A distributed algorithm to maintain and repair the trail networks of arboreal ants

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm's success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots

Fuzzy clustering with balance constraint

We study equality in fuzzy clustering algorithms where an equality constraint is added to the existing model. Equality is being used in various areas, such as districting (either zonal or political), industries (distribution companies). We focus on wireless sensor networks problem. Existing protocols do not pay too much attention to the cluster head selection step and equality of workload of the clusters. These two issues have significant e ect on the consumption of energy in a network where increasing lifetime of network is critical. A solution approach based on the Lagrangean relaxation is developed. The proposed algorithm is compared with the popular LEACH protocol. Results show that in the same simulated environment, our algorithm works better

Comparative Phylogeography of a Coevolved Community: Concerted Population Expansions in Joshua Trees and Four Yucca Moths

Comparative phylogeographic studies have had mixed success in identifying common phylogeographic patterns among co-distributed organisms. Whereas some have found broadly similar patterns across a diverse array of taxa, others have found that the histories of different species are more idiosyncratic than congruent. The variation in the results of comparative phylogeographic studies could indicate that the extent to which sympatrically-distributed organisms share common biogeographic histories varies depending on the strength and specificity of ecological interactions between them. To test this hypothesis, we examined demographic and phylogeographic patterns in a highly specialized, coevolved community – Joshua trees (Yucca brevifolia) and their associated yucca moths. This tightly-integrated, mutually interdependent community is known to have experienced significant range changes at the end of the last glacial period, so there is a strong a priori expectation that these organisms will show common signatures of demographic and distributional changes over time. Using a database of >5000 GPS records for Joshua trees, and multi-locus DNA sequence data from the Joshua tree and four species of yucca moth, we combined paleaodistribution modeling with coalescent-based analyses of demographic and phylgeographic history. We extensively evaluated the power of our methods to infer past population size and distributional changes by evaluating the effect of different inference procedures on our results, comparing our palaeodistribution models to Pleistocene-aged packrat midden records, and simulating DNA sequence data under a variety of alternative demographic histories. Together the results indicate that these organisms have shared a common history of population expansion, and that these expansions were broadly coincident in time. However, contrary to our expectations, none of our analyses indicated significant range or population size reductions at the end of the last glacial period, and the inferred demographic changes substantially predate Holocene climate changes

Into Complexity. A Pattern-oriented Approach to Stakeholder Communities

The NWO-programme ”the societal aspects of genomics”, has called for stronger means of collaboration and deliberative involvement between the various stakeholders of genomics research. Within the project group assembled at the University for Humanistics, this call was translated to the ‘lingua democratica’, in which the prerequisites of such deliberative efforts were put to scrutiny. The contribution of this thesis has taken a more or less abstract angle to this task, and sought to develop a vocabulary that can be shared amongst various stakeholders with different backgrounds, interests and stakes for any complex theme, although genomics has more or less been in focus throughout the research. As ‘complexity thinking’ is currently a theme in both the ‘hard’ sciences as the social sciences and the humanities, and has always been an issue for professionals, this concept was pivotal in achieving such an inclusive angle. However, in order to prevent that complexity would become fragmented due to disciplinary boundaries, it is essential that those aspects of complexity that seem to return in many discussions would be made clear, and stand out with respect to the complexities of specialisation. The thesis has argued that the concept of ‘patterns’ applies for these aspects, and they form the backbone of the vocabulary that has been developed. Especially patterns of feedback have been given much attention, as this concept is pivotal for many complex themes. However, although patterns are implicitly or explicitly used in many areas, there is little methodological (and philosophical) underpinning of what they are and why they are able to do what they do. As a result, quite some attention has been given to these issues, and how they relate to concepts such as ‘information’,‘order’ and complexity itself. From these explorations, the actual vocabulary was developed, including the methodological means to use this vocabulary. This has taken the shape of a recursive development of a so-called pattern-library, which has crossed disciplinary boundaries, from technological areas, through biology, psychology and the social sciences, to a topic that is typical of the humanities. This journey across the divide of C.P. Snow’s ‘two cultures’ is both a test for a lingua democratica, as well as aimed to demonstrate how delicate, and balanced such a path must be in order to be effective, especially if one aims to retain certain coherence along the way. Finally, the methodology has been applied in a very practical way, to a current development that hinges strongly on research in genomics, which is trans-humanist movement

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches

Early subtropical forest growth is driven by community mean trait values and functional diversity rather than the abiotic environment

While functional diversity (FD) has been shown to be positively related to a number of ecosystem functions including biomass production, it may have a much less pronounced effect than that of environmental factors or species specific properties. Leaf and wood traits can be considered particularly relevant to tree growth, as they reflect a trade-off between resources invested into growth and persistence. Our study focussed on the degree to which early forest growth was driven by FD, the environment (11 variables characterizing abiotic habitat conditions), and community-weighted mean (CWM) values of species traits in the context of a large-scale tree diversity experiment (BEF-China). Growth rates of trees with respect to crown diameter were aggregated across 231 plots (hosting between one and 23 tree species) and related to environmental variables, FD, and CWM, the latter two of which were based on 41 plant functional traits. The effects of each of the three predictor groups were analyzed separately by mixed model optimization and jointly by variance partitioning. Numerous single traits predicted plot-level tree growth, both in the models based on CWMs and FD, but none of the environmental variables was able to predict tree growth. In the best models, environment and FD explained only 4 and 31% of variation in crown growth rates, respectively, while CWM trait values explained 42%. In total, the best models accounted for 51% of crown growth. The marginal role of the selected environmental variables was unexpected, given the high topographic heterogeneity and large size of the experiment, as was the significant impact of FD, demonstrating that positive diversity effects already occur during the early stages in tree plantations