Natural swimming speed of Dascyllus reticulatus increases with water temperature

Abstract

Recent research on the relationship between coral reef water temperature and fish swimming activity has stated that swimming speed is inversely correlated with temperature above a species' optimum temperature (Johansen, J. L., and Jones, G. P. 2011. Increasing ocean temperature reduces the metabolic performance and swimming ability of coral reef damselfishes. Global Change Biology, 17: 2971–2979; Johansen, J. L., Messmer,V., Coker, D. J., Hoey, A. S., and Pratchett, M. S. 2014. Increasing ocean temperatures reduce activity patterns of a large commercially important coral reef fish. Global Change Biology, 20: 1067–1074). For tropical coral reefs, one anticipated consequence of global warming is an increase of ≥3°C in average water temperature in addition to greater thermal fluctuations [IPCC (Intergovernmental Panel on Climate Change). 2007. Summary for policymakers. In Climate Change 2007: The Physical Science Basis. Contribution of Working, Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Ed. by S. Solomon, D. Qin, and M. Manning et al. Cambridge University Press, Cambridge, UK; Lough, J. 2007. Climate and climate change on the Great Barrier Reef. In Climate Change and the Great Barrier Reef. Ed. by J. Johnson and P. A. Marshall, pp. 15–50. Great Barrier Reef Marine Park Authority and Australian Greenhouse Office, Townsville, Qld, Australia; Johansen and Jones, 2011]. Evaluating the behaviour of coral reef associated fish species at different temperatures can help to assess their sensitivity to climate change. In this study, the speed of freely swimming fish in a natural setting is investigated as a function of seasonal changes in water temperature, as contrasted with systematic temperature increases in a fish tank. We show that Dascyllus reticulatus swim faster as a function of increased water temperature over the range 20.9–30.3°C. The experiments were carried out using ∼3.6 million fish trajectories observed at the Kenting National Park in Taiwan. Fish speed was computed by detecting and tracking the fish through consecutive video frames, then converting image speeds to scene speeds. Temperatures were grouped into 10 intervals. The data reveal an ∼2 mm s−1 increase in average speed per additional temperature degree over the range of 20.9–30.3°C. The Mann–Kendall test using the mean and median speed for each interval revealed that there is a speed increase trend as temperature increases at the 0.05 significance level, rather than a random increase. Our results complement previous studies that investigated the effect of temperature on the swimming performance of different fish species in the laboratory (Johansen and Jones, 2011; Myrick, C. A. and Cech, J. J. 2000. Swimming performance of four California stream fishes: temperature effects. Environmental Biology of Fishes, 58: 289–295; Ojanguren, A. F. and Braña, F. 2000. Thermal dependence of swimming endurance in juvenile brown trout. Journal of Fish Biology, 56: 1342–1347; Lough 2007; Johansen et al., 2014).

Extracting statistically significant behaviour from fish tracking data with and without large dataset cleaning

“…In their analysis, a fish tank model, which allows to modify the temperature of the water [8], was used in contrast to our study which explores the data obtained from underwater videos in a natural setting that reflects the seasonal changes in water temperature. Some of our results have been reported previously in [7] from an ecological perspective where interested readers can find a deeper discussion regarding our findings and the findings of other studies. In [7], it was shown that one can still obtain statistically sound results, even with a dataset that contains examples of all the errors listed above.…”

“…Some of our results have been reported previously in [7] from an ecological perspective where interested readers can find a deeper discussion regarding our findings and the findings of other studies. In [7], it was shown that one can still obtain statistically sound results, even with a dataset that contains examples of all the errors listed above. It shows that by a combination of data binning and robust statistics over a large amount of data, statistically sound inferences can be made from the very noisy data (i.e.…”

“…Herein, a dataset consisting of about 4 million fish trajectories is analysed to explore how fish speed varied with respect to water temperature. Investigating the behaviour of coral reef fish species at different temperatures can help to assess their sensitivity to climate change [7]. There are marine biology studies [8, 9] which explored the fish activity in different water temperatures.…”

Survey of Fish Behavior Analysis by Computer Vision

“…The computer vision was first applied in monitoring fish swimming behavior with the purpose of observing the relationship between swimming and the climate specifically. Beyan C et al, obtained the continuous video frames through the computer vision to detect and track the changing speed of fish in different temperature, it is concluded that the relationship between climate and swimming [107]. Two previous studies [108][109][110] used cameras to detect Atlantic salmon swimming patterns to find that fish swimming speed not only varied with season and time of day but also is influenced by light intensity and feeding activity, too.…”

Mutualistic damselfish induce higher photosynthetic rates in their host coral

“…Finally, given that water temperature influences both coral photosynthesis (e.g. Borell et al, 2008) and damselfish movement (Johansen and Jones, 2011;Beyan et al, 2015), the effects of rising water temperatures on the physiological aspects of fish-coral mutualisms are in need of further investigation. …”