Billfishes are considered to be among the fastest swimmers in the oceans. Previous studies have estimated maximum speed of sailfish and black marlin at around 35 m s−1 but theoretical work on cavitation predicts that such extreme speed is unlikely. Here we investigated maximum speed of sailfish, and three other large marine pelagic predatory fish species, by measuring the twitch contraction time of anaerobic swimming muscle. The highest estimated maximum swimming speeds were found in sailfish (8.3±1.4 m s−1), followed by barracuda (6.2±1.0 m s−1), little tunny (5.6±0.2 m s−1) and dorado (4.0±0.9 m s−1); although size-corrected performance was highest in little tunny and lowest in sailfish. Contrary to previously reported estimates, our results suggest that sailfish are incapable of exceeding swimming speeds of 10-15 m s−1, which corresponds to the speed at which cavitation is predicted to occur, with destructive consequences for fin tissues.
Sea cucumber populations around the globe are experiencing marked declines caused by overexploitation and habitat degradation. Fisheries-independent data used to manage these ecologically and economically important species are frequently collected using diver- or snorkeler-based surveys, which have a number of limitations, including small spatial coverage and observer biases. In the present study, we explored how pairing traditional transect surveys with unmanned aerial vehicles (UAVs) and machine learning could improve sea cucumber density estimation in shallow environments. In July 2018, we conducted 24 simultaneous snorkeler–UAV transects in Tetiaroa, French Polynesia. All UAV images were independently reviewed by three observers and a convolution neural network (CNN) model: ResNet50. All three methods (snorkelers, manual review of UAV images, and ResNet50) produced similar counts, except at relatively high densities (∼75 sea cucumber 40 m−2), where UAVs and CNNs began to underestimate. Using a UAV-derived photomosaic of the study site, we simulated potential transect locations and determined a minimum of five samples were required to reliably estimate densities, while sample variance plateaued after 25 transects. Collectively, these results illustrate UAVs’ ability to survey small invertebrate species, while saving time, money, and labour compared to traditional methods, and highlights their potential to maximize efficiency when designing transect surveys.
To navigate well through three-dimensional environments, animals must in some way gauge the distances to objects and features around them. Humans use a variety of visual cues to do this, but insects, with their small size and rigid eyes, are constrained to a more limited range of possible depth cues. For example, insects attend to relative image motion when they move, but cannot change the optical power of their eyes to estimate distance. On clear days, the horizon is one of the most salient visual features in nature, offering clues about orientation, altitude and, for humans, distance to objects. We set out to determine whether flying fruit flies treat moving features as farther off when they are near the horizon. Tethered flies respond strongly to moving images they perceive as close. We measured the strength of steering responses while independently varying the elevation of moving stimuli and the elevation of a virtual horizon. We found responses to vertical bars are increased by negative elevations of their bases relative to the horizon, closely correlated with the inverse of apparent distance. In other words, a bar that dips far below the horizon elicits a strong response, consistent with using the horizon as a depth cue. Wide-field motion also had an enhanced effect below the horizon, but this was only prevalent when flies were additionally motivated with hunger. These responses may help flies tune behaviors to nearby objects and features when they are too far off for motion parallax.
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