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Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.

We used GPS tracking to monitor the grazing patterns of Brown Swiss and Alpine grey lactating cows on an alpine summer pasture (2038 m a.s.l.; SD = 146) in the Dolomites. The pasture (171 ha) was managed with a continuous grazing system (0.52 LU/ha) with morning and evening milking in the barn, guided grazing during the ‘day’, and free grazing at ‘night’. GPS positions were collected from 8 Brown Swiss multiparous and 9 Alpine Grey (4 primiparous and 5 multiparous) cows every two minutes. We inferred behaviours (grazing, resting, walking) from movement metrics, activity sensors and direct behavioural observations. After excluding milking periods, the cows grazed for 8 h/d, rested 10–11 h/d, and walked for 1.5/d. Grazing extended into late evening after milking, and resting prevailed throughout the ‘night’ until the morning milking. When grazing and resting, cows mainly used grasslands as the preferred habitat, but forest and sparse shrub were also used remarkably without consistent negative or positive selection. The pasture use was highly heterogeneous, with higher animal loads close to the barn, especially at night, and in areas with gentler slopes. Alpine Grey primiparous cows were less limited by slope and distance from the barn in their movement but were more selective in habitat use than multiparous cows. Differences between multiparous cows of the two breeds were less marked. Further studies should help understand the internal and external drivers of cattle grazing patterns to devise management practices combining animals’ productivity and welfare with the conservation of the grassland ecosystem services.

Mountain regions and the important ecosystem services they provide are considered to be very vulnerable to the current warming, and recent studies suggest that high-mountain environments experience more rapid changes in temperature than environments at lower elevations. Here we analysed weather records for the period 1975-2010 from the Eastern Italian Alps that show that warming occurred both at high and low elevations, but it was less pronounced at high elevations. This negative elevation-dependent trend was consistent for mean, maximum and minimum air temperature. Global radiation data measured at different elevations, surface energy fluxes measured above an alpine grassland and above a coniferous forest located at comparable elevations for nine consecutive years as well as remote sensing data (MODIS) for cloud cover and aerosol optical depth were analysed to interpret this observation. Increasing global radiation at low elevations turned out to be a potential driver of this negative elevation-dependent warming, but also contributions from land use and land cover changes at high elevations (abandonment of alpine pastures, expansion of secondary forest succession) were taken into account. We emphasise though, that a negative elevation-dependent warming is not universal and that future research and in particular models should not neglect the role of land use changes when determining warming rates over elevation.

Key messageIn Abies alba Mill. (silver fir), the frequency of intra-annual density fluctuations (IADFs) increases along the latitudinal transect, from North to South, is higher in pure than in mixed stands, and their formation is linked to spring and/or summer drought conditions.Trees respond to climate, recording information in tree rings and their anatomical features, such as IADFs. The IADFs are regions within the tree ring where density changes in response to tree physiological processes and environmental conditions. The objective of this study was to assess the effect of different forest-stand structures and of climate on IADF formation and frequency in populations of silver fir along a latitudinal gradient in Italy (Trentino, Molise and Calabria regions). In doing so, we aimed to compare the frequency of IADFs in pure or mixed stands and to understand the ability of silver fir to cope with environmental fluctuations. Results showed higher frequency of IADFs in pure than mixed stands (in Trentino and Calabria) and in younger trees (namely in the pure stand in Calabria), because of the higher sensitivity to environmental variability. The formation of IADFs in silver fir emerged as a response to cope with drought. Summer precipitation (both pure and mixed stands in Trentino) and early spring/summer (both pure and mixed stands in Calabria) played a key role in the formation of IADF-type E+. The formation of IADF-type L+, on the other hand, was related to temperature/precipitation in late summer and early autumn (both pure and mixed stands in Molise) and to precipitation in summer (pure stand in Calabria). Our findings support the theory that IADFs are an important structural/functional mechanism for responding to climate fluctuations.

The study investigates the potential of a commercially available proximal sensing system – based on a 16-band multispectral sensor – for monitoring mean midday gross ecosystem production (GEPm) in a subalpine grassland of the Italian Alps equipped with an eddy covariance flux tower. Reflectance observations were collected for 5 consecutive years, characterized by different climatic conditions, together with turbulent carbon dioxide fluxes and their meteorological drivers. Different models based on linear regression (vegetation indices approach) and on multiple regression (reflectance approach) were tested to estimateGEPm from optical data. The overall performance of this relatively low-cost system was positive. Chlorophyll-related indices including the red-edge part of the spectrum in their formulation (red-edge normalized difference vegetation index, NDVIred-edge; chlorophyll index, CIred-edge) were the best predictors of GEPm, explaining most of its variability during the observation period. The use of the reflectance approach did not lead to considerably improved results in estimating GEPm: the adjusted R2 (adjR2) of the model based on linear regression – including all the 5 years – was 0.74, while the adjR2 for the multiple regression model was 0.79. Incorporating mean midday photosynthetically active radiation (PARm) into the model resulted in a general decrease in the accuracy of estimates, highlighting the complexity of the GEPm response to incident radiation. In fact, significantly higher photosynthesis rates were observed under diffuse as regards direct radiation conditions. The models which were observed to perform best were then used to test the potential of optical data for GEPm gap filling. Artificial gaps of three different lengths (1, 3 and 5 observation days) were introduced in the GEPm time series. The values of adjR2 for the three gap-filling scenarios showed that the accuracy of the gap filling slightly decreased with gap length. However, on average, the GEPm gaps were filled with an accuracy of 73% with the model fed with NDVIred-edge, and of 76% with the model using reflectance at 681, 720 and 781 nm and PARm data.

Regional ecosystem productivity is highly sensitive to inter-annual climate variability, both within and outside the primary carbon uptake period. However, Earth system models lack sufficient spatial scales and ecosystem processes to resolve how these processes may change in a warming climate. Here, we show, how for the European Alps, mid-latitude Atlantic ocean winter circulation anomalies drive high-altitude summer forest and grassland productivity, through feedbacks among orographic wind circulation patterns, snowfall, winter and spring temperatures, and vegetation activity. Therefore, to understand future global climate change influence to regional ecosystem productivity, Earth systems models need to focus on improvements towards topographic downscaling of changes in regional atmospheric circulation patterns and to lagged responses in vegetation dynamics to non-growing season climate anomalies.

Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH.

Interactions between tree species determine the dynamics of forest communities. Spatial and temporal changes in resource availability, variation in species composition and spatial distribution of trees may alter competitive interactions between species and, therefore, affect tree growth and forest productivity. In this study, we analyzed the intra and inter-specific interactions between European beech (Fagus sylvatica L.) and silver fir (Abies alba Mill.) in southern Italy (Molise and Calabria regions), and how these interactions affect basal area increments in mixed-species and pure stands. Results showed that intra-specific interactions have a negative effect on the basal area increment, both in pure and mixed-species stands of Molise and Calabria. Basal area increment was higher influenced by intra-specific interactions in pure stands than in mixed-species stands. Silver fir in Molise showed higher basal area increment in mixed-species stand, probably in relation with stand structure and space occupation that resulted in less competition between individual trees. European beech showed high values of intra-specific interactions in pure stands, likely related to the low self-tolerance of this species and to the spatial arrangement of trees, due to canopy closure. The absence of inter-specific interactions in mixed-species stands could be explained by the sub-dominant position of European beech, which may have limited the benefit derived from niche separation and complementarity for silver fir.

Global vegetation models require the photosynthetic parameters, maximum carboxylation capacity (Vcm), and quantum yield (α) to parameterize their plant functional types (PFTs). The purpose of this work is to determine how much the scaling of the parameters from leaf to ecosystem level through a seasonally varying leaf area index (LAI) explains the parameter variation within and between PFTs. Using Fluxnet data, we simulate a seasonally variable LAIF for a large range of sites, comparable to the LAIM derived from MODIS. There are discrepancies when LAIF reach zero levels and LAIM still provides a small positive value. We find that temperature is the most common constraint for LAIF in 55% of the simulations, while global radiation and vapor pressure deficit are the key constraints for 18% and 27% of the simulations, respectively, while large differences in this forcing still exist when looking at specific PFTs. Despite these differences, the annual photosynthesis simulations are comparable when using LAIF or LAIM (r2 = 0.89). We investigated further the seasonal variation of ecosystem‐scale parameters derived with LAIF. Vcm has the largest seasonal variation. This holds for all vegetation types and climates. The parameter α is less variable. By including ecosystem‐scale parameter seasonality we can explain a considerable part of the ecosystem‐scale parameter variation between PFTs. The remaining unexplained leaf‐scale PFT variation still needs further work, including elucidating the precise role of leaf and soil level nitrogen. Key Points We present an analysis of the ecosystem photosynthetic parameter variation The seasonal parameters are only partly explained by LAI Key meteorological constraints are derived, which are useful as addition to PFTs

In this paper we explore the skill of hyperspectral reflectance measurements and vegetation indices (VIs) derived from these in estimating carbon dioxide (CO2) fluxes of grasslands. Hyperspectral reflectance data, CO2 fluxes and biophysical parameters were measured at three grassland sites located in European mountain regions using standardized protocols. The relationships between CO2 fluxes, ecophysiological variables, traditional VIs and VIs derived using all two-band combinations of wavelengths available from the whole hyperspectral data space were analysed. We found that VIs derived from hyperspectral data generally explained a large fraction of the variability in the investigated dependent variables but differed in their ability to estimate midday and daily average CO2 fluxes and various derived ecophysiological parameters. Relationships between VIs and CO2 fluxes and ecophysiological parameters were site-specific, likely due to differences in soils, vegetation parameters and environmental conditions. Chlorophyll and water-content-related VIs explained the largest fraction of variability in most of the dependent variables. Band selection based on a combination of a genetic algorithm with random forests (GA–rF) confirmed that it is difficult to select a universal band region suitable across the investigated ecosystems. Our findings have major implications for upscaling terrestrial CO2 fluxes to larger regions and for remote- and proximal-sensing sampling and analysis strategies and call for more cross-site synthesis studies linking ground-based spectral reflectance with ecosystem-scale CO2 fluxes.