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Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally. Here we show that we can map global trait patterns by complementing vascular plant observations from the global citizen science project iNaturalist with measurements from the plant trait database TRY. We evaluate these maps using sPlotOpen, a global collection of vegetation plot data. Our results show high correlations between the iNaturalist- and sPlotOpen-based maps of up to 0.69 (r) and higher correlations than to previously published trait maps. As citizen science data collections continue to grow, we can expect them to play a significant role in further improving maps of plant functional traits.

Compound heat waves and drought events draw our particular attention as they become more frequent. Co-occurring extreme events often exacerbate impacts on ecosystems and can induce a cascade of detrimental consequences. However, the research to understand these events is still in its infancy. DeepExtremes is a project funded by the European Space Agency (https://rsc4earth.de/project/deepextremes/) aiming at using deep learning to gain insight into Earth surface under extreme climate conditions. Specifically, the goal is to forecast and explain extreme, multi-hazard, and compound events. To this end, the project leverages the existing Earth observation archive to help us better understand and represent different types of hazards and their effects on society and vegetation. The project implementation involves a multi-stage process consisting of 1) global event detection; 2) intelligent subsampling and creation of mini-data-cubes; 3) forecasting methods development, interpretation, and testing; and 4) cloud deployment and upscaling. The data products will be made available to the community following the reproducibility and FAIR data principles. By effectively combining Earth system science with explainable AI, the project contributes knowledge to advancing the sustainable management of consequences of extreme events. This presentation will show the progress made so far and specifically introduce how to participate in the challenges about spatio-temporal extreme event prediction in DeepExtremes.

A variety of Earth system data streams are being captured and derived from remote sensing observations and modelling approaches. Since the spatial and temporal resolutions of these datasets continuously rise, global and local insights become more difficult to obtain and only specialists are able to effectively access and explore the data.Here we present the Leipzig Explorer of Earth Data Cubes (lexcube.org), the first fully interactive viewer for large Earth system data cubes, enabling the exploration and visualization of terabytes of data through space and time. Lexcube runs in the web browser and on many modern devices, including phones and tablets, works with a weak network connection and requires no coding skills. It can also be used to support field research by displaying the current geolocation of the user device in the visualization, allowing to compare past Earth system data to the current real-world situation in the field. Currently, lexcube.org allows to explore the Earth System Data Cube with 73 parameters from various domains, the ECMWF CAMS global reanalysis of atmospheric composition EAC4 and a data set of 97 different spectral indices from the national park Hainich in Germany. As of January 2023, lexcube.org has seen over 2,500 users who have generated over 145,000 API requests since its release in May 2022. Utilizing the open-source library xarray, Lexcube is capable of browsing any supported gridded data set in space and time, integrating into the existing data cube open-source ecosystem. Lexcube itself will be released in 2023 as an accessible, easy-to-use open-source package.

Vegetation carbon uptake is a major sink for anthropogenic greenhouse gas emissions, yet inferring longer-term behaviour of ecosystems as carbon sinks or sources is still difficult. Here, using a time series decomposition technique and eddy covariance data, we show that while at subseasonal scales net ecosystem CO2 uptake (NEP) is closely related to photosynthetic uptake, there is an increased importance of ecosystem respiration for determining NEP on longer time scales. The interannual evolution of net ecosystem CO2 uptake (NEP) is insufficiently understood and often not well captured in state-of-the-art vegetation models and data products. This lack of understanding may in part be due to different drivers between interannual and seasonal scales affecting the two terms balancing NEP - photosynthetic uptake of CO2 (GPP) and ecosystem respiration (Reco).Here, we extract timescale specific carbon flux dynamics at 20 long-running FLUXNET eddy covariance sites (>13 years) using time series decomposition to relate variability in GPP and Reco to NEP at subseasonal to interannual timescales. The results indicate that relations between NEP and GPP or Reco, respectively, are not constant across different timescales, but that GPP and Reco exert differential control on NEP between sub-seasonal, seasonal, and longer timescales. Overall, the fraction of variance in NEP explained by GPP variance is decreased at longer timescales, while the fraction of NEP variance explained by Reco variance and by the covariance of GPP and Reco generally increases at longer timescales. Regarding GPP, we find that the slopes between NEP and GPP, which could be interpreted as scale specific apparent carbon use efficiencies (NEP/GPP) are highest and most consistent at subseasonal scales, while generally smaller in magnitude and less constrained at interannual scales. This indicates that GPP and NEP are generally more strongly and directly linked at the subseasonalscale. Regarding Reco, we find a positive relationship between NEP and Reco at the seasonal scale. This is counterintuitive given NEP = GPP – Reco, but similar to spatial relations in other studies and likely related to GPP seasonality as a common driver. In contrast, the subseasonal and interannual NEP-Reco relations are mostly negative, as would be expected since higher respiratory loss would generally indicate lower ecosystem carbon retention, i.e. lower NEP. The timescale specific relations extracted here based on direct ecosystem CO2 exchange measurements suggest an increased importance of ecosystem respiration for long-term carbon source or sink behavior for some ecosystems. These results give insight into ecosystem functioning, as well as demonstrate the utility of time series decomposition as a diagnostic of ecosystem dynamics at different timescales. Such information may eventually serve as a basis to infer turnover times of ecosystem carbon pools and better characterize interannual ecosystem carbon dynamics.

Hot temperature extremes are changing in intensity and frequency. Quantifying these changes is key for developing adaptation and mitigation strategies. The conventional approach to study changes in hot extremes is based on air temperatures. However, many biogeochemical processes, i.e. decomposition of organic material and release of CO2, are triggered by soil temperature and it remains unclear whether it changes as does air temperature. Here, we demonstrate that soil hot extremes are intensifying and becoming even more frequent faster than air hot extremes over central eastern and western Europe. Based on existing model simulations, we also show that the increase in hot soil extremes could amplify or spread future heat waves by releasing sensible heat during hot days. We find an increase of 3 (7) % in the number of hot days with a contribution of heat from the soil under a warming level of 2.0 (3.0) °C than under a warming level of 1.5 °C. Furthermore, defining intensity and frequency extreme indices based on soil and air temperatures leads to a difference of more than 1 °C in intensity and 10% in frequency regionally during the last decades of the 21st century under the SPP5 8.5 emission scenario. In light of these results, maximum soil temperatures should be included in ecological risk studies as a complementary perspective to the conventional approach using extreme indices based on air temperatures.

Extreme events are on the rise. The 2022 compound heatwave and drought event caused significant vegetation mortality and serious ecosystem destruction in Europe that urgently need to be investigated. In this study, we used climate data (ERA5-Land air temperature at 2 m and precipitation) and remote sensing products (kNDVI derived from MODIS and ESA CCI Land Cover product) to investigate the dynamics of the 2022 extreme events and vegetation responses. Furthermore, we compared the effects of this year to other normal as well as abnormal years in Europe. We propose a ranking-based approach that compares cumulative sums of climate variables and kNDVI over the growing season to determine extreme areas and compound intensity over the last 23 years. The results show that the 2022 event is a widespread compound heatwave and drought event, with a similar spatial pattern to the 2018 extreme event, but less severe. Vegetational response differed among land cover classes, grassland was more affected while deciduous trees were barely affected in the 2022 event. In general, vegetation recovered relatively quickly after the 2022 event. Our ranking-based approach enables an effective comparison and characterization of climate extremes and their effects on vegetation over different years. A more in-depth analysis of spatial and temporal patterns can contribute to the development of targeted measures and support decision-makers in responding to climate extremes.

Persistence is an important characteristic of many complex systems in nature and of the Earth system in particular. Relating this statistical concept to physical properties of ecosystems is rather elusive, but reflects how long the system remains at a certain state before changing to a different one and is measured via the memory and dependence of values on past states [1]. Characterizing persistence in the terrestrial biosphere is very relevant to understand intrinsic properties of the system such as legacy effects of extreme climate events [2]. Such memory effects are often highly non-linear and therefore challenging to detect in observational records and poorly represented in Earth system models. This study estimates long and short-term non-linear persistence in eddy-covariance flux measurements and remote sensing products in European forests and the corresponding hydro-meteorological data. Characterizing persistence in the data allows us to make inferences on the interaction between Drought-Heat events, forest dynamics, and ecosystem resilience [3]. The comparison of in-situ and Earth Observation (EO) data allows us to infer how meaningful EO data are for monitoring complex dynamics in ecosystems.For short-term, spatio-temporal persistence, we use echo state networks using the technique suggested in [4] as an explainable AI (XAI) technique. In this context, the persistence of the system can be estimated by the model's response when the input fades abruptly. For the characterization of long-term persistence, we introduce a novel kernel extension of the well-established Detrended Fluctuation Analysis (DFA) [5], a method widely used in atmospheric sciences [1]. The DFA method is a scaling analysis that provides a simple quantitative parameter (the scaling exponent) to represent the correlation properties of a signal and a characteristic time of the event of interest. Unlike DFA, the proposed kernel DFA method can handle non-linear time-scales interactions. Estimating the non-linear persistence of forests and climate data allows us to relate characteristic times, crossover points between different scaling exponents, and short-term memory parameters with the duration and intensity of the events, as well as an indicator of change in the vegetation response to hydro-climatic conditions. [1] Salcedo-Sanz, S., et al. “Persistence in complex systems”. Physics Reports 957, 1-73, (2022).[2] Bastos, Ana, et al. “Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity." Science advances 6.24 (2020)[3] Scheffer, M., Carpenter, S. R., Dakos, V. & van Nes, E. H. Generic indicators of ecological resilience: inferring the chance of a critical transition. Annu. Rev. Ecol. Evol. Syst. 46, 145–167 (2015).[4] Barredo Arrieta, A., Gil-Lopez, S., Laña, I. et al. On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification. Neural Comput & Applic 34, 10257–10277 (2022).[5] Peng, C‐K., et al. "Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series." Chaos: an interdisciplinary journal of nonlinear science 5.1 (1995): 82-87.

Identifying the thresholds of drought that, if crossed, suppress vegetation functioning is vital for accurate quantification of how land ecosystems respond to climate variability and change. We present a globally applicable framework to identify drought thresholds for vegetation responses to different levels of known soil-moisture deficits using four remotely sensed vegetation proxies spanning 2001–2018. The thresholds identified represent critical inflection points for changing vegetation responses from highly resistant to highly vulnerable in response to drought stress, and as a warning signal for substantial vegetation impacts. Drought thresholds varied geographically, with much lower percentiles of soil-moisture anomalies in vegetated areas covered by more forests, corresponding to a comparably stronger capacity to mitigate soil water deficit stress in forested ecosystems. Generally, those lower thresholds are detected in more humid climates. State-of-the-art land models, however, overestimated thresholds of soil moisture (i.e. overestimating drought impacts), especially in more humid areas with higher forest covers and arid areas with few forest covers. Based on climate model projections, we predict that the risk of vegetation damage will increase by the end of the twenty-first century in some hotspots like East Asia, Europe, Amazon, southern Australia and eastern and southern Africa. Our data-based results will inform projections on future drought impacts on terrestrial ecosystems and provide an effective tool for drought management.

The inter-annual variability (IAV) of the global carbon cycle (C-cycle) is prone to large uncertainty, which in turn, affects uncertainty in future climate projections. Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is a key endeavour to improve our understanding of C-cycle dynamics.C-cycle IAV mainly results from the balance of carbon uptake by gross primary productivity, carbon release from respiration processes, and other disturbance-induced fluxes. These processes are largely controlled by temperature, water availability, and incoming solar radiation, which are modulated by large-scale modes of atmospheric circulation such as the El Niño/Southern Oscillation (ENSO) or the Pacific Decadal Oscillation (PDO). Here, we use a data-driven approach [1] to quantify the fraction of IAV in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability, by using spatio-temporal sea level pressure-a proxy for large-scale atmospheric circulation-as a predictor in ridge regression models of carbon cycle IAV. We use a regularisation approach [1] to curb the problems of overfitting and multicollinearity due to the limited time interval and large number of spatial predictors (spatial gridded time-series of SLP anomalies). We find that the model based on SLP anomalies can achieve high skill in predictions of the IAV in atmospheric growth rate and global land sink, with Pearson correlations between original and predicted test values of 0.7-0.84. The coefficients of the regression indicate two dominant regions contributing to C-cycle IAV: one in the tropical Pacific corresponding to the well-known influence of ENSO, another one located in the western Pacific. We test how the prediction skill depends on the length of the time-series and show that for short time-series (15-20 years) the correlation of predicted vs. observed test values is strongly dependent on the particular period considered, while it is more stable for periods longer than 30 years. These results indicate that the influence of atmospheric circulation variability on IAV of the C-cycle can limit our ability to draw robust conclusions when using short observational records. [1] Sippel, S., Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A. G., Fischer, E., and Knutti, R.: Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning, Journal of Climate, 32(17), 5677–5699, https://doi.org/10.1175/JCLI-D-18-0882.1, 2019.

The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, with a hypothesis test being performed for each grid cell. When the whole image or a set of grid cells are analyzed for a global effect, the problem of multiple testing arises. When no global effect is present, we expect $$ \alpha $$α% of all grid cells to be false positives, and spatially autocorrelated data can give rise to clustered spurious rejections that can be misleading in an analysis of spatial patterns. In this work, we review standard solutions for the multiple testing problem and apply them to spatio-temporal environmental data. These solutions are independent of the test statistic, and any test statistic can be used (e.g., tests for trends or change points in time series). Additionally, we introduce permutation methods and show that they have more statistical power. Real-world data are used to provide examples of the analysis, and the performance of each method is assessed in a simulation study. Unlike other simulation studies, our study compares the statistical power of the presented methods in a comprehensive simulation study. In conclusion, we present several statistically rigorous methods for analyzing spatio-temporal environmental data and controlling the false positives. These methods allow the use of any test statistic in a wide range of applications in environmental sciences and remote sensing.

Ecosystems are projected to face extreme high temperatures more frequently in the near future. Various biotic coping strategies exist to prevent heat stress. Controlled experiments have recently provided evidence for continued transpiration in woody plants during high air temperatures, even when photosynthesis is inhibited. Such a decoupling of photosynthesis and transpiration would represent an effective strategy (‘known as leaf or canopy cooling’) to prevent lethal leaf temperatures. At the ecosystem scale, continued transpiration might dampen the development and propagation of heat extremes despite further desiccating soils. However, at the ecosystem scale, evidence for the occurrence of this decoupling is still limited. Here, we aim to investigate this mechanism using eddy-covariance data of thirteen woody ecosystems located in Australia and a causal graph discovery algorithm. Working at half-hourly time resolution, we find evidence for a decoupling of photosynthesis and transpiration in four ecosystems which can be classified as Mediterranean woodlands. The decoupling occurred at air temperatures above 35 ∘C. At the nine other investigated woody sites, we found that vegetation CO2 exchange remained coupled to transpiration at the observed high air temperatures. Ecosystem characteristics suggest that the canopy energy balance plays a crucial role in determining the occurrence of a decoupling. Our results highlight the value of causal-inference approaches for the analysis of complex physiological processes. With regard to projected increasing temperatures and especially extreme events in future climates, further vegetation types might be pushed to threatening canopy temperatures. Our findings suggest that the coupling of leaf-level photosynthesis and stomatal conductance, common in land surface schemes, may need be re-examined when applied to high-temperature events.

One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p >= 800, each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n < 60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Nino-Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern., We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30-54 years. For shorter time series (n < 30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices., Earth System Dynamics, 13 (4), ISSN:2190-4987, ISSN:2190-4979

How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates X on a real-valued response Y. We introduce a procedure for estimating causal effects and a nonparametric hypothesis test for these effects being zero. Our application is based on geospatial information on conflict events and remote-sensing-based data on forest loss between 2000 and 2018 in Colombia. Across the entire country, we estimate the effect to be slightly negative (conflict reduces forest loss) but insignificant (P = 0.578), while at the provincial level, we find both positive effects (e.g., La Guajira, P = 0.047) and negative effects (e.g., Magdalena, P = 0.004). The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time. Our theoretical findings are supported by simulations, and code is available online.

Much uncertainty remains in measuring the inter-annual and longer-term dynamics of vegetation gross and net primary productivity (GPP, NPP) and the connected land carbon sink. Potential for better GPP estimation lies in newer satellite products representing different processes or vegetation states, but how they capture interannual GPP dynamics remains to be explored. Here, we differentiate shorter- and longer-term vegetation dynamics and their drivers for several Earth-observation-based vegetation proxies and a GPP estimate using time series decomposition. We find that relations between proxies can significantly differ between time scales, along land cover and climate gradients. For GPP estimated at FLUXNET sites, seasonal and multiannual slopes differ by at least 25% for half of the cases investigated, indicating considerable mismatch if multiannual relations were derived from seasonal slopes. Considering time-scale-specific sensitivities between proxies of vegetation productivity may improve estimates of interannual variability in vegetation productivity. publishersversion published

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles., Nature Ecology & Evolution, 6 (1), ISSN:2397-334X

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., Three key axes of variation of ecosystem functional changes and their underlying causes are identified from a dataset of surface gas exchange measurements across major terrestrial biomes and climate zones.

Datos disponibles en https://github.com/fmsabatini/sPlotOpen_Code, EU H2020 project BACI, Grant No. 640176 (...), Sabatini, F.M., Lenoir, J., Hattab, T., Arnst, E.A., Chytrý, M., Dengler, J., De Ruffray, P., Hennekens, S.M., Jandt, U., Jansen, F., Jiménez-Alfaro, B., Kattge, J., Levesley, A., Pillar, V.D., Purschke, O., Sandel, B., Sultana, F., Aavik, T., Aćić, S., Acosta, A.T.R., Agrillo, E., Alvarez, M., Apostolova, I., Arfin Khan, M.A.S., Arroyo, L., Attorre, F., Aubin, I., Banerjee, A., Bauters, M., Bergeron, Y., Bergmeier, E., Biurrun, I., Bjorkman, A.D., Bonari, G., Bondareva, V., Brunet, J., Čarni, A., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Ćušterevska, R., De Bie, E., de Gasper, A.L., De Sanctis, M., Dimopoulos, P., Dolezal, J., Dziuba, T., El-Sheikh, M.A.E.-R.M., Enquist, B., Ewald, J., Fazayeli, F., Field, R., Finckh, M., Gachet, S., Galán-de-Mera, A., Garbolino, E., Gholizadeh, H., Giorgis, M., Golub, V., Alsos, I.G., Grytnes, J.-A., Guerin, G.R., Gutiérrez, A.G., Haider, S., Hatim, M.Z., Hérault, B., Hinojos Mendoza, G., Hölzel, N., Homeier, J., Hubau, W., Indreica, A., Janssen, J.A.M., Jedrzejek, B., Jentsch, A., Jürgens, N., Kącki, Z., Kapfer, J., Karger, D.N., Kavgacı, A., Kearsley, E., Kessler, M., Khanina, L., Killeen, T., Korolyuk, A., Kreft, H., Kühl, H.S., Kuzemko, A., Landucci, F., Lengyel, A., Lens, F., Lingner, D.V., Liu, H., Lysenko, T., Mahecha, M.D., Marcenò, C., Martynenko, V., Moeslund, J.E., Monteagudo Mendoza, A., Mucina, L., Müller, J.V., Munzinger, J., Naqinezhad, A., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G.E., Pärtel, M., Pauchard, A., Peet, R.K., Peñuelas, J., Pérez-Haase, A., Peterka, T., Petřík, P., Peyre, G., Phillips, O.L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rivas-Torres, G., Rodwell, J.S., Ruprecht, E., Rūsiņa, S., Samimi, C., Schmidt, M., Schrodt, F., Shan, H., Shirokikh, P., Šibík, J., Šilc, U., Sklenář, P., Škvorc, Ž., Sparrow, B., Sperandii, M.G., Stančić, Z., Svenning, J.-C., Tang, Z., Tang, C.Q., Tsiripidis, I., Vanselow, K.A., Vásquez Martínez, R., Vassilev, K., Vélez-Martin, E., Venanzoni, R., Vibrans, A.C., Violle, C., Virtanen, R., von Wehrden, H., Wagner, V., Walker, D.A., Waller, D.M., Wang, H.-F., Wesche, K., Whitfeld, T.J.S., Willner, W., Wiser, S.K., Wohlgemuth, T., Yamalov, S., Zobel, M., Bruelheide, H.

Understanding the dependencies of the terrestrial carbon and water cycle with meteorological conditions is a prerequisite to anticipate their behaviour under climate change conditions. However, terrestrial ecosystems and the atmosphere interact via a multitude of variables across temporal and spatial scales. Additionally these interactions might differ among vegetation types or climatic regions. Today, novel algorithms aim to disentangle the causal structure behind such interactions from empirical data. The estimated causal structures can be interpreted as networks, where nodes represent relevant meteorological variables or land-surface fluxes and the links represent the dependencies among them (possibly including time lags and link strength). Here we derived causal networks for different seasons at 119 eddy covariance flux tower observations in the FLUXNET network. We show that the networks of biosphere–atmosphere interactions are strongly shaped by meteorological conditions. For example, we find that temperate and high-latitude ecosystems during peak productivity exhibit biosphere–atmosphere interaction networks very similar to tropical forests. In times of anomalous conditions like droughts though, both ecosystems behave more like typical Mediterranean ecosystems during their dry season. Our results demonstrate that ecosystems from different climate zones or vegetation types have similar biosphere–atmosphere interactions if their meteorological conditions are similar. We anticipate our analysis to foster the use of network approaches, as they allow for a more comprehensive understanding of the state of ecosystem functioning. Long-term or even irreversible changes in network structure are rare and thus can be indicators of fundamental functional ecosystem shifts.

Global greening trends have been widely reported based on long‐term remote sensing data of terrestrial ecosystems. Typically, a hypothesis test is performed for each grid cell; this leads to multiple hypothesis testing and false positive trend detection. We reanalyze global greening and account for this issue with a novel statistical method that allows robust inference on greening regions. Based on leaf area index (LAI) data, our methods reduce the detected greening from 35.2% to 15.3% of the terrestrial land surface; this reduction is most notable in nonwoody vegetation. Our results confirm several greening regions (China, India, Europe, Sahel, North America, Brazil, and Siberia), that are also supported by independent data products. We also report evidence for an increasing seasonal amplitude in LAI north of 35°N. Considering the widespread use of spatially replicated trend tests in global change research, we recommend adopting the proposed multiple testing procedure to control false positive outcomes., Plain Language Summary: Using satellite data, recent studies have detected an increase in vegetation greenness around the globe. These studies attribute this vegetation increase to different factors, such as warming or land use change. However, we argue that the commonly used analysis method is detecting too many regions with trends. In this work, we reanalyze vegetation data using the leaf area index, which measures the area occupied by leaves in any given area. With our refined methods, we too detect greening regions around the world, however these regions are smaller and less abundant. Our research introduces a step in the statistical analysis that increases the reliability of the detected vegetation greening. This can help establish more consensus on what the main contributing factors are for the observed vegetation increase., Key Points: Many studies have consistently reported on global greening trends, but repeatedly without rigorous significance testing. Although global greening has been overestimated, significant greening can still be rigorously detected. We observe an increase in the seasonal amplitude of leaf area index around the glob.

Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data. Reference: "A Unified Vegetation Index for Quantifying the Terrestrial Biosphere", Gustau Camps-Valls, Manuel Campos-Taberner, Álvaro Moreno-Martı́nez, Sophia Walther, Grégory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Muñoz-Marı́, Francisco Javier Garcı́a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. Science Advances, in press, 2021