Document Type : Research Paper
Authors
1 Dept. of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2 Dept. of Rangland Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
10.22059/jrwm.2024.335991.1633
Abstract
Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six predictive models: Artificial Neural Networks, Random Forest, Classification Tree Analysis, Surface Range Envelope, Generalized Boosting Machines, and Generalized Linear Models. To evaluate the interactions between topographic factors and other variables, two environmental datasets were quantified and used for model calibration. The first dataset includes eleven factors covering topographic, climatic, edaphic, and remote sensing variables. Meanwhile, the second dataset contains six factors, focusing on climatic, edaphic, and remote-sensing variables. Model accuracy was evaluated using the True Skill Statistic (TSS), the area under the curve of the Receiver Operating Characteristics (ROC), and the Accuracy Index. The evaluation indices indicate that the Generalized Boosting Machine (GBM) model predicted the ecological niche of F. gummosa more accurately than the other methods. Additionally, the results showed that removing topographical variables reduced the model accuracy by 11 to 25%. The slope, NDVI, wetness, and soil groups were found to be the most important factors in mapping potentially suitable habitats for the target plant. According to the results obtained from the GBM model, approximately 45% of the Ghorkhoud area is in excellent condition. This knowledge can aid in the selection of predictors for practical Species Distribution Model (SDM) applications and provide information on which modeling techniques are most useful for a group of species.
Keywords
Ahmadi, k., Mahmoodi, S., Chandra Pal, S., Saha, A., Chowdhuri, I., Trong Nguyen, T., Jarvie, S., Szostak, M., Socha, J., & Thai, V. (2023). Improving species distribution models for dominant trees in climate data-poor forests using high-resolution remote sensing. Ecological Modelling, 475, 110190. doi: 10.1016/j.ecolmodel.2022.110190.
Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43, 1223-1232.
Akhter, S., Mcdonald, MA., van Breugel, P., Sohel, S., Kjær, ED., & Mariott, R. (2017). Habitat distribution modelling to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb. of Bangladesh. Land Use policy, 60, 223–232. doi: 10.1016/j.landusepol.2016.10.027
Amiri, M.S., Yazdi, M.E.T., & Rahnama, M. (2021). Medicinal plants and phytotherapy in Iran: Glorious history, current status and future prospects. Plant Sci. Today, 8, 95–111.
Amiri, M., Tarkesh, M., & Jafari, R. (2019). Predicting the climatic Ecological Niche of Artemisia aucheri Boiss in Central Iran using species distribution modeling. Iranian Journal of Applied Ecology, 8(2), 61-79. (In Persian)
Andaryani, S., Sloan, S., Nourani, V., & Keshtkar, H. (2021). The utility of a hybrid GEOMOD-Markov Chain model of land-use change in the context of highly water-demanding agriculture in a semi-arid region. Ecological Informatics, 64, 101332. doi: 10.1016/j.ecoinf.2021.101332.
Araujo, MB., & Guisan, A. (2006). Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677-1688. doi:10.1111/j.1365-2699.2006. 01584.x
Austin, MP. (2007). Species distribution models and ecological theory: a critical assessment and some possible new approaches, Ecological Modelling, 200, 1–19.
Buri, A., Cianfrani, C., Pinto-Figueroa, E., Yashiro, E., Spangenberg, J. E., Adatte, T., Verrecchia, E., Guisan, A., & Pradervand, J.N. (2017). Soil factors improve predictions of plant species distribution in a mountain environment. Progress in Physical Geography: Earth and Environment, 41(6), 703-722. https://doi.org/10.1177/0309133317738162
Balazy, R., Kamińska, A., Ciesielski, M., Socha, J., & Pierzchalski, M. (2019). Modeling the Effect of Environmental and Topographic Variables Affecting the Height Increment of Norway Spruce Stands in Mountainous Conditions with the Use of LiDAR Data. Remote Sensing, 11 (20), 2407.
Beauregard, F., & de Blois, S. (2014). Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models. PLoS ONE, 9(3), e92642. doi: 10.1371/journal.pone.0092642
Dashti, M., Mirdavoudi, H., Ghasemi Arian, A. & Azizi, N. (2021). Effects of Topography and Soil Variables on Abundance of Onobrychis chorassanica Bunge. in Kardeh and Kurtian Rangelands, Mashhad, Iran. Journal of Rangeland Science, 11(3), 283-300.
Deckers, J., Driessen, P., Nachtergaele, F., Spaargaren, O. (2001). World reference base for soil resourcesein a nutshell. Eur. Soil Bur. Rep. 7, 173e181.
Franklin, J. (2009). Mapping Species Distributions: Spatial Inference and Prediction. Cambridge: Cambridge University Press.
Ghorbani, A., Porghorban, N., Moamerin, M., Ghafarin, S., Bidarlordn, M., Taheri Niarin, MM. (2022). Effects of topographic variables on plant species diversity in rangelands of Hir County, Ardabil Province, Iran. ECOPERSIA, 10(4), 285-295. doi:20.1001.1.23222700.2022.10.4.3.2
Guiquan, S., Jiali, F., Shuai, G. (2023). Geographic distribution and impacts of climate change on the suitable habitats of Rhamnus utilis Decne in China. BMC Plant Biol, 23, 592. Doi:10.1186/s12870-023-04574-4
Guisan, A. (2013). Predicting species distributions for conservation decisions. Ecol. Lett, 16, 1424–1435. doi: 10.1111/ele.12189.
Keshtkar, H., Azarnivand, H., Etemad, V., & Mosavi, S. (2008). Seed dormancy-breaking and germination requirements of Ferula ovina and Ferula gummosa. Desert, 13, 45-51.
Keshtkar, H. R., Yeganeh, B. H., & Jabarzare, A. (2011). Floristic studies and life forms of GhorKhood protected area. Iranian Journal of Biology. 24(3) 421-431. (In Persian)
Keshtkar, H., & Poormohammad, P. (2022). Mapping spatial patterns of plant species based on machine-learning and regression models. Desert, 27(1), 167-181. doi:10.22059/JDESERT.2022.88514.
Kosanic, A., Anderson, K., Harrison, S., Turkington, T., & Bennie, J. (2018). Changes in the geographical distribution of plant species and climatic variables on the West Cornwall peninsula (South West UK). PloS One, 13(2), e0191021.
Konowalik, K., Nosol, A. (2021). Evaluation metrics and validation of presence-only species distribution models based on distributional maps with varying coverage. Sci Rep, 11, 1482. Doi:10.1038/s41598-020-80062-1
Kontsiotis, V.J., Chatzigiovanakis, S., Valsamidis, E., Xofis, P., & Liordos, V. (2023). Normalized Difference Vegetation Index as a Proxy of Urban Bird Species Presence and Distribution at Different Spatial Scales. Diversity, 15, 1139. Doi:10.3390/d15111139
Leveau, L.M. & Isla, F.I. (2021). Predicting bird species presence in urban areas with NDVI: An assessment within and between cities. Urban Forestry & Urban Greening, 63, 127199. Doi: 10.1016/j.ufug.2021.127199.
Maharjan, SK., Sterck, FJ., Raes, N. & Poorter, L. (2022). Temperature and soils predict the distribution of plant species along the Himalayan elevational gradient. Journal of Tropical Ecology 38, 58–70. Doi:10.1017/S026646742100050X
Mahboubi, M. (2016). Ferula gummosa, a traditional medicine with novel applications. J. Diet. Suppl., 13, 700–718.
Moradi, P., Aghajanloo, F., Moosavi, A., Monfared, H.H., Khalafi, J., Taghiloo, M., Khoshzaman, T., Shojaee, M., & Mastinu, A. (2021). Anthropic Effects on the Biodiversity of the Habitats of Ferula gummosa. Sustainability, 13, 7874. Doi:10.3390/su13147874
Najmabadi, A., Fakhira, A., Fathabadi, A., and Qolipour, M. (2016). Modeling the habitat desirability of Ferula gummosa species based on methods based on the presence of maximum entropy and factor analysis of the ecological niche of a case study (Buanlu watershed, Shirvan city). National Conference of New Researches in Agricultural Engineering, Environment and Natural Resources, Karaj, 1-11. (In Persian)
Niu, Y., Zhou, J., Yang, S., Chu, B., Ma, S., Zhu, H. & Hua, L. (2019). The effects of topographical factors on the distribution of plant communities in a mountain meadow on the Tibetan Plateau as a foundation for target-oriented management. Ecological Indicators, 106, 105532. doi: 10.1016/j.ecolind.2019.105532.
Norberg, A. (2019). A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs, 89, 1–24. Doi:10.1002/ecm.1370
Oppel, S., Meirinho, A., Ramírez, I., Gardner, B., O’Connell, AF., Miller, PI., & Louzao, M. (2012). Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biological Conservation, 156, 94-104.
Parviainen, M., Luoto, M., Ryttäri, T. & Heikkinen, RK. (2008). Modelling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. Journal of Biogeography, 35, 1888-1905.
Peng, LP., Cheng, FY., Hu, XG. (2019). Modelling environmentally suitable areas for the potential introduction and cultivation of the emerging oil crop Paeonia ostii in China. Sci Rep, 9, 3213. doi:10.1038/s41598-019-39449-y.
Poormohammadi, S., Malekinezhad, H., & Rahimian, M. (2010). Investigating the role of physiographical factors on temperature-related parameters affecting evapotranspiration (Case study: Yazd province). Journal of Arid Biome, 1(2), 9-19. (In Persian)
Rajpoot, R., Adhikari, D., Verma, S., Saikia, P., Kumar, A, & Grant, KR. (2020). Climate models predict a divergent future for the medicinal tree Boswellia serrata Roxb. In India. Global Ecology and Conservation, 23, e01040.
Ruheili, A.M., Al Sariri, T., & Al Subhi, A.M. (2022). Predicting the potential habitat distribution of parthenium weed (Parthenium hysterophorus) globally and in Oman under projected climate change. Journal of the Saudi Society of Agricultural Sciences, 21(7), 469-478. doi: 10.1016/j.jssas.2021.12.004
Schwager, P., & Berg, C. (2021). Remote sensing variables improve species distribution models for alpine plant species. Basic and Applied Ecology, 54,1-13. https://doi.org/10.1016/j.baae.2021.04.002.
Shrestha, N. (2020). Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics, 8, 39-42. DOI: 10.12691/ajams-8-2-1
Slater, H., & Michael, E. (2012). Predicting the Current and Future Potential Distributions of Lymphatic Filariasis in Africa Using Maximum Entropy Ecological Niche Modelling. PLoS ONE, 7(2), e32202. doi: 10.1371/journal.pone.0032202
Thuiller, W., Georges, D., Engler, R., Breiner, F., Georges, MD., & Thuiller, CW. (2016). Package ‘biomod2’. https://cran.r-project.org/package= biomod2.
Verdian, S., Jafarian, Z., rastgar, S., & kargar, M. (2022). Habitat Determination of Ferula gummosa Boiss. Using Generalized Additive Model in Lar Rangeland, Tehran Province. Journal of Environmental Science Studies, 7(1), 4512-4520. doi: 10.22034/jess.2022.142079 (In Persian)
Xu, Y., Huang, Y., Zhao, H., Yang, M., Zhuang, Y., & Ye, X. (2021). Modelling the Effects of Climate Change on the Distribution of Endangered Cypripedium japonicum in China. Forests, 12(4), 429. 429. DOI:10.3390/f12040429
Yang, J., El-Kassaby, Y.A. & Guan, W. (2020). The effect of slope aspect on vegetation attributes in a mountainous dry valley, Southwest China. Sci Rep 10, 16465. doi:10.1038/s41598-020-73496-0
Zare Chahouki, M.A., Karami, P., & Piri Sahragard, H. (2022). Ensemble Modeling Approach to Predict the Potential Distribution of Artemisia sieberi in Desert Rangelands of Yazd Province, Central Iran. Journal of Rangeland Science, 12(4), 326-340. doi: 10.30495/RS.2022.685569
Zhang, Q., Fang, R., Deng, C., Zhao, H., Shen, M., & Wang, Q. (2022). Slope aspect effects on plant community characteristics and soil properties of alpine meadows on Eastern Qinghai-Tibetan plateau. Ecological Indicators, 143, 109400. doi: 10.1016/j.ecolind.2022.109400.
Zhang, H.T., & Wang, W.T. (2023). Prediction of the Potential Distribution of the Endangered Species Meconopsis punicea Maxim under Future Climate Change Based on Four Species Distribution Models. Plants, 12, 1376. https://doi.org/10.3390/plants12061376
Zhou, H., Feng, L., & Fu, L. (2023). Modelling the effects of topographic heterogeneity on distribution of Nitraria tangutorum Bobr. species in deserts using LiDAR-data. Sci Rep, 13, 13673. doi:10.1038/s41598-023-40678-5
Zhu, Y., Wei, W., Li, H., Wang, B.,Yang, X., & Liu, Y. (2018). Modelling the potential distribution and shifts of three v0arieties of Stipa tianschanica in the eastern Eurasian Steppe under multiple climate change scenarios. Global Ecology and Conservation, 16, e00501. doi: 10.1016/j.gecco. 2018.e00501
)