Hemi‐marsh concept prevails? Kaminski and Prince (1981) revisited
Bibliographic record
Abstract
Abstract The hemi‐marsh concept has prevailed in avian habitat selection and wetland ecology and management since the 1970s. Hemi‐marsh is a stage of wetland succession when approximately equal proportions of emergent vegetation and open water occur. In 1981, researchers reported on a field experiment in Delta Marsh, Manitoba, Canada, where they artificially created interspersion levels of emergent vegetation and open water (30%:70%, 50:50 [hemi‐marsh], and 70:30) by mowing or rototilling to test for hypothesized differences in indicated breeding pair indices (IBPs/ha; pairs + lone males) of dabbling ducks and species diversity among treatment combinations. Greatest dabbler densities and species diversity were in 50:50 hemi‐marsh plots. This research has been widely cited; however, the experiment was pseudo‐replicated because of non‐independence of IBP counts within and among weeks and years of surveys. We reanalyzed data from the study to support or refute original results. More robust and generalized analyses revealed vegetation:water interspersion characteristics influenced IBP densities of blue‐winged teal (Spatula discors), mallard (Anas platyrhynchos), gadwall (Mareca strepera), northern shoveler (S. clypeata), and species diversity. We estimated greatest IBP densities and species diversity in 50:50 hemi‐marsh plots, agreeing with the previous study. Including nested random effects to account for temporal pseudo‐replication improved model fits by 2–30%. Despite accounting for temporal variation in dabbler density and species diversity, 61–93% of variation remained unexplained, indicating unresolved complexities of breeding dabbler habitat use. Nonetheless, our reanalysis generalized and strengthened the original finding that dabbling duck densities and species diversity were greatest in hemi‐marsh plots. Managers may strive to reproduce hemi‐marsh conditions for dabbling ducks and evaluate other waterbird responses. Our reanalysis also emphasized the need for researchers to account for or prevent temporal and spatial pseudo‐replication, examine model fit, and provide reproducible documentation of data and code, especially now that analytical tools and databases are freely available.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".