1. Introduction
Citrus fruits are considered to be a major crop across the world, with about 60 million megatons each year produced according to the Food and Agriculture Organization of the United Nations (FAO) (2015) data. Witches’ broom diseases (WBDs) have negatively impacted several high-value agricultural products in Oman, such as acid lime (
Citrus aurantifolia L.) trees and the alfalfa (
Medicago sativa L) crop. These diseases are caused by phytoplasmas related to the 16SrII-B and 16SrII-D subgroups [
1,
2]. The 16SrII-D phytoplasma is more aggressive than the 16SrII-B type. Plants infected with 16SrII-D phytoplasma show many symptoms including phyllody and witches’ broom. Also, 16SrII-D phytoplasma infects wild plant hosts from different species. In Oman, 16SrII-D phytoplasma was reported in more than 25 plant hosts from economic crops and wild plants [
3].
Phytoplasma are a group of bacteria that belongs to the Mollicutes class [
4]. In Oman, leafhoppers—
Hishimonus phycitis,
Austroagallia avicula, and
Empoasca sp.—and also
Diaphorina citri, the Asian citrus psyllid, are the main and putative vectors in transmitting 16SrII-B [
5] and 16SrII-D [
6] phytoplasmas. The insect vectors of phytoplasmas were recorded from all areas infected with phytoplasmas in Oman [
5,
6]. The most common symptoms caused by phytoplasmas are witches’ broom, yellowing leaves, inhibited growth, big buds, leaf deformation, virescence, phyllody, purple color, bolting, the formation of bunchy fibrous secondary roots and discoloration, reducing yield, decline, and dieback [
4,
7]. A diseased tree takes about six months for its symptoms to develop [
8].
Witches’ broom disease of acid lime trees (WBDL) is due to a phytoplasma infection that belongs to the 16SrII-B subgroup phytoplasma type, which was first found in Omani lime trees in the late 1970s and early 1980s [
9]. The 16SrII-B subgroup phytoplasma, which is the causal agent of WBD, spread to other countries such as the United Arab Emirates (UAE) by 1989 and the southeastern part of Iran by 1997 [
10]. As the disease progressed, the lime industry in Oman was severely impacted, and many trees were brought down to prevent the disease from spreading [
11]. Acid lime trees constitute 4% of the fruit crops grown in Oman [
12]. According to Al-Yahyai et al. [
13], however, the spread of the disease is particularly acute in Oman, as 98% of acid limes were found to be infected with the 16SrII-B subgroup phytoplasma. In addition, more than half a million acid lime trees have been destroyed because of WBDL in Oman since 1990. This has resulted in the loss of more than 75% of acid lime production [
14]. WBD affecting acid lime trees in Oman has, therefore, resulted in the loss of more than 50% of the cultivated acid lime area during the last four decades. WBDL (16SrII-B) kills acid lime trees in less than five years, and these trees’ production cannot be quickly restored [
8].
Alfalfa is one of the main forage crops in Oman, with a value of US
$120 million per year [
6]. Alfalfa, which is produced in a limited capacity in Oman, is a reported host for the 16SrII-D phytoplasma disease. According to Khan et al. [
15], phytoplasma disease results in a 25% loss in alfalfa production, leading to a loss of US
$30 million per year. The main symptoms of infected alfalfa are an excessive increase in the number of shoots and the yellowing of leaves, which reduce the marketability of the crop [
6].
Alfalfa witches’ broom (AlfWB), which affects crops across the world, was first reported in the 1990s from all regions of Oman [
15]. Several causal agents have been documented for WBD in alfalfa, such as ‘
Candidatus Phytoplasma asteris’ in the USA, ‘
Ca. Phytoplasma trifolii’ in Canada, ‘
Ca. phytoplasma fraxini’ in Argentina, and the 16SrII-D subgroup in Oman and Iran [
3,
16]. 16SrII-D phytoplasma causes WBD in Alfalfa in Saudi Arabia and the UAE [
10,
15]. In Oman, the WBD has been found all across the country, though fewer infections are reported from the southern part of Oman [
11]. Due to the importance of acid lime trees and alfalfa crops to the Omani economy, this research focuses on exploring the environmental factors that contribute to phytoplasmas. Although disease identification, symptoms, and hosts have already been extensively researched, very few studies have been carried out to assess the results of climate change using modeling tools to create different climate-change scenarios and consider the host plants. Several anthropogenic practices contribute to producing greenhouse gases, which have changed the patterns of temperature and precipitation around the world [
17]. Therefore, the primary aim of this study is focused on implementing MaxEnt to explore the environmental factors that contribute to the phytoplasmas that affect acid lime trees and alfalfa crops. The MaxEnt model is a robust and maximum entropy-based model that is capable of simulating the species’ spatial distribution using relevant environmental variables and species occurrence information.
4. Discussion
This study was the first of its kind to look into the impacts created by bioclimatic factors in association with projected climate-change scenarios on the geographical distribution of phytoplasmas for 16SrII-D (AlfWB) and 16SrII-B (WBDL) diseases across Oman using MaxEnt modeling. MaxEnt modeling has been frequently used by many researchers due to its rapid processing ability and its capacity to provide comprehensive results concerning the current and future occurrences of a target species [
35].
Al-Ghaithi et al. [
8] documented the possibility of the impact of environmental factors on the distribution of 16SrII-B phytoplasmas—the causal agent of WBDL disease in acid lime trees—but they were unable to identify these environmental factors. This study modeled the potential distribution of 16SrII-D and 16SrII-B subgroup phytoplasma diseases and showed the possibly impacted area under both current and future climate-change scenarios. The MaxEnt model resulted in an “excellent” rating for the AUC value of 0.826 for AlfWB (16SrII-D) and 0.859 for WBDL (16SrII-B) under the 2021–2040 climate scenario. Donkersley et al. [
36] suggested establishing nurseries in areas where most of the phytoplasma infection of acid lime trees can be found.
Moreover, the MaxEnt model provided values for all the projected climatic scenarios and also predicted the potential distribution of the disease under different climatic conditions. For 16SrII-B phytoplasma disease, it provided AUC values of 0.8598, 0.900, 0.931, and 0.913 for the periods of 2021–2040, 2041–2060, 2061–2080, and 2081–2100 respectively. For the 16SrII-D phytoplasma disease, the MaxEnt model provided AUC values of 0.8268, 0.837, 0.858, and 0.894, respectively, for the same four periods. These results are available as a baseline for future studies that focus on mapping the distribution of other phytoplasma types of other hosts in Oman. Due to MaxEnt’s ability to detect localities with similar conditions for occurrence, this study provided a good proxy for a suitable habitat for the 16SrII-B and 16SrII-D vectors.
The simulation of the potential distribution of 16SrII-B and 16SrII-D phytoplasmas are based on data obtained from native regions rather than data from exotic areas. The simulation in this study should, therefore, be regarded as a realized niche instead of a fundamental niche [
37]. It is worth mentioning, moreover, that this study is based on climatic variables (20 climatic variables: bio1–bio19 and DEM) rather than on other abiotic factors, such as soil, hydro-geology, and other variables. According to Li et al. [
38], bioclimatic variables should be considered as critical factors in controlling the redevelopment and spread of natural populations. For example, a study conducted by Nagler et al. [
39] showed the impact and the significant contribution of bioclimatic variables on the distribution of
Elaeagnus angustifolia.
The result of the MaxEnt modeling has revealed and predicted the different distribution of suitable habitats for 16SrII-B and 16SrII-D phytoplasmas of WBDL and AlfWB diseases. As shown on the map in
Figure 3, the coastal area had the potential for the distribution of 16SrII-B phytoplasma across the various climatic scenarios, even in the southern part of the country. Although some of the collected occurrence samples were asymptotic, the area along the coast will still be a hotspot for the disease. However, for 16SrII-D phytoplasma, it was found that while the coastal area in the north was a highly suitable habitat for the distribution of the disease, the southern coast of Oman was not so. Nevertheless, the areas of moderately suitable and highly suitable habitat kept decreasing, and their distribution reduced across Oman for the period for all future scenarios except for the period between 2021 and 2040. Global warming will, therefore, greatly influence the distribution of 16SrII-D phytoplasma disease by causing shifts or contractions in the ranges of the disease in specific areas (
Figure 4). On the other hand, MaxEnt predictions showed that potentially highly suitable climatic distributions for 16SrII-B phytoplasmas will expand under all future climate scenarios as shown in
Table 5. That being said, the 16SrII-B and 16SrII-D subgroups phytoplasmas were registered in more than 25 plant hosts including economic crops, and medicinal and wild plants in Oman [
1,
3,
9,
18,
40,
41,
42,
43]. Therefore, studying the phytoplasma groups and the impacts of environment factors on these phytoplasmas in Oman ultimately will help the decision-maker in controlling the phytoplasma diseases and agricultural practices in Oman. In addition, the 16SrII-B and 16SrII-D distribution under various climatic projections could serve as a proxy of the host because the model is built on these environmental variables.
Furthermore, the results in
Table 6 and
Table 7 show that under different climatic scenarios, there were no similarities that could be attributed to factors such as the occurrence of data from samples collected from the northern part of the country. The selection of bio-environment variables might also be a source of uncertainty because there might be overlapping results. In addition, the global climate models used in this study were of coarse resolution, which could create uncertainties about their validity. However, this study has helped in analyzing the environmental variables that could contribute to the distribution of disease, thereby enabling the development of possible ways to stop, slow down, or reverse the negative impacts of climate change on 16SrII-D and 16SrII-B phytoplasmas. Therefore, studying the effects of climate change on 16SrII-D and 16SrII-B phytoplasmas is essential to establishing a reliable decision-making process to guide plant breeding research that can select the genetic strains best suited for specific areas in Oman. Moreover, this study will help decision makers determine suitable areas for growing acid lime trees and alfalfa in Oman during the coming 80 years.
MaxEnt proved its ability to make predictions about 16SrII-B and 16SrII-D distributions based on the environmental variables. This model can be used as a tool for land managers to predict the likelihood of presence of this disease based on small data samples.
5. Conclusions
This is the first study that evaluated the environmental variables that affect the 16SrII-D and 16SrII-B distribution of phytoplasmas diseases in Oman. In addition, this study has also predicted the effect of climate change on the distribution of the disease for the periods between 2021 and 2100.
The models produced reliable results based on the current distribution of the diseases. According to the model, isothermality (bio3), temperature annual range (bio7), minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation of the driest month (bio14) play a major role in the distribution of the WBDL (16SrII-B) phytoplasma disease. Similarly, isothermality (bio3) and precipitation of the driest month (bio14) played a significant role in AlfWB (16SrII-D) phytoplasma disease distribution. In addition, the mean diurnal range (bio2), seasonal precipitation (bio15), mean temperature of the wettest quarter (bio8), precipitation of the warmest quarter (bio18), and DEM played a significant role in 16SrII-D phytoplasma distribution.
On an overall basis, climate change will make more areas vulnerable to these two diseases. Therefore, this study will help in producing suitable strategies to control the disease spatial distribution and management.
The results generated in this research will be useful for Oman’s neighboring nations, where phytoplasma diseases are also prevalent.
This study identified hotspots and vulnerable areas that can help in mapping and delineating those places, and in developing new strategies to control the spread of the disease across Oman and other countries that face similar challenges.
Finally, this study would be the first attempt in spatial modeling of witches’ broom disease distribution in Oman. In other words, this study has opened windows of opportunities for research and development (R&D) in this area. Data collection and monitoring campaigns are important areas that efforts can be directed to, especially at local scales. Notably, in the nearest future, we will attempt further R&D in understanding the insect vectors of 16SrII-D and 16SrII-B phytoplasma disease. This will strengthen our understanding and providing a better picture of the distribution of phytoplasma disease in Oman under a changing climatic scenario.