Application of the Acoustic Propagation Model to a deep‐water cross‐shelf curtain

A good understanding of acoustic coverage and temporal variation relative to environmental conditions is crucial for accurate interpretation of results from acoustic tracking studies and ensuing appropriate management recommendations. In their recent paper, Gjelland & Hedger ( Methods in Ecology and Evolution , 2017) suggest that the general detection probability model proposed by Gjelland & Hedger ( Methods in Ecology and Evolution , 2013, 4, 665–674) was not appropriately used in Huveneers et al. ( Methods in Ecology and Evolution , 2016, 7, 825–835). The intent of the comparison in Huveneers et al . (2016) was to evaluate the Acoustic Propagation Model (APM) in a situation when parameterisation is not logistically feasible. This can be the case when reference tags have not been deployed, or when the distance between tagged animals and receivers cannot be accurately estimated or cannot be obtained under sufficiently varied weather conditions. Even when parameterisation is possible, there will be situations where the APM does not account for all factors affecting detection probability, e.g. deep-water receivers where density gradients can affect sound propagation. Re-parameterisation of the APM based on clarification in Gjelland & Hedger (2017) and application to a deep-water cross-shelf receiver array showed similar detection range to a logistic model until about ∼750 m distance, after which the APM resulted in unrealistically high detection probability estimates. The need for large amounts of data to parameterise the APM and achieve good statistical fit negates the value of the theoretical propagation model. Detection range can also be affected by a broad range of factors, many of which are not included within the APM. The complexity of the way different environmental factors can influence acoustic detections and the variability of environments in which acoustic tracking studies are undertaken make it challenging to develop a general model applicable across environments. We support further improvement of the APM and recommend the use of reference tags to collect necessary data to parameterise the APM and assess factors influencing detection probability.