Modeling the distribution of rare and interesting moss species of the family Orthotrichaceae (Bryophyta) in Tajikistan and Kyrgyzstan

• Autorzy: Cihal L. , Kalab O. , Plasek V.
• Języki publikacji: EN

Abstrakty

EN

Bryological research carried out from 2008 in Tajikistan and Kyrgyzstan brought interesting data on the occurrence of epiphytic bryophytes which have not been recorded yet there. One of the species was recently described as a new (Orthotrichum pamiricum) and some of the other newly recorded species are considered as rare or endangered in the region of Middle Asia. To make detailed field monitoring of the species with the aim of mapping their distribution in a wild and complex mountainous terrain, it was necessary in the first instance to identify the area with suitable conditions for the occurrence of these species. We present an innovative modeling program MaxEnt (maximum entropy modeling), which have not previously been used for modeling either epiphytic bryophytes or in the Middle Asia region. Using 205 samples (presence-only data), percent tree cover, and seven uncorrelated bioclimatic variables, regions suitable for the occurrence of the studied species were identified. Distribution models for eight most interesting species of Orthotrichum are presented here (O. affine, O. anomalum, O. crenulatum, O. cupulatum, O. pallens, O. pamiricum, O. pumilum, and O. speciosum). They indicated appropriate areas for the most probable occurrence of the species in western Tajikistan, and southwestern and northeastern Kyrgyzstan. These results could serve as guides for future survey expeditions, and aid in the conservation of target species and our understanding of their ecology. Different environmental variables for various species were selected as the most important for modeling. However, for most species higher minimum temperatures and higher precipitation in the wettest month and mean diurnal range were the variables with the greatest contribution to the models.

• Wydawca: -
• Czasopismo: Acta Societatis Botanicorum Poloniae
• Rocznik: 2017
• Tom: 86
• Numer: 2
• Opis fizyczny: Article 3543 [15p.],fig.,ref.

Twórcy

autor

Cihal L.

• Silesian Museum, Nadrazni okruh 31, 746 01 Opava, Czech Republic

autor

Kalab O.

• University of Ostrava, Chittussiho 10, 710 00 Ostrava, Czech Republic

autor

Plasek V.

• University of Ostrava, Chittussiho 10, 710 00 Ostrava, Czech Republic

Bibliografia

• 1. Blockeel TL, Bednarek-Ochyra H, Ochyra R, Cykowska B, Esquivel MG, Lebouvier M, et al. New national and regional bryophyte records, 21. J Bryol. 2013;31(2):132–139. https://doi.org/10.1179/174328209x431213
• 2. Ellis L, Akhoondi Darzikolaei S, Shirzadian S, Bakalin V, Bednarek-Ochyra H, Ochyra R, et al. New national and regional bryophyte records, 29. J Bryol. 2011;33(4):316–323. https://doi.org/10.1179/1743282011Y.0000000031
• 3. Ellis L, Alegro A, Bansal P, Nath V, Cykowska B, Bednarek-Ochyra H, et al. New national and regional bryophyte records, 32. J Bryol. 2012;34(3):231–246. https://doi.org/10.1179/1743282012y.0000000019
• 4. Ellis L, Bayliss J, Bruggeman-Nannenga M, Cykowska B, Ochyra R, Gremmen N, et al. New national and regional bryophyte records, 38. J Bryol. 2014;36(1):61–72. https://doi.org/10.1179/1743282013Y.0000000085
• 5. Ellis L, Afonina OM, Asthana A, Gupta R, Sahu V, Nath V, et al. New national and regional bryophyte records, 39. J Bryol. 2014;36(2):134–151. https://doi.org/10.1179/1743282014Y.0000000100
• 6. Ellis L, Aleffi M, Tacchi R, Alegro A, Alonso M, Asthana A, et al. New national and regional bryophyte records, 41. J Bryol. 2014;36(4):306–324. https://doi.org/10.1179/1743282014Y.0000000123
• 7. Ellis L, Aleffi M, Bakalin VA, Bednarek-Ochyra H, Bergamini A, Beveridge P, et al. New national and regional bryophyte records, 42. J Bryol. 2015;37(1):68–79. https://doi.org/10.1179/1743282014y.0000000132
• 8. Ellis L, Asthana A, Srivastava A, Bakalin VA, Bednarek-Ochyra H, Cano MJ, et al. New national and regional bryophyte records, 43. J Bryol. 2015;37(2):128–147. https://doi.org/10.1179/1743282015Y.0000000003
• 9. Ellis LT, Alegro A, Šegota V, Bakalin VA, Barone R, Borovichev EA, et al. New national and regional bryophyte records, 44. J Bryol. 2015;37(3):228–241. https://doi.org/10.1179/1743282015Y.0000000014
• 10. Plášek V, Sawicki J, Číhal L. Orthotrichum pamiricum (Bryophyta), a new epiphytic moss species from Pamir Mountains in Central Asia. Turk J Botany. 2014;38(4):754–762. https://doi.org/10.3906/bot-1312-23
• 11. Yu J, Ma YH, Guo SL. Modeling the geographic distribution of the epiphytic moss Macromitrium japonicum in China. Ann Bot Fenn. 2013;50(1–2):35–42. https://doi.org/10.5735/085.050.0105
• 12. Raxworthy CJ, Martinez-Meyer E, Horning N, Nussbaum RA, Schneider GE, Ortega- Huerta MA, et al. Predicting distributions of known and unknown reptile species in Madagascar. Nature. 2003;426(6968):837–841. https://doi.org/10.1038/nature02205
• 13. Bourg NA, McShea WJ, Gill DE. Putting a CART before the search: successful habitat prediction for a rare forest herb. Ecology. 2005;86(10):2793–2804. https://doi.org/10.1890/04-1666
• 14. Kruijer HJ, Raes N, Stech M. Modelling the distribution of the moss species Hypopterygium tamarisci (Hypopterygiaceae, Bryophyta) in Central and South America. Nova Hedwigia. 2010;91(3–4):399–420. https://doi.org/10.1127/0029-5035/2010/0091-0399
• 15. Sérgio C, Figueira R, Draper D, Menezes R, Sousa AJ. Modelling bryophyte distribution based on ecological information for extent of occurrence assessment. Biol Conserv. 2007;135(3):341–351. https://doi.org/10.1016/j.biocon.2006.10.018
• 16. Desamore A, Laenen B, Stech M, Papp B, Hedenäs L, Mateo RG, et al. How do temperate bryophytes face the challenge of a changing environment? Lessons from the past and predictions for the future. Glob Chang Biol. 2012;18(9):2915–2924. https://doi.org/10.1111/j.1365-2486.2012.02752.x
• 17. Yu J, Tang YX, Guo SL. Comparison of the geographical distribution of Racomitrium and Grimmia in China using ArcGis and MaxEnt software. Plant Sci J. 2012;30(5):443–458. https://doi.org/10.3724/sp.j.1142.2012.50443
• 18. Mateo RG, Vanderpoorten A, Muñoz J, Laenen B, Désamoré A. Modeling species distributions from heterogeneous data for the biogeographic regionalization of the European bryophyte flora. PLoS One. 2013;8(2):e55648. https://doi.org/10.1371/journal.pone.0055648
• 19. Poncet R, Hugonnot V, Vergne T. Modelling the distribution of the epiphytic moss Orthotrichum rogeri to assess target areas for protected status. Cryptogam Bryol. 2015;36(1):3–17. https://doi.org/10.7872/cryb.v36.iss1.2015.3
• 20. Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr. 2007;34(1):102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x
• 21. Shcheglovitova M, Anderson RP. Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecol Modell. 2013;269:9–17. https://doi.org/10.1016/j.ecolmodel.2013.08.011
• 22. Anderson RP, Dudík M, Ferrier S, Guisan A, J Hijmans R, Huettmann F, et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006;29(2):129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
• 23. Phillips S. A Brief tutorial on MaxEnt. Lessons in Conservation. 2006;3:108–135.
• 24. Phillips SJ, Dudík M. Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography. 2008;31(2):161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
• 25. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Divers Distrib. 2011;17(1):43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
• 26. Halvorsen R. A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling. Sommerfeltia. 2013;36:1–132. https://doi.org/10.2478/v10208-011-0016-2
• 27. Renner IW, Warton DI. Equivalence of MaxEnt and Poisson point process models for species distribution modeling in ecology. Biometrics. 2013;69(1):274–281. https://doi.org/10.1111/j.1541-0420.2012.01824.x
• 28. Halvorsen R, Mazzoni S, Bryn A, Bakkestuen V. Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt. Ecography. 2015;38(2):172–183. https://doi.org/10.1111/ecog.00565
• 29. Anderson RP, Gonzalez I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with MaxEnt. Ecol Modell. 2011;222(15):2796–2811. https://doi.org/10.1016/j.ecolmodel.2011.04.011
• 30. Kumar S, Stohlgren TJ. MaxEnt modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. Journal of Ecology and the Natural Environment. 2009;1(4):94–98.
• 31. Goffinet B, Buck WR, Wall MA. Orthotrichum freyanum (Orthotrichaceae), a new epiphytic moss from Chile. Nova Hedwigia 2007;131:1–11.
• 32. Lara F, Garilleti R, Mazimpaka V. A peculiar new Orthotrichum species (Orthotrichaceae, Bryopsida) from central Argentina. Bot J Linn Soc. 2007;155(4):477–482. https://doi.org/10.1111/j.1095-8339.2007.00720.x
• 33. Medina R, Lara F, Mazimpaka V, Garilleti R. Orthotrichum norrisii (Orthotrichaceae), a new epiphytic Californian moss. Bryologist. 2008;111(4):670–675. https://doi.org/10.1639/0007-2745-111.4.670
• 34. Lara F, Garilleti R, Mazimpaka V. Orthotrichum karoo (Orthotrichaceae), a new species with hyaline-awned leaves from southwestern Africa. Bryologist. 2009;112(1):194–201. https://doi.org/10.1639/0007-2745-112.1.194
• 35. Lara F, Garilleti R, Medina R, Mazimpaka V. A new key to the genus Orthotrichum Hedw. in Europe and the Mediterranean region. Cryptogam Bryol. 2009;30(1):129–142.
• 36. Plášek V, Sawicki J, Trávníčková V, Pasečná M. Orthotrichum moravicum (Orthotrichaceae), a new moss species from the Czech Republic. Bryologist. 2009;112(2):329–336. https://doi.org/10.1639/0007-2745-112.2.329
• 37. Fedosov V, Ignatova E. Orthotrichum dagestanicum sp. nov. (Orthotrichaceae, Musci) – a new species from Dagestan (Eastern Caucasus). Arctoa. 2010;19:69–74. https://doi.org/10.15298/arctoa.19.05
• 38. Garilleti R, Shevock JR, Norris DH, Lara F. Orthotrichum mazimpakanum sp. nov. and O. anodon (Orthotrichaceae), two similar species from California. Bryologist. 2011;114(2):346–355. https://doi.org/10.1639/0007-2745-114.2.346
• 39. Medina R, Lara F, Mazimpaka V, Shevock JR, Garilleti R. Orthotrichum pilosissimum (Orthotrichaceae), a new moss from arid areas of Nevada with unique axillary hairs. Bryologist. 2011;114(2):316–324. https://doi.org/10.1639/0007-2745.114.2.316
• 40. Medina R, Lara F, Goffinet B, Garilleti R, Mazimpaka V. Integrative taxonomy successfully resolves the pseudo-cryptic complex of the disjunct epiphytic moss Orthotrichum consimile s. l. (Orthotrichaceae). Taxon. 2012;61(6):1180–1198.
• 41. Sawicki J, Plášek V, Szczecińska M. Molecular studies resolve Nyholmiella (Orthotrichaceae) as a separate genus. J Syst Evol. 2010;48(3):183–194. https://doi.org/10.1111/j.1759-6831.2010.00076.x
• 42. Plášek V. Klíč pro determinaci zástupců rodů Orthotrichum a Nyholmiella v České republice. Bryonora. 2012;50:17–33.
• 43. Lewinsky J. A synopsis of the genus Orthotrichum Hedw. (Musci, Orthotrichaceae). Bryobrothera. 1993;2:1–59.
• 44. Rivas-Martínez S, Rivas-Sáenz S, Penas A. Worldwide bioclimatic classification system. Global Geobotany. 2011;1:1–634. https://doi.org/10.5616/gg110001
• 45. Peterson AT. Niches and geographic distributions. In: Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, et al., editors. Ecological niches and geographic distributions. Princeton, NJ: Princeton University Press; 2011. p. 23–46. (Monographs in Population Biology; vol 49). https://doi.org/10.1515/9781400840670.23
• 46. Guillera-Arroita G, Lahoz-Monfort JJ, Elith J, Gordon A, Kujala H, Lentini PE, et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr. 2015;24(3):276–292. https://doi.org/10.1111/geb.12268
• 47. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl. 2009;19(1):181–197. https://doi.org/10.1890/07-2153.1
• 48. Fourcade Y, Engler JO, Rödder D, Secondi J. Mapping species distributions with MaxEnt using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One. 2014;9(5):e97122. https://doi.org/10.1371/journal.pone.0097122
• 49. QGIS Development Team. QGIS Geographic Information System [Internet]. 2016 [cited 2017 Apr 28]. Available from: http://www.qgis.org
• 50. Boria RA, Olson LE, Goodman SM, Anderson RP. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Modell. 2014;275:73– 77. https://doi.org/10.1016/j.ecolmodel.2013.12.012
• 51. Anderson RP, Raza A. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J Biogeogr. 2010;37(7):1378–1393. https://doi.org/10.1111/j.1365-2699.2010.02290.x
• 52. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005;25(15):1965–1978. https://doi.org/10.1002/joc.1276
• 53. Chang KT. Introduction to geographic information systems. Boston, MA: McGraw-Hill Higher Education; 2008.
• 54. GDAL – Geospatial Data Abstraction Library: Version 2.1.0 [Internet]. 2016 [cited 2017 Apr 28]. Available from: http://www.gdal.org
• 55. Warren DL, Glor RE, Turelli M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution. 2008;62(11):2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x
• 56. Warren DL, Glor RE, Turelli M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography. 2010;33(3):607–611. https://doi.org/10.1111/j.1600-0587.2009.06142.x
• 57. Mbatudde M, Mwanjololo M, Kakudidi EK, Dalitz H. Modelling the potential distribution of endangered Prunus africana (Hook. f.) Kalkm. in East Africa. Afr J Ecol. 2012;50(4):393–403. https://doi.org/10.1111/j.1365-2028.2012.01327.x
• 58. Pradhan P, Dutta A, Roy A, Basu S, Acharya K. Inventory and spatial ecology of macrofungi in the Shorea robusta forest ecosystem of lateritic region of West Bengal. Biodiversity. 2012;13(2):88–99. https://doi.org/10.1080/14888386.2012.690560
• 59. Pradhan P. Strengthening MaxEnt modelling through screening of redundant explanatory bioclimatic variables with variance inflation factor analysis. Researcher. 2016;8(5):29–34.
• 60. Chefaoui RM, Lobo JM. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol Modell. 2008;210(4):478–486. https://doi.org/10.1016/j.ecolmodel.2007.08.010
• 61. Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Modell. 2011;222(11):1810–1819. https://doi.org/10.1016/j.ecolmodel.2011.02.011
• 62. Acevedo P, Jiménez‐Valverde A, Lobo JM, Real R. Delimiting the geographical background in species distribution modelling. J Biogeogr. 2012;39(8):1383–1390. https://doi.org/10.1111/j.1365-2699.2012.02713.x
• 63. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol Evol. 2012;3(2):327–338. https://doi.org/10.1111/j.2041-210x.2011.00172.x
• 64. VanDerWal J, Shoo LP, Graham C, Williams SE. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Modell. 2009;220(4):589–594. https://doi.org/10.1016/j.ecolmodel.2008.11.010
• 65. Longton R. Reproductive biology and life-history strategies. Advances in Bryology. 1997;6(65):101.
• 66. Wyatt R. Population ecology of bryophytes. Journal of the Hattori Botanical Laboratory. 1982;52:179–198.
• 67. Lönnell N, Hylander K, Jonsson BG, Sundberg S. The fate of the missing spores patterns of realized dispersal beyond the closest vicinity of a sporulating moss. PLoS One. 2012;7(7):e41987. https://doi.org/10.1371/journal.pone.0041987
• 68. Sundberg S. Spore rain in relation to regional sources and beyond. Ecography. 2013;36(3):364–373. https://doi.org/10.1111/j.1600-0587.2012.07664.x
• 69. Stoneburner A, Lane DM, Anderson LE. Spore dispersal distances in Atrichum angustatum (Polytrichaceae). Bryologist. 1992;95(3):324–328. https://doi.org/10.2307/3243491
• 70. Snäll T, Fogelqvist J, Ribeiro P, Lascoux M. Spatial genetic structure in two congeneric epiphytes with different dispersal strategies analysed by three different methods. Mol Ecol. 2004;13(8):2109–2119. https://doi.org/10.1111/j.1365-294x.2004.02217.x
• 71. Sundberg S. Larger capsules enhance short-range spore dispersal in Sphagnum, but what happens further away? Oikos. 2005;108(1):115–124. https://doi.org/10.1111/j.0030-1299.2005.12916.x
• 72. Miles C, Longton R. Deposition of moss spores in relation to distance from parent gametophytes. J Bryol. 1992;17(2):355–368. https://doi.org/10.1179/jbr.1992.17.2.355
• 73. Soro A, Sundberg S, Rydin H. Species diversity, niche metrics and species associations in harvested and undisturbed bogs. J Veg Sci. 1999;10(4):549–560. https://doi.org/10.2307/3237189
• 74. Miller NG, McDaniel SF. Bryophyte dispersal inferred from colonization of an introduced substratum on Whiteface Mountain, New York. Am J Bot. 2004;91(8):1173– 1182. https://doi.org/10.3732/ajb.91.8.1173
• 75. Hutsemekers V, Dopagne C, Vanderpoorten A. How far and how fast do bryophytes travel at the landscape scale? Divers Distrib. 2008;14(3):483–492. https://doi.org/10.1111/j.1472-4642.2007.00454.x
• 76. Radosavljevic A, Anderson RP. Making better MaxEnt models of species distributions: complexity, overfitting and evaluation. J Biogeogr. 2014;41(4):629–643. https://doi.org/10.1111/jbi.12227
• 77. Zhang L, Cao B, Bai C, Li G, Mao M. Predicting suitable cultivation regions of medicinal plants with MaxEnt modeling and fuzzy logics: a case study of Scutellaria baicalensis in China. Environ Earth Sci. 2016;75(5):361. https://doi.org/10.1007/s12665-015-5133-9
• 78. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285– 1293. https://doi.org/10.1126/science.3287615
• 79. Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv. 1997;24(1):38–49. https://doi.org/10.1017/s0376892997000088
• 80. Benítez Á, Prieto M, Aragón G. Large trees and dense canopies: key factors for maintaining high epiphytic diversity on trunk bases (bryophytes and lichens) in tropical montane forests. Forestry. 2015;88(5):521–527. https://doi.org/10.1093/forestry/cpv022
• 81. Glime JM. Bryophyte ecology. Vol 1. Physiological ecology. Ebook sponsored by Michigan Technological University and the International Association of Bryologists [Internet]. 2007 [cited 2017 May 12]. Available form: http://www.bryoecol.mtu.edu/
• 82. Bates J, Roy D, Preston C. Occurrence of epiphytic bryophytes in a tetrad transect across southern Britain. 2. Analysis and modelling of epiphyte-environment relationships. J Bryol. 2004;26(3):181–197. https://doi.org/10.1179/037366804x5288
• 83. Marini L, Nascimbene J, Nimis PL. Large-scale patterns of epiphytic lichen species richness: photobiont-dependent response to climate and forest structure. Sci Total Environ. 2011;409(20):4381–4386. https://doi.org/10.1016/j.scitotenv.2011.07.010
• 84. Sumarga E. A comparison of logistic regression, geostatistics and MaxEnt for distribution modeling of a forest endemic; a pilot study on lobel’s maple at Mt. Pizzalto, Italy [Master thesis]. Enschede: University of Twente; 2011.

Identyfikatory

DOI

10.5586/asbp.3543