Land management trumps the effects of climate change and elevated <scp>CO</scp><sub>2</sub> on grassland functioning
Bibliographic record
Abstract
Summary Grasslands cover ˜30% of the Earth's terrestrial surface and provide many ecosystem services. Many grasslands are heavily managed to maximize these services for human benefit, but the outcome of management is anticipated to be increasingly influenced by various aspects of climate change and elevated atmospheric CO 2 . The relative importance of global change vs. land management on grasslands is largely unknown. A meta‐analysis is used here to examine drivers at both scales primarily targeting services provided by grasslands relating to plant productivity (above‐ and below‐ground biomass) and soil processes (nutrients and soil respiration) in 38 manipulative experiments published in the last decade. We specifically target effects of (i) single and combined land management practices ( LM s), (ii) single and combined factors relating to broad‐scale climate change and elevated CO 2 , and (iii) combined management practices and changes to climate and CO 2 . Collectively, this examines the general efficacy of global change models in predicting changes to grassland functioning. We found that combinations of management practices had approximately double the explanatory power for variation in grassland services compared with individual or interactive effects of factors associated with climate change and CO 2 . These interacting management practices such as nutrient additions and defoliation predominantly influenced functions associated with productivity or biomass both below and above ground. The effects of interacting factors of climate and CO 2 influenced a wider range of ecosystem functions, but the magnitude of these effects was relatively smaller. Interactions between management practices or between climate change/ CO 2 factors always had higher explanatory power than any factor in isolation indicating that multivariate synergistic models of environmental change can better describe impacts on ecosystem function in plant communities (e.g. relative to univariate climate‐based models). Given that the magnitude and direction (positive or negative) of the interactions varied widely, this also implies that the outcomes of these multivariate interactions can vary spatially, temporally or by immediate context (e.g. management prescriptions). Synthesis . Although our work confirms how climate change and CO 2 can affect many ecosystem‐based functional attributes, it suggests that combinations of LM s remain the dominant set of factors in determining the performance of grassland plant communities. Land management may thus be critical for influencing projected responses to future climate change and elevated CO 2 in models of grassland function at least for factors relating to primary production.
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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 | 0.001 | 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) | 0.000 | 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".