Measuring Community Similarity with Phylogenetic Networks
Bibliographic record
Abstract
Environmental drivers of biodiversity can be identified by relating patterns of community similarity to ecological factors. Community variation has traditionally been assessed by considering changes in species composition and more recently by incorporating phylogenetic information to account for the relative similarity of taxa. Here, we describe how an important class of measures including Bray-Curtis, Canberra, and UniFrac can be extended to allow community variation to be computed on a phylogenetic network. We focus on phylogenetic split systems, networks that are produced by the widely used median network and neighbor-net methods, which can represent incongruence in the evolutionary history of a set of taxa. Calculating β diversity over a split system provides a measure of community similarity averaged over uncertainty or conflict in the available phylogenetic signal. Our freely available software, Network Diversity, provides 11 qualitative (presence-absence, unweighted) and 14 quantitative (weighted) network-based measures of community similarity that model different aspects of community richness and evenness. We demonstrate the broad applicability of network-based diversity approaches by applying them to three distinct data sets: pneumococcal isolates from distinct geographic regions, human mitochondrial DNA data from the Indonesian island of Nias, and proteorhodopsin sequences from the Sargasso and Mediterranean Seas. Our results show that major expected patterns of variation for these data sets are recovered using network-based measures, which indicates that these patterns are robust to phylogenetic uncertainty and conflict. Nonetheless, network-based measures of community similarity can differ substantially from measures ignoring phylogenetic relationships or from tree-based measures when incongruent signals are present in the underlying data. Network-based measures provide a methodology for assessing the robustness of β-diversity results in light of incongruent phylogenetic signal and allow β diversity to be calculated over widely used network structures such as median networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".