Skip to main content
Download PDF

The influence of climate change on the future distribution of two Thymus species in Iran: MaxEnt model-based prediction

BMC Plant Biology volume 24, Article number: 269 (2024) Cite this article

Abstract

Within a few decades, the species habitat was reshaped at an alarming rate followed by climate change, leading to mass extinction, especially for sensitive species. Species distribution models (SDMs), which estimate both present and future species distribution, have been extensively developed to investigate the impacts of climate change on species distribution and assess habitat suitability. In the West Asia essential oils of T. daenensis and T. kotschyanus include high amounts of thymol and carvacrol and are commonly used as herbal tea, spice, flavoring agents and medicinal plants. Therefore, this study aimed to model these Thymus species in Iran using the MaxEnt model under two representative concentration pathways (RCP 4.5 and RCP 8.5) for the years 2050 and 2070. The findings revealed that the mean temperature of the warmest quarter (bio10) was the most significant variable affecting the distribution of T. daenensis. In the case of T. kotschyanus, slope percentage was the primary influencing factor. The MaxEnt modeling also demonstrated excellent performance, as indicated by all the Area Under the Curve (AUC) values exceeding 0.9. Moreover, based on the projections, the two mentioned species are expected to undergo negative area changes in the coming years. These results can serve as a valuable achievement for developing adaptive management strategies aimed at enhancing protection and sustainable utilization in the context of global climate change.

Peer Review reports

Introduction

Within a few decades, the species habitat was reshaped at an alarming rate by climate change, leading to mass extinction, especially for sensitive species [1]. Biodiversity, agricultural production, and food security are predicted to alter expressively in response to a changing future climate globally [2, 3]. For example, high-diverse ecosystems in Melanesia Islands, which have the most diversified terrestrial ecosystems on the planet and hold more than half of the world’s coral species, have been vulnerable to climate change, habitat degradation, fragmentation, and loss over the last 50 years [4]. De Frenne et al. [5] revealed how forest microclimates will be influenced by climate change by exploring the interactions with land use changes across different biomes. Their results indicate the importance of global warming to ecological functions in local habitats, and biodiversity and ecosystem services cannot be ignored. Forecasting the impact of climate change on plant communities is an important area of study because it plays a crucial role in alerting scientists to make informed decisions in the face of future crises [6, 7]. Many new methods have been used to explore the species distribution pattern on the basis of global warming scenarios. Understanding how species will respond to climate change, including their distribution under future climate change scenarios, is vital for the effective management and conservation of biodiversity [8]. Species distribution models (SDMs) have been used extensively, with remarkable success, in understanding the influence of climate change on potential distribution of species [9, 10]. Among SDMs, the maximum entropy model (MaxEnt), which has shown superior performance compared to other models when dealing with limited sample sizes and presence-only data [11,12,13], has been widely used to assess ecological requirements, environmental responses, and habitat suitability of species. Iran with about 1.65 million square kilometer area, located on the Iranian Plateau, is a large country and after Turkey is the richest country of plant diversity in the Middle East [14]. Traditional medicine has always been very important in the Iranian culture and traditions, documented by many historical books describing Iranian traditional medicine as one of the oldest and richest alternative medicines [15]. Several valuable medicinal plants such as Thymus spp. distributed in Iran’s meadows [16]. Because they contain high levels of thymol and carvacrol, T. daenensis Celak and T. kotschyanus Boiss. & Hohen are valuable species in the genus [17, 18]. The populations of these species have severely declined in recent years, probably as a result of climate changes as well as excessive harvesting for food and medical purposes. So, understanding how species will adapt to climate change (e.g., how they will be distributed in future climate change scenarios) is crucial for effective biodiversity management and conservation [10, 19]. While SDMs have been widely employed for various purposes in Iran [20,21,22,23,24], to the best of the authors’ knowledge, the majority of prior studies on mentioned species were carried out locally, and no research has investigated the impact of climate change on the distribution pattern of T. daenensis and T. kotschyanus in the scale of Iran [21, 25,26,27,28,29,30,31]. As a result, the current study was aimed at achieving the following goals for the aforementioned Thymus species in Iran: (1) Identifying the most important climatic and ecological variables influencing the distribution pattern of these species; (2) predicting the appropriate areas of the Thymus spp. distribution pattern in Iran based on current climatic/environmental variables; (3) estimating possibly appropriate areas and Thymus spp. change trends under different climate conditions in the 2050s and 2070s; (4) Achievement to the aims provide valuable information for decision-makers involved in the future conservation planning.

Materials and methods

Study area

The study area is located within Iran and spans a vast surface area of 1.65 million square kilometers between 44°-64° E latitude and 25°-40° N longitude. Iran, situated in the arid zone of Middle East, holds the distinction of being the second-largest nation in the region. Iran’s altitude ranges from − 27 m above sea level (m.a.s.l) in the Caspian Sea basin to 5,670 m.a.s.l in the Alborz Mountain range. The country experiences an average annual rainfall of around 250 mm, as reported by Jamshidi and Samani [32]. The soil, topography, elevation, and water availability all affect the vegetation’s character. Iran with more than 8,000 species of vascular plants is the subject of this study. Over 30% of these species are endemic of Iran [33]. The country has a tenth of its land covered in forests [34]. There are several broad-leaved evergreens and broad-leaved deciduous trees in the area including; Carpinus betulus L. Juglanse regia L., Ulmus glabra Hudson, Tilia platyphyllosa Scop, Quercus castaneaefolia C. A. Mey, and Fagus orientalis Lipsky and thorny shrubs are also abundant. Woodlands, together with Ulmus minor Miller., Pyrus spp., Celtis australis L., Juglanse regia, Pistacia atlantica Desf., Astragalus spp. Ferula spp. and Amygdalus scoparia Soach. cover the Zagros Mountains. This area is a hot spot for crop wild relative and habitats of some medicinal plants such as Thymus spp [35, 36]. The ravines are host to many different species of creepers as well as Salix excelsa S. G. Gmelin, Populus nigra L., and Platanus orientalis L. On the intermediate dry plateau, thin stands of Juniperus excelsa M.Bieb., Prunus lycioides (Spach) C.K.Schneid., Berberis vulgaris L., Cotoneaster spp., and wild fruit trees are found. In the steppes, thorny plants like Astragalus spp. and Acantholimon spp. make up the ground cover, while in the hills and arid plains, medium altitudes are habitat to species of Artemisia spp., or wormwood. Below 900 m.a.s.l., Ziziphus spina-chirsti (L.) Willd. Accacia spp. and scattered plants are abundant. The water-retaining desert dunes support dense colonies of vegetation—waters, whether surface or subsurface, flow through forests. Vitis vinifera L., Tamarix spp., Phoenix dactylifera L., Myrtus communis, Nerium oleander L., and Salix spp., are all supported by desert [37].

Species occurrence data

The genus Thymus, belonging to the Lamiaceae family and includes more than 215 species worldwide, is presented in Iranian flora by 18 species [38, 39]. Thymus species are mainly found in Iran’s western and northern highlands, including West Azerbaijan, East Azerbaijan, Ardabil, Zanjan, Kurdistan, Mazandaran, Golestan, Tehran, Hamadan, Markazi, and North Khorasan provinces [38, 39]. The carminative, digestive, antispasmodic, anti-inflammatory, emmenagogic, and tonic properties of thyme leaves and flowering parts are widely used in the folk medicine [40,41,42]. Essential oils are a group of remarkable substances found in Thymus spp. that have antifungal, phytotoxic, and insecticidal properties, encouraging their investigation and prospective usage in agricultural and food-related fields. T. daenensis Celak and T. kotschyanus Boiss. & Hohen, (Fig. 1) essential oil contains more than 60% of the valuable thymol and carvacrol compound [17, 18].

T. daenensis is a bushy perennial plant characterized by little hairs. It typically grows to a height of 15–30 cm and has opposite lanceolate to linear leaves with 18–20 mm long and 2–5 mm wide. Leaves have 2–3 pairs of dorsal vines. The inflorescence can be found at the end of the stem branches and consists of spherical or elongated clusters of reddish flowers. The calyx measures approximately 3–4.5 mm, tubiform or campanulate. The corolla is bilobed and measures 5–6 mm [38].

T. kotschanus is a perennial woody pulvinate plant with dense branches. It typically grows to a height of 6–12 cm and has opposite ovate leaves with 8–17 mm long and 4.5–7 mm wide. The inflorescence can be found at the end of the stem branches and consists of spherical clusters of white flowers. The calyx measures approximately 4.5–6 mm, tubiform or more and less campanulate. The corolla is bilobed and measures 6–7 mm [38].

Fig. 1
figure 1

Photographs of T. kotschyanus (A) and T. daenensis (B) in the habitat

Full size image

The presence points of the stated species were identified through an extensive range of sources, including direct field research, historical data available in the herbaria of HSBU, IRAN and TARI, (the herbarium acronyms follow the study of [43] and literature review [38, 39] [www.gbif.org]. It is essential to note that all points obtained from the literature and herbaria were confirmed through field verification. As reliable absence data for species distribution were not available, this study relied solely on presence data. After collecting the data occurrence, in order to avoid spatial autocorrelation among occurrence points, any points less than 1 km was removed initially and before modelling. For remove duplicate points we filtered occurrence data by randomly selecting a presence point within a single grid cell (i.e., 1 × 1 km) using ‘sdm’ package in R environment (Ver. 4.2.2). Finally, for T. kotschyanus, 32 occurrence points and for T. daenensis, 48 occurrence points were used to generate SDMs respectively (Fig. 2).

Fig. 2
figure 2

Distribution pattern of the two Thymus spp. in Iran (based on elevation map)

Full size image

Environmental variables

A total of 37 environmental variables including bioclimatic (www.worldclim.org), edaphic (www.soilgrid.org), and topographic variables (www.worldgrids.org) were initially selected at a spatial resolution of 30 arc-seconds (approximately 1 × 1 km). Afterwards, according to the ecology of species, available literature [17, 25, 29, 30, 32,33,34,35, 41, 42], expert judgment and correlation test, only five climatic factors, and one topographic factor were chosen to distribution modeling of the two Thymus species (Table 1). It is essential to be indicated that the co-linearity among variables was assessed using Pearson’s correlation coefficient (r), as suggested by previous researchers [6, 11, 44,45,46,47]. If two variables exhibited a high correlation (r>|0.70|), one of them was excluded to mitigate co-linearity [46].

Table 1 The most important environmental variables used in modeling
Full size table

Distribution modeling

The MaxEnt model [48] was utilized for modeling the current and future habitat suitability of species. MaxEnt (Ver. 1.0–3) was utilized through the “dismo” package (Ver.1.3-9( (https://rspatial.org/raster/sdm/; Hijmans et al. [49] in the R programming environment (Ver. 4.2.2) [50]. The models were evaluated using 10-fold cross-validation to estimate errors and assess model consistency [51, 52]. In cross-validation, data were randomly divided into ten parts; nine parts were used for model fitting, and the fitted model was then used to evaluate the holdout part [53, 54]. The equal training sensitivity and specificity of MaxEnt output were considered as the proper threshold for the model’s prediction [55]. For predicting the current and future suitability, a single MaxEnt model with a full dataset was re-fitted. It is essential to be indicated that the process of modeling was repeated ten times with 10,000 background points in order to represent the variation in the environmental covariates and have stable model predictive performance [56].

To represent the future potential distributions of the Thymus spp., projected climate variables for 2050 (average for 2041–2060) and 2070 (average for 2061–2080) were utilized. The projections were derived from the average of 16 general circulation models (GCMs) under two greenhouse-gas emissions scenarios: semi-optimistic (RCP4.5) and pessimistic (RCP8.5). In fact, in order to reduce the uncertainty, the average of 16 general circulation models (GCMs) was applied. The environmental parameters were standardized to a common spatial resolution of 30 s of latitude/longitude. Suitability maps for each species under present and future climate scenarios were generated using ArcGIS software (Ver. 10.2).

Model evaluation

To assess the accuracy of the modeling results, we computed the area under the curve (AUC) of the receiver operating characteristic (ROC) [57]. The AUC score is a powerful tool for measuring model performance because of its independence from threshold selection [58, 59]. The AUC shows the power of the model to discriminate presences from a random background [60]. The AUC ranges between 0 and 1.0, with 0.5 showing a random prediction performance and 1.0 indicating perfect discrimination. Values under 0.50 indicate models worse than random [46]. The percent contribution of the Jackknife test was used to assess the relative importance of each environmental variable influencing the models of specific species under current climate conditions [48], which is the best index for small sample sizes [61]. This analysis helps to understand the relative importance of different environmental factors in shaping the species’ habitats [62]. Additionally, the permutation importance (PI) was used to indicate the model’s dependence on each variable [48].

Results

The model evaluation

According to the AUC values, the modeling performance for the studied species was 0.927 for T. daenensis and 0.957 for T. kotschyanus according to training data (Figures not shown). The AUC showed that the performance of the model was excellent [63].

Variables importance

Following the Pearson correlation test, each species was retained for modeling, with a total of six variables chosen for two species (Table 2). Given the present contribution (PC) values, the importance of the variables for each species varied remarkably (Table 2). The most important variables for T. daenensis were mean temperature of warmest quarter (bio10) (52.7%), the slope percentage (20.6%), and precipitation of driest month (bio14) (18.3%) (Fig. 3 panels B, D and F; Table 2). In addition, the model indicated that slope percentage (60.8%) and mean temperature of the coldest quarter (bio11) (28.5%) were the most restricting environmental variables based on the existing distribution pattern of T. kotschyanus (Fig. 3 panels F and C; Table 2;). According to Table 2; Fig. 3A and C, min temperature of the coldest month (bio6) was unique to T. daenensis, and mean temperature of the coldest quarter (bio11) was unique to T. kotschyanus, whereas the other four of the variables were shared by both species (Fig. 3).

Table 2 Percent contribution (PC) and permutation importance (PI) of the environmental variables for the Thymus spp
Full size table
Fig. 3
figure 3

Environmental variables for modeling of the Thymus spp. Abbreviations are described in Table 1

Full size image

Predicted current potential distribution pattern

MaxEnet modeling indicates that the current most suitable habitats for T. daenensis (Fig. 4A) are mountains of northwest and north of Iran (i.e., Qazvin, Zanjan, Kordestan, Kermanshah, Ilam, Hamadan, Markazi, Lorestan, Esfahan, Chaharmahal and Bakhtiari, Kohgiloyeh and Buyer Ahmad, Fars, Kerman, South Khorasan and Razvi Khorasan Provinces). The current most suitable habitats for T. kotschyanus (Fig. 4B) are mountains of west and northwest and north of Iran (i.e., West Azarbaijan, Eest Azarbaijan, Ardabil, Kordestan, Lorestan, Hamadan Tehran, Gilan, Mazandran, Golestan and North Khorasan Provinces). In summary, the current potential range of the two species exceeds their actual habitat by a substantial margin (Fig. 4).

Fig. 4
figure 4

Map for potential current habitat suitability of the Thymus spp in Iran

Full size image

Predicted future potential distribution pattern

It is obvious that there is a gradual transition between scenarios and time ranges for each species (Figs. 5 and 6). Cwompared to the current potential habitat, tow Thymus spp. are projected to experience negative range changes in 2050 and 2070 under different RCP4.5 and RCP8.5 scenarios (Figures: 5 and 6), albeit with varying percentages (see Table 3). T. daenensis and T. kotschyanus have current distribution areas of 484,070 Km2 and 332,231 Km2, respectively. They will face the greatest losses in the RCP8.5 scenarios, reaching 41,682 Km2 and 142,805 Km2, or their distribution ranges decline 91.39% and 57.02%, respectively (Figs. 5 and 6; Table 3).

Fig. 5
figure 5

The future distribution map changes of Thymus daenensis in 2050 (average for 2041–2060) and 2070 (average for 2061–2080) under semi-optimistic (RCP4.5) and pessimistic (RCP8.5) scenarios

Full size image

In compared to the current condition, provinces such as Qazvin, Kermanshah, Markazi, Kohgiloyeh and Buyer Ahmad, Fars, Kerman, and South Khorasan will be completely affected in RCP 8.5_2070, resulting the most adverse distribution range changes for T. daenensis, while this species’ distribution range will decline in lower altitudes of Zanjan, Kordestan, Lorestan, Hamadan, Esfahan, Chaharmahal and Bachtiari, and Razavi Khorasan provinces (Figs. 5 and 6). In contrast, new limited habitats in the highlands north of Tehran and south of Mazandaran provinces will be suitable for the spread of the aforementioned species. For T. Kotschyanus distribution range will decline in lower altitude parts of West Azarbaijan, East Azarbaijan, Ardabil, Kordestan, Lorestan, Hamadan Tehran, Gilan, Mazandran, Golestan, and North Khorasan provinces, and will be restricted to highlands of the mentioned areas (Fig. 6).

Fig. 6
figure 6

The future distribution map changes of Thymus kotschyanus in 2050 (average for 2041–2060) and 2070 (average for 2061–2080) under semi-optimistic (RCP4.5) and pessimistic (RCP8.5) scenarios

Full size image
Table 3 The gain, loss, and range change of the two Thymus spp. under semi-optimistic (RCP4.5) and pessimistic (RCP8.5) climate change scenarios of 2050 (average for 2041–2060) and 2070 (average for 2061–2080)
Full size table

Discussion

Environmental variables affecting the distribution pattern of Thymus spp

It is crucial to recognize the key environmental factors affecting the geographical distribution pattern of a species in terms of an ecological perspective [64, 65]. Based on the value of contribution, the projected findings revealed that the variables involved in suitable habitat for the two Thymus species were as follows. Slope percentage and mean temperature of the coldest quarter (bio11) were the dominant variables influencing distribution pattern of T. Kotschyanus (60.8% and 28.5%) respectively., while the mean temperature of the warmest quarter (bio10) (52.7%) and slope percentage (20.8%) were the dominant variables influencing dispersion pattern of T. daenensis (Table 3). According to Esfanjani et al. [66] the most important environmental factors affecting distribution pattern of T. kotschyanus were pH, elevation, precipitation and temperature variation. Consistent with our findings tow studied species are distributed in the slopes of the mountainous in the heights of the Iranian and Turanian regions (Fig. 3F) and it is very important that the soils are well drained [40]. In mountainous areas like Iran slope percentage as a key topographic factor is vital in regulating the spatial arrangement of soil nutrient, soil stability [67, 68], water [69, 70], heat, and sunlight, creating diverse microclimates with unique soil properties [11, 71,72,73]. According to studies conducted in the West and North-West of Iran T. kotschyanus grows in spots with sandy-loam and sandy clay-loam soils, with annual precipitation ranging from 161 to 832 mm, mean temperatures ranging from 10.5 to 19.1oC, and elevation varying from 1000 up to 3000 m.a.s.l [25, 26, 29, 74]. Consistent with our findings, temperature was the most important factor influencing the presence of Thymus species, followed by elevation and precipitation (for T. kotschyanus, precipitation was more important than elevation) [75]. According to Larti et al. [25], T. kotschyanus can grow in a wide range of elevation, PH, slope, and soil texture, particularly in sandy and rocky mountains. This species requires less annual precipitation than T. daenensis and is able to grow in arid mountains, but it requires a cold winter to grow (Fig. 3C). Corticchiato et al. [76] stated that the main factors affecting the distribution pattern of Thymus species involve climate, altitude, soil type, soil texture, organic matter and calcium content of soil in east regions of Spain. Boira & Blanquer [77] pointed out that some factors involving elevation, soil texture, and climate affect development of Thymus piperella in Spain.

Our finding indicated, mean temperature of the warmest quarter (bio10) (52.7%), slope percentage (20.8%) and precipitation of the driest month (18.3%) were the dominant variable influencing the distribution pattern of T. daenensis (see Table 3). According to Arvin et al. [78] research, this species occurs in alpine regions at altitudes ranging from 1000 to 2400 m.a.s.l., with an average annual temperature of 11.9–17.7 oC, an annual minimum temperature of 4.4–10.7 oC, and precipitation ranging from 200 to 700 mm/year. Altitude, slope percentage and minimum mean temperature are important ecological variables in the mentioned and other Thymus species dispersion pattern [26, 27, 78,79,80].

Predicting the current distribution potential of Thymus spp

The MaxEnt model has been widely used in ecology, conservation biology, evolutionary biology, and invasive species management [48]. Previous studies reported that the MaxEnt model was commonly implemented to predict potential distributions in many plant species in several national-scale studies, from Iran [36], Tunisia [81], China [82] and east Asia [83]. Thymus spp. are widely regarded as one of the most important spices and food preservatives in the food industry. Plant characteristics such as essential oil content and yield, and physiological and morphological traits may be affected by a variety of factors including climatic, soil conditions and genetics [75, 80, 84, 85]. These characteristics changes clearly depend on mean temperature and temperature extremes in plant’s ecosystem [80].

The MaxEnt model results identified that the current highly suitable habitats for T. daenensis were mostly in the west, center and the northeast of the country but, the actual presence range of the species does not include the northeast. These regions are characterized by elevation 1000–2500 m.a.s.l, precipitation of driest month (bio14) < 10 mm, mean temperature of the warmest quarter between 15 and 28 Co and slope percentage > 3 < 23% (see Fig. 3). Nadjafi et al. [86] found that the optimal temperature for T. daenensis seeds germination was 20 °C. Temperatures above the optimum (22 °C) appear to be the outcome of secondary dormancy in the seeds of the mentioned species. Consistent with our findings according to Majd et al. [30] research, the best-growing conditions for the aforementioned species are foothills with average minimum and maximum temperatures of 12 and 23 degrees Celsius, followed by mountain areas with average minimum and maximum temperatures of 9 and 24 oC.

Based on modeling suitable habitats for T. kotschyanus are in the west, north, and northeast of the country. However, the actual presence range of the species does not include the northeast (Fig. 3). These regions characterized by slope percentage > 5%, altidude > 1500 m.a.s.l and mean temperature of the coldest quarter < 5 Co (Fig. 6). According to Fig. 4B, regions with high summer rainfall are unsuitable for this species’ growth, so in the north, regions like the Caspian Sea coast, which has a low elevation and high summer precipitation, are not suitable for this species’ dispersal. Consist whit our finding T. kotschyanus thrives in rocky slopes of the Irano-Turanian mountains, where it can reach elevations of up to 3000 m. These mountains have calcareous sandy loam soils and a cold, dry climate [25, 38].

Prediction of the future potential distribution pattern of Thymus spp

In compared to the current condition, provinces such as Qazvin, Kermanshah, Markazi, Kohgiloyeh and Buyer Ahmad, Fars, Kerman, and South Khorasan will be completely affected in.

In the previous research, it has been observed that global climate change influences the future distribution pattern of plant species, with varied responses among different species [87]. The MaxEnt model revealed that the suitable habitats of the tow Thymus species reduce in 2050 and 2070 under semi-optimistic (RCP4.5) and pessimistic (RCP8.5) scenarios (Table 3). The distribution areas of T. daenensis and T. kotschyanus changed the most under the RCP 8.5–2070 scenario. According to RCP 8.5–2070, climate change will cause T. daenensis to lose approximately 91.39% of its current range, reducing it from 484,070 to 41,682 km2 (Table 3). While this species’ distribution range will decline in lower altitudes of Zanjan, Kordestan, Lorestan, Hamadan, Esfahan, Chaharmahal and Bachtiari, and Razavi Khorasan provinces (Fig. 5). In contrast, new limited habitats in the highlands north of Tehran and south of Mazandaran provinces will be suitable for the spread of the aforementioned species. Climate change, as projected by the RCP 8.5–2070 scenario, will reduce T. kotschyanus’ distribution range by 57%, from 332,231 to 142,805 km2 (Table 3). T. Kotschyanus distribution range will decline in lower altitudes of West Azarbaijan, East Azarbaijan, Ardabil, Kordestan, Lorestan, Hamadan Tehran, Gilan, Mazandran, Golestan, and North Khorasan provinces, and will be restricted to highlands of the mentioned areas (Fig. 6).

Thymus species have a limited ecological niche and do not prefer marginal habitats [27]. A diverse range of mountainous plant communities have been reported around the world [88, 89]. A logical consequence is a shift in the spatial distribution of the species towards higher elevations to benefit from higher precipitation and cooler temperatures, resulting in more adapted plant development. Previous research has also detected an elevational shift in plant community distribution pattern under climate change scenarios in Iran and around the world, especially in mountain habitats [21, 88, 90]. The investigated Thymus species’ habitats are declining in the foothills, particularly at lower latitudes. T. kotschyanus is an alpine species found in sandy and rocky mountains at elevations ranging from 1500 to 3000 m above sea level. Temperature is the most important factor influenced the presence of Thymus species, followed by elevation and precipitation but for T. kotschyanus, the importance of precipitation is greater than elevation [75].

Highland environments remain suitable for alpine species, while for T. daenensis cool foothills with annual precipitation > 250 mm, annual mean temperature < 15 °C, annual minimum temperature < 9 °C, and elevation ranges between 1670 and 2000 m.a.s.l. are suitable areas [30, 78]. As a foothill’s species, climate change is having a significant impact on the distribution range of the species that are unable to migrate to the highlands. Germination behavior is an important part of a species’ regeneration strategy [91], and it is influenced by a variety of inherent and external factors [92, 93]. Temperature is the most vital of these elements, influencing the maximum germination percentage and rate of germination [94]. If climatic factors such as temperature and precipitation change in a region beyond the tolerance of a species’ plasticity, changes in species distribution may be unavoidable. It has been shown that plant species have shifted their altitudinal and latitudinal ranges in response to climate change [95], but there is also likely to be a rapid increase in extinction risk [96]. Increasing evidence indicates that the global average temperature is rising and precipitation is declining, in part due to increased greenhouse gas emissions [97]. Nadjafi et al. [86] found that 20 °C was the optimal temperature for T. daenensis seeds. It seems that increasing temperature over the optimum (22 °C) is the result of secondary dormancy in seeds of the species. Based on Sharifi Ashoorabadi et al. [98] studies highest germination percentage of thyme seeds occurs at temperatures ranging from − 4 to 4 degrees, and at temperatures ranging from − 8 to more than 4, the germination ratio decreases significantly. Thymus species are most common in cool to moderate annual temperatures in elevated area [75]. So, maybe climate change has a negative impact on thyme distribution range by reducing seed germination.

Plants with wider ecological niches such as T. kotschyanus, in particular, will be better able to adapt to climate change than species with limited ecological niches like T. daenensis [99, 100]. For instance, global warming has improved the habitat suitability of Ruscus aculeatus in Sardinia [101] and Homonoia riparia in China [58]. Future climate change was predicted to result in a significant reduction in suitable habitats for many other species, including Fritillaria cirrhosa in China [102], Artemisia aucheri, A. sieberi, and Daphne mucronata in Iran [99, 103]. This study employed two different climate change scenarios, RCP 4.5 and RCP 8.5, to evaluate the potential impacts of climate change on Thymus species in the future. RCP4.5 represents a relatively moderate and plausible scenario, considering greenhouse gas emissions mitigation, while RCP8.5 portrays a high-emission scenario. Considering the substantial differences between these two scenarios, the projected outcomes for the Thymus species are different. Under the RCP8.5 scenario, there is a higher projected loss of suitable areas for T. daenensis and T. kotschyanus compared to the RCP 4.5 scenario for both study periods, as indicated in Table 3. This suggests that the more severe greenhouse gas emissions scenario leads to severe adverse effects on the suitable habitats of these two Thymus species. However, range changes, rather than retractions, are the most important information to consider when effective conservation measures are planned [59, 104].

Research limitations

We must recognize and acknowledge several limitations in our study. MaxEnt modeling has proven to be very effective at determining habitat use and species distributions for a variety of species and localities. Because it relies only on presence data, it lacks many of the complications associated with presence-absence analytical methods. MaxEnt modeling has frequently outperformed a number of other approaches that rely on presence-only data, it is relatively insensitive to spatial errors associated with location data, and it can produce useful models with as few as five locations. However, MaxEnt needs to develop methodology for selecting the best approximating model, and establish protocol for assessing habitat selection based on repeated sampling of individuals [105]. Another limitation of this research was that the chosen set of 6 environmental variables may not encompass all the factors influencing the geographic distribution pattern of Thymus spp. In other words, apart from the selected environmental variables, various other factors, such as species interactions [106], human activities such as over grazing, the habitat fragmentation and other land- use changes [107], pasture fire and distance from streams [11], may exert a critical influence on the distribution pattern of the Thymus spp. However, it is crucial to understand that the predicted suitable habitat area from the model may not always perfectly coincide with the actual habitat where the species is found [108, 109]. This discrepancy can arise due to uncertainties and internal assumptions within the species distribution models, highlighting the need for further research and consideration of these factors to improve the accuracy of such predictions.

Conclusions

The leaves, flowers, and essential oils of T. daenensis and T. kotschyanus are widely used as herbal tea, spices, flavoring agents, and medicinal herbs. Thymus oils and extracts are also frequently utilized in the pharmaceutical, perfume, and cosmetic sectors, as well as for flavoring and protecting food products. Climate change has significant effects on the suitable habitats of T. kotschyanus and T. daenensis, causing these species to lose the majority of their distribution range in the future. More than half of the Thymus species are still obtained from wild land in the country, and they are frequently overharvested. The identification and analysis of appropriate habitats for the species production, along with promoting the cultivation of these valuable species in these habitats through support programs such as insurance, will have multiple benefits. Firstly, it will aid in the restoration of the species’ natural habitats by reducing the frequency of harvests. Secondly, it will foster an increase in the population of these species, thereby reducing the likelihood of their extinction. In this regard, it is suggested that large-scale cultivation of the two Thymus spp. in rangelands is required. The first step to expand cultivation of plants is the selection of new desirable areas. we can conclude that the best place for the production of quantity effective materials this plant in order to attain the best results, is the height between 1600 and 2400 m above sea level in north of Fars, Eshahan, Hamadan, Kordestan and North Khorasan provinces for T. daenensis and for T. Kotschyanus, West Azarbaijan, East Azarbaijan, Ardabil, Kordestan, Lorestan, Hamadan Tehran, Gilan, Mazandran, Golestan, and North Khorasan provinces. Moreover, by implementing protected areas in the mentioned provinces, it is possible to preserve the genetic diversity of the species and guarantee their survival.

Data availability

The raw data of this article will be made available by corresponding authors, according to the personal requests.

References

  1. Muluneh MG. Impact of climate change on biodiversity and food security: a global perspective—a review article. Agric Food Secur. 2021;10(1):1–25. https://doi.org/10.1186/s40066-021-00318-5.

    Article  Google Scholar 

  2. Vermeulen SJ, Campbell BM, Ingram JSI. Climate change and food systems. Annu Rev Environ Resour. 2012;37:195–222.

    Article  Google Scholar 

  3. Wiebe K, Robinson S, Cattaneo A. Climate change, agriculture and food security: impacts and the potential for adaptation and mitigation. Sustainable food Agric, pp. 55–74, 2019.

  4. Bhattarai U. Impacts of climate change on biodiversity and ecosystem services: direction for future research. Hydro Nepal, pp. 41–8, 2017.

  5. De Frenne P, et al. Forest microclimates and climate change: importance, drivers and future research agenda. Glob Change Biol. 2021;27(11):2279–97.

    Article  Google Scholar 

  6. Peng Z, Zhang Y, Zhu L. Spatial and temporal patterns of the sensitivity of radial growth response by Picea schrenkiana to regional climate change in the Tianshan Mountains. J Res. 2023;34:1669–81. https://doi.org/10.1007/s11676-023-01629-y.

    Article  Google Scholar 

  7. Rahman W, Magos Brehm J, Maxted N. The impact of climate change on the future distribution of priority crop wild relatives in Indonesia and implications for conservation planning. J Nat Conserv. 2023;73:126368. https://doi.org/10.1016/j.jnc.2023.126368.

    Article  Google Scholar 

  8. Beridze B, et al. Biodiversity protection against anthropogenic climate change: conservation prioritization of Castanea sativa in the South Caucasus based on genetic and ecological metrics. Ecol Evol. 2023;13(5):e10068.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hama AA, Khwarahm NR. Predictive mapping of two endemic oak tree species under climate change scenarios in a semiarid region: range overlap and implications for conservation. Ecol Inf. 2023;73:101930.

    Article  Google Scholar 

  10. Naudiyal N, et al. Potential distribution of Abies, Picea, and Juniperus species in the sub-alpine forest of Minjiang headwater region under current and future climate scenarios and its implications on ecosystem services supply. Ecol Ind. 2021;121:107131. https://doi.org/10.1016/j.ecolind.2020.107131.

    Article  Google Scholar 

  11. Momeni Damaneh J, Ahmadi J, Rahmanian S, Sadeghi SMM, Nasiri V, Borz SA. Prediction of wild pistachio ecological niche using machine learning models. Ecol Inf. 2022;72:101907.

    Article  Google Scholar 

  12. Khan AM, et al. MaxEnt modelling and impact of climate change on habitat suitability variations of economically important Chilgoza Pine (Pinus gerardiana Wall.) In South Asia. Forests. 2022;13(5):715.

    Article  Google Scholar 

  13. Ahmadi M, Hemami M, Kaboli M, Shabani F. MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models. Ecol Evol. 2023;13(2):e9827.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jafari SM, Akhani H. Plants of jahan nama protected area, Golestan Province, N. Iran. Pak J Bot. 2008;40(4):1533–54.

    Google Scholar 

  15. Ameri A, Heydarirad G, Mahdavi Jafari J, Ghobadi A, Rezaeizadeh H, Choopani R. Medicinal plants contain mucilage used in traditional persian medicine (TPM). Pharm Biol. 2015;53(4):615–23.

    Article  PubMed  Google Scholar 

  16. Nobakht SZ, Akaberi M, Mohammadpour AH, Moghadam AT, Emami SA. Hypericum perforatum: traditional uses, clinical trials, and drug interactions. Iran J Basic Med Sci. 2022;25(9):1045.

    PubMed  PubMed Central  Google Scholar 

  17. Ghasemi G, et al. Composition, antifungal, phytotoxic, and insecticidal activities of thymus kotschyanus essential oil. Molecules. 2020;25(5):1152. https://doi.org/10.3390/molecules25051152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bahreininejad B, Mirza M. Effects of ecological factors on essential oil components of several genotypes of Thymus daenensis celak using ordination technique. Iran J Med Aromatic Plants Res. 2019;35(2):196–208.

    Google Scholar 

  19. Shi X, et al. Prediction of the potentially suitable areas of Litsea cubeba in China based on future climate change using the optimized MaxEnt model. Ecol Ind. 2023;148:110093.

    Article  Google Scholar 

  20. Asgarian A, Soffianian A. Past and potential future distribution of white mangroves in an arid estuarine environment: integration of Maxent and CA-Markov models. Mar Policy. 2023;147:105345.

    Article  Google Scholar 

  21. Behmanesh B, Tabasi E, Fakhireh A, Khalasi Ahvazi L. Modeling the distribution of medicinal plant species of Thymus kotschyanus and Achillea millefolium using ENFA and Logistic Regression. J Plant Ecosyst Conserv. 2019;6(13):91–120.

    Google Scholar 

  22. Bazrmanesh A, Tarkesh M, Bashari H, Poormanafi S. Effect of climate change on the ecological niches of the climate of Bromus Tomentellus Boiss using Maxent in Isfahan province. J Range Watershed Managment. 2019;71(4):857–67.

    Google Scholar 

  23. Mohammady M, et al. Modeling and prediction of Habitat Suitability for Ferula gummosa Medicinal Plant in a Mountainous Area. Nat Resour Res. 2021;30(6):4861–84. https://doi.org/10.1007/s11053-021-09940-3.

    Article  Google Scholar 

  24. Zeraatkar A, Khajoei Nasab F. Mapping the habitat suitability of endemic and sub-endemic almond species in Iran under current and future climate conditions. Environ Dev Sustain, pp. 1–18, 2023.

  25. Larti M, Ghasempour S, Sharifi Ashorabadi E, Alizadeh B. The study of some ecological characteristics of Thymus kotschyanus Boiss. Et hohen and Thymus pubescens Boiss. & Kotschy ex Celak in West Azarbaijan. Iran J Med Aromatic Plants Res. 2013;29(2):412–24.

    Google Scholar 

  26. Darvishi L, Zare Chahouki MA, Jafari M, Azarnivand H, Yousefi Valikchali M. Study on the Environmental Factors Contributing to distribution of Thymus kotschyanus in Taleghan Basin, Iran. J Rangel Sci. 2013;4(1):82–90.

    Google Scholar 

  27. Zare Chahouki MA, Abasi M. Habitat suitability modeling for Thymus kotschyanus Boiss. & Hohen. Using ecological-niche factor analysis (case study: rangeland of middle Taleghan). Iran J Med Aromatic Plants Res. 2016;32(4):561–73.

    Google Scholar 

  28. Nazari S, Jafarian Z, Alavi J, Naghi poor AA. The impact of Climate Change on the Geographic distribution of Thymus kotschyanus (Boiss and Hohen) using ensemble modelling. Desert Manage. 2021;9(3):1–16. https://doi.org/10.22034/JDMAL.2021.526831.1338.

    Article  Google Scholar 

  29. Yari R, Dashti M, Zeynabi M, Parsa Mohebi SM, Azizi N. Autecology study of Thymus kotschyanus in Rangeland ecosystems of Boshrouyeh city, South Khorasan Province. Technol Med Aromatic Plants Iran. 2021;4(1):18–32.

    Google Scholar 

  30. Majd M, Khoshhal Dastjerdi J, Sefidkon F, Lebasschi M, Baratian A. Determination of the effects of climate on growth and phonological stage of Daenensis Thymuse for cultivation in different geographical regions. Phys Geogr Res Q. 2021;53(2):195–211.

    Google Scholar 

  31. Jamshidi Z, Samani N. Mapping the spatiotemporal diversity of precipitation in Iran using multiple statistical methods. Theoret Appl Climatol. 2022;150(1–2):893–907.

    Article  Google Scholar 

  32. Jamshidi Z, Samani N. Mapping the Spatiotemporal Diversity of Precipitation in Iran, 2021.

  33. Noroozi J, Moser D, Essl F. Diversity, distribution, ecology and description rates of alpine endemic plant species from Iranian mountains. Alp Bot. 2016;126:1–9.

    Article  Google Scholar 

  34. Sharifi M. An Overview of Ecological Potential and the Outstanding Universal Value of Forests Resources of I.R. Iran with respect to Climate Change, Forests, Rangelands and Climate Change in the Near East Region. Natural Parks and Protected Areas High Council Member of FRWO. Regional Workshop. 20–22 September 2011, 2011.

  35. Sayadi S, Mehrabian A, Mostafavi H. Diversity centers and distribution patterns of Eudicot crop wild relatives of Iran: priorities for conservation and important plant areas. J Wildl Biodivers. 2022;6(1):1–19. https://doi.org/10.22120/jwb.2021.526979.1219.

    Article  Google Scholar 

  36. Hosseini N, Mehrabian A, Mostafavi H. The distribution patterns and priorities for conservation of monocots Crop Wild relatives (CWRs) of Iran. J Wildl Biodivers. 2021;5(2):28–43.

    Google Scholar 

  37. White F, Léonard J. Phytogeographical links between Africa and Southwest Asia. Flora et vegetatio Mundi. 1991;9:229–46.

    Google Scholar 

  38. Jamzad Z. FLORA OF IRAN(lamiaceae). Volume 76. Iran, Tehran: Research Institute of Forests and Rangelands; 2012.

    Google Scholar 

  39. Jalas J. Thymus in KH Rechinger, editor, Fl. Iran. vol. 150: 532–551. Graz, 1982.

  40. Ghasemi Pirbalouti A, Emami Bistghani Z, Malekpoor F. An overview on genus Thymus. J Med Herbs. 2015;6(2):93–100.

    Google Scholar 

  41. Manukyan A. Secondary metabolites and their antioxidant capacity of caucasian endemic thyme (Thymus transcaucasicus Ronn.) As affected by environmental stress. J Appl Res Med Aromatic Plants. 2019;13:100209. https://doi.org/10.1016/j.jarmap.2019.100209.

    Article  Google Scholar 

  42. Elahian F, Yazdinezhad A, Moein-Albokay Tusi N, Nouri Z, Mirzaei SA. Variety of antibacterial and antifungal activity of Thymus kotschyanus essential oil collected from fourteen regions of Iran. J Birjand Univ Med Sci. 2020;27(3):275–90.

    Google Scholar 

  43. Thiers B. Index Herbariorum: a global directory of public herbaria and associated staff. New York Garden’s Virtual Herbarium, New York Garden‘s Virtual Herbariumhttp://sweetgum.nybg.org/ih, 2022.

  44. Aminzadeh M, Amiri F, Abadi EA, Mahdevi K, Fadai S. Factors affecting on essential chemical composition of Thymus kotschyanus in Iran. World Appl Sci J. 2010;8(7):847–56.

    CAS  Google Scholar 

  45. Elith J, et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006;29(2):129–51. https://doi.org/10.1111/j.2006.0906-7590.04596.x.

    Article  Google Scholar 

  46. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Divers Distrib. 2011;17(1):43–57.

    Article  Google Scholar 

  47. Wu Y-M, Shen X-L, Tong L, Lei F-W, Mu X-Y, Zhang Z-X. Impact of past and future climate change on the potential distribution of an endangered montane shrub Lonicera Oblata and its conservation implications. Forests. 2021;12(2):125.

    Article  Google Scholar 

  48. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model. 2006;190:3–4.

    Article  Google Scholar 

  49. Hijmans RJ, Phillips S, Leathwick J, Elith J, Hijmans MRJ. Package ‘dismo,’ Circles, vol. 9, no. 1, pp. 1–68, 2017.

  50. Team RC. R: A Language and Environment for Statistical Computing, Vienna, Austria, http://www.R-project.org/. 2018.

  51. Makki T et al. Impacts of climate change on the distribution of riverine endemic fish species in Iran, a biodiversity hotspot region. Freshw Biol, 2023.

  52. Maruthadurai R, Das B, Ramesh R. Predicting the invasion risk of rugose spiraling whitefly, Aleurodicus Rugioperculatus, in India based on CMIP6 projections by MaxEnt. Pest Manag Sci. 2023;79(1):295–305.

    Article  CAS  PubMed  Google Scholar 

  53. Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G. blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models, Methods in Ecology and Evolution, vol. 10, no. 2, pp. 225–232, 2019, https://doi.org/10.1111/2041-210X.13107.

  54. Makki T, Mostafavi H, Matkan A, Aghighi H. Modelling climate-change impact on the spatial distribution of Garra rufa (Heckel, 1843)(Teleostei: Cyprinidae). Iran J Sci Technol Trans A: Sci. 2021;45:795–804.

    Article  Google Scholar 

  55. Liu C, White M, Newell G. Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr. 2013;40(4):778–89. https://doi.org/10.1111/jbi.12058.

    Article  Google Scholar 

  56. Valavi R, Guillera-Arroita G, Lahoz-Monfort JJ, Elith J. Predictive performance of presence-only species distribution models: a benchmark study with reproducible code. Ecol Monogr. 2022;92(1). https://doi.org/10.1002/ecm.1486.

  57. Lobo JM, Jiménez-Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008;17(2):145–51.

    Article  Google Scholar 

  58. jun Yi Y, Cheng X, Yang ZF, Zhang SH. Maxent modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecol Eng. 2016;92:260–9. https://doi.org/10.1016/j.ecoleng.2016.04.010.

    Article  Google Scholar 

  59. Fois M, Cuena-Lombraña A, Fenu G, Bacchetta G. Using species distribution models at local scale to guide the search of poorly known species: review, methodological issues and future directions. Ecol Model. 2018;385:124–32.

    Article  Google Scholar 

  60. Phillips SJ, et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl. 2009;19(1):181–97. https://doi.org/10.1890/07-2153.1.

    Article  PubMed  Google Scholar 

  61. Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr. 2007;34(1):102–17.

    Article  Google Scholar 

  62. Abdelaal M, Fois M, Fenu G, Bacchetta G. Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa Arabica Crép. In Egypt. Ecol Inf. 2019;50:68–75.

    Article  Google Scholar 

  63. Phillips SJ, Dudík M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 2008;31(2):161–75. https://doi.org/10.1111/j.0906-7590.2008.5203.x.

    Article  Google Scholar 

  64. Mirinejad S, Jahantab E, Mahmoudi MR, Navaei MN, Rahimi MM, Sharafatmandrad M. Investigating the impact of some habitat characteristics on distribution of Stachys pilifera benth using the BMLR model in Iran. Pol J Environ Stud. 2018;27(5):2171–8. https://doi.org/10.15244/pjoes/79719.

    Article  Google Scholar 

  65. Yan X, Wang S, Duan Y, Han J, Huang D, Zhou J. Current and future distribution of the deciduous shrub Hydrangea macrophylla in China estimated by MaxEnt. Ecol Evol. 2021;11(22):16099–112.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Esfanjani J, Ghorbani A, Moameri M, Zare Chahouki MA, Esmali Ouri A, Ghasemi ZS. Application of modeling techniques for the identification the relationship between environmental factors and plant species in rangelands of Iran. Ecol Inf. 2021;61:101229. https://doi.org/10.1016/j.ecoinf.2021.101229.

    Article  Google Scholar 

  67. Abdi E, Saleh HR, Majnonian B, Deljouei A. Soil fixation and erosion control by Haloxylon Persicum roots in arid lands, Iran. J Arid Land. 2019;11:86–96.

    Article  Google Scholar 

  68. Deljouei A, Cislaghi A, Abdi E, Borz SA, Majnounian B, Hales TC. Implications of hornbeam and beech root systems on slope stability: from field and laboratory measurements to modelling methods. Plant Soil. 2023;483(1–2):547–72.

    Article  CAS  Google Scholar 

  69. Farahnak M, et al. Soil hydraulic conductivity differences between upslope and downslope of two coniferous trees on a hillslope. J For Res. 2019;24(3):143–52.

    Article  Google Scholar 

  70. Oke OA, Thompson KA. Distribution models for mountain plant species: the value of elevation. Ecol Model. 2015;301:72–7.

    Article  Google Scholar 

  71. Santos X, et al. Inferring habitat-suitability areas with ecological modelling techniques and GIS: a contribution to assess the conservation status of Vipera latastei. Biol Conserv. 2006;130(3):416–25.

    Article  Google Scholar 

  72. Douaihy CB, Restoux G, Machon N, Dagher-Kharrat MB. Ecological characterization of the Juniperus excelsa stands in Lebanon. Ecologia Mediterranea. 2013;39(1):169–80.

    Article  Google Scholar 

  73. Zeng XH, Zhang WJ, Song YG, Shen HT. Slope aspect and slope position have effects on plant diversity and spatial distribution in the hilly region of Mount Taihang, North China. J Food Agric Environ. 2014;12:391–7.

    Google Scholar 

  74. Najafzadeh R, Rashidi Z, Shokri B, Abdi H. Investigation of morphological and ecological and essential oil content variation of some populations of thyme species (Thymus spp.) in the northwest and west of Iran. Iran J Rangelands Forests Plant Breed Genetic Res. 2020;27(2):291–306.

    Google Scholar 

  75. Yousefzadeh S, Abedi R, Mokhtassi-Bidgoli A. Which environmental factors are more important for geographic distributions of Thymus species and their physio-morphological and phytochemical variations? Arab J Geosci. 2021;14(18):1864. https://doi.org/10.1007/s12517-021-08253-2.

    Article  Google Scholar 

  76. Corticchiato M, Tomi F, Bernardini AF, Casanova J. Composition and infraspecific variability of essential oil from Thymus herba barona Lois. Biochem Syst Ecol. 1998;26(8):915–32.

    Article  CAS  Google Scholar 

  77. Boira H, Blanquer A. Environmental factors affecting chemical variability of essential oils in Thymus piperella L. Biochem Syst Ecol. 1998;26(8):811–22.

    Article  CAS  Google Scholar 

  78. Arvin AA, Khodagholi M, Moazeni S. Investigation of the bio-climatic needs of Thymus daenensis celak: the case of Isfahan Province. J Range Watershed Managment. 2020;73(2):257–72.

    Google Scholar 

  79. Pirbalouti AG, Karimi A, Yousefi M, Enteshari S, Golparvar AR. Diversity of Thymus daenensis Celak in central and west of Iran. J Med Plants Res. 2011;5(4):319–23.

    Google Scholar 

  80. Tolyat MA, Tavakkol Afshari R, Jahansoz MR, Nadjafi F, Naghdibadi HA. Determination of cardinal germination temperatures of two ecotypes of Thymus daenensis subsp. daenensis. Seed Sci Technol. 2014;42(1):28–35. https://doi.org/10.15258/sst.2014.42.1.03.

    Article  Google Scholar 

  81. Soilhi Z, Sayari N, Benalouache N, Mekki M. Predicting current and future distributions of Mentha pulegium L. in Tunisia under climate change conditions, using the MaxEnt model. Ecol Inf. 2022;68:101533.

    Article  Google Scholar 

  82. Gao X, Liu J, Huang Z. The impact of climate change on the distribution of rare and endangered tree Firmiana kwangsiensis using the Maxent modeling. Ecol Evol. 2022;12(8):1–10. https://doi.org/10.1002/ece3.9165.

    Article  Google Scholar 

  83. Bin Dong P, et al. Distributional response of the Rare and Endangered Tree species Abies chensiensis to Climate Change in East Asia. Biology. 2022;11(11):1659. https://doi.org/10.3390/biology11111659.

    Article  Google Scholar 

  84. Tohidi B, Rahimmalek M, Arzani A. Essential oil composition, total phenolic, flavonoid contents, and antioxidant activity of Thymus species collected from different regions of Iran. Food Chem. 2017;220:153–61. https://doi.org/10.1016/j.foodchem.2016.09.203.

    Article  CAS  PubMed  Google Scholar 

  85. Tohidi B, Rahimmalek M, Trindade H. Review on essential oil, extracts composition, molecular and phytochemical properties of Thymus species in Iran. Ind Crops Prod. 2019;134:89–99.

    Article  CAS  Google Scholar 

  86. Nadjafi F, Tabrizi L, Shabahang J, Damghani AMM. Cardinal germination temperatures of some medicinal plant species. Seed Technol, pp. 156–63, 2009.

  87. Kong F, Tang L, He H, Yang F, Tao J, Wang W. Assessing the impact of climate change on the distribution of Osmanthus fragrans using Maxent. Environ Sci Pollut Res. 2021;28(26):34655–63. https://doi.org/10.1007/s11356-021-13121-3.

    Article  Google Scholar 

  88. Khwarahm NR. Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq. Ecol Processes. 2020;9(1):1–16.

    Google Scholar 

  89. Hamid M, Khuroo AA, Charles B, Ahmad R, Singh CP, Aravind NA. Impact of climate change on the distribution range and niche dynamics of himalayan birch, a typical treeline species in Himalayas. Biodivers Conserv. 2019;28:2345–70.

    Article  Google Scholar 

  90. Naghipour Borj AA, Ostovar Z, Asadi E. The influence of climate change on distribution of an endangered medicinal plant (Fritillaria Imperialis L.) in central Zagros. J Rangel Sci. 2019;9(2):159–71.

    Google Scholar 

  91. Baskin CC, Baskin JM. Seeds: ecology, biogeography, and, evolution of dormancy and germination. Elsevier; 1998.

  92. Shim SI, Moon J-C, Jang CS, Raymer P, Kim W. Effect of potassium nitrate priming on seed germination of seashore paspalum. HortScience. 2008;43(7):2259–62.

    Article  Google Scholar 

  93. Koutecká E, Lepš J. Effect of light and moisture conditions and seed age on germination of three closely related Myosotis species. Folia Geobotanica. 2009;44(2):109–30. https://doi.org/10.1007/s12224-009-9038-9.

    Article  Google Scholar 

  94. Phartyal SS, Thapliyal RC, Nayal JS, Joshi G. Seed storage physiology of himalayan elm (Ulmus Wallichiana): an endangered tree species of tropical highlands. Seed Sci Technol. 2003;31(3):651–8.

    Article  Google Scholar 

  95. Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems, nature, vol. 421, no. 6918, pp. 37–42, 2003.

  96. Thomas CD, et al. Extinction risk from climate change. Nature. 2004;427(6970):145–8.

    Article  CAS  PubMed  Google Scholar 

  97. Muñoz AR, Márquez AL, Real R. Updating known distribution models for forecasting Climate Change Impact on Endangered species. PLoS ONE. 2013;8(6):e65462. https://doi.org/10.1371/journal.pone.0065462.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Sharifi Ashoorabadi E, Mackizadeh Tafti M, Hasani J, Lebaschy MH. Effect of temperature and humidity on seed germination of six different Thymus species. Iran J Seed Sci Technol. 2021;10(3):1–15. https://doi.org/10.22092/ijsst.2020.128012.1296.

    Article  Google Scholar 

  99. Abolmaali SM-R, Tarkesh M, Bashari H. MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran. Ecol Inf. 2018;43:116–23.

    Article  Google Scholar 

  100. Khanum R, Mumtaz AS, Kumar S. Predicting impacts of climate change on medicinal asclepiads of Pakistan using Maxent modeling. Acta Oecol. 2013;49:23–31.

    Article  Google Scholar 

  101. Fois M, Cuena-Lombraña A, Fenu G, Cogoni D, Bacchetta G. Does a correlation exist between environmental suitability models and plant population parameters? An experimental approach to measure the influence of disturbances and environmental changes. Ecol Ind. 2018;86:1–8.

    Article  Google Scholar 

  102. Zhao Q, Li R, Gao Y, Yao Q, Guo X, Wang W. Modeling impacts of climate change on the geographic distribution of medicinal plant Fritillaria Cirrhosa D. Don. Plant Biosystems-An Int J Dealing all Aspects Plant Biology. 2018;152(3):349–55.

    Article  Google Scholar 

  103. Sanjerehei MM, Rundel PW. The impact of climate change on habitat suitability for Artemisia sieberi and Artemisia aucheri (Asteraceae)—A modeling approach. Pol J Ecol. 2017;65(1):97–109.

    Google Scholar 

  104. Koch R, Almeida-Cortez JS, Kleinschmit B. Revealing areas of high nature conservation importance in a seasonally dry tropical forest in Brazil: combination of modelled plant diversity hot spots and threat patterns. J Nat Conserv. 2017;35:24–39.

    Article  Google Scholar 

  105. Baldwin RA. Use of maximum entropy modeling in wildlife research. Entropy. 2009;11(4):854–66.

    Article  Google Scholar 

  106. Qin A, et al. Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja Sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecol Conserv. 2017;10:139–46. https://doi.org/10.1016/j.gecco.2017.02.004.

    Article  Google Scholar 

  107. Tucker MA, et al. Moving in the Anthropocene: global reductions in terrestrial mammalian movements. Science. 2018;359(6374):466–9.

    Article  CAS  PubMed  Google Scholar 

  108. Li GQ, Liu CC, Liu YG, Yang J, Zhang XS, Guo K. Advances in theoretical issues of species distribution models. Acta Ecol Sin. 2013;33(16):4827–35.

    Article  Google Scholar 

  109. Lissovsky AA, Dudov SV. Species-distribution modeling: advantages and limitations of its application. 2. MaxEnt. Biology Bull Reviews. 2021;11(3):265–75.

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

Not applicable

Author information

Authors and Affiliations

  1. Department of Medicinal Plants, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Iran

    Naser Hosseini & Mansour Ghorbanpour

  2. Department of Biodiversity and Ecosystem Management, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran

    Hossein Mostafavi

Authors
  1. Naser Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mansour Ghorbanpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossein Mostafavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N. H. supervised the research, performed the experiment and analyzed the data. M. G. and H. M. advised the research and revised the manuscript critically. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Naser Hosseini or Mansour Ghorbanpour.

Ethics declarations

Ethics approval and consent to participate

All methods performed in this study were in compliance with the relevant institutional, national, and international guidelines and legislation.

Consent to participate

Not Applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

Identification of the collected plant materials

The identification and authentication of thyme as a species was done according to Botanical Standards (Rechinger 1982) and vouchered in Arak University Herbarium (A1x).

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hosseini, N., Ghorbanpour, M. & Mostafavi, H. The influence of climate change on the future distribution of two Thymus species in Iran: MaxEnt model-based prediction. BMC Plant Biol 24, 269 (2024). https://doi.org/10.1186/s12870-024-04965-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-024-04965-1

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us