Authors:
Z. NazariQuantitative Plant Ecology and Biodiversity Research Lab, Department of Biology Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran

Search for other papers by Z. Nazari in
Current site
Google Scholar
PubMed
Close
,
H. EjtehadiQuantitative Plant Ecology and Biodiversity Research Lab, Department of Biology Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran

Search for other papers by H. Ejtehadi in
Current site
Google Scholar
PubMed
Close
,
O. MirshamsiInstitute of Applied Zoology (RDZI), Faculty of Science Ferdowsi University of Mashhad, Mashhad, Iran
Department of Biology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran

Search for other papers by O. Mirshamsi in
Current site
Google Scholar
PubMed
Close
, and
F. MemarianiHerbarium FUMH, Department of Botany, Research Centre for Plant Science Ferdowsi University of Mashhad, Mashhad, Iran

Search for other papers by F. Memariani in
Current site
Google Scholar
PubMed
Close
Restricted access

Ferula microcolea (Boiss.) Boiss. is an endemic plant in Iran that some of its habitats have been destroyed in recent decades. Since the bioclimatic variables which determine its potential distribution, are poorly defined, a specific analysis is needed. In this study, the species distribution modelling was used for reaching these goals: (i) identifying the bioclimatic factors that constrain the distribution of this species in Iran, (ii) generating a potential habitat suitability map for F. microcolea using Maxent (iii) determining the high suitable areas where this species could be present (iv) evaluating the final model. In all, 66 records of F. microcolea in Iran were used as the occurrence data. Nineteen bioclimatic variables were obtained from the WorldClim database and collinear variables were removed in a sequential manner with regard to the ecological knowledge of the plant. The maxent parameters were optimised with ENMeval R package. For evaluating the performance of the Maxent model, the Area under curve value (AUC) was calculated. The results showed that the model performance was excellent. Analysis of variable contribution demonstrated that the distribution of this species is most influenced by the Annual Mean Temperature. We revealed that the area about 22,005.5 km2 is highly suitable for F. microcolea that is principally located in Chaharmahal and Bakhtiari province. Although this region is rich in biodiversity, greater focus should be paid to its conservation. Our findings provide a scientific basis for the habitats conservation of this species in Iran.

  • Amiri, H. (2014): Chemical composition and antioxidant activity of essential oil and methanolic extracts of Ferula microcolea (Boiss.) Boiss (Apiaceae). –Int. J. Food Prop. 17(4): 722730.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, R. P. and Gonzalez, I. (2011): Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. –Ecol. Model. 222(15): 27962811.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, S. (1994): Area and endemism. –Quart. Rev. Biol. 69(4): 451471.

  • Ashcroft, M. B., Gollan, J. R., Warton, D. I. and Ramp, D. (2012): A novel approach to quantify and locate potential microrefugia using topoclimate, climate stability, and isolation from the matrix.–Global Change Biol. 18: 18661879.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Austin, M. P. (2002): Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. –Ecol. Model. 157(2–3): 101118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bai, D.-F., Chen, P.-J., Atzeni, L., Cering, L., Li, Q. and Shi, K. (2018): Assessment of habitat suitability of the snow leopard (Panthera uncia) in Qomolangma National Nature Reserve based on MaxEnt modeling. –Zool. Res. 39(6): 373386.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Block, W. M. and Brennan, L. A. (1993): The habitat concept in ornithology. Theory and applications. Springer, Boston, MA.–Current Ornithology 11: 3591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boissier, E. (1872): Flora orientalis.–Genevae & Basileae, 2: 574582.

  • Bucklin, D. N., Basille, M., Benscoter, A. M., Brandt, L. A., Mazzotti, F. J., Romañach, S. S., Speroterra, C. and Watling, J. I. (2015): Comparing species distribution models constructed with different subsets of environmental predictors. –Divers. Distrib. 21(1): 2335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chamberlain, D. F. and Rechinger, K. H. (1987): Ferula. – In: Rechinger, K. H. (ed.): Flora Iranica 162: 387426.

  • Chang, Y. L., Xia, Y., Peng, M. W., Chu, G. M. and Wang, M. (2020): Maxent modelling for predicting impacts of climate change on the potential distribution of Anabasis aphylla in northwestern China. –Appl. Ecol. Env. Res. 18: 16371648.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Convertino, M., Muñoz-Carpena, R., Chu-Agor, M. L., Kiker, G. A. and Linkov, I. (2014): Untangling drivers of species distributions: global sensitivity and uncertainty analyses of MaxEnt. –Environ. Model. Softw. 51: 296309.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daneshvar, M. R. M., Ebrahimi, M. and Nejadsoleymani, H. (2019): An overview of climate change in Iran: facts and statistics. –Environ. Syst. Res. 8(1): 7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., García Marquéz, J. R., Gruber, B., Lafourcade, B., Leitao, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D. and Lautenbach, S. (2013): Col-linearity: a review of methods to deal with it and a simulation study evaluating their performance. –Ecography 36(1): 2746.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dubuis, A., Giovanettina, S., Pellissier, L., Pottier, J., Vittoz, P. and Guisan, A. (2013): Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variable. –J. Veg. Sci. 24: 593606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E. and Yates, C. J. (2011): A statistical explanation of Maxent for ecologists. –Divers. Distrib. 17(1): 4357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, X., Gebresenbet, F. and Walker, C. (2017): Shifting from closed-source graphical-interface to open-source programming environment: a brief tutorial on running Maxent in R.–PeerJ Preprints 5: e3346v1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Franklin, J. (2010): Mapping species distributions: spatial inference and prediction. – Cambridge University Press, Cambridge.

  • Galante, P. J., Alade, B., Muscarella, R., Jansa, S. A., Goodman, S. M. and Anderson, R. P. (2018): The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity. –Ecography 41(5): 726736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghazimoradi, L., Tarkesh, M., Bashari, H. and Wahhabi, M. R. (2016): Determination of potential habitat of Ferula ovina Boiss. using generalized additive model (GAM) in Fereidunshahr area of Isfahan province.–Rangeland and Watershed Management 69(3): 677689.

    • Search Google Scholar
    • Export Citation
  • Guisan, A. and Zimmermann, N. E. (2000): Predictive habitat distribution models in ecology. –Ecol. Model. 135(2–3): 147186.

  • Henderson, E. B., Ohmann, J. L., Gregory, M. J., Roberts, H. M. and Zald, H. (2014): Species distribution modelling for plant communities: stacked single species or multivariate modelling approaches?Appl. Veg. Sci. 17(3): 516527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hernandez, P. A., Graham, C. H., Master, L. L. and Albert, D. L. (2006): The effect of sample size and species characteristics on performance of different species distribution modeling methods. –Ecography 29(5): 773785.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. and Jarvis, A. (2005): Very high resolution interpolated climate surfaces for global land areas. –Int. J. Climat. 25(15): 19651978.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hijmans, R. J., Etten, J. V., Cheng, J., Sumner, M., Mattiuzzi, M. Greenberg, J. A. and Lamigueiro, O. P. (2015): Package ‘raster’. – R package version 2.5-8.

    • Search Google Scholar
    • Export Citation
  • Jalili, A. and Jamzad, Z. (1999): Red Data Book of Iran, a preliminary survey of endemic, rare & endangered plant species in Iran. – Research Institute of Forest & Rangelands, Ministry of Jahad-e Sazandegi (Publ. 215), 748 pp.

    • Search Google Scholar
    • Export Citation
  • Kaky, E. and Gilbert, F. (2019): Assessment of the extinction risks of medicinal plants in Egypt under climate change by integrating species distribution models and IUCN Red List criteria. –J. Arid Environ. 170, Art. 103988.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalinski, C. E. (2019): Building better species distribution models with machine learning: assessing the role of covariate scale and tuning in Maxent models. – Doctoral dissertation, University of Southern California.

    • Search Google Scholar
    • Export Citation
  • Khalaj, A. and Mortazavi, S. M. (2016): Climatic zoning of precipitation and temperature in Chaharmahal and Bakhtiari Province using geographic information system (GIS). –Res. J. Appl. Sci. 11(7): 496507.

    • Search Google Scholar
    • Export Citation
  • Li, J., Fan, G. and He, Y. (2020): Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the Max-Ent model and chemical analysis. –Sci. Total Environm. 698: 134141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mazangi, A., Ejtehadi, H. and Mirshamsi, O., Ghassemzadeh, F. and Hosseinianyou sefkhani, S. S. (2016): Effects of climate change on the distribution of endemic Ferula xylorhachis Rech. f. (Apiaceae: Scandi ceae) in Iran: Predictions from ecological niche models. –Russ. J. Ecol. 47: 349354.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mozaffarian, V. (1983): The family of Umbelliferae in Iran. –Pub. Res. Inst. Forest Rangelands 35: 146147.

  • Mozaffarian, V. (1996): A dictionary of Iranian plant names. – Farhang Moaser, Tehran, 396 pp.

  • Mozaffarian, V. (2007): Umbelliferae. – In: Assadi, M. et al. (eds): Flora of Iran. Vol. 54. Research Institute of Forests and Rangelands, Tehran, pp. 280296.

    • Search Google Scholar
    • Export Citation
  • Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M. and Anderson, R. P. (2014): ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. –Methods Ecol.Evol. 5(11): 11981205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naimi, B. (2015): Usdm: Uncertainty analysis for species distribution models.–R package version, 1, 112.

  • Noroozi, J., Akhani, H. and Breckle, S. W. (2008): Biodiversity and phytogeography of the alpine flora of Iran. –Biodiv. Conserv. 17(3): 493521.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, A. T., Papeş, M. and Soberón, J. (2008): Rethinking receiver operating characteristic analysis applications in ecological niche modelling. –Ecol. Model. 213(1): 6372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, S. J. and Dudík, M. (2008): Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. –Ecography 31(2): 161175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, S. J., Anderson, R. P. and Schapire, R. E. (2006): Maximum entropy modeling of species geographic distributions. –Ecol. Model. 190(3–4): 231259.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pimenov, M. G. and Leonov, M. V. (2004): The Asian Umbelliferae biodiversity database (ASIUM) with particular reference to South-West Asian taxa. –Turkish J.Bot. 28(1–2): 139145.

    • Search Google Scholar
    • Export Citation
  • Pradhan, P. (2016): Strengthening MaxEnt modelling through screening of redundant explanatory bioclimatic variables with variance inflation factor analysis. –Researcher 8(5): 2934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Razmi, R., Balyani, S. and Daneshvar, M. R. M. (2017): Geo-statistical modeling of mean annual rainfall over the Iran using ECMWF database. –Spat. Inf. Res. 25(2): 219227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saadatfar, A., Tavassolian, I. and Jafari, S. H. (2018): Determining the potential habitat of Ferula assafoetida using hierarchical analysis and geographic information system (Case study: Chatroud region, Kerman).–RS & GIS for Nat. Res. 9(4): 139155.

    • Search Google Scholar
    • Export Citation
  • Safaian, N. and Shokri, M. (1992): Botanical and ecological study of species of the genus Ferula (Medicinal Plants) in Mazandaran province.–WOCMAP I. Medicinal and Aromatic Plants Conference (ISHS 333), pp. 159164.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahebkar, A. and Iranshahi, M. (2010): Biological activities of essential oils from the genus Ferula (Apiaceae).–Asian Biomed. 4(6): 835847.

  • Sony, R. K., Sen, S., Kumar, S., Sen, M. and Jayahari, K. M. (2018): Niche models inform the effects of climate change on the endangered Nilgiri Tahr (Nilgiritragus hylocrius) populations in the southern Western Ghats, India.–Ecol. Engineer. 120: 355363.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharifi, Y. M., Shahmoradi, A. and Zare, K. (2014): Ecological study of Ferula ovina Boiss in Kerman province. –Renew. Nat. Resour. Res. 5(2): 5768.

    • Search Google Scholar
    • Export Citation
  • Urbani, F., D’Alessandro, P. and Biondi, M. (2017): Using Maximum Entropy Modeling (MaxEnt) to predict future trends in the distribution of high altitude endemic insects in response to climate change. –B. Insectol. 70(2): 189200.

    • Search Google Scholar
    • Export Citation
  • Valenzuela-Ceballos, S., Castañeda, G., Rioja-Paradela, T., Carrillo-Reyes, A. and Bastiaans, E. (2015): Variation in the thermal ecology of an endemic iguana from Mexico reduces its vulnerability to global warming. –J. Therm. Biol. 48: 5664.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., Liu, H., Li, Y. and Zhang, H. (2019): Habitat quality of overwintering red-crowned cranes based on ecological niche modeling. –Arab. J. Geosci. 12(24), 750.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warren, D. L. and Seifert, S. N. (2011): Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. –Ecol. Appl. 21(2): 335342.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wei, B., Wang, R., Hou, K., Wang, X. and Wu, W. (2018): Predicting the current and future cultivation regions of Carthamus tinctorius L. using MaxEnt model under climate change in China. –Global Ecol.Conserv. 16: e00477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., Jiang, F., Li, G., Qin, W., Li, S., Gao, H., Cai, Z., Lin, G. and Zhang, T. (2019): Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China.–Ecol. Evol. 9(11): 66436654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
  • Top

 

The author instruction is available in PDF.
Please, download the file from HERE.

 

 

 

Senior editors

Managing Editors

Editorial Board

  • Gy. BORBÉLY (Debrecen)
  • A. ČARNY (Ljubljana)
  • A. CSERGŐ (Dublin)
  • B. CZÚCZ (Paris)
  • M. HÖHN (Budapest)
  • K. T. KISS (Budapest)
  • A. KUZEMKO (Uman)
  • Z. LOSOSOVÁ (Brno)
  • I. MÁTHÉ (Szeged)
  • E. MIHALIK (Szeged)
  • S. ORBÁN (Eger)
  • R. PÁL (Butte)
  • Gy. PINKE (Mosonmagyaróvár)
  • T. PÓCS (Eger)
  • K. PRACH (České Budejovice)
  • E. S. RAUSCHERT (Cleveland)
  • E. RUPRECHT (Cluj Napoca)
  • G. SRAMKÓ (Debrecen)
  • A. T. SZABÓ (Veszprém)
  • É. SZŐKE (Budapest)
  • B. TOKARSKA-GUZIK (Katowice)
  • B. TÓTHMÉRÉSZ (Debrecen)
  • P. TÖRÖK (Debrecen)

Botta-Dukát, Zoltán
E-mail: botta-dukat.zoltan@okologia.mta.hu

or

Lőkös, László
E-mail: acta@bot.nhmus.hu
Institute: Botanical Department, Hungarian Natural History Museum
Address: Könyves K. krt. 40. H-1097 Budapest, Hungary

  • Scopus
  • Biological Abstracts
  • BIOSIS Previews
  • CAB Abstracts
  • Chemical Abstracts
  • Global Health
  • Referativnyi Zhurnal

 

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
23
Scimago
Journal Rank
0,392
Scimago Quartile Score Plant Science (Q2)
Ecology, Evolution, Behavior and Systematics (Q3)
Scopus  
Scopus
Cite Score
2,5
Scopus
CIte Score Rank
Plant Science 205/482 (Q2)
Ecology, Evolution, Behavior and Systematics 322/687 (Q2)
Scopus
SNIP
1,046

2020  
Scimago
H-index
19
Scimago
Journal Rank
0,417
Scimago
Quartile Score
Plant Science Q2
Ecology, Evolution, Behavior and Systematics Q3
Scopus
Cite Score
155/89=1,7
Scopus
Cite Score Rank
Plant Science 221/445 (Q2)
Ecology, Evolution, Behavior and Systematics 374/647 (Q3)
Scopus
SNIP
0,838
Scopus
Cites
260
Scopus
Documents
22
Days from submission to acceptance 127
Days from acceptance to publication 132
Acceptance
Rate
36%

 

2019  
Scimago
H-index
17
Scimago
Journal Rank
0,404
Scimago
Quartile Score
Plant Science Q2
Ecology, Evolution, Behavior and Systematics Q3
Scopus
Cite Score
164/91=1,8
Scopus
Cite Score Rank
Plant Science 209/431 (Q2)
Ecology, Evolution, Behavior and Systematics 358/629 (Q3)
Scopus
SNIP
0,699
Scopus
Cites
215
Scopus
Documents
23
Acceptance
Rate
30%

 

Acta Botanica Hungarica
Publication Model Hybrid
Submission Fee none
Article Processing Charge 900EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2022 Online subsscription: 594 EUR / 740 USD
Print + online subscription: 676 EUR / 844 USD
Subscription fee 2023 Online subsscription: 612 EUR / 740 USD
Print + online subscription: 696 EUR / 844 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

Acta Botanica Hungarica
Language English
French
German
Russian
Spanish
Size B5
Year of
Foundation
1954
Volumes
per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 0236-6495 (Print)
ISSN 1588-2578 (Online)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Apr 2022 69 4 6
May 2022 53 8 3
Jun 2022 20 4 0
Jul 2022 28 6 0
Aug 2022 19 2 0
Sep 2022 11 0 0
Oct 2022 0 0 0