New insights on the distributions of freshwater turtles in southern Ontario using environmental DNA
Bibliographic record
Abstract
Quantifying species geographical distributions and understanding factors that underlie them, has been a long-standing focus in ecology. However, obtaining unbiased, accurate species occurrence data can be challenging, especially when the organism has low abundance or is cryptic. Environmental DNA (eDNA) has emerged as a fast, sensitive, and non-invasive survey tool to infer species presence. In aquatic ecosystems, eDNA has been widely applied to inform conservation of species at risk or for early detection of invasive species, but its potential for answering more fundamental ecological questions remains relatively unexplored. In my thesis, I used eDNA and community observations to explore the northern distributions and range limits of freshwater turtles, an understudied taxon in the current era of rapid climate change: I centered my thesis on three freshwater turtles in southern Ontario, Canada: midland painted turtle (Chrysemys picta marginata), northern map turtle (Graptemys geographica), and common musk turtle (Sternotherus odoratus). In chapter II, I focused on G. geographica and a single lake during winter, applying eDNA to detect communal overwintering sites below the ice. My work confirmed that eDNA can locate overwintering turtles and revealed a new site, which was further confirmed using a remotely controlled underwater vehicle. Chapter III focused on S. odoratus, where I used eDNA to sample waterbodies in southeastern Ontario within and beyond its known northern range. I modelled its distribution using Maximum Entropy (MaxEnt) with parameters fully optimized. I integrated eDNA detections with community observations and gained new insights on environmental variables that shape and constrain the turtle’s distribution. In chapter IV, I modelled the northern distributions of all three turtles again using optimized MaxEnt. I addressed the issue of overfitting and improved the biological interpretability of MaxEnt models by implementing variable collinearity reduction protocols. Overall, my models suggested that distributions of the turtles are shaped by thermal conditions, characteristics of aquatic habitats, and topographical features. Collectively, my work sheds new light on aspects of spatial ecology of three temperate freshwater turtles and contributes to an expanding toolbox of methods for surveying species, identifying critical habitat, and better understanding the factors that shape or limit species distributions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".