بوم‌شناسی جنگل‏‌های ایران

1. Adhikari, B., Subedi, S. C., Bhandari, S., Baral, K., Lamichhane, S., & Maraseni, T. (2023). Climate‐driven decline in the habitat of the endemic spiny babbler (Turdoides nipalensis). Ecosphere, 14(6), e4584. https://doi.org/10.1002/ecs2.4584 [DOI:https://doi.org/10.1002/ecs2.4584]

2. Aertsen, W., Kint, V., van Orshoven, J., Özkan, K., & Muys, B. (2010). Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221(8), 1119-1130. [DOI:10.1016/j.ecolmodel.2010.01.007]

3. Akbary, M., & Sayad, V. (2021). Analysis of climate change studies in Iran. Physical Geography Research, 53(1), 37-74. [DOI:10.22059/JPHGR.2021.301111.1007528 [In Persian]]

4. Akyol, A., Örücü, Ö. K., & Arslan, E. S. (2020). Habitat suitability mapping of stone pine (Pinus pinea L.) under the effects of climate change. Biologia, 75, 2175-2187. https://doi.org/10.2478/s11756-020-00594-9 [DOI:https://doi.org/10.2478/s11756-020-00594-9]

5. Alavi, S. J., Ahmadi, K., Hosseini, S. M., Tabari Kouchaksaraei, 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]]

6. 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. https://doi.org/10.1111/j.1365-2664.2006.01214.x [DOI:10.1111/j.1365-2664.2006.01214.x.]

7. Amiri, M., Dargahi, D., Habashi, H., & Mohammadi, J. (2008). Effect of geographic situation on natural regeneration of oak (Quercus castaneifoila CA Mey) in Loveh Forest. Journal of Pajouhesh-va-Sazandegi, 116-123. [In Persian]

8. Amiri, M., Tarkesh, M., Jafari, R., & Jetschke, G. (2020). Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling? Ecological Informatics, 57, 101060. [DOI:10.1016/j.ecoinf.2020.101060]

9. Ardestani, E. G., Tarkesh, M., Bassiri, M., & Vahabi, M. R. (2015). Potential habitat modeling for reintroduction of three native plant species in central Iran. Journal of Arid Land, 7, 381-390. [DOI:10.1007/s40333-014-0050-4]

10. Babalik, A. A., Sarikaya, O., & Orucu, O. K. (2021). The Current and future compliance areas of Kermes Oak (Quercus coccifera L.) under climate change in Turkey. Fresenius Environmental Bulletin, 30(01), 406-413.

11. Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., & Courchamp, F. (2012). Impacts of climate change on the future of biodiversity. Ecology Letters, 15(4), 365-377. [DOI:10.1111/j.1461-0248.2011.01736.x]

12. Bladon, A. J., Donald, P. F., Collar, N. J., Denge, J., Dadacha, G., Wondafrash, M., & Green, R. E. (2021). Climatic change and extinction risk of two globally threatened Ethiopian endemic bird species. PloS one, 16(5), e0249633. [DOI:10.1371/journal.pone.0249633]

13. Bogoni, J. A., Percequillo, A. R., Ferraz, K. M., & Peres, C. A. (2023). The empty forest three decades later: Lessons and prospects. Biotropica, 55(1), 13-18. https://doi.org/10.1111/btp.13188 [DOI:https://doi.org/10.1111/btp.13188]

14. Breiman , L. (2001). Random forests. Machine Learning, 5-32. [DOI:10.1023/A:1010950718922]

15. Breiman, L., Friedman, F., Olshen, F., & Stone, C. (1984). Classification and Regression Trees. Wadsworth, Pacific Grove. New York. https://doi.org/10.1201/9781315139470 [DOI:https://doi.org/10.1201/9781315139470]

16. Dalmaris, E., Ramalho, C. E., Poot, P., Veneklaas, E. J., & Byrne, M. (2015). A climate change context for the decline of a foundation tree species in south-western Australia: insights from phylogeography and species distribution modelling. Annals of Botany, 116(6), 941-952. [DOI:10.1093/aob/mcv044]

17. Desta, F., Colbert, J. J., Rentch, J. S., & Gottschalk, K. W. (2004). Aspect induced differences in vegetation, soil, and microclimatic characteristics of an Appalachian watershed. Castanea, 69(2), 92-108. https://doi.org/10.2179/0008-7475(2004)069<0092:AIDIVS>2.0.CO;2 https://doi.org/10.2179/0008-7475(2004)069<0092:AIDIVS>2.0.CO;2 [DOI:10.2179/0008-7475(2004)0692.0.CO;2]

18. Di Cola, V., Broennimann, O., Petitpierre, B., Breiner, F. T., d'Amen, M., Randin, C., Engler, R., Pottier, J., Pio, D., & Dubuis, A. (2017). ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography, 40(6), 774-787. [DOI:10.1111/ecog.02671]

19. Estoque, R. C., Ooba, M., Togawa, T., & Hijioka, Y. (2020). Projected land-use changes in the Shared Socioeconomic Pathways: Insights and implications. Ambio, 49, 1972-1981. [DOI:10.1007/s13280-020-01338-4]

20. Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 19(1), 1-67. [DOI:10.1214/aos/1176347963]

21. Gorji Bahri, Y., Kiadaliri, S., & Faraji Poul, R. A. (2013). Study on growth and silvicultural analysis of young stand of Quercus castaneifolia CAM in Neyrang forest, Nowshahr. Iranian Journal of Forest and Poplar Research, 21(3), 387-395. [DOI:10.22092/IJFPR.2014.4720 [In Persian]]

22. Haidarian Aghakhani, M., Tamartash, R., Jafarian, Z., Tarkesh Esfahani, M., & Tatian, M. (2017). Predicting the impacts of climate change on Persian oak (Quercus brantii) using Species Distribution Modelling in Central Zagros for conservation planning. Journal of Environmental Studies, 43(3), 497-511. [DOI:10.22059/JES.2017.233756.1007441 [In Persian]]

23. Hajjarian, M., Hosseinzadeh, O., & Khalledi, F. (2016). Using combined MADM approach for Hyrcanian forests management. Environmental Sciences, 14(3), 1-12 [DOI:10.22059/JES.2017.233756.1007441 [In Persian]]

24. IPCC. (2007). Summary for policymakers. In: Solomon, S., Qin, D., Manning, M., Chen, Z., & others (eds) Climate change. 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

25. IPCC. (2021). Summary for policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R. & Zhou, B. (eds.)]. Cambridge University Press, Cambridge.

26. IPCC. (2023). sixth assessment report (AR6) "Climate Change 2023" Synthesis Report. Switzerland.

27. Iverson, L.R., Prasad, A.M., Matthews, S.N. & Peters, M. (2008). Estimating potential habitat for eastern US tree species under six climate scenarios. Forest Ecology and Management, (254), 390-406. [DOI:10.1016/j.foreco.2007.07.023]

28. Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 1-20. [DOI:10.1038/sdata.2017.122]

29. Khalatbari Limaki, M., Es-hagh Nimvari, M., Alavi, S. J., Mataji, A., & Kazemnezhad, F. (2021). Potential elevation shift of oriental beech (Fagus orientalis L.) in Hyrcanian mixed forest ecoregion under future global warming. Ecological Modelling, 455, 109637. [DOI:10.1016/j.ecolmodel.2021.109637]

30. Khodagholi, M., Motamedi, J., & Saboohi, R. (2023). Effects of climate change on the distribution of Bromus tomentellus. Iran Nature, 7(6), 17-25. https:// 10.22092/IRN.2023.128386 [In Persian]

31. Khwarahm, N. R. (2020). Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq. Ecological Processes, 9(1), 1-16. [DOI:10.1186/s13717-020-00259-0]

32. Lek, S., & Guégan, J.-F. (1999). Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 120(2-3), 65-73. [DOI:10.1016/S0304-3800(99)00092-7]

33. Liao, Z., Nobis, M. P., Xiong, Q., Tian, X., Wu, X., Pan, K., Zhang, A., Wang, Y., & Zhang, L. (2021). Potential distributions of seven sympatric sclerophyllous oak species in Southwest China depend on climatic, non-climatic, and independent spatial drivers. Annals of Forest Science, 78, 1-22. 5 [DOI:10.1007/s13595-020-01012-]

34. López-Tirado, J., & Hidalgo, P. J. (2016). Predictive modelling of climax oak trees in southern Spain: insights in a scenario of global change. Plant Ecology, 217, 451-463. [DOI:10.1007/s11258-016-0589-6]

35. López-Tirado, J. H. (2016). Predictive modelling of climax oak trees in southern Spain: insights in a scenario of global change. Plant Ecology, (217), 451-463. [DOI:10.1007/s11258-016-0589-6]

36. Madsen, C. L., Kjær, E. D., & Ræbild, A. (2021). Climatic criteria for successful introduction of Quercus species identified by use of Arboretum data. Forestry: An International Journal of Forest Research, 94(4), 526-537. [DOI:10.1093/forestry/cpab006]

37. Malekian, M., & Sadeghi, M. (2020). Predicting impacts of climate change on the potential distribution of two interacting species in the forests of western Iran. Meteorological Applications, 27(1), e1800. [DOI:10.1002/met.1800]

38. Mehrnia, M., Assadi, M., & Moradi, A. (2022). The conservation status of Quercus castaneifolia CA Mey in Iran. Iran Nature, 7(5), 137-146. [DOI:10.22092/IRN.2022.356479.1414 [In Persian]]

39. Mirhashemi, H., Heydari, M., Ahmadi, K., Karami, O., Kavgaci, A., Matsui, T., & Heung, B. (2023). Species distribution models of Brant's oak (Quercus brantii Lindl.): The impact of spatial database on predicting the impacts of climate change. Ecological Engineering, 194, 107038. [DOI:10.1016/j.ecoleng.2023.107038]

40. Moghbel Esfahani, F., Alavi, S. J., Hosseini, S. M., & Tabari Kochaksarai, M. (2023). Determining the habitat suitability of Quercus castaneifolia CA Mey In order to plan restoration using species distribution modeling. Forest Research and Development, 9(3), 419-436. [DOI:10.30466/JFRD.2023.54577.1654 [In Persian]]

41. Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University-Computer and Information Sciences, 35(2), 757-774. [DOI:10.1016/j.jksuci.2023.01.014]

42. Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., & Toxopeus, A. G. (2014). Where is positional uncertainty a problem for species distribution modelling? Ecography, 37(2), 191-203. [DOI:10.1111/j.1600-0587.2013.00205.x]

43. Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized Linear Models. Journal of the Royal Statistical Society. Series A (General), 135(3), 370-384. https://doi.org/10.2307/2344614 [DOI:https://doi.org/10.2307/2344614]

44. Nicotra, A. B., Atkin, O. K., Bonser, S. P., Davidson, A. M., Finnegan, E. J., Mathesius, U., Poot, P., Purugganan, M. D., Richards, C. L., & Valladares, F. (2010). Plant phenotypic plasticity in a changing climate. Trends in Plant Science, 15(12), 684-692. [DOI:10.1016/j.tplants.2010.09.008]

45. Özcan, A. U., Gülçin, D., Tuttu, G., Velázquez, J., Ayan, S., Stephan, J., Tuttu, U., Varlı, A., & Çiçek, K. (2024). The Future Possible Distribution of Kasnak Oak (Quercus vulcanica Boiss. & Heldr. ex Kotschy) in Anatolia under Climate Change Scenarios. Forests, 15(9), 1551. [DOI:10.3390/f15091551]

46. Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259. [DOI:10.1016/j.ecolmodel.2005.03.026]

47. Radmehr, A., Soosani, J., Ghalebahmani, S. M., Balapour, S., & Sepahvand, A. (2015). Effects of climate variables (temperature and precipitation) on the width of rings-growth in Persian coppice oak in the central Zagros (case study: Khoramabad). Wood and Forest Science and Technology, 22(1), 93-110. [In Persian]

48. Ribeiro, B., & Shapira, P. (2019). Anticipating governance challenges in synthetic biology: Insights from biosynthetic menthol. Technological Forecasting and Social Change, 139, 311-320. [DOI:10.1016/j.techfore.2018.11.020]

49. Sabeti, H. (1976). Forests, trees and shrubs of Iran. Yazd University Press [In Persian]

50. Safaei, M., Rezayan, H., & Zeaiean Firouzabadi, P. (2022). Modelling potential impacts of climate change on the oak spatial distribution (Case study: Ilam and Lorestan provinces). Applied researches in Geographical Sciences, 22(65), 247-264. [DOI:10.52547/jgs.22.65.247]

51. Sagheb Talebi, K., Sajedi, T., & Pourhashemi, M. (2014). Forests of Iran: A Treasure from the Past, a Hope for the Future. Springer. [DOI:10.1007/978-94-007-7371-4]

52. Shahnaseri, G., Malekian, M., & Pourmoghadam, K. (2023). Habitat loss of the chestnut-leaved oak (Quercus castaneifolia) in the Hyrcanian forests of Iran: impacts of anthropogenic factors on forest thinning and degradation. Global Ecology and Conservation, 46, e02600. [DOI:10.1016/j.gecco.2023.e02600]

53. Shiravand, H., & Hosseini, S. A. (2020). A new evaluation of the influence of climate change on Zagros oak forest dieback in Iran. Theoretical and Applied Climatology, 141, 685-697. [DOI:10.1111/ele.12889]

54. Sicard, P., Agathokleous, E., De Marco, A., & Paoletti, E. (2022). Ozone-reducing urban plants: Choose carefully. Science, 377(6606), 585-585. [DOI:10.1126/science.add9734]

55. Sierra-Morales, P., Rojas-Soto, O., Ríos-Muñoz, C. A., Ochoa-Ochoa, L. M., Flores-Rodríguez, P., & Almazán-Núñez, R. C. (2021). Climate change projections suggest severe decreases in the geographic ranges of bird species restricted to Mexican humid mountain forests. Global Ecology and Conservation, 30, e01794. [DOI:10.1016/j.gecco.2021.e01794]

56. Šimková, M., Vacek, S., Šimůnek, V., Vacek, Z., Cukor, J., Hájek, V., Bílek, L., Prokůpková, A., Štefančík, I., & Sitková, Z. (2023). Turkey oak (Quercus cerris L.) resilience to climate change: Insights from coppice forests in Southern and Central Europe. Forests, 14(12), 2403. [DOI:10.3390/f14122403]

57. Stevens‐Rumann, C. S., Kemp, K. B., Higuera, P. E., Harvey, B. J., Rother, M. T., Donato, D. C., Morgan, P., & Veblen, T. T. (2018). Evidence for declining forest resilience to wildfires under climate change. Ecology letters, 21(2), 243-252. [DOI:10.3390/f14122403]

58. Sun, S., Zhang, Y., Huang, D., Wang, H., Cao, Q., Fan, P., Yang, N., Zheng, P., & Wang, R. (2020). The effect of climate change on the richness distribution pattern of oaks (Quercus L.) in China. Science of the Total Environment, 744, 140786. [DOI:10.1016/j.scitotenv.2020.140786]

59. Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293. [DOI:10.1126/science.3287615]

60. Taleshi, H., Jalali, S. G., Alavi, S. J., Hosseini, S. M., & Naimi, B. (2020). Projection of climate change impacts on potential distribution of chestnut-leaved oak (Quercus castaneifolia CAM) using ensemble modeling in the Hyrcanian forests of Iran. Ecology of Iranian Forest, 8(15), 10-21. https://doi.org/10.52547/ifej.8.15.10 [DOI:10.52547/ifej.8.15.10 [In Persian]]

61. Thuiller, W., Georges, D., Engler, R., & Breiner, F. (2016). biomod2: Ensemble Platform for Species Distribution Modeling.

62. Thuiller, W., Georges, D., Gueguen, M., Engler, R., & Breiner, F. (2021). Package 'biomod2'.

63. Thuiller, W., Lavergne, S., Roquet, C., Boulangeat, I., Lafourcade, B., & Araujo, M. B. (2011). Consequences of climate change on the tree of life in Europe. Nature, 470(7335), 531-534. [DOI:10.1038/nature09705]

64. Valavi, R., Shafizadeh-Moghadam, H., Matkan, A., Shakiba, A., Mirbagheri, B., & Kia, S. H. (2019). Modelling climate change effects on Zagros forests in Iran using individual and ensemble forecasting approaches. Theoretical and Applied Climatology, 137, 1015-1025. [DOI:10.1007/s00704-018-2625-z]

65. Vannini, A., Lucero, G., Anselmi, N., & Vettraino, A. M. (2009). Response of endophytic Biscogniauxia mediterranea to variation in leaf water potential of Quercus cerris. Forest Pathology, 39(1), 8-14. [DOI:10.1111/j.1439-0329.2008.00554.x]