This week I have focused my efforts on analyzing the videos that I captured last week. I’m excited to see what knowledge can be gained from my data, but I must admit that going through almost four hours of footage was a bit draining. The protocol that I developed for analyzing the videos was straightforward, although it turned out to be time consuming. It took me a while to comb through each fifteen-minute video to find five periods in which the organisms were swimming so that I could perform my analysis. It also took a long time to calculate swimming speed. The method I used involved making a measurement of the position of the organism every so often during a three second swimming period, calculating the distance traveled between measurements, and then dividing that distance by the time elapsed. I was originally making a position measurement every 10 frames, but quickly realized that was going to take way too much time if I had to do it 75 times. I then changed my protocol to take position measurements every 20 frames, which sped up the process quite a bit and allowed me to analyze all fifteen videos this week. I also measured the length of each organism. This allowed me to normalize my swimming speed data since it is known that body size is positively correlated with swimming speed in Pleurobrachia (Matsumoto 1991). I have yet to run statistical analysis on my data, but based on some preliminary plotting I think that there is a positive, linear relationship between beat frequency and swimming speed but I’m not sure yet if the slope of the regression line is significantly different from zero. Out of curiosity, I used Excel to calculate the correlation coefficient, R value, for my data. R values can be used to assess the magnitude and direction of a correlation between two variables, but they are not indicative of a causal relationship. The R value for my data was about 0.89, which indicates that the variables are strongly correlated. In addition to collecting data related to my main question, I also measured the average length of the ctenes for each organism and the average ctene spacing. This will allow me to assess how ctene length and spacing changes with body size, which may provide some explanatory information about the observed relationship between beat frequency and swimming speed. I also measured the average inflection point, the point at which the ctene bends, for each organism to gain further insight into how ctene behavior modulates swimming speed. I plan to continue making observations of ctene behavior to supplement my quantitative data as well as run my statistical analyses next week. Hopefully, I will be able to show you all my plots and other figures as well as let you know if my results are significant in my next blog post.
References: Matsumoto, G. I. (1991). Swimming movements of ctenophores, and the mechanics of propulsion by ctene rows. In Coelenterate Biology: Recent Research on Cnidaria and Ctenophora (pp. 319–325). Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3240-4_46
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AuthorMy name is Wyatt Heimbichner Goebel and I am a marine biology major at Western Washington University. I love biology, specifically marine mammal ecology and biomechanics. I’m always up for conversations about music, poetry, and weird biology facts. Archives
August 2018
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