MétaCan
Menu
Back to cohort
Record W7133282526

Trapping Methods for the Invasive European Green Crab in Canada

2022· other· en· W7133282526 on OpenAlexaboutno aff
Cynthia H. McKenzie, Kyle Matheson, Philip S. Sargent, Michael Piersiak, Renée Y. Bernier, Nathalie Simard, Thomas W. Therriault

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTrap (plumbing)HabitatInvasive speciesIntroduced speciesTrappingEcosystem
DOInot available

Abstract

fetched live from OpenAlex

European Green Crab (EGC; Carcinus maenas) is a voracious aquatic invasive species (AIS) that threatens Canada’s Atlantic and Pacific marine and estuarine ecosystems. It preys on and competes with commercial and recreational shellfish, negatively impacts commercial fisheries, and destroys ecologically- and biologically-significant habitat for native species. Fisheries and Oceans Canada (DFO) has developed substantial knowledge of EGC, particularly regarding trapping for early detection, monitoring, research, and physical removal for control. Information on trapping method considerations (deployment, environment, behaviour, catch) and goal focussed protocols for different objectives including control measures and mitigation strategies has been compiled to provide advice on detection and control of EGC. Trapping EGC is critical for early detection, determining impacts on native species and habitat, and rapid response and control efforts to prevent ecosystem degradation and commercial fishery loss. A review of 69 peer reviewed studies and unpublished projects on EGC trapping were reviewed to examine trap types used in Canada (46 studies) and elsewhere (23 studies). Fifteen traps were categorized by type and usage in Canada and 13 additional traps that were used in North America and other parts of the world. The Fukui collapsible crab trap was the most utilized trap in Canada based on the review. Other traps have proven effective and direct trap type comparisons have been conducted in several regions. Trap selection must consider the trapping objective, in particular, the targeted portion of the EGC population, as some trap types can disproportionately catch large adult EGC due to trap design and intraspecific EGC behaviours. Several factors are key in selecting an appropriate trap type based on trapping objectives, which can include habitat type, depth and site location, deployment method, life stage of targeted population, bycatch considerations and available resources. Trapping is an effective methodology for monitoring relative changes in EGC abundances and population dynamics, including changes in co-occurring native species (e.g., rock crab, lobster, and some fish depending on the trap type) that may be impacted by the invasion. Trapping for rapid response and control can effectively reduce EGC numbers and alter population dynamics. Outcomes could include reduction of EGC mean body size and recovery of impacted native species and habitat, but trapping efforts may need to be sustained to maintain low impacts of EGC on ecosystem components. Knowledge gaps identified include a lack of information on trapping juvenile EGC and determining effective threshold levels or numbers for control to prevent environmental and fishery impacts. This EGC trapping advice could be incorporated by managers into a decision making tool for guiding action related to early detection, rapid response, and control management activities.

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)0.006
metaresearch head score (Gemma)0.015
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 score0.147
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du CanadaFrench-language works237,207