Assessing the relationship between propagule pressure and probability of establishment for the aquatic invader «Bythotrephes longimanus» using two complementary approaches
Bibliographic record
Abstract
Estimating the probability of establishment of non-indigenous species is a crucial element in managing their spread. In this thesis, I use two approaches to estimate the probability of establishment of Bythotrephes longimanus, a predatory cladoceran that is invading lakes in Ontario and the surrounding American states. At a watershed level, I develop a vector based model to predict the probability of establishment of B. longimanus over time. I use metrics of propagule pressure from anthropogenic and natural dispersal to estimate spread, and extend the model to incorporate spatial and temporal gaps in knowledge of the invasion status of lakes. I found that recreational boating traffic is the dominant vector of spread and that most risk to lakes is due to static hubs of invasion - the five largest lakes in the watershed. Next, at the scale of a local population introduction, I investigate probability of establishment empirically. I follow B. longimanus populations over their entire life cycle and look for evidence of early invasion dynamics that may affect establishment, including Allee effects, demographic and environmental stochasticity, windows of opportunity and bottlenecks during sexual reproduction. I found that populations introduced at low doses exhibit weak Allee effects during sexual reproduction and that these effects strengthen over the season. Further, probability of establishment is positively related to propagule pressure; however, the relation is highly stochastic. The insights obtained on the characteristics of the relation between propagule pressure and probability of establishment at population and watershed scales can be linked in management plans aimed at slowing the spread of B. longimanus in inland lakes.
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 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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".