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Record W4416592830 · doi:10.1093/fshmag/vuaf096

Angler intel on the state of recreational fisheries

2025· article· en· W4416592830 on OpenAlexaff
Joel Zhang

Bibliographic record

VenueFisheries · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsCarleton University
Fundersnot available
KeywordsState (computer science)Recreational fishingFishingRecreationFisheries management

Abstract

fetched live from OpenAlex

While not often considered in this way, recreational fishing may be contributing to “hidden” declines (where declines are happening over time but are unnoticed). Catch-and-release fishing, popularized in the 1970s, aims to protect fish as they are released instead of harvested; thus, theoretically allowing fish populations to be maintained for the benefit of ecosystems and human recreation. Despite this, there is now substantial evidence that catch-and-release fishing can cause some level of delayed mortality and other potential impacts, including changes to behavior, growth, and reproduction. Over time, these impacts contribute to “hidden declines” within a population and render certain catch-and-release fishing regulations ineffective. To safeguard a fishery against hidden declines, it is important to conduct population monitoring to assess long-term trends. For various reasons, not all exploited fish populations are monitored, due to inherent technical challenges or lack of resources. In Ontario, Canada, this is the case for Largemouth Bass Micropterus nigricans and Smallmouth Bass M. dolomieu. Although there are monitoring programs for fish populations in Ontario (which is itself remarkable, given the vast number of water bodies), the methods used are not optimal for monitoring the status of populations of black bass Micropterus spp. Alternative monitoring strategies, such as the use of knowledge from long-time anglers (serving as key informants), has proven to be useful in a variety of places around the world, and is often used for hard to monitor species. With a lack of monitoring for black bass in Ontario, we used knowledge from anglers to determine if they are perceiving changes in the populations (abundance and size) of Largemouth Bass and Smallmouth Bass. Anecdotal reports of decreases in black bass size and ­numbers have existed for some time, and this is the first study that aimed to assess the validity of these reports. To that end, a survey was distributed to anglers that target black bass on various waters in eastern Ontario. As part of the survey, we asked each angler to rank their fishing experience on each lake over different time periods. In total, 354 people responded to the survey. We determined that across the past 50 years, the average perceived number and size of both Largemouth Bass and Smallmouth Bass has decreased across all 16 of the study waters, with sharp decreases occurring after 2005. Through previous research studies, we know that catch-and-release fishing has impacts on the number of surviving juvenile fish in our study area and that an increasing level of illegal fishing could have caused some of these declines. Linking studies that monitored the impacts of fishing during the spawning season with the survey results showing that anglers perceive a decline in Ontarian black bass fisheries offers unique insights for assessing the overall health of black bass populations. This leads to two main conclusions: (1) Monitoring programs need to do a better job of assessing impacts and trends, and show how even catch-and-release angling for black bass can impact overall health of the population, and (2) the use of local and expert knowledge provides valuable insight, especially when monitoring is insufficient or doesn’t exist. To avoid situations where we are “too little, too late,” we may need to use local knowledge from key informants to stay ahead of potential declines. It is only with these insights that we can begin to implement effective management interventions that benefit fish and people. I would like to acknowledge and thank Dr. Steven Cooke, Dr. David Philipp, Julie Claussen and Dr. Cory Suski for their work in recreational fisheries and their support for the use of alternative monitoring and management.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0290.003

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 head0.020
GPT teacher head0.243
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractno

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