Systematics of harvester ants ( <i>Messor</i> ) in Israel based on integrated morphological, genetic, and ecological data
Bibliographic record
Abstract
ABSTRACT Harvester ants of the genus Messor are considered ecosystem engineers, whose distribution is broadly influenced by a variety of environmental factors. Although distinct Messor species have been reported to inhabit different habitats, their taxonomy in Israel remains largely ambiguous, hampering the proper ecological characterization of these species. Here, we applied an integrative species delimitation approach combining morphology-based identification, phylogenetic analyses of nuclear and mitochondrial genes, and ecological niche modelling to investigate the phylogenetic relationships among Messor species in the small but ecologically diverse region of Israel. Our analyses of mitochondrial genes revealed the presence of at least 13 well-defined lineages, whereas only seven were supported by the analysis of the nuclear genes. However, the concatenated tree that included all the three markers supported 11 lineages. Among two of the lineages-in M. semirufus and in a group of ants closest in resemblance to M. grandinidus- we identified 3-4 clades that were well established on most trees, inviting further study. In addition, we reveal three undescribed species and raise two subspecies to species rank, highlighting the high diversity of harvester ants in Israel. Ecological niche modelling consistently supported the observed distribution of species, with soil type and average annual temperature being the most influential factors. These results demonstrate that species distribution modelling can serve as a valuable component of integrative species delimitation. We call for future studies to investigate these fascinating lineages of one of the most prominent and ecologically important genera of ants in the Mediterranean Basin.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".