Predictive distribution of Aculus schlechtendali (Acari: Eriophyidae) in southern Brazil

ABSTRACT

The potential (predictive) distribution modelling is a forecasting tool for species distribution, using occurrence records and correlating them with the interaction of algorithms and environmental variables. Aculus schlechtendali (Nalepa) is a phytophagous mite of economic importance, associated with losses in apple trees. The present study aimed to predict the potential distribution of A. schlechtendali in southern Brazil. Available information on species occurrence in Brazil was compiled in the database of previous reports and additional data from the Laboratory of Acarology – Univates. The MaxEnt algorithm and bioclimatic variables (from Bio1 to Bio19) were used to limit the predictive model. Current records of A. schlechtendali have indicated that it is present in 17 sites, among the states of Rio Grande do Sul, Santa Catarina, and Paraná; the major producers of apple in Brazil. The most significant bioclimatic variables evaluated were Mean Temperature of the Coldest Quarter (Bio11) and Annual Mean Temperature (Bio1). Using the modelling tool, areas with environmental suitability for this mite were predicted and established in southern Brazil. These results benefit apple agricultural chain, guiding the farmers in mite management, seeking mechanisms to prevent the establishment of this species in the areas indicated as suitable for their occupation.

KEYWORDS:

• Apple rust mite
• biogeography
• economic importance
• suitability
• predictive modelling

Acknowledgments

We would like to thank our colleagues at the Laboratory of Acarology - Univates, who have participated, at some point, either directly or indirectly in the study. Particular thanks are due to Ministério da Agricultura, Pecuária e Abastecimento (MAPA), for the partnership in field activities and Embrapa - Estação Experimental de Fruticultura de Clima Temperado - Vacaria (RS). Luiz L. C. Corrêa thanks the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Darliane E. Silva is grateful to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), for their post doctorate scholarships. Noeli J. Ferla is supported by CNPq productivity research scholarship (310035/2017-1). Finally, we are grateful to the anonymous reviewers for suggestions on the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).