Predicting potential climate change impacts with bioclimate envelope models: a palaeoecological perspective
Bibliographic record
Abstract
ABSTRACT Aim We assess the realism of bioclimate envelope model projections for anticipated future climates by validating ecosystem reconstructions for the late Quaternary with fossil and pollen data. Specifically, we ask: (1) do climate conditions with no modern analogue negatively affect the accuracy of ecosystem reconstructions? (2) are bioclimate envelope model projections biased towards under‐predicting forested ecosystems? (3) given a palaeoecological perspective, are potential habitat projections for the 21st century within model capabilities? Location Western North America. Methods We used an ensemble classifier modelling approach (RandomForest) to spatially project the climate space of modern ecosystem classes throughout the Holocene (at 6000, 9000, 11,000, 14,000, 16,000, and 21,000 YBP) using palaeoclimate surfaces generated by two general circulation models (GFDL and CCM1). The degree of novel arrangement of climate variables was quantified with the multivariate Mahalanobis distance to the nearest modern climatic equivalent. Model projections were validated against biome classifications inferred from 1460 palaeoecological records. Results Model accuracy assessed against independent palaeoecology data is generally low for the present day, increases for 6000 YBP, and then rapidly declines towards the last glacial maximum, primarily due to the under‐prediction of forested biomes. Misclassifications were closely correlated with the degree of climate dissimilarity from the present day. For future projections, no‐analogue climates unexpectedly emerged in the coastal Pacific Northwest but were absent throughout the rest of the study area. Main conclusions Bioclimate envelope models could approximately reconstruct ecosystem distributions for the mid‐ to late‐Holocene but proved unreliable in the Late Pleistocene. We attribute this failure to a combination of no‐analogue climates and a potential lack of niche conservatism in tree species. However, climate dissimilarities in future projections are comparatively minor (similar to those of the mid‐Holocene), and we conclude that no‐analogue climates should not compromise the accuracy of model predictions for the next century.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".