Vegetation associations of riparian birds in successional woodlands along the regulated Missouri River
Bibliographic record
Abstract
River regulation by dams on the Missouri River has modified riparian forest successional patterns, with decreases in early and increases in later seral stages and higher occurrence of invasive tree species, including Russian olive (Elaeagnus angustifolia) and eastern red cedar (Juniperus virginiana). The effects of these altered successional trajectories on bird biodiversity are difficult to quantify because of limited data on bird-habitat associations. We surveyed riparian shrubland and forest bird species across a gradient of riparian forest ages along two segments of the regulated Missouri River in South Dakota and Nebraska, USA and explored relationships between bird abundance and patch- and landscape-scale vegetation characteristics for 46 bird species. Predicted abundances at sites assigned to five vegetation classes, estimated from Bayesian binomial N-mixture models, identified 11 early successional bird species and 19 forest bird species. Abundances of early successional bird species were similar at cottonwood-willow sites and Russian olive sites and were positively correlated with cottonwood (Populus deltoides) importance values for only one species, Willow Flycatcher (Empidonax traillii). Abundances of forest bird species were similar at sites in the three forest vegetation classes, although Ovenbird (Seiurus aurocapilla) and Baltimore Oriole (Icterus galbula) showed some affinity for mid- or late successional cottonwood sites over late-successional non-cottonwood sites. Abundances of three forest species, including Baltimore Oriole, were positively correlated with cottonwood or negatively correlated with eastern red cedar importance values. Fifteen species were positively correlated with shrubland land cover, whereas 21 species were positively correlated with forest land cover. For most bird species, correlations were strongest with land cover within a 200-m buffer compared to 400 or 1200 m. These data suggest that the trends in riparian forest change due to river regulation along the middle Missouri River may produce a mix of positive and negative effects on riparian bird species. While management plans to promote regeneration of early successional cottonwood-willow stands are likely to benefit conservation of early successional bird species, Russian olive may also provide suitable bird habitat for the majority those species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".