A new approach to integrate phylogenetic structure and partner availability to study biotic specialization in ecological networks
Bibliographic record
Abstract
Summary Biotic specialization holds information about the assembly, evolution and stability of biological communities. Phylogenetic diversity metrics have been used to quantify biotic specialization, but their current implementations do not adequately account for the availability of the interacting partners. Also, the overdispersed pattern of phylogenetic specialization has been misinterpreted as an attribute of generalists. We developed an approach that resolves these issues by accounting for partner availability to quantify the phylogenetic structure of specialization (i.e., clustered, overdispersed, or random) in ecological networks. We showed that our approach avoids biases of previous methods. We also implemented it on empirical networks of host–parasite, avian seed-dispersal, lichenized fungi– cyanobacteria and coral–dinoflagellate interactions. We found a large proportion of taxa that interact with phylogenetically random partners, in some cases to a larger extent than detected with an existing method that does not account for partner availability. We also found many taxa that interact with phylogenetically clustered partners, while taxa with overdispersed partners were rare. Our results highlight the important role of randomness in shaping interaction networks, even in highly intimate symbioses, and provide a much-needed quantitative framework to assess the role that evolutionary history and symbiotic specialization play in shaping patterns of biodiversity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".