Brooks Parsimony Analysis is a cladistic biogeographic method that aims to extract biogeographic information from phylogenetic trees, depicting from a group of cladograms a general pattern of relationships among the areas the taxa inhabit. We present here BuM 2.0, an online framework to automatically create matrices for Brooks Parsimony Analysis (BPA) using Baum & Ragan’s algorithm for Matrix Representation with Parsimony.
The most common methods for combining different phylogenetic trees with uneven but overlapping taxon sampling are the Matrix Representation with Parsimony (MRP) and consensus tree methods. Although straightforward, some steps of MRP are time-consuming and risky when manually performed, especially the preparation of the matrix representations from the original topologies, and the creation of the single matrix containing all the information of the individual trees. Here we present Building MRP-Matrices (BuM), a free online tool for generating a combined matrix, following Baum and Ragan coding scheme, from files containing phylogenetic trees in parenthetical format.
Norms of negative consequences might be practiced in a society for a long time. These norms are mostly related to strong beliefs that prevent individuals from violating or abandoning them. In this case, these norms must be removed to reach a stable and benevolent society. Negative norms removal is presented in social science as a part of society evolution, yet it is not modeled in normative multi-agent systems. Norm removal in social science is done in two main stages. The first stage is called collective belief change. The second stage is called collective action. In this paper, we model these two stages into five processes which are, norm negativity realization, collective belief change, norm removal decision, choosing removal monitoring authority, and removal process. When the five stages are completed, agents make their own decision either to delete or not to delete the norm from their cognitive structure depending on their internal system status.
Summary iTUPA is a free online application for automatizing the Topographic-Unit Parsimony Analysis (TUPA), which identifies areas of endemism based on topography. iTUPA generates species-occurrences matrices based on user-defined topographic units (TUs) and provides a parsimony analysis of the generated matrix. We tested iTUPA after a proposal of regionalization for the Brazilian Atlantic Forest. iTUPA can handle millions of species registers simultaneously and uses Google Earth high-definition maps to visually explore the endemism data. We believe iTUPA is a useful tool for further discussions on biodiversity conservation. Availability and implementation iTUPA is hosted on Google cloud and freely available at http://nuvem.ufabc.edu.br/itupa. iTUPA is implemented using R (version 3.5.1), with RStudio 1.1.453 used as the implementation IDE, Shiny 1.1.0 web framework, and Google Maps® API version 3.36.
Aim: Cladistic biogeography is all about congruence: when individual area cladograms coincide, they result in a general area cladogram that reveals shared history. However, the complexities of the natural world hamper the reconstruction of fully solved biogeographical patterns. Herein, we present SAMBA (super area-cladogram after resolving multiple biogeographical ambiguities), a pattern-based method combining supertrees and area cladograms to depict the relationships among areas. We also present a prototypical implementation of SAMBA as a web-based framework named iSAMBA. Location: Global.Taxon: Any taxon can be analysed with SAMBA.Methods: SAMBA is based on phylogenetic supertrees, a technique that combines previously calculated phylogenetic trees to produce a general area cladogram representing conciliatory and non-ambiguous patterns of relationships. In our method, the input topologies are individual area cladograms. SAMBA is implemented through a web-based framework named iSAMBA. We analysed a theoretical and a real scenario to compare SAMBA with primary Brooks parsimony analysis (BPA), component analysis, three area statement analysis (TAS) and the transparent method.Results: SAMBA produces area cladograms that converge with the actual history of fragmentations of both hypothetical and real scenarios used as examples of implementation of the method. Primary BPA, component analysis, TAS and the transparent method are much more affected by the "biogeographical noise" (e.g. multiple areas in a single terminal, paralogies and missing areas) than SAMBA.Main Conclusions: SAMBA results in more informative general area cladograms than other pattern-based biogeographical methods. SAMBA reveals shared patterns of biotic distribution without generating multiple unreliable area cladograms. The main advantage of SAMBA is the simplicity of using a single technique to extract biogeographical information from individual area cladograms and combine them to depict a non-ambiguous general pattern of relationships among areas.
Social norms main objective is to regulate autonomous agents' behaviour in an open normative multi-agent system. Norms in these societies are dynamically created and disappeared according to the society's needs. Consequently, norms effects on agents or on the environment are not observable at the moment of creation. Norms practicing consequences might be either positive, like increasing the educational level of a society by conducting social discussions. Or negative, like causing money loss in gambling. Or the norm might have neutral consequences. In this paper, we propose a technique to detect negative norms in an open normative multi-agent system. Our technique has two main stages: i) Observation and ii) Analysis. The observation stage relies on the overhearing approach of monitoring where the messages that are exchanged between agents are observable. All observations are then analysed in order to detect negative norms. Negativity of a norm is based on its effect on agents or on the environment. In this technique, we adopted ATN concept to represent norms. This technique is implemented using Java and JADE. Testing results of this technique shows that it works properly, and detects negative norms according to the defined negativity threshold.
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