DNA barcoding taxonomy and diversity of the Mediterranean sharks and rays

Elasmobranchs are an important by-catch of commercial fisheries targeting bony fishes. Fisheries targeting sharks are rare, but usually almost all specimen bycatched are marketed. They risk extinction if current fishing pressure continues (Ferretti et al., 2008). Accurate species identification is critical for the design of sustainable fisheries and appropriate management plans, especially since not all species are equally sensitive to fishing pressure (Walker & Hislop 1998). The identification of species constitutes the first basic step for biodiversity monitoring and conservation (Dayrat B et al., 2005). More recently, mtDNA sequencing has also been used for species identification and its use has become widespread under the DNA Barcode initiative (e.g. Hebert et al. 2003a, 2003b; Ward et al. 2005, 2008a; Moura et al 2008; Steinke et al. 2009). The aims of this work were: 1) identify sharks and skates species using DNA barcode; 2) compare species of different provenance; 3) use DNA barcode for misidentified species. Using DNA barcode 15 species of sharks (Alopias vulpinus, Centrophorus granulosus, Cetorhinus maximus, Dalatias licha, Etmopterus spinax, Galeorhinus galeus, Galeus melastomus, Heptranchias perlo, Hexanchus griseus, Mustelus mustelus, Mustelus punctulatus, Oxynotus centrina, Scyliorhinus canicula Squalus acanthias, Squalus blainville), 1 species of chimaera (Chimaera monstrosa) and 21 species of rays/skayes (Dasyatis centroura, Dasyatis pastinaca, Dasyatis sp., Dipturus nidarosiensis, Dipturus oxyrinchus, Leucoraja circularis, Leucoraja melitensis, Myliobatis aquila, Pteromylaeus bovinus, Pteroplatytrygon violacea, Raja asterias, Raja brachyura, Raja clavata, Raja miraletus, Raja montagui, Raja radula, Raja polystigma, Raja undulata, Rostroraja alba, Torpedo marmorata, Torpedo nobiliana, Torpedo torpedo) was identified.

Flower cover and bee diversity estimate by drone RGB images

Worldwide, biodiversity is decreasing due to climate change, habitat fragmentation and agricultural intensification. Bees are essential crops pollinator, but their abundance and diversity are decreasing as well. For their conservation, it is necessary to assess the status of bee population. Field data collection methods are expensive and time consuming thus, recently, new methods based on remote sensing are used. In this study we tested the possibility of using flower cover diversity estimated by UAV images (FCD-UAV) to assess bee diversity and abundance in 10 agricultural meadows in the Netherlands. In order to do so, field data of flower and bee diversity and abundance were collected during a campaign in May 2021. Furthermore, RGB images of the areas have been collected using Unmanned Aerial Vehicle (UAV) and post-processed into orthomosaics. Lastly, Random Forest machine learning algorithm was applied to estimate FCD of the species detected in each field. Resulting FCD was expressed with Shannon and Simpson diversity indices, which were successively correlated to bee Shannon and Simpson diversity indices, abundance and species richness. The results showed a positive relationship between FCD-UAV and in-situ collected data about bee diversity, evaluated with Shannon index, abundance and species richness. The strongest relationship was found between FCD (Shannon Index) and bee abundance with R2=0.52. Following, good correlations were found with bee species richness (R2=0.39) and bee diversity (R2=0.37). R2 values of the relationship between FCD (Simpson Index) and bee abundance, species richness and diversity were slightly inferior (0.45, 0.37 and 0.35, respectively). Our results suggest that the proposed method based on the coupling of UAV imagery and machine learning for the assessment of flower species diversity could be developed into valuable tools for large-scale, standardized and cost-effective monitoring of flower cover and of the habitat quality for bees.