پیش‌بینی خصوصیات محیطی رویشگاه با استفاده از ترکیب پوشش گیاهی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه علوم و مهندسی جنگل، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران

2 دانشجوی کارشناسی ارشد، گروه علوم و مهندسی جنگل، دانشکدۀ منابع طبیعی دانشگاه تربیت مدرس، نور، ایران

3 دانشجوی دکتری، گروه علوم و مهندسی جنگل، دانشکدۀ منابع طبیعی، دانشگاه تربیت مدرس، نور، ایران

چکیده

در این پژوهش، کارایی دو روش نزدیک‌ترین همسایه (KNN) و میانگین‌ وزنی (WA) در برآورد غیرمستقیم خصوصیات محیطی جوامع گیاهی ارزیابی شد. برای این منظور از اطلاعات ترکیب پوشش گیاهی تعداد 324 قطعه‌نمونه 400 متر مربعی مربوط به پایگاه اطلاعاتی سرخدار در جنگل‌های هیرکانی استفاده شد. سپس با استفاده از دو روش KNN و WA و بهره‌گیری از دو سری از اطلاعات ترکیب پوشش گیاهی (حضور- غیاب و درصد تاج‌پوشش) و مقادیر اولیه داده‌های محیطی، اقدام به برآورد غیرمستقیم متغیرهای محیطی (ارتفاع از سطح دریا، شیب و جهت دامنه، درصد کربن آلی، درصد ازت، درجه واکنش و بافت خاک) در هر قطعه‌نمونه شد. ارزیابی صحت-سنجی مدل‌ها با استفاده از تحلیل رگرسیون و مقدار عددی ضریب تبیین انجام شد. نتایج برآورد خصوصیات محیطی در رویشگاه‌های مورد بررسی نشان داد استفاده از روش KNN با استفاده از داده‌های درصد تاج‌پوشش گونه‌ها به‌دلیل بهره‌مندی از بالاترین مقدار ضریب تبیین نسبت به سه حالت دیگر در اولویت است. برآورد نقطه‌ای متغیرها با استفاده از دو رویکرد متفاوت درون‌یابی (KNN) و برون‌یابی (WA) به‌عنوان عامل اصلی اختلاف این دو روش ارزیابی شد. عملکرد مناسب‌تر روش KNN در برآورد نقطه‌ای خصوصیات محیطی نسبت به روش WA به‌دلیل استفاده از اطلاعات محیطی قطعات‌نمونه‌ با بالاترین درجه مشابهت ترکیب گونه‌ای نسبت به نقطه مزبور است. در حالی‌که نتایج روش WA متاثر از دامنه تغییرات متغیرهای محیطی در سطح کل رویشگاه قرار دارد که این مسئله، افزایش میزان خطا در برآورد غیرمستقیم داده‌های محیطی را منجر می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting environmental variables using vegetation composition

نویسندگان [English]

  • Omid Esmailzadeh 1
  • Meysam Soofi 2
  • Rezgar Darvand 3
1 Associ., Prof., Department of Forest Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, I.R. Iran
2 Mcs. Student, Department of Forest Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, I.R. Iran
3 PhD. Student, Department of Forest Science and Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, I.R. Iran
چکیده [English]

In this study, the efficiency of the two nearest neighbor (KNN) and weighted average (WA) methods was evaluated for indirect estimation of environmental variables in plant communities. For this purpose, vegetation composition data of 324 relevés with an area 400 m2 of the Hyrcanian yew forests database were used. Then, environmental variables in each relevés were indirectly estimated by using KNN and WA methods based on two kind of vegetation data (incidence based and abundance based of floristic data) as well as the initial values of that environmental variables.Validation of the models were evaluated using determinant coefficient of linear regression analysis, which done based on the initial values and followed by estimated one of each environmental variables as the predictor and response variables. Results showed that using KNN method based on abundance data due to having the highest determination coefficient value has the priority in comparison to another three algorithms. The main reason of the differences between KNN and WA was influenced by different approaches of interpolation (KNN) and extrapolation (WA) in the process of environmental variables point estimation. The better performance of the KNN compared with WA in the point estimating of environmental variables is due to using the environmental data of the only adjacent plot data with the most similarly floristically features to each points in the KNN, while the results of the WA are globally affected by the range of each environmental variables at the whole of the dataset.

کلیدواژه‌ها [English]

  • Indicator value
  • Taxus baccata
  • Weighted Average (WA)
  • Nearest Neighbor (KNN)
[1]. Boyer, K. E., Kertesz, J. S., and Bruno, J. F. (2009). Biodiversity effects on productivity and stability of marine macroalgal communities: The role of environmental context. Oikos, 118(7):1062-1072.
[2]. Geert Hiddink, J., Wynter Davies, T., Perkins, M., Machairopoulou, M., and Neill, S. P. (2009). Context dependency of relationships between biodiversity and ecosystem functioning is different for multiple ecosystem functions. Oikos, 118(12):1892-1900.
[3]. Hajek, P., Kurjak, D., von Wühlisch, G., Delzon, S., and Schuldt, B. (2016). Intraspecific variation in wood anatomical, hydraulic, and foliar traits in ten european beech provenances differing in growth yield. Frontiers in plant science, 7(791.
[4]. Lavorel, S., Grigulis, K., Lamarque, P., Colace, M. P., Garden, D., Girel, J., Pellet, G., and Douzet, R. (2011). Using plant functional traits to understand the landscape distribution of multiple ecosystem services. Journal of Ecology, 99(1):135-147.
[5]. McGill, B. J., Enquist, B. J., Weiher, E., and Westoby, M. (2006). Rebuilding community ecology from functional traits. Trends in ecology & evolution, 21(4):178-185.
[6]. Shipley, B. (2010). From plant traits to vegetation structure: Chance and selection in the assembly of ecological communities, Cambridge University Press,
[7]. ter Braak, C. J. (2019). New robust weighted averaging‐and model‐based methods for assessing trait–environment relationships. Methods in Ecology and Evolution, 10(11):1962-1971.
[8]. Funk, J. L., Larson, J. E., Ames, G. M., Butterfield, B. J., Cavender‐Bares, J., Firn, J., Laughlin, D. C., Sutton‐Grier, A. E., Williams, L., and Wright, J. (2017). Revisiting the h oly g rail: Using plant functional traits to understand ecological processes. Biological Reviews, 92(2):1156-1173.
[9]. Lavorel, S., and Garnier, E. (2002). Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the holy grail. Functional ecology, 16(5):545-556.
[10]. Violle, C., Navas, M. L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., and Garnier, E. (2007). Let the concept of trait be functional! Oikos, 116(5):882-892.
[11]. Ellenberg, H. H. (1988). Vegetation ecology of central europe, Cambridge University Press,
[12]. Green, R. H. (1971). A multivariate statistical approach to the hutchinsonian niche: Bivalve molluscs of central canada. Ecology, 52(4):543-556.
[13]. Suominen, L., Ruokolainen, K., Tuomisto, H., Llerena, N., and Higgins, M. A. (2013). Predicting soil properties from floristic composition in western amazonian rain forests: Performance of k‐nearest neighbour estimation and weighted averaging calibration. Journal of Applied Ecology, 50(6):1441-1449.
[14]. Zonneveld, I. (1983). Principles of bio-indication, In Ecological indicators for the assessment of the quality of air, water, soil, and ecosystemspp 207-217,Springer.
[15]. Kusbach, A., Long, J. N., Van Miegroet, H., and Shultz, L. M. (2012). Fidelity and diagnostic species concepts in vegetation classification in the rocky mountains, northern utah, USA. Botany, 90(8):678-693.
[16]. Juggins, S., and Birks, H. J. B. (2012). Quantitative environmental reconstructions from biological data, In Tracking environmental change using lake sedimentspp 431-494,Springer.
[17]. ter Braak, C. J., and van Dame, H. (1989). Inferring ph from diatoms: A comparison of old and new calibration methods. Hydrobiologia, 178(3):209-223.
[18]. Cristóbal, E., Ayuso, S. V., Justel, A., and Toro, M. (2014). Robust optima and tolerance ranges of biological indicators: A new method to identify sentinels of global warming. Ecological Research, 29(1):55-68.
[19]. Ter Braak, C. J., and Barendregt, L. G. (1986). Weighted averaging of species indicator values: Its efficiency in environmental calibration. Mathematical Biosciences, 78(1):57-72.
[20]. Birks, H. J. B., Heiri, O., Seppä, H., and Bjune, A. E. (2010). Strengths and weaknesses of quantitative climate reconstructions based on late-quaternary. The Open Ecology Journal, 3(1):
[21]. Jackson, S. T., and Williams, J. W. (2004). Modern analogs in quaternary paleoecology: Here today, gone yesterday, gone tomorrow? Annual Review of Earth and Planetary Sciences, 32(495-537.
[22]. Sirén, A., Tuomisto, H., and Navarrete, H. (2013). Mapping environmental variation in lowland amazonian rainforests using remote sensing and floristic data. International Journal of Remote Sensing, 34(5):1561-1575.
[23]. Zuquim, G., Tuomisto, H., Jones, M. M., Prado, J., Figueiredo, F. O., Moulatlet, G. M., Costa, F. R., Quesada, C. A., and Emilio, T. (2014). Predicting environmental gradients with fern species composition in brazilian amazonia. Journal of Vegetation Science, 25(5):1195-1207.
[24]. Tavakoli, S., Ejtehadi, H., and Esmailzadeh, O. (2019). Elenberg's ecological indicator values to predict some soil factors in salaheddinkola forests of nowshahr, iran. Taxonomy & Biosystematics, 11(38):1-20.
[25]. Karami-Kordalivand, P., Esmailzadeh, O., Willner, W., Noroozi, J., and Alavi, S. Classification of forest communities (co-) dominated by taxus baccata in the hyrcanian forests (northern iran) and their comparison with southern europe. European Journal of Forest Research, 1-14.
[26]. Braun-Blanquet, J. (1932). Plant sociology. The study of plant communities. Plant sociology. The study of plant communities. First ed.,
[27]. Tavakoli, S., Ejtehadi, H., and Esmailzadeh, O. (2020). Optimizing the classification of species composition data by combining multiple objective evaluators toward selecting the best method and optimum number of clusters. Phytocoenologia, 50(2):163-172.
[28]. Zarrinkafsh, M. (2001). Forest soils. Forests and Rangelands Research Institute, Tehran, Iran.
[29]. Juggins, S., and Juggins, M. S. (2020). Package ‘rioja’. An R Package for the Analysis of Quaternary Science Data., 0.9, 26.
[30]. Sathicq, M. B., Gelis, M. M. N., and Cochero, J. (2019). Optimos prime: An r package for autoecological (optima and tolerance range) data calculation. bioRxiv, 654152.
[31]. Simpson, G. L., Oksanen, J., and Simpson, M. G. L. (2020). Package ‘analogue’.
[32]. .ter Braak, C. J., and Juggins, S. (1993). Weighted averaging partial least squares regression (wa-pls): An improved method for reconstructing environmental variables from species assemblages, In Twelfth international diatom symposium, pp 485-502, Springer.
[33]. Tuomisto, H., Ruokolainen, K., Poulsen, A. D., Moran, R. C., Quintana, C., Cañas, G., and Celi, J. (2002). Distribution and diversity of pteridophytes and melastomataceae along edaphic gradients in yasuní national park, ecuadorian amazonia1. Biotropica, 34(4):516-533.
[34]. Higgins, M. A., Ruokolainen, K., Tuomisto, H., Llerena, N., Cardenas, G., Phillips, O. L., Vásquez, R., and Räsänen, M. (2011). Geological control of floristic composition in amazonian forests. Journal of biogeography, 38(11):2136-2149.