Machine‐learning algorithms predict soil seed bank persistence from easily available traits-Reference-Cited by-同舟云学术

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Machine‐learning algorithms predict soil seed bank persistence from easily available traits

Published:2022-04 Issue:2 Volume:25 Page:
ISSN:1402-2001
Container-title:Applied Vegetation Science
language:en
Short-container-title:Applied Vegetation Science

Author:

Rosbakh Sergey¹^ORCID,Pichler Maximilian²^ORCID,Poschlod Peter¹^ORCID

Affiliation:

1. Ecology and Nature Conservation Biology University of Regensburg Regensburg Germany

2. Theoretical Ecology University of Regensburg Regensburg Germany

Publisher

Wiley

Subject

Management, Monitoring, Policy and Law,Nature and Landscape Conservation,Ecology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/avsc.12660

Reference74 articles.

1. Visualizing the effects of predictor variables in black box supervised learning models

2. Persistent soil seed bank and standing vegetation at a high alpine site in the central Chilean Andes

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Influence of seed mass and shape on light plasticity of germination of alpine plants on the Tibetan Plateau: The role of photoblastic taxa, dispersal ability, and life history;Global Ecology and Conservation;2024-06

2. Large and non-spherical seeds are less likely to form a persistent soil seed bank;Proceedings of the Royal Society B: Biological Sciences;2024-06

3. The omission of seed banks in demography as an example of bias in ecology;BioScience;2024-05-28

4. Climate and plant traits alter the relationship between seed dispersal and seed dormancy in alpine environment;Environmental and Experimental Botany;2024-03

5. Pace and parity predict the short‐term persistence of small plant populations;Ecology and Evolution;2024-02

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