# A Collection of Best Practices for the Collection and Analysis of Bioacoustic Data
## 2 Species Identification  
Monitoring animal species in the wild, which is critical to many studies in biology, is undertaken with increasing frequency using passive acoustic methods (sampling animals by recording their sounds but without further engagement). Some taxa in which such methods have been applied include birds [42; 73], marine mammals [43; 58; 74], bats [59; 75; 76], amphibians [56; 77], insects [78], and primates [57]. A first step in analyzing passive acoustic recordings is to identify which species produced each sound detected. When sounds cannot be readily identified visually using spectrograms or other representations of sound or aurally, researchers use statistical classifiers or machine learning algorithms [79; 80; 81; 82; 83]. A growing number of bioacoustic analysis and passive acoustic monitoring software tools now integrate machine learning methods directly into the processes of call classification and species identification [84; 83; 85]. Species classification algorithms are trained using datasets of acoustic recordings in which the identities of species producing the recorded sounds are known. Many species have calls that are difficult to distinguish from those of other species, and so decisions about species identity must often be validated with visual observations [86]. This can be difficult to do, particularly in situations where recorders are left in remote locationsor species exhibit cryptic behavior. As a result, visual validation of species identities is sometimes not performed, and this leads to uncertainty in the validity of some tools for species identification. When using a species classifier, it is crucial to ensure that the classifier was trained using data that can unequivocally be attributed to the species included in the classifier. In addition, when compiling training and testing datasets, a sufficiently large set of recordings should be collected to capture the breadth of temporal, environmental, geographic, and behavioral variability in vocalizations of each species to be classified. In addition, care must be taken when selecting those samples used to train the classifier and those used to test the performance of the classifier within a dataset. For example, including vocalizations produced by a given group of animals in both the training and testing datasets can skew the results due to similarities in vocalizations produced by particularly vocal individuals in the group. If similarities exist, this approach will provide an unrealistically high estimate of classifier performance. Therefore, it is important to ensure independence of training and testing datasets by grouping all calls from any given individual into just one of the two datasets, when possible. When using existing species classifiers on novel data, it is important to consider how and where the classifier training data were collected. Recorder characteristics such as sampling rate, sensitivity, and placement in the environment can all affect data quality and therefore impact classifier performance [87; 88; 89]. The same can be true of acoustic analysis parameters such as window length, window type, and step-size for spectrogram computation. Another factor that can impact classifier outcomes is geographic location. Many species exhibit geographic variation in their signals [90; 91; 92; 93], and different locations often comprise different assemblages of species. Because of these factors, a classifier trained using data from one location will not necessarily be accurate when applied to data collected in a different location. An example of a study that has taken these issues into consideration to produce a robust acoustic classifier is [PERSON] et al. [58]. These authors trained a random forest classifier to identify, based on acoustic parameters, five deplinid species that inhabit the California Current ecosystem.