Habitat and Time Are More Important Predictors of Weed Seed Predation than Space on a Diversified Vegetable Farm in Maine, USA
Bibliographic record
Abstract
Postdispersal weed seed predation is a significant source of weed mortality in agroecosystems. The magnitude of seed predation, however, is variable. Understanding the relative importance of factors driving variability in seed predation rates will increase the potential utility of seed predation to farmers. We conducted landscape-scale field experiments to quantify and compare the effects of space, time of sampling, and habitat on weed seed predation. Seed predation assays, with and without vertebrate exclosures, measured seed predation rates at spatially explicit sample sites across 8.5 ha of crop and noncrop habitats on a diversified organic vegetable farm in Maine. Total and invertebrate seed predation averaged 8% and 3% d−1, respectively. Vertebrate seed predators detected by motion-sensing cameras included small mammals and birds. A ground beetle,Harpalus rufipes, was highly dominant in pitfall traps, comprising 66% of invertebrate seed predators captured within crop fields. Seed predation was randomly distributed in space. However, time of sampling and habitat were highly significant predictors of seed predation. Variance partitioning indicated that habitat factors explained more variation than did time of sampling. Total seed predation was greater in crop and riparian forest habitats than in mowed grass, meadow, or softwood forest. Generally, invertebrate seed predation was greatest at sites with an intermediate degree of vegetative cover, whereas habitat type was the chief biotic determinant of vertebrate seed predation rates. These results suggest cover cropping and wetland conservation as practices that may bolster seed predation rates.
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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".