شناسایی مهم‌ترین عوامل مؤثر بر پراکنش گونۀ ون (.Fraxinus excelsior L) و رویشگاه‌های با پتانسیل آن در جنگل خیرودکنار نوشهر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد مدیریت جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران

2 استاد گروه جنگلداری و اقتصاد جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران

3 دانشیار گروه جنگلداری، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس

4 دانشیار گروه جنگلداری و اقتصاد جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران

10.22034/ijf.2022.337489.1863

چکیده

پژوهش حاضر با هدف شناسایی مناطق مطلوب رویشگاهی گونۀ ون (Fraxinus excelsior L.) و عوامل محیطی مؤثر بر پراکنش آن در جنگل خیرود نوشهر با مساحت تقریبی 8200 هکتار انجام گرفت. بدین منظور طی جنگل‌گردشی مختصات جغرافیایی 1004 پایۀ ون با دستگاه GPS ثبت شد و همراه با متغیرهای محیطی حاصل‌شده از خصوصیات اقلیمی، ویژگی‌های اولیه و ثانویۀ توپوگرافی با کمترین مقدار همبستگی در مدل‌های خطی و جمعی تعمیم‌یافته، جنگل تصادفی و آنتروپی بیشینه مدل‌سازی تعیین شد. آماده‌سازی متغیرها و تحلیل آنها در نرم‌افزارهای Arc GIS, SAGA, R انجام گرفت. به‌منظور مقایسۀ عملکرد حاصل از چهار مدل، متغیرهای ورودی همۀ مدل‌ها یکسان در نظر گرفته شد. مدل‌ها با معیارهای آمارۀ مهارت درست، ضریب کاپا و سطح زیرمنحنی ارزیابی شدند. نتایج نشان داد که مدل جنگل تصادفی با بیشترین مقدار ضریب کاپا (973/0)، سطح زیرمنحنی (997/0) و آمارۀ مهارت درست (973/0) دارای بهترین عملکرد است. براساس مدل جنگل تصادفی تأثیرگذارترین متغیرها بر حضور گونۀ ون به‌ترتیب عمق دره، انحنای پروفیل، شیب و شاخص موقعیت توپوگرافی هستند که نشان‌دهندۀ مناطقی با خاک غنی، رطوبت کافی و زهکش مناسب (غیرراکد بودن آب)، شیب کمتر از 45 درصد و نور کافی (مناسب با مرحلۀ رویشی) است و در گسترۀ وسیعی از سری‌های بهاربن، چلیر و منیاسنگ قرار دارند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Identifying the most important factors affecting the distribution of Ash (Fraxinus excelsior L.) and detect potential habitats areas in Kherudkanar Nowshahr forest

نویسندگان [English]

  • A Moridpour 1
  • M Namiranian 2
  • S.J. Alavi 3
  • V Etemad 4
1 MSc. Student of Forest Management, Faculty of Natural Resources, University of Tehran, I. R. Iran
2 Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, I. R. Iran
3 3Associate Prof., Dept. of Forestry, Faculty of Natural Resources, Tarbiat Modares University, Noor, I. R. Iran
4 Associate Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, I. R. Iran
چکیده [English]

The aim of this research was to identify the optimal habitat areas of ash species (Fraxinus excelsior L.) in Kheyrud Nowshahr forest, which covers an area of approximately 8,224 hectares. We recorded the geographical coordinates of 1004 ash individuals using a GPS device, along with environmental variables such as climate variables and primary and secondary topographic predictors extracted from DEM. We used the predictors with the least degree of correlation as input in the Generalized Linear Model, Generalized Additive Model, Random Forest, and maxent models. We prepared and analyzed the variables in ArcGIS, SAGA, and R software. To compare the performance of the four models, we considered the input variables in all models to be the same. We evaluated the models using kappa coefficient (K), Area Under Curve (AUC), and True Skill Statistic (TSS) measures. The RF model had the highest K (0.973), AUC (0.997), and TSS (0.973), indicating the best performance among the models. According to the RF model, the most important variables were valley depth, profile curvature, slope, and topographic position index. These variables indicate the areas with rich soil, sufficient moisture, proper drainage (non-stagnant water), slope less than 45%, and sufficient light (suitable for the vegetative stage). We identified the most suitable areas in the Baharbon, Chelir, and Menia-Sang districts due to optimal conditions for these effective variables.

کلیدواژه‌ها [English]

  • Habitat suitability model
  • Environmental variables
  • Statistical methods
 
Ahmadi, K., Hosseini, S., Tabari, M., & Nouri, Z. (2019). Modeling the potential habitat of English yew (Taxus baccata L.) in the Hyrcanian forests of Iran. Forest Research and Development, 5(4), 513-525. doi: 10.30466/jfrd.2019.120791. (In persian)
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 ecology43(6), 1223-1232. DOI:10.1111/j.1365-2664.2006.01214.x.
Aliniabengar, GH., Payam, H., & Fallahchaie, M. (2013). Quantities and Qualitative Assessment of Natural European Ash in Hyrcanian Forests of Iran. ciences and Techniques in Natural Resources, 7(4), 1-10. (In persian)
Araújo, M.B. & New, M., (2007). Ensemble forecasting of species distributions. Trends in ecology & evolution, 22(1), pp.42-47. https://doi.org/10.1016/j.tree.2006.09.010.
Anderson, R.P., Lew, D., & Peterson, A.T. (2003). Evaluating predictive models of species’ distributions  criteria for selecting optimal models. Ecological modelling162(3), 211-232. https://doi.org/10.1016/S0304-3800(02)00349-6.
Braunisch, V., Coppes, J., Arlettaz, R., Suchant, R., Schmid, H., & Bollmann, K. (2013). Selecting from correlated climate variables  a major source of uncertainty for predicting species distributions under climate change. Ecography36(9), 971-983. https://doi.org/10.1111/j.1600-0587.2013.00138.x.
Boyce, M.S., & McDonald, L.L. (1999). Relating populations to habitats using resource selection functions. Trends in ecology & evolution, 14(7), 268-272. https://doi.org/10.1016/S0169-5347(99)01593-1.
Brotons, L., Thuiller, W., Araújo, M.B., & Hirzel, A.H. (2004). Presence‐absence versus presence‐only modelling methods for predicting bird habitat suitability. Ecography27(4), 437-448. https://doi.org/10.1111/j.0906-7590.2004.03764.x.
Buri, A., Grand, S., Yashiro, E., Adatte, T., Spangenberg, J.E., Pinto‐Figueroa, E., ... & Guisan, A. (2020). What are the most crucial soil variables for predicting the distribution of mountain plant species? A comprehensive study in the Swiss Alps. Journal of Biogeography, 47(5), 1143-1153. https://doi.org/10.1111/jbi.13803.
Camaclang, A.E., Maron, M., Martin, T.G., & Possingham, H.P. (2015). Current practices in the identification of critical habitat for threatened species. Conservation Biology, 29(2), 482-492. DOI: 10.1111/cobi.12428.
Cutler, D.R., Edwards Jr, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., & Lawler, J.J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783-2792. https://doi.org/10.1890/07-0539.1.
Elith, J., & Leathwick, J.R. (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics40(1), 677-697. https://doi.org/10.1146/annurev.ecolsys.110308.120159.
Elith, J., & Franklin, J. (2013). Species distribution modeling. In Encyclopedia of Biodiversity: Second Edition (pp. 692-705). Elsevier Inc.DOI: 10.1016/B978-0-12-384719-5.00318-X.
Etemad, V., Namiranian, M., Zobeiri, M., Majnounian, B., & Moradi, G. (2013). Qualitative and Quantitative Variation of Forest Stands after one Period of Forest Management Plan (Case study: Namkhane DistrictKheyrud Forest). Forest and Wood Products66(3), 243-256. doi: 10.22059/jfwp.2013.36110.DOI: 10.22059/JFWP.2013.36110. (In persian)
Fois, M., Cuena-Lombraña, A., Fenu, G., & Bacchetta, G. (2018). Using species distribution models at local scale to guide the search of poorly known species  Review, methodological issues and future directions. Ecological Modelling385, 124-132. https://doi.org/10.1016/j.ecolmodel.2018.07.018.
Fortunel, C., Paine, C.T., Fine, P.V., Kraft, N.J., & Baraloto, C. (2014). Environmental factors predict community functional composition in A mazonian forests. Journal of Ecology102(1), 145-155. https://doi.org/10.1111/1365-2745.12160.
Frew, J.E., & Dozier, J. (2012). Environmental informatics. Annual Review of Environment and Resources37(1), 449-472. https://doi.org/10.1146/annurev-environ-042711-121244.
Franklin, J. (2010). Moving beyond static species distribution models in support of conservation biogeography. Diversity and Distributions16(3), 321-330. https://doi.org/10.1111/j.1472-4642.2010.00641.x.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No.. New York  Springer series in statistics.
Fukuda, S., De Baets, B., Waegeman, W., Verwaeren, J., & Mouton, A. M. (2013). Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environmental modelling & software47, 1-6.  https://doi.org/10.1016/j.envsoft.2013.04.005.
Ghareghan, F., Ghanbarian, G., Pourghasemi, H.R., & Safaeian, R. (2020). Prediction of habitat suitability of Morina persica L. species using artificial intelligence techniques. Ecological Indicators112, 106096. https://doi.org/10.1016/j.ecolind.2020.106096.
Gogol-Prokurat, M. (2011). Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecological Applications, 21(1), 33-47. DOI: 10.1890/09-1190.1.
Gorsevski, P.V., Gessler, P.E., Foltz, R.B., & Elliot, W.J. (2006). Spatial prediction of landslide hazard using logistic regression and ROC analysis. Transactions in GIS10(3), 395-415. https://doi.org/10.1111/j.1467-9671.2006.01004.x.
Guisan, A., Edwards Jr, T.C., & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2-3), 89-100. https://doi.org/10.1016/S0304-3800(02)00204-1.
Guisan, A., Thuiller, W., & Zimmermann, N.E. (2017). Habitat suitability and distribution models  with applications in R. Cambridge University Press.
Hastie, T., & Tibshirani, R. (1987). Non‐parametric logistic and proportional odds regression. Journal of the Royal Statistical Society  Series C (Applied Statistics)36(3), 260-276.
Heubes, J., Schmidt, M., Stuch, B., Márquez, J.R.G., Wittig, R., Zizka, G., ... & Hahn, K. (2013). The projected impact of climate and land use change on plant diversityAn example from West Africa. Journal of arid environments96, 48-54. https://doi.org/10.1016/j.jaridenv.2013.04.008.
Jafari, A., Alipour, M., abbasi, M., & Soltani, A. (2019). Distribution Modeling of Hawthorn (Crataegus azarolus L.) in Chaharmahal & Bakhtiari Province using the maximum entropy method. Enviromental Studies, 45(2), 223-235.doi.10.22059/JES.2019.280556.1007855.(In persian)
Jafarian, Z., & Kargar, M. (2017). Distribution Modeling of  Protective and Valuable Plant Species in the Tourist Area of Polour Using Generalized Linear Model (GLM) and Generalized Additive Model(GAM). Geography and Development Iranian Journal, 15(46), 117-132. .(In persian)
Landis, J.R., & Koch, G.G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.
Lawler, J.J., Wiersma, Y.F., & Huettmann, F. (2011). Using species distribution models for conservation planning and ecological forecasting. In Predictive species and habitat modeling in landscape ecology (pp. 271-290). Springer, New York, NY.
Leroy, B., Delsol, R., Hugueny, B., Meynard, C.N., Barhoumi, C., Barbet‐Massin, M., & Bellard, C. (2018). Without quality presence–absence data, discrimination metrics such as TSS can be misleading measures of model performance. Journal of Biogeography, 45(9), 1994-2002. https://doi.org/10.1111/jbi.13402.
Li, X., & Wang, Y. (2013). Applying various algorithms for species distribution modelling. Integrative Zoology8(2), 124-135. https://doi.org/10.1111/1749-4877.12000.
Márcia Barbosa, A., Real, R., Muñoz, A.R., & Brown, J.A. (2013). New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions19(10), 1333-1338. https://doi.org/10.1111/ddi.12100.
McSHEA, W.J. (2014). What are the roles of species distribution models in conservation planning. Environmental Conservation, 41(2), 93-96. https://doi.org/10.1017/S0376892913000581.
Moisen, G.G., & Frescino, T.S. (2002). Comparing five modelling techniques for predicting forest characteristics. Ecological modelling157(2-3), 209-225. https://doi.org/10.1016/S0304-3800(02)00197-7.
Mokarram, M., & Negahban, S. (2015). LandformClassificationusing Topographic Position Index (Case study  Southern Darabcity). Scientific- Research Quarterly of Geographical Data, 23(92), 57-65. https://doi.org/10.22131/sepehr.2015.13507.
Moore, I.D., & Hutchinson, M.F. (1991). Spatial extension of hydrologic process modelling. In National Conference Publication- Institute of Engineers. Australia, 3(91) 803-808.
Moore, I.D., Grayson, R.B., & Ladson, A.R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes5(1), 3-30.
Namiranian, M. (2005). An Investigation of the Ash species inventory at Gorazbon  District in the Educational and Research Forests of  Kheiroodkenar. Natural resources of Iran, 57(4), 689-702.(In persian).
Olaya Marín, E.J., Martinez-Capel, F., García Bartual, R.L., & Vezza, P. (2016). Modelling critical factors affecting the distribution of the vulnerable endemic Eastern Iberian barbel (Luciobarbus guiraonis) in Mediterranean rivers. Mediterranean Marine Science17(1), 264-279. http://dx.doi.org/10.12681/mms.1351.
Pearson, R.G. (2007). Species’ distribution modeling for conservation educators and practitioners. Synthesis. American Museum of Natural History50, 54-89.
Phillips, S.J., Anderson, R.P., & Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modeling, 190, 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026.
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Rodriguez de Rivera, O., & López-Quílez, A. (2017). Development and comparison of species distribution models for forest inventories. ISPRS International Journal of Geo-Information, 6(6), 176. https://doi.org/10.3390/ijgi6060176.
Santika, T., & Hutchinson, M.F. (2009). The effect of species response form on species distribution model prediction and inference. Ecological Modelling220(19), 2365-2379. https://doi.org/10.1016/j.ecolmodel.2009.06.004.
Salehi, M., Vazirinasab, H., Khoshgam, M., Rafati, N. (2012). Application of the generalized additive model in determination of the retinopathy risk factors relation types for Tehran diabetic patients. Razi Journal of Medical Sciences, 19(97), 1-9. (In persian).
Schwartz, M.W. (2008). The performance of the endangered species act. Annual Review of Ecology, Evolution, and Systematics, 279-299. https://doi.org/10.1146/annurev.ecolsys.39.110707.173538.
Silva, L.D., Costa, H., de Azevedo, E.B., Medeiros, V., Alves, M., Elias, R.B., & Silva, L. (2014), February). Modeling native and invasive woody species comparison of ENFA and MaxEnt applied to the Azorean forest. In International Conference on Dynamics, Games, and Science (pp. 415-444). Springer, Cham.
Tabari, M., Jazirehei, M., Asadullahi, F., & Hajimirsadeghi, M. (2002). Study of forest communities and environmental needs of European Ash (Fraxinus excelsior. L) in the northern forests of Iran. Pajohesh & Sazandegi, 15(2), 94-101.Pajohesh & Sazandegi. (In persian).
Teimoori Asl, S., Naghipoor, A.,  Ashrafzadeh, M., & Heydarian, M. (2020). Predicting the impact of climate change on potential habitats of Stipa hohenackeriana Trin & Rupr in Central Zagros. rangeland, 14(3), 523-538. ‎DOI: 20.1001.1.20080891.1399.14.3.12.4. (In persian).
Thessen, A. (2016). Adoption of machine learning techniques in ecology and earth science. One Ecosystem1, e8621.
Wang, T., Wang, G., Innes, J., Nitschke, C., & Kang, H. (2016). Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia–Pacific region. Forest Ecology and Management360, 357-366. https://doi.org/10.1016/j.foreco.2015.08.004.
Xu, W., Du, Q., Yan, S., Cao, Y., Liu, X., Guan, D.X., & Ma, L.Q. (2022). Geographical distribution of As-hyperaccumulator Pteris vittata in China  Environmental factors and climate changes. Science of The Total Environment803, 149864. https://doi.org/10.1016/j.scitotenv.2021.149864.
Zhang, K., Zhang, Y., Zhou, C., Meng, J., Sun, J., Zhou, T., & Tao, J. (2019). Impact of climate factors on future distributions of Paeonia ostii across China estimated by MaxEnt. Ecological Informatics, 50(1), 62-67. https://doi.org/10.1016/j.ecoinf.2019.01.004.
Zurell, D., Franklin, J., König, C., Bouchet, P.J., Dormann, C.F., Elith, J., ... & Merow, C. (2020). A standard protocol for reporting species distribution models. Ecography43(9), 1261-1277. https://doi.org/10.1111/ecog.04960.