Foraging depth depicts resource partitioning and contamination level in a pelagic shark assemblage: Insights from mercury stable isotopes.

The decline of shark populations in the world ocean is affecting ecosystem structure and function in an unpredictable way and new ecological information is today needed to better understand the role of sharks in their habitats. In particular, the characterization of foraging patterns is crucial to understand and foresee the evolution of dynamics between sharks and their prey. Many shark species use the mesopelagic area as a major foraging ground but the degree to which different pelagic sharks rely on this habitat remains overlooked. In order to depict the vertical dimension of their trophic ecology, we used mercury stable isotopes in the muscle of three pelagic shark species (the blue shark Prionace glauca, the shortfin mako shark Isurus oxyrinchus and the smooth hammerhead shark Sphyrna zygaena) from the northeastern Pacific region. The Δ199Hg values, ranging from 1.40 to 2.13‰ in sharks, suggested a diet mostly based on mesopelagic prey in oceanic habitats. We additionally used carbon and nitrogen stable isotopes (δ13C, δ15N) alone or in combination with Δ199Hg values, to assess resource partitioning between the three shark species. Adding Δ199Hg resulted in a decrease in trophic overlap estimates compared to those based on δ13C/δ15N alone, demonstrating that multi-isotope modeling is needed for accurate trophic description of the three species. Mainly, it reveals that they forage at different average depths and that resource partitioning is mostly expressed through the vertical dimension within pelagic shark assemblages. Concomitantly, muscle total mercury concentration (THg) differed between species and increased with feeding depth. Overall, this study highlights the key role of the mesopelagic zone for shark species foraging among important depth gradients and reports new ecological information on trophic competition using mercury isotopes. It also suggests that foraging depth may play a pivotal role in the differences between muscle THg from co-occurring high trophic level shark species.

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.