Using Latent Selection Difference to Model Persistence in a Declining Population
Bibliographic record
Abstract
Population persistence is a direct measure of the viability of a population. Monitoring the distribution of declining populations or subpopulations over time can yield estimates of persistence, which we show can be modeled as a latent selection difference (LSD) contrasting attributes of sites where populations have persisted versus those that have not. Predicted persistence can be modeled with predictor covariates to identify factors correlated with species persistence. We demonstrate how to model persistence based on changes in occupancy that can include adjustments for detection probability. Using a known historical distribution of the western grebe (Aechmophorus occidentalis), we adapted methods originally developed for occupancy modeling to evaluate how environmental covariates including emergent vegetation and human developments have affected western grebe persistence in Alberta. The relative probability of persistence was correlated with the extent of shoreline bulrush (Scirpus lacustris), which is important vegetation for nesting cover. We also documented that western grebe populations were less likely to persist on lakes in the boreal forest, primarily located on the northern boundary of the species' range. Factors influencing occupancy were different than those determining persistence by western grebes; persistence and occupancy were not correlated. Persistence was more likely on lakes with recreational development, reflecting reliance by grebes on the larger, fish-bearing waterbodies that also are attractive for lakeshore development. Unfortunately, the correlation with recreational development on Alberta's lakes puts grebes at risk for loss of brood-rearing habitats--primary threats to altricial birds--if steps are not taken to prevent disturbance to bulrush stands. Identifying factors related to the persistence of a species--especially one in decline--is a fundamental step in conservation management.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".