Nest-site availability, selection and reuse in a cavity-nesting community in forests of interior British Columbia
Bibliographic record
Abstract
Cavity nesting communities are structured in a complex hierarchy of interdependencies based on the creation of and competition for nest-sites. This structure has been called a nest web. Although cavities are persistent and may be used multiple times, few studies have examined cavity use in relation to availability, or patterns of nest-site reuse. My objectives were to: 1) determine cavity availability and use in continuous and naturally-fragmented forests, and 2) examine nest-site reuse by cavity nesting guilds and species. To examine cavity availability and use, I measured nest-site characteristics in continuous forests and naturally-fragmented aspen (Populus tremuloides) stands in British Columbia. I examined cavity selection at the community, guild and species levels using resource selection indices. Continuous forests had fewer cavities and lower cavity occupancy rates (9-10%) than fragments (35-44%). However, cavity characteristics did not differ between those habitats. Overall, cavity nesters preferred live, unhealthy trees with few holes. Nest-site selection was influenced by tree and habitat attributes, rather than cavity characteristics such as orientation. Low overall occupancy rates suggested that there was a surplus of cavities. To examine nest-site reuse, 193 cavities were monitored between 1995-1999. Eight percent of cavities were destroyed during the study, mainly due to tree blowdown. Cavities were occupied two years in a row, rather than intermittently. Reuse rates were highest for cavities occupied by secondary cavity nesters (48%) and were lowest for those used by weak excavators (17%). Although woodpeckers were the main providers of cavities for secondary cavity nesters in the community, only 28% of cavities used by woodpeckers were occupied the following year. Reuse rates varied considerably among species within all guilds. Deep cavities with large entrances and those in aspen were reused most often, as were those in aspen groves and close to forest edges. I suggest that large-scale attributes such as proximity to foraging habitat are better indicators of nest-site suitability than microhabitat characteristics. Thus cavity-nesting communities should be managed at a larger scale than individual nest trees or cavities. Because cavity abundance does not reflect cavity suitability, counts of holes will not predict the ability of a habitat to sustain cavity-nester populations.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".