Exogenous and endogenous factors acting on the spatial distribution of a chrysomelid in extensively managed blueberry fields
Bibliographic record
Abstract
Abstract The role of endogenous (i.e. limited dispersal, intraspecific competition, aggregation) and exogenous (i.e. resource patchiness, heterogeneous landscapes, spatially structured habitat) factors on the spatial distribution of herbivores can be inferred from theoretical models in intensively managed or heterogeneous landscapes but not in extensively managed crops. In the present study, we examined aggregation patterns and the influence of environmental and spatial factors on the distribution of Altica sylvia M alloch larvae within extensively managed blueberry fields to determine how exogenous and endogenous factors affect this defoliator. Altica sylvia larvae and defoliation exhibited clumped within‐field distributions. A lack of correspondence between larval density and defoliation indicated that oviposition habitat selection is occasionally suboptimal in this species. Clumped distribution patterns were not explained by endogenous factors or associations with spatially structured variables. Conversely, exogenous factors acting at the patch scale affected distribution patterns because larvae were less abundant in patches located close to forest edges, as well as in patches where weeds or a nonhost blueberry plant occurred. As a result of exogenously‐produced variability in A. sylvia spatial distribution, we suggest that focused sampling methods be used to monitor this herbivore. We emphasize the need for similar assessments for herbivores that forage in extensively managed crops or somewhat heterogeneous monocultures.
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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".