Bioenergetics and Population Dynamics of Flannelmouth Sucker and Bluehead Sucker in Grand Canyon as Evidenced by Tag Recapture Observations
Bibliographic record
Abstract
Abstract Flannelmouth suckers Catostomus latipinnus and bluehead suckers C. discolobus in the Grand Canyon of the Colorado River are among the few native species to persist after river conditions changed with the completion of Glen Canyon Dam in 1963. To evaluate the effectiveness of adaptive management manipulations to the system for recovering native species, it is necessary to estimate basic demographic and population dynamic parameters for the species of interest, such as growth, mortality, and recruitment. With this in mind, we present a two‐stage analysis in which we first estimated growth and individual ages using a field‐based bioenergetics model and then input the age estimates into age‐structured mark–recapture (ASMR) models to estimate mortality and recruitment. The analysis was based on 18,500 flannelmouth suckers and 13,975 bluehead suckers that had been tagged with passive integrated transponder tags in the main‐stem Colorado River in Grand Canyon and its tributaries since 1989. The bioenergetics estimates of growth suggest that the growth of flannelmouth suckers is faster and that of bluehead suckers slower than in the tributaries reported in other studies. The results from the bioenergetics model provide necessary parameters for input into future ecosystem models. The ASMR models that include the effects of age‐specific gear vulnerability indicate that annual natural mortality rates are about 0.2 for flannelmouth suckers and 0.4 for bluehead suckers, which is in agreement with independent estimates from growth parameters and longevity. The estimates of age‐1 sucker recruitment and adult abundance correspond well with independent electrofishing catch rate data. The estimated recruitment and abundance estimates provide insights into the efficacy of adaptive management actions targeted to benefit native fishes over the past two decades.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".