Projecting the Potential Global Distribution of Carpomya vesuviana (Diptera: Tephritidae), Considering Climate Change and Irrigation Patterns

Potential Parasitoids for Biocontrol of the Ber Fruit Fly, Carpomya vesuviana Costa (Diptera: Tephritidae)

The ber fruit fly (BFF), Carpomya vesuviana Costa, 1854 (Diptera: Tephritidae), is an important key pest of the jujube, Ziziphus jujuba Miller. The main control measures against this pest are to use chemical control, but the first survey for its natural enemies was performed in Iran. Here, we report eight species of parasitic wasps of the BFF from five different families. The family Eurytomidae with three species, the families Pteromalidae and Mutillidae with two species each, and the families Braconidae and Diapriidae with one species each are associated with different immature stages of the BFF, of which Eurytoma pineticola Zerova (Eurytomidae) and Cyrtoptyx lichtensteini (Masi) (Pteromalidae) were the most abundant parasitoid species. Fopius carpomyiae (Silvestri,) was not reared on BFF on the jujube during this survey, but it was reported on Ziziphus spina-christi (L.) with a high parasitism rate. Therefore, it seems to be the most important parasitoid of BFF in Iran. The parasitoid community of BFF in Khorasan, Eastern Iran, is reviewed, and an identification key to these species is proposed.

Fungus under a Changing Climate: Modeling the Current and Future Global Distribution of Fusarium oxysporum Using Geographical Information System Data

The impact of climate change on biodiversity has been the subject of numerous research in recent years. The multiple elements of climate change are expected to affect all levels of biodiversity, including microorganisms. The common worldwide fungus Fusarium oxysporum colonizes plant roots as well as soil and several other substrates. It causes predominant vascular wilt disease in different strategic crops such as banana, tomato, palm, and even cotton, thereby leading to severe losses. So, a robust maximum entropy algorithm was implemented in the well-known modeling program Maxent to forecast the current and future global distribution of F. oxysporum under two representative concentration pathways (RCPs 2.6 and 8.5) for 2050 and 2070. The Maxent model was calibrated using 1885 occurrence points. The resulting models were fit with AUC and TSS values equal to 0.9 (±0.001) and 0.7, respectively. Increasing temperatures due to global warming caused differences in habitat suitability between the current and future distributions of F. oxysporum, especially in Europe. The most effective parameter of this fungus distribution was the annual mean temperature (Bio 1); the two-dimensional niche analysis indicated that the fungus has a wide precipitation range because it can live in both dry and rainy habitats as well as a range of temperatures in which it can live to certain limits. The predicted shifts should act as an alarm sign for decision makers, particularly in countries that depend on such staple crops harmed by the fungus.

Biogeography of Black Mold Aspergillus niger: Global Situation and Future Perspective under Several Climate Change Scenarios Using MaxEnt Modeling

Climate change impacts represent one of the most important ecological and medical issues during this century. Several fungal species will change their distribution through space and time as a response to climate changes. This will rearrange many fungal diseases throughout the world. One of the most important and very common fungi is the black mold Aspergillus niger. The COVID-19 pandemic reforms the way in which mycologists think about this fungus as an emerging healthy issue. Through this work, about one thousand records of Aspergillus niger were used to model its current and future global distribution using 19 bioclimatic variables under several climate change scenarios. Maximum entropy implemented in Maxent was chosen as the modeling tool, especially with its accuracy and reliability over the other modeling techniques. The annual mean temperature (bio 1) forms the most contributed climatological parameter to black mold distribution. The produced current distribution model came compatible with the real distribution of the species with a cosmopolitan range. The rise of temperature due to global warming will form a limitation to Aspergillus niger through several parts of its range. The generated maps of the future status of this fungus under two different RCPs for 2050 and 2070, indicate several parts that become free from black mold due to temperature limitations. The present results need more intensive future evaluation using data science and GIS, especially on a local scale including more ecological parameters other than climatological data.

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

Simple Summary

The greater wax moth (GWM) is a common pest of bee colonies throughout the world. This study highlighted the global habitat suitability of GWM using the statistical power of Maxent to model its current and future distribution under climate-change scenarios in 2050 and 2070. Our study shed light on the major environmental factors that manage the habitat suitability of GWM. To the best of our knowledge, this is the first modeling study of wax moths. In brief, this pest can cause severe indirect damage to the global honey market totalling millions of dollars; therefore, developing prompt monitoring or control strategies is advised.

Abstract

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.

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

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.

Predicting the habitat suitability of Schistosoma intermediate host Bulinus truncatus, its predatory aquatic insect Odonata nymph, and the associated aquatic plant Ceratophyllum demersum using MaxEnt

Schistosomiasis is one of the most important parasitic diseases in tropical and subtropical areas. Its prevalence is associated with the distribution of freshwater snails, which are their intermediate hosts. Thus, control of freshwater snails is the solution to reduce the transmission of this disease. This will be achieved by understanding the relationship between the snails and their habitats including natural enemies and associated aquatic plants as well as the factors affecting their distribution. In this study, Maximum Entropy model (MaxEnt) was used for mapping and predicting the possible geographic distribution of Bulinus truncatus snail (the intermediate host of Schistosoma haematobium), Odonata nymph (predatory aquatic insect), and Ceratophyllum demersum (the associated aquatic plant) in Egypt based on topographic and climatic factors. The models of the investigated species were evaluated using the area under receiver operating characteristic curve. The results showed that the potential risk areas were along the banks of the Nile River and its irrigation canals. In addition, the MaxEnt models revealed some similarities in the distribution pattern of the vector, the predator, and the aquatic plant. It is obvious that the predictive distribution range of B. truncatus was affected by altitude, precipitation seasonality, isothermality, and mean temperature of warmest quarter. The presence of B. truncatus decreases with the increase of altitude and precipitation seasonality values. It could be concluded that the MaxEnt model could help introducing a predictive risk map for Schistosoma haematobium prevalence and performing better management strategies for schistosomiasis.

Modeling the Invasion of the Large Hive Beetle, Oplostomusfuligineus, into North Africa and South Europe under a Changing Climate

Simple Summary

Large Hive Beetles (LHBs) are common pests of honeybee colonies, especially in the African continent. The ability of this pest to invade new regions in North Africa and Europe is highlighted in the present study using a species distribution modeling technique in current and future climate change scenarios in 2050 and 2070. In brief, this pest will be a new burden on the beekeeping sector outside Africa, and therefore the development of early monitoring strategies is recommended.

Abstract

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.