A mechanism for population self‐regulation: Social density suppresses GnRH expression and reduces reproductivity in voles
Bibliographic record
Abstract
Nearly 100 years ago, Charles Elton described lemming and vole population cycles as ecological models for understanding population regulation in nature. Yet, the mechanisms driving these cycles are still not fully understood. These rodent populations can continue to cycle in the absence of predation and with food supplementation, and represent a major unsolved problem in population ecology. It has been hypothesized that the social environment at high population density can drive selection for a low-reproduction phenotype, resulting in population self-regulation as an intrinsic mechanism driving the cycles. However, a physiological mechanism for this self-regulation has not been demonstrated. We manipulated population density in wild meadow voles Microtus pennsylvanicus using large-scale field enclosures over 3 years and examined reproductive performance and physiology. Within the field enclosures, we assessed the proportion of breeding animals, mass at sexual maturation, and faecal androgen and oestrogen metabolites. We then collected brain tissue from juvenile voles born at high or low density, quantified mRNA expression of gonadotropin-releasing hormone (GnRH) and oestrogen receptor alpha (ERα) and measured DNA methylation at six CpG sites in a region that was highly conserved with the mouse GnRH promoter. At high density, there was a lower proportion of reproductive animals. Juvenile voles born at high densities had reduced expression of GnRH in the hypothalamus, accompanied by marginally lower faecal sex hormone metabolites. Female juvenile voles born at high density also had higher methylation levels at two CpG sites while males did not, aligning with prior observations that females (but not males) from high-density environments retain reduced reproduction long term. Our results support a physiological basis for population self-regulation in vole cycles, as altering population density alone induced reproductive downregulation at the hypothalamic level. Our results demonstrate that altering the early-life social environment can fundamentally impact reproductive function in the brain. This, in turn, can drive population demography changes in wild animals.
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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 | 0.000 | 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) | 0.001 | 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".