Trait clustering offers expanded insight into crustacean zooplankton metacommunity structuring processes in boreal lakes
Bibliographic record
Abstract
Functional trait similarity in metacommunities, important for community assembly and ecological resilience in the face of environmental changes on landscapes, is structured by niche and stochastic processes. Yet, uncertainties about the importance of these processes remain because opposing responses of different species to environmental variables can obscure the detection of niche patterns related to environmental variation. To counter this, we propose examining metacommunity assembly patterns detected within clusters of species that share similar functional traits. We focused on groups of crustacean zooplankton species in boreal lake metacommunities that are similar in body size, feeding guild, and phylogenetic distance. The crustacean zooplankton species composition of lake metacommunities in three regions of Quebec, Canada (Saguenay, Côte-Nord and Schefferville) were surveyed using organismal bulk sample cytochrome oxidase I (COI) metabarcoding. Species were clustered according to their similarity using the three traits, and redundancy analysis and variation partitioning were applied to parse the relative influences of niche processes (environmental variables) and stochastic processes (spatial vectors) in the structuring of the communities within-cluster, between-clusters, and unclustered. We found that stochastic processes were the main source of influence in all three regions, but especially in Schefferville, the northernmost region. Cladocerans were more strongly influenced by niche processes than copepods. Finally, we found that analyzing the community assembly processes for all zooplankton species together offered less explanatory power and missed environmental variables that were key in the structuring of the different groups of ecologically similar species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".