Data underlying the publication: Pinpointing stage-specific causes of recruitment bottlenecks to optimize seed-based wetland restoration

doi: 10.4121/21071089.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21071089
Datacite citation style:
Zhiyuan Zhao; Zhang, Liquan; Lin Yuan; Bouma, Tjeerd (2022): Data underlying the publication: Pinpointing stage-specific causes of recruitment bottlenecks to optimize seed-based wetland restoration. Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

In this study, we intended to make seed-based wetland restoration predictive and inform management by (1) seeking integrated experimental evidence generalizing stage-specific causative factors for demographic loss/mortality and (2) developing predictors oriented toward site-specific bottlenecks. Specifically, this study is focused on seed retention and seedling emergence, since they represent the most vulnerable life stages that follow sowing. Firstly, by means of large-scale field experiments, we tested how seed retention and seedling emergence were affected by varied management options (i.e., seed-planting depth and species selection) and a wide range of physical settings (i.e., elevation, hydrodynamic intensity, bed-level dynamics and sediment properties). Variable screening was then implemented to identify stage-specific governing factors. Secondly, the resulting insights and dataset were used to develop stage-specific predictive models using machine learning. Model experiments under various scenarios were then conducted to assess site-specific feasibility of potential seed-based restoration practices.

These files include the data used to create each figure in the manuscript, organized as follows:

1. Field experiments

a) Physical conditions at all study locations

b) Manipulated experiment concerning seed retention

c) Manipulated experiment concerning seedling emergence

2. Machine learning

a) Developing machine learning predictors on seed retention

b) Developing machine learning predictors on seedling emergence

For a complete description, see 'Data description.docx'

  • 2022-11-02 first online, published, posted
Compressed file package, containing documents (*.docx), tables (*.xlsx), and images (*.png, *.tif).
  • This work was supported by the project “Coping with deltas in transition” within the Programme of Strategic Scientific Alliances between China and the Netherlands (Project no. PSA-SA-E-02). Tjeerd J. Bouma was financially supported by the CoE-Perkpolder financed by RWS and the CoE-Zuidgors project financed by the Province of Zeeland, both focused on marsh restoration. Lin Yuan and Liquan Zhang were funded by the National Natural Science Foundation of China (41876093), the Scientific Research Project of Shanghai & Technology Committee(22dz1202700), and the Central Guidance on Local Science and Technology Development Fund of Shanghai (YDZX20213100002003). Zhiyuan Zhao was supported by the China Scholarship Council.
Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems
East China Normal University, State Key Laboratory of Estuarine and Coastal Research


files (1)