Direct and indirect drivers of plant diversity responses to climate and clipping across northern temperate grassland
Bibliographic record
Abstract
It is well known that climate can influence plant community assembly via a multitude of indirect and direct pathways. However, interpretations of plant diversity responses to simulated climate change experiments, and subsequent predictions of plant communities under future climate scenarios, rarely address the importance of indirect effects. Networks of direct and indirect effects are also critical in understanding linkages between climate and grazing, a common land use of grasslands, and implications for plant diversity. We characterized the roles of indirect vs. direct effects in determining plant diversity responses to climate and grazing using data from three northern temperate grasslands in which we conducted factorial experiments manipulating precipitation, air temperature, and clipping intensity. Utilizing a structural equation modeling framework to address the multivariate networks, we found warming operated directly, causing species loss at all sites. We identified shoot biomass as the key indirect driver of diversity loss in response to both precipitation and clipping, regardless of site. However, site‐specific contingencies in the network of interactions were important for understanding varied precipitation effects. At the driest site only, shoot biomass was resistant to reduced precipitation, and diversity was consequently unaltered. Similarly, disconnect between primary drivers and responses explained relatively idiosyncratic responses of evenness compared to richness. Importantly, the finding of widespread, directly controlled plant diversity loss with warming aligns with concerns about declining biodiversity under climate change. However, using a framework of network interactions also allowed us to pinpoint the source of variability in response across systems. Looking forward, we can use the identification of this key indirect pathway to guide an understanding of community assembly under factors likely to control shoot biomass. Viewing a multifactorial, multisite experimental approach through a framework of network interactions allowed us to both identify generalized responses and distill the complexity of contingent responses. This, along with the practical need to identify diversity responses to climate change and grazing, underscores the importance of understanding both indirect and direct drivers of ecosystem responses to global change factors.
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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 | 0.001 |
| 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".