Relative Importance of Nesting Habitat and Measures of Connectivity in Predicting the Occurrence of a Forest Songbird in Fragmented Landscapes Importance relative de l'habitat de nidification et du degré de connectivité comme prédicteurs de l'occurrence d'un passereau forestier en paysages fragmentés
Bibliographic record
Abstract
Theoretical and empirical studies suggest that well-connected networks of forest habitat facilitate animal movement and contribute to species' persistence and thereby the maintenance of biodiversity. Many structural and functional connectivity metrics have been proposed, e.g., distance to nearest neighboring patch or graph-based measures, but the relative importance of these measures in contrast to nesting habitat at fine spatial scales is not well established. With graph-based measures of connectivity, Euclidean distances between forest patches can be directly related to the preferred gap crossing distances of a bird (functional connectivity). We determined the relative predictive power of nesting habitat, forest cover, and structural or functional connectivity measures in describing the breeding distribution of Hooded Warblers (Setophaga citrina) over two successive breeding seasons in a region highly fragmented by agriculture in southern Ontario. Logistic regression models of nesting occurrence patterns were compared using Akaike's information criterion and relative effect sizes were compared using odds ratios. Our results provide support for the expectation that nest-site characteristics are indeed related to the breeding distribution of S. citrina. However, models based on nesting habitat alone were 4.7 times less likely than a model including functional connectivity as a predictor for the breeding distribution of S. citrina. Models of nest occurrence in relation to surrounding forest cover had lower model likelihoods than models that included graph-based functional connectivity, but these measures were highly confounded. Graph-based measures of connectivity explained more variation in nest occurrence than structural measures of forest connectivity, in both 2004 and 2005. These results suggest that S. citrina selected nesting areas that were functionally connected at their preferred gap crossing distances, but nesting habitat was a critically important predictor of nest occurrence patterns.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".