Wäldchen, J.; Wittich, H. C.; Rzanny, M.; Fritz, A.; Mäder, P.: Towards more effective identification keys: A study of people identifying plant species characters. People and Nature 4 (6), pp. 1603 - 1615 (2022)
Katal, N.; Rzanny, M.; Mäder, P.; Wäldchen, J.: Deep learning in plant phenological research: A systematic literature review. Frontiers in Plant Science 13, 805738 (2022)
Schmid, B.; Schmitz, M.; Rzanny, M.; Scherer-Lorenzen, M.; Mwangi, P. N.; Weisser, W. W.; Hector, A.; Schmid, R.; Flynn, D. F. B.: Removing subordinate species in a biodiversity experiment to mimic observational field studies. Grassland Research 1 (1), pp. 53 - 62 (2022)
Mahecha, M. D.; Rzanny, M.; Kraemer, G.; Mäder, P.; Seeland, M.; Wäldchen, J.: Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. Ecography 44 (8), pp. 1131 - 1142 (2021)
Mäder, P.; Boho, D.; Rzanny, M.; Seeland, M.; Wittich, H. C.; Deggelmann, A.; Wäldchen, J.: The Flora Incognita app – interactive plant species identfication. Methods in Ecology and Evolution 12 (7), pp. 1335 - 1342 (2021)
Rzanny, M.; Mäder, P.; Deggelmann, A.; Chen, M.; Wäldchen, J.: Flowers, leaves or both? How to obtain suitable images for automated plant identification. Plant Methods 15, 77 (2019)
Hines, J.; Giling, D. P.; Rzanny, M.; Voigt, W.; Meyer, S. T.; Weisser, W. W.; Eisenhauer, N.; Ebeling, A.: A meta‐food web for invertebrate species collected in a European grassland. Ecology 100 (6), e02679 (2019)
Seeland, M.; Rzanny, M.; Boho, D.; Wäldchen, J.; Mäder, P.: Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinformatics 20, 4 (2019)
Wittich, H. C.; Seeland, M.; Wäldchen, J.; Rzanny, M.; Mäder, P.: Recommending plant taxa for supporting on-site species identification. BMC Bioinformatics 19, 190 (2018)
Ebeling, A.; Rzanny, M.; Lange, M.; Eisenhauer, N.; Hertzog, L. R.; Meyer, S. T.; Weisser, W. W.: Plant diversity induces shifts in the functional structure and diversity across trophic levels. Oikos 127 (2), pp. 208 - 219 (2018)
Rzanny, M.; Seeland, M.; Wäldchen, J.; Mäder, P.: Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods 13, 97 (2017)
Seeland, M.; Rzanny, M.; Alaqraa, N.; Wäldchen, J.; Mäder, P.: Plant species classification using flower images—A comparative study of local feature representations. PLoS One 12 (2), e0170629 (2017)
Begehold, H.; Rzanny, M.; Winter, S.: Patch patterns of lowland beech forests in a gradient of management intensity. Forest Ecology and Management 360, pp. 69 - 79 (2016)
The new research project "PollenNet" aims to use artificial intelligence to accurately predict the spread of pollen. In order to improve allergy prevention, experts are bringing together the latest interdisciplinary findings from a wide range of fields.
If rivers overflow their banks, the consequences can be devastating. Using methods of explainable machine learning, researchers at the Helmholtz Centre for Environmental Research (UFZ) have shown that floods are more extreme when several factors are involved in their development.
Europe is the fastest warming continent in the world. According to the European Environment Agency’s assessment, many of these risks have already reached critical levels and could become catastrophic without urgent and decisive action.
Plant observations collected with plant identification apps such as Flora Incognita allow statements about the developmental stages of plants - both on a small scale and across Europe.
We have gained a new external member: Prof. Dr. Christian Wirth has been appointed by the Senate of the Max Planck Society as External Scientific Member. As a former group leader and later fellow at the institute, Prof. Wirth initiated and supported the development of the TRY database, the world's largest collection on plant traits.
Removing a tonne of CO2 from the air and thus undoing a tonne of emissions? Doesn't quite work, says a study. And provides four objections in view of Earth systems.
The new report by the Global Carbon Project shows: Fossil CO2 emissions will reach a record high in 2023. If emissions remain this high, the carbon budget that remains before reaching the 1.5°C limit will probably be used up in seven years. Although emissions from land use are decreasing slightly, they are still too high to be compensated by renewable forests and reforestation.
Global experts have unveiled the annual 10 New Insights in Climate Science report. The report equips policymakers with the latest and most pivotal climate science research from the previous 18 months, synthesised to help inform negotiations at COP28 and policy implementation through 2024 and beyond.
In the annual ranking of the world's most cited and thus most influential scientists, five authors from our institute are once again represented in 2023.
Vegetation can respond to drought through different mechanisms, including changes in the plants’ structure and physiology. By analyzing state-of-the-art satellite-derived datasets with explainable machine learning methods, an international team around Wantong Li and René Orth showed that the vegetation’s physiology in many ecosystems has deviated from its structure under drought on a global scale.