Published on 01 January 2017
DEMNA : Freshwater fish occurrences from monitorings on the biological quality of water bodies in Wallonia
View DatasetDescription
WARNING: SOME ERRORS WHERE SPOTED WITHIN THE DATASET. USE IS NOT RECOMMENDED BEFORE 2018 UPDATE. This occurrence dataset countains fresh water fish occurrences in wallonia (Belgium) compiled by Hydrobiology unit at the Department of Study of the Natural and Agricultural Environment (DEMNA- Public Service of Wallonia). The data come from an ongoing monitoring network on the biological quality of water bodies in Wallonia, and previous/alternative samplings events performed by DEMNA's hydrobiology unit. Opportunistic observations made during non-fish-target activities has also been included into the dataset. The Current monitoring network is composed of more than 440 sites, distributed among small streams to the largest rivers/canals from the hydrographic districts of the Meuse, the Scheldt, the Rhine and the Seine. The network was set up in order to perform the biological monitoring of water courses (Fishes, diatoms - doi:10.15468/vvkk9a -, macroinvertebrates - doi:10.15468/ew00u8 - and macrphytes- doi:10.15468/cumtkq) required, among other missions and activites of the DEMNA, for the Natura 2000 and Water Framework directive reportings. The updates will be realised annually, in order to implement previous-year validated data. We intend to integrate associated biometrics to this publication into the post-2018 updates. Results from the water masses monitoring can be accessed here : http://geoapps.wallonie.be/CigaleInter/. More information is available upon request adressed to the DEMNA, and please note that this dataset does not replace any official convention on the provision of biological data delivered by the department.
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Publication Details
DOI
Publisher
Service Public de Wallonie – Département d’Etude du Milieu Naturel et Agricole (SPW – DEMNA)
Subfield
Aquatic Science
Field
Agricultural and Biological Sciences
Domain
Life Sciences
Confidence Score
44%
Source
Scholar Data Model