An empirically based simulation of group foraging in the harvesting ant, Messor pergandei.

We present an empirically based group model of foraging interactions in Messor pergandei, the Sonoran desert harvesting ant. M. pergandei colonies send out daily foraging columns consisting of tens of thousands of individual ants. Each day, the directions of the columns may change depending on the resource availability and the neighbor interactions. If neighboring columns meet, ants fight, and subsequent foraging is suppressed. M. pergandei colonies face a general problem which is present in many systems: dynamic spatial partitioning in a constantly changing environment, while simultaneously minimizing negative competitive interactions with multiple neighbors. Our simulation model of a population of column foragers is spatially explicit and includes neighbor interactions. We study how different behavioral strategies influence resource exploitation and space use for different nest distributions and densities. Column foraging in M. pergandei is adapted to the spatial and temporal properties of their natural habitat. Resource and space use is maximized both at the colony and the population level by a model with a behavioral strategy including learning and fast forgetting rates.

Tuning positive feedback for signal detection in noisy dynamic environments.

Learning from previous actions is a key feature of decision-making. Diverse biological systems, from neuronal assemblies to insect societies, use a combination of positive feedback and forgetting of stored memories to process and respond to input signals. Here we look how these systems deal with a dynamic two-armed bandit problem of detecting a very weak signal in the presence of a high degree of noise. We show that by tuning the form of positive feedback and the decay rate to appropriate values, a single tracking variable can effectively detect dynamic inputs even in the presence of a large degree of noise. In particular, we show that when tuned appropriately a simple positive feedback algorithm is Fisher efficient, in that it can track changes in a signal on a time of order L(h)=(|h|/σ)(-2), where |h| is the magnitude of the signal and σ the magnitude of the noise.

A mathematical model of foraging in a dynamic environment by trail-laying Argentine ants.

Ants live in dynamically changing environments, where food sources become depleted and alternative sources appear. Yet most mathematical models of ant foraging assume that the ants' foraging environment is static. Here we describe a mathematical model of ant foraging in a dynamic environment. Our model attempts to explain recent empirical data on dynamic foraging in the Argentine ant Linepithema humile (Mayr). The ants are able to find the shortest path in a Towers of Hanoi maze, a complex network containing 32,768 alternative paths, even when the maze is altered dynamically. We modify existing models developed to explain ant foraging in static environments, to elucidate what possible mechanisms allow the ants to quickly adapt to changes in their foraging environment. Our results suggest that navigation of individual ants based on a combination of one pheromone deposited during foraging and directional information enables the ants to adapt their foraging trails and recreates the experimental results.

Structure and formation of ant transportation networks.

Many biological systems use extensive networks for the transport of resources and information. Ants are no exception. How do biological systems achieve efficient transportation networks in the absence of centralized control and without global knowledge of the environment? Here, we address this question by studying the formation and properties of inter-nest transportation networks in the Argentine ant (Linepithema humile). We find that the formation of inter-nest networks depends on the number of ants involved in the construction process. When the number of ants is sufficient and networks do form, they tend to have short total length but a low level of robustness. These networks are topologically similar to either minimum spanning trees or Steiner networks. The process of network formation involves an initial construction of multiple links followed by a pruning process that reduces the number of trails. Our study thus illuminates the conditions under and the process by which minimal biological transport networks can be constructed.

Evolution of mitochondrial gene orders in echinoderms.

A comprehensive analysis of the mitochondrial gene orders of all previously published and two novel Antedon mediterranea (Crinoidea) and Ophiura albida (Ophiuroidea) complete echinoderm mitochondrial genomes shows that all major types of rearrangement operations are necessary to explain the evolution of mitochondrial genomes. In addition to protein coding genes we include all tRNA genes as well as the control region in our analysis. Surprisingly, 7 of the 16 genomes published in the GenBank database contain misannotations, mostly unannotated tRNAs and/or mistakes in the orientation of tRNAs, which we have corrected here. Although the gene orders of mt genomes appear very different, only 8 events are necessary to explain the evolutionary history of echinoderms with the exception of the ophiuroids. Only two of these rearrangements are inversions, while we identify three tandem-duplication-random-loss events and three transpositions.

CREx: inferring genomic rearrangements based on common intervals.

SUMMARY: We present the web-based program CREx for heuristically determining pairwise rearrangement events in unichromosomal genomes. CREx considers transpositions, reverse transpositions, reversals and tandem-duplication-random-loss (TDRL) events. It supports the user in finding parsimonious rearrangement scenarios given a phylogenetic hypothesis. CREx is based on common intervals, which reflect genes that appear consecutively in several of the input gene orders. AVAILABILITY: CREx is freely available at http://pacosy.informatik.uni-leipzig.de/crex