Pelagic bioregionalisation using open-access data for better planning of marine protected area networks

Roberson, Leslie A.; Lagabrielle, Erwann; Lombard, Amanda T.; Sink, Kerry; Livingstone, Tamsyn; Grantham, Hedley S.; Harris, Jean M.

doi:10.1016/j.ocecoaman.2017.08.017

Cited by 25 publications

(11 citation statements)

References 100 publications

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“…Cluster analysis has been commonly used to identify bioregions and is still widely used today (Milligan and Cooper, 1987;Ebach et al, 2015;Roberson et al, 2017;Bloomfield et al, 2018). For the Southern Ocean, physical and biological variables, including the water temperature, salinity, depth, chlorophyll, and sea-ice information, were used to obtain the bioregions to facilitate systematic planning for the protection of marine habitat diversity (Grant et al, 2006;Sharp et al, 2010;Teschke et al, 2016;Godet et al, 2020)..…”

Section: Methodsmentioning

confidence: 99%

“…Bioregionalization is one method used to define ecosystems. Under this approach, regions are defined based on physical and biological properties, the method can be defined as the process of delineating the continuous spatial coverage of contiguous spatial units that support distinct biological assemblages (Costello, 2009;Koubbi et al, 2011;Roberson et al, 2017). Usually, the spatial units are delineated using geophysical and biological observation data, modeled data, or a combination of both (Grantham et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Variations in and environmental controls of primary productivity in the Amundsen Sea

Feng

Zhang

et al. 2021

Preprint

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Abstract. The Amundsen Sea is one of the regions with the highest primary productivity in the Antarctic. To better understand the role of the Southern Ocean in the global carbon cycle and in climate regulation, a better understanding of the variations in and environmental controls of primary productivity is needed. Using cluster analysis, the Amundsen Sea was divided into nine bioregions. The biophysical differences among bioregions enhanced confidence to identify priorities and regions to study the temporal and spatial variations in primary productivity. Four nearshore bioregions with high net primary productivity or rapidly increasing rates were selected to analyze temporal and spatial variations in primary productivity in the Amundsen Sea. Due to changes in net solar radiation and sea ice, primary production had significant seasonal variation in these four bioregions. The phenology had changed at two bioregions (3 and 5), which has the third and fourth highest primary production, due to changes in the dissolved iron, nitrate, phosphate, and silicate concentrations. Annual primary production showed increasing trends in these four bioregions. The variation in primary production in the bioregion (9), which has the highest primary production, was mainly affected by variations in sea surface temperatures. In the bioregion, which has the second-highest primary production (8), the primary production was significantly positively correlated with sea surface temperature and significantly negatively correlated with sea ice thickness. The long-term changes of primary productivity in bioregions 3 and 5 were thought to be related to changes in the dissolved iron, nitrate, phosphate, and silicate concentrations, and dissolved iron was the limiting factor in these two bioregions. Bioregionalization not only disentangle multiple factors that control the spatial differences, but also disentangle limiting factors that affect the phenology, decadal and long-term changes in primary productivity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variations in and environmental controls of primary productivity in the Amundsen Sea

Feng

Zhang

et al. 2021

Preprint

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“…Despite this, it is possible to build such datasets with limited information and resources. Coastal habitat types can be mapped from Google Earth imagery (Harris et al 2013), and offshore habitat types can be delineated by combining bathymetric data (e.g., from the General Bathymetric Chart of the Oceans (GEBCO), available at: www.gebco.net) with a pelagic bioregionalisation based on a cluster analysis of multiple physical variables that can be measured using remote sensing (Roberson et al 2017).…”

Section: Ecologically or Biologically Significant Area Identificationmentioning

confidence: 99%

Systematic Conservation Planning as a Tool to Advance Ecologically or Biologically Significant Area and Marine Spatial Planning Processes

Harris

Holness

Finke

et al. 2019

Maritime Spatial Planning

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Marine Spatial Planning (MSP) intends to create an improved, rational use of the ocean to reduce conflict among competing uses to achieve social, economic and environmental objectives. Systematic Conservation Planning (SCP) can contribute to MSP because it is also spatially explicit, deliberately seeks to reduce conflict and generates an evidence-based prioritisation of ocean-space use. Importantly, SCP includes biodiversity representation and persistence, is underpinned by quantitative targets and uses complementarity to achieve targets efficiently. Therefore, designing Ecologically or Biologically Significant Areas (EBSAs) using SCP improves their identification and delineation compared to current expert-based approaches, with greater likelihood of uptake in MSP because their SCP-based design deliberately avoids competing activities where possible. These principles are demonstrated in a case study of the Benguela Current Large Marine Ecosystem.

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“…Early marine bioregionalizations drew on data from limited biological collections and expert knowledge to draw spatial boundaries (Ekman, 1953; Hedgpeth, 1957) and many global or large‐scale bioregionalizations still rely heavily on the input of expert knowledge in various forms (GOODS: UNESCO, 2009; MEOW: Spalding et al., 2007). Since the widespread availability of remotely sensed data, many bioregionalizations have used statistical methods to classify environmental data into distinct groups (Raymond, 2014; Roberson et al., 2017; Sayre et al., 2017). The assumption underlying this approach is that different environments are representative of distinct habitats and should contain different assemblages of species, thus reflecting biogeographic patterns.…”

Section: Introductionmentioning

confidence: 99%

Determining marine bioregions: A comparison of quantitative approaches

et al. 2020

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As human pressures on natural systems increase, understanding and predicting the distribution of biodiversity has become vital for managing marine habitats. One important task is to define coherent and ecologically meaningful spatial units that can aid in planning, evaluating and implementing spatial management options. These spatial units are particularly useful for a diverse range of applications including: designing monitoring efforts, managing human activities (especially in marine protected area designation) and informing the relative scales required for ecosystem based assessments (Baker

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Pelagic bioregionalisation using open-access data for better planning of marine protected area networks

Cited by 25 publications

References 100 publications

Variations in and environmental controls of primary productivity in the Amundsen Sea

Variations in and environmental controls of primary productivity in the Amundsen Sea

Systematic Conservation Planning as a Tool to Advance Ecologically or Biologically Significant Area and Marine Spatial Planning Processes

Determining marine bioregions: A comparison of quantitative approaches

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