Search Results

1 - 10 of 24 items :

Clear All

References Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. - In: Petrov, B.N. & Csaki, F. (eds), Second international symposium on information theory, Akademiai Kiado, Budapest, pp. 267-281. *Anderson, R.P. & Gonzalez, I.J. 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. - Ecol. Modelling 222: 2796-2811. *Anderson, R.P. & Raza, A. 2010. The effect of the extent of the study region on GIS models of species geographic distributions and

of Applied Ecology, 43: 1223–1232, https://doi.org/10.1111/j.1365-2664.2006.01214.x . Booth, T.H., Nix, H.A., Busby, J.R. and Hutchinson, M.F. 2014. Bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions, 20: 1–9, https://doi.org/10.1111/ddi.12144 . Bosso, L., Russo, D., Di Febbraro, M., Cristinzio, G. and Zoina, A. 2016. Potential distribution of Xylella fastidiosa in Italy: a maximum entropy model. Phytopathologia Mediterranea , [S.l.], v. 55, n. 1, p. 62

). Vol. 2.1.4. Geoscientific Model Development , 8 , 1991–2007. Coroiu, I., Kryštufek, B., Vohralík, V. 2016. Mus spicilegus . The IUCN Red List of Threatened Species 2016: e.T13984A544549. http://dx.doi.org/10.2305/IUCN.UK.2016-3.RLTS.T13984A544549.en 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 , 11 (1), 43–57. Evstafiev, I. 2015. Results of a thirty-year study of small mammals of Crimea. Part 1. Introduction, composition of fauna, home ranges

tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecological Modelling , 222 (15), 2796-2811. https://doi.org/10.1016/j.ecolmodel.2011.04.011 Anderson, R. P., Lew, D., & Peterson, A. T. (2003). Evaluating predictive models of species distributions: criteria for selecting optimal models. Ecological Modelling , 162 (3), 211-232. https://doi.org/10.1016/S0304-3800(02)00349-6 Austin, M. P. (2002). Spatial prediction of species distribution: an interface between ecological theory and statistical modelling

References Anderson R.P. & Gonzalez I. (2011): Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. – Ecological Modelling 222(15): 2796-2811. Atherton I., Bosanquet, Sam D.S. & Lawley M. (2010): Mosses and liverworts of Britain and Ireland: a field guide. British Bryological Society, Plymouth, 848 pp. Bates J., Roy D. & Preston C. (2004): Occurrence of epiphytic bryophytes in a’tetrad’transect across southern Britain. 2. Analysis and modelling of epiphyte-environment relationships

-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. – Ecol. Modelling 222: 2796–2811. Anderson, R.P., Peterson, A.T. & Gómez-Laverde, M. 2002. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. – Oikos 98: 3–16. Araújo, M.B. & Guisan, A. 2006. Five (or so) challenges for species distribution modelling. –J. Biogeogr. 33: 1677–1688. Araújo, M.B. & Luoto, M. 2007. The importance of biotic interactions for modelling species

. Wisz, and N. E. Zimmermann. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29,129–151. Elith, J., S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17,43–57. Engler, R., A. Guisan, and L. Rechsteiner. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology 41,263–274. Feeley, K. J., and M. R. Silman. 2011a. The data

, J., L eathwick , J.R., 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics , 40 (1): 677–697. E lith , J., P hillips , S., H astie , T., D udik , M., C hee , Y., Y ates , C., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions , 17: 43–57. F ielding , A.H., B ell , J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation , 24 (1): 38–49. F ranklin , J

): Applied logistic regression analysis. Vol. 106. Sage. Naimi B., Hamm N.A., Groen T.A., Skidmore A.K. & Toxopeus A.G. (2014): Where is positional uncertainty a problem for species distribution modelling? – Ecography 37(2): 191-203. Phillips S.J., Dudík M. & Schapire R.E. (2017): Maxent software for modeling species niches and distributions (Version 3.4.1). Available from url: http://biodiversityinformatics.amnh.org/open_source/maxent/ . Accessed 2018-10-24. Plášek V. (2004): The moss Buxbaumia viridis (Bryopsida, Buxbaumiaceae) in the Czech part of the Western