Edge Effects in the Great Tit: Analyses of Long‐term Data with GIS Techniques
Bibliographic record
Abstract
In contemporary fragmented landscapes, edges are commonplace, and understanding the effects of edge environments is thus essential for the conservation of forest communities. The reproductive output of forest passerines is often reduced close to forest edges. Possible explanations include overcrowding by conspecifics, elevated rates of predation, and the occurrence of lower-quality habitat and/or individuals at forest edges. We attempted to separate these processes by examining edge effects in the absence of nest predation and by effectively controlling for differences in breeding density and the quality of habitats and individuals. We used an edge distance index (EDI), which accounts for the number and distribution of edges in close proximity to a breeding location, to help explain variation in breeding density, nesting success, and reproductive traits of 8308 pairs of Great Tits (Parus major) breeding between 1965 and 2005, in Wytham, near Oxford, United Kingdom. Results from linear mixed modeling confirmed higher breeding density and a higher proportion of immigrant individuals at forest edges. Nevertheless, independently of these effects, we also found that birds laying later, with smaller clutches but larger eggs, were typical of edge environments. The number of offspring recruited to the breeding offspring per breeding attempt was also reduced at edges, both directly and mediated through changes in clutch size and laying date. Edge effects on life histories were detectable within individual females and up to 500 m from the woodland edge. Woodland edges are increasingly common in contemporary fragmented landscapes. Therefore these results, which suggest a pervasive effect of edges on reproduction, are of considerable importance to the management and conservation of forest communities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".