One hundred and six years of change in a <scp>S</scp>onoran <scp>D</scp>esert plant community: Impact of climate anomalies and trends in species sensitivities
Bibliographic record
Abstract
A major restriction in predicting plant community response to future climate change is a lack of long-term data needed to properly assess species and community response to climate and identify a baseline to detect climate anomalies. Here, we use a 106-year dataset on a Sonoran Desert plant community to test the role of extreme temperature and precipitation anomalies on community dynamics at the decadal scale and over time. Additionally, we tested the climate sensitivity of 39 desert plant species and whether sensitivity was associated with growth form, longevity, geographic range, or local dominance. We found that desert plant communities had shifted directionally over the 106 years, but the climate had little influence on this directional change primarily due to nonlinear shifts in precipitation anomalies. Decadal-scale climate had the largest impact on species richness, species relative density, and total plant cover, explaining up to 26%, 45%, and 55% of the variance in each, respectively. Drought and the interaction between the frequency of freeze events and above-average summer precipitation were among the most influential climate factors. Increased drought frequency and wetter periods with frequent freeze events led to larger reductions in total plant cover, species richness, and the relative densities of dominant subshrubs Ambrosia deltoidea and Encelia farinosa. More than 80% of the tested species were sensitive to climate, but sensitivity was not associated with a species' local dominance, longevity, geographic range, or growth form. Some species appear to exhibit demographic buffering, where when they have a higher sensitivity to drought, they also tend to have a higher sensitivity to favorable (i.e., wetter and hotter) conditions. Overall, our results suggest that, while decadal-scale climate variation substantially impacts these desert plant communities, directional change in temperature over the last century has had little impact due to the relative importance of precipitation and drought. With projections of increased drought in this region, we may see reductions in total vegetation cover and species richness due to the loss of species, possibly through a breakdown in their ability to demographically buffer climatic variation, potentially changing community dynamics through a change in facilitative and competitive processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".