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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (12): 1730-1743    DOI: 10.1007/s40333-024-0036-9    
Research article     
Environmental factors influencing the distribution of endangered endemic species Hedysarum criniferum Boiss in arid and semi-arid rangelands, Iran
Javid HAYATI, Hossein BASHARI*(), Seyed H MATINKHAH, Hamid R KARIMZADEH, Mostafa TARKESH
Department of Natural Resources, Isfahan University of Technology, Isfahan 8415683111, Iran
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Abstract  

Understanding the factors influencing the distribution of plant species is crucial for enhancing the management of endangered ecosystems. This study investigated the response of Hedysarum criniferum Boiss, an endemic and endangered species to 25 environmental variables within its habitats with an area of 2.95×105 km² in arid and semi-arid rangelands of Iran. The purpose of this research is to identify the key environmental factors affecting the distribution and habitat preferences of H. criniferum for further conservation and restoration of the species. To predict the occurrence of H. criniferum and explore its relationship with environmental factors, we employed the best subset regression analysis, the hierarchical classification, and the extended Huisman-Olf-Fresco (eHOF) model. The results showed that four environmental variables, i.e., gravel content, pH, annual minimum temperature, and mean annual temperature showed significant correlations with the canopy cover of H. criniferum (P<0.05). The probability of H. criniferum occurrence increased with higher precipitation and elevation, while it decreased with higher mean annual temperature, annual minimum temperature, and gravel content. The species' response curves and their optimal values, as assessed by the eHOF model, indicated that the response to mean annual temperature, ranging from 12°C to 16°C, was optimal at 13°C. The response to mean annual precipitation, within a range of 150-650 mm, was optimal at 650 mm. Elevation responses, spanning from 1546 to 2450 m, showed an optimum at 2450 m. Regarding soil characteristics, the response to gravel content, ranging from 13.0%-48.0%, demonstrated an optimal value at 20.0%. The pH levels, varying from 7.5 to 8.2, prompted a sine-shaped response with an optimal pH of 8.0. These findings provide valuable insights for predicting species occurrence and identifying suitable locations for restoration programs. Our study underscores the importance of considering multiple environmental variables in habitat suitability assessments. By incorporating these broader considerations, we can further refine predictive models and enhance conservation efforts aimed at restoring habitats conducive to the luxuriance of endangered species like H. criniferum.

Key words： habitat suitability      species response curves      best subset regression      extended Huisman-Olf-Fresco (eHOF) model      hierarchical classification     
Received: 30 April 2024      Published: 31 December 2024
Corresponding Authors: *Hossein BASHARI (E-mail: hbashari@iut.ac.ir)
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Javid HAYATI
Hossein BASHARI
Seyed H MATINKHAH
Hamid R KARIMZADEH
Mostafa TARKESH
Cite this article:

Javid HAYATI, Hossein BASHARI, Seyed H MATINKHAH, Hamid R KARIMZADEH, Mostafa TARKESH. Environmental factors influencing the distribution of endangered endemic species Hedysarum criniferum Boiss in arid and semi-arid rangelands, Iran. Journal of Arid Land, 2024, 16(12): 1730-1743.

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http://jal.xjegi.com/10.1007/s40333-024-0036-9     OR     http://jal.xjegi.com/Y2024/V16/I12/1730

Fig. 1 Geographical location of the natural habitat sites of Hedysarum criniferum Boiss in Iran. DEM, digital elevation model.
Site number Site
name		 Province Latitude Longitude Elevation (m) Slope (%) Mean annual precipitation (mm)
1 Sepidan Fars 29°53′25′′N 51°57′36′′E 2050 45.0 560
2 Damane Isfahan 33°00′14′′N 50°37′01′′E 2450 60.0 550
3 Moote 1 Isfahan 33°47′00′′N 50°47′30′′E 1943 70.0 150
4 Moote 2 Isfahan 33°46′58′′N 50°46′24′′E 2090 65.0 150
5 Chadegan Isfahan 33°42′25′′N 50°28′18′′E 2114 70.0 343
6 Avaj Ghazvin 34°32′23′′N 49°12′50′′E 2118 45.0 412
7 Gahvare Kermanshah 34°20′59′′N 46°21′37′′E 1546 33.0 430
8 Gonbad Hamedan 35°34′35′′N 48°41′46′′E 2266 27.0 329
Table 1 Main features of selected natural habitats sites of H. criniferum in Iran
Variable Abbreviation Plant height Canopy cover
R2 P R2 P
Gravel Gr -0.690 0.000** -0.620 0.001**
Lime - 0.050 0.840 -0.320 0.120
Soil organic carbon SOC -0.220 0.300 -0.380 0.070
Soil pH pH -0.410 0.040* -0.590 0.002**
Electrical conductivity EC -0.640 0.001** -0.510 0.010*
Soil calcium Ca -0.800 0.000** -0.660 0.000**
Soil magnesium Mg -0.530 0.008** -0.520 0.010**
Soil sodium Na -0.670 0.000** -0.540 0.006**
Soil potassium K -0.390 0.060 -0.540 0.007**
Soil phosphorus P -0.150 0.470 -0.090 0.690
Soil nitrogen N -0.370 0.070 -0.180 0.400
Calcium sulfate CaSO4 -0.090 0.690 -0.160 0.460
Sodium adsorption ratio SAR -0.230 0.280 -0.270 0.270
Sand - -0.040 0.840 0.110 0.620
Silt - -0.030 0.910 -0.040 0.850
Clay - 0.090 0.690 -0.130 0.550
Elevation - 0.630 0.010* 0.590 0.010**
Aspect - 0.310 0.210 -0.400 0.060
Slope - 0.020 0.940 -0.370 0.080
Mean annual precipitation MAP 0.670 0.000** 0.500 0.010*
Mean wind speed MWS -0.590 0.003** -0.510 0.010*
Maximum wind speed MxWS 0.140 0.520 0.390 0.070
Annual maximum temperature AMT_max -0.260 0.230 -0.070 0.750
Annual minimum temperature AMT_min -0.710 0.000** -0.570 0.004**
Mean annual temperature MAT 0.630 0.002** -0.620 0.006**
Table 2 Correlation of environmental factors with plant height and canopy cover of H. criniferum in the arid and semi-arid rangelands of Iran
Plant height
R2 Adj R2 MSE (cm) Cp Gr pH EC Ca Mg Na Elevation MAP MWS AMT_min MAT
0.639 0.623 61.85 5.899 *
0.762 0.739 42.81 4.886 * *
0.784 0.751 40.82 3.462 * * *
0.806 0.765 38.53 0.065 * * * *
0.816 0.765 38.50 1.189 * * * * *
0.822 0.759 39.52 2.787 * * * * * *
0.829 0.755 40.28 4.268 * * * * * * *
0.832 0.743 42.17 6.040 * * * * * * * *
0.833 0.725 45.12 8.025 * * * * * * * * *
0.833 0.704 48.55 10.015 * * * * * * * * * *
0.833 0.680 52.54 12.000 * * * * * * * * * * *
Canopy cover
R2 Adj R2 MSE (%) Cp Gr pH EC Ca Mg Na Elevation MAP MWS AMT_min MAT
0.440 0.415 70.37 23.560 *
0.770 0.748 30.35 13.230 * *
0.787 0.756 29.39 4.541 * * *
0.807 0.766 28.11 1.026 * * * *
0.822 0.772 27.37 1.867 * * * * *
0.831 0.772 27.46 3.138 * * * * * *
0.836 0.764 28.41 4.789 * * * * * * *
0.847 0.765 28.29 5.942 * * * * * * * *
0.853 0.759 29.00 7.424 * * * * * * * *
0.856 0.746 30.58 9.188 * * * * * * * * *
0.858 0.728 32.65 11.025 * * * * * * * * * *
0.859 0.704 35.54 13.000 * * * * * * * * * *
Table 3 Regression analysis for plant height and canopy cover of H. criniferum with environmental variables
Factor Plant height
pH Elevation MAT
Elevation -0.303 (P=0.465)
MAP -0.158 (P=0.709) 0.133 (P=0.754)
AMT_min -0.398 (P=0.328) -0.218 (P=0.603) 0.638 (P=0.089)
Factor Canopy cover
Gravel pH AMT_min
pH 0.560 (P=0.149)
MAP -0.667 (P=0.071) -0.398 (P=0.328)
AMT_min -0.374 (P=0.361) -0.172 (P=0.685) -0.136 (P=0.747)
Table 4 Correlation between variables of the best regression model to predict plant height and canopy cover of H. criniferum
Fig. 2 Results of hierarchical partitioning (HP) analysis for the variances of plant height (a) and canopy cover (b) of H. criniferum
Variable No response trend (Model I) Monotone increasing or decreasing trend
(Model II)		 Increasing or decreasing trend below a maximum attainable response
(Model III)		 Symmetrical unimodal response (Model IV) Asymmetrical unimodal response (Model V) Symmetrical bimodal response (Model VI) Skewed relations with optimal value (Model VII)
MAT 31.04 21.66 13.61 7.34* 10.36 10.36 13.78
AMT_min 31.04 19.43 17.47 16.81* 18.22 19.83 23.23
MAP 31.04 32.97 28.82 26.52 26.05* 29.54 32.27
Elevation 31.04 31.93 28.06 18.66 18.57* 21.58 24.98
Gravel 31.04 33.47 28.49 23.07 22.67* 26.10 29.49
pH 28.09 26.44 24.40 20.31* 21.69 23.56 27.32
Table 5 Akaike information criterion (AIC) values of each model for each environmental variable based on the extended Huisman-Olf-Fresco (eHOF) model
Fig. 3 Response curves and optimal presence points of H. criniferum with respect to environmental parameters. (a), MAT; (b), MAP; (c), AMT_min; (d), elevation; (e), gravel; (f), pH. The descriptions of Models I-VII are provided in Table 5. In the response curve plots, the upper box plots illustrate the variability of presence points, while the lower box plots represent the variability of absence points, highlighting the median and variance of the species' distribution. The gray lines reflect model uncertainty, with narrower lines indicating lower uncertainty and wider lines indicating higher uncertainty.
[1]   Adel M N, Pourbabaei H, Alavi S J, et al. 2017. Response curves of seventeen woody species to soil factors along a riparian forest in northern Iran. Russian Journal of Ecology, 48(3): 219-225.
[2]   Anderson M J, Walsh D C, Sweatman W L, et al. 2022. Non-linear models of species' responses to environmental and spatial gradients. Ecology Letters, 25(12): 2739-2752.
doi: 10.1111/ele.14121 pmid: 36269686
[3]   Austin M. 2007. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling, 200(1-2): 1-9.
[4]   Bartholomée O, Dwyer C, Tichit P, et al. 2023. Shining a light on species coexistence: visual traits drive bumblebee communities. Proceedings of the Royal Society, 290(1996): 20222548, doi: 10.1098/rspb.2022.2548.
[5]   Berini J L, Brockman S A, Hegeman AD, et al. 2018. Combinations of abiotic factors differentially alter production of plant secondary metabolites in five woody plant species in the boreal-temperate transition zone. Frontiers in Plant Science, 9: 1257, doi: 10.3389/fpls.2018.01257.
pmid: 30233611
[6]   Bremner J M. 1960. Determination of nitrogen in soil by the Kjeldahl method. Journal of Agricultural Science, 55(1): 11-33.
[7]   Burak K, Kashlak A. 2024. Bootstrapping generalized linear models to accommodate overdispersed count data. Statistical Papers, 65: 3769-3788.
[8]   Cao Y, Wang L Z. 2023. How to statistically disentangle the effects of environmental factors and human disturbances: A review. Water, 15(4): 734, doi: 10.3390/w15040734.
[9]   Choi B H, Nemoto T, Ohashi H. 1999. Anatomy of nodal regions and leaves in Hedysarum and related genera (Leguminosae). Journal of Japanese Botany, 74(4): 236-250.
[10]   Cochran W G. 1977. The estimation of sample size. In: Cochran W G (3rd ed.). Sampling Techniques. New York: John Wiley & Sons, 72-86.
[11]   Davis E L, Hager H A, Gedalof Z E. 2018. Soil properties as constraints to seedling regeneration beyond alpine treelines in the Canadian Rocky Mountains. Arctic, Antarctic, and Alpine Research, 50(1): e1415625, doi: 10.1080/15230430.2017.1415625.
[12]   Dianati-Tilaki G A, Keshavarz A, Amiri B. 2016. Physiological and biochemical adaptations of Hedysarum coronarium L. and Hedysarum criniferum Boiss to salinity stress. Range Management and Agroforestry, 37(1): 32-38.
[13]   Dixon P M. 2020. The bootstrap and the jackknife:Describing the precision of ecological indices. In: Scheiner S M, Gurevitch J. Design and Analysis of Ecological Experiments. New York: Chapman and Hall/CRC, 290-318.
[14]   Gao J B, Li S C, Zhao Z Q, et al. 2012. Investigating spatial variation in the relationships between NDVI and environmental factors at multi-scales: A case study of Guizhou Karst Plateau, China. International Journal of Remote Sensing, 33(7): 2112-2129.
[15]   Hartemink A E, Barrow N J. 2023. Soil pH-nutrient relationships: The diagram. Plant and Soil, 486(1): 209-215.
[16]   Hayati J, Matinkhah S H, Bashari H, et al. 2022. Predicting habitat suitability for the endangered species Hedysarum criniferum Boiss using a fuzzy model in the semi-arid zone in Iran. Arid Land Research and Management, 36(3): 344-369.
[17]   Heegaard E. 2002. The outer border and central border for species-environmental relationships estimated by non-parametric generalised additive models. Ecological Modelling, 157(2-3): 131-139.
[18]   Heikkinen J, Mäkipää R. 2010. Testing hypotheses on shape and distribution of ecological response curves. Ecological Modelling, 221(3): 388-399.
[19]   Huisman J, Olff H, Fresco L F. 1993. A hierarchical set of models for species response analysis. Journal of Vegetation Science, 4(1): 37-46.
[20]   James G, Witten D, Hastie T, et al. 2013. An Introduction to Statistical Learning:With Applications in R. New York: Springer.
[21]   Jankowski S J, Freiser H. 1961. Flame photometric methods of determining the potassium tetraphenylborate. Analytical Chemistry, 33(6): 773-775.
[22]   Jansen F, Oksanen J. 2013. How to model species responses along ecological gradients-Huisman-Olff-Fresco models revisited? Journal of Vegetation Science, 24(6): 1108-1117.
[23]   Lawesson J E, Oksanen J. 2002. Niche characteristics of Danish woody species as derived from coenoclines. Journal of Vegetation Science, 13(2): 279-290.
[24]   Mac Nally R. 1996. Hierarchical partitioning as an interpretative tool in multivariate inference. Australian Journal of Ecology, 21(2): 224-228.
[25]   McLean E O. 1982. Soil pH and lime requirement. In: Page A L, Miller R H, Keeney D R. Methods of Soil Analysis, part 2. Chemical and Microbiological Properties. Madison: Soil Science Society of America, 199-224.
[26]   Michaelis J, Diekmann M R. 2017. Biased niches-species response curves and niche attributes from Huisman-Olff-Fresco models change with differing species prevalence and frequency. PLoS ONE, 12(8): e0183152, doi: 10.1371/journal.pone.0183152.
[27]   Mueller-Dombois D, Ellenberg H. 1974. Aims and Methods of Vegetation Ecology. New York: Wiley.
[28]   Nelson D W, Sommers L E. 1996. Total carbon, organic carbon, and organic matter. In: Sparks D L, Page A L, Helmke P A, et al. Methods of Soil Analysis: Part 3 Chemical Methods. Madison: Soil Science Society of America, 961-1010.
[29]   Oksanen J, Minchin P R. 2002. Continuum theory revisited: What shape are species responses along ecological gradients? Ecological Modelling, 157(2-3): 119-129.
[30]   Olsen S R. 1954. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate (No. 939). Washington: US Department of Agriculture.
[31]   R Development Core Team. 2015. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
[32]   Redente E F. 1982. Sweetvetch seed germination Hedysarum boreale. Journal of Range Management, 35(4): 469-472.
[33]   Santika T, Hutchinson M F. 2009. The effect of species response form on species distribution model prediction and inference. Ecological Modelling, 220(19): 2365-2379.
[34]   Schroeder T A, Hamann A, Wang T L, et al. 2010. Occurrence and dominance of six Pacific Northwest conifer species. Journal of Vegetation Science, 21(3): 586-596.
[35]   Shahbazi A, Matinkhah S, Khajeali J, et al. 2017. The effects of pollinators and seed predators (Bruchidius koenigi Schilsky) on the breeding biology of Hedysarum criniferum Boiss. Plant Species Biology, 32(1): 36-44.
[36]   Sheikhzadeh A, Bashari H, Matinkhah S H, et al. 2016. The effect of twenty years of exclosure on parametric and non-parametric diversity indices in Chadegan rangelands-Isfahan. Iranian Journal of Applied Ecology, 17(5): 25-36. (in Persian)
[37]   Sheikhzadeh A, Bashari H, Tarkesh Esfahani M, et al. 2019. Investigation of rangeland indicator species using parametric and non-parametric methods in hilly landscapes of central Iran. Journal of Mountain Science, 16(6): 1408-1418.
[38]   van der Veen B, Hui F K C, Hovstad K A, et al. 2021. Model-based ordination for species with unequal niche widths. Methods in Ecology and Evolution, 12(7): 1288-1300.
[39]   Westerband A C, Funk J L, Barton K E. 2021. Intraspecific trait variation in plants: a renewed focus on its role in ecological processes. Annals of Botany, 127(4): 397-410.
doi: 10.1093/aob/mcab011 pmid: 33507251
[40]   Xu W H, Prescott C E. 2024. Can assisted migration mitigate climate-change impacts on forests? Forest Ecology and Management, 556: 121738, doi: 10.1016/j.foreco.2024.121738.
[41]   Xue J G, Wang X G, Du X G, et al. 2012. Influence of salinity and temperature on the germination of Hedysarum scoparium Fisch. et Mey. African Journal of Biotechnology, 11(14): 3244-3249.
[42]   Zhang J T, Dong Y R. 2010. Factors affecting species diversity of plant communities and the restoration process in the loess area of China. Ecological Engineering, 36(3): 345-350.
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