How do genes flow? Identifying potential dispersal mode for the semi-aquatic lichen Dermatocarpon luridum using spatial modelling and photobiont markers
Bibliographic record
Abstract
BACKGROUND: Landscape genetics is an interdisciplinary field that combines tools and techniques from population genetics with the spatially explicit principles from landscape ecology. Spatial variation in genotypes is used to test hypotheses about how landscape pattern affects dispersal in a wide range of taxa. Lichens, symbiotic associations between mycobionts and photobionts, are an entity for which little is known about their dispersal mechanism. Our objective was to infer the dispersal mechanism in the semi-aquatic lichen Dermatocarpon luridum using spatial models and the spatial variation of the photobiont, Diplosphaera chodatii. We sequenced the ITS rDNA and the β-actin gene regions of the photobiont and mapped the haplotype spatial distribution in Payuk Lake. We subdivided Payuk Lake into subpopulations and applied four spatial models based on the topography and hydrology to infer the dispersal mechanism. RESULTS: Genetic variation corresponded with the topography of the lake and the net flow of water through the waterbody. A lack of isolation-by-distance suggests high gene flow or dispersal within the lake. We infer the dispersal mechanism in D. luridum could either be by wind and/or water based on the haplotype spatial distribution of its photobiont using the ITS rDNA and β-actin markers. CONCLUSIONS: We inferred that the dispersal mechanism could be either wind and/or water dispersed due to the conflicting interpretations of our landscape hypotheses. This is the first study to use spatial modelling to infer dispersal in semi-aquatic lichens. The results of this study may help to understand lichen dispersal within aquatic landscapes, which can have implications in the conservation of rare or threatened lichens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".