Improving rigour and efficiency of use‐availability habitat selection analyses with systematic estimation of availability
Bibliographic record
Abstract
Summary Animal habitat selection analyses often rely on comparisons of habitat use and availability to infer selection. Random locations are commonly used to assess availability despite inefficiency and potential uncertainty associated with random sampling. Herein, I propose a systematic approach to estimate habitat availability to reduce sampling error and computing time associated with GIS‐based estimation of habitat availability using random locations. I used Euclidean distance analysis (EDA) as a model technique to demonstrate the sensitivity of use‐availability analyses to insufficient random sampling and to evaluate the proposed systematic approach. I re‐analysed data from a previous study of habitat selection of Florida panthers (Puma concolor coryi) and compared results of analyses in which distance‐based habitat availability (i.e. expected distance) was estimated with a range in sample sizes of random locations, and also systematically. My results demonstrate that expected distances and statistical results of EDA based on random sampling can be unreliable with low and arbitrary numbers of random points, vary if increasing numbers of points are used, and approach results obtained systematically at greater numbers of points (i.e. with sufficient sampling). The systematic approach efficiently measures habitat availability by making calculations from all possible locations, at a specified resolution, across the scale of interest. Thus, it eliminates uncertainty due to sampling error and is considerably faster. The systematic approach improves rigour and efficiency of animal habitat selection analyses that rely on comparisons of habitat use and availability and ensures repeatability of results for practical and theoretical applications.
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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 | 0.030 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".