Next Article in Journal
Development of Functional Rubber-Based Impact-Absorbing Pavements for Cyclist and Pedestrian Injury Reduction
Previous Article in Journal
Ecological Risk Assessment of Potential Toxic Elements in Salt Marshes on the East Coast of the Red Sea: Differential Physiological Responses and Adaptation Capacities of Dominant Halophytes
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 13
Issue 20
10.3390/su132011275
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Projected Impact of Climate Change on Habitat Suitability of a Vulnerable Endemic Vachellia negrii (pic.serm.) kyal. & Boatwr (Fabaceae) in Ethiopia

by
Arayaselassie Abebe Semu
1,2,*,
Tamrat Bekele
1,
Ermias Lulekal
1,
Paloma Cariñanos
3,4 and
Sileshi Nemomissa
1
1
Department of Plant Biology and Biodiversity Management, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa 1176, Ethiopia
2
Range Ecology and Biodiversity Program, College of Agriculture and Environmental Sciences, Haramaya University, Dire Dawa 3000, Ethiopia
3
Andalusian Institute for Earth System Research (IISTA-CEAMA), 18002 Granada, Spain
4
Department of Botany, University of Granada, 18002 Granada, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(20), 11275; https://doi.org/10.3390/su132011275
Submission received: 27 July 2021 / Revised: 4 September 2021 / Accepted: 6 September 2021 / Published: 13 October 2021
Browse Figures
Review Reports Versions Notes

Abstract

:
Species tend to shift their suitable habitat both altitudinally and latitudinally under climate change. Range shift in plants brings about habitat contraction at rear edges, forcing leading edge populations to explore newly available suitable habitats. In order to detect these scenarios, modeling of the future geographical distribution of the species is widely used. Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. is endemic to Ethiopia and was assessed as vulnerable due to changes to its habitat by anthropogenic impacts. It occurs in upland wooded grassland from 2000–3100 m.a.s.l. The main objective of this study is to model the distribution of Vachellia negrii in Ethiopia by using Maxent under climate change. Nineteen bioclimatic variables were downloaded from an open source. Furthermore, topographic position index (tpi), solar radiation index (sri) and elevation were used. Two representative concentration pathways were selected (RCP 4.5 and RC P8.5) for the years 2050 and 2070 using the Community Climate System Model (CCSM 5). A correlation analysis of the bioclimatic variables has resulted in the retention of 10 bioclimatic variables for modeling. Forty-eight occurrence points were collected from herbarium specimens. The area under curve (AUC) is 0.94, indicating a high-performance level of the model. The distribution of the species is affected by elevation (26.4%), precipitation of the driest month (Bio 14, 21.7%), solar radiation (12.9%) and precipitation seasonality (Bio15, 12.2%). Whereas the RCP 8.5 has resulted in decrease of suitable areas of the species from the current 4,314,153.94 ha (3.80%) to 4,059,150.90 ha (3.58%) in 2050, this area will shrink to 3,555,828.71 ha in 2070 under the same scenario. As climate change severely affects the environment, highly suitable areas for the growth of the study subject will decrease by 758,325 ha. The study’s results shows that this vulnerable, endemic species is facing habitat contraction and requires interventions to ensure its long-term persistence.

1. Introduction

Climate change affects species by pushing them to their ecological limit, resulting in range shifts. Climate change is a key challenge for biodiversity and the functions of ecosystems [1,2,3]. Various studies elsewhere have shown that climate change results in the redistribution of species [4,5,6,7]. Species track suitable habitats at their leading edge [8] and remain in their warm range edge due to phenotypic plasticity [9,10]. In cases where these two options are not available to species, they are destined to extinction [11]. A recent review of studies on climate change and biodiversity has revealed a taxonomic bias where less than 2% of plants were examined for the effects of climate change [12]. The observed rate of past climate change is slower than the future rate [13] and so are the rates of range shifts [14]. Climate change is not the only factor for species range shift [15,16,17]. Habitat degradation, loss and invasive species intensify the effects of climate change on species [13,18].
Climate change is part of the history of our planet, and habitat contraction and expansion has been well documented [19]. A recent example is the Pleistocene, where repeated changes in species ranges were recorded [20]. Whereas past environmental change has led to a cycle of species origination (speciation) and extinction, the impact of the current human-driven climate change is unidirectional range shifts [7] whereby species lose their genetic diversity at their rear edge. As a result, local extinctions of populations of species are common, triggering an extinction vortex [21]. On the other hand, leading edge populations tend to occupy novel ranges due to human-driving climate forcing [5]. How and when species under these new physical conditions compensate for the lost segments of their genetic diversity to ensure their long-term persistence is open to question [22,23].
Species of different taxonomic groups respond to climate change differently [1,2]. Butterflies have exhibited faster range shift than birds in Europe [18]. Latitudinal and altitudinal range shift in plants is gradual and this is relatively readily detectable [24,25]. The effects of climate change on range restricted species, e.g., endemics, leads the loss of their habitat due to warming [26,27,28,29]. Endemics have narrow geographic ranges and are often linked to a specialized environmental niche, limited dispersal capacities, smaller population sizes and limited adaptive capability [30]. Furthermore, regions with high endemism are more likely vulnerable to climate change at both the species, population and community levels [31,32].
Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. (Fabaceae) is endemic to Ethiopia and has been assessed as vulnerable due to anthropogenic impacts to its habitat [33]. This tree species has a flat crown and occurs in montane wooded grassland ranging in altitude from 2000–3100 m above sea level (m.a.s.l.) [34,35], as well as church forests [36]. V. negrii has a small seed size compared to its pod width [37]. A sample of the seeds of this species has been stored in Svalbard Global Seed Vault (Norway). It does not occur in the protected area network of the country. The species has a wide variety of economic importance such as forage for honeybees, building material for making plows, fuel wood and charcoal. It also improves soil fertility through nitrogen fixation and serves as shade for the domestic animals and people.
Species reach their ecological limits under continued climate change and either respond to survive or perish [11]. Modelling future potential suitable ranges under different climate change scenarios is useful to foster conservation and long-term persistence of species [1]. Maxent is a useful and widely used tool for predicting the current and future distribution of species and the performance of the model can be statistically evaluated [38,39,40,41]. The main aim of this study is to predict the current and future distribution the endemic Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. in Ethiopia under climate change.

2. Materials and Methods

Species Occurrence and Environmental Data

Presence data of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. were collected from the field observation in northern Wollo, the National Herbarium of Ethiopia, using published and unpublished materials. Forty-eight occurrence points were used in this study (Figure 1).
The 19 bioclimatic variables downloaded from Worldclim (www.worldclim.org, accessed on 20 May 2021) with 30 s (~1 km2) from the observation data version 2.1 and 3 topographic layers were used in this study (Table 1). To predict the future distribution of this species, the General Circulation Model (GCM) Community Climate System Model (CCSM ver.5) was used. The intermediate greenhouse gas scenario (RCP 4.5) and the highest emission scenario (RCP 8.5) from the fifth Report of the Intergovernmental Panel on Climate Change (IPCC5) were used. Furthermore, solar radiation data was downloaded from the www.worldclim.org (accessed on 20 May 2021) with the standardized tiff formats [42,43,44]. Topographic factors (altitude and slope) were downloaded from Aster DEM (space born thermal emission and reflection radiometer Digital Elevation Model) from www.gscloud.cn (accessed on 24 May 2021) [45].
Bioclimatic variables downloaded in TIFF file format and converted to Asci format by using SDM toolbox of ArcGIS version 10.5 [44]. In order to identify the multicollinearity among the 19bioclimatic and topographic variables, Pearson’s correlation was used [43]. A correlation among the bioclimatic and topographic variables was assessed using the ENM tool in R package [46]. A cut-off point of <0.8 was used to exclude variables with high correlation from further analysis to minimize the effect of multicollinearity and model overfitting [47]. Out of the 22 environmental variables, 13 were considered for further analyses in this study.

3. Data Analysis

Maxent software version 3.4.4 was downloaded from http://www.cs.princeton.edu/schapire/MaxEnt/ (accessed on 30 May 2021) and used for modeling the current and future distribution of the species. Maxent is not disposed to sample size and can produce species response curves. All environmental layers have been converted and overlaid onto the same pixel size of 30 m and projected as an ASCII raster grid format. The generated model was evaluated by calculating the AUC of ROC graph [48,49]. The data for species distribution classified as training and test data with proportion of 75% and 25% for the total occurrence record data.
AUC is an effective and efficient independent threshold index with the capacity of assessing the model’s capacity to distinguish the presence and absence. AUC values are categorized in to five different classes based on performance [50,51]. The performance classes are failing (0.5 to 0.6), bad (0.6 to 0.7), reasonable (0.7 to 0.8), good (0.8 to 0.9) and great (0.9 to 1). Models with values less than 0.5 indicates that the occurrence in the real-life scenario is rare or can be considered as a guesstimate [52] Jackknife was run to systematically exclude each variable or evaluate the leading bioclimatic or topographic variables. Jack knife evaluates the leading variables in determining the potential distribution of species [53]. The relationship between the environmental and topographic factors and the potential habitat for the species is determined from the created response curve from the model [54,55].

4. Result

4.1. Correlation among the Environmental Variables

The Pearson’s correlation has resulted in the removal of Bio 5, Bio 6, Bio 8, Bio 10, Bio 11, Bio 13 and Bio 16 from further analysis due to their high multicollinearity (Table 2).

4.2. Model Evaluation

Validation guarantees the reliability of modeling results. In this study, the AUC value showed that the Maxent model performed well (AUC = 0.940) under the current scenario (Figure 2). The distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. under the current climatic conditions is highly influenced by elevation, Bio 14 (precipitation of the driest month), Bio 1 Annual Mean Temperature, Bio 15 (precipitation seasonality) and solar radiation (Table 3). Furthermore, the contributions of these variables are higher than the others.

4.3. Response Curves of Bioclimatic Variables

The response curves show the relationships of probability of occurrence of Vachellianegrii (Pic.-Serm.) Kyal. & Boatwr. and each environmental variable (Figure 3). The species response curve describes the relationship between the topography and bioclimatic factors and species occurrence probability (Figure 3). Elevation, precipitation of the driest month (Bio 14), 26.4 percent and 21.7 percent, respectively, contributed the most to the spread of the species. The relevance of the bioclimatic and topographic variables of elevation and precipitation of the driest month (Bio 14) contribute the greatest percentages (30.4 and 27.2, respectively), followed by temperature seasonality (Bio 15), contributing13.9 in permutation (Table 3). The response curves for the important variables show that they have a greater influence on the distribution of Vachellianegrii (Pic.-Serm.) Kyal. & Boatwr. (Figure 4). The suitability for growth increases as the altitudinal range increases, and ideally fits in the range of 2000 to 3000 m, where the rainfall of the driest month is adequate for the species’ survival and growth ranges from 5 to 10m3 during the driest season.

4.4. Potential Distribution of Vachellia negrii

Modelling the potential distribution Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. shows that it occurs on both sides of the Rift Valley (Figure 5). It has a relatively higher range on the north and northwestern highlands of Ethiopia than on the southeastern highlands. In the latter, its highly suitable habitats are confined to the Bale Mountain range and Gara Muleta and its surroundings. In north and northwestern Ethiopia, highly suitable areas for this species are in moist and dry Afromontane forests. Moderately suitable habitats of this species occur at the fringes of the highly suitable areas and are much more extensive (Table 4).

4.5. Important Environmental Variables for Future Geographic Distribution of Vachellia negrii

The importance of environmental variables for the prediction of the distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr for in the scenarios (RCP 4.5 and 8.5) are different for mid-century and for2070 (Figure 6). There is also a within-scenarios variation. The distribution of this species is mainly determined by elevation, precipitation of the driest month (Bio 14) and precipitation of the warmest month (Bio 18) for RCP 4.5 in 2050. For RCP 4.5 in 2070, Bio 14 and elevation are the main factors restricting the distribution of this species. Whereas Bio 18 is the main limiting factor for RCP 8.5 in 2050, Bio 14 limits the distribution of this species. Elevation determines the distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. in both 2050 and 2070 for RCP 8.5.

4.6. Predicted Distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr.

The predicted distribution of highly suitable habitats for Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. decreases for both climate change scenarios (Figure 7). The species loses its northern range of highly suitable areas, and the southeastern range is more or less limited to Gara Muleta and its surroundings. Highly suitable habitats for this species decrease by 5.9% under RCP 4.5. in 2050 and 13.2% for RCP 8.5 in 2070 (Table 4). The decline of highly suitable habitats for this species is dramatic for RCP 8.5 in 2050 and 2070, i.e., 17.6% in 2050 and 38% in 2070. A similar trend was observed for moderately and low suitable habitats. An exception is a slight increase in moderately suitable habitats for RCP 8.5 in 2070.

5. Discussion

Forecasting the future of climate change and its influence on species distribution is essential due to the wide range of climate simulation findings [56,57]. The distribution and well-being of forest ecosystems will be altered in either a positive or negative way by future global warming. In Ethiopia, the spread of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. is now threatened. This vulnerable species is affected by both anthropogenic and climatic forces. The current impacts of climate change are affecting the whole world, especially in developing countries due to extreme population growth and illegal poaching [58]. Maxent modeling will have an effect on allocating the areas and the species which are endangered and in need of conservation [57].

5.1. Model Performance and Percent Contribution of Variables

According to Pearson et al., an AUC greater than 0.75 is a clear illustration of the strength and efficiency of a good species distribution (niche) model (2007). The model’s AUC of 0.94 indicates that it is within the acceptable limits [57,59]. Because AUC values greater than 0.75 are considered informative [50], our results show that the model performed well in distinguishing between true and false positives [60]. The exclusion of variables owing to autocorrelation resulted in a flawless model outcome, according to references [61,62]. The topographic and climatic factors were shown to be autocorrelated, resulting in (Table 2) topographic variables (elevation and slope), precipitation (Bio 12, Bio 14, Bio 15, Bio 18 and Bio 19), temperate (Bio 1, Bio2, Bio3, Bio4 and Bio7) and solar radiation. Elevation has a significant role in determining the range of a species’ habitat. As seen in Figure 4, the most suitable areas are found at elevations of 2500 m.a.s.l. When predicting the distribution of organisms, elevation is one of the most essential factors to consider [16].

5.2. Current Distribution

The species’ present distribution is limited to the highlands of Tigray, Amhara, Oromia and Dire Dawa. The spread of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr is constrained by elevation. Even a small number of bioclimatic factors [63,64] affects the distribution. Bioclimatic variables that were employed to anticipate the distribution of suitable land did not adequately determine microclimates [57,65,66,67]. Although elevation is the most important factor in the distribution model, solar radiation also has a role in the species’ future and current spread. Elevation influences the species’ microclimate, which has a direct impact on distribution [67,68].
Growing may also be possible at various elevations around the country [68,69,70]. Other variables that influence distribution, such as Bio14 and Bio15, also have a role. The temperature during the dry season and the precipitation throughout the dry and rainy seasons have an impact on species survival and recruitment. Because water is involved in all of the physiological activities of the species, the availability of water throughout the dry season promotes flowering and seed dispersal [71].

5.3. Future Distribution

Climate change has a significant impact on the geographic range of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. according to the IPCC climate change scenario. Highly suitable, moderately suitable, low suitable and unsuitable are the four categories of appropriate regions predicted by the model. Climate change would have a negative influence on the niche of the species [28,72]. As a result, the suitable habitat range decreases (Figure 8). References 43, 52 and 66 predict that the species will make a significant shift to a more appropriate habitat. Climate change is the most significant factor, although habitat degradation, invasive species invasions, biodiversity loss and land conversion also contribute to the species’ decreasing land mass [9,10].
Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. thrives in environments that are ideal for its distribution, which decrease by 73.86% in 2050 of RCP 4.5 and 7% in 2070of RCP 4.5. The species’ habitat suitability decreases in 2050 based on RCP 8.5, but increases in 2070 based on RCP 8.5. The Ethiopian highlands, similar to all other highlands, are undergoing climate change, which has a beneficial impact on species growth [67]. If the temperature rises on a regular basis, it will alter the seasonal growth of species [73,74].
Highly suitable areas for distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. decreases by 73.86% in 2050 of RCP 4.5 and an increase in 7% in 2070of RCP 4.5. The projection of the species in 2050 based on RCP 8.5 shows a decrease in the moderately suitable areas, but in the case of 2070 RCP 8.5, there is an increase in the habitat suitability. Ethiopian highlands, similar to any other highlands, are facing change in temperature, which affects the species growth positively [67]. The increase in temperature would also affect the seasonal growth of species if it occur son a continuous basis [73,74].

5.4. Range Shift

The moderate, low and high potentials from the landscape and regions with adequate habitats for growth and survival showed a decline in both the mid-century (2050s) and the end-of-century (2070s) periods (Figure 8). As a result, a rise in temperature may compel their habitat to move from a lower to a higher elevation, causing habitat contraction and expansion, in this case, contraction [57,75,76]. In addition, invading species tend to decrease and alter the distribution of native species in the region [21,71,77,78]. According to the findings, the species range decreases from the total land mass of 104,429,094.6 ha with no occupancy, whereas 5,753,201.233 ha is deemed as an appropriate habitat in both situations, but climate change has caused 2,915,668.9450000003 ha to be lost. Several new studies on climate change and global warming demonstrate that variations in temperature have an impact on biodiversity distribution [78,79].

5.5. Conservation Strategy

In forest and land management, the ultimate goal of species distribution modeling (SDM) is to create future information about species distribution. The (SDM’s) findings will encourage conservationists to develop and use coping mechanisms. The suitable region of the species’ geographical range has shifted, according to the current research. Environmental plans must be based on research findings and recommendations. The species should be included in a list that may be planted during the country’s yearly green movement, increasing the availability of the species in the country’s high lands [55]. During the country’s yearly greening, these designated appropriate regions would be used for planting. The government and stakeholders must focus on planting rare and endemic endangered species since the allocated sites for yearly plantation are ideal habitats for the species. In addition to the Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. plantation, the closure area would have an effect on safeguarding other treated plants and facilitating the establishment of additional species from seed banks [80,81].

6. Conclusions

Climate change is affecting the geographic distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. according to the present research. The model clearly demonstrates that the range of appropriate habitat for the species is shrinking. This has long-term consequences for the survival of the species. As a result, yearly tree-planting activities in Ethiopia should take into account predicted climatic conditions as well as the planting of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. in appropriate habitats. For the Ethiopian government’s annual plantation, the central highlands are now the most favored planting regions. Because these areas have been recognized as appropriate, it is suggested that these species be introduced. It is advised that these plants be planted on the hilltops since these areas have been designated as suitable. Efforts to conserve these severely endangered species should be made in general. Other endangered plant species, in addition to Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr., should be conserved, therefore seed banks, botanical gardens and other insitu conservation techniques should be prioritized. To assist in the rescue of these species from extinction in the wild, further physiological, ecological, and microclimate research should be conducted.

Author Contributions

Data collection, data analysis, writing and preparation of the document for publication: A.A.S. Data formatting, writing, editing: T.B. Writing, editing and nomenclature of species conformation: E.L. Editing GIS assistance and proof reading of the manuscript: P.C. Writing, data analysis, GIS assistance ans Maxent out put analysis and proof reading of the manuscript: S.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data can be obtained from the corresponding author via mail.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ehrlich, P.R.; Pringle, R.M. Where does biodiversity go from here? A grim business-as-usual forecast and a hopeful portfolio of partial solutions. Proc. Natl. Acad. Sci. USA 2008, 105, 11579–11586. [Google Scholar] [CrossRef] [Green Version]
  2. Rinawati, F.; Stein, K.; Lindner, A. Climate Change Impacts on Biodiversity—The Setting of a Lingering Global Crisis. Diversity 2013, 5, 114–123. [Google Scholar] [CrossRef] [Green Version]
  3. Pecl, G.T.; Araújo, M.B.; Bell, J.D.; Blanchard, J.; Bonebrake, T.C.; Chen, I.-C.; Clark, T.D.; Colwell, R.K.; Danielsen, F.; Evengård, B.; et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 2017, 355, eaai9214. [Google Scholar] [CrossRef] [PubMed]
  4. Parmesan, C. Ecological and Evolutionary Responses to Recent Climate Change. Annu. Rev. Ecol. Evol. Syst. 2006, 37, 637–669. [Google Scholar] [CrossRef] [Green Version]
  5. Chen, I.-C.; Hill, J.K.; Ohlemuller, R.; Roy, D.B.; Thomas, C.D. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 2011, 333, 1024–1026. [Google Scholar] [CrossRef]
  6. Bogale, G.A.; Tolossa, T.T. Climate change intensification impacts and challenges of invasive species and adaptation measures in Eastern Ethiopia. Sustain. Environ. 2021, 7, 1875555. [Google Scholar] [CrossRef]
  7. Chaudhary, C.; Richardson, A.J.; Schoeman, D.S.; Costello, M.J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl. Acad. Sci. USA 2021, 118. [Google Scholar] [CrossRef]
  8. Araújo, M.B.; Rahbek, C. How Does Climate Change Affect Biodiversity? Science 2006, 313, 1396–1397. [Google Scholar] [CrossRef] [PubMed]
  9. Ros, F.V.; Matesanz, S.; Guilhaumon, F.; Araújo, M.B.; Balaguer, L.; Benito-Garzón, M.; Cornwell, W.; Gianoli, E.; van Kleunen, M.; Naya, D.E.; et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 2014, 17, 1351–1364. [Google Scholar] [CrossRef] [Green Version]
  10. King, N.G.; McKeown, N.; Smale, D.A.; Moore, P. The importance of phenotypic plasticity and local adaptation in driving intraspecific variability in thermal niches of marine macrophytes. Ecography 2018, 41, 1469–1484. [Google Scholar] [CrossRef]
  11. Berg, M.P.; Kiers, T.; Driessen, G.; Van Der Heijden, M.; Kooi, B.; Kuenen, F.; Liefting, M.; Verhoef, H.A.; Ellers, J. Adapt or disperse: Understanding species persistence in a changing world. Glob. Chang. Biol. 2010, 16, 587–598. [Google Scholar] [CrossRef]
  12. Taheri, S.; Naimi, B.; Rahbek, C.; Araújo, M.B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 2021, 7, eabe1110. [Google Scholar] [CrossRef] [PubMed]
  13. IPCC. Summary for policymakers. In Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems; Shukla, P.R., Ed.; World Meteorological Organization: Geneva, Switzerland, 2019; pp. 1–43. [Google Scholar]
  14. Williams, J.E.; Blois, J.L. Range shifts in response to past and future climate change: Can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts? J. Biogeogr. 2018, 45, 2175–2189. [Google Scholar] [CrossRef]
  15. Bertrand, R.; Lenoir, J.; Piedallu, C.; Riofrío-Dillon, G.; De Ruffray, P.; Vidal, C.; Pierrat, J.-C.; Gégout, J.-C. Changes in plant community composition lag behind climate warming in lowland forests. Nat. Cell Biol. 2011, 479, 517–520. [Google Scholar] [CrossRef] [PubMed]
  16. Adhikari, D.; Barik, S.K.; Upadhaya, K. Habitat distribution modelling for reintroduction of Ilex khasianaPurk., a critically endangered tree species of northeastern India. Ecol. Eng. 2012, 40, 37–43. [Google Scholar] [CrossRef] [Green Version]
  17. Liu, X.; Pan, Y.; Zhu, X.; Li, S. Spatiotemporal variation of vegetation coverage in Qinling-Daba Mountains in relation to environmental factors. Acta Geogr. Sin. 2015, 5, 705–716. [Google Scholar] [CrossRef]
  18. Devictor, V.; Van Swaay, C.; Brereton, T.; Brotons, L.; Chamberlain, D.; Heliölä, J.; Herrando, S.; Julliard, R.; Kuussaari, M.; Lindström, Å.; et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Chang. 2012, 2, 121–124. [Google Scholar] [CrossRef]
  19. Hewitt, G.M. The genetic legacy of the Quaternary ice ages. Nature 2000, 405, 907–913. [Google Scholar] [CrossRef]
  20. Wershow, S.T.; DeChaine, E.G. Retreat to refugia: Severe habitat contraction projected for endemic alpine plants of the Olympic Peninsula. Am. J. Bot. 2018, 105, 760–778. [Google Scholar] [CrossRef] [Green Version]
  21. Staude, I.R.; Navarro, L.M.; Pereira, H.M. Range size predicts the risk of local extinction from habitat loss. Glob. Ecol. Biogeogr. 2020, 29, 16–25. [Google Scholar] [CrossRef] [Green Version]
  22. Engler, R.; Guisan, A.; Rechsteiner, L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 2004, 41, 263–274. [Google Scholar] [CrossRef]
  23. Niskanen, A.K.J.; Niittynen, P.; Aalto, J.; Väre, H.; Luoto, M. Lost at high latitudes: Arctic and endemic plants under threat as climate warms. Divers. Distrib. 2019, 25, 809–821. [Google Scholar] [CrossRef] [Green Version]
  24. Davis, M.B. Range Shifts and Adaptive Responses to Quaternary Climate Change. Science 2001, 292, 673–679. [Google Scholar] [CrossRef] [Green Version]
  25. Thuiller, W.; Midgley, G.; Hughes, G.O.; Bomhard, B.; Drew, G.; Rutherford, M.C.; Woodward, F.I. Endemic species and ecosystem sensitivity to climate change in Namibia. Glob. Chang. Biol. 2006, 12, 759–776. [Google Scholar] [CrossRef]
  26. Dullinger, S.; Dirnböck, T.; Grabherr, G. Modelling climate change-driven treeline shifts: Relative effects of temperature increase, dispersal and invasibility. J. Ecol. 2004, 92, 241–252. [Google Scholar] [CrossRef]
  27. Ranjitkar, S.; Xu, J.; Shrestha, K.K.; Kindt, R. Ensemble forecast of climate suitability for the Trans-Himalayan Nyctaginaceae species. Ecol. Model. 2014, 282, 18–24. [Google Scholar] [CrossRef]
  28. Chala, D.; Brochmann, C.; Psomas, A.; Ehrich, D.; Gizaw, A.; Masao, C.A.; Bakkestuen, V.; Zimmermann, N. Good-bye to tropical alpine plant giants under warmer climates? Loss of range and genetic diversity in Lobelia rhynchopetalum. Ecol. Evol. 2016, 6, 8931–8941. [Google Scholar] [CrossRef] [PubMed]
  29. Willis, K.J.; Bhagwat, S.A. Biodiversity and Climate Change. Science 2009, 326, 806–807. [Google Scholar] [CrossRef] [PubMed]
  30. Chichorro, F.; Juslén, A.; Cardoso, P. A review of the relation between species traits and extinction risk. Biol. Conserv. 2019, 237, 220–229. [Google Scholar] [CrossRef]
  31. Dirnböck, T.; Essl, F.; Rabitsch, W. Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob. Chang. Biol. 2011, 17, 990–996. [Google Scholar] [CrossRef]
  32. Enquist, B.J.; Feng, X.; Boyle, B.; Maitner, B.; Newman, E.A.; Jørgensen, P.M.; Roehrdanz, P.R.; Thiers, B.M.; Burger, J.R.; Corlett, R.T.; et al. The commonness of rarity: Global and future distribution of rarity across land plants. Sci. Adv. 2019, 5, eaaz0414. [Google Scholar] [CrossRef] [Green Version]
  33. Lucas, P.M.; Suarez, M.G.; Revilla, E. Range area matters, and so does spatial configuration: Predicting conservation status in vertebrates. Ecography 2019, 42, 1103–1114. [Google Scholar] [CrossRef] [Green Version]
  34. Bekele, T. Phyosociology and Ecology of Humid Afromontane Forest on the Central plateau of Ethiopia. J. Veg. Sci. 1994, 5, 87–98. [Google Scholar] [CrossRef] [Green Version]
  35. Teketay, D.; Senbeta, F.; Maclachlan, M.; Bekele, M.; Barklund, P. Edible Wild Plants in Ethiopia; Addis Ababa University Press: Addis Ababa, Ethiopia, 2010; p. 575. [Google Scholar]
  36. Wassie, A.; Sterck, F.J.; Bongers, F. Species structural diversity of church forests in a fragmented Ethiopian highlands. J. Veg. Sci. 2010, 21, 938–948. [Google Scholar] [CrossRef]
  37. Hunde, A.; Thulin, M. Fabaceae. In Flora of Ethiopia; Addis Ababa University Press: Addis Ababa, Ethiopia, 1989; Volume 3, p. 670. [Google Scholar]
  38. Vivero, J.L.; Kelbessa, E.; Demissew, S. The Red List of Endemic Trees & Shrubs of Ethiopia and Eritrea. In The Red List of Endemic Trees & Shrubs of Ethiopia and Eritrea; Unpublished.
  39. Phillips, S. A Brief Tutorial on Maxent. AT&T Research. Retrieved March 2019 from American Museum of Natural History. 2006. Available online: https://biodiversityinformatics.amnh.org/open_source/maxent/Maxent_tutorial2017.pdf (accessed on 11 June 2021).
  40. Remya, K.; Ramachandran, A.; Jayakumar, S. Predicting the current and future suitable habitat distribution of Myristica dactyloidesGaertn. using MaxEnt model in the Eastern Ghats, India. Ecol. Eng. 2015, 82, 184–188. [Google Scholar] [CrossRef]
  41. Sarikaya, O.; Karaceylan, I.B.; Sen, I. Maximum entropy modeling (maxent) of current and future distributions of ipsmannsfeldi (wachtl, 1879) (curculionidae: Scolytinae) in turkey. Appl. Ecol. Environ. Res. 2018, 16, 2527–2535. [Google Scholar] [CrossRef]
  42. Elith, J.; Graham, C.H.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef] [Green Version]
  43. Poloczanska, E.; Brown, C.; Sydeman, W.J.; Kiessling, W.; Schoeman, D.; Moore, P.; Brander, K.; Bruno, J.F.; Buckley, L.B.; Burrows, M.; et al. Global imprint of climate change on marine life. Nat. Clim. Chang. 2013, 3, 919–925. [Google Scholar] [CrossRef]
  44. Brown, J.L. SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 2014, 5, 694–700. [Google Scholar] [CrossRef]
  45. Gebrewahid, Y.; Abrehe, S.; Meresa, E.; Eyasu, G.; Abay, K.; Gebreab, G.; Kidanemariam, K.; Adissu, G.; Abreha, G.; Darcha, G. Current and future predicting potential areas of Oxytenantheraabyssinica (A. Richard) using MaxEnt model under climate Northern Ethiopia. Ecol. Process. 2020, 9, 1–15. [Google Scholar] [CrossRef] [Green Version]
  46. Warren, D.L.; Matzke, N.J.; Cardillo, M.; Baumgartner, J.B.; Beaumont, L.J.; Turelli, M.; Glor, R.E.; Huron, N.A.; Simões, M.; Iglesias, T.L.; et al. ENMTools 1.0: An R package for comparative ecological biogeography. Ecography 2021, 44, 504–511. [Google Scholar] [CrossRef]
  47. Schmidt, H.; Radinger, J.; Teschlade, D.; Stoll, S. The role of spatial units in modelling freshwater fish distributions: Comparing a subcatchment and river network approach using MaxEnt. Ecol. Model. 2020, 418, 108937. [Google Scholar] [CrossRef]
  48. Phillips, S.J.; Elith, J. POC plots: Calibrating species distribution models with presence-only data. Ecology 2010, 91, 2476–2484. [Google Scholar] [CrossRef] [PubMed]
  49. David, W.; Hosmer, J.; Lemeshow, S.; Sturdivant, R.X. Applied Logistic Regression, 3rd ed.; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2013. [Google Scholar]
  50. Phillips, S.J.; Dudík, M. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  51. Swets, J.A. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285–1293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Shcheglovitova, M.; Anderson, R.P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Model. 2013, 269, 9–17. [Google Scholar] [CrossRef]
  53. Yang, X.; Kushwaha, S.; Saran, S.; Xu, J.; Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 2013, 51, 83–87. [Google Scholar] [CrossRef]
  54. Vilà, M.; Gassó, N.; Thuiller, W.; Pino, J. Potential distribution range of invasive plant species in Spain. NeoBiota 2012, 12, 25–40. [Google Scholar] [CrossRef] [Green Version]
  55. Zhao, D.; He, H.; Wang, W.; Wang, L.; Du, H.; Liu, K.; Zong, S. Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region. Sustainability 2018, 10, 863. [Google Scholar] [CrossRef] [Green Version]
  56. Yi, Y.-J.; Zhou, Y.; Cai, Y.-P.; Yang, W.; Li, Z.-W.; Zhao, X. The influence of climate change on an endangered riparian plant species: The root of riparian Homonoia. Ecol. Indic. 2018, 92, 40–50. [Google Scholar] [CrossRef]
  57. Zhou, Y.; Zhang, Z.; Zhu, B.; Cheng, X.; Yang, L.; Gao, M.; Kong, R. MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China. Forests 2021, 12, 752. [Google Scholar] [CrossRef]
  58. He, J.; Yan, C.; Holyoak, M.; Wan, X.; Ren, G.; Hou, Y.; Xie, Y.; Zhang, Z. Quantifying the effects of climate and anthropogenic change on regional species loss in China. PLoS ONE 2018, 13, e0199735. [Google Scholar] [CrossRef] [Green Version]
  59. Chunco, A.J.; Phimmachak, S.; Sivongxay, N.; Stuart, B.L. Predicting Environmental Suitability for a Rare and Threatened Species (Lao Newt, Laotritonlaoensis) Using Validated Species Distribution Models. PLoS ONE 2013, 8, e59853. [Google Scholar] [CrossRef]
  60. Pearce, J.; Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 2000, 133, 225–245. [Google Scholar] [CrossRef] [Green Version]
  61. Fielding, A.H.; Bell, J.F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 1997, 24, 38–49. [Google Scholar] [CrossRef]
  62. Van Der Putten, W.H.; Macel, M.; Visser, M.E. Predicting species distribution and abundance responses to climate change: Why it is essential to include biotic interactions across trophic levels. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 2025–2034. [Google Scholar] [CrossRef]
  63. Yackulic, C.B.; Chandler, R.B.; Zipkin, E.F.; Royle, J.; Nichols, J.D.; Grant, E.H.C.; Veran, S. Presence-only modelling using MAXENT: When can we trust the inferences? Methods Ecol. Evol. 2012, 4, 236–243. [Google Scholar] [CrossRef]
  64. Thibaud, E.; Petitpierre, B.; Brönnimann, O.; Davison, A.C.; Guisan, A. Measuring the relative effect of factors affecting species distribution model predictions. Methods Ecol. Evol. 2014, 5, 947–955. [Google Scholar] [CrossRef]
  65. Pearson, R.G.; Raxworthy, C.J.; Nakamura, M.; Peterson, A.T. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 2006, 34, 102–117. [Google Scholar] [CrossRef]
  66. Çoban, H.O.; Örücü, Ö.K.; Arslan, E.S. MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier. Sustainability 2020, 12, 2671. [Google Scholar] [CrossRef] [Green Version]
  67. Çoban, H.O.; Çosgun, S. The role of topography in the spatial distribution of tree species in the Mediterranean region of Turkey. Fresenius Environ. Bull. 2020, 29, 1369–1378. [Google Scholar]
  68. Luoto, M.; Heikkinen, R.K. Disregarding topographical heterogeneity biases species turnover assessments based on bioclimatic models. Glob. Chang. Biol. 2007, 14, 483–494. [Google Scholar] [CrossRef]
  69. Dobrowski, S.Z. A climatic basis for microrefugia: The influence of terrain on climate. Glob. Chang. Biol. 2011, 17, 1022–1035. [Google Scholar] [CrossRef]
  70. Uzma, F.; Konappa, N.M.; Chowdappa, S. Diversity and extracellular enzyme activities of fungal endophytes isolated from medicinal plants of Western Ghats, Karnataka. Egypt. J. Basic Appl. Sci. 2016, 3, 335–342. [Google Scholar] [CrossRef] [Green Version]
  71. Zhong, L.; Ma, Y.; Salama, M.S.; Su, Z. Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau. Clim. Chang. 2010, 103, 519–535. [Google Scholar] [CrossRef]
  72. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model 2006, 190, 231–259. [Google Scholar] [CrossRef] [Green Version]
  73. Xu, M.; Xue, X. Analysis on the effects of climate warming on growth and phenology of alpine plants. J. Arid. Land Resour. Environ. 2013, 27, 137–141. [Google Scholar]
  74. Stohlgren, T.J.; Jarnevich, C.S.; Esaias, W.E.; Morisette, J.T. Bounding species distribution models. Curr. Zool. 2011, 57, 642–647. [Google Scholar] [CrossRef] [Green Version]
  75. Albano, P.G.; Steger, J.; Bošnjak, M.; Dunne, B.; Guifarro, Z.; Turapova, E.; Hua, Q.; Kaufman, D.S.; Rilov, G.; Zuschin, M. Native biodiversity collapse in the eastern Mediterranean. Proc. R. Soc. B Biol. Sci. 2021, 288, 20202469. [Google Scholar] [CrossRef]
  76. Oduor, A.M.; Leimu, R.; van Kleunen, M. Invasive plant species are locally adapted just as frequently and at least as strongly as native plant species. J. Ecol. 2016, 104, 957–968. [Google Scholar] [CrossRef] [Green Version]
  77. Sintayehu, D.W. Impact of climate change on biodiversity and associated key ecosystem services in Africa: A systematic review. Ecosyst. Health Sustain. 2018, 4, 225–239. [Google Scholar] [CrossRef] [Green Version]
  78. Sintayehu, D.W.; Dalle, G.; Bobasa, A.F. Impacts of climate change on current and future invasion of Prosopis juliflora in Ethiopia: Environmental and socio-economic implications. Heliyon 2020, 6, e04596. [Google Scholar] [CrossRef]
  79. Watts, G.; Battarbee, R.W.; Bloomfield, J.; Crossman, J.; Daccache, A.; Durance, I.; Elliott, J.A.; Garner, G.; Hannaford, J.; Hannah, D.; et al. Climate change and water in the UK—Past changes and future prospects. Prog. Phys. Geogr. Earth Environ. 2015, 39, 6–28. [Google Scholar] [CrossRef]
  80. Janowiak, M.K.; Iverson, L.; Fosgitt, J.; Handler, S.D.; Dallman, M.; Thomasma, S.; Hutnik, B.; Swanston, C.W. Assessing Stand-Level Climate Change Risk Using Forest Inventory Data and Species Distribution Models. J. For. 2017, 115, 222–229. [Google Scholar] [CrossRef]
  81. Pecchi, M.; Marchi, M.; Burton, V.; Giannetti, F.; Moriondo, M.; Bernetti, I.; Bindi, M.; Chirici, G. Species distribution modelling to support forest management. A literature review. Ecol. Model 2019, 411, 108817. [Google Scholar] [CrossRef]
Sustainability 13 11275 g001 550
Figure 1. Map of Africa and Ethiopia showing localities of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. occurrences.
Figure 1. Map of Africa and Ethiopia showing localities of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. occurrences.
Sustainability 13 11275 g001
Sustainability 13 11275 g002 550
Figure 2. The Receiver Operating Characteristic (ROC) curve result of the Maxent modelling.
Figure 2. The Receiver Operating Characteristic (ROC) curve result of the Maxent modelling.
Sustainability 13 11275 g002
Sustainability 13 11275 g003 550
Figure 3. Importance of environmental variables for distribution of Vachellianegrii (Pic.-Serm.) Kyal. & Boatwr.
Figure 3. Importance of environmental variables for distribution of Vachellianegrii (Pic.-Serm.) Kyal. & Boatwr.
Sustainability 13 11275 g003
Sustainability 13 11275 g004 550
Figure 4. Response curve of the major bioclimatic and topographic factors influencing the distribution of Vachellianegrii (Pic.-Serm.) Kyal. & Boatwr.
Figure 4. Response curve of the major bioclimatic and topographic factors influencing the distribution of Vachellianegrii (Pic.-Serm.) Kyal. & Boatwr.
Sustainability 13 11275 g004
Sustainability 13 11275 g005 550
Figure 5. Potential distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr.in Ethiopia.
Figure 5. Potential distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr.in Ethiopia.
Sustainability 13 11275 g005
Sustainability 13 11275 g006 550
Figure 6. Importance of environmental variables for predicting the distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. under different climate change scenarios.
Figure 6. Importance of environmental variables for predicting the distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. under different climate change scenarios.
Sustainability 13 11275 g006
Sustainability 13 11275 g007 550
Figure 7. Predicted distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. (a). RCP 4.5 in 2050 (b). RCP 8.5 in 2050 (c). RCP 4.5 in 2070 and (d). RCP 8.5 in 2070 under different climatic change scenarios.
Figure 7. Predicted distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr. (a). RCP 4.5 in 2050 (b). RCP 8.5 in 2050 (c). RCP 4.5 in 2070 and (d). RCP 8.5 in 2070 under different climatic change scenarios.
Sustainability 13 11275 g007
Sustainability 13 11275 g008 550
Figure 8. Change in the occupancy of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr.
Figure 8. Change in the occupancy of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr.
Sustainability 13 11275 g008
Table
Table 1. Bioclimatic and topographic variables used in the species distribution modeling.
Table 1. Bioclimatic and topographic variables used in the species distribution modeling.
VariableVariable TypeUnit
Bio 1Annual Mean Temperature°C
Bio 2Mean Diurnal Range (Mean of monthly (max temp–min temp))°C
Bio 3Isothermality (BIO2/BIO7) (×100)°C
Bio 4Temperature Seasonality (standard deviation ×100)°C
Bio 5Max Temperature of Warmest Month°C
Bio 6Min Temperature of Coldest Month°C
Bio 7Temperature Annual Range (BIO5-BIO6)°C
Bio 8Mean Temperature of Wettest Quarter°C
Bio 9Mean Temperature of Driest Quarter°C
Bio 10Mean Temperature of Warmest Quarter°C
Bio 11Mean Temperature of Coldest Quarter°C
Bio 12Annual Precipitation°C
Bio 13Precipitation of Wettest MonthMm
Bio 14Precipitation of Driest MonthMm
Bio 15Precipitation Seasonality (Coefficient of Variation)Mm
Bio 16Precipitation of Wettest QuarterMm
Bio 17Precipitation of Driest QuarterMm
Bio 18Precipitation of Warmest QuarterMm
Bio 19Precipitation of Coldest QuarterMm
ElevationAltitudeM
Solar radiationSolar radiation
SlopeSlope%
Table
Table 2. Correlation result of bioclimatic and topographic variables.
Table 2. Correlation result of bioclimatic and topographic variables.
layerSriBio1Bio2Bio3Bio4Bio5Bio6Bio7Bio8Bio9Bio10Bio11Bio12Bio13Bio14Bio15Bio16Bio17Bio18Bio19ElvAsp
Sri10.25–0.150.33−0.490.130.34−0.410.290.230.130.35−0.45−0.44−0.230.36−0.47−0.23−0.035−0.36−0.31−0.25
Bio1 1−0.45−0.530.410.960.96−0.040.970.970.980.98−0.63−0.618−0.320.16−0.60−0.36−0.54−0.31−0.96−0.43
Bio2 10.40−0.05−0.26−0.630.70−0.50−0.47−0.42−0.460.360.460.100.030.440.130.280.220.580.22
Bio3 1−0.87−0.620.42−0.35−0.56−0.46−0.620.390.380.340.290.080.310.290.410.180.450.15
Bio4 10.580.230.630.400.340.530.25−0.21−0.10−0.25−0.04−0.080.1370.28−0.02−0.28−0.04
Bio5 10.850.220.930.900.970.91−0.60−0.55−0.350.16−0.540.290.41−0.30−0.89−0.38
Bio6 1−0.310.940.940.910.97−0.633−0.64−0.290.18−0.63−0.38−0.38−0.341−0.96−0.43
Bio7 1−0.07−0.120.06−0.160.0770.19−0.10−0.040.20−0.33−0.530.080.190.11
Bio8 10.910.9520.94−0.72−0.71−0.330.15−0.70−0.07−0.012−0.45−0.94−0.44
Bio9 10.950.96−0.52−0.55−0.230.10−0.52−0.36−0.49−0.19−0.94−0.39
Bio10 10.94−0.59−0.55−0.340.15−0.53−0.27−0.53−0.27−0.93−0.39
Bio11 1−0.63−0.60−0.300.20−0.59−0.38−0.51−0.31−0.97−0.44
Bio12 10.850.57−0.310.91−0.340.440.770.610.35
Bio13 10.120.110.980.560.230.720.630.37
Bio14 1−0.670.260.1770.460.300.300.24
Bio15 10.010.974−0.35−0.09−0.14−0.09
Bio16 1−0.750.250.780.610.35
Bio17 10.520.260.330.26
Bio18 10.120.490.21
Bio19 10.330.23
Elv 10.46
Asp 1
Note: The bold once are auto correlated variables need to be removed from the model inorder to avoid over fitting of model.
Table
Table 3. Percent contribution of variables and species response to different climatic scenarios (%c = percent contribution and PI = permutation importance).
Table 3. Percent contribution of variables and species response to different climatic scenarios (%c = percent contribution and PI = permutation importance).
VariablesCodeCurrent20502070
RCP 4.5RCP 8.5RCP 4.5RCP 8.5
%cPI%cPI%cPI%cPI%cPI
Annual Mean TemperatureBio 10.20.904.500.804.702.2
Mean Diurnal Range (Mean of monthly (max temp–min temp))Bio 22.92.100.409.50000
Isothermality (P2/P7) × (100)Bio 30.10.105.503.70000
Temperature Seasonality (standard deviation × 100)Bio 44.613.9016.70.2007.503.7
Temperature Annual Range (P5–P6)Bio 70.10.206.303.807.10.84.7
Annual PrecipitationBio120.12.402.50004.102.7
Precipitation of Driest MonthBio 1421.727.232.110.9010.326.8
Precipitation of Seasonality (Coefficient of Variation)Bio 1512.26.82.011.611.1012.7233.9
Precipitation of Warmest QuarterBio 1812.76.53.024.7114.10102.1
Precipitation of Coldest QuarterBio 1941.102.3210.6012.222.4
ElevationElev26.430.45017.16022.479.421.17027.4
Solar radiationSr12.96.829.23.732.832.316.117.21610.5
SlopeSlp2.11.512.8 20.94.526.73.7
Table
Table 4. Suitable areas for the distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr.
Table 4. Suitable areas for the distribution of Vachellia negrii (Pic.-Serm.) Kyal. & Boatwr.
Climatic PeriodRCPClasses of Suitable Habitats
Highly SuitableModerately SuitableLow SuitableUnsuitable
Hectares%Hectares%Hectares%Hectares%
Current 4,314,153.9413.809,394,670.6998.2918,423,842.6816.2681,177,180.271.64
2050RCP 4.54,059,150.9013.588,491,245.8637.4917,529,132.2815.4783,230,318.4873.45
RCP 8.53,745,769.5953.37,303,036.6126.4516,657,887.6214.2885,603,153.775.97
2070RCP 4.53,555,828.7113.136,675,597.1495.8914,063,493.7612.4189,014,927.978.55
RCP 8.52,676,601.2452.3610,035,562.548.8514,171,535.8612.5086,426,147.8776.27
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Semu, A.A.; Bekele, T.; Lulekal, E.; Cariñanos, P.; Nemomissa, S. Projected Impact of Climate Change on Habitat Suitability of a Vulnerable Endemic Vachellia negrii (pic.serm.) kyal. & Boatwr (Fabaceae) in Ethiopia. Sustainability 2021, 13, 11275. https://doi.org/10.3390/su132011275

AMA Style

Semu AA, Bekele T, Lulekal E, Cariñanos P, Nemomissa S. Projected Impact of Climate Change on Habitat Suitability of a Vulnerable Endemic Vachellia negrii (pic.serm.) kyal. & Boatwr (Fabaceae) in Ethiopia. Sustainability. 2021; 13(20):11275. https://doi.org/10.3390/su132011275

Chicago/Turabian Style

Semu, Arayaselassie Abebe, Tamrat Bekele, Ermias Lulekal, Paloma Cariñanos, and Sileshi Nemomissa. 2021. "Projected Impact of Climate Change on Habitat Suitability of a Vulnerable Endemic Vachellia negrii (pic.serm.) kyal. & Boatwr (Fabaceae) in Ethiopia" Sustainability 13, no. 20: 11275. https://doi.org/10.3390/su132011275

APA Style

Semu, A. A., Bekele, T., Lulekal, E., Cariñanos, P., & Nemomissa, S. (2021). Projected Impact of Climate Change on Habitat Suitability of a Vulnerable Endemic Vachellia negrii (pic.serm.) kyal. & Boatwr (Fabaceae) in Ethiopia. Sustainability, 13(20), 11275. https://doi.org/10.3390/su132011275

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop