Jaguar Range‐Wide Connectivity: Prioritising Core Areas and Corridors Between and Within Protected Areas and Indigenous Lands - Guilherme Costa Alvarenga; Caroline C. Sartor; Ana Carolina Srbek‐Araujo; Ana Cristina Mendes‐Oliveira; Bart Harmsen; Carlos De Angelo; Carolina Franco Esteves; Claudia Bueno de Campos; Daiana Jeronimo Polli; Diego F. Passos Viana; Diogo Maia Gräbin; Emiliano Donadio; et al. (Global Change Biology, 2026)
Anthropogenic habitat transformation has led to population declines and increasing isolation of populations of many species, particularly top predators, where small and fragmented populations often experience limited dispersal and reduced genetic diversity. Jaguars ( Panthera onca ), for example, have lost half of their historical range and most populations outside the Amazon biome are small, isolated and highly threatened. Here, we assess jaguar population connectivity across the species' range by identifying and ranking core areas and dispersal corridors, including linkages among Protected Areas and Indigenous Lands (hereafter, PAs). We used cumulative resistant kernels and factorial Least‐Cost Paths (fLCP) in two complementary approaches: core area‐based and PA‐based connectivity. We identified 307 core areas for jaguar movement, covering ~7.75 million km 2 (55% of the current range), of which approximately half lies within PAs. The largest and highest‐ranked core area spans ~6.5 million km 2 across four biomes (Amazon, Llanos, Chaco and Pantanal). We identified 176 dispersal corridors connecting core areas, although connectivity was not continuous across the entire range. Northern populations (Mexico and Central America) showed several connectivity gaps, whereas South America displayed a large, highly interconnected network linking the Amazon, Llanos, Chaco, Pantanal, Cerrado, Caatinga, Iberá and the Atlantic Forest's Green Corridor. In contrast, coastal Atlantic Forest patches appeared largely isolated. The PA‐based analysis predicted 507 potential corridors connecting 905 PAs, including potential links absent from the core area model, particularly across Central America and between the Darién Gap, Amazon and coastal Atlantic Forest. The core‐area approach provides a more biologically realistic representation of current connectivity, while the PA‐based approach complements it by identifying pragmatic opportunities for long‐term connectivity conservation. Together, these approaches identify priority areas and regions of uncertainty, providing a spatial framework to guide jaguar conservation and connectivity planning under accelerating global change.

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)

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)

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.

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)

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.

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)

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)

Journals must expand access to peer review data - Olivia M. Smith; Kayla L. Davis; Robin Waterman; Riley B. Pizza; Caitlin Mack; Emily E. Conway; Kara C. Dobson; Brianna Foster; Ani E. Hristova; Julie C. Jarvey; Nan Nourn; Courtney L. Davis (Trends in Ecology & Evolution, 2024)

Two Novel Variants in MITF and PAX3 Associated With Splashed White Phenotypes in Horses - Aiden McFadden; Katie Martin; Gabriel Foster; Micaela Vierra; Erica W. Lundquist; Robin E. Everts; Erik Martin; Erin Volz; Kaitlyn McLoone; Samantha A. Brooks; Christa Lafayette (Journal of Equine Veterinary Science, 2023)

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)

5′UTR Variant in KIT Associated With White Spotting in Horses - Aiden McFadden; Katie Martin; Gabriel Foster; Micaela Vierra; Erica W. Lundquist; Robin E. Everts; Erik Martin; Erin Volz; Kaitlyn McLoone; Samantha A. Brooks; Christa Lafayette (Journal of Equine Veterinary Science, 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.

Demographic composition, not demographic diversity, predicts biomass and turnover across temperate and tropical forests - Jessica F. Needham; Daniel J. Johnson; Kristina J. Anderson‐Teixeira; Norman Bourg; Sarayudh Bunyavejchewin; Nathalie Butt; Min Cao; Dairon Cárdenas; Chia‐Hao Chang‐Yang; Yu‐Yun Chen; George Chuyong; Handanakere S. Dattaraja; et al. (Global Change Biology, 2022)
The growth and survival of individual trees determine the physical structure of a forest with important consequences for forest function. However, given the diversity of tree species and forest biomes, quantifying the multitude of demographic strategies within and across forests and the way that they translate into forest structure and function remains a significant challenge. Here, we quantify the demographic rates of 1961 tree species from temperate and tropical forests and evaluate how demographic diversity (DD) and demographic composition (DC) differ across forests, and how these differences in demography relate to species richness, aboveground biomass (AGB), and carbon residence time. We find wide variation in DD and DC across forest plots, patterns that are not explained by species richness or climate variables alone. There is no evidence that DD has an effect on either AGB or carbon residence time. Rather, the DC of forests, specifically the relative abundance of large statured species, predicted both biomass and carbon residence time. Our results demonstrate the distinct DCs of globally distributed forests, reflecting biogeography, recent history, and current plot conditions. Linking the DC of forests to resilience or vulnerability to climate change, will improve the precision and accuracy of predictions of future forest composition, structure, and function.

A review of a decade of lessons from one of the world’s largest MPAs: conservation gains and key challenges - Graeme C. Hays; Heather J. Koldewey; Samantha Andrzejaczek; Martin J. Attrill; Shanta Barley; Daniel T. I. Bayley; Cassandra E. Benkwitt; Barbara Block; Robert J. Schallert; Aaron B. Carlisle; Pete Carr; Taylor K. Chapple; et al. (Marine Biology, 2020)

Risk management to prioritise the eradication of new and emerging invasive non-native species - Olaf Booy; Aileen C. Mill; Helen E. Roy; Alice Hiley; Niall Moore; Pete Robertson; Simon Baker; Matt Brazier; Mathilde Bue; Richard Bullock; Steve Campbell; Dominic Eyre; et al. (Biological Invasions, 2017)

CTFS ‐Forest GEO : a worldwide network monitoring forests in an era of global change - Kristina J. Anderson‐Teixeira; Stuart J. Davies; Amy C. Bennett; Erika B. Gonzalez‐Akre; Helene C. Muller‐Landau; S. Joseph Wright; Kamariah Abu Salim; Angélica M. Almeyda Zambrano; Alfonso Alonso; Jennifer L. Baltzer; Yves Basset; Norman A. Bourg; et al. (Global Change Biology, 2015)
Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long‐term forest dynamics research sites ( CTFS ‐Forest GEO ) useful for characterizing forest responses to global change. Within very large plots (median size 25 ha), all stems ≥1 cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS ‐Forest GEO spans 25°S–61°N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS ‐Forest GEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61 °C), changes in precipitation (up to ±30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8 g N m −2 yr −1 and 3.1 g S m −2 yr −1 ), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5 km). The broad suite of measurements made at CTFS ‐Forest GEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFS ‐Forest GEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

Task Specialization and Odor Effects on Proboscis Extension Conditioning in Bumblebees (Bombus huntii) - Susannah Hannaford; Amelia Sattler; Jessica Siegel; Robin L. Foster (Journal of Insect Behavior, 2013)

Experimental analysis of worker division of labor in bumblebee nest thermoregulation (Bombus huntii, Hymenoptera: Apidae) - Kathryn E. Gardner; Robin L. Foster; Sean O’Donnell (Behavioral Ecology and Sociobiology, 2007)

Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests - Helene C. Muller‐Landau; Richard S. Condit; Jerome Chave; Sean C. Thomas; Stephanie A. Bohlman; Sarayudh Bunyavejchewin; Stuart Davies; Robin Foster; Savitri Gunatilleke; Nimal Gunatilleke; Kyle E. Harms; Terese Hart; et al. (Ecology Letters, 2006)
The theory of metabolic ecology predicts specific relationships among tree stem diameter, biomass, height, growth and mortality. As demographic rates are important to estimates of carbon fluxes in forests, this theory might offer important insights into the global carbon budget, and deserves careful assessment. We assembled data from 10 old‐growth tropical forests encompassing censuses of 367 ha and > 1.7 million trees to test the theory's predictions. We also developed a set of alternative predictions that retained some assumptions of metabolic ecology while also considering how availability of a key limiting resource, light, changes with tree size. Our results show that there are no universal scaling relationships of growth or mortality with size among trees in tropical forests. Observed patterns were consistent with our alternative model in the one site where we had the data necessary to evaluate it, and were inconsistent with the predictions of metabolic ecology in all forests.

Comparing tropical forest tree size distributions with the predictions of metabolic ecology and equilibrium models - Helene C. Muller‐Landau; Richard S. Condit; Kyle E. Harms; Christian O. Marks; Sean C. Thomas; Sarayudh Bunyavejchewin; George Chuyong; Leonardo Co; Stuart Davies; Robin Foster; Savitri Gunatilleke; Nimal Gunatilleke; et al. (Ecology Letters, 2006)
Tropical forests vary substantially in the densities of trees of different sizes and thus in above‐ground biomass and carbon stores. However, these tree size distributions show fundamental similarities suggestive of underlying general principles. The theory of metabolic ecology predicts that tree abundances will scale as the −2 power of diameter. Demographic equilibrium theory explains tree abundances in terms of the scaling of growth and mortality. We use demographic equilibrium theory to derive analytic predictions for tree size distributions corresponding to different growth and mortality functions. We test both sets of predictions using data from 14 large‐scale tropical forest plots encompassing censuses of 473 ha and > 2 million trees. The data are uniformly inconsistent with the predictions of metabolic ecology. In most forests, size distributions are much closer to the predictions of demographic equilibrium, and thus, intersite variation in size distributions is explained partly by intersite variation in growth and mortality.

Effects of stage of colony cycle, context, and intercolony distance on conspecific tolerance by paper wasps (Polistes fuscatus) - George J. Gamboa; Robin L. Foster; Julie A. Scope; Angela M. Bitterman (Behavioral Ecology and Sociobiology, 1991)