Quantifying the temporal stability in seasonal habitat for sage‐grouse using regression and ensemble tree approaches
Bibliographic record
Abstract
Abstract Identifying and quantifying the extent to which landscape‐level habitat variables drive the spatial distribution of individuals across a region can provide fundamental insights into a species ecology and be essential to wildlife management and conservation plans. Although the preferences for habitat resources and the resources themselves are not static over time, most research at large spatial scales does not consider seasonal effects nor quantify annual temporal variability in the spatial distribution of habitat resources. In this study, we used a machine learning (boosted regression trees [BRTs]) and generalized linear mixed model (GLMM) approach to quantify seasonal habitat selection across three life stages (nest, late brood, and winter habitat) of sage‐grouse and estimated annual stability across a 13‐year dataset in south‐central Wyoming. Generalized linear mixed models had high area under the curve (AUC) values, but were not as high as the BRT models that had mean AUC values of 0.86, 0.81, and 0.87 for nest, late brood, and winter habitat, respectively. Generalized linear mixed models and BRT result provided similar results, but because of the higher validation values of the BRT models, we assessed annual variation by predicting the BRT models across years. We found significant spatial trends in the distribution of nesting habitat, with general decreases in the relative probability of use across the core of the study area and corresponding increases in selection on the periphery. The primary temporally shifting variables for the nesting BRT models were development, Normalized Difference Vegetation Index, and topographic wetness, suggesting they were shifting out of preferable ranges for these variables as habitat suitability was decreased over the course of our study. Winter habitat appeared to have similar spatial changes in probability of selection, but these changes were likely related to changes in winter precipitation and snow depth, which were the primary contributors to the winter BRT models. The annual dynamics of habitat selection are seldom addressed in large‐scale research but can have potentially dramatic influences on our identification of preferred habitats.
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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 | 0.001 | 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) | 0.001 | 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 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".