Assessing the impact of climate change on the worldwide distribution of Dalbulus maidis (DeLong) using MaxEnt

Abstract

BACKGROUND For the first time, a model was applied at the global scale to investigate the effects of climate change on Dalbulus maidis. D. maidis is the main vector of three plant pathogens of maize crops and has been reported as one of the most important maize pests in Latin America. We modelled the effects of climate change on this pest using three Global Climate Models under two Representative Concentration Pathways (RCPs) using MaxEnt software. RESULTS Overall, climate change will lead to a decrease in suitable areas for D. maidis. In South America, climate change will decrease the areas suitable for the pest, especially in Brazil. However, Argentina, Chile, Colombia, Ecuador, Peru and Venezuela will have small areas that are highly suitable for the corn leafhopper. Outside the pest's range, Ethiopia, Kenya, Rwanda, Burundi and South Africa also should be concerned about the risk of corn leafhopper invasions in the future because they are projected to have conditions that are highly suitable for this insect in some areas. CONCLUSION This study allows the relevant countries to increase their quarantine measures and guide researchers to develop new Zea mays varieties that are resistant or tolerant to D. maidis. In addition, the maize‐stunting pathogens for the areas are highlighted in this modelling. © 2019 Society of Chemical Industry

An Evaluation of Habitat Uses and Their Implications for the Conservation of the Chinese Bumblebee Bombus pyrosoma (Hymenoptera: Apidae)

“…The adjacent modeled sample sites were thinned where the distance was less than 30 km (Supplementary Figures 1, 2). Data thinning was done by the spThin R package (0.2.0 Version) (de Oliveira et al, 2014;Aiello-Lammens et al, 2015;Santana et al, 2019).…”

“…Twenty-three bioclimatic/environmental variables, including nineteen bioclimatic variables at a 2.5 min resolution (∼5 km) and four environmental variables for the current period , were downloaded from the WorldClim-Global climate dataset 1 (Santana et al, 2019; Supplementary Table 1). The four environmental variables, related to bumblebee distribution, were growing degree days (30 arc min (∼50 km), Atlas of the Biosphere, The Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison) 2 , rainy_days (5 arc min (∼10 km), Climate Change Agriculture And Food Security, CCAFS) 3 , global reference evapotranspiration [Global-ET0, 30 arc sec (∼1 km), Consultative Group for International Agriculture Research (CGIAR)] 4 , and radiation of warmest quarter (bio_26) (10 arc min (∼20 km), Global climatologies for bioclimatic modeling, CliMond) 5 (Williams et al, 2014).…”

“…The MaxEnt software is used to perform the Jackknife test to determine the relative importance and contribution of different variables in the distribution of species. Three approaches are used for this analysis: (1) Models created using all variables except excluding one, (2) models created using one variable, and all the other variables were excluded, (3) models created using all variables (Phillips et al, 2006;Santana et al, 2019). The response curves were calculated to determine the influence of climate change on the distribution of B. pyrosoma.…”

Phylo‐biogeographical distribution of whitefly Bemisia tabaci (Insecta: Aleyrodidae) mitotypes in Ecuador

“…Ecological niche modeling has become increasingly useful for assessing the impact of ecological processes on insect population dynamics, including determining the potential geographic distributions of species. The models of the correlative type, such as MaxEnt, have been used to predict effects of climate change on habitat loss for the leafhopper Dalbulus maidis in the Americas (Santana et al 2019), and the risk of pest establishment for the mealybug Phenacoccus solenopsis in India (Kumar et al 2014a), the whitefly B. tabaci in Europe (Gilioli et al 2014), and other insect species (Kumar et al 2014b(Kumar et al , 2015. MaxEnt is a species distribution model based on a machinelearning framework that uses presence-only records and a collection of background points for the study area to estimate and compare probability densities based on a vector of environmental covariates relevant to habitat suitability (Elith et al 2011).…”

“…MaxEnt is a species distribution model based on a machinelearning framework that uses presence-only records and a collection of background points for the study area to estimate and compare probability densities based on a vector of environmental covariates relevant to habitat suitability (Elith et al 2011). For example, using this approach has been pivotal in decision-making processes to establish quarantine strategies and conservation planning (Kumar and Stohlgren 2009, Costa et al 2010, Gilioli et al 2014, Kumar et al 2014a, b, 2015, Santana et al 2019, making it useful for studying the displacement of endemic B. tabaci by introduced mitotypes, based on ecological factors.…”