02175nas a2200145   4500008004100000245013300041210006900174260001300243300001400256520165200270100002501922700002301947700001801970856004101988       2018                        eng d00aMachine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (Hydrobates leucorhous) Vocalizations0 aMachine Learning Techniques for Quantifying Geographic Variation  bSpringer  a295–3123 a<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>
1 aHumphries, Grant, RW1 aBuxton, Rachel, T.1 aJones, Ian, L  uhttps://bio.acousti.ca/es/node/53265