Incorporating existing thermal tolerance into projections of compositional turnover under climate change
Bibliographic record
Abstract
Abstract Aim Observed, realized niche space often underestimates species’ physiological tolerances due to interactions with other species, dispersal constraints, and because some combinations of influential environmental factors do not currently exist in the real world. Conversely, correlative ecological niche models rely on the assumption that the range of environmental conditions encompassed by a species’ geographic distribution accurately reflects their environmental tolerances, including community‐level approaches like generalized dissimilarity modelling (GDM). We extend GDM to better understand what effect broader environmental tolerances could have on compositional turnover under climate change. Innovation We show how GDM can be adjusted as a function of best‐available estimates of the average ratio between realized and potential niche widths to modify projected temporal turnover. We demonstrate this approach by using the estimated niche ratios of Australian plant species (n = 7,184) relative to thermal extremes, and the rate at which this ratio varied with temperature. The modified GDMs showed existing thermal tolerance could reduce the turnover predicted by standard models under climate change by up to 11%. We further show how the reduction in expected turnover by 2090 will influence where a greater proportion of the current community will persist in a region. Main conclusions We suggest that standard spatial GDMs and their modified versions represent the extremes of ecological niche perspectives (i.e., realized and potential) and the range of tolerance communities may have when responding to environmental change. GDM projections therefore identify the range of uncertainty associated with a critical model assumption, and as climate change continues, ongoing community monitoring could be used to validate the balance between the two possibilities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".