Computational Ecology and Phylogenetics

Computational ecology and phylogenetics merge mathematical modelling, high‐throughput data analysis and evolutionary inference to understand patterns of biodiversity, community structure and macroevolutionary history. In ecological applications, techniques range from species‐distribution models driven by environmental covariates to agent‐based and network simulations of trophic interactions. In parallel, phylogenetic methods—distance‐based clustering, maximum‐likelihood and Bayesian inference—reconstruct the tree of life using genome‐scale or multilocus datasets. Advances in machine learning enable automated trait extraction from remote‐sensing imagery and prediction of species’ responses to global change. Bayesian phylodynamics integrates time‐stamped pathogen genomes with coalescent and birth–death models to infer transmission dynamics. Spatial phylogenetics embeds lineage diversity into geographic information systems to identify evolutionary hotspots and guide reserve design. Together, these computational frameworks illuminate the drivers of species richness, forecast range shifts under climate scenarios, optimise conservation priorities and unravel the tempo and mode of diversification across kingdoms.

Research from Nature Portfolio

Phylogenomic analyses of extant and fossil echinoids have redefined relationships within irregular sea urchins by combining multi‐locus datasets with ancestral‐range reconstruction. Three new superfamilies of sand dollars were proposed and Late Cretaceous to Paleogene diversification was traced to the tropical western Pacific. In plant systems, long‐read sequencing of the Durio zibethinus chloroplast genome uncovered an atypical plastome structure lacking the large inverted repeat. Comparative analysis across 24 varieties resolved subfamily relationships in Malvaceae, placing Helicteroideae sister to Tilioideae and revealing plasticity in plastid architecture. In viral epidemiology, a sequential Monte Carlo approach using the trajectory of segregating sites enabled rapid inference of basic reproduction numbers and epidemic origin dates for emerging SARS-CoV-2 lineages, demonstrating that summary genetic statistics can yield robust epidemiological insights even with sparse case reporting.

Research from all publishers

A detailed multilocus study of the powdery mildew genus Golovinomyces used five gene regions to untangle a morphologically heterogeneous complex on Asteraceae hosts. Phylogenetic and morphological evidence supported the delimitation of three discrete species differing in host range and distribution, resolving long‐standing taxonomic ambiguities. A complementary effort introduced secondary barcodes—fragments of CAM, GAPDH, GS and RPB2 loci—to enhance resolution within Erysiphaceae species complexes that are indistinguishable using ribosomal DNA alone, laying the groundwork for a standardised fungal barcode reference. In marine invertebrates, extensive sampling of the nemertean genus Nipponnemertes across intertidal to bathyal depths combined mitochondrial and nuclear markers to reveal three deeply divergent clades, each associated with distinctive head morphology, and resulted in the description of ten new species, thereby substantially expanding known regional and global ribbon‐worm diversity.

Computational Ecology and Phylogenetics publication trend

The graph below shows the total number of articles in computational ecology and phylogenetics across all publications each year (not limited to Nature Index journals).

Technical terms

Species‐distribution model (SDM): A statistical or machine learning model that predicts a species’ geographic range based on environmental predictors and occurrence records.

Birth–death model: A phylogenetic framework that models lineage diversification through speciation (birth) and extinction (death) rates over time.

Coalescent model: A population‐genetic approach that traces the ancestry of sampled alleles backward in time to infer demographic history and effective population size.

Phylodynamics: The integration of epidemiological models with phylogenetic trees to infer transmission patterns, reproduction numbers and epidemic origins from pathogen sequence data.

Bayesian inference: A statistical method combining prior distributions and likelihoods to estimate posterior probabilities of phylogenetic trees and model parameters.

Segregating site: A nucleotide position in an alignment at which two or more sequences differ, used as a summary statistic in genetic diversity and phylodynamic analyses.

Phylogenetic diversity (PD): The total branch length spanned by a set of taxa on a phylogenetic tree, used as a measure of evolutionary breadth in conservation planning.

Spatial phylogenetics: The mapping of phylogenetic diversity and endemism metrics onto geographic grids to identify evolutionary hotspots and priority areas for protection.

References

1. Phylogenomic analyses of echinoid diversification prompt a re-evaluation of their fossil record. eLife (2022).
2. Phylogeny, ancestral ranges and reclassification of sand dollars. Scientific Reports (2023).
3. Assembly of the durian chloroplast genome using long PacBio reads. Scientific Reports (2020).
4. Epidemiological inference for emerging viruses using segregating sites. Nature Communications (2023).
5. Multi-locus phylogeny and taxonomy of an unresolved, heterogeneous species complex within the genus Golovinomyces (Ascomycota, Erysiphales), including G. ambrosiae, G. circumfusus and G. spadiceus. BMC Microbiology (2020).
6. Secondary DNA Barcodes (CAM, GAPDH, GS, and RpB2) to Characterize Species Complexes and Strengthen the Powdery Mildew Phylogeny. Frontiers in Ecology and Evolution (2022).
7. Molecular Phylogeny of the Genus Nipponnemertes (Nemertea: Monostilifera: Cratenemertidae) and Descriptions of 10 New Species, With Notes on Small Body Size in a Newly Discovered Clade. Frontiers in Marine Science (2022).