Comparing resource selection and demographic models for predicting animal density
Bibliographic record
Abstract
ABSTRACT Habitat‐based prediction of population density relies on relationships between landscape configuration (i.e., abundance of land‐cover types) and equilibrium density. This may be accomplished by estimating resource selection probability functions (RSPFs) based on presence–absence data, or by relating carrying capacity to landscape covariates. We used RSPFs for moose (Alces alces) from 2 study sites and carrying capacities from 34 wildlife management units across northern Ontario, Canada, to create 2 estimators of moose density. We compared the predictions of both models to moose density in a novel site obtained via aerial census. We also projected the RSPF across 34 management units and compared predicted density to estimated carrying capacity of each unit. The RSPF and carrying capacity models predicted moose equilibrium densities that were statistically indistinguishable from the estimated density of moose at the novel study site, but the carrying capacity model generated uninformatively broad prediction intervals. The RSPF failed to predict carrying capacities in management units across Ontario; however, the differences between RSPF estimates and carrying capacities varied predictably with differences in covariates related to forage availability, suggesting habitat selection strength and RSPF transferability vary with landscape quality. Estimating densities using RSPFs relies on a consistent relationship between habitat selection and animal density; thus, RSPF applicability across space will depend heavily on similarity between the novel and original sites. Demographic projection benefits from broad spatiotemporal datasets that improve reliability but that are relatively rare and subject to broad error. Our findings suggest that selection‐based population estimation is preferable to demographically based models because of increased precision of estimates, the immediacy of available data (e.g., single survey or radio‐telemetry in multiple sites vs. many generations of population estimates in a time series across space), and the capacity to predict fine‐scale patterns of distribution and abundance. © 2016 The Wildlife Society.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".