Phenology and Abundance of Northern Tamarisk Beetle, <i>Diorhabda carinulata</i> Affecting Defoliation of <i>Tamarix</i>
Bibliographic record
Abstract
Timing and spatial dynamics of tamarisk (Tamarix spp. L.) defoliation by the biological control agent Diorhabda carinulata (Desbrochers) were evaluated. Relative abundance of D. carinulata and the phenology of tamarisk along the San Juan and Colorado rivers were recorded in 2011–2012. D. carinulata began reproducing in the spring when temperatures were >15°C. Variation in spring temperature-rise affected the timing of development of larvae of the first summer generation and initial defoliation of tamarisk at each site. Shortening day lengths in mid- to late-summer cued D. carinulata to enter reproductive diapause resulting in cessation of defoliation. The critical day length for inducing reproductive diapause was 33–47 minutes shorter than that of populations of D. carinulata released into North America in 2001. Variation in spring temperature-rise combined with timing of shortening day length resulted in differences in D. carinulata voltinism per site. During the active season, larvae were less likely to establish in areas where defoliation was >70%. Lack of reestablishment of larvae led to temporary loss of D. carinulata from the locations and allowed tamarisks to sprout new canopies. Defoliation of tamarisk was dictated by environmental cues and abundance of D. carinulata, and in turn large amounts of defoliation negatively affected abundance of D. carinulata.
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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 | 0.000 | 0.000 |
| 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".