
Affiliation:
University of Cambridge
David F. R. P. Burslem
Affiliation:
University of Aberdeen
William D. Simonson
Affiliation:
University of Cambridge
Get access

Summary

Introduction

Increasing atmospheric carbon dioxide, changing climates, nitrogen deposition and other aspects of anthropogenic global change are hypothesised to be changing forest productivity and biomass stocks in tropical forests and elsewhere (Clark 2004; Lewis, Malhi & Phillips 2004; Lewis et al. 2009a; Luo, 2007; Myeni et al. 1997). These hypotheses continue to be much debated, with contrary views on the plausibility of particular mechanisms and on the status of current evidence for or against them (Clark 2007; Friedlingstein et al. 2006; Holtum & Winter 2010; Körner 2009; Wright 2005, 2010). The influence of atmospheric and climate change on forest biomass is of particular interest because of the potential for positive or negative feedbacks. Increases in forest biomass and associated carbon pools would slow the rise in atmospheric carbon dioxide, producing a negative feedback, whereas decreases in forest biomass would have the opposite effect. Uncertainty surrounding these feedbacks is considerable at the global scale, with important implications for global carbon budgets (Luo 2007).

In view of this, it is essential to know whether forests are experiencing changes in productivity and biomass in excess of those typical for their age. Successional forests, those regrowing after disturbances, increase in biomass over time, with the trajectory and duration of this increase varying with forest type (Bormann & Likens 1979; Odum 1969). In the absence of global change, such forests are expected to eventually reach a dynamic equilibrium in which biomass gains from growth and recruitment are balanced by biomass losses from tree death and branchfall, and these old-growth forests thus experience no directional changes in biomass (Odum 1969; Yang, Luo & Finzi 2011). Accordingly, detection of directional changes in biomass in old-growth forests is generally considered evidence of global change influences. When and where such changes are detected, the next critical question concerns prediction of future net carbon fluxes and ultimate carbon stocks of such altered forests.

Type
Chapter
Information
Forests and Global Change , pp. 381 - 416
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, T. W. & Goodman, L. A. (1957) Statistical inference about Markov chains. Annals of Mathematical Statistics, 28, 89–110.CrossRefGoogle Scholar
Asner, G. P., Mascaro, J., Muller-Landau, H. C. et al. (2011) A universal airborne LIDAR approach for tropical forest carbon mapping. Oecologia, 168, 1147–1160.CrossRefGoogle ScholarPubMed
Baker, T. R., Phillips, O. L., Malhi, Y. et al. (2004) Increasing biomass in Amazonian forest plots. Philosophical Transactions of the Royal Society Series B, 359, 353–365.CrossRefGoogle ScholarPubMed
Bormann, F. H. & Likens, G. E. (1979) Pattern and Process in a Forested Ecosystem. New York: Springer.CrossRefGoogle Scholar
Chave, J. (1999) Study of structural, successional and spatial patterns in tropical rain forests using TROLL, a spatially explicit forest model. Ecological Modelling, 124, 233–254.CrossRefGoogle Scholar
Chave, J., Andalo, C., Brown, S. et al. (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145, 87–99.CrossRefGoogle ScholarPubMed
Chave, J., Condit, R., Aguilar, S. et al. (2004) Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society Series B, 359, 409–420.CrossRefGoogle ScholarPubMed
Chave, J., Condit, R., Lao, S. et al. (2003) Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama. Journal of Ecology, 91, 240–252.CrossRefGoogle Scholar
Chave, J., Condit, R., Muller-Landau, H. C. et al. (2008) Assessing evidence for a pervasive alteration in tropical tree communities. PLoS Biology, 6, 455–462.CrossRefGoogle ScholarPubMed
Clark, D. A. (2002) Are tropical forests an important carbon sink? Reanalysis of the long-term plot data. Ecological Applications, 12, 3–7.CrossRefGoogle Scholar
Clark, D. A. (2004) Tropical forests and climate change. Proceedings of the Royal Society Series B, 359, 477–491.Google Scholar
Clark, D. A. (2007) Detecting tropical forests’ responses to global climatic and atmospheric change: current challenges and a way forward. Biotropica, 39, 4–19.CrossRefGoogle Scholar
Condit, R. (1998) Tropical Forest Census Plots. Berlin: Springer, and Georgetown, TX: R. G. Landes Company.CrossRefGoogle Scholar
De Gruijter, J., Brus, D. J., Bierkens, M. F. P. & Knotters, M. (2006) Sampling for Natural Resource Monitoring. Berlin: Springer.CrossRefGoogle Scholar
Facelli, J. M. & Pickett, S. T. A. (1990) Markovian chains and the role of history in succession. Trends in Ecology & Evolution, 5, 27–30.CrossRefGoogle ScholarPubMed
Fisher, J. I., Hurtt, G. C., Thomas, R. Q. & Chambers, J. Q. (2008) Clustered disturbances lead to bias in large-scale estimates based on forest sample plots. Ecology Letters, 11, 554–563.CrossRefGoogle ScholarPubMed
Friedlingstein, P., Bopp, L., Rayner, P. et al. (2006) Climate-carbon cycle feedback analysis, results from the C4MIP model intercomparison. Journal of Climate, 19, 3337–3353.CrossRefGoogle Scholar
Gloor, M., Phillips, O. L., Lloyd, J. J. et al. (2009) Does the disturbance hypothesis explain the biomass increase in basin-wide Amazon forest plot data?Global Change Biology, 15, 2418–2430.CrossRefGoogle Scholar
Haan, P. (1999) On the use of density kernels for concentration estimations within particle and puff dispersion models. Atmospheric Environment, 33, 2007–2021.CrossRefGoogle Scholar
Higuchi, N., Dos Santos, J. & Jardim, F. C. S. (1982) Tamanho de parcela amostral para invenários florestais. Acta Amazonica, 12, 91–103.CrossRefGoogle Scholar
Holtum, J. A. M. & Winter, K. (2010) Elevated [CO2] and forest vegetation: more a water issue than a carbon issue?Functional Plant Biology, 37, 694–702.CrossRefGoogle Scholar
Horn, H. S. (1975) Markovian properties of forest succession. In Ecology and Evolution of Communities (eds. Cody, M. L. & Diamond, J. M.), pp. 196–211. Cambridge, MA: Belknap Press/Harvard University Press.Google Scholar
Jansen, P. A., Van Der Meer, P. J. & Bongers, F. (2008) Spatial contagiousness of canopy disturbance in tropical rain forest: an individual-tree-based test. Ecology, 89, 3490–3502.CrossRefGoogle ScholarPubMed
Keller, M., Palace, M. & Hurtt, G. (2001) Biomass estimation in the Tapajos National Forest, Brazil: Examination of sampling and allometric uncertainties. Forest Ecology and Management, 154, 371–382.CrossRefGoogle Scholar
Kellner, J. R., Clark, D. B. & Hubbell, S. P. (2009) Pervasive canopy dynamics produce short-term stability in a tropical rain forest landscape. Ecology Letters, 12, 155–164.CrossRefGoogle Scholar
Körner, C. (2003) Slow in, rapid out – carbon flux studies and Kyoto targets. Science, 300, 1242–1243.CrossRefGoogle Scholar
Körner, C. (2009) Responses of humid tropical trees to rising CO2. Annual Review of Ecology and Systematics, 40, 61–79.CrossRefGoogle Scholar
Lewis, S. L., Lloyd, J., Sitch, S., Mitchard, E. T. A. & Laurance, W. F. (2009a) Changing ecology of tropical forests: evidence and drivers. Annual Review of Ecology Evolution and Systematics, 529–549.CrossRefGoogle Scholar
Lewis, S. L., Lopez-Gonzalez, G., Sonke, B. et al. (2009b) Increasing carbon storage in intact African tropical forests. Nature, 457, 1003–1006.CrossRefGoogle ScholarPubMed
Lewis, S. L., Malhi, Y. & Phillips, O. L. (2004) Fingerprinting the impacts of global change on tropical forests. Philosophical Transactions of the Royal Society Series B, 359, 437–462.CrossRefGoogle ScholarPubMed
Lewis, S. L., Phillips, O. L., Baker, T. R. et al. (2004) Concerted changes in tropical forest structure and dynamics: evidence from 50 South American long-term plots. Philosophical Transactions of the Royal Society Series B, 359, 421–436.CrossRefGoogle ScholarPubMed
Luo, Y. Q. (2007) Terrestrial carbon-cycle feedback to climate warming. Annual Review of Ecology Evolution and Systematics, 683–712.CrossRefGoogle Scholar
Macarthur, R. H. & Wilson, E. O. (1967) The Theory of Island Biogeography. Princeton, NJ: Princeton University Press.Google Scholar
Martin, A. R. & Thomas, S. C. (2011) A reassessment of carbon content in tropical trees. PLoS One, 6, e23533.CrossRefGoogle ScholarPubMed
Mascaro, J., Asner, G. P., Muller-Landau, H. C. et al. (2011a) Controls over aboveground forest carbon density on Barro Colorado Island, Panama. Biogeosciences, 8, 1615–1629.CrossRefGoogle Scholar
Mascaro, J., Detto, M., Asner, G. P. & Muller-Landau, H. C. (2011b) Evaluating uncertainty in mapping forest carbon with airborne LiDAR. Remote Sensing of Environment, 115, 3770–3774.CrossRefGoogle Scholar
Metcalf, C. J. E., Clark, J. S. & Clark, D. A. (2009) Tree growth inference and prediction when the point of measurement changes: modelling around buttresses in tropical forests. Journal of Tropical Ecology, 25, 1–12.CrossRefGoogle Scholar
Moffat, A. M., Papale, D., Reichstein, M. et al. (2007) Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agricultural and Forest Meteorology, 147, 209–232.CrossRefGoogle Scholar
Myeni, R., Keeling, C., Tucker, C., Asrar, G. & Nemani, R. (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698–702.CrossRefGoogle Scholar
Odum, E. P. (1969) The strategy of ecosystem development. Science, 164, 262–270.CrossRefGoogle ScholarPubMed
Pacala, S. W., Canham, C. D., Saponara, J. et al. (1996) Forest models defined by field measurements: estimation, error analysis and dynamics. Ecological Monographs, 66, 1–43.CrossRefGoogle Scholar
Phillips, O. L. (1996) Long-term environmental change in tropical forests: Increasing tree turnover. Environmental Conservation, 23, 235–248.CrossRefGoogle Scholar
Phillips, O. L., Aragao, L., Lewis, S. L. et al. (2009) Drought sensitivity of the Amazon rainforest. Science, 323, 1344–1347.CrossRefGoogle ScholarPubMed
Phillips, O. L., Lewis, S. L., Baker, T. R., Chao, K. J. & Higuchi, N. (2008) The changing Amazon forest. Philosophical Transactions of the Royal Society Series B, 363, 1819–1827.CrossRefGoogle ScholarPubMed
Phillips, O. L., Malhi, Y., Higuchi, N. et al. (1998) Changes in the carbon balance of tropical forests: Evidence from long-term plots. Science, 282, 439–442.CrossRefGoogle ScholarPubMed
Phillips, O. L., Malhi, Y., Vinceti, B. et al. (2002) Changes in growth of tropical forests: Evaluating potential biases. Ecological Applications, 12, 576–587.CrossRefGoogle Scholar
Ricklefs, R. E. (2004) A comprehensive framework for global patterns in biodiversity. Ecology Letters, 7, 1–15.CrossRefGoogle Scholar
Rüger, N., Berger, U., Hubbell, S. P., Vieilledent, G. & Condit, R. (2011) Growth strategies of tropical tree species: Disentangling light and size effects. PLoS One, 6, e25330.CrossRefGoogle ScholarPubMed
Rustad, L. E., Campbell, J. L., Marion, G. M. et al. (2001) A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warmingOecologia, 126, 543–562.CrossRefGoogle ScholarPubMed
Saugier, B., Roy, J. & Mooney, H. A. (2001) Terrestrial Global Productivity. London: Academic Press.Google Scholar
Schimel, D. S. (1995) Terrestrial ecosystems and the carbon cycle. Global Change Biology, 1, 77–91.CrossRefGoogle Scholar
Schnitzer, S. A., Dalling, J. W. & Carson, W. P. (2000) The impact of lianas on tree regeneration in tropical forest canopy gaps: Evidence for an alternative pathway of gap-phase regeneration. Journal of Ecology, 88, 655–666.CrossRefGoogle Scholar
Scott, D. W. (1992) Multivariate Density Estimation: Theory, Practice, and Visualisation. New York: Wiley.CrossRefGoogle Scholar
Sheil, D. (1995) A critique of permanent plot methods and analysis with examples from Budongo-forest, Uganda. Forest Ecology and Management, 77, 11–34.CrossRefGoogle Scholar
Shugart, H. H. (1984) A Theory of Forest Dynamics. New York: Springer.CrossRefGoogle Scholar
Silverman, B. W. (1986) Density Estimation for Statistics and Data Analysis. London: Chapman and Hall.CrossRefGoogle Scholar
Sironen, S., Kangas, A. & Maltamo, M. (2010) Comparison of different non-parametric growth imputation methods in the presence of correlated observations. Forestry, 83, 39–51.CrossRefGoogle Scholar
Sironen, S., Kangas, A., Maltamo, M. & Kalliovirta, J. (2008) Localization of growth estimates using non-parametric imputation methods. Forest Ecology and Management, 256, 674–684.CrossRefGoogle Scholar
Sole, R. V. & Manrubia, S. C. (1995) Are rainforests self-organized in a critical state?Journal of Theoretical Biology, 173, 31–40.CrossRefGoogle Scholar
Usher, M. B. (1979) Markovian approaches to ecological succession. Journal of Animal Ecology, 48, 413–426.CrossRefGoogle Scholar
Waggoner, P. E. & Stephens, G. R. (1970) Transition probabilities for a forest. Nature, 225, 1160–1161.CrossRefGoogle ScholarPubMed
Wagner, F., Rutishauser, E., Blanc, L. & Herault, B. (2010) Effects of plot size and census interval on descriptors of forest structure and dynamics. Biotropica, 42, 664–671.CrossRefGoogle Scholar
Wolf, A., Field, C. B. & Berry, J. A. (2011) Allometric growth and allocation in forests: a perspective from FLUXNET. Ecological Applications, 21, 1546–1556.CrossRefGoogle ScholarPubMed
Wright, S. J. (2005) Tropical forests in a changing environment. Trends in Ecology & Evolution, 20, 553–560.CrossRefGoogle Scholar
Wright, S. J. (2010) The future of tropical forests. Annals of the New York Academy of Sciences, 1195, 1–27.CrossRefGoogle ScholarPubMed
Wright, S. J., Kitajima, K., Kraft, N. J. B. et al. (2010) Functional traits and the growth-mortality tradeoff in tropical trees. Ecology, 91, 3664–3674.CrossRefGoogle Scholar
Yang, Y. H. & Luo, Y. Q. (2011) Isometric biomass partitioning pattern in forest ecosystems: evidence from temporal observations during stand development. Journal of Ecology, 99, 431–437.Google Scholar
Yang, Y. H., Luo, Y. Q. & Finzi, A. C. (2011) Carbon and nitrogen dynamics during forest stand development: a global synthesis. New Phytologist, 190, 977–989.CrossRefGoogle ScholarPubMed
Zuidema, P. A., Jongejans, E., Chien, P. D., During, H. J. & Schieving, F. (2010) Integral Projection Models for trees: a new parameterization method and a validation of model output. Journal of Ecology, 98, 345–355.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×