Implementing area-based management of vulnerable marine ecosystems in areas beyond national jurisdiction: A South Pacific case study - Ashley A. Rowden; Fabrice Stephenson; Martin Cryer; Carolyn J. Lundquist; Owen F. Anderson; Matthew Bennion; Andrew Biggerstaff; Tiffany Bock; Tom Brough; Lee Georgeson; Alexandra Macdonald; Marco Milardi; et al. (Ocean & Coastal Management, 2026)

Migratory species strongly affect seabird biomass in seasonal assemblages off northeast Aotearoa/New Zealand - Nicholas W. Daudt; Marta Guerra; Tom Brough; Sarah L. Dwyer; Jochen R. Zaeschmar; Matthew R. Schofield; Robert O. Smith; Leandro Bugoni; Eric J. Woehler; William J. Rayment (Journal of Marine Systems, 2026)

The importance of sample preparation techniques for use in mine site operational mineralogy - C. Garner; J. Strongman; B. Staniforth; J. Fletcher; C. Brough (Minerals Engineering, 2024)

Implications for the conservation of deep-water corals in the face of multiple stressors: A case study from the New Zealand region - Fabrice Stephenson; Ashley A. Rowden; Owen F. Anderson; Joanne I. Ellis; Shane W. Geange; Tom Brough; Erik Behrens; Judi E. Hewitt; Malcolm R. Clark; Dianne M. Tracey; Savannah L. Goode; Grady L. Petersen; et al. (Journal of Environmental Management, 2023)

A seafloor bioregionalisation for New Zealand - Fabrice Stephenson; Ashley A. Rowden; Jordi Tablada; Karen Tunley; Tom Brough; Carolyn J. Lundquist; David A. Bowden; Shane Geange (Ocean & Coastal Management, 2023)

Prey and habitat characteristics contribute to hotspots of distribution for an endangered coastal dolphin - Tom E. Brough; William J. Rayment; Liz Slooten; Steve Dawson (Frontiers in Marine Science, 2023)
Introduction Understanding the characteristics of hotspots of species distribution provides opportunities for habitat-based management; a vital and often missing component in the conservation of mobile marine species. Correlates of species distribution derived from species distribution models (SDMs) are assumed to represent the characteristics of important habitat, which often include physical and biological (i.e., prey) components. In this study, we integrate surveys of Hector’s dolphin ( Cephalorhynchus hectori ) with in-situ data on environmental characteristics and prey to identify the statistical correlates of distribution and to assess ‘what makes hotspots unique’. Methods Between 2014 and 2017, ca. 300 surveys were carried out at hotspots and areas not routinely used by dolphins at Banks Peninsula on the east coast of New Zealand’s South Island. A broad range of prey and environmental variables were explored as drivers of dolphin distribution using generalized additive models, and principal component analysis was employed to determine a key environmental signature for hotspots. Results The relative abundance of dolphins was strongly correlated with prey abundance and a range of environmental variables representing habitat type and oceanographic conditions. The combination of high prey abundance and sandy, shallow, high current and low turbidity habitat was strongly represented at hotspots. Discussion These characteristics are also likely attributes of habitat with high ecological value generally, being related to high biodiversity, productivity, naturalness and ecosystem function. This study showcases the importance of targeted investigations into the characteristics of species hotspots to better guide the management of important areas for the conservation of both species and ecosystems.

Prediction of disability-free survival in healthy older people - Johannes Tobias Neumann; Le T. P. Thao; Anne M. Murray; Emily Callander; Prudence R. Carr; Mark R. Nelson; Rory Wolfe; Robyn L. Woods; Christopher M. Reid; Raj C. Shah; Anne B. Newman; Jeff D. Williamson; et al. (GeroScience, 2022)
Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people. Trial registration Clinicaltrials.gov (NCT01038583)

Development of a Seafloor Community Classification for the New Zealand Region Using a Gradient Forest Approach - Fabrice Stephenson; Ashley A. Rowden; Tom Brough; Grady Petersen; Richard H. Bulmer; John R. Leathwick; Andrew M. Lohrer; Joanne I. Ellis; David A. Bowden; Shane W. Geange; Greig A. Funnell; Debbie J. Freeman; et al. (Frontiers in Marine Science, 2022)
To support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest models was developed using a broad suite of biotic and high-resolution environmental predictor variables. Gradient Forest modeling uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover. A total of 630,997 records (39,766 unique locations) of 1,716 taxa living on or near the seafloor were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in four biotic groups and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. We refer to this classification as the New Zealand “Seafloor Community Classification” (SCC). Associated uncertainty estimates of compositional turnover for each of the biotic groups and overall community were also produced, and an added measure of uncertainty – coverage of the environmental space – was developed to further highlight geographic areas where predictions may be less certain owing to low sampling effort. Environmental differences among the deep-water New Zealand SCC groups were relatively muted, but greater environmental differences were evident among groups at intermediate depths in line with well-defined oceanographic patterns observed in New Zealand’s oceans. Environmental differences became even more pronounced at shallow depths, where variation in more localised environmental conditions such as productivity, seafloor topography, seabed disturbance and tidal currents were important differentiating factors. Environmental similarities in New Zealand SCC groups were mirrored by their biological compositions. The New Zealand SCC is a significant advance on previous numerical classifications and includes a substantially wider range of biological and environmental data than has been attempted previously. The classification is critically appraised and considerations for use in spatial management are discussed.

Use of Iodine Supplements by Breastfeeding Mothers Is Associated with Better Maternal and Infant Iodine Status - Ying Jin; Jane Coad; Shao J Zhou; Sheila Skeaff; Cheryl Benn; Louise Brough (Biological Trace Element Research, 2021)

Drivers of Spatial Distributions of Basking Shark (Cetorhinus maximus) in the Southwest Pacific - Brittany Finucci; Clinton A. J. Duffy; Tom Brough; Malcolm P. Francis; Marco Milardi; Matthew H. Pinkerton; Grady Petersen; Fabrice Stephenson (Frontiers in Marine Science, 2021)
Basking sharks ( Cetorhinus maximus ) were widely reported throughout New Zealand waters. Once commonly observed, and sometimes in large numbers, basking sharks are now infrequently reported. Basking shark observations are known to be highly variable across years, and their distribution and occurrence have been shown to be influenced by environmental predictors such as thermal fronts, chl- a concentration, and the abundance of prey (zooplankton). Little is known of basking sharks in the South Pacific and more information on distribution, habitat use, and migratory patterns is required to better understand the species’ regional ecology. Here, we used bootstrapped Habitat Suitability Models [HSM, ensembled from Boosted Regression Tree (BRT) and Random Forest (RF) models] to determine the drivers of basking shark distribution, predict habitat suitability and estimated uncertainty in the South Pacific for the first time. High−resolution environmental (1 km 2 grid resolution) and biotic data, including inferred prey species, and all available basking shark records across New Zealand’s Exclusive Economic Zone (EEZ) were included in the ensemble HSMs. The most influential driver of modeled basking shark distribution was vertical flux of particulate organic matter at the seabed, which may indicate higher levels of primary production in the surface ocean and higher prey density in the mesopelagic zone and at the seafloor. The BRT and RF models had good predictive power (AUC and TSS > 0.7) and both models performed similarly with low variability in the model fit metrics. Areas of high basking shark habitat suitability included the east and west coasts of the South Island, Puysegur Ridge, and Auckland Island slope. The outputs produced here could be incorporated into future management framework for assessing threat and conservation needs (e.g., spatially explicit risk assessment) for this regionally protected species, as well as providing guidance for future research efforts (e.g., areas of interest for sampling).

Transmission from Seed to Seedling and Secondary Spread of Xanthomonas campestris pv. Campestris in Brassica Transplants: Effects of Dose and Watering Regime - S.J. Roberts; L.H. Hiltunen; P.J. Hunter; J. Brough (European Journal of Plant Pathology, 1999)