Divergent long‐term trends and interannual variation in ecosystem resource use efficiencies of a southern boreal old black spruce forest 1999–2017
Bibliographic record
Abstract
Abstract Long‐term trends in ecosystem resource use efficiencies (RUEs) and their controlling factors are key pieces of information for understanding how an ecosystem responds to climate change. We used continuous eddy covariance and microclimate data over the period 1999–2017 from a 120‐year‐old black spruce stand in central Saskatchewan, Canada, to assess interannual variability, long‐term trends, and key controlling factors of gross ecosystem production (GEP) and the RUEs of carbon (CUE = net primary production [NPP]/GEP), light (LUE = GEP/absorbed photosynthetic radiation [APAR]), and water (WUE = GEP/evapotranspiration [ E ]). At this site, annual GEP has shown an increasing trend over the 19 years ( p < 0.01), which may be attributed to rising atmospheric CO 2 concentration. Interannual variability in GEP, aside from its increasing trend, was most strongly related to spring temperatures. Associated with the significant increase in annual GEP were relatively small changes in NPP, APAR, and E , so that annual CUE showed a decreasing trend and annual LUE and WUE showed increasing trends over the 19 years. The long‐term trends in the RUEs were related to the increasing CO 2 concentration. Further analysis of detrended RUEs showed that their interannual variation was impacted most strongly by air temperature. Two‐factor linear models combining CO 2 concentration and air temperature performed well ( R 2 ~0.60) in simulating annual RUEs. LUE and WUE were positively correlated both annually and seasonally, while LUE and CUE were mostly negatively correlated. Our results showed divergent long‐term trends among CUE, LUE, and WUE and highlighted the need to account for the combined effects of climatic controls and the ‘CO 2 fertilization effect’ on long‐term variations in RUEs. Since most RUE‐based models rely primarily on one resource limitation, the observed patterns of relative change among the three RUEs may have important implications for RUE‐based modeling of C fluxes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".