Vegetation and sheep population dynamics
Bibliographic record
Abstract
Introduction The relationship between the sheep and their food supply is a key element in understanding the population dynamics of Soay sheep on Hirta. This island population of Soay sheep provides an ideal model system for the study of plant–herbivore dynamics: there are no vertebrate predators like foxes or buzzards, and no competitors like rabbits or voles. The vegetation is relatively unpolluted by atmospheric nutrient inputs, there are no confounding management operations, and the population is closed to immigration or emigration. Because the sheep population is evidently food-limited, we expect that grazing will have a major impact on the biomass, spatial structure and botanical composition of the vegetation. In this chapter, we describe the relationship between the sheep and their food supply, and discuss the consequences of sheep grazing for plant performance and longer-term vegetation dynamics. In a plant–herbivore interaction where there are no competing herbivores and no vertebrate predators, we expect that herbivore numbers will be determined by the food supply available to the sheep during winter (Crawley 1983). Our study follows a long tradition of monitoring the response of vegetation to changes in the numbers of vertebrate herbivores: e.g. relaxation of rabbit grazing on chalk grasslands following the myxoma epidemic (Thomas 1960), African elephants (Cumming 1981), ungulate guilds in Serengeti (McNaughton 1985), introduced reindeer on South Georgia (Leader-Williams et al. 1987; Leader-Williams 1998), livestock in the New Forest (Putman et al . 1989) desert rodents in the USA (Brown and Heske 1990), moose on Isle Royale (McLaren and Peterson 1994), sheep on heather moorland (Welch and Scott 1995), lemmings in arctic tundra (Virtanen et al . 1997), whitetailed deer in North American forests (Cornett et al . 2000), kangaroos in Australia (Newsome et al . 2001) red deer on Rum (Virtanen et al . 2002) and many more.
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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".