From broadscale patterns to fine‐scale processes: habitat structure influences genetic differentiation in the pitcher plant midge across multiple spatial scales
Bibliographic record
Abstract
The spatial scale at which samples are collected and analysed influences the inferences that can be drawn from landscape genetic studies. We examined genetic structure and its landscape correlates in the pitcher plant midge, Metriocnemus knabi, an inhabitant of the purple pitcher plant, Sarracenia purpurea, across several spatial scales that are naturally delimited by the midge's habitat (leaf, plant, cluster of plants, bog and system of bogs). We analysed 11 microsatellite loci in 710 M. knabi larvae from two systems of bogs in Algonquin Provincial Park (Canada) and tested the hypotheses that variables related to habitat structure are associated with genetic differentiation in this midge. Up to 54% of variation in individual-based genetic distances at several scales was explained by broadscale landscape variables of bog size, pitcher plant density within bogs and connectivity of pitcher plant clusters. Our results indicate that oviposition behaviour of females at fine scales, as inferred from the spatial locations of full-sib larvae, and spatially limited gene flow at broad scales represent the important processes underlying observed genetic patterns in M. knabi. Broadscale landscape features (bog size and plant density) appear to influence oviposition behaviour of midges, which in turn influences the patterns of genetic differentiation observed at both fine and broad scales. Thus, we inferred linkages among genetic patterns, landscape patterns and ecological processes across spatial scales in M. knabi. Our results reinforce the value of exploring such links simultaneously across multiple spatial scales and landscapes when investigating genetic diversity within a 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".