Development of inland lakes as hubs in an invasion network
Bibliographic record
Abstract
Summary The ability to predict spatially explicit dispersal by non‐indigenous species is a difficult but increasingly important undertaking as it allows management efforts to be focused around areas identified as susceptible to invasion. Lakes may serve as useful models for these studies because the habitats are well defined, and vectors of spread may be readily identified and quantified. In this study, we examined patterns of spread of the non‐indigenous spiny waterflea Bythotrephes longimanus to inland lakes in Ontario, Canada, to identify lakes for which management efforts to reduce traffic would be most effective. We surveyed people using lakes for recreational purposes to quantify movements of trailered boats and other risky activities, to model relative vector traffic from invaded lakes to non‐invaded and other invaded lakes. Non‐linear functions were developed to describe the cumulative number of invaded and non‐invaded destination lakes visited by people leaving five important lakes already invaded by the spiny waterflea (Huron, Simcoe, Muskoka, Panache and Kashagawigamog). The relative difference in these functions was used to identify which lakes will develop into future invasion hubs and will therefore be most important to future dispersal of the species. In the recent past, Lake Muskoka has been an important hub from which the spiny waterflea has invaded other lakes. It is unlikely to continue to be a source for waterflea invasion as most outbound traffic is to previously invaded lakes. Conversely, most outbound traffic from Lakes Kashagawigamog and Simcoe is to non‐invaded lakes and, therefore, these lakes are likely to develop into hubs in the future. Synthesis and applications . These data on zooplankton in lake systems and associated mechanisms of transport indicate patterns not only of intrinsic value to lake management, but also of potential importance in understanding invasions more generally. Frequency distributions of the number of outbound connections to both invaded and non‐invaded destinations from invaded sources follow a power function, consistent with scale‐free networks. These networks indicate that small proportions of sources function as hubs. Management efforts targeted to remove developing hubs from the invasion network, rather than equal effort applied to outbound vector traffic from all sources, may reduce the predicted rate of new invasions.
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 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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".