Birds and wine grapes: foraging activity causes small‐scale damage patterns in single vineyards
Bibliographic record
Abstract
Summary Crop damage caused by pest bird species is an economic problem in agricultural areas world‐wide. Previous research in North America has focused on estimating regional loss of yield for economic purposes, and largely ignored small‐scale variation in crop damage. If a bird depredation problem is perceived, farmers need to know how to identify areas of their farms that are most susceptible to bird damage so that they may focus their deterrent efforts most efficiently. We developed sensitive sampling and analysis techniques to allow the identification of spatial and temporal patterns in bird damage to wine grapes at the level of single vineyards. We used visual estimation techniques and novel data collection and management procedures to detect small‐scale spatiotemporal patterns in bird damage to Baco Noir and Vidal (ice‐wine) grape varieties in the St Catharine’s area of Ontario, Canada, during the 1998 and 1999 ripening seasons. We detected three overall trends in study vineyards: (i) bird damage was greatest on the edges of vineyards and decreased with distance towards the centre; (ii) bird damage was vertically stratified in vineyards, with grape clusters near the top of vines sustaining more damage than those close to the ground; and (iii) bird damage increased at specific times during the ripening season. An exotic species, the European starling, was responsible for most of the crop damage. Starlings foraged by making short forays into vineyards from perches in adjacent vegetation. This kind of foraging behaviour was reflected in the spatial damage patterns measured in our study plots. We suggest that the data presented here are more useful than estimates of total loss of yield to the managers of individual farms, because they identify the areas of vineyards most susceptible to bird damage. Future field experiments should evaluate the utility of focusing deterrent measures only in the most highly susceptible areas of crop fields. More detailed knowledge of where birds concentrate their foraging efforts, when crops become susceptible, and which species are responsible will allow farmers to focus their deterrent efforts most effectively, while attenuating conflict with non‐offending species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".