Analytical Approaches

This report outlines how the ABMI calculates animal density from remote camera image data. The report describes in detail the components of this density estimate (how the ABMI collects the necessary information, results, assumptions, tests), other factors that need to be considered in some designs, and provides further discussion on the basic assumptions made and dealing with skewed sampling distribution.

The objective of this report is to address some of this question using a variety of datasets where multiple recordings from ARUs placed in the same area have been listened to for extended periods. The structure of the report is a series of questions with a methods and results specific to the question. A general conclusion is presented at the end of the report and a discussion of next steps for settling on a standardized protocol for ARU listening.

Point counts are one of the most commonly used methods for assessing bird abundance. Autonomous recording units (ARUs) are increasingly being used as a replacement for human-based point counts. Previous studies have compared the relative benefits of human versus ARU-based point count methods, primarily with the goal of understanding differences in species richness and the abundance of individuals over an unlimited distance. What has not been done is evaluate how to standardize these two types of data so that they can be compared in the same analysis, especially when there are differences in the area sampled.

We compared detection distances between human observers in the field and four commercially available recording devices (Wildlife Acoustics SM2, SM3, RiverForks, and Zoom H1) by simulating vocalizations of various avian species at different distances and amplitudes. We also investigated the relationship between sound amplitude and detection to simplify ARU calibration. We used these data to calculate correction factors that can be used to standardize detection distances of ARUs relative to each other and human observers. In general, humans in the field could detect sounds at greater distances than an ARU although detectability varied depending on species song characteristics. We provide correction factors for four commonly used ARUs and propose methods for calibrating ARUs relative to each other and human observers.

Automated recognition is increasingly used to extract species detections from audio recordings; however, the time required to manually review each detection can be prohibitive. We developed a flexible protocol called “validation prediction” that uses machine learning to predict whether recognizer detections are true or false positives and can be applied to any recognizer type, ecological application, or analytical approach. Validation prediction uses a predictable relationship between recognizer score and the energy of an acoustic signal but can also incorporate any other ecological or spectral predictors (e.g., time of day, dominant frequency) that will help separate true from false-positive recognizer detections. First, we documented the relationship between recognizer score and the energy of an acoustic signal for two different recognizer algorithm types (hidden Markov models and convolutional neural networks). Next, we demonstrated our protocol using a case study of two species, the Common Nighthawk (Chordeiles minor) and Ovenbird (Seiurus aurocapilla). We reduced the number of detections that required validation by 75.7% and 42.9%, respectively, while retaining at least 98% of the true-positive detections. Validation prediction substantially improves the efficiency of using automated recognition on acoustic data sets. Our method can be of use to wildlife monitoring and research programs and will facilitate using automated recognition to mine bioacoustic data sets.

Automated recognition is increasingly used to extract information about species vocalizations from audio recordings. During processing, recognizers calculate the probability of correct classification (“score”) for each acoustic signal assessed. Our goal was to investigate the implications of recognizer score for ecological research and monitoring. We trained four recognizers with clips of Common Nighthawk (Chordeiles minor) calls recorded at different distances: near, midrange, far, and mixed distances. We found distance explained 49% and 41% of the variation in score for the near and mixed-distance recognizers, but only 3% and 6% of the variation for the midrange and far recognizers.

Wildlife practitioners are increasingly moving to non-invasive and passive monitoring technology, such as autonomous recording units (ARUs) to survey wildlife. Additionally, recent trends in ecological research are to investigate patterns at scales much larger than a single monitoring program is typically capable of (i.e., regional or continental scales). These large-scale studies often require collaboration and the sharing or integration of data from multiple sources to address research questions and objectives over large areas.