Forests play a crucial yet poorly understood role in the global carbon cycle. Developing a greater understanding of the carbon flux of forests will help determine the extent to which forested areas can serve as a sink for carbon, how this could help to mitigate anthropogenic increases in atmospheric greenhouse gas levels, and how forests in turn may respond to climate change.
A decade ago, David Coomes was involved in designing a national system to monitor carbon fluxes using integrated ground-based and satellite remote-sensing approaches1. The group is now active is developing expertise in LiDAR and hyperspectral imagery as a way of examining large-scale forest structure and composition and its role in the carbon cycle. Working at multiple scales allows us to get a broad view of carbon fluxes while increasing the accuracy needed to employ policies like Reducing Emissions from Deforestation and forest Degradation (REDD+). We hope to inform such policy and to refine the methods used by the international community for measuring and monitoring forest carbon.
Our studies are looking to integrate information not just across scales but along environmental gradients. Our studies have found that disturbance plays a critical role in driving forest carbon fluxes over decadal time scales2, 3. In boreal systems, we found that carbon continues to accumulate in ecosystem (particularly in soil) for many centuries following fire, indicating that anthropogenic fire suppression in this region may have potential as a climate change mitigation strategy (albeit a difficult one)4. We have reviewed the literature on how herbivore disturbance can reduce terrestrial carbon stocks; it seems likely that herbivores contribute little to carbon emissions compared to fossil fuel usage, although we identified major gaps in knowledge so our conclusions remain preliminary4.
One shortcoming of current carbon stock and flux estimates is the degree of uncertainty inherent in statistical models being used, so we are also attempting to improve the accuracy of these models. For instance, some models predict aboveground allometry to be invariant of environment, but our studies have shown this not to be the case, and that allometry can vary across environmental gradients such as rainfall 5. Wood density is also an important predictor in carbon stock estimates, yet our gaps in knowledge of species-specific wood densities creates error in scaled-up estimations of forest carbon. We have developed and tested models for estimating wood densities when such data are missing6.
- Coomes, D.A., Allen, R.B., Scott, N.A., Goulding, C., Beets, P. (2002) Designing systems to monitor carbon stocks in forests and shrublands. Forest Ecology and Management, 164, 89-108. click
- Coomes, D.A., Holdaway, R. J., Kobe, R. K., Lines, E. R., & Allen, R. B. (2012) A general integrative framework for modelling woody biomass production and carbon sequestration rates in forests. Journal of Ecology, 100, 42-64.
- Wardle, D.A., Hornberg, G., Zackrisson, O., Kalela-Brundin, M., & Coomes, D.A. (2003) Long-term effects of wildfire on ecosystem properties across an island area gradient. Science, 300, 972-975. click
- Tanentzap, A. J., & Coomes, D.A. (2012) Carbon storage in terrestrial ecosystems: Do browsing and grazing herbivores matter? Biological Reviews, 87(1), 72-94. DOI link
- Lines, E. R., Zavala, M. A., Purves, D. W., & Coomes, D.A. (2012) Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Global Ecology and Biogeography, 21, 1017–1028. DOI link
- Flores, O., & Coomes, D.A. (2011). Estimating the wood density of species for carbon stock assessments. Methods in Ecology and Evolution, 2(2), 214-220. DOI link