Managing for delicious ecosystem service under climate change: can United States sugar maple (<i>Acer saccharum</i>) syrup production be maintained in a warming climate?
Bibliographic record
Abstract
Sugar maple (Acer saccharum) is a highly valued tree in United States (US) and Canada, and its sap when collected from taps and concentrated, makes a delicious syrup. Understanding how this resource may be impacted by climate change and other threats is essential to continue management for maple syrup into the future. Here, we evaluate the current distribution of maple syrup production across twenty-three states within the US and estimate the current potential sugar maple resource based on tree inventory data. We model and project the potential habitat responses of sugar maple using a species distribution model with climate change under two future General Circulation Models (GCM) and emission scenarios and three time periods (2040, 2070, 2100). Our results show that under GFDL-A1Fi (high CO2 emissions), sugar maple habitat is projected to decline (mean ratio of future habitat to current habitat per state = 0.46, sd ± 0.33), which could lead to reduced maple syrup production per tree and nearly 5 million additional taps required to maintain current projection levels. If global emissions are reduced and follow a lower trajectory of warming (under PCM-B1), then habitat for the species may be maintained but would still require management intervention. Finally, our results point to regions, particularly along the northern tier, where both climate change impacts and currently developing sugar maple habitat may signify viable opportunities to increase maple syrup production.EDITED BY Christine Fürst EDITED BY Christine Fürst
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".