Environmental drivers alter PUFA content in littoral macroinvertebrate assemblages via changes in richness and abundance
Bibliographic record
Abstract
Shallow littoral areas in lakes are productive and highly diverse ecotonal zones, providing habitats for both invertebrate and vertebrate species. We developed a Bayesian modeling framework to elucidate the relationships between environmental drivers (lake typology, habitat, water chemistry, and latitude) and taxon richness, abundance, as well as the content of polyunsaturated fatty acids (PUFAs) in littoral macroinvertebrate communities in 95 boreal lakes. PUFAs, particularly arachidonic acid (ARA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), are critical micronutrients to maintain normal physiological functions in consumers. Lake typology was a significant predictor for PUFA content in the invertebrate assemblages, which was connected to taxon richness and/or abundance. Benthic communities in large humus-poor or nutrient-rich lakes displayed higher abundance, taxon richness, and more PUFA-rich taxa, whereas those in medium- and large-sized humic (color 30-90 mg Pt/L) and humus-rich lakes (color >90 mg Pt/L) were characterized by decreased abundance and subsequently low PUFA content. The abundance, taxon richness, and nutritional quality of the communities were also strongly related to latitude. Lakes with lower pH were characterized by lower benthic invertebrate diversity and low frequency of taxa with high somatic EPA and DHA content. The complexity of littoral habitats dominated by various macrophyte assemblages creates an environment that favors higher benthic abundance and increased presence of taxonomic groups with high PUFA content. Nutritional quality of benthic invertebrates for upper trophic levels can be modulated by a complex interplay between external stressors and abiotic factors that typically shape the structure of littoral benthic communities. Supplementary Information: The online version contains supplementary material available at 10.1007/s00027-023-00996-2.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".