Carbon allocation in boreal black spruce forests across regions varying in soil temperature and precipitation
Bibliographic record
Abstract
Abstract A common hypothesis for northern ecosystems is that low soil temperatures inhibit plant productivity. To address this hypothesis, we reviewed how separate components of ecosystem carbon (C) cycling varied along a soil temperature gradient for nine well‐drained, relatively productive boreal black spruce ( Picea mariana Mill. [B.S.P.]) forests in Alaska, USA, and Saskatchewan and Manitoba, Canada. Annual soil temperature [expressed as soil summed degree days (SDD)] was positively correlated with aboveground net primary productivity (ANPP), while negatively correlated with total belowground carbon flux (TBCF). The partitioning of C to ANPP at the expense of root processes represented a nearly 1 : 1 tradeoff across the soil temperature gradient, which implied that the amount of C cycling through these black spruce ecosystems was relatively insensitive to variation in SDD. Moreover, the rate at which C accumulated in the ecosystem since the last stand replacing fire was unrelated to SDD, but SDD was positively correlated to the ratio of spruce‐biomass : forest‐floor‐mass. Thus, plant partitioning of C and the distribution of ecosystem C were apparently affected by soil temperature, although across regions, precipitation co‐varied with soil temperature. These two factors likely correlated with one another because of precipitation's influence on soil heat balance, suggesting that a soil temperature–precipitation interaction could be responsible for the shifts in C allocation. Nonetheless, our results highlight that for this boreal ecosystem, ANPP and TBCF can be negatively correlated. In tropical and temperate forests, TBCF and ANPP have been reported as positively correlated, and our results may reflect the unique interactions between soil temperature, forest floor accumulation, rooting depth, and nutrient availability that characterize the black spruce forest type.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".