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Abstract
The structure of the phytoplankton community in surface waters is the consequence of complex interactions between physical and chemical properties of the upper water column as well as interaction within the general biological community. Understanding the structure of phytoplankton communities is especially challenging in highly variable and dynamic marine environments. A variety of strategies have been employed to delineate marine planktonic habitats, including both biogeochemical and water mass based approaches. These methods have led to fundamental improvements in our understanding of marine phytoplankton distributions, but they are often difficult to apply to systems with physical and chemical properties and forcings that vary greatly over relatively short spatial or temporal scales. In this study, we have developed a method of dynamic habitat delineation based on environmental variables that are biologically relevant, that integrate over varying time scales, and that are derived from standard oceanographic measurements. As a result, this approach is widely applicable, simple to implement, and effective in resolving the spatial distribution of phytoplankton communities. As a test of our approach, we have applied it to the Amazon River influenced Western Tropical North Atlantic (WTNA) and to the South China Sea (SCS), which is influenced by both the Mekong River and seasonal coastal upwelling. These two systems differ substantially in their spatial and temporal scales, nutrient sources/sinks, and hydrographic complexity, providing a robust test of the applicability of our analysis. Despite their significant differences in scale and character, our approach generated statistically robust habitat classifications that were clearly relevant to surface phytoplankton communities. Additional analysis of the habitat-defining variables themselves can provide insight into the processes acting to shape phytoplankton communities in each habitat. Finally, by demonstrating the biological relevance of the generated habitats, we gain insights into the conditions promoting the growth of distinct communities and the factors that lead to mismatches between environmental conditions and phytoplankton community structure.
Highlights
The role of physical and chemical factors in shaping marine phytoplankton communities and in generating distinct marine biogeographic provinces has long been recognized (Sverdrup et al, 1942; Platt et al, 1991; Longhurst et al, 1995; Sathyendranath et al, 1995)
In the Western Tropical North Atlantic (WTNA), the first two principal components of the principal component analysis (PCA) described a total of 83.2% of the overall variation in the system (Figure 2)
nitrate availability index (NAI) and mixed layer depth (MLD) contributed roughly to both PCs, but were the primary drivers of PC2, which accounted for 14.0% of the total variation
Summary
The role of physical and chemical factors in shaping marine phytoplankton communities and in generating distinct marine biogeographic provinces has long been recognized (Sverdrup et al, 1942; Platt et al, 1991; Longhurst et al, 1995; Sathyendranath et al, 1995). The resulting provinces can be used to summarize and extrapolate sparse measurements to larger oceanic regions and were defined with geographically malleable boundaries that reflect seasonal and climactic fluctuations, though challenges remain in objectively achieving this categorization (e.g., Oliver and Irwin, 2008; Reygondeau et al, 2013). The resulting interactions are often nonlinear and manifest as emergent properties at the community or ecosystem level
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