Data and code for: Evolutionarily distinct species in frugivore networks have fewer links and more specialized partners
Bibliographic record
Abstract
Abstract Ecological networks describe the complex set of interconnections among species and their environment, and network structure can inform the stability, resilience, and functioning of ecosystems. Increasing attention is being paid to the mechanisms that determine species interactions. Phylogeny has informed our understanding of connections within networks, mostly by describing the strength of phylogenetic conservation of interactions. Here, we examine how the phylogenetic position of a species relates to its functional position within a network, testing the relationship between phylogenetic and network topologies. Time period: Early 20th century to present. We used a large dataset of frugivore interactions to calculate partner degree and degree, and bird and plant phylogenies to calculate local evolutionary distinctiveness (ED), evolutionary distinctiveness calculated on a community-level phylogeny. We then fit binomial Bayesian models to estimate the effect of evolutionary distinctiveness on partner degree and degree, comparing the effects of known bird traits from AVONET. The partners of both high ED birds and plants are less likely to interact with other members of the community, and birds with high ED tend to interact with fewer plant species. In birds, the effect size of ED is comparable to or larger than measured bird traits. Our analysis illustrates how phylogenetic relationships affect present-day ecologies and underscores the unique ecological contribution of evolutionary outliers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.515 | 0.258 |
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".