Many fishers diversify their income by participating in multiple fisheries, which has been shown to significantly reduce year-to-year variation in income. The ability of fishers to diversify has become increasingly constrained in the last few decades, and catch share programs could further reduce diversification as a result of consolidation. This could increase income variation and thus financial risk. However, catch shares can also offer fishers opportunities to enter or increase participation in catch share fisheries by purchasing or leasing quota. Thus, the net effect on diversification is uncertain. We tested whether diversification and variation in fishing revenues changed after implementation of catch shares for 6,782 vessels in 13 US fisheries that account for 20% of US landings revenue. For each of these fisheries, we tested whether diversification levels, trends, and variation in fishing revenues changed after implementation of catch shares, both for fishers that remained in the catch share fishery and for those that exited but remained active in other fisheries. We found that diversification for both groups was nearly always reduced. However, in most cases, we found no significant change in interannual variation of revenues, and, where changes were significant, variation decreased nearly as often as it increased.diversification | risk | catch shares | fisheries
Estimates of recreational fishing harvest are often unavailable until after a fishing season has ended. This lag in information complicates efforts to stay within the quota. The simplest way to monitor quota within the season is to use harvest information from the previous year. This works well when fishery conditions are stable, but is inaccurate when fishery conditions are changing. We develop regression-based models to “nowcast” intraseasonal recreational fishing harvest in the presence of changing fishery conditions. Our basic model accounts for seasonality, changes in the fishing season, and important events in the fishery. Our extended model uses Google Trends data on the internet search volume relevant to the fishery of interest. We demonstrate the model with the Gulf of Mexico red snapper fishery where the recreational sector has exceeded the quota nearly every year since 2007. Our results confirm that data for the previous year works well to predict intraseasonal harvest for a year (2012) where fishery conditions are consistent with historic patterns. However, for a year (2013) of unprecedented harvest and management activity our regression model using search volume for the term “red snapper season” generates intraseasonal nowcasts that are 27% more accurate than the basic model without the internet search information and 29% more accurate than the prediction based on the previous year. Reliable nowcasts of intraseasonal harvest could make in-season (or in-year) management feasible and increase the likelihood of staying within quota. Our nowcasting approach using internet search volume might have the potential to improve quota management in other fisheries where conditions change year-to-year.
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