Modelling the occupancy of two bird species of conservation concern in a managed Acadian Forest landscape: Applications for forest management
Bibliographic record
Abstract
Measuring and modeling bird occupancy in managed forest landscapes can provide useful information for achieving sustainable forestry practices and for informing conservation. Occupancy models are useful tools to describe the habitat use of species and predict how changes in the habitat and landscape may affect future occupancy. Specifically, these models can help support changes in forest management practices to improve habitat quality for species, including species of conservation concern. We used occupancy modelling to quantify the habitat use of two species of conservation concern, Canada warbler (Cardellina canadensis) and olive-sided flycatcher (Contopus cooperi) in the managed Black Brook district forest in northwestern New Brunswick, Canada as case studies to demonstrate an approach for improving sustainability of forest management and improving outcomes for species including those of conservation concern. Bird observations were collected during the breeding season using autonomous recording units over multiple days in plots stratified by forest type and development stage. We extracted environmental variables from forest resource inventory, a digital elevation model, and LiDAR data and classified variables as informing composition, structure, and landscape. The landscape variables were made up of composition and structure variables measured at extents beyond the sampling area. The AIC supported model for Canada warbler included percentage of softwood (+), wetness (+), understory cover(+) and heterogeneity of canopy height (+). However, some of these relationships were weak, with 95 % confidence intervals that included zero. The AIC supported models for olive-sided flycatcher included percentage of softwood (+), understory(+), wetness(+), stand height variation(+) and heterogeneity at 6 m height (-), although none of the variables had 95 % confidence intervals excluding zero. Models for both species were able to effectively classify habitat suitability, and met the criteria for acceptable classification performance. For both species, none of the supported models included landscape level variables. We recommend monitoring areas where the occupancy is predicted to be the highest and uncertainty is lowest to improve identification of occupied habitat. This habitat should then be integrated into forest management planning to support the persistence of these species. Results from the models also suggest habitat features that can be created through forest management to increase the overall amount of good quality habitat for these species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".