Combined modelling of distribution and niche in invasion biology: a case study of two invasive Tetramorium ant species
Bibliographic record
Abstract
Spatial modelling of species distributions has become an important tool in the study of biological invasions. Here, we examine the utility of combining distribution and ecological niche modelling for retrieving information on invasion processes, based on species occurrence data from native and introduced ranges. Specifically, we discuss questions, concerning (1) the global potential to spread to other ranges, (2) the potential to spread within established invasions, (3) the detectability of niche differences across ranges, and (4) the ability to infer invasion history through data from the introduced range. We apply this approach to two congeneric pavement ants, Tetramorium sp.E (formerly T. caespitum (Linnaeus 1758)) and T. tsushimae Emery 1925, both introduced to North America. We identify (1) the potential of both species to inhabit ranges worldwide, and (2) the potential of T. sp.E and T. tsushimae, to spread to 23 additional US states and to five provinces of Canada, and to 24 additional US states and to one province of Canada, respectively. We confirm that (3) niche modelling can be an effective tool to detect niche shifts, identifying an increased width of T. sp.E and a decreased width of T. tsushimae following introduction, with potential changes in niche position for both species. We make feasible that (4) combined modelling could become an auxiliary tool to reconstruct invasion history, hypothesizing admixture following multiple introductions in North America for T. sp.E, and a single introduction to North America from central Japan, for T. tsushimae. Combined modelling represents a rapid means to formulate testable explanatory hypotheses on invasion patterns and helps approach a standard in predictive invasion research.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".