Inference for stationary Log-Gaussian Cox point processes using Bayesian deep learning: Application to human oral microbiome image data - Shuwan Wang; Christopher K. Wikle; Athanasios C. Micheas; Jessica L. Mark Welch; Jacqueline R. Starr; Kyu Ha Lee (Spatial Statistics, 2026)

Spatial statistics: Climate and the environment - Christopher K. Wikle; Mevin B. Hooten; William Kleiber; Douglas W. Nychka (Spatial Statistics, 2024)

Space, uncertainty, and the environment: honoring the distinguished career of Noel Cressie - Alfred Stein; Christopher K. Wikle (Spatial Statistics, 2024)

Echo state network-enhanced symbolic regression for spatio-temporal binary stochastic cellular automata - Nicholas Grieshop; Christopher K. Wikle (Spatial Statistics, 2024)

Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics - Nicholas Grieshop; Christopher K. Wikle (Spatial Statistics, 2024)

Nitrogen fertilizer and pronitridine rates for corn production in the Midwest U.S. - Harpreet Kaur; Kelly A. Nelson; Christopher K. Wikle; Richard Ferguson; Gurbir Singh (Field Crops Research, 2024)

Deep learning and spatial statistics - Christopher K. Wikle; Jorge Mateu; Andrew Zammit-Mangion (Spatial Statistics, 2023)

Using echo state networks to inform physical models for fire front propagation - Myungsoo Yoo; Christopher K. Wikle (Spatial Statistics, 2023)

Time-varying effects of local weather on behavior and probability of breeding deferral in two Arctic-nesting goose populations - Stephanie A. Cunningham; Toryn L. J. Schafer; Christopher K. Wikle; Jay A. VonBank; Bart M. Ballard; Lei Cao; Stuart Bearhop; Anthony D. Fox; Geoff M. Hilton; Alyn J. Walsh; Larry R. Griffin; Mitch D. Weegman (Oecologia, 2023)
Arctic-nesting geese face energetic challenges during spring migration, including ecological barriers and weather conditions (e.g., precipitation and temperature), which in long-lived species can lead to a trade-off to defer reproduction in favor of greater survival. We used GPS location and acceleration data collected from 35 greater white-fronted geese of the North American midcontinent and Greenland populations at spring migration stopovers, and novel applications of Bayesian dynamic linear models to test daily effects of minimum temperature and precipitation on energy expenditure (i.e., overall dynamic body acceleration, ODBA) and proportion of time spent feeding (PTF), then examined the daily and additive importance of ODBA and PTF on probability of breeding deferral using stochastic antecedent models. We expected distinct responses in behavior and probability of breeding deferral between and within populations due to differences in stopover area availability. Time-varying coefficients of weather conditions were variable between ODBA and PTF, and often did not show consistent patterns among birds, indicating plasticity in how individuals respond to conditions. An increase in antecedent ODBA was associated with a slightly increased probability of deferral in midcontinent geese but not Greenland geese. Probability of deferral decreased with increased PTF in both populations. We did not detect any differentially important time periods. These results suggest either that movements and behavior throughout spring migration do not explain breeding deferral or that ecological linkages between bird decisions during spring and subsequent breeding deferral were different between populations and across migration but occurred at different time scales than those we examined.

A two‐species occupancy model accommodating simultaneous spatial and interspecific dependence - Christopher T. Rota; Christopher K. Wikle; Roland W. Kays; Tavis D. Forrester; William J. McShea; Arielle W. Parsons; Joshua J. Millspaugh (Ecology, 2016)
Occupancy models are popular for estimating the probability a site is occupied by a species of interest when detection is imperfect. Occupancy models have been extended to account for interacting species and spatial dependence but cannot presently allow both factors to act simultaneously. We propose a two‐species occupancy model that accommodates both interspecific and spatial dependence. We use a point‐referenced multivariate hierarchical spatial model to account for both spatial and interspecific dependence. We model spatial random effects with predictive process models and use probit regression to improve efficiency of posterior sampling. We model occupancy probabilities of red fox ( Vulpes vulpes ) and coyote ( Canis latrans ) with camera trap data collected from six mid‐Atlantic states in the eastern United States. We fit four models comprising a fully factorial combination of spatial and interspecific dependence to two‐thirds of camera trapping sites and validated models with the remaining data. Red fox and coyotes each exhibited spatial dependence at distances >0.8 and 0.4 km, respectively, and exhibited geographic variation in interspecific dependence. Consequently, predictions from the model assuming simultaneous spatial and interspecific dependence best matched test data observations. This application highlights the utility of simultaneously accounting for spatial and interspecific dependence.

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove - Mevin B. Hooten; Christopher K. Wikle (Environmental and Ecological Statistics, 2008)

Shifts in the spatio-temporal growth dynamics of shortleaf pine - Mevin B. Hooten; Christopher K. Wikle (Environmental and Ecological Statistics, 2007)

Mapping pre-European settlement vegetation at fine resolutions using a hierarchical Bayesian model and GIS - Hong S. He; Daniel C. Dey; Xiuli Fan; Mevin B. Hooten; John M. Kabrick; Christopher K. Wikle; Zhaofei Fan (Plant Ecology, 2007)

Spatio‐temporal processes in ecology: a gentle introduction - Mevin B. Hooten; Christopher K. Wikle (Journal of Biogeography, 2006)

Efficient statistical mapping of avian count data - J. Andrew Royle; Christopher K. Wikle (Environmental and Ecological Statistics, 2005)

Hierarchical Bayesian space-time models - CHRISTOPHER K. Wikle; L. Mark Berliner; Noel Cressie (Environmental and Ecological Statistics, 1998)