Grazing alters the sensitivity of plant productivity to precipitation in northern temperate grasslands
Bibliographic record
Abstract
Abstract Questions Inter‐annual variability in precipitation is expected to increase in grasslands, potentially causing additional stress to systems already impacted by anthropogenic activities such as livestock grazing, which can induce changes to grassland vegetation. Yet, the sensitivity of key ecosystem functions to these co‐occurring factors is often overlooked. Here, we determine: (a) the effects of grazing on the sensitivity of above‐ground net primary productivity (ANPP sensitivity) to inter‐annual variation in water‐year precipitation (the sum of precipitation from September through to the following August); (b) whether ANPP sensitivity to precipitation is associated with shifts induced by grazing in functional group biomass (grass vs forb) contribution to total ANPP, litter, and species richness, and mean annual water‐year precipitation; and (c) whether the impacts of grazing on ANPP vary between dry and wet years. Location Native grasslands in Alberta, Canada. Methods We used long‐term (14–28 years) ANPP and precipitation data from 31 grazed grasslands, each with a paired non‐grazed livestock exclosure. ANPP was sampled annually within exclosures and adjacent grazed locations at each site. Results We found that grazing increased ANPP sensitivity to inter‐annual changes in precipitation. Increased ANPP sensitivity to precipitation in grazed, relative to non‐grazed locations was associated with both an increase in the contribution of forbs to total ANPP and a decrease in the contribution of grasses to total ANPP; reduced litter also increased ANPP sensitivity to precipitation. Species richness was not associated with ANPP sensitivity in both grazed and non‐grazed locations. Arid grasslands were more sensitive to inter‐annual variation in precipitation when grazed than were mesic grasslands. Similarly, grazing reduced ANPP during dry years but had no effect during wet years. Conclusions Overall, these findings suggest that grazed grasslands are more vulnerable to reductions in primary productivity in dry years, which may present a challenge for maintaining ecosystem services in an era of increasing precipitation variability.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".