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349 results on '"Scholten T"'

3. Spatial prediction of organic matter quality in German agricultural topsoils

Author

Sakhaee, A., Scholten, T., Taghizadeh-Mehrjardi, R., Ließ, Mareike, Don, A., Sakhaee, A., Scholten, T., Taghizadeh-Mehrjardi, R., Ließ, Mareike, and Don, A.

Abstract

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties’ distribution in Germany.

Published
2024

4. Conductance spectroscopy of a proximity induced superconducting topological insulator

Author

Snelder, M., Stehno, M. P., Golubov, A. A., Molenaar, C. G., Scholten, T., Wu, D., Huang, Y. K., van der Wiel, W. G., Golden, M. S., and Brinkman, A.

Subjects
Condensed Matter - Superconductivity, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

We study the proximity effect between the fully-gapped region of a topological insulator in direct contact with an s-wave superconducting electrode (STI) and the surrounding topological insulator flake (TI) in Au/Bi$_{1.5}$Sb$_{0.5}$Te$_{1.7}$Se$_{1.3}$/Nb devices. The conductance spectra of the devices show the presence of a large induced gap in the STI as well as the induction of superconducting correlations in the normal part of the TI on the order of the Thouless energy. The shape of the conductance modulation around zero-energy varies between devices and can be explained by existing theory of s-wave-induced superconductivity in SNN' (S is a superconductor, N a superconducting proximized material and N' is a normal metal) devices. All the conductance spectra show a conductance dip at the induced gap of the STI.

Published
2015

6. Chapter 2 - Subdued mountains of Central Europe

Author

Kleber, A., Terhorst, B., Bullmann, H., Damm, B., Dietze, M., Döhler, S., Felix-Henningsen, P., Heinrich, J., Heinrich, S., Hülle, D., Leopold, M., Menke, M., Meyer-Heintze, S., Raab, T., Sauer, D., Scholten, T., Thiemeyer, H., and Frechen, M.

Published
2024
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7. Sustainable nitrogen use in agriculture

Author

Scholten, T., Frede, H.-G., Hülsbergen, K.-J., Kögel-Knabner, I., Liehr, S., Möckel, Stefan, Nacke, E., Navé, E., Reisch, L., Weisser, W., Zaehle, S., Scholten, T., Frede, H.-G., Hülsbergen, K.-J., Kögel-Knabner, I., Liehr, S., Möckel, Stefan, Nacke, E., Navé, E., Reisch, L., Weisser, W., and Zaehle, S.

Abstract

What form should agriculture in Germany take in the future? Scientists, policymakers and the general public are currently discussing this question intensively. Sustainable nitrogen use is an important part of this discussion, though it has received little public attention so far. Agriculture adds approximately 1.5 million metric tonnes of reactive nitrogen to the environment in Germany every year. Nitrogen in the form of various compounds is a significant contributor to climate change, biodiversity loss and soil, air and water pollution. As a result, nitrogen inputs from agriculture into the environment are estimated to incur societal costs between €30 billion and €70 billion a year. These problems have been known for decades, and extensive research has been performed in this area. However, measures implemented to date have not been effective, as indicated by the slow decline of nitrogen inputs from agriculture. This acatech POSTION PAPER considers the entire value chain, from agricultural production right up to the end consumer. This provides the basis for a series of recommendations geared towards more efficient and sustainable resource utilisation and a reduction of nitrogen inputs into the environment.

Published
2023

8. Carbon–biodiversity relationships in a highly diverse subtropical forest [Dataset]

Author

Schuldt, A., Liu, X., Buscot, Francois, Bruelheide, H., Erfmeier, A., He, J.-S., Klein, A.-M., Ma, K., Scherer-Lorenzen, M., Schmid, B., Scholten, T., Tang, Z., Trogisch, S., Wirth, C., Wubet, Tesfaye ; orcid:0000-0001-8572-4486, Staab, M., Schuldt, A., Liu, X., Buscot, Francois, Bruelheide, H., Erfmeier, A., He, J.-S., Klein, A.-M., Ma, K., Scherer-Lorenzen, M., Schmid, B., Scholten, T., Tang, Z., Trogisch, S., Wirth, C., Wubet, Tesfaye ; orcid:0000-0001-8572-4486, and Staab, M.

Abstract

Carbon-focused climate mitigation strategies are becoming increasingly important in forests. However, with ongoing biodiversity declines we require better knowledge of how much such strategies account for biodiversity. We particularly lack information across multiple trophic levels and on established forests, where the interplay between carbon stocks, stand age, and tree diversity might influence carbon–biodiversity relationships. Using a large dataset (>4600 heterotrophic species of 23 taxonomic groups) from secondary, subtropical forests, we tested how multitrophic diversity and diversity within trophic groups relate to aboveground, belowground, and total carbon stocks at different levels of tree species richness and stand age. Our study revealed that aboveground carbon, the key component of climate-based management, was largely unrelated to multitrophic diversity. By contrast, total carbon stocks—that is, including belowground carbon—emerged as a significant predictor of multitrophic diversity. Relationships were nonlinear and strongest for lower trophic levels, but nonsignificant for higher trophic level diversity. Tree species richness and stand age moderated these relationships, suggesting long-term regeneration of forests may be particularly effective in reconciling carbon and biodiversity targets. Our findings highlight that biodiversity benefits of climate-oriented management need to be evaluated carefully, and only maximizing aboveground carbon may fail to account for biodiversity conservation requirements.

Published
2023

9. Carbon–biodiversity relationships in a highly diverse subtropical forest

Author

Schuldt, A., Liu, X., Buscot, Francois, Bruelheide, H., Erfmeier, A., He, J.-S., Klein, A.-M., Ma, K., Scherer-Lorenzen, M., Schmid, B., Scholten, T., Tang, Z., Trogisch, S., Wirth, C., Wubet, Tesfaye, Staab, M., Schuldt, A., Liu, X., Buscot, Francois, Bruelheide, H., Erfmeier, A., He, J.-S., Klein, A.-M., Ma, K., Scherer-Lorenzen, M., Schmid, B., Scholten, T., Tang, Z., Trogisch, S., Wirth, C., Wubet, Tesfaye, and Staab, M.

Abstract

Carbon-focused climate mitigation strategies are becoming increasingly important in forests. However, with ongoing biodiversity declines we require better knowledge of how much such strategies account for biodiversity. We particularly lack information across multiple trophic levels and on established forests, where the interplay between carbon stocks, stand age, and tree diversity might influence carbon–biodiversity relationships. Using a large dataset (>4600 heterotrophic species of 23 taxonomic groups) from secondary, subtropical forests, we tested how multitrophic diversity and diversity within trophic groups relate to aboveground, belowground, and total carbon stocks at different levels of tree species richness and stand age. Our study revealed that aboveground carbon, the key component of climate-based management, was largely unrelated to multitrophic diversity. By contrast, total carbon stocks—that is, including belowground carbon—emerged as a significant predictor of multitrophic diversity. Relationships were nonlinear and strongest for lower trophic levels, but nonsignificant for higher trophic level diversity. Tree species richness and stand age moderated these relationships, suggesting long-term regeneration of forests may be particularly effective in reconciling carbon and biodiversity targets. Our findings highlight that biodiversity benefits of climate-oriented management need to be evaluated carefully, and only maximizing aboveground carbon may fail to account for biodiversity conservation requirements.

Published
2023

10. Das Leben im Boden

Author

Aleixandre Jiménez, M. Pilar, Barral, M. Teresa, Díaz Fierros, F., Rubio Antón, José Luis, Díaz-Raviña, Montserrat, Eichholz, M., Werner, L., and Scholten, T.

Abstract

Der galizische Kulturrat hat den Comic "Vivir no solo" in galizischer Sprache veréiffentlicht, um junge Menschen für die Wichtigkeit des Bodens zu sensibilisieren. Ebenfalls soll auf die Notwendigkeit, ihn zu schützen, aufmerksam gemacht werden. Anlasslich des lntemationalen Jahres der Bodenwissenschaften (2015) und um seine intemationale Verbreitung zu erhéihen, wurde der Comic angepasst und auf Spanisch "Vivir en el suelo" und Englisch "Living in the soil" veréiffentlicht. Dieser Band "Das Leben im Boden"soll die lntemationale Bodendekade (2015-2024) der lntemationalen Vereinigung der Bodenwissenschaften (IUSS) feiem. Er ist eine Anpassung und Übersetzung der englischen Ausgabe ins Deutsche, die in Zusammenarbeit mit der Spanischen Gesellschaft íür Bodenkunde (SECS, Delegation von Galicien) und der Deutschen Bodenkundlichen Gesellschaft (DBG) durchgeführt wurde.

Published
2022

13. European small portable rainfall simulators: A comparison of rainfall characteristics

Author

Iserloh, T., Ries, J.B., Arnáez, J., Boix-Fayos, C., Butzen, V., Cerdà, A., Echeverría, M.T., Fernández-Gálvez, J., Fister, W., Geißler, C., Gómez, J.A., Gómez-Macpherson, H., Kuhn, N.J., Lázaro, R., León, F.J., Martínez-Mena, M., Martínez-Murillo, J.F., Marzen, M., Mingorance, M.D., Ortigosa, L., Peters, P., Regüés, D., Ruiz-Sinoga, J.D., Scholten, T., Seeger, M., Solé-Benet, A., Wengel, R., and Wirtz, S.

Published
2013
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16. Abiotic and biotic drivers of tree trait effects on soil microbial biomass and soil carbon concentration [Dataset]

Author

Beugnon, R., Bu, W., Bruelheide, H., Davrinche, A., Du, J., Haider, S., Kunz, M., von Oheimb, G., Perles-Garcia, M.D., Saadani, M., Scholten, T., Seitz, S., Singavarapu, Bala, Trogisch, S., Wang, Y., Wubet, Tesfaye ; orcid:0000-0001-8572-4486, Xue, K., Yang, B., Cesarz, S., Eisenhauer, N., Beugnon, R., Bu, W., Bruelheide, H., Davrinche, A., Du, J., Haider, S., Kunz, M., von Oheimb, G., Perles-Garcia, M.D., Saadani, M., Scholten, T., Seitz, S., Singavarapu, Bala, Trogisch, S., Wang, Y., Wubet, Tesfaye ; orcid:0000-0001-8572-4486, Xue, K., Yang, B., Cesarz, S., and Eisenhauer, N.

Abstract

Forests are critical ecosystems to understand the global carbon budget, due to their carbon sequestration potential in both above- and belowground compartments, especially in species-rich forests. Soil carbon sequestration is strongly linked to soil microbial communities, and this link is mediated by the tree community, likely due to modifications of micro-environmental conditions (i.e., biotic conditions, soil properties, and microclimate). We studied soil carbon concentration and the soil microbial biomass of 180 local neighborhoods along a gradient of tree species richness ranging from 1 to 16 tree species per plot in a Chinese subtropical forest experiment (BEF-China). Tree productivity and different tree functional traits were measured at the neighborhood level. We tested the effects of tree productivity, functional trait identity and dissimilarity on soil carbon concentrations, and their mediation by the soil microbial biomass and micro-environmental conditions. Our analyses showed a strong positive correlation between soil microbial biomass and soil carbon concentrations. Besides, soil carbon concentration increased with tree productivity and tree root diameter while it decreased with litterfall C:N content. Moreover, tree productivity and tree functional traits (e.g. root fungal association and litterfall C:N ratio) modulated micro-environmental conditions with substantial consequences for soil microbial biomass. We also showed that soil history and topography should be considered in future experiments and tree plantations, as soil carbon concentrations were higher where historical (i.e., at the beginning of the experiment) carbon concentrations were high, themselves being strongly affected by the topography. Altogether, these results imply that the quantification of the different soil carbon pools is critical for understanding microbial community–soil carbon stock relationships and their dependence on tree diversity and micro-environmental conditions.

Published
2022

17. Global maps of soil temperature

Author

Lembrechts, J. J. (Jonas J.), van den Hoogen, J. (Johan), Aalto, J. (Juha), Ashcroft, M. B. (Michael B.), De Frenne, P. (Pieter), Kemppinen, J. (Julia), Kopecky, M. (Martin), Luoto, M. (Miska), Maclean, I. M. (Ilya M. D.), Crowther, T. W. (Thomas W.), Bailey, J. J. (Joseph J.), Haesen, S. (Stef), Klinges, D. H. (David H.), Niittynen, P. (Pekka), Scheffers, B. R. (Brett R.), Van Meerbeek, K. (Koenraad), Aartsma, P. (Peter), Abdalaze, O. (Otar), Abedi, M. (Mehdi), Aerts, R. (Rien), Ahmadian, N. (Negar), Ahrends, A. (Antje), Alatalo, J. M. (Juha M.), Alexander, J. M. (Jake M.), Allonsius, C. N. (Camille Nina), Altman, J. (Jan), Ammann, C. (Christof), Andres, C. (Christian), Andrews, C. (Christopher), Ardo, J. (Jonas), Arriga, N. (Nicola), Arzac, A. (Alberto), Aschero, V. (Valeria), Assis, R. L. (Rafael L.), Assmann, J. J. (Jakob Johann), Bader, M. Y. (Maaike Y.), Bahalkeh, K. (Khadijeh), Barancok, P. (Peter), Barrio, I. C. (Isabel C.), Barros, A. (Agustina), Barthel, M. (Matti), Basham, E. W. (Edmund W.), Bauters, M. (Marijn), Bazzichetto, M. (Manuele), Marchesini, L. B. (Luca Belelli), Bell, M. C. (Michael C.), Benavides, J. C. (Juan C.), Benito Alonso, J. L. (Jose Luis), Berauer, B. J. (Bernd J.), Bjerke, J. W. (Jarle W.), Bjork, R. G. (Robert G.), Bjorkman, M. P. (Mats P.), Bjornsdottir, K. (Katrin), Blonder, B. (Benjamin), Boeckx, P. (Pascal), Boike, J. (Julia), Bokhorst, S. (Stef), Brum, B. N. (Barbara N. S.), Bruna, J. (Josef), Buchmann, N. (Nina), Buysse, P. (Pauline), Camargo, J. L. (Jose Luis), Campoe, O. C. (Otavio C.), Candan, O. (Onur), Canessa, R. (Rafaella), Cannone, N. (Nicoletta), Carbognani, M. (Michele), Carnicer, J. (Jofre), Casanova-Katny, A. (Angelica), Cesarz, S. (Simone), Chojnicki, B. (Bogdan), Choler, P. (Philippe), Chown, S. L. (Steven L.), Cifuentes, E. F. (Edgar F.), Ciliak, M. (Marek), Contador, T. (Tamara), Convey, P. (Peter), Cooper, E. J. (Elisabeth J.), Cremonese, E. (Edoardo), Curasi, S. R. (Salvatore R.), Curtis, R. (Robin), Cutini, M. (Maurizio), Dahlberg, C. J. (C. Johan), Daskalova, G. N. (Gergana N.), Angel de Pablo, M. (Miguel), Della Chiesa, S. (Stefano), Dengler, J. (Juergen), Deronde, B. (Bart), Descombes, P. (Patrice), Di Cecco, V. (Valter), Di Musciano, M. (Michele), Dick, J. (Jan), Dimarco, R. D. (Romina D.), Dolezal, J. (Jiri), Dorrepaal, E. (Ellen), Dusek, J. (Jiri), Eisenhauer, N. (Nico), Eklundh, L. (Lars), Erickson, T. E. (Todd E.), Erschbamer, B. (Brigitta), Eugster, W. (Werner), Ewers, R. M. (Robert M.), Exton, D. A. (Dan A.), Fanin, N. (Nicolas), Fazlioglu, F. (Fatih), Feigenwinter, I. (Iris), Fenu, G. (Giuseppe), Ferlian, O. (Olga), Fernandez Calzado, M. R. (M. Rosa), Fernandez-Pascual, E. (Eduardo), Finckh, M. (Manfred), Higgens, R. F. (Rebecca Finger), Forte, T. G. (T'ai G. W.), Freeman, E. C. (Erika C.), Frei, E. R. (Esther R.), Fuentes-Lillo, E. (Eduardo), Garcia, R. A. (Rafael A.), Garcia, M. B. (Maria B.), Geron, C. (Charly), Gharun, M. (Mana), Ghosn, D. (Dany), Gigauri, K. (Khatuna), Gobin, A. (Anne), Goded, I. (Ignacio), Goeckede, M. (Mathias), Gottschall, F. (Felix), Goulding, K. (Keith), Govaert, S. (Sanne), Graae, B. J. (Bente Jessen), Greenwood, S. (Sarah), Greiser, C. (Caroline), Grelle, A. (Achim), Guenard, B. (Benoit), Guglielmin, M. (Mauro), Guillemot, J. (Joannes), Haase, P. (Peter), Haider, S. (Sylvia), Halbritter, A. H. (Aud H.), Hamid, M. (Maroof), Hammerle, A. (Albin), Hampe, A. (Arndt), Haugum, S. V. (Siri, V), Hederova, L. (Lucia), Heinesch, B. (Bernard), Helfter, C. (Carole), Hepenstrick, D. (Daniel), Herberich, M. (Maximiliane), Herbst, M. (Mathias), Hermanutz, L. (Luise), Hik, D. S. (David S.), Hoffren, R. (Raul), Homeier, J. (Juergen), Hörtnagl, L. (Lukas), Hoye, T. T. (Toke T.), Hrbacek, F. (Filip), Hylander, K. (Kristoffer), Iwata, H. (Hiroki), Jackowicz-Korczynski, M. A. (Marcin Antoni), Jactel, H. (Herve), Jarveoja, J. (Jarvi), Jastrzebowski, S. (Szymon), Jentsch, A. (Anke), Jimenez, J. J. (Juan J.), Jonsdottir, I. S. (Ingibjorg S.), Jucker, T. (Tommaso), Jump, A. S. (Alistair S.), Juszczak, R. (Radoslaw), Kanka, R. (Robert), Kaspar, V. (Vit), Kazakis, G. (George), Kelly, J. (Julia), Khuroo, A. A. (Anzar A.), Klemedtsson, L. (Leif), Klisz, M. (Marcin), Kljun, N. (Natascha), Knohl, A. (Alexander), Kobler, J. (Johannes), Kollar, J. (Jozef), Kotowska, M. M. (Martyna M.), Kovacs, B. (Bence), Kreyling, J. (Juergen), Lamprecht, A. (Andrea), Lang, S. I. (Simone, I), Larson, C. (Christian), Larson, K. (Keith), Laska, K. (Kamil), Maire, G. I. (Guerric Ie), Leihy, R. I. (Rachel, I), Lens, L. (Luc), Liljebladh, B. (Bengt), Lohila, A. (Annalea), Lorite, J. (Juan), Loubet, B. (Benjamin), Lynn, J. (Joshua), Macek, M. (Martin), Mackenzie, R. (Roy), Magliulo, E. (Enzo), Maier, R. (Regine), Malfasi, F. (Francesco), Malis, F. (Frantisek), Man, M. (Matej), Manca, G. (Giovanni), Manco, A. (Antonio), Manise, T. (Tanguy), Manolaki, P. (Paraskevi), Marciniak, F. (Felipe), Matula, R. (Radim), Clara Mazzolari, A. (Ana), Medinets, S. (Sergiy), Medinets, V. (Volodymyr), Meeussen, C. (Camille), Merinero, S. (Sonia), Guimaraes Mesquita, R. d. (Rita de Cassia), Meusburger, K. (Katrin), Meysman, F. J. (Filip J. R.), Michaletz, S. T. (Sean T.), Milbau, A. (Ann), Moiseev, D. (Dmitry), Moiseev, P. (Pavel), Mondoni, A. (Andrea), Monfries, R. (Ruth), Montagnani, L. (Leonardo), Moriana-Armendariz, M. (Mikel), di Cella, U. M. (Umberto Morra), Moersdorf, M. (Martin), Mosedale, J. R. (Jonathan R.), Muffler, L. (Lena), Munoz-Rojas, M. (Miriam), Myers, J. A. (Jonathan A.), Myers-Smith, I. H. (Isla H.), Nagy, L. (Laszlo), Nardino, M. (Marianna), Naujokaitis-Lewis, I. (Ilona), Newling, E. (Emily), Nicklas, L. (Lena), Niedrist, G. (Georg), Niessner, A. (Armin), Nilsson, M. B. (Mats B.), Normand, S. (Signe), Nosetto, M. D. (Marcelo D.), Nouvellon, Y. (Yann), Nunez, M. A. (Martin A.), Ogaya, R. (Roma), Ogee, J. (Jerome), Okello, J. (Joseph), Olejnik, J. (Janusz), Olesen, J. E. (Jorgen Eivind), Opedal, O. H. (Oystein H.), Orsenigo, S. (Simone), Palaj, A. (Andrej), Pampuch, T. (Timo), Panov, A. V. (Alexey V.), Pärtel, M. (Meelis), Pastor, A. (Ada), Pauchard, A. (Aníbal), Pauli, H. (Harald), Pavelka, M. (Marian), Pearse, W. D. (William D.), Peichl, M. (Matthias), Pellissier, L. (Loïc), Penczykowski, R. M. (Rachel M.), Penuelas, J. (Josep), Petit Bon, M. (Matteo), Petraglia, A. (Alessandro), Phartyal, S. S. (Shyam S.), Phoenix, G. K. (Gareth K.), Pio, C. (Casimiro), Pitacco, A. (Andrea), Pitteloud, C. (Camille), Plichta, R. (Roman), Porro, F. (Francesco), Portillo-Estrada, M. (Miguel), Poulenard, J. (Jérôme), Poyatos, R. (Rafael), Prokushkin, A. S. (Anatoly S.), Puchalka, R. (Radoslaw), Pușcaș, M. (Mihai), Radujković, D. (Dajana), Randall, K. (Krystal), Ratier Backes, A. (Amanda), Remmele, S. (Sabine), Remmers, W. (Wolfram), Renault, D. (David), Risch, A. C. (Anita C.), Rixen, C. (Christian), Robinson, S. A. (Sharon A.), Robroek, B. J. (Bjorn J. M.), Rocha, A. V. (Adrian V.), Rossi, C. (Christian), Rossi, G. (Graziano), Roupsard, O. (Olivier), Rubtsov, A. V. (Alexey V.), Saccone, P. (Patrick), Sagot, C. (Clotilde), Sallo Bravo, J. (Jhonatan), Santos, C. C. (Cinthya C.), Sarneel, J. M. (Judith M.), Scharnweber, T. (Tobias), Schmeddes, J. (Jonas), Schmidt, M. (Marius), Scholten, T. (Thomas), Schuchardt, M. (Max), Schwartz, N. (Naomi), Scott, T. (Tony), Seeber, J. (Julia), Segalin De Andrade, A. C. (Ana Cristina), Seipel, T. (Tim), Semenchuk, P. (Philipp), Senior, R. A. (Rebecca A.), Serra-Diaz, J. M. (Josep M.), Sewerniak, P. (Piotr), Shekhar, A. (Ankit), Sidenko, N. V. (Nikita V.), Siebicke, L. (Lukas), Siegwart Collier, L. (Laura), Simpson, E. (Elizabeth), Siqueira, D. P. (David P.), Sitková, Z. (Zuzana), Six, J. (Johan), Smiljanic, M. (Marko), Smith, S. W. (Stuart W.), Smith-Tripp, S. (Sarah), Somers, B. (Ben), Sørensen, M. V. (Mia Vedel), Souza, J. J. (José João L. L.), Souza, B. I. (Bartolomeu Israel), Dias, A. S. (Arildo Souza), Spasojevic, M. J. (Marko J.), Speed, J. D. (James D. M.), Spicher, F. (Fabien), Stanisci, A. (Angela), Steinbauer, K. (Klaus), Steinbrecher, R. (Rainer), Steinwandter, M. (Michael), Stemkovski, M. (Michael), Stephan, J. G. (Jörg G.), Stiegler, C. (Christian), Stoll, S. (Stefan), Svátek, M. (Martin), Svoboda, M. (Miroslav), Tagesson, T. (Torbern), Tanentzap, A. J. (Andrew J.), Tanneberger, F. (Franziska), Theurillat, J.-P. (Jean-Paul), Thomas, H. J. (Haydn J. D.), Thomas, A. D. (Andrew D.), Tielbörger, K. (Katja), Tomaselli, M. (Marcello), Treier, U. A. (Urs Albert), Trouillier, M. (Mario), Turtureanu, P. D. (Pavel Dan), Tutton, R. (Rosamond), Tyystjärvi, V. A. (Vilna A.), Ueyama, M. (Masahito), Ujházy, K. (Karol), Ujházyová, M. (Mariana), Uogintas, D. (Domas), Urban, A. V. (Anastasiya V.), Urban, J. (Josef), Urbaniak, M. (Marek), Ursu, T.-M. (Tudor-Mihai), Vaccari, F. P. (Francesco Primo), Van De Vondel, S. (Stijn), Van Den Brink, L. (Liesbeth), Van Geel, M. (Maarten), Vandvik, V. (Vigdis), Vangansbeke, P. (Pieter), Varlagin, A. (Andrej), Veen, G. F. (G. F.), Veenendaal, E. (Elmar), Venn, S. E. (Susanna E.), Verbeeck, H. (Hans), Verbrugggen, E. (Erik), Verheijen, F. G. (Frank G. A.), Villar, L. (Luis), Vitale, L. (Luca), Vittoz, P. (Pascal), Vives-Ingla, M. (Maria), Von Oppen, J. (Jonathan), Walz, J. (Josefine), Wang, R. (Runxi), Wang, Y. (Yifeng), Way, R. G. (Robert G.), Wedegärtner, R. E. (Ronja E. M.), Weigel, R. (Robert), Wild, J. (Jan), Wilkinson, M. (Matthew), Wilmking, M. (Martin), Wingate, L. (Lisa), Winkler, M. (Manuela), Wipf, S. (Sonja), Wohlfahrt, G. (Georg), Xenakis, G. (Georgios), Yang, Y. (Yan), Yu, Z. (Zicheng), Yu, K. (Kailiang), Zellweger, F. (Florian), Zhang, J. (Jian), Zhang, Z. (Zhaochen), Zhao, P. (Peng), Ziemblińska, K. (Klaudia), Zimmermann, R. (Reiner), Zong, S. (Shengwei), Zyryanov, V. I. (Viacheslav I.), Nijs, I. (Ivan), Lenoir, J. (Jonathan), Lembrechts, J. J. (Jonas J.), van den Hoogen, J. (Johan), Aalto, J. (Juha), Ashcroft, M. B. (Michael B.), De Frenne, P. (Pieter), Kemppinen, J. (Julia), Kopecky, M. (Martin), Luoto, M. (Miska), Maclean, I. M. (Ilya M. D.), Crowther, T. W. (Thomas W.), Bailey, J. J. (Joseph J.), Haesen, S. (Stef), Klinges, D. H. (David H.), Niittynen, P. (Pekka), Scheffers, B. R. (Brett R.), Van Meerbeek, K. (Koenraad), Aartsma, P. (Peter), Abdalaze, O. (Otar), Abedi, M. (Mehdi), Aerts, R. (Rien), Ahmadian, N. (Negar), Ahrends, A. (Antje), Alatalo, J. M. (Juha M.), Alexander, J. M. (Jake M.), Allonsius, C. N. (Camille Nina), Altman, J. (Jan), Ammann, C. (Christof), Andres, C. (Christian), Andrews, C. (Christopher), Ardo, J. (Jonas), Arriga, N. (Nicola), Arzac, A. (Alberto), Aschero, V. (Valeria), Assis, R. L. (Rafael L.), Assmann, J. J. (Jakob Johann), Bader, M. Y. (Maaike Y.), Bahalkeh, K. (Khadijeh), Barancok, P. (Peter), Barrio, I. C. (Isabel C.), Barros, A. (Agustina), Barthel, M. (Matti), Basham, E. W. (Edmund W.), Bauters, M. (Marijn), Bazzichetto, M. (Manuele), Marchesini, L. B. (Luca Belelli), Bell, M. C. (Michael C.), Benavides, J. C. (Juan C.), Benito Alonso, J. L. (Jose Luis), Berauer, B. J. (Bernd J.), Bjerke, J. W. (Jarle W.), Bjork, R. G. (Robert G.), Bjorkman, M. P. (Mats P.), Bjornsdottir, K. (Katrin), Blonder, B. (Benjamin), Boeckx, P. (Pascal), Boike, J. (Julia), Bokhorst, S. (Stef), Brum, B. N. (Barbara N. S.), Bruna, J. (Josef), Buchmann, N. (Nina), Buysse, P. (Pauline), Camargo, J. L. (Jose Luis), Campoe, O. C. (Otavio C.), Candan, O. (Onur), Canessa, R. (Rafaella), Cannone, N. (Nicoletta), Carbognani, M. (Michele), Carnicer, J. (Jofre), Casanova-Katny, A. (Angelica), Cesarz, S. (Simone), Chojnicki, B. (Bogdan), Choler, P. (Philippe), Chown, S. L. (Steven L.), Cifuentes, E. F. (Edgar F.), Ciliak, M. (Marek), Contador, T. (Tamara), Convey, P. (Peter), Cooper, E. J. (Elisabeth J.), Cremonese, E. (Edoardo), Curasi, S. R. (Salvatore R.), Curtis, R. (Robin), Cutini, M. (Maurizio), Dahlberg, C. J. (C. Johan), Daskalova, G. N. (Gergana N.), Angel de Pablo, M. (Miguel), Della Chiesa, S. (Stefano), Dengler, J. (Juergen), Deronde, B. (Bart), Descombes, P. (Patrice), Di Cecco, V. (Valter), Di Musciano, M. (Michele), Dick, J. (Jan), Dimarco, R. D. (Romina D.), Dolezal, J. (Jiri), Dorrepaal, E. (Ellen), Dusek, J. (Jiri), Eisenhauer, N. (Nico), Eklundh, L. (Lars), Erickson, T. E. (Todd E.), Erschbamer, B. (Brigitta), Eugster, W. (Werner), Ewers, R. M. (Robert M.), Exton, D. A. (Dan A.), Fanin, N. (Nicolas), Fazlioglu, F. (Fatih), Feigenwinter, I. (Iris), Fenu, G. (Giuseppe), Ferlian, O. (Olga), Fernandez Calzado, M. R. (M. Rosa), Fernandez-Pascual, E. (Eduardo), Finckh, M. (Manfred), Higgens, R. F. (Rebecca Finger), Forte, T. G. (T'ai G. W.), Freeman, E. C. (Erika C.), Frei, E. R. (Esther R.), Fuentes-Lillo, E. (Eduardo), Garcia, R. A. (Rafael A.), Garcia, M. B. (Maria B.), Geron, C. (Charly), Gharun, M. (Mana), Ghosn, D. (Dany), Gigauri, K. (Khatuna), Gobin, A. (Anne), Goded, I. (Ignacio), Goeckede, M. (Mathias), Gottschall, F. (Felix), Goulding, K. (Keith), Govaert, S. (Sanne), Graae, B. J. (Bente Jessen), Greenwood, S. (Sarah), Greiser, C. (Caroline), Grelle, A. (Achim), Guenard, B. (Benoit), Guglielmin, M. (Mauro), Guillemot, J. (Joannes), Haase, P. (Peter), Haider, S. (Sylvia), Halbritter, A. H. (Aud H.), Hamid, M. (Maroof), Hammerle, A. (Albin), Hampe, A. (Arndt), Haugum, S. V. (Siri, V), Hederova, L. (Lucia), Heinesch, B. (Bernard), Helfter, C. (Carole), Hepenstrick, D. (Daniel), Herberich, M. (Maximiliane), Herbst, M. (Mathias), Hermanutz, L. (Luise), Hik, D. S. (David S.), Hoffren, R. (Raul), Homeier, J. (Juergen), Hörtnagl, L. (Lukas), Hoye, T. T. (Toke T.), Hrbacek, F. (Filip), Hylander, K. (Kristoffer), Iwata, H. (Hiroki), Jackowicz-Korczynski, M. A. (Marcin Antoni), Jactel, H. (Herve), Jarveoja, J. (Jarvi), Jastrzebowski, S. (Szymon), Jentsch, A. (Anke), Jimenez, J. J. (Juan J.), Jonsdottir, I. S. (Ingibjorg S.), Jucker, T. (Tommaso), Jump, A. S. (Alistair S.), Juszczak, R. (Radoslaw), Kanka, R. (Robert), Kaspar, V. (Vit), Kazakis, G. (George), Kelly, J. (Julia), Khuroo, A. A. (Anzar A.), Klemedtsson, L. (Leif), Klisz, M. (Marcin), Kljun, N. (Natascha), Knohl, A. (Alexander), Kobler, J. (Johannes), Kollar, J. (Jozef), Kotowska, M. M. (Martyna M.), Kovacs, B. (Bence), Kreyling, J. (Juergen), Lamprecht, A. (Andrea), Lang, S. I. (Simone, I), Larson, C. (Christian), Larson, K. (Keith), Laska, K. (Kamil), Maire, G. I. (Guerric Ie), Leihy, R. I. (Rachel, I), Lens, L. (Luc), Liljebladh, B. (Bengt), Lohila, A. (Annalea), Lorite, J. (Juan), Loubet, B. (Benjamin), Lynn, J. (Joshua), Macek, M. (Martin), Mackenzie, R. (Roy), Magliulo, E. (Enzo), Maier, R. (Regine), Malfasi, F. (Francesco), Malis, F. (Frantisek), Man, M. (Matej), Manca, G. (Giovanni), Manco, A. (Antonio), Manise, T. (Tanguy), Manolaki, P. (Paraskevi), Marciniak, F. (Felipe), Matula, R. (Radim), Clara Mazzolari, A. (Ana), Medinets, S. (Sergiy), Medinets, V. (Volodymyr), Meeussen, C. (Camille), Merinero, S. (Sonia), Guimaraes Mesquita, R. d. (Rita de Cassia), Meusburger, K. (Katrin), Meysman, F. J. (Filip J. R.), Michaletz, S. T. (Sean T.), Milbau, A. (Ann), Moiseev, D. (Dmitry), Moiseev, P. (Pavel), Mondoni, A. (Andrea), Monfries, R. (Ruth), Montagnani, L. (Leonardo), Moriana-Armendariz, M. (Mikel), di Cella, U. M. (Umberto Morra), Moersdorf, M. (Martin), Mosedale, J. R. (Jonathan R.), Muffler, L. (Lena), Munoz-Rojas, M. (Miriam), Myers, J. A. (Jonathan A.), Myers-Smith, I. H. (Isla H.), Nagy, L. (Laszlo), Nardino, M. (Marianna), Naujokaitis-Lewis, I. (Ilona), Newling, E. (Emily), Nicklas, L. (Lena), Niedrist, G. (Georg), Niessner, A. (Armin), Nilsson, M. B. (Mats B.), Normand, S. (Signe), Nosetto, M. D. (Marcelo D.), Nouvellon, Y. (Yann), Nunez, M. A. (Martin A.), Ogaya, R. (Roma), Ogee, J. (Jerome), Okello, J. (Joseph), Olejnik, J. (Janusz), Olesen, J. E. (Jorgen Eivind), Opedal, O. H. (Oystein H.), Orsenigo, S. (Simone), Palaj, A. (Andrej), Pampuch, T. (Timo), Panov, A. V. (Alexey V.), Pärtel, M. (Meelis), Pastor, A. (Ada), Pauchard, A. (Aníbal), Pauli, H. (Harald), Pavelka, M. (Marian), Pearse, W. D. (William D.), Peichl, M. (Matthias), Pellissier, L. (Loïc), Penczykowski, R. M. (Rachel M.), Penuelas, J. (Josep), Petit Bon, M. (Matteo), Petraglia, A. (Alessandro), Phartyal, S. S. (Shyam S.), Phoenix, G. K. (Gareth K.), Pio, C. (Casimiro), Pitacco, A. (Andrea), Pitteloud, C. (Camille), Plichta, R. (Roman), Porro, F. (Francesco), Portillo-Estrada, M. (Miguel), Poulenard, J. (Jérôme), Poyatos, R. (Rafael), Prokushkin, A. S. (Anatoly S.), Puchalka, R. (Radoslaw), Pușcaș, M. (Mihai), Radujković, D. (Dajana), Randall, K. (Krystal), Ratier Backes, A. (Amanda), Remmele, S. (Sabine), Remmers, W. (Wolfram), Renault, D. (David), Risch, A. C. (Anita C.), Rixen, C. (Christian), Robinson, S. A. (Sharon A.), Robroek, B. J. (Bjorn J. M.), Rocha, A. V. (Adrian V.), Rossi, C. (Christian), Rossi, G. (Graziano), Roupsard, O. (Olivier), Rubtsov, A. V. (Alexey V.), Saccone, P. (Patrick), Sagot, C. (Clotilde), Sallo Bravo, J. (Jhonatan), Santos, C. C. (Cinthya C.), Sarneel, J. M. (Judith M.), Scharnweber, T. (Tobias), Schmeddes, J. (Jonas), Schmidt, M. (Marius), Scholten, T. (Thomas), Schuchardt, M. (Max), Schwartz, N. (Naomi), Scott, T. (Tony), Seeber, J. (Julia), Segalin De Andrade, A. C. (Ana Cristina), Seipel, T. (Tim), Semenchuk, P. (Philipp), Senior, R. A. (Rebecca A.), Serra-Diaz, J. M. (Josep M.), Sewerniak, P. (Piotr), Shekhar, A. (Ankit), Sidenko, N. V. (Nikita V.), Siebicke, L. (Lukas), Siegwart Collier, L. (Laura), Simpson, E. (Elizabeth), Siqueira, D. P. (David P.), Sitková, Z. (Zuzana), Six, J. (Johan), Smiljanic, M. (Marko), Smith, S. W. (Stuart W.), Smith-Tripp, S. (Sarah), Somers, B. (Ben), Sørensen, M. V. (Mia Vedel), Souza, J. J. (José João L. L.), Souza, B. I. (Bartolomeu Israel), Dias, A. S. (Arildo Souza), Spasojevic, M. J. (Marko J.), Speed, J. D. (James D. M.), Spicher, F. (Fabien), Stanisci, A. (Angela), Steinbauer, K. (Klaus), Steinbrecher, R. (Rainer), Steinwandter, M. (Michael), Stemkovski, M. (Michael), Stephan, J. G. (Jörg G.), Stiegler, C. (Christian), Stoll, S. (Stefan), Svátek, M. (Martin), Svoboda, M. (Miroslav), Tagesson, T. (Torbern), Tanentzap, A. J. (Andrew J.), Tanneberger, F. (Franziska), Theurillat, J.-P. (Jean-Paul), Thomas, H. J. (Haydn J. D.), Thomas, A. D. (Andrew D.), Tielbörger, K. (Katja), Tomaselli, M. (Marcello), Treier, U. A. (Urs Albert), Trouillier, M. (Mario), Turtureanu, P. D. (Pavel Dan), Tutton, R. (Rosamond), Tyystjärvi, V. A. (Vilna A.), Ueyama, M. (Masahito), Ujházy, K. (Karol), Ujházyová, M. (Mariana), Uogintas, D. (Domas), Urban, A. V. (Anastasiya V.), Urban, J. (Josef), Urbaniak, M. (Marek), Ursu, T.-M. (Tudor-Mihai), Vaccari, F. P. (Francesco Primo), Van De Vondel, S. (Stijn), Van Den Brink, L. (Liesbeth), Van Geel, M. (Maarten), Vandvik, V. (Vigdis), Vangansbeke, P. (Pieter), Varlagin, A. (Andrej), Veen, G. F. (G. F.), Veenendaal, E. (Elmar), Venn, S. E. (Susanna E.), Verbeeck, H. (Hans), Verbrugggen, E. (Erik), Verheijen, F. G. (Frank G. A.), Villar, L. (Luis), Vitale, L. (Luca), Vittoz, P. (Pascal), Vives-Ingla, M. (Maria), Von Oppen, J. (Jonathan), Walz, J. (Josefine), Wang, R. (Runxi), Wang, Y. (Yifeng), Way, R. G. (Robert G.), Wedegärtner, R. E. (Ronja E. M.), Weigel, R. (Robert), Wild, J. (Jan), Wilkinson, M. (Matthew), Wilmking, M. (Martin), Wingate, L. (Lisa), Winkler, M. (Manuela), Wipf, S. (Sonja), Wohlfahrt, G. (Georg), Xenakis, G. (Georgios), Yang, Y. (Yan), Yu, Z. (Zicheng), Yu, K. (Kailiang), Zellweger, F. (Florian), Zhang, J. (Jian), Zhang, Z. (Zhaochen), Zhao, P. (Peng), Ziemblińska, K. (Klaudia), Zimmermann, R. (Reiner), Zong, S. (Shengwei), Zyryanov, V. I. (Viacheslav I.), Nijs, I. (Ivan), and Lenoir, J. (Jonathan)

Abstract

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0‐5 and 5‐15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km² pixels (summarized from 8519 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10° degrees C (mean = 3.0 +/‐ 2.1° degrees C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 +/‐2.3° degrees C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (‐0.7 +/‐ 2.3° degrees C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological

Published
2022

18. 3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

Author

Gerke, H.H., Vogel, Hans-Jörg, Weber, T.K.D., van der Meij, W.M., Scholten, T., Gerke, H.H., Vogel, Hans-Jörg, Weber, T.K.D., van der Meij, W.M., and Scholten, T.

Abstract

A 3–4D soil model represents a logical step forward from one-dimensional soil columns (1D), two-dimensional soil maps (2D), and three-dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3–4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context. This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3–4D soil model. A 3–4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes. A 3–4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil in

Published
2022

19. Monitoring and integrating the changes in vegetated areas with the rate of groundwater use in arid regions

Author

Morsy, Mona, Michaelides, S., Scholten, T., Dietrich, Peter, Morsy, Mona, Michaelides, S., Scholten, T., and Dietrich, Peter

Abstract

Frequent water table measurements are crucial for sustainable groundwater management in arid regions. Such monitoring is more important in areas that are already facing an acute problem with excessive groundwater withdrawal. In the majority of these locations, continuous readings of groundwater levels are lacking. Therefore, an approximate estimate of the rate of increase or decrease in water consumption over time may serve as a proxy for the missing data. This could be achieved by tracking the changes in vegetated areas that generally correlate with changes in the rate of water use. The technique proposed in this paper is based on two remote sensing datasets: Landsat 7 and 8 from 2001 to 2021, and Sentinel 2A from 2015 to 2021, as well as five vegetation indices: Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Transformed Vegetation Index (TVI). The findings have shown that the datasets chosen performed best for small-scale land farms at the research location, which was chosen to be the El-Qaa plain, in the southwestern corner of the Sinai Peninsula in Egypt. Landsat 7 data with a resolution of 30 m revealed a substantial increase in land farms from 2.9 km2 in 2001 to 23.3 km2 in 2021. By using the five indices based on Sentinel 2A data, vegetated areas were categorized as heavy, moderate, or light. In addition, the expansion of each class area from 2015 to 2021 was tracked. Additionally, the NDVI index was modified to better reflect the arid environment (subsequently naming this new index as the Arid Vegetation Index: AVI). Rough scenarios of the increase in water consumption rate at the research site were generated by observing the increase in vegetated areas and collecting rough information from the farmers regarding the crop types.

Published
2022

20. Abiotic and biotic drivers of tree trait effects on soil microbial biomass and soil carbon concentration

Author

Beugnon, R., Bu, W., Bruelheide, H., Davrinche, A., Du, J., Haider, S., Kunz, M., von Oheimb, G., Perles-Garcia, M.D., Saadani, M., Scholten, T., Seitz, S., Singavarapu, Bala, Trogisch, S., Wang, Y., Wubet, Tesfaye, Xue, K., Yang, B., Cesarz, S., Eisenhauer, N., Beugnon, R., Bu, W., Bruelheide, H., Davrinche, A., Du, J., Haider, S., Kunz, M., von Oheimb, G., Perles-Garcia, M.D., Saadani, M., Scholten, T., Seitz, S., Singavarapu, Bala, Trogisch, S., Wang, Y., Wubet, Tesfaye, Xue, K., Yang, B., Cesarz, S., and Eisenhauer, N.

Abstract

Forests are critical ecosystems to understand the global carbon budget, due to their carbon sequestration potential in both above- and belowground compartments, especially in species-rich forests. Soil carbon sequestration is strongly linked to soil microbial communities, and this link is mediated by the tree community, likely due to modifications of micro-environmental conditions (i.e., biotic conditions, soil properties, and microclimate). We studied soil carbon concentration and the soil microbial biomass of 180 local neighborhoods along a gradient of tree species richness ranging from 1 to 16 tree species per plot in a Chinese subtropical forest experiment (BEF-China). Tree productivity and different tree functional traits were measured at the neighborhood level. We tested the effects of tree productivity, functional trait identity and dissimilarity on soil carbon concentrations, and their mediation by the soil microbial biomass and micro-environmental conditions. Our analyses showed a strong positive correlation between soil microbial biomass and soil carbon concentrations. Besides, soil carbon concentration increased with tree productivity and tree root diameter while it decreased with litterfall C:N content. Moreover, tree productivity and tree functional traits (e.g. root fungal association and litterfall C:N ratio) modulated micro-environmental conditions with substantial consequences for soil microbial biomass. We also showed that soil history and topography should be considered in future experiments and tree plantations, as soil carbon concentrations were higher where historical (i.e., at the beginning of the experiment) carbon concentrations were high, themselves being strongly affected by the topography. Altogether, these results imply that the quantification of the different soil carbon pools is critical for understanding microbial community–soil carbon stock relationships and their dependence on tree diversity and micro-environmental conditions.

Published
2022

21. Zevende Nederlandse Bosinventarisatie : Methoden en resultaten

Author

Schelhaas, M.J., Teeuwen, S., Oldenburger, J., Beerkens, G., Velema, G., Kremers, J., Lerink, B., Paulo, M.J., Schoonderwoerd, H., Daamen, W., Dolstra, F., Lusink, M., van Tongeren, K., Scholten, T., Pruijsten, I., Voncken, F., Clerkx, A.P.P.M., Schelhaas, M.J., Teeuwen, S., Oldenburger, J., Beerkens, G., Velema, G., Kremers, J., Lerink, B., Paulo, M.J., Schoonderwoerd, H., Daamen, W., Dolstra, F., Lusink, M., van Tongeren, K., Scholten, T., Pruijsten, I., Voncken, F., and Clerkx, A.P.P.M.

Abstract

The Seventh National Forest Inventory (NBI-7) of the Netherlands, commissioned by the Ministry of Agriculture, Nature and Food Safety, was carried out in the period 2017–2021. This report describes the methods and basic results of the inventory. Forests cover 11% of the total land area of the Netherlands, but the total area decreased slightly in the period 2013–2021. Trends observed in previous inventories continue: on average, Dutch forests are getting older, more mixed and more uneven-aged. The average volume of living and dead wood continues to increase, but at a slower rate due to the dry summers in 2018–2020. Ash is affected greatly by ash dieback, while Norway spruce is suffering from drought and spruce bark beetle attacks. Gross annual increment has decreased, while fellings remained about constant. The shift from conifers to broadleaves is continuing, with broadleaves for the first time occupying more than half of the forested area., In de periode 2017-2021 is in opdracht van het ministerie van Landbouw, Natuur en Voedselkwaliteit de zevende Nederlandse Bosinventarisatie (NBI-7) uitgevoerd. Dit rapport beschrijft de onderliggende methoden en de basisresultaten. Bos beslaat 11% van het grondgebruik in Nederland, maar de oppervlakte daalde licht. Trends uit de voorgaande inventarisaties zetten door: het Nederlandse bos wordt gemiddeld ouder, meer gemengd en meer ongelijkjarig. De gemiddelde voorraad levend en dood hout blijft toenemen, maar minder snel door de effecten van de droge zomers in de periode 2018-2020. De es staat duidelijk onder druk door de essentaksterfte en de fijnspar door de combinatie van droogte en aantasting door de letterzetter. De gemiddelde bijgroei is gedaald, bij ongeveer gelijkblijvende kap. De verschuiving van naaldboomsoorten naar loofboomsoorten zet door, waarbij nu loofboomsoorten voor het eerst het grootste aandeel hebben.

Published
2022

24. Global maps of soil temperature

Author

Lembrechts, JJ, van den Hoogen, J, Aalto, J, Ashcroft, MB, De Frenne, P, Kemppinen, J, Kopecký, M, Luoto, M, Maclean, IMD, Crowther, TW, Bailey, JJ, Haesen, S, Klinges, DH, Niittynen, P, Scheffers, BR, Van Meerbeek, K, Aartsma, P, Abdalaze, O, Abedi, M, Aerts, R, Ahmadian, N, Ahrends, A, Alatalo, JM, Alexander, JM, Nina Allonsius, C, Altman, J, Ammann, C, Andres, C, Andrews, C, Ardö, J, Arriga, N, Arzac, A, Aschero, V, Assis, RL, Johann Assmann, J, Bader, MY, Bahalkeh, K, Barančok, P, Barrio, IC, Barros, A, Barthel, M, Basham, EW, Bauters, M, Bazzichetto, M, Belelli Marchesini, L, Bell, MC, Benavides, JC, Luis Benito Alonso, J, Berauer, BJ, Bjerke, JW, Björk, RG, Björkman, MP, Björnsdóttir, K, Blonder, B, Boeckx, P, Boike, J, Bokhorst, S, Brum, BNS, Brůna, J, Buchmann, N, Buysse, P, Luís Camargo, J, Campoe, OC, Candan, O, Canessa, R, Cannone, N, Carbognani, M, Carnicer, J, Casanova‐Katny, A, Cesarz, S, Chojnicki, B, Choler, P, Chown, SL, Cifuentes, EF, Čiliak, M, Contador, T, Convey, P, Cooper, EJ, Cremonese, E, Curasi, SR, Curtis, R, Cutini, M, Johan Dahlberg, C, Daskalova, GN, Angel de Pablo, M, Della Chiesa, S, Dengler, J, Deronde, B, Descombes, P, Di Cecco, V, Di Musciano, M, Dick, J, Dimarco, RD, Dolezal, J, Dorrepaal, E, Dušek, J, Eisenhauer, N, Eklundh, L, Erickson, TE, Erschbamer, B, Eugster, W, Ewers, RM, Exton, DA, Fanin, N, Fazlioglu, F, Feigenwinter, I, Fenu, G, Ferlian, O, Rosa Fernández Calzado, M, Fernández‐Pascual, E, Finckh, M, Finger Higgens, R, Forte, TGW, Freeman, EC, Frei, ER, Fuentes‐Lillo, E, García, RA, García, MB, Géron, C, Gharun, M, Ghosn, D, Gigauri, K, Gobin, A, Goded, I, Goeckede, M, Gottschall, F, Goulding, K, Govaert, S, Jessen Graae, B, Greenwood, S, Greiser, C, Grelle, A, Guénard, B, Guglielmin, M, Guillemot, J, Haase, P, Haider, S, Halbritter, AH, Hamid, M, Hammerle, A, Hampe, A, Haugum, SV, Hederová, L, Heinesch, B, Helfter, C, Hepenstrick, D, Herberich, M, Herbst, M, Hermanutz, L, Hik, DS, Hoffrén, R, Homeier, J, Hörtnagl, L, Høye, TT, Hrbacek, F, Hylander, K, Iwata, H, Antoni Jackowicz‐Korczynski, M, Jactel, H, Järveoja, J, Jastrzębowski, S, Jentsch, A, Jiménez, JJ, Jónsdóttir, IS, Jucker, T, Jump, AS, Juszczak, R, Kanka, R, Kašpar, V, Kazakis, G, Kelly, J, Khuroo, AA, Klemedtsson, L, Klisz, M, Kljun, N, Knohl, A, Kobler, J, Kollár, J, Kotowska, MM, Kovács, B, Kreyling, J, Lamprecht, A, Lang, SI, Larson, C, Larson, K, Laska, K, le Maire, G, Leihy, RI, Lens, L, Liljebladh, B, Lohila, A, Lorite, J, Loubet, B, Lynn, J, Macek, M, Mackenzie, R, Magliulo, E, Maier, R, Malfasi, F, Máliš, F, Man, M, Manca, G, Manco, A, Manise, T, Manolaki, P, Marciniak, F, Matula, R, Clara Mazzolari, A, Medinets, S, Medinets, V, Meeussen, C, Merinero, S, de Cássia Guimarães Mesquita, R, Meusburger, K, Meysman, FJR, Michaletz, ST, Milbau, A, Moiseev, D, Moiseev, P, Mondoni, A, Monfries, R, Montagnani, L, Moriana‐Armendariz, M, Morra di Cella, U, Mörsdorf, M, Mosedale, JR, Muffler, L, Muñoz‐Rojas, M, Myers, JA, Myers‐Smith, IH, Nagy, L, Nardino, M, Naujokaitis‐Lewis, I, Newling, Emily, Nicklas, L, Niedrist, G, Niessner, A, Nilsson, MB, Normand, S, Nosetto, MD, Nouvellon, Y, Nuñez, MA, Ogaya, R, Ogée, J, Okello, J, Olejnik, J, Eivind Olesen, J, Opedal, Ø, Orsenigo, S, Palaj, A, Pampuch, T, Panov, AV, Pärtel, M, Pastor, A, Pauchard, A, Pauli, H, Pavelka, M, Pearse, WD, Peichl, M, Pellissier, L, Penczykowski, RM, Penuelas, J, Petit Bon, M, Petraglia, A, Phartyal, SS, Phoenix, GK, Pio, C, Pitacco, A, Pitteloud, C, Plichta, R, Porro, F, Portillo‐Estrada, M, Poulenard, J, Poyatos, R, Prokushkin, AS, Puchalka, R, Pușcaș, M, Radujković, D, Randall, K, Ratier Backes, A, Remmele, S, Remmers, W, Renault, D, Risch, AC, Rixen, C, Robinson, SA, Robroek, BJM, Rocha, AV, Rossi, C, Rossi, G, Roupsard, O, Rubtsov, AV, Saccone, P, Sagot, C, Sallo Bravo, J, Santos, CC, Sarneel, JM, Scharnweber, T, Schmeddes, J, Schmidt, M, Scholten, T, Schuchardt, M, Schwartz, N, Scott, T, Seeber, J, Cristina Segalin de Andrade, A, Seipel, T, Semenchuk, P, Senior, RA, Serra‐Diaz, JM, Sewerniak, P, Shekhar, A, Sidenko, NV, Siebicke, L, Siegwart Collier, L, Simpson, E, Siqueira, DP, Sitková, Z, Six, J, Smiljanic, M, Smith, SW, Smith‐Tripp, S, Somers, B, Vedel Sørensen, M, João L. L. Souza, J, Israel Souza, B, Souza Dias, A, Spasojevic, MJ, Speed, JDM, Spicher, F, Stanisci, A, Steinbauer, K, Steinbrecher, R, Steinwandter, M, Stemkovski, M, Stephan, JG, Stiegler, C, Stoll, S, Svátek, M, Svoboda, M, Tagesson, T, Tanentzap, AJ, Tanneberger, F, Theurillat, J, Thomas, HJD, Thomas, AD, Tielbörger, K, Tomaselli, M, Albert Treier, U, Trouillier, M, Dan Turtureanu, P, Tutton, R, Tyystjärvi, VA, Ueyama, M, Ujházy, K, Ujházyová, M, Uogintas, D, Urban, AV, Urban, J, Urbaniak, M, Ursu, T, Primo Vaccari, F, Van de Vondel, S, van den Brink, L, Van Geel, M, Vandvik, V, Vangansbeke, P, Varlagin, A, Veen, GF, Veenendaal, E, Venn, Susanna, Verbeeck, H, Verbrugggen, E, Verheijen, FGA, Villar, L, Vitale, L, Vittoz, P, Vives‐Ingla, M, von Oppen, J, Walz, J, Wang, R, Wang, Y, Way, RG, Wedegärtner, REM, Weigel, R, Wild, J, Wilkinson, M, Wilmking, M, Wingate, L, Winkler, M, Wipf, S, Wohlfahrt, G, Xenakis, G, Yang, Y, Yu, Z, Yu, K, Zellweger, F, Zhang, J, Zhang, Z, Zhao, P, Ziemblińska, K, Zimmermann, R, Zong, S, Zyryanov, VI, Nijs, I, Lenoir, J, Lembrechts, JJ, van den Hoogen, J, Aalto, J, Ashcroft, MB, De Frenne, P, Kemppinen, J, Kopecký, M, Luoto, M, Maclean, IMD, Crowther, TW, Bailey, JJ, Haesen, S, Klinges, DH, Niittynen, P, Scheffers, BR, Van Meerbeek, K, Aartsma, P, Abdalaze, O, Abedi, M, Aerts, R, Ahmadian, N, Ahrends, A, Alatalo, JM, Alexander, JM, Nina Allonsius, C, Altman, J, Ammann, C, Andres, C, Andrews, C, Ardö, J, Arriga, N, Arzac, A, Aschero, V, Assis, RL, Johann Assmann, J, Bader, MY, Bahalkeh, K, Barančok, P, Barrio, IC, Barros, A, Barthel, M, Basham, EW, Bauters, M, Bazzichetto, M, Belelli Marchesini, L, Bell, MC, Benavides, JC, Luis Benito Alonso, J, Berauer, BJ, Bjerke, JW, Björk, RG, Björkman, MP, Björnsdóttir, K, Blonder, B, Boeckx, P, Boike, J, Bokhorst, S, Brum, BNS, Brůna, J, Buchmann, N, Buysse, P, Luís Camargo, J, Campoe, OC, Candan, O, Canessa, R, Cannone, N, Carbognani, M, Carnicer, J, Casanova‐Katny, A, Cesarz, S, Chojnicki, B, Choler, P, Chown, SL, Cifuentes, EF, Čiliak, M, Contador, T, Convey, P, Cooper, EJ, Cremonese, E, Curasi, SR, Curtis, R, Cutini, M, Johan Dahlberg, C, Daskalova, GN, Angel de Pablo, M, Della Chiesa, S, Dengler, J, Deronde, B, Descombes, P, Di Cecco, V, Di Musciano, M, Dick, J, Dimarco, RD, Dolezal, J, Dorrepaal, E, Dušek, J, Eisenhauer, N, Eklundh, L, Erickson, TE, Erschbamer, B, Eugster, W, Ewers, RM, Exton, DA, Fanin, N, Fazlioglu, F, Feigenwinter, I, Fenu, G, Ferlian, O, Rosa Fernández Calzado, M, Fernández‐Pascual, E, Finckh, M, Finger Higgens, R, Forte, TGW, Freeman, EC, Frei, ER, Fuentes‐Lillo, E, García, RA, García, MB, Géron, C, Gharun, M, Ghosn, D, Gigauri, K, Gobin, A, Goded, I, Goeckede, M, Gottschall, F, Goulding, K, Govaert, S, Jessen Graae, B, Greenwood, S, Greiser, C, Grelle, A, Guénard, B, Guglielmin, M, Guillemot, J, Haase, P, Haider, S, Halbritter, AH, Hamid, M, Hammerle, A, Hampe, A, Haugum, SV, Hederová, L, Heinesch, B, Helfter, C, Hepenstrick, D, Herberich, M, Herbst, M, Hermanutz, L, Hik, DS, Hoffrén, R, Homeier, J, Hörtnagl, L, Høye, TT, Hrbacek, F, Hylander, K, Iwata, H, Antoni Jackowicz‐Korczynski, M, Jactel, H, Järveoja, J, Jastrzębowski, S, Jentsch, A, Jiménez, JJ, Jónsdóttir, IS, Jucker, T, Jump, AS, Juszczak, R, Kanka, R, Kašpar, V, Kazakis, G, Kelly, J, Khuroo, AA, Klemedtsson, L, Klisz, M, Kljun, N, Knohl, A, Kobler, J, Kollár, J, Kotowska, MM, Kovács, B, Kreyling, J, Lamprecht, A, Lang, SI, Larson, C, Larson, K, Laska, K, le Maire, G, Leihy, RI, Lens, L, Liljebladh, B, Lohila, A, Lorite, J, Loubet, B, Lynn, J, Macek, M, Mackenzie, R, Magliulo, E, Maier, R, Malfasi, F, Máliš, F, Man, M, Manca, G, Manco, A, Manise, T, Manolaki, P, Marciniak, F, Matula, R, Clara Mazzolari, A, Medinets, S, Medinets, V, Meeussen, C, Merinero, S, de Cássia Guimarães Mesquita, R, Meusburger, K, Meysman, FJR, Michaletz, ST, Milbau, A, Moiseev, D, Moiseev, P, Mondoni, A, Monfries, R, Montagnani, L, Moriana‐Armendariz, M, Morra di Cella, U, Mörsdorf, M, Mosedale, JR, Muffler, L, Muñoz‐Rojas, M, Myers, JA, Myers‐Smith, IH, Nagy, L, Nardino, M, Naujokaitis‐Lewis, I, Newling, Emily, Nicklas, L, Niedrist, G, Niessner, A, Nilsson, MB, Normand, S, Nosetto, MD, Nouvellon, Y, Nuñez, MA, Ogaya, R, Ogée, J, Okello, J, Olejnik, J, Eivind Olesen, J, Opedal, Ø, Orsenigo, S, Palaj, A, Pampuch, T, Panov, AV, Pärtel, M, Pastor, A, Pauchard, A, Pauli, H, Pavelka, M, Pearse, WD, Peichl, M, Pellissier, L, Penczykowski, RM, Penuelas, J, Petit Bon, M, Petraglia, A, Phartyal, SS, Phoenix, GK, Pio, C, Pitacco, A, Pitteloud, C, Plichta, R, Porro, F, Portillo‐Estrada, M, Poulenard, J, Poyatos, R, Prokushkin, AS, Puchalka, R, Pușcaș, M, Radujković, D, Randall, K, Ratier Backes, A, Remmele, S, Remmers, W, Renault, D, Risch, AC, Rixen, C, Robinson, SA, Robroek, BJM, Rocha, AV, Rossi, C, Rossi, G, Roupsard, O, Rubtsov, AV, Saccone, P, Sagot, C, Sallo Bravo, J, Santos, CC, Sarneel, JM, Scharnweber, T, Schmeddes, J, Schmidt, M, Scholten, T, Schuchardt, M, Schwartz, N, Scott, T, Seeber, J, Cristina Segalin de Andrade, A, Seipel, T, Semenchuk, P, Senior, RA, Serra‐Diaz, JM, Sewerniak, P, Shekhar, A, Sidenko, NV, Siebicke, L, Siegwart Collier, L, Simpson, E, Siqueira, DP, Sitková, Z, Six, J, Smiljanic, M, Smith, SW, Smith‐Tripp, S, Somers, B, Vedel Sørensen, M, João L. L. Souza, J, Israel Souza, B, Souza Dias, A, Spasojevic, MJ, Speed, JDM, Spicher, F, Stanisci, A, Steinbauer, K, Steinbrecher, R, Steinwandter, M, Stemkovski, M, Stephan, JG, Stiegler, C, Stoll, S, Svátek, M, Svoboda, M, Tagesson, T, Tanentzap, AJ, Tanneberger, F, Theurillat, J, Thomas, HJD, Thomas, AD, Tielbörger, K, Tomaselli, M, Albert Treier, U, Trouillier, M, Dan Turtureanu, P, Tutton, R, Tyystjärvi, VA, Ueyama, M, Ujházy, K, Ujházyová, M, Uogintas, D, Urban, AV, Urban, J, Urbaniak, M, Ursu, T, Primo Vaccari, F, Van de Vondel, S, van den Brink, L, Van Geel, M, Vandvik, V, Vangansbeke, P, Varlagin, A, Veen, GF, Veenendaal, E, Venn, Susanna, Verbeeck, H, Verbrugggen, E, Verheijen, FGA, Villar, L, Vitale, L, Vittoz, P, Vives‐Ingla, M, von Oppen, J, Walz, J, Wang, R, Wang, Y, Way, RG, Wedegärtner, REM, Weigel, R, Wild, J, Wilkinson, M, Wilmking, M, Wingate, L, Winkler, M, Wipf, S, Wohlfahrt, G, Xenakis, G, Yang, Y, Yu, Z, Yu, K, Zellweger, F, Zhang, J, Zhang, Z, Zhao, P, Ziemblińska, K, Zimmermann, R, Zong, S, Zyryanov, VI, Nijs, I, and Lenoir, J

Published
2021

25. Effects of climate and atmospheric nitrogen deposition on early to mid-term stage litter decomposition across biomes

Author

Kwon, T., Shibata, H., Kepfer-Rojas, S., Schmidt, I. K., Larsen, K. S., Beier, C., Berg, B., Verheyen, K., Lamarque, J. F., Hagedorn, F., Eisenhauer, N., Djukic, I., Caliman, A., Paquette, A., Gutiérrez-Girón, A., Petraglia, A., Augustaitis, A., Saillard, A., Ruiz-Fernández, A. C., Sousa, A. I., Lillebø, A. I., Da Rocha Gripp, A., Lamprecht, A., Bohner, A., Francez, A. J., Malyshev, A., Andrić, A., Stanisci, A., Zolles, A., Avila, A., Virkkala, A. M., Probst, A., Ouin, A., Khuroo, A. A., Verstraeten, A., Stefanski, A., Gaxiola, A., Muys, B., Gozalo, B., Ahrends, B., Yang, B., Erschbamer, B., Rodríguez Ortíz, C. E., Christiansen, C. T., Meredieu, C., Mony, C., Nock, C., Wang, C. P., Baum, C., Rixen, C., Delire, C., Piscart, C., Andrews, C., Rebmann, C., Branquinho, C., Jan, D., Wundram, D., Vujanović, D., Adair, E. C., Ordóñez-Regil, E., Crawford, E. R., Tropina, E. F., Hornung, E., Groner, E., Lucot, E., Gacia, E., Lévesque, E., Benedito, E., Davydov, E. A., Bolzan, F. P., Maestre, F. T., Maunoury-Danger, F., Kitz, F., Hofhansl, F., Hofhansl, G., De Almeida Lobo, F., Souza, F. L., Zehetner, F., Koffi, F. K., Wohlfahrt, G., Certini, G., Pinha, G. D., Gonzlez, G., Canut, G., Pauli, H., Bahamonde, H. A., Feldhaar, H., Jger, H., Serrano, H. C., Verheyden, H., Bruelheide, H., Meesenburg, H., Jungkunst, H., Jactel, H., Kurokawa, H., Yesilonis, I., Melece, I., Van Halder, I., Quirós, I. G., Fekete, I., Ostonen, I., Borovsk, J., Roales, J., Shoqeir, J. H., Jean-Christophe Lata, J., Probst, J. L., Vijayanathan, J., Dolezal, J., Sanchez-Cabeza, J. A., Merlet, J., Loehr, J., Von Oppen, J., Löffler, J., Benito Alonso, J. L., Cardoso-Mohedano, J. G., Peñuelas, J., Morina, J. C., Quinde, J. D., Jimnez, J. J., Alatalo, J. M., Seeber, J., Kemppinen, J., Stadler, J., Kriiska, K., Van Den Meersche, K., Fukuzawa, K., Szlavecz, K., Juhos, K., Gerhtov, K., Lajtha, K., Jennings, K., Jennings, J., Ecology, P., Hoshizaki, K., Green, K., Steinbauer, K., Pazianoto, L., Dienstbach, L., Yahdjian, L., Williams, L. J., Brigham, L., Hanna, L., Hanna, H., Rustad, L., Morillas, L., Silva Carneiro, L., Di Martino, L., Villar, L., Fernandes Tavares, L. A., Morley, M., Winkler, M., Lebouvier, M., Tomaselli, M., Schaub, M., Glushkova, M., Torres, M. G. A., De Graaff, M. A., Pons, M. N., Bauters, M., Mazn, M., Frenzel, M., Wagner, M., Didion, M., Hamid, M., Lopes, M., Apple, M., Weih, M., Mojses, M., Gualmini, M., Vadeboncoeur, M., Bierbaumer, M., Danger, M., Scherer-Lorenzen, M., Ruek, M., Isabellon, M., Di Musciano, M., Carbognani, M., Zhiyanski, M., Puca, M., Barna, M., Ataka, M., Luoto, M., H. Alsafaran, M., Barsoum, N., Tokuchi, N., Korboulewsky, N., Lecomte, N., Filippova, N., Hlzel, N., Ferlian, O., Romero, O., Pinto-Jr, O., Peri, P., Dan Turtureanu, P., Haase, P., Macreadie, P., Reich, P. B., Petk, P., Choler, P., Marmonier, P., Ponette, Q., Dettogni Guariento, R., Canessa, R., Kiese, R., Hewitt, R., Weigel, R., Kanka, R., Gatti, R. C., Martins, R. L., Ogaya, R., Georges, R., Gaviln, R. G., Wittlinger, S., Puijalon, S., Suzuki, S., Martin, S., Anja, S., Gogo, S., Schueler, S., Drollinger, S., Mereu, S., Wipf, S., Trevathan-Tackett, S., Stoll, S., Lfgren, S., Trogisch, S., Seitz, S., Glatzel, S., Venn, S., Dousset, S., Mori, T., Sato, T., Hishi, T., Nakaji, T., Jean-Paul, T., Camboulive, T., Spiegelberger, T., Scholten, T., Mozdzer, T. J., Kleinebecker, T., Runk, T., Ramaswiela, T., Hiura, T., Enoki, T., Ursu, T. M., Di Cella, U. M., Hamer, U., Klaus, V., Di Cecco, V., Rego, V., Fontana, V., Piscov, V., Bretagnolle, V., Maire, V., Farjalla, V., Pascal, V., Zhou, W., Luo, W., Parker, W., Parker, P., Kominam, Y., Kotrocz, Z., Utsumi, Y., Kwon, T., Shibata, H., Kepfer-Rojas, S., Schmidt, I. K., Larsen, K. S., Beier, C., Berg, B., Verheyen, K., Lamarque, J. F., Hagedorn, F., Eisenhauer, N., Djukic, I., Caliman, A., Paquette, A., Gutiérrez-Girón, A., Petraglia, A., Augustaitis, A., Saillard, A., Ruiz-Fernández, A. C., Sousa, A. I., Lillebø, A. I., Da Rocha Gripp, A., Lamprecht, A., Bohner, A., Francez, A. J., Malyshev, A., Andrić, A., Stanisci, A., Zolles, A., Avila, A., Virkkala, A. M., Probst, A., Ouin, A., Khuroo, A. A., Verstraeten, A., Stefanski, A., Gaxiola, A., Muys, B., Gozalo, B., Ahrends, B., Yang, B., Erschbamer, B., Rodríguez Ortíz, C. E., Christiansen, C. T., Meredieu, C., Mony, C., Nock, C., Wang, C. P., Baum, C., Rixen, C., Delire, C., Piscart, C., Andrews, C., Rebmann, C., Branquinho, C., Jan, D., Wundram, D., Vujanović, D., Adair, E. C., Ordóñez-Regil, E., Crawford, E. R., Tropina, E. F., Hornung, E., Groner, E., Lucot, E., Gacia, E., Lévesque, E., Benedito, E., Davydov, E. A., Bolzan, F. P., Maestre, F. T., Maunoury-Danger, F., Kitz, F., Hofhansl, F., Hofhansl, G., De Almeida Lobo, F., Souza, F. L., Zehetner, F., Koffi, F. K., Wohlfahrt, G., Certini, G., Pinha, G. D., Gonzlez, G., Canut, G., Pauli, H., Bahamonde, H. A., Feldhaar, H., Jger, H., Serrano, H. C., Verheyden, H., Bruelheide, H., Meesenburg, H., Jungkunst, H., Jactel, H., Kurokawa, H., Yesilonis, I., Melece, I., Van Halder, I., Quirós, I. G., Fekete, I., Ostonen, I., Borovsk, J., Roales, J., Shoqeir, J. H., Jean-Christophe Lata, J., Probst, J. L., Vijayanathan, J., Dolezal, J., Sanchez-Cabeza, J. A., Merlet, J., Loehr, J., Von Oppen, J., Löffler, J., Benito Alonso, J. L., Cardoso-Mohedano, J. G., Peñuelas, J., Morina, J. C., Quinde, J. D., Jimnez, J. J., Alatalo, J. M., Seeber, J., Kemppinen, J., Stadler, J., Kriiska, K., Van Den Meersche, K., Fukuzawa, K., Szlavecz, K., Juhos, K., Gerhtov, K., Lajtha, K., Jennings, K., Jennings, J., Ecology, P., Hoshizaki, K., Green, K., Steinbauer, K., Pazianoto, L., Dienstbach, L., Yahdjian, L., Williams, L. J., Brigham, L., Hanna, L., Hanna, H., Rustad, L., Morillas, L., Silva Carneiro, L., Di Martino, L., Villar, L., Fernandes Tavares, L. A., Morley, M., Winkler, M., Lebouvier, M., Tomaselli, M., Schaub, M., Glushkova, M., Torres, M. G. A., De Graaff, M. A., Pons, M. N., Bauters, M., Mazn, M., Frenzel, M., Wagner, M., Didion, M., Hamid, M., Lopes, M., Apple, M., Weih, M., Mojses, M., Gualmini, M., Vadeboncoeur, M., Bierbaumer, M., Danger, M., Scherer-Lorenzen, M., Ruek, M., Isabellon, M., Di Musciano, M., Carbognani, M., Zhiyanski, M., Puca, M., Barna, M., Ataka, M., Luoto, M., H. Alsafaran, M., Barsoum, N., Tokuchi, N., Korboulewsky, N., Lecomte, N., Filippova, N., Hlzel, N., Ferlian, O., Romero, O., Pinto-Jr, O., Peri, P., Dan Turtureanu, P., Haase, P., Macreadie, P., Reich, P. B., Petk, P., Choler, P., Marmonier, P., Ponette, Q., Dettogni Guariento, R., Canessa, R., Kiese, R., Hewitt, R., Weigel, R., Kanka, R., Gatti, R. C., Martins, R. L., Ogaya, R., Georges, R., Gaviln, R. G., Wittlinger, S., Puijalon, S., Suzuki, S., Martin, S., Anja, S., Gogo, S., Schueler, S., Drollinger, S., Mereu, S., Wipf, S., Trevathan-Tackett, S., Stoll, S., Lfgren, S., Trogisch, S., Seitz, S., Glatzel, S., Venn, S., Dousset, S., Mori, T., Sato, T., Hishi, T., Nakaji, T., Jean-Paul, T., Camboulive, T., Spiegelberger, T., Scholten, T., Mozdzer, T. J., Kleinebecker, T., Runk, T., Ramaswiela, T., Hiura, T., Enoki, T., Ursu, T. M., Di Cella, U. M., Hamer, U., Klaus, V., Di Cecco, V., Rego, V., Fontana, V., Piscov, V., Bretagnolle, V., Maire, V., Farjalla, V., Pascal, V., Zhou, W., Luo, W., Parker, W., Parker, P., Kominam, Y., Kotrocz, Z., and Utsumi, Y.

Abstract

Litter decomposition is a key process for carbon and nutrient cycling in terrestrial ecosystems and is mainly controlled by environmental conditions, substrate quantity and quality as well as microbial community abundance and composition. In particular, the effects of climate and atmospheric nitrogen (N) deposition on litter decomposition and its temporal dynamics are of significant importance, since their effects might change over the course of the decomposition process. Within the TeaComposition initiative, we incubated Green and Rooibos teas at 524 sites across nine biomes. We assessed how macroclimate and atmospheric inorganic N deposition under current and predicted scenarios (RCP 2.6, RCP 8.5) might affect litter mass loss measured after 3 and 12 months. Our study shows that the early to mid-term mass loss at the global scale was affected predominantly by litter quality (explaining 73% and 62% of the total variance after 3 and 12 months, respectively) followed by climate and N deposition. The effects of climate were not litter-specific and became increasingly significant as decomposition progressed, with MAP explaining 2% and MAT 4% of the variation after 12 months of incubation. The effect of N deposition was litter-specific, and significant only for 12-month decomposition of Rooibos tea at the global scale. However, in the temperate biome where atmospheric N deposition rates are relatively high, the 12-month mass loss of Green and Rooibos teas decreased significantly with increasing N deposition, explaining 9.5% and 1.1% of the variance, respectively. The expected changes in macroclimate and N deposition at the global scale by the end of this century are estimated to increase the 12-month mass loss of easily decomposable litter by 1.1-3.5% and of the more stable substrates by 3.8-10.6%, relative to current mass loss. In contrast, expected changes in atmospheric N deposition will decrease the mid-term mass loss of high-quality litter by 1.4-2.2% and that of l

Published
2021

26. Comparative analysis of TMPA and IMERG precipitation datasets in the arid environment of El-Qaa Plain, Sinai

Author

Morsy, Mona, Scholten, T., Michaelides, S., Borg, E., Sherief, Y., Dietrich, Peter, Morsy, Mona, Scholten, T., Michaelides, S., Borg, E., Sherief, Y., and Dietrich, Peter

Abstract

The replenishment of aquifers depends mainly on precipitation rates, which is of vital importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in the Sinai Peninsula is a region that experiences constant population growth. This study compares the performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial resolution TMPA (Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) data were retrieved and analyzed, employing appropriate statistical metrics. The best-performing data set was determined as the data source capable to most accurately bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events and more rare heavy-intensity events. With light-intensity events, the corresponding satellite-based data sets differ the least and correlate more, while the greatest differences and weakest correlations are noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior performance than TMPA in all rainfall intensities.

Published
2021

27. Potentials and perspectives of food self-sufficiency in urban areas – a case study from Leipzig

Author

Rüschhoff, Judith Sophia, Hubatsch, Carl, Priess, Jörg, Scholten, T., Egli, Lukas, Rüschhoff, Judith Sophia, Hubatsch, Carl, Priess, Jörg, Scholten, T., and Egli, Lukas

Abstract

Regionalization of food systems is a potential strategy to support environmental, economic and social sustainability. However, local preconditions need to be considered to assess the feasibility of such a transformation process. To better understand the potentials and perspectives of food self-sufficiency in urban and peri-urban areas, we determined the food self-sufficiency level (SSL) of a German metropolitan region, i.e., the percentage of the food demand that could be potentially provided on existing agricultural land. Main input parameters were actual food demand, agricultural productivity and its temporal variability and land availability. Furthermore, we considered changes in diet, food losses and land management. Based on current diets and agricultural productivity, the administrative region of Leipzig achieved a mean SSL of 94%, ranging from 77 to 116%. Additionally, an area of 26,932 ha, representing 12% of the regionally available agricultural land, was needed for commodities that are not cultivated regionally. Changes in food demand due to a diet shift to a more plant-based diet and reduced food losses would increase the SSL by 29 and 17%, respectively. A shift to organic agriculture would decrease the SSL by 34% due to lower crop yields compared with conventional production. However, a combination of organic agriculture with less food loss and a more plant-based diet would lead to a mean SSL of 95% (75–115%). Our results indicate the feasibility of food system regionalization in the study area under current and potential near future conditions. Addressing a combination of multiple dimensions, for example plant-based and healthier diets combined with reduced food loss and organic farming, is the most favorable approach to increase food self-sufficiency in urban and peri-urban areas and simultaneously provide synergies with social and environmental objectives.

Published
2021

28. The potential of using satellite-related precipitation data sources in arid regions

Author

Michaelides, S., Morsy, Mona, Dietrich, Peter, Scholten, T., Borg, E., Sherief, Y., Michaelides, S., Morsy, Mona, Dietrich, Peter, Scholten, T., Borg, E., and Sherief, Y.

Abstract

In this chapter the general factors affecting the equilibrium and stability of arid regions are discussed, with particular focus given to rainfall, the primary factor influencing the continuity and development of these environments. Unfortunately, the scarcity or complete lack of rain gauges in such areas complicates the collection of sufficient rainfall data. Consequently, systematic steps are proposed toward the development of alternative analyses of the spatiotemporal distribution of rainfall in arid regions. The foremost approach is the incorporation of remote sensing data, along with the appropriate classifiers, into the indirect evaluation of rainfall. For the purpose of assessing the suitability of the proposed methods, a detailed study on rainfall in the eastern side of the Gulf of Suez in Egypt is presented.

Published
2021

29. Optimization of rain gauge networks for arid regions based on remote sensing data

Author

Morsy, Mona, Taghizadeh-Mehrjardi, R., Michaelides, S., Scholten, T., Dietrich, Peter, Schmidt, K., Morsy, Mona, Taghizadeh-Mehrjardi, R., Michaelides, S., Scholten, T., Dietrich, Peter, and Schmidt, K.

Abstract

Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site.

Published
2021

30. What’s in a colluvial deposit? Perspectives from archaeopedology

Author

Scherer, S., primary, Deckers, K., additional, Dietel, J., additional, Fuchs, M., additional, Henkner, J., additional, Höpfer, B., additional, Junge, A., additional, Kandeler, E., additional, Lehndorff, E., additional, Leinweber, P., additional, Lomax, J., additional, Miera, J., additional, Poll, C., additional, Toffolo, M.B., additional, Knopf, T., additional, Scholten, T., additional, and Kühn, P., additional

Published
2021
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31. Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

Author

Taghizadeh-Mehrjardi, R, Schmidt, K, Amirian-Chakan, A, Rentschler, T, Zeraatpisheh, M, Sarmadian, F, Valavi, R, Davatgar, N, Behrens, T, Scholten, T, Taghizadeh-Mehrjardi, R, Schmidt, K, Amirian-Chakan, A, Rentschler, T, Zeraatpisheh, M, Sarmadian, F, Valavi, R, Davatgar, N, Behrens, T, and Scholten, T

Abstract

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning

Published
2020

32. 3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning

Author

Rentschler, T., Werban, Ulrike, Ahner, M., Behrens, T., Gries, P., Scholten, T., Teuber, S., Schmidt, K., Rentschler, T., Werban, Ulrike, Ahner, M., Behrens, T., Gries, P., Scholten, T., Teuber, S., and Schmidt, K.

Abstract

Soil organic C (SOC) and soil moisture (SM) affect the agricultural productivity of soils. For sustainable food production, knowledge of the horizontal as well as vertical variability of SOC and SM at field scale is crucial. Machine learning models using depth‐related data from multiple electromagnetic induction (EMI) sensors and a gamma‐ray spectrometer can provide insights into this variability of SOC and SM. In this work, we applied weighted conditioned Latin hypercube sampling to calculate 25 representative soil profile locations based on geophysical measurements on the surveyed agricultural field, for sampling and modeling. Ten additional random profiles were used for independent model validation. Soil samples were taken from four equal depth increments of 15 cm each. These were used to approximate polynomial and exponential functions to reproduce the vertical trends of SOC and SM as soil depth functions. We modeled the function coefficients of the soil depth functions spatially with Cubist and random forests with the geophysical measurements as environmental covariates. The spatial prediction of the depth functions provides three‐dimensional (3D) maps of the field scale. The main findings are (a) the 3D models of SOC and SM had low errors; (b) the polynomial function provided better results than the exponential function, as the vertical trends of SOC and SM did not decrease uniformly; and (c) the spatial prediction of SOC and SM with Cubist provided slightly lower error than with random forests. Hence, we recommend modeling the second‐degree polynomial with Cubist for 3D prediction of SOC and SM at field scale.

Published
2020

33. Subdued Mountains of Central Europe

Author

Kleber, A., primary, Terhorst, B., additional, Bullmann, H., additional, Hülle, D., additional, Leopold, M., additional, Müller, S., additional, Raab, T., additional, Sauer, D., additional, Scholten, T., additional, Dietze, M., additional, Felix-Henningsen, P., additional, Heinrich, J., additional, Spies, E.-D., additional, and Thiemeyer, H., additional

Published
2013
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38. Abstracts from the 1st International Symposium on Decision Support in Anaesthesia and Intensive Care: ESCTAIC 7th Annual Meeting — SCCCPMA 17th Annual Meeting September 25–28, 1996, Mainz, Germany, Johannes Gutenberg University Medical School

Author

Streifert, D., Lutter, N., Van der Vorst, E., Mulier, J., Schwilk, B., Bothner, U., Muche, R., Friesdorf, W., Ruskin, K. J., de Geus, A. F., Wiersma, G. E., Huet, R., Neuffer, H., Fischer, F., Christensen, U. J., Jensen, P. F., Jacobsen, J., Ørding, H., Brambrink, A. M., Goel, V., Hanley, D. F., Becker, K., Shaffner, H. D., Martin, L. J., Thakor, N. V., Koehler, R. C., Traystman, R. J., Quintel, M., Apin, M., Martin, J., Messelken, M., Dieterle-Paterakis, R., Hiller, J., Milewski, P., Gross, H., Foehring, U., Weiler, N., Eberle, B., Heinrichs, W., Höltermann, W., van Wickern, M., Linton, D. M., Ross, J. J., Mason, D. G., Pullman, M. D., Edwards, N. D., Doi, M., Gajraj, R. J., Mantzardis, H., Kenny, G. N. C., Markgraf, R., Deutschinoff, G., Pientka, L., Scholten, T., Maljers, J., Walther, St., Santevecci, A., and Ranieri, R.

Published
1997
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40. Paleoclimate and weathering of the Tokaj (NE Hungary) loess-paleosol sequence: a comparison of geochemical weathering indices and paleoclimate parameters

Author

Schatz, A.-K., Scholten, T., and Kühn, P.

Abstract

The Tokaj loess-paleosol sequence in NE Hungary is one of the key sites for detailed paleoclimate reconstructions of the Quaternary in SE Europe. In this study, the geochemical composition of samples from the upper part of the sequence (45–21 ka) was analyzed and a variety of commonly used weathering indices and element ratios were applied to estimate weathering intensity. Further, similarities and differences between these weathering indices and their sensitivity to changes in paleoclimatic conditions were assessed. Results indicate that all of them accurately track changes in weathering intensity and are, with minor exceptions, very similar to each other. Based on different transfer functions for major and trace element concentrations (XRF), magnetic susceptibility (MS) and δ13C data, we calculated mean annual paleotemperature and mean annual paleoprecipitation for the time intervals of paleosol formation (45–27 ka) and dust deposition (27–21 ka). Results differ depending on the respective transfer function and method but largely agree with previously published paleoclimate data of the region. XRF- and δ13C-based results converge to a MAT of 8–10°C (paleosol) and 8–9°C (loess) and show a~MAP range of 685–879 mm a-1 (paleosol) and 572–700 mm a-1 (loess). MS-based results are most reliable with MATs of 8.4°C (paleosol) and 6.7°C (loess) and MAPs of 325–441 mm a-1 (paleosol) and 224 mm a-1 (loess).

Published
2018

41. Data supplement to 'Pedogenic and microbial interrelations to regional climate and local topography: New insights from a climate gradient (arid to humid) along the Coastal Cordillera of Chile'

Author

Bernhard, N., Moskwa, L., Schmidt, K., Oeser, R., Aburto, F., Bader, M., Baumann, K., von Blanckenburg, F., Boy, J., van den Brink, L., Brucker, E., Büdel, B., Canessa, R., Dippold, M., Ehlers, T., Fuentes, J., Godoy, R., Jung, P., Karsten, U., Köster, M., Kuzyakov, Y., Leinweber, P., Neidhardt, H., Matus, F., Mueller, C., Oelmann, Y., Oses, R., Osses, P., Paulino, L., Samolov, E., Schaller, M., Schmid, M., Spielvogel, S., Spohn, M., Stock, S., Stroncik, N., Tielbörger, K., Übernickel, K., Scholten, T., Seguel, O., Wagner, D., and Kühn, P.

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Data_MISCELLANEOUS
Abstract

Dataset

Published
2018

42. Data supplement to: Chemistry and microbiology of the Critical Zone along a steep climate and vegetation gradient in the Chilean Coastal Cordillera

Author

Oeser, R., Stroncik, N., Moskwa, L., Bernhard, N., Schaller, M., Canessa, R., van den Brink, L., Köster, M., Brucker, E., Stock, S., Fuentes, J., Godoy, R., Matus, F., Oses Pedraza, R., Osses McIntyre, P., Paulino, L., Seguel, O., Bader, M., Boy, J., Dippold, M., Ehlers, T., Kühn, P., Kuzyakov, Y., Leinweber, P., Scholten, T., Spielvogel, S., Spohn, M., Übernickel, K., Tielbörger, K., Wagner, D., and von Blanckenburg, F.

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Data_MISCELLANEOUS
Abstract

Dataset

Published
2018

44. Experimental evidence of functional group-dependent effects of tree diversity on soil fungi in subtropical forests

Author

Weißbecker, Christina, Wubet, Tesfaye, Lentendu, Guillaume, Kühn, P., Scholten, T., Bruelheide, H., Buscot, Francois, Weißbecker, Christina, Wubet, Tesfaye, Lentendu, Guillaume, Kühn, P., Scholten, T., Bruelheide, H., and Buscot, Francois

Abstract

Deconvoluting the relative contributions made by specific biotic and abiotic drivers to soil fungal community compositions facilitates predictions about the functional responses of ecosystems to environmental changes, such as losses of plant diversity, but it is hindered by the complex interactions involved. Experimental assembly of tree species allows separation of the respective effects of plant community composition (biotic components) and soil properties (abiotic components), enabling much greater statistical power than can be achieved in observational studies. We therefore analyzed these contributions by assessing, via pyrotag sequencing of the internal transcribed spacer (ITS2) rDNA region, fungal communities in young subtropical forest plots included in a large experiment on the effects of tree species richness. Spatial variables and soil properties were the main drivers of soil fungal alpha and beta-diversity, implying strong early-stage environmental filtering and dispersal limitation. Tree related variables, such as tree community composition, significantly affected arbuscular mycorrhizal and pathogen fungal community structure, while differences in tree host species and host abundance affected ectomycorrhizal fungal community composition. At this early stage of the experiment, only a limited amount of carbon inputs (rhizodeposits and leaf litter) was being provided to the ecosystem due to the size of the tree saplings, and persisting legacy effects were observed. We thus expect to find increasing tree related effects on fungal community composition as forest development proceeds.

Published
2018

45. Biodiversity across trophic levels drives multifunctionality in highly diverse forests

Author

Schuldt, A., Assmann, T., Brezzi, M., Buscot, Francois, Eichenberg, D., Gutknecht, Jessica, Härdtle, W., He, J.-S., Klein, A.-M., Kühn, P., Liu, X., Ma, K., Niklaus, P.A., Pietsch, K.A., Purahong, Witoon, Scherer-Lorenzen, M., Schmid, B., Scholten, T., Staab, M., Tang, Z., Trogisch, S., von Oheimb, G., Wirth, C., Wubet, Tesfaye, Zhu, C.-D., Bruelheide, H., Schuldt, A., Assmann, T., Brezzi, M., Buscot, Francois, Eichenberg, D., Gutknecht, Jessica, Härdtle, W., He, J.-S., Klein, A.-M., Kühn, P., Liu, X., Ma, K., Niklaus, P.A., Pietsch, K.A., Purahong, Witoon, Scherer-Lorenzen, M., Schmid, B., Scholten, T., Staab, M., Tang, Z., Trogisch, S., von Oheimb, G., Wirth, C., Wubet, Tesfaye, Zhu, C.-D., and Bruelheide, H.

Abstract

Human-induced biodiversity change impairs ecosystem functions crucial to human well-being. However, the consequences of this change for ecosystem multifunctionality are poorly understood beyond effects of plant species loss, particularly in regions with high biodiversity across trophic levels. Here we adopt a multitrophic perspective to analyze how biodiversity affects multifunctionality in biodiverse subtropical forests. We consider 22 independent measurements of nine ecosystem functions central to energy and nutrient flow across trophic levels. We find that individual functions and multifunctionality are more strongly affected by the diversity of heterotrophs promoting decomposition and nutrient cycling, and by plant functional-trait diversity and composition, than by tree species richness. Moreover, cascading effects of higher trophic-level diversity on functions originating from lower trophic-level processes highlight that multitrophic biodiversity is key to understanding drivers of multifunctionality. A broader perspective on biodiversity-multifunctionality relationships is crucial for sustainable ecosystem management in light of non-random species loss and intensified biotic disturbances under future environmental change.

Published
2018

46. Impacts of species richness on productivity in a large-scale subtropical forest experiment

Author

Huang, Y., Chen, Y., Castro-Izaguirre, N., Baruffol, M., Brezzi, M., Lang, A., Li, Y., Härdtle, W., von Oheimb, G., Yang, X., Liu, X., Pei, K., Both, S., Yang, B., Eichenberg, D., Assmann, T., Bauhus, J., Behrens, T., Buscot, Francois, Chen, X.-Y., Chesters, D., Ding, B.-Y., Durka, Walter, Erfmeier, A., Fang, J., Fischer, M., Guo, L.-D., Guo, D., Gutknecht, J.L.M., He, J.-S., He, C.-L., Hector, A., Hönig, L., Hu, R.-Y., Klein, A.-M., Kühn, P., Liang, Y., Li, S., Michalski, Stefan, Scherer-Lorenzen, M., Schmidt, K., Scholten, T., Schuldt, A., Shi, X., Tan, M.-Z., Tang, Z., Trogisch, S., Wang, Z., Welk, E., Wirth, C., Wubet, Tesfaye, Xiang, W., Yu, M., Yu, X.-D., Zhang, J., Zhang, S., Zhang, N., Zhou, H.-Z., Zhu, C.-D., Zhu, L., Bruelheide, H., Ma, K., Niklaus, P.A., Schmid, B., Huang, Y., Chen, Y., Castro-Izaguirre, N., Baruffol, M., Brezzi, M., Lang, A., Li, Y., Härdtle, W., von Oheimb, G., Yang, X., Liu, X., Pei, K., Both, S., Yang, B., Eichenberg, D., Assmann, T., Bauhus, J., Behrens, T., Buscot, Francois, Chen, X.-Y., Chesters, D., Ding, B.-Y., Durka, Walter, Erfmeier, A., Fang, J., Fischer, M., Guo, L.-D., Guo, D., Gutknecht, J.L.M., He, J.-S., He, C.-L., Hector, A., Hönig, L., Hu, R.-Y., Klein, A.-M., Kühn, P., Liang, Y., Li, S., Michalski, Stefan, Scherer-Lorenzen, M., Schmidt, K., Scholten, T., Schuldt, A., Shi, X., Tan, M.-Z., Tang, Z., Trogisch, S., Wang, Z., Welk, E., Wirth, C., Wubet, Tesfaye, Xiang, W., Yu, M., Yu, X.-D., Zhang, J., Zhang, S., Zhang, N., Zhou, H.-Z., Zhu, C.-D., Zhu, L., Bruelheide, H., Ma, K., Niklaus, P.A., and Schmid, B.

Abstract

Biodiversity experiments have shown that species loss reduces ecosystem functioning in grassland. To test whether this result can be extrapolated to forests, the main contributors to terrestrial primary productivity, requires large-scale experiments. We manipulated tree species richness by planting more than 150,000 trees in plots with 1 to 16 species. Simulating multiple extinction scenarios, we found that richness strongly increased stand-level productivity. After 8 years, 16-species mixtures had accumulated over twice the amount of carbon found in average monocultures and similar amounts as those of two commercial monocultures. Species richness effects were strongly associated with functional and phylogenetic diversity. A shrub addition treatment reduced tree productivity, but this reduction was smaller at high shrub species richness. Our results encourage multispecies afforestation strategies to restore biodiversity and mitigate climate change.

Published
2018

47. Spatial modelling with Euclidean distance fields and machine learning

Author

Behrens, T., Schmidt, K., Viscarra Rossel, Raphael, Gries, P., Scholten, T., MacMillan, R., Behrens, T., Schmidt, K., Viscarra Rossel, Raphael, Gries, P., Scholten, T., and MacMillan, R.

Abstract

This study introduces a hybrid spatial modelling framework, which accounts for spatial non-stationarity, spatial autocorrelation and environmental correlation. A set of geographic spatially autocorrelated Euclidean distance fields (EDF) was used to provide additional spatially relevant predictors to the environmental covariates commonly used for mapping. The approach was used in combination with machine-learning methods, so we called the method Euclidean distance fields in machine-learning (EDM). This method provides advantages over other prediction methods that integrate spatial dependence and state factor models, for example, regression kriging (RK) and geographically weighted regression (GWR). We used seven generic (EDFs) and several commonly used predictors with different regression algorithms in two digital soil mapping (DSM) case studies and compared the results to those achieved with ordinary kriging (OK), RK and GWR as well as the multiscale methods ConMap, ConStat and contextual spatial modelling (CSM). The algorithms tested in EDM were a linear model, bagged multivariate adaptive regression splines (MARS), radial basis function support vector machines (SVM), Cubist, random forest (RF) and a neural network (NN) ensemble. The study demonstrated that DSM with EDM provided results comparable to RK and to the contextual multiscale methods. Best results were obtained with Cubist, RF and bagged MARS. Because the tree-based approaches produce discontinuous response surfaces, the resulting maps can show visible artefacts when only the EDFs are used as predictors (i.e. no additional environmental covariates). Artefacts were not obvious for SVM and NN and to a lesser extent bagged MARS. An advantage of EDM is that it accounts for spatial non-stationarity and spatial autocorrelation when using a small set of additional predictors. The EDM is a new method that provides a practical alternative to more conventional spatial modelling and thus it enhances the DSM toolbox.

Published
2018

48. Contributors

Author

Bullmann, H., Chifflard, P., Damm, B., Dietze, M., Döhler, S., Felix-Henningsen, P., Frechen, M., Heinrich, J., Heinrich, S., Hübner, R., Hülle, D., Kleber, A., Leopold, M., Lorz, C., Maerker, K., Mailänder, R., Menke, M., Meyer-Heintze, S., Moldenhauer, K.-M., Ottner, F., Phillips, J.D., Raab, T., Richter-Krautz, J., Sauer, D., Scholten, T., Terhorst, B., Thiemeyer, H., Völkel, J., Vonlanthen, C., Waroszewski, J., and Yang, F.

Published
2024
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