No abstract

Most hosts, including humans, are simultaneously or sequentially infected with several parasites. A key question is whether patterns of coinfection arise because infection by one parasite species affects susceptibility to others or because of inherent differences between hosts. We used timeseries data from individual hosts in natural populations to analyze patterns of infection risk for a microparasite community, detecting large positive and negative effects of other infections. Patterns remain once variations in host susceptibility and exposure are accounted for. Indeed, effects are typically of greater magnitude, and explain more variation in infection risk, than the effects associated with host and environmental factors more commonly considered in disease studies. We highlight the danger of mistaken inference when considering parasite species in isolation rather than parasite communities.Macroparasites (helminths and arthropods) and microparasites (viruses, bacteria, and protozoa) are integral components of the ecological communities that include their hosts (1), and it is likely that most hosts, most of the time, are infected with more than one parasite species (2). Interactions between parasites in natural populations, however, have been studied only rarely. A community ecology perspective is particularly relevant for studies of coinfection, as parasites may interact directly by competing for resources or indirectly via the host immune system (3). Interactions may be antagonistic to at least one of the parasites, either as a result of resource shortage or where there are cross-effective immune responses, or they may be beneficial to one or both parasites, as a result of parasite-induced immunosuppression or down-regulation of all or part of the immune system (1). Table 1 for details of their biology). Infection risk will depend on both the probability of encountering an infectious dose and the probability of infection given exposure (host susceptibility). We aimed to determine whether susceptibility to infection by a microparasite was influenced by other microparasites. Therefore, for each microparasite, we investigated whether the other microparasites influenced the probability that a susceptible animal became infected at a given time point (t 0 ), adding infection status for these other microparasites as explanatory variables to baseline statistical models that accounted for environmental and individual variables (e.g., sex and season) (12) (table S1). Thus, we guard against detecting spurious associations, which, in reality, reflect correlated exposure risk (e.g., a positive association simply because both parasites are most prevalent in late summer). For parasites causing self-limiting infections, infection status at both t 0 and/ or the previous month (t −1 ) were considered as explanatory variables, whereas for B. microti infections, which are chronic, a three-level covariate was used: uninfected, newly infected (infected at t 0 but not before), and chronically infected (first infected prior ...

Summary Fundamental ecological research is both intrinsically interesting and provides the basic knowledge required to answer applied questions of importance to the management of the natural world. The 100th anniversary of the British Ecological Society in 2013 is an opportune moment to reflect on the current status of ecology as a science and look forward to high‐light priorities for future work. To do this, we identified 100 important questions of fundamental importance in pure ecology. We elicited questions from ecologists working across a wide range of systems and disciplines. The 754 questions submitted (listed in the online appendix) from 388 participants were narrowed down to the final 100 through a process of discussion, rewording and repeated rounds of voting. This was done during a two‐day workshop and thereafter. The questions reflect many of the important current conceptual and technical pre‐occupations of ecology. For example, many questions concerned the dynamics of environmental change and complex ecosystem interactions, as well as the interaction between ecology and evolution. The questions reveal a dynamic science with novel subfields emerging. For example, a group of questions was dedicated to disease and micro‐organisms and another on human impacts and global change reflecting the emergence of new subdisciplines that would not have been foreseen a few decades ago. The list also contained a number of questions that have perplexed ecologists for decades and are still seen as crucial to answer, such as the link between population dynamics and life‐history evolution. Synthesis. These 100 questions identified reflect the state of ecology today. Using them as an agenda for further research would lead to a substantial enhancement in understanding of the discipline, with practical relevance for the conservation of biodiversity and ecosystem function.

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticks Ixodes ricinus on red grouse Lagopus lagopus scoticus chicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

Suggestions of collapse in small herbivore cycles since the 1980s have raised concerns about the loss of essential ecosystem functions. Whether such phenomena are general and result from extrinsic environmental changes or from intrinsic process stochasticity is currently unknown. Using a large compilation of time series of vole abundances, we demonstrate consistent cycle amplitude dampening associated with a reduction in winter population growth, although regulatory processes responsible for cyclicity have not been lost. The underlying syndrome of change throughout Europe and grass-eating vole species suggests a common climatic driver. Increasing intervals of low-amplitude small herbivore population fluctuations are expected in the future, and these may have cascading impacts on trophic webs across ecosystems.

Summary 1.A geographical gradient in the relative impact of generalist and specialist predators on small rodent populations has been hypothesized to be responsible for the gradient in cyclicity found in Fennoscandia. Population oscillations resulting from weasel±vole interactions are said to be dampened by the increasing stabilizing impact of generalist predators in southern Fennoscandia resulting from: (i) a greater abundance and diversity of predators sustained by alternative prey; (ii) the absence of signi®cant snow cover leading to constant exposure of voles to generalist predators; and (iii) a heterogeneous habitat that makes dispersing voles more vulnerable to predators. 2. Changes in the abundance of ®eld voles (Microtus agrestis L.) in a man-made spruce forest in northern England were recorded during 1984±98 using sign indices at 14±18 sites calibrated with capture±recapture estimates of vole density. 3. Field vole populations exhibited cyclic dynamics which were in many ways similar to those reported from Fennoscandia, including population declines taking place during the breeding season and long periods with no recovery in numbers following population crashes. 4. The density dependence structure of the time series was explored by means of partial autocorrelation functions, which suggested second-order density dependence. Analyses based on two density estimates per year (spring and autumn) reveal signi®cant negative values for lags of 1, 1´5 and 2 years, suggesting that the time-lag might be somewhat shorter than 2 years. 5. Estimates of predation on ®eld voles by red foxes and tawny owls at high vole density were above the value predicted for this site and for the whole generalist predator community by a published model assuming that predation by generalist predators stabilizes vole populations. However, empirical estimates of the parameter used both for designing and testing the model are inherently imprecise. 6. A qualitative evaluation of the three variables (see 1) correlated to the Fennoscandian gradient and assumed to contribute to variations in generalist predation pressure did not support the hypothesis that low predation rates by generalist predators are necessary for vole dynamics to be dominated by the destabilizing impact of weasel±vole interactions. The specialist/generalist predation hypothesis must therefore be modi®ed to account for the regular population cycles occurring in northern Britain.

A key aim in epidemiology is to understand how pathogens spread within their host populations. Central to this is an elucidation of a pathogen's transmission dynamics. Mathematical models have generally assumed that either contact rate between hosts is linearly related to host density (density-dependent) or that contact rate is independent of density (frequency-dependent), but attempts to confirm either these or alternative transmission functions have been rare. Here, we fit infection equations to 6 years of data on cowpox virus infection (a zoonotic pathogen) for 4 natural populations to investigate which of these transmission functions is best supported by the data. We utilize a simple reformulation of the traditional transmission equations that greatly aids the estimation of the relationship between density and host contact rate. Our results provide support for an infection rate that is a saturating function of host density. Moreover, we find strong support for seasonality in both the transmission coefficient and the relationship between host contact rate and host density, probably reflecting seasonal variations in social behavior and/or host susceptibility to infection. We find, too, that the identification of an appropriate loss term is a key component in inferring the transmission mechanism. Our study illustrates how time series data of the hostpathogen dynamics, especially of the number of susceptible individuals, can greatly facilitate the fitting of mechanistic disease models.cowpox ͉ disease ͉ population cycles ͉ Markov chain Monte Carlo T he seminal studies of Anderson and May (1, 2) introduced a framework for modeling the dynamics of pathogens and their hosts that has since underpinned most predictive models of host-pathogen dynamics. It has been standard practice when modeling the dynamics of host-microparasite interactions (viral and bacterial infections) to represent the rate of change of infected hosts I(t) at time t by dI͑t͒ dt ϭ transmission rate ͑infection͒ Ϫ loss rate ͑death ϩ recovery͒.[1]However, empirically based identification of appropriate functional forms for the ''transmission rate'' and ''loss rate'' terms has not generally been possible for systems with host dynamics because of a lack of sufficient data (although refs. 3 and 4 have recently done this for infectious diseases of human populations).To date, most studies have used transmission rate terms that are either density-dependent or frequency-dependent (5, 6). The underlying difference between these is the assumption about how host contact rate c, varies with host density [(N(t))/A], where N(t) is host abundance and A, the area occupied by the population, is usually assumed constant and omitted from the equations (6). For density-dependent transmission, host contact rate varies linearly with density [typically adopted for directly transmitted diseases such as measles (7) and foot and mouth disease (8)], whereas for frequencydependent transmission it is constant [typically adopted for sexually transmitted diseases such as HIV in ...

We demonstrate evidence for the presence of travelling waves in a cyclic population of field voles in northern Britain by fitting simple, empirical models to spatially referenced time series data. Population cycles were broadly synchronous at all sites, but use of Mantel correlations suggested a strong spatial pattern along one axis at a projection line 72 degrees from North. We then fitted a generalized additive model to log population density assuming a fixed-form travelling wave in one spatial dimension for which the density at each site was offset in time by a constant amount from a standard density-time curve. We assumed that the magnitude of this offset would be proportional to the spatial separation between any given site and the centroid of the sampling sites, where separation is the distance between sites in a fixed direction. After fitting this model, we estimated that the wave moved at an average speed of 19 km yr-1, heading from West to East at an angle of 78 degrees from North. Nomadic avian predators which could synchronize populations over large areas are scarce and the travelling wave may be caused by density-dependent dispersal by field voles and/or predation by weasels, both of which act at a suitably small spatial scale.