Limited impacts of adaptive multi‐paddock grazing systems on plant diversity in the Northern Great Plains
Bibliographic record
Abstract
Abstract Grassland plant community structure and function are dependent on the type, timing, frequency and intensity of disturbance. Grazing systems employing dense herds of livestock for short periods of time (e.g. Adaptive Multi‐paddock Grazing; AMP) are gaining popularity as a potentially sustainable practice. Effects of AMP systems on plant diversity and composition are unknown, though theory provides some expectations. Spatially homogeneous grazing used by AMP may be a uniform ecological filter, thereby lowering plant diversity; alternatively, the AMP practice of using multiple paddocks might enhance habitat heterogeneity. Maintaining plant diversity, particularly of native species, is a key aspect of sustainability. As such, an understanding of the real‐world effects of AMP grazing is needed. We studied grasslands within 18 pairs of ranches across the northern Great Plains. Ranches managed under AMP were paired with a neighbouring ranch (N‐AMP) using regionally representative grazing practices. We collected surveys of management practices and conducted 2 years of on‐farm sampling to identify plant composition and diversity. Ranch management practices used by self‐identified AMP ranchers differed significantly (p < 0.1) from those used on neighbouring ranches, with higher stocking densities (number of animals per area at a single time) but not stocking rates (total number of animals per unit time per area) on AMP relative to N‐AMP ranches. There were fewer plant species in AMP grasslands at both the plot and landscape scales compared to N‐AMP ranches despite no overall difference in plant community composition. Management type did not alter the variability of plant community composition (beta diversity) or plot‐level species evenness. Although there were trends for lower diversity of native and introduced species at both spatial scales, a significant effect was found only for native species at the landscape scale. Synthesis and applications. The impacts of AMP grazing system management were limited to a minor reduction in plant diversity, with a modest decline in native species richness. We conclude that the benefits of AMP grazing in the northern Great Plains do not include the maintenance of plant diversity, and this system could hinder the conservation of remaining native plant species.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".