In the last few years, significant changes in climate have had a disparate effect on biodiversity. The influences of these changes are random and unpredictable. The resurgence of insect pests, especially of medical and veterinary importance, often corresponds with climate changes. The Old World screwworm, Chrysomya bezziana, is one of the most important myiasis-causing flies that parasitize warm-blooded animals in the Eastern Hemisphere. We used a spatial distribution modeling approach to estimate the consequences of climatic changes on the potential geographic distribution of this insect throughout the world currently and in the future. A Maxent model used occurrence data from 104 localities and 19 climatic factors to predict the suitable habitat regions throughout the world. Two representative concentration pathways 2.6 and 8.5, were used to forecast the future distribution of C. bezziana in 2050 and 2070. The Maxent model for C. bezziana provided a satisfactory result, with a high value of the Area Under Curve equal to 0.855 (±0.001). Furthermore, the True Skilled Statistics value is equal to 0.67. These values indicate the significant influence on the model of the ecology of this fly species. Jackknife test indicated that temperature variables play a significant role in C. bezziana dynamics. The resultant models indicated the areas at risk of invasion by potential serious medical/veterinary issues, especially in countries with a large livestock production.

Beekeeping is essential for the global food supply, yet honeybee health and hive numbers are increasingly threatened by habitat alteration, climate change, agrochemical overuse, pathogens, diseases, and insect pests. However, pests and diseases that have unknown spatial distribution and influences are blamed for diminishing honeybee colonies over the world. The greater wax moth (GWM), Galleria mellonella, is a pervasive pest of the honeybee, Apis mellifera. It has an international distribution that causes severe loss to the beekeeping industry. The GWM larvae burrow into the edge of unsealed cells that have pollen, bee brood, and honey through to the midrib of the wax comb. Burrowing larvae leave behind masses of webs that cause honey to leak out and entangle emerging bees, resulting in death by starvation, a phenomenon called galleriasis. In this study, the maximum entropy algorithm implemented in (Maxent) model was used to predict the global spatial distribution of GWM throughout the world. Two representative concentration pathways (RCPs) 2.6 and 8.5 of three global climate models (GCMs), were used to forecast the global distribution of GWM in 2050 and 2070. The Maxent models for GWM provided a high value of the Area Under Curve equal to 0.8 ± 0.001, which was a satisfactory result. Furthermore, True Skilled Statistics assured the perfection of the resultant models with a value equal to 0.7. These values indicated a significant correlation between the models and the ecology of the pest species. The models also showed a very high habitat suitability for the GWM in hot-spot honey exporting and importing countries. Furthermore, we extrapolated the economic impact of such pests in both feral and wild honeybee populations and consequently the global market of the honeybee industry.

The Egyptian cotton leafworm, Spodoptera littoralis is a highly invasive insect pest that causes extensive damage to many of the primary food crops. Considering the recent challenges facing global food production including climate change, knowledge about the invasive potential of this pest is essential. In this study, the maximum entropy model (MaxEnt) was used to predict the current global spatial distribution of the pest and the future distribution using two representative concentration pathways (RCPs) 2.6 and 8.5 in 2050 and 2070. High AUC and TSS values indicated model accuracy and high performance. Response curves showed that the optimal temperature for the S.littoralis is between 10 and 28 °C. The pest is currently found in Africa and is widely distributed across the Middle East and throughout Southern Europe. MaxEnt results revealed that the insect will shift towards Northern Europe and the Americas. Further, China was seen to have a suitable climate. We also extrapolated the impact of these results on major producing countries and how this affects trade flow, which help decision makers to take the invasiveness of such destructive pest into their account.

Climate change is expected to influence the geographic distribution of many taxa, including insects. Chrysomya albiceps is one of the most pervasive calliphorid fly with apparent ecological, forensic, and medical importance. However, the global habitat suitability is varied due to climate change. Models that forecast species spatial distribution are increasingly being used in wildlife management, highlighting the need for trustworthy techniques to assess their accuracy. So, we used the maximum entropy implemented in Maxent to predict the current and future potential global geographic distribution of C. albiceps and algorithms of DIVA-GIS to confirm the predicted current model. The Maxent model was calibrated using 2177 occurrence records. Based on the Jackknife test, four bioclimatic variables along with altitude were used to develop the final models. For future models, two representative concentration pathways (RCPs), 2.6 and 8.5, for 2050 and 2070 were used. The area under curve (AUC) and true skill statistics (TSS) were used to evaluate the resulted models with values equal to 0.92 (±0.001) and 0.7, respectively. Two-dimensional niche analysis illustrated that the insect can adapt to low and high temperatures (9 °C to 27 °C), and the precipitation range was 0 mm to 2500 mm. The resulted models illustrated the global distribution of C. albiceps with alteration to its distribution in the future, especially on the Mediterranean coasts of Europe and Africa, Florida in the USA, and the coasts of Australia. Such predicted shifts put decision makers against their responsibilities to prevent destruction in economic, medical, and ecological sectors.

Some beetle species can attack honeybee colonies, causing severe damage to beekeeping. These pests include Oplostomus fuligineus, which is also known as the Large Hive Beetle (LHB). This beetle is native to Sub-Saharan Africa and has recently also been recorded in some parts of North Africa. It feeds mainly on young bee larvae and stored food within the colonies, causing severe damage to weak colonies. The present work sheds light on the current and future distribution (from 2050 to 2070) of this beetle in Africa and South Europe using species distribution modeling. Maxent was used to model the invasion of LHB. The Shared Socioeconomic Pathways (SSPs) 126 and 585 were used to model the future distribution of LHB. The Maxent models showed satisfactory results with a high Area Under Curve (AUC) value (0.85 ± 0.02). Furthermore, the True Skill Statistics (TSS) value was equal to 0.87. The current and future maps showed a high risk of invasion because of temperature variation in most of the parts of North Africa and South Europe. The maps also predicted the future invasion of LHB into other countries, mainly through southern Europe. These predictive risk maps will help quarantine authorities in highly relevant countries to prevent the expansion of this pest outside of its natural range.