The influence of shark behavior and environmental conditions on baited remote underwater video survey results
04. March 2021
The influence of shark behavior and environmental conditions on baited remote underwater video survey results
James P. Kilfoil, Matthew D. Campbell, Michael R. Heithaus, Yuying Zhang
ABSTRACT:
Baited remote underwater video systems (BRUVS) have become an important and frequently used tool by resource managers to monitor relative abundances for a variety of marine species. As with any abundance survey method, fundamental assumptions of the technique are that counts derived from videos accurately reflect true changes to abundances, while being robust to changes in density-independent factors. We tested these assumptions using a newly developed spatially-explicit individual-based simulation model for two of the most commonly used video survey metrics of relative abundance; MaxN and MeanCount. Simulating a 1-km2 area over a 60-min BRUV deployment targeting elasmobranch species, we evaluated how resulting estimates of each metric were influenced by swimming speed, relative directness of movement patterns, relative attraction strength to bait, bait plume size, and camera visibility range while shark density was held constant. By simulating both standard (120°) and full-spherical (FS; 360°) fields of view (FOV), we were also able to explore if newly developed FS cameras could help reduce the impact of these density-independent factors. Results from both generalized linear models and visual inspection of simulations indicated that density as well as non-density-related factors were highly significant in predicting camera counts, regardless of the camera’s FOV or count metric used. Collectively, these findings illustrate that video survey metrics are sensitive to factors unrelated to changes in density, and researchers should carefully consider their potential influence on survey results as well as potential management decisions based on these data.
Ecological Modelling, Volume 447, 1 May 2021, DOI: 10.1016/j.ecolmodel.2021.109507