Publications

Peters, J.; Waegeman, W.; Van Doninck, J.; Ducheyne, E.; Calvete, C.; Lucientes, J.; Verhoest, N. E. C.; De Baets, B. (2014). Predicting spatio-temporal Culicoides imicola distributions in Spain based on environmental habitat characteristics and species dispersal. ECOLOGICAL INFORMATICS, 22, 69-80.

Abstract
The use of distance variables expressing the likelihood of species occurrence at a given site in relation to the distance to observed species presence is demonstrated to improve species distribution models, especially when combined with environmental variables which relate species occurrence to the environmental habitat characteristics. In this study we developed models to predict the spatio-temporal distribution of Culicoides imicola, which is the main transmission vector for the bluetongue virus in the Mediterranean region. We investigated (i) the importance of the environmental habitat characterization by means of bioclimatic variables, (ii) the effect of different distance variables to model the dispersal process, and (iii) the suitability of two different parameter identification procedures to determine the distance variables for species distribution modeling. Results showed that niche-based species distribution models, which only use environmental data, could estimate the occurrence of Culicoides imicola accurately, given that environmental data of the period of high species abundance (April until October) was included. The use of these models may therefore be hampered for predictive risk assessment aiming to estimate the probabilities and magnitude of undesired effects caused by the occurrence of C. imicola. Species distribution models accounting for species dispersal in addition to the environmental habitat characteristics, i.e. hybrid models, did provide accurate predictions of C. imicola distributions well before the onset of the season of high species abundance. A Gaussian or negative exponential function of the distance to presence locations was most suitable to model insect dispersal. The enhanced predictive capacity of these models potentially leads towards an increased model applicability in risk assessment and disease control. (C) 2014 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.ecoinf.2014.05.006

ISSN:
1574-9541; 1878-0512