Role of landscape composition and geographical location on breeding philopatry in grassland passerines : a stable isotope approach
Bibliographic record
Abstract
Grassland bird populations in North America are in steady decline.Despite declining faster and more consistently than any other group of birds, grassland songbird populations are relatively understudied and little is known about factors driving breeding-site philopatry and dispersal.Landscape and habitat composition may influence fidelity of grassland songbirds to a breeding area.As predicted by the theory of the Ideal Free Distribution, high-quality sites are likely to have a higher percentage of return breeders than low-quality sites because higher quality sites should have more or better-quality resources birds need for improved fitness.Using stable hydrogen isotope (D) analysis, I approximated minimum fidelity rates of two grassland songbirds to two landscape (grass-vs crop-dominated landscapes) and two habitat (native grass vs planted grass) types.I hypothesized that grassland songbirds would return more readily to higher quality sites.For Sprague's Pipit (Anthus spragueii), a habitat specialist, this would mean returning more readily to native grass habitat in grass dominated landscapes.I expected no difference in return rate of Savannah Sparrow (Passerculus sandwichensis), a habitat generalist, to either habitat or landscape.However, I found that the proportion of non-returning breeders was not influenced by landscape or habitat for either species.Furthermore, I examined attributes (distance from capture point to nearest crop and to the nearest road, as well as the percentage of native grass, planted grass, water and woody vegetation within landscape and territory buffers around the capture point) of the landscape and territory of each individual to determine if specific landscape or territory characteristics influenced their return rate to a breeding area.Neither species showed an affinity or aversion to any of the landscape or territory characteristics considered.At a larger scale, geographical position within the breeding range may influence dispersal rates of migratory songbirds.Given that environmental factors often change in a clinal manner, data, respectively.Many thanks to the staff of the Last Mountain Lake National Wildlife Area for their assistance and interest in
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".