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The beetle genus Pharaxonotha Reitter (Coleoptera: Erotylidae: Pharaxonothinae) is found in the cones of cycad genera in the New World, including species of Dioon Lindl., Ceratozamia Brongn., Microcycas (Miq.) A.DC and Zamia L. In this paper nine new species found in Dioon are described by Skelley, Tang and Pérez-Farrera: Pharaxonotha bicolor, P. dimorpha, P. fawcettae, P. gigantea, P. novoai, P. occidentalis, P. sclerotiza, P. woodruffi, P. vovidesi. A key to described species of Pharaxonotha inhabiting Dioon is presented, along with an account of Pharaxonotha kirschii Reitter for comparison.
The beetle genus Pharaxonotha Reitter (Coleoptera: Erotylidae: Pharaxonothinae) is found in the cones of cycad genera in the New World, including species of Ceratozamia Brongn., Dioon Lindl., Microcycas (Miq.) A.DC and Zamia L. Its presence and diversity are analyzed for the 17 species of Zamia known to occur in Panama. Nine species are recognized, and five new species are described: Pharaxonotha clarkorum Pakaluk, P. confusa Pakaluk, P. fortunensis Tang, Skelley and Taylor new species, P. holzmani Tang, Skelley and Taylor new species, P. kirschii Reitter, P. manicatae Tang, Skelley and Taylor new species, P. panamensis Tang, Skelley and Taylor new species, P. pseudoparasitica Tang, Skelley and Taylor new species, and P. taylori Skelley and Tang. A key to species of Pharaxonotha in Panama is presented.
ZooBank registration. urn:lsid:zoobank.org:pub:C054B0D4-FD00-4AE7-BBA7-C75A12368561
A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.