Next Article in Journal
Fine-Tuned Ecological Niche Models Unveil Climatic Suitability and Association with Vegetation Groups for Selected Chaetocnema Species in South Africa (Coleoptera: Chrysomelidae)
Next Article in Special Issue
The Spreading in Europe of the Non-Indigenous Species Oenothera speciosa Nutt. Might Be a Threat to the Autochthonous Moth Macroglossum stellatarum (Linnaeus, 1758)? A New Case Study from Italy
Previous Article in Journal
Genetic Differentiation among Subspecies of Banksia nivea (Proteaceae) Associated with Expansion and Habitat Specialization
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

1
Department of Entomology, Faculty of Science, Ain Shams University, Cairo 11566, Egypt
2
Biology Department, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
3
Department of Biology, College of Science, Ha’il University, Ha’il 55211, Saudi Arabia
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(2), 99; https://doi.org/10.3390/d14020099
Submission received: 11 January 2022 / Revised: 26 January 2022 / Accepted: 26 January 2022 / Published: 30 January 2022

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.

1. Introduction

The livestock industry forms one of the most effective economic sectors in both agricultural and non-agricultural countries [1,2,3]. In some cases, it can reach half of the total agricultural economy (e.g., Brazil and the USA) [4,5,6]. By 2050, global demand for animal products is predicted to quadruple, owing primarily to rising global living standards [7]. Several constraints facing such a rising economy include low genetic outcomes of native animals, water shortages, lack of marketing infrastructure, and global warming [8,9,10].
Greenhouse gas (GHG) emissions, which are produced due to several industrial and anthropogenic activities, are the primary cause of global climate change [11]. Climate change and global warming have a severe negative effect on the livestock industry [12,13]. The changing climate destroys natural pastures throughout the world and affects animal fitness and health, especially in tropical and subtropical regions, and encourages the resurgence and expansion of livestock pests in temperate and sub-cold areas [14,15,16]. The formation of a new climatological niche encourages many species of livestock pests to invade new geographical regions where their ancestors have never previously been present [17]. Flies of order Diptera form the largest share of livestock pests [18]. With their strong flying ability, their likelihood of expanding to a new area through either their own capability or through trade and transportations networks is predictable [19].
Screwworm flies of the family Calliphoridae are considered a major destructive veterinary pest, and can be fatal to all warm-blooded animals [20]. There are two economically important species of screwworms throughout the globe: the New World screwworm (NWS), Cochliomyia hominivorax (Coquerel, 1858), which is native to central and south America, and Old-World screwworm (OWS), Chrysomya bezziana (Villeneuve, 1914), which is native to Sub Saharan Africa, the Indian sub-continent, and Southeast Asia as far as Papua New Guinea and is invasive in the Gulf region, including Iraq [21,22]. The larval instars of these flies form the economic stage, as they induced a disease called myiasis [23]. Myiasis is caused due to the burrowing of larva laid by females on animal wounds or natural openings in the flesh of animals. This mode of life supports bacterial infection and encourages other species of flies to lay their eggs [24]. The world organization for animal health (OIE) classifies the myiasis caused by NWS and OWS on List B of diseases, which includes animal illnesses that are of public health and/or socioeconomic importance [25,26].
Females of the OWS deposit their eggs in masses ranging from 150 to 500 eggs at the edge of wounds or near body orifices. Under hot and wet climatic conditions, the hatched larvae developed into the third instar in two days. The screwworm burrows deep in the wound until only the caudal ends are observable. The entire larval stage lasts 5 to 6 days and the pupal stage remain for about 7 to 9 days [23]. The prevention of infestation by OWS is established by environmental hygiene. Keeping areas free of decaying organic matter such as trash, decaying plants, and excreta can prevent the flies from being attracted to target animals [24].
In the Eastern Hemisphere, particularly in Iraq during the 1990s, the FAO estimated the economic loss to the livestock industry due to OWS invasion to be USD 8,555,000 US [22]. Other research studies have estimated annual losses in the livestock industry in Australia to be equal to AUD 500 million due to the possible incursion of the OWS to Australian territory from nearby Papua New Guinea or through animal trading with the Gulf region [27]. The cases of Australia and Japan are very similar with respect to possible invasion by the OWS from nearby countries due to climatic changes; however, the Japanese case has never studied for invasion of this pest before.
Japan is an island country located in East Asia. It boarded to the west by the Sea of Japan, to the north by the Sea of Okhotsk, by the East China Sea and Taiwan to the south and the Pacific Ocean to the east. Japan is divided into eight administrative regions and forty-seven prefectures (Figure 1) [28]. As one of the world’s most highly industrialized countries, Japan must take into account the global food supply and security situation and shift toward the safe production of high-quality beef utilizing indigenous pasture resources, therefore reducing its reliance on imported meat [29]. In 2018, the net production amount of the agricultural livestock industry in Japan was estimated to be around JPY 3.21 trillion [30]. This number was said to be enlarged by the spread of animal farms throughout different parts of the country, especially the southern islands [31]. The OWS has never been recorded in Japan, although it is a widespread pest in neighboring China [32]. A recent study modeling the status of OWS through the world using geographical information systems (GIS) and mathematical modeling indicates the critical situation of Japan [33].
Models based on GIS and mathematical modeling can be used as an early warning of the redistribution of animal pests during the climate change process [34]. CLIMEX, GARP, HABITAT, and MaxEnt are some of the most widely used programs for estimating the current and future distribution patterns of specific species under various climate change scenarios [35,36,37]. MaxEnt is the most efficient and precise modeling program using Maximum Entropy [38,39]. One of the most pressing immediate goals of such modeling techniques is to develop risk maps for economically relevant livestock and human health pests [33,40]. Therefore, the present study aims to evaluate the possibility of the future invasion of Japan and its growing livestock production sector with OWS C. bezziana under different climate change scenarios in 2050 and 2070.

2. Materials and Methods

2.1. Occurrence Records

All existing records on adult flies and larvae of medical and veterinary OWS cases were collected from the literature [41,42,43,44]. The OWS records in the digital database (www.cabi.org (accessed on 10 May 2021)) were taken into account as well [45]. Duplicated records and those with high spatial uncertainty were removed [33,39]. The data were transformed into comma-delimited (CSV) forms and used to assess the likelihood of invasion of OWS in Japan [33].

2.2. Bioclimatic Data Layers

Bioclimatic data with a spatial resolution of around 5 km2 were downloaded from www.worldclim.org (accessed on 18 January 2021). To illustrate the current global climate, a total of fifteen climatic variables were used. These covariates were originally obtained from monthly temperature and rainfall measurements and collected from meteorological stations between 1950 and 2000.
For current data, all bioclimatic layers were converted into the ASCII format using ArcGIS v 10.7. Due to known spatial artifacts, bioclimatic layers 8–9 and 18–19 were eliminated [33,46]. In addition, Pearson’s correlation was used to hinder auto-correlation and multicollinearity among bioclimatic variables at (r2 |0.8|) [39]. This was accomplished using ArcGIS 10.3’s SDM Tools feature (Universal tool: Explore climate data: Remove highly correlated variable) [9,39].
For future data, in order to account for the future distribution of OWS based on carbon dioxide emission in 2050 (average of estimates for 2041–2060) and 2070 (average of predictions for 2061–2080) we used parallel datasets from two representative concentration paths (RCPs), 2.6 and 8.5 (https://www.worldclim.org/data/cmip6/cmip6climate.html) (accessed on 18 January 2021) [33,47]. These future data layers were converted to the ASCII format via ArcGIS v 10.7. The climatic data for the selected variables were clipped to Japanese territory. The Meteorological Research Institute’s global climate model (MRI-CGCM3) (https://www.worldclim.org/data/cmip6/cmip6_clim5m.html) (accessed on 18 January 2021) for the years 2050 and 2070 was used to analyze the effects of climate change on future OWS invasion in Japan. These statistics are part of the IPCC’s Fifth Assessment Report’s current GCM climate estimates.

2.3. Invasion Modeling

MaxEnt package has previously been used to predict the future invasion of OWS in Japan. Yet, the artificial intelligence of maximum entropy is often regarded as the most widely used software for simulating species distributions using presence-only data [33,39,48]. Furthermore, even with little occurrence data on an invasive pest, MaxEnt can estimate the probability distribution [48]. In our models, 75% of the occurrence records were utilized to train the model, whereas 25% of the records were used to test the model. The maximum number of background points was 10,000, while the maximum number of iterations was 1000. In addition, ten-fold cross-validation was performed on the procedure, which increased the model’s performance [49]. After removing the correlation, and based on the Jackknife function of MaxEnt, a collection of five biologically significant bioclimatic variables were chosen to construct the final projected models in the future: (Bio 1) Annual mean temperature; (Bio 3) Isothermality; (Bio 6) Minimum temperature for the coldest month; (Bio 10) Mean temperature for the warmest quarter; and (Bio 11) Mean temperature for the coldest quarter. The five most important variables were related to temperature. The current world bioclimatic factors chosen were then entered as Environmental data in the MaxEnt interface while the clipped Japanese future bioclimatic data were pathed through the projection found in the MaxEnt interface during each program run in order to generate future risk maps for Japan.

2.4. Performance of Models

The area under the curve (AUC) was used to measure model performance, with its value ranging from 0 (random discrimination) to 1 (perfect discrimination) [50]. AUC values below 0.5 suggested a poor fit, whilst AUC values above 0.75 showed a good fit [50]. In addition, True Skill Statistics (TSS) was used to measure the accuracy of the projected models [33,51]. TSS values varied from 0 to 1, with positive values near 1 indicating a strong link between the predictive model and the distribution and negative values indicating a weak association [51].

3. Results

A total of 104 localities with OWS occurrence records were used to evaluate the potential invasion of such pests to Japan under changing climatic conditions. All localities were distributed through sub-Saharan Africa and southern Asia.

3.1. Modeling Evaluation

The model performance as evaluated using the Area Under Curve (AUC) was equal to 0.89 (±0.001). The AUC value was generally higher in continuous species distribution modeling than in discontinuous modeling. The true Skill Statistics (TSS) results supported the high performance of the model, where it was equal to 0.7.

3.2. OWS Risk Maps under Two Climate Change Scenarios: 2050

From two representative concentration pathways (RCPs), 2.6 and 8.5, for 2050, two risk maps of OWS through Japanese territory were generated (Figure 2). The overall distribution pattern of OWS indicates the unsuitability of northern Japan as a supposed habitat of this species in the future. Regions such as Hokkaido and Tohoku will not be at any risk of OWS invasion. The Southern coasts of the main island show high and very high suitability of OWS, respectively especially for RCP 2.6. The southern part of the country is at high and very high risk of OWS invasion (Figure 3a,b), with Kyushu and Okinawa presenting a perfect habitat for OWS. Their nearness to the Asian mainland increases the risk of OWS invasion through this region.

3.3. OWS Risk Maps under Two Climate Change Scenarios: 2070

The generated risk maps of RCPs 2.6 and 8.5 for 2070 are not significantly different from those generated for 2050 (Figure 2). Northern Japan will not be at risk, although many of its parts transfer from unsuitable to very low risk, especially in the Tohoku region. Southern Japan shows a high to very high risk of OWS invasion (Figure 3c,d). The risk map of RCP 2.6 shows the worst-case scenario, particularly for most of the main island.

4. Discussion

The old-world screwworm is a very dangerous veterinary parasite with fatal potentiality in all warm-blooded animals [52,53]. The most common effect of its parasitic lifestyle is tissue destruction. The maggots of OWS burrow into and feed on the living tissue, causing severe injury and even death in untreated cases [24]. Such an effect on animal tissue opens the way to secondary infection by bacteria and encourages other parasitic flies to lay their eggs on the affected body part [25]. Throughout its native range in Africa and Asia the OWS is considered a neglected veterinary issue, with very little literature available on its biology and ecology [33]. The only socioeconomic study on OWS was done in Iraq, where the fly was introduced as invasive species and caused great economic loss to the livestock industry in the country [54,55]. Recently, the species distribution modeling technique has been used to evaluate the current and future status of OWS throughout the world, indicating Australia and Japan as especially vulnerable countries on the periphery of the screwworm’s natural range [33]. The annual losses to Australia’s livestock industry in the event of prospective OWS incursion are projected to be around AUD 500 million, while Japan is not discussed as a risk area for OWS invasion.
Japan’s dairy and beef industries are worth more than USD 10 billion, with 4.56 million cattle, 9.61 million pigs, 294 million poultry, and 11,000 sheep [56]. This growing livestock industry faces the unevaluated risk of OWS invasion due to climate change [57]. Meanwhile, climate change is expected to influence feed crops and forage quality, animal and milk production, livestock illnesses, and animal reproduction as well [23]. The present work represents the first early warning of OWS invasion in Japan and assesses the suitability of Japanese territory as habitat for such pests in the near (2050) and long (2070) future term.
The results of the generated risk maps indicate that the livestock industry in Hokkaido, in northern Japan, is safe from OWS invasion. The growth of the livestock sector of the northern island will not face any risk of myiases in either the short or the long term. The cold weather of the region, even accounting for climate change, will continue to limit OWS expansion, as the annual mean temperature (Bio 1) forms the main factor affecting OWS distribution [33]. On the other hand, climate change can be expected to produce new ecological niches in the southern part of the country. Kyushu and Okinawa form very suitable habitats for the OWS in all generated risk maps. Kagoshima (Figure 1, 46) is the main agriculture production prefecture in Kyushu. With about 200 islands, it occupies 600 Km in southern Japan, all of which show a very high risk of future OWS invasion. The small island of Yakushima (Figure 3) in Kagoshima prefecture, for example, is home to livestock production worth about JPY 250 million, 20% of the livestock economy of Kagoshima [31]. In the future this growing livestock economy will be at risk of myiasis in both the short and long term due to climate change. In the southern islands, the high and very high suitability of the southern coasts of Japan’s main island under future climate change scenarios is unlikely to have an effect, as most of this region has no livestock farms; however, attention should be taken to imported live animals received through the harbors on this coast in order to prevent the establishment of a small population of OWS that could subsequently transfer to southern Japan.
The four produced risk maps of OWS show a non-significant difference in the potential invasion of this pest through Japanese territory; from analysis of them, we can see that the rise of temperature due to climate change and global warming will favor species expansion up to a certain limit. The map generated for high CO2 emission at RCP 8.5 in 2070 shows less suitability than that for CO2 emissions at RCP 2.6 in 2070; such a result indicates that at certain very high temperatures, the OWS will not have the ability to extend its range.
The produced maps have several limitations; they are solely dependent on climatological data in the absence of clear future data about human population, land cover, and host animal distribution. This does not, however, reduce the importance of these maps as a very early warning to the Japanese quarantine authorities to take care in preventing any invasion of OWS into the islands. Furthermore, it encourages additional studies about this destructive veterinary pest throughout its native range, as well as the socioeconomic effect of this pest, in case it invades the land of Samurai.

Author Contributions

Conceptualization, M.N. and E.M.H.; methodology, E.M.H., M.N. and S.A.-A.; software, M.N., D.S. and E.M.H.; validation, E.M.H., M.N. and S.A.-A.; formal analysis, D.S. and E.M.H.; investigation, E.M.H., M.N. and S.A.-A.; resources, A.A.A.-K. and K.A.A.-S.; data curation, E.M.H. and M.N.; writing—original draft preparation, E.M.H. and M.N.; writing—review and editing, E.M.H., M.N., S.A.-A., A.A.A.-K., K.A.A.-S. and D.S.; visualization, M.N.; supervision, M.N. and D.S.; project administration, A.A.A.-K. and K.A.A.-S.; funding acquisition, A.A.A.-K. and K.A.A.-S. All authors have read and agreed to the published version of the manuscript.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R37).

Institutional Review Board Statement

Did not require ethical approval.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors acknowledge the support from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R37), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. We appreciate the help and valuable advice of Mohamed W. Negm College of Agriculture, Ibaraki University, Mito, Japan.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kim, K.R.; Jung, H.D.; Choi, Y.S. Study on economy-wide effects of livestock industry. Korean J. Agric. Manag. Policy 2006, 32, 692–709. [Google Scholar]
  2. Aleme, A.; Zemedu, L.; Adigrat, E. The contribution of livestock sector in Ethiopian economy. Rev. Adv. Life Sci. Technol. 2015, 29, 79–90. [Google Scholar]
  3. Islam, M.M.; Anjum, S.; Modi, R.J.; Wadhwani, K.N. Scenario of livestock and poultry in India and their contribution to national economy. Int. J. Sci. Environ. Technol. 2016, 5, 956–965. [Google Scholar]
  4. Grannis, J.L.; Bruch, M.L. The role of USDA-APHIS in livestock disease management within the USA. In The Economics of Livestock Disease Insurance: Concepts, Issues and International Case Studies; CABI: Wallingford, UK, 2006; pp. 19–28. [Google Scholar]
  5. Temple, G. (Ed.) Livestock Handling and Transport; CABI: Wallingford, UK, 2007; p. 5. [Google Scholar]
  6. Williams, G.W.; David, P.A. The Latin American Livestock Industry: Growth and Challenges. Choices 2020, 34, 1–11. [Google Scholar]
  7. Rojas-Downing, M.M.; Nejadhashemi, A.P.; Harrigan, T.; Woznicki, S. Climate change and livestock: Impacts, adaptation, and mitigation. Clim. Risk Manag. 2017, 16, 145–163. [Google Scholar] [CrossRef]
  8. Adesehinwa, A.O.K.; Okunola, J.O.; Adewumi, M.K. Socio-economic characteristics of ruminant livestock farmers and their production constraints in some parts of South-western Nigeria. Livest. Res. Rural. Dev. 2004, 16, 8. [Google Scholar]
  9. Liao, I.C.; Chao, N.-H. Aquaculture and food crisis: Opportunities and constraints. Asia Pac. J. Clin. Nutr. 2009, 18, 564–569. [Google Scholar]
  10. Hanh, H.Q.; Ton, V.D.; Lebailly, P. Dynamics and constraints of livestock production systems in Cam Giang district, Hai Duong Province, North Vietnam. Livest. Res. Rural. Dev. 2013, 25, 9. [Google Scholar]
  11. IPCC. Climate Change 2013: The physical science basis. In Contribution of Working Group I to the 5th Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Qin, D.H., Plattner, G.K., Tignor, M., Eds.; Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
  12. Gomez-Zavaglia, A.; Mejuto, J.; Simal-Gandara, J. Mitigation of emerging implications of climate change on food production systems. Food Res. Int. 2020, 134, 109256. [Google Scholar] [CrossRef]
  13. Nissim, S.; Koluman, N. Impact of climate change on the dairy industry in temperate zones: Predications on the overall negative impact and on the positive role of dairy goats in adaptation to earth warming. Small Rumin. Res. 2015, 123, 27–34. [Google Scholar]
  14. Tadeusz, K.; Blanes-Vidal, V.; Li, B.; Gates, R.S.; de Alencar Naas, I.; Moura, D.J.; Berckmans, D.; Banhazi, T.M. Impact of global climate change on the health, welfare and productivity of intensively housed livestock. Int. J. Agric. Biol. Eng. 2011, 4, 1–22. [Google Scholar]
  15. Lacetera, N. Impact of climate change on animal health and welfare. Anim. Front. 2019, 9, 26–31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Ojima, D.S.; Aicher, R.; Archer, S.R.; Bailey, D.W.; Casby-Horton, S.M.; Cavallaro, N.; Reyes, J.J.; Tanaka, J.A.; Washington-Allen, R.A. A climate change indicator framework for rangelands and pastures of the USA. Clim. Chang. 2020, 163, 1733–1750. [Google Scholar] [CrossRef]
  17. Early, R.; Bradley, B.; Dukes, J.S.; Lawler, J.J.; Olden, J.; Blumenthal, D.M.; Gonzalez, P.; Grosholz, E.D.; Ibañez, I.; Miller, L.; et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 2016, 7, 12485. [Google Scholar] [CrossRef]
  18. Gerhardt, R.R.; Lawrence, J.H. Flies (Diptera). In Medical and Veterinary Entomology; Academic Press: Oxford, UK, 2019; pp. 171–190. [Google Scholar]
  19. Kynkäänniemi, S.-M.; Kortet, R.; Härkönen, L.; Kaitala, A.; Paakkonen, T.; Mustonen, A.-M.; Nieminen, P.; Härkönen, S.; Ylönen, H.; Laaksonen, S. Threat of An Invasive Parasitic Fly, the Deer Ked (Lipoptena cervi), to the Reindeer (Rangifer Tarandus Tarandus): Experimental Infection and Treatment. Ann. Zoöl. Fenn. 2010, 47, 28–36. [Google Scholar] [CrossRef] [Green Version]
  20. Scholl, P.J.; Douglas, D.C.; Cepeda-Palacios, R. Myiasis (Muscoidea, Oestroidea). In Medical and Veterinary Entomology; Academic Press: Oxford, UK, 2019; pp. 383–419. [Google Scholar]
  21. Hall, M.J.R. Screwworm flies as agents of wound myiasis. World Anim. Rev. 1991, 8–17. Available online: http://forensicentomologist.com/wp-content/uploads/2008/05/screwworm-agents-of-myiasis.pdf (accessed on 10 May 2021).
  22. Ali, A.; Zaidi, F.; Fatima, S.H.; Munir, S. Modeling the occurrence and spatial distribution of screwworm species in Northern Pakistan. Environ. Monit. Assess. 2021, 193, 1–13. [Google Scholar] [CrossRef]
  23. Zumpt, F. Myiasis in Man and Animals in the Old World; Butterworths: London, UK, 1965. [Google Scholar]
  24. Spradbery, J.P. Screw-worm fly: A tale of two species. Agric. Zool. Rev. 1994, 6, 1–62. [Google Scholar]
  25. Reichard, R. Case studies of emergency management of screwworm. Rev. Sci. Tech. l’OIE 1999, 18, 145–163. [Google Scholar] [CrossRef]
  26. Anonymous Screwworm (Old World & NEW World). Available online: https://www.oie.int/fileadmin/Home/eng/Animal_Health_in_the_World/docs/pdf/Disease_cards/SCREWWORM.pdf (accessed on 10 May 2021).
  27. Fruean, S.N.; East, I.J. Spatial analysis of targeted surveillance for screw-worm fly (C hrysomya bezziana or Cochliomyia hominivorax) in A ustralia. Aust. Vet. J. 2014, 92, 254–262. [Google Scholar] [CrossRef]
  28. Umeda, A. Japan Atlas: A Bilingual Guide, 3rd ed.; Kodanshausa: New York, NY, USA, 2012. [Google Scholar]
  29. Smith, S.B.; Gotoh, T.; Greenwood, P.L. Current situation and future prospects for global beef production: Overview of special issue. Asian-Australas. J. Anim. Sci. 2018, 31, 927–932. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Engelmann, J. Livestock Production Value of Agricultural Sector in Japan 2009–2018. Available online: https://www.statista.com/statistics/644994/japan-livestock-production-value/2020 (accessed on 10 July 2021).
  31. Tominaga, S. Agriculture in the Islands of Kagoshima; ‏ Kagoshima University Research Center for the Pacific Islands: Kagoshima, Japan, 2013. [Google Scholar]
  32. Hall, M.J.R.; Edge, W.; Testa, J.M.; Adams, Z.J.O.; Ready, P.D. Old World screwworm fly, Chrysomya bezziana, occurs as two geographical races. Med. Vet. Èntomol. 2001, 15, 393–402. [Google Scholar] [CrossRef] [PubMed]
  33. Hosni, E.M.; Nasser, M.; Al-Ashaal, S.; Rady, M.H.; Kenawy, M.A. Modeling current and future global distribution of Chrysomya bezziana under changing climate. Sci. Rep. 2020, 10, 4947. [Google Scholar] [CrossRef] [PubMed]
  34. Midgley, G.F.; Hannah, L.; Millar, D.; Rutherford, M.C.; Powrie, L.W. Assessing the vulnerability of species richness to anthropogenic climate change in a biodiversity hotspot. Glob. Ecol. Biogeogr. 2002, 11, 445–451. [Google Scholar] [CrossRef]
  35. Guo, S.; Ge, X.; Zou, Y.; Zhou, Y.; Wang, T.; Zong, S. Projecting the Potential Global Distribution of Carpomya vesuviana (Diptera: Tephritidae), Considering Climate Change and Irrigation Patterns. Forests 2019, 10, 355. [Google Scholar] [CrossRef] [Green Version]
  36. Byeon, D.H.; Jung, J.M.; Jung, S.; Lee, W.H. Prediction of global geographic distribution of Metcalfa pruinosa using CLIMEX. Entomol. Res. 2018, 48, 99–107. [Google Scholar] [CrossRef]
  37. Wei, B.; Wang, R.; Hou, K.; Wang, X.; Wu, W. Predicting the current and future cultivation regions of Carthamus tinctorius L. using MaxEnt model under climate change in China. Glob. Ecol. Conserv. 2018, 16, e00477. [Google Scholar] [CrossRef]
  38. Zurell, D.; Franklin, J.; König, C.; Bouchet, P.J.; Dormann, C.F.; Elith, J.; Fandos, G.; Feng, X.; Guillera-Arroita, G.; Guisan, A.; et al. A standard protocol for reporting species distribution models. Ecography 2020, 43, 1261–1277. [Google Scholar] [CrossRef]
  39. Abou-Shaara, H.; Alashaal, S.A.; Hosni, E.M.; Nasser, M.G.; Ansari, M.J.; Alharbi, S.A. Modeling the Invasion of the Large Hive Beetle, Oplostomus fuligineus, into North Africa and South Europe under a Changing Climate. Insects 2021, 12, 275. [Google Scholar] [CrossRef]
  40. Ge, X.; He, S.; Wang, T.; Yan, W.; Zong, S. Potential Distribution Predicted for Rhynchophorus ferrugineus in China under Different Climate Warming Scenarios. PLoS ONE 2015, 10, e0141111. [Google Scholar] [CrossRef] [PubMed]
  41. Wardhana, A.; Hall, M.; Mahamdallie, S.; Muharsini, S.; Cameron, M.; Ready, P. Phylogenetics of the Old World screwworm fly and its significance for planning control and monitoring invasions in Asia. Int. J. Parasitol. 2012, 42, 729–738. [Google Scholar] [CrossRef] [PubMed]
  42. Morgan, J.A.; Urech, R. An improved real-time PCR assay for the detection of Old World screwworm flies. Acta Trop. 2014, 138, S76–S81. [Google Scholar] [CrossRef] [PubMed]
  43. Stevens, J.R. The evolution of myiasis in blowflies (Calliphoridae). Int. J. Parasitol. 2003, 33, 1105–1113. [Google Scholar] [CrossRef]
  44. Zaidi, F.; Fatima, S.H.; Khisroon, M.; Gul, A. Distribution Modeling of three screwworm species in the ecologically diverse landscape of North West Pakistan. Acta Trop. 2016, 162, 56–65. [Google Scholar] [CrossRef] [PubMed]
  45. CABI. Chrysomya Bezziana (Old-World Screwworm). In Invasive Species Compendium; CABI: Wallingford, UK, 2021; Available online: https://www.cabi.org/isc/datasheet/88417 (accessed on 10 May 2021).
  46. Escobar, L.E.; Lira-Noriega, A.; Medina-Vogel, G.; Peterson, A.T. Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: Use of Maxent and NicheA to assure strict model transference. Geospat. Health 2014, 9, 221–229. [Google Scholar] [CrossRef]
  47. Mohammadi, S.; Ebrahimi, E.; Moghadam, M.S.; Bosso, L. Modelling current and future potential distributions of two desert jerboas under climate change in Iran. Ecol. Inform. 2019, 52, 7–13. [Google Scholar] [CrossRef]
  48. Phillips, S.J.; Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  49. Kessler, W.H.; Ganser, C.; Glass, G.E. Modeling the Distribution of Medically Important Tick Species in Florida. Insects 2019, 10, 190. [Google Scholar] [CrossRef] [Green Version]
  50. Mulieri, P.R.; Patitucci, L.D. Using ecological niche models to describe the geographical distribution of the myiasis-causing Cochliomyia hominivorax (Diptera: Calliphoridae) in southern South America. Parasitol. Res. 2019, 118, 1077–1086. [Google Scholar] [CrossRef]
  51. Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]
  52. Wardhana, A.; Cecchi, G.; Muharsini, S.; Cameron, M.; Ready, P.; Hall, M. Environmental and phylogeographical determinants of the distribution of the Old World screwworm fly in Indonesia. Acta Trop. 2014, 138, S62–S68. [Google Scholar] [CrossRef] [PubMed]
  53. Nasser, M.G.; Hosni, E.M.; Kenawy, M.A.; Alharbi, S.A.; Almoallim, H.S.; Rady, M.H.; Merdan, B.A.; Pont, A.C.; Al-Ashaal, S.A. Evolutionary profile of the family Calliphoridae, with notes on the origin of myiasis. Saudi J. Biol. Sci. 2021, 28, 2056–2066. [Google Scholar] [CrossRef] [PubMed]
  54. Siddig, A.; Al Jowary, S.; Al Izzi, M.; Hopkins, J.; Hall, M.J.R.; Slingenbergh, J. Seasonality of Old World screwworm myiasis in the Mesopotamia valley in Iraq. Med. Vet. Èntomol. 2005, 19, 140–150. [Google Scholar] [CrossRef] [PubMed]
  55. Al-Taweel, A.A.; Okaily, R.A.; Salman, Q.S.; Al-Temimi, F.A.; Al-Adhadh, B.N.; Hamad, B.S.; Urech, R. Elative performance of surveys for the Old World screwworm fly, Chrysomya bezziana, in Iraq based on fly trapping and myiasis monitoring. Acta Trop. 2014, 138, S56–S61. [Google Scholar] [CrossRef] [PubMed]
  56. Greenwood, P.L.; E Gardner, G.; Ferguson, D.M. Current situation and future prospects for the Australian beef industry—A review. Asian-Australas. J. Anim. Sci. 2018, 31, 992–1006. [Google Scholar] [CrossRef]
  57. Forman, S.; Hungerford, N.; Yamakawa, M.; Yanase, T.; Tsai, J.; Joo, Y.-S.; Yang, D.-K.; Nha, J.-J. Efectos del cambio climático y riesgos zoosanitarios en Asia. Rev. Sci. Tech. l’OIE 2008, 27, 581–597. [Google Scholar] [CrossRef]
Figure 1. Map showing detailed administrative regions and the forty-seven prefectures of Japan.
Figure 1. Map showing detailed administrative regions and the forty-seven prefectures of Japan.
Diversity 14 00099 g001
Figure 2. Risk map of future potential invasion of OWS to Japan under two representative concentration pathways: (a) RCP 2.6 in 2050, (b) RCP 8.5 in 2050, (c) RCP 2.6 in 2070, and (d) RCP 8.5 in 2070.
Figure 2. Risk map of future potential invasion of OWS to Japan under two representative concentration pathways: (a) RCP 2.6 in 2050, (b) RCP 8.5 in 2050, (c) RCP 2.6 in 2070, and (d) RCP 8.5 in 2070.
Diversity 14 00099 g002
Figure 3. Illustrated zonation of the Southern regions of Japan with a high and very high risk of invasion with OWS in the future: (a) RCP 2.6 in 2050, (b) RCP 8.5 in 2050, (c) RCP 2.6 in 2070, and (d) RCP 8.5 in 2070.
Figure 3. Illustrated zonation of the Southern regions of Japan with a high and very high risk of invasion with OWS in the future: (a) RCP 2.6 in 2050, (b) RCP 8.5 in 2050, (c) RCP 2.6 in 2070, and (d) RCP 8.5 in 2070.
Diversity 14 00099 g003
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Hosni, E.M.; Nasser, M.; Al-Khalaf, A.A.; Al-Shammery, K.A.; Al-Ashaal, S.; Soliman, D. Invasion of the Land of Samurai: Potential Spread of Old-World Screwworm to Japan under Climate Change. Diversity 2022, 14, 99. https://doi.org/10.3390/d14020099

AMA Style

Hosni EM, Nasser M, Al-Khalaf AA, Al-Shammery KA, Al-Ashaal S, Soliman D. Invasion of the Land of Samurai: Potential Spread of Old-World Screwworm to Japan under Climate Change. Diversity. 2022; 14(2):99. https://doi.org/10.3390/d14020099

Chicago/Turabian Style

Hosni, Eslam M., Mohamed Nasser, Areej A. Al-Khalaf, Kholoud A. Al-Shammery, Sara Al-Ashaal, and Doaa Soliman. 2022. "Invasion of the Land of Samurai: Potential Spread of Old-World Screwworm to Japan under Climate Change" Diversity 14, no. 2: 99. https://doi.org/10.3390/d14020099

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop