Social and Ecological Context Shape Biological Invasions in Complex Landscapes - George L. W. Perry; James M. R. Brock; Cameron B. Guy; Casey Hamilton; Finnbar Lee; Dion L. Pou; Elizabeth Williams; Erica A. H. Smithwick (Current Landscape Ecology Reports, 2025)

‘Earth system engineers’ and the cumulative impact of organisms in deep time - Simon A.F. Darroch; Michelle M. Casey; Alison T. Cribb; Amanda E. Bates; Matthew E. Clapham; Dori L. Contreras; Matthew Craffey; Ivo A.P. Duijnstee; William Gearty; Nicholas J. Gotelli; Marcus J. Hamilton; Riley F. Hayes; et al. (Trends in Ecology & Evolution, 2025)

Parental perspectives on the use of tumor molecular profiling and germline genetic testing during their children's cancer treatment - Marcelo M. Sleiman; Muriel R. Statman; Mary Rose Yockel; Yelena P. Wu; Jada G. Hamilton; Wendy K. Kohlmann; Jennie Vagher; Soren Feola; Luke D. Maese; Jing Chen; Burton Appel; Casey J. Mehrhoff; et al. (Familial Cancer, 2025)

Is trauma associated with plant-based diet choice? - Faith O. Nomamiukor; Madeline A. Smethurst; Molly R. Franz; Evelyn G. Hamilton; Anissa Garza; Diana Padilla-Medina; Maxine Davis; Casey T. Taft (Appetite, 2025)

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.

Creation of a high spatio‐temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC‐21) - Stuart E. Hamilton; Daniel Casey (Global Ecology and Biogeography, 2016)
Aim To provide high‐resolution local, regional, national and global estimates of annual mangrove forest area from 2000 through to 2012 with the goal of driving mangrove research questions pertaining to biodiversity, carbon stocks, climate change, functionality, food security, livelihoods, fisheries support and conservation that have been impeded until now by a lack of suitable data. Location Global, covering 99% of all mangrove forests. Methods We synthesized the Global Forest Change database, the Terrestrial Ecosystems of the World database and the Mangrove Forests of the World database to extract mangrove forest cover at high spatial and temporal resolutions. We then used the new database to monitor mangrove cover at the global, national and protected area scales. Results Countries showing relatively high amounts of mangrove loss include Myanmar, Malaysia, Cambodia, Indonesia and Guatemala. Indonesia remains by far the largest mangrove‐holding nation, containing between 26% and 29% of the global mangrove inventory with a deforestation rate of between 0.26% and 0.66% per year. We have made our new database, CGMFC‐21, freely available. Main conclusions Global mangrove deforestation continues but at a much reduced rate of between 0.16% and 0.39% per year. Southeast Asia is a region of concern with mangrove deforestation rates between 3.58% and 8.08%, this in a region containing half of the entire global mangrove forest inventory. The global mangrove deforestation pattern from 2000 to 2012 is one of decreasing rates of deforestation, with many nations essentially stable, with the exception of the largest mangrove‐holding region of Southeast Asia. We provide a standardized spatial dataset that monitors mangrove deforestation globally at high spatio‐temporal resolutions. These data can be used to drive the mangrove research agenda, particularly as it pertains to monitoring of mangrove carbon stocks and the establishment of baseline local mangrove forest inventories required for payment for ecosystem service initiatives.