Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model - Elizabeth C. Fine; Julie L. McClean; Detelina P. Ivanova; Anthony P. Craig; Alan J. Wallcraft; Eric P. Chassignet; Elizabeth C. Hunke (Ocean Modelling, 2023)

Genomic regions associated with resistance to anthracnose in the Guatemalan climbing bean (Phaseolus vulgaris L.) germplasm collection - C. R. Maldonado-Mota; S. M. Moghaddam; S. Schröder; O. P. Hurtado-Gonzales; P. E. McClean; J. Pasche; R. Lamppa; M. A. Pastor-Corrales; M. G. Tobar-Piñón; J. M. Osorno (Genetic Resources and Crop Evolution, 2021)

Evidence gaps and biodiversity threats facing the marine environment of the United Kingdom’s Overseas Territories - Bethan C. O’Leary; Philip Fieldhouse; Colin J. McClean; Adriana E. S. Ford; Polly Burns; Julie P. Hawkins; Callum M. Roberts (Biodiversity and Conservation, 2019)

Racial differences in the relationship between tobacco, alcohol, and the risk of head and neck cancer: pooled analysis of US studies in the INHANCE Consortium - Kristin J. Voltzke; Yuan-Chin Amy Lee; Zuo-Feng Zhang; Jose P. Zevallos; Guo-Pei Yu; Deborah M. Winn; Thomas L. Vaughan; Erich M. Sturgis; Elaine Smith; Stephen M. Schwartz; Stimson Schantz; Joshua Muscat; et al. (Cancer Causes & Control, 2018)

Co-segregation analysis and mapping of the anthracnose Co-10 and angular leaf spot Phg-ON disease-resistance genes in the common bean cultivar Ouro Negro - M. C. Gonçalves-Vidigal; A. S. Cruz; G. F. Lacanallo; P. S. Vidigal Filho; L. L. Sousa; C. M. N. A. Pacheco; P. McClean; P. Gepts; M. A. Pastor-Corrales (Theoretical and Applied Genetics, 2013)

Lipopeptide biosurfactants from Paenibacillus polymyxa inhibit single and mixed species biofilms - Gerry A. Quinn; Aaron P. Maloy; Stephen McClean; Brian Carney; John W. Slater (Biofouling, 2012)

Novel PCR primers for detection of genetically diverse virulent Agrobacterium tumefaciens biovar 1 strains - Lani E. Yakabe; M. Malendia Maccree; Padma Sudarshana; Ali E. McClean; Shane R. Parker; William P. Wechter; Gernot Presting; Mizuri Marutani-Hert; Daniel A. Kluepfel (Journal of General Plant Pathology, 2012)

An examination of male and female odds ratios by BMI, cigarette smoking, and alcohol consumption for cancers of the oral cavity, pharynx, and larynx in pooled data from 15 case–control studies - Jay H. Lubin; Joshua Muscat; Mia M. Gaudet; Andrew F. Olshan; Maria Paula Curado; Luigino Dal Maso; Victor Wünsch-Filho; Erich M. Sturgis; Neonilia Szeszenia-Dabrowska; Xavier Castellsague; Zuo-Feng Zhang; Elaine Smith; et al. (Cancer Causes & Control, 2011)

Linkage mapping of the Phg-1 and Co-1 4 genes for resistance to angular leaf spot and anthracnose in the common bean cultivar AND 277 - Maria Celeste Gonçalves-Vidigal; Anelise S. Cruz; Alexandre Garcia; J. Kami; Pedro S. Vidigal Filho; Lorenna L. Sousa; P. McClean; P. Gepts; M. A. Pastor-Corrales (Theoretical and Applied Genetics, 2011)

Role of lipase in Burkholderia cepacia complex (Bcc) invasion of lung epithelial cells - T. Mullen; K. Markey; P. Murphy; S. McClean; M. Callaghan (European Journal of Clinical Microbiology & Infectious Diseases, 2007)

Comparison of antibiotic susceptibility of Burkholderia cepacia complex organisms when grown planktonically or as biofilm in vitro - E. Caraher; G. Reynolds; P. Murphy; S. McClean; M. Callaghan (European Journal of Clinical Microbiology & Infectious Diseases, 2007)

Model‐based uncertainty in species range prediction - Richard G. Pearson; Wilfried Thuiller; Miguel B. Araújo; Enrique Martinez‐Meyer; Lluís Brotons; Colin McClean; Lera Miles; Pedro Segurado; Terence P. Dawson; David C. Lees (Journal of Biogeography, 2006)
Aim Many attempts to predict the potential range of species rely on environmental niche (or ‘bioclimate envelope’) modelling, yet the effects of using different niche‐based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy‐guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence‐only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main conclusions We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy‐guiding applications along with a full appreciation of uncertainty.

Cloning and homology modelling of a Pto-like protein kinase family of common bean (Phaseolus vulgaris L.) - G. Vallad; M. Rivkin; C. Vallejos; P. McClean (Theoretical and Applied Genetics, 2001)

Genetic variance, coefficient of parentage, and genetic distance of six soybean populations - T. Helms; G. Vallad; P. McClean; J. Orf (Theoretical and Applied Genetics, 1997)