Abstract:

Horsetails are a complex taxonomic and systematic group. Therefore, the study of the geographical distribution of these species is necessary for a better understanding of the phylogeny of this family. We concluded an analysis of the distribution of 5 species of horsetail of the subgenus Equisetum (Equisetum, Equisetaceae): E. arvense L., E. fluviatile L., E. palustre L., E. pratense Ehrh., E. sylvaticum L. using the maximum entropy method implemented in the MaxEnt program. Modeling was carried out using climate variables from the WorldClim global climate base. Simulation results show good simulation quality. In 3 out of 5 species, the AUC of the test sample was in the range of 0.9–1, and in 2 species — 0.8–0.9. In general, for most species, a plausible picture of their intended distribution has developed. The obtained models suggest that the territory of Russia is favorable enough for the growth of horsetails. Analysis of the contribution of 14 bioclimatic variables to the distribution of the studied species revealed that the most important variables are: annual mean temperature, isotermality, temperature seasonality, max temperature of warmest month, temperature annual range, mean temperature of warmest quarter, mean temperature of driest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation seasonality, precipitation of driest quarter, precipitation of warmest quarter, and precipitation of coldest quarter. Key words: Equisetum; species distribution modeling; siberia; maxEnt References Barbet-Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3(2), 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77. https://doi.org/10.1016/j.ecolmodel.2013.12.012 Broennimann, O., Treier, U. A., Müller-Schärer, H., Thuiller, W., Peterson, A. T., & Guisan, A. (2007). Evidence of climatic niche shift during biological invasion. Ecology Letters, 10(8), 701–709. https://doi.org/10.1111/j.1461-0248.2007.01060.x Brown, J. L. (2014). SDMtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution, 5(7), 694–700. https://doi.org/10.1111/2041-210X.12200 Cord, A. F., Klein, D., Gernandt, D. S., de la Rosa, J. A., & Dech, S. (2014). Remote sensing data can improve predictions of species richness by stacked species distribution models: a case study for Mexican pines. Journal of Biogeography, 41(4), 736–748. https://doi.org/10.1111/jbi.12225 Dobrowski, S. Z., Thorne, J. H., Greenberg, J. A., Safford, H. D., Mynsberge, A. R., Crimmins, S. M., & Swanson, A. K. (2011). Modeling plant ranges over 75 years of climate change in California, USA: Temporal transferability and species traits. Ecological Monographs, 81(2), 241–257. https://doi.org/10.1890/10-1325.1 Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x Engler, J. O., Rödder, D., Elle, O., Hochkirch, A., & Secondi, J. (2013). Species distribution models contribute to determine the effect of climate and interspecific interactions in moving hybrid zones. Journal of Evolutionary Biology, 26(11), 2487–2496. https://doi.org/10.1111/jeb.12244 Farwell, O. A. (1916). The genus Hippochaete in North America, north of Mexico. Mem NY Bot Gard, 6, 461–472. Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence / absence models. Environmental Conservation, 24(1), 38–49. https://doi.org/10.1017/S0376892997000088 Guisan, A., Zimmermann, N. E., Elith, J., Graham, C. H., Phillips, S., & Peterson, A. T. (2007). What matters for predicting the occurrences of trees: Techniques, data, or species’ characteristics? Ecological Monographs, 77(4), 615–630. https://doi.org/10.1890/06-1060.1 Guisan, A, & Thuiller, W. (2007). Predicting species distribution: offering more than simple habitat models (vol 8, pg 993, 2005). Ecology Letters, 10(5), 435. https://doi.org/10.1111/j.1461-0248.2005.00792.x Predictive habitat distribution models in ecology, 135 Ecological Modelling 147 (2000). https://doi.org/10.1016/S0304-3800(00)00354-9 Guo, Q., & Liu, Y. (2010). ModEco???: an integrated software package for ecological niche modeling. Ecography, March, 1–6. https://doi.org/10.1111/j.1600-0587.2010.06416.x Hauke, R. L. (1963). A Taxonomic Monograph of the Genus Equisetum Subgenus Hippochaete. Beihefte Zur Nova Hedwigia, 8, 1–123. http://kbd.kew.org/kbd/detailedresult.do?id=25673 Hauke, R. L. (1978). A taxonomic monograph of Equisetum subgenus Equisetum. Nova Hedwigia, 385–456. Hijmans, R. J. (2012). Cross-validation of species distribution models: Removing spatial sorting bias and calibration with a null model. Ecology, 93(3), 679–688. https://doi.org/10.1890/11-0826.1 Hijmans, R. J., & Elith, J. (2013). Species distribution modeling with R Introduction. In October. https://doi.org/10.1016/S0550-3213(02)00216-X Morán-Ordóñez, A., Suárez-Seoane, S., Elith, J., Calvo, L., & de Luis, E. (2012). Satellite surface reflectance improves habitat distribution mapping: A case study on heath and shrub formations in the Cantabrian Mountains (NW Spain). Diversity and Distributions, 18(6), 588–602. https://doi.org/10.1111/j.1472-4642.2011.00855.x Peterson, E. B. (2005). Estimating cover of an invasive grass (Brontus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM + data. International Journal of Remote Sensing, 26(12), 2491–2507. http://www.tandfonline.com/doi/abs/10.1080/01431160500127815 Phillips, S. B., P. Anderson, R., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 31(2), 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x Rothmaler, W. H. P. (1944). Pteridophyten studien. I. In Feddes Repertorium (Vol. 54, pp. 55–82). http://onlinelibrary.wiley.com/doi/10.1002/fedr.19440540106/abstract Smith, M., Oliveira-Filho, A. T., Bachman., S., Moat, J., Lughada, E. M. N., & Lucas, E. J. (2008). Plant Diversity Hotspost in the atlantic Coastal Forests of Brazil. Conservation, 23(1), 151–163. http://onlinelibrary.wiley.com/doi/10.1111/j.1523-1739.2008.01075.x/full Stigall, A. (2012). Using ecological niche modeling to evaluate niche stability in deep time. Journal of Biogeography, 39, 772–781. http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2699.2011.02651.x/full Stockwell, D. (1999). The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13(2), 143–158. https://doi.org/10.1080/136588199241391 Stockwell, D. R. ., & Peterson, A. T. (2002). Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148(1), 1–13. https://doi.org/10.1016/S0304-3800(01)00388-X Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science (New York, N.Y.), 240(4857), 1285–1293. https://doi.org/10.1126/science.3287615 Veloz, S. D. (2009). Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography, 36(12), 2290–2299. https://doi.org/10.1111/j.1365-2699.2009.02174.x Warren, D. L., & Seifert, S. N. (2011). Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecological Applications, 21(2), 335–342. https://doi.org/10.1890/10-1171.1

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