[Aiello-Lammens, M.E, Boria, R.A., Radosavljevic, A., Vilela, B., Anderson, R.P. 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. – Ecography, 38, 541–545.10.1111/ecog.01132]Search in Google Scholar
[Albert, C.H., Yoccoz, N.G., Edwards, T.C. Jr, Graham, C.H., Zimmermann, N.E., Thuiller, W. 2010. Sampling in ecology and evolution – bridging the gap between theory and practice. – Ecography, 33, 1028–1037.]Search in Google Scholar
[Barry, S.C., Welsh, A.H. 2002. Generalized additive modelling and zero inflated count data. – Ecological Modelling, 157, 179–188.]Search in Google Scholar
[Boria, R.A., Olson, L.O., 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.]Search in Google Scholar
[Bornand, C.N., Kéry, M., Bueche, L., Fischer, M. 2014. Hide-and-seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence. – Methods in Ecology and Evolution, 5, 433–442.]Search in Google Scholar
[Boscolo, D., Metzger, J.P. 2009. Is bird incidence in Atlantic forest fragments influenced by landscape patterns at multiple scales? – Landscape Ecology, 24, 907–918.]Search in Google Scholar
[Boulangeat, I., Gravel, D., Thuiller, W. 2012. Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances. – Ecology Letters, 15, 584–593.]Search in Google Scholar
[Clarke, K.D., Lewis, M., Brandle, R., Ostendorf, B. 2012. Non-detection errors in a survey of persistent, highly-detectable vegetation species. – Environmental Monitoring and Assessment, 184, 625–635.]Search in Google Scholar
[Dormann, C.F. 2011. Modelling species’ distributions. – Jopp, F. et al. (eds.). Modelling complex ecological dynamics: an introduction into ecological modelling for students, teachers & scientists. Springer, 179–196.]Search in Google Scholar
[Elith, J., Graham, C.H. 2009. Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. – Ecography, 32, 66–77.]Search in Google Scholar
[Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. – Ecography, 29, 129–151.]Search in Google Scholar
[Elith, J., Leathwick, J. 2007. Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. – Diversity and Distributions, 13, 265–275.]Search in Google Scholar
[Elkington, T.T., Woodell, S.R.J. 1963. Potentilla fruticosa L. (Dasiphora fruticosa (L.) Rydb.). – Journal of Ecology, 51, 769–781.]Search in Google Scholar
[Ficetola, G.F., Bonardi, A., Mucher, C.A., Gilissen, N.L.M., Padoa-Schioppa, E. 2014. How many predictors in species distribution models at the landscape scale? Land use versus LiDAR-derived canopy height. – International Journal of Geographical Information Science, 28, 1723–1739.]Search in Google Scholar
[Ghosh, A., Fassnacht, F.E., Joshi, P.K., Koch, B. 2014. A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. – International Journal of Applied Earth Observation and Geoinformation, 26, 49–63.]Search in Google Scholar
[Giljohann, K.M., Hauser, C.E., Williams, N.S.G., Moore, J.L. 2011. Optimizing invasive species control across space: willow invasion management in the Australian Alps. – Journal of Applied Ecology, 48, 1286–1294.]Search in Google Scholar
[Gogol-Prokurat, M. 2011. Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. – Ecological Applications, 21, 33–47.]Search in Google Scholar
[Gorchakovskiy, P.L. 1960. On the distribution and habitat conditions of Dasiphora fruticosa (L.) Rydb. in connection with relict character of its localities in the Ural Mountains. – Zapiski Sverdlovskogo Otdeleniya Vsesoyuzhnogo Botanicheskogo Obshestva, 1, 3–22. (In Russian).]Search in Google Scholar
[Graf, R.F., Bollman, K., Suter, W., Bugmann, H. 2005. The importance of spatial scale in habitat models: capercaillie in the Swiss Alps. – Landscape Ecology, 20, 703–717.]Search in Google Scholar
[Guisan, A., Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. – Ecology Letters, 8, 993–1009.]Search in Google Scholar
[Hanssen, A.W., Kuipers, W.J.A. 1965. On the relationship between frequency of rain and various meteorological parameters. – Mededelingen van de Verhandlungen, 81, 2–15.]Search in Google Scholar
[Heilbron, D. 1994. Zero-altered and other regression models for count data with added zeros. – Biometrical Journal, 36, 531–547.]Search in Google Scholar
[Holland, J.D., Bert, D.G., Fahrig, L. 2004. Determining the spatial scale of species’ response to habitat. – BioScience, 54, 227–233.]Search in Google Scholar
[Jiménez-Valverde, A., Lobo, J.M., Hortal, J. 2008. Not as good as they seem: the importance of concepts in species distribution modelling. – Diversity and Distributions, 14, 885–890.]Search in Google Scholar
[Ju, J., Gopal, S., Kolaczyk, E.D. 2005. On the choice of spatial and categorical scale in remote sensing land cover classification. – Remote Sensing of Environment, 96, 62–77.]Search in Google Scholar
[Kadmon, R., Farber, O., Danin, A. 2004. Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. – Ecological Applications, 14, 401–413.]Search in Google Scholar
[Kass, G.V. 1980. An exploratory technique for investigating large quantities of categorical data. – Applied Statistics, 29, 119–127.]Search in Google Scholar
[Kéry, M., Gardner, B., Monnerat, C. 2010. Predicting species distributions from checklist data using site-occupancy models. – Journal of Biogeography, 37, 1851–1862.]Search in Google Scholar
[Kéry, M., Royle, J.A., Schmid, H. 2005. Modeling avian abundance from replicated counts using binomial mixture models. – Ecological Applications, 15, 1450–1461.]Search in Google Scholar
[Kéry, M., Spillmann, J.H., Troung, C., Holderegger, R. 2006. How biased are estimates of extinction probability in revisitation studies? – Journal of Ecology, 94, 980–986.]Search in Google Scholar
[Lahoz-Monfort, J.J., Guillera-Arroita, G., Wintle, B.A. 2014. Imperfect detection impacts the performance of species distribution models. – Global Ecology and Biogeography, 23, 504–515.]Search in Google Scholar
[Latimer, A.M., Wu, S., Gelfand, A.E., Silander, J.A. 2006. Building statistical models to analyze species distributions. – Ecological Applications, 16, 33–50.]Search in Google Scholar
[Liang, Y., He, H.S., Fraser, J.S., Wu, Z. 2013. Thematic and Spatial Resolutions Affect Model-Based Predictions of Tree Species Distribution. – PLoS ONE 8(7).10.1371/journal.pone.0067889370165023861828]Search in Google Scholar
[Lobo, J.M., Jiménez-Valverde, A., Hortal, J. 2010. The uncertain nature of absences and their importance in species distribution modelling. – Ecography, 33, 103–114.]Search in Google Scholar
[Lonati, M., Pascale, M., Operti, B., Lombardi, G. 2014 Synecology, conservation status and IUCN assessment of Potentilla fruticosa L. in the Italian Alps. – Acta Botanica Gallica, 161, 159–173.]Search in Google Scholar
[Luoto, M., Kuussaari, M., Rita, H., Salminen, J., von Bondsdorff, T. 2001. Determinants of distribution and abundance in the clouded apollo butterfly: a landscape ecological approach. – Ecography, 24, 601–617.]Search in Google Scholar
[Mack, R.N., Harper, J.L. 1977. Interference in dune annuals: spatial pattern and neighbourhood effects. – Journal of Ecology, 65, 345–363.]Search in Google Scholar
[Manceur, A.M., Kühn, I. 2014. Inferring model-based probability of occurrence from preferentially sampled data with uncertain absences using expert knowledge. – Methods in Ecology and Evolution, 5, 739–750.]Search in Google Scholar
[Marceau, D.J., Gratton, D.J., Fournier, R.A., Fortin, J.-P. 1994. Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution. – Remote Sensing of Environment, 49, 105–117.]Search in Google Scholar
[Marosz, A. 2004. Effect of soil salinity on nutrient uptake, growth, and decorative value of four ground cover shrubs. – Journal of Plant Nutrition, 27, 977–989.]Search in Google Scholar
[McCarthy, M.A., Moore, J.L., Morris, W.K., Parris, K.M., Garrard, G.E., Vesk, P.A., Rumpff, L., Giljohann, K., Camac, J., Bau, S.S., Friend, T., Harrison, B., Yue, B. 2012. The influence of abundance on detectability. – Oikos, 122, 717–726.]Search in Google Scholar
[McPherson, J.M., Jetz, W., Rogers, D.J. 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? – Journal of Applied Ecology, 41, 811–823.]Search in Google Scholar
[Mullahy, J. 1986. Specification and testing of some modified count data models. – Journal of Econometrics, 33, 341–365.]Search in Google Scholar
[O’Neill, R.V., DeAngelis, D.L., Waide, J.B., Allen, T.F.H. 1986. A Hierarchical Concept of Ecosystems. Princeton University Press, Princeton, NJ.]Search in Google Scholar
[Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. – Ecological Applications, 19, 181–197.]Search in Google Scholar
[Phillips, S.J., Elith, J. 2013. On estimating probability of presence from use–availability or presence–background data. – Ecology, 94, 1409–1419.]Search in Google Scholar
[Reier, Ü., Leht, M. 1999. Potentilla fruticosa L. in Estonia and Latvia: origin, present situation and taxonomic position. – Sander, H. (ed.). Dendroloogilised uurimused Eestis 1, 23–36.]Search in Google Scholar
[Remm, K. 2004. Case-based predictions for species and habitat mapping. – Ecological Modelling, 177, 259–281.]Search in Google Scholar
[Remm, K. 2015. Classifications of areal categories in the Estonian Topographical Database, in the 1: 50 000 historical map from 1930ies, in the Estonian 1: 10 000 soil map, at three different thematic scales as used in the shrubby cinquefoil (Dasiphora fruticosa (L.) Rydb.) distribution studies in north-western Estonia. [WWW document]. – URL http://doi.org/10.13140/RG.2.1.3283.6645 [Accessed 3 May 2016].]Search in Google Scholar
[Remm, K. 2016. Shrubby cinquefoil (Dasiphora fruticosa) cover records from a study site in the NW Estonia. [WWW document]. – URL http://doi.org/10.13140/RG.2.1.4987.6724 [Accessed 3 May 2016].]Search in Google Scholar
[Remm, K., Kelviste, T. 2014. An online calculator for spatial data and its applications. – Computational Ecology and Software, 4, 22–34.]Search in Google Scholar
[Remm, K., Linder, M., Remm, L. 2009. Relative density of finds for assessing similarity-based maps of orchid occurrence. – Ecological Modelling, 220, 294−309.]Search in Google Scholar
[Roland, A.E., Smith, E.C. 1969. The flora of Nova Scotia, part II: The dicotyledons. – Proceedings of the Nova Scotian Institute of Science, 26, 277–743.]Search in Google Scholar
[Roleček, J., Chytrý, M., Hájek, M., Lvončík, S., Tichy, L. 2007. Sampling design in large-scale vegetation studies: Do not sacrifice ecological thinking to statistical purism! – Folia Geobotanica, 42, 199–208.10.1007/BF02893886]Search in Google Scholar
[Steege, H. ter, Haripersaud, P.P., Banki, O.S., Schieving, F. 2011. A model of botanical collectors’ behavior in the field: never the same species twice. – American Journal of Botany, 98, 31–37.]Search in Google Scholar
[Thompson, C.M., McGarigal, K. 2002. The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). – Landscape Ecology, 17, 569–586.]Search in Google Scholar
[Thuiller, W., Araújo, M.B., Lavorel, S. 2003. Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales. – Journal of Vegetation Science, 14, 669–680.]Search in Google Scholar
[Titeux, N., Dufrene, M., Radoux, J., Hirzel, A.H., Defourny, P. 2007. Fitness-related parameters improve presence-only distribution modelling for conservation practice: the case of the red-backed shrike. – Biological Conservation, 138, 207–223.10.1016/j.biocon.2007.04.019]Search in Google Scholar
[Turner, M.G., Constanza, R., Sklar, F.H. 1989. Methods to evaluate the perfomance of spatial simulation models. – Ecological Modelling, 48, 1–18.]Search in Google Scholar
[Václavík, T., Meentemeyer, R.K. 2009. Invasive species distribution modeling (iSDM): are absence data and dispersal constraints needed to predict actual distributions? – Ecological Modelling, 220, 3248–3258.10.1016/j.ecolmodel.2009.08.013]Search in Google Scholar
[Vale, C.G., Tarroso, P., Brito, J.C. 2014. Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara–Sahel transition zone. – Diversity and Distributions, 20, 20–33.10.1111/ddi.12115]Search in Google Scholar
[Welsh, A.H., Cunningham, R.B., Donnelly, C.F., Lindenmayer, D.B. 1996. Modelling the abundance of rare species: statistical models for counts with extra zeros. – Ecological Modelling, 88, 297–308.]Search in Google Scholar
[Wiens, J.A. 1989. Spatial scaling in ecology. – Functional Ecology, 3, 385–397.]Search in Google Scholar
[Zhou, W., Qian, Y., Li, X., Li, W., Han, L. 2014. Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. – Landscape Ecology, 29, 153–167.10.1007/s10980-013-9950-5]Search in Google Scholar
[Zurell, D., Jeltsch, F., Dormann, C.F., Schröder, B. 2009. Static species distribution models in dynamically changing systems: how good can predictions really be? – Ecography, 32, 733–744.10.1111/j.1600-0587.2009.05810.x]Search in Google Scholar
