Vegetation and Soil Biodiversity Across Perennial Grassland-Annual Cropland Edges
Bibliographic record
Abstract
The agroecosystem is composed of a mosaic of land uses and management types. As such, edges are prevalent and can have biological and physical effects on the surrounding area. For a deeper understanding of edge effects, both aboveground and belowground processes must be considered. To address edge effects in the agroecosystem, we investigated both aboveground and belowground properties across perennial grassland-annual cropland edges in central Saskatchewan, Canada. Specifically, we examined aboveground vegetation, belowground soil properties, belowground vegetation, and soil microbial community composition across edges of managed perennial grasslands and croplands. An a priori structural equation model (SEM) was created to analyze relationships between aboveground and belowground changes across the edge, specifically looking at drivers of the soil microbial community. Overall, our SEMs demonstrated that soil total nitrogen positively influenced bacterial richness and bacterial richness negatively influenced fungal richness. Belowground plant richness, rather than aboveground plant richness, had a positive relationship with fungal richness. Aboveground living biomass was a positive driver of soil total carbon and total nitrogen. At the community level, soil bacteria and fungi appear to be directly influenced by soil properties and microbial interactions, rather than plants directly. However, further investigation into the fungal community revealed specific fungal genera abundance was influenced by plant richness, while some were not; and may be due to specific plant associations. Understanding edge effects in the agroecosystem may aid in developing better management practices, bringing benefits to both the producer and agroecosystem health and resilience.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".