Modeling Habitat Suitability of Anser Anser in Iran

Document Type : Research Paper

Authors

1 Department of Environmental Sciences, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran

2 Faculty of Geo- information Science and Earth Observation (ITC), University of Twente, Netherlands

Abstract

Biodiversity is one of the most important indicators of ecosystem diversity and dynamism. Birds, as a clinker indicator of ecosystem biodiversity, are considered as the habitat suitability and other necessary living conditions for any species. Therefore, the study of birds, especially migratory birds, is of particular importance as a clinker indicator. Due to the need for studies in this field, the present study was conducted to investigate the desirability of the habitat and identify the most important environmental variables affecting the distribution of the Anser anser species as a migratory and index species in Iran. Using 23 environmental variables and the nine models in the BIOMOD software package under R software, the Anser anser species is distributed in three types of habitats including winter-passing, summer-passing and breeding and stopeover modeling. The findings from species modeling showed that the models, used in species distribution modeling, have high accuracy in studying species distribution. In general, temperature and precipitation variables are the most important, while the variables such as vegetation and distance to roads are less important in the distribution of Anser anser species in Iran.  According to the results, 15.91% of the surface of Iran was identified as a desirable habitat for Anser anser species, which overlaps with 15.95% of the protected areas. Therefore, the used method in this study identifies the desired habitats of the species correctly. Besides, it can be applied as a suitable method to model the habitat suitability of similar species, which is essential from the perspective of conservation providing comprehensive and practical wildlife management programs.
Extended Abstract
1-Introduction
Bird watching or the scientific study of birds is one of the oldest environmental sciences. The migration of birds, especially aquatic species, has long been of interest to many researchers. Birds, as a clinker indicator of ecosystem biodiversity, are considered as the habitat suitability and other necessary living conditions for any species. Therefore, the study of birds, especially migratory waterfowl, is of particular importance. Due to the need for studies in this field, the present study aims to investigate the desirability of the habitat and identify the most important environmental variables affecting the distribution of Anser anser species as a migratory and index aquatic species in Iran.
2-Materials and Methods
In this study, 10 presence spots for the summer-passing and breeding population, 116 presence spots for overwintering population and 25 presence spots for the passing population of Anser anser have been applied in order to model the distribution of Anser anser species in three habitat types including winter-passing, summer-passing and hatchery, as well as stopping places in Iran. These spots were obtained from the bird census reports of the Environmental Protection Agency. Three groups of environmental variables including topography, climate and land use / land cover were used to investigate the factors affecting the distribution of Anser anser species. Finally, 23 environmental variables were called for modeling in the biomod software package.
3-Results and Discussion
The values ​​of accuracy evaluation indices reveal that models such as FDA and RF have high accuracy in the modeling process out of the nine models implemented in modeling Anser anser stopover. Moreover, variables such as seasonal rainfall, distance to rural areas and the amount of rainfall in the least rainy season play an important role in the selection of stopover by the Anser anser. According to the results of the overlap analysis of the total area of ​​Iran, only 20.99% is known as desirable habitats of this species, which includes 18.69% of the total desirable habitats in protected areas.
Based on from modeling the habitat of summer Anser anser, all the models used in this study have high accuracy in studying the species distribution. On the other hand, the distribution of this species in Iran depends on factors such as distance to wetlands, distance to forest and distance to streams. According to the findings, 3.22% of the total area of ​​Iran is known as a suitable habitat summer Anser anser and overlaps with the presence of species, which is 4.09% of the total summer habitats. Optimal laying and hatching of this species is covered by protected areas.
The results of modeling the Anser anser species in the surface of wintering habitats indicate that all models have high accuracy. The distribution of this species in the surface of wintering habitats is affected by factors such as rainfall, distance to the city and the warmest rainfall of the year. Optimal habitats of this species cover 23.52% of Iran, which is 25.09% of the total desirable habitats of the studied species overlap with protected areas.
Examining the distribution of species at the level of ecological nests and understanding the relationship between environmental variables and the distribution of species using a biomod software package show how species respond to environmental changes at the present time. The findings from evaluating the algorithms used in modeling the habitat types of the Anser anser species reveal that the biomod software package has a high ability to predict the optimal habitats of this species. Thus, it has identified desirable species habitats at the present time, habitats that can be used in the future and have favorable conditions for species introduction, as well as habitats that had ideal conditions in the past. In this regard, the species of Anser anser Choose to spend winters in areas such as Urmia Lake, Fars province, Sistan and Baluchestan, Azerbaijan, Kurdistan and the southern parts of the Caspian Sea and in some eastern and northern areas of the country that have ideal biological conditions. According to the frindings from modeling, the Anser anser is mainly present in summers in Azerbaijan province, especially Urmia Lake and the surrounding wetlands. These areas are more preferred than other parts of the country due to favorable weather conditions, adequate security and availability of food resources. Locations of Anser anser in the country include areas such as the southern shores of the Caspian Sea, parts of the west and northwest, as well as northeastern areas. Proper identification of stops and providing applications to protect these areas is important due to the functional role of stopping places in meeting the needs of migratory species. In general, in the present study, it is found that 15.95% of the total habitats of the Anser anser species are located in protected areas. This indicates the existence of a large part of the desirable habitats of this species outside the protected areas. Therefore, the results of the present study show the need to reconsider the demarcation of protected areas in the future more than in the past in Iran.
4-Conclusion
The current study aims to investigate the distribution of Anser anser species and the effect of environmental variables on the distribution of this species in Iran using a biomod software package.  Based on the results, the method used in this study correctly identifies the desired habitats of the species and can be used as a suitable method to model the habitat suitability of similar species and also to study the biodiversity of habitats. This is essential from the point of view of conservation and the presentation of comprehensive and practical wildlife management programs.

Keywords


References
Araújo, M. B. & Peterson, A. T. (2012). Uses and misuses of bioclimatic envelope modeling. Ecology, 93 (7), 1527-1539.
Asadian, M., Aliabadian, M. & Riazi, B. (2014) The role of climatic factors, vegetation and altitude on the geographical distribution of bird species richness in Sarakhs. Conservation and Exploitation of Natural Resources, 2 (1), 65-76 (In Persian).
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.‏
Berthold, P. (2000). Vogelzug. Eine aktuelle Gesamtübersicht. Darmstadt: Wissenschaftliche Buchgesellschaft.‏
Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984). Classification and regression trees. Wadsworth Int. Group, 37 (15), 237-251.‏
Brommer, J. E., Lehikoinen, A. & Valkama, J. (2012). The breeding ranges of Central European and Arctic bird species move poleward. PLoS One, 7 (9), e43648.
Bruner, A. G., Gullison, R. E., Rice, R. E. & Da Fonseca, G. A. (2001). Effectiveness of parks in protecting tropical biodiversity. Science, 291 (5501), 125-128.
Chudzińska, M. E., van Beest, F. M., Madsen, J. & Nabe‐Nielsen, J. (2015). Using habitat selection theories to predict the spatiotemporal distribution of migratory birds during stopover–a case study of pink‐footed geese Anser brachyrhynchus. Oikos, 124 (7), 851-860.‏
Corsi, F., Duprè, E. & Boitani, L. (1999). A large‐scale model of wolf distribution in Italy for conservation planning. Conservation Biology, 13 (1), 150-159.
Eklund, J., Arponen, A., Visconti, P. & Cabeza, M. (2011). Governance factors in the identification of global conservation priorities for mammals. Philosophical Transactions of the Royal Society B: Biological Sciences, 366 (1578), 2661-2669.
Elith, J. & Franklin, J. (2013). Species distribution modeling. Encyclopedia of biodiversity. Academic Press, Waltham, MA. Elith, J., Graham, CH, Anderson, RP et al.(2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, 129-151.‏
Faaborg, J., Holmes, R. T., Anders, A. D., Bildstein, K. L., Dugger, K. M., Gauthreaux, 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, S. I., Sherry, T. W., Sillett, T. S., Thompson, F. R. & Warnock, N. (2010). Conserving migratory land birds in the New World: dowe know enough?. Ecol Appl., 20 (2), 398-418.
Farashi, A. & Halakouhi, L. (2018). Migratory waterfowls as indicators to assess the protection efficiency in Iran. Acta Ecologica Sinica, 38 (6), 429-443.
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 (2), 38-49.
Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19 (1), 1-67.
Gauthier, G., Beˆty, J. & Hobson, K. A. (2003). Are greater snow geese capital breeders?New evidence from a stable-isotope model. Ecology, 84, 3250-3264.
Guisan, A. & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135, 86-147.
Habibzadeh, N. & Hassan Alizadeh, R. (2015). Multi-scale modeling of the feeding habitat of Egyptian vultures in the Arasbaran Protected Area. Applied ecology, 6 (3), 1-13 (In Persian).
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.
Huntley, B., Collingham, Y. C., Willis, S. G. & Green, R. E. (2008). Potential impacts of climatic change on European breeding birds. PloS one, 3 (1), e1439.
IPCC (2001), Climate change 2001: IPCC Special Report on Emissions Scenarios. A Special Report of IPCC Working Group III, Intergovernmental Panel on Climate Change, ISBN: 92-9169, 113-115.
‏Jarvis, A. M. & Robertson, A. (1999). Predicting population sizes and priority conservation areas for 10 endemic Namibian bird species. Biological Conservation, 88 (1), 121-131.
Jenni, L. & Kéry, M. (2003). Timing of autumn bird migration under climate change: advances in long–distance migrants, delays in short-distance migrants. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270 (1523), 1467-1471.‏
Jetz, W. & Rahbeck, C. (2002). Geographic range size and deter-minants of avian species richness. Science, 297, 1548-1551.
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, Series Nova, 39 (1), 63-102.‏
Liu, G., Lizhi, Z., Dong, Y., Zhang, F. & Rong, F. (2019). The complete mitochondrial genome of natural hybridization of Anser albifrons and Anser fabalisthe (Anser albifrons× Anser fabalis). Mitochondrial DNA Part B, 4 (1), 1077-1078.‏
Mehlman, D. W., Mabey, S. E., Ewert, D. N., Duncan, C., Abel, B., Cimprich, D., Sutter, R. D. & Woodrey, M. (2005). Conserving stopover sites for forest-dwellingmigratory landbirds. Auk 122, 1281-1290.
Moreno, R., Zamora, R., Molina, J. R., Vasquez, A. & Herrera, M. Á. (2011). Predicti modelingmicrohabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Ecological Informatics, 6 (6), 364-37.
Newton, L. (2008). The Migration Ecology of Birds. Academic Press is an imprint of Elsevier.
Nix, H. A. (1986). A biogeographic analysis of Australian elapid snakes. Atlas of elapid snakes of Australia, 7, 4-15.‏
Nouri Jangi, M. & Nouri Jangi, A. (2014), Assessing the Status and Threats of Biodiversity in Iran, The Second National and Specialized Conference on Environmental Research in Iran, Hamadan. https://civilica.com/doc/293025 (In Persian).
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.
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 (s1), 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, 14-23.
Rastegar-Pouyani, N., Kami, H. G., Rajabzadeh, H. R., Shafiei, S. & Anderson, S. C. (2008). Annotated checklist of amphibians and reptiles of Iran. Iranian Journal of Animal Biosystematics, 4 (1), 7-30.‏
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, 10 (3),307-313.
Scribner, K. T., Arntzen, J. W., Cruddace, N., Oldham, R. S. & Burke, T. (2001). Environmental correlates of toad abundance and population genetic diversity. Biological conservation, 98 (2), 201-210.
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. (2009). Biomod –a platform for ensemble forecasting of species distributions. Ecography, 32 (3), 369-373.
Walther, G. R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J. & Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416 (6879), 389-395.