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.
The increased availability of global scale environmental and biological data is enabling more objective, data‐driven, classifications of the oceans. However, comparisons of seasonal differences at a global scale have been limited to ocean color and derived variables. Here, we used long‐term averages of 18 ocean variables in summer and winter to classify the seasonal ocean surface waters through principle components analysis and k‐means clustering. We identified 11 distinct areas that fit the definition of “ecosystems,” which is enduring regions demarcated by environmental characteristics. Overall, the “ecosystems” retained their general distribution with latitude boundaries in each season, while there were some variations in latitude and some more complex differences. Of all the variables, temperature had the greatest importance and correlated with many other variables in both seasons. Some variables had uniquely significant effects on the classification in both seasons as well, such as calcite, land distance, depth, and surface current. Thus while the present seasonal classification is robust for available data, future analyses with variables not presently available may improve it. Nevertheless, the results indicate that species will need to either move or have adapted to live in greater ecosystem variation in mid temperature and subtropical latitudes where ecosystem boundaries move seasonally. In contrast, because conditions vary less in the tropics and polar seas, species in these latitudes may be more sensitive to climatic change.
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