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Modeling of the distribution of Purple-flowered Rhododendron (Rhododendron ponticum L.) under the current and future climate conditions

Year 2021, Volume: 22 Issue: 1, 26 - 41, 12.05.2021

Ömer K. Örücü , Derya Gülçin , İrem Özçifçi , E. Seda Arslan

https://doi.org/10.17474/artvinofd.834174
Cited By: 1

Abstract

This study aims to model the present and future potential distribution of Rhododendron ponticum L. species according to diverse climate scenarios using maximum entropy. Carried out in two stages, the present study utilized presence data representing natural distribution of R. ponticum L. species in Turkey, Georgia, and Russia. In the first stage, we determined variables of the climate models and focused on 19 bioclimatic variables (in 2.5 minute, or approximately 20 km2, spatial resolution in Wordclim version 2.1) obtained for presence data from sample points. In order to prevent from high correlation and multi-collinearity, bioclimatic variables were reduced to 8 variables by performing Pearson correlation analysis. In the second stage, CNRM-CM6-1 climate change model, which is one of the CMIP6 models, was used to determine how the distribution areas of the species will be affected by climate change. Within this scope, the potential distribution areas of the species under the SSP2 4.5 and SSP5 8.5 scenarios in the periods 2041-2060 and 2081-2100 were modelled by means of the MaxEnt 3.4.1 software. Furthermore, spatial differences between the present and future potential distribution of the species were assessed by change analysis. In conclusion, this study suggested using produced knowledge and transforming them from theory to practice for underpinning sustainable landscape management.

Keywords

Rhododendron ponticum L. CNRM-1 Species distribution model Change analysis MaxEnt

References

  • Abdelaal M, Fois M, Fenu G, Bacchetta G (2019) Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt. Ecological Informatics 50:68–75. https://doi.org/10.1016/j.ecoinf.2019.01.003
  • Akkemik Ü (2014) Türkiye’nin doğal-egzotik ağaç ve çalıları I. Orman Genel Müdürlüğü Yayınları, Ankara
  • Akyol A, Orucu OK, Arslan ES (2020) Habitat suitability mapping of stone pine (Pinus pineaL.) under the effects of climate change. Biologia
  • Arslan ES, Akyol A, Örücü ÖK, Sarıkaya AG (2020) Distribution of rose hip (Rosa canina L.) under current and future climate conditions. Reg Environ Change 20:107. https://doi.org/10.1007/s10113-020-01695-6
  • Austin M (2007) Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling 200:1–19. https://doi.org/10.1016/j.ecolmodel.2006.07.005
  • BIYOD (2020) BİYOD - Biyolojik Çeşitlilik ve Odun DIşı Orman Ürünleri Veri Tabanı. Tarım ve Orman Bakanlığı Orman Genel Müdürlüğü, Ankara
  • Bouchard M, Aquilué N, Périé C, Lambert M-C (2019) Tree species persistence under warming conditions: A key driver of forest response to climate change. Forest Ecology and Management 442:96–104. https://doi.org/10.1016/j.foreco.2019.03.040
  • Cao B, Bai CK, Zhang LL, et al (2016) Modeling habitat distribution of Cornus officinalis with Maxent modeling and fuzzy logics in China. J Plant Ecol 9:742–751. https://doi.org/10.1093/jpe/rtw009
  • Chen I-C, Hill JK, Ohlemüller R, et al (2011) Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 333:1024–1026. https://doi.org/10.1126/science.1206432
  • Çoban HO, Örücü ÖK, Arslan ES (2020) MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier. Sustainability 12:2671. https://doi.org/10.3390/su12072671
  • Dagnino D, Guerrina M, Minuto L, et al (2020) Climate change and the future of endemic flora in the South Western Alps: relationships between niche properties and extinction risk. Reg Environ Change 20:121. https://doi.org/10.1007/s10113-020-01708-4
  • Dai J, Roberts DA, Stow DA, et al (2020) Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal. Remote Sensing of Environment 250:112037. https://doi.org/10.1016/j.rse.2020.112037
  • Davis PH (1965) Flora of Turkey and The East Aegean Islands - I. Edinburgh University Press, Edinburgh
  • Dawson TP, Jackson ST, House JI, et al (2011) Beyond Predictions: Biodiversity Conservation in a Changing Climate. Science 332:53–58. https://doi.org/10.1126/science.1200303
  • Dimobe K, Ouédraogo A, Ouédraogo K, et al (2020) Climate change reduces the distribution area of the shea tree (Vitellaria paradoxa CF Gaertn.) in Burkina Faso. Journal of Arid Environments 181:104237
  • Djalante R (2019) Key assessments from the IPCC special report on global warming of 1.5 °C and the implications for the Sendai framework for disaster risk reduction. Progress in Disaster Science 1:100001. https://doi.org/10.1016/j.pdisas.2019.100001
  • Dyderski MK, Paź S, Frelich LE, Jagodziński AM (2018) How much does climate change threaten European forest tree species distributions? Global Change Biology 24:1150–1163. https://doi.org/10.1111/gcb.13925
  • Ehrlén J, Morris WF (2015) Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters 18:303–314. https://doi.org/10.1111/ele.12410
  • Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annual review of ecology, evolution, systematics 40:677–697
  • Eyring V, Bony S, Meehl GA, et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
  • Fei S, Desprez JM, Potter KM, et al (2017) Divergence of species responses to climate change. Science Advances 3:e1603055. https://doi.org/10.1126/sciadv.1603055
  • Ferrarini A, Alsafran MHSA, Dai J, Alatalo JM (2019) Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study. Clim Dyn 52:1413–1423. https://doi.org/10.1007/s00382-018-4200-9
  • Fortunel C, Paine CET, Fine PVA, et al (2014) Environmental factors predict community functional composition in Amazonian forests. Journal of Ecology 102:145–155. https://doi.org/10.1111/1365-2745.12160
  • Garcia K, Lasco R, Ines A, et al (2013) Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Applied Geography 44:12–22. https://doi.org/10.1016/j.apgeog.2013.07.005
  • Garzón MB, Robson TM, Hampe A (2019) ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity. New Phytologist 222:1757–1765. https://doi.org/10.1111/nph.15716
  • Gassó N, Thuiller W, Pino J, Vilà M (2012) Potential Distribution Range of Invasive Plant Species in Spain. NeoBiota 12:25
  • GBIF (2020) Rhododendron ponticum L. in GBIF Secretariat (2019). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2020-12-01
  • Hausfather Z (2019) CMIP6: the next generation of climate models explained. In: Carbon Brief. https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained. Accessed 8 Oct 2020
  • Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x
  • Hosmer Jr DW, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons
  • iNaturalist (2020a) Gözlemci Grzegorz Grzejszczak Tarih: Mayıs 31, 2016 12:11 PM HST Yer: Adżaria, Gruzja (Google, OSM). In: iNaturalist. https://www.inaturalist.org/photos/15547467. Accessed 30 Nov 2020
  • iNaturalist (2020b) Gözlemci Grzegorz Grzejszczak Tarih: Mayıs 31, 2016 12:11 PM HST Yer: Adżaria, Gruzja (Google, OSM). In: iNaturalist. https://www.inaturalist.org/photos/15546233. Accessed 30 Nov 2020
  • IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland
  • Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics 60:101150. https://doi.org/10.1016/j.ecoinf.2020.101150
  • Kim J, Lee DK, Kim HG (2020) Suitable trees for urban landscapes in the Republic of Korea under climate change. Landscape and Urban Planning 204:103937. https://doi.org/10.1016/j.landurbplan.2020.103937
  • Kramer-Schadt S, Niedballa J, Pilgrim JD, et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. https://doi.org/10.1111/ddi.12096
  • Kumar P (2012) Assessment of impact of climate change on Rhododendrons in Sikkim Himalayas using Maxent modelling: limitations and challenges. Biodivers Conserv 21:1251–1266. https://doi.org/10.1007/s10531-012-0279-1
  • Li G, Huang J, Guo H, Du S (2020) Projecting species loss and turnover under climate change for 111 Chinese tree species. Forest Ecology and Management 477:118488. https://doi.org/10.1016/j.foreco.2020.118488
  • Manzoor SA, Griffiths G, Iizuka K, Lukac M (2018) Land Cover and Climate Change May Limit Invasiveness of Rhododendron ponticum in Wales. Front Plant Sci 9:. https://doi.org/10.3389/fpls.2018.00664
  • Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  • Moukrim S, Lahssini S, Rhazi M, et al (2019) Climate change impacts on potential distribution of multipurpose agro-forestry species: Argania spinosa (L.) Skeels as case study. Agroforest Syst 93:1209–1219. https://doi.org/10.1007/s10457-018-0232-8
  • Nascimbene J, Benesperi R, Casazza G, et al (2020) Range shifts of native and invasive trees exacerbate the impact of climate change on epiphyte distribution: The case of lung lichen and black locust in Italy. Science of the Total Environment 735:139537
  • Norberg A, Abrego N, Blanchet FG, et al (2019) A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs 89:e01370. https://doi.org/10.1002/ecm.1370
  • Oliver TH, Smithers RJ, Beale CM, Watts K (2016) Are existing biodiversity conservation strategies appropriate in a changing climate? Biological Conservation 193:17–26. https://doi.org/10.1016/j.biocon.2015.10.024
  • Örücü ÖK (2019) Phoenix theophrasti Gr.’nin iklim değişimine bağlı günümüz ve gelecekteki yayılış alanlarının MaxEnt Modeli ile tahmini ve bitkisel tasarımda kullanımı. Türkiye Ormancılık Dergisi 20:274–283. https://doi.org/10.18182/tjf.613205
  • Özbucak TB, Türkiş S, Çakmak A (2009) Ordu çevresinde yayılış gösteren bazı Rhododendron türleri üzerine ekolojik bir çalışma. Research Journal of Biology Sciences 2:71–77
  • Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A %J J of biogeography (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. 34:102–117
  • Peterson ML, Doak DF, Morris WF (2019) Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Global Change Biology 25:775–793. https://doi.org/10.1111/gcb.14562
  • Phillips SJ, Anderson RP, Schapire RE %J E modelling (2006) Maximum entropy modeling of species geographic distributions. 190:231–259
  • Phillips SJ, Elith J (2010) POC plots: calibrating species distribution models with presence‐only data. Ecology 91:2476–2484
  • Qin AL, Liu B, Guo QS, et al (2017) Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Glob Ecol Conserv 10:139–146. https://doi.org/10.1016/j.gecco.2017.02.004
  • Reside AE, Critchell K, Crayn DM, et al (2019) Beyond the model: expert knowledge improves predictions of species’ fates under climate change. Ecological Applications 29:e01824. https://doi.org/10.1002/eap.1824
  • Rogers BM, Jantz P, Goetz SJ (2017) Vulnerability of eastern US tree species to climate change. Global Change Biology 23:3302–3320. https://doi.org/10.1111/gcb.13585
  • Rojas Briceño NB, Cotrina Sánchez DA, Barboza Castillo E, et al (2020) Current and Future Distribution of Five Timber Forest Species in Amazonas, Northeast Peru: Contributions towards a Restoration Strategy. Diversity 12:305. https://doi.org/10.3390/d12080305
  • Romero GQ, Gonçalves-Souza T, Kratina P, et al (2018) Global predation pressure redistribution under future climate change. Nature Climate Change 8:1087–1091. https://doi.org/10.1038/s41558-018-0347-y
  • Sarıkaya A, Örücü ÖK (2019) Prediction Of Potential And Future Distribution Areas Of Anatolian Chesnut (Castanea Sativa Mill.) By Usıng Maximum Entropy (Maxent) Modeling Depending On Climate Change In Turkey. International Journal of Ecosystems and Ecology Science (IJEES) 9:699–708
  • Scheffers BR, Meester LD, Bridge TCL, et al (2016) The broad footprint of climate change from genes to biomes to people. Science 354:. https://doi.org/10.1126/science.aaf7671
  • Scheper J, Holzschuh A, Kuussaari M, et al (2013) Environmental factors driving the effectiveness of European agri-environmental measures in mitigating pollinator loss – a meta-analysis. Ecology Letters 16:912–920. https://doi.org/10.1111/ele.12128
  • Shcheglovitova M, Anderson RP (2013) Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecological Modelling 269:9–17. https://doi.org/10.1016/j.ecolmodel.2013.08.011
  • Sillero N (2011) What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling 222:1343–1346
  • Simi E, Moreno PI, Villa‐Martínez R, et al (2017) Climate change and resilience of deciduous Nothofagus forests in central–east Chilean Patagonia over the last 3200 years. Journal of Quaternary Science 32:845–856. https://doi.org/10.1002/jqs.2948
  • Thapa A, Wu RD, Hu YB, et al (2018) Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol Evol 8:10542–10554. https://doi.org/10.1002/ece3.4526
  • Timpane-Padgham BL, Beechie T, Klinger T (2017) A systematic review of ecological attributes that confer resilience to climate change in environmental restoration. PLOS ONE 12:e0173812. https://doi.org/10.1371/journal.pone.0173812
  • von Holle B, Yelenik S, Gornish ES (2020) Restoration at the landscape scale as a means of mitigation and adaptation to climate change. Curr Landscape Ecol Rep 5:85–97. https://doi.org/10.1007/s40823-020-00056-7
  • Walentowski H, Falk W, Mette T, et al (2017) Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany. Annals of Forest Research 60:101-126–126. https://doi.org/10.15287/afr.2016.789
  • Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and ecological surprises. Frontiers in Ecology and the Environment 5:475–482. https://doi.org/10.1890/070037
  • WorldClim (2020) Global climate and weather data — WorldClim. https://worldclim.org/data/index.html. Accessed 1 Dec 2020
  • Yang J, Pedlar JH, McKenney DW, Weersink AJ (2015) The development of universal response functions to facilitate climate smart regeneration of black spruce and white pine in Ontario, Canada. https://doi.org/10.1016/j.foreco.2014.12.001
  • Yang XQ, Kushwaha SPS, Saran S, et al (2013) Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol Eng 51:83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004
  • Yu F, Wang T, Groen TA, et al (2019) Climate and land use changes will degrade the distribution of Rhododendrons in China. Science of The Total Environment 659:515–528. https://doi.org/10.1016/j.scitotenv.2018.12.223
  • Zhang T, Liu G (2017) Study of methods to improve the temporal transfer ability of niche model. Journal of China Agricultural University 22:98–105
  • Zhao H, Zhang H, Xu C (2020) Study on Taiwania cryptomerioides under climate change: MaxEnt modeling for predicting the potential geographical distribution. Global Ecology and Conservation 24:e01313. https://doi.org/10.1016/j.gecco.2020.e01313

Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi

Year 2021, Volume: 22 Issue: 1, 26 - 41, 12.05.2021

Ömer K. Örücü , Derya Gülçin , İrem Özçifçi , E. Seda Arslan

https://doi.org/10.17474/artvinofd.834174
Cited By: 1

Abstract

Bu çalışmanın amacı mor çiçekli orman gülü Rhodendron ponticum L. 'nin maximum entropi algoritması kullanılarak günümüz ve gelecek potansiyel yayılış alanlarının iklim senaryolarına göre modellenmesidir. İki aşamalı olarak yürütülen bu çalışmanın birinci aşamasında R. ponticum L.’nin çalışma alanı (Türkiye, Gürcistan ve Rusya sınırları) içerisindeki yayılışını temsil eden örnek noktalara ait (presence data) veriler ve biyoklimatik değişkenler kullanılmıştır. Yüksek korelasyonu ve çoklu doğrusallığı önlemek amacıyla, Worldclim 2.1 versiyonu 2.5 dakika (yaklaşık 20 km2) konumsal çözünürlükteki 19 biyoklimatik değişken Pearson Korelasyon analizi yapılarak 8 değişkene indirgenmiştir. İkinci aşamada ise türün yayılış alanlarının iklim değişiminden nasıl etkileneceğini belirlemek için CMIP6 modellerinden olan CNRM-CM6-1 iklim değişikliği modeli kullanılmış, SSP2 4.5 ve SSP5 8.5’e senaryolarına göre 2041-2060 ve 2081-2100 periyotlarına ait potansiyel yayılış alanı MaxEnt 3.4.1 programı kullanılarak modellenmiştir. Ayrıca, tür için tahmin edilen günümüz ve gelecekteki potansiyel yayılış alanları arasındaki alansal ve konumsal farklar, değişim analizi ile ortaya konulmuştur. Sonuçta, R. ponticum L.’nin potansiyel yayılış alanlarına göre üretilen bilginin teoriden pratiğe dönüşmesindeki temel faydalar sürdürülebilir peyzaj yönetimi kapsamında tartışılmıştır.

Keywords

Rhododendron ponticum L. CNRM-1 tür dağılım modeli değişim analizi MaxEnt

References

  • Abdelaal M, Fois M, Fenu G, Bacchetta G (2019) Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt. Ecological Informatics 50:68–75. https://doi.org/10.1016/j.ecoinf.2019.01.003
  • Akkemik Ü (2014) Türkiye’nin doğal-egzotik ağaç ve çalıları I. Orman Genel Müdürlüğü Yayınları, Ankara
  • Akyol A, Orucu OK, Arslan ES (2020) Habitat suitability mapping of stone pine (Pinus pineaL.) under the effects of climate change. Biologia
  • Arslan ES, Akyol A, Örücü ÖK, Sarıkaya AG (2020) Distribution of rose hip (Rosa canina L.) under current and future climate conditions. Reg Environ Change 20:107. https://doi.org/10.1007/s10113-020-01695-6
  • Austin M (2007) Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling 200:1–19. https://doi.org/10.1016/j.ecolmodel.2006.07.005
  • BIYOD (2020) BİYOD - Biyolojik Çeşitlilik ve Odun DIşı Orman Ürünleri Veri Tabanı. Tarım ve Orman Bakanlığı Orman Genel Müdürlüğü, Ankara
  • Bouchard M, Aquilué N, Périé C, Lambert M-C (2019) Tree species persistence under warming conditions: A key driver of forest response to climate change. Forest Ecology and Management 442:96–104. https://doi.org/10.1016/j.foreco.2019.03.040
  • Cao B, Bai CK, Zhang LL, et al (2016) Modeling habitat distribution of Cornus officinalis with Maxent modeling and fuzzy logics in China. J Plant Ecol 9:742–751. https://doi.org/10.1093/jpe/rtw009
  • Chen I-C, Hill JK, Ohlemüller R, et al (2011) Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 333:1024–1026. https://doi.org/10.1126/science.1206432
  • Çoban HO, Örücü ÖK, Arslan ES (2020) MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier. Sustainability 12:2671. https://doi.org/10.3390/su12072671
  • Dagnino D, Guerrina M, Minuto L, et al (2020) Climate change and the future of endemic flora in the South Western Alps: relationships between niche properties and extinction risk. Reg Environ Change 20:121. https://doi.org/10.1007/s10113-020-01708-4
  • Dai J, Roberts DA, Stow DA, et al (2020) Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal. Remote Sensing of Environment 250:112037. https://doi.org/10.1016/j.rse.2020.112037
  • Davis PH (1965) Flora of Turkey and The East Aegean Islands - I. Edinburgh University Press, Edinburgh
  • Dawson TP, Jackson ST, House JI, et al (2011) Beyond Predictions: Biodiversity Conservation in a Changing Climate. Science 332:53–58. https://doi.org/10.1126/science.1200303
  • Dimobe K, Ouédraogo A, Ouédraogo K, et al (2020) Climate change reduces the distribution area of the shea tree (Vitellaria paradoxa CF Gaertn.) in Burkina Faso. Journal of Arid Environments 181:104237
  • Djalante R (2019) Key assessments from the IPCC special report on global warming of 1.5 °C and the implications for the Sendai framework for disaster risk reduction. Progress in Disaster Science 1:100001. https://doi.org/10.1016/j.pdisas.2019.100001
  • Dyderski MK, Paź S, Frelich LE, Jagodziński AM (2018) How much does climate change threaten European forest tree species distributions? Global Change Biology 24:1150–1163. https://doi.org/10.1111/gcb.13925
  • Ehrlén J, Morris WF (2015) Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters 18:303–314. https://doi.org/10.1111/ele.12410
  • Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annual review of ecology, evolution, systematics 40:677–697
  • Eyring V, Bony S, Meehl GA, et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
  • Fei S, Desprez JM, Potter KM, et al (2017) Divergence of species responses to climate change. Science Advances 3:e1603055. https://doi.org/10.1126/sciadv.1603055
  • Ferrarini A, Alsafran MHSA, Dai J, Alatalo JM (2019) Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study. Clim Dyn 52:1413–1423. https://doi.org/10.1007/s00382-018-4200-9
  • Fortunel C, Paine CET, Fine PVA, et al (2014) Environmental factors predict community functional composition in Amazonian forests. Journal of Ecology 102:145–155. https://doi.org/10.1111/1365-2745.12160
  • Garcia K, Lasco R, Ines A, et al (2013) Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Applied Geography 44:12–22. https://doi.org/10.1016/j.apgeog.2013.07.005
  • Garzón MB, Robson TM, Hampe A (2019) ΔTraitSDMs: species distribution models that account for local adaptation and phenotypic plasticity. New Phytologist 222:1757–1765. https://doi.org/10.1111/nph.15716
  • Gassó N, Thuiller W, Pino J, Vilà M (2012) Potential Distribution Range of Invasive Plant Species in Spain. NeoBiota 12:25
  • GBIF (2020) Rhododendron ponticum L. in GBIF Secretariat (2019). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2020-12-01
  • Hausfather Z (2019) CMIP6: the next generation of climate models explained. In: Carbon Brief. https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained. Accessed 8 Oct 2020
  • Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785. https://doi.org/10.1111/j.0906-7590.2006.04700.x
  • Hosmer Jr DW, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & Sons
  • iNaturalist (2020a) Gözlemci Grzegorz Grzejszczak Tarih: Mayıs 31, 2016 12:11 PM HST Yer: Adżaria, Gruzja (Google, OSM). In: iNaturalist. https://www.inaturalist.org/photos/15547467. Accessed 30 Nov 2020
  • iNaturalist (2020b) Gözlemci Grzegorz Grzejszczak Tarih: Mayıs 31, 2016 12:11 PM HST Yer: Adżaria, Gruzja (Google, OSM). In: iNaturalist. https://www.inaturalist.org/photos/15546233. Accessed 30 Nov 2020
  • IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland
  • Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics 60:101150. https://doi.org/10.1016/j.ecoinf.2020.101150
  • Kim J, Lee DK, Kim HG (2020) Suitable trees for urban landscapes in the Republic of Korea under climate change. Landscape and Urban Planning 204:103937. https://doi.org/10.1016/j.landurbplan.2020.103937
  • Kramer-Schadt S, Niedballa J, Pilgrim JD, et al (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379. https://doi.org/10.1111/ddi.12096
  • Kumar P (2012) Assessment of impact of climate change on Rhododendrons in Sikkim Himalayas using Maxent modelling: limitations and challenges. Biodivers Conserv 21:1251–1266. https://doi.org/10.1007/s10531-012-0279-1
  • Li G, Huang J, Guo H, Du S (2020) Projecting species loss and turnover under climate change for 111 Chinese tree species. Forest Ecology and Management 477:118488. https://doi.org/10.1016/j.foreco.2020.118488
  • Manzoor SA, Griffiths G, Iizuka K, Lukac M (2018) Land Cover and Climate Change May Limit Invasiveness of Rhododendron ponticum in Wales. Front Plant Sci 9:. https://doi.org/10.3389/fpls.2018.00664
  • Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  • Moukrim S, Lahssini S, Rhazi M, et al (2019) Climate change impacts on potential distribution of multipurpose agro-forestry species: Argania spinosa (L.) Skeels as case study. Agroforest Syst 93:1209–1219. https://doi.org/10.1007/s10457-018-0232-8
  • Nascimbene J, Benesperi R, Casazza G, et al (2020) Range shifts of native and invasive trees exacerbate the impact of climate change on epiphyte distribution: The case of lung lichen and black locust in Italy. Science of the Total Environment 735:139537
  • Norberg A, Abrego N, Blanchet FG, et al (2019) A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs 89:e01370. https://doi.org/10.1002/ecm.1370
  • Oliver TH, Smithers RJ, Beale CM, Watts K (2016) Are existing biodiversity conservation strategies appropriate in a changing climate? Biological Conservation 193:17–26. https://doi.org/10.1016/j.biocon.2015.10.024
  • Örücü ÖK (2019) Phoenix theophrasti Gr.’nin iklim değişimine bağlı günümüz ve gelecekteki yayılış alanlarının MaxEnt Modeli ile tahmini ve bitkisel tasarımda kullanımı. Türkiye Ormancılık Dergisi 20:274–283. https://doi.org/10.18182/tjf.613205
  • Özbucak TB, Türkiş S, Çakmak A (2009) Ordu çevresinde yayılış gösteren bazı Rhododendron türleri üzerine ekolojik bir çalışma. Research Journal of Biology Sciences 2:71–77
  • Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A %J J of biogeography (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. 34:102–117
  • Peterson ML, Doak DF, Morris WF (2019) Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Global Change Biology 25:775–793. https://doi.org/10.1111/gcb.14562
  • Phillips SJ, Anderson RP, Schapire RE %J E modelling (2006) Maximum entropy modeling of species geographic distributions. 190:231–259
  • Phillips SJ, Elith J (2010) POC plots: calibrating species distribution models with presence‐only data. Ecology 91:2476–2484
  • Qin AL, Liu B, Guo QS, et al (2017) Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Glob Ecol Conserv 10:139–146. https://doi.org/10.1016/j.gecco.2017.02.004
  • Reside AE, Critchell K, Crayn DM, et al (2019) Beyond the model: expert knowledge improves predictions of species’ fates under climate change. Ecological Applications 29:e01824. https://doi.org/10.1002/eap.1824
  • Rogers BM, Jantz P, Goetz SJ (2017) Vulnerability of eastern US tree species to climate change. Global Change Biology 23:3302–3320. https://doi.org/10.1111/gcb.13585
  • Rojas Briceño NB, Cotrina Sánchez DA, Barboza Castillo E, et al (2020) Current and Future Distribution of Five Timber Forest Species in Amazonas, Northeast Peru: Contributions towards a Restoration Strategy. Diversity 12:305. https://doi.org/10.3390/d12080305
  • Romero GQ, Gonçalves-Souza T, Kratina P, et al (2018) Global predation pressure redistribution under future climate change. Nature Climate Change 8:1087–1091. https://doi.org/10.1038/s41558-018-0347-y
  • Sarıkaya A, Örücü ÖK (2019) Prediction Of Potential And Future Distribution Areas Of Anatolian Chesnut (Castanea Sativa Mill.) By Usıng Maximum Entropy (Maxent) Modeling Depending On Climate Change In Turkey. International Journal of Ecosystems and Ecology Science (IJEES) 9:699–708
  • Scheffers BR, Meester LD, Bridge TCL, et al (2016) The broad footprint of climate change from genes to biomes to people. Science 354:. https://doi.org/10.1126/science.aaf7671
  • Scheper J, Holzschuh A, Kuussaari M, et al (2013) Environmental factors driving the effectiveness of European agri-environmental measures in mitigating pollinator loss – a meta-analysis. Ecology Letters 16:912–920. https://doi.org/10.1111/ele.12128
  • Shcheglovitova M, Anderson RP (2013) Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecological Modelling 269:9–17. https://doi.org/10.1016/j.ecolmodel.2013.08.011
  • Sillero N (2011) What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling 222:1343–1346
  • Simi E, Moreno PI, Villa‐Martínez R, et al (2017) Climate change and resilience of deciduous Nothofagus forests in central–east Chilean Patagonia over the last 3200 years. Journal of Quaternary Science 32:845–856. https://doi.org/10.1002/jqs.2948
  • Thapa A, Wu RD, Hu YB, et al (2018) Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol Evol 8:10542–10554. https://doi.org/10.1002/ece3.4526
  • Timpane-Padgham BL, Beechie T, Klinger T (2017) A systematic review of ecological attributes that confer resilience to climate change in environmental restoration. PLOS ONE 12:e0173812. https://doi.org/10.1371/journal.pone.0173812
  • von Holle B, Yelenik S, Gornish ES (2020) Restoration at the landscape scale as a means of mitigation and adaptation to climate change. Curr Landscape Ecol Rep 5:85–97. https://doi.org/10.1007/s40823-020-00056-7
  • Walentowski H, Falk W, Mette T, et al (2017) Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany. Annals of Forest Research 60:101-126–126. https://doi.org/10.15287/afr.2016.789
  • Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and ecological surprises. Frontiers in Ecology and the Environment 5:475–482. https://doi.org/10.1890/070037
  • WorldClim (2020) Global climate and weather data — WorldClim. https://worldclim.org/data/index.html. Accessed 1 Dec 2020
  • Yang J, Pedlar JH, McKenney DW, Weersink AJ (2015) The development of universal response functions to facilitate climate smart regeneration of black spruce and white pine in Ontario, Canada. https://doi.org/10.1016/j.foreco.2014.12.001
  • Yang XQ, Kushwaha SPS, Saran S, et al (2013) Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol Eng 51:83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004
  • Yu F, Wang T, Groen TA, et al (2019) Climate and land use changes will degrade the distribution of Rhododendrons in China. Science of The Total Environment 659:515–528. https://doi.org/10.1016/j.scitotenv.2018.12.223
  • Zhang T, Liu G (2017) Study of methods to improve the temporal transfer ability of niche model. Journal of China Agricultural University 22:98–105
  • Zhao H, Zhang H, Xu C (2020) Study on Taiwania cryptomerioides under climate change: MaxEnt modeling for predicting the potential geographical distribution. Global Ecology and Conservation 24:e01313. https://doi.org/10.1016/j.gecco.2020.e01313
There are 72 citations in total.

Details

Primary Language Turkish
Subjects Environmental Sciences
Journal Section Research Article
Authors

Ömer K. Örücü 0000-0002-2162-7553

Derya Gülçin 0000-0001-7118-0174

İrem Özçifçi 0000-0002-7095-012X

E. Seda Arslan 0000-0003-1592-5180

Publication Date May 12, 2021
Acceptance Date December 28, 2020
Published in Issue Year 2021 Volume: 22 Issue: 1

Cite

APA Örücü, Ö. K., Gülçin, D., Özçifçi, İ., Arslan, E. S. (2021). Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, 22(1), 26-41. https://doi.org/10.17474/artvinofd.834174
AMA Örücü ÖK, Gülçin D, Özçifçi İ, Arslan ES. Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi. ACUJFF. May 2021;22(1):26-41. doi:10.17474/artvinofd.834174
Chicago Örücü, Ömer K., Derya Gülçin, İrem Özçifçi, and E. Seda Arslan. “Mor Çiçekli Ormangülünün (Rhododendron Ponticum L.) günümüz Ve Gelecekteki Iklim koşullarına göre yayılış alanlarının Modellenmesi”. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi 22, no. 1 (May 2021): 26-41. https://doi.org/10.17474/artvinofd.834174.
EndNote Örücü ÖK, Gülçin D, Özçifçi İ, Arslan ES (May 1, 2021) Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi 22 1 26–41.
IEEE Ö. K. Örücü, D. Gülçin, İ. Özçifçi, and E. S. Arslan, “Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi”, ACUJFF, vol. 22, no. 1, pp. 26–41, 2021, doi: 10.17474/artvinofd.834174.
ISNAD Örücü, Ömer K. et al. “Mor Çiçekli Ormangülünün (Rhododendron Ponticum L.) günümüz Ve Gelecekteki Iklim koşullarına göre yayılış alanlarının Modellenmesi”. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi 22/1 (May 2021), 26-41. https://doi.org/10.17474/artvinofd.834174.
JAMA Örücü ÖK, Gülçin D, Özçifçi İ, Arslan ES. Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi. ACUJFF. 2021;22:26–41.
MLA Örücü, Ömer K. et al. “Mor Çiçekli Ormangülünün (Rhododendron Ponticum L.) günümüz Ve Gelecekteki Iklim koşullarına göre yayılış alanlarının Modellenmesi”. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, vol. 22, no. 1, 2021, pp. 26-41, doi:10.17474/artvinofd.834174.
Vancouver Örücü ÖK, Gülçin D, Özçifçi İ, Arslan ES. Mor Çiçekli Ormangülünün (Rhododendron ponticum L.) günümüz ve gelecekteki iklim koşullarına göre yayılış alanlarının modellenmesi. ACUJFF. 2021;22(1):26-41.

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