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Table of Contents
RESEARCH ARTICLE
Year : 2021  |  Volume : 58  |  Issue : 4  |  Page : 335-345

The potential future change of the suitability patterns of six leishmaniasis vectors in Iran


Sustainability Solutions Research Lab, University of Pannonia, Egyetem utca 10, H-8200, Veszprém, Hungary

Date of Submission12-May-2020
Date of Acceptance15-Aug-2020
Date of Web Publication25-Mar-2022

Correspondence Address:
Dr Attila J Trajer
Sustainability Solutions Research Lab, University of Pannonia, Egyetem utca 10, H-8200, Veszprém
Hungary
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0972-9062.316277

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  Abstract 

Background & objectives: Visceral and cutaneous leishmaniasis are endemic in Iran. The aim of this study was to model the changing suitability patterns of five confirmed and one suspected leishmaniasis vector Phlebotomus species resident in the country.
Methods: The potential present and future suitability patterns of the sandfly species in Iran were modelled using climate envelope forecasting method for the reference period 1970–2000 and the future period 2041–2060. Results: The reference period climate of Iran seemed to be the most suitable for Phlebotomus perfiliewi and Phlebotomus tobbi and less suitable for Phlebotomus simili, while Phlebotomus neglectus, Phlebotomus papatasi and Phlebotomus sergenti showed intermediate values among the studied sandfly species. The modelled changes in the suitability values show a similar pattern in the case of the six species, even the exact magnitude of the modelled values varied. The model results indicate that climate change could decrease the sandfly habitability in the present-day arid regions in Central Iran. The Iranian sandfly populations will move to higher elevation regions, and the suitability values of the sandfly species are predicted to increase in the foothills of the mountainous regions in the northern and the western part of the country.
Interpretation & conclusion: The increase of the maximally suitable areas in Iran was found which was predicted to be accompanied by the parallel shrinkage of the sandfly-inhabited areas in the arid regions of the country. Topographical conditions could strongly influence the suitability patterns of the vectors in Iran.

Keywords: Climate change; Phlebotomine sandflies; Leishmaniasis; Ecological forecasting


How to cite this article:
Trajer AJ. The potential future change of the suitability patterns of six leishmaniasis vectors in Iran. J Vector Borne Dis 2021;58:335-45

How to cite this URL:
Trajer AJ. The potential future change of the suitability patterns of six leishmaniasis vectors in Iran. J Vector Borne Dis [serial online] 2021 [cited 2022 May 21];58:335-45. Available from: https://www.jvbd.org/text.asp?2021/58/4/335/316277




  Introduction Top


Climate change is predicted to induce the altitudinal and latitudinal shift of arthropod vector populations, and consequently, the changing landscape ofvector-borne diseases[1] . Although it is a widely accepted and generalized idea, the potential effects of global warming on the specific vector-borne varies by vectors[2]. It means that though vector-borne diseases are frequently held as a symptom of climate change, it does not mean that the effect generally will be positive for vector populations in all regions[3]. Such factors like the changing land use patterns - including the alterations of the forest cover and urbanization - the increasing intensity of travelling and trade, can also strongly influence the spread of the vectors[4]. Furthermore, the biological diversity of vectors and vector-borne diseases also influence the changing global risk of the emerging and re-emerging vector-borne diseases[2]. The interpretation of the potential effects of climate change on vector-borne diseases requires an area-sensitive approach. It is a good example for this phenomenon, that while in temperate Europe, global warming is predicted to cause the northward expansion of the vectors of leishmaniasis[5],[6], Gonzalez et al. 2014 predicted the altitudinal shifts and the reduced spatial distribution of sandfly species in tropical Colombia[7].

Leishmaniasis is one of the most important emerging parasitic diseases among travellers and local inhabitants[8]. It is a parasitic disease entity which is caused by different Leishmania protozoans and transmitted by Old and New World sandfly species. Leishmaniasis can be either zoonotic or anthroponotic according to the reservoirs and cutaneous, mucocutaneous and visceral forms regarding the predominantly affected organs. It is ranked among the seven most important tropical diseases in the World[9]. The annual incidence of cutaneous leishmaniasis is about 700,000-1 million new cases; in the case of visceral leishmaniasis, this value is about 300,000 per year[10]. Without any treatment, the mortality rate of visceral leishmaniasis is 95–100%” and causes the death of about 40,000 people each year. Over 8.1% of the Earth’s human population lives in areas at risk of visceral and over 5.7% at risk of cutaneous leishmaniasis infection[10]. Studies in recent years have shown that the spectra of leishmaniasis and the number of affected people in the world can be greater as it was previously assumed[12]. For example, in the tropical regions, cutaneous and mucocutaneous leishmaniasis can stand in the background of the activation and reactivation of rheumatoid arthritis in many patients[13].

Roughly two-thirds of the leishmaniasis cases occur in the countries of the WHO Eastern Mediterranean Region[10]. This area has diverse sandfly fauna. It can be traced back in time to the Tertiary evolutionary history of the genus Phlebotomus, because the Middle Eastern Eurasian Mountain ranges could be the diversification hotspot of sandfly species during the late Paleogene and Neogene eras[14]. In Iran (officially: The Islamic Republic of Iran), more than 20 Phlebotomine sandfly species were recorded in the last decades[15]. At least 3% of the country live in areas at risk of cutaneous and about 2.5% at risk of cutaneous leishmaniasis infection[16]. While the reported annual number of cutaneous leishmaniasis is about 20,000 in Iran, Norouzinezhad et al. 2016 assumed that the actual number of cases could be four to five times higher than the observed[17].

Aims

The abundancy of leishmaniasis in Iran indicates that it is an important task to predict future changes in the ranges of Leishmania vector sandflies in the country. However, studies were performed to model the present development and distribution patterns of the vectors[18],[19],[20] and rarely the future suitability of the parasites[21], the prediction of the potential future changes of the vectors was not conducted yet for Iran. For this reason, it was aimed to model the reference period (1970–2000) and the future (2041–2060) potential distribution of the selected six confirmed and suspected vector sandfly species of cutaneous and visceral leishmaniasis in Iran using Climate Envelope Modelling ecological forecasting method.


  Material & Methods Top


The important physical factors of sandfly distribution in Iran

The climatic and topographical conditions in Iran are various. Only the climate of the higher mountain ranges of the Zagros and the Kopet-Dag Mts. are characterized by the cold, dry and hot or warm summer temperate climates (Dsa and Dsb) according to the Köppen-Geiger climatic classification system[22]. The rest part of Iran mainly belongs to the hot or cold semi-arid and arid climates (BWk, BWh, BSh, BSk). In certain regions of the southern foothills of the Zagros Mts. and on the coasts of the Caspian Sea, the hot or warm summer Mediterranean (Cfa, Csa) climates are characteristic. In the arid and semi-arid regions, the arid deserts, the arid desert shrub, and the forest- steppe are the characteristic vegetation. In the Mediterranean climate areas, forests and woodlands represent the natural (potential) vegetation. The highly populated areas mainly can be found in the forest-steppe regions [Figure 1].
Figure 1: The topographic map with the ten most populated cities (A) and the Köppen-Geiger climatic areas[22] of Iran (B).

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The involved sandfly species

From the reported 22 Phlebotomine sandfly species of Iran[15], six sandfly vectors were involved in the present study. Four of them is the confirmed vector of L. infantum in the country. Although the modelled distribution of the six sandfly species cannot cover the entire distribution of the Iranian sandfly fauna, the use of these six species as indicators can be accepted. The involved vector sandfly species were as follows: Phlebotomus (Larroussius) neglectus Tonnoir, 1921, Phlebotomus (Phlebotomus) papatasi Scopoli 1786, Phlebotomus (Lar.) perfilievi Perfiliev, 1937, Phlebotomus (Paraphlebotomus) similis Perfiliew 1963, Phlebotomus (Paraphlebotomus) sergenti Parrot 1917 and Phlebotomus (Larroissius) tobbi Adler and Theodor, 1930. As shown in [Table 1], the molecularly proved Leishmania vector status of the species is various and cover almost the entire leishmania forms in the country. Among the six species, five are the confirmed vectors of leishmaniasis in Iran, and one (Ph. similis) is a suspected vector of leishmaniasis.
Table 1: The molecularly confirmed vector status of the modelled six sandfly species (*no data in Iran).

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Climate envelope modelling

The impact of global warming on the potential distribution of the selected sandfly species was modelled with climate envelope modelling (CEM)[3]. This ecological forecasting method is also known as niche-based modelling or correlative modelling. Ecological forecasting models relate the current occurrence data of species to the patterns of the ecological (biotic, abiotic, anthroponotic) factors to surmise models of climatic resilience[33]. Among the forecasting models, CEM uses the climatic factors to create the models. Naturally, other ecological modelling techniques also exists, for example, the deep learning-based, neural algorithms, like MaxEnt or the GARP platform, which were also successfully used in the modelling of sandfly species in many studies[34],[35],[36],[37]. Perhaps, the only disadvantage of these techniques is their black-box nature that can make it difficult to gain independent climatic distribution factors.

The first step of CEM is predicting responses of species to the changing climate by drawing an envelope around the domain of climatic variables where the given species has been recently found. In the first, preparatory phase of modelling, CEM tries to find statistical correlations between the climatic factors and the distributions of species[38],[39]. Then, identifying areas predicted to fall within that domain under future climatic conditions the potential future distribution patterns can be modelled[40]. The inner logic of this step hypothesizes that both present and future ranges are primarily dependent on the climatic factors[41]. Then, CEM models the future (or the past) distribution according to the scenarios (or the past climate models) based on the present spatial correspondence between the input climatic and the output occurrence variables[42]. If the climatic distribution limiting values are known, as in this study, the performance of the first step can be omitted.

To what extent climatic factors alone can explain the observed distributions of the species differences is somewhat doubtful[43]. However, it is known that practically all the important environmental variables like the edaphic conditions (in the case of plant and soil-dwelling species), the potential (natural) vegetation type, the annual biorhythm of plants and animals and the potential land use forms are all strongly and mutually dependent on the climate[44],[45]. The occurrence patterns of sandfly species can be also confidently modelled based on climatic factors[5]. The activity and abundance of sandflies are strongly influenced by such climatic factors like the relative humidity and the ambient temperature[46]. These factors also determine the local breeding habitat choice of sandfly species[47].

To summarize the procedure described above, the logical outline of modelling was as follows:

The suitable bioclimatic and aridity index parameters were selected for suitability modelling (Step 1).

The monthly temperature and precipitation factors were converted into bioclimatic variables and aridity indices (see below) if it was possible (Step 2).

The bioclimatic values were used to characterize the environmental (climatic) suitability of an area for an individual sandfly species (Step 3). The model outcomes were displayed solely for the territory of Iran.

Climate data

The WordClim database[48] served as the source of the reference period (1970–2000) and future climate period’s (2041–2060) georeferenced climate data. The reference period data was gained from the WordClim database Version 2, future climatic data were gained from WordClim database Version 1.4. The CMIP5-based future scenarios were based on the downscaled global climate model (GCM) data. The predictions of all the four representative concentration pathways (RCPs) were used in the modelling, namely the models based on the rcps 2.6, 4.5, 6.0 and 8.5 scenarios. The Community Climate System Model 4 (CCSM4) coupled climate model[49] was the original simulation environment of the used 2.5 min spatial resolutions projections.

The used extrema

The study of Trájer et al. 2013[6] was the source of the used climatic extrema of the six Phlebotomus species. The original extrema have temperature and precipitation nature and available in monthly resolution. The factors were gained in a CEM environment by the iterative run of the modelled distribution of selected Phlebotomus species and compared them to the recorded distributions to investigate the optimal number of percentiles to be left from the upper and lower values of the climatic factors. The authors[6] calculated cumulative distribution functions for the monthly temperature and precipitation climatic parameters. Because the used climatic models and scenarios of the present study contain bioclimatic variables, at the first step, the primarily climatic variables were converted to bioclimatic variables. Only those climatic factors were involved, which could be converted into bioclimatic variables. Some bioclimatic variables are not convertible from primary temperature and precipitation variables due to the not clear interpretation of the seasonal affiliation of the factors. For example, the precipitation sums of the driest and the wettest quarter cannot be generalized, because e.g., in the temperate regions of Eurasia, frequently the first quarter is the driest and the second is the wettest in the year; in contrast, in the warm temperate-subtropical climate regions, commonly the third quarter of the year is the driest and the fourth quarter is the wettest.

The upper and lower extrema of six bioclimatic variables could be produced from the temperature and precipitation distribution limiting factors published by Trájer et al. 2013[6]. Furthermore, the extrema of three derived aridity factors were produced. The used bioclimatic variables were as follows: the lower and upper limits of the annual mean of the temperature (bio1lower;upper; °C*10), the lower and upper limits of the mean temperature of the warmest quarter (bio10lower;upper; °C*10); the lower and upper limits of the mean temperature of the coldest quarter (bio11lower;upper; °C*10); the lower and upper limits of the annual precipitation sum (bio12lower;upper; mm); the precipitation of the warmest quarter (bio18lower;upper; mm) and the lower and upper limits of the precipitation of coldest quarter (bio19lower;upper; mm).

To make aridity-sensitive the model, which can be important regarding the general climatic conditions of Iran, the number of factors were enhanced by the introduction of 3x2 aridity factors. These factors were as follows: the lower and upper limits of the mean annual Thornth- waite agrometeorological aridity index (TAIalower;upper; mm°C-1); the lower and upper limits of the Thornthwaite agrometeorological aridity index of the warmest quarter (TAIwlower;upper; mm°C-1) and the lower and upper limits of the Thornthwaite agrometeorological aridity index of the coldest quarter (TAIclower;upper; mm°C-1).

The Thornthwaite agrometeorological aridity index (sing. abbr.: TAIa) values directly can be derived from the precipitation and temperature values[50]. The use of the aridity index is justified by the fact that sandfly species are highly sensitive to the coexistence of the notable solar radiation and atmospheric drought[47],[51].

The common formula of the TAI index is the following:



where

TAI is the Thornthwaite agrometeorological aridity index in mm°C-1,

P is the monthly precipitation sum in mm,

T is the monthly mean temperature in °C.

This means 9x2 bioclimatic factors in modelling in the case of sandfly each species which is an acceptable number for distribution modelling purposes. For the numerical modelling of the potential distribution areas of species, these above-described 9x2 extrema of bioclimatic and aridity-nature climatic factors were used. [Table 2] shows the values of the used climatic factors.
Table 2: The used lower and upper extrema of the bioclimatic factors (bio) and the Thornthwaite Agrometeorological indices (TAIs) (Ph negl: Phlebotomus neglectus, Ph pap: Phlebotomus papatasi, Ph perf: Phlebotomus perfiliewi, Ph serg: Phlebotomus sergenti, Ph sim: Phlebotomus similis, Ph tobb: Phlebotomus tobbi).

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Model identification

Modelling was performed by Quantum GIS 3.4.4 software[52] with GrassGis7.4.1 program[53]. As the projection system, the Lambert Azimuthal Equal Area (EPSG:3035) was selected.

Modelling was based on the binary logic of the Boolean algebra that means the mathematical formalism of the deterministic unit step functions also should be similar:



Where bio1 represents the lower and upper limits of the annual mean of the temperature, the bio10 is the georeferenced climate model data of the lower and upper limits of the mean temperature of the warmest quarter, bio11 is the georeferenced climate model data of the lower and upper limits of the mean temperature of the coldest quarter. Factor bio12 represents the georeferenced climate model data of the annual precipitation sum, bio18 is the georeferenced climate model data of the precipitation of warmest quarter and bio19 represents the lower and upper limits of the precipitation of coldest quarter. Factor TAIa is the mean annual Thornthwaite agrometeorological aridity index, TAIw is the Thornthwaite agrometeorological aridity index of the warmest quarter and TAIc is the Thornthwaite agrometeorological aridity index of the coldest quarter.

The potential areas can be determined according to the following mathematical formalism:



Where A(bio1; bio10; bio11; bio12; bio18; bio19; TAIa; TAIw; TAIc) shows the potential distribution area of the given species, which contains the remaining areas after taking into consideration the temperature and precipitation limitations. The modelled number of the fulfilled factors in each 2.5x2.5 min grid was converted into percentage (%) values. Hereinafter, these values were held as the habitat-suitability factors of the modelled sandfly species. The satisfied 9x2 factor number means the 100%, the 0 satisfied factor represents the 0% suitability value. As an upper limit, the 89% suitability value was applied in the analyses.


  Results Top


Reference period

In the reference period, the suitability patterns for the six sandfly species showed similar patterns; even the exact suitability values were different. The lowest modelled suitability values (<78%) were modelled for the deserted areas such as the Dasht-e Kavir, the Dasht-e Lut, the Central Makran Range and the Mesopotamian part of Iran around the city Ahvaz. The highest values (94%<) were projected in the Mountainous areas of the Elburz (Alborz) Mts., the Zagros Mts. and the Kopet Dag Mts. The west parts of the country seemed to be more suitable for the modelled Phlebotomus species than the east regions. The higher suitability values (94%<) were modelled to the cold arid desert (BWk), cold arid steppe (BSk), temperate dry and hot summer (Mediterranean) climate areas of Iran. The modelled values were the lowest (72%>) in the arid, hot desert climate regions. The highest suitability values could be seen in the case of Ph. perfiliewi and Ph. tobbi, the lowest in the case of Ph. similis. The order of the species according to the average modelled suitability values were as follows: Ph. similis (mean: 67.7%, SD: 28.3%) < Ph. neglectus (mean: 69.8%, SD: 28.1) < Ph. perfiliewi (mean: 84.7%, SD: 12.0%) < Ph. papatasi (mean: 84.9%, SD: 11.1%) < Ph. tobbi (mean: 85.0%, SD: 12.1%) = Ph. sergenti (mean: 85.0%, SD: 11.3%; [Figure 2]).
Figure 2: The modelled reference period's suitability values of the modelled six sandfly species in Iran in 1970–2000.

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Future periods

For the future periods 2041–2060, the suitability values of each species showed increased values compared to the reference period values along with the mountainous ranges. However, depending on the RCPs, in the Central Iranian regions, the decrease of the suitability values could be observed in the case of all species. In general, the decreasing trends were the strongest according to the worsening order of the representative pathway scenarios. The most notable decreases were projected in the deserted areas of Central and East Iranian. The modelled suitability values showed similar but increasing trends in the mountainous ranges, except for the Central Makran Range. The highest suitability values were projected in the northwest areas of Iran, namely in the region of urbanized areas of Urmia, Tabriz and the agglomeration of the Capital, Tehran including Karaj where the suitability values reached the 94-100% values, depending on the model conditions. The major cities along the Zagros Ranges, like Kerman-shah, Isfahan and Shiraz were also influenced [Figure 3].
Figure 3: The projected mean suitability values upon the modelled six sandfly species in Iran in 2041–2060.

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In general, increasing values could be observed in the mountain ranges of the west and the north parts of the country, including the Northwest Zagros, the Elburz and the Kopet Dag Mts. The decreasing suitability values were predominantly characteristic to the lee side of the mountain ranges in the desert and semi-desert areas of Central and Southeast Iran. Because the main wind direction is northeast to southwest in the winter half-year, when most of the annual rainfall occurs, the most notable projected decrease in the suitability values was modelled in the southwest exposed valley sides. These effects are expressly visible in Central Iran. Parallelly, the most notable increases in the suitability values generally could be seen at the north or northeast sides of the mountain ranges. The models predicted the general decreasing trend in the average suitability values. Such cities which are close to the deserted areas, like Qom or Ahvaz, seemed to be less suitable for Phlebotomus species in the future [Figure 4].
Figure 4: The average change of the projected suitability values compared to the reference period's patterns upon the modelled six sandfly species in Iran in 2041–2060.

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In general, the highest modelled suitability values were observed in the case of Ph. papatasi, Ph. sergenti and Ph. tobbi and the lowest values were modelled for Ph. neglectus and Ph. similis [Figure 5]. There were no notable differences in the orders of the modelled values by species regarding the scenarios and periods. The differences in the suitability values between the RCP scenarios were also not notable, in the case of a species and a period, generally, it could not exceed the 3% value [Table 3].
Figure 5: The projected suitability values and the changes compared to the reference period's patterns upon the modelled six sandfly species in Iran in 2041–2060.

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Table 3: The modelled minimum and maximum values of the future (2041–2060) suitability values of the six modelled sandfly species averaged for Iran from rcp2.6 to rcp8.5 scenarios.

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The order of the changes both in the above the 89% and the average suitability values were as follows: cc26(2050) >cc60(2050)>cc45(2050)>cc60(2070)>cc85(2050). The changes in the average suitability values ranged from mild to moderate, but the suitability trend was clearly decreasing [Table 4].
Table 4: The modelled changes of the suitable areas for the modelled Phlebotomus species by periods and RCPs.

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Topographical patterns

Looking at the change of the values according to the elevation categories above the sea level by 100 m steps, between the sea level to the 2200 m, the average values showed a decreasing tendency. Above 2300 m a.s.l., the average values showed an increasing tendency, in general. The absolute value of the changes was greater as the number of the representative pathway scenarios increases. The negative peak was the most expressed at an altitude of 700 and 1100 m a.s.l. [Figure 6].
Figure 6: The averaged changes for 2041–2060 in the modelled suitability values of the six Phlebotomus species compared to the reference period values by 100 m elevation interval bars (the -100 to 0 m elevation interval represents the parts of Iran in the Caspian lowland; the lowest altitudinal value: -27m).

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  Discussion Top


The current findings indicated the dominant role of topographic factors in the determination of the occurrence of sandfly species in Iran. The direct factor, of course, was climate and the climate-influenced other environmental factors, but the topography influenced so strongly the spatial patterns of these factors that this circumstance made the modelled suitability values very similar for all sandfly species both for the present and the future periods. Notable differences were not predicted between the suitability patterns of the vectors of the cutaneous and visceral leishmanias neither for the present nor the future periods. The situation is predicted to be different for example in Europe, where notable differences could be observed in the modelled distribution areas of the same sandfly species[5]. Although the models predicted the extension of the maximally suitable areas, the suitability values averaged for the whole area of the country showed a slight, decreasing trend in the future periods.

It is important to compare the reference period model results with the observed occurrences for validation. Karimi et al 2014[54] presented occurrence data about Ph. neglectus. Ph. papatasi. Ph. perfiliewi. Ph. sergenti. As far as can be seen from the published occurrence data provided, the deserted central areas of the country are less affected and the surrounding mountain ranges are more influenced similarly to the modelled distributions in the present study. It is more convincing in comparison with the model results of Sofizadeh et al. 2017[34],[35],[36] and Hanafi- Bojd et al. 2015[55] who showed highly similar patterns for Phlebotomus major s.l., Ph. perfiliewi, Phlebotomus alexandri Sinton, 1928 and Ph. tobbi to the model results of this study. It is a confirming circumstance because the authors used different modelling technique approach. The modelled reference period suitability patterns of the potential vectors of visceral leishmaniasis, namely of Ph. neglectus. Ph. papatasi. Ph. perfiliewi and Ph. tobbi showeds correlations with the observed incidence patterns of the L. infantum and Leishmania donovani Laveran and Mesnil, 1903 caused visceral leishmaniasis in Iran. Based on the distribution of incidence of visceral leishmaniasis in Iran in 1983–2012 the highest incidence values (0.4-0.73 per 100.000 inhabitants) were observed in Northwest Iran, in the western ranges of the Kopet Dag Mts. and in the southwest ranges of the Zagros Mts[54],[55],[56]. In contrast, less than 0.02 per 100.000 incidences were observed in the arid regions of Central and South Iran. This finding well corresponds to the modelled low suitability values of the deserted regions. The very widespread distribution of cutaneous leishmaniasis showed fewer similarities with the modelled reference period range of the single member of the modelled Leishmania major vectors. Naturally, Ph. neglectus is only one of the potential vectors of this parasite.

The dry and hot summer Mediterranean Csa climates which are characteristic to the north foredeep of the Elburz Mts. (the Caspian coastline) and the southwest ranges of the Zagros Mts. can be favourable regions for the Leishmania vector sandfly species because the development and the overwintering of sandfly species require relatively warm summers and mild winters[57],[58]. In such predominantly Mediterranean climate countries like Italy or Greece, the northward spread or re-emergence of leishmaniasis was observed in the last decade[59],[60] which changes occurred parallelly to the warming of the global climate from the 1980s. Similar changes also can be expected in Iran in the future. In contrast, a major part of the central and east part of Iran, the Dasht-e Kavir, the Dasht-e Lut and the Central Makran Ranges belong to the hot, arid desert climate (BWh); the climate of the foothills bordering these arid zones of Central Iran belongs to the cold arid desert (BWk), the cold and hot arid steppe (BSh and BSk) climates. Although sandfly species can be sensitive to solar radiation combined with dry atmospheric conditions[47], they can survive in dry environments by exploiting the microclimatic conditions of the environment in deserted ecosystems[61]. Sandfly populations exist in wide areas of the world in the arid and semi-arid regions and the factors, that influence the leishmaniasis incidences are similar that factors were described above. For example, in the governorate of Sidi Bouzid, Tunisia, under arid desert (BWh) conditions, Talmoudi et al. 2017[62] found a significant positive correlation between the incidence of zoonotic cutaneous leishmaniasis incidence and rodent density, temperature and the amount of rainfall, but negative correlation with air humidity. In the hyper-xeric environment of the Arava Valley and in general in the Negev Desert, in Israel, Wasserberg et al. 2003[63] found that high sandfly densities were linked to moister soils that also underscores that the aridification of the already arid areas can negatively impact the resident sandfly populations. These facts may indicate that the presence of leishmaniasis in the arid regions highly dependent on the local environmental factors and the sandfly populations of these regions can be vulnerable to even a milder change of the climatic conditions.

Comparing the modelled changes in the suitability values and the altitude patterns, a further characteristic phenomenon is conceivable related to the topography. As it was demonstrated above, the sign of the predicted changes of the suitability values showed characteristic patterns along the elevation gradient. Besides this pattern, it could also be observed that in many cases, the northeast sides of the mountain ranges showed greater values than the southwest sides. Because the used climate models were sensible to such topographical effects like the lapse rate or the foehn effect, in the modelled suitability patter changes, both effects appeared. [Figure 7] shows a part of Central Iran, where on the luv side, which is the north-northeast side of the mountains in Central Iran, the projected changes showed slightly positive values. In contrast, on the lee sides, which are the south-southwest sides of the Iranian mountains, the projected changes were negatives in general.
Figure 7: Comparison of the topography and the projected changes between the average suitability values of the six sandfly species in Central Iran in 1970–2000 and 2041–2060 according to the rcp8.5 scenario.

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In a larger scale, the spatial distribution of rainfall over Iran is mainly determined by the geographical position of the Arabian anticyclone and the mid-tropospheric trough over the Middle East[64]. In a mesoclimatic level, the interplay of the regional cyclones and the topography is very important in the production of precipitation. Modelling the foehn effect in the Alborz Mountains related to an extreme forest fire, Mofidi et al. 2015 found that foehn event occurs due to the presence of high-pressure air masses over the central areas of the country and lee cyclone over the southern Caspian Sea area in winter[65]. It results in a south to north pressure gradient and wind across the Mountain Ranges which has mainly east to west (e.g. the Alborz Mts.) or southeast to a northwest direction (e.g. the Zagros Mts.). It can be concluded that the higher precipitation sum of the mountain slopes exposed to winds can be a potentially important factor of the changing distribution of the vectors of leishmaniasis, although further studied are needed to clarify the extent of this effect.


  Conclusion Top


The climatic models predicted the increase of the maximally suitable areas of the selected sandfly vectors in Iran. In contrast, the shrinkage of the sandfly-inhabited areas in the present arid regions in Central Iran was predicted due to the increasing future desertification of the country. Topography - which indirectly included in climate models - could be an unavoidable factor of ecological modelling of the resident sandfly fauna in the country.

Conflicts of interest: None


  Acknowledgements Top


This work was supported by the Széchenyi 2020 project of the Hungarian Ministry of Innovation and Technology under grant number NKFIH-471-3/2021 project.



 
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