2022-08-05T16:54:30 n32677

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

Partner institution

L'Institut Français de Recherche pour l'Exploitation de la Mer https://wwz.ifremer.fr/en/

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

Has association with
Paul Wu  (Researcher)
Kerrie Mengersen  (Researcher)

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