John Bryden

Chronic sublethal stress causes bee colony failure

Current bee population declines and colony failures are well documented yet poorly understood and no single factor has been identified as a leading cause. The evidence is equivocal and puzzling: for instance, many pathogens and parasites can be found in both failing and surviving colonies and field pesticide exposure is typically sublethal. Here, we investigate how these results can be due to sublethal stress impairing colony function. We mathematically modelled stress on individual bees which impairs colony function and found how positive density dependence can cause multiple dynamic outcomes: some colonies fail while others thrive. We then exposed bumblebee colonies to sublethal levels of a neonicotinoid pesticide. The dynamics of colony failure, which we observed, were most accurately described by our model. We argue that our model can explain the enigmatic aspects of bee colony failures, highlighting an important role for sublethal stress in colony declines.

Interspecific competition in honeybee intracellular gut parasites is asymmetric and favours the spread of an emerging infectious disease

There is increasing appreciation that hosts in natural populations are subject to infection by multiple parasite species. Yet the epidemiological and ecological processes determining the outcome of mixed infections are poorly understood. Here, we use two intracellular gut parasites (Microsporidia), one exotic and one co-evolved in the western honeybee (Apis mellifera), in an experiment in which either one or both parasites were administered either simultaneously or sequentially. We provide clear evidence of within-host competition; order of infection was an important determinant of the competitive outcome between parasites, with the first parasite significantly inhibiting the growth of the second, regardless of species. However, the strength of this ‘priority effect’ was highly asymmetric, with the exotic Nosema ceranae exhibiting stronger inhibition of Nosema apis than vice versa. Our results reveal an unusual asymmetry in parasite competition that is dependent on order of infection. When incorporated into a mathematical model of disease prevalence, we find asymmetric competition to be an important predictor of the patterns of parasite prevalence found in nature. Our findings demonstrate the wider significance of complex multi-host–multi-parasite interactions as drivers of host–pathogen community structure.

DYNAMIC TRANSMISSION, HOST QUALITY, AND POPULATION STRUCTURE IN A MULTIHOST PARASITE OF BUMBLEBEES

The evolutionary ecology of multihost parasites is predicted to depend upon patterns of host quality and the dynamics of transmission networks. Depending upon the differences in host quality and transmission asymmetries, as well as the balance between intra‐ and interspecific transmission, the evolution of specialist or generalist strategies is predicted. Using a trypanosome parasite of bumblebees, we ask how host quality and transmission networks relate to parasite population structure across host species, and thus the potential for the evolution of specialist strains adapted to different host species. Host species differed in quality, with parasite growth varying across host species. Highly asymmetric transmission networks, together with differences in host quality, likely explain local population structure of the parasite across host species. However, parasite population structure across years was highly dynamic, with parasite populations varying significantly from one year to the next within individual species at a given site. This suggests that, while host quality and transmission may provide the opportunity for short‐term host specialization by the parasite, repeated bottlenecking of the parasite, in combination with its own reproductive biology, overrides these smaller scale effects, resulting in the evolution of a generalist parasite.

Stability in flux: community structure in dynamic networks.

The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.

Word usage mirrors community structure in the online social network Twitter

BackgroundLanguage has functions that transcend the transmission of information and varies with social context. To find out how language and social network structure interlink, we studied communication on Twitter, a broadly-used online messaging service.ResultsWe show that the network emerging from user communication can be structured into a hierarchy of communities, and that the frequencies of words used within those communities closely replicate this pattern. Consequently, communities can be characterised by their most significantly used words. The words used by an individual user, in turn, can be used to predict the community of which that user is a member.ConclusionsThis indicates a relationship between human language and social networks, and suggests that the study of online communication offers vast potential for understanding the fabric of human society. Our approach can be used for enriching community detection with word analysis, which provides the ability to automate the classification of communities in social networks and identify emerging social groups.

Twitter users change word usage according to conversation-partner social identity

This paper investigates how people express social identity at a large scale on a social network. We looked at communities of users on the Twitter website, and tested two established social-psychology theories that are usually performed at local scale. We found evidence of Communication Accommodation Theory, where community members vary their language characteristics depending on which community they are communicating with. We also found the level of linguistic variation correlated with how isolated a community was: evidence that there is Convergence between linked members. This demonstrates the power of methods which analyse subtle human behaviour on social networks.

The impact of clonal mixing on the evolution of social behaviour in aphids.

Reports of substantial clonal mixing measured in social aphid colonies seem, on the face of it, to rule out population structure as an explanation of this enigmatic insects social behaviour. To clarify how selection operates in aphids, and to disentangle direct and indirect fitness components, we present a model of the life cycle of a typical colony-dwelling aphid. The model incorporates ecological factors and includes a trade-off between investing in social behaviour and investing in reproduction. Our focus on inclusive fitness contrasts with previous approaches that optimize colony output. Through deriving a variant of Hamiltons rule, we show that a simple relationship can be established between the patch-carrying capacity and immigration rates into patches. Our results indicate that the levels of clonal mixing reported are not inconsistent with social behaviour. We discuss our model in terms of the evolutionary origins of social behaviour in aphids.

How humans transmit language: horizontal transmission matches word frequencies among peers on Twitter

Language transmission, the passing on of language features such as words between people, is the process of inheritance that underlies linguistic evolution. To understand how language transmission works, we need a mechanistic understanding based on empirical evidence of lasting change of language usage. Here, we analysed 200 million online conversations to investigate transmission between individuals. We find that the frequency of word usage is inherited over conversations, rather than only the binary presence or absence of a word in a persons lexicon. We propose a mechanism for transmission whereby for each word someone encounters there is a chance they will use it more often. Using this mechanism, we measure that, for one word in around every hundred a person encounters, they will use that word more frequently. As more commonly used words are encountered more often, this means that it is the frequencies of words which are copied. Beyond this, our measurements indicate that this per-encounter mechanism is neutral and applies without any further distinction as to whether a word encountered in a conversation is commonly used or not. An important consequence of this is that frequencies of many words can be used in concert to observe and measure language transmission, and our results confirm this. These results indicate that our mechanism for transmission can be used to study language patterns and evolution within populations.