Phenology and yield do not scale from single row to whole plot in chickpea and lentil - L. Lake; R. Gimenez; R. Ortega Martinez; J. L. Weller; M. Javid; J. B. Butler; L. E. James; M. F. Dreccer; R. French; C. M. Cossani; J. E. Hayes; V. O. Sadras (Euphytica, 2025)
Reliable phenotyping is critical for pre-breeding and breeding. Some traits such as herbicide tolerance are more likely to scale from plant to crop than others such as yield. Here we compared phenology, yield and its components in two arrangements—single rows and whole plots—for 10 chickpea and 10 lentil cultivars in 11 (chickpea) and 10 (lentil) Australian environments resulting from the combination of location, season, and sowing date. The cultivars were characterised for key genetic loci for phenology: Elf3a , GI and the FT gene cluster in chickpea, and GWAS-chr2 and the FTb gene cluster in lentil. Across environments, yield of chickpea ranged from 33 to 268 g per lineal m (g m −1 ) in single rows and 5 to 77 g m −1 in whole plots, and yield of lentil ranged from 20 to 174 g m −1 in single rows and 9 to 104 g m −1 in whole plots. Across environments and genotypes, time to flowering was later in 207 of 275 chickpea whole plots compared with single rows and in 175 out of 234 lentil whole plots compared with single rows. In both chickpea and lentil, flowering and podding varied with the interaction between genotype, arrangement, and environment, resulting in altered genotypic rankings between single row and whole plot within and between environments. Yield components were variably affected by the three-way interaction; biomass was the only trait showing no interaction in either crop. Broad sense heritability of seed size fell from 0.60 in whole plots to 0.37 in single rows for chickpea, and from 0.87 to 0.62 in lentil. We highlight the need to understand scaling for agronomically important traits to avoid wasteful or counterproductive phenotyping and breeding efforts.

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Animal population decline and recovery after severe fire: Relating ecological and life history traits with expert estimates of population impacts from the Australian 2019-20 megafires - Michelle Ensbey; Sarah Legge; Chris J. Jolly; Stephen T. Garnett; Rachael V. Gallagher; Mark Lintermans; Dale G. Nimmo; Libby Rumpff; Ben C. Scheele; Nick S. Whiterod; John C.Z. Woinarski; Shane T. Ahyong; et al. (Biological Conservation, 2023)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Productivity dominated by picoplankton in a macro‐tidal tropical estuary, Darwin Harbour - D. Purcell‐Meyerink; J. Fortune; E. C. V. Butler (New Zealand Journal of Botany, 2017)
Productivity around the tropical coast of North Australia has been considered to be dominated by phytoplankton. The phytoplankton encompasses a large taxon which, dependent on the source definition, can vary significantly. Traditionally, they would only include pelagic photosynthetic organisms (autotrophic), yet more recently the definition is being revised to include heterotrophic organisms. Recent advancements in oceanography over the past three decades have found the prevalence in the global ocean of cyanobacteria of sizes ranging from 0.5–1.5 µm. Two cyanobacterial genera Synechoccocus and Prochlorococcus are now recognised to dominate phytoplankton cell counts and biomass specifically in the oligotrophic tropical and subtropical oceans. This study is the first to identify the importance of picoplankton as a foremost contributor to productivity in a macro‐tidal ecosystem in Northern Tropical Australia. Picoplankton was found to be the dominant group of primary producers (> 99% or 6 × 10 9 cells/L) within the phytoplankton in Darwin Harbour during the dry season of 2013. The larger phytoplankton > 2 µm contributed ( 2 cells/L) of the population. This study further defines the influences on this dynamic system that would allow the picoplankton community to thrive, highlighting the complexity of this nitrogen‐limited system, where nutrient cycling, including nitrogen fixation mediated possibly through the microbial community, plays a pertinent role. Other environmental influences include spring tides, spatial variation and increased turbidity which benefits heterotrophy over autotrophy in this ecosystem.

Reconstructing global overturning from meridional density gradients - E. D. Butler; K. I. C. Oliver; J. J.-M. Hirschi; J. V. Mecking (Climate Dynamics, 2016)

Application of stable isotope mixing models for defining trophic biomagnification pathways of mercury and selenium - H. J. Jones; K. M. Swadling; E. C. V. Butler; L. A. Barry; C. K. Macleod (Limnology and Oceanography, 2014)
Trophic models based on nitrogen stable isotope ratios ( δ 15 N) have been shown to predict changes in mercury (Hg) concentrations in fish; however, they are usually applied at the ecosystem scale and are rarely directed at known trophic pathways. We discuss a novel approach in which we combined gut contents analysis and stable isotope analyses ( δ 15 N and δ 13 C) into a Bayesian isotopic mixing model to provide a quantitative estimate of Hg and selenium (Se) biomagnification in an estuarine food web. Estimates of the relationship between total mercury (THg) and methylmercury (MeHg) were significantly improved in mixing model‐adjusted food webs over models that included all known prey sources. Spatial differences in dietary composition and MeHg bioavailability offer strong evidence that local food webs can have a significant effect on the biomagnification of Hg within benthic fish species. While no evidence of Se biomagnification was found, lower Se : Hg ratios at higher trophic levels could be attributed to increasing trophic Hg concentration. Furthermore, stable isotope analysis suggested Hg and Se biotransfer from benthic sources to fish. Overall, the findings highlight that isotope mixing models can be a significant aid in assessments of contaminant biomagnification, particularly when it is important to define food pathways to top predators.

Multi-Decadal Changes in Tundra Environments and Ecosystems: Synthesis of the International Polar Year-Back to the Future Project (IPY-BTF) - Terry V. Callaghan; Craig E. Tweedie; Jonas Åkerman; Christopher Andrews; Johan Bergstedt; Malcolm G. Butler; Torben R. Christensen; Dorothy Cooley; Ulrika Dahlberg; Ryan K. Danby; Fred J. A. Daniёls; Johannes G. de Molenaar; et al. (Ambio, 2011)

Sectional notes - H. W. Poulsen; H. G. Zuckerman; C. H. Metzger; W. B. Ward; W. C. Edmundson; E. R. Bennet; R. A. Jehle; A. B. Beaumont; John S. Gardner; E. L. Newdick; Frank W. Hussey; E. J. Wheeler; et al. (American Journal of Potato Research, 1934)