Species‐level predation network uncovers high prey specificity in a Neotropical army ant community
Bibliographic record
Abstract
Army ants are among the top arthropod predators and considered keystone species in tropical ecosystems. During daily mass raids with many thousand workers, army ants hunt live prey, likely exerting strong top-down control on prey species. Many tropical sites exhibit a high army ant species diversity (>20 species), suggesting that sympatric species partition the available prey niches. However, whether and to what extent this is achieved has not been intensively studied yet. We therefore conducted a large-scale diet survey of a community of surface-raiding army ants at La Selva Biological Station in Costa Rica. We systematically collected 3,262 prey items from eleven army ant species (genera Eciton, Nomamyrmex and Neivamyrmex). Prey items were classified as ant prey or non-ant prey. The prey nearly exclusively consisted of other ants (98%), and most booty was ant brood (87%). Using morphological characters and DNA barcoding, we identified a total of 1,103 ant prey specimens to the species level. One hundred twenty-nine ant species were detected among the army ant prey, representing about 30% of the known local ant diversity. Using weighted bipartite network analyses, we show that prey specialization in army ants is unexpectedly high and prey niche overlap very small. Besides food niche differentiation, we uncovered a spatiotemporal niche differentiation in army ant raid activity. We discuss competition-driven multidimensional niche differentiation and predator-prey arms races as possible mechanisms underlying prey specialization in army ants. By combining systematic prey sampling with species-level prey identification and network analyses, our integrative approach can guide future research by portraying how predator-prey interactions in complex communities can be reliably studied, even in cases where morphological prey identification is infeasible.
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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) | 0.000 | 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".