Benjamin D. Dalziel

Are there general mechanisms of animal home range behaviour? A review and prospects for future research

Home range behaviour is a common pattern of space use, having fundamental consequences for ecological processes. However, a general mechanistic explanation is still lacking. Research is split into three separate areas of inquiry - movement models based on random walks, individual-based models based on optimal foraging theory, and a statistical modelling approach - which have developed without much productive contact. Here we review recent advances in modelling home range behaviour, focusing particularly on the problem of identifying mechanisms that lead to the emergence of stable home ranges from unbounded movement paths. We discuss the issue of spatiotemporal scale, which is rarely considered in modelling studies, as well as highlighting the need to consider more closely the dynamical nature of home ranges. Recent methodological and theoretical advances may soon lead to a unified approach, however, conceptually unifying our understanding of linkages among home range behaviour and ecological or evolutionary processes.

Fitting Probability Distributions to Animal Movement Trajectories: Using Artificial Neural Networks to Link Distance, Resources, and Memory

Animal movement paths are often thought of as a confluence of behavioral processes and landscape patterns. Yet it has proven difficult to develop frameworks for analyzing animal movement that can test these interactions. Here we describe a novel method for fitting movement models to data that can incorporate diverse aspects of landscapes and behavior. Using data from five elk (Cervus canadensis) reintroduced to central Ontario, we employed artificial neural networks to estimate movement probability kernels as functions of three landscape‐behavioral processes. These consisted of measures of the animals’ response to the physical spatial structure of the landscape, the spatial variability in resources, and memory of previously visited locations. The results support the view that animal movement results from interactions among elements of landscape structure and behavior, motivating context‐dependent movement probabilities, rather than from successive realizations of static distributions, as some traditional models of movement and resource selection assume. Flexible, nonlinear models may thus prove useful in understanding the mechanisms controlling animal movement patterns.

Human mobility patterns predict divergent epidemic dynamics among cities

The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fraction of workers travel to work in a few focal locations. Similarly sized cities also vary in the level of organization in their mobility patterns, equivalent on average to the variation expected from a 2.64-fold change in population size. Systematic variation in mobility patterns is sufficient to cause significant differences among cities in infectious disease dynamics—even among cities of the same size—according to an individual-based model of airborne pathogen transmission parametrized with the mobility data. This suggests that differences among cities in host contact patterns are sufficient to drive differences in infectious disease dynamics and provides a framework for testing the effects of host mobility patterns in city-level disease data.

Spatial and temporal dynamics of superspreading events in the 2014-2015 West Africa Ebola epidemic

Significance For many infections, some infected individuals transmit to disproportionately more susceptibles than others, a phenomenon referred to as “superspreading.” Understanding superspreading can facilitate devising individually targeted control measures, which may outperform population-level measures. Superspreading has been described for a recent Ebola virus (EBOV) outbreak, but systematic characterizations of its spatiotemporal dynamics are still lacking. We introduce a statistical framework that allows us to identify core characteristics of EBOV superspreading. We find that the epidemic was largely driven and sustained by superspreadings that are ubiquitous throughout the outbreak and that age is an important demographic predictor for superspreading. Our results highlight the importance of control measures targeted at potential superspreaders and enhance understanding of causes and consequences of superspreading for EBOV. The unprecedented scale of the Ebola outbreak in Western Africa (2014–2015) has prompted an explosion of efforts to understand the transmission dynamics of the virus and to analyze the performance of possible containment strategies. Models have focused primarily on the reproductive numbers of the disease that represent the average number of secondary infections produced by a random infectious individual. However, these population-level estimates may conflate important systematic variation in the number of cases generated by infected individuals, particularly found in spatially localized transmission and superspreading events. Although superspreading features prominently in first-hand narratives of Ebola transmission, its dynamics have not been systematically characterized, hindering refinements of future epidemic predictions and explorations of targeted interventions. We used Bayesian model inference to integrate individual-level spatial information with other epidemiological data of community-based (undetected within clinical-care systems) cases and to explicitly infer distribution of the cases generated by each infected individual. Our results show that superspreaders play a key role in sustaining onward transmission of the epidemic, and they are responsible for a significant proportion (∼61%) of the infections. Our results also suggest age as a key demographic predictor for superspreading. We also show that community-based cases may have progressed more rapidly than those notified within clinical-care systems, and most transmission events occurred in a relatively short distance (with median value of 2.51 km). Our results stress the importance of characterizing superspreading of Ebola, enhance our current understanding of its spatiotemporal dynamics, and highlight the potential importance of targeted control measures.

Spatial variability of stand-scale residuals in Ontario’s boreal forest fires

Knowledge of postfire residuals in boreal forest landscapes is increasingly important for ecological applications and forest management. While many studies provide useful insight, knowledge of stand-scale postfire residual occurrence and variability remains fragmented and untested as formal hypotheses. We examined the spatial variability of stand-scale postfire residuals in boreal forests and tested hypotheses of their spatial associations. Based on the literature, we hypothesized that preburn forest cover characteristics, site conditions, proximity to water and fire edge, and local fire intensity influence the spatial variability of postfire residuals. To test these hypotheses, we studied live-tree and snag residuals in 11 boreal Ontario forest fires, using 660 sample points based on high resolution photography (1:408) captured immediately after the fires. The abundance of residuals varied considerably within and among these fires, precluding attempts to generalize estimates. Based on a linear mixed-effe...

Contact heterogeneity, rather than transmission efficiency, limits the emergence and spread of canine influenza virus.

Host-range shifts in influenza virus are a major risk factor for pandemics. A key question in the study of emerging zoonoses is how the evolution of transmission efficiency interacts with heterogeneity in contact patterns in the new host species, as this interplay influences disease dynamics and prospects for control. Here we use a synergistic mixture of models and data to tease apart the evolutionary and demographic processes controlling a host-range shift in equine H3N8-derived canine influenza virus (CIV). CIV has experienced 15 years of continuous transfer among dogs in the United States, but maintains a patchy distribution, characterized by sporadic short-lived outbreaks coupled with endemic hotspots in large animal shelters. We show that CIV has a high reproductive potential in these facilities (mean R0 = 3.9) and that these hotspots act as refugia from the sparsely connected majority of the dog population. Intriguingly, CIV has evolved a transmission efficiency that closely matches the minimum required to persist in these refugia, leaving it poised on the extinction/invasion threshold of the host contact network. Corresponding phylogenetic analyses show strong geographic clustering in three US regions, and that the effective reproductive number of the virus (Re) in the general dog population is close to 1.0. Our results highlight the critical role of host contact structure in CIV dynamics, and show how host contact networks could shape the evolution of pathogen transmission efficiency. Importantly, efficient control measures could eradicate the virus, in turn minimizing the risk of future sustained transmission among companion dogs that could represent a potential new axis to the human-animal interface for influenza.

Persistent Chaos of Measles Epidemics in the Prevaccination United States Caused by a Small Change in Seasonal Transmission Patterns.

Epidemics of infectious diseases often occur in predictable limit cycles. Theory suggests these cycles can be disrupted by high amplitude seasonal fluctuations in transmission rates, resulting in deterministic chaos. However, persistent deterministic chaos has never been observed, in part because sufficiently large oscillations in transmission rates are uncommon. Where they do occur, the resulting deep epidemic troughs break the chain of transmission, leading to epidemic extinction, even in large cities. Here we demonstrate a new path to locally persistent chaotic epidemics via subtle shifts in seasonal patterns of transmission, rather than through high-amplitude fluctuations in transmission rates. We base our analysis on a comparison of measles incidence in 80 major cities in the prevaccination era United States and United Kingdom. Unlike the regular limit cycles seen in the UK, measles cycles in US cities consistently exhibit spontaneous shifts in epidemic periodicity resulting in chaotic patterns. We show that these patterns were driven by small systematic differences between countries in the duration of the summer period of low transmission. This example demonstrates empirically that small perturbations in disease transmission patterns can fundamentally alter the regularity and spatiotemporal coherence of epidemics.

Detecting collective behaviour in animal relocation data, with application to migrating caribou

Summary Collective behaviour can allow populations to have emergent responses to uncertain environments, driven by simple interactions among nearby individuals. High-throughput ethological studies, where individual behaviour is closely observed in each member of a population (typically in the laboratory or by simulation), have revealed that collective behaviour in populations requires only rudimentary cognitive abilities in individuals and could therefore represent a widespread adaptation to life in an uncertain world. However, the ecological significance of collective behaviour is not yet well understood, as most studies to date have been confined to specialized situations that allow intensive monitoring of individual behaviour. Here, we describe a way to screen for collective behaviour in ecological data that is sampled at a coarser resolution than the underlying behavioural processes. We develop and test the method in the context of a well-studied model for collective movement in a noisy environmental gradient. The large-scale distribution patterns associated with collective behaviour are difficult to distinguish from the aggregated responses of independent individuals in this setting because independent individuals also align to track the gradient. However, we show that collective idiosyncratic deviations from the mean gradient direction have high predictive value for detecting collective behaviour. We describe a method of testing for these deviations using the average normalized velocity of the population. We demonstrate the method using data from satellite tracking collars on the migration patterns of caribou (Rangifer tarandus), recovering evidence that collective behaviour is a key driver of caribou migration patterns. We find moreover that the relative importance of collective behaviour fluctuates seasonally, concurrent with the timing of migration and reproduction. Collective behaviour is a potentially widespread dynamic property of populations that can, in some cases, be detected in coarsely sampled ecological data.

Spread of Canine Influenza A(H3N2) Virus, United States

A canine influenza A(H3N2) virus emerged in the United States in February–March 2015, causing respiratory disease in dogs. The virus had previously been circulating among dogs in Asia, where it originated through the transfer of an avian-origin influenza virus around 2005 and continues to circulate. Sequence analysis suggests the US outbreak was initiated by a single introduction, in Chicago, of an H3N2 canine influenza virus circulating among dogs in South Korea in 2015. Despite local control measures, the virus has continued circulating among dogs in and around Chicago and has spread to several other areas of the country, particularly Georgia and North Carolina, although these secondary outbreaks appear to have ended within a few months. Some genetic variation has accumulated among the US viruses, with the appearance of regional-temporal lineages. The potential for interspecies transmission and zoonotic events involving this newly emerged influenza A virus is currently unknown.

Fitting Dynamic Models to Animal Movement Data: The Importance of Probes for Model Selection, a Reply to Franz and Caillaud

Analyzing animal movement can provide a useful perspective on the interface between landscape patterns and individual behavior (Patterson et al. 2008; Schick et al. 2008). In Dalziel et al. (2008) we proposed a method of fitting dynamic movement models to trajectory and landscape data. The goal was to explore how disparate landscape-behavior processes combine to generate animal movement patterns. As an example of the approach, we used a genetic algorithm (GA) to fit artificial neural network (ANN) models to observations of elk (Cervus canadensis) movement. We formulated seven ANN models that encompassed a factorial combination of three different types of landscapebehavior interaction: the distance from an elk’s current location to each point on the landscape, d(x); the resource structure at that point relative to the rest of the home range, r(x); and the estimated memory of previous visits to that point, m(x), where x represents the coordinates of the landscape. The models used this information to estimate a dynamic redistribution kernel that, at each time point, gave the probability that an elk would move to a given location x ∗ at the next time step. Thus, ∗ P(x p x) p k[d(x), r(x), m(x)],