Mendeley EndNote BibTex Kaynak Göster

Yıl 2020, Cilt 44, Sayı 4, 427 - 440, 01.08.2020
https://doi.org/10.3906/bot-2001-33

Öz

Kaynakça

  • Akçakaya HR, Butchart SH, Watson JE, Pearson RG (2014). Preventing species extinctions resulting from climate change. Nature Climate Change 4 (12): 1048-1049. doi: 10.1038/nclimate2455
  • 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. doi: 10.1111/j.1365-2664.2006.01214.x
  • Araújo MB, Whittaker RJ, Ladle RJ, Erhard M (2005). Reducing uncertainty in projections of extinction risk from climate change. Global Ecology and Biogeography 14 (6): 529-538. doi: 10.1111/j.1466-822x.2005.00182.x
  • Attorre F, Abeli T, Bacchetta G, Farcomeni A, Fenu G et al. (2018). How to include the impact of climate change in the extinction risk assessment of policy plant species? Journal for Nature Conservation 44: 43-49. doi: 10.1016/j.jnc.2018.06.004
  • Baldwin R (2009). Use of maximum entropy modeling in wildlife research. Entropy 11 (4): 854-866. doi: 10.3390/e11040854
  • Baskin CC, Baskin JM (1998). Seeds: ecology, biogeography, and evolution of dormancy and germination. San Diego, USA: Academic Press.
  • Blackmore S, Oldfield S (2017). Plant Conservation Science and Practice: The Role of Botanic Gardens. Cambridge, England: Cambridge University Press.
  • Chen I, Hill JK, Ohlemuller R, Roy DB, Thomas CD (2011). Rapid range shifts of species associated with high Llvels of climate warming. Science 333 (6045): 1024-1026. doi: 10.1126/ science.1206432
  • De Marco P, Nóbrega CC (2018). Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation. Plos One 13 (9): e0202403. doi: 10.1371/ journal.pone.0202403
  • Diniz-Filho JA, Mauricio Bini L, Fernando Rangel T, Loyola RD, Hof C et al. (2009). Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32 (6): 897-906. doi: 10.1111/j.1600- 0587.2009.06196.x
  • Dobrowski SZ, Abatzoglou J, Swanson AK, Greenberg JA, Mynsberge AR et al. (2012). The climate velocity of the contiguous United States during the 20th century. Global Change Biology 19 (1): 241-251. doi: 10.1111/gcb.12026
  • Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S et al. (2006). Novel methods improve prediction of species’ distribution from occurrence data. Ecography 29 (2): 129-151. doi: 10.1111/j.2006.0906-7590.04596.x
  • Elith J, Leathwick JR (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40 (1): 677- 697. doi: 10.1146/annurev.ecolsys.110308.120159
  • Fick SE, Hijmans RJ (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. doi: 10.1002/ joc.5086
  • Fielding AH, Bell JF (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24 (1): 38-49. doi: 10.1017/s0376892997000088
  • Grime JP, Mason G, Curtis AV, Rodman J, Band SR (1981). A comparative study of germination characteristics in a local flora. The Journal of Ecology 69 (3): 1017. doi: 10.2307/2259651
  • Guisan A, Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters 8 (9): 993- 1009. doi: 10.1111/j.1461-0248.2005.00792.x
  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25 (15): 1965-1978. doi: 10.1002/joc.1276
  • Hirzel AH, Hausser J, Chessel D, Perrin N (2002). Ecologicalniche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83 (7): 2027-2036. doi: 10.1890/0012-9658(2002)083[2027:enfaht]2.0.co;2
  • IPCC (2014). Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. doi: 10.1017/ CBO9781107415324
  • Jena P, Azad S, Rajeevan M (2016). CMIP5 projected changes in the annual cycle of Indian monsoon rainfall. Climate 4 (1): 14. doi: 10.3390/cli4010014
  • Jungclaus JH, Fischer N, Haak H, Lohmann K, Marotzke J et al. (2013). Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model. Journal of Advances in Modeling Earth Systems 5 (2): 422-446. doi: 10.1002/jame.20023
  • Işık K (2011). Rare and endemic species: why are they prone to extinction? Turkish Journal of Botany 35: 411-417. doi: 10.3906/bot-1012-90
  • Kandemir A, Hedge IC (2007). An anomalous new Ferulago (Apiaceae) from eastern Turkey. Willdenowia 37 (1): 273-276. doi: 10.3372/wi.37.37115
  • Kandemir A, Sarı İ (2019). Impacts of insect herbivory on reproductive success of Ferulago glareosa (Apiaceae). Biological Diversity and Conservation 12 (1): 160-166. doi: 10.5505/ biodicon.2019.21939
  • Kempel A, Rindisbacher A, Fischer M, Allan E (2018). Plant soil feedback strength in relation to large-scale plant rarity and phylogenetic relatedness. Ecology 99 (3): 597-606. doi: 10.1002/ecy.2145
  • Kumar S, Spaulding SA, Stohlgren TJ, Hermann KA, Schmidt TS et al. (2009). Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US. Frontiers in Ecology and the Environment 7 (8): 415-420. doi: 10.1890/080054
  • Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W et al. (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 / R. Pachauri R, Meyer LA (editors). Geneva, Switzerland. ISBN: 978-92-9169-143-2.
  • Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2006). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography 34 (1): 102-117. doi: 10.1111/j.1365-2699.2006.01594.x
  • Peterson AT, Soberón J (2012). Species distribution modeling and ecological niche modeling: getting the concepts right. Natureza & Conservação 10 (2): 102-107. doi: 10.4322/natcon.2012.019
  • Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E et al. (2011). Ecological Niches and Geographic Distributions (MPB-49). Princeton, NJ, USA: Princeton University Press. Phillips SJ, Anderson RP, Schapire RE (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190 (3-4): 231-259. doi: 10.1016/j. ecolmodel.2005.03.026
  • Phillips SJ, Dudík M (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31 (2): 161-175. doi:10.1111/j.0906- 7590.2007.5203.x
  • Porfirio LL, Harris RM, Lefroy EC, Hugh S, Gould SF et al. (2014). Improving the use of species distribution models in conservation planning and management under climate change. Plos One 9 (11): e113749. doi: 10.1371/journal.pone.0113749
  • Pramanik M, Paudel U, Mondal B, Chakraborti S, Deb P (2018). Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India. Climate Risk Management 19: 94-105. doi: 10.1016/j.crm.2017.11.002
  • Primack RB (2006). Essentials of Conservation Biology. Sunderland, MA, USA: Sinauer Associates, Inc.
  • R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
  • Roberts HA (1979). Periodicity of seedling emergence and seed survival in some Umbelliferae. Journal of Applied Ecology 16 (1): 195. doi: 10.2307/2402738
  • Solomon S, Qin D, Manning M, Chen Z, Marquis M et al. (2007). Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the IPCC. Cambridge, UK and New York, USA: Cambridge University Press.
  • Srinivasa RK, Nagesh Kumar D (2016). Selection of global climate models for India using cluster analysis. Journal of Water and Climate Change 7 (4): 764-774. doi: 10.2166/wcc.2016.112
  • Süel H, Şentürk Ö, Mert A, Özdemir S, Yalçınkaya B (2018). Habitat Suitability Modeling and Mapping. In: V. International Multidisciplinary Congress of Eurasia; Barcelona, Spain. p. 74. Swets J (1988). Measuring the accuracy of diagnostic systems. Science 240 (4857): 1285-1293. doi: 10.1126/ science.3287615
  • Thuiller W (2004). Patterns and uncertainties of species’ range shifts under climate change. Global Change Biology 10 (12): 2020- 2027. doi: 10.1111/j.1365-2486.2004.00859.x
  • Thuiller W, Lavorel S, Araujo MB (2005). Niche properties and geographical extent as predictors of species sensitivity to climate change. Global Ecology and Biogeography 14 (4): 347- 357. doi: 10.1111/j.1466-822x.2005.00162.x
  • Vandelook F, Bolle N, Van Assche JA (2009). Morphological and physiological dormancy in seeds of Aegopodium podagraria (Apiaceae) broken successively during cold stratification. Seed Science Research 19 (2): 115-123. doi: 10.1017/s0960258509301075
  • Wamelink GW, Goedhart W, Frissel JY (2014). Why some plant species are rare. Plos One 9 (7): e102674. doi: 10.1371/journal. pone.0102674

Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?

Yıl 2020, Cilt 44, Sayı 4, 427 - 440, 01.08.2020
https://doi.org/10.3906/bot-2001-33

Öz

Turkey is one of the most important temperate countries on Earth in terms of plant diversity. There is a growing interest in understanding habitat suitability and future distributions of species in the scientific world. Because climate change has impacted ecosystems with major consequences, species are shifting and declining much faster than in the past. Some global climate models used for predicting climate in the future better represent and have higher reliability for some climate types.Ferulago glareosa , which lives in Turkey, is a rare endemic plant species. In this study, we investigated current and future distributions of the species determined to be habitat-specific to lead to future studies on conservation. The Maxent model was used to map the current and future potential distribution of the species for Turkey. HadGEM2-ES and MPI-ESM-LR global climate models based on predicted future suitability of Ferulago glareosa for 2050 and 2070 were examined. Models were constructed based on 20 presence points of the species and 2 abiotic variables. The current species distribution modeling of Ferulago glareosa predicted by the model produced very high success rates with training and test AUC values of 0.970 and 0.968, respectively. The true skill statistics value of the model 0.8245 indicated excellent model performance. In the end, we have demonstrated how predictions obtained from a highly reliable global climate model for a region's climate could provide more dependable insights into the future distribution of narrow-spread endemic species.

Kaynakça

  • Akçakaya HR, Butchart SH, Watson JE, Pearson RG (2014). Preventing species extinctions resulting from climate change. Nature Climate Change 4 (12): 1048-1049. doi: 10.1038/nclimate2455
  • 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. doi: 10.1111/j.1365-2664.2006.01214.x
  • Araújo MB, Whittaker RJ, Ladle RJ, Erhard M (2005). Reducing uncertainty in projections of extinction risk from climate change. Global Ecology and Biogeography 14 (6): 529-538. doi: 10.1111/j.1466-822x.2005.00182.x
  • Attorre F, Abeli T, Bacchetta G, Farcomeni A, Fenu G et al. (2018). How to include the impact of climate change in the extinction risk assessment of policy plant species? Journal for Nature Conservation 44: 43-49. doi: 10.1016/j.jnc.2018.06.004
  • Baldwin R (2009). Use of maximum entropy modeling in wildlife research. Entropy 11 (4): 854-866. doi: 10.3390/e11040854
  • Baskin CC, Baskin JM (1998). Seeds: ecology, biogeography, and evolution of dormancy and germination. San Diego, USA: Academic Press.
  • Blackmore S, Oldfield S (2017). Plant Conservation Science and Practice: The Role of Botanic Gardens. Cambridge, England: Cambridge University Press.
  • Chen I, Hill JK, Ohlemuller R, Roy DB, Thomas CD (2011). Rapid range shifts of species associated with high Llvels of climate warming. Science 333 (6045): 1024-1026. doi: 10.1126/ science.1206432
  • De Marco P, Nóbrega CC (2018). Evaluating collinearity effects on species distribution models: an approach based on virtual species simulation. Plos One 13 (9): e0202403. doi: 10.1371/ journal.pone.0202403
  • Diniz-Filho JA, Mauricio Bini L, Fernando Rangel T, Loyola RD, Hof C et al. (2009). Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32 (6): 897-906. doi: 10.1111/j.1600- 0587.2009.06196.x
  • Dobrowski SZ, Abatzoglou J, Swanson AK, Greenberg JA, Mynsberge AR et al. (2012). The climate velocity of the contiguous United States during the 20th century. Global Change Biology 19 (1): 241-251. doi: 10.1111/gcb.12026
  • Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S et al. (2006). Novel methods improve prediction of species’ distribution from occurrence data. Ecography 29 (2): 129-151. doi: 10.1111/j.2006.0906-7590.04596.x
  • Elith J, Leathwick JR (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40 (1): 677- 697. doi: 10.1146/annurev.ecolsys.110308.120159
  • Fick SE, Hijmans RJ (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. doi: 10.1002/ joc.5086
  • Fielding AH, Bell JF (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24 (1): 38-49. doi: 10.1017/s0376892997000088
  • Grime JP, Mason G, Curtis AV, Rodman J, Band SR (1981). A comparative study of germination characteristics in a local flora. The Journal of Ecology 69 (3): 1017. doi: 10.2307/2259651
  • Guisan A, Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters 8 (9): 993- 1009. doi: 10.1111/j.1461-0248.2005.00792.x
  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25 (15): 1965-1978. doi: 10.1002/joc.1276
  • Hirzel AH, Hausser J, Chessel D, Perrin N (2002). Ecologicalniche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83 (7): 2027-2036. doi: 10.1890/0012-9658(2002)083[2027:enfaht]2.0.co;2
  • IPCC (2014). Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. doi: 10.1017/ CBO9781107415324
  • Jena P, Azad S, Rajeevan M (2016). CMIP5 projected changes in the annual cycle of Indian monsoon rainfall. Climate 4 (1): 14. doi: 10.3390/cli4010014
  • Jungclaus JH, Fischer N, Haak H, Lohmann K, Marotzke J et al. (2013). Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model. Journal of Advances in Modeling Earth Systems 5 (2): 422-446. doi: 10.1002/jame.20023
  • Işık K (2011). Rare and endemic species: why are they prone to extinction? Turkish Journal of Botany 35: 411-417. doi: 10.3906/bot-1012-90
  • Kandemir A, Hedge IC (2007). An anomalous new Ferulago (Apiaceae) from eastern Turkey. Willdenowia 37 (1): 273-276. doi: 10.3372/wi.37.37115
  • Kandemir A, Sarı İ (2019). Impacts of insect herbivory on reproductive success of Ferulago glareosa (Apiaceae). Biological Diversity and Conservation 12 (1): 160-166. doi: 10.5505/ biodicon.2019.21939
  • Kempel A, Rindisbacher A, Fischer M, Allan E (2018). Plant soil feedback strength in relation to large-scale plant rarity and phylogenetic relatedness. Ecology 99 (3): 597-606. doi: 10.1002/ecy.2145
  • Kumar S, Spaulding SA, Stohlgren TJ, Hermann KA, Schmidt TS et al. (2009). Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US. Frontiers in Ecology and the Environment 7 (8): 415-420. doi: 10.1890/080054
  • Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W et al. (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 / R. Pachauri R, Meyer LA (editors). Geneva, Switzerland. ISBN: 978-92-9169-143-2.
  • Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2006). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography 34 (1): 102-117. doi: 10.1111/j.1365-2699.2006.01594.x
  • Peterson AT, Soberón J (2012). Species distribution modeling and ecological niche modeling: getting the concepts right. Natureza & Conservação 10 (2): 102-107. doi: 10.4322/natcon.2012.019
  • Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E et al. (2011). Ecological Niches and Geographic Distributions (MPB-49). Princeton, NJ, USA: Princeton University Press. Phillips SJ, Anderson RP, Schapire RE (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190 (3-4): 231-259. doi: 10.1016/j. ecolmodel.2005.03.026
  • Phillips SJ, Dudík M (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31 (2): 161-175. doi:10.1111/j.0906- 7590.2007.5203.x
  • Porfirio LL, Harris RM, Lefroy EC, Hugh S, Gould SF et al. (2014). Improving the use of species distribution models in conservation planning and management under climate change. Plos One 9 (11): e113749. doi: 10.1371/journal.pone.0113749
  • Pramanik M, Paudel U, Mondal B, Chakraborti S, Deb P (2018). Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India. Climate Risk Management 19: 94-105. doi: 10.1016/j.crm.2017.11.002
  • Primack RB (2006). Essentials of Conservation Biology. Sunderland, MA, USA: Sinauer Associates, Inc.
  • R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
  • Roberts HA (1979). Periodicity of seedling emergence and seed survival in some Umbelliferae. Journal of Applied Ecology 16 (1): 195. doi: 10.2307/2402738
  • Solomon S, Qin D, Manning M, Chen Z, Marquis M et al. (2007). Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the IPCC. Cambridge, UK and New York, USA: Cambridge University Press.
  • Srinivasa RK, Nagesh Kumar D (2016). Selection of global climate models for India using cluster analysis. Journal of Water and Climate Change 7 (4): 764-774. doi: 10.2166/wcc.2016.112
  • Süel H, Şentürk Ö, Mert A, Özdemir S, Yalçınkaya B (2018). Habitat Suitability Modeling and Mapping. In: V. International Multidisciplinary Congress of Eurasia; Barcelona, Spain. p. 74. Swets J (1988). Measuring the accuracy of diagnostic systems. Science 240 (4857): 1285-1293. doi: 10.1126/ science.3287615
  • Thuiller W (2004). Patterns and uncertainties of species’ range shifts under climate change. Global Change Biology 10 (12): 2020- 2027. doi: 10.1111/j.1365-2486.2004.00859.x
  • Thuiller W, Lavorel S, Araujo MB (2005). Niche properties and geographical extent as predictors of species sensitivity to climate change. Global Ecology and Biogeography 14 (4): 347- 357. doi: 10.1111/j.1466-822x.2005.00162.x
  • Vandelook F, Bolle N, Van Assche JA (2009). Morphological and physiological dormancy in seeds of Aegopodium podagraria (Apiaceae) broken successively during cold stratification. Seed Science Research 19 (2): 115-123. doi: 10.1017/s0960258509301075
  • Wamelink GW, Goedhart W, Frissel JY (2014). Why some plant species are rare. Plos One 9 (7): e102674. doi: 10.1371/journal. pone.0102674

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Article
Yazarlar

İdris SARI
Erzincan Social Sciences High School, Erzincan, Turkey
0000-0003-2475-8759


Ali KANDEMİR Bu kişi benim
Department of Biology, Faculty of Art and Science, Erzincan Binali Yıldırım University, Erzincan, Turkey

Yayımlanma Tarihi 1 Ağustos 2020
Yayınlandığı Sayı Yıl 2020, Cilt 44, Sayı 4

Kaynak Göster

Bibtex @ { tbtkbotany783725, journal = {Turkish Journal of Botany}, issn = {1300-008X}, eissn = {1303-6106}, address = {}, publisher = {TÜBİTAK}, year = {2020}, volume = {44}, number = {4}, pages = {427 - 440}, doi = {10.3906/bot-2001-33}, title = {Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?}, key = {cite}, author = {Sarı, İdris and Kandemir, Ali} }
APA Sarı, İ. & Kandemir, A. (2020). Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future? . Turkish Journal of Botany , 44 (4) , 427-440 . DOI: 10.3906/bot-2001-33
MLA Sarı, İ. , Kandemir, A. "Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?" . Turkish Journal of Botany 44 (2020 ): 427-440 <https://dergipark.org.tr/tr/pub/tbtkbotany/issue/56432/783725>
Chicago Sarı, İ. , Kandemir, A. "Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?". Turkish Journal of Botany 44 (2020 ): 427-440
RIS TY - JOUR T1 - Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future? AU - İdris Sarı , Ali Kandemir Y1 - 2020 PY - 2020 N1 - doi: 10.3906/bot-2001-33 DO - 10.3906/bot-2001-33 T2 - Turkish Journal of Botany JF - Journal JO - JOR SP - 427 EP - 440 VL - 44 IS - 4 SN - 1300-008X-1303-6106 M3 - doi: 10.3906/bot-2001-33 UR - https://doi.org/10.3906/bot-2001-33 Y2 - 2022 ER -
EndNote %0 Turkish Journal of Botany Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future? %A İdris Sarı , Ali Kandemir %T Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future? %D 2020 %J Turkish Journal of Botany %P 1300-008X-1303-6106 %V 44 %N 4 %R doi: 10.3906/bot-2001-33 %U 10.3906/bot-2001-33
ISNAD Sarı, İdris , Kandemir, Ali . "Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?". Turkish Journal of Botany 44 / 4 (Ağustos 2020): 427-440 . https://doi.org/10.3906/bot-2001-33
AMA Sarı İ. , Kandemir A. Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?. Turkish Journal of Botany. 2020; 44(4): 427-440.
Vancouver Sarı İ. , Kandemir A. Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?. Turkish Journal of Botany. 2020; 44(4): 427-440.
IEEE İ. Sarı ve A. Kandemir , "Will Ferulago glareosa Kandemir and Hedge Apiaceae be extinct in the near future?", Turkish Journal of Botany, c. 44, sayı. 4, ss. 427-440, Ağu. 2020, doi:10.3906/bot-2001-33