Quantifying the age-structure of free-ranging delphinid populations: testing the accuracy of Unoccupied Aerial System-photogrammetry

Understanding the population health status of long-lived and
slow-reproducing species is critical for their management. However, it can
take decades with traditional monitoring techniques to detect
population-level changes in demographic parameters. Early detection of the
effects of environmental and anthropogenic stressors on vital rates would
aid in forecasting changes in population dynamics and therefore inform
management efforts. Changes in vital rates strongly correlate with
deviations in population growth, highlighting the need for novel
approaches that can provide early warning signs of population decline
(e.g., changes in age-structure). We tested a novel and frequentist
approach, using Unoccupied Aerial System- (UAS) photogrammetry, to assess
the population age-structure of small delphinids. First, we measured the
precision and accuracy of UAS-photogrammetry in estimating total body
length (TL) of trained bottlenose dolphins (Tursiops truncatus). Using a
log-transformed linear model, we estimated TL using the
blowhole-to-dorsal-fin-distance (BHDF) for surfacing animals. To test the
performance of UAS-photogrammetry to age-classify individuals, we then
used length measurements from a 35-year dataset from a free-ranging
bottlenose dolphin community to simulate UAS-estimates of BHDF and TL. We
tested five age-classifiers and determined where young individuals
(<10 years) were assigned when misclassified. Finally, we tested
whether UAS-simulated BHDF only or the associated TL estimates provided
better classifications. TL of surfacing dolphins was overestimated by 3.3%
±3.1% based on UAS-estimated BHDF. Our age-classifiers performed best in
predicting age-class when using broader and fewer (two and three)
age-class bins with ~80% and ~72% assignment performance, respectively.
Overall, 72.5-93% of the individuals were correctly classified within two
years of their actual age-class bin. Similar classification performances
were obtained using both proxies. UAS-photogrammetry is a non-invasive,
inexpensive, and effective method to estimate TL and age-class of
free-swimming dolphins. UAS-photogrammetry can facilitate the detection of
early signs of population changes, which can provide important insights
for timely management decisions.