Adaptation to climate change through seasonal migration revealed by climatic versus demographic niche models
Bibliographic record
Abstract
Predicting the geographic range of species and their response to climatic variation and change are entwined goals in conservation and evolutionary ecology. Species distribution models (SDMs) are foundational in this effort and used to visualize the geographic range of species as the spatial representation of its realized niche. SDMs are also used to forecast range shifts under climate change, but often in the absence of empirical evidence that climate limits population growth. We explored the influence of climate on demography, seasonal migration, and the extent of the geographic range in song sparrows (Melospiza melodia), a species thought to display marked local adaptation to regional climate. To do so, we developed SDMs to predict the demographic and climate niches of migratory and resident song sparrows across our study area in western North America from California to Alaska, using 48 years of demographic data from a focal population in British Columbia and 1.2 million continental-scale citizen science observations. Spatial agreement of our demographic and climate niche models in the region of our focal population was strong (76%), supporting the hypothesis that demographic performance and the occurrence of seasonal migration varied predictably with climatic conditions. In contrast, agreement at the northern (58%) and southern (40%) extents of our study area was lower, as expected if the factors limiting population growth vary regionally. Our results support the hypothesis that local climate drives spatial variation in the occurrence of seasonal migration in song sparrows by limiting the fitness of year-round residents, and suggest that climate warming has favored range expansions and facilitated an upward shift in elevational range song sparrows that forgo seasonal migration. Our work highlights the potential role of seasonal migration in climate adaptation and limits on the reliability of climate niche models not validated with demographic data.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".