Fine-scale information on the occurrence of coastal cetaceans is required to support regulation of offshore energy developments and marine spatial planning. In particular, the EU Habitats Directive requires an understanding of the extent to which animals from Special Areas of Conservation (SAC) use adjacent waters, where survey effort is often sparse. Designing survey regimes that can be used to support these assessments is especially challenging because visual sightings are expected to be rare in peripheral parts of a population's range. Consequently, even intensive visual line-transect surveys can result in few encounters. Static passive acoustic monitoring (PAM) provides new opportunities to extend survey effort by using echolocation click detections to quantify levels of occurrence of coastal dolphins, but this does not provide information on species identity. In NE Scotland, assessments of proposed offshore energy developments required information on spatial patterns of occurrence of bottlenose dolphins in waters in and next to the Moray Firth SAC. Here, we illustrate how this can be achieved by integrating data from broad-scale PAM arrays with presence-only data from visual surveys. Generalized estimating equations were used with PAM data to model the occurrence of dolphins in relation to depth, distance to coast, slope, and sediment, and to predict the spatial variation in the cumulative occurrence of all dolphin species across a 4 × 4 km grid of the study area. Classification tree analysis was then applied to available visual sightings data to estimate the likely species identity of dolphins sighted in each grid cell in relation to local habitat. By multiplying these probabilities, it was possible to provide advice on spatial variation in the probability of encountering bottlenose dolphins from this protected population at a regional scale, complementing data from surveys that estimate average density or overall abundance within a region.
Locatsoin fo passive acoustic monitoring sites
Data on the the number of days that dolphins were detected at each PAM site in each of the 3 years studied
Visual sightings data used in classification tree to predict the liklely species identifity of dolphins detcted in different parts of the study area
Model outputs from the GEE and classification TREE with predictions of the likely occurrence of bottlenose dolphins in different grid squares within the study area.
Hourly PAM detections of dolphins at each site with co-variates used in the GEE to estimate spatio-temporal variability in the probability of detecting dolphins
Information on field names used in the 5 data files
This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.
|Date made available
|22 May 2015
|Dryad Digital Repository
|Date of data production
|11 Jul 2014
|Scotland, Moray Firth