The 'rotiferologist' effect and other global correlates of species richness in monogonont rotifers

Global biodiversity patterns are often driven by diff erent environmental variables at diff erent scales. However, it is still controversial whether there are general trends, whether similar processes are responsible for similar patterns, and/or whether confounding eff ects such as sampling bias can produce misleading results. Our aim is twofold: 1) assessing the global correlates of diversity in a group of microscopic animals little analysed so far, and 2) inferring the infl uence of sampling intensity on biodiversity analyses. As a case study, we choose rotifers, because of their high potential for dispersal across the globe. We assembled and analysed a new worldwide dataset of records of monogonont rotifers, a group of microscopic aquatic animals, from 1960 to 1992. Using spatially explicit models, we assessed whether the diversity patterns conformed to those commonly obtained for larger organisms, and whether they still held true after controlling for sampling intensity, variations in area, and spatial structure in the data. Our results are in part analogous to those commonly obtained for macroorganisms (habitat heterogeneity and precipitation emerge as the main global correlates), but show some divergence (potential absence of a latitudinal gradient and of a large-scale correlation with human population). Moreover, the eff ect of sampling eff ort is remarkable, accounting for 50% of the variability; this strong eff ect may mask other patterns such as latitudinal gradients. Our study points out that sampling bias should be carefully considered when drawing conclusions from large-scale analyses, and calls for further faunistic work on microorganisms in all regions of the world to better understand the generality of the processes driving global patterns in biodiversity.

Classi cation of new electricity customers based on surveys and smart metering data

This paper proposes a process for the classifi cation of new residential electricity customers. The current state of the art is extended by using a combination of smart metering and survey data and by using model-based feature selection for the classifi cation task. Firstly, the normalized representative consumption profi les of the population are derived through the clustering of data from households. Secondly, new customers are classifi ed using survey data and a limited amount of smart metering data. Thirdly, regression analysis and model-based feature selection results explain the importance of the variables and which are the drivers of diff erent consumption profi les, enabling the extraction of appropriate models. The results of a case study show that the use of survey data signi ficantly increases accuracy of the classifi cation task (up to 20%). Considering four consumption groups, more than half of the customers are correctly classifi ed with only one week of metering data, with more weeks the accuracy is signifi cantly improved. The use of model-based feature selection resulted in the use of a signifi cantly lower number of features allowing an easy interpretation of the derived models.