Supercolonial structure of invasive populations of the tawny crazy ant Nylanderia fulva in the US

Background: Social insects are among the most serious invasive pests in the world, particularly successful at monopolizing environmental resources to outcompete native species and achieve ecological dominance. The invasive success of some social insects is enhanced by their unicolonial structure, under which the presence of numerous queens and the lack of aggression against non-nestmates allow high worker densities, colony growth, and survival while eliminating intra-specific competition. In this study, we investigated the population genetics, colony structure and levels of aggression in the tawny crazy ant, Nylanderia fulva, which was recently introduced into the United States from South America. Results: We found that this species experienced a genetic bottleneck during its invasion lowering its genetic diversity by 60%. Our results show that the introduction of N. fulva is associated with a shift in colony structure. This species exhibits a multicolonial organization in its native range, with colonies clearly separated from one another, whereas it displays a unicolonial system with no clear boundaries among nests in its invasive range. We uncovered an absence of genetic differentiation among populations across the entire invasive range, and a lack of aggressive behaviors towards conspecifics from different nests, even ones separated by several hundreds of kilometers. Conclusions: Overall, these results suggest that across its entire invasive range in the U.S.A., this species forms a single supercolony spreading more than 2000 km. In each invasive nest, we found several, up to hundreds, of reproductive queens, each being mated with a single male. The many reproductive queens per nests, together with the free movement of individuals between nests, leads to a relatedness coefficient among nestmate workers close to zero in introduced populations, calling into question the stability of this unicolonial system in which indirect fitness benefits to workers is apparently absent.Fil: Eyer, Pierre André. Texas A&M University; Estados UnidosFil: McDowell, Bryant. Texas A&M University; Estados UnidosFil: Johnson, Laura N. L.. Texas A&M University; Estados UnidosFil: Calcaterra, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para el Estudio de Especies Invasivas; ArgentinaFil: Fernández, María Belén. Fundación para el Estudio de Especies Invasivas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Shoemaker, Dewayne. University of Tennessee; Estados UnidosFil: Puckett, Robert T.. Texas A&M University; Estados UnidosFil: Vargo, Edward L.. Texas A&M University; Estados Unido

Spatial clustering of twig-nesting ants corresponds with metacommunity assembly processes

El concepto de metacomunidad y sus modelos asociados están pobremente integrados en el campo de la ecología de paisaje. Una manera de promover una síntesis es identificar situaciones en donde modelos específicos de metacomunidad correspondan a patrones específicos y explícitos en la distribución de comunidades a través del espacio. Exploramos esta posible relación usando un mapeo de las comunidades de hormigas que anidan en cafetos en un agroecosistema de café en el sur de México. Trabajos previos han demostrado que el modelo de ordenamiento de especies predomina para especies comunes y el de efecto de masa para especies raras. Estudiamos si los patrones diferenciales de la agrupación espacial entre las especies dominantes y subdominantes corresponden a un modelo de ordenamiento de especies y de efecto de masa, respectivamente. Encontramos una agrupación significativa entre las especies subdominantes en dos de los seis sitios y no agrupación entre los dominantes. A nivel de especie, observamos una agrupación significativa en 23% de los casos. Estos resultados sustentan parcialmente nuestra hipótesis y pueden ser explicado mecánicamente por la hipótesis intersticial; por lo cual, las especies subdominantes persisten en ‘aberturas’ entre las especies dominantes. Al examinar a nuestro nivel de escala espacial, no encontramos sustento para la hipótesis de mosaico en hormigas. Nuestros resultados sugieren que más estudios vinculando a modelos de metacomunidad con patrones espaciales específicos y explícitos pueden aportar conocimientos sobre patrones y procesos relacionados en paisajes.The metacommunity concept and associated models are poorly integrated with the field of landscape ecology. One way to promote synthesis is to identify situations in which specific metacommunity models correspond to specific and explicit spatial patterns in the distribution of communities across space. We explore this possible link using mapped communities of twig-nesting ants on coffee plants from a plantation in southern Mexico. Previous work has shown species sorting to predominate among common species and mass effects among rare species. We test whether differential patterns of spatial clustering among dominant and subdominant ant species correspond to a species sorting and mass effects model, respectively. We find significant clustering among subdominant species in two of six sites and no clustering among dominants. At the species level, significant clustering was observed in 23% of cases. These results partially support our hypothesis and may be explained mechanistically by the interstitial hypothesis, whereby rare species persist in “gaps” among dominants. At the spatial scales we examined, we found no support for the ant-mosaic. Our results suggest further study linking metacommunity models to specific and explicit spatial patterns may yield insights on pattern and process relationships in landscapes

Temporal analysis of honey bee interaction networks based on spatial proximity

The BeesBook system provides high-resolution data about bee movements within a single colony by automatically tracking individual honey bees inside a hive over their entire life. This thesis focuses on the process of designing and implementing a network pipeline to extract interaction networks from this data. Spatial proximity is used as an indicator for interactions between bees. Social network analysis methods were applied to investigate the static and dynamic properties of the resulting social networks of honey bees on a global, intermediate and local level. The resulting networks were characterized by a low hierarchical structure and a high density. The global structure of the colony seems to be stable over time. The local structure is highly dynamic, as bees change communities as they age. Communities in the honey bee network are formed by age groups that show a high spatial fidelity. The findings are in line with the established state of research that colonies are organized around age-based task division. The results of the analysis validate the implemented pipeline and the inferred networks. Consequently, this work provides an excellent foundation for future research focusing on temporal network analysis

Finding and tracking multi-density clusters in an online dynamic data stream

The file attached to this record is the author's final peer reviewed version.Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise

An Order-based Algorithm for Minimum Dominating Set with Application in Graph Mining

Dominating set is a set of vertices of a graph such that all other vertices have a neighbour in the dominating set. We propose a new order-based randomised local search (RLS$_o$) algorithm to solve minimum dominating set problem in large graphs. Experimental evaluation is presented for multiple types of problem instances. These instances include unit disk graphs, which represent a model of wireless networks, random scale-free networks, as well as samples from two social networks and real-world graphs studied in network science. Our experiments indicate that RLS$_o$ performs better than both a classical greedy approximation algorithm and two metaheuristic algorithms based on ant colony optimisation and local search. The order-based algorithm is able to find small dominating sets for graphs with tens of thousands of vertices. In addition, we propose a multi-start variant of RLS$_o$ that is suitable for solving the minimum weight dominating set problem. The application of RLS$_o$ in graph mining is also briefly demonstrated

From metacommunity dynamics to rapid biodiversity assessment: DNA-based approaches expand horizons in both fundamental and applied ecology

Molecular tools have long been recognised as having enormous potential to expand horizons in ecology, but the promise remains substantially unfulfilled. In this thesis, I apply genetic approaches to two ecological problems that have proved difficult to solve using traditional techniques. Chapters 1 and 2 apply molecular tools to a community ecology problem to ask what mechanisms govern the persistence of an ant-plant metacommunity. I first use molecular data to clarify the number of coexisting ant species, and then employ population genetic techniques to investigate dispersal scale and other elements of life-history in the three most common species. Where hostplant density is high, a clear dispersal hierarchy is detected, which correlates positively with ant body size and negatively with fecundity, consistent with the hypothesis of a dispersal-fecundity trade-off. The hierarchy is less clear when hostplant density is low because one species shows dispersal plasticity, dispersing longer distances when hostplants are scarce. Results are discussed in the context of mechanisms that allow the coexistence of multiple symbionts with a single plant host. Chapters 3 to 8 address the use of molecular tools for informing decision-making in environmental management and biodiversity conservation. COI metabarcoding data are used to analyse patterns of arthropod diversity in the contexts of sustainable forest management (Chapter 5), agricultural management (Chapter 6), and habitat restoration (Chapter 7). It is shown that this potentially revolutionary technique can detect even fine-scale environmental changes, accurately characterise the biodiversity response to management variables, and be used to test the usefulness of convenient indicator variables. COI data is shown to outperform 18S data in recovering alpha and beta diversity information, and reference-based OTU-picking is demonstrated to be a useful approach where there is interest in the responses of a particular set of species. Potential applications and current limitations are discussed in Chapter 8

A Social Network Image Classification Algorithm Based on Multimodal Deep Learning

The complex data structure and massive image data of social networks pose a huge challenge to the mining of associations between social information. For accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning. Firstly, a social network association clustering model (SNACM) was established, and used to calculate trust and similarity, which represent the degree of similarity between users. Based on artificial ant colony algorithm, the SNACM was subject to weighted stacking, and the social network image association network was constructed. After that, the social network images of three modes, i.e. RGB (red-green-blue) image, grayscale image, and depth image, were fused. Finally, a three-dimensional neural network (3D NN) was constructed to extract the features of the multimodal social network image. The proposed algorithm was proved valid and accurate through experiments. The research results provide a reference for applying multimodal deep learning to classify the images in other fields