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Record W4290102368 · doi:10.1139/cjfas-2022-0095

Poor downstream passage at a dam creates an ecological trap for migratory fish

2022· article· en· W4290102368 on OpenAlexvenueno aff
Haley A. Ohms, Dereka Chargualaf, Gabriel T. Brooks, Cory Hamilton, Eric P. Palkovacs, David A. Boughton

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersSouthwest Fisheries Science CenterNational Oceanic and Atmospheric Administration
KeywordsFish migrationDownstream (manufacturing)Spawn (biology)SpillwayFisheryEnvironmental scienceHabitatEcologyPopulationTrap (plumbing)Rainbow troutFish <Actinopterygii>BiologyGeology

Abstract

fetched live from OpenAlex

Ecological traps can be caused when partial restoration leads organisms to make maladaptive habitat choices. One example of this is fishways (e.g., fish ladders) that provide upstream passage at dams but are not paired with adequate downstream passage. We tested the hypothesis that attracting anadromous fishes to spawn above a dam, but blocking downstream passage of their offspring leads to an ecological trap. Using passive integrated transponder (PIT) tags, we monitored the movements of steelhead ( Oncorhynchus mykiss) at a dam and fishway on the central California coast. We found that downstream passage for juveniles and kelts was limited by four factors: migration delay, loss in the reservoir, avoidance of the downstream bypass, and water depths on the spillway. Based on the spillway-passage depth thresholds, we estimated that the ability for fish to pass downstream was limited to only half of the migration season in 55% of the past 20 years (2002–2021). Our results support the ecological trap hypothesis, which may explain why restoration using fishways has failed to produce recovery gains in this population and elsewhere.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.213
Teacher spread0.195 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→