Differential habitat selection in boreal songbirds influences estimates of population size and distribution
Bibliographic record
Abstract
Abstract Aim Most large‐scale species distribution models assume spatially constant habitat selection throughout a species' geographic range. However, there is evidence this assumption may not be valid for a number of boreal bird species, which could lead to biased predictions of density and distribution in range‐wide models. Our goal was to test for and quantify differential habitat selection (DHS) in songbirds among regions of the Canadian boreal forest. Location Northern Alberta, western Ontario and southern Quebec, Canada. Methods We used hierarchical analysis of covariance models with region‐specific parameter estimates to test for differential selection of forest attributes among three regions for six boreal bird species. We used the results of these models to quantify intraspecific niche overlap between regions and compared posterior predictive accuracy to models that did not account for DHS. Results We found a generally large standardized effect size (median effect size = 1.674) of region on selection of specific habitat variables for all six species, although there was high variability among species, variables and regional comparisons. The proportion of niche overlap between regions was generally low (mean overlap = 0.309 for all pairwise comparisons), with no spatial pattern to the overlap. Models accounting for DHS had significantly higher posterior predictive accuracy according to the Watanabe–Akaike information criterion. Main Conclusions We found strong evidence for DHS among regions for six boreal songbird species in individual habitat attributes and overall niche space. The higher predictive accuracy of our DHS models suggests that failure to account for spatial variability in habitat selection can lead to biased estimates of density and spatial distribution. Models that did not account for DHS overestimated density relative to DHS models. We conclude that large‐scale species distribution models should account for regional variation in habitat selection in order to obtain accurate estimates of population size and distribution.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".