The ecology, distribution and population genetics of amphibians on Haida Gwaii, British Columbia
Bibliographic record
Abstract
Amphibian populations are declining worldwide due to many threats including habitat destruction, disease, non-native species and climate change, and are in dire need of informed conservation strategies. Island populations are particularly at risk. Invasive species are a major threat to amphibians on islands, along with habitat destruction and reduced genetic diversity in island populations. Additionally, species’ ranges are naturally shifting due to climate change, bringing the definition of a non-native species into flux. In this thesis, I aimed to improve the understanding of population-level dynamics and provide insights for conservation of amphibians using tools from invasion biology, experimental ecology, population genomics and conservation biology. I focussed on two amphibians of conservation concern in British Columbia (BC), Canada - the Northern Red-legged Frog (Rana aurora; NRLF), introduced to the Haida Gwaii Archipelago but native to southwest BC, and the Western Toad (Anaxyrus boreas boreas, called Hlk’yáan Ḵ’ust’áan in the X̱aad Kíl dialect of the Haida language (X̱aad Kíl Née; Haida Language Council), native to both areas. I modelled the distribution of the NRLF under current and future climate, conducted a mesocosm experiment examining the effect of co-occurrence history and naïveté on competition between the Western Toad and NRLF using populations from Haida Gwaii and southwest BC, and conducted genomic analyses of diversity and divergence of 27 Western Toad (hereafter “toad”) populations on Haida Gwaii and southwest BC. Together, my research suggests that: 1) the introduced NRLF has spread since its discovery on Haida Gwaii in 2002, and will likely spread further on Haida Gwaii, especially with climate change; 2) Haida Gwaii toad tadpoles are weaker competitors with the introduced NRLF compared to southwest BC toad tadpoles, likely due to their short history of co-occurrence (but it is unknown whether competition with NRLFs affects toad population persistence); and finally, 3) Haida Gwaii toad populations are genetically distinct and less diverse compared to southwest BC toad populations. The observed toad genetic patterns could be due to a historical population bottleneck or founder effect as well as contemporary small effective population size. This thesis provides knowledge that can be used to help conserve amphibians.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".