Temporal variation in saproxylic beetle assemblages in a Mediterranean ecosystem

One of the main challenges in biological conservation has been to understand species distribution across space and time. Over the last decades, many diversity and conservation surveys have been conducted that have revealed that habitat heterogeneity acts as a major factor that determines saproxylic assemblages. However, temporal dynamics have been poorly studied, especially in Mediterranean forests. We analyzed saproxylic beetle distribution at inter and intra-annual scales in a “dehesa” ecosystem, which is a traditional Iberian agrosilvopastoral ecosystem that is characterized by the presence of old and scattered trees that dominate the landscape. Significant differences in effective numbers of families/species and species richness were found at the inter-annual scale, but this was not the case for composition. Temperature and relative humidity did not explain these changes which were mainly due to the presence of rare species. At the intra-annual scale, significant differences in the effective numbers of families/species, species richness and composition between seasons were found, and diversity partitioning revealed that season contributed significantly to gamma-diversity. Saproxylic beetle assemblages exhibited a marked seasonality in richness but not in abundance, with two peaks of activity, the highest between May and June, and the second between September and October. This pattern is mainly driven by the seasonality of the climate in the Mediterranean region, which influences ecosystem dynamics and imposes a marked seasonality on insect assemblages. An extended sampling period over different seasons allowed an overview of saproxylic dynamics, and revealed which families/species were restricted to particular seasons. Recognizing that seasons act as a driver in modelling saproxylic beetle assemblages might be a valuable tool in monitoring and for conservation strategies in Mediterranean forests.

A Computational Model of the Belief System Under the Scope of Social Communication

This paper presents an approach to the belief system based on a computational framework in three levels: first, the logic level with the definition of binary local rules, second, the arithmetic level with the definition of recursive functions and finally the behavioural level with the definition of a recursive construction pattern. Social communication is achieved when different beliefs are expressed, modified, propagated and shared through social nets. This approach is useful to mimic the belief system because the defined functions provide different ways to process the same incoming information as well as a means to propagate it. Our model also provides a means to cross different beliefs so, any incoming information can be processed many times by the same or different functions as it occurs is social nets.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.