Colony dehydration and water collection by specialized caste in the leaf-cutting ant Atta sexdens rubropilosa

Social organization enables leaf-cutting ants to keep appropriate micro-ecological nest conditions for the fungus garden (their main food), eggs, larvae and adults. To maintain stability while facing changing conditions, individual ants must perceive destabilising factors and produce a proper behavioral response. We investigated behavioral responses to experimental dehydration in leaf-cutting ants to verify if task specialization exists, and to quantify the ability of ant sub-colonies for water management. Our setup consisted of fourteen sub-colonies, ten of which were randomly assigned to different levels of experimental dehydration with silica gel, whereas the remaining four were controls. The ten experimental sub-colonies were split into two groups, so that five of them had access to water. Diverse ant morphs searched for water in dehydrated colonies, but mainly a caste of small ants collected water after sources had been discovered. Size specialization for water collection was replicable in shorter experiments with three additional colonies. Ants of dehydrated colonies accumulated leaf-fragments on the nest entrance, and covering the fungus garden. Behaviors that may enhance humidity within the nests were common to all dehydration treatments. Water availability increased the life span of dehydrated colonies.

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved.