Abstract
Acoustic sampling methods are becoming increasingly important in biological monitoring. Sound attenuation is one of the most important dynamics affecting the utility of bioacoustic data as it directly affects the probability of detection of individuals from bioacoustic arrays and especially the localization of acoustic signals necessary in telemetry studies. Therefore, models of sound attenuation are necessary to make efficient use of bioacoustic data in ecological monitoring and assessment applications. Models of attenuation in widespread use are based on Euclidean distance between source and sensor, which is justified under spherical attenuation of sound waves in homogeneous environments. In some applications there are efforts to evaluate the detection range of sensors in response to local environmental characteristics at the sensor or at sentinel source locations with known environmental characteristics. However, attenuation is a function of the total environment between source and sensor, not just their locations. In this paper I develop a model of signal attenuation based on a non-Euclidean cost-weighted distance metric which contains resistance parameters that relate to environmental heterogeneity in the vicinity of an array. Importantly, these parameters can be estimated by maximum likelihood using experimental data from an array of fixed sources, thus allowing investigators who use bioacoustic methods to devise explicit models of sound attenuation in situ. In addition, drawing on analogy with classes of models known as spatial capture-recapture, I show that parameters of the non-Euclidean model of attenuation can be estimated when source locations are unknown. Thus, the models can be applied to real field studies which require localization of signals in heterogeneous environments.
- Posted January 13, 2018.
