Ravines in Angola. Causes and restraint methods

The Earth we know today was not always so. Over millions of years have undergone significant ch an g e s brought about by numerous geological phenomena aimed at your balance, some internal order, creating new geological formations and other external order smoothing formations previously created. From t h e tectonic standpoint, Angola is located in a relatively stable area which gives it a certain p ri v i l e g e w h e n compared with some Asian countries or even Americans where quite often occur earthquakes and volcanic eruptions. However, the same cannot be said in relation to the occurrence of an external geodynamics phenomena, such as the ravines, which in recent years has taken shape in many provinces, especially due to anthropogenic activity, giving rise to geological hazards, increasing the risk of damage in buildings and others infrastructures, losses direct or indirect in economic activities and loss of human lives. We understand that the reducing of these risks starts, in particular, by their identification, for later take preventive measures. This work is the result of some research work carried out by the authors through erosion courses of s o i l and stabilization of soils subject to erosion phenomena, carried out by Engineering Laboratory of Angola (LEA). For the realization of this work, we resorted to cartographic data query, literature, listening to s o m e o f the provincial representatives and local residents, as well as the observation in lo co o f s o m e af f e ct ed areas. The results allow us to infer that the main provinces affected by ravine phenomenon are located in Central and Northern highlands, as well as in the eastern region, and more recently in Cuando-Cub an go province. Not ruling out, however, other regions, such as in Luanda and Cabinda [1]. Relatively the causes, we can say that the ravines in Angola are primarily due to the combination of three natural factors: climate, topography and type of soil [2]. When we add the anthropogenic activit y , namely the execution of construction works, the drainage system obstructio n, exploration of m i n e ral s, agriculture and fires, it is verified an increasing of the phenomenon, often requiring immedi at e act i o n . These interventions can be done through structural or engineering measures and by the stabilization measures on the degraded soil cover [3]. We present an example of stabilization measures throu g h t h e deployment of a local vegetation called Pennisetum purpureum. It is expected that the results may contribute to a better understanding of the causes of the ravine phenomenon in Angola and that the adopted stabilization method can be adapted in other affected provinces in order to prevent and making the contention of the ravines.

How well can models predict changes in species distributions? A 13-year-old otter model revisited

Species distribution and ecological niche models are increasingly used in biodiversity management and conservation. However, one thing that is important but rarely done is to follow up on the predictive performance of these models over time, to check if their predictions are fulfilled and maintain accuracy, or if they apply only to the set in which they were produced. In 2003, a distribution model of the Eurasian otter (Lutra lutra) in Spain was published, based on the results of a country-wide otter survey published in 1998. This model was built with logistic regression of otter presence-absence in UTM 10 km2 cells on a diverse set of environmental, human and spatial variables, selected according to statistical criteria. Here we evaluate this model against the results of the most recent otter survey, carried out a decade later and after a significant expansion of the otter distribution area in this country. Despite the time elapsed and the evident changes in this species’ distribution, the model maintained a good predictive capacity, considering both discrimination and calibration measures. Otter distribution did not expand randomly or simply towards vicinity areas,m but specifically towards the areas predicted as most favourable by the model based on data from 10 years before. This corroborates the utility of predictive distribution models, at least in the medium term and when they are made with robust methods and relevant predictor variables.