Precipitation alters interactions in a grassland ecological community
Bibliographic record
Abstract
Climate change is transforming precipitation regimes world-wide. Changes in precipitation regimes are known to have powerful effects on plant productivity, but the consequences of these shifts for the dynamics of ecological communities are poorly understood. This knowledge gap hinders our ability to anticipate and mitigate the impacts of climate change on biodiversity. Precipitation may affect fauna through direct effects on physiology, behaviour or demography, through plant-mediated indirect effects, or by modifying interactions among species. In this paper, we examined the response of a semi-arid ecological community to a fivefold change in precipitation over 7 years. We examined the effects of precipitation on the dynamics of a grassland ecosystem in central California from 2007 to 2013. We conducted vegetation surveys, pitfall trapping of invertebrates, visual surveys of lizards and capture-mark-recapture surveys of rodents on 30 plots each year. We used structural equation modelling to evaluate the direct, indirect and modifying effects of precipitation on plants, ants, beetles, orthopterans, kangaroo rats, ground squirrels and lizards. We found pervasive effects of precipitation on the ecological community. Although precipitation increased plant biomass, direct effects on fauna were often stronger than plant-mediated effects. In addition, precipitation altered the sign or strength of consumer-resource and facilitative interactions among the faunal community such that negative or neutral interactions became positive or vice versa with increasing precipitation. These findings indicate that precipitation influences ecological communities in multiple ways beyond its recognized effects on primary productivity. Stochastic variation in precipitation may weaken the average strength of biotic interactions over time, thereby increasing ecosystem stability and resilience to climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".