How geographic distance and depth drive ecological variability and isolation of demersal fish communities in an archipelago system (Cape Verde, Eastern Atlantic Ocean)

Cape Verde is a tropical oceanic ecosystem, highly fragmented and dispersed, with islands physically isolated by distance and depth. To understand how isolation affects the ecological variability in this archipelago, we conducted a research project on the community structure of the 18 commercially most important demersal fishes. An index of ecological distance based on species relative dominance (Di) is developed from Catch Per Unit Effort, derived from an extensive database of artisanal fisheries. Two ecological measures of distance between islands are calculated: at the species level, DDi, and at the community level, DD (sum of DDi). A physical isolation factor (Idb) combining distance (d) and bathymetry (b) is proposed. Covariance analysis shows that isolation factor is positively correlated with both DDi and DD, suggesting that Idb can be considered as an ecological isolation factor. The effect of Idb varies with season and species. This effect is stronger in summer (May to November), than in winter (December to April), which appears to be more unstable. Species react differently to Idb, independently of season. A principal component analysis on the monthly (DDi) for the 12 islands and the 18 species, complemented by an agglomerative hierarchical clustering, shows a geographic pattern of island organization, according to Idb. Results indicate that the ecological structure of demersal fish communities of Cape Verde archipelago, both in time and space, can be explained by a geographic isolation factor. The analytical approach used here is promising and could be tested in other archipelago systems.

Una Red de Sensores para las Smart Cities

El objetivo del trabajo fin de Master consiste en el estudio de redes de sensores inalámbricos para las Smart Cities. En particular, se implementa una red inalámbrica (tecnología ZIGBEE como protocolo de comunicación inalámbrica con micrófonos como sensores, para recolectar niveles de ruido. Hemos elegido el Arduino como plataforma de cálculo para controlar cada nodo, y XBee como módulos de comunicación inalámbrico. La red sensorial cuenta con tres nodos sensores que capturan el ruido y lo envían a un nodo receptor, que hace de estación central encargado de monitorizar los niveles de ruido. Dicho coordinador está conectado mediante USB a un PC, donde podemos comprobar las medidas recibidas por el XBee. Finalmente estos datos se muestran en una interfaz gráfica en el computador conectado al coordinador. De esta forma, puede monitorizarse el nivel de ruido de tres lugares diferentes en tiempo real.