Go to content
UR Home

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

URN to cite this document:
DOI to cite this document:
Rosbakh, Sergey ; Pichler, Maximilian ; Poschlod, Peter ; Török, Péter
License: Creative Commons Attribution Non-commercial 4.0
PDF - Published Version
Date of publication of this fulltext: 24 May 2022 06:54

This publication is part of the DEAL contract with Wiley.


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 ...


Owner only: item control page
  1. Homepage UR

University Library

Publication Server


Publishing: oa@ur.de
0941 943 -4239 or -69394

Dissertations: dissertationen@ur.de
0941 943 -3904

Research data: datahub@ur.de
0941 943 -5707

Contact persons