{"doi":"10.1093/molbev/msae073","title":"Comparison of Bayesian Coalescent Skyline Plot Models for Inferring Demographic Histories","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Bayesian coalescent skyline plot models are widely used to infer demographic histories. The first (non-Bayesian) coalescent skyline plot model assumed a known genealogy as data, while subsequent models and implementations jointly inferred the genealogy and demographic history from sequence data, including heterochronous samples. Overall, there exist multiple different Bayesian coalescent skyline plot models which mainly differ in two key aspects: (i) how changes in population size are modeled through independent or autocorrelated prior distributions, and (ii) how many change-points in the demographic history are used, where they occur and if the number is pre-specified or inferred. The specific impact of each of these choices on the inferred demographic history is not known because of two reasons: first, not all models are implemented in the same software, and second, each model implementation makes specific choices that the biologist cannot influence. To facilitate a detailed evaluation of Bayesian coalescent skyline plot models, we implemented all currently described models in a flexible design into the software RevBayes. Furthermore, we evaluated models and choices on an empirical dataset of horses supplemented by a small simulation study. We find that estimated demographic histories can be grouped broadly into two groups depending on how change-points in the demographic history are specified (either independent of or at coalescent events). Our simulations suggest that models using change-points at coalescent events produce spurious variation near the present, while most models using independent change-points tend to over-smooth the inferred demographic history.</jats:p>","journal":"Molecular Biology and Evolution","year":2024,"id":676031,"datarank":0.40371156202386044,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.07412787542342748,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.07412787542342748,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":8,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":330419,"name":"Sebastian Höhna","orcid":"0000-0001-6519-6292","position":1,"is_corresponding":false},{"id":1766417,"name":"Ronja J Billenstein","orcid":"0009-0003-1812-4212","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Comparison of Bayesian Coalescent Skyline Plot Models for Inferring Demographic Histories","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Bayesian coalescent skyline plot models are widely used to infer demographic histories. The first (non-Bayesian) coalescent skyline plot model assumed a known genealogy as data, while subsequent models and implementations jointly inferred the genealogy and demographic history from sequence data, including heterochronous samples. Overall, there exist multiple different Bayesian coalescent skyline plot models which mainly differ in two key aspects: (i) how changes in population size are modeled through independent or autocorrelated prior distributions, and (ii) how many change-points in the demographic history are used, where they occur and if the number is pre-specified or inferred. The specific impact of each of these choices on the inferred demographic history is not known because of two reasons: first, not all models are implemented in the same software, and second, each model implementation makes specific choices that the biologist cannot influence. To facilitate a detailed evaluation of Bayesian coalescent skyline plot models, we implemented all currently described models in a flexible design into the software RevBayes. Furthermore, we evaluated models and choices on an empirical dataset of horses supplemented by a small simulation study. We find that estimated demographic histories can be grouped broadly into two groups depending on how change-points in the demographic history are specified (either independent of or at coalescent events). Our simulations suggest that models using change-points at coalescent events produce spurious variation near the present, while most models using independent change-points tend to over-smooth the inferred demographic history.</jats:p>","is_dataset_classified":null,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38630635","pmcid":"PMC11068272","openalex_id":"https://openalex.org/W4394869052","authors":[],"funders":[{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"HO 6201/1–1","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"HO 6201/2-1","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"HO 6201/1-1","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"313688472/SPP 1991","title":"Taxon-Omics: New approaches for discovering and naming biodiversity"},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"unidentified","title":"unidentified"}],"total_grants":5,"fwci":3.1046,"citation_percentile":0.91314389,"influential_citations":0,"citation_trend":[{"year":2024,"count":1},{"year":2025,"count":6},{"year":2026,"count":1}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://academic.oup.com/mbe/advance-article-pdf/doi/10.1093/molbev/msae073/57259537/msae073.pdf","host_type":"journal"},{"url":"https://academic.oup.com/mbe/advance-article-pdf/doi/10.1093/molbev/msae073/57259537/msae073.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/mbe/article-pdf/41/5/msae073/58027021/msae073.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1093/molbev/msae073","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38630635","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11068272","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11068272/pdf/msae073.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11068272","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11068272?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1093/molbev/msae073","host_type":""}],"fields_of_study":["Genetic and phenotypic traits in livestock","Veterinary Equine Medical Research","Genetic diversity and population structure","0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":["Animals","Bayes Theorem","Computer Simulation","Demography","Genetics, Population","Horses","Models, Genetic","Population Density","Software"],"keywords":["Coalescent theory","Approximate Bayesian computation","Plot (graphics)","Demographic history","Skyline","Bayesian probability","Biology","Spurious relationship","Population","Statistics","Econometrics","Evolutionary biology","Computer science","Mathematics","Data mining","Artificial intelligence","Inference","Phylogenetic tree","Demography","Genetics","Coalescent","Demographic Histories","Revbayes","Heterochronous Samples","Population Density","Genetics, Population","Models, Genetic","Methods","Animals","Bayes Theorem","Computer Simulation","Horses","Software"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-17T01:59:27.604323Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}