02448nas a2200241 4500008004100000022001400041245008000055210006900135260001500204300000900219490000700228520175800235653001801993653002002011653001402031653002002045653002202065653002102087653001702108100002002125700001802145856004302163 2020 eng d a2075-445000aSoil Health and Arthropods: From Complex System to Worthwhile Investigation0 aSoil Health and Arthropods From Complex System to Worthwhile Inv c16/01/2020 a1-210 v113 a

The dramatic increase in soil degradation in the last few decades has led to the need to identify methods to define not only soil quality but also, in a holistic approach, soil health. In the past twenty years, indices based on living communities have been proposed alongside the already proven physical-chemical methods. Among them, some soil invertebrates have been included in monitoring programs as bioindicators of soil quality. Being an important portion of soil fauna, soil arthropods are involved in many soil processes such as organic matter decomposition and translocation, nutrient cycling, microflora activity regulation and bioturbation. Many studies have reported the use of soil arthropods to define soil quality; among taxa, some have been explored more in depth, typically Acari and Collembola, while generally less abundant groups, such as Palpigradi or Embioptera, have not been investigated much. This paper aims to evaluate and compare the use of different soil microarthropod taxa in soil degradation/quality studies to highlight which groups are the most reported for soil monitoring and which are the most sensitive to soil degradation. We have decided not to include the two most present and abundant taxa, Acari and Collembola, in this paper in consideration of the vast amount of existing literature and focus the discussion on the other microarthropod groups. We reported some studies for each taxon highlighting the use of the group as soil quality indicator. A brief section reporting some indices based on soil microarthropods is proposed at the end of this specific discussion. This paper can be considered as a reference point in the use of soil arthropods to estimate soil quality and health.

10abioindicators10amacroarthropods10amesofauna10amicroarthropods10asoil biodiversity10asoil degradation10asoil quality1 aMenta, Cristina1 aRemelli, Sara uhttps://www.mdpi.com/2075-4450/11/1/5403914nas a2200505 4500008004100000022001400041245012800055210006900183260001500252300000900267490000700276520241600283653002402699653001302723653002302736653002102759653001802780653001802798653002102816653001202837653001902849100002702868700002002895700002202915700002102937700002202958700001802980700001702998700001703015700001803032700002303050700002003073700002303093700002403116700001803140700002403158700002803182700002703210700001903237700002003256700001403276700001903290700002103309856007803330 2020 eng d a1471-214800aFour myriapod relatives – but who are sisters? No end to debates on relationships among the four major myriapod subgroups0 aFour myriapod relatives but who are sisters No end to debates on c04/11/2020 a1-150 v203 a

Background: Phylogenetic relationships among the myriapod subgroups Chilopoda, Diplopoda, Symphyla and Pauropoda are still not robustly resolved. The first phylogenomic study covering all subgroups resolved phylogenetic relationships congruently to morphological evidence but is in conflict with most previously published phylogenetic trees based on diverse molecular data. Outgroup choice and long-branch attraction effects were stated as possible explanations for these incongruencies. In this study, we addressed these issues by extending the myriapod and outgroup taxon sampling using transcriptome data.

Results: We generated new transcriptome data of 42 panarthropod species, including all four myriapod subgroups and additional outgroup taxa. Our taxon sampling was complemented by published transcriptome and genome data resulting in a supermatrix covering 59 species. We compiled two data sets, the first with a full coverage of genes per species (292 single-copy protein-coding genes), the second with a less stringent coverage (988 genes). We inferred phylogenetic relationships among myriapods using different data types, tree inference, and quartet computation approaches. Our results unambiguously support monophyletic Mandibulata and Myriapoda. Our analyses clearly showed that there is strong signal for a single unrooted topology, but a sensitivity of the position of the internal root on the choice of outgroups. However, we observe strong evidence for a clade Pauropoda+Symphyla, as well as for a clade Chilopoda+Diplopoda.

Conclusions: Our best quartet topology is incongruent with current morphological phylogenies which were supported in another phylogenomic study. AU tests and quartet mapping reject the quartet topology congruent to trees inferred with morphological characters. Moreover, quartet mapping shows that confounding signal present in the data set is sufficient to explain the weak signal for the quartet topology derived from morphological characters. Although outgroup choice affects results, our study could narrow possible trees to derivatives of a single quartet topology. For highly disputed relationships, we propose to apply a series of tests (AU and quartet mapping), since results of such tests allow to narrow down possible relationships and to rule out confounding signal.

 

10aArthropod phylogeny10aconflict10aConfounding signal10aInternal rooting10aPhylogenetics10aphylogenomics10aQuartet topology10aRNA-Seq10aTranscriptomes1 aSzucsich, Nikolaus, U.1 aBartel, Daniela1 aBlanke, Alexander1 aBöhm, Alexander1 aDonath, Alexander1 aFukui, Makiko1 aGrove, Simon1 aLiu, Shanlin1 aMacek, Oliver1 aMachida, Ryuichiro1 aMisof, Bernhard1 aNakagaki, Yasutaka1 aPodsiadlowski, Lars1 aSekiya, Kaoru1 aTomizuka, Shigekazu1 aVon Reumont, Björn, M.1 aWaterhouse, Robert, M.1 aWalzl, Manfred1 aMeng, Guanliang1 aZhou, Xin1 aPass, Günther1 aMeusemann, Karen uhttps://bmcevolbiol.biomedcentral.com/articles/10.1186/s12862-020-01699-0