Effect of Different Species of Prorocentrum Genus on the Japanese Oyster Crassostrea gigas Proteomic Profile.

This paper assesses the effects of exposure to toxic concentrations (1200 to 6000 cells/mL) of the dinoflagellates Prorocentrum lima, Prorocentrum minimum, and Prorocentrum rhathymum and several concentrations of aqueous and organic extracts obtained from the same species (0 to 20 parts per thousand) on the Crassostrea gigas (5-7 mm) proteomic profile. Through comparative proteomic map analyses, several protein spots were detected with different expression levels, of which eight were selected to be identified by liquid chromatography-mass spectrometry (LC-MS/MS) analyses. The proteomic response suggests that, after 72 h of exposure to whole cells, the biological functions of C. gigas affected proteins in the immune system, stress response, contractile systems and cytoskeletal activities. The exposure to organic and aqueous extracts mainly showed effects on protein expressions in muscle contraction and cytoskeleton morphology. These results enrich the knowledge on early bivalve developmental stages. Therefore, they may be considered a solid base for new bioassays and/or generation of specific analytical tools that allow for some of the main effects of algal proliferation phenomena on bivalve mollusk development to be monitored, characterized and elucidated.

Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery.

Chlorophyll-a (Chl-a) concentration is a key parameter to describe water quality in marine and freshwater environments. Nowadays, several products with Chl-a have derived from satellite imagery, but they are not available or reliable sometimes for coastal and/or small water bodies. Thus, in the last decade several methods have been described to estimate Chl-a with high-resolution (30 m) satellite imagery, such as Landsat, but a standardized method to estimate Chl-a from Landsat imagery has not been accepted yet. Therefore, this study evaluated the predictive performance of regression models (Simple Linear Regression [SLR], Multiple Linear Regression [MLR] and Generalized Additive Models [GAMs]) to estimate Chl-a based on Landsat imagery, using in situ Chl-a data collected (synchronized with the overpass of Landsat 8 satellite) and spectral reflectance in the visible light portion (bands 1-4) and near infrared (band 5). These bands were selected because of Chl-a absorbance/reflectance properties in these wavelengths. According to goodness of fit, GAM outperformed SLR and MLR. However, the model validation showed that MLR performed better in predicting log-transformed Chl-a. Thus, MLR, constructed by using four spectral bands (1, 2, 3, and 5), was considered the best method to predict Chl-a. The coefficients of this model suggested that log-transformed Chl-a concentration had a positive linear relationship with bands 1 (coastal/aerosol), 3 (green), and 5 (NIR). On the other hand, band 2 (blue) suggested a negative relationship, which implied high coherence with Chl-a absorbance/reflectance properties measured in the laboratory, indicating that Landsat 8 images could be applied effectively to estimate Chl-a concentrations in coastal environments.