Invasion of the Land of Samurai: Potential Spread of Old-World Screwworm to Japan under Climate Change

Abstract

Temperatures have fluctuated dramatically throughout our planet’s long history, and in recent decades, global warming has become a more visible indicator of climate change. Climate change has several effects on different economic sectors, especially the livestock industry. The Old-world screwworm (OWS), Chrysomya bezziana (Villeneuve, 1914), is one of the most destructive insect pests which is invading new regions as a result of climate change. The economic loss in livestock business due to invasion of OWS was previously assessed by FAO in Iraq to be USD 8,555,000. Other areas at risk of invasion with OWS in the future include Japan. Therefore, maximum entropy implemented in MaxEnt was used to model predictive risk maps of OWS invasion to Japan based on two representative concentration pathways (RCPs), 2.6 and 8.5, for 2050 and 2070. The Area Under Curve (AUC) indicates high model performance, with a value equal to 0.89 (±0.001). In addition, the True Skill Statistics (TSS) value was equal to 0.7. The resulting models indicate the unsuitability of the northern territory of Japan for invasion by OWS. The main island’s southern costs show high and very high invasion suitability, respectively, and both Kyushu and Okinawa are at high risk of invasion with OWS. The predicted risk maps can be considered a warning sign for the Japanese quarantine authority to hasten a control program in order to protect the livestock industry from this devastating pest.

Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate

“…The models developed in this study analyzed the effect of climate change on the existing and future distribution of the GWM using just climatological factors. For this objective, several papers only used climate factors [ 22 , 27 , 40 ]. Considering other environmental variables, such as human population, land cover, vegetation index, and host animal distribution, could help to improve them.…”

“…Bioclimatic layers 8–9 and 18–19 were eliminated due to known spatial artifacts [ 22 ]. We applied the Pearson correlation coefficient to judge the correlation between each pair of covariates (r 2 ≥ |0.8|) to reduce collinearity between variables [ 27 , 40 ]. This coefficient removed the correlation among the covariates through the function of SDM Tools in ArcGIS 10.7 (Universal tool; Explore climate data; Remove highly correlated variable) [ 40 ].…”

“…We applied the Pearson correlation coefficient to judge the correlation between each pair of covariates (r 2 ≥ |0.8|) to reduce collinearity between variables [ 27 , 40 ]. This coefficient removed the correlation among the covariates through the function of SDM Tools in ArcGIS 10.7 (Universal tool; Explore climate data; Remove highly correlated variable) [ 40 ]. Based on this, only five bioclimatic covariates were selected to produce final models: annual mean temperature (Bio 1), mean diurnal range (Bio 2), annual temperature range (Bio 7), annual precipitation (Bio 12), and precipitation of the driest month (Bio 14).…”

“…Thus, Maxent v 3.4.1 was used to model the current and future global distribution of the GWM. In our models, 75% of the occurrence records were used to train the model, whereas 25% of the records were used to test it [ 40 ]. The number of background points and iterations were set at 10,000 and 1000, respectively [ 46 ].…”

Evaluation of Climate Change Impacts on the Global Distribution of the Calliphorid Fly Chrysomya albiceps Using GIS

“…Pearson correlation coefficient was used to reduce the multicollinearity among bioclimatic variables at a value equal to (|r| ≥ 0.8) (Tables S2 and S3) [23][24][25]. This coefficient hinders the correlation among the covariates through the function of SDM Tools in ArcGIS 10.7 (universal tool; explore climate data; remove highly correlated variable) [26]. Finally, four bioclimatic covariates along with altitude were selected for further analysis.…”