Integrated population models facilitate ecological understanding and improved management decisions
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Integrated population models (IPMs) represent a formal statistical methodology for combining multiple data sets such as population counts, band recoveries, and fecundity estimates into a single unified analysis with dual objectives: better estimating population size, trajectory, and vital rates; and formally describing the ecological processes that generated these patterns. Although IPMs have been used in population ecology and fisheries management, their use in wildlife management has been limited. Data sets available for North American waterfowl are unprecedented in terms of time span (>60 years) and geographic coverage, and are especially well‐suited for development of IPMs that could improve the understanding of population ecology and help guide future harvest and habitat management decisions. In this overview, we illustrate 3 potential benefits of IPMs: integration of multiple data sources (i.e., population counts, mark‐recapture data, and fecundity estimates), increased precision of parameter estimates, and ability to estimate missing demographic parameters by reanalyzing results from a historical study of canvasbacks ( Aythya valisineria ). Drawing from our own published and unpublished work, we demonstrate how IPMs could be used to identify the critical vital rates that have had the greatest influence on population change in lesser scaup ( Aythya affinis ), evaluate potential mechanisms of harvest compensation for American black ducks ( Anas rubripes ), or prioritize the most appropriate places to conduct habitat management to benefit northern pintails ( Anas acuta ). Integrated population models provide a powerful platform for evaluating alternative hypotheses about population regulation and they have potential to advance the understanding of wildlife ecology and help managers make ecologically based decisions. © 2017 The Wildlife Society.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it