Meng, M.; Ni, J.; Zong, M. J.: Impacts of changes in climate variability on regional vegetation in China: NDVI-based analysis from 1982 to 2000. Ecological Research 26 (2), pp. 421 - 428 (2011)
Ashiq, M. W.; Zhao, C. Y.; Ni, J.; Akhtar, M.: GIS-based high-resolution spatial interpolation of precipitation in mountain-plain areas of Upper Pakistan for regional climate change impact studies. Theoretical and Applied Climatology 99 (3-4), pp. 239 - 253 (2010)
Ni, J.; Yu, G.; Harrison, S. P.; Prentice, I. C.: Palaeovegetation in China during the late Quaternary: Biome reconstructions based on a global scheme of plant functional types. Palaeogeography, Palaeoclimatology, Palaeoecology 289 (1-4), pp. 44 - 61 (2010)
Ni, J.; Wang, G. H.; Bai, Y. F.; Li, X. Z.: Scale-dependent relationships between plant diversity and above-ground biomass in temperate grasslands, south-eastern Mongolia. Journal of Arid Environments 68 (1), pp. 132 - 142 (2007)
Ni, J.; Harrison, S. P.; Prentice, I. C.; Kutzbach, J. E.; Sitch, S.: Impact of climate variability on present and Holocene vegetation: A model-based study. Ecological Modelling 191 (3-4), pp. 469 - 486 (2006)
Wang, G.-H.; Ni, J.: Responses of plant functional types to an environmental gradient on the Northeast China Transect. Ecological Research 20 (5), pp. 563 - 572 (2005)
Wang, Q.; Ni, J.; Tenhunen, J.: Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global Ecology and Biogeography 14 (4), pp. 379 - 393 (2005)
Ni, J.: Corrigendum to "Net primary productivity in forests of China: scaling-up of national inventory data and comparison with model predictions" (Forest Ecology and Management, vol 187 (2003), 485-495). Forest Ecology and Management 194 (1-3), p. 413 (2004)
Ni, J.: Estimating net primary productivity of grasslands from field biomass measurements in temperate northern China. Plant Ecology 174 (2), pp. 217 - 234 (2004)
Ni, J.: Forest productivity of the Altay and Tianshan Mountains in the dryland, northwestern China. Forest Ecology and Management 202 (1-3), pp. 13 - 22 (2004)
Ni, J.: Plant functional types and climate along a precipitation gradient in temperate grasslands, north-east China and south-east Mongolia. Journal of Arid Environments 53 (4), pp. 501 - 516 (2003)
Ni, J.: Net primary productivity in forests of China: scaling-up of national inventory data and comparison with model predictions. Forest Ecology and Management 176 (1-3), pp. 485 - 495 (2003)
Ni, J.; Ding, S.-Y.: Modeling the large-scale distribution of plant diversity: a possibility inferred from climate and productivity. Acta Phytoecologica Sinica 26 (5), pp. 568 - 574 (2002)
Clark, D. A.; Brown, S.; Kicklighter, D. W.; Chambers, J. Q.; Thomlinson, J. R.; Ni, J.: Measuring net primary production in forests: Concepts and field methods. Ecological Applications 11 (2), pp. 356 - 370 (2001)
Clark, D. A.; Brown, S.; Kicklighter, D. W.; Chambers, J. Q.; Thomlinson, J. R.; Ni, J.; Holland, E. A.: NPP in tropical forests: an evaluation and synthesis of existing field data. Ecological Applications 11: 371-384. Appendix 1. Estimates from the literature of net primary productivity in tropical forests. Ecological Archives A011-006-A1, pp. 1 - 19 (2001)
Thanks to FLUXCOM-X, the next generation of data driven, AI-based earth system models, scientists can now see the Earth’s metabolism at unprecedented detail – assessed everywhere on land and every hour of the day.
A study by Leipzig University, the German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig (iDiv) and the MPI for Biogeochemistry shows that gaps in the canopy of a mixed floodplain forest have a direct influence on the temperature and moisture in the forest soil, but only a minor effect on soil activity.
EU funds the international research project AI4PEX to further improve Earth system models and thus scientific predictions of climate change. Participating scientists from 9 countries met at the end of May 2024 to launch the project at the MPI for Biogeochemistry in Jena, which is leading the project.
From the Greek philosopher Aristotle to Charles Darwin to the present day, scientists have dealt with this fundamental question of biology. Contrary to public perception, however, it is still largely unresolved. Scientists have now presented a new approach for the identification and delimitation of species using artificial intelligence (AI).
A research team led by the German Centre for Integrative Biodiversity Research (iDiv) and Leipzig University has developed an algorithm that analyses observational data from the Flora Incognita app. The novel can be used to derive ecological patterns that could provide valuable information about the effects of climate change on plants.
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.
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.
A new study shows a natural solution to mitigate the effects of climate change such as extreme weather events. Researchers found that a diverse plant community acts as a buffer against fluctuations in soil temperature. This buffer, in turn, can have a decisive influence on important ecosystem processes.
The plant identification app Flora Incognita receives this year's Sonja Bernadotte Award for its importance in nature education for all age groups and its high scientific standards and usefulness.