Using a temperature-dependent population model to predict the population growth rates of grass carp across North America
Bibliographic record
Abstract
• With available data models predict increased population growth with temperature. • Temperature dependence in other traits alters the direction of change in growth rate. • Adult survival is likely a good target for management action. • More temperature-dependent, species-specific data is needed for future research. Invasion risk and impact are related to the population growth rate of newly introduced species. We parameterized a temperature dependent age- and size-structured integral projection model (IPM) to predict the population growth rate of invasive grass carp ( Ctenopharyngodon idella ) in North America. We formulated models using available data on temperature dependence in the age at maturity and fecundity for grass carp and found a small increase in population growth rate at higher temperatures. However, these models did not include potential temperature-dependence in other life history variables (e.g., somatic growth rate, maximum size, and survival) for which there is no data specific to grass carp. Inclusion of simulated temperature dependence in these important variables can reverse the trend in population growth rate and temperature, depending on which combination of life history traits are temperature-dependent. Elasticity analysis highlighted adult survival as a good management target to keep population growth rates small in all cases. We suggest that future studies regarding climate impacts on population growth will require detailed study of the impacts of temperature dependence on various life history traits in order to reach robust conclusions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".