Abstract:Almost all classifications of the world ocean are based on expert opinion or ad hoc management areas. A quantitative analysis of environmental variables may provide a more objective basis for mapping and classifying the oceans to support data management, reporting, and conservation efforts. Here, we used long‐term averages of 20 ocean variables to classify the ocean surface waters using PCA and k‐means clustering. We identified seven distinct areas that fit the definition of “ecosystems,” that is, enduring reg… Show more
“…However, due to the lack of works for this region, comparisons between the present results and other studies were very limited. In other studies, authors have proposed a similar number of dominant regimes on a large scale in the tropical Pacific (Fereday et al, 2008), for the determination of robust modes of Northern Hemisphere sea ice variability (Fučkar et al, 2016), and for ocean mapping from environmental data (Zhao et al, 2020).…”
Section: Performance Indices and Clustering Qualitymentioning
Abstract. The massive Sargassum algae beachings observed over the past decade are a
new natural hazard currently impacting the island states of the Caribbean
region (human health, environmental damages, and economic losses). This
study aims to improve the prediction of the surface current dynamic leading
to beachings in the Lesser Antilles using clustering analysis methods. The
input surface currents were derived from the Mercator model and the Hybrid
Coordinate Ocean Model (HYCOM) outputs in which we integrated the windage
effect. Past daily observations of Sargassum beaching on Guadeloupe coasts and
satellite-based Sargassum offshore abundance were also integrated. Four
representative current regimes were identified for both Mercator and HYCOM
data. The analysis of the current sequences leading to beachings showed that
the recurrence of two current regimes is related to the beaching peaks
respectively observed in March and August. The performance score of the
predictive model showed that the HYCOM data seem more suitable to assess
coastal Sargassum hazard in the Lesser Antilles. For 1 year of tests (i.e., 2021),
the decision tree accuracy respectively reached 70.1 % and 58.2 % for
HYCOM and Mercator with a temporal uncertainty range ±3 d around the
forecast date. The present clustering analysis predictive system, requiring
lower computational resources compared to conventional forecast models, would
help improve this risk management in the islands of the region.
“…However, due to the lack of works for this region, comparisons between the present results and other studies were very limited. In other studies, authors have proposed a similar number of dominant regimes on a large scale in the tropical Pacific (Fereday et al, 2008), for the determination of robust modes of Northern Hemisphere sea ice variability (Fučkar et al, 2016), and for ocean mapping from environmental data (Zhao et al, 2020).…”
Section: Performance Indices and Clustering Qualitymentioning
Abstract. The massive Sargassum algae beachings observed over the past decade are a
new natural hazard currently impacting the island states of the Caribbean
region (human health, environmental damages, and economic losses). This
study aims to improve the prediction of the surface current dynamic leading
to beachings in the Lesser Antilles using clustering analysis methods. The
input surface currents were derived from the Mercator model and the Hybrid
Coordinate Ocean Model (HYCOM) outputs in which we integrated the windage
effect. Past daily observations of Sargassum beaching on Guadeloupe coasts and
satellite-based Sargassum offshore abundance were also integrated. Four
representative current regimes were identified for both Mercator and HYCOM
data. The analysis of the current sequences leading to beachings showed that
the recurrence of two current regimes is related to the beaching peaks
respectively observed in March and August. The performance score of the
predictive model showed that the HYCOM data seem more suitable to assess
coastal Sargassum hazard in the Lesser Antilles. For 1 year of tests (i.e., 2021),
the decision tree accuracy respectively reached 70.1 % and 58.2 % for
HYCOM and Mercator with a temporal uncertainty range ±3 d around the
forecast date. The present clustering analysis predictive system, requiring
lower computational resources compared to conventional forecast models, would
help improve this risk management in the islands of the region.
“…However, due to the lack of works for this region, comparisons between the present results and other studies were very limited. In other studies, authors have proposed a similar number of dominant regimes on a large scale, in the tropical Pacific (Fereday et al, 2008), for the determination of robust modes of Northern Hemisphere Sea ice variability (Fučkar et al, 2016), or for ocean mapping from environmental data (Zhao et al, 2020).…”
Section: Performance Indices and Clustering Qualitymentioning
Abstract. The massive Sargassum algae strandings observed over the past decade are the new natural hazard that currently impacts the island states of the Caribbean region (human health, environmental damages, and economic losses). This study aims to improve the prediction of the surface current dynamic leading to beachings in the Lesser Antilles, using clustering analysis methods. The input surface currents including windage effect were derived from the Mercator model and the Hybrid Coordinate Ocean Model (HYCOM). Past daily observations of Sargassum stranding on Guadeloupe coasts were also integrated. Four representative current regimes were identified for both Mercator and HYCOM data. The analysis of the backward current sequences leading to strandings showed that the recurrence of two current regimes is related to the beaching peaks observed respectively in March and in August. A decision tree classifier was built and its accuracy reaches 73.3 % with 0.04°-scale HYCOM data and 50.8 % with 0.08°-scale Mercator data. This significant accuracy difference highlights the need of very small-scale current data (i.e., lower than 5 km scale) to assess coastal Sargassum hazard in the Lesser Antilles. The present clustering analysis predictive system would help improve this risk management in the islands of this region.
“…Such global targets recognize that the percentage area of seascapes covered by protected areas alone may not be enough to indicate effective protection of marine systems, owing to the use of poor delineation methods or a lack of on‐the‐ground management or monitoring (Watson et al, 2014; Visconti et al, 2019). Therefore, to deliver effective conservation outcomes and avoid the problem of ‘paper parks’ (Wilhelm et al, 2014; Di Minin & Toivonen, 2015), it is necessary to prioritize action at relevant sites of importance for biodiversity (IPBES, 2019; Visconti et al, 2019; Zhao, Basher & Costello, 2020).…”
1. There are growing pressures on marine biodiversity. Seabirds in particular are one the most-threatened groups. The black-vented shearwater (Puffinus opisthomelas) is endemic to Mexican islands and the only shearwater living its entire life cycle in the California Current System, one of the most productive large marine ecosystems in the world. Marine Protected Areas (MPAs) in this region, however, were designed without consideration of accurate data on seabird distributions.2. Here, 57 black-vented shearwaters were GPS-tracked from their main breeding colony (95% of the global population) over four seasons (2016)(2017)(2018)(2019) to estimate their at-sea distribution. Two methods were applied to identify priority conservation areas: the approach developed by BirdLife International to identify marine Important Bird and Biodiversity Areas and a method using expectationmaximization binary clustering to identify core foraging areas.3. One potential marine Important Bird and Biodiversity Area close to the breeding colony and five core foraging areas were identified. These priority conservation areas were largely beyond the bounds of the current MPA network in the region. 4. Our results detail opportunities for improving the implementation of conservation and management measures in the California Current System region with respect to seabirds. The approach of combining site identification methods can be applied to other seabird species for which high-resolution tracking data are available and can help guide conservation action plans and MPA design.
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