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

URN to cite this document:
urn:nbn:de:bvb:355-epub-522928
DOI to cite this document:
10.5283/epub.52292
Rosbakh, Sergey ; Pichler, Maximilian ; Poschlod, Peter ; Török, Péter
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License: Creative Commons Attribution Non-commercial 4.0
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Date of publication of this fulltext: 24 May 2022 06:54

This publication is part of the DEAL contract with Wiley.


Abstract

Question Soil seed banks (SSB), i.e. pools of viable seeds in the soil and on its surface, play a crucial role in plant biology and ecology. Information on seed persistence in soil is of great importance for fundamental and applied research, yet compiling data sets on this trait still requires enormous efforts. We asked whether the machine-learning (ML) approach could be used to infer and predict ...

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