| Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence C Mi, F Huettmann, Y Guo, X Han, L Wen PeerJ 5, e2849, 2017 | 262 | 2017 |
| Australian wheat production expected to decrease by the late 21st century B Wang, DL Liu, GJ O'Leary, S Asseng, I Macadam, R Lines‐Kelly, ... Global change biology 24 (6), 2403-2415, 2018 | 76 | 2018 |
| Multi-model ensemble projections of future extreme heat stress on rice across southern China L He, J Cleverly, B Wang, N Jin, C Mi, DL Liu, Q Yu Theoretical and Applied Climatology 133, 1107-1118, 2018 | 58 | 2018 |
| Global protected areas as refuges for amphibians and reptiles under climate change C Mi, L Ma, M Yang, X Li, S Meiri, U Roll, O Oskyrko, D Pincheira-Donoso, ... Nature Communications 14 (1), 1389, 2023 | 48 | 2023 |
| Spatio-temporal distribution of sugarcane potential yields and yield gaps in Southern China Q Zu, C Mi, D Li Liu, L He, Z Kuang, Q Fang, D Ramp, L Li, B Wang, ... European Journal of Agronomy 92, 72-83, 2018 | 47 | 2018 |
| Classification and regression with random forests as a standard method for presence-only data SDMs: a future conservation example using China tree species L Zhang, F Huettmann, S Liu, P Sun, Z Yu, X Zhang, C Mi Ecological Informatics 52, 46-56, 2019 | 46 | 2019 |
| The use of classification and regression algorithms using the random forests method with presence-only data to model species’ distribution L Zhang, F Huettmann, X Zhang, S Liu, P Sun, Z Yu, C Mi MethodsX 6, 2281-2292, 2019 | 45 | 2019 |
| The surface-atmosphere exchange of carbon dioxide in tropical rainforests: Sensitivity to environmental drivers and flux measurement methodology Z Fu, T Gerken, G Bromley, A Araújo, D Bonal, B Burban, D Ficklin, ... Agricultural and Forest Meteorology 263, 292-307, 2018 | 41 | 2018 |
| Ancient demographics determine the effectiveness of genetic purging in endangered lizards HX Xie, XX Liang, ZQ Chen, WM Li, CR Mi, M Li, ZJ Wu, XM Zhou, WG Du Molecular biology and evolution 39 (1), msab359, 2022 | 39 | 2022 |
| Possible impact of climate change on apple yield in Northwest China M Li, J Guo, J He, C Xu, J Li, C Mi, S Tao Theoretical and Applied Climatology 139, 191-203, 2020 | 34 | 2020 |
| The effect of NDVI time series density derived from spatiotemporal fusion of multisource remote sensing data on crop classification accuracy R Sun, S Chen, H Su, C Mi, N Jin ISPRS International Journal of Geo-Information 8 (11), 502, 2019 | 34 | 2019 |
| Conservation prioritization with machine learning predictions for the black-necked crane Grus nigricollis, a flagship species on the Tibetan Plateau for 2070 X Han, F Huettmann, Y Guo, C Mi, L Wen Regional Environmental Change 18 (7), 2173-2182, 2018 | 31 | 2018 |
| Climate envelope predictions indicate an enlarged suitable wintering distribution for Great Bustards (Otis tarda dybowskii) in China for the 21st century C Mi, H Falk, Y Guo PeerJ 4, e1630, 2016 | 31 | 2016 |
| Combining occurrence and abundance distribution models for the conservation of the Great Bustard C Mi, F Huettmann, R Sun, Y Guo PeerJ 5, e4160, 2017 | 28 | 2017 |
| Latitudinal embryonic thermal tolerance and plasticity shape the vulnerability of oviparous species to climate change B Sun, L Ma, Y Wang, C Mi, LB Buckley, O Levy, H Lu, WG Du Ecological Monographs 91 (3), e01468, 2021 | 26 | 2021 |
| Machine learning model analysis of breeding habitats for the black-necked crane in Central Asian Uplands under anthropogenic pressures X Han, Y Guo, C Mi, F Huettmann, L Wen Scientific reports 7 (1), 6114, 2017 | 25 | 2017 |
| Satellite tracking reveals a new migration route of black-necked cranes (Grus nigricollis) in Qinghai-Tibet Plateau Y Wang, C Mi, Y Guo PeerJ 8, e9715, 2020 | 21 | 2020 |
| Global patterns of climate change impacts on desert bird communities L Ma, SR Conradie, CL Crawford, AS Gardner, MR Kearney, ... Nature communications 14 (1), 211, 2023 | 18 | 2023 |
| Effects of climate and human activity on the current distribution of amphibians in China BS Chunrong Mi, Falk Huettmann, Xinhai Li, Zhongwen Jiang, Weiguo Du Conservation Biology, 2022 | 17 | 2022 |
| Obtaining the best possible predictions of habitat selection for wintering Great Bustards in Cangzhou, Hebei Province with rapid machine learning analysis C Mi, F Huettmann, Y Guo Chinese Science Bulletin 59, 4323-4331, 2014 | 17 | 2014 |
Falk Huettmann在 alaska.edu 的电子邮件经过验证
