Plant biotype interacting grazing activity shapes grassland ecosystem functions
Bibliographic record
Abstract
Abstract Grasslands play an essential role in maintaining the health of planet Earth, but many grasslands have lost their ecosystem services due to unsustainable management practices, such as overgrazing. Little is known about how grazing activity interacts with plant biotypes, impacting grassland ecosystem services. Here, we (1) assessed the relative performance of five plant biotypes in response to grazing activities and (2) determined the effectiveness of grazing exclusion in enhancing soil physiochemical properties in grasslands. The synthesis of 39,214 observations on plant‐, soil‐, and anthropogeny‐related factors from 88 published studies revealed that grazing exclusion increased aboveground plant biomass accumulation by 100.4% (±4.2 SE), belowground biomass by 70.2% (±25.7), total soil C content by 21.4% (±1.7), and soil organic carbon (SOC) concentration by 14.3% (±0.8), on average, as compared to moderate‐to‐heavy (MtH) grazing. Plant biotypes responded to grazing activities differently; alpine meadows increased total soil C content by 107.2%, alpine steppes increased SOC by 52.2%, but desert steppes decreased total C content by 21.8% under the grazing exclusion. All plant biotypes reduced soil bulk density by 6.4%–19.4% under grazing exclusion. Soil microbial community diversity responded to grazing activities inconsistently, ranging from an 18% decrease to a 26% increase in soil microbial diversity compared to MtH grazing. We conclude that selecting appropriate plant biotypes alongside improved grazing management will enhance grassland ecosystem functions and services as plant biotypes affect aboveground and belowground biomass and interface with soil physiochemical properties.
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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 | 0.000 |
| 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) | 0.001 | 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".