Social niche construction: evolutionary explanations for cooperative group formation

Cooperative behaviours can be defined as those that benefit others at an apparent cost to self. How these kinds of behaviours can evolve has been a topic of great interest in evolutionary biology, for at first sight we would not expect one organism to evolve to help another. Explanations for cooperation rely on the presence of a population structure that clusters cooperators together, such that they enjoy the benefits of each others' actions. But, the question that has been left largely unaddressed is, how does this structure itself evolve? If we want to really explain why organisms cooperate, then we need to explain not just their adaptation to their social environment, but why they live in that environment.It is well-known that individual genetic traits can affect population structure; an example is extracellular matrix production by bacteria in a biofilm. Yet, the concurrent evolution of such traits with social behaviour is very rarely considered. We show here that social behaviour can exert indirect selection pressure on population structure-modifying traits, causing individuals to adaptively modify their population structure to support greater cooperation. Moreover, we argue that any component of selection on structure modifying traits that is due to social behaviour must be in the direction of increased cooperation; that component of selection cannot be in favour of the conditions for greater selfishness. We then examine the conditions under which this component of selection on population structure exists. Thus, we argue that not only can population structure drive the evolution of cooperation, as in classical models, but that the benefits of greater cooperation can in turn drive the evolution of population structure - a positive feedback process that we call social niche construction.We argue that this process is necessary in providing an adaptive explanation for some of the major transitions in evolution (such as from single- to multi- celled organisms, and from solitary insects to eusocial colonies). Any satisfactory account of these transitions must explain how the individuals came to live in a population structure that supported high degrees of cooperation, as well as showing that cooperation is individually advantageous given that structure

Moderate contact between sub-populations promotes evolved assortativity enabling group selection

Group selection is easily observed when spatial group structure is imposed on a population. In fact, spatial structure is just a means of providing assortative interactions such that the benefits of cooperating are delivered to other cooperators more than to selfish individuals. In principle, assortative interactions could be supported by individually adapted traits without physical grouping. But this possibility seems to be ruled-out because any ‘marker’ that cooperators used for this purpose could be adopted by selfish individuals also. However, here we show that stable assortative marking can evolve when sub-populations at different evolutionarily stable strategies (ESSs) are brought into contact. Interestingly, if they are brought into contact too quickly, individual selection causes loss of behavioural diversity before assortative markers have a chance to evolve. But if they are brought into contact slowly, moderate initial mixing between sub-populations produces a pressure to evolve traits that facilitate assortative interactions. Once assortative interactions have become stablished, group competition between the two ESSs is facilitated without any spatial group structure. This process thus illustrates conditions where individual selection canalises groups that are initially spatially defined into stable groups that compete without the need for continued spatial separation

Mate choice and speciation : perspectives from adaptive dynamics and population genetics

Speciation theory is undergoing a renaissance period, largely due to the new methods developed in molecular biology as well as advances in the mathematical theory of evolution. In this thesis, I explore mathematical techniques applicable to the evolution of traits relevant to speciation processes. Some of the theory is further developed and is part of a general framework in the research of evolution. In nature, sister species may coexist in close geographical proximity. However, the question as to whether a speciation event has been a local event driven by the interactions (perhaps complex ones) of individuals that affect their survival and reproduction, has not yet been satisfactorily answered. This is the key issue I address in my thesis. The emphasis is given to the role of non-random mating in an environment where individuals experience diversifying ecological selection. Firstly, I investigate the role of assortative mating, and find that assortative mating works against the speciation process in the initial stages of diversification. However, once the population has diversified, ecological and sexual selection drives the population to a state of reproductive isolation. Secondly, I explore a scheme where individuals choose mates according to the level of adaptation of the mate. I find, that when the level of adaptation to the environment depends on the structure of the population in a frequency-dependent manner, the dynamics of the population may be highly complex and even chaotic. Furthermore, this setting does not facilitate reproductive isolation when mating happens across the habitats. However, if mate choice takes into account the survival and reproduction of the progeny, reproductive isolation can be maintained. Finally, some advances are made in the theory of adaptive dynamics, which along with the theory of population genetics, has been a focal tool in this thesis. My contribution to adaptive dynamics is to resolve an open question on the coexistence of similar strategies near so-called singular points. Singular points play a central role in the theory of adaptive dynamics and their existence is essential to a continuous diversification process.Uusien lajien synty on monimutkainen prosessi, joka edellyttää monen biologisen mekanismin kehittymistä. Jos populaatio kuitenkin jakaantuu maantieteellisesti niin että populaatiot eivät ole missään yhteydessä keskenään, ajan kuluessa nämä mekanismit kehittyvät jo pelkästään satunnaisten mutaatiotapahtumien seurauksena. Kun populaatiot eivät pysty vaihtamaan geneettistä materiaalia, mutaatiot kummassakin populaatiossa muokkaavat populaation omaa geneettistä materiaalia yhteensopimattomaksi toisen populaation kanssa. Evoluution tutkijoiden yksi ratkaisematon onglema onkin, voivatko uudet lajit syntyä vaikka populaatio ei jakaantuisikaan eri osiin. Väitöskirjassani etsin vastausta juuri tähän tilanteeseen, ja tälläistä uusien lajien syntyä kutsutaan sympatriseksi lajiutumiseksi. Välttämätön ehto sympatriselle lajiutumiselle on (kun kyseessä on seksuaalisesti lisääntyvät organismit) assortatiivinen parivalinta. Näin on, koska jos organismit pariutuisivat satunnaisesti niin geneettinen materiaali olisi koko populaatiossa homogeeninen. Tässä työssä keskeisessä roolissa ovat siis pariutumiseen liittyvät mallit. Artikkeleissani käytän jo olemassa olevia sekä kehitän uusia pariutumiseen liittyviä malleja. Tärkeimpiä matemaattisia työkaluja ovat populaatiogenetiikka ja adaptiivininen dynamiikka, jotka kummatkin nojautuvat vahvasti dynaamisten systeemien teoriaan. Työssäni osoitan, että jo olemassa olevat mallit mahdollistavat sympatrisen lajiutumisen varsin tiukkojen ehtojen sanelemana; luonnonvalinnan täytyy olla suhteessa seksuaaliseen valintaan voimakas. Tämän takia esitimme uuden mallin, jonka oletukset perustuvat vahvemmin ekologiaan. Lisätutkimukselle on kuitenkin tarve, koska vaikka osoitimme että assortatiivinen pariutuminen on optimaalinen strategia, emme vielä tiedä voiko se olla evoluution tulos. Lopuksi vielä todettakkoon, että väitöskirjassani ratkaisin erään adaptiivisen dynamiikkaan liittyvän avoimen ongelman. Ongelma koski kahden eri strategian, esimerkiksi kahden eri fenotyypin, samanaikaista olemassoloa ns. singulaaristen pisteiden läheisyydessä

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Linear and nonlinear approaches to unravel dynamics and connectivity in neuronal cultures

[eng] In the present thesis, we propose to explore neuronal circuits at the mesoscale, an approach in which one monitors small populations of few thousand neurons and concentrates in the emergence of collective behavior. In our case, we carried out such an exploration both experimentally and numerically, and by adopting an analysis perspective centered on time series analysis and dynamical systems. Experimentally, we used neuronal cultures and prepared more than 200 of them, which were monitored using fluorescence calcium imaging. By adjusting the experimental conditions, we could set two basic arrangements of neurons, namely homogeneous and aggregated. In the experiments, we carried out two major explorations, namely development and disintegration. In the former we investigated changes in network behavior as it matured; in the latter we applied a drug that reduced neuronal interconnectivity. All the subsequent analyses and modeling along the thesis are based on these experimental data. Numerically, the thesis comprised two aspects. The first one was oriented towards a simulation of neuronal connectivity and dynamics. The second one was oriented towards the development of linear and nonlinear analysis tools to unravel dynamic and connectivity aspects of the measured experimental networks. For the first aspect, we developed a sophisticated software package to simulate single neuronal dynamics using a quadratic integrate–and–fire model with adaptation and depression. This model was plug into a synthetic graph in which the nodes of the network are neurons, and the edges connections. The graph was created using spatial embedding and realistic biology. We carried out hundreds of simulations in which we tuned the density of neurons, their spatial arrangement and the characteristics of the fluorescence signal. As a key result, we observed that homogeneous networks required a substantial number of neurons to fire and exhibit collective dynamics, and that the presence of aggregation significantly reduced the number of required neurons. For the second aspect, data analysis, we analyzed experiments and simulations to tackle three major aspects: network dynamics reconstruction using linear descriptions, dynamics reconstruction using nonlinear descriptors, and the assessment of neuronal connectivity from solely activity data. For the linear study, we analyzed all experiments using the power spectrum density (PSD), and observed that it was sufficiently good to describe the development of the network or its disintegration. PSD also allowed us to distinguish between healthy and unhealthy networks, and revealed dynamical heterogeneities across the network. For the nonlinear study, we used techniques in the context of recurrence plots. We first characterized the embedding dimension m and the time delay δ for each experiment, built the respective recurrence plots, and extracted key information of the dynamics of the system through different descriptors. Experimental results were contrasted with numerical simulations. After analyzing about 400 time series, we concluded that the degree of dynamical complexity in neuronal cultures changes both during development and disintegration. We also observed that the healthier the culture, the higher its dynamic complexity. Finally, for the reconstruction study, we first used numerical simulations to determine the best measure of ‘statistical interdependence’ among any two neurons, and took Generalized Transfer Entropy. We then analyzed the experimental data. We concluded that young cultures have a weak connectivity that increases along maturation. Aggregation increases average connectivity, and more interesting, also the assortativity, i.e. the tendency of highly connected nodes to connect with other highly connected node. In turn, this assortativity may delineates important aspects of the dynamics of the network. Overall, the results show that spatial arrangement and neuronal dynamics are able to shape a very rich repertoire of dynamical states of varying complexity.[cat] L’habilitat dels teixits neuronals de processar i transmetre informació de forma eficient depèn de les propietats dinàmiques intrínseques de les neurones i de la connectivitat entre elles. La present tesi proposa explorar diferents tècniques experimentals i de simulació per analitzar la dinàmica i connectivitat de xarxes neuronals corticals de rata embrionària. Experimentalment, la gravació de l’activitat espontània d’una població de neurones en cultiu, mitjançant una càmera ràpida i tècniques de fluorescència, possibilita el seguiment de forma controlada de l’activitat individual de cada neurona, així com la modificació de la seva connectivitat. En conjunt, aquestes eines permeten estudiar el comportament col.lectiu emergent de la població neuronal. Amb l’objectiu de simular els patrons observats en el laboratori, hem implementat un model mètric aleatori de creixement neuronal per simular la xarxa física de connexions entre neurones, i un model quadràtic d’integració i dispar amb adaptació i depressió per modelar l’ampli espectre de dinàmiques neuronals amb un cost computacional reduït. Hem caracteritzat la dinàmica global i individual de les neurones i l’hem correlacionat amb la seva estructura subjacent mitjançant tècniques lineals i no–lineals de series temporals. L’anàlisi espectral ens ha possibilitat la descripció del desenvolupament i els canvis en connectivitat en els cultius, així com la diferenciació entre cultius sans dels patològics. La reconstrucció de la dinàmica subjacent mitjançant mètodes d’incrustació i l’ús de gràfics de recurrència ens ha permès detectar diferents transicions dinàmiques amb el corresponent guany o pèrdua de la complexitat i riquesa dinàmica del cultiu durant els diferents estudis experimentals. Finalment, a fi de reconstruir la connectivitat interna hem testejat, mitjançant simulacions, diferents quantificadors per mesurar la dependència estadística entre neurona i neurona, seleccionant finalment el mètode de transferència d’entropia gereralitzada. Seguidament, hem procedit a caracteritzar les xarxes amb diferents paràmetres. Malgrat presentar certs tres de xarxes tipus ‘petit món’, els nostres cultius mostren una distribució de grau ‘exponencial’ o ‘esbiaixada’ per, respectivament, cultius joves i madurs. Addicionalment, hem observat que les xarxes homogènies presenten la propietat de disassortativitat, mentre que xarxes amb un creixent nivell d’agregació espaial presenten assortativitat. Aquesta propietat impacta fortament en la transmissió, resistència i sincronització de la xarxa

On the Dynamics and Structure of Multiple Strain Epidemic Models and Genotype Networks

Mathematical disease modeling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen. This paradigm has been useful in simplifying the biological reality of epidemics and has allowed the modeling community to focus on the complexity of other factors such as contact structure and interventions. However, there is an increasing amount of evidence that the strain diversity of pathogens, and their interplay with the host immune system, can play a large role in shaping the dynamics of epidemics. This body of work first explores the role of strain-transcending immunity in mathematical disease models, and how genotype networks may be used to explore the evolution of multistrain pathogens. A model is introduced to follow multistrain epidemics with an underlying genotype network. Consequently, the genotype network structure of the antigenic hemagglutinin protein of influenza A (H3N2) is analyzed, suggesting the important role of strain-transcending immunity in the evolution of the virus. The unique structure of the influenza genotype network is then explored with age-weighted preferential attachment models, utilizing approximate Bayesian computation of the network growth mechanisms. Finally, multistrain vaccination strategies are identified through the application of a genetic algorithm towards minimization of super-critical strains. Altogether, we show the impact of genotype networks on multistrain disease modeling, explore the role of empirical genotype network structure, and identify applications that include network generative models and vaccine strain selection

The social and spatial behaviour of caribou Rangifer tarandus

All animals are social at some point in their life. The causes and consequences of animal social behaviour are widely studied, but the integration of space use and spatial features of the landscape within our understanding of social behaviour is not widely studied. My thesis broadly addresses the role of spatial features of the landscape and individual-level space use traits as potential drivers of emergent social behaviour in caribou (Rangifer tarandus). First, I present a theoretical framework linking social and spatial behaviour within the context of evolutionary and behavioural ecology theory. Next, I assess the relationship between social behaviour and space use across scales, from fine-scale foraging and interactions to coarse-scale examination of how individuals and groups vary social behaviour through space and time. Overall, I found that caribou social behaviour is linked to space use and spatial behaviour in four important ways. First, I found that collective movement was an important predictor for patterns of habitat selection, where caribou tend to select foraging habitat (i.e. lichen) while alone, but to move collectively between foraging patches. Second, despite high home range overlap between caribou, and thus potential to associate, sub-groups of individuals had strong social preference for one another and formed distinct social communities. Third, based on a thirty year dataset of caribou group size, I found that group sizes varied spatially and temporally. In contrast to our expectation, groups decreased in size as a function of increasing population density, while groups tended to be larger in winter compared to summer, presumably as a result of seasonal access to foraging opportunities. Finally, I found that social network strength and habitat specialization were density-dependent, while more social individuals were habitat generalists. However, habitat specialization had a greater effect on fitness, where habitat specialists had higher fitness than habitat generalists, but only at high density. My thesis addresses questions about the relationship between social and spatial behaviour and provides a theoretical framework for future studies to address similar questions. Throughout my thesis I also argue for the integration of various diverse ecological fields, including socioecology, spatial ecology, movement ecology, and conservation biology

The influence of social networks on welfare and productivity in dairy cattle

Cattle are gregarious animals that form stable social groups based on affiliative and dominance relationships. However the husbandry practices of the modern dairy industry typically do not take social relationships into consideration, despite a growing body of evidence demonstrating important effects of social relationships on health and fitness in wild animals. Keeping cattle in large, unstable groups can lead to reduced welfare and productivity due to social stress and further research is needed to provide a beneficial social environment that can instead provide stress buffering effects. Social network analysis (SNA) is becoming an increasingly popular method to study animal social groups but until very recently has not been applied in animal welfare studies, where it can offer great advantages. This thesis uses SNA to investigate the social structure of a dynamic group of dairy cattle, and to explore the connection between social network position, and health and productivity. Social data was collected using spatial proximity loggers, allowing remote, continuous recording of associations between cattle. This approach was also used to measure relationships between young calves, investigating the effects of the early social environment. First, proximity loggers were tested and found to exhibit a significant sampling bias, which had consequences for SNA; a correction method was developed to improve their robustness. The social network structure of 110 lactating dairy cows on a commercial farm was then quantified, over four one-month periods. The network was highly centralised and social stability was low, however there were heterogeneous relationships between cows and we found evidence for assortment by traits. Social network position was linked to the health and productivity of cows; more gregarious individuals had higher milk yields and higher somatic cell counts which may represent a cost-benefit trade-off. Another study assessed the effects of pair-housing calves on weaning stress, health and production during pen rearing. Calves that were paired with a social companion showed a lower stress response to weaning than those housed individually. This effect was further reduced for calves paired earlier, suggesting that social bond strength is important for social support in cattle. The social networks of calves when grouped together showed some stability and relationships were heterogeneous, with social associations being influenced by prior familiarity. Advancing our understanding of the social requirements of dairy cattle is fundamental for their welfare, and for productivity, and is particularly important in light of recent farming intensification.Department for Environment Food & Rural AffairsUniversity of ExeterDairyC