{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T23:12:18Z","timestamp":1791328338395,"version":"4.3.1"},"posted":{"date-parts":[[2025,6,12]]},"group-title":"Evolutionary Biology","reference-count":85,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:00:00Z","timestamp":1749686400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"US National Institutes of Health","doi-asserted-by":"crossref","award":["R01 AI162611"],"award-info":[{"award-number":["R01 AI162611"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"US National Institutes of Health","doi-asserted-by":"crossref","award":["R01 AI153044"],"award-info":[{"award-number":["R01 AI153044"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100005895","name":"Fred Hutch Cancer Center","doi-asserted-by":"crossref","award":["S10OD028685"],"award-info":[{"award-number":["S10OD028685"]}],"id":[{"id":"10.13039\/100005895","id-type":"DOI","asserted-by":"crossref"}]},{"award":["G0E1420N"],"award-info":[{"award-number":["G0E1420N"]}],"id":[{"id":"https:\/\/ror.org\/03qtxy027","id-type":"ROR","asserted-by":"publisher"}]},{"award":["G098321N"],"award-info":[{"award-number":["G098321N"]}],"id":[{"id":"https:\/\/ror.org\/03qtxy027","id-type":"ROR","asserted-by":"publisher"}]},{"award":["101102733"],"award-info":[{"award-number":["101102733"]}],"id":[{"id":"https:\/\/ror.org\/019w4f821","id-type":"ROR","asserted-by":"publisher"}]},{"award":["101094685"],"award-info":[{"award-number":["101094685"]}],"id":[{"id":"https:\/\/ror.org\/019w4f821","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,12,5]]},"abstract":"<jats:p>\n                  Phylodynamic analysis has been instrumental in elucidating epidemiological and evolutionary dynamics of pathogens. Bayesian phylodynamics integrates out phylogenetic uncertainty, which is typically substantial in phylodynamic datasets due to limited genetic diversity. Phylodynamic inference does not, however, scale with modern datasets, partly due to difficulties in traversing tree space. Here, we characterize tree space and landscape in phylodynamic inference and assess its impacts on analysis difficulty and key biological estimates. By running extensive Bayesian analyses of 15 classic large phylodynamic datasets and carefully analyzing the posterior samples, we find that the posterior tree landscape is diffuse yet rugged, leading to widespread tree sampling problems that usually stem from sequences in a small part of the tree. We develop clade-specific diagnostics to show that a few sequences\u2014including putative recombinants and recurrent mutants\u2014frequently drive the ruggedness and sampling problems, although existing data-quality tests show limited power to detect them. The sampling problems can significantly impact phylodynamic inferences or distort major biological conclusions; the impact is usually stronger on \u201clocal\u201d estimates (\n                  <jats:italic>e.g<\/jats:italic>\n                  ., introduction history) associated with particular clades than on \u201cglobal\u201d parameters (\n                  <jats:italic>e.g<\/jats:italic>\n                  ., demographic trajectory) governed by general tree shape. We evaluate existing and newly-developed MCMC diagnostics, and offer strategies for optimizing phylodynamic analysis settings and mitigating sampling problem impacts. Our findings highlight the need and directions to develop efficient traversal over rugged tree landscapes, ultimately advancing scalable and reliable phylodynamics.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>Significance Statement<\/jats:title>\n                  <jats:p>Bayesian phylodynamics is central to epidemiological studies, but exploring the vast and complex tree space is computationally challenging. Phylodynamic datasets comprise many highly similar sequences, sampled through time, creating a uniquely structured landscape of optimal trees. Here, we show that phylodynamic tree landscapes are often highly rugged, with multiple peaks separated by difficult-to-cross valleys. These features lead to widespread sampling problems which are often driven by a few sequences. These problems can significantly impact phylodynamic estimates, especially those associated with particular clades, distorting biological conclusions. We develop diagnostics to identify problematic sequences and provide solutions to mitigate their impacts. We offer strategies to optimize phylodynamic analysis workflows and to develop algorithms for navigating rugged landscapes, thereby advancing infectious disease investigation.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.06.10.657742","type":"posted-content","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T14:25:11Z","timestamp":1749738311000},"source":"Crossref","is-referenced-by-count":3,"title":["Biological causes and impacts of rugged tree landscapes in phylodynamic inference"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-828X","authenticated-orcid":false,"given":"Jiansi","family":"Gao","sequence":"first","affiliation":[{"name":"Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marius","family":"Brusselmans","sequence":"additional","affiliation":[{"name":"Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luiz M.","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, United Kingdom"},{"name":"School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro, Brazil"},{"name":"Department of Biostatistics, University of North Carolina Chapel Hill, Chapel Hill, NC 27599"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc A.","family":"Suchard","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095"},{"name":"Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095"},{"name":"Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy","family":"Baele","sequence":"additional","affiliation":[{"name":"Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0607-6025","authenticated-orcid":false,"suffix":"IV","given":"Frederick A.","family":"Matsen","sequence":"additional","affiliation":[{"name":"Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109"},{"name":"Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109"},{"name":"Department of Genome Sciences, University of Washington, Seattle, WA 98109"},{"name":"Department of Statistics, University of Washington, Seattle, WA 98109"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026041203160348000_2025.06.10.657742v2.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.11.020"},{"key":"2026041203160348000_2025.06.10.657742v2.2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05200-3"},{"key":"2026041203160348000_2025.06.10.657742v2.3","doi-asserted-by":"publisher","DOI":"10.1126\/science.abf2946"},{"key":"2026041203160348000_2025.06.10.657742v2.4","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msac159"},{"key":"2026041203160348000_2025.06.10.657742v2.5","doi-asserted-by":"publisher","DOI":"10.1126\/science.aah6266"},{"key":"2026041203160348000_2025.06.10.657742v2.6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1009175"},{"key":"2026041203160348000_2025.06.10.657742v2.7","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msaa314"},{"key":"2026041203160348000_2025.06.10.657742v2.8","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-033021-112532"},{"key":"2026041203160348000_2025.06.10.657742v2.9","unstructured":"G Baele , et al., BEAST X for Bayesian phylogenetic, phylogeographic and phylodynamic inference. Nat. Methods pp. 1\u20134 (2025)."},{"key":"2026041203160348000_2025.06.10.657742v2.10","doi-asserted-by":"publisher","DOI":"10.1080\/10635150390196993"},{"key":"2026041203160348000_2025.06.10.657742v2.11","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syw025"},{"key":"2026041203160348000_2025.06.10.657742v2.12","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syv006"},{"key":"2026041203160348000_2025.06.10.657742v2.13","doi-asserted-by":"publisher","DOI":"10.1126\/science.1206357"},{"key":"2026041203160348000_2025.06.10.657742v2.14","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1038\/s41559-017-0280-x","article-title":"A biologist\u2019s guide to Bayesian phylogenetic analysis","volume":"1","year":"2017","journal-title":"Nat. Ecol. & Evol"},{"key":"2026041203160348000_2025.06.10.657742v2.15","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syy008"},{"key":"2026041203160348000_2025.06.10.657742v2.16","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syy032"},{"key":"2026041203160348000_2025.06.10.657742v2.17","doi-asserted-by":"publisher","DOI":"10.1093\/gbe\/evw171"},{"key":"2026041203160348000_2025.06.10.657742v2.18","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msaa295"},{"key":"2026041203160348000_2025.06.10.657742v2.19","doi-asserted-by":"publisher","DOI":"10.1111\/2041-210X.13727"},{"key":"2026041203160348000_2025.06.10.657742v2.20","first-page":"565","article-title":"How trustworthy is your tree? Bayesian phylogenetic effective sample size through the lens of Monte Carlo error","volume":"19","year":"2024","journal-title":"Bayesian Anal"},{"key":"2026041203160348000_2025.06.10.657742v2.21","doi-asserted-by":"publisher","DOI":"10.1093\/ve\/veae081"},{"key":"2026041203160348000_2025.06.10.657742v2.22","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/40.3.315"},{"key":"2026041203160348000_2025.06.10.657742v2.23","doi-asserted-by":"publisher","DOI":"10.1080\/106351501753462902"},{"key":"2026041203160348000_2025.06.10.657742v2.24","doi-asserted-by":"publisher","DOI":"10.1126\/science.1115493"},{"key":"2026041203160348000_2025.06.10.657742v2.25","doi-asserted-by":"publisher","DOI":"10.1080\/10635150600812544"},{"key":"2026041203160348000_2025.06.10.657742v2.26","doi-asserted-by":"publisher","DOI":"10.1038\/nature22040"},{"key":"2026041203160348000_2025.06.10.657742v2.27","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2024670"},{"key":"2026041203160348000_2025.06.10.657742v2.28","doi-asserted-by":"publisher","DOI":"10.1038\/nature13016"},{"key":"2026041203160348000_2025.06.10.657742v2.29","doi-asserted-by":"publisher","DOI":"10.1038\/nature14460"},{"key":"2026041203160348000_2025.06.10.657742v2.30","doi-asserted-by":"publisher","DOI":"10.1126\/science.1256739"},{"key":"2026041203160348000_2025.06.10.657742v2.31","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-33112-3"},{"key":"2026041203160348000_2025.06.10.657742v2.32","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.66448"},{"key":"2026041203160348000_2025.06.10.657742v2.33","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.add7437"},{"key":"2026041203160348000_2025.06.10.657742v2.34","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19122-z"},{"key":"2026041203160348000_2025.06.10.657742v2.35","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2019.07.018"},{"key":"2026041203160348000_2025.06.10.657742v2.36","doi-asserted-by":"publisher","DOI":"10.1126\/science.abp8337"},{"key":"2026041203160348000_2025.06.10.657742v2.37","doi-asserted-by":"publisher","DOI":"10.1126\/science.abd2161"},{"key":"2026041203160348000_2025.06.10.657742v2.38","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03754-2"},{"key":"2026041203160348000_2025.06.10.657742v2.39","doi-asserted-by":"publisher","DOI":"10.1093\/ve\/vey016"},{"key":"2026041203160348000_2025.06.10.657742v2.40","doi-asserted-by":"publisher","DOI":"10.1080\/10635150590946961"},{"key":"2026041203160348000_2025.06.10.657742v2.41","doi-asserted-by":"publisher","DOI":"10.1016\/0025-5564(81)90043-2"},{"key":"2026041203160348000_2025.06.10.657742v2.42","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syu023"},{"key":"2026041203160348000_2025.06.10.657742v2.43","doi-asserted-by":"crossref","first-page":"btaf488","DOI":"10.1093\/bioinformatics\/btaf488","article-title":"HIPSTR: highest independent posterior subtree reconstruction in TreeAnnotator X","volume":"41","year":"2025","journal-title":"Bioinformatics"},{"key":"2026041203160348000_2025.06.10.657742v2.44","doi-asserted-by":"crossref","first-page":"e1012789","DOI":"10.1371\/journal.pcbi.1012789","article-title":"Accurate Bayesian phylogenetic point estimation using a tree distribution parameterized by clade probabilities","volume":"21","year":"2025","journal-title":"PLOS Comput. Biol"},{"key":"2026041203160348000_2025.06.10.657742v2.45","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177011136"},{"key":"2026041203160348000_2025.06.10.657742v2.46","doi-asserted-by":"publisher","DOI":"10.1080\/10635150801886156"},{"key":"2026041203160348000_2025.06.10.657742v2.47","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau9343"},{"key":"2026041203160348000_2025.06.10.657742v2.48","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl474"},{"key":"2026041203160348000_2025.06.10.657742v2.49","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msx263"},{"key":"2026041203160348000_2025.06.10.657742v2.50","doi-asserted-by":"publisher","DOI":"10.1093\/ve\/vew007"},{"key":"2026041203160348000_2025.06.10.657742v2.51","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/mss265"},{"key":"2026041203160348000_2025.06.10.657742v2.52","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-023-01368-0"},{"key":"2026041203160348000_2025.06.10.657742v2.53","doi-asserted-by":"crossref","unstructured":"P Varilly , et al., Delphy: scalable, near-real-time Bayesian phylogenetics for outbreaks. bioRxiv pp. 2025\u201303 (2025).","DOI":"10.1101\/2025.03.25.645253"},{"key":"2026041203160348000_2025.06.10.657742v2.54","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syw101"},{"key":"2026041203160348000_2025.06.10.657742v2.55","doi-asserted-by":"crossref","first-page":"0126","DOI":"10.1038\/s41559-017-0126","article-title":"Contentious relationships in phylogenomic studies can be driven by a handful of genes","volume":"1","year":"2017","journal-title":"Nat. Ecol. & Evol"},{"key":"2026041203160348000_2025.06.10.657742v2.56","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syab100"},{"key":"2026041203160348000_2025.06.10.657742v2.57","doi-asserted-by":"publisher","DOI":"10.1137\/110845045"},{"key":"2026041203160348000_2025.06.10.657742v2.58","doi-asserted-by":"publisher","DOI":"10.1007\/s00285-021-01685-0"},{"key":"2026041203160348000_2025.06.10.657742v2.59","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s11538-023-01244-2","article-title":"Ranked subtree prune and regraft","volume":"86","year":"2024","journal-title":"Bull. Math. Biol"},{"key":"2026041203160348000_2025.06.10.657742v2.60","unstructured":"T Xie , H Richman , J Gao , IFA Matsen , C Zhang , PhyloVAE: Unsupervised learning of phylogenetic trees via variational autoencoders in The Thirteenth International Conference on Learning Representations. (2025)."},{"key":"2026041203160348000_2025.06.10.657742v2.61","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syr074"},{"key":"2026041203160348000_2025.06.10.657742v2.62","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syt014"},{"key":"2026041203160348000_2025.06.10.657742v2.63","unstructured":"M Hunt , et al., Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny. bioRxiv (2024)."},{"key":"2026041203160348000_2025.06.10.657742v2.64","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msaa047"},{"key":"2026041203160348000_2025.06.10.657742v2.65","doi-asserted-by":"publisher","DOI":"10.1080\/10635150290102357"},{"key":"2026041203160348000_2025.06.10.657742v2.66","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2213913120"},{"key":"2026041203160348000_2025.06.10.657742v2.67","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.molbev.a025604"},{"key":"2026041203160348000_2025.06.10.657742v2.68","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/sys078"},{"key":"2026041203160348000_2025.06.10.657742v2.69","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syab099"},{"key":"2026041203160348000_2025.06.10.657742v2.70","doi-asserted-by":"crossref","unstructured":"J Klawitter , RR Bouckaert , AJ Drummond , Skeletons in the forest: Using entropy-based rogue detection on Bayesian phylogenetic tree distributions. bioRxiv pp. 2024\u201309 (2024).","DOI":"10.1101\/2024.09.25.615070"},{"key":"2026041203160348000_2025.06.10.657742v2.71","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syaa002"},{"key":"2026041203160348000_2025.06.10.657742v2.72","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syab004"},{"key":"2026041203160348000_2025.06.10.657742v2.73","doi-asserted-by":"publisher","DOI":"10.1186\/s12862-020-01679-4"},{"key":"2026041203160348000_2025.06.10.657742v2.74","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008322"},{"key":"2026041203160348000_2025.06.10.657742v2.75","doi-asserted-by":"crossref","unstructured":"RR Bouckaert , P. Weidem\u00fcller , LRE Gomez, N. M\u00fcller, Improving the scalability of Bayesian phylodynamic inference through efficient MCMC proposals. bioRxiv pp. 2025\u201306 (2025).","DOI":"10.1101\/2025.06.18.660471"},{"key":"2026041203160348000_2025.06.10.657742v2.76","doi-asserted-by":"publisher","DOI":"10.2807\/1560-7917.ES.2017.22.13.30494"},{"key":"2026041203160348000_2025.06.10.657742v2.77","doi-asserted-by":"publisher","DOI":"10.1093\/sysbio\/syz020"},{"key":"2026041203160348000_2025.06.10.657742v2.78","unstructured":"R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria), (2024)."},{"key":"2026041203160348000_2025.06.10.657742v2.79","first-page":"667","article-title":"Rank-normalization, folding, and localization: An improved \n                  \n                for assessing convergence of MCMC (with discussion)","volume":"16","year":"2021","journal-title":"Bayesian analysis"},{"key":"2026041203160348000_2025.06.10.657742v2.80","unstructured":"Stan Development Team, RStan: the R interface to Stan (2024) R package version 2.32.6."},{"key":"2026041203160348000_2025.06.10.657742v2.81","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq706"},{"key":"2026041203160348000_2025.06.10.657742v2.82","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006650"},{"key":"2026041203160348000_2025.06.10.657742v2.83","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty633"},{"key":"2026041203160348000_2025.06.10.657742v2.84","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msaa015"},{"key":"2026041203160348000_2025.06.10.657742v2.85","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msz197"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2025.06.10.657742","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:03:30Z","timestamp":1782277410000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2025.06.10.657742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,12]]},"references-count":85,"URL":"https:\/\/doi.org\/10.1101\/2025.06.10.657742","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1073\/pnas.2510938123","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2025,6,12]]},"subtype":"preprint"}}