The effect of local habitat and spatial connectivity on urban seed predation
Bibliographic record
Abstract
PREMISE: During the last centuries, the area covered by urban landscapes is increasing all over the world. Urbanization can change local habitats and decrease connectivity among these habitats, with important consequences for species interactions. While several studies have found a major imprint of urbanization on plant-insect interactions, the effects of urbanization on seed predation remain largely unexplored. METHODS: We investigated the relative impact of sunlight exposure, leaf litter, and spatial connectivity on predation by moth and weevil larvae on acorns of the pedunculate oak across an urban landscape during 2018 and 2020. We also examined whether infestations by moths and weevils were independent of each other. RESULTS: While seed predation varied strongly among trees, seed predation was not related to differences in sunlight exposure, leaf litter, or spatial connectivity. Seed predation by moths and weevils was negatively correlated at the level of individual acorns in 2018, but positively correlated at the acorn and the tree level in 2020. CONCLUSIONS: Our study sets the baseline expectation that urban seed predators are unaffected by differences in sunlight exposure, leaf litter, and spatial connectivity. Overall, our findings suggest that the impact of local and spatial factors on insects within an urban context may depend on the species guild. Understanding the impact of local and spatial factors on biodiversity, food web structure, and ecosystem functioning can provide valuable insights for urban planning and management strategies aimed at promoting urban insect diversity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".