Assessing density-dependent establishment and dispersal: an example using caddisfly larvae
Bibliographic record
Abstract
Density dependence in colonization is poorly understood. We studied colonization by a benthic, stream-dwelling caddisfly, Hydropsyche slossonae, through experiments varying conspecific densities and environmental conditions. A model of larval acceptance or rejection of a locality (dispersal from the locality) was developed and fitted to the data to estimate the relative strengths of density-dependent and density-independent processes underlying dispersal. In spring and fall, we varied density, substrate size, and current velocity in laboratory experiments and varied density in field experiments. In the laboratory, dispersal of fifth instars was always density dependent, but the strength of density dependence was highest in spring when the proportion dispersing was lowest. Dispersal in field experiments was density dependent only in spring. Proportion dispersing was highest under low flow. The model fit to laboratory data suggested that stronger density dependence in spring occurred because of a reduction in density-independent dispersal stimuli with no change in density-dependent stimuli. In contrast, a change in density-dependent stimuli did appear to cause differences between the proportions dispersing under the two flow regimes. The model reveals the potential for density-independent dispersal stimuli to modify the strength of density dependence detectable at the population level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".