Automated signal recognition software is increasingly used to extract species detection data from acoustic recordings collected using autonomous recording units (ARUs), but there is little practical guidance available for ecologists on the application of this technology. Performance evaluation is an important part of employing automated acoustic recognition technology because the resulting data quality can vary with a variety of factors. We reviewed the bioacoustic literature to summarize performance evaluation and found little consistency in evaluation, metrics employed, or terminology used. We also found that few studies examined how score threshold, i.e., cutoff for the level of confidence in target species classification, affected performance, but those that did showed a strong impact of score threshold on performance. We used the lessons learned from our literature review and best practices from the field of machine learning to evaluate the performance of five readily-available automated signal recognition programs. We used the Common Nighthawk (Chordeiles minor) as our model species because it has simple, consistent, and frequent vocalizations. We found that automated signal recognition was effective for determining Common Nighthawk presence-absence and call rate, particularly at low score thresholds, but that occupancy estimates from the data processed with recognizers were consistently lower than from data generated by human listening and became unstable at high score thresholds. Of the five programs evaluated, our convolutional neural network (CNN) recognizer performed best, with recognizers built in Song Scope and MonitoR also performing well. The RavenPro and Kaleidoscope recognizers were moderately effective, but produced more false positives than the other recognizers. Finally, we synthesized six general recommendations for ecologists who employ automated signal recognition software, including what to use as a test benchmark, how to incorporate score threshold, what metrics to use, and how to evaluate efficiency. Future studies should consider our recommendations to build a body of literature on the effectiveness of this technology for avian research and monitoring. Recommandations pour l'évaluation des performances de reconnaissance acoustique et application à cinq programmes courants de reconnaissance automatisée de signaux sonores RÉSUMÉ. Les logiciels de reconnaissance automatisée de signaux sonores sont de plus en plus utilisés pour extraire les données de détection des espèces d'enregistrements acoustiques récoltés au moyen d'unités d'enregistrement autonomes (ARU en anglais), mais il existe peu d'instructions pratiques sur l'utilisation de cette technologie pour les écologistes. L'évaluation de la performance est une étape importante dans l'utilisation d'une technologie de reconnaissance acoustique automatisée parce que la qualité des résultats peut varier en fonction de divers facteurs. Nous avons passé en revue la littérature sur la bioacoustique afin de résumer les critères d'évaluation de ...
The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.
Distance sampling is widely used to estimate animal population densities by accounting for imperfect detection of individuals with increasing distance from an observer. Distance sampling assumes that distances are measured without error; however, it is often applied to human estimated distances, which are known to be inconsistent, inaccurate, and biased. We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show the error is less than human estimated error extracted from a literature case study. RSL predicted distances can be obtained by manual measurement in sound viewing software, or automatically with automated signal recognition software. We built calibration datasets of Ovenbirds (Seiurus aurocapilla) and Common Nighthawks (Chordeiles minor) recorded at known distances and used regression of RSL from those recordings to predict distance. There was no error bias of RSL predicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and error from all RSL predicted distances was less than human estimated error extracted from the literature. We then simulated ARU point count surveys with a known density and estimated that density with distance sampling to test whether RSL distance prediction does not violate the assumption that distances are measured without error. There was no difference in density estimates from known distance and density estimates obtained from RSL predicted distance, while density estimates contaminated with human estimated error were significantly lower than density estimates from known distance. We found that a calibration dataset of approximately 300 vocalizations was suitable to minimize error for both species, and so conclude that RSL distance prediction is an accessible method of improving distance estimates relative to human estimation. We provide general recommendations on how to collect calibration recordings for the application of RSL distance prediction to other species and areas.
Research666 the probability that our method missed peaks (spatial: 0.12, temporal: 0.18) or detected false peaks (spatial: 0.11, temporal: 0.37) due to data gaps and showed that our approach remains useful even for sparse and/or sporadic location data. Our study presents a generalizable approach to evaluating migratory connectivity across the full annual cycle that can be used to focus migratory bird conservation towards places and times of the annual cycle where populations are more likely to be limited.
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