Temporal variability in the way local habitat affects duck population growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Climate change is expected to lead to greater temporal climatic variability across broad spatial extents. A potential consequence is that shifts in climatic conditions might alter how local habitat affects the population growth of animals dependent on those habitats for at least part of their life cycle. We tested whether such a phenomenon occurred when the North American Prairie Pothole Region transitioned through periods of wet and dry conditions by modeling the population growth of seven duck species over 52 years (1961–2012). We found that the influence of local habitat quality—indexed by wetland availability—on duck population growth varied in magnitude and direction on an annual basis. While the effect of wetlands was relatively small in most years, there were some years in which wetlands strongly affected duck population growth in both positive and negative directions (e.g., negative in 2002 and positive in 2008). Contrary to our expectation, inter‐annual variability in the effect of wetlands on duck population growth did not depend on regional precipitation. We also found that for two species—American Wigeon ( Anas americana ) and Green‐winged Teal ( A. carolinensis )—duck population growth in the presence of wetlands rarely differed from what would be expected solely under density dependence. Our study is the first to demonstrate that the effect of local habitat on population growth varies over time even if the cause of that variation remains unexplained. Consequently, any study that attempts to identify a species’ critical habitat using time series abundance data must consider that local relationships are non‐stationary. More complicated measures of climate change may reveal how local drivers of population growth depend on broader temporal climatic patterns.
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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 | 0.000 | 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) | 0.013 | 0.001 |
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