Influence of weather variables in temporal variation of density of Dichroplus maculipennis (Melanoplinae) and Borellia bruneri (Gomphocerinae), pest species in the Pampas.

CEPEDA ROSANA; LANGE, CARLOS E.; MARIOTTINI YANINA; DE WYSIECKI MARÍA LAURA; MARINELLI CLAUDIA

Illheus

Congreso; 12th International Congress of Orthopterology; 2016

Orthopterists Society

Grasshoppers are important invertebrate herbivores in grasslands worldwide. The composition and abundance of species in each community and the dynamics of populations respond to a combination of interacting abiotic and biotic factors that vary spatially and temporally. Extensive research has been conducted on grasshopper dynamics to understand the underlying mechanisms promoting outbreaks. It is well known that weather is one of the most influential factors in the population dynamics of the different species of grasshoppers. Since late 2005 to the present monitoring is being conducted in representative plant communities of the southern Pampas, an area affected by grasshopper pests. Major outbreaks of the Dichroplus maculipennis and the Borellia bruneri were recorded from late 2008 to 2010 affecting a variety of crops and natural grasslands in an area of ca 2.5 million ha. The main objective of this study was to evaluate the relationship between temporal variation in density of D. maculipennis and B. bruneri with climatic variables associated with precipitation and temperature. To carry out this study, specific density data obtained from sampling carried out during December and January 2005-2015 (Ten consecutive years) in natural grasslands of Laprida (Buenos Aires province) were considered. In order to achieve the proposed objective and assuming independence between density of each season, an ANCOVA (Analysis of Covariance) was performed using as covariates those environmental variables that minimized CME (Mean Square Error) in a multiple linear regression modeling as response the density of species. The variables included in the analysis were: 1) Mean September temperature (prior to sampling), 2) Mean October temp, 3) Mean November temp., 4) Mean December temp, 5) Cumulative rainfall until October, 6) Rainfall of sampled month, 7) Rainfall over the year, and 8) Number of rainy days (from September to sampling date). The regression model that best adjusted for D. maculipennis included variables 1, 2, 3, 5, 6, 7, and 8. While it was observed that all variables significantly affected density, some coefficient variables resulted near to zero and dependence between them was observed. According to ANCOVA, significant differences between the samples of 2008 and 2010 occurred. The variables that better explained the observed density increase for D. maculipennis were the accumulated rainfall in the year (p