Mangrove genera influence bed shear stress and coastal protection ecosystem services - Alice J. Twomey; Dylan Cowley; Simon J. Murphy; David P. Callaghan; Marc Koh; Neil Saintilan; Catherine E. Lovelock (Water Research, 2026)

Investigating coral rubble dynamics through tilting base and flume experiments - Wen Deng; Cyprien Simonnet; Lauren Nolan; David P. Callaghan; Tom E. Baldock (Coastal Engineering, 2026)

Interdisciplinary solutions to enable nature-based solutions for coastal protection achieve ecological and engineering outcomes - Alice J. Twomey; Katherine Erickson; Melanie J. Bishop; Kris Boody; David P. Callaghan; Toni Cannard; Catherine E. Lovelock; Mariana Mayer-Pinto; Rebecca L. Morris; Andrew W.M. Pomeroy; Megan I. Saunders; Andy Steven; et al. (Environmental Science & Policy, 2025)

Discoveries on coastal retrogressive breach failures inspired by failure of an underwater fixed platform - Xiamei Man; William R. Dally; David P. Callaghan; Peter Nielsen (Marine Geology, 2025)

Quantifying the impact of future climate change on the risk of coral rubble instability across the Great Barrier Reef by 2100 - Dongfang Liu; David P. Callaghan; Tom E. Baldock (Journal of Environmental Management, 2025)

A probabilistic coral rubble mechanical instability model applied with field observations from the Great Barrier reef - Dongfang Liu; David P. Callaghan; Ananth Wuppukondur; Tom E. Baldock (Coastal Engineering, 2025)

Structural and hydrodynamic modelling of the probability of breakage of branching and plate coral colonies - Wen Deng; Tania Kenyon; Karen Eigeland; David P. Callaghan; Tom E. Baldock (Coastal Engineering, 2025)

Using valve gape analysis to compare sensitivity of native Mytilus edulis to invasive Magallana gigas when exposed to heavy metal contamination - David B. Kelleghan; Luke O'Callaghan; Freddie Huggard; Tasman P. Crowe; Paul R. Brooks (Marine Environmental Research, 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.

Phenotypic plasticity as a cause and consequence of population dynamics - Dominic P. Brass; Christina A. Cobbold; David A. Ewing; Bethan V. Purse; Amanda Callaghan; Steven M. White (Ecology Letters, 2021)
Predicting complex species‐environment interactions is crucial for guiding conservation and mitigation strategies in a dynamically changing world. Phenotypic plasticity is a mechanism of trait variation that determines how individuals and populations adapt to changing and novel environments. For individuals, the effects of phenotypic plasticity can be quantified by measuring environment–trait relationships, but it is often difficult to predict how phenotypic plasticity affects populations. The assumption that environment–trait relationships validated for individuals indicate how populations respond to environmental change is commonly made without sufficient justification. Here we derive a novel general mathematical framework linking trait variation due to phenotypic plasticity to population dynamics. Applying the framework to the classical example of Nicholson's blowflies, we show how seemingly sensible predictions made from environment–trait relationships do not generalise to population responses. As a consequence, trait‐based analyses that do not incorporate population feedbacks risk mischaracterising the effect of environmental change on populations.

Corrigendum: OceanGliders: A Component of the Integrated GOOS - Pierre Testor; Brad de Young; Daniel L. Rudnick; Scott Glenn; Daniel Hayes; Craig M. Lee; Charitha Pattiaratchi; Katherine Hill; Emma Heslop; Victor Turpin; Pekka Alenius; Carlos Barrera; et al. (Frontiers in Marine Science, 2021)

Habitat maps to enhance monitoring and management of the Great Barrier Reef - Chris M. Roelfsema; Eva M. Kovacs; Juan Carlos Ortiz; David P. Callaghan; Karlo Hock; Mathieu Mongin; Kasper Johansen; Peter J. Mumby; Magnus Wettle; Mike Ronan; Petra Lundgren; Emma V. Kennedy; et al. (Coral Reefs, 2020)

OceanGliders: A Component of the Integrated GOOS - Pierre Testor; Brad de Young; Daniel L. Rudnick; Scott Glenn; Daniel Hayes; Craig M. Lee; Charitha Pattiaratchi; Katherine Hill; Emma Heslop; Victor Turpin; Pekka Alenius; Carlos Barrera; et al. (Frontiers in Marine Science, 2019)
The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health, and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in real-time and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintain and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.

Coastal retreat and improved water quality mitigate losses of seagrass from sea level rise - Megan I. Saunders; Javier Leon; Stuart R. Phinn; David P. Callaghan; Katherine R. O'Brien; Chris M. Roelfsema; Catherine E. Lovelock; Mitchell B. Lyons; Peter J. Mumby (Global Change Biology, 2013)
The distribution and abundance of seagrass ecosystems could change significantly over the coming century due to sea level rise ( SLR ). Coastal managers require mechanistic understanding of the processes affecting seagrass response to SLR to maximize their conservation and associated provision of ecosystem services. In Moreton Bay, Queensland, Australia, vast seagrass meadows supporting populations of sea turtles and dugongs are juxtaposed with the multiple stressors associated with a large and rapidly expanding human population. Here, the interactive effects of predicted SLR , changes in water clarity, and land use on future distributions of seagrass in Moreton Bay were quantified. A habitat distribution model of present day seagrass in relation to benthic irradiance and wave height was developed which correctly classified habitats in 83% of cases. Spatial predictions of seagrass and presence derived from the model and bathymetric data were used to initiate a SLR inundation model. Bathymetry was iteratively modified based on SLR and sedimentary accretion in seagrass to simulate potential seagrass habitat at 10 year time steps until 2100. The area of seagrass habitat was predicted to decline by 17% by 2100 under a scenario of SLR of 1.1 m. A scenario including the removal of impervious surfaces, such as roads and houses, from newly inundated regions, demonstrated that managed retreat of the shoreline could potentially reduce the overall decline in seagrass habitat to just 5%. The predicted reduction in area of seagrass habitat could be offset by an improvement in water clarity of 30%. Greater improvements in water clarity would be necessary for larger magnitudes of SLR . Management to improve water quality will provide present and future benefits to seagrasses under climate change and should be a priority for managers seeking to compensate for the effects of global change on these valuable habitats.

Multiple Effects of Changes in Arctic Snow Cover - Terry V. Callaghan; Margareta Johansson; Ross D. Brown; Pavel Ya. Groisman; Niklas Labba; Vladimir Radionov; Raymond S. Bradley; Sylvie Blangy; Olga N. Bulygina; Torben R. Christensen; Jonathan E. Colman; Richard L. H. Essery; et al. (Ambio, 2011)

Symposia lectures - F. Parak; A. Ostermann; G. U. Nienhaus; Nobuo Niimura; William A. Eaton; Stephen J. Hagen; Eric R. Henry; James Hofrichter; Gouri Jas; Lisa Lapidus; Victor Muñoz; Chih-chen Wang; et al. (Journal of Biosciences, 1999)