Volume 6, Issue 2 (2018)
ECOPERSIA 2018, 6(2): 139-145 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Kargar M, Jafarian Z, Tamartash R, Alavi S. Prediction of Spatial Distribution of Plant Species Richness in the Valdarreh Rangelands, Mazandaran by Macroecological Modelling and Stacked Species Distribution Models. ECOPERSIA 2018; 6 (2) :139-145
URL: http://ecopersia.modares.ac.ir/article-24-16475-en.html
URL: http://ecopersia.modares.ac.ir/article-24-16475-en.html
1- Range Management Department, Natural Resource Faculty, Sari Agricultural of Sciences & Natural Resources University, Sari, Iran
2- Range Management Department, Natural Resource Faculty, Sari Agricultural of Sciences & Natural Resources University, Sari, Iran , jafarian79@yahoo.com
3- Forestry Department, Natural Resource Faculty, Tarbiat Modares University, Noor, Iran
2- Range Management Department, Natural Resource Faculty, Sari Agricultural of Sciences & Natural Resources University, Sari, Iran , jafarian79@yahoo.com
3- Forestry Department, Natural Resource Faculty, Tarbiat Modares University, Noor, Iran
Abstract: (4900 Views)
Aims: The information on species richness (SR) can be used to help establish conservation strategies or to predict future patterns of biodiversity under global change. The aim of the present study was the prediction of spatial distribution of plant species richness in the Valdarreh Rangelands, Mazandaran, Iran by Macroecological Modelling (MEM) and Stacked Species Distribution Models (S-SDM).
Materials & Methods: This experimental study was carried out in the Valdarreh rangelands. In the present study compared the direct, macroecological approach for modeling species richness with the more recent approach of stacking predictions from individual species distributions. Both approaches performed in reproducing observed patterns of species richness along an elevation gradient were evaluated. MEM was implemented by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually, assuming a binomial distribution.
Findings: The direct MEM approach yielded nearly unbiased predictions centered around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by The S-SDM approaches. This method also cannot provide any information on species identity and, thus community composition. Predicted SR by S-SDM was correlated by a Spearman p of 0.76 with the observed SR. The MEM-predicted SR achieved a Spearman rank correlation of 0.32 with S-SDM. The species richness along the elevational gradient for MEM and S-SDM were 0.21 and 0.82, respectively.
Conclusion: MEM and S-SDM have complementary strengths and both can be used in combination to obtain better species richness predictions.
Materials & Methods: This experimental study was carried out in the Valdarreh rangelands. In the present study compared the direct, macroecological approach for modeling species richness with the more recent approach of stacking predictions from individual species distributions. Both approaches performed in reproducing observed patterns of species richness along an elevation gradient were evaluated. MEM was implemented by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually, assuming a binomial distribution.
Findings: The direct MEM approach yielded nearly unbiased predictions centered around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by The S-SDM approaches. This method also cannot provide any information on species identity and, thus community composition. Predicted SR by S-SDM was correlated by a Spearman p of 0.76 with the observed SR. The MEM-predicted SR achieved a Spearman rank correlation of 0.32 with S-SDM. The species richness along the elevational gradient for MEM and S-SDM were 0.21 and 0.82, respectively.
Conclusion: MEM and S-SDM have complementary strengths and both can be used in combination to obtain better species richness predictions.
Article Type: مقاله Ø§Ø³ØªØ®Ø±Ø§Ø Ø´Ø¯Ù‡ از پایان نامه |
Subject:
Aquatic Ecology
Received: 2017/10/14 | Accepted: 2018/07/17 | Published: 2018/07/17
Received: 2017/10/14 | Accepted: 2018/07/17 | Published: 2018/07/17
References
1. Dubuis A, Pottier J, Rion V, Pellissier L, Theurillat JP, Guisan A. Predicting spatial patterns of plant species richness: A comparison of direct macroecological and species stacking modelling approach. Divers Distrib. 2011;17(6):1122-31. [Link] [DOI:10.1111/j.1472-4642.2011.00792.x]
2. Asadpour Ousalou YA. Identifying the benthic organisms diversity in Shahrechay river and Dam lake, Western Azerbaijan province, Iran. Ecopersia. 2015;3(2):1013-21. [Link]
3. Whittaker RH. Evolution and measurement of species diversity. Taxon. 1972;21(2-3):231-51. [Link]
4. Algar AC, Kharouba HM, Young ER, Kerr JT. Predicting the future of species diversity: Macroecological theory, climate change, and direct tests of alternative forecasting methods. Ecography. 2009;32(1):22-33. [Link] [DOI:10.1111/j.1600-0587.2009.05832.x]
5. Ferrier S, Guisan A. Spatial modelling of biodiversity at the community level. J Appl Ecol. 2006;43(3):393-404. [Link]
6. Ebraimi M, Masoudipour AR, Rigi M. Role of soil and topographic features in distribution of plant species (Case study: Sanib Taftan Watershed). Ecopersia. 2015;3(1):917-32. [Link]
7. Hutchinson GE. Concluding remarks. Cold Spring Harb Symp Quant Biol. 1957;22:415-27. [Link] [DOI:10.1101/SQB.1957.022.01.039]
8. Moser D, Dullinger S, Englisch T, Niklfeld H, Plutzar C, Sauberer N, et al. Environmental determinants of vascular plant species richness in the Austrian Alps. J Biogeogr. 2005;32(7):1117-27. [Link]
9. McCullagh P, Nelder JA. Generalized linear models, second edition. Boca Raton: CRC Press; 1989. [Link]
10. Dubuis A. Predicting spatial patterns of plant biodiversity: From species to communities [Dissertation]. Lausanne: University of Lausanne; 2013:p295. [Link]
11. D'Amen M, Dubuis A, Fernandes RF, Pottier J, Pellissier L, Guisan A. Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models. J Biogeogr. 2015;42(7):1255-66. [Link] [DOI:10.1111/jbi.12485]
12. Wu J, Qian J, Hou X, Busso CA, Liu Z, Xing B. Spatial variation of plant species richness in a sand dune field of northeastern Inner Mongolia, China. J Arid Land. 2016;8(3):434-42. [Link] [DOI:10.1007/s40333-016-0001-3]
13. Pineda E, Lobo JM. Assessing the accuracy of species distribution models to predict amphibian species richness patterns. J Anim Ecol. 2009;78(1):182-90. [Link] [DOI:10.1111/j.1365-2656.2008.01471.x]
14. Pottier J, Dubuis A, Pellisier L, Maiorano L, Rossier L, Randin CF, et al. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients. Glob Ecol Biogeogr. 2013;22(1):52-63. [Link]
15. Zurell D, Zimmermann NE, Sattler T, Nobis MP, Schröder B. Effects of functional traits on the prediction accuracy of species richness models. Divers Distrib. 2016;22(8):905-17. [Link] [DOI:10.1111/ddi.12450]
16. Lehmann A, Leathwick JR, Overton JM. Assessing New Zealand fern diversity from spatial predictions of species assemblages. Biodivers Conserv. 2002;11(12):2217-38. [Link] [DOI:10.1023/A:1021398729516]
17. Zhang R, Liu T, Zhang JL, Sun QM. Spatial and environmental determinates of plant species diversity in a temperate desert. J Plant Ecol. 2016;9(2):124-31. [Link] [DOI:10.1093/jpe/rtv053]
18. Kargar Chigani H, Javadi SA, Zahedi Amiri Gh, Khajeddin SJ, Jafari M. Vegetation composition differentiation and species-environment relationships in the northern part of Isfahan province, Iran. J Arid Land. 2017;9(2):161-75. [Link]
19. Movaghari M, Arzani H, Tavili A, Azarnivand H, Saravi M, Farahpoor M. Suitability of medicinal plants in rangelands of Lasem Watershed (Amol-Mazandaran province). Iran J Med Aromat Plants. 2015;30(6):898-914. [Persian] [Link]
20. Hirzel A, Guisan A. Which is the optimal sampling strategy for habitat suitability modelling?. Ecol Model. 2002;157(2-3):331-41. [Link] [DOI:10.1016/S0304-3800(02)00203-X]
21. Distler T, Schuetz JG, Velásquez-Tibatá J, Langham GM. Stacked species distribution models and macroecological models provide congruent projections of avian species richness under climate change. J Biogeogr. 2015;42(5):976-88. [Link]
22. Hastie T, Tibshirani R. Generalized Additive Models. London: Chapman and Hall; 1999. [Link]
23. Newbold T, Gilbert F, Zalat S, El-Gabbas A, Reader T. Climate-based models of spatial patterns of species richness in Egypt's butterfly and mammal fauna. J Biogeogr. 2009;36(11):2085-95. [Link] [DOI:10.1111/j.1365-2699.2009.02140.x]
24. Thuiller W, Lafourcade B, Engler R, Araújo MB. BIOMOD - a platform for ensemble forecasting of species distributions. Ecography. 2009;32(3):369-73. [Link] [DOI:10.1111/j.1600-0587.2008.05742.x]
25. Heikkinen RK. Predicting patterns of vascular plant species richness with composite variables: A meso-scale study in Finnish Lapland. Vegetatio. 1996;126(2):151-65. [Link] [DOI:10.1007/BF00045601]
26. Guisan A, Rahbek C. SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J Biogeogr. 2011;38(8):1433-44. [Link] [DOI:10.1111/j.1365-2699.2011.02550.x]
27. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, et al. Effects of sample size on the performance of species distribution models. Divers Distrib. 2008;14(5):763-73. [Link]
28. Guisan A, Thuiller W. Predicting species distribution: Offering more than simple habitat models. Ecol Lett. 2005;8(9):993-1009. [Link] [DOI:10.1111/j.1461-0248.2005.00792.x]
29. Pausas JG, Austin MP. Patterns of plant species richness in relation to different environments: An appraisal. J Veg Sci. 2001;12(2):153-66. [Link] [DOI:10.2307/3236601]
30. Zuo X, Wang S, Zhao XY, Lian J. Scale dependence of plant species richness and vegetation-environment relationship along a gradient of dune stabilization in Horqin Sandy Land, Northern China. J Arid Land. 2014;6(3):334-42. [Link] [DOI:10.1007/s40333-013-0221-8]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |