I am very satisfied with the research experience I have gained in the CURE lab with Professor Laura May-Collado. The course has given me a taste of what it truly takes to work with real data in a lab. I gained a strong appreciation for the amount of time and hard work that goes in to creating a finalized product. This course will make you work very hard, but at the end it is the most rewarding feeling knowing that you are capable of making a significant impact in the scientific community. This course has definitely sparked my interest in research and in marine sciences in general.
This semester, I worked with another student in the class to analyze the song structure of the humpback whales off of the coast of Guerrero, Mexico. We worked with an acoustic analysis program crafted by Cornell University, called Raven. This program is commonly used in research labs, and so I think it was a valuable experience working closely with Raven and messing around with all of the different settings to see what the program is capable of. Dr. May-Collado gave us a lot of freedom with Raven, giving us a brief introduction to the program in the beginning, showing us the just basics. This allowed us to explore Raven much further on our own, giving us a more in-depth and personal experience with the program. For our project, we used Raven to visualize the structure of the humpback whale song. We worked with a data set of about 650 30-minute sound files. In each sound file, we made note when we detected the presence of humpback whales or boats. We then went back into the sound files where we heard whale song and analyzed the structure of the whale song. We were hoping to make conclusions about the variation of the humpback whale song based on the structural changes in the song over the breeding season. This process is easier said than done. Based on the data analysis my partner and I had done in previous science labs, we didn’t expect the whale presence/absence analysis to take as long as it did. We definitely spent more time in the lab doing our analysis than we initially expected. We mentioned at the end of our study how we may have achieved more statistically significant data had we been given more data to work with. Our data only represented a mere two weeks. Realistically speaking, if we had more data representing a larger portion of the humpback whale breeding season, we probably wouldn’t have finished our study in time! This really put the time commitment of research into perspective for me. It is very tedious, time consuming work. However, the end result is extremely rewarding, especially when you find your results are significant. We found that about 36% of the variation in the Guerrero humpback whale song was due to boat traffic. For me, this made me 100x more interested in continuing to research this population of whales.
This semester, I worked with another student in the class to analyze the song structure of the humpback whales off of the coast of Guerrero, Mexico. We worked with an acoustic analysis program crafted by Cornell University, called Raven. This program is commonly used in research labs, and so I think it was a valuable experience working closely with Raven and messing around with all of the different settings to see what the program is capable of. Dr. May-Collado gave us a lot of freedom with Raven, giving us a brief introduction to the program in the beginning, showing us the just basics. This allowed us to explore Raven much further on our own, giving us a more in-depth and personal experience with the program. For our project, we used Raven to visualize the structure of the humpback whale song. We worked with a data set of about 650 30-minute sound files. In each sound file, we made note when we detected the presence of humpback whales or boats. We then went back into the sound files where we heard whale song and analyzed the structure of the whale song. We were hoping to make conclusions about the variation of the humpback whale song based on the structural changes in the song over the breeding season. This process is easier said than done. Based on the data analysis my partner and I had done in previous science labs, we didn’t expect the whale presence/absence analysis to take as long as it did. We definitely spent more time in the lab doing our analysis than we initially expected. We mentioned at the end of our study how we may have achieved more statistically significant data had we been given more data to work with. Our data only represented a mere two weeks. Realistically speaking, if we had more data representing a larger portion of the humpback whale breeding season, we probably wouldn’t have finished our study in time! This really put the time commitment of research into perspective for me. It is very tedious, time consuming work. However, the end result is extremely rewarding, especially when you find your results are significant. We found that about 36% of the variation in the Guerrero humpback whale song was due to boat traffic. For me, this made me 100x more interested in continuing to research this population of whales.
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