Spatio-Temporal Event Model for Cyber-Physical Systems

The emerging Cyber-Physical Systems (CPSs) are envisioned to integrate computation, communication and control with the physical world. Therefore, CPS requires close interactions between the cyber and physical worlds both in time and space. These interactions are usually governed by events, which occur in the physical world and should autonomously be reflected in the cyber-world, and actions, which are taken by the CPS as a result of detection of events and certain decision mechanisms. Both event detection and action decision operations should be performed accurately and timely to guarantee temporal and spatial correctness. This calls for a flexible architecture and task representation framework to analyze CP operations. In this paper, we explore the temporal and spatial properties of events, define a novel CPS architecture, and develop a layered spatiotemporal event model for CPS. The event is represented as a function of attribute-based, temporal, and spatial event conditions. Moreover, logical operators are used to combine different types of event conditions to capture composite events. To the best of our knowledge, this is the first event model that captures the heterogeneous characteristics of CPS for formal temporal and spatial analysis.

Regional differences in the spatial and temporal heterogeneity of oceanographic habitat used by Steller sea lions

Over the past three decades, the decline and altered spatial distribution of the western stock of Steller sea lions (Eumetopias jubatus) in Alaska have been attributed to changes in the distribution or abundance of their prey due to the cumulative effects of fisheries and environmental perturbations. During this period, dietary prey occurrence and diet diversity were related to population decline within metapopulation regions of the western stock of Steller sea lions, suggesting that environmental conditions may be variable among regions. The objective of this study, therefore, was to examine regional differences in the spatial and temporal heterogeneity of oceanographic habitat used by Steller sea lions within the context of recent measures of diet diversity and population trajectories. Habitat use was assessed by deploying satellite-depth recorders and satellite relay data loggers on juvenile Steller sea lions (n = 45) over a five-year period (2000–2004) within four regions of the western stock, including the western, central, and eastern Aleutian Islands, and central Gulf of Alaska. Areas used by sea lions during summer months (June, July, and August) were demarcated using satellite telemetry data and characterized by environmental variables (sea surface temperature [SST] and chlorophyll a [chl a]), which possibly serve as proxies for environmental processes or prey. Spatial patterns of SST diversity and Steller sea lion population trends among regions were fairly consistent with trends reported for diet studies, possibly indicating a link between environmental diversity, prey diversity, and distribution or abundance of Steller sea lions. Overall, maximum spatial heterogeneity coupled with minimal temporal variability of SST appeared to be beneficial for Steller sea lions. In contrast, these patterns were not consistent for chl a, and there appeared to be an ecological threshold. Understanding how Steller sea lions respond to measures of environmental heterogeneity will ultimately be useful for implementing ecosystem management approaches and developing additional conservation strategies.

Fire History at the Southwestern Great Plains Margin, Capulin Volcano National Monument

This study documents historic fire events at Capulin Volcano National Monument over the last four centuries using dendrochronologically dated fire scars at two sites: the lower volcano lava flows (the Boca) and the adjacent canyon slopes (Morrow Ranch). The mean fire interval (MFI) was 12 years at the Boca site (before 1890) and 5.4 years (1600-1750) and 19.1 years (1751-1890) at the Morrow Ranch site. Data from the Boca and Morrow Ranch sites combined with the extremely pyrogenic landscape position of the volcano slopes indicate that the volcano slopes likely burned more frequently (e.g., MFI <5 yr). Around 1750, the fire regime appeared to transition to longer fire intervals, greater temporal synchrony among fire-scarred trees, and a higher proportion of trees scarred in fire years. Temporal variability in the fire regime at Capulin Volcano may reflect changes in human populations, climate, and land use.

Space–time zero-inflated count models of Harbor seals

Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models.

Seeking a Second Opinion: Uncertainty in Disease Ecology

Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy.