BACKCASTING AND FORECASTING BIOLOGICAL INVASIONS OF INLAND LAKES
Bibliographic record
Abstract
Human introduction of nonindigenous species constitutes a serious threat to many ecosystems, particularly lakes. Recent attempts to predict invasions have focused on the supply of propagules of nonindigenous species to recipient ecosystems from source populations. Here we develop a spatially explicit “gravity” model to test this concept for Bythotrephes longimanus , a crustacean waterflea from Eurasia that is rapidly invading lakes in Ontario, Canada. The gravity model predicted spread of Bythotrephes based upon seven identified risk factors (e.g., use of contaminated fishing or boat anchor line) that may allow dispersal of either live individuals or their resting eggs from invaded to noninvaded lakes, as well as based on the spatial arrangement of invaded and noninvaded lakes in Ontario. Discriminant analysis of lake gravity scores successfully identified invasion status for 74% of 170 inland lakes. A retrospective analysis of 31 invaded lakes revealed that the order in which lakes were invaded was directly related to the magnitude of vector inflows from invaded sources. Analysis of the dominant vector inflow to each invaded lake revealed a “stepping stone” pattern in which at least five lakes were sequentially invaded from the source population in Lake Huron. One invaded lake (Muskoka) apparently served as an invasion “hub,” resulting in up to 18 additional direct and 17 indirect invasions. Species spread occurred via a combination of dominant, local diffusion (median distance 12.5 km) and rare, long‐distance (>100 km) dispersal. Eleven of 131 lakes that were not invaded in 2000 were reported invaded in 2001. Gravity scores of these lakes were significantly higher than those of other noninvaded systems, indicating that susceptibility to invasion can be related to the magnitude of vector inflows. A GIS model based on gravity scores indicated that distribution of Bythotrephes is expected to expand to eastern and northwestern Ontario, although most new invasions are expected to occur in the central region of the province. Our results indicate that quantitative analysis of human dispersal vectors provides a robust starting point with which to assess vulnerability of discrete ecosystems to invasion. Management efforts focused on reducing the number and magnitude of human‐mediated dispersal vectors may reduce the rate of invasion of new ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".