Macrophomina phaseolina (Tassi) Goid. is one of the most devastating necrotrophic, seed- and soil-borne pathogenic fungus that belongs to the Botryosphaeriaceae family (Dell’Olmo et al., 2022; Ortiz et al., 2023). It infects more than 800 plant species over the world, including economically important crops such as Glycine max (L.) Merr. (soybean), Helianthus giganteus E. Watson (sunflower), Vicia faba L. (bean), Gossypium hirsutum L. (cotton), Sesamum indicum L. (sesame), and cereal plants causing charcoal rot, dry root rot, wilt, blight, and damping-off diseases (Farr and Rossman, (2022); Tančić Živanov et al., 2019; Kaur et al., 2023). This fungus is characterized by forming a spherical aggregating mass of hyphae called microsclerotia, which can survive up to 15 years in soil and crop debris as a resistant structure to overcome several inadequate environmental conditions, making disease control a challenge (Marquez et al., 2021).

Climate has a major role in the prevalence of M. phaseolina which is thermophilic in nature and reflects a critical correlation with soil and environmental factors (Bashir, 2017). For disease occurrence, high temperature and low moisture played a pivotal role in the development and distribution of this fungus, where maximum disease was observed at 25-32°C air and 23-35°C soil temperature (Bashir, 2017; Marquez et al., 2021). Some other factors are also responsible for the occurrence of disease such as different pathogen strains, inconsistency in disease resistance and susceptibility, and soil physical and chemical characteristics that alter the interaction between pathogen and host (Bashir, 2017).

Generally, M. phaseolina is geographically distributed in tropical and sub-tropical areas with semi-arid weather (Wrather et al., 2001; Sarr et al., 2014). Despite the fact that M. phaseolina is a disease that thrives in warm climates, it has been observed in recent years to spread in a number of different locations and is now ubiquitous all over the world (Veverka et al., 2008). According to the Intergovernmental Panel on Climate Change (IPCC), one of the primary reasons for its appearance is climate change. According to the IPCC, global warming is likely to continue, and the average temperature of the earth’s surface is expected to rise by 0.3–4.5 degrees Celsius (Chen et al., 2022). Thus, this phenomenon has disparate effects on biodiversity and has altered fungal attributes, resulting in yield losses and economic damage (Ghini et al., 2012; Dell’Olmo et al., 2022). Nowadays, the fungus can adapt to various agroecological conditions, and its aggressivity fluctuates depending on different environmental factors (biotic and abiotic) and geographic areas (Mihail and Taylor, 1995; Fang et al., 2011; Borer et al., 2016).

Climate change has introduced additional hurdles in safeguarding crops from fungal infections, jeopardizing food security. Understanding fungal dispersal and predicting appropriate future environments are essential for implementing preventative measures for its prevention and control (Dietzel et al., 2019; Savary et al., 2019). Examining the spatial distribution of infections yields critical insights into their prevalence and the influence of environmental conditions on phytopathogens and epidemics. A variety of spatial statistical approaches have been employed to characterize the spread of fungal diseases and affected plants (Wu et al., 2001; Taliei et al., 2013). The clarification of the present distribution status of M. phaseolina in relation to climate change and the constraints of current mitigation efforts is crucial since there are numerous opportunities for future research on this fungus (Pandey and Basandrai, 2020).

Geographic Information Systems (GIS) facilitate the mapping of pathogen distribution and the spatial modeling of environmental factors influencing disease occurrence by describing, analyzing, and visualizing data associated with geographic coordinates (Fischer and Nijkamp, 1992; Murad and Khashoggi, 2020). It is regarded as an advantageous instrument for forecasting species proliferation and assessing infestation impacts (Alkhalifah et al., 2022). Conserving species in their native habitats needed comprehension of their spatial distribution patterns and ecological interconnectedness. Distribution Models (SDMs) integrate geographical data from various sources utilizing GIS tools (Balakrishnan et al., 2018).

Species distribution modeling (SDM) is a crucial technique that delineates the specific habitat of each species (Runquist et al., 2019; Deneu et al., 2022). In recent years, numerous modeling software applications utilizing various mathematical methods have been created to achieve this objective; however, MaxEnt (Maximum Entropy Model) and DIVA-GIS are the most effective and precise tools employed to assess the impact of climate change on diverse fungal species. CLIMEX, GARP, and HABITAT are widely utilized methods for assessing the future distribution of certain species in response to climate change; nonetheless, their efficacy has been noted to be inferior to that of MaxEnt (Shabani et al., 2014; Hosni et al., 2020; Zurell et al., 2020).

This study aims to examine the effects of climate change on the worldwide occurrence of Macrophomina phaseolina (Tassi) Goid., a critical soil-borne fungal disease that jeopardizes food security in many crops. This research aims to estimate the current and future distribution of M. phaseolina under various climatological scenarios by leveraging a comprehensive dataset of occurrence records of this pathogen and employing species distribution modeling techniques, notably the Maximum Entropy (MaxEnt) model. The research aims to discover critical environmental factors affecting the habitat appropriateness of the fungus and to evaluate how shifting climate conditions may impact its regional distribution. This study seeks to address the crucial question: How will climate change influence the distribution and prevalence of Macrophomina phaseolina in the forthcoming decades?

Undoubtedly, climate change has become the center of world attention in recent years. This phenomenon affects the biodiversity of living organisms, including fungi, leading to the extinction of some species, and increasing the aggressivity of others (Sax et al., 2013; Nnadi and Carter, 2021; Mora et al., 2022). One Health (OH) is the concept that the health of humans, animals, plants, and their shared ecosystem is inextricably linked (Centers for Disease Control and Prevention (CDC), 2022). This approach tackles burgeoning problems such as food safety (Garcia et al., 2020). Plants supply over 80% of the food consumed by humans and are the main source of nutrition for livestock, however, plant diseases often threaten the availability of plants for humans and animals (Savary et al., 2017; Food and Agriculture Organization of the United Nations (FAO), 2020). In addition, agricultural crops may act as carriers of many human pathogens and harmful fungal-based toxins, making these plants the main origin of foodborne outbreaks (Rizzo et al., 2021). Increasing the studies in these fields will demonstrate the value of the OH approach for the perception and mitigation of the negative impacts of these issues.

Macrophomina phaseolina can cause substantial yield losses in crops such as Glycine max (L.) Merr. (soybean), Sorghum bicolor (L.) Moench (sorghum), and several cereals crops under high temperatures and low soil moisture (below 60%), impacting the incomes of farmers and threatening global food security (Kaur et al., 2012; Ghosh et al., 2018). Also, this fungus produces several types of mycotoxins, such as phaseolinone, mullein, kojic acid, and moniliformin, which negatively impact food safety for humans and animals (Khambhati et al., 2020). So, studies on species range changes in the near and long future are crucial for implementing effective management measures and conserving valuable species.

The present study forms a step for better elucidation of the habitat requirements of M. phaseolina and how it will respond to climate change. The results showed that the choice of environmental variables has a certain effect on the prediction of niche models. Many researchers who use the MaxEnt model to predict the distribution of species non-selectively use all the environmental factors or most environmental factors. The environmental variables, sourced from the WorldClim database, are derived from temperature and precipitation data tailored to the specific requirements of occurrence calculations. Consequently, there are unavoidable correlations between the autocorrelation of these variables and other matters (Merow and Silander, 2014; Remya et al., 2015; Yi et al., 2016). The predominant method employed for assessing model accuracy is the ROC curve method (AUC method), which is now acknowledged as a specialized model evaluator. It delivers performance evaluation data across all threshold ranges, as it is unaffected by diagnostic thresholds (Wang et al., 2018). The generated habitat suitability of our models coincided closely with the actual occurrence of fungus records with a high AUC value equal to 0.902, implying a close association between the model and the species’ ecology. Furthermore, the TSS value of 0.8 indicated that the model predictions and the dispersion of the fungus were in perfect accord (Naeem et al., 2018).

The most important influencing parameter to M. phaseolina distribution is the temperature, agreed with previous studies in microbial and biological organisms (Makori et al., 2017; Sohail et al., 2020). Temperature is a key factor in fungal growth dynamics and understanding the effect of temperature on fungal growth is an essential part of fungal physiology (Ancin-Murguzur et al., 2018; Mustafa et al., 2023). The majority of fungi are mesophilic where they can grow at temperatures within the range from 15°C to 37°C with an optimal temperature of 25–30°C (Aguilar-Paredes et al., 2023). Understanding the impact of temperature on fungal communities will aid the design of effective management strategies against climate change and associated microbial risks (Ibáñez et al., 2023).

Here are a few essential considerations emphasizing the significance of limitation factor maps in SDM (Warren et al., 2008; Elith et al., 2010): Limitation factor maps assist in identifying the principal environmental factors that affect species dispersal. Through the examination of the correlation between species occurrence data and environmental variables, researchers can identify the most significant elements influencing habitat suitability for species. Incorporating limiting constraints into species distribution models enhances their accuracy and predictive capability. III) They offer insights on the ecological necessities and tolerances of a species. By delineating the variables that constrain its spread, scientists can enhance their comprehension of the species’ niche and the spectrum of environmental conditions in which it can endure. This knowledge enhances our comprehensive understanding of species-environment interactions and aids in evaluating species’ responses to environmental alterations. IV) They serve as essential instruments for conservation planning and management. They can assist in identifying regions with high habitat appropriateness for a species, places presently unsuited but capable of becoming suitable with appropriate interventions, and regions expected to remain unsuitable in the future due to constraints imposed by specific causes. This information is essential for prioritizing conservation initiatives, identifying crucial habitats, and executing successful management techniques (Moritz et al., 2012; Nabil et al., 2020).

The latest prediction map ( Figure 7 ) indicates the global dissemination of M. phaseolina, corroborating numerous prior studies on its ecology and distribution (Marquez et al., 2021). Western Europe exhibits high to extremely high habitat suitability, whilst the colder nations in Eastern Europe demonstrate low suitability. This may result from the snowy conditions that inhibit fungal growth. Numerous studies have examined the impact of temperature on fungal development (Cesondes et al., 2007; Das et al., 2008; Cesondes et al., 2012). In Africa, the fungus exhibits exceptional climate suitability in equatorial, subequatorial, and tropical regions, where it endures elevated temperatures due to microsclerotia (Manici et al., 1995; Akhtar et al., 2011; Lodha et al., 2022). Sahara deserts and regions with elevated precipitation levels have limited habitat appropriateness, with insufficient soil moisture identified as a significant predisposing factor for the fungus (Goudarzi et al., 2008; Lodha and Mawar, 2020). India, China, and Southeast Asia exhibit exceptional compatibility for M. phaseolina. The fungus is predominantly found in the Old World; nevertheless, the results suggest that its habitat is also suitable in temperate regions of North America, southeastern South America, and western Australia.

The future predictive modeling and calibration maps indicate the spread of the fungus towards Eastern Europe, specifically Russia and Ukraine. The minimum temperature of the coldest month (bio_6) facilitated the fungus’s adaptability to low temperatures (3.3°C to 10°C). This disruption may result from the adverse effects of global warming, which will certainly jeopardize the future production of numerous commodities, including Triticum aestivum L. (wheat) and Zea mays L. (maize) in Russia and Ukraine, representing over 30% of global wheat trade (US Department of Agriculture World Agricultural Supply and Demand Estimates (WASDE), 2023). Conversely, the fungus will forfeit the majority of its habitats in Africa due to the anticipated high temperature increase in equatorial regions and other areas that would be above the fungus’s tolerance threshold. In the New World, the prevalence of fungus remains largely unchanged. These maps and data will assist specialists in plant pathology and management in assessing the future distribution risk of M. phaseolina.

The current study provides updated and detailed maps about the global prevalence of M. phaseolina under changing climate through the present and future periods, where this work is considered the first modeling to anticipate the global prevalence of this fungus using the robust predictive powers of MaxEnt and DIVA-GIS. In comparison to other works related to the distribution of fungi, (Alkhalifah et al., 2023). reported differences in habitat suitability between the current and future distributions of Fusarium oxysporum, especially in Europe, due to global warming. The most effective climatological parameter of F. oxysporum distribution was the annual mean temperature (Bio-1), which disagreed with our work. Also, the annual mean temperature (bio-1) formed the most contributed bioclimatological parameter to Aspergillus niger distribution (Alkhalifah et al., 2022). On the other hand, A. niger will gain new habitat in several parts of the world (the Eastern part of Europe and Central Asia) where it will form emergence medical and agricultural issues (Alkhalifah et al., 2022). From the limited works about the distribution of fungi responding to climate change, Europe will be a good habitat for emerging fungi. Also, the study of (Wu et al., 2024) utilized the MaxEnt model and ArcGIS to map suitable habitats for the invasive weeds Avena sterilis and Avena ludoviciana in Asia, highlighting the significant risk they pose to dryland crops under climate change.

Considering other environmental covariates involving human population, land cover, host animal distribution, and vegetation index could aid in ameliorating them (Hosni et al., 2022). However, the decreasing of future data on these variables may limit their utility in researching the effect of climate change on present distribution models. Despite it all, our research contributes to a greater understanding of the current and future distribution of M. phaseolina worldwide. The models generated in this study analyzed the impact of climate change on the existing and future prevalence of the fungus using bioclimatological factors.

When it comes to forecasting the future distribution of dangerous fungus, SDM is an important and powerful tool. Based on the findings of this research, it was determined that M. phaseolina will continue to spread towards Eastern Europe and some regions of South America, whereas it will be eradicated from certain locations in Africa and Asia. The current findings of this research serve as a cautionary tale about the ways in which climate change can potentially alter the geographic distribution of M. phaseolina across the globe. Therefore, in order to improve our ability to predict the spread of this fungus, we need to conduct additional research on it, with a particular focus on the habitat suitability that influences its invasion pattern. In order to combat the phenomenon of fungal adaptability to a variety of environmental conditions, it is of the utmost importance to create advanced monitoring and control measures.