Tracing the Drivers of Range‐Wide Bowhead Whale Genomic Structure and Diversity - Michael V. Westbury; Nicholas A. Freymueller; Andrea A. Cabrera; Lutz Bachmann; Steven H. Ferguson; Mads Peter Heide‐Jørgensen; Kit M. Kovacs; Christian Lydersen; Olga Shpak; Øystein Wiig; Damien A. Fordham; Eline D. Lorenzen (Molecular Ecology, 2026)
In a rapidly warming Arctic, genetic variation might serve to buffer organisms against the effects of environmental change, such as sea ice loss and ocean warming. Yet, this concept remains largely unexplored because comprehensive genome‐wide studies across the full ranges of Arctic marine taxa are rare. Bowhead whales ( Balaena mysticetus ) have a strong association with sea ice and polar water masses, a long history of human exploitation, and a circumpolar distribution, making them a valuable model for evaluating how past environmental and anthropogenic factors have shaped contemporary population variation. We analysed both nuclear and mitochondrial genomes from bowhead whales sampled across the species' range, encompassing all four recognised stocks. Firstly, our results indicate the existence of three genetic groupings instead of four: (1) Okhotsk Sea; (2) East Greenland‐Svalbard‐Barents Sea; and (3) a population containing two recognised stocks—the Bering‐Chukchi‐Beaufort and East Canada‐West Greenland. We utilised high‐resolution ecological niche modelling and bowhead whale movement data to reconstruct inter‐stock habitat connectivity over the last 11,700 years, finding that this explains our identified genetic groupings. Bowhead whale populations exhibit little‐to‐no evidence of recent inbreeding and retain high genetic diversity relative to other mammalian species despite centuries of intensive commercial whaling. The most vulnerable population is that in the Okhotsk Sea, which has the lowest genetic diversity, most inbreeding, and the highest realised genetic load. Collectively, our findings elucidate the recent history and dynamics of bowhead whales, offering valuable baseline data and context on present‐day genetic structure and diversity to support effective conservation and management strategies.

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.

Mangrove blue carbon in Laguna de Términos, México, a protected area: Spatial variability, sources and factors related - Jorge A. Herrera-Silveira; Mónica Pech-Cárdenas; Siuling G. Cinco-Castro; Eunice Pech-Poot; Flor Árcega-Cabrera; Raquel Iturria-Dawn; Andrés Canul-Cabrera; Juan E. Mendoza-Martínez; Azucena Dótor-Almazán; John W. Day; Steven W.J. Canty; Miguel Cifuentes-Jara; et al. (Regional Studies in Marine Science, 2025)

Mechanistic pathways of tick exposure risk in native and invaded plant communities - Drew Hiatt; Whalen W. Dillon; Allison Gardner; Steven Cabrera; Michael Dietze; Brian F. Allan; S. Luke Flory (Ecology, 2025)
Plant invasions may alter disease vector abundance by several mechanistic pathways, including modifying microclimates that influence vector survival or changing habitats to influence host use. Here, we used a field experiment and observational data to evaluate multiple mechanistic pathways (tick survival and host abundance) by which plant invasions may alter vector‐borne disease risk using the common disease vector lone star tick ( Amblyomma americanum ), its preeminent host white‐tailed deer ( Odocoileus virginianus ), and the widespread invasive cogongrass ( Imperata cylindrica ) in the southeastern United States. In the field experiment, ticks survived over 50% longer in areas dominated by the invasive plant compared to those with only native plant species. Invaded areas had lower temperatures and higher relative humidity, yielding a lower vapor pressure deficit (VPD) that likely reduced tick desiccation. The observational study showed similar average tick abundance in native and invaded plant communities and no difference in wildlife host (white‐tailed deer) activity between plant communities. However, there was a positive relationship between tick abundance and white‐tailed deer activity, but only in native areas. Together, these results suggest that more favorable microclimate conditions resulting in greater tick longevity are the dominant driver of tick abundance in invaded areas, while tick abundance in native‐dominated areas may be promoted, at least in part, by white‐tailed deer activity. Our results demonstrate that plant invasions can affect multiple, potentially counteracting mechanistic pathways that contribute to tick exposure risk. The complexity of these relationships highlights the need for a better understanding of how invasive species and other global change drivers influence disease vectors and, ultimately, disease transmission.

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.

Midstory removal of encroaching species has minimal impacts on fuels and fire behavior regardless of burn season in a degraded pine-oak mixture - Steven Cabrera; Heather D. Alexander; John L. Willis; Christopher J. Anderson (Forest Ecology and Management, 2023)

Pronounced Seasonal and Spatial Variability in Determinants of Phytoplankton Biomass Dynamics along a Near–Offshore Gradient in the Southern North Sea - Viviana Otero; Steven Pint; Klaas Deneudt; Maarten De Rijcke; Jonas Mortelmans; Lennert Schepers; Patricia Martin-Cabrera; Koen Sabbe; Wim Vyverman; Michiel Vandegehuchte; Gert Everaert (Journal of Marine Science and Engineering, 2023)
Marine phytoplankton biomass dynamics are affected by eutrophication, ocean warming, and ocean acidification. These changing abiotic conditions may impact phytoplankton biomass and its spatiotemporal dynamics. In this study, we used a nutrient–phytoplankton–zooplankton (NPZ) model to quantify the relative importance of the bottom-up and top-down determinants of phytoplankton biomass dynamics in the Belgian part of the North Sea (BPNS). Using four years (2014–2017) of monthly observations of nutrients, solar irradiance, sea surface temperature, chlorophyll-a, and zooplankton biomass at ten locations, we disentangled the monthly, seasonal, and yearly variation in phytoplankton biomass dynamics. To quantify how the relative importance of determinants changed along a near–offshore gradient, the analysis was performed for three spatial regions, i.e., the nearshore region ( 30 km). We found that, from year 2014 to 2017, the phytoplankton biomass dynamics ranged from 1.4 to 23.1 mg Chla m−3. Phytoplankton biomass dynamics follow a general seasonal cycle, as is the case in other temperate regional seas, with a distinct spring bloom (5.3–23.1 mg Chla m−3) and a modest autumn bloom (2.9–5.4 mg Chla m−3). This classic bimodal bloom pattern was not observed between 2003 and 2010 in the BPNS. The seasonal pattern was most expressed in the nearshore region. The relative contribution of factors determining phytoplankton biomass dynamics varied spatially and temporally. Throughout a calendar year, solar irradiance and zooplankton grazing were the most influential determinants in all regions, i.e., they jointly explained 38–65% of the variation in the offshore region, 45–71% in the midshore region, and 56–77% in the nearshore region. In the near- and midshore regions, nutrients were the greatest limit on phytoplankton production in the month following the spring bloom (44–55%). Nutrients were a determinant throughout the year in the offshore region (27–62%). During winter, sea surface temperature was a determinant in all regions (15–17%). By the high-resolution spatiotemporal analysis of the relative contributions of different determinants, this study contributes to a better mechanistic understanding of the spatiotemporal dynamics of phytoplankton biomass in the southern North Sea. This detailed understanding is anticipated to contribute to the definition of targeted management strategies for the BPNS and to support sustainable development in Belgium’s blue economy.

Acute toxicity of pesticide mixtures to honey bees is generally additive, and well predicted by Concentration Addition - Verena Taenzler; Arnd Weyers; Christian Maus; Markus Ebeling; Steven Levine; Ana Cabrera; Daniel Schmehl; Zhenglei Gao; Ismael Rodea-Palomares (Science of The Total Environment, 2023)

Modeling surface residue decomposition and N release using the Cover Crop Nitrogen Calculator (CC-NCALC) - Resham Thapa; Miguel Cabrera; Chris Reberg-Horton; Carson Dann; Kip S. Balkcom; David Fleisher; Julia Gaskin; Rick Hitchcock; Aurelie Poncet; Harry H. Schomberg; Dennis Timlin; Steven B. Mirsky (Nutrient Cycling in Agroecosystems, 2022)

Resistance to amitraz in the parasitic honey bee mite Varroa destructor is associated with mutations in the β-adrenergic-like octopamine receptor - Carmen Sara Hernández-Rodríguez; Sara Moreno-Martí; Gabrielle Almecija; Krisztina Christmon; Josephine D. Johnson; Marie Ventelon; Dennis vanEngelsdorp; Steven C. Cook; Joel González-Cabrera (Journal of Pest Science, 2022)
Varroa destructor is considered a major reason for high loss rate of Western honey bee ( Apis mellifera ) colonies. To prevent colony losses caused by V. destructor, it is necessary to actively manage the mite population. Beekeepers, particularly commercial beekeepers, have few alternative treatments other than synthetic acaricides to control the parasite, resulting in intensive treatment regimens that led to the evolution of resistance in mite populations. To investigate the mechanism of the resistance to amitraz detected in V. destructor mites from French and U.S. apiaries, we identified and characterized octopamine and tyramine receptors (the known targets of amitraz) in this species. The comparison of sequences obtained from mites collected from different apiaries with different treatment regimens, showed that the amino acid substitutions N87S or Y215H in the OctβR were associated with treatment failures reported in French or U.S. apiaries, respectively. Based on our findings, we have developed and tested two high throughput diagnostic assays based on TaqMan technology able to accurately detect mites carrying the mutations in this receptor. This valuable information may be of help for beekeepers when selecting the most suitable acaricide to manage V. destructor .

Effects of moisture and temperature on C and N mineralization from surface-applied cover crop residues - Resham Thapa; Katherine L. Tully; Miguel L. Cabrera; Carson Dann; Harry H. Schomberg; Dennis Timlin; Chris Reberg-Horton; Julia Gaskin; Brian W. Davis; Steven B. Mirsky (Biology and Fertility of Soils, 2021)