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Sökning: WFRF:(Lens Frederic) > (2020-2023)

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1.
  • Hendriks, Kasper P., et al. (författare)
  • Global Brassicaceae phylogeny based on filtering of 1,000-gene dataset
  • 2023
  • Ingår i: Current Biology. - : Elsevier. - 0960-9822 .- 1879-0445. ; 33:19, s. 4052-4068
  • Tidskriftsartikel (refereegranskat)abstract
    • The mustard family (Brassicaceae) is a scientifically and economically important family, containing the model plant Arabidopsis thaliana and numerous crop species that feed billions worldwide. Despite its relevance, most phylogenetic trees of the family are incompletely sampled and often contain poorly supported branches. Here, we present the most complete Brassicaceae genus-level family phylogenies to date (Bras-sicaceae Tree of Life or BrassiToL) based on nuclear (1,081 genes, 319 of the 349 genera; 57 of the 58 tribes) and plastome (60 genes, 265 genera; all tribes) data. We found cytonuclear discordance between the two, which is likely a result of rampant hybridization among closely and more distantly related lineages. To eval-uate the impact of such hybridization on the nuclear phylogeny reconstruction, we performed five different gene sampling routines, which increasingly removed putatively paralog genes. Our cleaned subset of 297 genes revealed high support for the tribes, whereas support for the main lineages (supertribes) was moder-ate. Calibration based on the 20 most clock-like nuclear genes suggests a late Eocene to late Oligocene origin of the family. Finally, our results strongly support a recently published new family classification, dividing the family into two subfamilies (one with five supertribes), together representing 58 tribes. This includes five recently described or re-established tribes, including Arabidopsideae, a monogeneric tribe accommodating Arabidopsis without any close relatives. With a worldwide community of thousands of researchers working on Brassicaceae and its diverse members, our new genus-level family phylogeny will be an indispensable tool for studies on biodiversity and plant biology.
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2.
  • Kattge, Jens, et al. (författare)
  • TRY plant trait database - enhanced coverage and open access
  • 2020
  • Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
  • Tidskriftsartikel (refereegranskat)abstract
    • Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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