Using classification tree analysis to reveal causes of mortality in an insect population
Bibliographic record
Abstract
Invasive species pose significant threats to native and managed ecosystems. However, it may not always be possible to perform rigorous, long‐term studies on invaders to determine the factors that influence their population dynamics, particularly when time and resources are limited. We applied a novel approach to determine factors associated with mortality in larvae of the sawfly Profenusa thomsoni Konow, a leafminer of birch, and a relatively recent invader of urban and rural birch forests in Alaska. Classification tree analysis was applied to reveal relationships between qualitative and quantitative predictor variables and categorical response variables in a large data set of larval mortality observations. We determined the state (living or dead) of sawfly larvae in samples of individual leaves. Each leaf was scored for variables reflecting the intensity of intra‐specific competition and leaf quality for leafminers, year of collection and degree‐days accumulated were recorded for each sample. We explored the association of these variables with larval state using classification tree analysis. Leafminer mortality was best explained by a combination of competition and resource exhaustion and our analysis revealed a possible advantage to group feeding in young larvae that may explain previously observed patterns of resource overexploitation in this species. Dead larvae were disproportionately found in smaller leaves, which highlights the potential effect of competition on mortality and suggests that smaller‐leaved species of birch will better able to resist leafminer damage. We show that classification tree analysis may be useful in situations where urgency and/or limited resources prohibit traditional life‐table studies.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".