6 research outputs found
Is coexistence between non-native and native Erythrinidae species mediated by niche differentiation or environmental filtering? A case study in the upper Paraná River floodplain
ABSTRACT The limiting similarity theory predicts that divergence in the functional traits of native and introduced species is an essential component in species establishment, as introduced species must occupy a niche that is unoccupied by resident species. On the other hand, the environmental filtering hypothesis predicts convergence between introduced and native species, as both possess traits that make them adapted to the local abiotic environment. Morphology, spatial co-occurrence, diet, feeding selectivity, and niche breadth and overlap of Erythrinidae were evaluated to detect possible mechanisms acting in the coexistence between non-native and native species. Native (Hoplias sp. B and Hoplias cf. malabaricus) and non-native (Hoplerythrinus unitaeniatus and Hoplias mbigua) species presented differences in morphological traits, spatial co-occurrence, diet, selectivity, and niche breadth and overlap. The mechanisms mediating species coexistence seem to vary according to species. The absence of spatial and feeding overlap suggests that non-native species H. unitaeniatus occupy a different niche than native species, supporting its successful establishment without eliminating the native species. However, low feeding overlap and similar morphologies between non-native and native species of Hoplias point to environmental filters; in this case, the non-native H. mbigua is able to establish due to similarities in functional traits
A Novel Architecture for Situation Awareness Systems
Abstract. Situation Awareness (SA) is the problem of comprehending elements of an environment within a volume of time and space. It is a crucial factor in decision-making in dynamic environments. Current SA systems support the collection, filtering and presentation of data from different sources very well, and typically also some form of low-level data fusion and analysis, e.g., recognizing patterns over time. However, a still open research challenge is to build systems that support higher-level information fusion, viz., to integrate domain specific knowledge and automatically draw conclusions that would otherwise remain hidden or would have to be drawn by a human operator. To address this challenge, we have developed a novel system architecture that emphasizes the rôle of formal logic and automated theorem provers in its main components. Additionally, it features controlled natural language for operator I/O. It offers three logical languages to adequately model different aspects of the domain. This allows to build SA systems in a more declarative way than is possible with current approaches. From an automated reasoning perspective, the main challenges lay in combining (existing) automated reasoning techniques, from low-level data fusion of time-stamped data to semantic analysis and alert generation that is based on linear temporal logic. The system has been implemented and interfaces with Google-Earth to visualize the dynamics of situations and system output. It has been successfully tested on realistic data, but in this paper we focus on the system architecture and in particular on the interplay of the different reasoning components.