The dynamic predator–prey relations in the food web are vital for understanding the function and structure of ecosystems. Dietary estimation is a research hotspot of quantitative ecology, providing key insights into predator–prey relationships. One of the most promising approaches is quantitative fatty acid signature analysis (QFASA), which is the first generation of statistical tools to estimate the quantitative trophic predator–prey relationships by comparing the fatty acid (FA) signatures among predators and their prey. QFASA has been continuously widely applied, refined and extended since its introduction. This article reviewed the research progress of QFASA from development and application. QFASA reflects the long-term diet of predator, and provides the quantitative dietary composition of predator, but it is sensitive to the metabolism of predator. The calibration coefficients (CCs) and the FA subset are two crucial parameters to explain the metabolism of predators, but the incorrect construction or improper use of CCs and the FA subset may cause bias in dietary estimation. Further study and refinement of the QFASA approach is needed to identify recommendations for which CCs and subsets of FA work best for different taxa and systems.

The molecular mechanism underlying modulation of metamorphosis of the bivalve Mytilus coruscus by bacteria remains unclear. Here, the functional role of the thioesterase gene tesA of the bacterium Pseudoalteromonas marina in larval metamorphosis was examined. The aim was to determine whether inactivation of the tesA gene altered the biofilm-inducing capacity, bacterial cell motility, biopolymers, or the intracellular c-di-GMP levels. Complete inactivation of tesA increased the c-di-GMP content in P. marina, accompanied by a reduced fatty acid content, weaker motility, upregulation of bacterial aggregation, and biofilm formation. The metamorphosis rate of mussel larvae on ΔtesA biofilms was reduced by ∼ 80% compared with those settling on wild-type P. marina. Exogenous addition of a mixture of extracted fatty acids from P. marina into the ΔtesA biofilms promoted the biofilm-inducing capacity. This study suggests that the bacterial thioesterase gene tesA altered the fatty acid composition of ΔtesA P. marina biofilms (BF) through regulation of its c-di-GMP, subsequently impacting mussel metamorphosis.

Dynamic responses of tuna purse seine to currents were numerically studied with regard to the vertical subsidence and global load distribution, and determinant attributes were identified. We rebuilt the submerged geometry of a purse seine net using the lumped mass method with hydrodynamic coefficients obtained from measurements of the prototype material, as well as a mesh grouping method, which corrects the twine diameter and netting material density for the equivalent net by introducing the compensation coefficient. Uneven tension distribution showed that it was vulnerable to high loads in the bunt area at shooting and along the lead line at pursing. High loads were present at the convex sections of net circles in the direction of the current. Higher shooting speeds resulted in well-balanced sinking, while a reduced hanging ratio of netting panels was beneficial to faster sinking. While large mesh sizes reduce water resistance and increase sinking velocity, extensive use should be cautioned in terms of the sensibility to stress. Comparing two options of different proportions of large-mesh panels, mesh sizes doubled for 15 strips versus 5 strips, suggesting that the 15 strip option would pose a higher risk of strand vulnerability, while the 5 strip option may be a more balanced alternative with a lower mesh density and a lower mesh stress.

Tuna fish school detection provides information on the fishing decisions of purse seine fleets. Here, we present a recognition system that included fish shoal image acquisition, point extraction, point matching, and data storage. Points are a crucial characteristic for images of free-swimming tuna schools, and point algorithm analysis and point matching were studied for their applications in fish shoal recognition. The feature points were obtained by using one of the best point algorithms (scale invariant feature transform, speeded up robust features, oriented fast and rotated brief). The k-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new points, which means that new data points will be assigned a value based on how closely they match the points that exist in the database. Finally, we tested the model, and the experimental results show that the proposed method can accurately and effectively recognize tuna free-swimming schools.