Influence of different land uses on groundwater quality in southern Portugal

In Alentejo region, southern Portugal, differences in groundwater samples from six groundwater bodies covered with different land uses were analysed based on the monitoring plan of the Alqueva multi-purpose project, created in the sequence of the construction of the Alqueva Dam on the Guadiana River, in South Portugal. For most of the groundwater bodies there is a statistical significant difference between magnesium, sulphate, chloride, and phosphate. All of these ions are strongly correlated with land use management. Groundwater, where land is covered by olive groves, has high levels of electric conductivity, calcium, potassium, sulphate, and phosphate. Dry land crops are correlated with calcium, magnesium, chloride and consequently, electric conductivity, phosphates and sulphate. Vineyards are strongly correlated with high sulphate and phosphate levels. This study clearly shows that different land uses within a certain groundwater body influence the water quality in a different way. Therefore, an appropriate soil management should be adjusted to each situation, taking into account the aquifer matrix and the overlying soil.

Occurrence and abundance models of threatened plant species: Applications to mitigate the impact of hydroelectric power dams

Species occurrence and abundance models are important tools that can be used in biodiversity conservation, and can be applied to predict or plan actions needed to mitigate the environmental impacts of hydropower dams. In this study our objectives were: (i) to model the occurrence and abundance of threatened plant species, (ii) to verify the relationship between predicted occurrence and true abundance, and (iii) to assess whether models based on abundance are more effective in predicting species occurrence than those based on presence–absence data. Individual representatives of nine species were counted within 388 randomly georeferenced plots (10 m × 50 m) around the Barra Grande hydropower dam reservoir in southern Brazil. We modelled their relationship with 15 environmental variables using both occurrence (Generalised Linear Models) and abundance data (Hurdle and Zero-Inflated models). Overall, occurrence models were more accurate than abundance models. For all species, observed abundance was significantly, although not strongly, correlated with the probability of occurrence. This correlation lost significance when zero-abundance (absence) sites were excluded from analysis, but only when this entailed a substantial drop in sample size. The same occurred when analysing relationships between abundance and probability of occurrence from previously published studies on a range of different species, suggesting that future studies could potentially use probability of occurrence as an approximate indicator of abundance when the latter is not possible to obtain. This possibility might, however, depend on life history traits of the species in question, with some traits favouring a relationship between occurrence and abundance. Reconstructing species abundance patterns from occurrence could be an important tool for conservation planning and the management of threatened species, allowing scientists to indicate the best areas for collection and reintroduction of plant germplasm or choose conservation areas most likely to maintain viable populations.