My co-authors and I are pleased to announce our new publication:

Pérez-Jorge, S., Oliveira, C., Rivas, E.I., Prieto, R., Cascão, I., Wensveen, P.J., Miller, P.J.O., Silva, M. Predictive model of sperm whale prey capture attempts from time-depth data. Movement Ecology 11, 33 (2023).

It can be freely downloaded here:

Background: High-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.
Methods: A 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.
Results: Average 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%.
Conclusions: These 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.

Please do contact me if you have any questions.

Thank you very much, 

Sergi

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