Principal Component Analysis (PCA).

<p>The climatic niche of <i>Eschscholzia californica</i> in the native range in California (green) and in the invaded range in central Chile (red). The blue area corresponds to niche areas shared in both ranges (niche stability).The solid and dashed lines show 100% and 75% of the climatic envelope from the native (green) and from the invaded range (red), respectively. The green area is the unfilled climatic niche space in the invaded range, and the red area, is the expanded climatic niche in the invaded range. The more intense blue cells represent zones with higher occurrence densities in the invaded niche (central Chile). In the PCA analysis the first axis accounts for 47.66% of the total variance and mainly represents mean annual precipitation, precipitation during the warmest quarter and altitude; the second PCA axis accounts for 29.68% of the total variance and mainly represents precipitation seasonality. In the correlation circle, the hidden label (behind bio7) corresponds to temperature seasonality (bio4).</p

Species distribution models (SDMs) for <i>Eschscholzia californica</i> in central Chile.

<p>A) Generated with occurrences recorded in the invaded range (central Chile). B) Generated with occurrences recorded in the native range (California). C) Overlap of both SDMs. It shows that 99.9% of the invaded range predicted from central Chile (blue) is included in the area predicted from California; 65% of the area predicted from California (green) is not predicted by the SDM from central Chile; only a small proportion of area (0.1%) in Chile (red) is not predicted from California. AUC values for these models are displayed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105025#pone-0105025-t001" target="_blank">Table 1</a>.</p

Uribe-Rivera et al 2017 DataSet: High resolution bioclimatic layers for southwest of South America for three recent past periods (1970, 1990 and 2010)

<p>These files were generated as part of the article "Dispersal and extrapolation on the accuracy of temporal predictions from distribution models for the Darwin’s frog" (Uribe-Rivera et al. 2017; accepted in Ecological Applications)</p> <p>We used point data of meteorological stations between 34°-48°S and 70°-75°W, to generate new climatic surfaces for three recent past periods (1970; 1990; 2010). Meteorological data encompassed 293 weather stations, and were extracted from three databases: Dirección Meteorológica de Chile (DMC); Dirección General de Aguas de Chile (DGA); and the FAOClim-NET Agroclimatic database management system (FAO 2001), recording monthly records of mean daily minimum temperature, mean daily maximum temperature and total rainfall for 5-year periods (1965-1969 for 1970 climatic conditions; 1985-1989 for 1990 climatic conditions; and 2005-2009 for 2010 climatic conditions). For each period monthly mean values of each climatic variable were interpolated to generate surfaces using Anusplin v.4.4 (Hutchinson and Xu 2006), which applies the same algorithm used to derive the WorldClim bioclimatic surfaces (Hijmans et al. 2005). Interpolations were fitted following Pliscoff et al. (2014) at a ~1x1 Km resolution, with elevation as an independent variable using the GTOPO30 global digital elevation model (USGS, 1996). Because some weather stations do not have information for every month, we used the option of non-data of Anusplin. The quality of interpolations of climatic data was assessed calculating the Pearson correlation coefficient at the cell level between the monthly climatic values from the CRU-TS v3.10.01 Historic Climate Database for GIS (Climatic Research Unit - Time Series, 2012), and the monthly climatic values from the new climatic layers. Finally, surfaces of 19 bioclimatic variables were generated using the dismo package in R (Hijmans et al. 2014).</p> <p>All bioclimatic layers were uploaded in a single compressed ZIP file. Individual layers can be found inside it as georeferenced ASCII raster files, and nominated primarily by time period, and secundarily by the number of bioclimatic layer, following the worldclim nomenclature (http://www.worldclim.org/bioclim).</p