3-OH-FA- and branched GDGT-based temperature calibrations from a transect of lakes in the eastern USA - Changhai Hou; Michael L. Griffiths; Alice Hardman; Osamu Seki; Aifeng Zhou; Sarah Greene; Elizabeth W. Patterson; Roland Indriksons; Sabrina Ryan; Michael DaSilva; Matthew Allison; Lin Chen; et al. (Organic Geochemistry, 2026)

Including Fitness and Health Proxies Can Alter Our Understanding of Habitat Selection - Marie Auger‐Méthé; Fanny Dupont; Alyssa Eby; Kyle H. Elliott; Nigel E. Hussey; Devin A. Lyons; Marianne Marcoux; Allison Patterson; Shabnam Shadloo; Courtney R. Shuert (Ecology Letters, 2026)
Habitat selection analyses, which discern the environmental conditions individuals select, often inform conservation planning. Through a literature review, we demonstrate that recent habitat selection studies rarely include fitness and health information. With a simulation study, we show that ignoring such information could support the protection of sink habitats. Our case studies demonstrate how health and fitness proxies can modify our understanding of habitat selection: (1) incorporating mass gain of thick‐billed murres shows the energetic benefit of areas deemed secondary by a naive resource selection function; (2) including number of chicks in a step selection function (SSF) exposes the complex relationships glaucous‐winged gulls have with landscapes impacted by humans; and (3) including external signs of trauma in the movement kernel of SSFs demonstrates other ways in which narwhal distribution can be altered. We urge movement ecologists to collect and use health and fitness data to improve ecological inference and conservation action.

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.

Warming conditions increase foraging effort and alter nutritional state in two species of guano seabirds - Francis van Oordt; Jaime Silva; Allison Patterson; Ruedi G. Nager; Kyle H. Elliott (Marine Biology, 2026)

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.

Drivers of Interspecific Spatial Segregation in Two Closely‐Related Seabird Species at a Pan‐Atlantic Scale - Anne‐Sophie Bonnet‐Lebrun; Jason Matthiopoulos; Rémi Lemaire‐Patin; Tanguy Deville; Robert Barrett; Maria I. Bogdanova; Mark Bolton; Signe Christensen‐Dalsgaard; Francis Daunt; Nina Dehnhard; Sébastien Descamps; Kyle Elliott; et al. (Journal of Biogeography, 2025)
Aim Ecologically similar species living in sympatry are expected to segregate to reduce the effects of competition where resources are limiting. Segregation from heterospecifics commonly occurs in space, but it is often unknown whether such segregation has underlying environmental causes. Indeed, species could segregate because of different fundamental environmental requirements (i.e., ‘niche divergence’), because competitive exclusion at sympatric sites can force species to either change the habitat use they would have at allopatric sites (i.e., ‘niche displacement’) or to avoid certain areas, independently of habitat (i.e., ‘spatial avoidance’). Testing these hypotheses requires the comparison between sympatric and allopatric sites. Understanding the competitive mechanisms that underlie patterns of spatial segregation could improve predictions of species responses to environmental change, as competition might exacerbate the effects of environmental change. Location North Atlantic and Arctic. Taxa Common guillemots Uria aalge and Brünnich's guillemots Uria lomvia. Methods Here, we examine support for these explanations for spatial segregation in two closely‐related seabird species, common guillemots ( Uria aalge ) and Brünnich's guillemots ( U. lomvia ). For this, we collated a pan‐Atlantic data set of breeding season foraging tracks from 1046 individuals, collected from 20 colonies (8 sympatric and 12 allopatric). These were analysed with habitat models in a spatially transferable framework to compare habitat preferences between species at sympatric and allopatric sites. Results We found no effect of the distribution of heterospecifics on local habitat preferences of the focal species. We found differences in habitat preferences between species, but these were not sufficient to explain the observed levels of spatial segregation at sympatric sites. Main Conclusions Assuming we did not omit any relevant environmental variables, these results suggest a mix of niche divergence and spatial avoidance produces the observed patterns of spatial segregation.

Spatial distribution of selenium-mercury in Arctic seabirds - Marta Cruz-Flores; Jérémy Lemaire; Maud Brault-Favrou; Signe Christensen-Dalsgaard; Carine Churlaud; Sébastien Descamps; Kyle Elliott; Kjell Einar Erikstad; Alexey Ezhov; Maria Gavrilo; David Grémillet; Gaël Guillou; et al. (Environmental Pollution, 2024)

Offshore decommissioning horizon scan: Research priorities to support decision-making activities for oil and gas infrastructure - Sarah M. Watson; Dianne L. McLean; Brian J. Balcom; Silvana N.R. Birchenough; Alison M. Brand; Elodie C.M. Camprasse; Jeremy T. Claisse; Joop W.P. Coolen; Tom Cresswell; Bert Fokkema; Susan Gourvenec; Lea-Anne Henry; et al. (Science of The Total Environment, 2023)

Corrigendum to “Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification” [Biol. Conserv. 281 (2023) 109994] - Martin Beal; Paulo Catry; Richard A. Phillips; Steffen Oppel; John P.Y. Arnould; Maria I. Bogdanova; Mark Bolton; Ana P.B. Carneiro; Corey Clatterbuck; Melinda Conners; Francis Daunt; Karine Delord; et al. (Biological Conservation, 2023)

Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification - Martin Beal; Paulo Catry; Richard A. Phillips; Steffen Oppel; John P.Y. Arnould; Maria I. Bogdanova; Mark Bolton; Ana P.B. Carneiro; Corey Clatterbuck; Melinda Conners; Francis Daunt; Karine Delord; et al. (Biological Conservation, 2023)

Tracking Cairns: Biologging Improves the Use of Seabirds as Sentinels of the Sea - Emile Brisson-Curadeau; Allison Patterson; Shannon Whelan; Thomas Lazarus; Kyle H. Elliott (Frontiers in Marine Science, 2017)

Consulting Forestry / Certification and Ecosystem Services - James Coufal; Carl Wiedemann; Jacob Gorman; Larry Teeter; Yaoqi Zhang; Michael Kilgore; Kristell Miller; Stephanie Snyder; Sun Chang; Miriam Davis; Stanley Asah; J. Mark Fly; et al. (Journal of Forestry, 2011)
45The industrialization of the United States throughout the 19th century resulted in the exploitation of millions of acres of timberland across the country. Logging during that era was described by critics as “cut out and get out” because the land was usually abandoned after the merchantable timber was exhausted. Concern about future timber supplies and the effect of logging on watersheds spurred the development of professional forestry after the Civil War. The first forestry leaders, including Bernhard Fernow and Gifford Pinchot, encouraged private non-industrial forest owners to sustainably manage their woodlands and specifically warned against destructive logging practices—including “cutting the best and leaving the rest”—aka high-grading. During the 1920s and 30s both the Forest Service and SAF spurred initiatives to encourage sustainable timber harvesting practices on private forestland. Although great progress has been made in forest management, logging on most private non-industrial forest land in the east has not changed significantly since the 19th century. Landowners usually sell timber without using the services of a forester, allowing the buyer to selectively cut the most valuable trees in the woodlot. Although this can reduce future value by 70-90%, it remains common practice because both the landowner and the timber buyer maximize short term income. The timber value and productivity of millions of acres of woodland throughout the United States have been significantly degraded as a result. This paper will trace the history of high-grading as described by forestry leaders from the 19th century through the 21st and will show that much work remains unfinished.