The recent increase in international fish trade leads to the need for improving the traceability of fishery products. In relation to this, consistent monitoring of the production chain focusing on technological developments, handling, processing and distribution via global networks is necessary. Molecular barcoding has therefore been suggested as the gold standard in seafood species traceability and labelling. This review describes the DNA barcoding methodology for preventing food fraud and adulteration in fish. In particular, attention has been focused on the application of molecular techniques to determine the identity and authenticity of fish products, to discriminate the presence of different species in processed seafood and to characterize raw materials undergoing food industry processes. In this regard, we herein present a large number of studies performed in different countries, showing the most reliable DNA barcodes for species identification based on both mitochondrial (COI, cytb, 16S rDNA and 12S rDNA) and nuclear genes. Results are discussed considering the advantages and disadvantages of the different techniques in relation to different scientific issues. Special regard has been dedicated to a dual approach referring to both the consumer’s health and the conservation of threatened species, with a special focus on the feasibility of the different genetic and genomic approaches in relation to both scientific objectives and permissible costs to obtain reliable traceability.

The Anopheles maculipennis complex consists of several mosquito species, including some primary malaria vectors. Therefore, the presence of a species in a particular area significantly affects public health. In this study, 1252 mosquitoes were collected in northern Italy, representing four identified species of the Anopheles maculipennis complex (Anopheles daciae sp. inq., Anopheles maculipennis s. s., Anopheles atroparvus and Anopheles melanoon). The sequences of two DNA markers, mitochondrial cytochrome c oxidase I (COI) and nuclear internal transcribed spacer 2 (ITS2), were generated. DNA-based species delimitation analyses were performed, incorporating public sequences, with distance-based and coalescent tree-based methods to confirm the actual species boundaries within the complex. While some morphospecies were unequivocally delimited by all methods and markers, COI analysis splitted An. maculipennis s. s. into two well-supported groups. However, molecular delimitation failed in recognizing An. daciae sp. inq. and An. messeae as two separate evolutionary entities. Species delimitation was further tested with a morphometric approach, which clearly differentiated species collected in the survey area. These findings underscore how challenging the characterization of the taxonomy of the complex is, providing evidence of potential introgression events in An. maculipennis s. s. and suggesting the need for robust evidence to support An. daciae sp. inq. and An. messeae as distinct species.

Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences.