Spatial and Temporal Structures of the Macrozoobenthos from the Intertidal Zone of the Kneiss Islands (Central Mediterranean Sea)

The present study analyses the spatio-temporal variability of the macrozoobenthos from the intertidal zone of the Kneiss Islands (Gulf of Gabès, Tunisia). Samples were collected once from 34 stations, while seasonal variations were studied by sampling four times at 12 stations over the period 2013-2014. A total of 159 macrobenthos taxa associated with intertidal Zostera noltei beds are identified from the 34 stations, with a taxonomic dominance of crustaceans (32%), molluscs (29%) and annelids (27%). Abundance varies from 9244 to 36,844 ind·m-2 with a mean value of 14,346 ind·m-2. Analysis of the trophic structure shows that the majority of stations are strongly represented by carnivores (41%), followed by the non-selective deposit feeders (16%). Cluster analysis and multidimensional scaling allow identification of three main benthic assemblages based on species abundance, corresponding to different sediment types and organic matter contents. The seasonal variability in abundance, diversity and community structure is mainly due to spring and summer recruitment. The biotic indices (i.e. AMBI, BO2A and BENTIX) show that the intertidal area of Kneiss Islands exhibits a good ecological status.

Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia

Multispectral and hyperspectral sensor data of the bio-optical parameters with a high spatial resolution are important for monitoring and mapping of the coastal ecosystems and estuarine areas, such as the Kneiss Islands in the Gulf of Gabes. Sentinel 2 S2A and Hyperion Earth observing-1 (EO1) imaging sensors reflectance data have been used for water quality determination and mapping of turbidity TU and chlorophyll Chl-a in shallow waters. First, we applied a tidal swing area mask based on uncorrelated pixel via 2D scatter plot between 665 nm and 865 nm to eliminate the overestimation of the concentration of water quality parameters due to the effect of the bottom reflection. The processing for mapping and validating Chl-a, Turbidity S2A, and EO1 were performed using a relation between reflectance bands and in situ measurements. Therefore, we were able to validate the performance of the case 2 regional coast colour processor (C2RCC) as well as our region-adapted empirical optical remote sensing algorithms. Turbidity was mapped based on the reflectance of 550 nm band for EO1 (R2 = 0.63) and 665 nm band for S2A (R2 = 0.70). Chlorophyll was mapped based on (457/528 nm) reflectance ratio (R2 = 0.57) for EO1 and (705/665 nm) reflectance ratio (R2 = 0.72) for the S2A.