Community assembly processes restrict the capacity for genetic adaptation under climate change
Bibliographic record
Abstract
Given the magnitude and rate of ongoing climate change, the physiological capacity of species to tolerate extreme conditions will play a key role in influencing outcomes for biodiversity. It is also possible that species will respond to changes in climate by shifting their physiological tolerances, through genetic adaptation. How these processes influence biodiversity outcomes will be crucial in determining the most suitable management responses to retain diversity into the future. Here we assess how accounting for physiological tolerances, genetic adaptation and community assembly processes such as species replacement, influence projected climate change outcomes for the flora of Tasmania (all 2051 plant species). We incorporate these processes into the M‐SET metacommunity model and compare four different assumptions of species niches: realized niches, broader physiological tolerances and low or high capacity for genetic adaptation. Accounting for physiological tolerances rather than realized niches had the largest impact on projected outcomes, with 358 fewer species extinctions in the hottest climate scenario (mean = 30 extinctions). In contrast, adding the capacity for species physiological tolerances to shift through genetic adaptation resulted in little additional benefits for biodiversity outcomes, even under an optimistic level of adaptive capacity. We find that this is due largely to community assembly processes such as species replacement restricting the ability of species to persist and adapt in situ, as has been suggested from theoretical metacommunity models applied in simple artificial settings. Our results highlight the importance of accounting for species physiological tolerances and community‐level processes in biodiversity projections, while the potential role for genetic adaptation may be small, requiring further exploration in alternative contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".