Skip to main content
NCBI home page
Ecology and Evolution logoLink to Ecology and Evolution
. 2026 Apr 30;16:e73566. doi: 10.1002/ece3.73566

Climate‐Driven Habitat Suitability Modeling for the Vulnerable Species Euryops pinifolius A. Rich in Ethiopia: Implications for Conservation

Liyew Birhanu 1,2,3,✉, Heiko Balzter 2,4, Laura Basell 5, Moya Burns 6, Wale Arega 3, Yilkal Gebeyehu 7
PMCID: PMC13130346  PMID: 42078666

ABSTRACT

Globally, rare and threatened species are a high conservation priority due to their increased risk of extinction. Euryops pinifolius is a vulnerable plant species found within restricted ranges in the Afroalpine Ecosystem of Ethiopia. The species has been seriously threatened due to overexploitation for fuelwood collection by the communities living adjacent to the mountains. Given the socio‐economic importance as well as the increasing vulnerability of the species, it is important to understand how extinction may be prevented in the face of climate change. The present study, therefore, aimed to model and map habitat suitability of the species under climate change scenarios in Ethiopia. In this study, we used the MaxEnt model to predict the current and future distribution of E. pinifolius in Ethiopia under different climate scenarios. The results demonstrated excellent simulation quality by the MaxEnt model (AUC = 0.985). The major environmental factors predicting the current and future distribution of E.pinifolius were Mean Temperature of the Driest Quarter (Bio9), Altitude (Alt), Vegetation Cover (Veg), Precipitation Seasonality (Bio15), and Mean Temperature of the Wettest Quarter (Bio8). Under current climatic conditions, the potential distribution of E. pinifolius is primarily concentrated in the Ethiopian highlands, especially in the northern, northwestern, and central parts of the country. We therefore recommend that both current and projected future suitable areas be given conservation priority to safeguard this species.

Keywords: climate change, conservation planning, Ethiopia, Euryops pinifolius, habitat suitability, MaxEnt model

Euryops pinifolius, vulnerable plant species in Ethiopia's Afroalpine ecosystem, faces serious threats from overexploitation for fuelwood. Using the MaxEnt model (AUC = 0.985), this study identified key environmental factors‐such as temperature, altitude, vegetation cover, and precipitation seasonality‐affecting its current and future habitat suitability. The species' suitable habitats are mainly in the northern, northwestern, and central Ethiopian highlands, and these areas should be prioritized for conservation under current and future climate scenarios.

graphic file with name ECE3-16-e73566-g009.jpg

1. Introduction

Biodiversity loss is an urgent global concern, with numerous species disappearing due to anthropogenic activities (Sala et al. 2000; Pereira et al. 2010; Johnson et al. 2017; IPBES 2019). Land‐use change, habitat fragmentation, overexploitation, pollution, invasive species, and climate change are widely recognized as major drivers of this problem (Fahrig 2003; Watson et al. 2005; Wilson et al. 2016; Newbold 2018; IPCC 2021). However, the relative importance of these stressors is often context‐specific, and their interactions can be complex and poorly understood (Nešić and Bjedov 2021). In many ecosystems, addressing more immediate and tangible pressures, such as fragmentation and land‐use conversion, may offer more achievable entry points for conservation and mitigation efforts, while acknowledging that broader forces, such as climate change, exacerbate these underlying challenges (Muluneh 2021). Numerous species are therefore facing extinction, driven by the combined influence of these human‐induced changes (Tilman et al. 2017).

Likewise, plant distribution patterns are influenced by environmental factors such as climate, soil nutrients, human disturbances, and topographic features, which vary across different regions (Liu et al. 2003; Pugnaire et al. 2012; Brown et al. 2019). Global climate change poses significant risks to species and ecosystems, causing shifts in species distribution (Chen et al. 2011), threatening their viability via range reduction (Thomas et al. 2004), and altering the representation of species in protected areas (Velásquez‐Tibatá et al. 2012). Species confined to mountain tops are at significant risk of extinction, as they may not have enough suitable habitats they can migrate to in response to climate change (Lenoir et al. 2008; Markham 2014). Consequently, understanding the impact of climate change and other anthropogenic pressures on plant distribution is essential for effective conservation planning and management (Urban et al. 2016; Charney et al. 2021).

The Horn of Africa is one of 36 global biodiversity hotspots, incorporating many endemic and threatened plants, yet climate impact studies in this region are scarce (Myers et al. 2000; Mittermeier et al. 2005). Africa will face temperature rises of 3°C–4°C between 2080 and 2099, approximately 1.5 times higher than the global mean (IPCC 2021). Climate change poses a threat to a substantial portion of tropical African flora, with many species potentially facing extinction (Stévart et al. 2019). These impacts are expected to drive major shifts in species distributions, particularly affecting endemic and montane ecosystems, and to increase the risk of habitat loss across the continent (Warren et al. 2013; Midgley and Bond 2015). Specifically, it is estimated that over 5000 African plant species will lose their climatically suitable habitats by 2085 (McClean et al. 2005; Yebeyen et al. 2022). Mapping and monitoring species' responses to ongoing change for effective management is therefore essential.

Of 6027 plant species recorded in the Flora of Ethiopia and Eritrea, 647 (10.74%) are endemic, and many of these cluster in the Afroalpine and sub‐Afroalpine regions (Friis et al. 2010; Demissew et al. 2021). Threats to endemic Ethiopian species vary, as demonstrated by studies of Lobelia rhynchopetalum, which is at risk of extinction due to climate warming, and Echinops kebericho, which is heavily exploited for its medicinal value (Vivero et al. 2005; Chala et al. 2016; Tafesse et al. 2023).

Euryops pinifolius A. Rich. (Family: Asteraceae) is native to Ethiopia, grows primarily in the seasonally dry tropical biome, and was chosen for our study based on its threatened status and narrow distribution ranges, making it ideally suited for modeling (Vivero et al. 2005). Euryops pinifolius is a shrub found in montane meadows at altitudes of 3200–3700 m. asl often alongside genus lobelia on thin soil, rocks, and cliff margins. It is economically valued by local communities for its medicinal uses and as fuelwood (Kelbessa et al. 1992). Medicinal qualities include pain relief by chewing the leaf, especially for stomachache (kurba), and it has been demonstrated to have antioxidant properties (Meragiaw et al. 2016; Rehman et al. 2022). In some regions of Ethiopia, it is the most collected firewood species because it can be easily burned without drying (Ashenafi 2001). Parts of the plant are also used for feeding livestock and for ornamental purposes (esthetics and recreational values) (Guassa Area General Management Plan (GMP) 2005).

The International Union for Conservation of Nature (IUCN) has listed the plant as vulnerable due to anthropogenic impacts on its habitat, leading to legislation restricting harvesting in some areas (Vivero et al. 2005; Steger et al. 2020). To determine the best conservation strategies for E. pinifolius, an understanding of the environmental factors that affect its geographical distribution is required. Despite the clear importance of this shrub, the effects of global climate change on its distribution remain unclear.

Among the most important research problems for ecologists is understanding how species interact with environmental factors to predict how these factors will change species distributions (Cao et al. 2016). An important consideration in predicting the habitat suitability of species under climate change is that it provides critical information for the conservation of endemic and threatened plant species. Tools include species distribution models (SDMs) for investigating habitat suitability, identifying environmental variables driving species patterns, and identifying areas where urgent conservation measures are needed (Elith et al. 2011; Oyebanji et al. 2021).

A wide range of species distribution modeling techniques is available, with varying performance and data type requirements (Elith et al. 2006). Among the best algorithms, MaxEnt (Maximum Entropy) is one of the most useful techniques for modeling endemic, rare, and threatened species with limited habitat ranges and scant presence‐only occurrence data (Phillips and Dudík 2008; Dyderski et al. 2018; Chen et al. 2022). In this study, the MaxEnt model was employed to predict potentially suitable habitats for E. pinifolius under various climate change scenarios in Ethiopia. The objectives of this research are to: (a) estimate the current and future habitat suitability for E. pinifolius under these climate change scenarios; (b) identify the significant environmental factors influencing the geographical distribution of E. pinifolius; and (c) recommend priority areas for effective future conservation of the species.

2. Materials and Methods

2.1. Species Occurrence Data

E. pinifolius A. Rich is a member of Asteraceae, a shrub that grows between 3200 and 3700 m above sea level in the Sub‐Afroalpine vegetation of Ethiopia (Vivero et al. 2005). It grows on the edges of cliffs, on rocks, and in thin soil alongside Lobelia. We obtained 151 occurrence points for E. pinifolius from the Global Biodiversity Information Facility (https://www.gbif.org), Addis Ababa University's National Herbarium of Ethiopia, field surveys, and previously published literature (Table S1). To ensure the accuracy of occurrence data, a rigorous multi‐step cleaning and rarefaction process was applied. Species names and geographic coordinates were carefully verified using herbarium records, ArcGIS, and Google Earth. To reduce spatial autocorrelation, occurrence points were spatially rarefied at a 1km2 resolution using SDM Toolbox v2.6 (Brown 2014; Guisan et al. 2017). Additionally, spatial thinning was conducted to remove duplicate records (Aiello‐Lammens et al. 2015). Finally, a total of 136 occurrence points for E. pinifolius were retained for the final analysis to generate the potential distribution using MaxEnt modeling. The flowering and seed dispersal stages of E. pinifolius are shown in Figure 1. The occurrence‐point data were saved in a CSV file, organized by species name, longitude, and latitude, and subsequently plotted on a map (Figure 2).

FIGURE 1.

FIGURE 1

Open in a new tab

Eyuryops pinifolius A. Rich (A) flowering stage (B) seed dispersal stage (Dagnachew et al. 2023).

FIGURE 2.

FIGURE 2

Open in a new tab

The location of 151 occurrence points of E. pinifolius in Ethiopia.

2.2. Environmental Variables

To determine the habitat suitability of E. pinifolius, 19 bioclimatic variables, 3 biophysical factors (elevation, slope, and aspect), as well as land use/land cover, and vegetation data were used. We obtained climate data for the current period (1970–2000) and future bioclimatic projections for the 2050s (2041–2060) and 2070s (2061–2080) from WorldClim 2.1 (https://www.worldclim.org/), with a spatial resolution of 30 arc sec (approximately 1 km2). Two shared socioeconomic pathways (the worst SSP8.5 and the intermediate emission pathway SSP4.5) developed by HadGEM2‐Es global circulation models (GCMs) were used during the 2050s (2041–2060) and 2070s (2061–2080) (Fick and Hijmans 2017). Topographic variables were derived from the Shuttle Radar Topography Mission Digital Elevation Model (SRTM‐DEM) (Jarvis et al. 2008).

Ethiopian vegetation types were derived from the World Agroforestry Centre (http://landscapeportal.org/layers/geonode:veg), whereas the land use land cover map was obtained from (https://cds.climate.copernicus.eu/). Using spatial analyst tools in ArcGIS version 10.7 at a resolution of 1 km2, all environmental variables were processed to have the same extent, projection, and resolution (Phillips et al. 2006). The environmental data were then converted into “asc” files, which are necessary for Maxent modeling, using the ArcGIS software. To reduce multicollinearity among the 24 environmental variables, highly correlated variables (r ≥ 0.70, Pearson correlation coefficient) were eliminated from further models (Guisan et al. 2017). Finally, 13 environmental variables were used to model the geographical distribution for E. pinifolius (Table 1).

TABLE 1.

List of Environmental variables (13 variables with the code shown in bold font were chosen for the MaxEnt modeling study). Capitalized codes (e.g., Alt, ASP, Slope, LULC, Vegetation) represent topographic and land‐cover variables, while Bio1–Bio19 represent bioclimatic variables.

Code Environmental variables Unit
Bio1 Annual mean temperature °C
Bio2 Mean diurnal range °C
Bio3 Isothermality —
Bio4 Temperature seasonality —
Bio5 Maximum temperature of the warmest month °C
Bio6 Minimum temperature of the coldest month °C
Bio7 Temperature annual range °C
Bio8 Mean temperature of the wettest quarter °C
Bio 9 Mean temperature of driest quarter °C
Bio10 Mean temperature of the warmest quarter °C
Bio11 Mean temperature of the coldest quarter °C
Bio12 Annual precipitation mm
Bio13 Precipitation of the wettest month mm
Bio14 Precipitation of the driest month mm
Bio15 Precipitation seasonality —
Bio16 Precipitation of the wettest quarter mm
Bio17 Precipitation of the driest quarter mm
Bio18 Precipitation of the warmest quarter mm
Bio19 Precipitation of the coldest quarter mm
Alt Altitude M
ASP Aspect Degree
Slope Slope %
LULC Land use Land cover %
Vegetation Vegetation cover %
Open in a new tab

2.3. Species Distribution Modeling

We employed the maximum‐entropy algorithm (Maxent version 3.4.1) species distribution modeling algorithm to predict the current and future habitat suitability of E. pinifolis. In the modeling, 75% of the data was used for training the model, whereas 25% of the data was used for model testing (Phillips et al. 2006). The performance of the MaxEnt models was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) value. The AUC values range from 0 to 1, where high AUC values imply a good model fit. The model's predictions become more accurate, and confidence increases as the AUC value approaches 1 (Swets 1988). Therefore, models with an AUC > 0.90 are generally considered to be highly accurate, while those with 0.70 < AUC ≤ 0.90 are viewed as good. AUC values between 0.50 and 0.70 suggest lower accuracy, and an AUC ≤ 0.50 indicates performance no better than random chance (Swets 1988; Elith et al. 2011). Jackknife analyses were performed to determine variables that significantly influence the model reliability. Referring to the classification proposed by Gao et al. (2022), four habitat classes were generated: unsuitable habitat (0–0.20); lowly suitable habitat (0.21–0.50); moderately suitable habitat (0.51–0.70); highly suitable habitat (> 0.70). The area for optimal distribution, classified as low, medium, and high suitable habitats, was calculated for each model. The overall methodological framework of the study is illustrated in Figure 3.

FIGURE 3.

FIGURE 3

Open in a new tab

Flow chart diagram methodology used in species distribution modeling.

3. Results

3.1. Model Performance and Accuracy

Our model demonstrated excellent performance, achieving an average AUC value of 0.985. This indicates that the MaxEnt model was highly reliable in predicting the potential geographical distribution areas of E. pinifolius in Ethiopia (Figure 4).

FIGURE 4.

FIGURE 4

Open in a new tab

The area under the receiver operating characteristic curve for E. pinifolius obtained via MaxEnt modeling.

3.2. Environmental Variable Contributions for Euryops pinifolius Distribution in Ethiopia

The MaxEnt analyses identified key environmental variables that contributed to the distribution model of E. pinifolius in Ethiopia under different climate change scenarios. Mean Temperature of the Driest Quarter (Bio9) had the highest contribution in the model, followed by Altitude (Alt), Vegetation Cover (Veg), Precipitation Seasonality (Bio15), and Mean Temperature of the Wettest Quarter (Bio8) (Table 2).

TABLE 2.

Relative percent contribution of environmental factors to MaxEnt modeling for Euryops pinifolius distribution in Ethiopia.

Variable Percent contribution
Current 2050 2070
SSP4.5 SSP8.5 SSP4.5 SSP8.5
Bio 9 46.4 49.71 43.88 46.09 40.99
Alt 36.86 30 38.32 24.87 38.69
Veget 4.90 5.67 6.30 5.76 5.82
Bio8 3.24 4.65 2.61 13.86 2.14
Bio15 2.85 3.6 3.2 3.47 2.78
Bio5 2.11 3.33 1.46 2.71 6.31
Bio19 1.08 1.45 1.33 1.39 1.34
LULC 0.728 0.44 0.74 0.561 0.46
Slope 0.63 0.42 0.78 0.44 0.30
Bio18 0.56 0.43 0.39 0.30 0.57
ASP 0.52 0.25 0.57 0.25 0.34
Bio4 0.25 0.40 0.27 0.21 0.14
Bio14 0.06 0.06 0.08 0.03 0.06
Open in a new tab

The jackknife analysis also revealed that altitude (ALT) and the Mean Temperature of the Driest Quarter (Bio9) were the most influential environmental variables when used individually, indicating their important role in predicting the geographical distribution of E. pinifolius (Figure 5). Other significant contributors included Bio8 (Mean Temperature of Wettest Quarter), Bio5 (Maximum Temperature of Warmest Month), vegetation cover, Bio18 (Precipitation of Warmest Quarter), Bio14 (Precipitation of Driest Month), Bio15 (Precipitation Seasonality), land cover, and slope. In contrast, aspect showed a negligible impact on the model's predictions (Figures 5 and 6).

FIGURE 5.

FIGURE 5

Open in a new tab

Predictive power of different environmental variables based on the jackknife of regularized training gain.

FIGURE 6.

FIGURE 6

Open in a new tab

Result of jackknife AUC test of the MaxEnt model for evaluating the relative importance of bioclimatic environmental variables for E. pinifolius.

3.3. Response of E. pinifolius A. Rich to the Main Contributing Environmental Variables

The impact of each predictor variable on the MaxEnt model projection was demonstrated by the response curves (Figure 7). Each curve is a distinct model generated with the help of the corresponding variable. A MaxEnt model constructed using just the corresponding variable is represented by each curve listed below. These graphs show how predicted appropriateness depends on the chosen variable and on dependencies arising from correlations between the chosen variable and other factors.

FIGURE 7.

FIGURE 7

Open in a new tab

Response curve of the major bioclimatic and topographic factors influencing the distribution of E. pinifolius A. Rich (A‐F). These key variables showed an initial increase in suitability, followed by a decline as each variable approached its optimal range. Specifically, Alt showed the highest suitability at an elevation range between 3000 and 4000 m, Mean Temperature of the Driest Quarter (BIO9) was most favorable at 10°C.

3.4. Current and Future Habitat Suitability of Euryops pinifolius in Ethiopia

The current potential distribution of E. pinifolius is primarily concentrated in specific highland and mountainous regions in the central, northern, and northwestern parts of the country, including the Amhara and Tigray regions, as well as parts of Oromia and Southern Nations, Nationalities, and Peoples' Region (SNNPR) (Figure 8). The total suitable area accounted for 13,420.48 km2. Of this, 9310.06 km2 is classified as lowly suitable habitat, 4110.42 km2 as moderately suitable habitat, and 4327.75 km2 as highly suitable habitat (Table 3, Figure 8).

FIGURE 8.

FIGURE 8

Open in a new tab

Current potential suitable habitats of Euryops pinifolius in Ethiopia.

TABLE 3.

Suitable areas for the distribution of E. pinifolius.

Period SSP Classes of suitable habitats
Highly suitable Moderately suitable Lowly suitable Unsuitable
Area (km2) % Area (km2) % Area (km2) % Area (km2) %
Current — 4327.75 0.38 4110.42 0.36 9310.06 0.82 1,124,629.22 99
2050 4.5 871.881 0.08 4283.26 0.38 12,334.69 1.1 1,124,887.62 98.9
8.5 4680.27 0.82 3823.79 0.33 8987.49 0.78 1,124,885.90 98
2070 4.5 4972.89 0.43 3952.99 0.34 10,128.89 0.89 1,123,322.68 99
8.5 927.497 0.08 4602.41 0.4 12,143.89 1.07 1,124,703.66 99
Open in a new tab

For 2050, under the SSP 4.5 scenario, the total suitable area for E. pinifolius increases to 17,489.83 km2, with 12,334.69 km2 classified as low suitability, 4283.26 km2 as moderately suitability, and 871.88 km2 as highly suitability. Under the SSP8.5 scenario, the total suitable area is 17,491.54 km2, with 8987.49 km2 of low suitability, 3823.79 km2 of moderately suitability, and 4680.27 km2 of highly suitability. For 2070, under the SSP4.5 scenario, the total suitable area for E. pinifolius is projected to be 19,054.77 km2. Of this, 10,128.89 km2 is classified as lowly suitable habitat, 3952.99 km2 as moderately suitable habitat, and 4972.89 km2 as highly suitable habitat. Under the SSP8.5 scenario, the total suitable area is expected to be 17,673.79 km2, with 12,143.89 km2 categorized as lowly suitability habitat, 4602.41 km2 as moderately suitable habitat, and 927.50 km2 as highly suitable habitat (Table 3, Figure 9).

FIGURE 9.

FIGURE 9

Open in a new tab

Maps indicating the predicted potential suitable habitat distribution for Euryops pinifolius under different climate scenarios: a1: SSP4.5_250; a2: SSP8.5_250; b1: SSP4.5_270; b2: SSP8.5_270. The a1 map (SSP4.5_250) shows habitat suitability by 2050 under the intermediate emission scenario, a2 (SSP8.5_250) shows habitat suitability by 2050 under the worst‐case emission scenario, b1 (SSP4.5_270) shows habitat suitability by 2070 under the intermediate emission scenario, and b2 (SSP8.5_270) shows habitat suitability by 2070 under the worst‐case emission scenario.

Compared to the current habitat suitability, future scenarios under both SSPs show an overall increase in total suitable area, although the distribution of suitability levels varies across years and scenarios. In 2050, under SSP4.5, lowly suitable (+32.5%) and moderately suitable (+4.2%) areas increase, while highly suitable areas decline (−79.8%). Under SSP8.5, highly suitable areas slightly increase (+8.2%), whereas moderately (−7.0%) and lowly suitable (−3.5%) areas decline. By 2070, SSP4.5 predicts gains in highly suitable (+14.9%) and lowly suitable (+8.8%) areas, with a slight decrease in moderately suitable areas (−3.8%). In SSP8.5, highly suitable areas again decrease substantially (−78.6%), while moderately (+12.0%) and lowly suitable (+30.4%) areas expand. Unsuitable areas remain nearly unchanged across all scenarios (Table 4, Figure 9).

TABLE 4.

Percentage changes in habitat suitability classes of E. pinifolius between current and future climate scenarios (SSP4.5 and SSP8.5) for 2050 and 2070.

Climatic period Scenarios Change (%) compared to the current suitability
Highly suitable Moderately suitable Lowly suitable Unsuitable
Current — — — — —
2050 SSP4.5 −79.8 +4.2 +32.5 +0.02
SSP8.5 +8.2 −7.0 −3.5 +0.02
2070 SSP4.5 +14.9 −3.8 +8.8 −0.12
SSP8.5 −78.6 +12.0 +30.4 +0.01
Open in a new tab

4. Discussion

E. pinifolius habitat suitability in Ethiopia was predicted with a high degree of accuracy using the MaxEnt model. The model exhibited excellent predictive performance, with an AUC of 0.985, indicating strong reliability in forecasting the species' potential habitats and distribution patterns across the region. This high performance is consistent with both regional and global applications of MaxEnt, further demonstrating its robustness for modeling habitat suitability under current and future climate scenarios. For instance, Mekasha et al. (2026) applied MaxEnt to Commiphora africana in Ethiopia and reported strong model predictions that inform conservation priorities under future climate change scenarios. Similarly, a MaxEnt study on Oxytenanthera abyssinica in northern Ethiopia yielded AUC values exceeding 0.90, placing the model's performance in the “excellent” category and corroborating its utility for predicting habitat shifts under climate change (Gebrewahid et al. 2020). By contrast, Gebrehiwot et al. (2022) modeled the distribution of Podocarpus falcatus with a moderate AUC of ~0.78, illustrating how species‐specific ecology and data quality can influence model accuracy while still providing useful insights for landscape‐scale restoration planning. At a global scale, Li, Geng, et al. (2025); Li, Zhang, et al. (2025) used MaxEnt to project macadamia habitat suitability under future climate scenarios and achieved an average AUC of 0.98, highlighting the model's capacity to reliably discriminate suitable versus unsuitable areas across diverse biogeographic contexts. Collectively, these studies support MaxEnt's effectiveness when calibrated with robust occurrence data and pertinent bioclimatic predictors, reinforcing its role in guiding conservation and management strategies.

Mean Temperature of the Driest Quarter (BIO9) is the most influential variable shaping the habitat suitability of E. pinifolius. In our study, E. pinifolius showed optimal suitability at moderate temperatures, around 10°C during the driest quarter. Suitability remained low at temperatures below ~5°C, while at temperatures above 30°C, suitability markedly declined (Figure 7, E), which may be due to moisture limitations affecting establishment and survival. Temperature during the driest period is likely a critical ecological constraint, as plants in montane ecosystems experience combined stress from limited moisture availability and temperature fluctuations (Brochmann et al. 2022; Li, Geng, et al. 2025; Li, Zhang, et al. 2025). These factors strongly influence plant physiology, phenology, and seedling establishment, thereby shaping species distribution patterns (Harrison and Prentice 2003; Körner 2007). Recent studies on alpine and montane plants similarly highlight temperature‐related variables as major drivers of species distributions because they regulate metabolic processes, growth, and regeneration (Zu et al. 2024).

Altitude also played a key role in determining suitable habitats. Elevation acts as an integrative environmental gradient influencing temperature, precipitation, solar radiation, soil development, and moisture availability (Hijmans et al. 2005; Graham and Hijmans 2006). For many alpine and montane species, elevational gradients strongly constrain species distributions because they define ecological niches characterized by specific climatic conditions. Elevation and temperature gradients are among the most important factors shaping alpine plant distribution and range limits (Felkel et al. 2023; Zu et al. 2024). The predicted spatial distribution of suitable habitats for E. pinifolius was mainly concentrated in the mountainous regions of the Ethiopian highlands, particularly in the northern, northwestern, and central parts of the country. This spatial pattern is consistent with previous botanical records, indicating that the species occurs predominantly in Afroalpine vegetation zones (Vivero et al. 2005). Afroalpine ecosystems are generally restricted to elevations between approximately 3200 and 4533 m above sea level and are characterized by unique climatic conditions, including low temperatures, high solar radiation, and strong diurnal temperature fluctuations (Hedberg 1995). These harsh environmental conditions restrict the distribution of many plant species but provide specialized habitats for cold‐adapted taxa. A study conducted by Gebrehiwot et al. (2020) reported that in the Afroalpine vegetation of the Abune Yosef massif, Euryops pinifolius forms a dominant shrub layer at 3834–4168 m a.s.l., co‐occurring with herbaceous species such as Kniphofia foliosa, Cardamine hirsuta , Artemisia abyssinica, Festuca macrophylla, and Senecio farinaceus. These communities persist under extremely high‐elevation conditions, highlighting the species' adaptation to stress‐prone environments and its narrow ecological niche.

Under future climate change scenarios (SSP4.5 and SSP8.5 for 2050 and 2070), the model predicts an increase in climatically suitable habitats for E. pinifolius. This projected expansion may be related to the upward shift of climatic niches as global temperatures increase. Similar trends have been reported in other montane ecosystems, where warming temperatures allow plant species to colonize higher elevations or newly suitable environments (Lenoir et al. 2008; Gao et al. 2022). Climate change may cause shifts in alpine vegetation distributions toward higher altitudes or latitudes as species track suitable climatic conditions (Bueno de Mesquita et al. 2024; Reichmuth et al. 2025). Although detailed physiological studies on E. pinifolius are lacking, its occurrence across a relatively broad elevational range suggests some level of ecological tolerance to environmental variability. This adaptability may be supported by physiological plasticity or interactions with associated soil microbial communities that help plants cope with environmental stress in alpine ecosystems (Hou et al. 2024). Similar adaptive strategies have been observed in other Afroalpine taxa, including species of Lobelia, which exhibit specialized morphological and physiological traits allowing them to survive in extreme highland environments (Knox and Palmer 1995).

Under the moderate‐emission SSP4.5 scenario, highly suitable habitats for E. pinifolius decline by 2050 but recover by 2070, reflecting initial habitat loss within its current range followed by new suitable areas at higher elevations (Thuiller et al. 2011). Under the high‐emission SSP8.5 scenario, suitable habitats increase slightly by 2050 but decline sharply by 2070, as extreme warming exceeds physiological limits (Chen et al. 2011; IPCC 2021). A study by Dagnachew et al. (2023) showed that E. pinifolius seeds maintain high viability under cold and temperature‐variable conditions, which may support persistence and colonization of newly suitable habitats under moderate warming, yet extreme temperatures projected under SSP8.5 could limit successful establishment despite this trait. These findings highlight that moderate warming may temporarily facilitate range shifts in montane plants, whereas extreme climate scenarios pose a greater risk of habitat loss.

5. Limitations and Future Research Directions

This study provides an important step toward understanding the potential future distribution of a threatened and culturally significant species under climate change. However, several limitations should be considered. Although the modeling framework focused on abiotic drivers, species distributions are also shaped by complex ecological and evolutionary processes. Interactions with pollinators (Inouye 2020; Richman et al. 2020), competition with other plant species (Anthelme and Dangles 2012), and eco‐evolutionary dynamics (Hamann et al. 2021) can significantly influence range shifts. In addition, integrating genotype, phenotype, and behavioral traits has been identified as critical for predicting species' responses to environmental extremes (Buckley et al. 2023), while neighboring species interactions may further affect survival and adaptation under changing climates (Damschen 2022).

This study's reliance on a single global circulation model (HadGEM2‐ES) may have limited the robustness of the forecasts, as different emission scenarios and climate models can yield different outcomes (Zhang et al. 2019). Future research should therefore incorporate multiple climate models and emission pathways to improve projection reliability. Similarly, the use of a single modeling algorithm (MaxEnt), despite the availability of alternative approaches within the Biomod2 framework, may introduce algorithm‐specific bias. Adopting ensemble modeling techniques that integrate multiple algorithms would enhance predictive performance and reduce uncertainty. The occurrence data, derived from GBIF databases, may also be subject to spatial sampling bias. Despite applying spatial filtering and bias correction, these limitations can influence predictions, particularly in under‐sampled regions. Field‐based validation through targeted surveys in newly identified suitable habitats would help improve model accuracy and reliability.

Finally, even where climatic suitability increases, anthropogenic pressures, including agricultural expansion, deforestation, land degradation, and human settlement may restrict habitat availability and connectivity, especially in the Ethiopian highlands (Getachew et al. 2020; Tefera et al. 2022). Addressing these challenges requires integrating socio‐ecological dimensions into species distribution models. Incorporating local and indigenous knowledge through co‐produced research approaches can provide valuable historical and ecological insights (Steger et al. 2020).

6. Implications for Conservation Plans

We propose the following conservation measures based on our findings. First, habitats that remain suitable in all future scenarios may be vital refuges for E. pinifolius conservation efforts. Adaptive management will be informed by regular monitoring of these refugia, enabling early detection of changes in species performance. Although future climate scenarios suggest expansion of suitable areas, the ability of E. pinifolius may be constrained by land‐use patterns, fragmentation, agriculture, and overgrazing (Friis et al. 2010; Gebrehiwot et al. 2020; Getachew et al. 2020). Nevertheless, these areas offer opportunities for targeted reforestation and habitat restoration, especially in high and moderate suitability zones. The need to incorporate biodiversity conservation into regional land management policy is emphasized by highlighting highland areas in northern, northwestern, and central Ethiopia as important refugia. Incorporating local communities and indigenous knowledge alongside scientific modeling can further enhance conservation effectiveness, providing insights into historical species distributions, ecosystem dynamics, and potential responses to environmental change (Steger et al. 2020). Future research should refine predictive models by including species interactions, land‐use dynamics, and local ecological knowledge to better guide conservation planning for E. pinifolius and other Afroalpine species.

7. Conclusions

Species distribution modeling (SDM) is a valuable tool for assessing the impact of climate change on species that require monitoring and management. Our model identified altitude and mean temperature of the driest quarter as the most influential environmental factors for predicting the distribution of Euryops pinifolius, reflecting the species' preference for high‐elevation, cooler, and seasonally dry microclimates. Vegetation cover and precipitation‐related variables also contributed to shaping habitat suitability, indicating that both climatic and local environmental factors limit the species' distribution. The results show that suitable habitat for E. pinifolius is largely concentrated in the Ethiopian highlands, particularly in the central, northern, and northwestern regions. These areas face multiple future threats, including climate change, habitat fragmentation, and land‐use change, which could reduce the extent and connectivity of suitable habitats. Understanding future predicted distribution can be used in conjunction with socio‐economic factors and indigenous knowledge to better inform conservation planning.

Author Contributions

Liyew Birhanu: conceptualization (equal), data curation (equal), formal analysis (lead), investigation (equal), methodology (equal), resources (equal), software (equal), validation (equal), visualization (equal), writing – original draft (lead), writing – review and editing (equal). Heiko Balzter: conceptualization (equal), formal analysis (equal), funding acquisition (lead), investigation (equal), methodology (equal), project administration (lead), resources (equal), software (equal), supervision (lead), validation (equal), visualization (equal), writing – review and editing (equal). Laura Basell: conceptualization (equal), investigation (equal), methodology (equal), resources (equal), validation (equal), visualization (equal), writing – review and editing (equal). Moya Burns: conceptualization (equal), investigation (equal), methodology (equal), resources (equal), validation (equal), visualization (equal), writing – review and editing (equal). Wale Arega: conceptualization (equal), data curation (equal), resources (equal), visualization (equal), writing – review and editing (equal). Yilkal Gebeyehu: formal analysis (equal), resources (equal), writing – review and editing (equal).

Funding

This research was supported by University of Leicester, through the visiting research fellowship. H.B. was supported by the Natural Environment Research Council of the UK through the National Centre for Earth Observation. L.B. was supported by a British Academy Mid‐Career Fellowship Reference (MFSS24\240089).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1: Supporting Information.

ECE3-16-e73566-s001.pdf (65.2KB, pdf)

Acknowledgments

This study was supported by funding from the Institute for Environmental Futures at the University of Leicester. The author, Liyew Birhanu, also acknowledges the award of a Visiting Fellowship from the Institute for Environmental Futures, which made this research possible. Additionally, Liyew Birhanu also extends his sincere gratitude to the Council for At‐Risk Academics (CARA) and the School of Life Sciences and the Environment, Royal Holloway, University of London, UK, for their support of his research fellowship.

Data Availability Statement

The datasets used in this study are freely available from the following sources: Presence records of the 151 Euryops pinifolius are given in Table S1. Bioclimatic data: WorldClim (https://www.worldclim.org).

References

  1. Aiello‐Lammens, M. E. , Boria R. A., Radosavljevic A., Vilela B., and Anderson R. P.. 2015. “Spatial Thinning of Species Occurrence Records to Reduce Sampling Bias.” Ecography 38, no. 5: 541–545. [Google Scholar]
  2. Anthelme, F. , and Dangles O.. 2012. “Plant–Plant Interactions in Tropical Alpine Environments.” Perspectives in Plant Ecology, Evolution and Systematics 14, no. 6: 363–372. 10.1016/j.ppees.2012.05.002. [DOI] [Google Scholar]
  3. Ashenafi, Z. T. 2001. “Common Property Resource Management of an Afro‐Alpine Habitat: Supporting a Population of the Critically Endangered Ethiopian Wolf (Canis simensis).” (Doctoral dissertation, University of Kent, Canterbury, UK).
  4. Brochmann, C. , Gizaw A., Chala D., et al. 2022. “History and Evolution of the Afroalpine Flora: In the Footsteps of Olov Hedberg.” Alpine Botany 132: 65–87. 10.1007/s00035-021-00256-9. [DOI] [Google Scholar]
  5. Brown, J. H. , Stevens G. C., and Kaufman D. M.. 2019. “The Geographic Range: Size, Shape, Boundaries, and Internal Structure.” Annual Review of Ecology, Evolution, and Systematics 27: 597–623. [Google Scholar]
  6. Brown, J. L. 2014. “SDM Toolbox: A Python‐Based GIS Toolkit for Landscape Genetic, Biogeographic and Species Distribution Model Analyses.” Methods in Ecology and Evolution 5, no. 7: 694–700. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Buckley, L. B. , Carrington E., Dillon M. E., et al. 2023. “Characterizing Biological Responses to Climate Variability and Extremes to Improve Biodiversity Projections.” PLOS Clim 2, no. 6: e0000226. 10.1371/journal.pclm.0000226. [DOI] [Google Scholar]
  8. Bueno de Mesquita, C. P. , Elmendorf S. C., Smith J. G., and Suding K. N.. 2024. “Shifting Alpine Plant Distributions With Global Change: Testing the Environmental Matching Hypothesis.” Arctic, Antarctic, and Alpine Research 56, no. 1. 10.1080/15230430.2024.2393443. [DOI] [Google Scholar]
  9. Cao, B. , Bai C., Zhang L., Li G., and Mao M.. 2016. “Modelling Habitat Distribution of Cornus officinalis With Maxent Modeling and Fuzzy Logics in China.” Journal of Plant Ecology 9: 742–751. 10.1093/jpe/rtw044. [DOI] [Google Scholar]
  10. Chala, D. , Brochmann C., Psomas A., et al. 2016. “Good‐Bye to Tropical Alpine Plant Giants Under Warmer Climates? Loss of Range and Genetic Diversity in Lobelia Rhynchopetalum.” Ecology and Evolution 6, no. 24: 8931–8941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Charney, N. D. , Record S., Gerstner B. E., Merow C., Zarnetske P. L., and Enquist B. J.. 2021. “A Test of Species Distribution Model Transferability Across Environmental and Geographic Space for 108 Western North American Tree Species.” Frontiers in Ecology and Evolution 9: 689295. 10.3389/fevo.2021.689295. [DOI] [Google Scholar]
  12. Chen, I.‐C. , Hill J. K., Ohlemüller R., Roy D. B., and Thomas C. D.. 2011. “Rapid Range Shifts of Species Associated With High Levels of Climate Warming.” Science 333: 1024–1026. 10.1126/science.1206432. [DOI] [PubMed] [Google Scholar]
  13. Chen, Y. , Le X., Chen Y., et al. 2022. “Identification of the Potential Distribution Area of Cunninghamia lanceolata in China Under Climate Change Based on the MaxEnt Model.” Chinese Journal of Applied Ecology 33: 1207–1214. [DOI] [PubMed] [Google Scholar]
  14. Dagnachew, S. , Teketay D., Demissew S., and Awas T.. 2023. “Evaluation of Germination Under Different Storage Conditions of Four Endemic Plant Species From Ethiopia: Implications for Ex Situ Conservation in Seed Banks.” Seeds 2023, no. 2: 45–59. 10.3390/seeds2010005Academic. [DOI] [Google Scholar]
  15. Damschen, E. I. 2022. “The Role of Neighbouring Species in Survival as the Climate Changes.” Nature 611: 455–456. 10.1038/d41586-022-03459-0. [DOI] [PubMed] [Google Scholar]
  16. Demissew, S. , Friis I., and Weber O.. 2021. “Diversity and Endemism of the Flora of Ethiopia and Eritrea: State of Knowledge and Future Perspectives.” Rendiconti Lincei 32: 675–697. 10.1007/s12210-021-00999-x. [DOI] [Google Scholar]
  17. Dyderski, M. K. , Paz S., Frelich L. E., and Jagodziński A. M.. 2018. “How Much Does Climate Change Threaten European Forest Tree Species' Distributions?” Global Change Biology 24: 1150–1163. 10.1111/gcb.13925. [DOI] [PubMed] [Google Scholar]
  18. Elith, J. , Leathwick J. R., Lehmann A., et al. 2006. “Novel Methods Improve Prediction of Species' Distributions From Occurrence Data.” Ecography 29, no. 2: 129–151. 10.1111/j.2006.0906-7590.04596.x. [DOI] [Google Scholar]
  19. Elith, J. , Phillips S. J., Hastie T., and Dudík M.. 2011. “A Statistical Explanation of MaxEnt for Ecologists.” Diversity and Distributions 17: 43–57. 10.1111/j.1472-4642.2010.00725.x. [DOI] [Google Scholar]
  20. Fahrig, L. 2003. “Effects of Habitat Fragmentation on Biodiversity.” Annual Review of Ecology, Evolution, and Systematics 34: 487–515. 10.1146/annurev.ecolsys.34.011802.132419. [DOI] [Google Scholar]
  21. Felkel, S. , Tremets berger K., Moser D., Dohm J. C., Himmelbauer H., and Winkler M.. 2023. “Genome–Environment Associations Along Elevation Gradients in Two Snow Bed Species of the North‐Eastern Calcareous Alps.” BMC Plant Biology 23: 203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fick, S. E. , and Hijmans R. J.. 2017. “WorldClim 2: New 1‐Km Spatial Resolution Climate Surfaces for Global Land Areas.” International Journal of Climatology 37: 4302–4315. 10.1002/joc.5086. [DOI] [Google Scholar]
  23. Friis, I. , Demissew S., and van Breugel P.. 2010. Atlas of the Potential Vegetation of Ethiopia. Royal Danish Academy of Sciences and Letters. [Google Scholar]
  24. Gao, X. , Liu J., and Huang Z.. 2022. “The Impact of Climate Change on the Distribution of Rare and Endangered Tree Firmiana Kwangsiensis Using the Maxent Modeling.” Ecology and Evolution 12, no. 8: e9165. 10.1002/ece3.9165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gebrehiwot, K. , Woldu Z., Fekadu M., Teferi E., Desalegn T., and Demissew S.. 2020. “Classification and Ordination of Plant Communities in Abune Yosef Mountain Range, Ethiopia.” Acta Ecologica Sinica 40, no. 5: 398–411. 10.1016/j.chnaes.2019.12.001. [DOI] [Google Scholar]
  26. Gebrehiwot, T. , Mekonen A., and Tesfaye B.. 2022. “Modelling the Potential Distribution of Podocarpus falcatus Under Climate Change Scenarios in Ethiopia Using MaxEnt.” Environmental Systems Research 11, no. 1: 12. [Google Scholar]
  27. Gebrewahid, Y. , Abrehe S., Meresa E., et al. 2020. “Current and Future Predicting Potential Areas of Oxytenanthera abyssinica (A. Richard) Using MaxEnt Model Under Climate Change in Northern Ethiopia.” Ecol Process 9: 6. 10.1186/s13717-019-0210-8. [DOI] [Google Scholar]
  28. Getachew, F. , Teketay D., and Lemenih M.. 2020. “Drivers of Land‐Use/Land‐Cover Change and Its Impacts on Woody Plant Species Diversity in Human‐Modified Landscapes in South Central Ethiopia.” Heliyon 6, no. 11: e05561. 10.1016/j.heliyon.2020.e05561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Graham, C. H. , and Hijmans R. J.. 2006. “A Comparison of Methods for Mapping Species Ranges and Species Richness.” Global Ecology and Biogeography 15, no. 5: 578–587. 10.1111/j.1466-822x.2006.00257.x. [DOI] [Google Scholar]
  30. Guassa Area General Management Plan (GMP) . 2005. –2012. Guassa Community Conservation Area General Management Plan 2005–2012. Amhara National Regional State/Ethiopian Wildlife and Natural History Society. [Google Scholar]
  31. Guisan, A. , Thuiller W., and Zimmermann N.. 2017. Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press. [Google Scholar]
  32. Hamann, E. , Denney D., Day S., et al. 2021. “Plant Eco‐Evolutionary Responses to Climate Change: Emerging Directions.” Plant Science 304: 110737. 10.1016/j.plantsci.2020.110737. [DOI] [PubMed] [Google Scholar]
  33. Harrison, S. , and Prentice I. C.. 2003. “Climate and the Ecology of Vegetation.” In Ecology of Tropical Savannas, edited by Sarmiento J. L. and Hernández J. M., 165–186. Springer. [Google Scholar]
  34. Hedberg, O. 1995. “Features of Afroalpine Ecology.” Acta Phytogeographica Suecica 49: 1–144. [Google Scholar]
  35. Hijmans, R. J. , Cameron S. E., Parra J. L., Jones P. G., and Jarvis A.. 2005. “Very High‐Resolution Interpolated Climate Surfaces for Global Land Areas.” International Journal of Climatology 25, no. 15: 1965–1978. 10.1002/joc.1276. [DOI] [Google Scholar]
  36. Hou, M. , Leng C., Zhu J., et al. 2024. “Alpine and Subalpine Plant Microbiome Mediated Plants Adapt to the Cold Environment: A Systematic Review.” Environmental Microbiomes 19: 82. 10.1186/s40793-024-00614-. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Inouye, D. W. 2020. “Effects of Climate Change on Plant–Pollinator Interactions.” Current Opinion in Insect Science 38: 40–44. 10.1016/j.cois.2020.01.002.32088650 [DOI] [Google Scholar]
  38. IPBES . 2019. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services. IPBES Secretariat. [Google Scholar]
  39. IPCC . 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. [Google Scholar]
  40. Jarvis, A. , Reuter H. I., Nelson A., and Guevara E.. 2008. “Hole‐Filled SRTM for the Globe Version 4.” https://cgiarcsi.community/data/srtm‐90m‐digital‐elevation‐database‐v4‐1/.
  41. Johnson, C. N. , Balmford A., Brook B. W., et al. 2017. “Biodiversity Losses and Conservation Responses in the Anthropocene.” Science 356, no. 6335: 270–275. 10.1126/science.aam9317. [DOI] [PubMed] [Google Scholar]
  42. Kelbessa, E. , Demissew S., Woldu Z., and Edwards S.. 1992. “Some Threatened Endemic Plants of Ethiopia.” In The Status of Some Plant Resources in Parts of Tropical Africa, edited by Edwards S. and Asfaw Z.. Addis Ababa University. [Google Scholar]
  43. Knox, E. B. , and Palmer J. D.. 1995. “Chloroplast DNA Evidence on the Origin and Radiation of the Giant Senecios of Eastern Africa.” Proceedings of the National Academy of Sciences 92, no. 21: 10349–10353. 10.1073/pnas.92.21.10349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Körner, C. 2007. “The Use of ‘Altitude’ in Ecological Research.” Trends in Ecology & Evolution 22, no. 11: 569–574. 10.1016/j.tree.2007.09.006. [DOI] [PubMed] [Google Scholar]
  45. Lenoir, J. , Gégout J. C., Marquet P. A., de Ruffray P., and Brisse H.. 2008. “A Significant Upward Shift in Plant Species Optimum Elevation During the 20th Century.” Science 320, no. 5884: 1768–1771. 10.1126/science.1156831. [DOI] [PubMed] [Google Scholar]
  46. Li, N. , Geng Z., Huang X., et al. 2025. “A Review of Differential Plant Responses to Drought, Heat, and Combined Drought + Heat Stress.” Current Issues in Molecular Biology 47, no. 12: 975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Li, X. , Zhang Y., Chen W., and Huang L.. 2025. “Global Habitat Suitability Assessment of Macadamia Under Future Climate Scenarios Using MaxEnt.” Frontiers in Plant Science 16: 1658566. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Liu, S. L. , Ma K. M., and Fu B. J.. 2003. “The Relationship Between Landform, Soil Characteristics and Plant Community Structure in the Donglingshan Mountain Region, Beijing.” Acta Phytoecologica Sinica 27, no. 4: 496–502. [Google Scholar]
  49. Markham, J. 2014. “Rare Species Occupy Uncommon Niches.” Scientific Reports 4: 63–65. 10.1038/srep06012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. McClean, C. J. , Lovett J. C., Küper W., et al. 2005. “African Plant Diversity and Climate Change.” Annals of the Missouri Botanical Garden 92, no. 2: 139–152. [Google Scholar]
  51. Mekasha, Y. , Alemu A., and Tsegaye D.. 2026. “Predicting Current and Future Potential Distribution of Commiphora africana Under Climate Change Using MaxEnt in Ethiopia.” Springer Environmental Research Communications 8, no. 2: 45. [Google Scholar]
  52. Meragiaw, M. , Asfaw Z., and Argaw M.. 2016. “The Status of Ethnobotanical Knowledge of Medicinal Plants and the Impacts of Resettlement in Delanta, Northwestern Wello, Northern Ethiopia.” Evidence‐Based Complementary and Alternative Medicine 2016: 5060247. 10.1155/2016/5060247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Midgley, G. F. , and Bond W. J.. 2015. “Future of African Terrestrial Biodiversity and Ecosystems Under Anthropogenic Climate Change.” Nature Climate Change 5, no. 9: 823–829. [Google Scholar]
  54. Mittermeier, R. A. , Robles Gil P., Hoffman M., et al. 2005. Hotspots Revisited: Earth's Biologically Richest and Most Endangered Terrestrial Ecoregions. University of Chicago Press/Conservation International. https://press.uchicago.edu/ucp/books/book/distributed/H/bo3707156.html. [Google Scholar]
  55. Muluneh, M. G. 2021. “Impact of Climate Change on Biodiversity and Food Security: A Global Perspective—a Review Article.” Agric & Food Secur 10: 36. 10.1186/s40066-021-00318-5. [DOI] [Google Scholar]
  56. Myers, N. , Mittermeier R. A., Mittermeier C. G., da Fonseca G. A. B., and Kent J.. 2000. “Biodiversity Hotspots for Conservation Priorities.” Nature 403, no. 6772: 853–858. 10.1038/35002501. [DOI] [PubMed] [Google Scholar]
  57. Nešić, M. , and Bjedov I.. 2021. “Habitat Degradation: Pressures, Threats, and Conservation.” In Life on Land, edited by Leal Filho W., Azul A. M., Brandli L., Lange Salvia A., and Wall T., 501–514. Springer. 10.1007/978-3-319-95981-8_65. [DOI] [Google Scholar]
  58. Newbold, T. 2018. “Future Effects of Climate and Land‐Use Change on Terrestrial Vertebrate Community Diversity Under Different Scenarios.” Proceedings of the Royal Society B: Biological Sciences 285, no. 1881: 20180792. 10.1098/rspb.2018.0792. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Oyebanji, O. O. , Salako G., Nneji L. M., et al. 2021. “Impact of Climate Change on the Spatial Distribution of Endemic Legume Species of the Guineo‐Congolian Forest, Africa.” Ecological Indicators 122: 107229. 10.1016/j.ecolind.2020.107229. [DOI] [Google Scholar]
  60. Pereira, H. M. , Leadley P. W., Proença V., et al. 2010. “Scenarios for Global Biodiversity in the 21st Century.” Science 330: 1496–1501. 10.1126/science.1196624. [DOI] [PubMed] [Google Scholar]
  61. Phillips, S. J. , Anderson R. P., and Schapire R. E.. 2006. “Maximum Entropy Modelling of Species Geographic Distributions.” Ecological Modelling 190: 231–259. 10.1016/j.ecolmodel.2005.03.026. [DOI] [Google Scholar]
  62. Phillips, S. J. , and Dudík M.. 2008. “Modelling of Species Distributions With Maxent: New Extensions and Comprehensive Evaluation.” Ecography 31, no. 2: 161–175. 10.1111/j.0906-7590.2008.5203.x. [DOI] [Google Scholar]
  63. Pugnaire, F. I. , Armas C., and Maestre F. T.. 2012. “Positive Plant Interactions in the Iberian Southeast: Mechanisms.” Regional Environmental Change 13: 235–248. [Google Scholar]
  64. Rehman, N. U. , Shah M., Ullah S., et al. 2022. “ Enzymes Inhibition and Antioxidant Potential of Medicinal Plants Growing in Oman, Including Euryops pinifolius .” BioMed Research International 2022: 7880387. 10.1155/2022/7880387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Reichmuth, A. , Kühn I., Schmidt A., and Doktor D.. 2025. “Forested Natura 2000 Sites Under Climate Change: Effects of Tree Species Distribution Shifts.” Web Ecology 25: 59–89. [Google Scholar]
  66. Richman, S. K. , Irwin R. E., Nelson C. J., and Bronstein J. L.. 2020. “Experimental Warming Differentially Affects the Fitness Consequences of Mutualism and Antagonism for a High‐Elevation Plant.” Ecology Letters 23, no. 3: 494–503. 10.1111/ele.13446. [DOI] [Google Scholar]
  67. Sala, O. E. , Chapin F. S., Armesto J. J., et al. 2000. “Global Biodiversity Scenarios for the Year 2100.” Science 287, no. 5459: 1770–1774. 10.1126/science.287.5459.1770. [DOI] [PubMed] [Google Scholar]
  68. Steger, C. , Hirsch S., Evers C., et al. 2020. “Science With Society: Evidence‐Based Guidance for Best Practices in Environmental Transdisciplinary Work.” Global Environmental Change 65: 102137. 10.1016/j.gloenvcha.2020.102137. [DOI] [Google Scholar]
  69. Stévart, T. , Dauby G., Lowry P. P., et al. 2019. “A Third of the Tropical African Flora Is Potentially Threatened With Extinction.” Science Advances 5, no. 11: eaax9444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Swets, J. A. 1988. “Measuring the Accuracy of Diagnostic Systems.” Science 240: 1285–1293. [DOI] [PubMed] [Google Scholar]
  71. Tafesse, B. , Bekele T., Demissew S., Dullo B. W., Nemomissa S., and Chala D.. 2023. “Conservation Implications of Mapping the Potential Distribution of an Ethiopian Endemic Versatile Medicinal Plant, Echinops kebericho Mesfin.” Ecology and Evolution 13, no. 5: e10061. 10.1002/ece3.10061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Tefera, T. , Abebe T., and Zeleke G.. 2022. “Land Use and Land Cover Dynamics and Its Driving Forces in the Central Highlands of Ethiopia: Evidence From Remote Sensing and Key Informants.” Environmental Challenges 7: 100491. 10.1016/j.envc.2022.100491. [DOI] [Google Scholar]
  73. Thomas, C. D. , Cameron A., Green R. E., et al. 2004. “Extinction Risk From Climate Change.” Nature 427, no. 6970: 145–148. 10.1038/nature02121. [DOI] [PubMed] [Google Scholar]
  74. Thuiller, W. , Lavorel S., Araújo M. B., Sykes M. T., and Prentice I. C.. 2011. “Climate Change Threats to Plant Diversity in Europe.” Proceedings of the National Academy of Sciences 102, no. 23: 8245–8250. 10.1073/pnas.0409902102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Tilman, D. , Clark M., Williams D. R., Kimmel K., Polasky S., and Packer C.. 2017. “Future Threats to Biodiversity and Pathways to Their Prevention.” Nature 546, no. 7656: 73–81. 10.1038/nature22900. [DOI] [PubMed] [Google Scholar]
  76. Urban, M. C. , Bocedi G., Hendry A. P., et al. 2016. “Improving the Forecast for Biodiversity Under Climate Change.” Science 353, no. 6304: aad8466. 10.1126/science.aad8466. [DOI] [PubMed] [Google Scholar]
  77. Velásquez‐Tibatá, P. , Salaman C. H., and Graham C. H.. 2012. “Effects of Climate Change on Species Distribution, Community Structure, and Conservation of Birds in Protected Areas in Colombia.” Regional Environmental Change 13: 235–248. [Google Scholar]
  78. Vivero, J. L. , Kelbessa E., and Demissew S.. 2005. The Red List of Endemic Trees & Shrubs of Ethiopia and Eritrea, 14. Fauna & Flora International. [Google Scholar]
  79. Warren, R. , VanDer Wal J., Price J., et al. 2013. “Quantifying the Benefit of Early Climate Change Mitigation in Avoiding Biodiversity Loss.” Nature Climate Change 3, no. 7: 678–682. 10.1038/nclimate1887. [DOI] [Google Scholar]
  80. Watson, R. T. , Arico S., Bridgewater P., et al. 2005. Ecosystems and Human Well‐Being. World Health Organization. [Google Scholar]
  81. Wilson, M. C. , Chen X. Y., Corlett R. T., et al. 2016. “Habitat Fragmentation and Biodiversity Conservation: Key Findings and Future Challenges.” Landscape Ecology 31, no. 2: 219–227. 10.1007/s10980-015-0312-3. [DOI] [Google Scholar]
  82. Yebeyen, D. , Nemomissa S., Hailu B. T., et al. 2022. “Modeling and Mapping Habitat Suitability of Highland Bamboo Under Climate Change in Ethiopia.” Forests 13, no. 6: 859. 10.3390/f13060859. [DOI] [Google Scholar]
  83. Zhang, K. , Zhang Y., Zhou C., et al. 2019. “Impact of Climate Factors on Future Distributions of Paeonia Ostii Across China Estimated by MaxEnt.” Ecological Informatics 50: 62–67. [Google Scholar]
  84. Zu, K. , Chen F., Huang C., et al. 2024. “Elevational Distribution Patterns of Plant Diversity and Phylogenetic Structure Vary Geographically Across Eight Subtropical Mountains.” Ecology and Evolution 14, no. 12: e70722. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: Supporting Information.

ECE3-16-e73566-s001.pdf (65.2KB, pdf)

Data Availability Statement

The datasets used in this study are freely available from the following sources: Presence records of the 151 Euryops pinifolius are given in Table S1. Bioclimatic data: WorldClim (https://www.worldclim.org).

Articles from Ecology and Evolution are provided here courtesy of Wiley

RESOURCES