The spatial and temporal distribution of avian stick nests across a managed forest
Bibliographic record
Abstract
Stick nests (as created by several forest dwelling birds) are valuable habitat features. Consequently, forest management practices in Western Canada often call for stick nests and the surrounding habitat to be conserved where possible. I examined historical distributions of stick nests across a working-forest landscape in west-central Alberta, to determine if locations as amassed by forest workers (1999 -2017) appeared randomly-distributed across the landscape or were biased towards specific habitat metrics, and if so, did these metrics change over time? I worked with three sets of data compiled from 1999, 2003, and 20152017, respectively. Biologically relevant and important management habitat metrics were compiled using the most relevant GIS layers corresponding to the years of stick-nest reporting. These metrics were calculated at five spatial scales: 25 m, 50 m, 100 m, 250 m, and 500 m. Identical data were collected from generated random (reference) sites paired with each stick nest site. \nI used conditional logistic regression to isolate the best predictors of stick nest occurrence in each time period, at each spatial scale. Models were successfully fitted for four of five spatial scales only in the 1999 time period. Deciduous cover was found to be a strong explanatory variable for stick nest locations at the 25 m and 50 m scale. Increased area of land-use (primarily oil and gas developments) and a high component of deciduous cover were found significant at the 100 m scale. The model generated for the 500 m scale indicated an increased likelihood of stick nests in areas with increased area of land-use, probably a result of both nesting behaviour and observer effects. The results of this study did not support the notion that habitat metrics associated with stick nests have remained constant (or changed) between 1999 and 2017 in the forest management area. A consistent and more thorough stick-nest monitoring program is likely required to fully understand the factors (natural and anthropogenic) linked to the conservation of stick nests across a working-forest landscape. Moreover, investing in these monitoring programs may help improve sustainable management practices over time by enhancing understanding of the complex influences of landscape management on raptor nesting behaviour.
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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 | 0.000 | 0.000 |
| 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".