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Record W3006221282 · doi:10.1002/rse2.147

An unseen synchrony or recurrent resource pulse opportunity? linking fisheries with aeroecology

2020· article· en· W3006221282 on OpenAlexaff
Henry H. Hansen, Mark A. Pegg, Matthew S. Van Den Broeke, Doug Watkinson, Eva C. Enders

Bibliographic record

VenueRemote Sensing in Ecology and Conservation · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersNational Science Foundation
KeywordsMayflyEcologyForagingBiologyFreshwater fishPhenologyHabitatFisheryTemporal scalesNocturnalFreshwater ecosystemEcosystemFish <Actinopterygii>Nymph

Abstract

fetched live from OpenAlex

Abstract Understanding insect and fish interactions from a spatial and temporal perspective can have implications on large‐scale phenology in freshwater systems, yet current information is limited. We employed a novel approach of combining information from acoustic telemetry for six freshwater fish species and weather radar to assess the phenology of mayfly emergence and foraging patterns of freshwater fish. We hypothesized that freshwater fish conduct synchronous movements with annual mayfly hatches as a pulse resource opportunity. Generalized additive models were developed to assess movement distance as a function of species and time; before, during, and after annual mayfly hatch events. A cross‐section abundance index was also employed to quantify dynamics of aerial mayflies. Hatch dynamics revealed nocturnal emergence behaviour with annual variations in intensity, spatial extent, and origin. We found that the hatch was likely a pulse resource feeding opportunity for channel catfish, common carp, freshwater drum, and walleye instead of a synchronized feeding event. Bigmouth buffalo and lake sturgeon utilized riverine habitat away from the hatch and did not likely forage on the emerging mayflies. Remote sensing of fishes and emergent insects using our approach is the first attempt at bridging the capabilities of fisheries ecology and aeroecology to advance movement ecology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.964

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.230
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.

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

Citations10
Published2020
Admission routes1
Has abstractyes

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