Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems

Land carbon dynamics in temperate and boreal ecosystems are sensitive to environmental change. Accurately simulating gross primary productivity (GPP) and its seasonality is key for reliable carbon cycle projections. However, significant biases have been found in early spring GPP simulations of northern forests, where observations often suggest a later resumption of photosynthetic activity than predicted by models. Here, we used eddy covariance-based GPP estimates from 39 forest sites that differ by their climate and dominant plant functional types. We used a mechanistic and an empirical light use efficiency (LUE) model to investigate the magnitude and environmental controls of delayed springtime photosynthesis resumption (DSPR) across sites. We found DSPR reduced ecosystem LUE by 30-70% at many, but not all site-years during spring. A significant depression of LUE was found not only in coniferous but also at deciduous forests and was related to combined high radiation and low minimum temperatures. By embedding cold-acclimation effects on LUE that considers the delayed effects of minimum temperatures, initial model bias in simulated springtime GPP was effectively resolved. This provides an approach to improve GPP estimates by considering physiological acclimation and enables more reliable simulations of photosynthesis in northern forests and projections in a warming climate.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

[Simulated Ozone Damage on Gross Primary Productivity (GPP) in a Winter Wheat Field]

An eddy-covariance system combined with a semi-mechanistic model was used to analyze variations in gross primary productivity (GPP) and to simulate the impact of ozone (O₃) on GPP under different levels O₃ concentrations over a winter wheat field in Nanjing. The results showed that GPP was higher during the middle of the growth period and low during the early and late growth periods, reaching a maximum of 40 μmol·(m²·s)^-1. Using high and low ozone sensitivity settings,O₃-damage in 150, 100, 50 nL·L^-1 and control treatment (CK) reduced GPP by -72%, -36%, -6%, and -10%, and by -13%, -6%, -1%, and -2%, respectively. These results provide a scientific basis for formulating defense strategies for O₃ damage to crops.

Flux towers in the sky: global ecology from space

Global ecology - the study of the interactions among the Earth's ecosystems, land, atmosphere and oceans - depends crucially on global observations: this paper focuses on space-based observations of global terrestrial ecosystems. Early global ecology relied on an extrapolation of detailed site-level observations, using models of increasing complexity. Modern global ecology has been enabled largely by vegetation indices (greenness) from operational space-based imagery but current capabilities greatly expand scientific possibilities. New observations from spacecraft in orbit allowed an estimation of gross carbon fluxes, photosynthesis, biomass burning, evapotranspiration and biomass, to create virtual eddy covariance sites in the sky. Planned missions will reveal the dimensions of the diversity of life itself. These observations will improve our understanding of the global productivity and carbon storage, land use, carbon cycle-climate feedback, diversity-productivity relationships and enable improved climate forecasts. Advances in remote sensing challenge ecologists to relate information organised by biome and species to new data arrayed by pixels and develop theory to address previously unobserved scales.

Differential responses of ecotypes to climate in a ubiquitous Arctic sedge: implications for future ecosystem C cycling

The response of vegetation to climate change has implications for the carbon cycle and global climate. It is frequently assumed that a species responds uniformly across its range to climate change. However, ecotypes - locally adapted populations within a species - display differences in traits that may affect their gross primary productivity (GPP) and response to climate change. To determine if ecotypes are important for understanding the response of ecosystem productivity to climate we measured and modeled growing season GPP in reciprocally transplanted and experimentally warmed ecotypes of the abundant Arctic sedge Eriophorum vaginatum. Transplanted northern ecotypes displayed home-site advantage in GPP that was associated with differences in leaf area index. Southern ecotypes exhibited a greater response in GPP when transplanted. The results demonstrate that ecotypic differentiation can impact the morphology and function of vegetation with implications for carbon cycling. Moreover they suggest that ecotypic control of GPP may limit the response of ecosystem productivity to climate change. This investigation shows that ecotypes play a substantial role in determining GPP and its response to climate. These results have implications for understanding annual to decadal carbon cycling where ecotypes could influence ecosystem function and vegetation feedbacks to climate change.

[Spatiotemporal variation of productivity and carbon use efficiency of forests in Northeast China from 2000 to 2015.]

Understanding changes in vegetation productivity and carbon use efficiency and their responses to climate change is significant to accurately assess and predict regional carbon budget in Northeast forest area, a region being an important carbon sink and sensitive to global change. Based on MODIS monitoring data and vegetation type distribution data, I analyzed the spatiotemporal varia-tions of ecosystem productivity (net primary productivity (NPP), gross primary productivity (GPP)) and carbon use efficiency (NPP/GPP) of Northeast forest from 2000 to 2015. Results showed that the average NPP and GPP were 346.4 and 773 g C·m^-2·a^-1, respectively, and the average NPP/GPP was 0.45 during 2000 and 2015. NPP and GPP of different forest types were following the order: coniferous and broad-leaved mixed forests > deciduous broad-leaved forests > coniferous forests, while the difference in NPP/GPP was not significant among different forest types. NPP and GPP were high in Southeast part and low in Northwest part. From 2000 to 2015, the NPP, GPP and NPP/GPP of Northeast forest showed a fluctuating increase, suggesting the carbon sequestration capacity was gradually enhanced. However, the trends and rates of NPP, GPP and NPP/GPP showed spatial variation. NPP, GPP and NPP/GPP increased significantly in the southern part of the Daxing'anling while decreased significantly in the northern part of the Daxing'anling, and showed a weak increasing trend in the rest of Northeast forest. The increase of annual precipitation was the main factor driving the fluctuating increase of NPP, GPP and NPP/GPP in Northeast forest.

[Characters of ecosystem vulnerability in southwestern China based on vegetation productivity]

Southwestern China is one of the most important areas for global biodiversity conservation. Under the background of global climate change, the vulnerability of this area has showed an increasing trend. According to the IPCC concept of vulnerability, we calculated the spatial distribution of ecosystem's vulnerability in southwestern China based on gross primary productivity (GPP) and then analyzed the spatial variation of different levels of vulnerability. Besides, we analyzed the relationship between environmental factors and ecosystem vulnerability, including precipitation, temperature, altitude, slope and vegetation type. The results showed that ecosystem vulnerability in the southwestern China gradually increased from southeast to northwest, with most area within the region being slight and mild vulnerable area (together occupied 69% of the total). The vulnerability level decreased with the increasing of mean annual precipitation and temperature but increased with increasing elevation and slope. Karst area in southwest and borders between farming and ranging regions in northwest Mountain ecotone of the study area had higher vulnerability, being more easily affected by climate change or other disturbances. The vulnerabilities of needle-leaved forest, grassland, and shrubland were relatively higher than other vegetation types, with the potential to be more easily affected by climate change.

Quantifying soil moisture impacts on light use efficiency across biomes

Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub-humid, semi-arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly-based drought indices. Counter to common assumptions, fLUE reductions are largest in drought-deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought-related assessments.

Plant functional traits and canopy structure control the relationship between photosynthetic CO2 uptake and far-red sun-induced fluorescence in a Mediterranean grassland under different nutrient availability

Sun-induced fluorescence (SIF) in the far-red region provides a new noninvasive measurement approach that has the potential to quantify dynamic changes in light-use efficiency and gross primary production (GPP). However, the mechanistic link between GPP and SIF is not completely understood. We analyzed the structural and functional factors controlling the emission of SIF at 760 nm (F₇₆₀ ) in a Mediterranean grassland manipulated with nutrient addition of nitrogen (N), phosphorous (P) or nitrogen-phosphorous (NP). Using the soil-canopy observation of photosynthesis and energy (SCOPE) model, we investigated how nutrient-induced changes in canopy structure (i.e. changes in plant forms abundance that influence leaf inclination distribution function, LIDF) and functional traits (e.g. N content in dry mass of leaves, N%, Chlorophyll a+b concentration (Cab) and maximum carboxylation capacity (V_cmax )) affected the observed linear relationship between F₇₆₀ and GPP. We conclude that the addition of nutrients imposed a change in the abundance of different plant forms and biochemistry of the canopy that controls F₇₆₀ . Changes in canopy structure mainly control the GPP-F₇₆₀ relationship, with a secondary effect of Cab and V_cmax . In order to exploit F₇₆₀ data to model GPP at the global/regional scale, canopy structural variability, biodiversity and functional traits are important factors that have to be considered.

Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest

Although the analysis of flux data has increased our understanding of the interannual variability of carbon inputs into forest ecosystems, we still know little about the determinants of wood growth. Here, we aimed to identify which drivers control the interannual variability of wood growth in a mesic temperate deciduous forest. We analysed a 9-yr time series of carbon fluxes and aboveground wood growth (AWG), reconstructed at a weekly time-scale through the combination of dendrometer and wood density data. Carbon inputs and AWG anomalies appeared to be uncorrelated from the seasonal to interannual scales. More than 90% of the interannual variability of AWG was explained by a combination of the growth intensity during a first 'critical period' of the wood growing season, occurring close to the seasonal maximum, and the timing of the first summer growth halt. Both atmospheric and soil water stress exerted a strong control on the interannual variability of AWG at the study site, despite its mesic conditions, whilst not affecting carbon inputs. Carbon sink activity, not carbon inputs, determined the interannual variations in wood growth at the study site. Our results provide a functional understanding of the dependence of radial growth on precipitation observed in dendrological studies.

Association between sap flow-derived and eddy covariance-derived measurements of forest canopy CO2 uptake

The carbon sink intensity of the biosphere depends on the balance between gross primary productivity (GPP) of forest canopies and ecosystem respiration. GPP, however, cannot be directly measured and estimates are not well constrained. A new approach relying on canopy transpiration flux measured as sap flow, and water-use efficiency inferred from carbon isotope analysis (GPPSF ) has been proposed, but not tested against eddy covariance-based estimates (GPPEC ). Here we take advantage of parallel measurements using the two approaches at a semi-arid pine forest site to compare the GPPSF and GPPEC estimates on diurnal to annual timescales. GPPSF captured the seasonal dynamics of GPPEC (GPPSF  = 0.99 × GPPEC , r(2)  = 0.78, RMSE = 0.82, n = 457 d) with good agreement at the annual timescale (653 vs 670 g C m(-2)  yr(-1) ). Both methods showed that GPP ranged between 1 and 8 g C m(-2)  d(-1) , and the GPPSF /GPPEC ratio was between 0.5 and 2.0 during 82% of the days. Carbon uptake dynamics at the individual tree scale conformed with leaf scale rates of net assimilation. GPPSF can produce robust estimations of tree- and canopy-scale rates of CO2 uptake, providing constraints and greatly extending current GPPEC estimations.