Otolith and Genomic Data Reveal Temporal Insights Into Stocking Across a Large River Basin in a Mobile, Long‐Lived Australian Freshwater Fish Species - Katherine A. Harrisson; Luciano B. Beheregaray; Christopher M. Bice; Emily J. Booth; Chris J. Brauer; Gavin L. Butler; David Dawson; Olga Dudchenko; Benjamin G. Fanson; Graeme Hackett; Annique Harris; Matthew J. Jones; et al. (Molecular Ecology, 2025)
Freshwater ecosystems and their biota are under increasing pressure from anthropogenic stressors. In response to declining fish stocks, hatchery and stocking programmes are widely implemented as core components of restoration and management strategies, with positive outcomes for some wild populations. Despite this, stocking remains contentious due to potential genetic and ecological risks to wild populations. Monitoring and evaluation of stocking outcomes are critical to ensuring the long‐term sustainability of wild populations, but identification of stocked individuals post‐release remains a key challenge, particularly for mobile species. In this study, we combined otolith (natal origin and age) and genomic data to identify stocked individuals and evaluate the genetic implications of stocking for a culturally and socioeconomically important and mobile freshwater fish, golden perch Macquaria ambigua (family: Percichthyidae), across Australia's Murray–Darling Basin (MDB). We also generated a chromosome‐level genome assembly. Many close kin were detected across the MDB, increasing in prevalence over recent decades and mostly of hatchery origin. Rivers with many close kin were associated with low effective population sizes ( N e

Corrigendum to “Troubled waters in the land down under: Pervasive threats and high extinction risks demand urgent conservation actions to protect Australia's native freshwater fishes” [Biol. Conserv. 300 (2024), 110843] - Mark Lintermans; Maiko Lutz; Nick S. Whiterod; Bernd Gruber; Michael P. Hammer; Mark J. Kennard; David L. Morgan; Tarmo A. Raadik; Peter Unmack; Steven Brooks; Brendan C. Ebner; Dean Gilligan; et al. (Biological Conservation, 2025)

Troubled waters in the land down under: Pervasive threats and high extinction risks demand urgent conservation actions to protect Australia's native freshwater fishes - Mark Lintermans; Maiko Lutz; Nick S. Whiterod; Bernd Gruber; Michael P. Hammer; Mark J. Kennard; David L. Morgan; Tarmo A. Raadik; Peter Unmack; Steven Brooks; Brendan C. Ebner; Dean Gilligan; et al. (Biological Conservation, 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)

The conservation impacts of ecological disturbance: Time‐bound estimates of population loss and recovery for fauna affected by the 2019–2020 Australian megafires - Sarah Legge; Libby Rumpff; John C. Z. Woinarski; Nick S. Whiterod; Michelle Ward; Darren G. Southwell; Ben C. Scheele; Dale G. Nimmo; Mark Lintermans; Hayley M. Geyle; Stephen T. Garnett; Brittany Hayward‐Brown; et al. (Global Ecology and Biogeography, 2022)
Aim After environmental disasters, species with large population losses may need urgent protection to prevent extinction and support recovery. Following the 2019–2020 Australian megafires, we estimated population losses and recovery in fire‐affected fauna, to inform conservation status assessments and management. Location Temperate and subtropical Australia. Time period 2019–2030 and beyond. Major taxa Australian terrestrial and freshwater vertebrates; one invertebrate group. Methods From > 1,050 fire‐affected taxa, we selected 173 whose distributions substantially overlapped the fire extent. We estimated the proportion of each taxon’s distribution affected by fires, using fire severity and aquatic impact mapping, and new distribution mapping. Using expert elicitation informed by evidence of responses to previous wildfires, we estimated local population responses to fires of varying severity. We combined the spatial and elicitation data to estimate overall population loss and recovery trajectories, and thus indicate potential eligibility for listing as threatened, or uplisting, under Australian legislation. Results We estimate that the 2019–2020 Australian megafires caused, or contributed to, population declines that make 70–82 taxa eligible for listing as threatened; and another 21–27 taxa eligible for uplisting. If so‐listed, this represents a 22–26% increase in Australian statutory lists of threatened terrestrial and freshwater vertebrates and spiny crayfish, and uplisting for 8–10% of threatened taxa. Such changes would cause an abrupt worsening of underlying trajectories in vertebrates, as measured by Red List Indices. We predict that 54–88% of 173 assessed taxa will not recover to pre‐fire population size within 10 years/three generations. Main conclusions We suggest the 2019–2020 Australian megafires have worsened the conservation prospects for many species. Of the 91 taxa recommended for listing/uplisting consideration, 84 are now under formal review through national processes. Improving predictions about taxon vulnerability with empirical data on population responses, reducing the likelihood of future catastrophic events and mitigating their impacts on biodiversity, are critical.

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.

Analytical performance of a platform for point-of-care CRP testing in adults consulting for lower respiratory tract infection in primary care - Veerle Matheeussen; Viviane Van Hoof; Katherine Loens; Christine Lammens; Anouk Vanderstraeten; Samuel Coenen; Chris C. Butler; Paul Little; Theo J. M. Verheij; Herman Goossens; Margareta Ieven (European Journal of Clinical Microbiology & Infectious Diseases, 2018)

Research priorities for managing the impacts and dependencies of business upon food, energy, water and the environment - Jonathan M. H. Green; Gemma R. Cranston; William J. Sutherland; Hannah R. Tranter; Sarah J. Bell; Tim G. Benton; Eva Blixt; Colm Bowe; Sarah Broadley; Andrew Brown; Chris Brown; Neil Burns; et al. (Sustainability Science, 2017)

Assessing uncertainties in land cover projections - Peter Alexander; Reinhard Prestele; Peter H. Verburg; Almut Arneth; Claudia Baranzelli; Filipe Batista e Silva; Calum Brown; Adam Butler; Katherine Calvin; Nicolas Dendoncker; Jonathan C. Doelman; Robert Dunford; et al. (Global Change Biology, 2017)
Understanding uncertainties in land cover projections is critical to investigating land‐based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro‐economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.

Short-interval cortical inhibition and intracortical facilitation during submaximal voluntary contractions changes with fatigue - Sandra K. Hunter; Chris J. McNeil; Jane E. Butler; Simon C. Gandevia; Janet L. Taylor (Experimental Brain Research, 2016)

A missense variant in CST3 exerts a recessive effect on susceptibility to age-related macular degeneration resembling its association with Alzheimer’s disease - Joe M. Butler; Umar Sharif; Manir Ali; Martin McKibbin; Joseph P. Thompson; Richard Gale; Yit C. Yang; Chris Inglehearn; Luminita Paraoan (Human Genetics, 2015)