Species‐Habitat Relationships and Priority Areas for Marsh‐Breeding Birds in Ontario
Bibliographic record
Abstract
ABSTRACT Populations of marsh‐breeding birds have declined throughout the southern Laurentian Great Lakes basin. To advance conservation of these species, we used occupancy modeling, a regional prioritization scheme, and data from Birds Canada's Great Lakes Marsh Monitoring Program (2016–2018) to describe species‐habitat relationships and identify priority habitat areas for 7 obligate marsh‐breeding bird species in southern Ontario, Canada: American bittern ( Botaurus lentiginosus ), common gallinule ( Gallinula galeata ), least bittern ( Ixobrychus exilis ), marsh wren ( Cistothorus palustris ), pied‐billed grebe ( Podilymbus podiceps ), sora ( Porzana carolina ), and Virginia rail ( Rallus limicola ). Given these species respond to land cover at widely varying spatial scales, we initially identified the most informative scale (buffer = 100 m, 200 m, 400 m, 800 m, 1,600 m, 3,200 m, or 6,400 m) for marsh, urban, agricultural, and forest cover to increase model performance. We also considered climate variables, whether sample sites were along a Great Lakes coastline or inland, and covariates influencing detection. Occupancy was best explained by land cover at a wide range of spatial scales depending on the species. All species except Virginia rail responded positively to marsh cover; American bittern and Virginia rail responded negatively to urban cover; least bittern, pied‐billed grebe, and Virginia rail responded negatively and sora responded positively to agricultural cover; and American bittern, common gallinule, marsh wren, and pied‐billed grebe responded negatively and Virginia rail responded positively to forest cover. Only American bittern responded negatively to mean May–June temperature; only pied‐billed grebe responded positively to start of growing season; and only Virginia rail had higher occupancy at inland marshes compared to coastal. We combined predictions from the best model for each of 5 species with reasonably good model fit (we excluded sora and Virginia rail) to identify priority habitat areas for marsh‐breeding birds. Expansion of wetland conservation work from existing priority areas based on waterfowl to also include these new additional priority areas based on marsh‐breeding birds will be an important step towards conservation of all birds, and will help slow or maybe even reverse declining population trends. Some restoration activities outside but adjacent to priority areas will also be important for rebuilding marshes for these species across this intensively farmed and developed region. © 2020 The Wildlife Society.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.003 | 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 teacher head, 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".