Familiar neighbours, but not relatives, enhance fitness in a territorial mammal
Bibliographic record
Abstract
Summary One of the outstanding questions in evolutionary biology is the extent to which mutually beneficial interactions and kin-selection can facilitate the evolution of cooperation by mitigating conflict between interacting organisms. The indirect fitness benefits gained from associating with kin are an important pathway to conflict resolution [1], but conflict can also be resolved if individuals gain direct benefits from cooperating with one another (e.g. mutualism or reciprocity) [2]. Owing to the kin-structured nature of many animal societies, it has been difficult for previous research to assess the relative importance of these mechanisms [3–5]. However, one area that might allow for the relative roles of kin-selection and mutualistic benefits to be disentangled is in the resolution of conflict over territorial space [6]. While much research has focused on group-living species, the question of how cooperation can first be favoured in solitary, territorial species remains a key question. Using 22 years of data from a population of North American red squirrels, we assessed how kinship and familiarity with neighbours affected fitness in a territorial mammal. While living near kin did not enhance fitness, familiarity with neighbours increased survival and annual reproductive success. These fitness benefits were strong enough to compensate for the effects of aging later in life, with potential consequences for the evolution of senescence. We suggest that such substantial fitness benefits provide the opportunity for the evolution of cooperation between adversarial neighbours, offering insight into the role that mutually beneficial behaviours might play in facilitating and stabilizing social systems. Graphical Abstract
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".