Assessing Potential Data Sources for Landscape-scale Terrestrial Biodiversity Indicators - Tom Bradfer-Lawrence; Meriel Harrison; Adham Ashton-Butt; Sophie Bennett; Robin J. Boyd; Ella Browning; Graeme Buchanan; Adam Fraser; Sian E. Green; Robert Hawkes; Becky E. Heath; Pen-Yuan Hsing; et al. (Environmental Management, 2026)

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

Distribution and time budgets limit occupancy of breeding sites in the nonbreeding season in a colonial seabird - Sophie Bennett; Francis Daunt; Kate R. Searle; Mike P. Harris; Lila Buckingham; James Duckworth; Ruth E. Dunn; Sarah Wanless; Mark A. Newell; Jonathan A. Green (Animal Behaviour, 2024)

Marine reserve use by a migratory coastal shark, Carcharias taurus - Ross G. Dwyer; Michaela Rathbone; Deborah L. Foote; Mike Bennett; Paul A. Butcher; Nicholas M. Otway; Brett M. Louden; Fabrice R.A. Jaine; Craig E. Franklin; Carley Kilpatrick (Biological Conservation, 2023)

Satellite Tagging and Photographic Identification Reveal Connectivity Between Two UNESCO World Heritage Areas for Reef Manta Rays - Amelia J. Armstrong; Asia O. Armstrong; Frazer McGregor; Anthony J. Richardson; Michael B. Bennett; Kathy A. Townsend; Graeme C. Hays; Mike van Keulen; Jessica Smith; Christine L. Dudgeon (Frontiers in Marine Science, 2020)

The relationship between pain, analgesics and survival in patients with advanced cancer; a secondary data analysis of the international European palliative care Cancer symptom study. - Jason W Boland; Victoria Allgar; Elaine G Boland; Mike I Bennett; Stein Kaasa; Marianne Jensen Hjermstad; Miriam Johnson (European Journal of Clinical Pharmacology, 2020)
Purpose Opioids reduce cancer-related pain but an association with shorter survival is variably reported. Aim: To investigate the relationship between pain, analgesics, cancer and survival within the European Palliative Care Cancer Symptom (EPCCS) study to help inform clinical decision making. Methods Secondary analysis of the international prospective, longitudinal EPCCS study which included 1739 adults with advanced, incurable cancer receiving palliative care. In this secondary analysis, for all participants with date of death or last follow up, a multilevel Weibull survival analysis examined whether pain, analgesics, and other relevant variables are associated with time to death. Results Date of death or last follow-up was available for 1404 patients (mean age 65.7 [SD:12.3];men 50%). Secondary analysis of this group showed the mean survival from baseline was 46.5 (SD:1.5) weeks (95% CI:43.6–49.3). Pain was reported by 76%; 60% were taking opioids, 51% non-opioid analgesics and 24% co-analgesics. Opioid-use was associated with decreased survival in the multivariable model (HR = 1.59 (95% CI:1.38–1.84), p < 0.001). An exploratory subgroup analysis of those with C-reactive protein (CRP) measures ( n = 219) indicated higher CRP was associated with poorer survival ( p = 0.001). In this model, the strength of relationship between survival and opioid-use weakened ( p = 0.029). Conclusion Opioid-use and survival were associated; this relationship weakened in a small sensitivity-testing subgroup analysis adjusting for CRP. Thus, the observed relationship between survival and opioid-use may partly be due to tumour-related inflammation. Larger studies, measuring disease activity, are needed to confirm this finding to more accurately judge the benefits and risks of opioids in advanced progressive disease.

Gestation and size at parturition for Mobula kuhlii cf. eregoodootenkee - Matt K. Broadhurst; Betty J. L. Laglbauer; Mike B. Bennett (Environmental Biology of Fishes, 2019)

Testing the generality of above‐ground biomass allometry across plant functional types at the continent scale - Keryn I. Paul; Stephen H. Roxburgh; Jerome Chave; Jacqueline R. England; Ayalsew Zerihun; Alison Specht; Tom Lewis; Lauren T. Bennett; Thomas G. Baker; Mark A. Adams; Dan Huxtable; Kelvin D. Montagu; et al. (Global Change Biology, 2016)
Accurate ground‐based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost‐effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above‐ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power‐law models explained 84–95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand‐based biomass from allometric models of varying levels of generalization (species‐specific, plant functional type) were validated using whole‐plot harvest data from 17 contrasting stands (range: 9–356 Mg ha −1 ). Losses in efficiency of prediction were stand ‐level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost‐effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species‐specific models is only warranted when gains in accuracy of stand‐based predictions are relatively high (e.g. high‐value monocultures).

A seven-gene expression panel distinguishing clonal expansions of pre-leukemic and chronic lymphocytic leukemia B cells from normal B lymphocytes - Brian A. McCarthy; Sophia Yancopoulos; Mike Tipping; Xiao-jie Yan; Xue Ping Wang; Fiona Bennett; Wentian Li; Martin Lesser; Santanu Paul; Erin Boyle; Carolina Moreno; Rosa Catera; et al. (Immunologic Research, 2015)

Tiger shark (Galeocerdo cuvier) movement patterns and habitat use determined by satellite tagging in eastern Australian waters - Bonnie J. Holmes; Julian G. Pepperell; Shane P. Griffiths; Fabrice R. A. Jaine; Ian R. Tibbetts; Mike B. Bennett (Marine Biology, 2014)

Predicting the century‐long post‐fire responses of reptiles - Dale. G. Nimmo; Luke T. Kelly; Lisa M. Spence‐Bailey; Simon J. Watson; Angie Haslem; John G. White; Mike F. Clarke; Andrew F. Bennett (Global Ecology and Biogeography, 2012)
Aim We examined the century‐long post‐fire responses of reptiles to (1) determine the time‐scales over which fauna – fire relationships occur, (2) assess the capacity of a conceptual model to predict faunal response to fire, and (3) investigate the degree to which models of fauna – fire relationships can predict species occurrence and are transferable across space. Location A 104,000 km 2 area in the semi‐arid Murray Mallee region of south‐eastern Australia. Methods We surveyed reptiles at 280 sites across a century‐long post‐fire chronosequence. We developed generalized additive mixed models (GAMMs) of the relationship between time since fire and the occurrence of 17 species in two subregions, and compared modelled responses with predictions derived from the conceptual model. The predictive capacity of GAMMs was then assessed (1) within the subregion the model was developed and (2) when transferred into a novel subregion. Results Eleven species displayed a significant relationship with time‐since‐fire, with changes in species probability of occurrence continuing up to 100 years post‐fire. Predictions of the timing of species post‐fire peak in occurrence were accurate for 9 of 13 species models for which a significant fire response was detected, but little success was achieved in predicting the shape of a species' response. GAMMs predicted species occurrence more accurately when applied within the subregion in which they were developed than when transferred into a novel subregion, primarily due to some species responding to fire more strongly in one part of their geographic range. Main conclusions Fire influences the occurrence of reptiles in semi‐arid ecosystems over century‐long time frames. Habitat‐use conceptual models have value in predicting the peak occurrence of species following fire, particularly for species with distributions strongly shaped by fire. Species relationships with fire can differ across their geographic range, probably associated with variation in climatic influences on post‐fire succession and the consequent provision of habitat resources.

Electrophoretic variation as a measure of speciesdifferentiation between four species of the genus Lolium - Sarita Jane Bennett; Mike D. Hayward; Dave F. Marshall (Genetic Resources and Crop Evolution, 2002)