Can habitat selection predict abundance?
Bibliographic record
Abstract
. Habitats have substantial influence on the distribut ion and abundance of animals. Ani-\nmals’ selective mo vement yields their habitat use. Animals generally are more abundant in\nhabitats that are selected most strongly.\n2. Models of habitat selection can be used to distribute animals on the landscape or their\ndistribution can be modelled based on data of habitat use, occupancy, intensity of use or\ncounts of animals. When the population is at carrying capacity or in an ideal-free distri-\nbution, habitat selection and related metrics of habitat use can be used to estimate abun-\ndance.\n3. If the population is not at equilibrium, models have the flexibility to incorporate density\ninto models of habitat selection; but abundance might be influenced by factors influencing fit-\nness that are not directly related to habitat thereby compromising the use of habitat-based\nmodels for predicting population size.\n4. Scale and domain of the sampling fram e, both in time and space, are crucial consider-\nations limiting application of these models. Ultimately, identifying reliable models for predict-\ning abundance from habitat data requires an understanding of the mechanisms underlying\npopulation regulation and limitation.\nanimal movement, occupancy, population estimation, population size, presence-\nonly data, resource selection functions
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.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".