Grassland-herbivore interactions: How do grazers coexist?

We develop a new approach to modeling grazing systems that links foraging characteristics (intake and digestive constraints) with resource dynamics via the probability of encounter with different grass heights. Three complementary models are presented: the generation of a grass height structure through selective grazing; investigating the conditions for consumer coexistence; and, using a simplified resource structure, the consequences for consumer abundance. The main finding is that coexistence between grazers differing in body size is possible if a single-resource type becomes differentiated in its height structure. Large grazers can facilitate food availability for smaller species but with the latter being competitively dominant. The relative preference given to different resource partitions is important in determining the nature of population interactions. Large-body and small-body grazer populations can interact through competitive, parasitic, commensalist, or amensalist relationships, depending on the way they partition the resource as well as their relative populations and the dynamics of resource renewal. The models provide new concepts of multispecies carrying capacity (stock equilibrium) in grazed systems with implications for conservation and management. We conclude that consumer species are not substitutable; therefore, the use of rangeland management concepts such as "livestock units" may be inappropriate.

Genealogies of Partition: History, History-Writing and 'The Troubles' in Ireland

Contemporary political disputes have a long history of expression and contestation through the genre of history-writing in Ireland. The role of history writing and political science writing during the nearly 40 years of the so-called 'Troubles' has been no exception to this. Battles between competing versions of what the conflict 'is about', mediated through academic and popular texts have themselves in turn become constitutive of it. This builds upon centuries of the representation of the complicated politics of this island as 'an issue' in British domestic politics - first 'the Catholic question', then 'the Irish question'. The location of political power outside the island for centuries has created successive battles for the representation of sectional interests in a metropolitan centre. The skills of propaganda, history writing, newspaper writing have consequently been deployed at a remarkable level of skill and intensity. In the recent period one of the consequences of this has been the removal from the debate of the actuality of partition; this builds upon a particular historical representation of partition as an historical inevitability. To seek to restore partition to the debate is not to call for its undoing but to recognise that seeking to circumvent debates about its origins in the key period of democratisation in Irish politics (1880-1920) has been counter-productive. This essay examines the genealogies of partition in Irish and international contexts in the light of these battles for representation, and aims to return a lost dimension to the debate about the so-called 'Troubles'in Ireland. The genealogy of partition is the issue that has been marginalised in academic study and this has affected both policy and politics.

Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.