Abstract
In the last decade, we have experienced a huge loss of biodiversity on the planet. One of the possible ways to overcome this loss is to focus on places of human interest, monitoring the changes and the impacts on biodiversity. In general, impact assessments of hydroelectric power-plants on terrestrial invertebrates have been conducted after installation of the dam. Monitoring biodiversity is expensive, and shortcuts are often used to access this information. Therefore the use of the higher-taxon approach seems appropriate for this purpose. We investigated the effectiveness of ant genus-level at predicting ant species-level responses overtime in two grains of the spatial scale (site and plot) in the area of influence of a hydroelectric power-plant in the Amazon basin. We used a database from six sites, sampled over seven years in 14 ant collection events. The samplings were standardized, with five collection points along with 250 m plots. We tested whether the variation in the spatio-temporal scales affected the predictive power of genus-level for the metrics of richness and composition. We investigated if there is a difference in the predictive response of genera as surrogates for the species when using different spatial scales (site and plot) and also if the effectiveness of the prediction of genera changes over time. Also, we evaluated the effects of the species: genus ratio on the prediction of genera as a surrogate for species richness and composition.We recorded a high positive correlation between the richness of genera and species. Also, detected high congruence among the composition of genera and species, and no species:genus ratio effect in the evaluated spatio-temporal scales. Thus, the robustness of genus data to predict the species-level does not change over time at both spatial scales under the influence area of a hydroelectric power-plant in the Amazon basin. It reinforces the use of the higher-taxon approach, which can be a useful and efficient tool in biodiversity studies or long-term monitoring.
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