Engaging rural Australian communities in National Science Week helps increase visibility for women researchers

During a week-long celebration of science, run under the federally-supported National Science Week umbrella, the Catch a Rising Star: women in Queensland research (CaRS) program flew scientists who identify as women to regional and remote communities in the Australian State of Queensland. The aim of the project was twofold: first, to bring science to remote and regional communities in a large, economically diverse state; and second, to determine whether media and public engagement provide career advancement opportunities for women scientists. This paper focuses on the latter goal. The data show: 1) a substantial majority (> 80%) of researchers thought the training and experience provided by the program would help develop her career as a research scientist in the future; 2) the majority (65%) thought the program would help relate her research to end users, industry partners, or stakeholders in the future; and, 3) analytics can help create a compelling narrative around engagement metrics and help to quantify influence. During the weeklong project, scientists reached 600,000 impressions on one social media platform (Twitter) using a program hashtag. The breadth and depth of the project outcomes indicate funding bodies and employers could use similar data as an informative source of metrics to support hiring and promotion decisions. Although this project focused on researchers who identify as women, the lessons learned are applicable to researchers representing a diverse range of backgrounds. Future surveys will help determine whether the CaRS program provided long-term career advantages to participating scientists and communities

Extending dental mesowear analyses to Australian marsupials, with applications to six Plio-Pleistocene kangaroos from southeast Queensland

Mesowear analysis is a form of dental wear analysis used to infer the diets of herbivorous mammal species. It makes use of percentage indices of blunt, round and sharp cusp shape and high occlusal relief to classify the diet of species into one of three categories: browser, grazer or mixed feeder. Previously, this form of analysis has been limited to placental mammals, restricting the use of such analyses in Australia where the dominant herbivorous mammalian fauna consist of marsupials. In order to address this limitation, mesowear variables of extant marsupials were examined to determine whether their diets can accurately be predicted using mesowear analyses. Discriminant Function Analysis of mesowear variables and analysis of variance (ANOVA) of univariate mesowear scores for marsupial species demonstrate that mesowear analysis can be used to classify marsupial diets. A dataset of 24 typical marsupial species considered to be representative of the three dietary categories with respect to mesowear was generated and significantly increased cross-validated classification levels from 74.4% to 100% for the second molar. Mesowear analysis for marsupial species is most effective for the second molar with high predictive power also being evident for the first and third molars. When mesowear analysis was applied to six Plio-Pleistocene macropods (Marsupialia: Macropodidae) from the Darling Downs region, southeast Queensland, all species were classified as mixed feeders with the exception of Protemnodon roechus which was classified as a grazer. This study demonstrates the effectiveness of mesowear analysis as a dietary proxy for herbivorous marsupial species

Honouring Jagalingu Country. Bimblebox: art - science - nature National Touring Exhibition 2014 – 2016

This work focuses on creating an historical record of the flora and fauna that exists at the Bimblebox Nature Refuge in response to the threat to Jagalingu Aboriginal Country. A proposed multi-billion dollar mining development plan by Clive Palmer's Waratah Coal is to go through the Bimblebox Nature Refuge - a peaceful 8000 hectare sanctuary in central-west Queensland

New material of Gumardee pascuali Flannery et al., 1983 (Marsupialia: Macropodiformes) and two new species from the Riversleigh World Heritage area, Queensland, Australia

New specimens of the late Oligocene macropodiform, Gumardee pascuali, are described, as well as two new late Oligocene to early Miocene species, G. springae sp. nov. and G. richi sp. nov. Species of Gumardee exhibit a unique combination of features, including very long upper and lower third premolars, partially lophodont molars and, in dorsoventral plane, concave lower molar row and convex upper molar row. We combined two morphological matrices to assess the phylogenetic relationships of these species. Our analysis recovered species of Gumardee as a well-supported monophyletic group, nested within Potoroinae. Gumardee richi and G. pascuali appear to be more derived than G. springae in having a more strongly developed posthypocristid that is almost hypolophid-like

Cookeroo, a new genus of fossil kangaroo (Marsupialia, Macropodidae) from the Oligo-Miocene of Riversleigh, northwestern Queensland, Australia

A new genus of Oligo-Miocene kangaroo (Macropodiformes), Cookeroo, and two new species, Cookeroo bulwidarri and C. hortusensis, are described from the Riversleigh World Heritage Area, northern Australia. Species of Cookeroo are distinguished from other basal macropodids by possessing a unique combination of characters including an expanded masseteric canal confluent with the mandibular canal that extends to below m1, a sinuous i1 with enamel on the buccal surface only and with dorsal and ventral flanges present, a dentary with a marked inflection of the ventral border below m3, bilophodont molars, and an elongate third premolar. We assess the phylogenetic relationships of the genus using a combination of two previously published morphological matrices. Our analysis recovers Cookeroo species as early-branching members of a clade that also contains macropodines, sthenurines, and lagostrophines

Data from: Sharing is caring? measurement error and the issues arising from combining 3D morphometric datasets

Geometric morphometrics is routinely used in ecology and evolution and morphometric datasets are increasingly shared among researchers, allowing for more comprehensive studies and higher statistical power (as a consequence of increased sample size). However, sharing of morphometric data opens up the question of how much nonbiologically relevant variation (i.e., measurement error) is introduced in the resulting datasets and how this variation affects analyses. We perform a set of analyses based on an empirical 3D geometric morphometric dataset. In particular, we quantify the amount of error associated with combining data from multiple devices and digitized by multiple operators and test for the presence of bias. We also extend these analyses to a dataset obtained with a recently developed automated method, which does not require human-digitized landmarks. Further, we analyze how measurement error affects estimates of phylogenetic signal and how its effect compares with the effect of phylogenetic uncertainty. We show that measurement error can be substantial when combining surface models produced by different devices and even more among landmarks digitized by different operators. We also document the presence of small, but significant, amounts of nonrandom error (i.e., bias). Measurement error is heavily reduced by excluding landmarks that are difficult to digitize. The automated method we tested had low levels of error, if used in combination with a procedure for dimensionality reduction. Estimates of phylogenetic signal can be more affected by measurement error than by phylogenetic uncertainty. Our results generally highlight the importance of landmark choice and the usefulness of estimating measurement error. Further, measurement error may limit comparisons of estimates of phylogenetic signal across studies if these have been performed using different devices or by different operators. Finally, we also show how widely held assumptions do not always hold true, particularly that measurement error affects inference more at a shallower phylogenetic scale and that automated methods perform worse than human digitization