| title: |
| Predictors of Vibrio vulnificus occurrence: a machine learning approach |
|
| contributing persons: |
| David Riedinger[VerfasserIn] |
 |
1396402276 |
| Matthias Labrenz[AkademischeR BetreuerIn] |
|
13682028X |
|
Leibniz-Institut für Ostseeforschung Warnemünde |
| Holger Scholz[AkademischeR BetreuerIn] |
|
1272433781 |
|
Robert Koch Institut |
|
| contributing corporate bodies: |
| Universität Rostock[Grad-verleihende Institution] |
|
38329-6 |
| Universität Rostock. Mathematisch-Naturwissenschaftliche Fakultät[Grad-verleihende Institution] |
|
2147083-2 |
|
| |
| abstract: |
|
Vibrio vulnificus, a deadly marine bacterium, is expanding due to climate change,
warming waters, and eutrophication in estuarine environments. This thesis analyzes
its spread using global 16S rRNA data and machine learning models, identifying temperature,
salinity, and chlorophyll a as key predictors. A rapid, affordable detection method
was developed. In the Baltic Sea, eutrophication stimulates V. vulnificus more than
seagrass suppresses it. Reducing nutrient-driven blooms may be key to limiting its
growth worldwide.
[English] |
|
| document type: |
|
| institution: |
| Faculty of Mathematics and Natural Sciences |
|
| language: |
|
| subject class (DDC): |
| 550 Earth sciences |
| 570 Life science |
|
| extent: |
|
1 Online-Ressource (170 Seiten)
|
|
| |
publication /
production: |
|
Rostock: Universität Rostock
|
|
2024
|
|
| statement of responsibility: |
| vorgelegt von David Jeroen Riedinger |
|
| notes: |
| Enthält Zeitschriftenartikel |
|
| |
| identifiers: |
|
| |
| access condition: |
|
| license/rights statement: |
all rights reserved
This work may only be used under the terms of the German Copyright Law (Urheberrechtsgesetz). |
|
|
| RosDok id: |
rosdok_disshab_0000003509 |
| created / modified: |
16.04.2026 / 16.04.2026
|
| metadata license: |
The metadata of this document was dedicated to the public domain
(CC0 1.0 Universal Public Domain Dedication). |
access
viewer
fulltext