Living on the edge: the evolution of spatial cognition in food-caching chickadees
Bibliographic record
Abstract
There is a tremendous amount of intra- and inter-specific variation in animal cognitive abilities, and understanding the evolutionary mechanisms generating such variation is an important issue in evolutionary biology. Here we review our research on spatial cognition in food-caching chickadees, which use spatial cognitive abilities to recover previously made food caches to survive harsh winters. This work ranges from comparing multiple populations along environmental gradients to investigating the causes and consequences of individual variation in spatial learning and memory abilities, to describing the genetic basis of such variation. Our data show that (i) chickadees in harsher environments have better spatial cognitive abilities and a larger hippocampus with more hippocampal neurons, with differences between populations persisting in common garden experiments; (ii) chickadees tested in the wild show extensive individual variation in their learning and memory abilities; (iii) this variation is highly heritable and has a genetic basis; and (iv) this variation is associated with significant differences in survival, lifespan and reproductive success. Overall, our data provide strong evidence that spatial cognitive abilities in food-caching chickadees are shaped by natural selection, which generates differences among populations living across heterogeneous environmental conditions.This article is part of the Theo Murphy meeting issue 'Selection shapes diverse animal minds'.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".