LANDSCAPE PATTERNS OF AN AQUATIC INVADER: ASSESSING DISPERSAL EXTENT FROM SPATIAL DISTRIBUTIONS
Bibliographic record
Abstract
Assessing the spatial distribution of organisms across landscapes is a key step toward determining processes that produce observed patterns. The spatial distribution of an invasive aquatic mollusk, the zebra mussel (Dreissena polymorpha), was examined in two lake-rich areas (Belarus and midwestern United States) with contrasting invasion histories. Spatial distribution patterns of invaded lakes were determined using Ripley's K. Aggregation of invaded lakes was found at similar spatial extents (<50 km) in both regions; segregation was found at spatial extents >120 km in Belarus. The observed spatial extent of aggregation likely reflected the scale of secondary geographic spread, whereas the scale of long-distance dispersal events was reflected by the spatial extent of segregation. Isolated Belarus lakes were less likely to be invaded than those connected by waterways. Although one-dimensional aggregation of invaded lakes along connected Belarus waterways was not observed, nearest neighbor analysis indicated that zebra mussel dispersal occurred at distances <15 km within these waterways. Based on observed spatial pattern, we concluded that zebra mussels have not yet saturated European and North American lake landscapes, including many suitable lakes. Similar distribution patterns of invaded lakes in Belarus and North America suggest that similar processes have influenced zebra mussel spread in both landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.072 | 0.002 |
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; both teacher heads agree on what is shown here.
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".