Relationship between resource selection, distribution, and abundance: a test with implications to theory and conservation
Bibliographic record
Abstract
Abstract Much of applied and theoretical ecology is concerned with the interactions of habitat quality, animal distribution, and population abundance. We tested a technique that uses resource selection functions (RSF) to scale animal density to the relative probability of selecting a patch of habitat. Following an accurate survey of a reference block, the habitat‐based density estimator can be used to predict population abundance for other areas with no or unreliable survey data. We parameterized and tested the technique using multiple years of radiotelemetry locations and survey data collected for woodland caribou across four landscape‐level survey blocks. The habitat‐based density estimator performed poorly. Predictions were no better than those of a simple area estimator and in some cases deviated from the observed by a factor of 10. We developed a simulation model to investigate factors that might influence prediction success. We experimentally manipulated population density, caribou distribution, ability of animals to track carrying capacity, and precision of the estimation equation. Our simulations suggested that interactions between population density, the size of the reference block, and the pattern of distribution can lead to large discrepancies between observed and predicted population numbers. Over‐ or undermatching patch carrying capacity and precision of the estimator can influence predictions, but the effect is much less extreme. Although there is some empirical and theoretical evidence to support a relationship between animal abundance and resource selection, our study suggests that a number of factors can seriously confound these relationships. Habitat‐based density estimators might be effective where a stable, isolated population at equilibrium is used to generate predictions for areas with similar population parameters and ecological conditions.
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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 | 0.029 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".