Propagule pressure, Allee effects and the probability of establishment of an invasive species (<i>Bythotrephes longimanus</i>)
Bibliographic record
Abstract
Predicting establishment of exotic species is a central goal of invasion biology, and is dependent upon propagule pressure and population processes. We introduced invading spiny water fleas, Bythotrephes longimanus at different propagule pressures into 19 experimental enclosures, following populations over asexual generations, resting egg production, and emergence in the following year. We integrated experimental results with field data to generate a stochastic population model, predicting establishment in relation to propagule pressure and introduction date. Our results suggested that Allee effects are operational at higher densities or smaller volumes than previously predicted, that stochasticity plays an important role in establishment, and demographic stochasticity may be correlated between individuals. Further, our novel theoretical derivations suggest that organisms should modify their sex ratios to reduce Allee effects. The functional form using adaptive sex-ratios was consistent with both mesocosm and field data. Despite the occurrence of Allee effects and stochasticity, there was still no date during the growing season where we predict lakes to be entirely safe from Bythotrephes invasions. A single propagule had approximately a 0.15 establishment probability in our mesocosms, if introduced early in the season; propagule pressures of 10 had > 0.50 probability, regardless of introduction date.
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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 | 0.001 |
| 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".