Stability of native and exotic lady beetle populations in a diverse landscape
Bibliographic record
Abstract
Abstract The stability of populations is a central, yet challenging concept in ecology. Here, we examine the stability of annual abundance for populations of nine native and four exotic lady beetle species (Coccinellidae) in a mixed landscape in Michigan, USA, over 23 yr. The species have similar life histories, live together in the same habitats, and have been disturbed by the sequential introduction of exotic lady beetle species. The publicly available dataset was collected in a diverse landscape of three forest habitats, four annual crop habitats with different intensities of management, and three perennial crops. Replicated 1‐ha plots were sampled weekly with yellow sticky cards during the summer season for each habitat, three plots for each of the three forest types and six plots for each of the seven crop habitats. We quantified stability for each species and each species by habitat combination, using three parameters: trends in abundance, persistence, and temporal population variability. The most abundant lady beetle species were more stable than the uncommon and rare species. Habitat generalists had more stable population dynamics than specialists, but habitat breadth was confounded with lady beetle abundance. Exotic and native species with similar abundances had similar levels of stability. Exotic species disrupted the native populations, leading to declines in abundance for eight of nine species and increased population variability for the native species. The abundances of exotic lady beetles rose quickly to the long‐term mean. Our findings support the hypothesis that populations exhibit species‐specific levels of stability, not greatly affected by stochastic variations in environmental conditions or by habitat characteristics. We also show that exotic species can decrease the stability as well as the abundance of native species in a community of lady beetles.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".