Bibliographic record
Abstract
This thesis investigates the variation of life history traits within species, how they underpin population dynamics in woodland rodent populations and how they are effected by interactions between species. We ask how do life history traits differ between populations of the same species in similar habitats? We then go on to ask how two different species living in sympatry differ and the possible effects of interactions. We used data collected against three populations of Columbian ground squirrels (Urocitellus columbianus) in Canada and two species of woodland rodents from a site in the UK. Integral Projection Models or IPMs were used to compare the three populations of Columbain ground squirrels and identify differences between them. By using a form of perturbation analysis on the IPMs it was possible to identify the driving demographic and trait transition functions for the differences between the three populations. We then looked at interactions between wood mice (Apodemus sylvaticus) and the bank vole (Myodes glareolus) in the UK, and the possible effect on trapping bias. As data was limited for the two UK species, we could not construct a full IPM, but instead looked at the differences in growth rates of both species to determine what was the most important factors. Comparing the population level estimates from the IPMs for the Columbian ground squirrels, revealed significant differences between the populations. In particular the populations differed in growth rate (λ), generation length and R0. Perturbation analysis of the three IPMs revealed the adult survival function to be the major contributor to the differences between the three population. The inheritance function also had a large impact on the offspring estimates. For the two UK rodent species we found a large impact on the trapping bias due to interactions between the two species. With a significant increase in the chance of the same species being caught within a trap as previously caught. When analysing growth rates, we found that environmental factors only impacted growth for some groups, and we suggest that this may be due to the mitigation by the woodland of impacts of the environmental conditions. We did find that the density of a third species, the yellow necked mouse (Apodemus flavicollis), did have a large negative impact on growth rates on the other two species. In summary species population dynamics can very considerably between populations, even when the populations exist in potentially similar habitats. It is also possible for species living in sympatry to also have an impact on each other’s population dynamics. Extreme care should then be taken when making comparisons between species based solely on single population data.
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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) | 0.001 | 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".