Browsing by Author "Bunce M"

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    Author Correction: Dense sampling of bird diversity increases power of comparative genomics.
    (2021-04) Feng S; Stiller J; Deng Y; Armstrong J; Fang Q; Reeve AH; Xie D; Chen G; Guo C; Faircloth BC; Petersen B; Wang Z; Zhou Q; Diekhans M; Chen W; Andreu-Sánchez S; Margaryan A; Howard JT; Parent C; Pacheco G; Sinding M-HS; Puetz L; Cavill E; Ribeiro ÂM; Eckhart L; Fjeldså J; Hosner PA; Brumfield RT; Christidis L; Bertelsen MF; Sicheritz-Ponten T; Tietze DT; Robertson BC; Song G; Borgia G; Claramunt S; Lovette IJ; Cowen SJ; Njoroge P; Dumbacher JP; Ryder OA; Fuchs J; Bunce M; Burt DW; Cracraft J; Meng G; Hackett SJ; Ryan PG; Jønsson KA; Jamieson IG; da Fonseca RR; Braun EL; Houde P; Mirarab S; Suh A; Hansson B; Ponnikas S; Sigeman H; Stervander M; Frandsen PB; van der Zwan H; van der Sluis R; Visser C; Balakrishnan CN; Clark AG; Fitzpatrick JW; Bowman R; Chen N; Cloutier A; Sackton TB; Edwards SV; Foote DJ; Shakya SB; Sheldon FH; Vignal A; Soares AER; Shapiro B; González-Solís J; Ferrer-Obiol J; Rozas J; Riutort M; Tigano A; Friesen V; Dalén L; Urrutia AO; Székely T; Liu Y; Campana MG; Corvelo A; Fleischer RC; Rutherford KM; Gemmell NJ; Dussex N; Mouritsen H; Thiele N; Delmore K; Liedvogel M; Franke A; Hoeppner MP; Krone O; Fudickar AM; Milá B; Ketterson ED; Fidler AE; Friis G; Parody-Merino ÁM; Battley PF; Cox MP; Lima NCB; Prosdocimi F; Parchman TL; Schlinger BA; Loiselle BA; Blake JG; Lim HC; Day LB; Fuxjager MJ; Baldwin MW; Braun MJ; Wirthlin M; Dikow RB; Ryder TB; Camenisch G; Keller LF; DaCosta JM; Hauber ME; Louder MIM; Witt CC; McGuire JA; Mudge J; Megna LC; Carling MD; Wang B; Taylor SA; Del-Rio G; Aleixo A; Vasconcelos ATR; Mello CV; Weir JT; Haussler D; Li Q; Yang H; Wang J; Lei F; Rahbek C; Gilbert MTP; Graves GR; Jarvis ED; Paten B; Zhang G
    In Supplementary Table 1 of this Article, 23 samples (B10K-DU-029-32, B10K-DU-029-33, B10K-DU-029-36 to B10K-DU-029-44, B10K-DU- 029-46, B10K-DU-029-47, B10K-DU-029-49 to B10K-DU-029-53, B10K-DU- 029-75 to B10K-DU-029-77, B10K-DU-029-80, and B10K-DU-030-03; styled in boldface in the revised table) were assigned to the incorrect institution. Supplementary Table 1 has been amended to reflect the correct source institution for these samples, and associated data (tissue, museum ID/source specimen ID, site, state/province, latitude, longitude, date collected and sex) have been updated accordingly. The original table is provided as Supplementary Information to this Amendment, and the original Article has been corrected online.
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    Practitioner perspectives on informing decisions in One Health sectors with predictive models
    (Springer Nature Limited, 2025-12-01) Pepin KM; Carlisle K; Chipman RB; Cole D; Anderson DP; Baker MG; Benschop J; Bunce M; Binny RN; French N; Greenhalgh S; O’Neale DRJ; McDougall S; Morgan FJ; Muellner P; Murphy E; Plank MJ; Tompkins DM; Hayman DTS
    The continued emergence of challenges in human, animal, and environmental health (One Health sectors) requires public servants to make management and policy decisions about system-level ecological and sociological processes that are complex, poorly understood, and change over time. Relying on intuition, evidence, and experience for robust decision-making is challenging without a formal assimilation of these elements (a model), especially when the decision needs to consider potential impacts if an action is or is not taken. Models can provide assistance to this challenge, but effective development and use of model-based evidence in decision-making (‘model-to-decision workflow’) can be challenging. To address this gap, we examined conditions that maximize the value of model-based evidence in decision-making in One Health sectors by conducting 41 semi-structured interviews of researchers, science advisors, operational managers, and policy decision-makers with direct experience in model-to-decision workflows (‘Practitioners’) in One Health sectors. Broadly, our interview guide was structured to understand practitioner perspectives about the utility of models in health policy or management decision-making, challenges and risks with using models in this capacity, experience with using models, factors that affect trust in model-based evidence, and perspectives about conditions that lead to the most effective model-to-decision workflow. We used inductive qualitative analysis of the interview data with iterative coding to identify key themes for maximizing the value of model-based evidence in One Health applications. Our analysis describes practitioner perspectives for improved collaboration among modelers and decision-makers in public service, and priorities for increasing accessibility and value of model-based evidence in One Health decision-making. Two emergent priorities include establishing different standards for development of model-based evidence before or after decisions are made, or in real-time versus preparedness phases of emergency response, and investment in knowledge brokers with modeling expertise working in teams with decision-makers.
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    Tracing the international arrivals of SARS-CoV-2 Omicron variants after Aotearoa New Zealand reopened its border
    (Springer Nature Limited, 2022-10-29) Douglas J; Winter D; McNeill A; Carr S; Bunce M; French N; Hadfield J; de Ligt J; Welch D; Geoghegan JL
    In the second quarter of 2022, there was a global surge of emergent SARS-CoV-2 lineages that had a distinct growth advantage over then-dominant Omicron BA.1 and BA.2 lineages. By generating 10,403 Omicron genomes, we show that Aotearoa New Zealand observed an influx of these immune-evasive variants (BA.2.12.1, BA.4, and BA.5) through the border. This is explained by the return to significant levels of international travel following the border's reopening in March 2022. We estimate one Omicron transmission event from the border to the community for every ~5,000 passenger arrivals at the current levels of travel and restriction. Although most of these introductions did not instigate any detected onward transmission, a small minority triggered large outbreaks. Genomic surveillance at the border provides a lens on the rate at which new variants might gain a foothold and trigger new waves of infection.