Nowcasting Intraseasonal Recreational Fishing Harvest with Internet Search Volume

• Published in: PloS one Vol. 10; no. 9; p. e0137752
• Main Authors: Carter, David W, Crosson, Scott, Liese, Christopher
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.09.2015; Public Library of Science (PLoS)
• Subjects: Analysis; Animals; Commercial fishing; Conservation of Natural Resources; Economic statistics; Ecosystem; Estimates; Fish; Fisheries; Fisheries management; Fishes; Fishing; Fishing (Recreation); Internet; Lutjanus campechanus; Management; Methods; Mexico; Nowcasting; Online searching; Population Dynamics; Recreation; Regression analysis; Regression models; Science; Search engines; Seasonal variations; Seasons; Social networks; Social sciences; Sport fishing; Technology application; Time series; Trends; Mexico; United States; Gulf of Mexico; Florida; United States > US; Texas
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0137752

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.