Range expansion and establishment of a non-indigenous tunicate (<i>Diplosoma listerianum)</i> in thermal refugia is mediated by environmental variability in changing coastal environments
Bibliographic record
Abstract
Coastal marine non-indigenous species (NIS) have expanded their range into Atlantic Canada under more variable and anomalously warmer sea surface temperature (SST) conditions observed since 2012. In this study we apply species distribution modeling (SDM), in combination with empirical validation using annual field monitoring surveys, to assess the influence of anomalously warmer ocean conditions on the range expansion of NIS into coastal Nova Scotia using the recently established colonial tunicate Diplosoma listerianum as a test case. SDM accurately predicted D. listerianum’s introduction to Atlantic Canada, including its range expansion, contraction, and long-term persistence within thermal refugia as local environmental conditions generally warmed between 2012 and 2019. This tunicate’s ability to survive the winter was the primary constraint on its range expansion into Nova Scotia prior to 2012. The identification of local thermal refugia in which D. listerianum spread under anomalously warm winter conditions and persisted during colder winters provides new insights and a mechanism for range expansion and establishment of NIS. Ultimately, interannual modeling and long-term projections employed here can be applied to predict, monitor, and manage transient range expansion and long-term persistence of NIS within changing environments.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".