Data derived state probabilities for Z. noltei monitoring study. Observed variables were shoot density at four sites in this study
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Data-derived state probabilities for the seagrass monitoring study (Cognat et al., 2018) were used to validate the DBN model for Zostera noltei in Arcachon Bay. Shoot density was observed at four sites in this study.
Geographical area of data collection
kmlPolyCoords
-1.323089,44.848975 -0.920715,44.848975 -0.920715,44.556148 -1.323089,44.556148 -1.323089,44.848975
Publications
Paula Hatum, Kathryn McMahon, Kerrie Mengersen, et al. Guidelines for model adaptation: a study of the transferability of a general seagrass ecosystem DBN model. Authorea. January 12, 2022. DOI:
http://dx.doi.org/10.22541/au.164198621.13512002/v1
Research areas
Applied
mathematics
not
elsewhere
classified
Ecology
Complex
systems
Cite this collection
Hatum, Paula (2022), Data derived state probabilities for Z. noltei monitoring study. Observed variables were shoot density at four sites in this study, Dryad, Dataset,
https://doi.org/10.5061/dryad.7m0cfxpxd
Related information
QUT Centre for Data Science
https://www.qut.edu.au/research/centre-for-data-science
Partner institution
L'Institut Français de Recherche pour l'Exploitation de la Mer
https://wwz.ifremer.fr/en/
Access the data
Data file types
.csv (Microsoft Excel)
Licence
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Copyright
© Queensland University of Technology, 2022.
Dates of data collection
From 01/01/21 to 01/12/21
Connections
Contacts
Name: Paula Hatum
Other
Date record created:
2022-07-26T09:01:39
Date record modified:
2022-08-05T16:54:30
Record status:
Published - Open Access