Island in the Stream: Oceanography and Fisheries of the Charleston Bump

Understanding Environmental Influences on Movements and Depth Distributions of Tunas and Billfishes Can Significantly Improve Population Assessments

Richard W. Brill and Molly E. Lutcavage


Abstract.—The vulnerability of the highly mobile tunas (family Scombridae) and billfishes (families Istiophoridae and Xiphiidae) to various fishing gears and detection by aerial surveys is influenced by their depth distributions, travel speeds, residency times, and aggregation. As a result, understanding the effects of the physical environment on fish behavior is critical for robust population assessments. Numerous studies have attempted to understand the movements and habitat requirements of tunas and billfishes by correlating catch statistics with environmental conditions averaged over time and space. Such correlations do not necessarily elucidate the requisite relationships because the data are not gathered simultaneously, and because error terms are often too broad to demonstrate meaningful relationships. More important, using catch statistics to determine the effects of environmental conditions on catch statistics can never prove causation and result in tautology, unless independent measures offish abundance are available. The situation is not necessarily improved when catch statistics are correlated with satellite-derived sea surface temperature data. Tunas and billfish fish do not always live at the surface and, more importantly, regularly move through vertical thermal gradients (≈1°C m-1) that are orders of magnitude steeper than horizontal gradients (≈1°C km-1). Sea surface temperature gradients per se are, therefore, unlikely to influence horizontal movements or aggregation. Direct observations of tuna and billfish behaviors (collected via acoustic telemetry or electronic data-recording tags) can, however, be readily combined with information on their physiologically-based environmental tolerances, forage abundance, and appropriate oceanographic data. The resulting models can correct both traditional catch-per-unit effort data and aerial survey data for differences in gear vulnerability, and thus significantly improve population assessments.