How can we apply theories of habitat selection to wildlife conservation and management?
Bibliographic record
Abstract
Habitat-selection theory can be applied to solve numerous problems in the conservation and management of wildlife. Many of the solutions involve the use of habitat isodars, graphs of densities in pairs of habitats such that expected fitness is the same in both. For single species, isodars reflect differences in habitat quality, and specify the conditions when population density will, or will not, match the abundance of resources. When two or more species co-occur, isodars can be used to assess not only whether the species compete with one another, but also differences in habitat, in habitat selection, and in the functional form of density-dependent competition. Isodars have been applied to measure scales of habitat selection, the presence or absence of edge effects, as well as the number of habitats that species recognise in heterogeneous landscapes. Merged with foraging behaviour, isodars reveal the relative roles of habitat selection, spatial structure, and environmental stochasticity on local populations. Habitat-selection models can be linked similarly with theories of patch use to assess the underlying cause of source–sink dynamics. Isodars can detect and measure Allee effects, describe human habitat selection, and use human occupation of habitat as a leading indicator of threatened biodiversity. Even so, we have only begun to reveal the potential of habitat selection, and other optimal behaviours, to solve pressing problems in conservation and management.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".