Improving spatial predictions of animal resource selection to guide conservation decision making
Bibliographic record
Abstract
Resource selection is often studied by ecologists interested in the environmental drivers of animal space use and movement. These studies commonly produce spatial predictions, which are of considerable utility to resource managers making habitat and population management decisions. It is thus paramount that predictions from resource selection studies are accurate. We evaluated model building and fitting strategies for optimizing resource selection function predictions in a use-availability framework. We did so by simulating low- and high-intensity spatial sampling data that respectively predicted study area and movement-based resource selection. We compared one of the most commonly used forms of statistical regularization, Akaike's Information Criterion (AIC), with the lesser used least absolute shrinkage and selection operator (LASSO). LASSO predictions were less variable and more accurate than AIC and were often best when considering additive and interacting variables. We explicitly demonstrate the predictive equivalence using the logistic and Poisson likelihoods and how it is lost when the available sample is too small. Regardless of modeling approach, interpreting the sign of coefficients as a measure of selection can be misleading when optimizing for prediction.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".