Generalized estimating equations and generalized linear mixed‐effects models for modelling resource selection
Bibliographic record
Abstract
Summary Accurate resource selection functions (RSFs) are important for managing animal populations. Developing RSFs using data from GPS telemetry can be problematic due to serial autocorrelation, but modern analytical techniques can help to compensate for this correlation. We used telemetry locations from 18 woodland caribou Rangifer tarandus caribou in Saskatchewan, Canada, to compare marginal (population‐specific) generalized estimating equations (GEEs), and conditional (subject‐specific) generalized linear mixed‐effects models (GLMMs), for developing resource selection functions at two spatial scales. We evaluated the use of empirical standard errors, which are robust to misspecification of the correlation structure. We compared these approaches with destructive sampling. Statistical significance was strongly influenced by the use of empirical vs. model‐based standard errors, and marginal (GEE) and conditional (GLMM) results differed. Destructive sampling reduced apparent habitat selection. k‐fold cross‐validation results differed for GEE and GLMM, as it must be applied differently for each model. Synthesis and applications. Due to their different interpretations, marginal models (e.g. generalized estimating equations, GEEs) may be better for landscape and population management, while conditional models (e.g. generalized linear mixed‐effects models, GLMMs) may be better for management of endangered species and individuals. Destructive sampling may lead to inaccurate resource selection functions (RSFs), but GEEs and GLMMs can be used for developing RSFs when used with empirical standard errors.
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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 | 0.035 | 0.087 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".