Investigating the Distribution Pattern of Ten Halophytes Species Using Quadrate Indices and Discrete Probability Distribution: A Case Study of Eastern Iran’s Desert Ecosystems

Document Type : Original Article

Authors

1 Assistant Professor, Department of Rangeland and Watershed Management and Research Group of Drought and Climate Change, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran * Corresponding Author: rostampour@birjand.ac.ir

2 Khorasan-e-razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Mashhad, Iran.

‎10.22052/deej.2024.253729.1026

Abstract

Introduction: determining the spatial distribution pattern of species in their environment is regarded as an important aspect of quantitative plant ecology. On the other hand, evaluating vegetation and arid land reclamation attempts requires awareness of the type of distribution pattern. Moreover, the study of plant distribution patterns significantly contributes to the assessment of the plants’ environmental uniformity or heterogeneity, reproduction type, distribution, competition, and behavioral patterns, helping to determine appropriate methods for measuring quantitative properties of vegetation such as coverage and density. However, studies on the distribution patterns of plant species are mainly focused on forest and rangeland ecosystems, and desert ecosystems are under-researched in this regard. Therefore, this study sought to determine the distribution pattern of ten halophyte species in eastern Iran's desert ecosystems using quadrate methods and the probability distribution function.​
 
Materials and Method: This study was carried out in the halophyte habitats in the rangeland and desert ecosystems of Ferdows, Khosf, and Zirkouh in South Khorasan province and Zabol in Sistan and Baluchistan province. After visiting the study area and identifying its dominant species, the sampling of the region’s vegetation habitats was performed in the spring of 2022 using a random systematic method.To this end, sixty 4*4-meter quadrats were placed in the key area of each habitat, where the density of the dominant species was calculated by the counting method.Then, after measuring the density of ten species of dominant halophytes in each habitat under study, the mean and variance of species density were calculated.
After that, Index of Dispersion (ID), Lexis’s Index (IL), Charlier’s Index (ICh), Index of Cluster Size (ICS), Green’s Index (GI), Index of Cluster Frequency (ICF), Index of Mean Crowding (IMC), Index of Patchiness (IP), Morisita’s Index, (IM) and Standardized Morisita (Ip) were calculated based on mean (), variance (), and sampling plot number (n).
Moreover, coefficients a and b distribution patterns were determined for ten halophyte species based on Taylor’s power law model or the linear logarithmic relationship between mean ( ) and variance ( ). In addition, Iwao’s patchiness regression model was used to calculate the relationship between the mean ( ) and the Index of Mean Crowding (IMC), based on which α and β coefficients were determined. Finally, invers K indices of negative binomial distribution and deviation from the Poisson distribution (ID) were determined, and the fit of plant density data was examined regarding three discreet statistical distributions, including Poisson, binomial, and negative binomial distributions.
 
Results: The results of the analysis of quadrate indices and probability distribution function revealed that Salsloa tomenosa, Suaeda Monoica, Salsloa kali, Aeluropus Littoralis, and Tamarix Stricta had a clumped pattern, Sedilitzia Rosmarinus and Haloxylon Ammodendron followed a random pattern, and Salsola richteri, Hammada Salicornica, and Tamarix aphylla enjoyed a uniform pattern. Moreover, Aeluropus littoralis was found to have the highest amount of all the indices (except ICF).
On the other hand, the results of the analysis for IP and IM indices were close to each other. Furthermore, according to the halophyte species density data obtained from the analysis of the probability distribution function, Aeluropus littoralis and Suaeda monoica were the only species that followed the negative binomial distribution which indicates a clumped pattern. On the other hand, Sedilitzia rosmarinus and Haloxylon ammodendron were the only species that followed the Poisson distribution which indicates a random pattern. Moreover, Salsola richteri, Hammada Salicornica, and Tamarix aphylla were found to have followed both Poisson and binomial distribution functions. However, based on the linear relationship between mean logarithm and variance logarithm (R2adj = 0.94, P <0/0001) and the linear relationship between the mean and Index of Mean Crowding (IMC) of density data (R2adj = 0.94, p <0/0001), the overall distribution pattern of the studied halophyte species was identified to have a clumped pattern.
 
Discussions and Conclusion: This study used quadrate, regression, and possibility distribution methods to identify the plants’ distribution patterns. Accordingly, the results of the quadrate method indicated that out of the ten species studied, six species had a clumped pattern and four species enjoyed a uniform pattern. Moreover, almost all the methods used in the study area showed that Aeluropus littoralis and Suaeda monoica followed the clumped distribution pattern. On the other hand, Tamarix stricta were found to exert a negative influence on the soil due to salt accumulation and possibly prevent the growth and germination of the surrounding plants, thus contributing to the plant’s patchiness. The findings of the study also revealed that Sedilitzia rosmarinus and Haloxylon ammodendron followed a random distribution pattern. On the other hand, while a random distribution pattern is rarely observed in natural ecosystems, the distribution of seeds through wind and livestock, and germination and rapid reproduction of the Haloxylon ammodendronin the regional sites of the study area may be regarded as possible reasons for the random distribution of the species.
The results of the analysis of regression methods suggested that the halophyte species generally followed a clumped distribution pattern, indicating the patchiness of nutritional sources and the soil’s physical and chemical properties such as moisture, salinity, and texture. It should be noted soil moisture and salinity are considered the main constraining factors of plant communities in desert ecosystems that affect the physiological and morphological properties of halophyte and psammophyte plants and limit their distribution, probably contributing to the heterogeneous distribution of plant species. Therefore, the effects of environmental stress on the vegetative and functional properties of plant species and their distribution pattern should be taken into account in developing desert greening and saline land reclamation plans.

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Main Subjects

References

  1. Akafi, H., Ejtehadi, H., & Sepehri, A. (2017). Spatial Distribution Pattern of Certain Plant Species Use of Quadrate Indices in Ghamishloo National Park. Iranian Journal of Range and Desert Research, 24(4), 907-919.
  2. Anithakumari, P., Muralidharan, K., & Chandran, K.P. (2017). Red palm weevil incidence: Spatial pattern and implications in technology adoption. Journal of Plantation Crops, 45(2), 101-109.
  3. Antão, L.H., Magurran, A.E., & Dornelas, M. (2021). The Shape of Species Abundance Distributions across Spatial Scales. Ecol. Evol. 9,626730.
  4. Baldridge, E., Harris, D. J., Xiao, X., & White, E. P. (2016). An extensive comparison of species-abundance distribution models. PeerJ, 4, e2823.
  5. Baranian, E., Basiri, M., Bashari, H., & Tarkesh, M. (2011). Study of spatial pattern of plants using point pattern analysis, spatial and quadrate indices (rangeland of Fereidan in Isfahan Province). Journal of Rangeland, 5(3), 258-269.
  6. Bhatt, A., Bhat, N. R., &Thomas, M.T. (2019). Germination Behavior of Seidlitzia Rosmarinus Boiss., a Perennial Halophyte of Arabian Deserts. Agriculture and Natural Resources 53 (4), 348-54.
  7. Bidarnamani, F., Fahmideh, L., & Shabanipoor, M. (2019). Comparison of Distance-based and Quadrate-based Methods to Determine the Dispersion Methods of Calligonum polygonoides in Sistan and Baluchestan Province, Iran. Journal of Arid Biome, 9(1), 113-123.
  8. Bliss C.I., & Fisher R.A. (1953). Fitting the negative binomial distribution to biological data. Biometrics, 9,176-200.
  9. Callaghan, C. T., Borda-de-Água, L., van Klink, R., Rozzi, R., & Pereira, H. M. (2023). Unveiling global species abundance distributions. Nature ecology & evolution, 7(10), 1600–1609.
  10. Chen, G., Zeng, D., Chen, F., Fan, Z., & Geng, H. (2003). A research review on" fertile islands" of soils under woody plant canopy in arid and semi-arid regions. Journal of Applied Ecology, 14, 2295-2300
  11. Chen, J., & Shiyomi, M. (2019). A power law model for analyzing spatial patterns of vegetation abundance in terms of cover, biomass, density, and occurrence: derivation of a common rule. Journal of plant research, 132(4), 481–497.
  12. Cohen, J. (2020). Every variance function, including Taylor’s power law of fluctuation scaling, can be produced by any location-scale family of distributions with positive mean and variance. Theoretical Ecology, 13, 1-5.
  13. Damgaard, C., & Irvine, K. M. (2019). Using the beta distribution to analyze plant cover data. Journal of Ecology, 107(6): 2747-2759.
  14. David, F.N., &G. Moore. (1954). Notes on contagious distributions in plant populations. Annals of Botany of London,18:47-53.
  15. Douglas, J.B. (1975). Clustering and aggregation. Sankhya, Series B,37,398-417.
  16. Edwards, K. F., Kremer, C. T., Miller, E. T., Osmond, M. M., Litchman, E., & Klausmeier, C. A. (2018). Evolutionarily stable communities: a framework for understanding the role of trait evolution in the maintenance of diversity. Ecology letters, 21(12), 1853–1868.
  17. Fallahchay, M., & Khoshmanzar, S. (2019). Determination of spatial distribution pattern analysis of Acer velutinum species in two elevation classes using distance sampling methods (Case Study: Asalem Nav Forests, Series No. 2). Iranian Forest Ecology Journal, 7(13), 83-90.
  18. Fisher, R.A. (1915) Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika, 10, 507-521.
  19. Fisher, R. A. (1941). The negative binomial distribution. Annals of Eugenics, 11, 182–187.
  20. Fisher, R.A. (1962). The Simultaneous Distribution of Correlation Coefficients. Sankhya, Series A, 24, 1-8.
  21. Fu-Cheng, Qu, Z., Jiping, W., & Lei, J. (2012). Effect of distribution patterns of Artemisia sphaerocephala on soil wind erosion: A field evaluation of wind speed profile and roughness. Procedia Environmental Sciences, 12(20), 310–317.
  22. Gong, Y., Lv, G., Guo, Z., Chen, Y., & Cao, J. (2017). Influence of aridity and salinity on plant nutrients scales up from species to community level in a desert ecosystem. Scientific reports, 7(1), 6811.
  23. Green, R.H. (1966). Measurement of non-randomness in spatial distributions. Researches in Population Ecology8:1-7.
  24. Hawinkel, S., Rayner, J. C. W., Bijnens, L., & Thas, O, (2020). Sequence count data are poorly fit by the negative binomial distribution. PloS one, 15(4), e0224909.
  25. Hijbeek, R., Koedam, N., Khan, M. N., Kairo, J. G., Schoukens, J., & Dahdouh-Guebas, F. (2013). An Evaluation of Plotless Sampling Using Vegetation Simulations and Field Data from a Mangrove Forest. PloS one, 8(6), e67201.
  26. Iwao, S. (1968) A new regression method for analyzing the aggregation pattern of animal populations. Researches on Population Ecology, 10, 1–
  27. Jafari, M., & Rostampour, M. (2019). Soil and plant relationships. Volume one: ecology, statistics and analysis. University of Tehran Press, Tehran, Iran.
  28. Jahantab, E., Ghasemi Aryan, Y., Sepehri, A., Hanafi, B., & Yazdan Panah, E. A. (2012). Study on distribution pattern of dominant plant species of mountainous rangelands in Central Zagros (Case Study: Dyshmuk region in Kohgilouyeh and Boyerahmad province). Iranian Journal of Range and Desert Research, 19(3), 482-489.
  29. Jamali, H., Ghehsareh Ardestani, E., Ebrahimi, A., & Pordel, F. (2020). Comparing distance-based methods of measuring plant density in an arid sparse scrubland: testing field and simulated sampling. Environmental monitoring and assessment, 192(6), 343.
  30. Jalal, R., Sheikh, H., Alotaibi, M., Shami, A., Ashy, R., Baeshen, N., Abulfaraj, A., Baz, L., Refai, M., Baeshen, N., Fadhlina, A., Arifullah, M., & Baeshen M. (2022) The Microbiome of Suaeda monoica and Dipterygium glaucum From Southern Corniche (Saudi Arabia) Reveals Different Recruitment Patterns of Bacteria and Archaea. Frontiers in Marine Science, 9. 865834. 10.3389.
  31. Jiang, L., Lv, G., Gong, Y., Li, Y., Wang, H., & Wu, D. (2021). Characteristics and driving mechanisms of species beta diversity in desert plant communities. PloS one, 16(1): e0245249.
  32. Khan, M. N., Hijbeek, R., Berger, U., Koedam, N., Grueters, U., Islam, S. M., Hasan, M. A., & Dahdouh-Guebas, F. (2016). An Evaluation of the Plant Density Estimator the Point-Centred Quarter Method (PCQM) Using Monte Carlo Simulation. PloS one, 11(6), e0157985.
  33. Kiani, B., Fallah, A., Tabari Kouchaksarayi, M., Hosseini, S., & Irannezhad Parizi, M.H. (2013). Comparing distance-based and quadrate-based methods to identify spatial pattern of saxaul Haloxylon ammodenderonA.Mey (Siah-Kooh Region, Yazd Province). Forest and Wood Products, 65(4), 475-486.
  34. Krebs, C. J. (2014). Ecological Methodology. 3rd edition. Addison-Wesley Educational Publishers, Inc.
  35. Lawer E. A. (2023). Comparative analysis of plotless sampling methods for estimating woody plant density in a West African savanna agroforestry parkland. Environmental monitoring and assessment, 195(2), 263.
  36. Leong, S., Leong, S., Andrew, G., & Beattie, C. (2019). Dispersion pattern and sampling plan for Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae) in a citrus orchard. UKM Journal Article Repository, 24(2), 25-40.
  37. Liu, S., Baret, F., Allard, D., Jin, X., Andrieu, B., Burger, P., Hemmerlé, M., & Comar, A. (2017). A method to estimate plant density and plant spacing heterogeneity: application to wheat crops. Plant methods, 13, 38.
  38. Lloyd, M. (1967). Mean crowding. Journal of Animal Ecology, 36,1-30.
  39. Ludwig, J.A., & Reynolds, J.F. (1988). Statistical Ecology: A Primer on Methods and Computing. Wiley, New York, USA.
  40. Mahmoodian, A., Akharian, M., & Taher, M. N. (2021). Investigation of distribution of Aeluropus littoralis in Exclusion and grazed areas in the saline and alkaline rangelands of Golestan province. Iranian Journal of Natural Ecosystems, 12(2), 1-15.
  41. Malhado A.C., & Petrere, J.M. (2004). Behavior ofdispersion indices in pattern. Detection of a population of Angico, Andenathera peregrine. Barz. Journal of Biology, 64(2), 243-249.
  42. Mirzaie-Nodoushan, H., Asadi-Karam, F., & Mirhosseini, A. (2000). Investigation of the effective factors on Haloxylon sp. Seed germination. Iranian Journal of Rangelands and Forests Plant Breeding and Genetic Research, 4(1), 1-23.
  43. Moghaddam, M.R. (2001). Statistical and descriptive ecology of vegetation. Tehran University Press.
  44. Moghaddam M.R. (2005). Ecology of Terrestrial Plants. University of Tehran Press, Tehran, Iran.
  45. Mohebi, Z., Zare Chahouki, M.A., Tavili, A., Jafari, M. & Fahimipour, A. (2012). Comparing the efficiency of distance and quadrate indices in determining Artemisia sieberi and Astragalus ammodendron distribution pattern in Markazi Province. Watershed Management Researches (Pajouhesh-Va-Sazandegi), 25(1 (94)), 27-35.
  46. Morisita, M. (1959). Measuring of the dispersion of individuals and analysis of the distributional patterns. Memoirs of the Faculty of science, 2(4), 215–235.
  47. Mousaei Sanjerehei, M., & Bassiri, M. (2007). Comparison and evaluation of indices of dispersion patterns of plants on Artemisia sieberi shrublands in Yazd Province. Journal of Water and Soil Science, 11 (40), 483-495.
  48. Mousavi Kouhi, S.M., Moudi, M. Soltani Moghadam, E., & Sarchahi Moghadam, H. (2019). The investigating of sodium accumulation in some halophytic species of Zygophyllaceae, Polygonaceae, Asteraceae and Amaranthaceae. Nova Biologica Reperta , 6, 96-105.
  49. Moustafa, M., & Bakry, S. (2020). Spatial distribution of the plum scale insect, Parlatoria oleae (Colvee) (Hemiptera: Diaspididae) infesting mango trees in Egypt. International journal of Horticulture, Agriculture and Food science (IJHAF), 4(2): 14-20.
  50. R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
  51. Ranjan, R., & Klausmeier, C. A. (2022). How the resource supply distribution structures competitive communities. Journal of theoretical biology, 538, 111054.
  52. Research Institute of Forests and Rangelands. (2020). Rangeland Ecosystems Monitoring in different climatic regions of Iran.
  53. Rostampour, M., Jafari, M., Tavili, A., Azarnivand, H., & Eslami, S. V. (2017). Investigation of Plant species composition and diversity along a soil salinity gradient in margin rangelands of Petregan Playa, Southern Khorasan. Desert Ecosystem Engineering, 6(16), 11-24.
  54. Rozas, V., Zas, R., & Solla, A. (2009). Spatial structure of deciduous forest stands with contrasting human influence in northwest Spain. European Journal of Forest Research, 128(3), 273-285.
  55. Shaukat, S.S., & Siddiqu, I. A. (2004). Spatial pattern analysis of seeds of an arable soil seed bank and its relationship with above ground vegetation in an arid region. Journal of Arid Environment, 57(3), 311-327.
  56. Shen, G., Wang, X., & He, F. (2020). Distance-based methods for estimating density of nonrandomly distributed populations. Ecology, 101(10), e03143.
  57. Sheykholeslam, A., Asgardoon, S., & Yazdian, F. (2011). Investigation on spatial pattern of wild cherry (Cerasus avium) in hyrcanian forest (Case study: Pajim Forest, Behshahr). Research Journal of Forest Science and Engineering, 1(1), 35-42.
  58. Taylor, L. R. (1961). Aggregation, variance and the mean. Nature, 189(4766), 732–735.
  59. Vahidi, K., Gholinejad, B., & Karami, P. (2017). Comparing distance and quadrate indices in determining the distribution pattern of three shrub species (Case study: suburban rangeland of Kurdistan). Iranian Journal of Range and Desert Research, 23(4), 856-863.
  60. White, N. A., Engeman, R. M., Sugihara, R. T., & Krupa, H. W. (2008). A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets. BMC ecology, 8, 6.
  61. Yong-zhong, S., Xue-fen, W., Rong, Y., Xiao, Y., & Wen-jie, L. (2012). Soil fertility, salinity and nematode diversity influenced by Tamarix ramosissima in different habitats in an arid desert oasis. Environmental management, 50(2), 226–236.
  62. Yuan, Z., Wang, T., Zhu, X., Sha, Y., & Ye, (2011). Patterns of spatial distribution of Quercus variabilis in deciduous broadleaf forests in Baotianman Nature Reserve. Biodiversity Science, 19(2), 224-231.
  63. Zare Chahouki, M.A., & Naseri Hesar, N. (2018). Habitat distribution modeling of some plant species using logistic regression in the semi-arid rangelands. (Case study: Eshtehard rangelands). Journal of Plant Research (Iranian Journal of Biology), 31(1), 93-100.
  64. Zemunik, G., Turner, B. L., Lambers, H., & Laliberté, E. (2016). Increasing plant species diversity and extreme species turnover accompany declining soil fertility along a long-term chronosequence in a biodiversity hotspot. Journal of Ecology, 104(3), 792–805.
  65. Zhou, M., Li, L., Dunson, D., & Carin, L. (2012). Lognormal and Gamma Mixed Negative Binomial Regression. Proceedings of the 29th International Conference on International Conference on Machine Learning. Edinburgh, Scotland.
  66. Zhou, S. R., & Zhang, D. Y. (2008). A nearly neutral model of biodiversity. Ecology, 89(1), 248–258.
  67. Zolfaghari, Z., Moradi, M., Basiri, R., & Ghasemi, A. (2022). Evaluation of Tecomella undulata spatial distribution pattern in Bushehr province. Journal of Environmental Science and Technology, 24(3), 131-143.
Desert Ecosystem Engineering
Volume 12, Issue 41
September 2024
Pages 89-105

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