Similar Bird Communities Across Grazing Systems in the Nebraska Sandhills
Bibliographic record
Abstract
ABSTRACT Much of the remaining native rangeland in the Great Plains in the United States is privately owned and managed for beef production, and this single priority for land use may be contributing to declining avian biodiversity through a loss of structural heterogeneity. One proposed solution is to use multiple grazing systems across ranches, under the assumption that this approach will increase heterogeneity of vegetation structure and avian diversity across the landscape. We tested the relationship between grazing systems and avian diversity in the Nebraska Sandhills during 2014 and 2015 on a landscape that included 11 management units containing 5 different grazing systems. We used multivariate models to examine the relationship of bird diversity and communities to grazing systems at the management unit scale, and we used simulations to combine empirical data from ≥1 grazing system into virtual landscapes to test the hypothesis that multiple grazing systems would result in greater heterogeneity. The 5 most common avian species made up 84% of observations (28 species), and songbird richness was 5–6 species/7.06 ha at 53% of our plots. Variation in each of the diversity measures (Shannon diversity range = 0.41–2.2, Simpson's diversity range = 0.24–0.88) was best explained by the previous dormant season's stocking rate, and richness declined by about 1 species/plot with an increase in 1 animal unit month (AUM)/ha. Songbird community structure showed the most variance between management unit, but grazing system explained little community variation. None of the simulated landscapes consistently had greater structural heterogeneity of visual obstruction reading, litter depth, and cover of bare ground than others, and there was a limited level of heterogeneity overall in the simulated landscapes. In contrast to our predictions, a variety of grazing systems did not increase heterogeneity of vegetation structure across the landscape. Thus, conservation practitioners should encourage the use of other strategies to create structural heterogeneity, such as prescribed fires and extreme stocking rates, which will support a diverse grassland songbird community (i.e., a greater variety of bird species) across the landscape. © 2020 The Wildlife Society.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".