Studying the sensory ecology of frog‐biting midges (Corethrellidae: Diptera) and their frog hosts using ecological interaction networks
Bibliographic record
Abstract
Abstract Investigating species interaction networks has advanced the understanding of the robustness of ecosystems, the impact of species invasions, resource partitioning and the coevolution of interacting species. Here, we present a 10‐year study of the relationships between frog‐biting midges (Corethrella) and their frog hosts in tropical lowland forests across northern Borneo. We use quantitative bipartite host‐ectoparasite networks to explore this unusual relationship. Across northern Borneo, nine species of frog‐biting midges were found to bite 29 species of frogs. Overall, 378 individual Corethrella were found on 93 individual frogs. We compare the network structure between two forest types in Brunei Darussalam: a lowland mixed‐dipterocarp rainforest and a peatswamp forest. Results indicate that both antagonistic interaction networks were relatively specialized at the community level and show significant nestedness. The degree of specialization was higher in the rainforest than in the peatswamp suggesting higher diversity of midge sensory perception and frog avoidance strategies in the rainforest. At the species level, the specialization of midges as well as their frog hosts was highly variable in both networks suggesting that some frog species are better at avoiding being bitten by Corethrella than others. Frog species parasitized by midges had calls of low dominant frequencies suggesting that the spectral bandwidth of hearing in midges could influence host selection. Despite this, some frog species that call above 4 kHz have not escaped parasitism. We explore the role of habitat filtering, behavior and coevolution in shaping network structure.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".