| Machine learning algorithms to infer trait matching and predict species interactions in ecological networks M Pichler, V Boreux, AM Klein, M Schleuning, F Hartig Methods in Ecology and Evolution, 2019 | 107 | 2019 |
| A new joint species distribution model for faster and more accurate inference of species associations from big community data M Pichler, F Hartig Methods in Ecology and Evolution, 2021 | 38* | 2021 |
| Machine learning and deep learning—A review for ecologists M Pichler, F Hartig Methods in Ecology and Evolution 14 (4), 994-1016, 2023 | 36 | 2023 |
| Fixed or random? On the reliability of mixed‐effects models for a small number of levels in grouping variables J Oberpriller, M de Souza Leite, M Pichler Ecology and Evolution 12 (7), e9062, 2022 | 33 | 2022 |
| Linking functional traits and demography to model species-rich communities L Chalmandrier, F Hartig, DC Laughlin, H Lischke, M Pichler, DB Stouffer, ... Nature communications 12 (1), 2724, 2021 | 20 | 2021 |
| Machine‐learning algorithms predict soil seed bank persistence from easily available traits S Rosbakh, M Pichler, P Poschlod Applied Vegetation Science 25 (2), e12660, 2022 | 3 | 2022 |
| Novel community data in ecology-properties and prospects F Hartig, N Abrego, A Bush, JM Chase, G Guillera-Arroita, MA Leibold, ... Trends in Ecology & Evolution, 2024 | 1 | 2024 |
| Can predictive models be used for causal inference? M Pichler, F Hartig arXiv preprint arXiv:2306.10551, 2023 | | 2023 |
| cito: An R package for training neural networks using torch C Amesoeder, F Hartig, M Pichler arXiv preprint arXiv:2303.09599, 2023 | | 2023 |
| Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity Y Li, C Devenish, M Tosa, M Luo, D Bell, D Lesmeister, P Greenfield, ... bioRxiv, 2023.09. 07.556488, 2023 | | 2023 |
