Estimating zooplankton vertical distribution from combined LOPC and ZooScan observations on the Brazilian Coast
| Autor: | Rubens M. Lopes, George A. Jackson, Catarina R. Marcolin |
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| Rok vydání: | 2015 |
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| Zdroj: | ResearcherID |
| ISSN: | 1432-1793 0025-3162 |
| Popis: | Although the technology for sampling zooplankton is rapidly evolving, it remains a challenge to determine species distributions with high spatial resolution. This study reports on a technique developed to estimate the mesozooplankton vertical distribution by combining the spatial resolution possible with laser optical particle counter (LOPC) profiles with the taxonomic resolution possible with vertically integrated zooplankton net samples. Data obtained from different coastal and oceanic ecosystems off Brazil were used for our analysis. The aggregate signals from LOPC data sets were removed, and the distributions of the residual Zo-particles, believed to describe zooplankton, were calculated. Significant correlations were then identified between the abundances of the Zo-particles in different size classes and the abundance and size distributions of zooplankton collected in a vertical integrated net sample and classified with a ZooScan system. A principal component analysis was carried out to identify and exclude outliers. After these procedures, multiple linear regression analyses were used to detect relationships among abundances of different zooplankton groups and multiple Zo-particle size classes. The resulting relationships were used to estimate the vertical distributions of the dominant zooplankton taxa—calanoids, poecilostomatoids, shrimp-like, and appendicularians—through the water column from the LOPC data. The results achieved the goal of estimating distributions of dominant zooplankton taxa with an improved spatial resolution. The analytical technique described here enabled the analysis of zooplankton vertical structure in a wide range of marine ecosystems off Brazil and could serve as a basis to revisit historical data collected with similar methods elsewhere. |
| Databáze: | OpenAIRE |
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