Safeguarding the role of humans in conservation science in the age of AI - Amanda E. Bates; Bea Maas; Vanessa M. Adams; John F. Benson; Dominick A. DellaSala; Arash Ghoddousi; Varun R. Goswami; Janice Ser Huay Lee; Javier Nori; Valeria Rinaudo; Xingfeng Si; Orsolya Valkó; et al. (Biological Conservation, 2026)

Analysis of second-generation epigenetic clocks reveals further associations between disproportionate biological ageing and hippocampal volume - Lidija Milicic; Michael Vacher; Vincent Doré; Pierrick Bourgeat; Rosita Shishegar; James D. Doecke; Paul Maruff; Christopher C. Rowe; Victor L. Villemagne; Michael Weiner; Paul Aisen; Ronald Petersen; et al. (GeroScience, 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.

Surface-based morphometry reveals divergent aging trajectories in veterans with and without traumatic brain injury - Michelle C. Eliason; Amrutha Bindu Nagella; Cristian Cuadra; David S. Wack; Ghazala T. Saleem; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; John Q. Trojanowki; Arthur W. Toga; et al. (GeroScience, 2025)

Introducing ‘miniRECgap’ R package for simple gap-filling of missing eddy covariance CO2 flux measurements with classic nonlinear environmental response functions via GUI-supported R-scripts (case-study: In-sample gap-filling with ‘miniRECgap’ vs. MDS and an optimised shallow ANN in a ‘challenging’ peatland ecosystem) - Alina Premrov; Jagadeesh Yeluripati; Richard Slevin; Adam Bates; Magdalena Matysek; Stephen Barry; Kenneth A. Byrne; Rowan Fealy; Bernard Hyde; Gary Lanigan; Mark McCorry; Rachael Murphy; et al. (Environmental Modelling & Software, 2025)

Diversity in insect responses to landscape composition drives flower visitation across semi-natural grassland plants - Veronica Hederström; Theresia Krausl; Yuanyuan Quan; Adam H. Bates; Sandra Blasiusson; Rune Bygebjerg; Christoffer Fägerström; Kārlis Kenklis; Jadranka Rota; Ellen Sandström; Yann Clough (Agriculture, Ecosystems & Environment, 2025)

BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series - Maria Dornelas; Laura H. Antão; Amanda E. Bates; Viviana Brambilla; Jonathan M. Chase; Cher F. Y. Chow; Ada Fontrodona‐Eslava; Anne E. Magurran; Inês S. Martins; Faye Moyes; Alban Sagouis; Samuel Adu‐Acheampong; et al. (Global Ecology and Biogeography, 2025)
Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

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.

Voxel-wise insights into early Alzheimer’s disease pathology progression: the association with APOE and memory decline - Maha Wybitul; Nicolas Langer; Christoph Hock; Anton Gietl; Valerie Treyer; Michael Weiner; Paul Aisen; Ronald-Petersen; Clifford R. Jack; William Jagust; John Q. Trojanowski; Arthur W. Toga; et al. (GeroScience, 2025)
Longitudinal investigation of the Apolipoprotein E (APOE) genotype’s impact on Alzheimer’s disease (AD) biomarker progression, focusing on amyloid beta (Aβ) accumulation and gray matter (GM) atrophy, integrating cognitive decline and baseline levels. Longitudinal florbetapir-PET and T1-weighted MRI data from 100 cognitively normal (CN) and mild cognitive impaired (MCI) participants both with considerable global Aβ accumulation (“high Aβ accumulators”) were analyzed using a voxel-wise approach. Associations of APOE genotype and memory decline with Aβ accumulation and GM atrophy were examined separately for each neuroimaging modality, controlling for baseline Aβ levels and diagnosis. Alternatively, the effect of baseline diagnosis, while controlling for memory decline, was investigated. A multimodal analysis evaluated interactions between genotype, memory decline, and GM atrophy on Aβ accumulation. High Aβ accumulators displayed extensive Aβ pathology predominantly in the medial orbito-frontal cortex, cingulate cortex, and precuneus, along with GM atrophy in temporal, occipital, orbito-frontal, and parietal areas. ɛ4 carriers with memory decline exhibited greater Aβ accumulation and GM atrophy in selective regions compared to non-carriers with memory decline, while no genotype difference was observed in individuals without decline. No interaction effect was observed for MCI diagnosis. Regional associations between the two biomarkers were similarly dependent on genotype and memory decline. ɛ4 carriers exhibiting memory decline present an accelerated neurobiological pattern at predementia stages, supporting early ɛ4 carrier monitoring and interventions in this at-risk group. Importantly, memory decline might be more informative than MCI regarding AD pathology progression emphasizing the importance of repeated cognitive assessments.

Unexpected landscape-scale contemporary gene flow and fine-scale genetic diversity in rural hedgehogs - Hongli Yu; Lauren J. Moore; Axel Barlow; Louise K. Gentle; Deborah A. Dawson; Gavin J. Horsburgh; Lucy Knowles; Philip J. Baker; Adam Bates; Helen Hicks; Silviu Petrovan; Sarah Perkins; et al. (Conservation Genetics, 2025)
Agricultural intensification is one of the major forces driving populations of many traditionally common native species into smaller, fragmented populations which are prone to isolation and loss of genetic diversity. Identifying the spatial extent and characteristics of rural systems that support gene flow and promote genetic diversity for these species is thus essential for their long-term conservation. Here we used asymmetric autosomal genetic structure between sexes to investigate current gene flow among four neighbouring suburban populations of hedgehogs ( Erinaceus europaeus ) in England, which are separated by agricultural land. Contrary to expectations, we found that individuals belonged to a single genetic population despite the populations being separated by unoccupied agricultural land. Spatial autocorrelation was significant in adult female hedgehogs, but non-significant in adult males, revealing male driven contemporary gene flow between local populations. The results suggest that male hedgehogs are capable of moving between population patches separated by at least 3 km across the agricultural matrix. This finding is crucial to aid the development of a conservation strategy for hedgehogs as, for the first time, it shows the extent that previously assumed isolated populations across a perceived inhospitable landscape are connected by current gene flow. Higher within patch relatedness, and lower allelic richness were found from smaller suburban patches, largely reflecting local population size, indicating an early stage of genetic diversity loss due to habitat loss and associated fragmentation. Our study illustrates that considering current gene flow and local genetic diversity together is important to better understand habitat effects on genetic variation and to inform future conservation management.

Dynamic proportional loss of functional connectivity revealed change of left superior frontal gyrus in subjective cognitive decline: an explanatory study based on Chinese and Western cohorts - Luyao Wang; Wenjing Hu; Fan Dong; Can Sheng; Jinglong Wu; Ying Han; Jiehui Jiang; Michael W. Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2025)

Land Use Change Consistently Reduces α‐ But Not β‐ and γ‐Diversity of Bees - Toby P. N. Tsang; A. A. Amado De Santis; Gabriela Armas‐Quiñonez; John S. Ascher; Eva Samanta Ávila‐Gómez; András Báldi; Kimberly M. Ballare; Mario V. Balzan; Weronika Banaszak‐Cibicka; Svenja Bänsch; Yves Basset; Adam J. Bates; et al. (Global Change Biology, 2025)
Land use change threatens global biodiversity and compromises ecosystem functions, including pollination and food production. Reduced taxonomic α‐diversity is often reported under land use change, yet the impacts could be different at larger spatial scales (i.e., γ‐diversity), either due to reduced β‐diversity amplifying diversity loss or increased β‐diversity dampening diversity loss. Additionally, studies often focus on taxonomic diversity, while other important biodiversity components, including phylogenetic diversity, can exhibit differential responses. Here, we evaluated how agricultural and urban land use alters the taxonomic and phylogenetic α‐, β‐, and γ‐diversity of an important pollinator taxon—bees. Using a multicontinental dataset of 3117 bee assemblages from 157 studies, we found that taxonomic α‐diversity was reduced by 16%–18% in both agricultural and urban habitats relative to natural habitats. Phylogenetic α‐diversity was decreased by 11%–12% in agricultural and urban habitats. Compared with natural habitats, taxonomic and phylogenetic β‐diversity increased by 11% and 6% in urban habitats, respectively, but exhibited no systematic change in agricultural habitats. We detected a 22% decline in taxonomic γ‐diversity and a 17% decline in phylogenetic γ‐diversity in agricultural habitats, but γ‐diversity of urban habitats was not significantly different from natural habitats. These findings highlight the threat of agricultural expansions to large‐scale bee diversity due to systematic γ‐diversity decline. In addition, while both urbanization and agriculture lead to consistent declines in α‐diversity, their impacts on β‐ or γ‐diversity vary, highlighting the need to study the effects of land use change at multiple scales.

Complementary value of molecular, phenotypic, and functional aging biomarkers in dementia prediction - Andreas Engvig; Karl Trygve Kalleberg; Lars T. Westlye; Esten Høyland Leonardsen; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; John Q. Trojanowski; Arthur W. Toga; Laurel Beckett; et al. (GeroScience, 2024)
DNA methylation age (MA), brain age (BA), and frailty index (FI) are putative aging biomarkers linked to dementia risk. We investigated their relationship and combined potential for prediction of cognitive impairment and future dementia risk using the ADNI database. Of several MA algorithms, DunedinPACE and GrimAge2, associated with memory, were combined in a composite MA alongside BA and a data-driven FI in predictive analyses. Pairwise correlations between age- and sex-adjusted measures for MA (aMA), aBA, and aFI were low. FI outperformed BA and MA in all diagnostic tasks. A model including age, sex, and aFI achieved an area under the curve (AUC) of 0.94 for differentiating cognitively normal controls (CN) from dementia patients in a held-out test set. When combined with clinical biomarkers (apolipoprotein E ε4 allele count, memory, executive function), a model including aBA and aFI predicted 5-year dementia risk among MCI patients with an out-of-sample AUC of 0.88. In the prognostic model, BA and FI offered complementary value (both β s 0.50). The tested MAs did not improve predictions. Results were consistent across FI algorithms, with data-driven health deficit selection yielding the best performance. FI had a stronger adverse effect on prognosis in males, while BA’s impact was greater in females. Our findings highlight the complementary value of BA and FI in dementia prediction. The results support a multidimensional view of dementia, including an intertwined relationship between the biomarkers, sex, and prognosis. The tested MA’s limited contribution suggests caution in their use for individual risk assessment of dementia.

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)

Local molecular and connectomic contributions of tau-related neurodegeneration - Fardin Nabizadeh; Michael W. Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; John Q. Trojanowski; Arthur W. Toga; Laurel Beckett; Robert C. Green; Andrew J. Saykin; John Morris; et al. (GeroScience, 2024)

Regional-scale assessment of eutrophic condition indicators in subtropical temporarily closed estuaries - Daniel A. Lemley; Monique Nunes; Janine B. Adams; Guy C. Bate (Estuarine, Coastal and Shelf Science, 2024)

Sex differences in interacting genetic and functional connectivity biomarkers in Alzheimer’s disease - Jordan N. Williamson; Shirley A. James; Sean P. Mullen; Bradley P. Sutton; Tracey Wszalek; Beni Mulyana; Peter Mukli; Andriy Yabluchanskiy; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2024)

Glucose metabolism in posterior cingulate cortex has supplementary value to predict the progression of cognitively unimpaired to dementia due to Alzheimer’s disease: an exploratory study of 18F-FDG-PET - Qi Zhang; Chunqiu Fan; Luyao Wang; Taoran Li; Min Wang; Ying Han; Jiehui Jiang; Michael W. Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2023)

Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment - Jiehui Jiang; Can Sheng; Guanqun Chen; Chunhua Liu; Shichen Jin; Lanlan Li; Xueyan Jiang; Ying Han; Michael W. Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2022)

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume - Lidija Milicic; Michael Vacher; Tenielle Porter; Vincent Doré; Samantha C. Burnham; Pierrick Bourgeat; Rosita Shishegar; James Doecke; Nicola J. Armstrong; Rick Tankard; Paul Maruff; Colin L. Masters; et al. (GeroScience, 2022)
The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes.

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.

BioTIME: A database of biodiversity time series for the Anthropocene - Maria Dornelas; Laura H. Antão; Faye Moyes; Amanda E. Bates; Anne E. Magurran; Dušan Adam; Asem A. Akhmetzhanova; Ward Appeltans; José Manuel Arcos; Haley Arnold; Narayanan Ayyappan; Gal Badihi; et al. (Global Ecology and Biogeography, 2018)
Motivation The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km 2 (158 cm 2 ) to 100 km 2 (1,000,000,000,000 cm 2 ). Time period and grain BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format .csv and .SQL.

Overview and status of estuarine microphytobenthos ecological research in South Africa - T Dalu; JB Adams; JC Taylor; GC Bate; M Nunes; PW Froneman; RJ Wasserman (African Journal of Marine Science, 2018)

The OPAL bugs count survey: exploring the effects of urbanisation and habitat characteristics using citizen science - Adam J. Bates; Poppy Lakeman Fraser; Lucy Robinson; John C. Tweddle; Jon P. Sadler; Sarah E. West; Simon Norman; Martin Batson; Linda Davies (Urban Ecosystems, 2015)

Hydraulic modeling of the 2011 New Madrid Floodway activation: a case study on floodway activation controls - Adam Luke; Brad Kaplan; Jeff Neal; Jeremiah Lant; Brett Sanders; Paul Bates; Doug Alsdorf (Natural Hazards, 2015)

A review of the ecology and management of temporarily open/closed estuaries in South Africa, with particular emphasis on river flow and mouth state as primary drivers of these systems - AK Whitfield; GC Bate; JB Adams; PD Cowley; PW Froneman; PT Gama; NA Strydom; S Taljaard; AK Theron; JK Turpie; L van Niekerk; TH Wooldridge (African Journal of Marine Science, 2012)

SWITCH in Birmingham, UK: experimental investigation of the ecological and hydrological performance of extensive green roofs - Adam J. Bates; Rae Mackay; Richard B. Greswell; Jon P. Sadler (Reviews in Environmental Science and Bio/Technology, 2009)

Livestock Trampling Reduces the Conservation Value of Beetle Communities on High Quality Exposed Riverine Sediments - Adam J. Bates; Jon P. Sadler; Adrian P. Fowles (Biodiversity and Conservation, 2007)

Condition-dependent dispersal of a patchily distributed riparian ground beetle in response to disturbance - Adam J. Bates; Jon P. Sadler; Adrian P. Fowles (Oecologia, 2006)

The distribution and state of mangroves along the coast of Transkei, Eastern Cape Province, South Africa - J. B. Adams; B. M. Colloty; G. C. Bate (Wetlands Ecology and Management, 2004)