Equilibrium modeling of alpine plant distribution: how far can we go?

By Antoine Guisan, J. Theurillat. 2000

194 citations recorded by the source corpus.

Subfields: Nature and Landscape Conservation, Plant Science

View Paper: https://www.semanticscholar.org/paper/76ce555cc7a6229f1e45bfe092ccc694ca5b1445

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Citations in the corpus

SESAM – a new framework integrating macroecological and species distribution models for predicting spatio‐temporal patterns of species assemblages (2011, methodology): “It originated from the many attempts to map current patterns of species richness for reserve design (e.g. Young et al., 2009) and to forecast the likely impact of climate or land-use change on biodiversity (e.g. Guisan & Theurillat, 2000; see Ferrier & Guisan, 2006).”
Effects of incorporating spatial autocorrelation into the analysis of species distribution data (2007, background): “Guisan and Thuiller (2005, p. 1002) have argued that predictive distribution models therefore need to explicitly model processes responsible for spatial autocorrelation (see also González-Megías et al ., 2005). Along a different line of argument, Diniz-Filho et al . (2003) see the use of spatial aut”
A regional impact assessment of climate and land‐use change on alpine vegetation (2003, methodology): “…al., 1993, 1999; Brzeziecki et al., 1995; Huntley et al., 1995; Gottfried et al., 1998, 1999; Guisan et al., 1998; Sætersdal et al., 1998; Bolliger et al., 2000; Duckworth et al., 2000; Guisan & Theurillat, 2000; Richardson et al., 2000; Aber et al., 2001; Hansen et al., 2001; Lexer et al., 2002).”
Predicting reptile distributions at the mesoscale: relation to climate and topography (2003, methodology): “As a basic postulate, it is assumed with this type of modelling that, within a time frame of interest and considering a certain spatial scale, species’ distributions are in equilibrium with their surrounding environment (Guisan & Theurillat, 2000).”
Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales (2003, methodology): “Modelling plant distributions in relation to environmental factors has gained momentum with recent developments in the fields of GIS and statistical techniques applied to ecological analysis (e.g. Guisan & Theurillat 2000).”
The importance of snow in species distribution models of arctic vegetation (2018, background): “Some authors report snow as a moderately important variable (Guisan and Theurillat 2000, Beck et al. 2005, Carlson et al. 2015) but in some studies snow has not been deemed as influential (Dirnbock et al. 2003, Randin et al. 2009).”
An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data (2004, background): “2002) and for plant species assemblages and biodiversity reconstructed from superimposing individual species’ predictions (Guisan & Theurillat 2000; Lehmann, Overton & Leathwick 2002).”
Spatial prediction of species distribution: an interface between ecological theory and statistical modelling (2002, methodology): “However, statistical models currently used are static and contain no dynamic elements (Guisan and Zimmermann, 2000; Guisan and Theurillat, 2000).”
Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches (2011, methodology): “S-SDMs have already been used to predict current (Cumming, 2000; Parviainen et al., 2009; Pineda & Lobo, 2009) and future (Guisan & Theurillat, 2000b; Algar et al., 2009; Pineda & Lobo, 2009) distributions of SR, community composition (Pineda & Lobo, 2009) and species turnover rates (Thuiller et…”
GRASP: generalized regression analysis and spatial prediction (2002, background): “Species richness and natural community composition can, for instance, be estimated by summing several species predictions (Guisan and Theurillat, 2000; Leathwick, 2002; Lehmann et al., 2002).”
Potential Impact of Climate Change on Vegetation in the European Alps: A Review (2001, background): “For many alpine species, light is very likely to be an important limiting factor through competition with taller plants, as Guisan et al. (1998) showed for the alpine sedge Carex curvula.”
Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns (2002, background): “Ferrier and Watson (1997) cite this as an explanation for why they observed rare species to perform better in presence-only models than common species when often the opposite is the case (Iverson and Prasad, 1998; Guisan and Theurillat, 2000).”