Historical trends in cranberry pest abundances and their dependence on temperature
Bibliographic record
Abstract
Pest monitoring is crucial for early pest detection so that growers can engage in effective crop protection and management actions. Insect pest levels and crop losses are expected to rise alongside a warming climate. However, the effects may be species dependent, affecting our ability to generalize crop-specific risks. Canada is the second largest producer of cultivated cranberries (Vaccinium macrocarpon) globally, with British Columbia (BC) and Quebec accounting for approximately 95% of the Canadian market. The blackheaded fireworm (Rhopobota naevana) is a major cranberry pest and has a long monitoring history in BC by integrated pest management (IPM) practitioners. In this study, I first examined whether daily minimum temperature and accumulated degree days (ADD, daily temperature gained overtime) for R. naevana increased during the study in the Lower Mainland of BC (1991 – 2020). Then, I aggregated 30 years of long-term IPM monitoring data from various cranberry farms and climate records to determine associations between the ecology of R. naevana and temperature. Specifically, I examined whether ADD influenced the date of initial emergence time, abundance of emerging larvae, and the date growers first sprayed to control R. naevana. Annual ADD and daily minimum temperature, as measured by regional weather stations, did not increase during the duration of the study, although there were clear periods of high and low temperatures associated with the timing of the El Niño-Southern Oscillation. I did not detect an association between ADD and date of initial emergence. However, ADD was associated with emerging larvae abundance and first spray date. Warmer years with higher ADD could lead to higher spring larvae emergence and delayed first spray date. The high variability in the dataset due to differences in farms surveyed within and across years, the limited resolution of weather stations, and the potential for sampling error and observer bias within a multi-year data set could limit our ability to adequately detect some temperature driven effects. Longitudinal observations from the same set of cranberry farms over several years would likely reduce sources of external variation and help to further our understanding of the relationships between pest outbreaks, climate, and ecosystem interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".