The importance of geospatial inputs in assessing fine-scale landscape genetic patterns of a temperate treefrog
Bibliographic record
Abstract
Recent technological advancements in next generation sequencing techniques, enabling the use of thousands of genetic markers across an individual’s genome, and continued improvements to the spatial resolution and information content of remote sensing data, present a unique opportunity to investigate the finest geographic scale at which genetic structuring within and between populations becomes detectable. However, in order to exploit the integration of both high resolution genetic and geospatial data, an understanding of the uncertainties associated with both data sets, and the accuracy requirements of landscape genetic analyses of geospatial models, is required. In this thesis, I begin by highlighting some sources of uncertainty and bias in landscape models derived from high resolution LiDAR data which have the potential to impact downstream analyses of the correlation between patterns of genetic structuring and landscape heterogeneity. I then investigate the patterns of genetic structuring between breeding aggregations of the spring peeper, Pseudacris crucifer. I discovered that while different methodologies to derive land cover from airborne LiDAR data may result in similar overall accuracy, the configuration of landscape heterogeneity within the landscape, and class-specific recall and precision differed between models. A significant finding is that some classification methodologies did not accurately represent the contiguity of a road, which is often considered a putative barrier for amphibians. While ddRADseq could not resolve signatures of fine-scale genetic differentiation between breeding aggregations of Pseudacris crucifer within distances of <10 km, some differentiation between sampling locations separated by 60 km was detected. This grants some insight into the scale of genetic structuring of Pseudacris crucifer, and provides some representation of hylids in the population genetic literature. Ultimately this thesis highlights the importance for communication and collaboration between biologists and geospatial scientists to ensure the optimal modeling of heterogeneity with landscapes to address a wider array of applications in ecology and landscape genetics, as well as an accurate representation of uncertainty in geospatial models in ecological and landscape genetic analyses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".