Biology & Socio-Ecology (BS) Session 1
Time and Date: 16:15 - 18:00 on 19th Sep 2016
Room: G - Blauwe kamer
Chair: Jose Mateos
| Population density, social structure and disease spread in a carpenter ant colony
Abstract: Social behavior in human and animal populations gives mobility to infectious disease. In some cases, being in close proximity of an infected individual can be enough for transmission to occur. On the other hand, diseases which require more intimate contact depend on social behaviors rather than spatial factors. Here we map the social network of an ant colony and ask whether their behavior changes between high-density and low-density settings. We ask in particular: do ants structure their society in a way which mitigates the risk of epidemic disease? Only a minority of carpenter ants leave the nest. The foragers collect liquid food and, upon returning, regurgitate it into the mouths of their nest-mates. Typically, foragers will only give food to a small number of other ants; to feed the entire colony it gets passed through a complex network of feeding interactions. In a lab experiment we filmed several hours of nest activity. The ants were first given restricted space to live (high density), we then increased the size of the nest fourfold (low density). We observed some familiar network features such as degree heterogeneity and modularity. On average, the degree was lower in the high density setting despite the ants being physically closer. What did stay the same, however, was the ant's remarkable propensity to seek new connections rather than repeat previous ones, contrary to most other social animals. To address whether this behavior encourages disease spread we use a model in which we can adjust the heterogeneity of social tie strength. We couple this with a disease model where transmission depends non-linearly on the intensity of the interaction. Our analysis suggests that a rigid social structure determines the dynamics of communicable disease in carpenter ant colonies more so than spatial restrictions.
|Ewan Colman, Andreas Modlmeier, David Hughes and Shweta Bansal
| Social exchange of growth hormones and proteins through oral trophallaxis in ants
Abstract: Social insects frequently engage in oral fluid exchange–trophallaxis–during which they pass the contents of their social stomach(s) between adults, and between adults and larvae. Although trophallaxis is normally considered a simple food-sharing mechanism, we hypothesized that endogenous components of this social fluid might underlie a novel means of social communication. Through protein and small-molecule mass spectrometry and RNA sequencing in the ant Camponotus floridanus, we found that trophallaxis fluid contains a contingent of specific proteins, hydrocarbons, microRNAs, and Juvenile Hormone III, an important insect growth regulator. When C. floridanus nursing workers’ trophallaxis fluid was supplemented with juvenile hormone, the larvae they reared more often completed metamorphosis and became larger workers. Comparison of trophallaxis fluid proteins across social insect species revealed that many are regulators of growth and development. These results raise the possibility that trophallaxis is a communication mechanism that enables communal control of colony development.
|Adria Leboeuf, Richard Benton and Laurent Keller
| Parasite spreading in spatial ecological multiplex networks
Abstract: We propose a novel spatial multiplex framework for modelling multi-host parasite transmission in ecosystems. Our approach includes distinct mechanisms of parasite infection through both predation and blood-exchange between species populations in a given environment. In our multiplex network model, nodes have identities, i.e. predator, prey or parasite vectors, so that allowed interactions depend on them (e.g. predators feeding on prey). On this 2-layer multiplex topology, we model an SI infection dynamics which occurs on a given layer with a certain probability. Animal populations get infected in both layers either by preying on infected animals (on the food-web layer) or by getting in contact with vectors (on the vectorial layer). Simulations and analytical results indicate that embedding nodes in space, through a random geometric graph topology, rather than having them interacting on random graphs, slows down disease outbreaks in a 3-species (predator-prey-vector) reference model. We compare these results against a more realistic model with 20 species interacting according to empirical data relative to the Brazilian area of Serra de Canastra. In both models, we find that genuine multiplex measures such as the multiplex cartography powerfully predict which species type is crucial in accelerating the infection spread. In the 3-species model, prey populations participate more in the multiplex topology and therefore immunising them dramatically increases the rate of parasite diffusion within the networked ecosystem. On the other hand, in the 20-species model, a similar role of ecological importance is played by predators when the parasite diffusion mostly occurs on the trophic layer. In agreement with previous ecological studies, we provide a novel theoretical multiplex framework which shows that trophic and blood-exchanging interactions can be additive in sustaining multi-host parasite spread. Our methodology can be applied for investigating also spreading processes on social, transportation and brain networks.
|Massimo Stella, Cecilia Andreazzi, Sanja Selakovic, Alireza Goudarzi and Alberto Antonioni
| Farmers’ decision-making on disease management in potato production: An agent-based modelling approach
Abstract: In this project the host-pathogen system of potato-late blight (Phytophthora infestans) was analysed as a model system to study management of crop-disease interactions. Since disease incidence is influenced by biophysical processes as well as stakeholder behaviour, we focus not only on epidemiological processes but also on decision-making concerning disease management. To analyse the interactions and feedback mechanisms we used an agent-based modelling approach. Agent-based modelling (ABM) has been recognized as a useful tool to analyse human decision-making in a spatial environment in which biophysical processes occur. By analysing the systems dynamics we aim to identify decision-making strategies that lead to effective and sustainable disease control. Potato late blight is one of the main diseases in potato causing major losses in yield. This project focusses on the Netherlands which has a high potato density and because of favourable weather, frequently experiences outbreaks of the disease. Currently the use of fungicides is the most important control method but this is harmful for the environment. The use of resistant cultivars could improve sustainability of late blight management, however, additional management practices are required to prevent breakdown of resistance. An ABM was developed including processes on crop growth, disease dynamics and farmers’ decision-making on disease management. Based on social theories and interviews a framework was developed on farmers’ decision-making. The framework was based on strategic decision-making related to different objectives of farmers. Farmer interaction was related to three different decision-making strategies: individual, social and collective decision-making. In this way the effect of different types of decision-making strategies and objectives was analysed. This research will give more insight in the analysis of a social-ecological system using agent-based modelling. Furthermore it shows how a framework on decision-making was developed and implemented in an ABM to analyse management of potato late blight.
|Francine Pacilly, Jeroen Groot, Gert Jan Hofstede and Edith Lammerts van Bueren
| Identifying influential neighbors in animal flocking
Abstract: One important issue that is largely discussed in the community dealing with collective motion in animal groups concerns the number of neighbors each individual in a flock of birds or a school of fish is interacting with. Indeed, it has been shown that the properties that emerge at the level of a flock or a school largely depend on the number and position of neighbors each individual is paying attention to. Here we analyze the trajectories of individuals moving in groups in order to infer the pattern of influential neighbors. Experiments have been conducted with groups of 5, 8 and 10 individuals of the freshwater tropical fish Hemigrammus rhodostomus swimming in a ring-shaped tank. This species displays strong aggregative behavior leading to coherent and highly cohesive collective movements. We focus on 980 collective U-turns during which the group spontaneously changes its swimming direction from clockwise to anti-clockwise or vice-versa. Thus, we can minimize the effects of the constraining geometry and maximize the information transmission between individuals. To identify the influential neighbors, we first detect the correlation between the heading of a focal individual and the corresponding heading of individuals moving in its close vicinity with a certain time delay τ. Then, we analyze the time-average correlation value between a focal individual as a function of the spatial information and the number of neighbors. We find that fish react mainly to one or two neighbors at a time. Moreover, the most influential neighbor is not necessarily the nearest one. Indeed, we find no correlation between the distance rank of a neighbor and its likelihood to influence the behavior of a focal fish.
|Li Jiang, Luca Giuggioli, Ramón Escobedo, Valentin Lecheval, Clément Sire, Zhangang Han and Guy Theraulaz
| Heterogeneous SIS Model for Directed Networks and Optimal Curing Policy
Abstract: The core challenge in epidemiology is how to control epidemic outbreaks. This applies both to public health and to domains such as, e.g., malware protection. In this context, the contact network structure plays a crucial role in the diffusion of epidemics. In this paper we focus on the influence of network topology onto epidemic diffusion for an heterogeneous population. We hence specialize our analysis to community networks and efficient distribution of curing resources based on the network structure is provided. Target is to prevent an epidemics from persisting indefinitely in a population at minimum cost. In our model, the epidemic process develops over a directed weighted graph: a continuous-time individual-based susceptible--infected--susceptible (SIS) is analyzed using a first-order mean-field approximation. The epidemic threshold and the stability properties of the system are described for networks with general topology. The case of a community network is analyzed based on the graph-theoretical notion of equitable partition. For such structures, we show that the epidemic threshold can be computed using a lower-dimensional dynamical system. Moreover we can prove that the steady-state of the original system, that appears above the threshold, can also be computed using same reduced dynamical system. In the second part of the work, the model is used to derive the cost-optimal curing policy: not always curing nodes at uniform rate works efficiently in order to eradicate the infection. The optimal solution of this optimization problem is obtained by formulating a convex minimization problem on a general but symmetric weighted community network structure. Finally an algorithm with polynomial time complexity in the network size is devised for the case of a two-level optimal curing problem. It solves cases where the agents can be divided into two categories, e.g., male and female, small villages and cities, firewalls/gateways or clients in an enterprise network.
|Stefania Ottaviano, Francesco De Pellegrini, Stefano Bonaccorsi and Piet Van Mieghem