Uncovering the mechanisms underpinning divergent environmental change impacts on biodiversity and ecosystem functioning
Bibliographic record
Abstract
Abstract Environmental change drivers (ECDs) impact ecological communities in various ways, from enrichment that increases species' performance and abundance, to stressors that reduce their reproduction and growth. These effects can affect species coexistence as well as impact ecosystem functioning and the relationship between biodiversity and ecosystem function (BEF). Predicting the impact of ECDs on communities and BEF relationships requires understanding how ECDs affect fundamental population parameters, including intrinsic rate of increase (r), carrying capacity (K), and interspecific interactions (). Here, we use numerical simulations based on theoretical models to show the explicit links between these parameters and the nature of BEF relationships. Depending on the mean and variance of the effects of ECDs on community members, BEF relationships increase or decrease in both their intercept and slope. We further derive hypotheses about how BEF relationships will be affected by multiple ECDs or when we consider multiple ecosystem functions. Our simple approach to understanding how ECDs affect BEF relationships provides a robust framework to explain why disparate studies and meta‐analyses come to opposing conclusions about resilience or sensitivity of BEF relationships to anthropogenic influences. We show that modeling approaches offer a generalized and predictive understanding to guide biodiversity conservation, restoration, and green infrastructure design under environmental change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".