Within and Among Patch Variability in Patterns of Insect Herbivory Across a Fragmented Forest Landscape
Bibliographic record
Abstract
Fragmentation changes the spatial patterns of landscapes in ways that can alter the flow of materials and species; however, our understanding of the consequences of this fragmentation and flow alteration for ecosystem processes and ecosystem services remains limited. As an ecological process that affects many ecosystem services and is sensitive to fragmentation, insect herbivory is a good model system for exploring the role of fragmentation, and the resulting spatial patterns of landscapes, in the provision of ecosystem services. To refine our knowledge of how changes in landscape pattern affect insect herbivory, we quantified the combined influence of among patch (patch area and patch connectivity) and within patch (location within patch; canopy, edge, interior) factors on amounts of insect herbivory in a fragmented forest landscape. We measured herbivory in 20 forest patches of differing size and connectivity in southern Quebec (Canada). Within each patch, herbivory was quantified at the interior, edge, and canopy of sugar maple trees during the spring and summer of 2011 and 2012. Results show that connectivity affects herbivory differently depending on the location within the patch (edge, interior, canopy), an effect that would have gone unnoticed if samples were pooled across locations. These results suggest considering structure at both the patch and within patch scales may help to elucidate patterns when studying the effects of fragmentation on ecosystem processes, with implications for the services they support.
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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 | 0.001 | 0.001 |
| 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".