Spatial community concordance of summer phytoplankton and profundal macroinvertebrates in boreal lakes
Bibliographic record
Abstract
Reliance of profundal benthic macroinvertebrate assemblages on epilimnetic algal production — a key element of pelagic–benthic coupling — is a well-recognized phenomenon in the literature but the details are still poorly understood. In particular, the importance of taxonomic composition of algal communities and associated qualitative aspects of the settling material to benthic invertebrates is poorly known. We addressed this issue by investigating concordance of summer phytoplankton and profundal macroinvertebrate community composition using data from 47 boreal lakes. We examined community variation and environmental correlates of phytoplankton and macroinvertebrates with nonmetric multidimensional scaling ordinations. We also used cluster analysis to group sites according to their macroinvertebrate assemblages and evaluated the differences in phytoplankton composition and class-specific relative abundance among the clusters. Community concordance was evaluated with Procrustes rotation and partial Mantel tests, using oxygen concentration and phytoplankton biomass — the main drivers of macroinvertebrate community structure — as controlling factors. Procrustes and Mantel tests suggested congruence between macroinvertebrate and summer phytoplankton community composition. The concordance remained significant even after controlling for oxygen and biomass, by partial Mantel tests, suggesting a unique effect of phytoplankton species composition on macroinvertebrate assemblages. The four groups of lakes clustered by their macroinvertebrate assemblages also differed significantly in their phytoplankton taxa composition, and there were among-cluster differences in relative abundance of the seven most abundant phytoplankton classes. Our findings of community concordance of phytoplankton and profundal macroinvertebrates suggest a functional dependency of these two components, but further studies are needed to verify the actual causal mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".