Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Inferring causation from time series in Earth system sciences
Runge, Jakob ; Bathiany, Sebastian ; Bollt, Erik ; Camps-Valls, Gustau ; Coumou, Dim ; Deyle, Ethan ; Glymour, Clark ; Kretschmer, Marlene ; Mahecha, Miguel D. ; Muñoz-Marí, Jordi ; Nes, Egbert H. van; Peters, Jonas ; Quax, Rick ; Reichstein, Markus ; Scheffer, Marten ; Schölkopf, Bernhard ; Spirtes, Peter ; Sugihara, George ; Sun, Jie ; Zhang, Kun ; Zscheischler, Jakob - \ 2019
Nature Communications 10 (2019)1. - ISSN 2041-1723

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform to close the gap between method users and developers.

Modelling Landsurface Time-Series with Recurrent Neural Nets
Reichstein, Markus ; Besnard, Simon ; Carvalhais, Nuno ; Gans, Fabian ; Jung, Martin ; Kraft, Basil ; Mahecha, Miguel - \ 2018
- p. 7640 - 7643.
Machine learning tools and semi-empirical models have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere state and function. Yet, little work has been carried to explicitly model dynamic features accounting for memory effects, where in some cases hand-designed features (e.g. temperature sum, lagged precipitation) have been employed. Here, we explore the ability of recurrent neural network variants (RNN, LSTM) to model time series of dynamic variables 1) fPAR and NDVI, and 2) Carbon dioxide uptake and evapotranspiration, with meteorological variables as the only dynamic predictors. We show that the recurrent neural net approach excellently deals with this dynamic modelling challenge and outcompetes approaches where hand-designed features are complicated to conceive.
Global trait–environment relationships of plant communities
Bruelheide, Helge ; Dengler, Jürgen ; Purschke, Oliver ; Lenoir, Jonathan ; Jiménez-Alfaro, Borja ; Hennekens, Stephan M. ; Botta-Dukát, Zoltán ; Chytrý, Milan ; Field, Richard ; Jansen, Florian ; Kattge, Jens ; Pillar, Valério D. ; Schrodt, Franziska ; Mahecha, Miguel D. ; Peet, Robert K. ; Sandel, Brody ; Bodegom, Peter van; Altman, Jan ; Alvarez-Dávila, Esteban ; Arfin Khan, Mohammed A.S. ; Attorre, Fabio ; Aubin, Isabelle ; Baraloto, Christopher ; Barroso, Jorcely G. ; Bauters, Marijn ; Bergmeier, Erwin ; Biurrun, Idoia ; Bjorkman, Anne D. ; Blonder, Benjamin ; Čarni, Andraž ; Cayuela, Luis ; Černý, Tomáš ; Cornelissen, J.H.C. ; Craven, Dylan ; Dainese, Matteo ; Derroire, Géraldine ; Sanctis, Michele De; Díaz, Sandra ; Doležal, Jiří ; Farfan-Rios, William ; Feldpausch, Ted R. ; Fenton, Nicole J. ; Garnier, Eric ; Guerin, Greg R. ; Gutiérrez, Alvaro G. ; Haider, Sylvia ; Hattab, Tarek ; Henry, Greg ; Hérault, Bruno ; Ozinga, Wim A. - \ 2018
Nature Ecology & Evolution 2 (2018)12. - ISSN 2397-334X - p. 1906 - 1917.

Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. Here, we perform a global, plot-level analysis of trait–environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions.

Multivariate anomaly detection for Earth observations : A comparison of algorithms and feature extraction techniques
Flach, Milan ; Gans, Fabian ; Brenning, Alexander ; Denzler, Joachim ; Reichstein, Markus ; Rodner, Erik ; Bathiany, Sebastian ; Bodesheim, Paul ; Guanche, Yanira ; Sippel, Sebastian ; Mahecha, Miguel D. - \ 2017
Earth System dynamics 8 (2017)3. - ISSN 2190-4979 - p. 677 - 696.

Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no "gold standard" for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations
Zscheischler, Jakob ; Mahecha, Miguel D. ; Avitabile, Valerio ; Calle, Leonardo ; Carvalhais, Nuno ; Ciais, Philippe ; Gans, Fabian ; Gruber, Nicolas ; Hartmann, Jens ; Herold, Martin ; Ichii, Kazuhito ; Jung, Martin ; Landschützer, Peter ; Laruelle, Goulven G. ; Lauerwald, Ronny ; Papale, Dario ; Peylin, Philippe ; Poulter, Benjamin ; Ray, Deepak K. ; Regnier, Pierre ; Rödenbeck, Christian ; Roman-Cuesta, Rosa M. ; Schwalm, Christopher ; Tramontana, Gianluca ; Tyukavina, Alexandra ; Valentini, Riccardo ; Werf, Guido R. van der; West, Tristram O. ; Wolf, Julie E. ; Reichstein, Markus - \ 2017
Biogeosciences 14 (2017)15. - ISSN 1726-4170 - p. 3685 - 3703.
Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface–atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface–atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean ± 1 SD: 0.8 ± 0.1 PgC yr−1, positive numbers are sources to the atmosphere), Russia (0.1 ± 0.4 PgC yr−1), East Asia (1.6 ± 0.3 PgC yr−1), South Asia (0.3 ± 0.1 PgC yr−1), Australia (0.2 ± 0.3 PgC yr−1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of −5.4 ± 2.0 PgC yr−1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001–2010 indicates that the true value of NCE is a net CO2 source of 4.3 ± 0.1 PgC yr−1. This mismatch of nearly 10 PgC yr−1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water–atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.
Runoff initiation, soil detachment and connectivity are enhanced as a consequence of vineyards plantations
Cerdà, Artemi ; Keesstra, S.D. ; Rodrigo Comino, Jesús ; Novara, A. ; Pereira, P. ; Brevik, E.C. ; Giménez-Morera, A. ; Fernández-Raga, M. ; Mahecha-Pulido, Juan D. ; Prima, Simone Di; Jordán, Antonio - \ 2017
Journal of Environmental Management 202 (2017)1. - ISSN 0301-4797 - p. 268 - 275.
Connectivity - Detachment - Erosion - Rainfall simulation - Sediments - Water
Rainfall-induced soil erosion is a major threat, especially in agricultural soils. In the Mediterranean belt, vineyards are affected by high soil loss rates, leading to land degradation. Plantation of new vines is carried out after deep ploughing, use of heavy machinery, wheel traffic, and trampling. Those works result in soil physical properties changes and contribute to enhanced runoff rates and increased soil erosion rates. The objective of this paper is to assess the impact of the plantation of vineyards on soil hydrological and erosional response under low frequency – high magnitude rainfall events, the ones that under the Mediterranean climatic conditions trigger extreme soil erosion rates. We determined time to ponding, Tp; time to runoff, Tr; time to runoff outlet, Tro; runoff rate, and soil loss under simulated rainfall (55 mm h−1, 1 h) at plot scale (0.25 m2) to characterize the runoff initiation and sediment detachment. In recent vine plantations (<1 year since plantation; R) compared to old ones (>50 years; O). Slope gradient, rock fragment cover, soil surface roughness, bulk density, soil organic matter content, soil water content and plant cover were determined. Plantation of new vineyards largely impacted runoff rates and soil erosion risk at plot scale in the short term. Tp, Tr and Tro were much shorter in R plots. Tr-Tp and Tro-Tr periods were used as connectivity indexes of water flow, and decreased to 77.5 and 33.2% in R plots compared to O plots. Runoff coefficients increased significantly from O (42.94%) to R plots (71.92%) and soil losses were approximately one order of magnitude lower (1.8 and 12.6 Mg ha−1 h−1 for O and R plots respectively). Soil surface roughness and bulk density are two key factors that determine the increase in connectivity of flows and sediments in recently planted vineyards. Our results confirm that plantation of new vineyards strongly contributes to runoff initiation and sediment detachment, and those findings confirms that soil erosion control strategies should be applied immediately after or during the plantation of vines.
Impact of potentially contaminated river water on agricultural irrigated soils in an equatorial climate
Trujillo-González, Juan Manuel ; Mahecha-Pulido, Juan D. ; Torres-Mora, Marco Aurelio ; Brevik, Eric C. ; Keesstra, Saskia D. ; Jiménez-Ballesta, Raimundo - \ 2017
Agriculture 7 (2017)7. - ISSN 2077-0472
Agricultural land use - Equatorial area - Trace elements - Wastewater irrigation

Globally, it is estimated that 20 million hectares of arable land are irrigated with water that contains residual contributions from domestic liquids. This potentially poses risks to public health and ecosystems, especially due to heavy metals, which are considered dangerous because of their potential toxicity and persistence in the environment. The Villavicencio region (Colombia) is an equatorial area where rainfall (near 3000 mm/year) and temperature (average 25.6 °C) are high. Soil processes in tropical conditions are fast and react quickly to changing conditions. Soil properties from agricultural fields irrigated with river water polluted by a variety of sources were analysed and compared to non-irrigated control soils. In this study, no physico-chemical alterations were found that gave evidence of a change due to the constant use of river water that contained wastes. This fact may be associated with the climatic factors (temperature and precipitation), which contribute to fast degradation of organic matter and nutrient and contaminants (such as heavy metals) leaching, or to dilution of wastes by the river.

Detecting Changes in Essential Ecosystem and Biodiversity Properties: The H2020 project BACI
Mahecha, Miguel D. ; Flach, Milan ; Reichstein, Markus ; Herold, M. ; Reiche, J. - \ 2016
Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits
Musavi, Talie ; Migliavacca, Mirco ; Weg, Martine Janet van de; Kattge, Jens ; Wohlfahrt, Georg ; Bodegom, Peter M. van; Reichstein, Markus ; Bahn, Michael ; Carrara, Arnaud ; Domingues, Tomas F. ; Gavazzi, Michael ; Gianelle, Damiano ; Gimeno, Cristina ; Granier, André ; Gruening, Carsten ; Havránková, Kateřina ; Herbst, Mathias ; Hrynkiw, Charmaine ; Kalhori, Aram ; Kaminski, Thomas ; Klumpp, Katja ; Kolari, Pasi ; Longdoz, Bernard ; Minerbi, Stefano ; Montagnani, Leonardo ; Moors, Eddy ; Oechel, Walter C. ; Reich, Peter B. ; Rohatyn, Shani ; Rossi, Alessandra ; Rotenberg, Eyal ; Varlagin, Andrej ; Wilkinson, Matthew ; Wirth, Christian ; Mahecha, Miguel D. - \ 2016
Ecology and Evolution 6 (2016)20. - ISSN 2045-7758 - p. 7352 - 7366.
FLUXNET - Ecosystem functional property - Eddy covariance - Interannual variability - Photosynthetic capacity - Plant traits - Spatiotemporal variability - TRY database

The aim of this study was to systematically analyze the potential and limitations of using plant functional trait observations from global databases versus in situ data to improve our understanding of vegetation impacts on ecosystem functional properties (EFPs). Using ecosystem photosynthetic capacity as an example, we first provide an objective approach to derive robust EFP estimates from gross primary productivity (GPP) obtained from eddy covariance flux measurements. Second, we investigate the impact of synchronizing EFPs and plant functional traits in time and space to evaluate their relationships, and the extent to which we can benefit from global plant trait databases to explain the variability of ecosystem photosynthetic capacity. Finally, we identify a set of plant functional traits controlling ecosystem photosynthetic capacity at selected sites. Suitable estimates of the ecosystem photosynthetic capacity can be derived from light response curve of GPP responding to radiation (photosynthetically active radiation or absorbed photosynthetically active radiation). Although the effect of climate is minimized in these calculations, the estimates indicate substantial interannual variation of the photosynthetic capacity, even after removing site-years with confounding factors like disturbance such as fire events. The relationships between foliar nitrogen concentration and ecosystem photosynthetic capacity are tighter when both of the measurements are synchronized in space and time. When using multiple plant traits simultaneously as predictors for ecosystem photosynthetic capacity variation, the combination of leaf carbon to nitrogen ratio with leaf phosphorus content explains the variance of ecosystem photosynthetic capacity best (adjusted R2 = 0.55). Overall, this study provides an objective approach to identify links between leaf level traits and canopy level processes and highlights the relevance of the dynamic nature of ecosystems. Synchronizing measurements of eddy covariance fluxes and plant traits in time and space is shown to be highly relevant to better understand the importance of intra- and interspecific trait variation on ecosystem functioning.

Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks
Papale, Dario ; Black, T.A. ; Carvalhais, Nuno ; Cescatti, Alessandro ; Chen, Jiquan ; Jung, Martin ; Kiely, Gerard ; Lasslop, Gitta ; Mahecha, Miguel D. ; Margolis, Hank ; Merbold, Lutz ; Montagnani, Leonardo ; Moors, Eddy ; Olesen, J.E. ; Reichstein, Markus ; Tramontana, Gianluca ; Gorsel, Eva Van; Wohlfahrt, Georg ; Ráduly, Botond - \ 2015
Journal of Geophysical Research: Biogeosciences 120 (2015)10. - ISSN 2169-8953 - p. 1941 - 1957.
artificial neural networks - gross primary production - latent heat - representativeness - uncertainty - upscaling

Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input-output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m-2 d-1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7-1.41 gC m-2 d-1), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8-2.09 gC m-2 d-1). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty. Key Points Uncertainty due to spatial sampling is evaluated using ANNs and FLUXNET data GPP and LE budgets and IAV are analyzed with different site networks The uncertainty in upscaling due to spatial sampling is highly heterogeneous

A bHLH-Based Feedback Loop Restricts Vascular Cell Proliferation in Plants
Vera-Sirera, Francisco ; Rybel, B.P.M. de; Urbez, Cristina ; Kouklas, Evangelos ; Pesquera, Marta ; Alvarez-Mahecha, Juan Camilo ; Minguet, Eugenio ; Tuominen, Hanneke ; Carbonell, Juan ; Borst, J.W. ; Weijers, D. ; Blazquez, Miguel - \ 2015
Developmental Cell 35 (2015)4. - ISSN 1534-5807 - p. 432 - 443.
Control of tissue dimensions in multicellular organisms requires the precise quantitative regulation of mitotic activity. In plants, where cells are immobile,
tissue size is achieved through control of both cell division orientation and mitotic rate. The bHLH transcription factor heterodimer formed by TARGET OF
MONOPTEROS5 (TMO5) and LONESOME HIGHWAY (LHW) is a central regulator of vascular widthincreasing divisions. An important unanswered question
is how its activity is limited to specify vascular tissue dimensions. Here we identify a regulatory network that restricts TMO5/LHW activity. We show
that thermospermine synthase ACAULIS5 antagonizes TMO5/LHW activity by promoting the accumulation of SAC51-LIKE (SACL) bHLH transcription
factors. SACL proteins heterodimerize with LHW—therefore likely competing with TMO5/LHW interactions—prevent activation ofTMO5/LHWtarget genes,
and suppress the over-proliferation caused by excess TMO5/LHWactivity. These findings connect two thusfar disparate pathways and provide a mechanistic
understanding of the quantitative control of vascular tissue growth.
Trend change detection in NDVI time series: Effects of inter-annual variability and methodology
Forkel, M. ; Carvalhais, N. ; Verbesselt, J. ; Mahecha, M.D. ; Neigh, C. ; Reichstein, M. - \ 2013
Remote Sensing 5 (2013)5. - ISSN 2072-4292 - p. 2113 - 2144.
spectral vegetation indexes - satellite data - north-america - boreal forest - el-nino - alaska - modis - climate - disturbance - accuracy
Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite datase
Hot spots, hot moments and time-span of changes in drivers and their responses on carbon cycling in Europe
Tomelleri, E. ; Forkel, M. ; Fuchs, R. ; Jung, M. ; Mahecha, M.D. ; Reichstein, M. ; Weber, U. - \ 2012
State-dependent errors in a land surface model across biomes inferred from eddy covariance observations on multiple timescales
Wang, T. ; Brender, P. ; Ciais, P. ; Piao, S. ; Mahecha, M.D. ; Chevallier, F. ; Reichstein, M. ; Ottle, C. ; Maignan, F. ; Arain, A. ; Bohrer, G. ; Cescatti, A. ; Kiely, G. ; Law, B.E. ; Lutz, M. ; Montagnani, L. ; Moors, E.J. - \ 2012
Ecological Modelling 246 (2012). - ISSN 0304-3800 - p. 11 - 25.
net ecosystem exchange - artificial neural-networks - carbon-dioxide exchange - atmosphere co2 exchange - energy-balance closure - northern temperate grassland - boreal forest stands - water-vapor exchange - interannual variability - soil respiration
Characterization of state-dependent model biases in land surface models can highlight model deficiencies, and provide new insights into model development. In this study, artificial neural networks (ANNs) are used to estimate the state-dependent biases of a land surface model (ORCHIDEE: ORganising Carbon and Hydrology in Dynamic EcosystEms). To characterize state-dependent biases in ORCHIDEE, we use multi-year flux measurements made at 125 eddy covariance sites that cover 7 different plant functional types (PFTs) and 5 climate groups. We determine whether the state-dependent model biases in five flux variables (H: sensible heat, LE: latent heat, NEE: net ecosystem exchange, GPP: gross primary productivity and Reco: ecosystem respiration) are transferable within and between three different timescales (diurnal, seasonal–annual and interannual), and between sites (categorized by PFTs and climate groups). For each flux variable at each site, the spectral decomposition method (singular system analysis) was used to reconstruct time series on the three different timescales. At the site level, we found that the share of state-dependent model biases (hereafter called “error transferability”) is larger for seasonal–annual and interannual timescales than for the diurnal timescale, but little error transferability was found between timescales in all flux variables. Thus, performing model evaluations at multiple timescales is essential for diagnostics and future development. For all PFTs, climate groups and timescale components, the state-dependent model biases are found to be transferable between sites within the same PFT and climate group, suggesting that specific model developments and improvements based on specific eddy covariance sites can be used to enhance the model performance at other sites within the same PFT-climate group. This also supports the legitimacy of upscaling from the ecosystem scale of eddy covariance sites to the regional scale based on the similarity of PFT and climate group. However, the transferability of state-dependent model biases between PFTs or climate groups is not always found on the seasonal–annual and interannual timescales, which is contrary to transferability found on the diurnal timescale and the original time series.
TRY - a global database of plant traits
Kattge, J. ; Diaz, S. ; Lavorel, S. ; Prentices, I.C. ; Leadley, P. ; Bönisch, G. ; Garnier, E. ; Westobys, M. ; Reich, P.B. ; Wrights, I.J. ; Cornelissen, C. ; Violle, C. ; Harisson, S.P. ; Bodegom, P.M. van; Reichstein, M. ; Enquist, B.J. ; Soudzilovskaia, N.A. ; Ackerly, D.D. ; Anand, M. ; Atkin, O. ; Bahn, M. ; Baker, T.R. ; Baldochi, D. ; Bekker, R. ; Blanco, C.C. ; Blonders, B. ; Bond, W.J. ; Bradstock, R. ; Bunker, D.E. ; Casanoves, F. ; Cavender-Bares, J. ; Chambers, J.Q. ; Chapin III, F.S. ; Chave, J. ; Coomes, D. ; Cornwell, W.K. ; Craine, J.M. ; Dobrin, B.H. ; Duarte, L. ; Durka, W. ; Elser, J. ; Esser, G. ; Estiarte, M. ; Fagan, W.F. ; Fang, J. ; Fernadez-Mendez, F. ; Fidelis, A. ; Finegan, B. ; Flores, O. ; Ford, H. ; Frank, D. ; Freschet, T. ; Fyllas, N.M. ; Gallagher, R.V. ; Green, W.A. ; Gutierrez, A.G. ; Hickler, T. ; Higgins, S.I. ; Hodgson, J.G. ; Jalili, A. ; Jansen, S. ; Joly, C.A. ; Kerkhoff, A.J. ; Kirkup, D. ; Kitajima, K. ; Kleyer, M. ; Klotz, S. ; Knops, J.M.H. ; Kramer, K. ; Kühn, I. ; Kurokawa, H. ; Laughlin, D. ; Lee, T.D. ; Leishman, M. ; Lens, F. ; Lewis, S.L. ; Lloyd, J. ; Llusia, J. ; Louault, F. ; Ma, S. ; Mahecha, M.D. ; Manning, P. ; Massad, T. ; Medlyn, B.E. ; Messier, J. ; Moles, A.T. ; Müller, S.C. ; Nadrowski, K. ; Naeem, S. ; Niinemets, Ü. ; Nöllert, S. ; Nüske, A. ; Ogaya, R. ; Oleksyn, J. ; Onipchenko, V.G. ; Onoda, Y. ; Ordonez Barragan, J.C. ; Ozinga, W.A. ; Poorter, L. - \ 2011
Global Change Biology 17 (2011)9. - ISSN 1354-1013 - p. 2905 - 2935.
relative growth-rate - tropical rain-forest - hawaiian metrosideros-polymorpha - litter decomposition rates - leaf economics spectrum - old-field succession - sub-arctic flora - functional traits - wide-range - terrestrial biosphere
Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.
Comparing observations and process-based simulationsof biosphere-atmosphere exchanges on multiple timescales
Moors, E.J. ; Mahecha, M.D. ; Reichstein, M. ; Jung, M. ; Seneviratne, S.I. ; Zaehle, S. ; Beer, C. ; Braakhekke, M.C. ; Carvalhais, N. ; Lange, H. ; Maire, G. Le - \ 2010
Journal of Geophysical Research: Biogeosciences 115 (2010). - ISSN 2169-8953 - 21 p.
net ecosystem exchange - interannual time scales - global vegetation model - energy-balance closure - eddy covariance - stomatal conductance - climate-change - water-vapor - pine forest - long-term
Terrestrial biosphere models are indispensable tools for analyzing the biosphere-atmosphere exchange of carbon and water. Evaluation of these models using site level observations scrutinizes our current understanding of biospheric responses to meteorological variables. Here we propose a novel model-data comparison strategy considering that CO2 and H2O exchanges fluctuate on a wide range of timescales. Decomposing simulated and observed time series into subsignals allows to quantify model performance as a function of frequency, and to localize model-data disagreement in time. This approach is illustrated using site level predictions from two models of different complexity, Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and Lund-Potsdam-Jena (LPJ), at four eddy covariance towers in different climates. Frequency-dependent errors reveal substantial model-data disagreement in seasonal-annual and high-frequency net CO2 fluxes. By localizing these errors in time we can trace these back, for example, to overestimations of seasonal-annual periodicities of ecosystem respiration during spring greenup and autumn in both models. In the same frequencies, systematic misrepresentations of CO2 uptake severely affect the performance of LPJ, which is a consequence of the parsimonious representation of phenology. ORCHIDEE shows pronounced model-data disagreements in the high-frequency fluctuations of evapotranspiration across the four sites. We highlight the advantages that our novel methodology offers for a rigorous model evaluation compared to classical model evaluation approaches. We propose that ongoing model development will benefit from considering model-data (dis)agreements in the time-frequency domain
Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals
Richardson, A.D. ; Mahecha, M.D. ; Falge, E. ; Kattge, J. ; Moffat, A.M. ; Papale, D. ; Reichstein, M. ; Stauch, V.J. ; Braswell, B.H. ; Churkina, G. ; Kruijt, B. ; Hollinger, D.Y. - \ 2008
Agricultural and Forest Meteorology 148 (2008)1. - ISSN 0168-1923 - p. 38 - 50.
net ecosystem exchange - eddy-covariance measurements - carbon-dioxide exchange - long-term measurements - time-series data - spatial variability - spectral-analysis - soil respiration - turbulent fluxes - surface fluxes
Information about the uncertainties associated with eddy covariance measurements of surface-atmosphere CO2 exchange is needed for data assimilation and inverse analyses to estimate model parameters, validation of ecosystem models against flux data, as well as multi-site synthesis activities (e.g., regional to continental integration) and policy decision-making. While model residuals (mismatch between fitted model predictions and measured fluxes) can potentially be analyzed to infer data uncertainties, the resulting uncertainty estimates may be sensitive to the particular model chosen. Here we use 10 site-years of data from the CarboEurope program, and compare the statistical properties of the inferred random flux measurement error calculated first using residuals from five different models, and secondly using paired observations made under similar environmental conditions. Spectral analysis of the model predictions indicated greater persistence (i.e., autocorrelation or memory) compared to the measured values. Model residuals exhibited weaker temporal correlation, but were not uncorrelated white noise. Random flux measurement uncertainty, expressed as a standard deviation, was found to vary predictably in relation to the expected magnitude of the flux, in a manner that was nearly identical (for negative, but not positive, fluxes) to that reported previously for forested sites. Uncertainty estimates were generally comparable whether the uncertainty was inferred from model residuals or paired observations, although the latter approach resulted in somewhat smaller estimates. Higher order moments (e.g., skewness and kurtosis) suggested that for fluxes close to zero, the measurement error is commonly skewed and leptokurtic. Skewness could not be evaluated using the paired observation approach, because differencing of paired measurements resulted in a symmetric distribution of the inferred error. Patterns were robust and not especially sensitive to the model used, although more flexible models, which did not impose a particular functional form on relationships between environmental drivers and modeled fluxes, appeared to give the best results. We conclude that evaluation of flux measurement errors from model residuals is a viable alternative to the standard paired observation approach.
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