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

Spring wood phenology responds more strongly to chilling temperatures than bud phenology in European conifers

A comparative assessment of bud and wood phenology could aid a better understanding of tree growth dynamics. However, the reason for asynchronism or synchronism in leaf and cambial phenology remains unclear. To test the assumption that the temporal relationship between the budburst date and the onset date of wood formation is due to their common or different responses to environmental factors, we constructed a wood phenology dataset from previous literature, and compared it with an existing bud phenology dataset in Europe. We selected three common conifers (Larix decidua Mill., Picea abies (L.) H. Karst. and Pinus sylvestris L.) in both datasets and analyzed 909 records of the onset of wood formation at 47 sites and 238,720 records of budburst date at 3051 sites. We quantified chilling accumulation (CA) and forcing requirement (FR) of budburst and onset of wood formation based on common measures of CA and FR. We then constructed negative exponential CA-FR curves for bud and wood phenology separately. The results showed that the median, variance and probability distribution of CA-FR curves varied significantly between bud and wood phenology for three conifers. The different FR under the same chilling condition caused asynchronous bud and wood phenology. Furthermore, the CA-FR curves manifested that wood phenology was more sensitive to chilling than bud phenology. Thus, the FR of the onset of wood formation increases more than that of budburst under the same warming scenarios, explaining the stronger earlier trends in the budburst date than the onset date of woody formation simulated by the process-based model. Our work not only provides a possible explanation for asynchronous bud and wood phenology from the perspective of organ-specific responses to chilling and forcing, but also develops a phenological model for predicting both bud and wood phenology with acceptable uncertainties.

Biome-scale temperature sensitivity of ecosystem respiration revealed by atmospheric CO2 observations

The temperature sensitivity of ecosystem respiration regulates how the terrestrial carbon sink responds to a warming climate but has been difficult to constrain observationally beyond the plot scale. Here we use observations of atmospheric CO2 concentrations from a network of towers together with carbon flux estimates from state-of-the-art terrestrial biosphere models to characterize the temperature sensitivity of ecosystem respiration, as represented by the Arrhenius activation energy, over various North American biomes. We infer activation energies of 0.43 eV for North America and 0.38 eV to 0.53 eV for major biomes therein, which are substantially below those reported for plot-scale studies (approximately 0.65 eV). This discrepancy suggests that sparse plot-scale observations do not capture the spatial-scale dependence and biome specificity of the temperature sensitivity. We further show that adjusting the apparent temperature sensitivity in model estimates markedly improves their ability to represent observed atmospheric CO2 variability. This study provides observationally constrained estimates of the temperature sensitivity of ecosystem respiration directly at the biome scale and reveals that temperature sensitivities at this scale are lower than those based on earlier plot-scale studies. These findings call for additional work to assess the resilience of large-scale carbon sinks to warming.

Modeling the effect of adaptation to future climate change on spring phenological trend of European beech (Fagus sylvatica L.)

Temperate trees could cope with climate change through phenotypic plasticity of phenological key events or adaptation in situ via selection on genetic variation. However, the relative contribution of local adaptation and phenotypic plasticity to phenological change is unclear for many ecologically important tree species. Here, we analyzed the leaf-out data of European beech (Fagus sylvatica L.) from 50 provenances planted in 7 trial sites. We first constructed a function between chilling accumulation (CA) and photoperiod-associated heat requirement (PHR) of leaf-out date for each provenance and quantified the relationship between parameters of the CA-PHR function and climatic variables at provenance origins by using the random forest model. Furthermore, we used the provenance-specific CA-PHR function to simulate future leaf-out dates under two climate change scenarios (RCP 4.5 and 8.5) and two assumptions (no adaptation and adaptation). The results showed that both CA, provenance, and their interactions affected the PHR of leaf-out. The provenances from southeastern Europe exhibited a stronger response of PHR to CA and thus flushed earlier than northwestern provenances. The parameters of the CA-PHR function were connected with climatic variables (e.g., mean diurnal temperature range, temperature seasonality) at the originating sites of each provenance. If only considering the phenotypic plasticity, the leaf-out date of European beech in 2070-2099 will advance by 6.8 and 9.0 days on average relative to 1951-2020 under RCP 4.5 and RCP 8.5, respectively. However, if F. sylvatica adapts to future climate change by adopting the current strategy, the advance of the leaf-out date will weaken by 1.4 and 3.4 days under RCP 4.5 and RCP 8.5, respectively. Our results suggest that the European beech could slow down its spring phenological advances and reduce its spring frost risk if it adopts the current strategy to adapt to future climate change.

Plant community composition alters moisture and temperature sensitivity of soil respiration in semi-arid shrubland

Soil respiration (Rs) is the second largest carbon (C) flux to the atmosphere and our understanding of how Rs and its components shift with plant-community composition remains an important question. We used high-frequency soil respiration measurements and root exclusion to evaluate how Rs, autotrophic respiration (Ra) and heterotrophic respiration (Rh) vary between a semi-arid perennial shrub community and annual invasive community. Over two growing seasons, total Rs was 40% higher under annual vegetation compared to shrubs. Partitioning revealed consistently higher Ra under annual vegetation which accounted for most of the difference in Rs. Under annual vegetation, Ra increased soon after the first rain events and remained high despite cooling temperatures while shrub Ra increased only when soil temperature began to warm up. The Rh rates were similar between vegetation types when daily soil temperatures were lower than 20 °C. As soil temperatures increased and soil moisture dropped below 10%, Rh was consistently higher under annual vegetation than shrubs. Seasonal dynamics of Rs and Rh were best modeled with an interaction term between soil moisture and temperature with significantly different model parameters for each vegetation type. Differences in the timing and magnitude of Rs and Ra between vegetation types are consistent with phenological differences between shrubs and annuals. Under annuals, larger Rh at high temperatures suggests that expansion of annual vegetation and future hotter and drier conditions could lead to greater C losses from this semi-arid shrub system.

Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer.

The three major axes of terrestrial ecosystem function

The leaf economics spectrum^1,2 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 variability^4,5. 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 ecosystems^7,8.

COSORE: A community database for continuous soil respiration and other soil-atmosphere greenhouse gas flux data

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO₂ flux, commonly though imprecisely termed soil respiration (R_S ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency R_S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R_S , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Geomatics and EO Data to Support Wildlife Diseases Assessment at Landscape Level: A Pilot Experience to Map Infectious Keratoconjunctivitis in Chamois and Phenological Trends in Aosta Valley (NW Italy)

Geomatics and satellite remote sensing offer useful analysis tools for several technical-scientific fields. This work, with reference to a regional case of study, investigates remote sensing potentialities for describing relationships between environment and diseases affecting wildlife at landscape level in the light of climate change effects onto vegetation. Specifically, the infectious keratoconjunctivitis (IKC) of chamois (Rupicapra rupicapra L.) in Aosta Valley (NW Italy) was investigated at the regional level. IKC (Mycoplasma conjunctivae) is a contagious disease for domestic and wild ruminants (Caprinae and Ovinae). Two types of analysis were performed: one aimed at exploring by remotely sensed data phenological metrics (PMs) and evapotranspiration (ET) trends of vegetation in the area; one investigating the correlation between PMs and ET, versus IKC prevalence. The analysis was based on TERRA MODIS image time series ranging from 2000 to 2019. Ground data about IKC were available for a shorter time range: 2009–2019. Consequently, PMs and ET trend investigations were focused on the whole times range (2000–2019); conversely, correlation analysis was achieved with reference to the reduced 2009–2019 period. The whole study was based on freely available data from public archives. MODIS products, namely MOD13Q1 v.6 and MOD16A2, were used to derive PM and ET trends, respectively. Shuttle Radar Topography Mission (SRTM) Digital Terrain Model (DTM) was used to describe local topography; CORINE Land Cover map was adopted to describe land use classes. PMs and ET (as derivable from EO data) proved to significantly changed their values in the last 20 years, with a continuous progressive trend. As far as correlation analysis was concerned, ET and some PMs (specifically, End of Season (EOS) and Length of Season (LOS) proved significantly condition IKC prevalence. According to results, the proposed methodology can be retained as an effective tool for supporting public health and eco-pathological sectors. Specifically, it can be intended for a continuous monitoring of effects that climatic dynamics determine onto wild animals in the Alpine area, included diseases and zoonosis, moving future environmental management and planning towards the One Health perspective.

Nutrients and water availability constrain the seasonality of vegetation activity in a Mediterranean ecosystem

Anthropogenic nitrogen (N) deposition and resulting differences in ecosystem N and phosphorus (P) ratios are expected to impact photosynthetic capacity, that is, maximum gross primary productivity (GPP_max ). However, the interplay between N and P availability with other critical resources on seasonal dynamics of ecosystem productivity remains largely unknown. In a Mediterranean tree-grass ecosystem, we established three landscape-level (24 ha) nutrient addition treatments: N addition (NT), N and P addition (NPT), and a control site (CT). We analyzed the response of ecosystem to altered nutrient stoichiometry using eddy covariance fluxes measurements, satellite observations, and digital repeat photography. A set of metrics, including phenological transition dates (PTDs; timing of green-up and dry-down), slopes during green-up and dry-down period, and seasonal amplitude, were extracted from time series of GPP_max and used to represent the seasonality of vegetation activity. The seasonal amplitude of GPP_max was higher for NT and NPT than CT, which was attributed to changes in structure and physiology induced by fertilization. PTDs were mainly driven by rainfall and exhibited no significant differences among treatments during the green-up period. Yet, both fertilized sites senesced earlier during the dry-down period (17-19 days), which was more pronounced in the NT due to larger evapotranspiration and water usage. Fertilization also resulted in a faster increase in GPP_max during the green-up period and a sharper decline in GPP_max during the dry-down period, with less prominent decline response in NPT. Overall, we demonstrated seasonality of vegetation activity was altered after fertilization and the importance of nutrient-water interaction in such water-limited ecosystems. With the projected warming-drying trend, the positive effects of N fertilization induced by N deposition on GPP_max may be counteracted by an earlier and faster dry-down in particular in areas where the N:P ratio increases, with potential impact on the carbon cycle of water-limited ecosystems.

Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues

As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979–2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.

Cryptic phenology in plants: Case studies, implications, and recommendations

Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model

The application of detailed process-oriented simulation models for gross primary production (GPP) estimation is constrained by the scarcity of the data needed for their parametrization. In this manuscript, we present the development and test of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Normalized Difference Vegetation Index (NDVI) observations into a simple process-based model driven by basic meteorological variables (i.e., global radiation, temperature, precipitation and reference evapotranspiration, all from global circulation models of the European Centre for Medium-Range Weather Forecasts). The model is run at daily time-step using meteorological forcing and provides estimates of GPP and LAI, the latter used to simulate MODIS NDVI though the coupling with the radiative transfer model PROSAIL5B. Modelled GPP is compared with the remote sensing-driven MODIS GPP product (MOD17) and the quality of both estimates are assessed against GPP from European eddy covariance flux sites over crops and grasslands. Model performances in GPP estimation (R2 = 0.67, RMSE = 2.45 gC m−2 d−1, MBE = −0.16 gC m−2 d−1) were shown to outperform those of MOD17 for the investigated sites (R2 = 0.53, RMSE = 3.15 gC m−2 d−1, MBE = −1.08 gC m−2 d−1).

Widespread inhibition of daytime ecosystem respiration

The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance of these two processes, it is not possible to measure either at the ecosystem scale during the daytime. Eddy-covariance measurements are widely used as the closest 'quasi-direct' ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however, suggests that current estimates may be biased by up to 25%, due to a previously unaccounted for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the re-evaluation of global terrestrial carbon cycle models and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

The influence of tree species on small scale spatial heterogeneity of soil respiration in a temperate mixed forest

Soil respiration is the largest terrestrial carbon flux into the atmosphere, and different tree species could directly influence root derived respiration and indirectly regulate soil respiration rates by altering soil chemical and microbial properties. In this study, we assessed the small scale spatial heterogeneity of soil respiration and the microbial community below the canopy of three dominant tree species (Korean pine (Pinus koraiensis), Mongolian oak (Quercus mongolica), and Manchuria ash (Fraxinus mandshurica)) in a temperate mixed forest in Northeast China. Soil respiration differed significantly during several months and increased in the order of oak<ash<pine, while soil temperature was greater in the order of pine<oak<ash, suggesting that soil respiration variations among tree species were not mainly regulated by soil temperature. In addition, the lower N and higher C concentrations of pine litter resulted in a higher C/N ratio than ash and oak, which might lead to a higher recalcitrance and slower decomposition rate, and decreased heterotrophic respiration under pine. By contrast, fine root biomass was significantly higher under pine than ash and oak, which induced higher soil autotrophic respiration under pine compared to ash and oak. Tree species sharply regulated the bacterial communities through altering the litter and soil properties, while the fungal communities were relatively consistent among tree species. This study revealed the connection between species specific traits and soil respiration, which is crucial for understanding plant-soil feedbacks and improving forecasts of the global carbon cycle.

Evaluating of simulated carbon flux phenology over a cropland ecosystem in a semiarid area of China with SiBcrop

The cropland ecosystem in semiarid areas is sensitive to climate change. The accurate representation of crop phenology is important for predicting the carbon and water exchange process. The performance of a newly developed phenological model (SiBcrop) for simulations of carbon flux phenology in a semiarid area ecosystem was evaluated. The results showed that the SiBcrop improved the prediction for daily maximum gross primary production (GPP), and the days GPP reached the maximum value were closer to the observation, compared to SiB3. SiBcrop had a better prediction for both monthly total net ecosystem exchange (NEE) in the growing season than in the dormant season in semiarid areas. The day when the cumulative NEE predicted with SiBcrop became positive was closer to the observation. The observed start date of carbon uptake (CUstart) had a larger annual variation than did the end date of carbon uptake (CUend). SiBcrop had a better prediction for CUstart but poor for CUend, compared to SiB3. There was a longer carbon uptake period (CUP) predicted with SiBcrop than the observed results.

Does the different photosynthetic pathway of plants affect soil respiration in a subtropical wetland?

Plants with different photosynthetic pathways could produce different amounts and types of root exudates and debris which may affect soil respiration rates. Therefore, wetland vegetation succession between plants with different photosynthetic pathways may ultimately influence the wetland carbon budget. The middle and lower reaches of the Yangtze River has the largest floodplain wetland group in China. Tian'e Zhou wetland reserve (29°48'N, 112°33′E) is located in Shishou city, Hubei province and covers about 77.5 square kilometers. Hemathria altissima (C4) was found gradually being replaced by Carex argyi (C3) for several years in this place. An in situ experiment was conducted in Tian'e Zhou wetland to determine the change of soil respiration as the succession proceeds. Soil respiration, substrate‐induced respiration, and bacterial respiration of the C4 species was greater than those of the C3 species, but below‐ground biomass and fungal respiration of the C4 species was less than that of the C3 species. There were no significant differences in above‐ground biomass between the two species. Due to the higher photosynthesis capability, higher soil respiration and lower total plant biomass, we inferred that the C4 species, H. altissima, may transport more photosynthate below‐ground as a substrate for respiration. The photosynthetic pathway of plants might therefore play an important role in regulating soil respiration. As C. argyi replaces H. altissima, the larger plant biomass and lower soil respiration would indicate that the wetland in this area could fix more carbon in the soil than before.

Terrestrial Carbon Cycle Variability

A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO ₂ concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget.

The CO ₂ concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration.

At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO ₂, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y ^-1) with respect to a large and uncertain background (123 +/- 4 Pg-C y ^-1), and detected trends in global primary productivity are even smaller (33 Tg-C y ^-2). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.

Soil moisture controls on phenology and productivity in a semi-arid critical zone

The Earth's Critical Zone, where physical, chemical and biological systems interact, extends from the top of the canopy to the underlying bedrock. In this study, we investigated soil moisture controls on phenology and productivity of an Acacia woodland in semi-arid central Australia. Situated on an extensive sand plain with negligible runoff and drainage, the carry-over of soil moisture content (θ) in the rhizosphere enabled the delay of phenology and productivity across seasons, until conditions were favourable for transpiration of that water to prevent overheating in the canopy. Storage of soil moisture near the surface (in the top few metres) was promoted by a siliceous hardpan. Pulsed recharge of θ above the hardpan was rapid and depended upon precipitation amount: 150mm storm(-1) resulted in saturation of θ above the hardpan (i.e., formation of a temporary, discontinuous perched aquifer above the hardpan in unconsolidated soil) and immediate carbon uptake by the vegetation. During dry and inter-storm periods, we inferred the presence of hydraulic lift from soil storage above the hardpan to the surface due to (i) regular daily drawdown of θ in the reservoir that accumulates above the hardpan in the absence of drainage and evapotranspiration; (ii) the dimorphic root distribution wherein most roots were found in dry soil near the surface, but with significant root just above the hardpan; and (iii) synchronisation of phenology amongst trees and grasses in the dry season. We propose that hydraulic redistribution provides a small amount of moisture that maintains functioning of the shallow roots during long periods when the surface soil layer was dry, thereby enabling Mulga to maintain physiological activity without diminishing phenological and physiological responses to precipitation when conditions were favourable to promote canopy cooling.

The effects of grazing and watering on ecosystem CO2 fluxes vary by community phenology

Grazing profoundly influences vegetation and the subsequent carbon fluxes in various ecosystems. However, little effort has been made to explore the underlying mechanisms for phenological changes and their consequences on carbon fluxes at ecosystem level, especially under the coupled influences of human disturbances and climate change. Here, a manipulative experiment (2012-2013) was conducted to examine both the independent and interactive effects of grazing and watering on carbon fluxes across phenological phases in a desert steppe. Grazing advanced or delayed phenological timing, leading to a shortened green-up phase (GrP: 23.60 days) in 2013 and browning phase (BrP: 12.48 days) in 2012 from high grazing, and insignificant effects on the reproductive phase (ReP) in either year. High grazing significantly enhance carbon uptake, while light grazing reduce carbon uptake in ReP. Watering only delayed the browning time by 5.01 days in 2013, producing no significant effects on any phenophase. Watering promoted the net ecosystem exchange (NEE), ecosystem respiration (ER), and gross ecosystem productivity (GEP) only in the GrP. When calculating the yearly differences in phenophases and the corresponding carbon fluxes, we found that an extended GrP greatly enhanced NEE, but a prolonged ReP distinctly reduced it. The extended GrP also significantly promote GEP. Increases in growing season length appeared promoting ER, regardless of any phenophase. Additionally, the shifts in NEE appeared dependent of the variations in leaf area index (LAI).

Modeling net ecosystem carbon exchange of alpine grasslands with a satellite-driven model

Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.