Unravelling environmental drivers and patterns of Portuguese man o' war (Physalia physalis) blooms in two ocean regions: North Atlantic and the Southeast Pacific

被引:0
|
作者
Martins, Lara Colaco [1 ]
Gomes-Pereira, Jose Nuno [2 ,3 ,4 ]
Dionisio, Gisela [2 ,3 ,5 ,6 ]
Assis, Jorge [1 ,7 ]
机构
[1] Univ Algarve, Ctr Marine Sci CCMAR, CIMAR, Faro, Portugal
[2] Atlantic Naturalist Assoc, Mons Silveira Medeiros 6, P-9900021 Horta, Portugal
[3] Naturalist Sci & Tourism Lab, P-9900026 Horta, Portugal
[4] Univ Azores, Inst Marine Sci Okeanos, P-9901862 Horta, Portugal
[5] Univ Lisbon, Fac Ciencias, MARE Marine & Environm Sci Ctr, Ave Nossa Senhora Cabo 939, P-2750374 Cascais, Portugal
[6] Univ Lisbon, Fac Ciencias, ARNET Aquat Res Network, Ave Nossa Senhora Cabo 939, P-2750374 Cascais, Portugal
[7] Nord Univ, Fac Biosci & Aquaculture, Bodo, Norway
关键词
Jellyfish blooms; Physalia physalis; Predictive modeling; Timeseries; Global warming; Atlantic Ocean; Pacific Ocean; GELATINOUS ZOOPLANKTON; JELLYFISH BLOOMS; IMPACTS; FUTURE; COAST; ENVENOMATION; AGGREGATIONS; SYSTEMATICS; STRANDINGS; EVOLUTION;
D O I
10.1016/j.marpolbul.2024.117278
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Jellyfish blooms can significantly impact marine food webs, biochemical processes and human health, disrupting various economic sectors, including fisheries, aquaculture and tourism. Thus, understanding the regional drivers and patterns of jellyfish occurrence is key for developing effective management strategies. The Portuguese man o' war ( Physalia physalis) is a hazardous, cosmopolitan siphonophore of particular concern, requiring a deeper ecological understanding to effectively guide mitigation efforts. Our study reveals that the occurrence of P. physalis in both the North Atlantic (Azores, Portugal) and the Southeast Pacific (Australian East Coast) is driven by region-specific wind patterns and increased primary productivity (>30 % model contribution), with warming conditions emerging as an additional occurrence driver on the Australian East Coast (similar to 20 % model contribution). These insights resulted from machine learning models (Boosted Regression Trees) trained with high-resolution environmental data against field data describing the temporal variability of P. physalis occurrence (North Atlantic: 2008-2021; Southeast Pacific: 2016-2020). The models achieved excellent predictive performance (AUC North Atlantic: 1.00; AUC Southeast Pacific: 0.92) and allowed hindcasting occurrences over 30 years, uncovering contrasting trends between the two regions, with decadal fluctuations in the Azores and a significant increase in occurrence over time on the Australian East Coast. Overall, we provide a better understanding of the drivers and patterns of P. physalis occurrence, which can support the development of coastal management strategies. Importantly, the anticipated changes in productivity and temperature conditions in both regions may result in increased blooms in the years to come, further exerting impacts on the ecosystems, human health, and the economy.
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页数:9
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