Hippocampal volumes and neuron numbers increase along a gradient of environmental harshness: a large-scale comparison
Bibliographic record
Abstract
Environmental conditions may provide specific demands for memory, which in turn may affect specific brain regions responsible for memory function. For food-caching animals, in particular, spatial memory appears to be important because it may have a direct effect on fitness via the accuracy of cache retrieval. Animals living in more harsh environments should rely more on cached food, and thus theoretically should have better memory to support cache retrieval, which may be crucial for survival. Consequently, animals in harsh environments may benefit from more neurons within a larger hippocampus (Hp), a part of the brain involved in spatial memory. Here, we present the first large-scale test of the hypothesis that Hp structure is related to the severity of the environment within a single food-caching species (the black-capped chickadee, Poecile atricapillus) with a large range encompassing a great diversity of climatic conditions. Hp size in birds collected at five locations along a gradient of environmental harshness from Alaska to Kansas ranked perfectly with climatic severity. Birds from more harsh northern climates (defined by lower ambient temperature, shorter day length and more snow cover) had significantly larger Hp volumes and more Hp neurons (both relative to telencephalon volume) than those from more mild southern latitudes. Environmental pressures therefore seem capable of influencing specific brain regions independently, which may result in enhanced memory, and hence survival, in harsh climates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".