Recreational angling and spearfishing on social media: insights on harvesting patterns, social engagement and sentiments related to the distributional range shift of a marine invasive species

Abstract

Fisheries are among the human activities that are most strongly affected by ongoing climate-related changes in the presence and abundance of fish species across the globe. The ecological and social repercussions of such changes for recreational fisheries are however still poorly understood. Here, we compare selected ecological and social dimensions of both recreational angling and spearfishing targeting the bluefish (Pomatomus saltatrix) in Italy. The bluefish has undergone a northward expansion in the region over the last 20-30 years, during which it reached new areas and increased in abundance. Using digital videos and their associated data published by both recreational anglers and spearfishers on YouTube we characterized ecological and social dimensions using a culturomics approach. Specifically, we focused on harvesting patterns, social engagement and sentiments related to the bluefish. Our study revealed four major results: (i) similar harvesting patterns (i.e., declared mass and seasonal upload patterns) related to videos by both recreational anglers and spearfishers; (ii) higher social engagement (i.e., number of views and likes) for videos by recreational 2 anglers than spearfishers; (iii) differences in themes of discussion, with anglers being mainly interested in fishing strategy and gears and spearfishers being more interested in fishing actions shown on the videos; iv) positive and negative sentiments of both recreational anglers and spearfishers towards the invasiveness and aggressiveness of the species. The latter represents an interesting trade-off associated with recreational fishing of the bluefish: it is perceived as an invasive species, but it is also a valued target fish because its voracity contributes to the quality of the recreational fishing experience. Our study showcases the value of exploring social media and associated data to better understand the ecological and human dimensions of marine recreational fisheries in relation to distributional range shifts of species associated with climate change.

Digital fisheries data in the Internet age: Emerging tools for research and monitoring using online data in recreational fisheries

“…However, it is extremely important to develop modified versions of such dictionaries tuned to specific recreational fishers’ languages, which could strongly differ from standard word associations with sentiments (Lennox et al, 2018; Sbragaglia, Correia, et al, 2021). For example, Sbragaglia et al (2022) developed a customized dictionary for Italian marine recreational fishers and used it to quantify polarity and sentiments of comments posted on YouTube after watching videos related to catching bluefish ( Pomatomus saltatrix Pomatomidae), an invasive species in the area of study. These analytical pipelines allow emotions such as joy or anger to be quantified across space and time in different contexts, and support rapid assessment of stakeholders needs.…”

“…Sentiment analysis libraries also have bias due to lexical nuances; Lennox et al (2020) discussed issues related to species such as “shark” having negative loadings in sentiment libraries that could bias efforts to apply these tools to understand fisheries, for example, by downweighing the satisfaction of anglers catching and mentioning ‘sharks’ in photo captions. Similarly, Sbragaglia et al (2022) developed a customized reference library for Italian recreational fishers to reduce the biases associated with misassociations with sentiments and polarity of standard libraries in the context of recreational fisheries.…”

“…example,Sbragaglia et al (2022) developed a customized dictionary for Italian marine recreational fishers and used it to quantify polarity and sentiments of comments posted on YouTube after watching videos related to catching bluefish (Pomatomus saltatrix Pomatomidae), an invasive species in the area of study. These analytical pipelines allow emotions such as joy or anger to be quantified across space and time in different contexts, and support rapid assessment of stakeholders needs.…”

Reporting the accuracy of small-scale fishing data by simply applying Benford’s law

“…Likely biased data reduced from 30% in 2014 to 12% in 2015, reaching total conformity in 2016. Indeed, monitoring quality can be improved over the years by enhancing social engagement, infrastructure, and observer's experiences (Freed et al, 2016;Sbragaglia et al, 2022). However, since most likely biased data from non-motorized canoes are from remote harbors, it is harder to improve monitoring quality over time.…”

A machine learning based image classification method to estimate fish sizes from images without a specified reference object

“…Yet, the advent of digital technologies means that over the last decade citizen science, angler smart phone applications, social media and other similar platforms have accumulated millions of georeferenced fish images. This data flow is increasing exponentially and has already been used to provide knowledge on invasive species, species redistributions or angler catches [11,12]. However, these data have not been used in more formal population assessments, largely because the relevant information on individual body sizes is lacking.…”