Fishermen may stretch the truth about the size of their catch, but when it comes to understanding the currents flowing through their watery workspace, their boasts hold up. In a new study, scientists report that knowledge gleaned over several generations by Italian fishermen has helped reveal lake water movements that researchers might otherwise have overlooked.
The body of water in question is the Y-shaped Lake Como, nestled in the Italian Alps. About 30 full-time fishermen ply the lake in small boats and cast large, floating gill nets that can be 700 meters long and up to 9 meters tall. These mesh walls, which fishermen can lower to different depths, snag fish as they drift through the water overnight. Fishermen must predict how the currents will move their gear to prevent the nets from tangling. They can also read the water's motions in how it wraps their nets into curving shapes.
Sarah Laborde, a gradate student in physical limnology and cultural anthropology at the University of Western Australia in Perth, started talking to fishermen to better understand the flow of water in her field site of Lake Como. Laborde was surprised when the men expressed familiarity with complex physical features that she and her colleagues had previously observed in the lake. When the lake stratifies into two layers of different temperatures each summer, for example, the fishermen progressively lower their nets to catch fish that follow this water layer boundary as it deepens throughout the season. The men also described movements of their nets caused by waves below the lake's surface.
Confident in the accuracy of the fishermen's observations, Laborde decided to document the extent of their knowledge in a series of interviews. She became intrigued when they described physical features she had never seen on Lake Como, such as areas where the water swirled in gyres or flowed backwards against the current.
"As a scientist, it's impossible to have monitoring probes everywhere," Laborde says. But the fishermen who travel the lake every day could observe the water in places and at times that scientists could not. Laborde says that in addition to their insights of how water moves, the fishermen also knew about fish behavior and could describe the terrain of the lake bottom without ever having seen a map. "They know it from practice and from what their forebears have told them," she says. "It's a really deep traditional knowledge."
Laborde and her colleagues worked with fishermen to map the paths of their nets, which acted like the drifting instruments scientists usually deploy to study the movements of water. They then used data such as wind speed and water temperature from three lake monitoring stations to construct a hydrological model or blueprint of how water flows throughout the lake. The model confirmed the fishermen's observations of whirling gyres and backward flows, the team reports today in the Proceedings of the National Academy of Sciences. Such processes mix and transport nutrients and algae, so documenting them helps scientists better understand the lake's ecology.
"Fishers' knowledge can be a fast and efficient alternative to get more data and to improve the design of future surveys," says Renato Silvano, an ecologist at the Federal University of Rio Grande do Sul in Porto Alegre, Brazil, who was not involved in the study. Silvano says that over the past decade, he and other biologists have increasingly used fishermen's local knowledge to study fish migrations and track changes in species composition and abundance. This paper shows how even the most number-driven physical scientists who build complex numerical models can benefit from local lore, he says. "They can use fishers' knowledge to develop new hypotheses that can be tested with scientific data."
"I think this is fantastic," Ben Hodges, a physical limnologist at the University of Texas, Austin, says about the paper's use of traditional knowledge. "This is the sort of stuff that most physical scientists have been ignoring." Hodges says that physical scientists can use fishermen's observations to fill in gaps and improve the accuracy of their models. This paper "is breaking new ideas" by presenting descriptive local knowledge alongside quantitative data, he says. "I don't know of anyone else who has done this."