Niche partitioning of avian predators in northern grasslands amended by biosolids
Bibliographic record
Abstract
Abstract Many food webs are affected by bottom‐up nutrient addition, as additional biomass or productivity at a given trophic level can support more consumers. In turn, when prey are abundant, predators may converge on the same diets rather than partitioning food resources. Here, we examine the diets and habitat use of predatory and omnivorous birds in response to biosolids amendment of northern grasslands used as grazing range for cattle in British Columbia, Canada. From an ecosystem management perspective, we test whether dietary convergence occurred and whether birds preferentially used the pastures with biosolids. Biosolids treatments increased Orthoptera densities and our work occurred during a vole (Microtus spp.) population peak, so both types of prey were abundant. American Kestrels (Falco sparverius) consumed both small mammals and Orthoptera. Short‐eared Owls (Asio flammeus) and Long‐eared owls (Asio otus) primarily ate voles (>97% of biomass consumed) as did Northern Harriers (Circus hudsonius, 88% vole biomass). Despite high dietary overlap, these species had minimal spatial overlap, and Short‐eared Owls strongly preferred pastures amended with biosolids. Common Ravens (Corvus corax), Black‐billed Magpies (Pica hudsonia), and American Crows (Corvus brachyrhynchos) consumed Orthoptera, Coleoptera, vegetation, and only a few small mammals; crows avoided pastures with biosolids. Thus, when both insect and mammalian prey were abundant, corvids maintained omnivorous diets, whereas owls and Harriers specialized on voles. Spatial patterns were more complex, as birds were likely responding to prey abundance, vegetation structure, and other birds in this consumer guild.
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 | 0.000 | 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 | 0.001 | 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".