Seasonal migration as a life history trait facilitating adaptation to climate change
Bibliographic record
Abstract
Abstract Predicting the geographic range of species and their response to variation in climate 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, or when based only on climate, its climate niche. SDMs can also forecast shifts in species range given climate change, but often lack of empirical support for causal links between climate and demography, yielding uncertain predictions. We addressed such uncertainties whilst also exploring the role of migration and resident life-histories in climate adaptation in mobile animals using 48 years of detailed demographic and climate data for song sparrows ( Melospiza melodia ), a polytypic species that varies in migratory life history. We developed SDMs representing demographic and climate niches of migratory and resident populations in western North America from California (CA) to Alaska (AK) using data from a focal population in British Columbia (BC) and 1.2 million citizen science observations. Distributions of resident and migrant populations predicted by each model agreed strongly (72.8%) in the region of our focal population, but less well in regions with dissimilar climates. Mismatches were largest in CA, smaller in AK, but in all cases supported the hypothesis that climate influences the evolution of migration and limits year-round residency. Our results imply that migrants predominated in our focal population a century ago, but that climate change has favored range expansions by non-migratory phenotypes and facilitated an upward shift in the elevational range of residents. We suggest long-term studies are crucial to evaluating the predictions of SDMs positing causal links between climatic conditions and species demography. We found such links to be robust regionally and particularly useful to elucidating the potential for migration or residence to facilitate adaptation to climate change.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".