Integrating dynamic processes into waterfowl conservation prioritization tools
Bibliographic record
Abstract
Abstract Aim Traditional approaches for including species' distributions in conservation planning have presented them as long‐term averages of variation. Like these approaches, the main waterfowl conservation targeting tool in the United States Prairie Pothole Region (US PPR) is based primarily on long‐term averaged distributions of breeding waterfowl. While this tool has supported valuable conservation, it does not explicitly consider spatiotemporal changes in spring wetland availability and does not assess wetland availability during the brood rearing period. We sought to develop a modelling approach and targeting tool that incorporated these types of dynamics for breeding waterfowl and broods. This goal also presented an opportunity for us to compare predictions from a traditional targeting tool based on long‐term averages to predictions from spatiotemporal models. Such a comparison facilitated tests of the underlying assumption that this traditional targeting tool could provide an effective surrogate measure for conservation objectives such as brood abundance and climate refugia. Location US PPR. Methods We developed spatiotemporal models of breeding waterfowl and brood abundance within the US PPR. We compared the distributions predicted by these models and assessed similarity with the averaged pair data that is used to develop the current waterfowl targeting tool. Results Results demonstrated low similarity and correlation between the averaged pair data and spatiotemporal brood and breeding waterfowl models. The spatiotemporal breeding waterfowl model distributions did not serve as better surrogates for brood abundance than the averaged pair data. Main conclusions Our study underscored the contributions that the current targeting tool has made to waterfowl conservation but also suggested that conservation plans in the region would benefit from the consideration of inter‐ and intra‐annual dynamics. We suggested that using only the averaged pair data and derived products might result in the omission of 58% ‐ 88% of important breeding waterfowl and brood habitat from conservation plans. [Correction added on 5 February 2021, after first online publication: ‘Results’ text has been modified and the ‘Main conclusions’ omission percentages have been corrected.]
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".