Water chemistry, landscape, and spatial controls of δ<sup>13</sup>C and δ<sup>15</sup>N of zooplankton taxa in boreal lakes: One size does not fit all
Bibliographic record
Abstract
Abstract Carbon (δ13C) and nitrogen (δ15N) stable isotope ratios of zooplankton are potentially good indicators of energy and nutrient fluxes, and trophic interactions in lake food‐webs, yet, are poorly understood. Based on a synoptic survey of 233 boreal lakes, we evaluated the relationships of water chemistry, hydromorphological, and land cover variables (lake/catchment‐specific factors), and the spatial position of lakes in the landscape (representing potential regional factors) with the δ13C and δ15N of nine meso‐zooplankton taxa. The δ13C variation of most taxa was negatively related to water chemistry variables associated with allochthonous inputs (colour/dissolved organic carbon), and positively correlated with nutrient concentrations (together explaining 12–69% of variation). Most of the δ13C variation explained by significant hydromorphological and land cover variables (% peat area, drainage basin area:lake area, shoreline development index, and lake area, explaining 26–47% of variation) was shared by significant water chemistry variables. Together, this suggests that δ13C variation of zooplankton reflects different environmental influences on δ13C of lake primary producers. The δ15N variation of most taxa was significantly related to pH, total phosphorous concentration (water chemistry variables, explaining 8–64% of variation), and water retention time (hydromorphological variable, explaining 13–87% of variation). These relationships are probably reflective of the association of zooplankton δ15N with terrestrial organic matter flux, environmentally‐induced biogeochemical nitrogen transformations and phytoplankton 15N fractionation. Specific water chemistry, hydromorphological, and land cover predictors of δ13C and δ15N and the direction of their effects (i.e. positive or negative) were largely similar among taxa. However, their degree of importance varied among taxa particularly for δ13C, probably due to contrasting feeding selectivity and resulting differences in allochthony. This suggests that δ13C of different taxa may respond differently to changes in limnological gradients due to environmental perturbations affecting boreal regions. The lake/catchment‐specific factors were more important than regional factors (lithology, soil properties and atmospheric nitrogen deposition; which are represented by the spatial position of lakes) in explaining δ13C and δ15N variation of zooplankton. Hence, before inferring regional effects in stable isotope studies, the influence of lake/catchment‐specific factors needs to be explicitly quantified.
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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 | 0.000 | 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 | 0.001 | 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".