Large‐scale multi‐trophic co‐response models and environmental control of pelagic food webs in Québec lakes
Bibliographic record
Abstract
Environmental heterogeneity plays a fundamental role in driving species distributions by, for one, fostering niche dimensionality. Within lake ecosystems, species distributions and concordance patterns are driven by both local and regional heterogeneity, though their relative importance across trophic levels has rarely been explored. We developed a statistical framework to compare responses of taxa from different trophic levels to abiotic factors and determine how this affected multi‐trophic network structures. In particular, we used multi‐species concordance modelling (concordance analysis and RV coefficient) to determine species associations and correlations within and among three trophic levels (phytoplankton, zooplankton and fish communities sampled across 49 southern Québec lakes, covering eight hydrological regions). We then used multiple factor analysis, latent variable modelling and local contributions of sites to beta diversity to assess the relative importance of major environmental gradients in structuring species co‐responses and species interaction turnover across the landscape. Our analyses confirmed that concordant species within each trophic level varied jointly or segregated into different pelagic food webs in Québec lakes where important acidification and eutrophication took place. Some keynote species were indicators of different food web compartments and distinguished groups of lakes along multiple environmental niche dimensions. Among the three trophic levels examined, zooplankton depicted the highest proportion of species concordance and appeared to act as a trophic linkage between phytoplankton and fish. Ultimately, the losses or gains in species richness and species interactions were strongly driven by environmental gradients. This study provides for the first time a combined analysis of the effects of environmental heterogeneity on ecological communities belonging to three trophic levels sampled near simultaneously across an 800 km broad lacustrine landscape. The new framework developed in this study has a great potential to better understand the complex response of aquatic ecosystems in a world increasingly affected by multiple, cumulative stressors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".