@inbook {53265,
	title = {Machine Learning Techniques for Quantifying Geographic Variation in Leach{\textquoteright}s Storm-Petrel (Hydrobates leucorhous) Vocalizations},
	booktitle = {Machine Learning for Ecology and Sustainable Natural Resource Management},
	year = {2018},
	pages = {295{\textendash}312},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<p>Leach\&rsquo;s storm-petrel (Hydrobates leucorhous), a nocturnal seabird that breeds in the Northern hemisphere, has geographically separated populations in the North Atlantic and North Pacific. Although some mixing occurs during the non-breeding season, genetic evidence demonstrates that these populations are diverging. However, genetic information for the study of phylogenetics can be costly and time-consuming to obtain. Vocalizations could offer a more cost-effective way of obtaining similar information (or could be used in conjunction with it). In this chapter, we examine if the chatter call of the Leach\&rsquo;s storm-petrel can be used to classify the Atlantic and Pacific populations. We used a machine learning context by testing several implementations of random forests and boosted regression trees in the R and Python programming languages. We discuss the implementations with respect to accuracy, speed, and memory handling. We found that random forests from the h2o and \&lsquo;randomForest\&rsquo; packages in R performed best with regards to accuracy, \&lsquo;randomForest\&rsquo; and \&lsquo;gbm\&rsquo; performing best with regards to speed, and \&lsquo;tensor forest\&rsquo; and \&lsquo;h2o\&rsquo; implementations performing best with regards to memory. Furthermore, we were able to classify the Atlantic versus Pacific populations of Leach\&rsquo;s storm-petrel with AUC values \&gt;0.8 (generally considered \&lsquo;good\&rsquo; in ecology). We expect that this could be adapted on a larger scale to assist taxonomic classification without having to perform invasive DNA sampling on many individuals of sensitive populations.</p>
},
	author = {Humphries, Grant RW and Rachel T. Buxton and Jones, Ian L}
}
