Nowcasting Intraseasonal Recreational Fishing Harvest with Internet Search Volume

Abstract

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.

Angler apps as a source of recreational fisheries data: opportunities, challenges and proposed standards

“…It already appears that angler apps can replace some aspects of conventional surveys (Jiorle 2015;Papenfuss et al 2015), and similar results have been found for online social media and Internet search volume (Carter et al 2015). Although it is important to integrate with 'business-as-usual' approaches, angler apps are likely to render some conventional approaches obsolete, or at least consign them to supporting roles such as validation or calibration.…”

“…Although it is important to integrate with 'business-as-usual' approaches, angler apps are likely to render some conventional approaches obsolete, or at least consign them to supporting roles such as validation or calibration. It already appears that angler apps can replace some aspects of conventional surveys (Jiorle 2015;Papenfuss et al 2015), and similar results have been found for online social media and Internet search volume (Carter et al 2015). We must also be open to a new science and management paradigm that is relatively datarich, enlightened, nimble and user-defined.…”

Data mining on YouTube reveals fisher group-specific harvesting patterns and social engagement in recreational anglers and spearfishers

“…Available data are often obtained with methods that are resource intensive (e.g., creel surveys; random telephone surveys; Pollock et al, 1994;Board and Council, 2006;Hartill et al, 2012;Rocklin et al, 2014;Hartill and Edwards, 2015;Zarauz et al, 2015;Bellanger and Levrel, 2017;Holdsworth et al, 2018;Hyder et al, 2018;van Poorten and Brydle, 2018). As a complement to more traditional methods, less resource-demanding data sourced from digital applications and social media platforms are attracting increasing attention (Martin et al, 2014;Carter et al, 2015;Venturelli et al, 2017). However, it remains unclear to what extent such data can produce reliable insights of use in management and monitoring of recreational fisheries.…”

Analyzing publicly available videos about recreational fishing reveals key ecological and social insights: A case study about groupers in the Mediterranean Sea

“…iEcology and conservation culturomics are particularly interesting in the context of recreational fisheries because of the many people that are involved in this activity, of which a sizable fraction uses digital platforms on the internet. New monitoring and assessment methods tailored towards recreational fishing have been developed to collect and analyze data from digital platforms (e.g., Carter et al, 2015;Martin et al, 2014;Monkman et al, 2018b;Sbragaglia et al, 2019a;Venturelli et al, 2017). For example, digital information of catches by recreational fishers constitutes a widespread spatio-temporal network of samples that -if appropriately analyzed -can provide an unprecedented body of information, especially for aquatic environments where sampling is constrained across time and space.…”