![]() We deployed nets 500 m apart in areas with similar depths and bottom habitat. Nets were 400 m in length, 1.5 m deep, with 1 m tie downs and with 8 cm mesh (stretched diagonal). Experimental nets were illuminated with UV LEDs as described above, whereas inactive LEDs were similarly placed on control nets. We deployed pairs of control and experimental nets in a commercial bottom-set gillnet fishery. (b) Testing net illumination effects on total target fish catch and catch value We calculated sea turtle catch-per-unit-effort (CPUE) for each net as the number of turtles captured/() × (). Sea turtles were removed from the nets, tagged, measured (straight carapace length) and released. We conducted experiments near Punta Abreojos, because the area has high densities of sea turtles, which ensures enough sea turtle interactions for robust analysis. After sunset, we deployed nets within approximately 1 km of each other and retrieved them before sunrise. The nets were 95 m long and 3 m deep with 40 cm mesh (stretched diagonal). We used surface-set monofilament nets similar to those used to conduct green sea turtle population surveys. Control nets had inactive LEDs placed every 5 m. Experimental nets had UV LEDs (peak wavelength 396 nm) placed every 5 m on the floatlines. We deployed pairs of nets consisting of a control and an experimental net, to examine the effects of UV illumination on sea turtle catch rates. (a) Testing net illumination effects on turtle catch rates Separately, we also examined the effects of UV net illumination on target fish catch rates and catch value in a commercial gillnet fishery. In this study, we examined the effects of illuminating gillnets with UV LEDs on sea turtle interaction rates. Our goal is to develop a BRT that reduces sea turtle bycatch without reducing the total target catch or the market value of catch. Exploiting this disparity in visual capabilities between sea turtles that perceive UV light and fish without UV sensitivities may be a productive strategy in developing potential BRTs. Many of these fish species possess UV-absorbing compounds in their eyes that filter UV light and are thought to minimize damage from short-wavelength radiation. While some fish species are also sensitive to UV light, several commercially valuable fish species are not. Īnatomical, physiological and behavioural studies indicate that green, loggerhead ( Caretta caretta) and leatherback ( Dermochelys coriacea) sea turtles are sensitive to ultraviolet (UV) wavelengths. ![]() Experiments have shown that changing the visual cues associated with fishing gear, such as illuminating nets with green light-emitting diodes (LEDs) or chemical lightsticks, can reduce green sea turtle ( Chelonia mydas) interaction rates. Identifying sensory cues that influences an animal's behaviour around fishing gear and understanding the animal's underlying sensory constraints can guide the development of BRTs. ![]() One approach to developing BRTs for gillnet fisheries has been to better understand the sensory and behavioural ecology of sea turtles and target fish. Owing to the concern over high rates of sea turtle bycatch and mortality in several such fisheries, a variety of bycatch reduction technologies (BRTs), such as modifications to float lines, altering net tie-downs, use of at-sea advisory programmes and net illumination have been examined. Ĭoastal gillnet fisheries are ubiquitous. As such, fisheries bycatch is considered to be a barrier to the recovery of sea turtle populations and has become a motivating factor to improve the balance between species protection and commercial fishing interests. ![]() ![]() In particular, several studies have shown that small-scale coastal gillnet fisheries may have high levels of sea turtle bycatch. seabirds, sea turtles and elasmobranchs) are linked to population declines in several vulnerable species. Incidental interactions between commercial fisheries and marine animals (e.g. ![]()
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