A new methodology for reconstructing climate and vegetation from modern pollen assemblages: an example from British Columbia
Bibliographic record
Abstract
Abstract Aim We used modern pollen assemblages to develop a method for climate reconstruction that reduces the spatial autocorrelation of residuals and accounts for the strong topographic and climatic variation that occurs in British Columbia, Canada. Location British Columbia, Canada, including sites both on the mainland and on adjacent islands (Queen Charlotte Islands and Vancouver Island). Methods New pollen assemblages from surface‐sediment samples collected in British Columbia were combined with other published and unpublished samples (n = 284). Multivariate rank‐distances between sample sites and a randomized set of sites within the province were calculated for climate parameters to determine whether gaps in the current network of present‐day pollen sample sites exist. Lacustrine surface‐sediment pollen assemblages (n = 145) were ordinated using non‐metric multidimensional scaling (NMDS), and a generalized additive model (GAM) was used to reconstruct modern mean warmest month temperature (MWMT) and mean annual precipitation (MAP) from the NMDS ordination. The results were compared with standard climate reconstruction techniques, including the modern analogue technique, partial least squares, weighted averaging, weighted averaging–partial least squares and factor analysis. Results Reconstructions of MWMT and MAP using NMDS and GAM were comparable to those of existing models. When reconstructing MWMT, the NMDS/GAM method had a lower root‐mean‐squared error of prediction (RMSEP), lower spatial autocorrelation and higher correlation with observed temperature values than the other methods tested. When reconstructing MAP, the partial least squares method performed better than the NMDS/GAM method for RMSEP and correlation with observed values; however, the NMDS/GAM method had a lower spatial autocorrelation of residuals. Main conclusions NMDS reveals strong relationships among modern pollen assemblages, vegetation and climate parameters. Climate models using NMDS and GAM are comparable to other palaeoecological reconstruction models, but provide lower spatial autocorrelation of residuals for both parameters tested. An inverse distance‐weighted surface of multivariate rank‐climate distances generated from the network of pollen sample sites indicates that greater sampling intensity in north‐western and central‐interior British Columbia is required in order to obtain an accurate representation of climatic and vegetation diversity in the province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".