First evidence of Equus asinus L. in the Chalcolithic disputes the Phoenicians as the first to introduce donkeys into the Iberian Peninsula

Two equid species have been documented in the Pleistocene of the Iberian Peninsula, the horse Equus caballus, and the Eurasian hemione Equus hydruntinus. While the former survived the HoloceneePleistocene until now, the timing for the extinction of the latter is unclear. Scarce, fragmented archaeological remains assess the presence of small equids living in the Holocene of Iberia. Those could possibly correspond to the Eurasian hemione although unambiguous morphological identification is often not possible. With the find of an equid tooth from Leceia, a Chalcolithic fortified site in Portugal, and using both morphological and mitochondrial genome analyses, we demonstrate for the first time the presence of a new equid species in Holocene Iberia, namely a donkey (Equus asinus). Radiocarbon dating of the tooth to Cal 2340e2130, and 2080e2060 BC with 95% probability, demonstrates that donkeys were present in Iberia well before the arrival of Phoenicians in the first quarter of the first millennium BC (900 e750 years BC), which were considered so far as the first who introduced donkeys in the region

Mitogenome Phylogenetics: The Impact of Using Single Regions and Partitioning Schemes on Topology, Substitution Rate and Divergence Time Estimation

The availability of mitochondrial genome sequences is growing as a result of recent technological advances in molecular biology. In phylogenetic analyses, the complete mitogenome is increasingly becoming the marker of choice, usually providing better phylogenetic resolution and precision relative to traditional markers such as cytochrome b (CYTB) and the control region (CR). In some cases, the differences in phylogenetic estimates between mitogenomic and single-gene markers have yielded incongruent conclusions. By comparing phylogenetic estimates made from different genes, we identified the most informative mitochondrial regions and evaluated the minimum amount of data necessary to reproduce the same results as the mitogenome. We compared results among individual genes and the mitogenome for recently published complete mitogenome datasets of selected delphinids (Delphinidae) and killer whales (genus Orcinus). Using Bayesian phylogenetic methods, we investigated differences in estimation of topologies, divergence dates, and clock-like behavior among genes for both datasets. Although the most informative regions were not the same for each taxonomic group (COX1, CYTB, ND3 and ATP6 for Orcinus, and ND1, COX1 and ND4 for Delphinidae), in both cases they were equivalent to less than a quarter of the complete mitogenome. This suggests that gene information content can vary among groups, but can be adequately represented by a portion of the complete sequence. Although our results indicate that complete mitogenomes provide the highest phylogenetic resolution and most precise date estimates, a minimum amount of data can be selected using our approach when the complete sequence is unavailable. Studies based on single genes can benefit from the addition of a few more mitochondrial markers, producing topologies and date estimates similar to those obtained using the entire mitogenome

Synergistic roles of climate warming and human occupation in Patagonian megafaunal extinctions during the Last Deglaciation

The causes of Late Pleistocene megafaunal extinctions (60,000 to 11,650 years ago, hereafter 60 to 11.65 ka) remain contentious, with major phases coinciding with both human arrival and climate change around the world. The Americas provide a unique opportunity to disentangle these factors as human colonization took place over a narrow time frame (~15 to 14.6 ka) but during contrasting temperature trends across each continent. Unfortunately, limited data sets in South America have so far precluded detailed comparison. We analyze genetic and radiocarbon data from 89 and 71 Patagonian megafaunal bones, respectively, more than doubling the high-quality Pleistocene megafaunal radiocarbon data sets from the region.We identify a narrowmegafaunal extinction phase 12,280 ± 110 years ago, some 1 to 3 thousand years after initial human presence in the area. Although humans arrived immediately prior to a cold phase, the Antarctic Cold Reversal stadial, megafaunal extinctions did not occur until the stadial finished and the subsequent warming phase commenced some 1 to 3 thousand years later. The increased resolution provided by the Patagonian material reveals that the sequence of climate and extinction events in North and South America were temporally inverted, but in both cases, megafaunal extinctions did not occur until human presence and climate warming coincided. Overall, metapopulation processes involving subpopulation connectivity on a continental scale appear to have been critical for megafaunal species survival of both climate change and human impacts.Fil: Metcalf, Jessica L.. University of Adelaide; Australia. State University of Colorado Boulder; Estados UnidosFil: Turney, Chris. University of New South Wales; AustraliaFil: Barnett, Ross. University of Oxford; Reino Unido. Universidad de Copenhagen; DinamarcaFil: Martin, Fabiana. Universidad de Magallanes. Instituto de la Patagonia. Centro de Estudios del Hombre Austral; ChileFil: Bray, Sarah C.. University of Adelaide; Australia. University of South Australia; AustraliaFil: Vilstrup, Julia T.. Universidad de Copenhagen; DinamarcaFil: Orlando, Ludovic. Universidad de Copenhagen; DinamarcaFil: Salas-Gismondi, Rodolfo. Université de Montpellier. Institut des Sciences de l’Evolution; Francia. Universidad Nacional Mayor de San Marcos; PerúFil: Loponte, Daniel Marcelo. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Medina, Matias Eduardo. Centro de Estudios Históricos ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Nigris, Mariana Eleonor. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Civalero, Maria Teresa. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernández, Pablo Marcelo. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gasco, Alejandra Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Paleoecología Humana; ArgentinaFil: Duran, Victor Alberto. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Paleoecología Humana; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Seymour, Kevin L.. Royal Ontario Museum. Department of Natural History; CanadáFil: Otaola, Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Multidisciplinario de Historia y Ciencias Humanas; ArgentinaFil: Gil, Adolfo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla | Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Museo de Historia Natural de San Rafael - Ianigla; ArgentinaFil: Paunero, Rafael. Universidad Nacional de La Plata; ArgentinaFil: Prevosti, Francisco Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Bradshaw, Corey J. A.. University of Adelaide; AustraliaFil: Wheeler, Jane C.. Instituto de Investigación y Desarrollo de Camélidos Sudamericanos; PerúFil: Borrero, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Multidisciplinario de Historia y Ciencias Humanas; ArgentinaFil: Austin, Jeremy J.. University of Adelaide; AustraliaFil: Cooper, Alan. University of Adelaide; Australia. University of Oxford; Reino Unid

Mitogenomic phylogenetic analyses of the Delphinidae with an emphasis on the Globicephalinae

BACKGROUND: Previous DNA-based phylogenetic studies of the Delphinidae family suggest it has undergone rapid diversification, as characterised by unresolved and poorly supported taxonomic relationships (polytomies) for some of the species within this group. Using an increased amount of sequence data we test between alternative hypotheses of soft polytomies caused by rapid speciation, slow evolutionary rate and/or insufficient sequence data, and hard polytomies caused by simultaneous speciation within this family. Combining the mitogenome sequences of five new and 12 previously published species within the Delphinidae, we used Bayesian and maximum-likelihood methods to estimate the phylogeny from partitioned and unpartitioned mitogenome sequences. Further ad hoc tests were then conducted to estimate the support for alternative topologies. RESULTS: We found high support for all the relationships within our reconstructed phylogenies, and topologies were consistent between the Bayesian and maximum-likelihood trees inferred from partitioned and unpartitioned data. Resolved relationships included the placement of the killer whale (Orcinus orca) as sister taxon to the rest of the Globicephalinae subfamily, placement of the Risso's dolphin (Grampus griseus) within the Globicephalinae subfamily, removal of the white-beaked dolphin (Lagenorhynchus albirostris) from the Delphininae subfamily and the placement of the rough-toothed dolphin (Steno bredanensis) as sister taxon to the rest of the Delphininae subfamily rather than within the Globicephalinae subfamily. The additional testing of alternative topologies allowed us to reject all other putative relationships, with the exception that we were unable to reject the hypothesis that the relationship between L. albirostris and the Globicephalinae and Delphininae subfamilies was polytomic. CONCLUSION: Despite their rapid diversification, the increased sequence data yielded by mitogenomes enables the resolution of a strongly supported, bifurcating phylogeny, and a chronology of the divergences within the Delphinidae family. This highlights the benefits and potential application of large mitogenome datasets to resolve long-standing phylogenetic uncertainties

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]