Wetland management practices and secretive marsh bird habitat in the Mississippi Flyway: a review
Bibliographic record
Abstract
Abstract Management regimes on publicly owned freshwater wetlands in the Mississippi Flyway of North America (i.e., Flyway) have historically emphasized waterfowl, but there is limited information on how waterfowl‐focused wetland management affects other wetland‐dependent wildlife. Secretive marsh birds (SMBs) depend on wetlands with emergent vegetation throughout their migratory life cycle and often encounter vegetation and water conditions resulting from waterfowl‐focused management regimes. Thus, there is a need for better understanding of how SMBs are affected by wetland management and the extent to which waterfowl‐focused management regimes provide habitat for SMBs. In this review, we identify the vegetation and water conditions resulting from typical management objectives on freshwater emergent wetlands in the Flyway, review and qualitatively synthesize results from studies that directly evaluate how wetland management practices affect SMBs or their habitat, and assess how the vegetation and water conditions being produced for target species (mainly waterfowl) align with SMB habitat requirements. We searched online databases and used Google Scholar to locate peer‐reviewed literature, technical reports, and graduate theses that pertained to responses of SMBs or their habitat to water‐level manipulation, herbicide application, prescribed fire, disking, mowing, and planting crops. There are several management strategies that complement SMBs and waterfowl, such as reducing cover of woody species and providing flooded emergent vegetation. We also highlight management strategies that may not currently align with SMB life‐cycle needs and suggest adjustments that might promote habitat for SMBs while still achieving waterfowl population objectives. For example, adjusting the dates and duration of spring water‐level drawdowns on a portion of wetlands within a larger complex can provide for spring migrating waterfowl and ensure habitat for migrating and nesting SMBs. Ideally, future studies would address how modifications to management practices affect SMBs and monitor potential effects on waterfowl, resulting in a more holistic approach to wetland management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".