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Results of four additional map assay experiments, in which turtles discriminated between a magnetic field in which they were fed and a magnetic field in which they were not a–d, Turtles differentiated between magnetic fields that exist near: Delaware, USA, and Cuba (a; two-tailed Wilcoxon signed-rank test, w = 108, P = 0.04, Hedge’s g = 0.50, n = 16); Maine and Florida, USA (b; two-tailed Wilcoxon signed-rank test, w = 121, P = 0.004, Hedge’s g = 0.63, n = 16); Newfoundland, Canada and Virginia, USA (c; two-tailed Wilcoxon signed-rank test, w = 115, P = 0.01, Hedge’s g = 0.60, n = 16); and the Turks and Caicos Islands and Haiti (d; two-tailed Wilcoxon signed-rank test, w = 97, P = 0.003, Hedge’s g = 0.60, n = 14). The data in c represent a second conditioning experiment conducted with the same turtles used in b and thus indicate that turtles can learn magnetic fields that exist at multiple locations. Remaining conventions are as in Fig. 1. Extended Data Fig. 2 shows the same data plotted on a linear scale. The maps were created using Natural Earth (https://www.naturalearthdata.com; credit Tom Patterson and Nathaniel Vaughn Kelso). Source data
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Growing evidence indicates that migratory animals exploit the magnetic field of the Earth for navigation, both as a compass to determine direction and as a map to determine geographical position¹. It has long been proposed that, to navigate using a magnetic map, animals must learn the magnetic coordinates of the destination2,3, yet the pivotal hypo...
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Carotenoids are vital pigments influencing both the coloration and health of aquatic organisms, particularly in species such as the Pacific abalone (Haliotis discus hannai). In this study, we identified the major carotenoids in abalone foot muscle using targeted metabolomics. Through differential metabolite analysis, we selected metabolites that met the following criteria: p-value <0.05, variable importance in projection (VIP) score ≥ 1, and fold change (FC) ≥ 2 or FC ≤ 0.5. The results showed that zeaxanthin had the highest content among all foot muscle colors, with the most significant p-value of 0.0079. Thus, we confirmed that zeaxanthin is the predominant carotenoid contributing to the distinct coloration of the foot muscle. We then used a deep learning model to predict carotenoid content based on color measurements in the CIELAB color space, defined by the Commission Internationale de l'Eclairage (CIE), which includes three dimensions: lightness (L*), redness-greenness (a*), and yellowness-blueness (b*). Performance evaluation of 344 abalone samples showed that the Long Short-Term Memory (LSTM) model provided the best prediction results, with a root mean square error (RMSE) of 6.692 and a coefficient of determination (R2) of 0.415. Furthermore, we developed the Color-Based Carotenoid Estimation Suite (CCES). This software features a user-friendly graphical interface, enabling users to input colorimetric data, train models, and predict carotenoid content. Compared to traditional methods, CCES offers non-destructive, rapid carotenoid estimation, improving efficiency by 450 times and reducing costs by 47 to 77 times. This method provides an efficient and scalable tool for aquaculture breeding and quality control, with applications extending beyond abalone to other aquatic and terrestrial species.