Suitable ecological niches of invasive malaria vector under present and projected climatic conditions in South of Iran

doi:10.21203/rs.3.rs-4752152/v1

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Suitable ecological niches of invasive malaria vector under present and projected climatic conditions in South of Iran

https://doi.org/10.21203/rs.3.rs-4752152/v1

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Background

Unfortunately, the resurgence of malaria occurred in Iran after three years of free malaria conditions, from 2022. Efforts to control malaria through surveillance, diagnosis, treatment and prevention measures have shown progress, but climate change may pose challenges to these efforts, potentially increasing the epidemic potential of malaria in susceptible regions. The research look for to predict the current and future geographical range and suitability of Anopheles stephensi mosquitoes in southern Iran. This information is important for assessing the risk of disease transmission and developing successful strategies for controlling these vectors in the future.

Method

The study compiled a database of An. stephensi findings in Hormozgan province based on field studies and utilized various scientific databases to gather relevant data. Geographical coordinates and distribution data of the species were employed for mapping and forecasting its spread under current and future climate conditions. A total of 19 bioclimatic variables were used for ecological niche prediction by the Maximum Entropy Model. The MaxEnt software was employed to evaluate potential changes in the spatial distribution of An. stephensi in the future, with the model's performance assessed using ROC analysis and AUC values.

Results

Anopheles stephensi distribution in Hormozgan province was studied over the past three decades, with 101 locations reported. The MaxEnt model predicts changes in distribution under different climate scenarios. The model's strong performance was demonstrated by ROC analysis, with AUC values ranging from 0.81 to 0.85 for training data and 0.62 to 0.72 for test data. Five key bioclimatic variables were identified, with Isothermality being the most impactful. The study highlights the significant influence of the Mean Temperature of the Driest Quarter. The modeling outcomes indicate that roughly 19–27% of the province's territory has a significant likelihood of An. stephensi thriving and expanding.

Discussion

The model suggests that 19–27% of the province's land is highly conducive to An. stephensi, with concentrated areas of suitability in the western part of Minab County. The study emphasizes the importance of taking proactive steps to tackle the effects of climate change on diseases carried by vectors, such as malaria.

Health sciences/Health care/Health policy

Health sciences/Health care/Health services

Health sciences/Health care/Quality of life

Biological sciences/Ecology

Biological sciences/Microbiology

Biological sciences/Zoology

Earth and environmental sciences/Ecology

Earth and environmental sciences/Environmental sciences

Earth and environmental sciences/Environmental social sciences

Earth and environmental sciences/Natural hazards

Health sciences/Diseases

Health sciences/Health care

Health sciences/Medical research

Anopheles stephensi

ecological niche

MaxEnt

Hormozgan

Iran

In 2022, the World Health Organization reported 249 million new cases of malaria globally. Despite progress in eliminating malaria in Iran, 1439 cases were reported in the latter half of 2022[1]. In 2023, this number increased tenfold, indicating a resurgence of malaria. Public health officials attribute the outbreaks to factors such as the presence of foreign nationals, an increase in malaria cases in neighboring Pakistan, poor detection of new cases, heavy summer rains, focus on the Covid-19 pandemic, emerging of the invasive Aedes species, and sanctions affecting the purchase of insecticides. The regions most impacted in Iran are the south and southeast [2].

Over the last decade, Anopheles (Cel.) stephensi has extended its range to cover various areas of Asia and Africa. This species is a very effective vector of urban malaria. Its movement to multiple African countries presents a substantial potential risk to malaria elimination and control endeavors in southern Asia and Africa [3–9].

The influence of worldwide climate change on natural ecosystems has been well-documented, and recent findings continue to underscore its far-reaching effects. The most recent evaluation report from the United Nations Intergovernmental Panel on Climate Change (IPCC AR5) highlights a significant increase in global land and ocean temperatures, with a rise of 0.89°C (0.69–1.08°C) from 1901 to 2012 [10]. This temperature increase is anticipated to have profound implications, including the potential alteration of species' suitable ranges and the exacerbation of the negative impact of alien species in various regions [11, 12]. Climate change also affects the El Niño cycle, impacting the transmission of diseases like malaria, dengue, and Rift Valley fever. Moreover, the complex connection between the geographical distribution of malaria, local vectors, and climatic and geographic factors has become more apparent, directly impacting the local epidemiology of malaria. This underscores the importance of considering climate change in malaria control strategies [13, 14].

Efforts to control malaria through surveillance, early diagnosis, treatment, and prevention measures, such as insecticide-treated nets, have shown progress. The Global Malaria Action Plan aims for the elimination of malaria in several countries, but climate change may pose challenges to these efforts, potentially increasing the epidemic potential of malaria in susceptible regions. Therefore, maintaining strong surveillance and preparedness is crucial, especially in developing countries with limited resources. As temperatures continue to rise, the range and behavior of malaria vectors, such as An. stephensi, are likely to be impacted, potentially leading to shifts in their distribution and posing new challenges for malaria control and elimination efforts[15].

By using meteorological data and conducting geographic information system (GIS) analysis, it is possible to predict the potential spread areas for malaria vectors over time and in the future[16].

The Representative Concentration Pathways (RCPs) outline four distinct pathways for greenhouse gas (GHG) emissions, land use, air pollutant emissions, and atmospheric concentrations throughout the 21st century. They have been created using Integrated Assessment Models [17] to inform a variety of climate model simulations, which are then used to predict the potential impacts on the climate system. These projections are then utilized for assessments of potential impacts and adaptation strategies. The RCPs encompass a broad spectrum of GHG emissions found in the existing literature, incorporating a stringent mitigation scenario (RCP2.6), two intermediate scenarios (RCP4.5 and RCP6.0), and one scenario characterized by very high GHG emissions (RCP8.5). Scenarios that lack additional measures to restrict emissions (referred to as ''baseline scenarios'') result in pathways falling within the range of RCP6.0 to RCP8.5[18].

Ecological niche modeling (ENM) is an ecological technique that use computer algorithms to predict the distribution of species over geographic areas and time by utilizing environmental factors. [19]. It helps define the characteristics of a species in a particular area based on environmental factors, particularly those that influence the species' geographic range. [20]. Advancements in geographic information systems (GIS) and the greater accessibility of comprehensive geospatial environmental data, including remote sensing data, have greatly enhanced the application of ENM in environmental studies. [20]. The methodologies employed in ENM enable ecologists to investigate how environmental factors such as temperature, precipitation, digital elevation, land cover, and land use influence the distribution of observed species, facilitating comparisons of different species' ecological niches [21, 22]. Quantitative modeling techniques rooted in ecological niche theory are primarily utilized to comprehend the interplay between species and their environment [23]. Ecological niche modeling forecasts a species' range by leveraging its occurrence data and specific environmental parameters to delineate its niche within an ecosystem, which is then projected onto geographical landscapes [24]. When focused on mosquito vectors, it identifies suitable habitats for each species in areas lacking data, enabling evidence-based assessments of malaria transmission risk in regions not currently covered by interventions [25, 26].

Forecasting the presence of a vector in particular areas poses a difficulty for numerous disease control initiatives aiming to enhance the planning and execution of control strategies and adaptation measures [27]. The maximum entropy model (MaxEnt v3.4.1[28] ) is a species distribution prediction model grounded in the maximum entropy principle that exhibits notable predictive performance compared to various other models for predicting species distribution, particularly in scenarios where there is a lack of species distribution data points. [29], It often outperforms similar prediction models in such cases [30, 31].

The objective of this research was to gather all records data on An. stephensi in Hormozgan Province, South of Iran, over the past decades. Additionally, the study aimed to forecast the present and future distribution as well as the environmental suitability of these species for the years 2030s, 2050s, and 2070s. Understanding the spatial distribution pattern of these vectors is essential for evaluating disease transmission risk in various regions and for designing effective vector control programs.

1. Study area

Hormozgan province, situated in the southern region of Iran between 25°23' to 28°57' N and 52°41' to 59°15' E, covers an area of 70,000 Km². It is bordered by the coasts of the Oman Sea and the Persian Gulf, spanning approximately 900 Km in the south (Fig. 1). The province comprises 13 counties, 38 cities, and 2274 villages. The climate of the province falls within the subtropical category, characterized by warm air as the most prominent feature. Known as one of the hot and arid regions of Iran, it is heavily influenced by semi-desert and desert climates. Summers along the coastal strip are extremely hot and humid, with temperatures occasionally exceeding 52^℃. The average annual temperature stands at 27^℃. The province experiences a protracted hot season lasting nine months, starting from early March and reaching its peak in July and August. This is followed by a brief three-month cool season, beginning in early December and influenced by cool western air masses. Overall, the climate of Hormozgan Province closely resembles that of desert areas, with extremely low levels of atmospheric precipitation, resulting in a notable lack of vegetation [32].

2. Data Gathering

A database of An. stephensi finding areas in Hormozgan province was compiled based on field studies published in recent decades. The researcher conducted searches in various scientific databases such as Google Scholar, Researchgate, SID, Ovid Medline, Scopus, PubMed, Web of Science, Magiran and IranDoc using the scientific keywords "Anopheles", "Anopheles stephensi " and "An. stephensi". All documents published from 1970 were reviewed, and the X and Y coordinates reported in these studies were utilized for mapping and modeling. The distribution data of the target species were employed for mapping and forecasting the spread of this species under current climate conditions as well as those projected for the 2030s, 2050s, and 2070s.

3. Environmental and bioclimatic data

The 19 Bioclimatic characteristic layers consisting of variables maps based on the annual GCM region related to the Eastern Mediterranean were downloaded from the Intergovernmental Panel on Climate Change website for the present and future years including the 2030s, 2050s, and 2070s (Table 1). This data was downloaded from http://ccafs-climate.org/data_spatial_downscaling/ (for Current (1950 to 2000) and 2030s) and from http://www.worldclim.org/cmip5_30s (for 2050s, 2070s). The future years’ Bioclimatic characteristics (with 1 km2 spatial resolution) were based on GCM in RCP 2.6, RCP 4.5, and RCP 8.5), which represent low, median, and high levels of greenhouse gas (GHG) concentration scenarios in the future, respectively[18]. The data related to Hormozgan province was then cropped from these Eastern Mediterranean maps using GIS 10.7 version® software.

4. Ecological niche modeling

Data collected from field studies conducted in the past three decades in Hormozgan province concerning An. stephensi, combined with specific data projected for future years, were inputted into the MaxEnt software to assess potential changes in the spatial distribution of this species in the future. The MaxEnt software, version 3.3.3, generates a probability estimate of species presence, ranging from 0 ("unsuitable") to 0.99 ("best habitat suitability"). All environmental and bioclimatic variables selected for modeling were converted to ASCII format for the subsequent modeling step using the MaxEnt model[17].

The efficacy of the MaxEnt model was evaluated through a threshold-independent Receiver-Operating Characteristic analysis and calculation of Area under the Receiver-Operating Characteristic Curve (AUC) values, with 0.5 denoting random and 1 indicating perfect discrimination. A Jackknife analysis in the MaxEnt model was conducted to assess the contribution of environmental and bioclimatic variables, providing alternative estimates of the most significant variables in the model. Variables that yielded no contribution (0 values) in the analysis were excluded from the final evaluation [33, 34].

The ASCII output map representing the average model for the target species was imported into ArcGIS 10.7.1. The habitat suitability prediction models were categorized into five classes between 0–1 using natural breaks in the symbology tools to generate the habitat suitability model visualization. The percentage of the province's area for each scenario was computed based on the threshold for high-risk locations (probability of presence exceeding 60%). Additionally, GIS software was utilized to identify areas with the highest significance and pinpoint concentrated locations, enabling the identification of the most probable sites for vector presence and suitable ecological niches for vector activities.

Current distribution of Anopheles stephensi

Based on the results of the studies, in the three last decades, An. stephensi reported from 101 different locations in Hormozgan Province. After preparing the geographical coordinates data bank file, its distribution was mapped (Fig. 2).

Figure 3 displays the MaxEnt model output maps and refers to suitable ecological niches for An. stephensi establishment at current and future years in Hormozgan Province, based on three greenhouse gas emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The trend of habitat desirability leans towards warm colors, indicating that the darker the color (red = 1), the more desirable the habitat, while conversely, as the color becomes lighter (blue = 0), the desirability of the habitat decreases. The MaxEnt analysis suggests that the distribution area of An. stephensi are expected to change in the future years under different GCM scenarios (Fig. 3).

2- Validation of the model

The ROC curve demonstrates the model's strong performance and accuracy. Figure 4 illustrates the acceptable validity of the model for current and projected dispersion under three scenarios for the years 2030s, 2050s, and 2070s. The AUC range for the training data was between 0.81 and 0.85, while for the test data, it ranged from 0.62 to 0.72 (Fig. 4).

3- Jackknife tests

The Jackknife test is a method used to demonstrate the impact of each change in the model. Results obtained from this test are based on repeating the model setup. In the accompanying charts, the blue columns represent changes that have the most significant contribution to the model, while fewer green columns indicate that removing those elements results in a significant loss of modeling information. The red column illustrates the combined impact of all variables used in the model. The role of climatic variables in modeling the distribution of An. stephensi in Hormozgan province, both currently and in the future, along with 19 climatic characteristics used in the modeling, are detailed in Fig. 5.

Between 19 bioclimatic variables were used to predict the ecological niches for An. stephensi, according to Jackknife analysis, 5 variables including Isothermality, Mean Temperature of Wettest Quarter (^°C), Mean Temperature of Driest Quarter (^°C), Annual Precipitation (mm), and Precipitation Seasonality have the most significant impact.

In the current modeling, Isothermality stands out as the most impactful variable, exerting a significant effect on the model whether present or absent. In the RCP2.6 scenarios, Isothermality and Precipitation Seasonality emerge as the most influential with present or absent variables, respectively.

In the RCP4.5 scenarios, the Mean Temperature of the Driest Quarter (^°C) stands out as the most influential variable across all three time periods under study. The removal of the seasonal precipitation, Mean Temperature of the wettest quarter, and Annual Precipitation (mm) variables can significantly affect the model for the years 2030, 2050, and 2070, respectively.

In the RCP8.5 scenarios, the Mean Temperature of the Driest Quarter (^°C) is the most influential variable for 2030 and 2070, while for 2050, it is Isothermality. Notably, the removal of the Mean Temperature of the Driest Quarter (^°C) in 2030 and seasonal precipitation changes for 2050 and 2070 has the most significant effect. This study highlights the Mean Temperature of the Driest Quarter (^°C) as the most influential environmental factor in the model.

The ecological niche suitability for An. stephensi establishments in Hormozgan Province are depicted in Fig. 6, with their respective areas detailed in Table 2 (Fig. 6, Table 2). The MaxEnt modeling outcomes, across various scenarios and years, indicate that roughly 19–27% of the province's territory has a significant likelihood of An. stephensi thriving and expanding. Additionally, prone areas with a presence probability above 60% and the centers of gravity of these prone points were identified using GIS software and are visible on every map (Figs. 7, 8). Approximately 15989 Km² of the area in Hormozgan province, which is over 60 percent, is suitable for the establishment of An. stephensi. Furthermore, a map was created using the average geographic coordinates of 5 points of the center of gravity of areas prone to the presence of An. stephensi for different years and scenarios to assess potential changes in the displacement of these susceptible areas. These susceptible points were found to be concentrated in the west of Minab City with minimal variation across different years and scenarios.

Monitoring vectors is a crucial aspect of planning, executing, and assessing vector control measures. Gaining a comprehensive understanding of the distribution of malaria vector populations across a country is an invaluable asset in combating malaria transmission [35]. As global temperatures rise, it is crucial to comprehend and alleviate the impact of climate change on disease vectors to ensure effective public health interventions and the preservation of natural ecosystems.

Anopheles mosquitoes have demonstrated remarkable adaptability to their evolving natural habitats, creating favorable conditions for the proliferation of their species. However, this adaptability comes with consequences for mosquitoes and hosts, as they act as vectors for a range of human pathogens such as malaria parasites, filarial worms, and arboviruses [36, 37]. Particularly noteworthy are the An. stephensi species, which have evolved and adjusted to ecological changes, emerging as invasive and highly effective vector for malaria transmission in Africa and Asia [8, 9].

This study delved into the distribution patterns of the An. stephensi species in Hormozgan Province over the past three decades. The findings revealed a concentration of these species in the central and eastern regions of this province. This clustering may be attributed to the proximity to Sistan-Baluchistan Province, where malaria is highly prevalent, and the movement of malaria cases in the eastern part of the province. These factors likely contribute to the higher incidence of malaria in these areas, possibly due to more conducive climate conditions for An. stephensi establishment, especially when compared to the western regions.

Research in biological ecology, eco-biology, and bio-geosciences is crucial as it allows us to predict optimal growth conditions, and distribution in both present and future scenarios, as well as the potential expansion or contraction of biological distribution in the future [38, 39]. The Ecological Niche Model (ENM) is a widely used predictive tool for assessing suitable habitats for various organisms, with the MaxEnt model standing out as one of its popular variants. In the context of Hormozgan province, the MaxEnt model was utilized to evaluate potential ecological niches for An. stephensi, considers current and future years under three greenhouse gas emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The analysis suggests that the geographic range of An. stephensi is likely to change in the coming years across different GCM scenarios.

Moreover, the MaxEnt model has been instrumental in identifying suitable ecological niches for several crucial malaria vector species in the southern regions of the country, including An. stephensi, An. fluviatilis and An. culicifacies [40, 41]. This model has also been applied in diverse geographic regions and for various species. For instance, Fuller and colleagues used the MaxEnt model in 2012 to project the distribution of the An. albimanus complex in the Caribbean and Central America region until 2080. Sinka and colleagues employed the model in 2010 to predict the distribution of Anopheles mosquitoes in African, European, and Middle Eastern regions [36, 42]. Additionally, Foley and colleagues utilized the model in 2008 to forecast the distribution of the An. minimus complex, a primary vector in Southeast Asia, alongside the GARP model [43]. Furthermore, Zhoupeng and colleagues conducted a prediction of the distribution status of four species, An. dirus, An. minimus, An. sinensis, and An. lesteri, in China using GCM models and three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5) for the present, 2030s, and 2050s [33].

Among the 19 bioclimatic variables used for the ecological niche prediction of An. stephensi by the maximum entropy model, according to Jackknife analysis, Isothermality is the most influential variable in the model. Isothermality is defined as the quantification of day-to-night temperature oscillations in relation to the seasonal temperature oscillations. This metric is particularly relevant for tropical, insular, and maritime environments. Understanding Isothermality helps in assessing how temperature fluctuations within a month compared to the year can impact species distribution [44]. In MaxEnt modelling by using projected climatic variables, Isothermality (Bio3) became the most influential predictor in Mansonia africana mosquito (Vector of Rift Valley fever in Kenya) [45]. Isothermality (Bio3) was identified as the most influential among the 19 bioclimatic variables in the distribution of An. gambiae [35, 46].

Other variables such as Mean Temperature of Wettest Quarter (^°C), Mean Temperature of Driest Quarter (^°C), Annual Precipitation (mm), and Precipitation Seasonality were also found to have considerable impacts. This discovery suggests that the optimal temperature and Precipitation played critical role in determining the spatial distribution of An. stephensi. Anopheles and Plasmodium are both influenced by temperature sensitivity. Given that mosquitoes are ectothermic organisms, temperature plays a crucial role in the developmental and mortality rates of each life stage [47, 48]. Moreover, the gonotrophic cycle in adult female mosquitoes, which involves the blood meal-egg laying process, is also influenced by temperature fluctuations [38]. A notable correlation was observed between the monthly density of An. stephensi larvae and adults and the levels of precipitation and mean temperature [49].

Based on the MaxEnt modeling findings in this research, around 19–27% of the land in Hormozgan province is identified as highly suitable for the proliferation and dispersal of An. stephensi, with suitability levels exceeding 60%. These areas include the counties of Bandar Abbas, Minab, Jask, Bashagard, the islands of Qeshm, Kish, Hormoz, and a small area of Bandar Lengeh. In terms of elevation, these areas are more suitable for flat areas in the province compared to the topographic map of the province. This prediction perfectly matches with the results of the flat nature of the majority of An. stephensi species. The centers of gravity of areas suitable for the presence of An. stephensi were concentrated with very little difference for different years and scenarios in the western part of Minab County. This county is Located in Iran's tropical region [13] and experienced an average temperature of 27.4 ± 0.9 ºC over the past 25 years. The lowest recorded temperature, 19.7 ± 1.2 ºC, was noted in February, while the highest, 34.9 ± 0.7 ºC, was observed in July. Despite an annual average precipitation of around 196.8 mm/year, the district is crisscrossed by various rivers that originate from the northern mountains [50]. It appears that this city offers the most favorable conditions for the proliferation of the invasive Anopheles species under all circumstances within Hormozgan province, necessitating the implementation of a structured and all-encompassing strategy for managing the larval habitats in these regions.

The research outlines the geographic spread of primary malaria vectors in Hormozgan province and constructs an ecological niche model to fill knowledge gaps. The model offers data-driven forecasts to assist public health policymakers in shaping upcoming national surveillance and control initiatives. These findings highlight the pressing necessity for proactive actions to tackle the influence of climate change on vector-borne diseases like malaria.

This study carefully predicts the possible distribution of main malaria vector in the current and the impact of climate change on this distribution in future. Also, we tried to use ecological and climate models, such as the MaxEnt model, to help the decisions of politicians and health officials in planning malaria control programs. Therefore, this research shows that the need for preventive measures to deal with the effects of climate change on infectious diseases such as malaria is very urgent.

Ethical consideration

The protocol for this project has been approved by the ethics committee at the Research Deputy of Tehran University of Medical Sciences, Tehran, Iran, with Ethics Code: IR.TUMS.SPH.REC.1397.297.

Conflict of interest statement

The authors confirm that they have no conflicts of interest to declare.

Funding

Declaration

Author Contribution

Conceptualization, Data collection, Writing- Review and Editing: MA, AAHB. MRYE Writing- Review and Editing. All authors read and approved the final manuscript.

Acknowledgments

We express our gratitude to the Health Deputy, Hormozgan University of Medical Sciences, as well as the entomologists at Bandar Abbas Health Center, for their invaluable assistance in facilitating the field operations of our research. We also extend our heartfelt thanks to the residents of the villages under study for their wholehearted cooperation with our team of researchers

Data Availability

The author confirms that all data generated or analysed during this study are included in this published article. Furthermore, primary and secondary sources and data supporting the findings of this study were all publicly available at the time of submission.

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Table 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

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Suitable ecological niches of invasive malaria vector under present and projected climatic conditions in South of Iran

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Version 1

Abstract

Background

Method

Results

Discussion

Figures

Introduction

Material and Methods

1. Study area

2. Data Gathering

3. Environmental and bioclimatic data

4. Ecological niche modeling

Result

2- Validation of the model

3- Jackknife tests

Discussion

Conclusion

Declarations

Ethical consideration

Conflict of interest statement

Funding

Author Contribution

Acknowledgments

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1