Local forest structure, climate and human disturbance determine regional distribution of boreal bird species richness in <scp>A</scp>lberta, <scp>C</scp>anada
Bibliographic record
Abstract
Abstract Aim It is challenging to disentangle how local habitat structure, climate, and human disturbance interplay to determine broad‐scale variation of species richness. Here, we separated various measures of local forest structure and composition, abiotic factors, and human land cover that constrain species richness of bird guilds in the boreal forest. Location Boreal forest, western C anada. Methods Data on breeding birds, habitat structure, climate and human footprints in 206 sites were sampled, with each site centred on an area of 1 ha in size. The 206 sites cover a large geographical extent with a distance of c . 1000 km between the most distant sites. We modelled bird guild species richness in relation to forest structure and composition (woody plant richness, forest biomass, number of vegetation layers, canopy openness), abiotic environment (temperature, precipitation, elevation), and percentage area of human land cover. We classified bird species into different guilds based on dietary preference, habitat specialization and migratory status, and used structural equations to quantify effect strengths of predictor variables. Results We found that temperature, low levels of human land cover, woody plant richness and number of vegetation layers had strong positive correlations with overall bird species richness in the boreal forest. Moreover, local forest structure and composition showed a pronounced variation in their relationships with species richness of different guilds. Insectivores, old‐growth forest specialists, forest generalists, long‐distance migrants and winter residents showed strong positive correlations with woody plant richness, whereas old‐growth forest specialists and winter residents were strongly related to forest biomass as well. The number of vegetation layers was positively related to species richness of most guilds, whereas the response to canopy openness was most pronounced for old‐growth forest specialists and winter residents (being negatively correlated). Main conclusions In addition to climate and human disturbance, local forest structure and composition are important determinants of broad‐scale variation of bird species richness in boreal forest. However, the strength and direction (positive/negative) of determinants is guild‐specific, suggesting a strong functional component to community structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".