Els Ducheyne

Nationwide inventory of mosquito biodiversity (Diptera: Culicidae) in Belgium, Europe

To advance our restricted knowledge on mosquito biodiversity and distribution in Belgium, a national inventory started in 2007 (MODIRISK) based on a random selection of 936 collection points in three main environmental types: urban, rural and natural areas. Additionally, 64 sites were selected because of the risk of importing a vector or pathogen in these sites. Each site was sampled once between May and October 2007 and once in 2008 using Mosquito Magnet Liberty Plus traps. Diversity in pre-defined habitat types was calculated using three indices. The association between species and environmental types was assessed using a correspondence analysis. Twenty-three mosquito species belonging to traditionally recognized genera were found, including 21 indigenous and two exotic species. Highest species diversity (Simpson 0.765) and species richness (20 species) was observed in natural areas, although urban sites scored also well (Simpson 0.476, 16 species). Four clusters could be distinguished based on the correspondence analysis. The first one is related to human modified landscapes (such as urban, rural and industrial sites). A second is composed of species not associated with a specific habitat type, including the now widely distributed Anopheles plumbeus. A third group includes species commonly found in restored natural or bird migration areas, and a fourth cluster is composed of forest species. Outcomes of this study demonstrate the effectiveness of the designed sampling scheme and support the choice of the trap type. Obtained results of this first country-wide inventory of the Culicidae in Belgium may serve as a basis for risk assessment of emerging mosquito-borne diseases.

Absence reduction in entomological surveillance data to improve niche-based distribution models for Culicoides imicola.

Data-driven models for the prediction of bluetongue vector distributions are valuable tools for the identification of areas at risk for bluetongue outbreaks. Various models have been developed during the last decade, and the majority of them use linear discriminant analysis or logistic regression to infer vector-environment relationships. This study presents a performance assessment of two established models compared to a distribution model based on a promising ensemble learning technique called Random Forests. Additionally, the impact of false absences, i.e. data records of suitable vector habitat that are, for various reasons, incorrectly labelled as absent, on the model outcome was assessed using alternative calibration-validation schemes. Three reduction methods were applied to reduce the number of false absences in the calibration data, without loss of information on the environmental gradient of suitable vector habitat: random reduction and stratified reduction based on the distance between absence and presence records in geographical (Euclidean distance) or environmental space (Mahalanobis distance). The results indicated that the predicted vector distribution by the Random Forest model was significantly more accurate than the vector distributions predicted by the two established models (McNemar test, p<0.01) when the calibration data were not reduced with respect to false absences. The performance of the established models, however, increased considerably by application of stratified false absence reductions. Model validation revealed no significant difference between the performance of the three distinct Culicoides imicola distribution models for the majority of alternative stratified reduction schemes. The main conclusion of this study is that the application of Random Forests, or linear discriminant analysis and logistic regression on the condition that calibration data were first reduced on geographical or environmental information, potentially lead toward better vector distribution models.

Fitness inheritance in multiple objective evolutionary algorithms: A test bench and real-world evaluation

In many real-world applications of evolutionary algorithms, the fitness of an individual has to be derived using complex models and time-consuming computations. Especially in the case of multiple objective optimisation problems, the time needed to evaluate these individuals increases exponentially with the number of objectives due to the curse of dimensionality [J. Chen, D.E. Goldberg, S. Ho, K. Sastry, Fitness inheritance in multi-objective optimization, in: W.B. Langdon et al. (Eds.), GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9-13, Morgan Kaufmann Publishers, New York, 2002, pp. 319-326]. This in turn leads to a slower convergence of the evolutionary algorithms. It is not feasible to use time-consuming models with large population sizes unless the time to evaluate the objective functions is reduced. Fitness inheritance is an efficiency enhancement technique that was originally proposed by Smith et al. [R.E. Smith, B.A. Dike, S.A. Stegmann, Fitness inheritance in genetic algorithms, in: Proceedings of the 1995 ACM Symposium on Applied Computing, February 26-28, ACM, Nashville, TN, USA, 1995] to improve the performance of genetic algorithms. Sastry et al. [K. Sastry, D.E. Goldberg, M. Pelikan, Dont evaluate, inherit, in: L. Spector et al. (Eds.), GECCO 2001: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers, San Francisco, 2001, pp. 551-558] and Chen et al. [J. Chen, D.E. Goldberg, S. Ho, K. Sastry, Fitness inheritance in multi-objective optimization, in: W.B. Langdon et al. (Eds.), GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, July 9-13, Morgan Kaufmann Publishers, New York, 2002, pp. 319-326] have developed analytical models for fitness inheritance. In this paper, the usefulness of fitness inheritance for a set of popular and separable multiple objective test functions as well as a non-separable real-world problem is evaluated based on unary performance measures testing closeness to the Pareto-optimal front, uniform distribution along and extent of the obtained Pareto front. A statistical evaluation of the performance of an NSGA-II like algorithm on the basis of these unary performance measures suggests that especially for non-convex or non-continuous problems the use of fitness inheritance negatively affects the closeness to the Pareto-optimal front.