Expanding northward: influence of climate change, forest connectivity, and population processes on a threatened species' range shift
Bibliographic record
Abstract
Species' ranges are dynamic, shifting in response to a large number of interrelated ecological and anthropogenic processes. Climate change is thought to be one of the most influential drivers of range shifts, but the effects of other confounded ecological processes are often ignored even though these processes may modify expected range responses to climate change. To determine the relative effects of climate, forest availability, connectivity, and biotic processes such as immigration and establishment, we examine range changes occurring in a species of bird, the Hooded Warbler (Wilsonia citrina). We focus predominantly on the periphery of the species' northern range in Canada but we also examine data from the entire species' range. Nesting records in southern Ontario were obtained from two breeding bird Atlases of Ontario separated by a period of 20 years (1981–1985 and 2001–2005), and the rate of range expansion was estimated by comparing the number of occupied areas in each Atlas. Twelve hypotheses of the relationship between the rate of range expansion and factors known to influence range change were examined using model-selection techniques and a mixed modeling approach (zero-inflated Poisson's regression). Cooler temperatures were positively related to a lack of range expansion indicating that climate constrained the species' distribution. Establishment probability (based on the number of occupied, neighboring Atlas squares) and immigration from populations to the south (estimated using independent data from the North American Breeding Bird Survey) were also important predictors of range expansion. These biotic process variables can mask the effects of forest availability and connectivity on range expansion. Expansion due to climate change may be slower in fragmented systems, but the rate of expansion will be influenced largely by biotic processes such as proximity to neighboring populations.
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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 | 0.000 | 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) | 0.001 | 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 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".