Researchers at UC Santa Barbara focused on a particularly troublesome issue for sharks: tangles with the longline tuna fishery. Using data from regional fisheries management organizations and machine learning algorithms, the scientists were able to map out hotspots where shark species face the greatest threat from longline fishing. The findings, published in Frontiers in Marine Science, highlight key regions where sharks can be protected with minimal impact on tuna fisheries.
“Longline fishing gear is exactly what it sounds like: a long line with lots of hooks attached to it that are baited. And they can be left in the water, waiting for fish to bite, for a very long time,” explained co-lead author Darcy Bradley, who heads UC Santa Barbara’s Ocean & Fisheries Program at the Environmental Markets Lab (emLab). These baited hooks catch predators like tuna, but many nearby sharks will also converge on the bait.
Rather than simply report how many sharks were caught and where, the authors aimed to assess the relative risk sharks faced across different areas of the ocean. “One of the main questions was ‘Where is the risk for catching sharks the highest, and does that overlap with fishing effort?’” said co-lead author Echelle Burns, a project scientist at emLab.
To answer this, Burns, Bradley and co-author Lennon Thomas (also at emLab) went hunting for data on longline fisheries. The authors compiled data on shark catch from industrial longline fishing across all the world’s tuna fisheries into one comprehensive resource. This was quite a task. Each fisheries management organization operates differently, meaning their data isn’t always in the same format.
The authors paired spatial shark catch data with environmental data like sea-surface temperature and factors correlated with food abundance. They also included economic data like ex vessel price—the price that fishers receive directly for their catch—for different shark species each year. “Because you can’t catch a shark where it doesn’t live,” Bradley added, “we used species distribution models to delineate where different sharks actually live in the ocean to inform our risk assessment.”
Still, there were a lot of unknowns. So Burns, Bradley and Thomas used a model to fill in the gaps, recognize trends and draw conclusions from this incomplete data.
This was a new approach to estimating the interactions between fisheries and marine species. Using machine learning enabled the team to extrapolate trends from their messy datasets. First, the model assessed whether a shark species was present in an area, and if so, how likely it was to be caught there. Then it looked at how many sharks of each species were caught in an area.
The authors prioritized predictive power in this study. “Our goal was to identify where sharks are at the highest risk of being caught by tuna longline fisheries,” Bradley explained. “For this study, we were not trying to understand the extent to which various factors influence this risk.”
They used a random forest model, which combines the outputs of many decision trees. What emerged was a map of catch risk for shark populations across the globe.
Tunas and sharks are both predators and target similar prey, so they’re often found together. But while they may share some traits, sharks and tunas are fundamentally different types of animals. Tunas grow quickly and produce many offspring, while sharks mature relatively late and reproduce slowly. As a result, tuna can withstand much higher fishing pressures than sharks, and even a small impact on shark numbers can affect the population of a threatened species.
Fortunately, the scientists found that hotspots for longline-shark interactions didn’t correspond with preferred fishing grounds. “This suggests that we can design management strategies that can protect vulnerable and threatened shark species without having to close the most productive tuna fishing grounds,” Bradley said. The team found this particularly heartening, since it could encourage actions that help sharks while appealing to fishers as well.