The three major axes of terrestrial ecosystem function.

The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.

Long-term trend and interannual variation in evapotranspiration of a young temperate Douglas-fir stand over 2002-2022 reveals the impacts of climate change.

The shortage of decades-long continuous measurements of ecosystem processes limits our understanding of how changing climate impacts forest ecosystems. We used continuous eddy-covariance and hydrometeorological data over 2002-2022 from a young Douglas-fir stand on Vancouver Island, Canada to assess the long-term trend and interannual variability in evapotranspiration (ET) and transpiration (T). Collectively, annual T displayed a decreasing trend over the 21 years with a rate of 1% yr-1, which is attributed to the stomatal downregulation induced by rising atmospheric CO2 concentration. Similarly, annual ET also showed a decreasing trend since evaporation stayed relatively constant. Variability in detrended annual ET was mostly controlled by the average soil water storage during the growing season (May-October). Though the duration and intensity of the drought did not increase, the drought-induced decreases in T and ET showed an increasing trend. This pattern may reflect the changes in forest structure, related to the decline in the deciduous understory cover during the stand development. These results suggest that the water-saving effect of stomatal regulation and water-related factors mostly determined the trend and variability in ET, respectively. This may also imply an increase in the limitation of water availability on ET in young forests, associated with the structural and compositional changes related to forest growth.

Re-assessment of the climatic controls on the carbon and water fluxes of a boreal aspen forest over 1996-2016: Changing sensitivity to long-term climatic conditions.

Recent evidence suggests that the relationships between climate and boreal tree growth are generally non-stationary; however, it remains uncertain whether the relationships between climate and carbon (C) fluxes of boreal forests are stationary or have changed over recent decades. In this study, we used continuous eddy-covariance and microclimate data over 21 years (1996-2016) from a 100-year-old trembling aspen stand in central Saskatchewan, Canada to assess the relationships between climate and ecosystem C and water fluxes. Over the study period, the most striking climatic event was a severe, 3-year drought (2001-2003). Gross ecosystem production (GEP) showed larger interannual variability than ecosystem respiration (Re ) over 1996-2016, but Re was the dominant component contributing to the interannual variation in net ecosystem production (NEP) during post-drought years. The interannual variations in evapotranspiration (ET) and C fluxes were primarily driven by temperature and secondarily by water availability. Two-factor linear models combining precipitation and temperature performed well in explaining the interannual variation in C and water fluxes (R2 > .5). The temperature sensitivities of all three C fluxes (NEP, GEP and Re ) declined over the study period (p < .05), and, as a result, the phenological controls on annual NEP weakened. The decreasing temperature sensitivity of the C fluxes may reflect changes in forest structure, related to the over-maturity of the aspen stand at 100 years of age, and exacerbated by high tree mortality following the severe 2001-2003 drought. These results may provide an early warning signal of driver shift or even an abrupt status shift of aspen forest dynamics. They may also imply a universal weakening in the relationship between temperature and GEP as forests become over-mature, associated with the structural and compositional changes that accompany forest ageing.

Seasonal variation in the canopy color of temperate evergreen conifer forests.

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near-surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on-the-ground phenological observations, leaf-level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower-based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter-dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy-level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature-based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color-based vegetation indices.

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems.

Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta ) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought-induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes.

Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis.

Methane flux (FCH4 ) measurements using the eddy covariance technique have increased over the past decade. FCH4 measurements commonly include data gaps, as is the case with CO2 and energy fluxes. However, gap-filling FCH4 data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap-filling method for other fluxes, to FCH4 datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH4 . However, there is still no consensus regarding FCH4 gap-filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH4 datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH4 and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH4 datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA-derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap-filling uncertainty is much larger than measurement uncertainty in accumulated CH4 budget. Therefore, the approach used for FCH4 gap filling can have important implications for characterizing annual ecosystem-scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.

Divergent long-term trends and interannual variation in ecosystem resource use efficiencies of a southern boreal old black spruce forest 1999-2017.

Long-term trends in ecosystem resource use efficiencies (RUEs) and their controlling factors are key pieces of information for understanding how an ecosystem responds to climate change. We used continuous eddy covariance and microclimate data over the period 1999-2017 from a 120-year-old black spruce stand in central Saskatchewan, Canada, to assess interannual variability, long-term trends, and key controlling factors of gross ecosystem production (GEP) and the RUEs of carbon (CUE = net primary production [NPP]/GEP), light (LUE = GEP/absorbed photosynthetic radiation [APAR]), and water (WUE = GEP/evapotranspiration [E]). At this site, annual GEP has shown an increasing trend over the 19 years (p < 0.01), which may be attributed to rising atmospheric CO2 concentration. Interannual variability in GEP, aside from its increasing trend, was most strongly related to spring temperatures. Associated with the significant increase in annual GEP were relatively small changes in NPP, APAR, and E, so that annual CUE showed a decreasing trend and annual LUE and WUE showed increasing trends over the 19 years. The long-term trends in the RUEs were related to the increasing CO2 concentration. Further analysis of detrended RUEs showed that their interannual variation was impacted most strongly by air temperature. Two-factor linear models combining CO2 concentration and air temperature performed well (R2 ~0.60) in simulating annual RUEs. LUE and WUE were positively correlated both annually and seasonally, while LUE and CUE were mostly negatively correlated. Our results showed divergent long-term trends among CUE, LUE, and WUE and highlighted the need to account for the combined effects of climatic controls and the 'CO2 fertilization effect' on long-term variations in RUEs. Since most RUE-based models rely primarily on one resource limitation, the observed patterns of relative change among the three RUEs may have important implications for RUE-based modeling of C fluxes.

Observed increase in local cooling effect of deforestation at higher latitudes.

Deforestation in mid- to high latitudes is hypothesized to have the potential to cool the Earth's surface by altering biophysical processes. In climate models of continental-scale land clearing, the cooling is triggered by increases in surface albedo and is reinforced by a land albedo-sea ice feedback. This feedback is crucial in the model predictions; without it other biophysical processes may overwhelm the albedo effect to generate warming instead. Ongoing land-use activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback, and it is not known whether the intrinsic biophysical mechanism on its own can change the surface temperature in a consistent manner. Nor has the effect of deforestation on climate been demonstrated over large areas from direct observations. Here we show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85 ± 0.44 K (mean ± one standard deviation) northwards of 45° N and 0.21 ± 0.53 K southwards. Below 35° N there is weak evidence that deforestation leads to warming. Results are based on comparisons of temperature at forested eddy covariance towers in the USA and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectation of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models.

Application of biochar and nitrogen influences fluxes of CO2, CH4 and N2O in a forest soil.

Nitrogen (N) fertilization of forests for increasing carbon sequestration and wood volume is expected to influence soil greenhouse gas (GHG) emissions, especially to increase N2O emissions. As biochar application is known to affect soil GHG emissions, we investigated the effect of biochar application, with and without N fertilization, to a forest soil on GHG emissions in a controlled laboratory study. We found that biochar application at high (10%) application rates increased CO2 and N2O emissions when applied without urea-N fertilizer. At both low (1%) and high biochar (10%) application rates CH4 consumption was reduced when applied without urea-N fertilizer. Biochar application with urea-N fertilization did not increase CO2 emissions compared to biochar amended soil without fertilizer. In terms of CO2-eq, the net change in GHG emissions was mainly controlled by CO2 emissions, regardless of treatment, with CH4 and N2O together accounting for less than 1.5% of the total emissions.

Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests.

Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECO models can be substantially improved by considering the biotic dependency of RECO on the short-term productivity (e.g., daily gross primary production, GPP) in addition to the well-known environmental controls of temperature and water availability. Here, we use a model-data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECO at 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data-oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECO when compared to a version of the model that does not consider the physiological phenology. The reduction of the model-observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model-error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECO in deciduous forests by including the phenological cycle of the canopy.

Nitrogen enrichment potential of biochar in relation to pyrolysis temperature and feedstock quality.

Nitrogen (N) enrichment of biochar from both inorganic and organic waste N sources has the potential to add economic and environmental value through its use as a slow release N fertilizer. We investigated the sorption of N by, and its release from, biochar made at pyrolysis temperatures of 400, 500 and 600 °C from three feedstocks: poultry litter (PL with a carbon (C) to N ratio (C:N) of 14), softwood chips of spruce-pine-fir (SPF with a C:N of 470), and a 50:50 mixture of PL and SPF (PL/SPF). The prepared biochars were enriched with ammonium nitrate (AN) and urea ammonium nitrate (UAN). PL biochars had the lowest C content (50-56% C), but the highest pH (9.3-9.9), electrical conductivity (EC, 780-960 dS m(-1)), cation exchange capacity (CEC, 40-46 cmol kg(-1)), and N content (3.3-4.5%). While N content and hydrogen (H) to C atomic ratio (H:C) decreased with increasing pyrolysis temperature irrespective of the feedstock used, both pH and EC slightly increased with pyrolysis temperature for all feedstocks. The PL and SPF biochars showed similar H:C and also similar N sorption and N release at all pyrolysis temperatures. These biochars sorbed up to 5% N by mass, irrespective of the source of N. However, PL/SPF biochar performed poorly in sorbing N from either AN or UAN. Biochar H:C was found to be unrelated to N sorption rates, suggesting that physical adsorption on active surfaces was the main mechanism of N sorption in these biochars. There were minor differences between N sorbed from NO3-N and NH4-N among different biochars. Very small amounts of sorbed N (0.2-0.4 mg N g(-1) biochar) was released when extracted with 1 M KCl solution, indicating that the retained N was strongly held in complex bonds, more so for NH4-N because the release of NO3-N was 3-4 times greater than that of NH4-N. NH4-N sorption far exceeded the effective CEC of the biochars, thereby suggesting that most of the sorption may be due to physical entrapment of NH4(+) in biochar pores. The results of this study suggest that biochar can be used to remove excess N from poultry and dairy manure and be a good mitigation option for reducing N leaching and gaseous losses.

Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs.

Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C m(-2) day(-1), and an average Nash-Sutcliffe model efficiency coefficient (E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66%. SADM overestimated daily GPP by about 15% during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30%, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.

Dry-season length affects the annual ecosystem carbon balance of a temperate semi-arid shrubland.

Semi-arid ecosystems have been shown to dominate over tropical forests in determining the trend and interannual variability of land carbon (C) sink. However, the magnitude and variability of ecosystem C balance remain largely uncertain for temperate semi-arid shrublands at the decadal scale. Using eddy-covariance and micro-meteorological measurements, we quantified the interannual variation in net ecosystem production (NEP) and its components, gross primary production (GPP) and ecosystem respiration (Reco, i.e., the sum of autotrophic and heterotrophic respiration), in a semi-arid shrubland of the Mu Us Desert, northern China during 2012-2022. This shrubland was an overall weak C sink over the 11 years (NEP = 12 ± 46 g C m-2 yr-1, mean ± SD). Annual NEP ranged from -66 to 77 g C m-2 yr-1, with the ecosystem frequently switching between being an annual C sink and a C source. GPP was twice as sensitive as Reco to prolonged dry seasons, leading to a close negative relationship between annual NEP and dry-season length (R2 = 0.80, P < 0.01). Annual GPP (R2 = 0.51, P = 0.01) and NEP (R2 = 0.58, P < 0.01) were positively correlated with annual rainfall. Negative annual NEP (the ecosystem being a C source) tended to occur when the dry season exceeded 50 d yr-1 or rainfall dropped below 280 mm yr-1. Increases in dry-season length strengthened the effects of low soil moisture relative to high vapor pressure deficit in constraining NEP. Both GPP and NEP were more closely correlated with C uptake amplitude (annual maximum daily values) than with C uptake period. These findings indicate that dry-season extension under climate change may reduce the long-term C sequestration in semi-arid shrublands. Plant species adapted to prolonged dry seasons should be used in ecosystem restoration in the studied area to enhance ecosystem functions.

Portable chamber system for measuring chloroform fluxes from terrestrial environments--methodological challenges.

This study describes a system designed to measure chloroform flux from terrestrial systems, providing a reliable first assessment of the spatial variability of flux over an area. The study takes into account that the variability of ambient air concentrations is unknown. It includes quality assurance procedures, sensitivity assessments, and testing of materials used to ensure that the flux equation used to extrapolate from concentrations to fluxes is sound and that the system does not act as a sink or a source of chloroform. The results show that many materials and components commonly used in sampling systems designed for CO2, CH4, and N2O emit chloroform and other volatile chlorinated compounds (VOCls) and are thus unsuitable in systems designed for studies of such compounds. To handle the above-mentioned challenges, we designed a system with a non-steady-state chamber and a closed-loop air-circulation unit returning scrubbed air to the chamber. Based on empirical observations, the concentration increase during a deployment was assumed to be linear. Four samples were collected consecutively and a line was fitted to the measured concentrations. The slope of the fitted line and the y-axis intercept were input variables in the equation used to transform concentration change data to flux estimates. The soundness of the flux equation and the underlying assumptions were tested and found to be reliable by comparing modeled and measured concentrations. Fluxes of chloroform in a forest clear-cut on the east coast of Vancouver Island, BC, during the year were found to vary from -130 to 620 ng m(-2) h(-1). The study shows that the method can reliably detect differences of approximately 50 ng m(-2) h(-1) in chloroform fluxes. The statistical power of the method is still comparatively strong down to differences of 35 ng m(-2) h(-1), but for smaller differences, the results should be interpreted with caution.

Arctic soil methane sink increases with drier conditions and higher ecosystem respiration.

Arctic wetlands are known methane (CH4) emitters but recent studies suggest that the Arctic CH4 sink strength may be underestimated. Here we explore the capacity of well-drained Arctic soils to consume atmospheric CH4 using >40,000 hourly flux observations and spatially distributed flux measurements from 4 sites and 14 surface types. While consumption of atmospheric CH4 occurred at all sites at rates of 0.092 ± 0.011 mgCH4 m-2 h-1 (mean ± s.e.), CH4 uptake displayed distinct diel and seasonal patterns reflecting ecosystem respiration. Combining in situ flux data with laboratory investigations and a machine learning approach, we find biotic drivers to be highly important. Soil moisture outweighed temperature as an abiotic control and higher CH4 uptake was linked to increased availability of labile carbon. Our findings imply that soil drying and enhanced nutrient supply will promote CH4 uptake by Arctic soils, providing a negative feedback to global climate change.

Leaf-level coordination principles propagate to the ecosystem scale.

Fundamental axes of variation in plant traits result from trade-offs between costs and benefits of resource-use strategies at the leaf scale. However, it is unclear whether similar trade-offs propagate to the ecosystem level. Here, we test whether trait correlation patterns predicted by three well-known leaf- and plant-level coordination theories - the leaf economics spectrum, the global spectrum of plant form and function, and the least-cost hypothesis - are also observed between community mean traits and ecosystem processes. We combined ecosystem functional properties from FLUXNET sites, vegetation properties, and community mean plant traits into three corresponding principal component analyses. We find that the leaf economics spectrum (90 sites), the global spectrum of plant form and function (89 sites), and the least-cost hypothesis (82 sites) all propagate at the ecosystem level. However, we also find evidence of additional scale-emergent properties. Evaluating the coordination of ecosystem functional properties may aid the development of more realistic global dynamic vegetation models with critical empirical data, reducing the uncertainty of climate change projections.

Tracking plant physiological properties from multi-angular tower-based remote sensing.

Imaging spectroscopy is a powerful technique for monitoring the biochemical constituents of vegetation and is critical for understanding the fluxes of carbon and water between the land surface and the atmosphere. However, spectral observations are subject to the sun-observer geometry and canopy structure which impose confounding effects on spectral estimates of leaf pigments. For instance, the sun-observer geometry influences the spectral brightness measured by the sensor. Likewise, when considering pigment distribution at the stand level scale, the pigment content observed from single view angles may not necessarily be representative of stand-level conditions as some constituents vary as a function of the degree of leaf illumination and are therefore not isotropic. As an alternative to mono-angle observations, multi-angular remote sensing can describe the anisotropy of surface reflectance and yield accurate information on canopy structure. These observations can also be used to describe the bi-directional reflectance distribution which then allows the modeling of reflectance independently of the observation geometry. In this paper, we demonstrate a method for estimating pigment contents of chlorophyll and carotenoids continuously over a year from tower-based, multi-angular spectro-radiometer observations. Estimates of chlorophyll and carotenoid content were derived at two flux-tower sites in western Canada. Pigment contents derived from inversion of a CR model (PROSAIL) compared well to those estimated using a semi-analytical approach (r(2) = 0.90 and r(2) = 0.69, P < 0.05 for both sites, respectively). Analysis of the seasonal dynamics indicated that net ecosystem productivity was strongly related to total canopy chlorophyll content at the deciduous site (r(2) = 0.70, P < 0.001), but not at the coniferous site. Similarly, spectral estimates of photosynthetic light-use efficiency showed strong seasonal patterns in the deciduous stand, but not in conifers. We conclude that multi-angular, spectral observations can play a key role in explaining seasonal dynamics of fluxes of carbon and water and provide a valuable addition to flux-tower-based networks.

Modeling acclimation of photosynthesis to temperature in evergreen conifer forests.

• In this study, we used a canopy photosynthesis model which describes changes in photosynthetic capacity with slow temperature-dependent acclimations. • A flux-partitioning algorithm was applied to fit the photosynthesis model to net ecosystem exchange data for 12 evergreen coniferous forests from northern temperate and boreal regions. • The model accounted for much of the variation in photosynthetic production, with modeling efficiencies (mean > 67%) similar to those of more complex models. The parameter describing the rate of acclimation was larger at the northern sites, leading to a slower acclimation of photosynthesis to temperature. The response of the rates of photosynthesis to air temperature in spring was delayed up to several days at the coldest sites. Overall photosynthesis acclimation processes were slower at colder, northern locations than at warmer, more southern, and more maritime sites. • Consequently, slow changes in photosynthetic capacity were essential to explaining variations of photosynthesis for colder boreal forests (i.e. where acclimation of photosynthesis to temperature was slower), whereas the importance of these processes was minor in warmer conifer evergreen forests.

Looking deeper into the soil: biophysical controls and seasonal lags of soil CO2 production and efflux.

We seek to understand how biophysical factors such as soil temperature (Ts), soil moisture (theta), and gross primary production (GPP) influence CO2 fluxes across terrestrial ecosystems. Recent advancements in automated measurements and remote-sensing approaches have provided time series in which lags and relationships among variables can be explored. The purpose of this study is to present new applications of continuous measurements of soil CO2 efflux (F0) and soil CO2 concentrations measurements. Here we explore how variation in Ts, theta, and GPP (derived from NASA's moderate-resolution imaging spectroradiometer [MODIS]) influence F0 and soil CO2 production (Ps). We focused on seasonal variation and used continuous measurements at a daily timescale across four vegetation types at 13 study sites to quantify: (1) differences in seasonal lags between soil CO2 fluxes and Ts, theta, and GPP and (2) interactions and relationships between CO2 fluxes with Ts, theta, and GPP. Mean annual Ts did not explain annual F0 and Ps among vegetation types, but GPP explained 73% and 30% of the variation, respectively. We found evidence that lags between soil CO2 fluxes and Ts or GPP provide insights into the role of plant phenology and information relevant about possible timing of controls of autotrophic and heterotrophic processes. The influences of biophysical factors that regulate daily F0 and Ps are different among vegetation types, but GPP is a dominant variable for explaining soil CO2 fluxes. The emergence of long-term automated soil CO2 flux measurement networks provides a unique opportunity for extended investigations into F0 and Ps processes in the near future.

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover.

Soil respiration (R s) represents the largest flux of CO2 from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global R s from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in R s, we showed that land-cover change played a more important role in regulating R s changes in temperate and boreal regions during 2000-2014. Significant changes in R s occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global R s patterns and highlight the importance of land-cover change in driving global and regional R s changes.