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111 results on '"Mahecha, M."'

1. Biodiversity and Climate Extremes: Known Interactions and Research Gaps

Author

Mahecha, M. D., primary, Bastos, A., additional, Bohn, F. J., additional, Eisenhauer, N., additional, Feilhauer, H., additional, Hickler, T., additional, Kalesse‐Los, H., additional, Migliavacca, M., additional, Otto, F. E. L., additional, Peng, J., additional, Sippel, S., additional, Tegen, I., additional, Weigelt, A., additional, Wendisch, M., additional, Wirth, C., additional, Al‐Halbouni, D., additional, Deneke, H., additional, Doktor, D., additional, Dunker, S., additional, Duveiller, G., additional, Ehrlich, A., additional, Foth, A., additional, García‐García, A., additional, Guerra, C. A., additional, Guimarães‐Steinicke, C., additional, Hartmann, H., additional, Henning, S., additional, Herrmann, H., additional, Hu, P., additional, Ji, C., additional, Kattenborn, T., additional, Kolleck, N., additional, Kretschmer, M., additional, Kühn, I., additional, Luttkus, M. L., additional, Maahn, M., additional, Mönks, M., additional, Mora, K., additional, Pöhlker, M., additional, Reichstein, M., additional, Rüger, N., additional, Sánchez‐Parra, B., additional, Schäfer, M., additional, Stratmann, F., additional, Tesche, M., additional, Wehner, B., additional, Wieneke, S., additional, Winkler, A. J., additional, Wolf, S., additional, Zaehle, S., additional, Zscheischler, J., additional, and Quaas, J., additional

Published
2024
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2. A belowground perspective on the nexus between biodiversity change, climate change, and human well-being

Author

Eisenhauer, N., Frank, Karin, Weigelt, A., Bartkowski, Bartosz, Beugnon, R., Liebal, K., Mahecha, M., Quaas, M., Al-Halbouni, D., Bastos, A., Bohn, Friedrich, de Brito, Mariana Madruga, Denzler, J., Feilhauer, H., Fischer, R., Fritsche, I., Guimaraes-Steinicke, C., Hänsel, M., Haun, D.B.M., Herrmann, H., Huth, Andreas, Kalesse-Los, H., Koetter, M., Kolleck, N., Krause, M., Kretschmer, M., Leitão, P.J., Masson, T., Mora, K., Müller, Birgit, Peng, Jian, Pöhlker, M.L., Ratzke, L., Reichstein, M., Richter, S., Rüger, N., Sánchez-Parra, B., Shadaydeh, M., Sippel, S., Tegen, I., Thrän, Daniela, Umlauft, J., Wendisch, M., Wolf, K., Wirth, C., Zacher, H., Zaehle, S., Quaas, J., Eisenhauer, N., Frank, Karin, Weigelt, A., Bartkowski, Bartosz, Beugnon, R., Liebal, K., Mahecha, M., Quaas, M., Al-Halbouni, D., Bastos, A., Bohn, Friedrich, de Brito, Mariana Madruga, Denzler, J., Feilhauer, H., Fischer, R., Fritsche, I., Guimaraes-Steinicke, C., Hänsel, M., Haun, D.B.M., Herrmann, H., Huth, Andreas, Kalesse-Los, H., Koetter, M., Kolleck, N., Krause, M., Kretschmer, M., Leitão, P.J., Masson, T., Mora, K., Müller, Birgit, Peng, Jian, Pöhlker, M.L., Ratzke, L., Reichstein, M., Richter, S., Rüger, N., Sánchez-Parra, B., Shadaydeh, M., Sippel, S., Tegen, I., Thrän, Daniela, Umlauft, J., Wendisch, M., Wolf, K., Wirth, C., Zacher, H., Zaehle, S., and Quaas, J.

Abstract

Soil is central to the complex interplay among biodiversity, climate, and society. This paper examines the interconnectedness of soil biodiversity, climate change, and societal impacts, emphasizing the urgent need for integrated solutions. Human-induced biodiversity loss and climate change intensify environmental degradation, threatening human well-being. Soils, rich in biodiversity and vital for ecosystem function regulation, are highly vulnerable to these pressures, affecting nutrient cycling, soil fertility, and resilience. Soil also crucially regulates climate, influencing energy, water cycles, and carbon storage. Yet, climate change poses significant challenges to soil health and carbon dynamics, amplifying global warming. Integrated approaches are essential, including sustainable land management, policy interventions, technological innovations, and societal engagement. Practices like agroforestry and organic farming improve soil health and mitigate climate impacts. Effective policies and governance are crucial for promoting sustainable practices and soil conservation. Recent technologies aid in monitoring soil biodiversity and implementing sustainable land management. Societal engagement, through education and collective action, is vital for environmental stewardship. By prioritizing interdisciplinary research and addressing key frontiers, scientists can advance understanding of the soil biodiversity–climate change–society nexus, informing strategies for environmental sustainability and social equity.

Published
2024

3. A framework for benchmarking land models

Author

Luo, Y. Q, Randerson, J. T, Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., Ciais, P., Dalmonech, D., Fisher, J. B, Fisher, R., Friedlingstein, P., Hibbard, K., Hoffman, F., Huntzinger, D., Jones, C. D, Koven, C., Lawrence, D., Li, D. J, Mahecha, M., Niu, S. L, Norby, R., Piao, S. L, Qi, X., Peylin, P., Prentice, I. C, Riley, W., Reichstein, M., Schwalm, C., Wang, Y. P, Xia, J. Y, Zaehle, S., and Zhou, X. H

Subjects
atmosphere-biosphere interaction, benchmarking, biogeochemistry, biophysics, climate change, climate feedback, ecosystem modeling, ecosystem response, numerical model, prediction, spatiotemporal analysis, trace gas, vegetation dynamics
Abstract

Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

Published
2012

6. The three major axes of terrestrial ecosystem function

Author

Migliavacca, M, Musavi, T, Mahecha, M, Nelson, J, Knauer, J, Baldocchi, D, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, T, Blanken, P, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, A, El-Madany, T, Farella, M, Fernandez-Martinez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, C, Gockede, M, Ibrom, A, Ikawa, H, Janssens, I, Jung, M, Kattge, J, Keenan, T, Knohl, A, Kobayashi, H, Kraemer, G, Law, B, Liddell, M, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, D, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, R, Reich, P, Rossini, M, Rotenberg, E, Scott, R, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, I, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca M., Musavi T., Mahecha M. D., Nelson J. A., Knauer J., Baldocchi D. D., Perez-Priego O., Christiansen R., Peters J., Anderson K., Bahn M., Black T. A., Blanken P. D., Bonal D., Buchmann N., Caldararu S., Carrara A., Carvalhais N., Cescatti A., Chen J., Cleverly J., Cremonese E., Desai A. R., El-Madany T. S., Farella M. M., Fernandez-Martinez M., Filippa G., Forkel M., Galvagno M., Gomarasca U., Gough C. M., Gockede M., Ibrom A., Ikawa H., Janssens I. A., Jung M., Kattge J., Keenan T. F., Knohl A., Kobayashi H., Kraemer G., Law B. E., Liddell M. J., Ma X., Mammarella I., Martini D., Macfarlane C., Matteucci G., Montagnani L., Pabon-Moreno D. E., Panigada C., Papale D., Pendall E., Penuelas J., Phillips R. P., Reich P. B., Rossini M., Rotenberg E., Scott R. L., Stahl C., Weber U., Wohlfahrt G., Wolf S., Wright I. J., Yakir D., Zaehle S., Reichstein M., Migliavacca, M, Musavi, T, Mahecha, M, Nelson, J, Knauer, J, Baldocchi, D, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, T, Blanken, P, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, A, El-Madany, T, Farella, M, Fernandez-Martinez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, C, Gockede, M, Ibrom, A, Ikawa, H, Janssens, I, Jung, M, Kattge, J, Keenan, T, Knohl, A, Kobayashi, H, Kraemer, G, Law, B, Liddell, M, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, D, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, R, Reich, P, Rossini, M, Rotenberg, E, Scott, R, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, I, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca M., Musavi T., Mahecha M. D., Nelson J. A., Knauer J., Baldocchi D. D., Perez-Priego O., Christiansen R., Peters J., Anderson K., Bahn M., Black T. A., Blanken P. D., Bonal D., Buchmann N., Caldararu S., Carrara A., Carvalhais N., Cescatti A., Chen J., Cleverly J., Cremonese E., Desai A. R., El-Madany T. S., Farella M. M., Fernandez-Martinez M., Filippa G., Forkel M., Galvagno M., Gomarasca U., Gough C. M., Gockede M., Ibrom A., Ikawa H., Janssens I. A., Jung M., Kattge J., Keenan T. F., Knohl A., Kobayashi H., Kraemer G., Law B. E., Liddell M. J., Ma X., Mammarella I., Martini D., Macfarlane C., Matteucci G., Montagnani L., Pabon-Moreno D. E., Panigada C., Papale D., Pendall E., Penuelas J., Phillips R. P., Reich P. B., Rossini M., Rotenberg E., Scott R. L., Stahl C., Weber U., Wohlfahrt G., Wolf S., Wright I. J., Yakir D., Zaehle S., and Reichstein M.

Abstract

The leaf economics spectrum 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 species. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities. 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. Here we derive a set of ecosystem functions 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.

Published
2021

7. Performance of Singular Spectrum Analysis in Separating Seasonal and Fast Physiological Dynamics of Solar-Induced Chlorophyll Fluorescence and PRI Optical Signals

Author

Biriukova, K, Pacheco-Labrador, J, Migliavacca, M, Mahecha, M, Gonzalez-Cascon, R, Martin, M, Rossini, M, Biriukova K., Pacheco-Labrador J., Migliavacca M., Mahecha M. D., Gonzalez-Cascon R., Martin M. P., Rossini M., Biriukova, K, Pacheco-Labrador, J, Migliavacca, M, Mahecha, M, Gonzalez-Cascon, R, Martin, M, Rossini, M, Biriukova K., Pacheco-Labrador J., Migliavacca M., Mahecha M. D., Gonzalez-Cascon R., Martin M. P., and Rossini M.

Abstract

High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) and slow components (controlled by structural and biochemical properties) from PRI, far-red F (F760), and far-red apparent fluorescence yield (Fy∗760). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA-decomposed components of PRI and Fy∗760 showed high correlations with the reference variables (R2 = 0.97 and 0.96, respectively). Fast SSA-decomposed components of PRI and Fy∗760 were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m2 m−2. The method was also successfully applied to predict light-use efficiency (LUE) from the fast SSA-decomposed components of PRI (R2 = 0.70) and Fy∗760 (R2 = 0.68) when discarding data modeled with LAI < 1 m2 m−2 and short-wave radiation Rin < 250 W m−2. The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA-decomposed component of apparent LUE∗ showed a stronger correlation with the fast SSA-decomposed component of Fy∗760 (R2 = 0.42) than with original Fy∗760 (R2 = 0.01). SSA-based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems.

Published
2021

9. Briefing note on systemic risk

Author

Sillmann, J., Christensen, I., Hochrainer-Stigler, S., Huang-Lachmann, J., Juhola, S., Kornhuber, K., Mahecha, M., Mechler, R., Reichstein, M., Ruane, A., Schweizer, P., and Williams, S.

Published
2022

10. ISC-UNDRR-RISK KAN Briefing note on systemic risk

Author

Sillmann, J., Christensen, I., Hochrainer-Stigler, S., Huang-Lachmann, J., Juhola, S., Kornhuber, K., Mahecha, M., Mechler, R., Reichstein, M., Ruane, A.C., Schweizer, P.-J., Wiliiams, S., Sillmann, J., Christensen, I., Hochrainer-Stigler, S., Huang-Lachmann, J., Juhola, S., Kornhuber, K., Mahecha, M., Mechler, R., Reichstein, M., Ruane, A.C., Schweizer, P.-J., and Wiliiams, S.

Abstract

Systemic risk is associated with cascading impacts that spread within and across systems and sectors (e.g. ecosystems, health, infrastructure and the food sector) via the movements of people, goods, capital and information within and across boundaries (e.g. regions, countries and continents). The spread of these impacts can lead to potentially existential consequences and system collapse across a range of time horizons. Globalization contributes to systemic risk affecting people worldwide. The impacts of climate change or COVID-19 show how the challenges of addressing systemic risk go beyond conventional risk management and governance. Critical system interdependencies, amplified by underlying vulnerabilities, highlight that there is a growing need to better understand cascading impacts, systemic risks and the possible political (governance) and societal responses. This includes improving our understanding of the root causes of systemic risk, both biophysical and socio-economic, and related information needs. Addressing contemporary challenges in terms of systemic risk requires integrating different systems perspectives and fostering system thinking, while implementing key intergovernmental agendas, such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals. This Briefing Note represents an integrated perspective of climate, environmental and disaster risk science and practice regarding systemic risk. It provides an overview of the concepts of systemic risk that have evolved over time and identifies commonalities across terminologies and perspectives associated with systemic risk used in different contexts. Key attributes of systemic risk are outlined without prescribing a single definition, and information and data requirements that are essential for a better and more actionable understanding of the systemic nature of risk are discussed. Finally, the opportunities to connect research and policy for addressing

Published
2022

11. Briefing Note on Systemic Risk : Opportunities for research, policy and practice from the perspective of climate, environmental and disaster risk science and management

Author

Sillmann, J., Christensen, I., Hochrainer-Stigler, S., Huang-Lachmann, J., Juhola, S., Kornhuber, K., Mahecha, M., Mechler, R., Reichstein, M., Ruane, A., Schweizer, P., and Williams, S.

Abstract

Systemic risk is associated with cascading impacts that spread within and across systems and sectors (e.g. ecosystems, health, infrastructure and the food sector) via the movements of people, goods, capital and information within and across boundaries (e.g. regions, countries and continents). The spread of these impacts can lead to potentially existential consequences and system collapse across a range of time horizons. This Briefing Note represents an integrated perspective of climate, environmental and disaster risk science and practice regarding systemic risk. It provides an overview of the concepts of systemic risk that have evolved over time and identifies commonalities across terminologies and perspectives associated with systemic risk used in different contexts. Key attributes of systemic risk are outlined without prescribing a single definition, and information and data requirements that are essential for a better and more actionable understanding of the systemic nature of risk are discussed. Finally, the opportunities to connect research and policy for addressing systemic risk are highlighted, followed by recommendations for future work in science, policy and practice on systemic risk.

Published
2022
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14. sPlotOpen:an environmentally balanced, open-access, global dataset of vegetation plots

Author

Sabatini, F. M. (Francesco Maria), Lenoir, J. (Jonathan), Hattab, T. (Tarek), Arnst, E. A. (Elise Aimee), Chytry, M. (Milan), Dengler, J. (Juergen), De Ruffray, P. (Patrice), Hennekens, S. M. (Stephan M.), Jandt, U. (Ute), Jansen, F. (Florian), Jimenez-Alfaro, B. (Borja), Kattge, J. (Jens), Levesley, A. (Aurora), Pillar, V. D. (Valerio D.), Purschke, O. (Oliver), Sandel, B. (Brody), Sultana, F. (Fahmida), Aavik, T. (Tsipe), Acic, S. (Svetlana), Acosta, A. T. (Alicia T. R.), Agrillo, E. (Emiliano), Alvarez, M. (Miguel), Apostolova, I. (Iva), Arfin Khan, M. A. (Mohammed A. S.), Arroyo, L. (Luzmila), Attorre, F. (Fabio), Aubin, I. (Isabelle), Banerjee, A. (Arindam), Bauters, M. (Marijn), Bergeron, Y. (Yves), Bergmeier, E. (Erwin), Biurrun, I. (Idoia), Bjorkman, A. D. (Anne D.), Bonari, G. (Gianmaria), Bondareva, V. (Viktoria), Brunet, J. (Jorg), Carni, A. (Andraz), Casella, L. (Laura), Cayuela, L. (Luis), Cerny, T. (Tomas), Chepinoga, V. (Victor), Csiky, J. (Janos), Custerevska, R. (Renata), De Bie, E. (Els), de Gasper, A. L. (Andre Luis), De Sanctis, M. (Michele), Dimopoulos, P. (Panayotis), Dolezal, J. (Jiri), Dziuba, T. (Tetiana), El-Sheikh, M. A. (Mohamed Abd El-Rouf Mousa), Enquist, B. (Brian), Ewald, J. (Joerg), Fazayeli, F. (Farideh), Field, R. (Richard), Finckh, M. (Manfred), Gachet, S. (Sophie), Galan-de-Mera, A. (Antonio), Garbolino, E. (Emmanuel), Gholizadeh, H. (Hamid), Giorgis, M. (Melisa), Golub, V. (Valentin), Alsos, I. G. (Inger Greve), Grytnes, J.-A. (John-Arvid), Guerin, G. R. (Gregory Richard), Gutierrez, A. G. (Alvaro G.), Haider, S. (Sylvia), Hatim, M. Z. (Mohamed Z.), Herault, B. (Bruno), Hinojos Mendoza, G. (Guillermo), Hoelzel, N. (Norbert), Homeier, J. (Juergen), Hubau, W. (Wannes), Indreica, A. (Adrian), Janssen, J. A. (John A. M.), Jedrzejek, B. (Birgit), Jentsch, A. (Anke), Juergens, N. (Norbert), Kacki, Z. (Zygmunt), Kapfer, J. (Jutta), Karger, D. N. (Dirk Nikolaus), Kavgaci, A. (Ali), Kearsley, E. (Elizabeth), Kessler, M. (Michael), Khanina, L. (Larisa), Killeen, T. (Timothy), Korolyuk, A. (Andrey), Kreft, H. (Holger), Kuehl, H. S. (Hjalmar S.), Kuzemko, A. (Anna), Landucci, F. (Flavia), Lengyel, A. (Attila), Lens, F. (Frederic), Lingner, D. V. (Debora Vanessa), Liu, H. (Hongyan), Lysenko, T. (Tatiana), Mahecha, M. D. (Miguel D.), Marceno, C. (Corrado), Martynenko, V. (Vasiliy), Moeslund, J. E. (Jesper Erenskjold), Monteagudo Mendoza, A. (Abel), Mucina, L. (Ladislav), Muller, J. V. (Jonas V.), Munzinger, J. (Jerome), Naqinezhad, A. (Alireza), Noroozi, J. (Jalil), Nowak, A. (Arkadiusz), Onyshchenko, V. (Viktor), Overbeck, G. E. (Gerhard E.), Partel, M. (Meelis), Pauchard, A. (Anibal), Peet, R. K. (Robert K.), Penuelas, J. (Josep), Perez-Haase, A. (Aaron), Peterka, T. (Tomas), Petrik, P. (Petr), Peyre, G. (Gwendolyn), Phillips, O. L. (Oliver L.), Prokhorov, V. (Vadim), Rasomavicius, V. (Valerijus), Revermann, R. (Rasmus), Rivas-Torres, G. (Gonzalo), Rodwell, J. S. (John S.), Ruprecht, E. (Eszter), Rusina, S. (Solvita), Samimi, C. (Cyrus), Schmidt, M. (Marco), Schrodt, F. (Franziska), Shan, H. (Hanhuai), Shirokikh, P. (Pavel), Sibik, J. (Jozef), Silc, U. (Urban), Sklenar, P. (Petr), Skvorc, Z. (Zeljko), Sparrow, B. (Ben), Sperandii, M. G. (Marta Gaia), Stancic, Z. (Zvjezdana), Svenning, J.-C. (Jens-Christian), Tang, Z. (Zhiyao), Tang, C. Q. (Cindy Q.), Tsiripidis, I. (Ioannis), Vanselow, K. A. (Kim Andre), Vasquez Martinez, R. (Rodolfo), Vassilev, K. (Kiril), Velez-Martin, E. (Eduardo), Venanzoni, R. (Roberto), Vibrans, A. C. (Alexander Christian), Violle, C. (Cyrille), Virtanen, R. (Risto), von Wehrden, H. (Henrik), Wagner, V. (Viktoria), Walker, D. A. (Donald A.), Waller, D. M. (Donald M.), Wang, H.-F. (Hua-Feng), Wesche, K. (Karsten), Whitfeld, T. J. (Timothy J. S.), Willner, W. (Wolfgang), Wiser, S. K. (Susan K.), Wohlgemuth, T. (Thomas), Yamalov, S. (Sergey), Zobel, M. (Martin), Bruelheide, H. (Helge), Sabatini, F. M. (Francesco Maria), Lenoir, J. (Jonathan), Hattab, T. (Tarek), Arnst, E. A. (Elise Aimee), Chytry, M. (Milan), Dengler, J. (Juergen), De Ruffray, P. (Patrice), Hennekens, S. M. (Stephan M.), Jandt, U. (Ute), Jansen, F. (Florian), Jimenez-Alfaro, B. (Borja), Kattge, J. (Jens), Levesley, A. (Aurora), Pillar, V. D. (Valerio D.), Purschke, O. (Oliver), Sandel, B. (Brody), Sultana, F. (Fahmida), Aavik, T. (Tsipe), Acic, S. (Svetlana), Acosta, A. T. (Alicia T. R.), Agrillo, E. (Emiliano), Alvarez, M. (Miguel), Apostolova, I. (Iva), Arfin Khan, M. A. (Mohammed A. S.), Arroyo, L. (Luzmila), Attorre, F. (Fabio), Aubin, I. (Isabelle), Banerjee, A. (Arindam), Bauters, M. (Marijn), Bergeron, Y. (Yves), Bergmeier, E. (Erwin), Biurrun, I. (Idoia), Bjorkman, A. D. (Anne D.), Bonari, G. (Gianmaria), Bondareva, V. (Viktoria), Brunet, J. (Jorg), Carni, A. (Andraz), Casella, L. (Laura), Cayuela, L. (Luis), Cerny, T. (Tomas), Chepinoga, V. (Victor), Csiky, J. (Janos), Custerevska, R. (Renata), De Bie, E. (Els), de Gasper, A. L. (Andre Luis), De Sanctis, M. (Michele), Dimopoulos, P. (Panayotis), Dolezal, J. (Jiri), Dziuba, T. (Tetiana), El-Sheikh, M. A. (Mohamed Abd El-Rouf Mousa), Enquist, B. (Brian), Ewald, J. (Joerg), Fazayeli, F. (Farideh), Field, R. (Richard), Finckh, M. (Manfred), Gachet, S. (Sophie), Galan-de-Mera, A. (Antonio), Garbolino, E. (Emmanuel), Gholizadeh, H. (Hamid), Giorgis, M. (Melisa), Golub, V. (Valentin), Alsos, I. G. (Inger Greve), Grytnes, J.-A. (John-Arvid), Guerin, G. R. (Gregory Richard), Gutierrez, A. G. (Alvaro G.), Haider, S. (Sylvia), Hatim, M. Z. (Mohamed Z.), Herault, B. (Bruno), Hinojos Mendoza, G. (Guillermo), Hoelzel, N. (Norbert), Homeier, J. (Juergen), Hubau, W. (Wannes), Indreica, A. (Adrian), Janssen, J. A. (John A. M.), Jedrzejek, B. (Birgit), Jentsch, A. (Anke), Juergens, N. (Norbert), Kacki, Z. (Zygmunt), Kapfer, J. (Jutta), Karger, D. N. (Dirk Nikolaus), Kavgaci, A. (Ali), Kearsley, E. (Elizabeth), Kessler, M. (Michael), Khanina, L. (Larisa), Killeen, T. (Timothy), Korolyuk, A. (Andrey), Kreft, H. (Holger), Kuehl, H. S. (Hjalmar S.), Kuzemko, A. (Anna), Landucci, F. (Flavia), Lengyel, A. (Attila), Lens, F. (Frederic), Lingner, D. V. (Debora Vanessa), Liu, H. (Hongyan), Lysenko, T. (Tatiana), Mahecha, M. D. (Miguel D.), Marceno, C. (Corrado), Martynenko, V. (Vasiliy), Moeslund, J. E. (Jesper Erenskjold), Monteagudo Mendoza, A. (Abel), Mucina, L. (Ladislav), Muller, J. V. (Jonas V.), Munzinger, J. (Jerome), Naqinezhad, A. (Alireza), Noroozi, J. (Jalil), Nowak, A. (Arkadiusz), Onyshchenko, V. (Viktor), Overbeck, G. E. (Gerhard E.), Partel, M. (Meelis), Pauchard, A. (Anibal), Peet, R. K. (Robert K.), Penuelas, J. (Josep), Perez-Haase, A. (Aaron), Peterka, T. (Tomas), Petrik, P. (Petr), Peyre, G. (Gwendolyn), Phillips, O. L. (Oliver L.), Prokhorov, V. (Vadim), Rasomavicius, V. (Valerijus), Revermann, R. (Rasmus), Rivas-Torres, G. (Gonzalo), Rodwell, J. S. (John S.), Ruprecht, E. (Eszter), Rusina, S. (Solvita), Samimi, C. (Cyrus), Schmidt, M. (Marco), Schrodt, F. (Franziska), Shan, H. (Hanhuai), Shirokikh, P. (Pavel), Sibik, J. (Jozef), Silc, U. (Urban), Sklenar, P. (Petr), Skvorc, Z. (Zeljko), Sparrow, B. (Ben), Sperandii, M. G. (Marta Gaia), Stancic, Z. (Zvjezdana), Svenning, J.-C. (Jens-Christian), Tang, Z. (Zhiyao), Tang, C. Q. (Cindy Q.), Tsiripidis, I. (Ioannis), Vanselow, K. A. (Kim Andre), Vasquez Martinez, R. (Rodolfo), Vassilev, K. (Kiril), Velez-Martin, E. (Eduardo), Venanzoni, R. (Roberto), Vibrans, A. C. (Alexander Christian), Violle, C. (Cyrille), Virtanen, R. (Risto), von Wehrden, H. (Henrik), Wagner, V. (Viktoria), Walker, D. A. (Donald A.), Waller, D. M. (Donald M.), Wang, H.-F. (Hua-Feng), Wesche, K. (Karsten), Whitfeld, T. J. (Timothy J. S.), Willner, W. (Wolfgang), Wiser, S. K. (Susan K.), Wohlgemuth, T. (Thomas), Yamalov, S. (Sergey), Zobel, M. (Martin), and Bruelheide, H. (Helge)

Abstract

Motivation: Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained: Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain: Global, 0.01–40,000 m². Time period and grain: 1888–2015, recording dates. Major taxa and level of measuremen

Published
2021

15. Data challenges limit our global understanding of humanitarian disasters triggered by climate extremes

Author

Mahecha, M, Guha-Sapir, D., Smits, J.P.J.M., Gans, F, Kraemer, G, Sillmann, J., Sippel, S., Russo, S., UCL - SSS/IRSS - Institut de recherche santé et société, Sillmann, J., Sippel, S., and Russo, S.

Subjects
Poverty, Inequality, Natural resource economics, media_common.quotation_subject, Vulnerability, Humanitarian disasters, Climate hazads, Ecosystem services, Geography, EM-DAT, Order (exchange), Scale (social sciences), Natural disaster, Institute for Management Research, Discipline, media_common
Abstract

Compared to other natural disasters, climate-related extremes like droughts and heat waves have the largest impacts on human well-being at the global scale. Given that droughts are expected to intensify over the next decades, that other extreme events may happen more frequently, and that global population growth already now leads to a higher exposure and humanity faces major challenges. A fundamental question related to these critical global developments is whether increasing wealth levels could partly offset this phenomenon. In order to investigate this question systematically and at the global level we need to consolidate multiple data sets that are required in the respective global empirical analyses. In this contribution we review this challenge. We argue that an objective assessment of trends in global disaster risk due to climate extremes requires (1) accurate regional disaster impact data of extreme events, (2) local demographic and socioeconomic indicators, for example, on poverty, inequality, and education to understand the societal vulnerability, and (3) high-level information on the exact spatiotemporal dynamics of the climate extreme, its return-time probability, and its impacts on ecosystem services. We outline a path toward an integrated assessment of natural disasters that requires bridging the wide disciplinary gaps to understand the full cascade from climate extremes to human impacts.

Published
2020
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16. The Low Dimensionality of Development

Author

Kraemer, G., Reichstein, Markus, Camps-Valls, Gustau, Smits, Jeroen, Mahecha, M., Kraemer, G., Reichstein, Markus, Camps-Valls, Gustau, Smits, Jeroen, and Mahecha, M.

Abstract

Contains fulltext : 221404.pdf (publisher's version ) (Open Access)

Published
2020

17. EVALUATING THE POTENTIAL OF DESIS TO INFER PLANT TAXONOMICAL AND FUNCTIONAL DIVERSITIES IN EUROPEAN FORESTS.

Author

Pacheco-Labrador, J., Weber, U., Ma, X., Mahecha, M. D., Carvalhais, N., Wirth, C., Huth, A., Bohn, F. J., Kraemer, G., Heiden, U., FunDivEUROPE members, and Migliavacca, M.

Subjects
SPECTRAL reflectance, REMOTE sensing, ENVIRONMENTAL degradation, FOREST biodiversity, SENSE data, SURFACE of the earth
Abstract

Tackling the accelerated human-induced biodiversity loss requires tools able to map biodiversity and its changes globally. Remote sensing (RS) offers unique capabilities of characterizing Earth surfaces; therefore, it could map plant biodiversity continuously and globally. This approach is supported by the Spectral Variation Hypothesis (SVH), which states that spectra and species (taxonomic and trait) diversities are linked through environmental heterogeneity. In this work, we evaluate the capability of the DESIS hyperspectral imager to capture plant diversity patterns as measured in dedicated plots of the network FunDivEUROPE. We computed functional and taxonomical diversity metrics from field taxonomic, structural, and foliar measurements in vegetation plots sampled in Spain and Romania. In addition, we also computed functional diversity metrics both from the DESIS reflectance factors and from vegetation parameters estimated via inversion of a radiative transfer model. Results showed that only metrics computed from spectral reflectance were able to capture taxonomic variability in the area. However, the lack of sensitivity was related to the insufficient plot size and the lack of spatial match between remote sensing and field data, but also the differences between the information contained in the field traits and remote sensing data, and the potential uncertainties in the remote estimates of vegetation parameters. Thus, while DESIS showed some sensitivity to plant diversity, further efforts are needed to deploy suitable biodiversity evaluation and validation plots and networks that support the development of biodiversity remote sensing products. [ABSTRACT FROM AUTHOR]

Published
2021
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20. Causality analysis of ecological time series: a time-frequency approach

Author

Shadaydeh, M., Garcia, Y., Mahecha, M., Reichstein, M., and Denzler, J.

Abstract

Attribution in ecosystems aims to identify the cause-effect relationships between the variables involved. Time series of ecological variables most often contain multiple periodical components, e.g. daily and seasonal cycles, induced by the meteorological forcing variables. Such components can significantly mask the underling endogenous causality structure of the biogeochemical cycle when using time domain analysis. This motivates the use of time-frequency analysis techniques such as short time Fourier transform or wavelet where causality inference can be investigated at different frequency bands or different time scales. In this study, we use the parametric spectral representation of the multivariate autoregressive Granger causality based on the generalized Partial Directed Coherence (gPDC) to investigate the cause-effect relationships between the meteorological observations of global radiation and air temperature, and the CO2 land fluxes of gross primary productivity and ecosystem respiration, at Hainich National Park-Germany. Preliminary results show that spectral domain causality analysis based on gPDC has promising potential in handling the presence of periodic components and in identifying the time variant causeeffect intensities between these variables at different time scales.

Published
2018

21. Detecting impacts of extreme events with ecological in situ monitoring networks

Author

Mahecha, M., Gans, F., Sippel, S., Donges, J., Kaminski, T., Metzger, S., Migliavacca, M., Papale, D., Rammig, A., and Zscheischler, J.

Abstract

Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the eight largest (typically very large) extreme events; but only a ≥ 50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere. ISSN:1726-4170 ISSN:1726-4170

Published
2017

24. Global trait:environment relationships of plant communities

Author

Bruelheide, H. (Helge), Dengler, J. (Juergen), Purschke, O. (Oliver), Lenoir, J. (Jonathan), Jimenez-Alfaro, B. (Borja), Hennekens, S. M. (Stephan M.), Botta-Dukat, Z. (Zoltan), Chytry, M. (Milan), Field, R. (Richard), Jansen, F. (Florian), Kattge, J. (Jens), Pillar, V. D. (Valerio D.), Schrodt, F. (Franziska), Mahecha, M. D. (Miguel D.), Peet, R. K. (Robert K.), Sandel, B. (Brody), van Bodegom, P. (Peter), Altman, J. (Jan), Alvarez-Davila, E. (Esteban), Khan, M. A. (Mohammed A. S. Arfin), Attorre, F. (Fabio), Aubin, I. (Isabelle), Baraloto, C. (Christopher), Barroso, J. G. (Jorcely G.), Bauters, M. (Marijn), Bergmeier, E. (Erwin), Biurrun, I. (Idoia), Bjorkman, A. D. (Anne D.), Blonder, B. (Benjamin), Carni, A. (Andraz), Cayuela, L. (Luis), Cerny, T. (Tomas), Cornelissen, J. H. (J. Hans C.), Craven, D. (Dylan), Dainese, M. (Matteo), Derroire, G. (Geraldine), De Sanctis, M. (Michele), Diaz, S. (Sandra), Dolezal, J. (Jiri), Farfan-Rios, W. (William), Feldpausch, T. R. (Ted R.), Fenton, N. J. (Nicole J.), Garnier, E. (Eric), Guerin, G. R. (Greg R.), Gutierrez, A. G. (Alvaro G.), Haider, S. (Sylvia), Hattab, T. (Tarek), Henry, G. (Greg), Herault, B. (Bruno), Higuchi, P. (Pedro), Hoelzel, N. (Norbert), Homeier, J. (Juergen), Jentsch, A. (Anke), Juergens, N. (Norbert), Kacki, Z. (Zygmunt), Karger, D. N. (Dirk N.), Kessler, M. (Michael), Kleyer, M. (Michael), Knollova, I. (Ilona), Korolyuk, A. Y. (Andrey Y.), Kuehn, I. (Ingolf), Laughlin, D. C. (Daniel C.), Lens, F. (Frederic), Loos, J. (Jacqueline), Louault, F. (Frederique), Lyubenova, M. I. (Mariyana, I), Malhi, Y. (Yadvinder), Marceno, C. (Corrado), Mencuccini, M. (Maurizio), Mueller, J. V. (Jonas, V), Munzinger, J. (Jerome), Myers-Smith, I. H. (Isla H.), Neill, D. A. (David A.), Niinemets, U. (Ulo), Orwin, K. H. (Kate H.), Ozinga, W. A. (Wim A.), Penuelas, J. (Josep), Perez-Haase, A. (Aaron), Petrik, P. (Petr), Phillips, O. L. (Oliver L.), Partel, M. (Meelis), Reich, P. B. (Peter B.), Roemermann, C. (Christine), Rodrigues, A. V. (Arthur, V), Sabatini, F. M. (Francesco Maria), Sardans, J. (Jordi), Schmidt, M. (Marco), Seidler, G. (Gunnar), Silva Espejo, J. E. (Javier Eduardo), Silveira, M. (Marcos), Smyth, A. (Anita), Sporbert, M. (Maria), Svenning, J.-C. (Jens-Christian), Tang, Z. (Zhiyao), Thomas, R. (Raquel), Tsiripidis, I. (Ioannis), Vassilev, K. (Kiril), Violle, C. (Cyrille), Virtanen, R. (Risto), Weiher, E. (Evan), Welk, E. (Erik), Wesche, K. (Karsten), Winter, M. (Marten), Wirth, C. (Christian), Jandt, U. (Ute), Bruelheide, H. (Helge), Dengler, J. (Juergen), Purschke, O. (Oliver), Lenoir, J. (Jonathan), Jimenez-Alfaro, B. (Borja), Hennekens, S. M. (Stephan M.), Botta-Dukat, Z. (Zoltan), Chytry, M. (Milan), Field, R. (Richard), Jansen, F. (Florian), Kattge, J. (Jens), Pillar, V. D. (Valerio D.), Schrodt, F. (Franziska), Mahecha, M. D. (Miguel D.), Peet, R. K. (Robert K.), Sandel, B. (Brody), van Bodegom, P. (Peter), Altman, J. (Jan), Alvarez-Davila, E. (Esteban), Khan, M. A. (Mohammed A. S. Arfin), Attorre, F. (Fabio), Aubin, I. (Isabelle), Baraloto, C. (Christopher), Barroso, J. G. (Jorcely G.), Bauters, M. (Marijn), Bergmeier, E. (Erwin), Biurrun, I. (Idoia), Bjorkman, A. D. (Anne D.), Blonder, B. (Benjamin), Carni, A. (Andraz), Cayuela, L. (Luis), Cerny, T. (Tomas), Cornelissen, J. H. (J. Hans C.), Craven, D. (Dylan), Dainese, M. (Matteo), Derroire, G. (Geraldine), De Sanctis, M. (Michele), Diaz, S. (Sandra), Dolezal, J. (Jiri), Farfan-Rios, W. (William), Feldpausch, T. R. (Ted R.), Fenton, N. J. (Nicole J.), Garnier, E. (Eric), Guerin, G. R. (Greg R.), Gutierrez, A. G. (Alvaro G.), Haider, S. (Sylvia), Hattab, T. (Tarek), Henry, G. (Greg), Herault, B. (Bruno), Higuchi, P. (Pedro), Hoelzel, N. (Norbert), Homeier, J. (Juergen), Jentsch, A. (Anke), Juergens, N. (Norbert), Kacki, Z. (Zygmunt), Karger, D. N. (Dirk N.), Kessler, M. (Michael), Kleyer, M. (Michael), Knollova, I. (Ilona), Korolyuk, A. Y. (Andrey Y.), Kuehn, I. (Ingolf), Laughlin, D. C. (Daniel C.), Lens, F. (Frederic), Loos, J. (Jacqueline), Louault, F. (Frederique), Lyubenova, M. I. (Mariyana, I), Malhi, Y. (Yadvinder), Marceno, C. (Corrado), Mencuccini, M. (Maurizio), Mueller, J. V. (Jonas, V), Munzinger, J. (Jerome), Myers-Smith, I. H. (Isla H.), Neill, D. A. (David A.), Niinemets, U. (Ulo), Orwin, K. H. (Kate H.), Ozinga, W. A. (Wim A.), Penuelas, J. (Josep), Perez-Haase, A. (Aaron), Petrik, P. (Petr), Phillips, O. L. (Oliver L.), Partel, M. (Meelis), Reich, P. B. (Peter B.), Roemermann, C. (Christine), Rodrigues, A. V. (Arthur, V), Sabatini, F. M. (Francesco Maria), Sardans, J. (Jordi), Schmidt, M. (Marco), Seidler, G. (Gunnar), Silva Espejo, J. E. (Javier Eduardo), Silveira, M. (Marcos), Smyth, A. (Anita), Sporbert, M. (Maria), Svenning, J.-C. (Jens-Christian), Tang, Z. (Zhiyao), Thomas, R. (Raquel), Tsiripidis, I. (Ioannis), Vassilev, K. (Kiril), Violle, C. (Cyrille), Virtanen, R. (Risto), Weiher, E. (Evan), Welk, E. (Erik), Wesche, K. (Karsten), Winter, M. (Marten), Wirth, C. (Christian), and Jandt, U. (Ute)

Abstract

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.

Published
2018

25. Relationships between Parental Education and Overweight with Childhood Overweight and Physical Activity in 9-11 Year Old Children: Results from a 12-Country Study

Author

Muthuri, S. K., Onywera, V. O., Tremblay, M. S., Broyles, S. T., Chaput, J. P., Fogelholm, M., Hu, G., Kuriyan, R., Kurpad, A., Lambert, E. V., Maher, C., Maia, J., Matsudo, V., Olds, T., Sarmiento, O. L., Standage, M., Tudor Locke, C., Zhao, P., Church, T. S., Katzmarzyk, P. T., Lambert, D. G., Barreira, T., Broyles, S., Butitta, B., Champagne, C., Cocreham, S., Denstel, K. D., Drazba, K., Harrington, D., Johnson, W., Milauskas, D., Mire, E., Tohme, A., Rodarte, R., Amoroso, B., Luopa, J., Neiberg, R., Rushing, S., Lewis, L., Ferrar, K., Georgiadis, E., Stanley, R., Matsudo, V. K. R., Matsudo, S., Araujo, T., De Oliveira, L. C., Fabiano, L., Bezerra, D., Ferrari, G., Bélanger, P., Borghese, M., Boyer, C., Leblanc, A., Francis, C., Leduc, G., Diao, C., Li, W., Liu, E., Liu, G., Liu, H., Ma, J., Qiao, Y., Tian, H., Wang, Y., Zhang, T., Zhang, F., Sarmiento, O., Acosta, J., Alvira, Y., Diaz, M. P., Gamez, R., Garcia, M. P., Gómez, L. G., Gonzalez, L., Gonzalez, S., Grijalba, C., Gutierrez, L., Leal, D., Lemus, N., Mahecha, E., Mahecha, M. P., Mahecha, R., Ramirez, A., Rios, P., Suarez, A., Triana, C., Hovi, E., Kivelä, J., Räsänen, S., Roito, S., Saloheimo, T., Valta, L., Lokesh, D. P., D'Almeida, M. S., Mattilda R, A., Correa, L., Vijay, D., Wachira, L. J., Muthuri, S., Da Silva Borges, A., Sá Cachada, S. O., De Chaves, R. N., Gomes, T. N. Q. F., Pereira, S. I. S., De Vilhena E. Santos, D. M., Dos Santos, F. K., Da Silva, P. G. R., De Souza, M. C., Lambert, V., April, M., Uys, M., Naidoo, N., Synyanya, N., Carstens, M., Cumming, S., Drenowatz, C., Emm, L., Gillison, F., Zakrzewski, J., Braud, A., Donatto, S., Lemon, C., Jackson, A., Pearson, A., Pennington, G., Ragus, D., Roubion, R., Schuna, J., Wiltz, J. r., Batterham, A., Kerr, J., Pratt, M., Pietrobelli, Angelo, Muthuri, Stella K, Onywera, Vincent O, Tremblay, Mark S, Broyles, Stephanie T, Chaput, Jean-Philippe, Fogelholm, Mikael, Hu, Gang, Kuriyan, Rebecca, Kurpad, Anura, Lambert, Estelle V, Maher, Carol, Maia, José, Matsudo, Victor, Olds, Timothy, Sarmiento, Olga L, Standage, Martyn, Tudor-Locke, Catrine, Zhao, Pei, Church, Timothy S, Katzmarzyk, Peter T, MRC/UCT RU for Exercise and Sport Medicine, Faculty of Health Sciences, Department of Food and Nutrition, and Nutrition Science

Subjects
Gerontology, Male, Parents, Pediatric Obesity, Physiology, Economics, Physical fitness, Economics of Training and Education, SCHOOL-AGED CHILDREN, BODY-MASS INDEX, OBESITY, Social Sciences, lcsh:Medicine, physical activity, DETERMINANTS, RA773, Overweight, Pediatrics, Families, Fathers, 0302 clinical medicine, Sociology, Risk Factors, Medicine and Health Sciences, Public and Occupational Health, 030212 general & internal medicine, Child, lcsh:Science, Children, 2. Zero hunger, Human Capital, Family Characteristics, Multidisciplinary, Child Health, 3142 Public health care science, environmental and occupational health, FAMILY, TIME, 3. Good health, Multidisciplinary Sciences, YOUTH, Physiological Parameters, Population Surveillance, educational attainment, OBESITY, child health, Educational Status, ADIPOSITY, Female, SCHOOL-AGED CHILDREN, medicine.symptom, childhood obesity, TRANSITION, BEHAVIOR, Research Article, Childhood Obesity, Mothers, 030209 endocrinology & metabolism, fathers, Childhood obesity, Education, 03 medical and health sciences, children, medicine, Humans, Body Weights and Measures, economics of training and education, Socioeconomic status, Exercise, Life Style, Educational Attainment, business.industry, Body Weight, lcsh:R, Biology and Life Sciences, Odds ratio, Physical Activity, medicine.disease, Obesity, Educational attainment, BODY-MASS INDEX, mothers, Age Groups, People and Places, Population Groupings, lcsh:Q, business, Body mass index, Demography
Abstract

Background: Globally, the high prevalence of overweight and low levels of physical activity among children has serious implications for morbidity and premature mortality in adulthood. Various parental factors are associated with childhood overweight and physical activity. The objective of this paper was to investigate relationships between parental education or overweight, and (i) child overweight, (ii) child physical activity, and (iii) explore household coexistence of overweight, in a large international sample. Methods: Data were collected from 4752 children (9–11 years) as part of the International Study of Childhood Obesity, Lifestyle and the Environment in 12 countries around the world. Physical activity of participating children was assessed by accelerometry, and body weight directly measured. Questionnaires were used to collect parents' education level, weight, and height. Results: Maternal and paternal overweight were positively associated with child overweight. Higher household coexistence of parent-child overweight was observed among overweight children compared to the total sample. There was a positive relationship between maternal education and child overweight in Colombia 1.90 (1.23–2.94) [odds ratio (confidence interval)] and Kenya 4.80 (2.21–10.43), and a negative relationship between paternal education and child overweight in Brazil 0.55 (0.33–0.92) and the USA 0.54 (0.33–0.88). Maternal education was negatively associated with children meeting physical activity guidelines in Colombia 0.53 (0.33–0.85), Kenya 0.35 (0.19–0.63), and Portugal 0.54 (0.31–0.96). Conclusions: Results are aligned with previous studies showing positive associations between parental and child overweight in all countries, and positive relationships between parental education and child overweight or negative associations between parental education and child physical activity in lower economic status countries. Relationships between maternal and paternal education and child weight status and physical activity appear to be related to the developmental stage of different countries. Given these varied relationships, it is crucial to further explore familial factors when investigating child overweight and physical activity. Refereed/Peer-reviewed

Published
2016

27. The ESA's Earth Observation Open Science Program [Space Agencies]

Author

Mathieu, P.-P., Borgeaud, M., Desnos, Y.-L., Rast, M., Brockmann, C., See, L., Kapur, R., Mahecha, M., Benz, U., Fritz, S., Mathieu, P.-P., Borgeaud, M., Desnos, Y.-L., Rast, M., Brockmann, C., See, L., Kapur, R., Mahecha, M., Benz, U., and Fritz, S.

Abstract

The world of Earth observation (EO) data is rapidly changing, driven by exponential advances in sensor and digital technologies. Recent decades have seen the development of extraordinary new ways of collecting, storing, manipulating, and transmitting data that are radically transforming the way we conduct and organize science. This convergence of technologies creates new challenges for EO scientists and data and software providers to fully exploit large amounts of multivariate data from diverse sources. At the same time, these technological trends also generate huge opportunities to better understand our planet and turn big data into new types of information services. This article briefly describes some of the elements of the European Space Agency's (ESA) EO Open Science program, which aims to enable the digital transformation of the EO community and make the most of the large, complex, and diverse data delivered by the new generation of EO missions, such as the Copernicus Sentinels.

Published
2017

30. Climate-mediated spatiotemporal variability in terrestrial productivity across Europe

Author

Wu, X., Babst, F., Ciais, P., Frank, D., Reichstein, M., Wattenbach, M., Zang, C., Mahecha, M., Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Laboratory of Tree-Ring Research, University of Arizona, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), German Research Centre for Geosciences - Helmholtz-Centre Potsdam (GFZ), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
lcsh:Geology, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, lcsh:QH501-531, lcsh:QH540-549.5, lcsh:QE1-996.5, lcsh:Life, lcsh:Ecology, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment
Abstract

Quantifying the interannual variability (IAV) of the terrestrial ecosystem productivity and its sensitivity to climate is crucial for improving carbon budget predictions. In this context it is necessary to disentangle the influence of climate from impacts of other mechanisms underlying the spatiotemporal patterns of IAV of the ecosystem productivity. In this study we investigated the spatiotemporal patterns of IAV of historical observations of European crop yields in tandem with a set of climate variables. We further evaluated if relevant remote-sensing retrievals of NDVI (normalized difference vegetation index) and FAPAR (fraction of absorbed photosynthetically active radiation) depict a similar behaviour. Our results reveal distinct spatial patterns in the IAV of the analysed proxies linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions in both crop yield and remote-sensing observations. Our results further indicate that variations in the water balance during the active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity-related variables in temperate Europe. Overall, we observe a temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe during the 1975–2009 period. In the same regions, a simultaneous increase in the IAV of water availability was detected. These findings suggest intricate responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become potentially more sensitive to changes in water availability in the dry regions in Europe. The changing sensitivity of terrestrial productivity accompanied by the changing IAV of climate is expected to impact carbon stocks and the net carbon balance of European ecosystems. ISSN:1726-4170 ISSN:1726-4170

Published
2014
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32. An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets

Author

v. Buttlar, J., Zscheischler, J., and Mahecha, M. D.

Subjects
lcsh:Geophysics. Cosmic physics, lcsh:QC801-809, lcsh:Q, lcsh:Science, lcsh:Physics, lcsh:QC1-999
Abstract

Spatiotemporal observations in Earth System sciences are often affected by numerous and/or systematically distributed gaps. This data fragmentation is inherited from instrument failures, sparse measurement protocols, or unfavourable conditions (e.g. clouds or vegetation thickness in case of remote-sensing data). Missing values are problematic as they may cause analytic biases and often inhibit advanced statistical analyses. Hence, gapfilling is an undesired but necessary task in Earth System sciences. State-of-the-art gapfilling algorithms based on Singular Spectrum Analysis (SSA) exploit the information contained in periodic temporal patterns to fill gaps in the observations. Here we propose an extension of this method in order to additionally consider the spatial processes and patterns underlying most geoscientific datasets. The latter has been made possible by including a recently developed 2-D-SSA approach. Using both artificial and real-world test data, we show that simultaneously exploiting spatial and temporal patterns improves the gapfilling substantially. We outperform conventional approaches particularly for large and systematically recurring gaps. The new method is reasonably fast and can be applied with a minimum of a priori assumptions regarding the structure of the data and the distribution of gaps. The algorithm is available as a ready-to-use open source software package., Nonlinear Processes in Geophysics, 21 (1), ISSN:1023-5809, ISSN:1607-7946

Published
2014

33. TRY: a global database of plant traits

Author

Kattge, J., Diaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bonisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., van Bodegom, P. M., Reichstein, M., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., Atkin, O., Bahn, M., Baker, T. R., Baldocchi, D., Bekker, R., Blanco, C., Blonder, B., Bond, W., Bradstock, R., Bunker, D. E., Casanoves, F., Cavender-Bares, J., Chambers, J., Chapin, F. S., Chave, J., Coomes, D., Cornwell, W. K., Craine, J. M., Dobrin, B. H., Durka, W., Elser, J., Enquist, B. J., Esser, G., Estiarte, M., Fagan, W. F., Fang, J., Fernandez, F., Fidelis, A., Finegan, B., Flores, O., Ford, H., Frank, D., Freschet, G. T., Fyllas, N. M., Gallagher, R., Green, W., Gutierrez, A. G., Hickler, T., Higgins, S., Hodgson, J. G., Jalili, A., Jansen, S., Kerkhoff, A. J., Kirkup, D., Kitajima, K., Kleyer, M., Klotz, S., Knops, J. M. H., Kramer, K., Kuhn, I., Kurokawa, H., Laughlin, D., Lee, T. D., Leishman, M., Lens, F., Lenz, T., Lewis, S. L., Lloyd, J., Llusia, J., Louault, F., Ma, S., Mahecha, M. D., Manning, P., Massad, T., Medlyn, B., Messier, J., Moles, A., Muller, S., Nadrowski, K., Naeem, S., Niinemets, U., Nollert, S., Nuske, A., Ogaya, R., Joleksyn, J., Onipchenko, V. G., Onoda, Y., Ordonez, J., Overbeck, G., Ozinga, W., Patino, S., Paula, S., Pausas, J. G., Penuelas, J., Phillips, O. L., Pillar, V., Poorter, H., Poorter, L., Poschlod, P., Proulx, R., Rammig, A., Reinsch, S., Reu, B., Sack, L., Salgado, B., Sardans, J., Shiodera, S., Shipley, B., Sosinski, E., Soussana, J.-F., Swaine, E., Swenson, N., Thompson, K., Thornton, P., Waldram, M., Weiher, E., White, M., Wright, S. J., Zaehle, S., Zanne, A. E., and Wirth, C.

Abstract

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.

Published
2011

37. Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula

Author

CARVALHAIS N., REICHSTEIN M., COLLATZ G.j, MAHECHA M., MIGLIAVACCA MIRCO, NEIGH C.s.r., TOMELLERI Enrico, BENALI A.a., PAPALE D., and SEIXAS Julia

Subjects
010504 meteorology & atmospheric sciences, lcsh:QE1-996.5, lcsh:Life, Soil Carbon, 04 agricultural and veterinary sciences, Eddy covariance measurements, 15. Life on land, 01 natural sciences, Terrestrial Carbon, lcsh:Geology, lcsh:QH501-531, Light-Use Efficiency, Process Model, 13. Climate action, Carbon-dioxide exchange, lcsh:QH540-549.5, Atmospheric CO2, 040103 agronomy & agriculture, Organic-matter decomposition, 0401 agriculture, forestry, and fisheries, lcsh:Ecology, Global Satellite, Temperature-Dependence, 0105 earth and related environmental sciences
Abstract

Quantification of ecosystem carbon pools is a fundamental requirement for estimating carbon fluxes and for addressing the dynamics and responses of the terrestrial carbon cycle to environmental drivers. The initial estimates of carbon pools in terrestrial carbon cycle models often rely on the ecosystem steady state assumption, leading to initial equilibrium conditions. In this study, we investigate how trends and inter-annual variability of net ecosystem fluxes are affected by initial non-steady state conditions. Further, we examine how modeled ecosystem responses induced exclusively by the model drivers can be separated from the initial conditions. For this, the Carnegie-Ames-Stanford Approach (CASA) model is optimized at set of European eddy covariance sites, which support the parameterization of regional simulations of ecosystem fluxes for the Iberian Peninsula, between 1982 and 2006. The presented analysis stands on a credible model performance for a set of sites, that represent generally well the plant functional types and selected descriptors of climate and phenology present in the Iberian region - except for a limited Northwestern area. The effects of initial conditions on inter-annual variability and on trends, results mostly from the recovery of pools to equilibrium conditions; which control most of the inter-annual variability (IAV) and both the magnitude and sign of most of the trends. However, by removing the time series of pure model recovery from the time series of the overall fluxes, we are able to retrieve estimates of inter-annual variability and trends in net ecosystem fluxes that are quasi-independent from the initial conditions. This approach reduced the sensitivity of the net fluxes to initial conditions from 47% and 174% to -3% and 7%, for strong initial sink and source conditions, respectively. With the aim to identify and improve understanding of the component fluxes that drive the observed trends, the net ecosystem production (NEP) trends are decomposed into net primary production (NPP) and heterotrophic respiration (RH) trends. The majority (∼97%) of the positive trends in NEP is observed in regions where both NPP and RH fluxes show significant increases, although the magnitude of NPP trends is higher. Analogously, ∼83% of the negative trends in NEP are also associated with negative trends in NPP. The spatial patterns of NPP trends are mainly explained by the trends in fAPAR (r = 0.79) and are only marginally explained by trends in temperature and water stress scalars (r = 0.10 and r = 0.25, respectively). Further, we observe the significant role of substrate availability (r = 0.25) and temperature (r = 0.23) in explaining the spatial patterns of trends in RH. These results highlight the role of primary production in driving ecosystem fluxes. Overall, our study illustrates an approach for removing the confounding effects of initial conditions and emphasizes the need to decompose the ecosystem fluxes into its components and drivers for more mechanistic interpretations of modeling results. We expect that our results are not only specific for the CASA model since it incorporates concepts of ecosystem functioning and modeling assumptions common to biogeochemical models. A direct implication of these results is the ability of this approach to detect climate and phenology induced trends regardless of the initial conditions., JRC.DDG.H.2-Climate change and air quality

Published
2010
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38. Comparing observations and process-based simulationsof biosphere-atmosphere exchanges on multiple timescales

Author

Mahecha, M., Reichstein, M., Jung, M., Seneviratne, S., Zaehle, S., Beer, C., Braakhekke, M., Carvalhais, N., Lange, H., Le Maire, G., Moors, E., Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
Écologie, P40 - Météorologie et climatologie, net ecosystem exchange, water-vapor, Earth System Science, eau, Alterra - Centre for Water and Climate, eddy covariance, interannual time scales, CWK - Earth System Science and Climate Change, Wageningen Environmental Research, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Modélisation environnementale, Météorologie, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, long-term, WIMEK, U10 - Informatique, mathématiques et statistiques, 000 - Autres thèmes, Modèle de simulation, Cycle hydrologique, Évapotranspiration, CWC - Earth System Science and Climate Change, pine forest, global vegetation model, stomatal conductance, climate-change, Leerstoelgroep Aardsysteemkunde, Climatologie, P01 - Conservation de la nature et ressources foncières, energy-balance closure, Cycle du carbone, Dioxyde de carbone, Écosystème, Atmosphère, Alterra - Centrum Water en Klimaat
Abstract

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.

Published
2010
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40. Biosphere-atmosphere exchange of CO2 in relation to climate: a cross-biome analysis across multiple time scales

Author

Stoy, P. C., Richardson, A. D., Baldocchi, D. D., Katul, G. G., Stanovick, J., Mahecha, M. D., Reichstein, M., Detto, M., Beverly Law, Wohlfahrt, G., Arriga, N., Campos, J., Mccaughey, J. H., Montagnani, L., U, K. T. Paw, Sevanto, S., Williams, M., and Department of Physics

Subjects
PINE FOREST, MODEL INTERCOMPARISONS, Climate Change, SPATIAL VARIABILITY, education, lcsh:Life, CARBON-DIOXIDE, lcsh:QH540-549.5, Biome, Climate Variation, TEMPERATURE SENSITIVITY, 1172 Environmental sciences, Ecology, Evolution, Behavior and Systematics, Earth-Surface Processes, Terrestrial Ecosystem, EUROPEAN FORESTS, Data Set, NET ECOSYSTEM EXCHANGE, lcsh:QE1-996.5, Carbon Dioxide, WAVELET ANALYSIS, Research Work, lcsh:Geology, Ecosystem Response, lcsh:QH501-531, lcsh:Ecology, WATER-VAPOR EXCHANGE, Eddy Covariance, Ecosystem Modeling, SOIL RESPIRATION
Abstract

The net ecosystem exchange of CO2 (NEE) varies at time scales from seconds to years and longer via the response of its components, gross ecosystem productivity (GEP) and ecosystem respiration (RE), to physical and biological drivers. Quantifying the relationship between flux and climate at multiple time scales is necessary for a comprehensive understanding of the role of climate in the terrestrial carbon cycle. Orthonormal wavelet transformation (OWT) can quantify the strength of the interactions between gappy eddy covariance flux and micrometeorological measurements at multiple frequencies while expressing time series variance in few energetic wavelet coefficients, offering a low-dimensional view of the response of terrestrial carbon flux to climatic variability. The variability of NEE, GEP and RE, and their co-variability with dominant climatic drivers, are explored with nearly one thousand site-years of data from the FLUXNET global dataset consisting of 253 eddy covariance research sites. The NEE and GEP wavelet spectra were similar among plant functional types (PFT) at weekly and shorter time scales, but significant divergence appeared among PFT at the biweekly and longer time scales, at which NEE and GEP were relatively less variable than climate. The RE spectra rarely differed among PFT across time scales as expected. On average, RE spectra had greater low frequency (monthly to interannual) variability than NEE, GEP and climate. CANOAK ecosystem model simulations demonstrate that "multi-annual" spectral peaks in flux may emerge at low (4+ years) time scales. Biological responses to climate and other internal system dynamics, rather than direct ecosystem response to climate, provide the likely explanation for observed multi-annual variability, but data records must be lengthened and measurements of ecosystem state must be made, and made available, to disentangle the mechanisms responsible for low frequency patterns in ecosystem CO2 exchange. ISSN:1726-4170 ISSN:1726-4170

Published
2009
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41. The European terrestrial biosphere affected by the 2005 and 2003 climate anomalies: comparative analysis of processes and spatial patterns

Author

Markus Reichstein, Viovy, N., Philippe Ciais, Mahecha, M., Aubinet, M., Barcza, Z., Beer, C., Bernhofer, C., Alain Carrara, André Granier, Grünwald, T., Haszpra, L., Nathalie Jarosz, Kutsch, W., Richard Joffre, Denis Loustau, Nagy, Z., Ourcival, J., Papale, D., Pinter, K., Serge Rambal, Sanz, M., Zähle, S., Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Liège, Department of Meteorology [Budapest], Institute of Geography and Earth Sciences [Budapest], Faculty of Sciences [Budapest], Eötvös Loránd University (ELTE)-Eötvös Loránd University (ELTE)-Faculty of Sciences [Budapest], Eötvös Loránd University (ELTE)-Eötvös Loránd University (ELTE), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Centro de Estudios Ambientales del Mediterraneo, Ecologie et Ecophysiologie Forestières [devient SILVA en 2018] (EEF), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Hungarian Meteorological Service (OMSz), Écologie fonctionnelle et physique de l'environnement (EPHYSE), Institut National de la Recherche Agronomique (INRA), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Szent István University, Tuscia University, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Hungarian Meteorological Service (OMSZ), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Università degli studi della Tuscia [Viterbo]

Subjects
carbon and water fluxes, interannual variability, télédétection, [SDV]Life Sciences [q-bio], ecosystem modelling, écosystème, flux, remote sensing, climate extremes, [SDE]Environmental Sciences, carbone, ComputingMilieux_MISCELLANEOUS, modélisation
Abstract

International audience

Published
2006

44. A framework for benchmarking land models

Author

Luo, Y.Q., Randerson, J.T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., Ciais, P., Dalmonech, D., Fisher, J.B., Fisher, R., Friedlingstein, P., Hibbard, K., Hoffman, F., Huntzinger, D., Jones, C.D., Koven, C., Lawrence, D., Li, D.J., Mahecha, M., Niu, S.L., Norby, R., Piao, S.L., Qi, X., Peylin, P., Prentice, I.C., Riley, W., Reichstein, M., Schwalm, C., Wang, Y.P., Xia, J.Y., Zaehle, S., Zhou, X.H., Luo, Y.Q., Randerson, J.T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., Ciais, P., Dalmonech, D., Fisher, J.B., Fisher, R., Friedlingstein, P., Hibbard, K., Hoffman, F., Huntzinger, D., Jones, C.D., Koven, C., Lawrence, D., Li, D.J., Mahecha, M., Niu, S.L., Norby, R., Piao, S.L., Qi, X., Peylin, P., Prentice, I.C., Riley, W., Reichstein, M., Schwalm, C., Wang, Y.P., Xia, J.Y., Zaehle, S., and Zhou, X.H.

Abstract

Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to

Published
2012

45. TRY: a global database of plant traits

Author

Bradstock, Ross A, Ma, S, Fang, J, van Bodegom, P M, Reichstein, M, Lavorel, S, Blanco, C, Soudzilovskaia, N A, Prentice, I C, Cornelissen, J H C, Baldocchi, D, Violle, C, Bahn, M, Garnier, E, Atkin, O, Reich, P B, Westoby, M, Ackerly, D D, Leadley, P, Bond, W, Kattge, J, Blonder, B, Wright, I J, Diaz, S, Bonisch, G, Anand, M, Harrison, S P, Wright, S J, Zanne, A E, Wirth, C, White, S, Yguel, B, Weiher, E, Zaehle, S, White, M, Baker, T, Bekker, R, Enquist, B, Sosinski, E, Soussana, J F, Waldram, M, Thompson, K, Swenson, A, Thornton, P, Swaine, E, Moles, A T, Manning, P, Naeem, S, Medlyn, B E, Massad, T, Messier, J, Muller, S C, Nadrowski, K, Mahecha, M D, Ogaya, R, Onipchenko, V G, Ozinga, W A, Oleksyn, J, Onoda, Y, Patino, S, Nollert, S, Ordonez, J, Niimemets, U, Nuske, A, Overbeck, G, Pillar, V, Poschlod, P, Paula, S, Penuelas, J, Phillips, O L, Poorter, H, Poorter, L, Prinzing, A, Pausas, J G, Salgado-Negret, B, Reu, B, Reinsch, S, Rammig, A, Sardans, J, Siefert, A, Proulx, R, Sack, L, Shipley, B, Shiodera, S, Chambers, J, Chave, J, Chapin, F, Casanoves, F, Cavender-Bares, J, Bunker, D, Coomes, D, Craine, J M, Cornwell, W K, Dobrin, B H, Esser, G, Durka, W, Elser, J, Estiarte, M, Duarte, L, Fernandez-Mendez, F, Fagan, W F, Ford, H, Flores, O, Finegan, B, Fidelis, A, Frank, D, Gallaghers, R V, Green, W A, Freschet, G T, Gutierrez, A G, Fyllas, N M, Jalili, A, Hodgson, J G, Higgins, S I, Hickler, T, Jansen, S, Kramer, K, Kirkup, D, Knops, J M H, Kleyer, M, Klotz, S, Joly, C A, Kitajima, K, Khun, I, Kerkhoff, A J, Kurokawa, H, Llusia, J, Laughlin, D, Lenz, T, Lee, T D, Lewis, S L, Lloyd, J, Leishman, M, Lens, F, Louault, F, Bradstock, Ross A, Ma, S, Fang, J, van Bodegom, P M, Reichstein, M, Lavorel, S, Blanco, C, Soudzilovskaia, N A, Prentice, I C, Cornelissen, J H C, Baldocchi, D, Violle, C, Bahn, M, Garnier, E, Atkin, O, Reich, P B, Westoby, M, Ackerly, D D, Leadley, P, Bond, W, Kattge, J, Blonder, B, Wright, I J, Diaz, S, Bonisch, G, Anand, M, Harrison, S P, Wright, S J, Zanne, A E, Wirth, C, White, S, Yguel, B, Weiher, E, Zaehle, S, White, M, Baker, T, Bekker, R, Enquist, B, Sosinski, E, Soussana, J F, Waldram, M, Thompson, K, Swenson, A, Thornton, P, Swaine, E, Moles, A T, Manning, P, Naeem, S, Medlyn, B E, Massad, T, Messier, J, Muller, S C, Nadrowski, K, Mahecha, M D, Ogaya, R, Onipchenko, V G, Ozinga, W A, Oleksyn, J, Onoda, Y, Patino, S, Nollert, S, Ordonez, J, Niimemets, U, Nuske, A, Overbeck, G, Pillar, V, Poschlod, P, Paula, S, Penuelas, J, Phillips, O L, Poorter, H, Poorter, L, Prinzing, A, Pausas, J G, Salgado-Negret, B, Reu, B, Reinsch, S, Rammig, A, Sardans, J, Siefert, A, Proulx, R, Sack, L, Shipley, B, Shiodera, S, Chambers, J, Chave, J, Chapin, F, Casanoves, F, Cavender-Bares, J, Bunker, D, Coomes, D, Craine, J M, Cornwell, W K, Dobrin, B H, Esser, G, Durka, W, Elser, J, Estiarte, M, Duarte, L, Fernandez-Mendez, F, Fagan, W F, Ford, H, Flores, O, Finegan, B, Fidelis, A, Frank, D, Gallaghers, R V, Green, W A, Freschet, G T, Gutierrez, A G, Fyllas, N M, Jalili, A, Hodgson, J G, Higgins, S I, Hickler, T, Jansen, S, Kramer, K, Kirkup, D, Knops, J M H, Kleyer, M, Klotz, S, Joly, C A, Kitajima, K, Khun, I, Kerkhoff, A J, Kurokawa, H, Llusia, J, Laughlin, D, Lenz, T, Lee, T D, Lewis, S L, Lloyd, J, Leishman, M, Lens, F, and Louault, F

Abstract

Plant traits the morphological, anatomical, physiological, biochemical and phenological characteristics of plants andtheir 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 andfunctional ecology to biogeography. Here we present the global database initiative named TRY, which has united awide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far93 trait databases have been contributed. The data repository currently contains almost three million trait entries for69 000 out of the worlds 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative andregeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first dataanalysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges ofvariation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation isalso documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetationmodels, capture a substantial fraction of the observed variation but for several traits most variation occurs withinPFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented bystate variables rather than fixed parameter values. The improved availability of plant trait data in the unified globaldatabase is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities forsynthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrialvegetation in Earth system models

Published
2011

46. Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites

Author

Migliavacca, M, Reichstein, M, Richardson, A, Colombo, R, Sutton, M, Lasslop, G, Tomelleri, E, Wohlfahrt, G, Carvalhais, N, Cescatti, A, Mahecha, M, Montagnani, L, Papale, D, Zaehle, S, Arain, A, Arneth, A, Black, T, Carrara, A, Dore, S, Gianelle, D, Helfter, C, Hollinger, D, Kutsch, W, Lafleur, P, Nouvellon, Y, Rebmann, C, Humberto, R, Rodeghiero, M, Roupsard, O, Sebastià, M, Seufert, G, Soussana, J, Michiel, K, Richardson, AD, COLOMBO, ROBERTO, Sutton, MA, Mahecha, MD, Black, TA, Kutsch, WL, Lafleur, PM, Humberto R, Michiel, K., Migliavacca, M, Reichstein, M, Richardson, A, Colombo, R, Sutton, M, Lasslop, G, Tomelleri, E, Wohlfahrt, G, Carvalhais, N, Cescatti, A, Mahecha, M, Montagnani, L, Papale, D, Zaehle, S, Arain, A, Arneth, A, Black, T, Carrara, A, Dore, S, Gianelle, D, Helfter, C, Hollinger, D, Kutsch, W, Lafleur, P, Nouvellon, Y, Rebmann, C, Humberto, R, Rodeghiero, M, Roupsard, O, Sebastià, M, Seufert, G, Soussana, J, Michiel, K, Richardson, AD, COLOMBO, ROBERTO, Sutton, MA, Mahecha, MD, Black, TA, Kutsch, WL, Lafleur, PM, Humberto R, and Michiel, K.

Abstract

In this study we examined ecosystem respiration (RECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of RECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of RECOfailed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of RECO. The maximum seasonal leaf area index (LAIMAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature Tref=15 °C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r2=0.52, P<0.001, n=104) even within each PFT. Besides LAIMAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (Ndepo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAIMAX) which performed well in predicting the spatio-temporal variability of RECO, explaining >70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands. © 2010 Blackwell Publishing Ltd.

Published
2011

50. A framework of benchmarking land models

Author

Luo, Y. Q., primary, Randerson, J., additional, Abramowitz, G., additional, Bacour, C., additional, Blyth, E., additional, Carvalhais, N., additional, Ciais, P., additional, Dalmonech, D., additional, Fisher, J., additional, Fisher, R., additional, Friedlingstein, P., additional, Hibbard, K., additional, Hoffman, F., additional, Huntzinger, D., additional, Jones, C. D., additional, Koven, C., additional, Lawrence, D., additional, Li, D. J., additional, Mahecha, M., additional, Niu, S. L., additional, Norby, R., additional, Piao, S. L., additional, Qi, X., additional, Peylin, P., additional, Prentice, I. C., additional, Riley, W., additional, Reichstein, M., additional, Schwalm, C., additional, Wang, Y. P., additional, Xia, J. Y., additional, Zaehle, S., additional, and Zhou, X. H., additional

Published
2012
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