The benefits of using topographic features to predict climate-resilient habitat for migratory forest landbirds: An example for the Rusty Blackbird, Olive-sided Flycatcher, and Canada Warbler
Bibliographic record
Abstract
Abstract Maintaining a functionally connected network of high-quality habitat is one of the most effective responses to biodiversity loss. However, the spatial distribution of suitable habitat may shift over time in response to climate change. Taxa such as migratory forest landbirds are already undergoing climate-driven range shifts. Therefore, patches of climate-resilient habitat (also known as “climate refugia”) are especially valuable from a conservation perspective. Here, we performed maximum entropy (Maxent) species distribution modeling to predict suitable and potentially climate-resilient habitat in Nova Scotia, Canada, for 3 migratory forest landbirds: Rusty Blackbird (Euphagus carolinus), Olive-sided Flycatcher (Contopus cooperi), and Canada Warbler (Cardellina canadensis). We used a reverse stepwise elimination technique to identify covariates that influence habitat suitability for the target species at broad scales, including abiotic (topographic control of moisture and nutrient accumulation) and biotic (forest characteristics) covariates. As topography should be relatively unaffected by a changing climate and helps regulate the structure and composition of forest habitat, we posit that the inclusion of appropriate topographic features may support the identification of climate-resilient habitat. Of all covariates, depth to water table was the most important predictor of relative habitat suitability for the Rusty Blackbird and Canada Warbler, with both species showing a strong association with wet areas. Mean canopy height was the most important predictor for the Olive-sided Flycatcher, whereby the species was associated with taller trees. Our models, which comprise the finest-scale species distribution models available for these species in this region, further indicated that, for all species, habitat (1) remains relatively abundant and well distributed in Nova Scotia and (2) is often located in wet lowlands (a climate-resilient topographic landform). These findings suggest that opportunities remain to conserve breeding habitat for these species despite changing temperature and precipitation regimes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".