Differences in Rate and Direction of Shifts between Phytoplankton Size Structure and Sea Surface Temperature
نویسندگان
چکیده
Species distributions are changing with various rates and directions in response to recent global warming. The velocity of sea surface temperature (SST) has been used to predict species migration and persistence as an expectation of how species track their thermal niches; however, several studies have found that evidence for species shifts has deviated from the velocity of SST. This study investigated whether estimation of the velocity of shifts in phytoplankton size structure using remote sensing data could contribute to better prediction of species shifts. A chlorophyll-a (Chla) size distribution (CSD) model was developed by quantifying the relationships between the size structure of the phytoplankton community and the spectral features of the phytoplankton absorption coefficient (aph(λ)), based on the principal component analysis approach. Model validation demonstrated that the exponent of CSD (hereafter, CSD slope), which can describe the synoptic size structure of a phytoplankton community, was derived successfully with a relative root mean square error of 18.5%. The median velocity of CSD slope across the ocean was 485.2 km·decade−1, broadly similar to Chla (531.5 km·decade−1). These values were twice the velocity of SST, and the directions of shifts in CSD slope and Chla were quite different from that of SST. Because Chla is generally covariant with the size structure of a phytoplankton community, we believe that spatiotemporal changes in Chla can explain the variations of phytoplankton size structure. Obvious differences in both rate and direction of shifts were found between the phytoplankton size structure and SST, implying that shifts of phytoplankton size structure could be a powerful tool for assessing the distributional shifts of marine species. Our results will contribute to generate global and regional maps of expected species shifts in response to environmental forcing.
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ورودعنوان ژورنال:
- Remote Sensing
دوره 9 شماره
صفحات -
تاریخ انتشار 2017