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Collective behaviour is common in bacteria, plants and animals, and therefore occurs across ecosystems, from biofilms to cities. With collective behaviour, social interactions among individuals propagate to affect the behaviour of groups, whereas group-level responses in turn affect individual behaviour. These cross-scale feedback loops between individuals, populations and their environments can provide fitness benefits, such as the efficient exploitation of uncertain resources, as well as costs, such as increased resource competition. Although the social mechanics of collective behaviour are increasingly well-studied, its role in ecosystems remains poorly understood. Here we introduce collective movement into a model of consumer-resource dynamics to demonstrate that collective behaviour can attenuate consumer-resource cycles and promote species coexistence. We focus on collective movement as a particularly well-understood example of collective behaviour. Adding collective movement to canonical unstable ecological scenarios causes emergent social-ecological feedback, which mitigates conditions that would otherwise result in extinction. Collective behaviour could play a key part in the maintenance of biodiversity.

Positive correlations have been reported between wastewater SARS-CoV-2 concentrations and a community's burden of infection, disease or both. However, previous studies mostly compared wastewater to clinical case counts or nonrepresentative convenience samples, limiting their quantitative potential.This study examined whether wastewater SARS-CoV-2 concentrations could provide better estimations for SARS-CoV-2 community prevalence than reported cases of COVID-19. In addition, this study tested whether wastewater-based epidemiology methods could identify neighborhood-level COVID-19 hotspots and SARS-CoV-2 variants.Community SARS-CoV-2 prevalence was estimated from eight randomized door-to-door nasal swab sampling events in six Oregon communities of disparate size, location, and demography over a 10-month period. Simultaneously, wastewater SARS-CoV-2 concentrations were quantified at each community's wastewater treatment plant and from 22 Newport, Oregon, neighborhoods. SARS-CoV-2 RNA was sequenced from all positive wastewater and nasal swab samples. Clinically reported case counts were obtained from the Oregon Health Authority.Estimated community SARS-CoV-2 prevalence ranged from 8 to 1,687/10,000 persons. Community wastewater SARS-CoV-2 concentrations ranged from 2.9 toThe greater reliability of wastewater SARS-CoV-2 concentrations over clinically reported case counts was likely due to systematic biases that affect reported case counts, including variations in access to testing and underreporting of asymptomatic cases. With these advantages, combined with scalability and low costs, wastewater-based epidemiology can be a key component in public health surveillance of COVID-19 and other communicable infections. https://doi.org/10.1289/EHP10289.

Wastewater-based epidemiology uses pooled wastewater samples to monitor community health and has been used extensively during the COVID-19 pandemic to track SARS-CoV-2 RNA shed by infected individuals into wastewater. Wastewater concentrations of SARS-CoV-2 RNA have been positively correlated with contemporaneous counts of COVID-19 cases, making it useful for following relative disease burden trends within a community. However, the statistical associations are too weak for wastewater-based epidemiology to reliably predict reported case counts, limiting its potential. Here we show that wastewater SARS-CoV-2 concentrations are highly correlated with the community prevalence estimated from 8 randomized household community surveys in 6 Oregon communities over a 10-month period. We found that wastewater-based epidemiology is a significantly better predictor of COVID-19 community prevalence than reported case counts, which suffer from systematic biases including variations in access to testing and underreporting of asymptomatic cases, even after accounting for uncertainty inherent in the wastewater and prevalence estimates by using Monte Carlo simulations. Additionally, our results show that wastewater-based epidemiology can identify the rise and fall of neighborhood-scale COVID-19 hot spots and provide rapid information about the presence of SARS-CoV-2 variants at the neighborhood- and city-scale through sequence analyses of the wastewater. These results validate the potential of wastewater-based epidemiology to be a quantitative method to predict the prevalence of SARS-CoV-2 and identify the presence of variants of concern in a given community or neighborhood, independent of availability and access to individual-level testing. These advantages in combination with its scalability, relatively modest cost and low labor requirements, makes integrating permanent wastewater-based epidemiology infrastructure into public health systems a key component in creating pandemic-resilient cities in the future.

Forecasting influenza primes public health systems to respond, reducing transmission, morbidity and mortality. Most influenza forecasts to date have, by necessity, relied on spatially course-grained data (e.g. state-or country-level incidence), and have operated at time horizons of 12 weeks or less. If influenza outbreaks could be predicted farther in advance and with increased spatial precision, then limited public health resources could be adaptively managed to minimize spread and improve health outcomes. Here, we use real-time syndromic data from a distributed network of thermometers to construct city-specific forecasts of influenza-like illness (ILI) with a horizon of 30 weeks. Daily geolocated ILI data from the network allows for estimates of recurrent city-specific patterns in ILI transmission rates. These “transmission templates” are used to parameterize an ensemble of ILI forecasts that differ randomly in three parameters, representing city- and season-specific rates of susceptible depletion and reporting, as well as differences in influenza season onset timing. For nine cities across the US, the best-in-hindsight model matches the observed data, and the best forecast variants can be identified in the early season.

Containing outbreaks of infectious disease requires rapid identification of transmission hotspots, as the COVID-19 pandemic demonstrates. Focusing limited public health resources on transmission hotspots can contain spread, thus reducing morbidity and mortality, but rapid data on community-level disease dynamics is often unavailable. Here, we demonstrate an approach to identify anomalously elevated levels of influenza-like illness (ILI) in real-time, at the scale of US counties. Leveraging data from a geospatial network of thermometers encompassing more than one million users across the US, we identify anomalies by generating accurate, county-specific forecasts of seasonal ILI from a point prior to a potential outbreak and comparing real-time data to these expectations. Anomalies are strongly correlated with COVID-19 case counts and may provide an early-warning system to locate outbreak epicenters.One Sentence SummaryDistributed networks of smart thermometers track COVID-19 transmission epicenters in real-time.

Seasonal flu by ZIP code Influenza virus strikes communities in northern latitudes during winter, straining health care provision almost to the breaking point. Change in environmental humidity is a key driver, but many other seasonal and social factors contribute. Dalziel et al. obtained a geographical distribution of doctor visits for influenza-like illness for more than 600 U.S. cities (see the Perspective by Wallinga). Some ZIP codes regularly experienced sharply defined peaks of cases, or intense epidemics, and others showed a longer, more diffuse influenza season. The surges tended to occur in smaller cities with less residential density and lower household incomes. Larger, more densely populated cities had more-diffuse epidemics, presumably because of higher rates of personal contact, which makes influenza transmission less subject to climate variation. Science , this issue p. 75 ; see also p. 29

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.

Collective behaviour is common in bacteria, plants and animals, and therefore occurs across ecosystems, from biofilms to cities. With collective behaviour, social interactions among individuals propagate to affect the behaviour of groups, whereas group-level responses in turn affect individual behaviour. These cross-scale feedback loops between individuals, populations and their environments can provide fitness benefits, such as the efficient exploitation of uncertain resources, as well as costs, such as increased resource competition. Although the social mechanics of collective behaviour are increasingly well-studied, its role in ecosystems remains poorly understood. Here we introduce collective movement into a model of consumer-resource dynamics to demonstrate that collective behaviour can attenuate consumer-resource cycles and promote species coexistence. We focus on collective movement as a particularly well-understood example of collective behaviour. Adding collective movement to canonical unstable ecological scenarios causes emergent social-ecological feedback, which mitigates conditions that would otherwise result in extinction. Collective behaviour could play a key part in the maintenance of biodiversity.

We consider a modified Holling-type II predator-prey model, based on the premise that the search rate of predators is dependent on the prey density, rather than constant. A complete analysis of the global behavior of the model is presented, and shows that the model exhibits a dichotomy similar to the classical Holling-type II model: either the coexistence steady state is globally stable; or it is unstable, and then a unique, globally stable limit cycle exists. We discuss the similarities, but also important differences between our model and the Holling-type II model. The main differences are that: 1. The paradox of enrichment which always occurs in the Holling-type II model, does not always occur here, and 2. Even when the paradox of enrichment occurs, predators can adapt by lowering their search rate, and effectively stabilize the system.

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.

Avian-origin H3N2 canine influenza virus (CIV) transferred to dogs in Asia around 2005, becoming enzootic throughout China and South Korea before reaching the United States in early 2015. To understand the posttransfer evolution and epidemiology of this virus, particularly the cause of recent and ongoing increases in incidence in the United States, we performed an integrated analysis of whole-genome sequence data from 64 newly sequenced viruses and comprehensive surveillance data. This revealed that the circulation of H3N2 CIV within the United States is typified by recurrent epidemic burst–fade-out dynamics driven by multiple introductions of virus from Asia. Although all major viral lineages displayed similar rates of genomic sequence evolution, H3N2 CIV consistently exhibited proportionally more nonsynonymous substitutions per site than those in avian reservoir viruses, which is indicative of a large-scale change in selection pressures. Despite these genotypic differences, we found no evidence of adaptive evolution or increased viral transmission, with epidemiological models indicating a basic reproductive number, R0, of between 1 and 1.5 across nearly all U.S. outbreaks, consistent with maintained but heterogeneous circulation. We propose that CIV9s mode of viral circulation may have resulted in evolutionary cul-de-sacs, in which there is little opportunity for the selection of the more transmissible H3N2 CIV phenotypes necessary to enable circulation through a general dog population characterized by widespread contact heterogeneity. CIV must therefore rely on metapopulations of high host density (such as animal shelters and kennels) within the greater dog population and reintroduction from other populations or face complete epidemic extinction. IMPORTANCE The relatively recent appearance of influenza A virus (IAV) epidemics in dogs expands our understanding of IAV host range and ecology, providing useful and relevant models for understanding critical factors involved in viral emergence. Here we integrate viral whole-genome sequence analysis and comprehensive surveillance data to examine the evolution of the emerging avian-origin H3N2 canine influenza virus (CIV), particularly the factors driving ongoing circulation and recent increases in incidence of the virus within the United States. Our results provide a detailed understanding of how H3N2 CIV achieves sustained circulation within the United States despite widespread host contact heterogeneity and recurrent epidemic fade-out. Moreover, our findings suggest that the types and intensities of selection pressures an emerging virus experiences are highly dependent on host population structure and ecology and may inhibit an emerging virus from acquiring sustained epidemic or pandemic circulation.

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 ([Formula: see text]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.

Fire disturbance patterns influence forest communities at a range of spatial scales. Forest community structure may also influence fire disturbance patterns, because tree species vary in their fuel value and in their tolerance to fire damage. However, the influence of community structure on fire disturbance likely depends on latent ecological differences between fires and on the spatial scale at which patterns are observed. Using data on fire intensity, community structure, and post-fire tree survival in four systematically sampled boreal forest fires, we tested the hypotheses that: (1) patterns in post-fire tree survival reflect interactions between fire intensity and community structure; (2) these relationships change with the spatial scale of observation. To test the first hypothesis, we used information theoretic methods to compare eight generalized linear mixed effects models describing the influence of community structure and fire intensity on tree survival in a 500 m2 sample plot, accounting for latent fire-to-fire differences in response. To test the scaling hypothesis, we reaveraged the data at nine successively larger spatial resolutions up to approximately 2 km2, at each resolution tracking the parameter values of the best model. When fit to the plot-level data, the dominant feature of the best model was a strong intensity–survival correlation which varied from fire to fire, and depended on plot-level community structure. In some fires, community structure and survival became more tightly coupled at larger scales, whereas fire intensity became less important. These results support the view that fire disturbance patterns are influenced by cross-scale interactions between community structure and fire intensity.

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-effects model, our data did not support the hypotheses that preburn forest cover characteristics, site conditions, and proximity to water significantly affect the spatial variability of stand-scale residuals. The results do indicate, however, that stand-scale residual variability is associated with local fire intensity (strongly) and distance to fire edge (weakly).

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.

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., Author Summary Measles epidemics continue to pose a significant public health risk wherever vaccination coverage is low. In such populations transmission rates tend to fluctuate seasonally, mirroring patterns of human aggregation, due to the timing of school terms, and/or the migration of workers and their families. Here we show empirically that slight changes in the seasonal pattern of measles transmission can lead to massive shifts in the complexity of measles dynamics, in some cases driving epidemic patterns that resemble deterministic chaos. Our analysis is based on a comparison of 20-year biweekly measles incidence time series in 80 major cities in the prevaccination era United States and United Kingdom. The results are important in two ways: first, in contrast to previous theory, we show that subtle shifts in seasonal patterns of transmission can cause deterministic chaos in the epidemic dynamics of acute immunizing infections; second, we demonstrate that this new route to deterministic chaos is significantly more robust to stochastic extinction compared with previous chaotic models, suggesting chaotic dynamics may be more common in natural populations than previously thought.

Inducible defenses are important in the life strategies of many taxa. In some species of marine gastropods, water-borne chemical cues from potential predators induce defensive changes in shell form and differences in growth rate. We examined such phenotypic plasticity in the direct-developing snail, Littorina subrotundata (Carpenter, 1864). Among experimental field populations of L. subrotundata exposed to differing intensities of predation by the purple shore crab, Hemigrapsus nudus (Dana, 1851), snails collected from predation-intense environments often had more massive shells than closely related snails from adjacent environments where predation was negligible. Snails collected from both environments were raised in tanks containing cages of H. nudus that were feeding on conspecific snails and compared to a control group raised in the absence of this stimulus. Most snails developed significantly more massive shells in the presence of the crabs suggesting that adaptive phenotypic plasticity may account for some of the variation we observed in the field. In one case, snails from a predation-intense environment did not exhibit a statistically significant amount of plasticity, but instead grew a more massive shell irrespective of the laboratory stimulus. We interpret this as evidence for a genetic difference in the plasticity of shell form among experimental populations, caused by intense selection by H. nudus. There was no statistical difference in the growth rates of snails among treatments.

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)]

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.

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.

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.