نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه محیط‌زیست، دانشکده منابع‌طبیعی و محیط‌زیست، دانشگاه فردوسی مشهد، ایران

2 دانشیار، گروه محیط‌زیست، دانشکده منابع‌طبیعی و محیط‌زیست، دانشگاه فردوسی مشهد، ایران

3 دانش آموخته دکتری، گروه محیط‌زیست، دانشگاه تنت، هلند

10.22051/jab.2021.33529.1386

چکیده

گونه‌های مهاجر در مسیر مهاجرت خود در طیف وسیعی از محیط‌ها حرکت می‌کنند، اما پاسخ پویای آن‌ها به محیط پیرامونی و نحوه انتخاب زیستگاه توسط پرندگان مهاجر آبزی به ندرت مورد توجه قرار گرفته است. با توجه به ضرورت انجام مطالعات در این زمینه، پژوهش حاضر به منظور بررسی نحوه پراکنش و پارامترهای زیست‌محیطی موثر در انتخاب زیستگاه غاز پیشانی سفید (Anser albifrons, Scopoli 1769) به عنوان یک گونه‌ی مهاجر در ایران صورت گرفت. در این مطالعه از چهار گروه متغیر زیست‌محیطی شامل: متغیرهای توپوگرافیک، اقلیمی وکاربری اراضی / پوشش سرزمین استفاده شد. نقاط حضور با استفاده از گزارشات سازمان حفاظت محیط‌زیست به دست آمد. جهت مدلسازی از 9 الگوریتم موجود در پکیج BIOMOD تحت نرم افزار R استفاده شد. صحت مدلسازی با استفاده از شاخص‌های ROC و TSS مورد ازریابی قرار گرفت. نتایج نشان داد که پارامترهایی نظیر میزان بارش سالیانه، فاصله تا زمین‌های کشاورزی دیم، بارش گرمترین فصل سال و فاصله تا تالاب‌ها بیشترین تأثیر را در پراکنش غاز پیشانی سفیددارند، همچنین نتایج صحت‌سنجی نشان داد که مدل‌های مورد استفاده در این مطالعه از صحت بالایی در مدلسازی پراکنش گونه برخوردار هستند. روش پیشنهادی در این مدلسازی می‌تواند چگونگی درک پژوهشگران از نحوه پراکنش و انتخاب زیستگاه به جهت ارائه راهکارهای مدیریتی و حفاظتی گونه ها را افزایش می‌دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Habitat modeling of white-fronted goose (Anser albifrons) habitat in Iran

نویسندگان [English]

  • Bigleri Quchan Atiq Fatemeh 1
  • azita farashi 2
  • Mitra Shariati Najafabadi 3

1 Master Student, Department of Environment, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Iran

2 Associate Professor, Department of Environment, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Iran

3 PhD Student, Department of Environment, University of Tenet, The Netherlands

چکیده [English]

Migratory species move on their migration path in a wide range of environments, but their dynamic response to the environment and how migratory birds choose their habitat has rarely been considered. Due to the need for studies in this field, the present study was conducted to investigate the distribution and environmental parameters affecting the habitat selection of white-fronted goose (Anser albifrons, Scopoli 1769) as a migratory species in Iran.In this study, four groups of environmental variables including: topographic, climatic and land use / land cover variables were used. Points of presence were obtained using reports from the Environmental Protection Agency. For modeling, 9 algorithms in BIOMOD package under R software were used. The accuracy of modeling was evaluated using ROC and TSS indices. The results showed that parameters such as annual rainfall, distance to rainfed fields, rainfall in the warmest season and distance to wetlands have the greatest impact on the distribution of white-fronted goose. Also, the validation results showed that the models used in this study have high accuracy in modeling the distribution of species. The proposed method in this modeling can increase how researchers understand the distribution and habitat selection to provide management and conservation solutions for species.

کلیدواژه‌ها [English]

  • Migration
  • Environmental parameters
  • Anser albifrons
  • BIOMOD
Abraham, K.F., Jefferies, R.L. and Alisauskas, R.T. (2005). The dynamics of landscape change and snow geese in mid-continent North America. Glob. Chang. Biol., 11, 841–855.
Alerstam, T., Lindström, Å., )1990(. Optimal Bird Migration: the Relative Importance of Time, Energy, and Safety, Bird Migration. Springer, Berlin Heidelberg, pp. 331–351.
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(6), 1223-1232.
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 , Vol. 8 (2) , 61 - 79.
Asadian, M., Aliabadian, M., Riyazi, B., (2014). The Role of Environmental Factors on Species Richness Pattern of Birds in Sarakhs. Conservation and Utilization of Natural Resources, Vol. 2 (1)., http://ejang.gau.ac.ir.
Austin, M. P., Cunningham, R. B., & Fleming, P. M. (1984). New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. Vegetatio, 55(1), 11-27.
Breiman, L. Fried man JH, Olshen RA, Stone CJ. (1984). Classification and regression trees. Belmont, California. Wadsworth International Group.‏
de Carvalho, D. L., Sousa-Neves, T., Cerqueira, P. V., Gonsioroski, G., Silva, S. M., Silva, D. P., & Santos, M. P. D. (2017). Delimiting priority areas for the conservation of endemic and threatened Neotropical birds using a niche-based gap analysis. PloS one, 12(2).
de Roder, F. E., Bijlsma, R. G., & Klomp, J. (2008). Tweede broedgeval van de Zeearend Haliaeetus albicilla in Nederland. De takkeling, 16(2), 100-123.‏
Ebbinge, B. S., Vanbiezen, J. B., & Vandervoet, H. (1991). Estimation of annual adult survival rates of barnacle geese Branta-Leucopsis using multiple resightings of marked individuals. Ardea, 79(1), 73-112.‏
Elith, J., Franklin, J. (2013). Species Distribution Modeling, in Encyclopedia of Biodiversity (Second Edition), S.A. Levin, Editor. Academic Press, Waltham. p. 692-705.
Faaborg, J., Holmes, R. T., Anders, A. D., Bildstein, K. L., Dugger, K. M., Gauthreaux Jr, S. A., Heglund, P.,  Hobson, K. A., Jahn, A. E., Johnson, D. H., . Latta, S. C., Levey, D. J., Marra, p. p., Merkord, C. L., Nol, E., Rothstein, I., Sherry, T. W., Sillett, S., Thompson III, F. R., Warnock. N., (2010). Conserving migratory land birds in the New World: Do we know enough? Ecological applications, 20(2), 398-418.‏
Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental conservation, 24(1), 38-49.
Foden, W. B., Butchart, S. H., Stuart, S. N., Vié, J. C., Akçakaya, H. R., Angulo, A., DeVantier, L. M., Gutsche, A., Turak, E., Cao, L., Donner, S. D., Katariya, V., Bernard, R., Holland, R. A., Hughes, A. F., O’Hanlon, S. E., Garnett, S. T., Şekercioğlu, C. H., Mace, G. M., (2013). Identifying the world's most climate change vulnerable species: a systematic traitbased assessment of all birds, amphibians and corals. PloS one, 8(6).
Fox, A.D., Madsen, J., Boyd, H., Kuijken, E., Norriss D.W., Tombre, I.M. and Stroud D.A. (2005). Effects of agricultural change on abundance, fitness components and distribution of two arcticnesting goose populations. Glob. Chang. Biol., 11, 881–893.
Franklin, J. (2010). Mapping species distributions: spatial inference and prediction. Cambridge University Press.‏
Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics 19:1–67.
Gaudreau, J., Perez, L., & Harati, S. (2018). Towards Modelling Future Trends of Quebec’s Boreal Birds’ Species Distribution under Climate Change. ISPRS International Journal of Geo- Information, 7(9), 335.
Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology letters, 8(9), 993-1009.‏
Gutiérrez, E. E., Boria, R. A., & Anderson, R. P. (2014). Can biotic interactions cause allopatry? Niche models, competition, and distributions of South American mouse opossums. Ecography, 37(8), 741-753.‏
Haidarian Aghakhani M., Tamartash R., Jafarian Z., Tarkesh Esfahani M., Tatian M.R. (2017). Forecasts Of Climate Change Effects On Amygdalus Scoparia Potential Distribution By Using Ensemble Modeling In Central Zagros. journal Of Rs And Gis For Natural Resources , Vol, 8 (3), 1 - 14.
Hannah, L., Midgley, G., Andelman, S., Araújo, M., Hughes, G., Martinez-Meyer, Pearson, R.,  Williams, P. (2007). Protected area needs in a changing climate. Frontiers in Ecology and the Environment, 5(3), 131-138.
Harrell Jr, F. E., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in medicine, 15(4), 361-387.
Hastie, T., Tibshirani, R., & Buja, A. (1994). Flexible discriminant analysis by optimal scoring. Journal of the American statistical association, 89(428), 1255-1270.
IPCC, A. (2007). Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change, 1.
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  , Vol. 15 (46), 117 - 132.
Jankowiak, L., Antczak, M., Tryjanowski, P. (2008). Habitat use, food and the importance of poultry in the diet of the red fox Vulpes vulpes in extensive farmland in Poland. World Appl. Sci. J. 4, 886–890.
Jetz, W. & Rahbeck, C. (2002) Geographic range size and deter-minants of avian species richness. Science, 297, 1548 –1551.
Jiménez‐Valverde, A., Barve, N., Lira‐Noriega, A., Maher, S. P., Nakazawa, Y., Papeş, M., & Peterson, A. T. (2011). Dominant climate influences on North American bird distributions. Global Ecology and Biogeography, 20(1), 114-118.‏
Kaboli, M., Aliabadian, M., Tohidifar, M., Hashemi, A., Musavi, S. B., & Roselaar, C. C. (2016). Atlas of birds of Iran. Jahad Daneshgahi, Karazmi Branch.‏
Karami, M., Hutterer, R., Benda, P., Siahsarvie, R., Kryštufek, B. (2008). Annotated check-list of the mammals of Iran. Lynx (Praha), n. s., 39(1), 63-102.
Kear, J. (Ed.). (2005). Ducks, geese and swans: Species accounts (Cairina to Mergus) (Vol. 2). Oxford University Press.‏
Khaleghizadeh, A. (2020). Atlas of Birds of Iran. Mohammad Kaboli, Mansour Aliabadian, Mohammad Tohidifar, Alireza Hashemi, Seyed Babak Musavi and Cees C. Roselaar. Iran Department of the Environment, Tehran. 617 pp. Journal of Animal Diversity, 2(1), 100-103.‏
Leemans, R., Rounsevell, M. D. A., Midgley, G. F., Price, J. T., Fischlin, A., Dube, O. P., Turley, C. (2006). Ecosystems, their properties, goods and services.
Li, X., Tian, H., Wang, Y., Li, R., Song, Z., Zhang, F. & Li, D. (2013). Vulnerability of 208 endemic or endangered species in China to the effects of climate change. Regional Environmental Change, 13(4), 843-852.
Madadi, A., Madadi, D., (2011). Climate change on vegetation and ecosystem. National Conference on Climate change and its impact on agriculture and environment, Urmia. https://civilica.com/doc/123385.
Madsen, J., Cracknell, G. and Fox, A.D. (eds) (1999). Goose populations of the western Palearctic. Wetlands International Pub. No. 48. National Environmental Research Institute,Denmark.
Menu, S., Gauthier, G., & Reed, A. (2002). Changes in survival rates and population dynamics of greater snow geese over a 30-year period: implications for hunting regulations. Journal of Applied Ecology, 91-102.‏
Nix, H. A. (1986). A biogeographic analysis of Australian elapid snakes. Atlas of elapid snakes of Australia, 7, 4-15.‏
Nouri Jangi, M., 2014, Assessing the situation and threats to biodiversity in Iran. The second national and specialized conference on environmental research in Iran. Hamedan. Https://civilica.com/doc/293025.
Osborne, P. E., Alonso, J. C., & Bryant, R. G. (2001). Modelling landscape‐scale habitat use using GIS and remote sensing: a case study with great bustards. Journal of applied ecology, 38(2), 458-471.
Owen, M. (1980). Wild geese of the world.-London, BT Bastford Ltd.: 1-230.‏
Paradis, E., Baillie, S. R., Sutherland, W. J., Dudley, C., Crick, H. Q., & Gregory, R. D. (2000). Large‐scale spatial variation in the breeding performance of song thrushes Turdus philomelos and blackbirds T. merula in Britain. Journal of Applied Ecology, 37, 73-87.‏
Polakowski, M., & Kasprzykowski, Z. (2016). Differences in the use of foraging grounds by Greylag Goose Anser anser and White-fronted Goose Anser albifrons at a spring stopover site. Avian Biology Research, 9(4), 265-272.‏
Priti, H., Aravind, N. A., Shaanker, R. U., & Ravikanth, G. (2016). Modeling impacts of future climate on the distribution of Myristicaceae species in the Western Ghats, India. Ecological Engineering, 89, 14-28.
RASTEGAR, P. N., Kami, H. G., Rajabzadeh, M., Shafiei, S., & Anderson, S. C. (2008). Annotated checklist of amphibians and reptiles of Iran.
Rosin, Z. M., Skórka, P., Wylegała, P., Krąkowski, B., Tobolka, M., Myczko, Ł., & Tryjanowski, P. (2012). Landscape structure, human disturbance and crop management affect foraging ground selection by migrating geese. Journal of Ornithology, 153(3), 747-759.‏
Sangoony, H., Vahabi, M., Tarkesh, M., Eshghizadeh, H., Soltani, S (2017). Characterization of ecosystem’s climate and geographical distribution of two pasture species using random forest modeling in Central Zagros region. Journal of Plant Ecosystem Conservation, 5 (10),1-17.
Scott, D.A., Adhami, A., 2006. An updated checklist of the birds of Iran. Podoces 1(1/2), 1-16.
Scott, I., Mitchell, P. I., & Evans, P. R. (1996). How does Variation Body Composition Affect the Basal Metabolic Rates of Birds of Birds?. Functional Ecology, 307-313.
Shariati najafabadi, M., Wang, T., Skidmore, A.K., Toxopeus, A.G., Kölzsch, A., Nolet,B.A., Exo, K.M., Griffin, L., Stahl, J., Cabot, D., 2014. Migratory herbivorouswaterfowl track satellite-derived green wave index. PLoS One 9, e108331.
Sillett, T. S., & Holmes, R. T. (2002). Variation in survivorship of a migratory songbird throughout its annual cycle. Journal of Animal Ecology, 71(2), 296-308.
‏Stralberg D, Matsuoka SM, Hamann A, Bayne EM, Solymos P, Schmiegelow FKA, Wang X, Cumming SG, Song SJ (2015). Projecting boreal bird responses to climate change: the signal exceeds the noise. Ecological Applications 25:52–69.
Thuiller, W. (2003). Biomod–optimizing predictions of species distributions and projecting potential future shifts under global change. Global change biology, 9(10), 1353-1362.
Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009). Biomod –a platform for ensemble forecasting of species distributions. Ecography, 32(3), 369-373.
Valavi, R., Motkan, A, A.,  Shakiba, A., Mirbagheri, B., (2015). Simulation of Climate Change Impact on Zagros Oak Habitat Using Two Artificial Neural Network and Random Forest Algorithms. Fifth Regional Conference on Climate Change, Tehran. https://civilica.com/doc/557234.
Van der Graaf, A. J., Stahl, J., Klimkowska, A., Bakker, J. P., & Drent, R. H. (2006). Surfing on a green wave e How plant growth drives spring migration in the Barnacle Goose Branta leucopsis. Ardea, 94(3), 565e577.
Van Eerden, M.R., Drent, R.H., Stahl, J. and Bakke, J.P. (2005). Connecting seas: western Palearctic continental flyway for water birds in the perspective of changing land use and climate. Glob. Chang. Biol., 11, 894–908.
Wauchope, H. S., Shaw, J. D., Varpe, Ø., Lappo, E. G., Boertmann, D., Lanctot, R. B., & Fuller, R. A. (2017). Rapid climate‐ driven loss of breeding habitat for Arctic migratory birds. Global Change Biology, 23(3), 1085-1094.