Spatial turnover of multiple ecosystem functions is more associated with plant than soil microbial β‐diversity
Bibliographic record
Abstract
Abstract Biodiversity—both above‐ and belowground—influences multiple functions in terrestrial ecosystems. Yet, it is unclear whether differences in above‐ and belowground species composition (β‐diversity) are associated with differences in multiple ecosystem functions (e.g., spatial turnover in ecosystem function). Here, we partitioned the contributions of above‐ and belowground β‐diversity and abiotic factors (geographic distance, differences in environments) on the spatial turnover of multiple grassland ecosystem functions. We compiled a dataset of plant and soil microbial communities and six indicators of grassland ecosystem functions (i.e., plant aboveground live biomass, plant nitrogen [N], plant phosphorus [P], root biomass, soil total N, and soil extractable P) from 18 grassland sites on four continents contributing to the Nutrient Network experiment. We used Mantel tests and structural equation models to disentangle the relationship between above‐ and belowground β‐diversity and spatial turnover in grassland ecosystem functions. We found that the effects of abiotic factors on the spatial turnover of ecosystem functions were largely indirect through their influences on above‐ and belowground β‐diversity, and that spatial turnover of ecosystem function was more strongly associated with plant β‐diversity than with soil microbial β‐diversity. These results indicate that changes in above‐ and belowground species composition are one mechanism that interacts with environmental change to determine variability in multiple ecosystem functions across spatial scales. As grasslands face global threats from shrub encroachment, conversion to agriculture, or are lost to development, the functions and services they provide will more strongly converge with increased aboveground community homogenization than with soil microbial community homogenization.
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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 | 0.000 | 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) | 0.010 | 0.001 |
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".