Constructing Isoscapes of <scp> δ <sup>13</sup> C </scp> , <scp> δ <sup>15</sup> N </scp> and <scp> δ <sup>34</sup> S </scp> Baselines Within a River System: A Spatial Stream Network Modelling Approach
Bibliographic record
Abstract
ABSTRACT Stable isotopes of carbon (δ 13 C), nitrogen (δ 15 N), and sulphur (δ 34 S) provide insights into freshwater food webs, but spatial variation in values of organisms at lower trophic levels often complicate interpretations. This is especially true in river ecosystems, in which hierarchical networks, directional flows, and aquatic‐terrestrial linkages shape the ecosystem processes that determine the isotopic composition of bioavailable molecules at the base of food webs. To advance understanding of spatial variation in these natural tracers at or near the base of river food webs, we fit predictive spatial models relating the δ 13 C, δ 15 N and δ 34 S of epibenthic biofilms and macroinvertebrate taxa to landscape variables within a north‐temperate river ecosystem. Stable isotope ratios were determined in biofilms (δ 15 N and δ 13 C) and three macroinvertebrate taxa (δ 15 N, δ 13 C and δ 34 S) commonly used as baselines for isotopic food web analyses (heptageniid mayflies, hydrobiid snails, amphipod crustaceans) across 30 sites in the Boardman/Ottaway River (BOR), Michigan, USA, in the spring of 2022. Spatial stream network implementations of linear mixed‐effects regression models (SSNLMM) were used to estimate land cover covariation with the isotopic composition of biofilms (δ 13 C and δ 15 N) and macroinvertebrates (δ 13 C, δ 15 N and δ 34 S), and to generate isoscapes for each element across the river network. Spatial patterns in δ 13 C and δ 34 S were similar across macroinvertebrate taxa, whereas biofilm and macroinvertebrates differed substantially in δ 13 C. Patterns in δ 15 N were less consistent, associated most often with forest, wetland, and crop land covers. However, spatial and aspatial errors generally accounted for more variance than fixed effects. Variation in δ 15 N was 2‰–6‰ in biofilms and 2‰–9‰ in macroinvertebrate taxa, indicating that these macroinvertebrates are primary consumers or detritivores. Isoscapes predicted higher δ 13 C for biofilms (−28‰ to −17‰) than for macroinvertebrates (−39‰ to −25‰), indicating that macroinvertebrates consumed non‐biofilm food resources, although the difference between macroinvertebrate and biofilm δ 13 C varied across the river system. Isoscapes predicted variation in Heptageniidae δ 34 S from −5‰ to 0‰, although predictive performance was weak, possibly due to small sample size. These mayflies were strongly 13 C‐depleted relative to biofilms and to the expected δ 13 C of terrestrial inputs in areas of the BOR that also tended to be less 34 S‐depleted, suggesting the assimilation of material produced via an alternative energy flow pathway. SSNLMMs provided novel insights into the type and strength of land cover drivers of δ 13 C, δ 15 N and δ 34 S at fine spatial scales for basal food web taxa in temperate rivers. Together, isoscape maps and inferences about landscape influences help explain spatial and taxonomic variation in isotope baselines, facilitating the assessment of river food web responses to environmental change.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".