Predictive model of sperm whale prey capture attempts from time-depth data.

backgroundHigh-resolution sound and movement recording tags offer unprecedented insights into the fine-scale foraging behaviour of cetaceans, especially echolocating odontocetes, enabling the estimation of a series of foraging metrics. However, these tags are expensive, making them inaccessible to most researchers. Time-Depth Recorders (TDRs), which have been widely used to study diving and foraging behaviour of marine mammals, offer a more affordable alternative. Unfortunately, data collected by TDRs are bi-dimensional (time and depth only), so quantifying foraging effort from those data is challenging.

methodsA predictive model of the foraging effort of sperm whales (Physeter macrocephalus) was developed to identify prey capture attempts (PCAs) from time-depth data. Data from high-resolution acoustic and movement recording tags deployed on 12 sperm whales were downsampled to 1 Hz to match the typical TDR sampling resolution and used to predict the number of buzzes (i.e., rapid series of echolocation clicks indicative of PCAs). Generalized linear mixed models were built for dive segments of different durations (30, 60, 180 and 300 s) using multiple dive metrics as potential predictors of PCAs.

resultsAverage depth, variance of depth and variance of vertical velocity were the best predictors of the number of buzzes. Sensitivity analysis showed that models with segments of 180 s had the best overall predictive performance, with a good area under the curve value (0.78 ± 0.05), high sensitivity (0.93 ± 0.06) and high specificity (0.64 ± 0.14). Models using 180 s segments had a small difference between observed and predicted number of buzzes per dive, with a median of 4 buzzes, representing a difference in predicted buzzes of 30%.

conclusionsThese results demonstrate that it is possible to obtain a fine-scale, accurate index of sperm whale PCAs from time-depth data alone. This work helps leveraging the potential of time-depth data for studying the foraging ecology of sperm whales and the possibility of applying this approach to a wide range of echolocating cetaceans. The development of accurate foraging indices from low-cost, easily accessible TDR data would contribute to democratize this type of research, promote long-term studies of various species in several locations, and enable analyses of historical datasets to investigate changes in cetacean foraging activity.