Climate change-induced species distribution modeling in hyper-arid ecosystems

Taoufik Saleh Ksiksi; Remya K.; Mohamed T. Mousa; Shima K. Al-Badi; Salama K. Al Kaabi; Shamsa M. Alameemi; Sanad M. Fereaa; Fatima E. Hassan

doi:10.12688/f1000research.19540.1

Home Browse Climate change-induced species distribution modeling in hyper-arid...

ALL Metrics

-

Views

-

Downloads

Get PDF

Get XML

Export

▬

✚

Research Article

Climate change-induced species distribution modeling in hyper-arid ecosystems

[version 1; peer review: peer review discontinued]

Taoufik Saleh Ksiksi¹, Remya K.², Mohamed T. Mousa¹, [...] Shima K. Al-Badi¹, Salama K. Al Kaabi¹, Shamsa M. Alameemi¹, Sanad M. Fereaa¹, Fatima E. Hassan¹

Taoufik Saleh Ksiksi¹, Remya K.², [...] Mohamed T. Mousa¹, Shima K. Al-Badi¹, Salama K. Al Kaabi¹, Shamsa M. Alameemi¹, Sanad M. Fereaa¹, Fatima E. Hassan¹

PUBLISHED 27 Jun 2019

Author details Author details

¹ Department of Biology, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
² Centre for Climate Change and Adaptation Research, Center for Climate Change, Chennai, India

Taoufik Saleh Ksiksi
Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Remya K.
Roles: Formal Analysis, Writing – Original Draft Preparation

Mohamed T. Mousa
Roles: Formal Analysis, Methodology, Writing – Review & Editing

Shima K. Al-Badi
Roles: Data Curation, Methodology

Salama K. Al Kaabi
Roles: Data Curation, Methodology

Shamsa M. Alameemi
Roles: Data Curation, Methodology

Sanad M. Fereaa
Roles: Data Curation, Methodology

Fatima E. Hassan
Roles: Data Curation, Methodology

OPEN PEER REVIEW

PEER REVIEW DISCONTINUED

Abstract

Background: The impact of climate change on selected plant species from the hyper-arid landscape of United Arab Emirates (UAE) was assessed through modeling of their habitat suitability and distribution. Calotropis procera, Prosopis cineraria and Ziziphus spina-christi were used for this study. The specific objectives of this study were to identify the current and future (for 2050s and 2070s) suitable habitats distribution using MaxEnt, an Ecological Envelope Model.
Methods: The adopted method consists of extraction of current and future bioclimatic variables together with their land use cover and elevation for the study area. MaxEnt species distribution model was then used to simulate the distribution of the selected species. The projections are simulated for the current date, the 2050s and 2070s using Community Climate System Model version 4 with representative concentration pathway RCP4.5.
Results: The current distribution model of all three species evolved with a high suitable habitat towards the north eastern part of the country. For C. procera, an area of 1775 km² is modeled under highly suitable habitat for the current year, while it is expected to increase for both 2050s and 2070s. The current high suitability of P. cinararia was around an area of 1335 km² and the future projection revealed an increase of high suitability habitats. Z. spina-christi showed a potential area of 5083 km² under high suitability and it might increase in the future.
Conclusions: Precipitation of coldest quarter (BIO19) had the maximum contribution for all the three species under investigation.

Keywords

MaxEnt Model, Niche Assessment, Species Distribution Models

Corresponding author: Taoufik Saleh Ksiksi

Competing interests: No competing interests were disclosed.

Grant information: This project was funded by UAE University (Project: 31S347) as part of the Summer Undergraduate Research Experience (SURE+).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2019 Ksiksi TS et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Ksiksi TS, K. R, Mousa MT et al. Climate change-induced species distribution modeling in hyper-arid ecosystems [version 1; peer review: peer review discontinued]. F1000Research 2019, 8:978 (https://doi.org/10.12688/f1000research.19540.1) First published: 27 Jun 2019, 8:978 (https://doi.org/10.12688/f1000research.19540.1) Latest published: 27 Jun 2019, 8:978 (https://doi.org/10.12688/f1000research.19540.1)

Introduction

Climate change has exerted significant biological, spatial and temporal effects on terrestrial habitats^1,2. The increasing CO₂ trend during the past decades will continue for several decades, which will likely have major effects on animal and plant species³. Additionally, the inherent fragility of dry lands made them particularly vulnerable to climate change, even a small change in temperature and rainfall patterns may cause serious threat to their biodiversity⁴. IIED⁵ suggests that dry-land regions are projected to significant climate changes, but there still exist substantial variability and uncertainty in these estimates based on different scenarios. Many of the important United Arab Emirates (UAE) plant species, for instance, may be facing major challenges for their survival. Many of such native species are becoming restricted in their distributions because of overgrazing and habitat destruction. In addition to the natural prolonged drought, human activities are also threatening many native plants⁶ and their conservation⁷. The IPCC report⁸ also projected that deserts are going to become hotter and drier. Additionally and in relation to the Arab region, climate change-induced desertification has been prolonged, critically affecting the vulnerability of the local population. The projected warming would lead to seasonal changes in precipitation, evapotranspiration and radiation Hulme and Jenkins⁹. These changes can largely affect the distribution of species and habitat composition¹⁰. Thus, the implications of climate change for biodiversity have led to the use of bioclimatic models in order to forecast the species range shift under future climate change scenarios Araújo and New¹¹.

Both habitat quality and diversity have significant impact on species’ distribution and richness within the environment¹². Ecological envelope models or species distribution models (SDMs) have been widely used for projecting the habitat shift and distribution induced by climate change¹³. These models are also used to understand biological processes¹⁴ and climate change impacts on biodiversity^15–17. Understanding the future distribution of climatically suitable habitat allows the conservation planners to assess the vulnerability of species and ecosystem to climate change^18,19. The species occurrence records alone may not be sufficient for building robust conservation strategy in light of climate change. While combining occurrence records with an environmental suitability model can be a promising tool for to assess species distribution and the potential impacts of climate change^20,21.

Maximum entropy (MaxEnt) is one of the robust environmental suitability ecological niche/ ecological envelope) models for assessing species habitat suitability changes under the ongoing and future environmental challenges^22–24. MaxEnt is a species distribution model (SDM)²⁵ and it is used for the present study. The hypothesis is that shifting, contracting and expanding species habitats are affected by the ongoing climate change. For this, habitat suitability changes of the selected three species (i.e. Calotropis procera, Prosopis cineraria and Ziziphus spina-christi) in the current vs the future climate scenarios was modeled.

Methods

Study site

This study was conducted in the UAE. It covers a total land area of approximately 82,925 km², which was used as the basis for calculating the species distribution areas. The study area boundaries was converted to shape files and and then used to extract the environmental and climatic variables.

Species ecological characteristics

Calotropis procera, belonging to the Asclepiadaceae family, locally known as ash harbor or milkweed. It is a poisonous shrub that can cause harm to humans if its latex touches the skin or eyes. The latex is a milky white substance present within the shrub, and is released if the shrub was cut²⁶.

Prosopis cineraria is considered the national tree of UAE and belongs to the Fabaceae family. It can withstand a wide range of temperature, heat and drought and also can tolerate saline soil conditions. It commonly exists in India and Pakistan in large numbers. In the UAE it is found on the inland sand plains and low dunes of the Northern Emirates and eastern rim of Abu Dhabi emirate, and appears occasionally in wadi beds of the Hajar mountain range²⁷.

Ziziphus spina-christi, belongs to the Rhamnaceae family, is a species indigenous to the Arabian Peninsula. This species is known for delayed seed germination, which is due to the hard coat of its seeds, therefore lowering its ability to compete with other plants over resources. This tree has different importance to people and animals. The honey produced from its flowers is very valuable and its leaves has various medicinal uses. The leaves also contain various flavonoids, saponin, alkaloids, and allelochemicals.

Model requirements

We have extracted current and future 19 bioclimatic variables from the WorldClim website²⁸ and tested for colinearity using the SDM tool in ArcGis 10.1²⁹ (an open-source alternative is QGIS). Additionally, variables included were land use cover and elevation data for the study area. Even though the variables were highly correlating we have selected nine bioclimatic variables and the edaphic variables for the model development. Representative concentration pathway (RCP)4.5 was selected and for 2050s and 2070s, the Community Climate System Model version 4 climate model was used to download the bioclimatic variables. RCP4.5 stabilizes radiative forcing by 2100³⁰. It includes global emissions of greenhouse gases and land-use-land-cover. A resolution of approximately 1 km² (30 arc-seconds) was used for this predictive assessment.

All three species’ presence-only data was used and each species had 100 georeferenced location data. For the selected species, the model was trained using current bioclimatic data and projected using future bioclimatic variables. To visualize the bioclimatic suitability range shift in the future projection, this study has used clamping module while running the model. Final outputs of the model predictions were exported to ArcGIS 10.1 for further analysis. The exported model outputs were converted to raster files and then the predicted habitat suitability ranges were reclassified using three pre-defined probability classes: high, medium and low. Other model settings were kept unmodified and the Jackknife method was used to assess the variable contribution to the habitat distribution. Model fit was assessed using the area under the curve (AUC) values.

Environmental parameters

In addition to land use cover and elevation data, 19 variables were extracted from the WorldClim database³¹, which is a set of global climate layers generated through interpolation of climate data from weather stations on a 30 arc-seconds grid (about 1 km² resolution). The 19 environmental variables are coded as follows:

1. BIO1: Annual Mean Temperature

2. BIO2: Mean Diurnal Range (Mean of monthly (max temp - min temp))

3. BIO3: Isothermality (BIO2/BIO7) (* 100)

4. BIO4: Temperature Seasonality (standard deviation *100)

5. BIO5: Max Temperature of Warmest Month

6. BIO6: Min Temperature of Coldest Month

7. BIO7: Temperature Annual Range (BIO5-BIO6)

8. BIO8: Mean Temperature of Wettest Quarter

9. BIO9: Mean Temperature of Driest Quarter

10. BIO10: Mean Temperature of Warmest Quarter

11. BIO11: Mean Temperature of Coldest Quarter

12. BIO12: Annual Precipitation

13. BIO13: Precipitation of Wettest Month

14. BIO14: Precipitation of Driest Month

15. BIO15: Precipitation Seasonality (Coefficient of Variation)

16. BIO16: Precipitation of Wettest Quarter

17. BIO17: Precipitation of Driest Quarter

18. BIO18: Precipitation of Warmest Quarter

19. BIO19: Precipitation of Coldest Quarter

Modeling approach

The MaxEnt model Version 3.4.1²⁵ was used for this assessment of the three different tree/shrub species distribution. MaxEnt has been used to model species niches and distributions through machine-learning techniques referred to as maximum entropy modeling^17,32. It is very robust in cases of small sample size of species distribution and locations¹⁷.

MaxEnt uses a combination of a set of environmental (e.g. climatic) grids and geo-referenced presence points for the species under investigation³³. The model expresses a probability distribution where each grid cell is predictably suitable to the conditions for a species to occur³³. To improve model performance and minimize over-fitting, because of the impact of correlation among explanatory variables, a Pearson’s correlation coefficient test was performed for each pair of quantitative variables^34–36. MaxEnt²⁵ is a model for predicting the distribution of species based on environmental parameters and species’ presence data³⁷. It uses both continuous and categorical data²⁵. MaxEnt has been reported to be comparable to other highly accurate prediction methods, even when dealing with small sample sizes³⁸. This model was run for the current and future (2050–2070) distributions. MaxEnt model was evaluated through the use of the area under the curve (AUC)³⁹. While generating response curves, the MaxEnt model estimates the relative effect of each predictor⁴⁰.

Results

Model fit and variable contribution

Models for the three species performed better than the random, with an AUC values 0.97 (C. procera), 0.98 (P. cinararia), and 0.97 (Ziziphus spina-christi). The jackknife test of variable importance for C. procera concede that out of the 10 selected environmental variables for model execution, precipitation of the coldest quarter (36.3%) temperature annual range (27.2%) and annual precipitation (6.3%) exhibited the highest gain towards the habitat distribution (Figure 1a). Whereas for P. cinararia precipitation of the coldest quarter followed by temperature seasonality and mean diurnal range were the highly influencing variables for its distribution (Figure 1b). At the same time for Ziziphus spina-christi precipitation of wettest month (56.1%) influenced the most followed by precipitation of the coldest quarter and annual precipitation (Figure 1c). Locations for each species are available as Underlying data⁴¹.

Table 1. Variable contributions in percent for Calotropis procera, Prosopis cineraria and Ziziphus spina-christi.

Variable	Percent contribution
Variable	Calotropis Procera	Prosopis cineraria	Ziziphus spinachristi
bio19	26.8	38.9	44.8
bio12	25.2	36.6	18.1
bio2	14.4	11.7	16.2
bio11	6.8	8.5	9
Elevation	6.3	2.8	4.8
bio7	5.8	0.5	3.5
bio5	5.3	0.3	1.6
Land cover	4.8	0.3	0.9
bio13	2.1	0.2	0.8
bio16	1.3	0.1	0.3
bio4	1.2	0.1	0.1
bio14	0	0	0

Figure 1.

Jackknife variable contribution test for Calotropis procera (a), Prosopis cineraria (b) and Ziziphus spina-christi (c).

Current habitat suitability

Results from the current habitat distribution map (Figure 2) clearly indicate that the environmental conditions in the northern part of the UAE (Ras AlKhaimah, Al-Fujayrah, Sharjah, Ajman, Umm Al-Quwain, Dubai and Al-Ain) act as the most suitable habitat for the selected three species. The predicted habitat suitability was reclassified into three classes and this unfolded that only 1.9% of the country showed high habitat suitability (H) for C. procera (Figure 3) followed by 9.4% as medium suitability habitat (M). The remaining 88.6% of the country has low environmental suitability (L) for this particular species. For P. cinararia (Figure 3), 1.5% of the total area was predicted as a highly suitable habitat and 5.7% as medium suitability. Ziziphus spinachristi (Figure 3) revealed high and medium suitability habitat of around 5.7% and 11.5%, respectively.

Figure 2.

Current and future habitat distribution (current, 2050s and 2070s) for Calotropis procera (a), Prosopis cineraria (b) and Ziziphus spina-christi (c).

Figure 3. Habitat suitability distribution as affected by climate change (current, 2050 and 2070) for Calotropis procera, Prosopis cineraria and Ziziphus spina-christi.

Future habitat suitability

The future (2050s) habitat distribution of C. procera indicated a slight reduction in the high (1.6%) and medium (8.8%) suitability classes. As for 2070s, the model predicted a visible increase in the high suitability class to 7% and medium suitability to 11.9%. The northern part of the country remains the highest environmentally suitable habitat for C. procera. For P. cinararia it is predicted that by 2050s, high suitability will be 8.5% and medium suitability will be 12.3%. In contrast, and by 2070s the high- and medium-suitability classes will see a reduction to 7% and 10.3% of the total area, respectively. For Ziziphus spina-christi distribution, an increase of its high habitat suitability distribution for both 2050s and 2070s, as climate is expected to change.

Discussion

In this study we predicted the suitable habitat for three significant dry land species with the MaxEnt model. Both current and future habitat distribution models of the selected three species were efficiently projected by MaxEnt. Both C. procera and P. cinararia had less area in the high habitat suitability for the present scenario. Whereas Ziziphus spina-christi had comparatively high present suitable habitat in the country. All three species were projected to have an increased level of high habitat suitability in the future. Calotropis procera can easily adapt with harsh climate conditions especially they rapidly adjust with water availability and loss⁴². A study by Frosi et al.⁴³ suggested that C. procera has high water use efficiency because of its high photosynthetic rate, despite a reduced stomatal conductance, which acts as its fundamental strategy to survive harsh growth conditions. P. cineraria is the national tree of UAE and most of the artificial forest and road sides in the country are afforested by this plant. It normally grows on soils ranging from sandy to clay loam Kumawat⁴⁴.

The present study can be useful for selecting species like p. cinararia for forest and roadside plantation in the future floral conservation programs. Predicted areas for P. cineraria consist of a large area of artificial forest²⁷. In UAE, during the past few decades around 300,000 ha has been planted with forests⁴⁵. The main purpose of these plantation is to combat desertification by afforestation. These artificial forests utilizes numerous resources with no economic benefit.

Other than P. cineraria, these forests consists of many other native and invasive species including P. juliflora, Salvadora persica and Ziziphus spina-christi. P. cineraria can have a positive impact on specie diversity and density²⁷. Prosopis cineraria improves organic matter, soil physical conditions and water-holding capacity. Therefore, it outperforms other species in the same area. Soil nutrient content is higher beneath the P. cineraria trees²⁷. Growth and floral diversity are better under P. cineraria than on adjacent areas⁴⁶.

Ziziphus spina-christi can resist heat and drought and its deep taproot provide an extraordinary regenerative potentials. This might be the reason for larger area distribution of this species in the UAE. Yet these species’ future habitat suitability areas are important for the country to incorporate in the conservation strategies under climate change. The areas predicted to be suitable can be considered for afforestation programs in the UAE. For all the three species, and with climate change impact, maximum habitat suitability predictions will be in the north-eastern parts of the UAE. It is important to highlight that one of the most vulnerable areas to climate change are dry land ecosystems⁴⁷.

Conclusion

The MaxEnt model provides useful information about species and their satiable habitats, which can be used as a basis in planning future conservation and afforestation programs. Additionally, the model is also efficient in predicting species distribution under different climate change scenarios. Conservation and afforestation programs considering the species and their suitable habitats, therefore, will not only be easily manageable and sustainable but also economically efficient by using different SDMs. Finer spatial scales of species distribution modeling may be an appropriate next step toward species conservation under the potential changes in climatic conditions.

Data availability

Underlying data

Figshare: SURE2018.zip. https://doi.org/10.6084/m9.figshare.8233328.v1⁴¹. This project contains a zipped folder made up of the following files:

Calotropis procera.csv (location of Calotropis procera this study).
Prosopis cineraria.csv (location of Prosopis cineraria in this study).
Ziziphus spinachristi.csv (location of Ziziphus spinachristi in this study).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Grant information

This project was funded by UAE University (Project: 31S347) as part of the Summer Undergraduate Research Experience (SURE+).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgements

The authors are thankful to the UAE University for their support.

Faculty Opinions recommended

References

1. Bellard C, Bertelsmeier C, Leadley P, et al.: Impacts of climate change on the future of biodiversity. Ecol Lett. 2012; 15(4): 365–377. PubMed Abstract | Publisher Full Text | Free Full Text
2. Mori AS, Spies TA, Sudmeier-Rieux K, et al.: Reframing ecosystem management in the era of climate change: issues and knowledge from forests. Biol Conserv. 2013; 165: 115–127. Publisher Full Text
3. Ferrarini A, Rossi G, Mondoni A, et al.: Prediction of climate warming impacts on plant species could be more complex than expected. evidence from a case study in the himalaya. Ecol Complex. 2014; 20: 307–314. Publisher Full Text
4. Elasha BO: Mapping of climate change threats and human development impacts in the arab region. undp arab development report–research paper series, undp regiona bureau for the arab states. 2010. Reference Source
5. IIED: Climate Change and Drylands International Institute for Environmental Development- Commission on Climate Change and Development. IIED, 2008.
6. El-Keblawy A, Abdelfattah MA, Khedr AHA: Relationships between landforms, soil characteristics and dominant xerophytes in the hyper-arid northern united arab emirates. J Arid Environ. 2015; 117: 28–36. Publisher Full Text
7. Ksiksi T, Guenaoui C, Fawzi N: Early Growth Stages of the Rare Acridocarpus orientalis in the UAE-A First Step towards Conservation. Nat Resour. 2012; 3(1): 1–5. Publisher Full Text
8. IPCC Climate Change, et al.: The physical science basis. Contribution of working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 2007; 996. Reference Source
9. Hulme M, Jenkins G: Climate change scenarios for the united kingdom scientific report 1998. 1998. Reference Source
10. Pearson RG, Dawson TP, Berry PM: Species: a spatial evaluation of climate impact on the envelope of species. Ecol Model. 2002; 154(3): 289–300. Publisher Full Text
11. Araújo MB, New M: Ensemble forecasting of species distributions. Trends Ecol Evol. 2007; 22(1): 42–47. PubMed Abstract | Publisher Full Text
12. Yi YJ, Cheng X, Yang ZF, et al.: Maxent modeling for predicting the potential distribution of endangered medicinal plant (h. riparia lour) in yunnan, China. Ecol Eng. 2016; 92: 260–269. Publisher Full Text
13. Deb JC, Phinn S, Butt N, et al.: Climatic-induced shifts in the distribution of teak (tectona grandis) in tropical asia: implications for forest management and planning. Environ Manage. 2017; 60(3): 422–435. PubMed Abstract | Publisher Full Text
14. Leathwick J, Austin M: Competitive interactions between tree species in new zealand’s old-growth indigenous forests. Ecology. 2001; 82(9): 2560–2573. Publisher Full Text
15. Loiselle BA, Howell CA, Graham CH, et al.: Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol. 2003; 17(6): 1591–1600. Publisher Full Text
16. Guisan A, Lehmann A, Ferrier S, et al.: Making better biogeographical predictions of species’ distributions. J Appl Ecol. 2006; 43(3): 386–392. Publisher Full Text
17. Pearson RG, Raxworthy CJ, Nakamura M, et al.: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in madagascar. J Biogeogr. 2007; 34(1): 102–117. Publisher Full Text
18. Wang T, Wang G, Innes J, et al.: Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia–Pacific region. Forest Ecol Manag. 2016; 360: 357–366. Publisher Full Text
19. Fatemi SS, Rahimi M, Tarkesh M, et al.: Predicting the impacts of climate change on the distribution of juniperus excelsa m. bieb. in the central and eastern alborz mountains, iran. IForest. 2018; 11(5): 643–650. Publisher Full Text
20. Rondinini C, Wilson KA, Boitani L, et al.: Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol Lett. 2006; 9(10): 1136–1145. PubMed Abstract | Publisher Full Text
21. Guisan A, Tingley R, Baumgartner JB, et al.: Predicting species distributions for conservation decisions. Ecol Lett. 2013; 16(12): 1424–1435. PubMed Abstract | Publisher Full Text | Free Full Text
22. Phillips SJ, Anderson RP, Schapire RE: Maximum entropy modeling of species geographic distributions. Ecol Model. 2006; 190(3–4): 231–259. Publisher Full Text
23. Hernandez PA, Graham CH, Master LL, et al.: The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography. 2006; 29(5): 773–785. Publisher Full Text
24. Pearson RG: Raxworthy cj, nakamura m, townsend peterson a. Madagascar J Biogeogr. 2007; 1: 102–117.
25. Phillips SJ, Anderson RP, Schapire RE: Maximum entropy modeling of species geographic distributions. Ecol Model. 2006; 190(3–4): 231–259. Publisher Full Text
26. Al-Mezaine HS, Al-Rajhi AA, Al-Assiri A, et al.: Calotropis procera (ushaar) keratitis. Am J Ophthalmol. 2005; 139(1): 199–202. PubMed Abstract | Publisher Full Text
27. El-Keblawy A, Ksiksi T: Artificial forests as conservation sites for the native flora of the uae. Forest Ecol Manag. 2005; 213(1–3): 288–296. Publisher Full Text
28. Hijmans RJ, Cameron SE, Parra JL, et al.: The worldclim interpolated global terrestrial climate surfaces. version 1.3, 2004.
29. A ESRI: Arcgis 10.1. Environmental Systems Research Institute, Redlands, CA, USA, 2012.
30. Thomson AM, Calvin KV, Smith SJ, et al.: Rcp 4.5: a pathway for stabilization of radiative forcing by 2100. Clim Change. 2011; 109(1–2): 77. Publisher Full Text
31. Hijmans RJ, Cameron SE, Parra JL, et al.: Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005; 25(15): 1965–1978. Publisher Full Text
32. Anderson R, Dudík M, Ferrier S, et al.: Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006; 29(2): 129–151. Publisher Full Text
33. Waltari E, Hijmans RJ, Peterson AT, et al.: Locating pleistocene refugia: comparing phylogeographic and ecological niche model predictions. PLoS One. 2007; 2(7): e563. PubMed Abstract | Publisher Full Text | Free Full Text
34. Kumar S, Stohlgren TJ: Maxent modeling for predicting suitable habitat for threatened and endangered tree canacomyrica monticola in new caledonia. J Ecol Nat Environ. 2009; 1(4): 094–098. Reference Source
35. Raes N, Roos MC, Slik JW, et al.: Botanical richness and endemicity patterns of borneo derived from species distribution models. Ecography. 2009; 32(1): 180–192. Publisher Full Text
36. Mukherjee S, Krishnan A, Tamma K, et al.: Ecology driving genetic variation: a comparative phylogeography of jungle cat (Felis chaus) and leopard cat (Prionailurus bengalensis) in India. PLoS One. 2010; 5(10): e13724. PubMed Abstract | Publisher Full Text | Free Full Text
37. Elith J, Phillips SJ, Hastie T, et al.: A statistical explanation of maxent for ecologists. Divers Distrib. 2011; 17(1): 43–57. Publisher Full Text
38. Anderson RP, Gonzalez I Jr: Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with maxent. Ecol Model. 2011; 222(15): 2796–2811. Publisher Full Text
39. Allouche O, Tsoar A, Kadmon R: Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (tss). J Appl Ecol. 2006; 43(6): 1223–1232. Publisher Full Text
40. Khanum R, Mumtaz AS, Kumar S: Predicting impacts of climate change on medicinal asclepiads of pakistan using maxent modeling. Acta Oecol. 2013; 49: 23–31. Publisher Full Text
41. Ksiksi T: Sure2018.zip. 2019. Reference Source
42. Ramadan A, Sabir JS, Alakilli SY: Metabolomic response of Calotropis procera growing in the desert to changes in water availability. PLoS One. 2014; 9(2): e87895. PubMed Abstract | Publisher Full Text | Free Full Text
43. Frosi G, Teixeira Oliveira M, Almeida-Cortez J, et al.: Ecophysiological performance of calotropis procera: an exotic and evergreen species in caatinga, brazilian semi-arid. Acta Physiol Plant. 2013; 35(2): 335–344. Publisher Full Text
44. Kumawat RN: Managing rangelands: promoting sustainable tree species "prosopis cineraria": A wonder tree for agroforestry in arid and semi-arid areas". 2016. Reference Source
45. MOEAW: State of Environment Report of the United Arab Emirates 2015. The Ministry of environment and water-United Arab Emirates, 2015.
46. El-Keblawy A, Ksiksi T, El Alqamy H: Camel grazing affects species diversity and community structure in the deserts of the UAE. J Arid Environ. 2009; 73(3): 347–354. Publisher Full Text
47. MOCCAE: National Climate Change Plan of the United Arab Emirates 2017-2050. Ministry of climate change and environment, 2017. Reference Source

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 27 Jun 2019

Author details Author details

¹ Department of Biology, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
² Centre for Climate Change and Adaptation Research, Center for Climate Change, Chennai, India

Taoufik Saleh Ksiksi
Roles: Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Remya K.
Roles: Formal Analysis, Writing – Original Draft Preparation

Mohamed T. Mousa
Roles: Formal Analysis, Methodology, Writing – Review & Editing

Shima K. Al-Badi
Roles: Data Curation, Methodology

Salama K. Al Kaabi
Roles: Data Curation, Methodology

Shamsa M. Alameemi
Roles: Data Curation, Methodology

Sanad M. Fereaa
Roles: Data Curation, Methodology

Fatima E. Hassan
Roles: Data Curation, Methodology

Competing interests

No competing interests were disclosed.

Grant information

This project was funded by UAE University (Project: 31S347) as part of the Summer Undergraduate Research Experience (SURE+).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (1)

version 1

Published: 27 Jun 2019, 8:978

https://doi.org/10.12688/f1000research.19540.1

Copyright

© 2019 Ksiksi TS et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download

Export To

metrics

	Views	Downloads
F1000Research	-	-
PubMed Central Data from PMC are received and updated monthly.	-	-

Citations

0

SEE MORE DETAILS

CITE

how to cite this article

Ksiksi TS, K. R, Mousa MT et al. Climate change-induced species distribution modeling in hyper-arid ecosystems [version 1; peer review: peer review discontinued]. F1000Research 2019, 8:978 (https://doi.org/10.12688/f1000research.19540.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

track

receive updates on this article

Track an article to receive email alerts on any updates to this article.

Peer review discontinued

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 27 Jun 2019

Peer review discontinued

Peer review at F1000Research is author-driven. Currently no reviewers are being invited.

What does this mean?

Comments on this article

All Comments(0)

Add a comment

Sign up for content alerts

Browse by related subjects

[1] 1. Bellard C, Bertelsmeier C, Leadley P, et al.: Impacts of climate change on the future of biodiversity. Ecol Lett. 2012; 15(4): 365–377. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Mori AS, Spies TA, Sudmeier-Rieux K, et al.: Reframing ecosystem management in the era of climate change: issues and knowledge from forests. Biol Conserv. 2013; 165: 115–127. Publisher Full Text

[3] 3. Ferrarini A, Rossi G, Mondoni A, et al.: Prediction of climate warming impacts on plant species could be more complex than expected. evidence from a case study in the himalaya. Ecol Complex. 2014; 20: 307–314. Publisher Full Text

[4] 4. Elasha BO: Mapping of climate change threats and human development impacts in the arab region. undp arab development report–research paper series, undp regiona bureau for the arab states. 2010. Reference Source

[5] 5. IIED: Climate Change and Drylands International Institute for Environmental Development- Commission on Climate Change and Development. IIED, 2008.

[6] 6. El-Keblawy A, Abdelfattah MA, Khedr AHA: Relationships between landforms, soil characteristics and dominant xerophytes in the hyper-arid northern united arab emirates. J Arid Environ. 2015; 117: 28–36. Publisher Full Text

[7] 7. Ksiksi T, Guenaoui C, Fawzi N: Early Growth Stages of the Rare Acridocarpus orientalis in the UAE-A First Step towards Conservation. Nat Resour. 2012; 3(1): 1–5. Publisher Full Text

[8] 8. IPCC Climate Change, et al.: The physical science basis. Contribution of working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 2007; 996. Reference Source

[9] 9. Hulme M, Jenkins G: Climate change scenarios for the united kingdom scientific report 1998. 1998. Reference Source

[10] 10. Pearson RG, Dawson TP, Berry PM: Species: a spatial evaluation of climate impact on the envelope of species. Ecol Model. 2002; 154(3): 289–300. Publisher Full Text

[11] 11. Araújo MB, New M: Ensemble forecasting of species distributions. Trends Ecol Evol. 2007; 22(1): 42–47. PubMed Abstract | Publisher Full Text

[12] 12. Yi YJ, Cheng X, Yang ZF, et al.: Maxent modeling for predicting the potential distribution of endangered medicinal plant (h. riparia lour) in yunnan, China. Ecol Eng. 2016; 92: 260–269. Publisher Full Text

[13] 13. Deb JC, Phinn S, Butt N, et al.: Climatic-induced shifts in the distribution of teak (tectona grandis) in tropical asia: implications for forest management and planning. Environ Manage. 2017; 60(3): 422–435. PubMed Abstract | Publisher Full Text

[14] 14. Leathwick J, Austin M: Competitive interactions between tree species in new zealand’s old-growth indigenous forests. Ecology. 2001; 82(9): 2560–2573. Publisher Full Text

[15] 15. Loiselle BA, Howell CA, Graham CH, et al.: Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol. 2003; 17(6): 1591–1600. Publisher Full Text

[16] 16. Guisan A, Lehmann A, Ferrier S, et al.: Making better biogeographical predictions of species’ distributions. J Appl Ecol. 2006; 43(3): 386–392. Publisher Full Text

[17] 17. Pearson RG, Raxworthy CJ, Nakamura M, et al.: Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in madagascar. J Biogeogr. 2007; 34(1): 102–117. Publisher Full Text

[18] 18. Wang T, Wang G, Innes J, et al.: Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia–Pacific region. Forest Ecol Manag. 2016; 360: 357–366. Publisher Full Text

[19] 19. Fatemi SS, Rahimi M, Tarkesh M, et al.: Predicting the impacts of climate change on the distribution of juniperus excelsa m. bieb. in the central and eastern alborz mountains, iran. IForest. 2018; 11(5): 643–650. Publisher Full Text

[20] 20. Rondinini C, Wilson KA, Boitani L, et al.: Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol Lett. 2006; 9(10): 1136–1145. PubMed Abstract | Publisher Full Text

[21] 21. Guisan A, Tingley R, Baumgartner JB, et al.: Predicting species distributions for conservation decisions. Ecol Lett. 2013; 16(12): 1424–1435. PubMed Abstract | Publisher Full Text | Free Full Text

[22] 22. Phillips SJ, Anderson RP, Schapire RE: Maximum entropy modeling of species geographic distributions. Ecol Model. 2006; 190(3–4): 231–259. Publisher Full Text

[23] 23. Hernandez PA, Graham CH, Master LL, et al.: The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography. 2006; 29(5): 773–785. Publisher Full Text

[24] 24. Pearson RG: Raxworthy cj, nakamura m, townsend peterson a. Madagascar J Biogeogr. 2007; 1: 102–117.

[25] 25. Phillips SJ, Anderson RP, Schapire RE: Maximum entropy modeling of species geographic distributions. Ecol Model. 2006; 190(3–4): 231–259. Publisher Full Text

[26] 26. Al-Mezaine HS, Al-Rajhi AA, Al-Assiri A, et al.: Calotropis procera (ushaar) keratitis. Am J Ophthalmol. 2005; 139(1): 199–202. PubMed Abstract | Publisher Full Text

[27] 27. El-Keblawy A, Ksiksi T: Artificial forests as conservation sites for the native flora of the uae. Forest Ecol Manag. 2005; 213(1–3): 288–296. Publisher Full Text

[28] 28. Hijmans RJ, Cameron SE, Parra JL, et al.: The worldclim interpolated global terrestrial climate surfaces. version 1.3, 2004.

[29] 29. A ESRI: Arcgis 10.1. Environmental Systems Research Institute, Redlands, CA, USA, 2012.

[30] 30. Thomson AM, Calvin KV, Smith SJ, et al.: Rcp 4.5: a pathway for stabilization of radiative forcing by 2100. Clim Change. 2011; 109(1–2): 77. Publisher Full Text

[31] 31. Hijmans RJ, Cameron SE, Parra JL, et al.: Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005; 25(15): 1965–1978. Publisher Full Text

[32] 32. Anderson R, Dudík M, Ferrier S, et al.: Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006; 29(2): 129–151. Publisher Full Text

[33] 33. Waltari E, Hijmans RJ, Peterson AT, et al.: Locating pleistocene refugia: comparing phylogeographic and ecological niche model predictions. PLoS One. 2007; 2(7): e563. PubMed Abstract | Publisher Full Text | Free Full Text

[34] 34. Kumar S, Stohlgren TJ: Maxent modeling for predicting suitable habitat for threatened and endangered tree canacomyrica monticola in new caledonia. J Ecol Nat Environ. 2009; 1(4): 094–098. Reference Source

[35] 35. Raes N, Roos MC, Slik JW, et al.: Botanical richness and endemicity patterns of borneo derived from species distribution models. Ecography. 2009; 32(1): 180–192. Publisher Full Text

[36] 36. Mukherjee S, Krishnan A, Tamma K, et al.: Ecology driving genetic variation: a comparative phylogeography of jungle cat (Felis chaus) and leopard cat (Prionailurus bengalensis) in India. PLoS One. 2010; 5(10): e13724. PubMed Abstract | Publisher Full Text | Free Full Text

[37] 37. Elith J, Phillips SJ, Hastie T, et al.: A statistical explanation of maxent for ecologists. Divers Distrib. 2011; 17(1): 43–57. Publisher Full Text

[38] 38. Anderson RP, Gonzalez I Jr: Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with maxent. Ecol Model. 2011; 222(15): 2796–2811. Publisher Full Text

[39] 39. Allouche O, Tsoar A, Kadmon R: Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (tss). J Appl Ecol. 2006; 43(6): 1223–1232. Publisher Full Text

[40] 40. Khanum R, Mumtaz AS, Kumar S: Predicting impacts of climate change on medicinal asclepiads of pakistan using maxent modeling. Acta Oecol. 2013; 49: 23–31. Publisher Full Text

[41] 41. Ksiksi T: Sure2018.zip. 2019. Reference Source

[42] 42. Ramadan A, Sabir JS, Alakilli SY: Metabolomic response of Calotropis procera growing in the desert to changes in water availability. PLoS One. 2014; 9(2): e87895. PubMed Abstract | Publisher Full Text | Free Full Text

[43] 43. Frosi G, Teixeira Oliveira M, Almeida-Cortez J, et al.: Ecophysiological performance of calotropis procera: an exotic and evergreen species in caatinga, brazilian semi-arid. Acta Physiol Plant. 2013; 35(2): 335–344. Publisher Full Text

[44] 44. Kumawat RN: Managing rangelands: promoting sustainable tree species "prosopis cineraria": A wonder tree for agroforestry in arid and semi-arid areas". 2016. Reference Source

[45] 45. MOEAW: State of Environment Report of the United Arab Emirates 2015. The Ministry of environment and water-United Arab Emirates, 2015.

[46] 46. El-Keblawy A, Ksiksi T, El Alqamy H: Camel grazing affects species diversity and community structure in the deserts of the UAE. J Arid Environ. 2009; 73(3): 347–354. Publisher Full Text

[47] 47. MOCCAE: National Climate Change Plan of the United Arab Emirates 2017-2050. Ministry of climate change and environment, 2017. Reference Source

Climate change-induced species distribution modeling in hyper-arid ecosystems

Abstract

Keywords

Introduction

Methods

Study site

Species ecological characteristics

Model requirements

Environmental parameters

Modeling approach

Results

Model fit and variable contribution

Table 1. Variable contributions in percent for Calotropis procera, Prosopis cineraria and Ziziphus spina-christi.

Figure 1.

Current habitat suitability

Figure 2.

Figure 3. Habitat suitability distribution as affected by climate change (current, 2050 and 2070) for Calotropis procera, Prosopis cineraria and Ziziphus spina-christi.

Future habitat suitability

Discussion

Conclusion

Data availability

Underlying data

Grant information

Acknowledgements

References

Comments on this article Comments (0)

Peer review discontinued

Comments on this article Comments (0)

Peer review discontinued

Comments on this article

Browse by related subjects

Competing Interests Policy

Stay Updated