Management strategies for black spruce (Picea mariana (Mill.) B.S.P.) in the face of climate change: climatic niche, clines, climatypes, and seed transfer
Bibliographic record
Abstract
Over 200 000 forest inventory and ecological ground plots representing North America north of 36 degrees latitude were used to develop a climate niche model predicting the current distribution of black spruce (Picea mariana (Mill.)) across its natural range. The resulting 8-variable Random Forest algorithm had a 4.4 per cent overall error rate. This error was primarily a function of errors of commission, i.e. predicting presence of black spruce for plots in which it was absent (error = 6 per cent). In contrast, errors of omission, predicting an absence of black spruce when it was present, was 0.1 per cent. Height growth data from four disparate provenance test series containing a total of 316 populations were analysed using linear mixed model procedures to model the pattern of ecological genetic variation. The resulting model accounted for 62 per cent of the observed variation among populations. Climatic surfaces for the IPCC RCP6.0 scenario at three time steps (decades centred on 2030, 2060 and 2090) projected early and sustained geographic shifts in the realized climatic niche. Approximately 56 per cent of the contemporary distribution is predicted to be lost or threatened habitat by 2060. Mapped projections indicate the shift in the trailing edge encompasses the entire managed boreal forest in Canada. Emergent suitable habitat totalled 28 per cent. Projections of the ecological genetic model into the climate of the decade centred on 2060 indicate the challenge for forest management is in assuring a timely transfer of trailing edge populations to the future location of the climates for which they are optimally suited.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".