Multi‐scale biodiversity analyses identify the importance of continental watersheds in shaping lake zooplankton biogeography
Bibliographic record
Abstract
Abstract Aim We examined variation in crustacean zooplankton composition and diversity across Canada, the most lake‐rich country in the world. In addition to α‐diversity patterns, we explore mechanisms behind β‐diversity spatial variation, using taxonomic and functional metrics. Our goal was to explore geographical gradients and related mechanisms shaping zooplankton distribution across different spatial scales. Location Canada. Taxon Crustacean zooplankton. Methods Pelagic zooplankton was sampled in and characterized for 624 lakes across Canada, spanning 12 ecozones (defined by climatic, vegetation and geological differences) or 6 continental drainage basins as part of the NSERC Canadian Lake Pulse Network project. We compared taxon and trait distributions, as well as spatial (longitudinal and latitudinal) patterns of community composition and diversity. We computed taxonomic and functional spatial β‐diversity indices, decomposing these into taxon replacement and richness differences. Finally, species (or traits) and lake uniqueness (contributions) to β‐diversity (SCBD and LCBD) were estimated by ecozone and continental basin. Results In all, 90 crustacean zooplankton species were identified across the country. Differences in zooplankton taxonomic and functional composition were more distinct when considered by continental basin than by ecozone. α‐diversity varied greatly across space, with greatest diversity in eastern Canada. β‐diversity was greatest when based on taxonomy and was driven by richness differences across all spatial and biodiversity dimensions. Main conclusions Structuring factors influencing zooplankton taxon and trait assemblages vary across spatial scales, being more important at the broadest scale considering continental hydrodynamics. Our results point to the combined effects of physical barriers to longitudinal dispersal and climate change in shaping zooplankton taxonomic and functional biogeography across Canadian lakes.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".