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Record W4406693274 · doi:10.1093/fshmag/vuae001

Lessons learned from a new online dashboard about the sociocultural aspects of marine recreational fishing in British Columbia, Canada

2025· article· en· W4406693274 on OpenAlexafffundabout
Jesse S. Sayles, Natalie C. Ban, Kurtis E. Sobkowich, Pat Ahern, Owen Bird

Bibliographic record

VenueFisheries · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of GuelphUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaScience Foundation Ireland
KeywordsFishingRecreational fishingFisheryRecreationSociocultural evolutionGeographyCatch and releaseOceanographySociologyEcologyAnthropologyBiologyGeology

Abstract

fetched live from OpenAlex

As the saying goes, “You don’t manage fish; you manage people!” Nothing could be more true in recreational fishing, where fishers court a variety of fish species using lures, lines, boats, and bait. Recreational fishing is all about the social ties among friends, culture and tradition, and maybe bringing home fish to eat (Arlinghaus et al., 2019; Brownscombe et al., 2019; Hyder et al., 2020). Fishing also contributes to local economies through the sale of licenses, guided trips, and associate service and hospitality industries (Arlinghaus et al., 2019; Brownscombe et al., 2019; Hyder et al., 2020). Because of recreational fishing’s importance to fishers, communities, and fisheries management, we need to better understand the human dimensions of recreational fisheries (Cooke et al., 2019). Here we showcase our development of a dashboard that communicates results from a human dimensions survey of recreational fishers. This survey was motivated by the need to better understand the human dimensions of recreational fishing in coastal British Columbia (BC), Canada. The human dimensions of recreational fisheries science are understudied, yet essential for fisheries management (Holder et al., 2020). For example, closing areas to fishing can protect or enhance local fishing stocks and ecosystems by reducing fishing pressure, but may shift pressure elsewhere as fishers seek opportunities to fish, impacting both the environment (e.g., increased local fishing pressure) and sociocultural benefits of fishing (e.g., loss of enjoyment due to crowding and competition; Brownscombe et al., 2019). In turn, this loss of opportunity can motivate some to work to enhance fishing conditions and opportunities through citizen science and marine stewardship, while others may become discouraged and stop fishing (Brownscombe et al., 2019). Understanding recreational fishers’ motivations, values, and behaviors is, thus, important for good fisheries management. We developed a survey (UVic ethics approval #21-0534) to provide such information for coastal BC, where approximately 320,000 fishers purchase tidal saltwater recreational fishing licenses annually (see https://bit.ly/3XgxBIw). We developed and administered an online survey (n = 1,918 responses) following the 2022/2023 fishing season (April to March) to examine fishers’ activities, motivations, and beliefs about management, perceived impacts of management measures, and involvement in marine stewardship and citizen science in BC (Sayles et al., unpublished). Our project was a collaborative effort between researchers at the University of Victoria (UVic) and the Sport Fishing Institute of British Columbia (SFI), a nonprofit fishing society with the goal of ensuring and promoting sustainable natural resource use and sustainable angling opportunities. In order to communicate and disseminate the 2023 survey results, we developed an interactive, publicly available, online dashboard (Figures 1 and 2) that allows users to view data summaries, maps, and graphs, as well as filter and compare results by different criteria (e.g., participant demographics, species targeted, region fished, and fishing method). We chose a dashboard to communicate the results in order to allow people to explore the data beyond what would be possible in a static report (e.g., applying multiple custom filters and making group comparisons). The dashboard is available at https://recfish.uvic.ca/. We used several steps to develop and learn from the process of creating the dashboard (Figure 3). Screen capture of the online dashboard showing statistics of fisher’s preferred target species and if the catches were kept or released. Screen capture of the online dashboard showing a stacked bar graph ranking the importance of different motivations for recreational fishing. Infographic about the development and dissemination of the dashboard as well as a QR Code for the online dashboard. We started the dashboard by developing a “requirements document,” a common software and app development approach. The requirements document outlined what the dashboard must do, and what it must not do. The latter is very important to help avoid costly project scope creep (both in terms of time and money). It is also important to discuss any concerns over data hosting, privacy, access, software updates, and maintenance. Preparing the requirements document helped us ­narrow down how we would develop the dashboard and where we would host it. One of SFI’s concerns was about long-term hosting because there was no funding to support the project beyond our research grant cycle. Therefore, we did not pursue any subscription-based third party software and cloud hosting options and looked for alternatives. We ended up using open source RShiny (https://shiny.posit.co/)—a programming language that combines the computational powers of the R open source computing environment with modern web development languages (e.g., HTML and CSS). Almost any kind of data can be analyzed in R and RShiny. For example, our project utilized data about species caught, locations fished, fishing trip type, as well as data about values and beliefs recorded on ordinal data scales. With RShiny, users can develop custom visualizations and user interfaces, implement data filters and other ways of interacting with the data (e.g., geospatial map queries), and perform a wide variety of data analysis and summaries on the selections. We were fortunate to benefit from UVics’s membership in the Digital Research Alliance of Canada, BC DRI Group (alliancecan.ca), which provided free internet server infrastructure and technical support for hosting our RShiny dashboard. Without this support, we may still have had to pay for RShiny dashboard hosting, though it is possible for individuals and institutions to set up their own RShiny servers (see https://bit.ly/3zdbMln). While the UVic researchers did some of the RShiny dashboard development in-house, we also hired a third party developer with RShiny expertise. They developed more advanced ­features for our dashboard and were able to do so faster than the UVic research team. Following the recommendations of others that have written on developing digital tools, including for environmental monitoring and conservation (Andrachuk et al., 2019; Siegler et al., 2021; Teacher et al., 2013), the UVic research team included someone with RShiny coding and project management experience (author J.S.), which helped keep overall development costs down. This team member developed initial prototypes of the dashboard in-house, did product testing and quality control, and, in general, was able to communicate and collaborate with the third party ­developer (author K.S.) on both a project management and technical level. Once we developed and tested the dashboard, we communicated its existence widely. We presented it at the 2024 World Fisheries Congress in Seattle, to the Sport Fishing Advisory Board, which advises the Canadian fisheries management agency Fisheries and Oceans Canada, as well as to colleagues in the agency. We also sent a summary of the dashboard and URL link to all survey participants who provided their email addresses, posted it on the SFI’s social media, listserve, and Website, and wrote this essay. At the time of this writing, a manuscript showcasing the results of the survey is near completion. Initial feedback about the survey has been positive. It must be noted, however, that the following accounts are anecdotal and not a systematic evaluation of the tool. In our conversations, several government research colleagues noted that the survey results provide novel insights about questions they had and for which they were unable to collect data about because of certain limitations on the kinds of questions and surveys that the government can administer. Several recreational fishers that we spoke with said that the data really resonated with them and helped them better understand trends in the fishing community that they had assumptions about, but had yet been able to confirm with hard data. We learned several lessons along the way that can help others looking to do similar work. For example, since the developer (K.S.) was hired at the end of the project to work on the dashboard, and was not a fisheries expert, they were less familiar than the UVic/SFI research team with the survey content and subject matter. Small steps, such as providing the developer with an outline of the survey logic (e.g., if any responses led to questions being added or skipped) can help ensure that metrics and indices are accurately calculated. In our case, for example, people who said they fished in the past year were asked several additional questions that were not shown to people who did fish in the past year; thus, it was important to make sure that summary statistics and dashboard displays dynamically accounted for relative sample size. Likewise, having the developer document the inputs to calculations and indices in an easy-to-follow flow chart can help the project team understand what is going on “under the hood” and avoid miscommunications. Following industry best practices, we also piloted early versions of the dashboard with a small sample of potential users and made adjustments based on their feedback. We anticipate that the data and dashboard provide fisheries managers and the recreational fishing community with valuable insights into possible human responses to fisheries management measures that can inform the decision-making process. Additionally, we hope that our successful dashboard project can inspire other research groups interested in developing similar tools. This project was supported by the BC Salmon Restoration and Innovation Fund (BCSRIF), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Social Sciences and Humanities Research Council of Canada (SSHRC), the University of Victoria, the Sport Fishing Institute of British Columbia (SFI), and the BC DRI Group and the Digital Research Alliance of Canada (alliancecan.ca).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.224
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2025
Admission routes3
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