Showing papers by "Robert Clement published in 2017"

How do leaf and ecosystem measures of water-use efficiency compare?

Abstract: The terrestrial carbon and water cycles are intimately linked: the carbon cycle is driven by photosynthesis, while the water balance is dominated by transpiration, and both fluxes are controlled by plant stomatal conductance The ratio between these fluxes, the plant water-use efficiency (WUE), is a useful indicator of vegetation function WUE can be estimated using several techniques, including leaf gas exchange, stable isotope discrimination, and eddy covariance Here we compare global compilations of data for each of these three techniques We show that patterns of variation in WUE across plant functional types (PFTs) are not consistent among the three datasets Key discrepancies include the following: leaf-scale data indicate differences between needleleaf and broadleaf forests, but ecosystem-scale data do not; leaf-scale data indicate differences between C3 and C4 species, whereas at ecosystem scale there is a difference between C3 and C4 crops but not grasslands; and isotope-based estimates of WUE are higher than estimates based on gas exchange for most PFTs Our study quantifies the uncertainty associated with different methods of measuring WUE, indicates potential for bias when using WUE measures to parameterize or validate models, and indicates key research directions needed to reconcile alternative measures of WUE

The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how?

Abstract: Eddy covariance (EC) continues to provide invaluable insights into the dynamics of Earth's surface processes. However, despite its many strengths, spatial replication of EC at the ecosystem scale is rare. High equipment costs are likely to be partially responsible. This contributes to the low sampling, and even lower replication, of ecoregions in Africa, Oceania (excluding Australia) and South America. The level of replication matters as it directly affects statistical power. While the ergodicity of turbulence and temporal replication allow an EC tower to provide statistically robust flux estimates for its footprint, these principles do not extend to larger ecosystem scales. Despite the challenge of spatially replicating EC, it is clearly of interest to be able to use EC to provide statistically robust flux estimates for larger areas. We ask: How much spatial replication of EC is required for statistical confidence in our flux estimates of an ecosystem? We provide the reader with tools to estimate the number of EC towers needed to achieve a given statistical power. We show that for a typical ecosystem, around four EC towers are needed to have 95% statistical confidence that the annual flux of an ecosystem is nonzero. Furthermore, if the true flux is small relative to instrument noise and spatial variability, the number of towers needed can rise dramatically. We discuss approaches for improving statistical power and describe one solution: an inexpensive EC system that could help by making spatial replication more affordable. However, we note that diverting limited resources from other key measurements in order to allow spatial replication may not be optimal, and a balance needs to be struck. While individual EC towers are well suited to providing fluxes from the flux footprint, we emphasize that spatial replication is essential for statistically robust fluxes if a wider ecosystem is being studied.