Do correlated responses to multiple environmental changes exacerbate or mitigate species loss?
Bibliographic record
Abstract
Biological communities face multiple global changes simultaneously, and predicting how they will respond remains a key challenge. Co‐tolerance theory offers a framework for understanding how species‐level responses to multiple stressors affect community properties. Co‐tolerance theory predicts that positive correlations in species responses (i.e. species that are susceptible to one stressor are more likely to be highly susceptible to a second) lessen total species loss, essentially because species cannot be eliminated from a community twice. However, it is unclear whether several of the tenets of co‐tolerance theory describe real‐world communities, and what consequences result from such deviations. Here, we use an empirical dataset of bird community response to land‐use change over a climate gradient to examine co‐tolerance theory's tenet that environmental changes only harm species (not benefit them). We show that this tenet is not met, and then use simulations to examine how predictions of total species richness and community intactness vary when multiple environmental changes both harm and benefit particular species in the community. Finally, we conduct a sensitivity analysis, examining how the average species response to environmental change, as well as the variance among species, can further alter predictions. Overall, we find that predictions of co‐tolerance theory can break down when communities contain species that benefit from some environmental changes. As a result, the presence of multiple environmental changes can either compound or mitigate species loss when species’ responses are positively correlated, preventing a one‐size‐fits‐all statement regarding the effects of correlated responses. This finding highlights the need to carefully consider the underlying mechanisms of community change when making policy assessments regarding the consequences of correlations of species responses to environmental impacts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; both teacher heads agree on what is shown here.
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".