1. Myotis sodalis (Indiana bat) Bat no. 218PGC, 11Aug05
Joseph M. Szewczak
Humboldt State University
Arcata, CA
More reference recordings decrease, rather than increase, acoustic classification performance of Myotis sodalis and M. lucifugus
Myotis sodalis (Indiana bat) Bat no. 218PGC, 11Aug05
John D. Chenger
Bat Conservation and Mgmt
Carlisle, PA
Janet D. Tyburec
Bat Survey Solutions, Inc.
Tucson, AZ
Introduction
Initial attempts to use recordings from species-known tracked bats to identify Myotis sodalis and distinguish it from the acoustically similar congener M. lucifugus indicated some acoustically distinct parts of their respective call repertoires. This initial data collected from 2002–2007 yielded about an 80% correct classification rate, although lower than reported elsewhere (e.g., Britzke et al. 2002). This encouraged continued tracking and recording of these species in hope that a larger data set would increase classification performance and strengthen the statistical difference between their echolocation calls and provide more robust classification.
Analysis
To assess the influence of reference recording training sample size on classification performance, we randomly selected different size paired training subsets from the full data set (10,955 full-spectrum call samples). Subsets ranged from 25 sequences for each species (641–907 calls) to subsets of 100 sequences from each species (3156–3476 calls). We then built classifiers from each paired training data set and evaluated classification performance.
As a consequence of the inverse square law of sound attenuation, and that volume scales by the cube of distance, bat detectors record far more lower amplitude bat calls than strong, higher amplitude recordings that rise above ambient sound levels:
Results and Discussion
Overall, as the library of reference recordings grew from 2007–2016 it did not strengthen any statistically significant acoustic differences between these species. In fact, it weakened. More recordings introduced examples of one species filling in data space previously exclusive to the other to reveal that essentially each species could produce call types like the other:
Initial data revealed a suggestion of species discriminating differences, but mostly substantial overlapping quantitative characteristics:
Michael Durham ©2005
(Szewczak Bat Research News. 41:142)
tethered zipline flight
short calls
To best extract and faithfully render the most information content from recordings, we used SonoBat software that uses high resolution sonograms and an intelligent call trending algorithm able to track low signal strength calls against background noise and echoes, and with fine temporal resolution as shown here with a high signal strength call:
Materials and Methods
We selected recording sites throughout the M. sodalis range to collect the most complete repertoire of echolocation calls, also addressing the variety of geographic and micro-habitat elements where this species occurs:
Classification performance exhibited stochastic variation with subset size and selection and ranged from 95.0– 69.9% correct. Smaller subsets revealed greater range of performance. Collectively this analysis suggests a downward trend in classification performance with larger data sets, with a trend that if extrapolated would approach a meaningless 50% correct performance near 21,000 calls:
Spotlight tracking of released bats
better
Mylu range
Myso range
Recording sites •
Example comparisons of fine scale full spectrum call trending with zero-crossing trend analysis. Yellow lines overlaying the sonograms indicate the trends established from the analysis:
asymptote?
Alternatively, the trend in the classification performance with training set size may indicate the approach toward an asymptote representing the potential actual range-wide and species-wide classification performance.
We captured bats in mist nets and harp traps, determined species in hand, and carefully tracked all bats using a variety of methods (right) to acquire representative call types to fill out the call repertoires. We acquired all recordings in full spectrum using Binary Acoustic Technology, Pettersson, or Wildlife Acoustics detectors:
The dependency of classification performance on size and selection of training set revealed by this analysis implies caution when interpreting acoustic classification performance to discriminate M. sodalis vs. M. lucifugus. Apparently attractive classification performance can result from an artifact of a limited or specialized training library used to construct a classifier, and such a classifier will underperform on recordings outside of the limited data set, specialized conditions, or situations it represents.
The specialized conditions or situations that reduce the range-wide performance of a classifier do not necessarily result from geographic differences. Bats vary their call structure to suit the task at hand, for example foraging strategy. As prey selection varies throughout the season, and even over the course of a night, call structure may vary to match. Prey selection will also vary from site to site and perhaps on as small a scale as from one side of a meadow from another, depending upon prey activity.
Eventual confident classification performance for these species may entail consideration of all factors that influence call selection.
Measurements of call parameters in both time-frequency and time-amplitude domains followed from extracted trends. Ninety six measured parameters of both discrete values and continuous form functions contributed to the data used to build classifiers:
Acknowledgements
A cast of many patient field partners who assisted with captures, tracking and recording.