Interactive effects of climate change and biodiversity loss on ecosystem functioning
Bibliographic record
Abstract
Climate change and biodiversity loss are expected to simultaneously affect ecosystems, however research on how each driver mediates the effect of the other has been limited in scope. The multiple stressor framework emphasizes non-additive effects, but biodiversity may also buffer the effects of climate change, and climate change may alter which mechanisms underlie biodiversity-function relationships. Here, we performed an experiment using tank bromeliad ecosystems to test the various ways that rainfall changes and litter diversity may jointly determine ecological processes. Litter diversity and rainfall changes interactively affected multiple functions, but how depends on the process measured. High litter diversity buffered the effects of altered rainfall on detritivore communities, evidence of insurance against impacts of climate change. Altered rainfall affected the mechanisms by which litter diversity influenced decomposition, reducing the importance of complementary attributes of species (complementarity effects), and resulting in an increasing dependence on the maintenance of specific species (dominance effects). Finally, altered rainfall conditions prevented litter diversity from fueling methanogenesis, because such changes in rainfall reduced microbial activity by 58%. Together, these results demonstrate that the effects of climate change and biodiversity loss on ecosystems cannot be understood in isolation and interactions between these stressors can be multifaceted.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".