Phylogenetic structure of specialization: A new approach that integrates partner availability and phylogenetic diversity to quantify biotic specialization in ecological networks
Bibliographic record
Abstract
Biotic specialization holds information about the assembly, evolution, and stability of biological communities. Partner availabilities can play an important role in enabling species interactions, where uneven partner availabilities can bias estimates of biotic specialization when using phylogenetic diversity indices. It is therefore important to account for partner availability when characterizing biotic specialization using phylogenies. We developed an index, phylogenetic structure of specialization (PSS), that avoids bias from uneven partner availabilities by uncoupling the null models for interaction frequency and phylogenetic distance. We incorporate the deviation between observed and random interaction frequencies as weights into the calculation of partner phylogenetic α-diversity. To calculate the PSS index, we then compare observed partner phylogenetic α-diversity to a null distribution generated by randomizing phylogenetic distances among the same number of partners. PSS quantifies the phylogenetic structure (i.e., clustered, overdispersed, or random) of the partners of a focal species. We show with simulations that the PSS index is not correlated with network properties, which allows comparisons across multiple systems. We also implemented PSS on empirical networks of host-parasite, avian seed-dispersal, lichenized fungi-cyanobacteria, and hummingbird pollination interactions. Across these systems, a large proportion of taxa interact with phylogenetically random partners according to PSS, sometimes to a larger extent than detected with an existing method that does not account for partner availability. We also found that many taxa interact with phylogenetically clustered partners, while taxa with overdispersed partners were rare. We argue that species with phylogenetically overdispersed partners have often been misinterpreted as generalists when they should be considered specialists. 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. PSS is available as an R package at https://github.com/cjpardodelahoz/pss.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".