Integration of numerical models in geographic databases: the case of Sado estuary management

Geographic information systems (GIS) are now widely applied in coastal resource management. Their ability to organise and interface information from a large range of public and private data sources, and their ability to combine this information, using management criteria, to develop a comprehensive picture of the system explains the success of GIS in this area. The use of numerical models as a tool to improve coastal management is also widespread. Less usual is a GIS-based management to ol implementing a comprehensive management model and integrating a numerical modelling system into itself. In this paper such a methodology is proposed. A GIS-based management tool based on the DPSIR model is presented. An overview of the MOHID numerical modelling system is given and the method of integrating this model in the management tool is described. This system is applied to the Sado Estuary (Portugal). Some preliminary results of the integration are presented, demonstrating the capabilities of the management system.

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudoabsence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study showsthat ifwe do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.