# A Collection of Best Practices for the Collection and Analysis of Bioacoustic Data
## 8 Discussion
Supplemental data commonly includes information such as time and location of recordings, equipment and settings used, weather conditions, species present, and behavior exhibited. This is by no means an exhaustive list and prior to collecting acoustic data, researchers should spend time carefully considering the types of supplemental data required, how these data will be documented, and ways to best organize these data and link them to specific acoustic recordings [159]. Another recurring challenge when working with bioacoustic data collected in the field is the ability to identify species in the recordings with confidence. The gold-standard method for accomplishing this is to pair acoustic recordings with visual observations of the vocalizing animals [160, 161]. However, in many cases it is not possible to obtain visual observations of all (or even any) sound producers during the recording period. In such cases, species must be identified based on their vocalizations. Some species produce calls that are distinctive and thus easily recognized (such as fixed frequency relationships among notes in Black-capped Chickadees [146]) and for others, detailed call catalogs are available (e.g., killer whales, [162, 163]). In many cases, however, it can be helpful to utilize machine learning classifiers (see Section 1, Species Identification, and Section 2, Population Structure). When using machine learning methods for classification or detection, it is essential to design models and handle data carefully to avoid over-fitting and thus ensure that outcomes will be indicative of expected performance on future novel data. This requires separation of data into training, testing, and validation datasets. Models are trained on training data, with testing data used for evaluation and testing during system design to adjust model parameters. Validation data should be reserved for a single final evaluation. To avoid over-fitting so that expected results on the reserved validation data will be in line with the testing performance during model development, it is important that the model complexity and number of parameters be selected carefully in accordance with the size of the training data set. Once an acoustic dataset has been collected, effective data processing also requires application of best practices. Many data processing workflows include examination of spectrograms. The settings used to create spectrograms (e.g., FFT size, window length, windowing function) can have significant impacts on the visual appearance of sounds and thus affect how they are evaluated. For example, increasing FFT size results in a gain in frequency resolution but a loss in time resolution, and vice versa [110]. The optimal settings for a given project will depend on the frequencies being examined, the sample rate of the recordings, and the questions being asked. It is important to maintain consistent settings across an analysis and/or to carefully document when and why settings are changed. In many cases, acoustic recordings are processed before analysis to minimize noise and/or exclude low quality calls (e.g., calls that are low SNR, masked by other sounds, overlapping with each other, etc.). It is helpful to maintain consistency in these methods across recordings, as well as to consider the effects of pre-processing on the results of the analyses to be performed. For example, before removing low quality calls from a dataset, one should consider whether this might skew the results. This could occur if lower amplitude calls are produced by certain group members, or by individuals in certain behavior states or locations relative to the transducer. Additionally, clear rules for evaluating the quality of a call should be developed and applied. For example, in Section 2, Population Structure and Section 5, Signal Structure and Evolution: Ecological Effects, the authors of the case studies presented all clearly state that they did not include low quality calls in their analyses [68; 70; 71].