Introduction

Moringa oleifera Lam. and Moringa stenopetala (Baker f.) Cufod. are among the 13 or more known species in the monogeneric family Moringaceae1. Although the two species are similar in many ways, they have contrasting distributions, which makes them interesting both from a theoretical and practical perspective. How some species have wide distribution, while others are restricted in their distribution has fascinated biogeographers and ecologists for years. While M. oleifera is native to the Indian subcontinent, it is extensively cultivated in countries like the Philippines, Cambodia, and the Caribbean Islands2. On the other hand, the native range of M. stenopetala is restricted to a small part of East Africa including Ethiopia and northern Kenya3,4,5. Indeed, M. stenopetala is only known in the wild from northern Kenya, but it is widely cultivated in Ethiopia4. Reports from Djibouti, Malawi, Senegal, Somalia, Sudan and Uganda are probably based on recent introductions4.

Both species are promoted in the tropics as multipurpose species in agroforestry systems to provide animal fodder, human food and climate change mitigation6,7. For example, the Food and Agriculture Organization (FAO) has been promoting Moringa in agroforestry programs since the 1990s8. Moringa trees have several uses in different parts of the globe. Almost every part of the tree is edible and highly nutritious9 and contain many essential minerals and vitamins10,11. Moringa oleifera is known to be rich in proteins, vitamin A, minerals, essential amino acids, antioxidants, and flavonoids, as well as isothiocyanates. The extracts also have multiple nutraceutical or pharmacological functions including anti-inflammatory, antioxidant, anti-cancer, hepatoprotective, neuroprotective, hypoglycemic, and blood lipid-reducing functions12. Furthermore, the Moringa tree has a significant contribution to traditional medicines in Asia and Africa. Different parts of M. oleifera and M. stenopetala have been used in traditional medicine to treat several health issues such as ascites, rheumatism and snake bites, and cardiac and circulatory stimulants4,7,13. The leaves of M. oleifera can also be used as natural plant growth promoter as they contain several growth hormones and mineral elements14.

Moringa is well adapted to adverse conditions where other plants have a very low level of survival rate15. Recently, intercropping of Moringa with other crops has been promoted because it improves yields, providing food and cash16. In some regions, Moringa seeds are used for water purification, and this is gaining interest among researchers as chemical water treatment is costly especially in many developing nations17. The use of Moringa seed was shown to reduce the turbidity of water by up to 90% and microbial growth by 95%, and hence a cost-efficient solution for water pollution18. It was reported that the Moringa plant is more effective in removing water turbidity than other natural coagulants19. In addition, Moringa products also have high commercial value in many countries, and their cultivation can help farmers to generate income7,20,21.

The cultivation of M. oleifera and M. stenopetala is also increasingly recommended as a climate-smart solution16,22. As a result, planting of Moringa in agroforestry systems has increased22. In countries like Niger, Ethiopia and Indonesia, different Moringa-based agroforestry systems are being practised. In Ethiopia, root crops such as Ensete ventricosum, Ipomoea batatas, Colocasia esculenta, Manihot esculenta and Dioscorea alata are intercropped with Moringa oleifera23. Similarly, in Niger cereal crops, fruiting trees, henna, and lettuce are grown in Moringa-based agroforestry systems24. Food crops like peanuts, corn and cassava are being cultivated with Moringa oleifera tree in alley cropping system in Indonesia25. In India, several efforts are being made to popularise Moringa-based agroforestry systems22. Rathore et al.26 showed that Moringa-mung bean-potato could be one of the most productive agroforestry systems, which could produce goods up to 36.2 Mg ha−1.

While Moringa species have been hailed for their multipurpose uses, concerns have also been raised about their potential to become invasive alien species with their increasing commercialisation. For example, in South Africa, Moringa is on the Species Under Surveillance for Possible Eradication or Containment Targets, where it is classified as Category E (i.e., fully invasive)7.

Currently, the two species are being promoted outside their known geographic range without adequate knowledge of suitability of the target areas. Information is lacking on the current distribution of Moringa species and the future habitat suitability under climate change. In this study, we applied an ensemble of species distribution models (SDMs) to map potentially suitable habitats with the aim to inform conservation and promotion of the species. SDMs are an increasingly important tool in ecology, biogeography and conservation science27. SDMs are able to predict areas where environmental conditions are appropriate for the survival of a species, even where it is not currently present, which is called the potential distribution or fundamental niche28. SDMs are useful in quantifying the correlation between environmental factors and the distribution of species29,30. The use of ensemble models is also increasing in SDMs because of the opportunity they provide for evaluation of possible climate change impacts on plant species and identifying populations that are threatened and areas where urgent conservation measures are needed28. To predict an outcome, ensemble modelling creates multiple models. These models can use different algorithms (regression/machine learning) or datasets for training. The ensemble model combines the predictions of each base model into one final prediction for new data. The goal of ensemble modelling is to lower the prediction error31,32. There are not many studies on ensemble modelling of the distribution of Moringa species; the only one is on the mapping of Moringa oleifera in South Africa33. Hence, we assessed the predicted habitat suitability of these Moringa species in consideration of different environmental variables. Similarly, it is also hypothesized that both the Moringa species are expected to change their habitat ranges with the changing of environmental conditions. Therefore, this study aims to estimate the current and future habitat suitability of M. oleifera and M. stenopetala in the tropical regions under climate change scenarios and to identify the influential environmental factors affecting the spatio-temporal distribution of Moringa species. The results of this exercise are expected to inform development of good policies and practices for promoting these species and in the event that they become invasive34.

Results

Model performance

With an AUC of ≥ 0.85, the ensemble models used here demonstrated a moderate performance. The various performance metrics used for comparing the individual models and ensemble model are summarized in Table 1. In the case of M. stenopetala, RF, MaxEnt and BRT predicted the current suitability with an AUC of ≥ 0.93. Similarly, the TSS of the model indicated good predictive performance (TSS value ≥ 0.55) for the distribution of M. oleifera except for CART (TSS < 0.51). The TSS values for M. stenopetala indicated a very good performance (> 0.70) for all models except CART. Therefore, we excluded the CART model from the ensemble function for both species due to its lower accuracy. In M. oleifera, RF outperformed all other models, with a high COR (0.67) and a comparatively lower deviance (0.8). In the case of M. stenopetala, RF performed better (COR = 0.78; deviance = 0.12) than all other models.

Table 1 Performance evaluation of SDMs using different statistical parameters for the current distribution of Moringa oleifera and Moringa stenopetala in Tropical countries.
Full size table

Current habitat suitability

Currently M. oleifera is distributed over an area of 21.1 million km2 whereas M. stenopetala is distributed over 0.91 million km2 area. The current distribution of M. oleifera covers countries in Southeast Asia, Central Africa, Central America, South America, and Oceania (Fig. 1). On the other hand, M. stenopetala was chiefly distributed in its native range in Ethiopia and Kenya, and very few were recorded from Central America (Fig. 1). However, a few individuals of M. stenopetala were also reported from India. The ensemble model projections indicated high habitat suitability for M. oleifera in India, Ghana, Burkina Faso, Mexico, Parts of Venezuela, Colombia, and Australia. On the other hand, the model projected that only Ethiopia and parts of Kenya will be highly suitable for the future distribution of M. stenopetala. Under the different climate change scenarios, parts of Central America may also provide suitable areas for M. stenopetala but not M. oleifera.

Figure 1
figure 1

Map of locations of data used for the species distribution modelling (a) and the current distribution of Moringa oleifera and Moringa stenopetala in the tropical regions. All maps were generated by authors of this work using ArcGIS 10.8.2 (https://www.arcgis.com/index.html).

Full size image

The estimated potentially suitable habitat assuming the different climate scenarios for M. oleifera and M. stenopetala in tropical countries is presented in Supplementary Figs. S1 to S4. Tropical countries cover a total area of about 49.97 million km2, of which 4.06 million km2 (8.12%) and 0.07 million km2 (0.13%) was projected to be highly suitable for M. oleifera and M. stenopetala, respectively, in the current scenario. In addition, 6.02 million km2 was predicted to be moderately suitable, 11.03 million km2 was least suitable, and 28.86 million km2 was unsuitable for M. oleifera. On the other hand, 0.02 km2, 0.65 million km2, and 49.05 million km2 were deemed to be moderately suitable, least suitable and unsuitable for M. stenopetala under the current climate scenario. The model predicted that about 4.06–4.19 million km2 by 2050 and 3.63‒4.43 million km2 area will be highly suitable for M. oleifera by 2070 under different climate scenarios. On the other hand, the highly suitable area for M. stenopetala was predicted to be about 0.05‒0.13 million km2 by 2050 and 0.04‒0.17 million km2 by 2070 (Fig. 2). Habitat suitability for both M. oleifera and M. stenopetala was projected to change under the different climate change scenarios (Figs. 3, 4, 5, 6). By 2050, the highly suitable area will expand by up to 3.2% for M. oleifera under SSP2-4.5. Under SSP2-4.5, the least suitable area would decrease by up to 16.2% by 2050. These areas will decrease by 3.9% under SSP1-2.6, but under SSP2-4.5, SSP3-7.0 and SSP5-8.5 this area will increase from 1.3 to 10.9% by 2070. Under SSP2-4.5, SSP3-7.0 and SSP5-8.5, the overall habitat suitability area for M. stenopetala is projected to increase from 5.6 to 97.9%.

Figure 2
figure 2

Changes in habitat suitability area in current and future (2050 and 2070) climate scenarios for M. oleifera and M. stenopetala.

Full size image
Figure 3
figure 3

Changes in habitat suitability area of Moringa oleifera and Moringa stenopetala under SSP1-2.6 by 2050 (top) 2070 (bottom). All maps were generated by authors of this work using ArcGIS 10.8.2 (https://www.arcgis.com/index.html).

Full size image
Figure 4
figure 4

Changes in habitat suitability area of Moringa oleifera and Moringa stenopetala under SSP2-4.5 by 2050 (top) 2070 (bottom). All maps were generated by authors of this work using ArcGIS 10.8.2 (https://www.arcgis.com/index.html).

Full size image
Figure 5
figure 5

Changes in habitat suitability area of Moringa oleifera and Moringa stenopetala under SSP3-7.0 by 2050 (top) 2070 (bottom). All maps were generated by authors of this work using ArcGIS 10.8.2 (https://www.arcgis.com/index.html).

Full size image
Figure 6
figure 6

Changes in habitat suitability area of Moringa oleifera and Moringa stenopetala under SSP5-8.5 by 2050 (top) 2070 (bottom). All maps were generated by authors of this work using ArcGIS 10.8.2 (https://www.arcgis.com/index.html).

Full size image

Niche overlap and distribution area for both species

According to the ensemble model projection, the degree of niche overlap of M. oleifera and M. stenopetala was low in the suitable areas under the current climatic scenario. However, it is projected to increase in future epochs under SSP1-2.6. Under SSP2-4.5 and SSP3-7.0, the niche overlap is projected to decrease over time. The niche overlap is expected to increase under SSP2-4.5 by 2070. In the SSP5-8.5 scenario, the niche overlap of the suitable area is projected to increase significantly (Table 2). The suitable area for both species was found in parts of Africa, specifically in Ethiopia, Kenya, and some parts of Sudan, with overlapping areas increasing in the future under SSP1-2.6 2050, SSP2-4.5 2050, and SSP5-8.5 2050. However, the overlapping area is projected to decrease under SSP3-7.0 2050. Furthermore, the overlapping area is expected to increase under the SSP1-2.6 scenario for 2070, but decrease under SSP2-4.5, SSP3-7.0, and SSP5-8.5 (Figs. 7, 8). Areas where significant overlap is projected to occur by 2070 are mainly driven by soil salinity, water holding capacity, slope, isothermality, and mean temperature of the driest quarter.

Table 2 Ecological niche overlap of suitable habitat between M. oleifera and M. stenopetala.
Full size table
Figure 7
figure 7

Changes in the overlapping area of Moringa oleifera and Moringa stenopetala under SSP1-2.6 and SSP2-4.5 by 2050 and 2070. All maps were generated by authors of this work using ArcGIS 10.8.2 (https://www.arcgis.com/index.html).

Full size image
Figure 8
figure 8

Changes in the overlapping area of Moringa oleifera and Moringa stenopetala under SSP3-7.0 and SSP5-8.5 by 2050 and 2070. All maps were generated by authors of this work using ArcGIS 10.8.2 (https://www.arcgis.com/index.html).

Full size image

Relative variable importance

In the ensemble model, the mean temperature of the driest quarter (Bio 9), precipitation of the wettest month (Bio 13), and mean diurnal temperature range (Bio 2) were the most influential bioclimatic variables in the potential distribution of M. oleifera. Isothermality (Bio 3), annual temperature range (Bio 7), and temperature seasonality (Bio 4) were highly influential in the case of M. stenopetala (Fig. 9). Among soil variables, soil pH, elevation and soil water holding capacity had higher influence on the distribution of M. oleifera, whereas soil salinity, slope, and soil pH were more influential for the current distribution of M. stenopetala. The clay and sand contents had the least contribution to the distribution of both Moringa species (Fig. 9).

Figure 9
figure 9

Relative variable importance of Moringa oleifera and Moringa stenopetala. Soil1 = soil salinity, soil2 = water holding capacity, pH = soil pH, clay1 = , clay2 = , bio2 = Mean Diurnal Range, bio3 = Isothermality, bio4 = Temperature Seasonality, bio7 = Temperature Annual Range, bio8 = Mean Temperature of Wettest Quarter, bio9 = Mean Temperature of Driest Quarter, bio13 = Precipitation of Wettest Month, bio14 = Precipitation of Driest Month, bio15 = Precipitation Seasonality, bio18 = Precipitation of Warmest Quarter, bio19 = Precipitation of Coldest Quarter.

Full size image

Areas that are highly suitable for M. stenopetala were characterized by slightly saline soil of 1–2 dS m−1, a slope of 10–25°, isothermality of 60–90%, annual temperature range of 15–22 °C, and temperature seasonality of 10–20%. The highly suitable areas for M. oleifera were characterized by soil pH of 6–8, precipitation of wettest month of 150–300 mm, mean temperature of the driest quarter of 15–25 °C, mean diurnal temperature range of 8–15 °C, elevation of 100–3000 masl and water holding capacity of 20–50% (Table 3).

Table 3 Characterization of highly suitable areas for M. oleifera and M. stenopetala.
Full size table

Discussion

This study has established the current and future distribution of M. stenopetala and M. oleifera. The strength of this study lies in the use of an ensemble modelling approach to increase the accuracy of the predictions and the use of different performance metrics to assess the models’ performance35. The results have also pointed out areas which are likely to be affected in the event that they become invasive. In such cases, practitioners need to be prepared to manage invasions in areas identified as highly suitable.

The distribution of M. oleifera and M. stenopetala is mainly concentrated in the West and East African countries like Kenya, Uganda, Ethiopia, parts of Sudan, and in parts of North American countries like south of Mexico, Guatemala. In contrast, the distribution of the Moringa tree is limited in countries like Botswana, Namibia, South Africa, Swaziland, and Zambia36, although the extent of suitable areas identified in these countries by our modelling was large.

The larger areas covered by M. oleifera may be explained by its fast growth and adaptation to a wide range of climatic and soil conditions. M. oleifera takes short time to first flowering, which is about 11 months from planting37 compared to the 2–2.5 years taken by M. stenopetala from planting to first flowering37. In addition, M. oleifera has been shown to withstand a wide range of precipitation conditions (annual rainfall of 250‒3000 mm)17. On the other hand, the ideal range of rainfall required for the growth of M. stenopetala is between 500 and 1400 mm38. M. oleifera is also adapted to a wide range of elevations (0‒2000 masl)6 compared to M. stenopetala (400‒1200 m)39. Our models have predicted that M. oleifera can occur at the elevation of 100‒3000 masl. Furthermore, M. oleifera can tolerate and grow well under temperatures ranging between 12.6 and 40 °C, while M. stenopetala grows better where temperatures are 24‒30 °C. M. oleifera grows better in well-drained clay or clay loam soils without prolonged waterlogging38. While M. oleifera can grow in soil with a wide pH range (5‒9), M. stenopetala mostly grows in soils with neutral reaction38,40.

Our model predicted that the overall suitable area of M. oleifera will be reduced, although some expansion may occur by 2070. The habitat suitability of M. stenopetala will decrease under SSP1-2.6, and only Ethiopia and parts of Kenya will be highly suitable for its future distribution. This is because some regions are projected to experience a decrease in temperature and increases in precipitation. These changes may result in a reduction in the area suitable for some species, particularly those adapted to warmer and drier conditions. This is because the cooler and wetter conditions will become more favourable for other species to thrive, displacing species adapted to warmer and drier conditions. As a result, suitable areas for some species may shrink. M. stenopetala appears to be favoured by future climate change scenarios as the highly suitable area tended to increase substantially by 2050 and 2070 under SSP5-8.5. SSP5-8.5 is expected to lead to increased variability and extreme events. These changes may result in an expansion of suitable areas for some species, particularly those adapted to warmer conditions. Similarly, a study on the habitat suitability of Ficus squamosa and Ficus heterostyla revealed a decline in their overlapping areas. However, among the two species, F. heterostyla demonstrated a greater potential for climate change adaptability. This confirms that many species may detect climatic changes and respond in various ways41.

Our results showed relatively high current and future overlap in the distributions of M. oleifera and M. stenopetala. Several factors favour the overlapping of both the Moringa species. Slightly saline soil of 1–2 dS m−1, water holding capacity of 20–40%, a slightly sloppy area between 10°–30°, isothermality between 65–90% and mean temperature of driest quarter of 20–35 °C are some of the favourable factors for the growth of both M. oleifera and M. stenopetala. As a consequence, the overlapping area between M. oleifera and M. stenopetala may tend to increase under current and future climate conditions.

Our model predicted that the distribution of both species is influenced by multiple factors like soil pH, soil salinity, elevation etc., which is in agreement with previous reports. A recent study has reported that elevation is one of the factors affecting the distribution of Moringa species, as it directly or indirectly influences the temperature and soil characteristics42. Additionally, the slope of a site was a very influential factor in the distribution of Moringa species. The majority of Moringa habitat was found on gravelly slopes and rocky mountainous areas with a slope of 40–60% indicating that they prefer well-drained soils42. Soil texture, bulk density, and soil porosity are the controlling factors affecting the WHC of the soil43, and the WHC of soil can affect the growth of plant species. Although M. oleifera is a drought tolerant tree, WHC was one of the significant factors affecting its growth. M. oleifera can grow well on soil having more than 70% WHC compared to soil having a lower WHC44. Our result revealed that WHC between 20–50% can favour the growth of M. oleifera. In changing climatic conditions, temperature and precipitation may severely alter the habitat suitability of these species.

The current distribution of M. oleifera and M. stenopetala was also highly influenced by soil pH and soil salinity. Soil pH in the natural environment has an enormous influence on the soil biological, chemical and physical properties that influences plant growth45. In our analysis, soil having a pH of 6.5–8 had the highest contribution in the current distribution of M. oleifera. However, previous studies have suggested that the Moringa tree can grow well in soil with pH between 4.5 to 8.5; and pH between 6.3 to 7 can improve the growth of the Moringa tree33,46,47. Soil salinity hinders plant growth by lowering leaf water potential, causing physiological and morphological alterations, producing reactive oxygen species, raising osmotic stress and ion toxicity, and changing biochemical processes48. However, it was reported that the Moringa trees can grow well and germinate at lower soil salinity49 of 5 dS m−1.

Our results indicate that currently suitable areas may likely become unsuitable for the growth of M. oleifera and M. stenopetala in future climate change scenarios. A similar finding was also reported for a economically important plant Pinus gerardiana. Where the models predicted a remarkable decline in the potential habitat suitability in the future climate change scenarios50. Climate change influences the physiochemical, and biological properties of soil which relates to the functional properties of soil. Drivers of climate change affects the organic matter status, carbon and nutrient cycling, plant available water, and hence productivity, which in turn affect soil pH51. Furthermore, due to climate change, there will be spatial and temporal changes in temperature and rainfall52. These temperature changes may affect evapotranspiration, including the evaporation of water from soils. As a result, the salinity of the soil will increase that will hinder the growth of plants53. Additionally, erratic rainfall can greatly affect the groundwater table and soil salinity. Lower level of groundwater table can instigate capillary rise which causes the upward movement of salts from the water table to the soil surface, resulted in the accumulation of salinity at or near the soil surface, and may cause salinity stress in the plants42. Moreover, extreme events of anthropogenic climate change can also alter the WHC of soil; the WHC may decrease in warmer climate in the future54. This may possess a severe impact on the distribution of the species because WHC is an important factor for plant growth and can compensate for a lack of precipitation in dry years43.

The results of this study have significant implications for research and development. The baseline information on the current distribution will be helpful to guide future research on the two species. In terms of development, the information provided on habitat suitability would be useful in guiding the targeting of the species to areas where they can be successfully introduce. Several international organizations, governments, and NGOs are making efforts to popularise these species in different agroforestry interventions to improve productivity and human nutrition55. Governments of several developing countries like Ghana, Cuba, and India have focused on Moringa for combating malnutrition and encouraging its cultivation56. However, identifying suitable areas for the plantation and growth is crucial. Output of the present study can be considered for a more effective implementation of these policies through promotion of these species in in areas suitable for their planting. The Moringa trees can act as a good carbon sink, as they produces heavy flushes even during the dry season, and can help in reducing the level of atmospheric carbon dioxide (CO2)57. Furthermore, the Moringa tree can absorb fifty times more CO2 compared to the Japanese cedar tree dominated vegetation and twenty times more CO2 than other vegetation58. In future climate change scenarios, if the highly suitable area for Moringa trees increases, it will also help in the adaptation of these trees for their various beneficial properties. However, to achieve the different goals, these Moringa species need to be planted in suitable niches with careful consideration of their potential for invasivenes. We recommend concerned bodies to promote the species in suitable areas to guarantee success.

Conclusions

Using an ensemble model, we predict the current distribution and future habitat suitable for M. oleifera and M. stenopetala. The analysis has provided evidence that ensemble models can accurately predict the distributions of both M. oleifera and M. stenopetala. It is concluded that the habitat that is highly suitable for M. stenopetala will increase under future climate change scenarios. In contrast, the overall suitable area for M. oleifera will be reduced. Nevertheless, some expansion in the suitable area is likely to occur by 2070. It is also concluded that soil pH and water holding capacity were the major predictors of the current distribution of M. oleifera, whereas soil salinity, elevation and slope are key predictors of the current distribution of M. stenopetala. This study has provided baseline information on the current distribution and possible future habitat suitability. The results are hoped to help researchers, policymakers and practitioners to make informed choices when selecting areas for the promotion of these species under current and future climate change scenarios. Future research should aim to produce fine-scale climate projections that account for regional changes in temperature, precipitation, and extreme weather events. This would enable more precise estimation of viable habitats under various climate change scenarios. In addition, dynamic modelling and multi-species modelling of Moringa species with other species will also be beneficial in understanding the migration and adaptation patterns of Moringa species and how their habitat suitability interacts with other plant species. The maps we provided are also hoped to help in identifying appropriate niches for the cultivation of Moringa species for greater production.

Materials and methods

Study area

The present study covers tropical regions (from the Tropic of Cancer to the Tropic of Capricorn), comprising 122 countries. Of these, 51 are from Africa, 19 are Asian, 27 are North American, 13 are Oceanian, and 12 are from South America. This region accounts for about 36% of the earth’s landmass, and the mean annual temperature in the tropics ranges from 25 to 28 °C59.

The target species

Moringa oleifera is commonly known as a ‘drumstick tree’ or ‘horse-radish tree.’ Moringa oleifera is a fast-growing, drought-resistant, deciduous, dicotyledonous tree with a height of 5–10 m60. Moringa oleifera can grow well in the humid tropics and hot, dry lands11 and endure a range of rainfall from 250–3000 mm and a pH of 5–961. This species can be found at 0‒1000 masl elevation and can be adapted to various soil types38. The tree has a soft trunk, gummy bark, and a tripinnately compound leaf62.

Moringa stenopetala is popularly known as the African cabbage tree, a strongly branched tree with a thick base with white to pale grey or silvery bark. Its trunk can grow up to 60 cm in diameter at its breast, and the tree has smooth wood and soft leaves63. In Africa, Moringa stenopetala naturally grows with the Acacia tortilis–Delonix elata–Commiphora spp. vegetation complex and can be found at an altitude of 400–2100 m. M. stenopetala has no specific soil requirement for its growth38.

Geo-location data

The data on the geo-locations of M. oleifera and M. stenopetala was collected from Global Biodiversity Information Facility (GBIF)64 and published in peer-reviewed literature (Fig. 1a; Supplementary Tables S1 and S2). The GBIF is an international organisation that focuses on making scientific data on biodiversity available via the Internet using web services.

The literature survey was carried out using the search engine “Google Scholar”. Search strings were created comprising the keywords “Moringa oleifera,” “Moringa stenopetala,” “drumstick tree,” “horse-reddish tree,” individually or in various combinations such as “Moringa oleifera in Asia” or “Distribution of Moringa stenopetala”. Deka et al.65 used similar data curation methods to examine the possible effects of climate change on the distribution of the endangered white-winged wood duck (Asarcornis scutulata, 1882) in the Indian Eastern Himalayan region. A total of 1739 (n = 133 literature, n = 1606 GBIF) locations were collected, out of which 1692 were for M. oleifera, and 47 were for M. stenopetala which were considered for niche modelling. However, to prevent the model from over-fitting, the numerous present points were removed from one grid cell66. The spThin67 package was used in the R software version 4.1.3 (R Core Team, 2022). Eventually, 652 occurrence records of M. oleifera (n = 100 literature, n = 552 GBIF) and 43 occurrence records of M. stenopetala (n = 33 literature, n = 10 GBIF) were considered to build the models. These are listed in Supplementary Table S1.

Environmental data

The standard 19 bioclimatic variables and elevations were downloaded from WorldClim version 2.168 with 30 Arc seconds, which are the average for the years 1970–200069. These bioclimatic factors have often been utilized in SDMs for climate prediction based on tree species70. Moreover, 14 soil data layers were accessed from the International Soil Reference and Information Centre (https://www.isric.org)71. Slope data was created from the DEM in the ArcGIS spatial extension. The spatial resolution of all the environmental variables was 30 Arc seconds (~ 1 km). The complete list of variables are presented in Supplementary Table S2.

Among 35 variables (Table S2), a few (18 in M. oleifera and 24 in M. stenopetala) were filtered using variance inflation factor (VIF) to avoid the effect of multicollinearity (Supplementary Table S3). We used R package called “usdm” to calculate VIF. Values greater than VIF 10 signify that an unacceptably large quantity of collinearity has been eliminated70. Therefore, the modelling was performed using 17 variables for M. oleifera and 11 for M. stenopetala. Some variables were calculated from others. For example, mean diurnal temperature range was calculated as the difference between mean of monthly maximum temperature and minimum temperature.

Future climate scenario data

To predict habitat suitability under future climate scenarios, we used the Coupled Model Intercomparison Project Phase 6 (CMIP6) downloaded from the WorldClim dataset. The CMIP6 is a recent climate projections data with the same spatial resolution of current period data from WorldClim for 2050 (average for 2041–2060), and 2070 (average for 2061–2080), under four shared socio‐economic pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Among these scenarios, SSP3-7.0 was the new scenario combinations, and the SSP1-2.6, SSP2-4.5 and SSP5-8.5 were the updated version of the RCP scenarios71. In this study, we exclude SSP4-6.0 as it has the highest inequality, followed by SSP3-7.0. Both SSP1-2.6 and SSP5-8.5 feature relatively equitable development and a rapid catch-up of the world’s poorest countries over the coming century73. SSP1-2.6 is the lower end of radiative forcing expected to produce < 2 °C warming, and for brevity here we will refer to it as “low” emission scenario. SSP2-4.5 represents the medium range of future pathways, and for brevity here, we will refer to it as “medium”. SSP5-8.5 represents the high end of the range of future pathways72,73, and here we refer to it as “high” climate change/emission scenario. SSP2-4.5 and SSP5-8.5 project global temperature anomalies of 2.4 °C and 4.9 °C above pre-industrial levels by 2100 with atmospheric CO2 equivalents of 650 and 1370 ppm, respectively74,75. Finally, the SSP dataset were used for model the Moringa sp. for future climate scenarios.

Species distribution modelling

We applied an ensemble of six models implemented in the ‘sdm’ package76 in R (4.1.3 version). The models included one regression and five machine learning methods. We selected the multivariate adaptive regression splines (MARS) regression model and the support vector machine (SVM), boosted regression trees (BRT), random forest (RF), classification and regression tree (CART) and maximum entropy (MaxEnt) machine learning methods for their high predictive accuracy77.

The model was trained with 10 replicates and evaluated per algorithm through five cross-validations. The occurrence data were split into training (70%) and test (30%) data to explore the implications of different environmental variables. Presence–absence models used equal numbers of presences and pseudo-absences, within the grid cell around presence locations78. Model accuracy and validation were judged by the True Skill Statistic (TSS) and the area under the curve (AUC). AUC values typically range from 0 to 1, with values closer to 1 indicating a more potent model79,80; AUC values < 0.7 are considered poor, 0.7–0.9 moderate, and > 0.9 good81,82. In contrast, TSS is a threshold-dependent measure of model accuracy, with values of + 1 indicating complete agreement between predictions and observations and 0 or below indicating agreement no better than random classification83,84. TSS value was classified as poor (< 0.40), fair (0.40–0.55), good (0.55–0.70), very good (0.70–0.85), excellent (0.85–0.99) and perfect (0.99–1.0)85,86. The Pearson correlation coefficient (COR) and deviance statistic were also considered to evaluate the model performance. COR estimates the correlation between continuous prediction with observation87, and the Deviance statistic calculates the deviation between observed and fitted values88.

Based on the potential species distribution habitat suitability index (0–1), the ensemble output maps were divided into four classes: unsuitable (0.00–0.2), least (> 0.2–0.40), moderate (> 0.4–0.6), and high potential (> 0.6–1.00)89. The reclassified tool in ArcGIS 10.8.2 was used to classify the ensemble maps. A field calculator was used to determine the raster area of each class of map (Count area × Area pixel/1,000,000). The relative variable relevance for each model’s present prospective Moringa distribution was established using the averaged variable response curve. The percentage contribution of each variable to the ensemble model was used to determine the variables' relative relevance. It draws attention to significant environmental factors that had a key role in shaping the geographic range of the species.