A Three-Dimensional Mapping of the Ocean Based on Environmental Data

Sayre, Roger; Wright, Dawn J.; Breyer, Sean; Butler, Kevin A.; Graafeiland, Keith Van; Costello, Mark J.; Harris, Peter T.; Goodin, Kathleen; Guinotte, John M.; Basher, Zeenatul; Kavanaugh, Maria T.; Halpin, Patrick N.; Monaco, Mark E.; Cressie, Noel; Aniello, Peter; Frye, C. L.; Stephens, Drew

doi:10.5670/oceanog.2017.116

Cited by 96 publications

(77 citation statements)

References 46 publications

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“…To better understand how we can achieve such complex integration of variables, more research and data are needed (Broderick, 2015;Buhl-Mortensen et al, 2015;Hilário et al, 2015), in addition to cross-disciplinary collaborations that will improve the quantity and quality of information available for decision-making (Lent, 2015;Bisack and Magnusson, 2016). A promising endeavor in that vein is the Ecological Marine Units (EMUs) project (Sayre et al, 2017), which offers a baseline three-dimensional global map of marine ecosystems. Finally, habitat mapping methods that give a more accurate representation of the marine environment should be explored.…”

Section: Data Selection and Fitness For Usementioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.

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Section: Data Selection and Fitness For Usementioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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“…There are also important advances in the amount of global marine environmental data with a depth component, through the World Ocean Atlas (Boyer et al., ). All this information with a 3D component can be used to create species distribution models (Duffy & Chown, ) and ecological units (Sayre et al., ) in 3D, although this is still not common practice. Scientists are now able to better map cumulative anthropogenic impacts in the ocean, estimate whether these impacts are increasing or decreasing (Halpern et al., ), and how they can affect different stakeholders (Klein et al., ).…”

Section: Discussionmentioning

confidence: 99%

3D spatial conservation prioritisation: Accounting for depth in marine environments

et al. 2017

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While marine environments are three‐dimensional (3D) in nature, current approaches and tools for planning and prioritising actions in the ocean are predominantly two dimensional. Here, we develop a novel 3D marine spatial conservation prioritisation approach, which explicitly accounts for the inherent vertical heterogeneity of the ocean. This enables both vertical and horizontal spatial prioritisation to be performed simultaneously. To our knowledge, this is the first endeavour to develop prioritisation of conservation actions in 3D. We applied the 3D spatial conservation prioritisation approach to the Mediterranean Sea as a case study. We first subdivided the Mediterranean Sea into 3D planning units by assigning them a z coordinate (representing depth). We further partitioned these 3D planning units vertically into three depth layers; this allowed us to quantify biodiversity (1,011 species and 19 geomorphic features) and the cost of conservation actions at different depths. We adapted the prioritisation software Marxan to identify 3D networks of sites where biodiversity conservation targets are achieved for the minimum cost. Using the 3D approach presented here, we identified networks of sites where conservation targets for all biodiversity features were achieved. Importantly, these networks included areas of the ocean where only particular depth layers along the water column were identified as priorities for conservation. The 3D approach also proved to be more cost‐efficient than the traditional 2D approach. Spatial priorities within the networks of sites selected were considerably different when comparing the 2D and 3D approaches. Prioritising in 3D allows conservation and marine spatial planners to target specific threats to specific conservation features, at specific depths in the ocean. This provides a platform to further integrate systematic conservation planning into the wider ongoing and future marine spatial planning and ocean zoning processes.

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“…Both different underlying data and methods may result in different classifications, suggesting the relations between them. Thus, to explore the generality of the ecosystems defined in the present study we compared them to three contrasting global marine classifications: the EMUs (Sayre, Dangermond, et al, ; Sayre, Wright, et al, ) based on k ‐means clustering analysis by Euclidean Distance using temperature, salinity, oxygen, nitrate, phosphate, and silicate as variables; biogeographic realms (Realms) defined by cluster analysis of 65,000 marine species distributions (Costello et al, ); and biogeochemical provinces (Provinces) defined by expert interpretation of ocean color and other pelagic information (Longhurst, ).…”

Section: Methods and Theorymentioning

confidence: 99%

“…Thus, despite appearances, only two fully global classifications of the ocean based on quantitative environmental data analysis have been published. Oliver and Irwin () applied a method, which used hierarchical and k ‐means clustering on sea surface temperature and radiance to map 81 “provinces.” Sayre, Dangermond, et al (); Sayre, Wright, et al, () used k ‐means clustering on six variables (temperature, salinity, oxygen, nitrate, phosphate, and silicate) to a depth of 5,500 m. They distinguished 37 three‐dimensional regions named “Ecological Marine Units” (EMUs), of which 22 comprised 99% of the ocean volume. However, there are 20 variables now available for ocean surface waters at a global scale (Basher, Bowden, & Costello, ).…”

Section: Introductionmentioning

confidence: 99%

Mapping near surface global marine ecosystems through cluster analysis of environmental data

Zhao

Basher

Costello

2019

Ecological Research

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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 regions demarcated by environmental characteristics. Of all the variables, temperature had the greatest importance and correlated with many other variables. However, some variables had uniquely significant effects on the classification, namely slope, surface current, pH, and wave height. Thus, while the present classification is robust for available data, future analyses with variables not presently available may improve it. How the ecosystems correlate with species richness, endemicity, or abundance will inform on the factors that most influence species' abundance, and thus support global modeling of the effects of climate change, for example, with regard to biological carbon fluxes.

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A Three-Dimensional Mapping of the Ocean Based on Environmental Data

Cited by 96 publications

References 46 publications

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

3D spatial conservation prioritisation: Accounting for depth in marine environments

Mapping near surface global marine ecosystems through cluster analysis of environmental data

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