Freshwater invasions: using historical data to analyse spread
Bibliographic record
Abstract
ABSTRACT Aquatic invasive species cause deleterious environmental and economic impacts, and are rapidly spreading through ecosystems worldwide. Despite this, very few data sets exist that describe both the presence and the absence of invaders over long time periods. We have used Geographical Information Systems (GIS) to analyse time‐series data describing the spread of the freshwater invasive New Zealand mudsnail,Potamopyrgus antipodarum, in Victoria, Australia, over 110 years. We have mapped the snail's spread, estimated the percentage of stream length invaded through time, calculated the functional form of the spread rate, and investigated the role that the two proposed vectors — fish stocking and angling — have had in this invasion. Since it was first found in 1895,P. antipodarumhas expanded its range in Victoria and now occurs throughout much of the southern and central areas of the state. The north of the state is relatively less invaded than the south, with the division corresponding approximately to the presence of the Great Dividing Range. We show that the snail's range has been increasing at an approximately exponential rate and estimate that 20% of total Victorian stream length is currently invaded. We also show that using long‐term data can change the outcome of analyses of the relationship between vectors of spread and invasion status of separate catchments. When our time‐series data were aggregated through time, the total numbers of fish stocking events and angling activity were both correlated with invasion. However, when the time‐series data were used and the number of fish stocking events calculated up until the date of invasion, no relationships with stocking were found. These results underline the role that time‐series data, based on both presences and absences, have to play when investigating the spread of invasive species.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| 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".