Mate‐finding allee effect in spruce budworm population dynamics
Bibliographic record
Abstract
Abstract Allee effects can cause populations to decline due to decreasing population growth rates with decreasing density and play a major role in population dynamics. Mate‐finding failure, a common mechanism contributing to demographic Allee effects, is usually difficult to demonstrate because of the arduous nature of sampling individuals at very low densities. In a rising outbreak of the eastern spruce budworm, Choristoneura fumiferana (Clemens) (Lepidoptera: Tortricidae), we used caged and tethered virgin females in traps to measure mating success over population densities ranging from deep endemic to outbreak conditions. We found that mating success increased with increasing population density, and that at endemic population densities, females experienced difficulties attracting males and mating, demonstrating for the first time a mate‐finding Allee effect in the spruce budworm. The relationship between population density and mating success is nonlinear. As population density increased, the proportion of mated females eventually reached a plateau and mating success was not 100% even at the highest moth densities, probably due to female reluctance to mate and perhaps interference competition by males for access to females. Both laboratory‐reared and wild females were equally effective in synthesizing pheromone, attracting males, and mating. Our results strongly suggest that a mate‐finding Allee effect is involved in maintaining low‐density spruce budworm populations below an Allee threshold where they fail to grow. Factors such as changes in predation pressure and immigration could help populations overcome this Allee threshold.
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 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 | 0.000 |
| 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".