Fluctuating effects of genetic and plastic changes in body mass on population dynamics in a large herbivore
Bibliographic record
Abstract
Recent studies suggest that evolutionary changes can occur on a contemporary time scale. Hence, evolution can influence ecology and vice-versa. To understand the importance of eco-evolutionary dynamics in population dynamics, we must quantify the relative contribution of ecological and evolutionary changes to population growth and other ecological processes. To date, however, most eco-evolutionary dynamics studies have not partitioned the relative contribution of plastic and evolutionary changes in traits on population, community, and ecosystem processes. Here, we quantify the effects of heritable and non-heritable changes in body mass distribution on survival, recruitment, and population growth in wild bighorn sheep (Ovis canadensis) and compare their importance to the effects of changes in age structure, population density, and weather. We applied a combination of a pedigree-based quantitative genetics model, statistical analyses of demography, and a new statistical decomposition technique, the Geber method, to a long-term data set of bighorn sheep on Ram Mountain (Canada), monitored individually from 1975 to 2012. We show three main results: (1) The relative importance of heritable change in mass, non-heritable change in mass, age structure, density, and climate on population growth rate changed substantially over time. (2) An increase in body mass was accompanied by an increase in population growth through higher survival and recruitment rate. (3) Over the entire study period, changes in the body mass distribution of ewes, mostly through non-heritable changes, affected population growth to a similar extent as changes in age structure or in density. The importance of evolutionary changes was small compared to that of other drivers of changes in population growth but increased with time as evolutionary changes accumulated. Evolutionary changes became increasingly important for population growth as the length of the study period considered increased. Our results highlight the complex ways in which ecological and evolutionary changes can affect population dynamics and illustrate the large potential effect of trait changes on population processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".