Risks to biodiversity from temperature overshoot pathways
Bibliographic record
Abstract
Abstract Temperature overshoot pathways entail exceeding a specified global warming level (e.g. 1.5°C or 2°C) followed by a decline in warming, achieved through anthropogenically enhanced CO2 removal from the atmosphere. However, risks to biodiversity from temperature overshoot pathways are poorly described. Here, we explore biodiversity risks from overshoot by synthesizing existing knowledge and quantifying the dynamics of exposure and de-exposure to potentially dangerous temperatures for more than 30 000 species for a 2°C overshoot scenario. Our results suggest that climate risk to biodiversity from temperature overshoot pathways will arrive suddenly, but decrease only gradually. Peak exposure for biodiversity occurs around the same time as peak global warming, but the rate of de-exposure lags behind the temperature decline. While the global overshoot period lasts around 60 years, the duration of elevated exposure of marine and terrestrial biodiversity is substantially longer (around 100 and 130 years, respectively), with some ecological communities never returning to pre-overshoot exposure levels. Key biodiversity impacts may be irreversible and reliance on widespread CO2 removal to reduce warming poses additional risks to biodiversity through altered land use. Avoiding any temperature overshoot must be a priority for reducing biodiversity risks from climate change, followed by limiting the magnitude and duration of any overshoot. More integrated models that include direct and indirect impacts from overshoot are needed to inform policy. This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".