Breeding bird use and wetland characteristics of diked and undiked coastal marshes in Michigan
Bibliographic record
Abstract
Abstract Dikes were built on Great Lakes coastal wetlands to enable water level management for wetland wildlife, particularly waterfowl, but few studies have compared bird use of these areas to undiked sites. During 2005–2007, we evaluated 9 diked and 7 undiked coastal wetlands at the St. Clair Flats (Lake St. Clair) and Saginaw Bay (Lake Huron) of Michigan, USA. We compared bird use of diked and undiked wetlands via 605 10‐minute point counts at randomly selected locations of emergent marsh and 287 45‐minute surveys of randomly selected open water areas. We also measured wetland characteristics in 1,521 randomly selected 0.25‐m2 quadrats to compare vegetation and physical conditions between diked and undiked wetlands. Diked wetlands had greater coverage and density of cattail (Typha spp.), coverage of floating‐leaved plants, water depth, and organic sediment depth compared to nearby undiked sites, whereas undiked wetlands had greater coverage and density of common reed (Phragmites australis) and bulrush (Schoenoplectus spp.) than diked wetlands. Bird species richness and similarity indices indicated comparable breeding bird communities. We observed greater abundances of Canada goose (Branta canadensis), wood duck (Aix sponsa), American bittern (Botaurus lentiginosus), least bittern (Ixobrychus exilis), and common gallinule (Gallinula galeata) in diked wetlands. These species likely responded to the deep‐water cattail marsh and aquatic bed dominating most diked sites. American coot (Fulica americana), Forster's tern (Sterna forsteri), ring‐billed gull (Larus delawarensis), and herring gull (Larus argentatus) abundance indices were greater in undiked wetlands, likely related to nesting and foraging habitat provided by the shallower, more open wetlands and connecting lakes. Diked wetlands did not benefit the bird community to the degree expected and conditions in diked areas were indicative of deep marshes with stabilized water levels. Periodic late‐summer drawdowns could encourage growth of plants we found associated with greater abundance of some priority bird species and reduction of floating vegetation negatively associated with abundance of several species. However, effective control of invasive common reed is needed to reduce risk of expansion during impoundment dewatering. © 2013 The Wildlife Society.
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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 | 0.000 |
| 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) | 0.001 | 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".