Multivariate benthic ecosystem functioning in the Arctic – benthic fluxes explained by environmental parameters in the southeastern Beaufort Sea
Bibliographic record
Abstract
Abstract. The effects of climate change on Arctic marine ecosystems and their biogeochemical cycles are difficult to predict given the complex physical, biological and chemical interactions among the ecosystem components. We studied benthic biogeochemical fluxes in the Arctic and the influence of short-term (seasonal to annual), long-term (annual to decadal) and other environmental variability on their spatial distribution to provide a baseline for estimates of the impact of future changes. In summer 2009, we measured fluxes of dissolved oxygen, nitrate, nitrite, ammonia, soluble reactive phosphate and silicic acid at the sediment–water interface at eight sites in the southeastern Beaufort Sea at water depths from 45 to 580 m. The spatial pattern of the measured benthic boundary fluxes was heterogeneous. Multivariate analysis of flux data showed that no single or reduced combination of fluxes could explain the majority of spatial variation, indicating that oxygen flux is not representative of other nutrient sink–source dynamics. We tested the influence of eight environmental parameters on single benthic fluxes. Short-term environmental parameters (sinking flux of particulate organic carbon above the bottom, sediment surface Chl a) were most important for explaining oxygen, ammonium and nitrate fluxes. Long-term parameters (porosity, surface manganese and iron concentration, bottom water oxygen concentrations) together with δ13Corg signature explained most of the spatial variation in phosphate, nitrate and nitrite fluxes. Variation in pigments at the sediment surface was most important to explain variation in fluxes of silicic acid. In a model including all fluxes synchronously, the overall spatial distribution could be best explained (57%) by the combination of sediment Chl a, phaeopigments, δ13Corg, surficial manganese and bottom water oxygen concentration. We conclude that it is necessary to consider long-term environmental variability along with rapidly ongoing environmental changes to predict the flux of oxygen and nutrients across Arctic sediments even at short timescales. Our results contribute to improve ecological models predicting the impact of climate change on the functioning of marine ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".