Tree phenology and carbon dioxide fluxes - use of digital photography for process-based interpretation at the ecosystem scale

Vegetation phenology is an important indicator of climate change and climate variability and it is strongly connected to biospheric–atmospheric gas exchange. We aimed to evaluate the applicability of phenological information derived from digital imagery for the interpretation of CO2 exchange measurements. For the years 2005–2007 we analyzed seasonal phenological development of 2 temperate mixed forests using tower-based imagery from standard RGB cameras. Phenological information was jointly analyzed with gross primary productivity (GPP) derived from net ecosystem exchange data. Automated image analysis provided reliable information on vegetation developmental stages of beech and ash trees covering all seasons. A phenological index derived from image color values was strongly correlated with GPP, with a significant mean time lag of several days for ash trees and several weeks for beech trees in early summer (May to mid-July). Leaf emergence dates for the dominant tree species partly explained temporal behaviour of spring GPP but were also masked by local meteorological conditions. We conclude that digital cameras at flux measurement sites not only provide an objective measure of the physiological state of a forest canopy at high temporal and spatial resolutions, but also complement CO2 and water exchange measurements, improving our knowledge of ecosystem processes.

Climate extremes and the carbon cycle

The terrestrial biosphere is a key component of the global carbon cycle and its carbon balance is strongly influenced by climate. Continuing environmental changes are thought to increase global terrestrial carbon uptake. But evidence is mounting that climate extremes such as droughts or storms can lead to a decrease in regional ecosystem carbon stocks and therefore have the potential to negate an expected increase in terrestrial carbon uptake. Here we explore the mechanisms and impacts of climate extremes on the terrestrial carbon cycle, and propose a pathway to improve our understanding of present and future impacts of climate extremes on the terrestrial carbon budget.

Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes

There is a need for accurate predictions of ecosystem carbon (C) and water fluxes in field conditions. Previous research has shown that ecosystem properties can be predicted from community abundance-weighted means (CWM) of plant functional traits and measures of trait variability within a community (FDvar). The capacity for traits to predict carbon (C) and water fluxes, and the seasonal dependency of these trait-function relationships has not been fully explored. Here we measured daytime C and water fluxes over four seasons in grasslands of a range of successional ages in southern England. In a model selection procedure, we related these fluxes to environmental covariates and plant biomass measures before adding CWM and FDvar plant trait measures that were scaled up from measures of individual plants grown in greenhouse conditions. Models describing fluxes in periods of low biological activity contained few predictors, which were usually abiotic factors. In more biologically active periods, models contained more predictors, including plant trait measures. Field-based plant biomass measures were generally better predictors of fluxes than CWM and FDvar traits. However, when these measures were used in combination traits accounted for additional variation. Where traits were significant predictors their identity often reflected seasonal vegetation dynamics. These results suggest that database derived trait measures can improve the prediction of ecosystem C and water fluxes. Controlled studies and those involving more detailed flux measurements are required to validate and explore these findings, a worthwhile effort given the potential for using simple vegetation measures to help predict landscape-scale fluxes.

Estimating Annual CO2 Flux for Lutjewad Station Using Three Different Gap-Filling Techniques

Long-term measurements of CO2 flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2 flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2 fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements.

Central European hardwood trees in a high-CO 2 future: synthesis of an 8-year forest canopy CO 2 enrichment project

Rapidly increasing atmospheric CO2 is not only changing the climate system but may also affect the biosphere directly through stimulation of plant growth and ecosystem carbon and nutrient cycling. Although forest ecosystems play a critical role in the global carbon cycle, experimental information on forest responses to rising CO2 is scarce, due to the sheer size of trees. Here, we present a synthesis of the only study world-wide where a diverse set of mature broadleaved trees growing in a natural forest has been exposed to future atmospheric CO2 levels (c. 550ppm) by free-air CO2 enrichment (FACE). We show that litter production, leaf traits and radial growth across the studied hardwood species remained unaffected by elevated CO2 over 8years. CO2 enrichment reduced tree water consumption resulting in detectable soil moisture savings. Soil air CO2 and dissolved inorganic carbon both increased suggesting enhanced below-ground activity. Carbon release to the rhizosphere and/or higher soil moisture primed nitrification and nitrate leaching under elevated CO2; however, the export of dissolved organic carbon remained unaltered.Synthesis. Our findings provide no evidence for carbon-limitation in five central European hardwood trees at current ambient CO2 concentrations. The results of this long-term study challenge the idea of a universal CO2 fertilization effect on forests, as commonly assumed in climate-carbon cycle models.

Impacts of drought stress on water relations and carbon assimilation in grassland species at different altitudes

Grassland is an important ecosystem type which is not only used agriculturally, but also covers sites which cannot be used for other purposes, e.g. in very steep locations or above timberlines. Prolonged summer droughts in the mid-term future, as are predicted for Central Europe, are expected to have a major impact on such ecosystems. To address this topic, rainfall exclusion via shelters was performed on three grassland sites at different altitudes (393, 982 and 1978 m above sea level) in Switzerland. Diurnal drought treatment effects were studied at each study site on a completely sunny day towards the end of an 8–10 week shelter period. Ecophysiological parameters including gas exchange (An, gs and intrinsic WUE) and chlorophyll a fluorescence (Fv/Fm, ΦPSII and NPQ) were considered for several species. The lowland and the Alpine field site were more strongly affected by soil drought than the pre-Alpine site. At all sites, grasses showed different patterns of reductions in stomatal conductance under soil drought compared to legumes and forbs. In addition, grasses were significantly more affected by reductions in assimilation rates at all sites. Time courses of reductions in assimilation rates relative to controls differed between species at the Alpine site, as some species showed reduced assimilation rates at this site in the early morning. Thus, similar rainfall exclusion treatments can trigger different reactions in various species at different sites, which might not become obvious during mere midday measurements. Overall, results suggest strong impacts of prolonged summer drought on grassland net photosynthesis especially at the Alpine site and, within sites, for grasses

Meta-Analysis of Long-Term Land Management Effect on Soil Organic Carbon (SOC) in Ethiopia

The role of Soil Organic Carbon (SOC) in mitigating climate change, indicating soil quality and ecosystem function has created research interested to know the nature of SOC at landscape level. The objective of this study was to examine variation and distribution of SOC in a long-term land management at a watershed and plot level. This study was based on meta-analysis of three case studies and 128 surface soil samples from Ethiopia. Three sites (Gununo, Anjeni and Maybar) were compared after considering two Land Management Categories (LMC) and three types of land uses (LUT) in quasi-experimental design. Shapiro-Wilk tests showed non-normal distribution (p = 0.002, a = 0.05) of the data. SOC median value showed the effect of long-term land management with values of 2.29 and 2.38 g kg-1 for less and better-managed watersheds, respectively. SOC values were 1.7, 2.8 and 2.6 g kg-1 for Crop (CLU), Grass (GLU) and Forest Land Use (FLU), respectively. The rank order for SOC variability was FLU>GLU>CLU. Mann-Whitney U and Kruskal-Wallis test showed a significant difference in the medians and distribution of SOC among the LUT, between soil profiles (p<0.05, confidence interval 95%, a = 0.05) while it is not significant (p>0.05) for LMC. The mean and sum rank of Mann Whitney U and Kruskal Wallis test also showed the difference at watershed and plot level. Using SOC as a predictor, cross-validated correct classification with discriminant analysis showed 46 and 49% for LUT and LMC, respectively. The study showed how to categorize landscapes using SOC with respect to land management for decision-makers.