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Application Natura 2000 Data For The Invasive Plants Spread Prediction

* Supported by the Internal Grant Agency of the Czech University of Life Sciences Prague (IGA 2014), Project No. 422201312423170, and by DKR-Wetland Group, Project No. 4222013223243. REFERENCES Austin MP (2002): Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101–118. doi: 10.1016/S0304-3800(02)00205-3. Austin M (2007): Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling, 200, 1

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Species distribution models for critically endangered liverworts (Bryophyta) from the Czech Republic: a guide to future survey expeditions.

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

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How much is enough? Influence of number of presence observations on the performance of species distribution models

REFERENCES Austin, M. 2007. Species distribution models and ecological theory, a critical assessment and some possible new approaches. Ecological Modelling 200,1–19. Bakkestuen, V., Erikstad, L., Halvorsen, R. 2008. Step-less models for regional environmental variation in Norway. Journal of Biogeography 35,1906–1922. Chapman, A. D. 2009. Numbers of living species in Australia and the world. Department of the Environment, Water, Heritage and the Arts, Canberra, Australia. Chefaoui, R. M., J. Hortal, and J. M. Lobo. 2005. Potential

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A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling

-785. Hijmans, R.J. & Elith, J. 2011. Species distribution modelling with R. - http://cran.r-project.org/ web/packages/dismo/vignettes/sdm.pdf, The R foundation for statistical computing. *Hijmans, R.J. & Graham, C.H. 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. - Global Change Biol. 12: 2272-2281. Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. 2006. Evaluating the ability of habitat suitability models to predict species presences. - Ecol. Modelling 199: 142

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Compared impact of compost and digestate on priming effect and hydrophobicity of soils depending on textural composition

the exposure time of 1 day for the SCM, the soil-digestate-mixtures (SDM) and the untreated reference, the mixtures were stored at 10°C. Afterwards, one part of the amendments was again air-dried and sieved to ≤ 2 mm. From SCM, SDM and R, four repetitions were finely ground in the ball mill for the analysis of C org . To determine the sorptivity of water and ethanol into the soil, the remaining part of the mixtures was compacted to a bulk density of 1.45 g cm –1 for Ss or 1.4 g cm –1 for Ut3 in 6 cylinders (100 cm 3 ) for each amendment and soil texture. For

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