Fish spawning events stimulate trophic hotspots across freshwater food webs
Bibliographic record
Abstract
Project title: Fish spawning events stimulate trophic hotspots across freshwater food webs Location: Smoke Lake, Algonquin Provincial Park, Ontario Canada; Lake Opeongo, Algonquin Provincial Park, Ontario Canada; north-temperate fish populations Data Types: (1) qPCR assay data (run on benthic macroinvertebrate and fish samples collected in the field); (2) acoustic telemetry data (detections from individual receivers); (3) standardized natural history observations (collected from both motion activated camera traps and observers in an observation blind); (4) literature review of egg predation interactions between north-temperate freshwater fishes Timeline of Data Collection: (1) Samples collected in 2023, assayed in 2023-2024; (2) March to June, 2023; (3) May 2008 to August 2015; (4) no time restriction imposed Focal Taxa: (1) fish: smallmouth bass (Micropterus dolomieu), burbot (Lota lota), yellow perch (Perca flavescens), white sucker (Catostomus commersonii), common shiner (Luxilus cornutus), creek chub (Semotilus atromaculatus), bluntnose minnow (Pimephales notatus), brown bullhead (Ameiurus nebulosus), northern pearl dace (Margariscus margarita), northern redbelly dace (Chrosomus eos); benthic macroinvertebrates: Anisoptera, Decapoda, Ephemeroptera, Megaloptera, Plecoptera, Trichoptera; (2) Smallmouth bass and white sucker; (3) All species that visited sucker spawning sites; (4) over 30 species of fish that provision egg resources and over 40 species of fish that predate egg resources Methods of Data Collection: (1) Fish and benthic macroinvertebrates were sampled across three creek deltas in Smoke Lake, Ontario, Canada. At each site, authors deployed Gee (1 inch) and larger (1.5-2 inch aperture) funnel minnow traps baited with chicken-flavoured dog kibble for capturing small-bodied fishes, then rod-and-reel angling was used to capture large-bodied fishes, and dip netting and snorkelling was used to capture benthic macroinvertebrates. (2) Fish had previously received acoustic telemetry tags (69 kHz Innovasea acoustic transmitters) through surgical implantation, providing a total of 15 white sucker and 19 smallmouth bass tagged in the Smoke Lake system (including Smoke Lake, Tea Lake, and Canoe Lake). Acoustic telemetry receivers (Innovasea VR2W omnidirectional receivers) were present at each of the three sites and were recording detections of acoustically tagged fish throughout all of 2023. We focused our analysis on the period of white sucker arrival and departure associated with spawning activity (March to June, 2023). (3) Motion activated camera traps were deployed from early-mid May to early-mid June between 2008 to 2015 to capture animals visiting the sucker spawning aggregation at Wright Creek, Opeongo Lake, Ontario Canada. All photographs were reviewed by Ontario Ministry of Natural Resources and Forestry staff to identify species present and qualitatively describe observed behaviour. Observations were also recorded from an observation blind, occupied with one to two observers.During blind observations, all avian and terrestrial species that could be observed were recorded, regardless of whether they were observed to feed on suckers and eggs. We identified and counted all individuals present at the stream within the field of view every 10 minutes and used the intervening period to carry out additional focal animal sampling; (4) We conducted a review of the literature through Google Scholar, ISI Web of Science, and Scopus to investigate the prevalence of egg predation and the provisioning of egg resources (to other fish consumers) across north-temperate freshwater fishes. We then extracted approximate spawning and egg development timing information from Scott and Crossman (1998) to provided information on potential availability of egg resources for fish consumers. File Library [Metadata tabs are included in all data files]: Code: make_egg_predation_figure.R; Project-specific Data files: (1): Sucker_UpdatedData.xls; (2) Sucker_Site.xls, Bass_Site.xls, Ragged_detect.xls; (3) NaturalHistoryObservations.xlsx; (4) Egg_Portfolio.xls, EggPredFigure_new_obs.xls
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".