Evaluating elicited judgments of turtle captures for data‐limited fisheries management

Abstract

We compare judgments of green turtle (Chelonia mydas) captures elicited from local gillnet skippers and not-for-profit conservation organization employees operating in a small-scale fishery in Peru, to capture rates calculated from a voluntary at-sea observer program operating out of the same fishery. To reduce cognitive biases and more accurately quantify uncertainty in our experts' judgments, we followed the IDEA ("Investigate," "Discuss," "Estimate," and "Aggregate") structured elicitation protocol. The elicited mean monthly estimates of green turtle gillnet captures within summer and winter fishing seasons were higher than the equivalent green turtle capture rates calculated from the fisheries observer data; however, no statistically significant differences were identified when comparing the means of the datasets using bootstrap hypothesis tests (winter observed difference-in-means: 83.15, adj mean ± SD = 42.39 ± 32.59; summer observed difference-in-means: 68.58, adj mean ± SD = 54.06 ± 41.22). We investigated respondent performance in relation to the observer data capture rates. The not-for-profit employees scored high on accuracy and calibration performance metrics. The gillnet skippers' judgments ranked higher on informativeness yet lower on accuracy and calibration, potentially reflective of overconfident judgments. This research presents a new context for using the IDEA protocol, which may prove helpful for rapid, exploratory evaluations of capture and bycatch impact in data-limited small-scale fishery management scenarios.

A Mitigation Hierarchy Approach for Managing Sea Turtle Captures in Small-Scale Fisheries

“…In data-limited fisheries management situations such as the current study, it is often necessary to draw on elicited knowledge from fishers and local practitioners to support evaluations. Structured elicitation methods such as the IDEA protocol offer robust frameworks to reduce cognitive biases and more accurately quantify uncertainty (Hanea et al, 2016;Arlidge et al, 2020).…”

Assessing information-sharing networks within small-scale fisheries and the implications for conservation interventions

“…This finding indicates that the usual mechanisms that drive information sharing between gillnet skippers in the other fishing-related networks (and potentially social networks generally) are not at play in the sea turtle bycatch information-sharing network 54,55 . The lack of discussion about sea turtle bycatch between gillnet skippers with similar levels of bycatch may potentially occur if some San Jose gillnet skippers with higher rates of sea turtle bycatch do not realize or appreciate that they have higher bycatch than other gillnet skippers in the community 68 . Indeed, previous research and field observations have suggested that fishers with higher bycatch rates tend not to put much effort into actively avoiding sea turtles captures unless they are specifically incentivized to do so (i.e., through the local notfor-profit's trial bycatch reduction initiative) 63 .…”

“…The lack of positive incentives for fishers to mitigate bycatch in the study system 63 , coupled with a lack of fishers' understanding of vessel-level bycatch rates 68 , leaves the possibility that some fishers may take sea turtle bycatch and not discuss it with anyone. Fishing practices in general will affect rates of bycatch, for example, discussions about gear, fishing location, weather, or fishing finances (which can influence decisions about when and what to fish), despite whether bycatch itself is the topic discussed.…”