How can we apply theories of habitat selection to wildlife conservation and management?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it