Increasing density leads to generalization in both coarse‐grained habitat selection and fine‐grained resource selection in a large mammal
Bibliographic record
Abstract
Density is a fundamental driver of many ecological processes including habitat selection. Theory on density-dependent habitat selection predicts that animals should be distributed relative to profitability of habitat, resulting in reduced specialization in selection (i.e. generalization) as density increases and competition intensifies. Despite mounting empirical support for density-dependent habitat selection using isodars to describe coarse-grained (interhabitat) animal movements, we know little of how density affects fine-grained resource selection of animals within habitats [e.g. using resource selection functions (RSFs)]. Using isodars and RSFs, we tested whether density simultaneously modified habitat selection and within-habitat resource selection in a rapidly growing population of feral horses (Equus ferus caballus Linnaeus; Sable Island, Nova Scotia, Canada; 42% increase in population size from 2008 to 2012). Among three heterogeneous habitat zones on Sable Island describing population clusters distributed along a west-east resource gradient (west-central-east), isodars revealed that horses used available habitat in a density-dependent manner. Intercepts and slopes of isodars demonstrated a pattern of habitat selection that first favoured the west, which generalized to include central and east habitats with increasing population size consistent with our understanding of habitat quality on Sable Island. Resource selection functions revealed that horses selected for vegetation associations similarly at two scales of extent (total island and within-habitat zone). When densities were locally low, horses were able to select for sites of the most productive forage (grasslands) relative to those of poorer quality. However, as local carrying capacity was approached, selection for the best of available forage types weakened while selection for lower-quality vegetation increased (and eventually exceeded that of grasslands). Isodars can effectively describe coarse-grained habitat selection in large mammals. Our study also shows that the main predictions of density-dependent habitat selection are highly relevant to our interpretation of RSFs in space and time. At low but not necessarily high population size, density will be a leading indicator of habitat quality. Fitness maximization from specialist vs. generalist strategies of habitat and resource selection may well be apparent at multiple spatial extents and grains of resolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".