Lifetime reproductive success and density-dependent, multi-variable resource selection
Bibliographic record
Abstract
Individuals are predicted to maximize lifetime reproductive success (LRS) through selective use of resources; however, a wide range of ecological and social processes may prevent individuals from always using the highest-quality resources available. Resource selection functions (RSFs) estimate the relative amount of time an individual spends using a resource as a function of the proportional availability of that resource. We quantified the association between LRS and coefficients of individual-based RSFs describing lifetime resource selection for 267 female red deer (Cervus elaphus) of the Isle of Rum, Scotland, from 1970 to 2001. LRS was significantly related to first- and second-order effects of selection for Agrostis/Festuca grassland and proximity to the sea coast (quality of forage within Agrostis/Festuca grassland was highest nearest the coast (ratio of short:long grassland)). The benefits of selecting for quality in Agrostis/Festuca grassland, however, traded-off with increases in LRS gained by avoiding conspecific density. LRS was inversely associated with local density, which was highest along the coast, and reproductive benefits of selecting Agrostis/Festuca grassland diminished with increasing density. We discuss the relevance of these results to our understanding of the spatial distribution of red deer abundance, and potential applications of our approach to evolutionary and applied ecology.
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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 | 0.001 | 0.007 |
| 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) | 0.001 | 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".