stelfi: An R package for fitting Hawkes and log-Gaussian Cox point process models.

Modelling spatial and temporal patterns in ecology is imperative to understand the complex processes inherent in ecological phenomena. Log-Gaussian Cox processes are a popular choice among ecologists to describe the spatiotemporal distribution of point-referenced data. In addition, point pattern models where events instigate others nearby (i.e., self-exciting behaviour) are becoming increasingly popular to infer the contagious nature of events (e.g., animal sightings). While there are existing R packages that facilitate fitting spatiotemporal point processes and, separately, self-exciting models, none incorporate both. We present an R package, stelfi, that fits spatiotemporal self-exciting and log-Gaussian Cox process models using Template Model Builder through a range of custom-written C++ templates. We illustrate the use of stelfi's functions fitting models to Sasquatch (bigfoot) sightings data within the USA. The structure of these data is typical of many seen in ecology studies. We show, from a temporal Hawkes process to a spatiotemporal self-exciting model, how the models offered by the package enable additional insights into the temporal and spatial progression of point pattern data. We present extensions to these well-known models that include spatiotemporal self-excitation and joint likelihood models, which are better suited to capture the complex mechanisms inherent in many ecological data. The package stelfi offers user-friendly functionality, is open source, and is available from CRAN. It offers the implementation of complex spatiotemporal point process models in R for applications even beyond the field of ecology.

Using individual-based bioenergetic models to predict the aggregate effects of disturbance on populations: A case study with beaked whales and Navy sonar.

Anthropogenic activities can lead to changes in animal behavior. Predicting population consequences of these behavioral changes requires integrating short-term individual responses into models that forecast population dynamics across multiple generations. This is especially challenging for long-lived animals, because of the different time scales involved. Beaked whales are a group of deep-diving odontocete whales that respond behaviorally when exposed to military mid-frequency active sonar (MFAS), but the effect of these nonlethal responses on beaked whale populations is unknown. Population consequences of aggregate exposure to MFAS was assessed for two beaked whale populations that are regularly present on U.S. Navy training ranges where MFAS is frequently used. Our approach integrates a wide range of data sources, including telemetry data, information on spatial variation in habitat quality, passive acoustic data on the temporal pattern of sonar use and its relationship to beaked whale foraging activity, into an individual-based model with a dynamic bioenergetic module that governs individual life history. The predicted effect of disturbance from MFAS on population abundance ranged between population extinction to a slight increase in population abundance. These effects were driven by the interaction between the temporal pattern of MFAS use, baseline movement patterns, the spatial distribution of prey, the nature of beaked whale behavioral response to MFAS and the top-down impact of whale foraging on prey abundance. Based on these findings, we provide recommendations for monitoring of marine mammal populations and highlight key uncertainties to help guide future directions for assessing population impacts of nonlethal disturbance for these and other long-lived animals.

Discrete-space continuous-time models of marine mammal exposure to Navy sonar.

Assessing the patterns of wildlife attendance to specific areas is relevant across many fundamental and applied ecological studies, particularly when animals are at risk of being exposed to stressors within or outside the boundaries of those areas. Marine mammals are increasingly being exposed to human activities that may cause behavioral and physiological changes, including military exercises using active sonars. Assessment of the population-level consequences of anthropogenic disturbance requires robust and efficient tools to quantify the levels of aggregate exposure for individuals in a population over biologically relevant time frames. We propose a discrete-space, continuous-time approach to estimate individual transition rates across the boundaries of an area of interest, informed by telemetry data collected with uncertainty. The approach allows inferring the effect of stressors on transition rates, the progressive return to baseline movement patterns, and any difference among individuals. We apply the modeling framework to telemetry data from Blainville's beaked whale (Mesoplodon densirostris) tagged in the Bahamas at the Atlantic Undersea Test and Evaluation Center (AUTEC), an area used by the U.S. Navy for fleet readiness training. We show that transition rates changed as a result of exposure to sonar exercises in the area, reflecting an avoidance response. Our approach supports the assessment of the aggregate exposure of individuals to sonar and the resulting population-level consequences. The approach has potential applications across many applied and fundamental problems where telemetry data are used to characterize animal occurrence within specific areas.

Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis.

Diagnosis and prognosis of cancer are informed by the architecture inherent in cancer patient tissue sections. This architecture is typically identified by pathologists, yet advances in computational image analysis facilitate quantitative assessment of this structure. In this article, we develop a spatial point process approach to describe patterns in cell distribution within tissue samples taken from colorectal cancer (CRC) patients. In particular, our approach is centered on the Palm intensity function. This leads to taking an approximate-likelihood technique in fitting point processes models. We consider two Neyman-Scott point processes and a void process, fitting these point process models to the CRC patient data. We find that the parameter estimates of these models may be used to quantify the spatial arrangement of cells. Importantly, we observe characteristic differences in the spatial arrangement of cells between patients who died from CRC and those alive at follow up.

Alcohol and injury among attendees at a busy inner city New Zealand emergency department.

INTRODUCTION: We present a study that provides a contemporary view of alcohol-related injury prevalence amongst patients presenting to a New Zealand (NZ) emergency department (ED). METHODS: Adult injury patients presenting to Auckland City Hospital ED within 6 h of injury were invited to participate during three recruitment periods (2015-2016). An interviewer-administered questionnaire obtained information on demographic, injury, general health, and lifestyle factors. Breath alcohol samples were obtained. Descriptive and logistic regression analyses were conducted. RESULTS: 501 patients participated (71% response rate), 21% had consumed alcohol within 6 h of their injury. The majority were male, and overall falls were the most common mechanism of injury among all patients. Alcohol-related injuries most commonly occurred at home, and were significantly more likely to occur during the weekend (Friday-Sunday) and night hours (23:00-06:59). After controlling for the effects of confounding; 'poor' general health, engaging in leisure activities at the time of injury, and injuries resulting from assaults were associated with increasing the odds of alcohol-related injury. CONCLUSIONS: Acute alcohol use continues to play a considerable role in ED injury presentations in NZ. Continued policy, health promotion, and injury prevention efforts are required to reduce the harms associated with alcohol use.