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Highlights: [•] We analyzed zooplankton size structure in the highly dynamic East China Sea. [•] Size spectral slope shows no relationship with temperature or food availability. [•] Size diversity decreases with food and exhibits no relationship with temperature. [•] Linear fit of size spectral slope is stronger in oligotrophic conditions. [•] Using size structure as indicators to monitor environmental conditions needs caution. [ABSTRACT FROM AUTHOR]

The biodiversity-ecosystem functioning debate is a central topic in ecology. Recently, there has been a growing interest in size diversity because body size is sensitive to environmental changes and is one of the fundamental characteristics of organisms linking many ecosystem properties. However, how size diversity affects ecosystem functioning is an important yet unclear issue., To fill the gap, with large-scale field data from the East China Sea, we tested the novel hypothesis that increasing zooplankton size diversity enhances top-down control on phytoplankton (H1) and compared it with five conventional hypotheses explaining the top-down control: flatter zooplankton size spectrum enhances the strength of top-down control (H2); nutrient enrichment lessens the strength of top-down control (H3); increasing zooplankton taxonomic diversity enhances the strength of top-down control (H4); increasing fish predation decreases the strength of top-down control of zooplankton on phytoplankton through trophic cascade (H5); increasing temperature intensifies the strength of top-down control (H6)., The results of univariate analyses support the hypotheses based on zooplankton size diversity (H1), zooplankton size spectrum (H2), nutrient (H3) and zooplankton taxonomic diversity (H4), but not the hypotheses based on fish predation (H5) and temperature (H6). More in-depth analyses indicate that zooplankton size diversity is the most important factor in determining the strength of top-down control on phytoplankton in the East China Sea., Our results suggest a new potential mechanism that increasing predator size diversity enhances the strength of top-down control on prey through diet niche partitioning. This mechanism can be explained by the optimal predator-prey body-mass ratio concept. Suppose each size group of zooplankton predators has its own optimal phytoplankton prey size, increasing size diversity of zooplankton would promote diet niche partitioning of predators and thus elevates the strength of top-down control. [ABSTRACT FROM AUTHOR]

Abstract: The correspondence between variation in the physico-chemical properties of the water column and the taxonomic composition of zooplankton communities represents an important indicator of long-term and broad-scale change in marine systems. Evaluating and relating compositional change to various forms of perturbation demand routine taxonomic identification methods that can be applied rapidly and accurately. Traditional identification by human experts is accurate but very time-consuming. The application of automated image classification systems for plankton communities has emerged as a potential resolution to this limitation. The objective of this study is to evaluate how specific aspects of training set construction for the ZooScan system influenced our ability to relate variation in zooplankton taxonomic composition to variation of hydrographic properties in the East China Sea. Specifically, we compared the relative utility of zooplankton classifiers trained with the following: (i) water mass-specific and global training sets; (ii) balanced versus imbalanced training sets. The classification performance (accuracy and precision) of water-mass specific classifiers tended to decline with environmental dissimilarity, suggesting water-mass specificity However, similar classification performance was also achieved by training our system with samples representing all hydrographic sub-regions (i.e. a global classifier). After examining category-specific accuracy, we found that equal performance arises because the accuracy was mainly determined by dominant taxa. This apparently high classification accuracy was at the expense of accurate classification of rare taxa. To explore the basis for such biased classification, we trained our global classifier with an equal amount of training data for each category (balanced training). We found that balanced training had higher accuracy at recognizing rare taxa but low accuracy at abundant taxa. The errors introduced in recognition still pose a major challenge for automatic classification systems. In order to fully automate analyses of zooplankton communities and relate variation in composition to hydrographic properties, the recognition power of the system requires further improvements. [Copyright &y& Elsevier]