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Legacy of Fire Slows Carbon Accumulation in Amazonian Forest Regrowth

Legacy of Fire Slows Carbon Accumulation in Amazonian Forest Regrowth. Daniel Zarin, Eric Davidson, Eduardo Brondizio, Ima Vieira, Tatiana S á , Ted Feldpausch, Rita Mesquita, Emilio Moran, Patricia Delam ô nica, Mark Ducey, Cleber Salimon, Manfred Denich.

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Legacy of Fire Slows Carbon Accumulation in Amazonian Forest Regrowth

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  1. Legacy of Fire Slows Carbon Accumulation in Amazonian Forest Regrowth Daniel Zarin, Eric Davidson, Eduardo Brondizio, Ima Vieira, Tatiana Sá, Ted Feldpausch, Rita Mesquita, Emilio Moran, Patricia Delamônica, Mark Ducey, Cleber Salimon, Manfred Denich University of Florida, Woods Hole Research Center, Indiana University, Museu Goeldi, EMBRAPA-Amazonia Oriental, Cornell University, INPA, University of New Hampshire, University of Sao Paulo, University of Bonn.

  2. Global scale:

  3. Regional scale: Adapted from: Zarin, DJ et al. 2001. Ecosystems 4:658-668.

  4. Local scale: Land-use intensity affects forest regrowth carbon accumulation • Land-use intensity has a dramatic impact on carbon accumulation in 8-year-old forest regrowth in Paragominas. • But, land-use intensity gradients differ from place to place, and there is no regionally systematic, quantitative assessment of HOW MUCH land-use intensity affects carbon accumulation. Uhl et al. 1988. Journal of Ecology 76:663-681.

  5. Linking local and regional scales Foto: Manfred Denich Fire history as an index of land-use intensity Nutrient losses Reduced regeneration How much does a legacy of frequent burning reduce forest regrowth carbon accumulation? Photo from CJR Carvalho EMBRAPA- Amazonia Oriental

  6. Methods • Assemble data, mostly from LBA groups (beginning prior to San Francisco meeting in December 2002) • Compare observed and predicted carbon values, based on regionally validated model (Zarin et al. 2001). • Model calculations of aboveground biomass (B)* are a function of: • plot age (A) • the proportion of the year with monthly rainfall in excess of 100 mm (R) • the average temperature during that part of the year (T) • Assess relationship between plot residuals and fire history. * For carbon values divide by 2

  7. The Data: 93 plots from nine locations (defined as 1x1 degree grid cells) The model: Bnon-sandy = 0.67 + 0.43(ART) Bsandy = -131.05 + 34.70 ln(ART)

  8. Reduced carbon accumulation relative to model prediction is associated with fire history • y = - 0.119 – 0.213x • Intercept not significantly different from zero (P = 0.56) • Slope is highly significant (P < 0.001) • R2 = 0.11

  9. Reduction in carbon accumulation relative to model predictions for Amazonian forest regrowth with a legacy of many fires. • Plots burned five or more times accumulated carbon at a slower rate than plots that were either cleared without burning, or that were burned 1-2 times. • Relative to the model prediction, the reduction in carbon accumulation for plots with this legacy of five or more fires is 1.7 ± 0.7 Mg C ha-1 y-1, compared to predicted accumulation rates of 3.2 ± 0.6 Mg C ha-1 y-1

  10. Conclusions • Fire history alters the trajectory of carbon accumulation in forest regrowth. • Stands with a pronounced fire legacy have ~1/2 the predicted carbon accumulation rate • Those stands retain an enhanced susceptibility to wildfire for a longer period of time than regrowth stands with fewer previous burns. Uhl, C. & J.B. Kauffman. 1990. Ecology 71:437-449.

  11. Implications: • Without intervention, stands with a legacy of many fires are poor candidates for carbon credit programs. • Where a legacy of many fires is predominant: • …in landscapes dominated by large cattle ranches, permanent conversion to intensive agriculture, pasture, and/or tree plantations may be appropriate. • …in regions dominated by smallholder agriculture, enrichment planting can hasten the pace of forest regrowth, and may also enhance financial returns. • Where fires have burned over and over again, the alternative scenario (i.e. no intervention) is a degraded, fire-prone landscape.

  12. Acknowledgments • We thank the many students, field assistants, and farmers who helped with data collection. • Financial support provided by LBA-ECO, the Andrew Mellon Foundation, and NSF.

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