98 results on '"Langerwisch, Fanny"'
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2. Rice Ecosystem Services in South-East Asia: The LEGATO Project, Its Approaches and Main Results with a Focus on Biocontrol Services
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Settele, Josef, Spangenberg, Joachim H., Heong, Kong Luen, Kühn, Ingolf, Klotz, Stefan, Arida, Gertrudo, Burkhard, Benjamin, Bustamante, Jesus Victor, Cabbigat, Jimmy, Canh, Le Xuan, Catindig, Josie Lynn A., Van Chien, Ho, Cuong, Le Quoc, Escalada, Monina, Görg, Christoph, Grescho, Volker, Grossmann, Sabine, Hadi, Buyung A. R., Hai, Le Huu, Harpke, Alexander, Hass, Annika L., Hirneisen, Norbert, Horgan, Finbarr G., Hotes, Stefan, Jahn, Reinhold, Klotzbücher, Anika, Klotzbücher, Thimo, Langerwisch, Fanny, Magcale-Macandog, Damasa B., Manh, Nguyen Hung, Marion, Glenn, Marquez, Leonardo, Ott, Jürgen, Penev, Lyubomir, Rodriguez-Labajos, Beatriz, Sann, Christina, Sattler, Cornelia, Schädler, Martin, Scheu, Stefan, Schmidt, Anja, Schrader, Julian, Schweiger, Oliver, Seppelt, Ralf, Van Sinh, Nguyen, Stoev, Pavel, Stoll-Kleemann, Susanne, Tekken, Vera, Thonicke, Kirsten, Trisyono, Y. Andi, Truong, Dao Thanh, Tuan, Le Quang, Türke, Manfred, Václavík, Tomáš, Vetterlein, Doris, Villareal, Sylvia “Bong”, Westphal, Catrin, Wiemers, Martin, Schröter, Matthias, editor, Bonn, Aletta, editor, Klotz, Stefan, editor, Seppelt, Ralf, editor, and Baessler, Cornelia, editor
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- 2019
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3. Using Dynamic Global Vegetation Models (DGVMs) for Projecting Ecosystem Services at Regional Scales
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Boit, Alice, Sakschewski, Boris, Boysen, Lena, Cano-Crespo, Ana, Clement, Jan, Garcia Alaniz, Nashieli, Kok, Kasper, Kolb, Melanie, Langerwisch, Fanny, Rammig, Anja, Sachse, René, van Eupen, Michiel, von Bloh, Werner, Zemp, Delphine Clara, Thonicke, Kirsten, Schröter, Matthias, editor, Bonn, Aletta, editor, Klotz, Stefan, editor, Seppelt, Ralf, editor, and Baessler, Cornelia, editor
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- 2019
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4. D3.5 Farming System Archetypes for each CS
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Langerwisch, Fanny, primary, Václavík, Tomáš, additional, Bednář, Marek, additional, Ziv, Guy, additional, Gunning, Jodi, additional, Gosal, Arjan, additional, Paulus, Anne, additional, Brdar, Sanja, additional, Lugonja, Predrag, additional, Stojkovic, Stefanija, additional, Roilo, Stephanie, additional, and Cord, Anna, additional
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- 2023
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5. D5.4 Mapping of vegetation indices and metrics, and their utility in FSA mapping at CS scale
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Riembauer, Guido, primary, Metz, Markus, additional, Ziv, Guy, additional, Gunning, Jodi, additional, Bullock, James, additional, Evans, Paul, additional, Václavík, Tomáš, additional, Langerwisch, Fanny, additional, Bednář, Marek, additional, Brdar, Sanja, additional, and Lugonja, Predrag, additional
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- 2023
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6. D3.3 Ecosystem service, biodiversity and socio-economic models for each case study
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Cord, Anna, primary, Roilo, Stephanie, additional, Beckmann, Michael, additional, Paulus, Anne, additional, Schneider, Katharina, additional, Lugonja, Predrag, additional, Nikolić, Tijana, additional, Langerwisch, Fanny, additional, Bednář, Marek, additional, Václavík, Tomáš, additional, Evans, Paul, additional, Gosal, Arjan, additional, Wool, Rosemary, additional, Breckenridge, George, additional, Ziv, Guy, additional, and Gunning, Jodi, additional
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- 2023
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7. Rice ecosystem services in South-east Asia
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Settele, Josef, Heong, Kong Luen, Kühn, Ingolf, Klotz, Stefan, Spangenberg, Joachim H., Arida, Gertrudo, Beaurepaire, Alexis, Beck, Silke, Bergmeier, Erwin, Burkhard, Benjamin, Brandl, Roland, Bustamante, Jesus Victor, Butler, Adam, Cabbigat, Jimmy, Le, Xuan Canh, Catindig, Josie Lynn A., Ho, Van Chien, Le, Quoc Cuong, Dang, Kinh Bac, Escalada, Monina, Dominik, Christophe, Franzén, Markus, Fried, Oliver, Görg, Christoph, Grescho, Volker, Grossmann, Sabine, Gurr, Geoff M., Hadi, Buyung A. R., Le, Huu Hai, Harpke, Alexander, Hass, Annika L., Hirneisen, Norbert, Horgan, Finbarr G., Hotes, Stefan, Isoda, Yuzuru, Jahn, Reinhold, Kettle, Helen, Klotzbücher, Anika, Klotzbücher, Thimo, Langerwisch, Fanny, Loke, Wai-Hong, Lin, Yu-Pin, Lu, Zhongxian, Lum, Keng-Yeang, Magcale-Macandog, Damasa B., Marion, Glenn, Marquez, Leonardo, Müller, Felix, Nguyen, Hung Manh, Nguyen, Quynh Anh, Nguyen, Van Sinh, Ott, Jürgen, Penev, Lyubomir, Pham, Hong Thai, Radermacher, Nico, Rodriguez-Labajos, Beatriz, Sann, Christina, Sattler, Cornelia, Schädler, Martin, Scheu, Stefan, Schmidt, Anja, Schrader, Julian, Schweiger, Oliver, Seppelt, Ralf, Soitong, Kukiat, Stoev, Pavel, Stoll-Kleemann, Susanne, Tekken, Vera, Thonicke, Kirsten, Tilliger, Bianca, Tobias, Kai, Andi Trisyono, Y., Dao, Thanh Truong, Tscharntke, Teja, Le, Quang Tuan, Türke, Manfred, Václavík, Tomáš, Vetterlein, Doris, Villareal, Sylvia ’Bong’, Vu, Kim Chi, Vu, Quynh, Weisser, Wolfgang W., Westphal, Catrin, Zhu, Zengrong, and Wiemers, Martin
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- 2018
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8. The LEGATO cross-disciplinary integrated ecosystem service research framework: an example of integrating research results from the analysis of global change impacts and the social, cultural and economic system dynamics of irrigated rice production
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Spangenberg, Joachim H., Beaurepaire, Alexis L., Bergmeier, Erwin, Burkhard, Benjamin, Van Chien, Ho, Cuong, Le Quoc, Görg, Christoph, Grescho, Volker, Hai, Le Huu, Heong, Kong Luen, Horgan, Finbarr G., Hotes, Stefan, Klotzbücher, Anika, Klotzbücher, Thimo, Kühn, Ingolf, Langerwisch, Fanny, Marion, Glenn, Moritz, Robin F. A., Nguyen, Quynh Anh, Ott, Jürgen, Sann, Christina, Sattler, Cornelia, Schädler, Martin, Schmidt, Anja, Tekken, Vera, Thanh, Truong Dao, Thonicke, Kirsten, Türke, Manfred, Václavík, Tomáš, Vetterlein, Doris, Westphal, Catrin, Wiemers, Martin, and Settele, Josef
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- 2018
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9. D3.5 Farming System Archetypes for each CS
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Langerwisch, Fanny, Václavík, Tomáš, Bednář, Marek, Ziv, Guy, Gunning, Jodi, Gosal, Arjan, Paulus, Anne, Brdar, Sanja, Lugonja, Predrag, Stojković, Stefanija, Roilo, Stephanie, Cord, Anna, Langerwisch, Fanny, Václavík, Tomáš, Bednář, Marek, Ziv, Guy, Gunning, Jodi, Gosal, Arjan, Paulus, Anne, Brdar, Sanja, Lugonja, Predrag, Stojković, Stefanija, Roilo, Stephanie, and Cord, Anna
- Abstract
This deliverable provides an overview of the methods and data used for developing the Farming System Archetypes (FSAs) in the five case studies - Humber, Mulde, SouthMoravia, Bačka and Catalonia. Additionally, it discusses limitations as well as problems and presents solutions. The FSAs are a generalized typology of farming systems that are assumed to have similar response to policy change. FSAs are a major component of the BESTMAP modelling architecture because they provide linkages between many aspects of the project, especially connecting the biophysical and agent-based modelling in the case studies (CS), based on local data (e.g. IACS/LPIS, for explanation see Methodology), with the modelling of policy effects at the EU level, based on FADN micro-data within the FADN regions. The FSA framework defines the main farm characteristics determined by two main dimensions: firstly farm specialization and secondly economic size, both calculated and mapped for each farm in the CSs. ‘Farmer agents’ who belong to the same FSA are then assumed to have similar decision patterns regarding the adoption of agri-environmental schemes, based on the relationships revealed in the CS agent-based models.
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- 2023
10. Climate change impacts in Latin America and the Caribbean and their implications for development
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Reyer, Christopher P.O., Adams, Sophie, Albrecht, Torsten, Baarsch, Florent, Boit, Alice, Canales Trujillo, Nella, Cartsburg, Matti, Coumou, Dim, Eden, Alexander, Fernandes, Erick, Langerwisch, Fanny, Marcus, Rachel, Mengel, Matthias, Mira-Salama, Daniel, Perette, Mahé, Pereznieto, Paola, Rammig, Anja, Reinhardt, Julia, Robinson, Alexander, Rocha, Marcia, Sakschewski, Boris, Schaeffer, Michiel, Schleussner, Carl-Friedrich, Serdeczny, Olivia, and Thonicke, Kirsten
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- 2017
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11. Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges
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Reyer, Christopher P. O., Brouwers, Niels, Rammig, Anja, Brook, Barry W., Epila, Jackie, Grant, Robert F., Holmgren, Milena, Langerwisch, Fanny, Leuzinger, Sebastian, Lucht, Wolfgang, Medlyn, Belinda, Pfeifer, Marion, Steinkamp, Jörg, Vanderwel, Mark C., Verbeeck, Hans, and Villela, Dora M.
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- 2015
12. Forest resilience, tipping points and global change processes
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Reyer, Christopher P.O., Rammig, Anja, Brouwers, Niels, and Langerwisch, Fanny
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- 2015
13. Comment on esd-2022-4
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Langerwisch, Fanny, primary
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- 2022
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14. D1.3 Guidelines and protocols harmonizing activities across case studies
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Václavík, Tomáš, Langerwisch, Fanny, Ziv, Guy, Gunning, Jodi, Gosal, Arjan, Beckmann, Michael, Paulus, Anne, Wittstock, Felix, Cord, Anna, Roilo, Stephanie, Domingo-Marimon, Cristina, Sanchez, Anabel, Broekman, Annelies, and Vujaklija, Dajana
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case study ,geodata ,guidelines ,Stakeholder engagement ,management ,policy - Abstract
This document is the first version of the Guidelines and protocols harmonizing activities across case studies of the H2020 BESTMAP project. It is intended to be updated in month 40 (D1.8).
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- 2022
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15. D1.3 Guidelines and protocols harmonizing activities across case studies
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Václavík, Tomáš, primary, Langerwisch, Fanny, additional, Ziv, Guy, additional, Gunning, Jodi, additional, Gosal, Arjan, additional, Beckmann, Michael, additional, Paulus, Anne, additional, Wittstock, Felix, additional, Cord, Anna, additional, Roilo, Stephanie, additional, Domingo-Marimon, Cristina, additional, Sanchez, Anabel, additional, Broekman, Annelies, additional, and Vujaklija, Dajana, additional
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- 2022
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16. Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests
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Sakschewski, Boris, primary, von Bloh, Werner, additional, Drüke, Markus, additional, Sörensson, Anna Amelia, additional, Ruscica, Romina, additional, Langerwisch, Fanny, additional, Billing, Maik, additional, Bereswill, Sarah, additional, Hirota, Marina, additional, Oliveira, Rafael Silva, additional, Heinke, Jens, additional, and Thonicke, Kirsten, additional
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- 2021
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17. Tackling unresolved questions in forest ecology: The past and future role of simulation models
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Maréchaux, Isabelle, primary, Langerwisch, Fanny, additional, Huth, Andreas, additional, Bugmann, Harald, additional, Morin, Xavier, additional, Reyer, Christopher P.O., additional, Seidl, Rupert, additional, Collalti, Alessio, additional, Dantas de Paula, Mateus, additional, Fischer, Rico, additional, Gutsch, Martin, additional, Lexer, Manfred J., additional, Lischke, Heike, additional, Rammig, Anja, additional, Rödig, Edna, additional, Sakschewski, Boris, additional, Taubert, Franziska, additional, Thonicke, Kirsten, additional, Vacchiano, Giorgio, additional, and Bohn, Friedrich J., additional
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- 2021
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18. Supplementary material to "Variable tree rooting strategies improve tropical productivity and evapotranspiration in a dynamic global vegetation model"
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Sakschewski, Boris, primary, von Bloh, Werner, additional, Drüke, Markus, additional, Sörensson, Anna A., additional, Ruscica, Romina, additional, Langerwisch, Fanny, additional, Billing, Maik, additional, Bereswill, Sarah, additional, Hirota, Marina, additional, Oliveira, Rafael S., additional, Heinke, Jens, additional, and Thonicke, Kirsten, additional
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- 2020
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19. Variable tree rooting strategies improve tropical productivity and evapotranspiration in a dynamic global vegetation model
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Sakschewski, Boris, primary, von Bloh, Werner, additional, Drüke, Markus, additional, Sörensson, Anna A., additional, Ruscica, Romina, additional, Langerwisch, Fanny, additional, Billing, Maik, additional, Bereswill, Sarah, additional, Hirota, Marina, additional, Oliveira, Rafael S., additional, Heinke, Jens, additional, and Thonicke, Kirsten, additional
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- 2020
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20. LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description
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Schaphoff, Sibyll, Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, Hester, Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, and Waha, Katharina
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This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.
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- 2018
21. A social-ecological approach to identify and quantify biodiversity tipping points in South America's seasonal dry ecosystems
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Thonicke, Kirsten, primary, Langerwisch, Fanny, additional, Baumann, Matthias, additional, Leitão, Pedro J., additional, Václavík, Tomáš, additional, Alencar, Ane, additional, Simões, Margareth, additional, Scheiter, Simon, additional, Langan, Liam, additional, Bustamante, Mercedes, additional, Gasparri, Ignacio, additional, Hirota, Marina, additional, Börner, Jan, additional, Rajao, Raoni, additional, Soares-Filho, Britaldo, additional, Yanosky, Alberto, additional, Ochoa-Quinteiro, José-Manuel, additional, Seghezzo, Lucas, additional, Conti, Georgina, additional, and de la Vega-Leinert, Anne Cristina, additional
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- 2019
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22. Make EU trade with Brazil sustainable
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Kehoe, Laura, Reis, Tiago, Virah-Sawmy, Malika, Balmford, Andrew, Kuemmerle, Tobias, Knohl, Alexander, Antonelli, Alexandre, Hochkirch, Axel, Vira, Bhaskar, Massa, Bruno, Peres, Carlos A., Ammer, Christian, Goerg, Christoph, Schneider, Christoph, Curtis, David, de la Pena, Eduardo, Tello, Enric, Sperfeld, Erik, Corbera, Esteve, Morelli, Federico, Valladares, Fernando, Peterson, Garry, Hide, Geoff, Mace, Georgina, Kallis, Giorgos, Olsson, Gunilla Almered, Brumelis, Guntis, Alexanderson, Helena, Haberl, Helmut, Nuissl, Henning, Kreft, Holger, Ghazoul, Jaboury, Piotrowski, Jan A., Macdiarmid, Jennie, Newig, Jens, Fischer, Joern, Altringham, John, Gledhill, John, Nielsen, Jonas O., Mueller, Joerg, Palmeirim, Jorge, Barlow, Jos, Alonso, Juan C., Presa Asencio, Juan Jose, Steinberger, Julia K., Jones, Julia Patricia Gordon, Cabral, Juliano Sarmento, Dengler, Juergen, Stibral, Karel, Erb, Karlheinz, Rothhaupt, Karl-Otto, Wiegand, Kerstin, Cassar, Louis F., Lens, Luc, Rosalino, Luis Miguel, Wassen, M. J., Stenseke, Marie, Fischer-Kowalski, Marina, Diaz, Mario, Rounsevell, Mark, van Kleunen, Mark, Junginger, Martin, Kaltenpoth, Martin, Zobel, Martin, Weigend, Maximilian, Partel, Meelis, Schilthuizen, Menno, Bastos Araujo, Miguel, Haklay, Muki, Eisenhauer, Nico, Selva, Nuria, Mertz, Ole, Meyfroidt, Patrick, Borges, Paulo A. V., Kovar, Pavel, Smith, Pete, Verburg, Peter, Pysek, Petr, Seppelt, Ralf, Valentini, Riccardo, Whittaker, Robert J., Henrique Faria, Sergio, Ulgiati, Sergio, Loetters, Stefan, Bjorck, Svante, Larson, Sven Ake, Tscharntke, Teja, Domingos, Tiago, Krueger, Tobias, Pascual, Unai, Olsson, Urban, Kati, Vassiliki, Winiwarter, Verena, Reyes-Garcia, Victoria, Vajda, Vivi, Sutherland, William J., de Waroux, Yann le Polain, Buckley, Yvonne, Rammig, Anja, Kasimir, Asa, Crona, Beatrice, Sindicic, Magda, Persson, Martin, Lapka, Miloslav, Di Gregorio, Monica, Hahn, Thomas, Boonstra, Wiebren, Lipsky, Zdenek, Zucaro, A., Roeder, Achim, Lopez Baucells, Adria, Danet, Alain, Franco, Aldina, Nieto Roman, Alejandra, Lehikoinen, Aleksi, Collalti, Alessio, Keller, Alexander, Strugariu, Alexandru, Perrigo, Allison, Fernandez-Llamazares, Alvaro, Salaseviciene, Alvija, Hinsley, Amy, Santos, Ana M. C., Novoa, Ana, Rodrigues, Ana, Mascarenhas, Andre, Martins, Andrea Damacena, Holzschuh, Andrea, Meseguer, Andrea S., Hadjichambis, Andreas, Mayer, Andreas, Hacket-Pain, Andrew, Ringsmuth, Andrew, de Frutos, Angel, Stein, Anke, Heikkinen, Anna, Smith, Annabel, Bjoersne, Anna-Karin, Bagneres, Anne-Genevieve, Machordom, Annie, Kristin, Anton, Ghoddousi, Arash, Staal, Arie, Martin, Arnaud, Taylor, Astrid, Borrell, Asuncion, Marescaux, Audrey, Torres, Aurora, Helm, Aveliina, Bauer, Barbara, Smetschka, Barbara, Rodriguez-Labajos, Beatriz, Peco, Begona, Gambin, Belinda, Celine, Bellard, Phalan, Ben, Cotta, Benedetta, Rugani, Benedetto, Jarcuska, Benjamin, Leroy, Boris, Nikolov, Boris Petrov, Milchev, Boyan Petrov, Brown, Calum, Ritter, Camila Duarte, Gomes, Carmen Bessa, Meyer, Carsten, Munteanu, Catalina, Penone, Caterina, Friis, Cecilie, Teplitsky, Celine, Roemer, Charlotte, Orland, Chloe, Voigt, Christian C., Levers, Christian, Zang, Christian, Bacon, Christine D., Meyer, Christoph, Wordley, Claire, Grilo, Clara, Cattaneo, Claudio, Battisti, Corrado, Banks-Leite, Cristina, Zurell, Damaris, Challender, Dan, Mueller, Daniel, Matenaar, Daniela, Silvestro, Daniele, McKay, David Armstrong, Buckley, David, Frantz, David, Gremillet, David, Mateos, David Moreno, Sanchez-Fernandez, David, Vieites, David, Ascoli, Davide, Arlt, Debora, Louis, Deharveng, Zemp, Delphine Clara, Strubbe, Diederik, Gil, Diego, Llusia, Diego, Bennett, Dominic J., Chobanov, Dragan Petrov, Aguilera, Eduardo, Oliveira, Eduardo, Pynegar, Edwin L., Granda, Elena, Grieco, Elisa, Conrad, Elisabeth, Revilla, Eloy, Lindkvist, Emilie, Caprio, Enrico, zu Ermgassen, Erasmus, Berenguer, Erika, Ochu, Erinma, Polaina, Ester, Nuernberger, Fabian, Esculier, Fabien, de Castro, Fabio, Albanito, Fabrizio, Langerwisch, Fanny, Batsleer, Femke, Ascensao, Fernando, Moyano, Fernando Esteban, Sayol, Ferran, Buzzetti, Filippo Maria, Eiro, Flavio, Volaire, Florence, Gollnow, Florian, Menzel, Florian, Pilo, Francesca, Moreira, Francisco, Briens, Francois, Essl, Franz, Vlahos, George, Billen, Gilles, Vacchiano, Giorgio, Wong, Grace, Gruychev, Gradimir Valentinov, Fandos, Guillermo, Petter, Gunnar, Sinare, Hanna, Mumby, Hannah S., Cottyn, Hanne, Seebens, Hanno, Bjorklund, Heidi, Schroeder, Heike, Lopez Hernandez, Heriberto D., Rebelo, Hugo, Chenet, Hugues, De la Riva, Ignacio, Torre, Ignasi, Aalders, Inge, Grass, Ingo, Chuine, Isabelle, Goepel, Jan, Wieringa, Jan J., Engler, Jan O., Pergl, Jan, Schnitzler, Jan, Vavra, Jan, Medvedovic, Jasna, Cabello, Javier, Martin, Jean-Louis, Mutke, Jens, Lewis, Jerome, da Silva, Jessica Fonseca, Marull, Joan, Carvalho, Joana, Carnicer, Jofre, Enqvist, Johan, Simaika, John P., Noguera, Jose C., Blanco Moreno, Jose M., Bruna, Josef, Garnier, Josette, Fargallo, Juan A., Rocha, Juan Carlos, Carrillo, Juan D., Infante-Amate, Juan, Traba Diaz, Juan, Schleicher, Judith, Simon, Judy, Noe, Julia Le, Gerlach, Justin, Eriksson, K. Martin, Prince, Karine, Ostapowicz, Katarzyna, Stajerova, Katerina, Farrell, Katharine N., Snell, Katherine, Yates, Katherine, Fleischer, Katrin, Darras, Kevin, Schumacher, Kim, Orach, Kirill, Thonicke, Kirsten, Riede, Klaus, Heller, Klaus-Gerhard, Wang-Erlandsson, Lan, Pereira, Laura, Riggi, Laura, Florez, Laura V., Emperaire, Laure, Durieux, Laurent, Tatin, Laurent, Rozylowicz, Laurentiu, Latella, Leonardo, Andresen, Louise C., Cahen-Fourot, Louison, de Agua, Luis Borda, Boto, Luis, Lassaletta, Luis, Amo, Luisa, Sekerka, Lukas, Morales, Manuel B., Macia, Manuel J., Suarez, Manuela Gonzalez, Cabeza, Mar, Londo, Marc, Pollet, Marc, Schwieder, Marcel, Peters, Marcell K., D'Amico, Marcello, Casazza, Marco, Florencio, Margarita, Felipe-Lucia, Maria, Gebara, Maria Fernanda, Johansson, Maria, Garcia, Maria Mancilla, Piquer-Rodriguez, Maria, Tengo, Maria, Elias, Marianne, Leve, Marine, Conde, Marta, Winter, Marten, Koster, Martijn, Mayer, Martin, Salek, Martin, Schlerf, Martin, Sullivan, Martin, Baumann, Matthias, Pichler, Melanie, Marselle, Melissa, Oddie, Melissa, Razanajatovo, Mialy, Borregaard, Michael Krabbe, Theurl, Michaela C., Hernandez, Miguel, Krofel, Miha, Kechev, Mihail Ognianov, Clark, Mike, Rands, Mike, Antal, Miklos, Pucetaite, Milda, Islar, Mine, Truong, Minh-Xuan A., Vighi, Morgana, Johanisova, Nadia, Prat, Narcis, Escobar, Neus, Deguines, Nicolas, Rust, Niki, Zafra-Calvo, Noelia, Maurel, Noelie, Wagner, Norman, Fitton, Nuala, Ostermann, Ole, Panferov, Oleg, Ange, Olivia, Canals, Oriol, Englund, Oskar, De Smedt, Pallieter, Petridis, Panos, Heikkurinen, Pasi, Weigelt, Patrick, Henriksson, Patrik J. G., de Castro, Paula Drummond, Matos-Maravi, Pavel, Duran, Paz, Aragon, Pedro, Cardoso, Pedro, Leitao, Pedro J., Hosner, Peter A., Biedermann, Peter, Keil, Petr, Petrik, Petr, Martin, Philip, Bocquillon, Pierre, Renaud, Pierre-Cyril, Addison, Prue, Antwis, Rachael, Carmenta, Rachel, Barrientos, Rafael, Smith, Rebecca, Rocha, Ricardo, Fuchs, Richard, Felix, Rob, Kanka, Robert, Aguilee, Robin, Padro Caminal, Roc, Libbrecht, Romain, Lorrilliere, Romain, van der Ent, Ruud J., Henders, Sabine, Pueyo, Salvador, Roturier, Samuel, Jacobs, Sander, Lavorel, Sandra, Leonhardt, Sara Diana, Fraixedas, Sara, Villen-Perez, Sara, Cornell, Sarah, Redlich, Sarah, De Smedt, Sebastian, van der Linden, Sebastian, Perez-Ortega, Sergio, Petrovan, Silviu, Cesarz, Simone, Sjoberg, Sissel, Caillon, Sophie, Schindler, Stefan, Trogisch, Stefan, Taiti, Stefano, Oppel, Steffen, Lutter, Stephan, Garnett, Tara, Guedes, Thais, Wanger, Thomas Cherico, Kastner, Thomas, Worthington, Thomas, Daw, Tim, Schmoll, Tim, McPhearson, Timon, Engl, Tobias, Rutting, Tobias, Vaclavik, Tomas, Jucker, Tommaso, Robillard, Tony, Krause, Torsten, Ljubomirov, Toshko, Aavik, Tsipe, Richardson, Vanessa A., Masterson, Vanessa Anne, Seufert, Verena, Cathy, Vet Gibault, Colino Rabanal, Victor, Montade, Vincent, Thieu, Vincent, Sober, Virve, Morin, Xavier, Mehrabi, Zia, Gonzalez, Adriana Trompetero, Sanz-Cobena, Alberto, Christie, Alec Philip, Romero-Munoz, Alfredo, Dauriach, Alice, Queiroz, Allan Souza, Golland, Ami, Evans, Amy Louise, Cordero, Ana Maria Araujo, Dara, Andrey, Rilovic, Andro, Pedersen, Anna Frohn, Csergo, Anna Maria, Lewerentz, Anne, Monserand, Antoine, Valdecasas, Antonio G., Doherty, Anya, Semper-Pascual, Asuncion, Bleyhl, Benjamin, Rutschmann, Benjamin, Bongalov, Boris, Hankerson, Brett, Heylen, Brigitte, Alonso-Alvarez, Carlos, Comandulli, Carolina, Frossard, Carolina M., Mckeon, Caroline, Godde, Cecile, Palm, Celinda, Singh, Chandrakant, Sieger, Charlotte Sophie, Ohrling, Christian, Paitan, Claudia Parra, Cooper, Conor, Edler, Daniel, Roessler, Daniela C., Kessner-Beierlein, Daniela, Garcia del Amo, David, Lopez Bosch, David, Gueldner, Dino, Noll, Domink, Motivans, Elena, Canteri, Elisabetta, Garnett, Emma, Malecore, Eva, Brambach, Fabian, Ruedenauer, Fabian, Yin, Fang, Hurtado, Fernando, Mempel, Finn, de Freitas, Flavio Luiz Mazzaro, Pendrill, Florence, Leijten, Floris, Somma, Francesca, Schug, Franz, De Knijf, Geert, Peterson, Gustaf, Pe'er, Guy, Booth, Hollie, Rhee, Howon, Staude, Ingmar, Gherghel, Iulian, Vila Traver, Jaime, Kerner, Janika, Hinton, Jennifer, Hortal, Joaquin, Persson, Joel, Uddling, Johan, Coenen, Johanna, Geldmann, Jonas, Geschke, Jonas, Juergensen, Jonathan, Lobo, Jorge M., Skejo, Josip, Heinen, Julia Helena, Schuenzel, Julia, Daniel-Ferreira, Juliana, Christophe Piquet, Julien, Murtough, Katie L., Prevel, Leonie, Hissa, Leticia B. V., af Segerstad, Louise Hard, Willemse, Luc, Benavides, Lucia, Sovova, Lucie, Figueiredo, Ludmilla, Leidinger, Ludwig, Piemontese, Luigi, da Fonte, Luis Fernando Marin, Moreta, Lys Sanz, Bhan, Manan, Toledo-Hernandez, Manuel, Engert, Manuela, Davoli, Marco, Mas Navarro, Maria, Voigt, Maria, Zirion, Maria, Wandl, Marie-Theres, Kipson, Marina, Johnson, Mark D., Lukic, Marko, Goula, Marta, Jung, Martin, Nunes, Matheus Henrique, Alvarez, Matheus Rodriguez, van den Burg, Matthijs P., Guerrero, Mayra Daniela Pena, Greenfield, Michael, Lobmann, Michael, Nygren, Michelle, Guth, Miriam Karen, Koh, Niak, Stanek, Nicola, Roux, Nicolas, Karagouni, Niki, Tiralla, Nina, Mairota, Paola, Savaget, Paulo, von Doehren, Peer, Benyei, Petra, Lena, Philippe, Rufin, Philippe, Janke, Rebekka, Santagata, Remo, Motta, Renzo, Battiston, Roberto, Oyanedel, Rodrigo, Bernardo-Madrid, Ruben, Vasconcelos, Sasha, Henriques, Sergio, Bager, Simon L., Qin, Siyu, Ivkovic, Slobodan, Cooke, Sophia, Ernst, Stefan, Schmelzer, Stefan, da Silva, Sven, Faberova, Tamara, Enseroth, Tanja, De Marzo, Teresa, Pienkowski, Thomas, Engel, Thore, Boehnert, Tim, Swinfield, Tom, Kurdikova, Vendula, Chvatalova, Veronika, Lopez-Marquez, Violeta, Arlidge, William, Zhang, Zhijie, Kehoe, Laura, Reis, Tiago, Virah-Sawmy, Malika, Balmford, Andrew, Kuemmerle, Tobias, Knohl, Alexander, Antonelli, Alexandre, Hochkirch, Axel, Vira, Bhaskar, Massa, Bruno, Peres, Carlos A., Ammer, Christian, Goerg, Christoph, Schneider, Christoph, Curtis, David, de la Pena, Eduardo, Tello, Enric, Sperfeld, Erik, Corbera, Esteve, Morelli, Federico, Valladares, Fernando, Peterson, Garry, Hide, Geoff, Mace, Georgina, Kallis, Giorgos, Olsson, Gunilla Almered, Brumelis, Guntis, Alexanderson, Helena, Haberl, Helmut, Nuissl, Henning, Kreft, Holger, Ghazoul, Jaboury, Piotrowski, Jan A., Macdiarmid, Jennie, Newig, Jens, Fischer, Joern, Altringham, John, Gledhill, John, Nielsen, Jonas O., Mueller, Joerg, Palmeirim, Jorge, Barlow, Jos, Alonso, Juan C., Presa Asencio, Juan Jose, Steinberger, Julia K., Jones, Julia Patricia Gordon, Cabral, Juliano Sarmento, Dengler, Juergen, Stibral, Karel, Erb, Karlheinz, Rothhaupt, Karl-Otto, Wiegand, Kerstin, Cassar, Louis F., Lens, Luc, Rosalino, Luis Miguel, Wassen, M. J., Stenseke, Marie, Fischer-Kowalski, Marina, Diaz, Mario, Rounsevell, Mark, van Kleunen, Mark, Junginger, Martin, Kaltenpoth, Martin, Zobel, Martin, Weigend, Maximilian, Partel, Meelis, Schilthuizen, Menno, Bastos Araujo, Miguel, Haklay, Muki, Eisenhauer, Nico, Selva, Nuria, Mertz, Ole, Meyfroidt, Patrick, Borges, Paulo A. V., Kovar, Pavel, Smith, Pete, Verburg, Peter, Pysek, Petr, Seppelt, Ralf, Valentini, Riccardo, Whittaker, Robert J., Henrique Faria, Sergio, Ulgiati, Sergio, Loetters, Stefan, Bjorck, Svante, Larson, Sven Ake, Tscharntke, Teja, Domingos, Tiago, Krueger, Tobias, Pascual, Unai, Olsson, Urban, Kati, Vassiliki, Winiwarter, Verena, Reyes-Garcia, Victoria, Vajda, Vivi, Sutherland, William J., de Waroux, Yann le Polain, Buckley, Yvonne, Rammig, Anja, Kasimir, Asa, Crona, Beatrice, Sindicic, Magda, Persson, Martin, Lapka, Miloslav, Di Gregorio, Monica, Hahn, Thomas, Boonstra, Wiebren, Lipsky, Zdenek, Zucaro, A., Roeder, Achim, Lopez Baucells, Adria, Danet, Alain, Franco, Aldina, Nieto Roman, Alejandra, Lehikoinen, Aleksi, Collalti, Alessio, Keller, Alexander, Strugariu, Alexandru, Perrigo, Allison, Fernandez-Llamazares, Alvaro, Salaseviciene, Alvija, Hinsley, Amy, Santos, Ana M. C., Novoa, Ana, Rodrigues, Ana, Mascarenhas, Andre, Martins, Andrea Damacena, Holzschuh, Andrea, Meseguer, Andrea S., Hadjichambis, Andreas, Mayer, Andreas, Hacket-Pain, Andrew, Ringsmuth, Andrew, de Frutos, Angel, Stein, Anke, Heikkinen, Anna, Smith, Annabel, Bjoersne, Anna-Karin, Bagneres, Anne-Genevieve, Machordom, Annie, Kristin, Anton, Ghoddousi, Arash, Staal, Arie, Martin, Arnaud, Taylor, Astrid, Borrell, Asuncion, Marescaux, Audrey, Torres, Aurora, Helm, Aveliina, Bauer, Barbara, Smetschka, Barbara, Rodriguez-Labajos, Beatriz, Peco, Begona, Gambin, Belinda, Celine, Bellard, Phalan, Ben, Cotta, Benedetta, Rugani, Benedetto, Jarcuska, Benjamin, Leroy, Boris, Nikolov, Boris Petrov, Milchev, Boyan Petrov, Brown, Calum, Ritter, Camila Duarte, Gomes, Carmen Bessa, Meyer, Carsten, Munteanu, Catalina, Penone, Caterina, Friis, Cecilie, Teplitsky, Celine, Roemer, Charlotte, Orland, Chloe, Voigt, Christian C., Levers, Christian, Zang, Christian, Bacon, Christine D., Meyer, Christoph, Wordley, Claire, Grilo, Clara, Cattaneo, Claudio, Battisti, Corrado, Banks-Leite, Cristina, Zurell, Damaris, Challender, Dan, Mueller, Daniel, Matenaar, Daniela, Silvestro, Daniele, McKay, David Armstrong, Buckley, David, Frantz, David, Gremillet, David, Mateos, David Moreno, Sanchez-Fernandez, David, Vieites, David, Ascoli, Davide, Arlt, Debora, Louis, Deharveng, Zemp, Delphine Clara, Strubbe, Diederik, Gil, Diego, Llusia, Diego, Bennett, Dominic J., Chobanov, Dragan Petrov, Aguilera, Eduardo, Oliveira, Eduardo, Pynegar, Edwin L., Granda, Elena, Grieco, Elisa, Conrad, Elisabeth, Revilla, Eloy, Lindkvist, Emilie, Caprio, Enrico, zu Ermgassen, Erasmus, Berenguer, Erika, Ochu, Erinma, Polaina, Ester, Nuernberger, Fabian, Esculier, Fabien, de Castro, Fabio, Albanito, Fabrizio, Langerwisch, Fanny, Batsleer, Femke, Ascensao, Fernando, Moyano, Fernando Esteban, Sayol, Ferran, Buzzetti, Filippo Maria, Eiro, Flavio, Volaire, Florence, Gollnow, Florian, Menzel, Florian, Pilo, Francesca, Moreira, Francisco, Briens, Francois, Essl, Franz, Vlahos, George, Billen, Gilles, Vacchiano, Giorgio, Wong, Grace, Gruychev, Gradimir Valentinov, Fandos, Guillermo, Petter, Gunnar, Sinare, Hanna, Mumby, Hannah S., Cottyn, Hanne, Seebens, Hanno, Bjorklund, Heidi, Schroeder, Heike, Lopez Hernandez, Heriberto D., Rebelo, Hugo, Chenet, Hugues, De la Riva, Ignacio, Torre, Ignasi, Aalders, Inge, Grass, Ingo, Chuine, Isabelle, Goepel, Jan, Wieringa, Jan J., Engler, Jan O., Pergl, Jan, Schnitzler, Jan, Vavra, Jan, Medvedovic, Jasna, Cabello, Javier, Martin, Jean-Louis, Mutke, Jens, Lewis, Jerome, da Silva, Jessica Fonseca, Marull, Joan, Carvalho, Joana, Carnicer, Jofre, Enqvist, Johan, Simaika, John P., Noguera, Jose C., Blanco Moreno, Jose M., Bruna, Josef, Garnier, Josette, Fargallo, Juan A., Rocha, Juan Carlos, Carrillo, Juan D., Infante-Amate, Juan, Traba Diaz, Juan, Schleicher, Judith, Simon, Judy, Noe, Julia Le, Gerlach, Justin, Eriksson, K. Martin, Prince, Karine, Ostapowicz, Katarzyna, Stajerova, Katerina, Farrell, Katharine N., Snell, Katherine, Yates, Katherine, Fleischer, Katrin, Darras, Kevin, Schumacher, Kim, Orach, Kirill, Thonicke, Kirsten, Riede, Klaus, Heller, Klaus-Gerhard, Wang-Erlandsson, Lan, Pereira, Laura, Riggi, Laura, Florez, Laura V., Emperaire, Laure, Durieux, Laurent, Tatin, Laurent, Rozylowicz, Laurentiu, Latella, Leonardo, Andresen, Louise C., Cahen-Fourot, Louison, de Agua, Luis Borda, Boto, Luis, Lassaletta, Luis, Amo, Luisa, Sekerka, Lukas, Morales, Manuel B., Macia, Manuel J., Suarez, Manuela Gonzalez, Cabeza, Mar, Londo, Marc, Pollet, Marc, Schwieder, Marcel, Peters, Marcell K., D'Amico, Marcello, Casazza, Marco, Florencio, Margarita, Felipe-Lucia, Maria, Gebara, Maria Fernanda, Johansson, Maria, Garcia, Maria Mancilla, Piquer-Rodriguez, Maria, Tengo, Maria, Elias, Marianne, Leve, Marine, Conde, Marta, Winter, Marten, Koster, Martijn, Mayer, Martin, Salek, Martin, Schlerf, Martin, Sullivan, Martin, Baumann, Matthias, Pichler, Melanie, Marselle, Melissa, Oddie, Melissa, Razanajatovo, Mialy, Borregaard, Michael Krabbe, Theurl, Michaela C., Hernandez, Miguel, Krofel, Miha, Kechev, Mihail Ognianov, Clark, Mike, Rands, Mike, Antal, Miklos, Pucetaite, Milda, Islar, Mine, Truong, Minh-Xuan A., Vighi, Morgana, Johanisova, Nadia, Prat, Narcis, Escobar, Neus, Deguines, Nicolas, Rust, Niki, Zafra-Calvo, Noelia, Maurel, Noelie, Wagner, Norman, Fitton, Nuala, Ostermann, Ole, Panferov, Oleg, Ange, Olivia, Canals, Oriol, Englund, Oskar, De Smedt, Pallieter, Petridis, Panos, Heikkurinen, Pasi, Weigelt, Patrick, Henriksson, Patrik J. G., de Castro, Paula Drummond, Matos-Maravi, Pavel, Duran, Paz, Aragon, Pedro, Cardoso, Pedro, Leitao, Pedro J., Hosner, Peter A., Biedermann, Peter, Keil, Petr, Petrik, Petr, Martin, Philip, Bocquillon, Pierre, Renaud, Pierre-Cyril, Addison, Prue, Antwis, Rachael, Carmenta, Rachel, Barrientos, Rafael, Smith, Rebecca, Rocha, Ricardo, Fuchs, Richard, Felix, Rob, Kanka, Robert, Aguilee, Robin, Padro Caminal, Roc, Libbrecht, Romain, Lorrilliere, Romain, van der Ent, Ruud J., Henders, Sabine, Pueyo, Salvador, Roturier, Samuel, Jacobs, Sander, Lavorel, Sandra, Leonhardt, Sara Diana, Fraixedas, Sara, Villen-Perez, Sara, Cornell, Sarah, Redlich, Sarah, De Smedt, Sebastian, van der Linden, Sebastian, Perez-Ortega, Sergio, Petrovan, Silviu, Cesarz, Simone, Sjoberg, Sissel, Caillon, Sophie, Schindler, Stefan, Trogisch, Stefan, Taiti, Stefano, Oppel, Steffen, Lutter, Stephan, Garnett, Tara, Guedes, Thais, Wanger, Thomas Cherico, Kastner, Thomas, Worthington, Thomas, Daw, Tim, Schmoll, Tim, McPhearson, Timon, Engl, Tobias, Rutting, Tobias, Vaclavik, Tomas, Jucker, Tommaso, Robillard, Tony, Krause, Torsten, Ljubomirov, Toshko, Aavik, Tsipe, Richardson, Vanessa A., Masterson, Vanessa Anne, Seufert, Verena, Cathy, Vet Gibault, Colino Rabanal, Victor, Montade, Vincent, Thieu, Vincent, Sober, Virve, Morin, Xavier, Mehrabi, Zia, Gonzalez, Adriana Trompetero, Sanz-Cobena, Alberto, Christie, Alec Philip, Romero-Munoz, Alfredo, Dauriach, Alice, Queiroz, Allan Souza, Golland, Ami, Evans, Amy Louise, Cordero, Ana Maria Araujo, Dara, Andrey, Rilovic, Andro, Pedersen, Anna Frohn, Csergo, Anna Maria, Lewerentz, Anne, Monserand, Antoine, Valdecasas, Antonio G., Doherty, Anya, Semper-Pascual, Asuncion, Bleyhl, Benjamin, Rutschmann, Benjamin, Bongalov, Boris, Hankerson, Brett, Heylen, Brigitte, Alonso-Alvarez, Carlos, Comandulli, Carolina, Frossard, Carolina M., Mckeon, Caroline, Godde, Cecile, Palm, Celinda, Singh, Chandrakant, Sieger, Charlotte Sophie, Ohrling, Christian, Paitan, Claudia Parra, Cooper, Conor, Edler, Daniel, Roessler, Daniela C., Kessner-Beierlein, Daniela, Garcia del Amo, David, Lopez Bosch, David, Gueldner, Dino, Noll, Domink, Motivans, Elena, Canteri, Elisabetta, Garnett, Emma, Malecore, Eva, Brambach, Fabian, Ruedenauer, Fabian, Yin, Fang, Hurtado, Fernando, Mempel, Finn, de Freitas, Flavio Luiz Mazzaro, Pendrill, Florence, Leijten, Floris, Somma, Francesca, Schug, Franz, De Knijf, Geert, Peterson, Gustaf, Pe'er, Guy, Booth, Hollie, Rhee, Howon, Staude, Ingmar, Gherghel, Iulian, Vila Traver, Jaime, Kerner, Janika, Hinton, Jennifer, Hortal, Joaquin, Persson, Joel, Uddling, Johan, Coenen, Johanna, Geldmann, Jonas, Geschke, Jonas, Juergensen, Jonathan, Lobo, Jorge M., Skejo, Josip, Heinen, Julia Helena, Schuenzel, Julia, Daniel-Ferreira, Juliana, Christophe Piquet, Julien, Murtough, Katie L., Prevel, Leonie, Hissa, Leticia B. V., af Segerstad, Louise Hard, Willemse, Luc, Benavides, Lucia, Sovova, Lucie, Figueiredo, Ludmilla, Leidinger, Ludwig, Piemontese, Luigi, da Fonte, Luis Fernando Marin, Moreta, Lys Sanz, Bhan, Manan, Toledo-Hernandez, Manuel, Engert, Manuela, Davoli, Marco, Mas Navarro, Maria, Voigt, Maria, Zirion, Maria, Wandl, Marie-Theres, Kipson, Marina, Johnson, Mark D., Lukic, Marko, Goula, Marta, Jung, Martin, Nunes, Matheus Henrique, Alvarez, Matheus Rodriguez, van den Burg, Matthijs P., Guerrero, Mayra Daniela Pena, Greenfield, Michael, Lobmann, Michael, Nygren, Michelle, Guth, Miriam Karen, Koh, Niak, Stanek, Nicola, Roux, Nicolas, Karagouni, Niki, Tiralla, Nina, Mairota, Paola, Savaget, Paulo, von Doehren, Peer, Benyei, Petra, Lena, Philippe, Rufin, Philippe, Janke, Rebekka, Santagata, Remo, Motta, Renzo, Battiston, Roberto, Oyanedel, Rodrigo, Bernardo-Madrid, Ruben, Vasconcelos, Sasha, Henriques, Sergio, Bager, Simon L., Qin, Siyu, Ivkovic, Slobodan, Cooke, Sophia, Ernst, Stefan, Schmelzer, Stefan, da Silva, Sven, Faberova, Tamara, Enseroth, Tanja, De Marzo, Teresa, Pienkowski, Thomas, Engel, Thore, Boehnert, Tim, Swinfield, Tom, Kurdikova, Vendula, Chvatalova, Veronika, Lopez-Marquez, Violeta, Arlidge, William, and Zhang, Zhijie
- Published
- 2019
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- View/download PDF
23. A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region
- Author
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Rammig, Anja, Heinke, Jens, Hofhansl, Florian, Verbeeck, Hans, Baker, Timothy R., Christoffersen, Bradley, Ciais, Philippe, De Deurwaerder, Hannes, Fleischer, Katrin, Galbraith, David, Guimberteau, Matthieu, Huth, Andreas, Johnson, Michelle, Krujit, Bart, Langerwisch, Fanny, Meir, Patrick, Papastefanou, Phillip, Sampaio, Gilvan, Thonicke, Kirsten, von Randow, Celso, Zang, Christian, and Rödig, Edna
- Subjects
ddc - Published
- 2017
24. Variable tree rooting strategies improve tropical productivity and evapotranspiration in a dynamic global vegetation model.
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Sakschewski, Boris, Bloh, Werner von, Drüke, Markus, Sörensson, Anna A., Ruscica, Romina, Langerwisch, Fanny, Billing, Maik, Bereswill, Sarah, Hirota, Marina, Oliveira, Rafael S., Heinke, Jens, and Thonicke, Kirsten
- Subjects
EVAPOTRANSPIRATION ,PLANTS ,TREES - Abstract
Tree water access via roots is crucial for forest functioning and therefore forests have developed a vast variety of rooting strategies across the globe. However, Dynamic Global Vegetation Models (DGVMs), which are increasingly used to simulate forest functioning, often condense this variety of tree rooting strategies into biome-scale averages, potentially under- or overestimating forest response to intra- and inter-annual variability in precipitation. Here we present a new approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 DGVM and apply it to tropical and sub-tropical South-America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulated intra-annual productivity and evapotranspiration, and consequently forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and belowground biomass across the study region. [ABSTRACT FROM AUTHOR]
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- 2020
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25. A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region
- Author
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Rammig, Anja, primary, Heinke, Jens, additional, Hofhansl, Florian, additional, Verbeeck, Hans, additional, Baker, Timothy R., additional, Christoffersen, Bradley, additional, Ciais, Philippe, additional, De Deurwaerder, Hannes, additional, Fleischer, Katrin, additional, Galbraith, David, additional, Guimberteau, Matthieu, additional, Huth, Andreas, additional, Johnson, Michelle, additional, Krujit, Bart, additional, Langerwisch, Fanny, additional, Meir, Patrick, additional, Papastefanou, Phillip, additional, Sampaio, Gilvan, additional, Thonicke, Kirsten, additional, von Randow, Celso, additional, Zang, Christian, additional, and Rödig, Edna, additional
- Published
- 2018
- Full Text
- View/download PDF
26. Supplementary material to "A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region"
- Author
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Rammig, Anja, primary, Heinke, Jens, additional, Hofhansl, Florian, additional, Verbeeck, Hans, additional, Baker, Timothy R., additional, Christoffersen, Bradley, additional, Ciais, Phillipe, additional, De Deurwaerder, Hannes, additional, Fleischer, Katrin, additional, Galbraith, David, additional, Guimberteau, Matthieu, additional, Huth, Andreas, additional, Johnson, Michelle, additional, Krujit, Bart, additional, Langerwisch, Fanny, additional, Meir, Patrick, additional, Papastefanou, Phillip, additional, Sampaio, Gilvan, additional, Thonicke, Kirsten, additional, von Randow, Celso, additional, Zang, Christian, additional, and Rödig, Edna, additional
- Published
- 2018
- Full Text
- View/download PDF
27. LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description
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Schaphoff, Sibyll, primary, von Bloh, Werner, additional, Rammig, Anja, additional, Thonicke, Kirsten, additional, Biemans, Hester, additional, Forkel, Matthias, additional, Gerten, Dieter, additional, Heinke, Jens, additional, Jägermeyr, Jonas, additional, Knauer, Jürgen, additional, Langerwisch, Fanny, additional, Lucht, Wolfgang, additional, Müller, Christoph, additional, Rolinski, Susanne, additional, and Waha, Katharina, additional
- Published
- 2018
- Full Text
- View/download PDF
28. LPJmL4 model output for the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation
- Author
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Schaphoff, Sibyll, von Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, H., Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, Waha, Katharina, Schaphoff, Sibyll, von Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, H., Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, and Waha, Katharina
- Abstract
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). The data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2017a,). Additionally, these results are evaluated in the companion paper of Schaphoff et al. (2017b, http://doi.org/10.5194/gmd-2017-146). The data collection includes some key output variables made with different model setups described by Schaphoff et al. (2017b). The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for soil, vegetation, aboveground and litter carbon as well as for vegetation distribution, crop yields, sowing dates, maximum thawing depth, and fire carbon emissions. Vegetation distribution is given for each plant functional type (PFT), crop yields, and sowing dates are given for each crop functional type (CFT), respectively. Monthly data are provided for the carbon fluxes (net primary production, gross primary production, soil respiration) and the water fluxes (transpiration, evaporation, interception, runoff, and discharge) and for absorbed photosynthetically active radiation (FAPAR) and albedo.
- Published
- 2018
29. A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations : An example from the Amazon region
- Author
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Rammig, Anja, Heinke, Jens, Hofhansl, Florian, Verbeeck, Hans, Baker, Timothy R., Christoffersen, Bradley, Ciais, Philippe, De Deurwaerder, Hannes, Fleischer, Katrin, Galbraith, David, Guimberteau, Matthieu, Huth, Andreas, Johnson, Michelle, Krujit, Bart, Langerwisch, Fanny, Meir, Patrick, Papastefanou, Phillip, Sampaio, Gilvan, Thonicke, Kirsten, von Randow, Celso, Zang, Christian, Rödig, Edna, Rammig, Anja, Heinke, Jens, Hofhansl, Florian, Verbeeck, Hans, Baker, Timothy R., Christoffersen, Bradley, Ciais, Philippe, De Deurwaerder, Hannes, Fleischer, Katrin, Galbraith, David, Guimberteau, Matthieu, Huth, Andreas, Johnson, Michelle, Krujit, Bart, Langerwisch, Fanny, Meir, Patrick, Papastefanou, Phillip, Sampaio, Gilvan, Thonicke, Kirsten, von Randow, Celso, Zang, Christian, and Rödig, Edna
- Abstract
Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25% and up to 60%, respectively. Our comparison metrics provide a quantitative measure for model-data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biom
- Published
- 2018
30. Impacts of future deforestation and climate change on the hydrology of the Amazon Basin : A multi-model analysis with a new set of land-cover change scenarios
- Author
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Guimberteau, Matthieu, Ciais, Philippe, Pablo Boisier, Juan, Paula Dutra Aguiar, Ana, Biemans, Hester, De Deurwaerder, Hannes, Galbraith, David, Kruijt, Bart, Langerwisch, Fanny, Poveda, German, Rammig, Anja, Andres Rodriguez, Daniel, Tejada, Graciela, Thonicke, Kirsten, Von Randow, Celso, Randow, Rita, Zhang, Ke, and Verbeeck, Hans
- Subjects
Climate Resilience ,WIMEK ,Water and Food ,Klimaatbestendigheid ,Water en Voedsel ,Life Science - Abstract
Deforestation in Amazon is expected to decrease evapotranspiration (ET) and to increase soil moisture and river discharge under prevailing energy-limited conditions. The magnitude and sign of the response of ET to deforestation depend both on the magnitude and regional patterns of land-cover change (LCC), as well as on climate change and CO2 levels. On the one hand, elevated CO2 decreases leaf-scale transpiration, but this effect could be offset by increased foliar area density. Using three regional LCC scenarios specifically established for the Brazilian and Bolivian Amazon, we investigate the impacts of climate change and deforestation on the surface hydrology of the Amazon Basin for this century, taking 2009 as a reference. For each LCC scenario, three land surface models (LSMs), LPJmL-DGVM, INLAND-DGVM and ORCHIDEE, are forced by bias-corrected climate simulated by three general circulation models (GCMs) of the IPCC 4th Assessment Report (AR4). On average, over the Amazon Basin with no deforestation, the GCM results indicate a temperature increase of 3.3ĝ€°C by 2100 which drives up the evaporative demand, whereby precipitation increases by 8.5 %, with a large uncertainty across GCMs. In the case of no deforestation, we found that ET and runoff increase by 5.0 and 14ĝ€%, respectively. However, in south-east Amazonia, precipitation decreases by 10ĝ€% at the end of the dry season and the three LSMs produce a 6ĝ€% decrease of ET, which is less than precipitation, so that runoff decreases by 22 %. For instance, the minimum river discharge of the Rio Tapajós is reduced by 31ĝ€% in 2100. To study the additional effect of deforestation, we prescribed to the LSMs three contrasted LCC scenarios, with a forest decline going from 7 to 34ĝ€% over this century. All three scenarios partly offset the climate-induced increase of ET, and runoff increases over the entire Amazon. In the south-east, however, deforestation amplifies the decrease of ET at the end of dry season, leading to a large increase of runoff (up to +27ĝ€% in the extreme deforestation case), offsetting the negative effect of climate change, thus balancing the decrease of low flows in the Rio Tapajós. These projections are associated with large uncertainties, which we attribute separately to the differences in LSMs, GCMs and to the uncertain range of deforestation. At the subcatchment scale, the uncertainty range on ET changes is shown to first depend on GCMs, while the uncertainty of runoff projections is predominantly induced by LSM structural differences. By contrast, we found that the uncertainty in both ET and runoff changes attributable to uncertain future deforestation is low.
- Published
- 2017
31. Investigating potential transferability of place-based research in land system science
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Václavík, Tomáš, Langerwisch, Fanny, Cotter, Marc, Fick, Johanna, Häuser, Inga, Hotes, Stefan, Kamp, Johannes, Settele, Josef, Spangenberg, Joachim H., and Seppelt, Ralf
- Subjects
global datasets ,synthesis ,Case study ,land system archetypes ,ecosystem services ,land-use intensity ,sustainable land management - Abstract
Much of our knowledge about land use and ecosystem services in interrelated social-ecological systems is derived from place-based research. While local and regional case studies provide valuable insights, it is often unclear how relevant this research is beyond the study areas. Drawing generalized conclusions about practical solutions to land management from local observations and formulating hypotheses applicable to other places in the world requires that we identify patterns of land systems that are similar to those represented by the case study. Here, we utilize the previously developed concept of land system archetypes to investigate potential transferability of research from twelve regional projects implemented in a large joint research framework that focus on issues of sustainable land management across four continents. For each project, we characterize its project archetype, i.e. the unique land system based on a synthesis of more than 30 datasets of land-use intensity, environmental conditions and socioeconomic indicators. We estimate the transferability potential of project research by calculating the statistical similarity of locations across the world to the project archetype, assuming higher transferability potentials in locations with similar land system characteristics. Results show that areas with high transferability potentials are typically clustered around project sites but for some case studies can be found in regions that are geographically distant, especially when values of considered variables are close to the global mean or where the project archetype is driven by large-scale environmental or socioeconomic conditions. Using specific examples from the local case studies, we highlight the merit of our approach and discuss the differences between local realities and information captured in global datasets. The proposed method provides a blueprint for large research programs to assess potential transferability of place-based studies to other geographical areas and to indicate possible gaps in research efforts.
- Published
- 2016
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32. Combined effects of climate and land-use change on the provision of ecosystem services in rice agro-ecosystems
- Author
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Langerwisch, Fanny, primary, Václavík, Tomáš, additional, von Bloh, Werner, additional, Vetter, Tobias, additional, and Thonicke, Kirsten, additional
- Published
- 2017
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- View/download PDF
33. The LEGATO cross-disciplinary integrated ecosystem service research framework: an example of integrating research results from the analysis of global change impacts and the social, cultural and economic system dynamics of irrigated rice production
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Spangenberg, Joachim H., primary, Beaurepaire, Alexis L., additional, Bergmeier, Erwin, additional, Burkhard, Benjamin, additional, Van Chien, Ho, additional, Cuong, Le Quoc, additional, Görg, Christoph, additional, Grescho, Volker, additional, Hai, Le Huu, additional, Heong, Kong Luen, additional, Horgan, Finbarr G., additional, Hotes, Stefan, additional, Klotzbücher, Anika, additional, Klotzbücher, Thimo, additional, Kühn, Ingolf, additional, Langerwisch, Fanny, additional, Marion, Glenn, additional, Moritz, Robin F. A., additional, Nguyen, Quynh Anh, additional, Ott, Jürgen, additional, Sann, Christina, additional, Sattler, Cornelia, additional, Schädler, Martin, additional, Schmidt, Anja, additional, Tekken, Vera, additional, Thanh, Truong Dao, additional, Thonicke, Kirsten, additional, Türke, Manfred, additional, Václavík, Tomáš, additional, Vetterlein, Doris, additional, Westphal, Catrin, additional, Wiemers, Martin, additional, and Settele, Josef, additional
- Published
- 2017
- Full Text
- View/download PDF
34. Supplementary material to "LPJmL4 – a dynamic global vegetation model with managed land: Part I – Model description"
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Schaphoff, Sibyll, primary, von Bloh, Werner, additional, Rammig, Anja, additional, Thonicke, Kirsten, additional, Biemans, Hester, additional, Forkel, Matthias, additional, Gerten, Dieter, additional, Heinke, Jens, additional, Jägermeyr, Jonas, additional, Knauer, Jürgen, additional, Langerwisch, Fanny, additional, Lucht, Wolfgang, additional, Müller, Christoph, additional, Rolinski, Susanne, additional, and Waha, Katharina, additional
- Published
- 2017
- Full Text
- View/download PDF
35. LPJmL4 – a dynamic global vegetation model with managed land: Part I – Model description
- Author
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Schaphoff, Sibyll, primary, von Bloh, Werner, additional, Rammig, Anja, additional, Thonicke, Kirsten, additional, Biemans, Hester, additional, Forkel, Matthias, additional, Gerten, Dieter, additional, Heinke, Jens, additional, Jägermeyr, Jonas, additional, Knauer, Jürgen, additional, Langerwisch, Fanny, additional, Lucht, Wolfgang, additional, Müller, Christoph, additional, Rolinski, Susanne, additional, and Waha, Katharina, additional
- Published
- 2017
- Full Text
- View/download PDF
36. A social-ecological approach to identify and quantify biodiversity tipping points in South America's seasonal dry ecosystems.
- Author
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Thonicke, Kirsten, Langerwisch, Fanny, Baumann, Matthias, Leitão, Pedro J., Václavík, Tomáš, Alencar, Ane, Simões, Margareth, Scheiter, Simon, Langan, Liam, Bustamante, Mercedes, Gasparri, Ignacio, Hirota, Marina, Börner, Jan, Rajao, Raoni, Soares-Filho, Britaldo, Yanosky, Alberto, Ochoa-Quinteiro, José-Manuel, Seghezzo, Lucas, Conti, Georgina, and de la Vega-Leinert, Anne Cristina
- Subjects
TROPICAL dry forests ,ECOLOGICAL integrity ,BIODIVERSITY ,ECOSYSTEMS ,AGRICULTURAL intensification ,ECOLOGICAL resilience - Abstract
Tropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture. Inspired by social-ecological systems theory, our approach helps to explain: (i) how drivers of natural vegetation decline affect the functioning of ecosystems, and thus ecological integrity, (ii) under which conditions, where, and at which scales the loss of ecological integrity may lead to biodiversity tipping points, and (iii) how these biodiversity tipping points may impact human well-being. Implementing such an approach with the greater aim of furthering more sustainable land use in the Dry Diagonal requires a transdisciplinary collaborative network, which in a first step integrates extensive observational data from the field and remote sensing with advanced ecosystem and biodiversity models. Secondly, it integrates knowledge obtained from dialogue processes with local and regional actors as well as meta-models describing the actor network. The co-designed methodological framework can be applied not only to define, detect, and map biodiversity tipping points across spatial and temporal scales, but also to evaluate the effects of tipping points on ES and livelihoods. This framework could be used to inform policy making, enrich planning processes at various levels of governance, and potentially contribute to prevent biodiversity tipping points in the Dry Diagonal and beyond. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
37. Climate change impacts in Latin America and the Caribbean and their implications for development
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World Bank Group, Reyer, Christopher, Adams, Sophie, Albrecht, Torsten, Baarsch, Florent, Boit, Alice, Canales Trujillo, Nella, Cartsburg, Matti, Coumou, Dim, Eden, Alexander, Fernandes, Erick, Langerwisch, Fanny, Marcus, Rachel, Mengel, Mathias, Mira-Salam, Daniel, Perette, Mahé, Pereznieto, Paola, Rammig, Anja, Reinhardt, Julia, Robinson, Alexander, Rocha, Marcia, Sakschewski, Boris, Schaeffer, Michiel, Schleussner, Carl-Friedrich, Serdeczny, Olivia, Thonick, Kirsten, World Bank Group, Reyer, Christopher, Adams, Sophie, Albrecht, Torsten, Baarsch, Florent, Boit, Alice, Canales Trujillo, Nella, Cartsburg, Matti, Coumou, Dim, Eden, Alexander, Fernandes, Erick, Langerwisch, Fanny, Marcus, Rachel, Mengel, Mathias, Mira-Salam, Daniel, Perette, Mahé, Pereznieto, Paola, Rammig, Anja, Reinhardt, Julia, Robinson, Alexander, Rocha, Marcia, Sakschewski, Boris, Schaeffer, Michiel, Schleussner, Carl-Friedrich, Serdeczny, Olivia, and Thonick, Kirsten
- Abstract
This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Latin America and the Caribbean (LAC). Projections show increasing mean temperatures by up to 4.5 °C compared to pre-industrial by the end of this century across LAC. Associated physical impacts include altered precipitation regimes, a strong increase in heat extremes, higher risks of droughts and increasing aridity. Moreover, the mean intensity of tropical cyclones, as well as the frequency of the most intense storms, is projected to increase while sea levels are expected to rise by ~0.2–1.1 mm depending on warming level and region. Tropical glacier volume is found to decrease substantially, with almost complete deglaciation under high warming levels. The much larger glaciers in the southern Andes are less sensitive to warming and shrink on slower timescales. Runoff is projected to be reduced in Central America, the southern Amazon basin and southernmost South America, while river discharge may increase in the western Amazon basin and in the Andes in the wet season. However, in many regions, there is uncertainty in the direction of these changes as a result of uncertain precipitation projections and differences in hydrological models. Climate change will also reduce agricultural yields, livestock and fisheries, although there may be opportunities such as increasing rice yield in several LAC countries or higher fish catch potential in the southernmost South American waters. Species range shifts threaten terrestrial biodiversity, and there is a substantial risk of Amazon rainforest degradation with continuing warming. Coral reefs are at increasing risk of annual bleaching events from 2040 to 2050 onwards irrespective of the climate scenario. These physical and biophysical climate change impacts challenge human livelihoods through, e.g., decreasing income from fisheries, agric
- Published
- 2017
38. Deforestation in Amazonia impacts riverine carbon dynamics
- Author
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Langerwisch, Fanny, primary, Walz, Ariane, additional, Rammig, Anja, additional, Tietjen, Britta, additional, Thonicke, Kirsten, additional, and Cramer, Wolfgang, additional
- Published
- 2016
- Full Text
- View/download PDF
39. Investigating potential transferability of place-based research in land system science
- Author
-
Václavík, Tomáš, primary, Langerwisch, Fanny, additional, Cotter, Marc, additional, Fick, Johanna, additional, Häuser, Inga, additional, Hotes, Stefan, additional, Kamp, Johannes, additional, Settele, Josef, additional, Spangenberg, Joachim H, additional, and Seppelt, Ralf, additional
- Published
- 2016
- Full Text
- View/download PDF
40. Impacts of future deforestation and climate change on the hydrology of the Amazon basin: a multi-model analysis with a new set of land-cover change scenarios
- Author
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Guimberteau, Matthieu, primary, Ciais, Philippe, additional, Ducharne, Agnès, additional, Boisier, Juan Pablo, additional, Aguiar, Ana Paula Dutra, additional, Biemans, Hester, additional, De Deurwaerder, Hannes, additional, Galbraith, David, additional, Kruijt, Bart, additional, Langerwisch, Fanny, additional, Poveda, German, additional, Rammig, Anja, additional, Rodriguez, Daniel Andres, additional, Tejada, Graciela, additional, Thonicke, Kirsten, additional, Von Randow, Celso, additional, Von Randow, Rita C. S., additional, Zhang, Ke, additional, and Verbeeck, Hans, additional
- Published
- 2016
- Full Text
- View/download PDF
41. Supplementary material to "Impacts of future deforestation and climate change on the hydrology of the Amazon basin: a multi-model analysis with a new set of land-cover change scenarios"
- Author
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Guimberteau, Matthieu, primary, Ciais, Philippe, additional, Ducharne, Agnès, additional, Boisier, Juan Pablo, additional, Aguiar, Ana Paula Dutra, additional, Biemans, Hester, additional, De Deurwaerder, Hannes, additional, Galbraith, David, additional, Kruijt, Bart, additional, Langerwisch, Fanny, additional, Poveda, German, additional, Rammig, Anja, additional, Rodriguez, Daniel Andres, additional, Tejada, Graciela, additional, Thonicke, Kirsten, additional, Von Randow, Celso, additional, Von Randow, Rita C. S., additional, Zhang, Ke, additional, and Verbeeck, Hans, additional
- Published
- 2016
- Full Text
- View/download PDF
42. Large-scale impact of climate change vs. land-use change on future biome shifts in Latin America
- Author
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Boit, Alice, primary, Sakschewski, Boris, additional, Boysen, Lena, additional, Cano-Crespo, Ana, additional, Clement, Jan, additional, Garcia-alaniz, Nashieli, additional, Kok, Kasper, additional, Kolb, Melanie, additional, Langerwisch, Fanny, additional, Rammig, Anja, additional, Sachse, René, additional, van Eupen, Michiel, additional, von Bloh, Werner, additional, Clara Zemp, Delphine, additional, and Thonicke, Kirsten, additional
- Published
- 2016
- Full Text
- View/download PDF
43. The Role of climate and land use change on the riverine carbon fluxes in Amazonia
- Author
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Langerwisch, Fanny
- Subjects
Institut für Erd- und Umweltwissenschaften - Published
- 2012
44. Climate change impacts in Latin America and the Caribbean and their implications for development
- Author
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Reyer, Christopher P.O., primary, Adams, Sophie, additional, Albrecht, Torsten, additional, Baarsch, Florent, additional, Boit, Alice, additional, Canales Trujillo, Nella, additional, Cartsburg, Matti, additional, Coumou, Dim, additional, Eden, Alexander, additional, Fernandes, Erick, additional, Langerwisch, Fanny, additional, Marcus, Rachel, additional, Mengel, Matthias, additional, Mira-Salama, Daniel, additional, Perette, Mahé, additional, Pereznieto, Paola, additional, Rammig, Anja, additional, Reinhardt, Julia, additional, Robinson, Alexander, additional, Rocha, Marcia, additional, Sakschewski, Boris, additional, Schaeffer, Michiel, additional, Schleussner, Carl-Friedrich, additional, Serdeczny, Olivia, additional, and Thonicke, Kirsten, additional
- Published
- 2015
- Full Text
- View/download PDF
45. Net biome production of the Amazon Basin in the 21st century
- Author
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Poulter , Benjamin, Aragao , Luiz, Heyder , Ursula, Gumpenberger , Marlies, Heinke , Jens, Langerwisch , Fanny, Rammig , Anja, Thonicke , Kirsten, Cramer , Wolfgang, Institute on Ecosystems and Department of Ecology [Bozeman], Montana State University ( MSU ), School of Geography, University of Exceter, Potsdam Institute for Climate Impact Research ( PIK ), Institut méditerranéen de biodiversité et d'écologie marine et continentale ( IMBE ), Université d'Avignon et des Pays de Vaucluse ( UAPV ) -Aix Marseille Université ( AMU ) -Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique ( CNRS ), Montana State University (MSU), Potsdam Institute for Climate Impact Research (PIK), Institut méditerranéen de biodiversité et d'écologie marine et continentale (IMBE), Avignon Université (AU)-Aix Marseille Université (AMU)-Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UMR237-Aix Marseille Université (AMU)-Avignon Université (AU)
- Subjects
[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2010
- Full Text
- View/download PDF
46. LPJmL4 - a dynamic global vegetation model with managed land: Part I - Model description.
- Author
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Schaphoff, Sibyll, von Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, Hester, Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, and Waha, Katharina
- Subjects
MATHEMATICAL models of agricultural productivity ,VEGETATION & climate ,ECOSYSTEMS - Abstract
This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation in direct coupling with water and carbon fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within, and impacts upon, the terrestrial biosphere as increasingly shaped by human activities such as climate change and land-use change. Here we describe the core model structure including recently eveloped modules now unified in LPJmL4. Thereby we also summarize LPJmL model developments and evaluations (based on 34 earlier publications focused e.g. on improved representations of crop types, human and ecological water demand, and permafrost) and model applications (82 papers, e.g. on historical and future climate change impacts) since its first description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at a Gitlab server, we hope to stimulate the application and further development of LPJmL4 across scientific communities, not least in support of major activities such as the IPCC and SDG process. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
47. Impacts of future deforestation and climate change on the hydrology of the Amazon basin: a multi-model analysis with a new set of land-cover change scenarios.
- Author
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Guimberteau, Matthieu, Ciais, Philippe, Ducharne, Agnès, Boisier, Juan Pablo, Aguiar, Ana Paula Dutra, Biemans, Hester, De Deurwaerder, Hannes, Galbraith, David, Kruijt, Bart, Langerwisch, Fanny, Poveda, German, Rammig, Anja, Rodriguez, Daniel Andres, Tejada, Graciela, Thonicke, Kirsten, Von Randow, Celso, Von Randow, Rita C. S., Ke Zhang, and Verbeeck, Hans
- Abstract
Neglecting any atmospheric feedback to precipitation, deforestation in Amazon, i.e., replacement of trees by shallow rooted short vegetation, is expected to decrease evapotranspiration (ET). Under energy-limited conditions, this process should lead to higher soil moisture and a consequent increase in river discharge. The magnitude and sign of the response of ET to deforestation depends both on land-cover change (LCC), and on climate and CO2 concentration changes in the future. Using three regional LCC scenarios recently established for the Brazilian and Bolivian Amazon, we investigate the combined impacts of deforestation and climate change on the surface hydrology of the Amazon basin for this century at sub-basin scale. For each LCC scenario, three land surface models (LSMs), LPJmL-DGVM, INLAND-DGVM and ORCHIDEE, are forced by bias-corrected climate simulated by three General Circulation Models (GCMs)for different scenarios of the IPCC 4th Assessment Report (AR4). The GCM results indicate that by 2100, without deforestation, the temperature will have increased by a mean of 3.3 °C (a range of 1.7 to 4.5 °C) over the Amazon basin, intensifing the regional water cycle, whereby precipitation, ET and runoff increase by 8.5, 5.0 and 14%, respectively. However, under this same scenario in south-east Amazonia, precipitation decreases by 10% at the end of the dry season and the three LSMs estimate a 6% decrease of ET, which does not compensate for lower precipitation. Runoff in southeastern Amazonia decreases by 22%, reducing minimum river discharge from the Rio Tapajós catchment by 31% in 2100. The low LCC scenario projects a 7% decline in the area of Amazonian forest by 2100, as compared to 2009; for the high LCC scenario the projection is a 34% decline. In the extreme deforestation scenario, forest loss partly offsets (-2.5%) the positive effect of climate change on increasing ET and slightly amplifies (+3.0%) the increaseof runoff. Effects of deforestation are more pronounced in the southern part of the Amazon basin, in particular in the Rio Madeira catchment where up to 56% of the 2009 forest area is lost. The effect of deforestation on water budgets is more severe at the end of the dry season in the Tapajós and the Xingu catchments where the decrease of ET due to climate change is amplified by forest area loss. Deforestation enhances runoff during this period (+35%) offsetting the negative effect of climate change (-22%), and balances the decrease of low flows in the Rio Tapajós. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
48. Assessing carbon dynamics in Amazonia with the Dynamic Global Vegetation Model LPJmL — discharge evaluation
- Author
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Langerwisch, Fanny, primary, Rost, Stefanie, additional, Poulter, Ben, additional, Zimmermann-Timm, Heike, additional, and Cramer, Wolfgang, additional
- Published
- 2008
- Full Text
- View/download PDF
49. Tackling unresolved questions in forest ecology: The past and future role of simulation models
- Author
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Maréchaux, Isabelle, Langerwisch, Fanny, Huth, Andreas, Bugmann, Harald, Morin, Xavier, Reyer, Christopher P.O., Seidl, Rupert, Collalti, Alessio, Dantas de Paula, Mateus, Fischer, Rico, Gutsch, Martin, Lexer, Manfred J., Lischke, Heike, Rammig, Anja, Rödig, Edna, Sakschewski, Boris, Taubert, Franziska, Thonicke, Kirsten, Vacchiano, Giorgio, and Bohn, Friedrich J.
- Subjects
13. Climate action ,15. Life on land - Abstract
Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations. We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems. Using three widely applied but contrasting approaches – species distribution models, individual‐based forest models, and dynamic global vegetation models – as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade. Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change., Ecology and Evolution, 11 (9), ISSN:2045-7758
50. Deforestation in Amazonia impacts riverine carbon dynamics
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Langerwisch, Fanny, Walz, Ariane, Rammig, Anja, Tietjen, Britta, Thonicke, Kirsten, and Cramer, Wolfgang
- Subjects
13. Climate action ,15. Life on land ,6. Clean water - Abstract
Fluxes of organic and inorganic carbon within the Amazon basin are considerably controlled by annual flooding, which triggers the export of terrigenous organic material to the river and ultimately to the Atlantic Ocean. The amount of carbon imported to the river and the further conversion, transport and export of it depend on temperature, atmospheric CO2, terrestrial productivity and carbon storage, as well as discharge. Both terrestrial productivity and discharge are influenced by climate and land use change. The coupled LPJmL and RivCM model system (Langerwisch et al., 2016) has been applied to assess the combined impacts of climate and land use change on the Amazon riverine carbon dynamics. Vegetation dynamics (in LPJmL) as well as export and conversion of terrigenous carbon to and within the river (RivCM) are included. The model system has been applied for the years 1901 to 2099 under two deforestation scenarios and with climate forcing of three SRES emission scenarios, each for five climate models. We find that high deforestation (business-as-usual scenario) will strongly decrease (locally by up to 90 %) riverine particulate and dissolved organic carbon amount until the end of the current century. At the same time, increase in discharge leaves net carbon transport during the first decades of the century roughly unchanged only if a sufficient area is still forested. After 2050 the amount of transported carbon will decrease drastically. In contrast to that, increased temperature and atmospheric CO2 concentration determine the amount of riverine inorganic carbon stored in the Amazon basin. Higher atmospheric CO2 concentrations increase riverine inorganic carbon amount by up to 20% (SRES A2). The changes in riverine carbon fluxes have direct effects on carbon export, either to the atmosphere via outgassing or to the Atlantic Ocean via discharge. The outgassed carbon will increase slightly in the Amazon basin, but can be regionally reduced by up to 60% due to deforestation. The discharge of organic carbon to the ocean will be reduced by about 40% under the most severe deforestation and climate change scenario. These changes would have local and regional consequences on the carbon balance and habitat characteristics in the Amazon basin itself as well as in the adjacent Atlantic Ocean., Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, 535
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