Experience‐dependent recapture rates and reproductive success in male grey mouse lemurs (<i>Microcebus murinus</i>)
Bibliographic record
Abstract
Male mating tactics can vary according to the potential for scramble or contest competition but also as a consequence of individual characteristics, such as body condition and previous experience. The influence of experience, i.e., residency, on male recapture rates and reproductive success was studied in a population of free-living grey mouse lemurs. Long-term capture data from 320 individuals revealed that both sexes had very low recapture probabilities within their first year in the study population, but recapture rates declined less sharply during the following years. Capture results and telemetric analyses on 12 focal males revealed that resident males had larger body mass and larger home ranges than new males. Home range size correlated with the number of accessible females, indicating that resident males had higher probabilities to meet mates than new males. The reproductive success of 132 candidate fathers, representing both resident and new males, was determined by means of molecular genotyping. Paternity determination was successful in 38 cases (success rate: 19%). Sixteen resident males and seventeen new males sired offspring. However, in relation to the number of candidate fathers being present in the mating season, resident males were twice as likely to reproduce successfully as new males. These findings suggest experience-dependent reproductive tactics that most likely correspond to a differential spatial knowledge of resources, mates and potential threats. The results generally agree with the predictions made for a scramble competition regime and demonstrate substantial behavioral plasticity in a nocturnal primate species with a dispersed multi-male/multi-female mating system.
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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 | 0.001 | 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".