Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales

Frequent jellyfish blooms cause human health issues and closures of coastal areas, impacting different economic sectors like tourism, fisheries, aquaculture farms and industry. Understanding the drivers of jellyfish bloom and predicting their occurrence is therefore essential to develop effective ma...

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Bibliographic Details
Main Author: Martins, Lara Colaço
Other Authors: Assis, Jorge, Gomes-Pereira, José Nuno
Format: Master Thesis
Language:English
Published: 2022
Subjects:
Online Access:http://hdl.handle.net/10400.1/19365
id ftunivalgarve:oai:sapientia.ualg.pt:10400.1/19365
record_format openpolar
spelling ftunivalgarve:oai:sapientia.ualg.pt:10400.1/19365 2023-05-15T17:28:53+02:00 Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales Martins, Lara Colaço Assis, Jorge Gomes-Pereira, José Nuno 2022-12-02 http://hdl.handle.net/10400.1/19365 eng eng http://hdl.handle.net/10400.1/19365 203263979 openAccess http://creativecommons.org/licenses/by/4.0/ Physalia physalis Species distribution modelling Climate change Jellyfish blooms Drivers Domínio/Área Científica::Ciências Naturais::Outras Ciências Naturais masterThesis 2022 ftunivalgarve 2023-04-05T00:01:13Z Frequent jellyfish blooms cause human health issues and closures of coastal areas, impacting different economic sectors like tourism, fisheries, aquaculture farms and industry. Understanding the drivers of jellyfish bloom and predicting their occurrence is therefore essential to develop effective management plans. The Portuguese Man-of-War (Physalia physalis) is a dangerous cosmopolitan siphonophore and its ecology remains largely understudied. The objective of this study is to understand the main environmental drivers (e.g., temperature, productivity, wind and ocean patterns) that explain the occurrence of P. physalis at a macroecological scale (the North Atlantic Ocean) and at a regional scale (Faial Island from the Azores archipelago), and to predict its distribution and temporal trends. We implemented machine learning modelling that fed on high-resolution environmental data and occurrence data describing its distribution in the North Atlantic Ocean and long-term temporal variability in the Faial Island (Azores). Models retrieved high accuracy scores and showed that the distribution of P. physalis is mainly explained by primary productivity, temperature and currents direction at the macroecological scale and by primary productivity and wind patterns at the regional scale. The models also showed a higher probability of occurrence on both Atlantic coasts and offshore North-northwest Atlantic. Models fed on temporal datasets demonstrate decadal fluctuations rather than significant increases over time, contradicting the previously established hypothesis that jellyfish blooms are increasing. By using species distribution modelling, we provide a better understanding on how environmental variability shapes the occurrence of P. physalis at different spatial and temporal scales (macroecological and regional), which can be considered in management plans and policies. In the future, projected global warming and decreased primary productivity in the North Atlantic may cause significant poleward shifting of this species, ... Master Thesis North Atlantic Northwest Atlantic Universidade do Algarve: Sapienta
institution Open Polar
collection Universidade do Algarve: Sapienta
op_collection_id ftunivalgarve
language English
topic Physalia physalis
Species distribution modelling
Climate change
Jellyfish blooms
Drivers
Domínio/Área Científica::Ciências Naturais::Outras Ciências Naturais
spellingShingle Physalia physalis
Species distribution modelling
Climate change
Jellyfish blooms
Drivers
Domínio/Área Científica::Ciências Naturais::Outras Ciências Naturais
Martins, Lara Colaço
Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales
topic_facet Physalia physalis
Species distribution modelling
Climate change
Jellyfish blooms
Drivers
Domínio/Área Científica::Ciências Naturais::Outras Ciências Naturais
description Frequent jellyfish blooms cause human health issues and closures of coastal areas, impacting different economic sectors like tourism, fisheries, aquaculture farms and industry. Understanding the drivers of jellyfish bloom and predicting their occurrence is therefore essential to develop effective management plans. The Portuguese Man-of-War (Physalia physalis) is a dangerous cosmopolitan siphonophore and its ecology remains largely understudied. The objective of this study is to understand the main environmental drivers (e.g., temperature, productivity, wind and ocean patterns) that explain the occurrence of P. physalis at a macroecological scale (the North Atlantic Ocean) and at a regional scale (Faial Island from the Azores archipelago), and to predict its distribution and temporal trends. We implemented machine learning modelling that fed on high-resolution environmental data and occurrence data describing its distribution in the North Atlantic Ocean and long-term temporal variability in the Faial Island (Azores). Models retrieved high accuracy scores and showed that the distribution of P. physalis is mainly explained by primary productivity, temperature and currents direction at the macroecological scale and by primary productivity and wind patterns at the regional scale. The models also showed a higher probability of occurrence on both Atlantic coasts and offshore North-northwest Atlantic. Models fed on temporal datasets demonstrate decadal fluctuations rather than significant increases over time, contradicting the previously established hypothesis that jellyfish blooms are increasing. By using species distribution modelling, we provide a better understanding on how environmental variability shapes the occurrence of P. physalis at different spatial and temporal scales (macroecological and regional), which can be considered in management plans and policies. In the future, projected global warming and decreased primary productivity in the North Atlantic may cause significant poleward shifting of this species, ...
author2 Assis, Jorge
Gomes-Pereira, José Nuno
format Master Thesis
author Martins, Lara Colaço
author_facet Martins, Lara Colaço
author_sort Martins, Lara Colaço
title Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales
title_short Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales
title_full Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales
title_fullStr Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales
title_full_unstemmed Modelling the occurrence of Physalia physalis in the North Atlantic Ocean at different spatial and temporal scales
title_sort modelling the occurrence of physalia physalis in the north atlantic ocean at different spatial and temporal scales
publishDate 2022
url http://hdl.handle.net/10400.1/19365
genre North Atlantic
Northwest Atlantic
genre_facet North Atlantic
Northwest Atlantic
op_relation http://hdl.handle.net/10400.1/19365
203263979
op_rights openAccess
http://creativecommons.org/licenses/by/4.0/
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