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```{asis, echo = {{envs_ecoclimate_knit}}, eval = {{envs_ecoclimate_knit}}, include = {{envs_ecoclimate_knit}}}
Using Ecoclimate (http://www.ecoclimate.org) dataset at resolution of 0.5 degrees.
```r}, include = {{envs_ecoclimate_knit}}} # R code to get environmental data from Ecoclimate envs_{{spAbr}} <- envs_ecoClimate( bcAOGCM = "{{bcAOGCM_rmd}}", bcScenario = "{{bcScenario_rmd}}", ecoClimSel = {{ecoClimSel_rmd}}) ##Add envrionmental values to occurrences table occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')] occs_vals_{{spAbr}} <- as.data.frame(raster::extract(envs_{{spAbr}}, occs_xy_{{spAbr}}, cellnumbers = TRUE)) # Remove duplicated same cell values occs_{{spAbr}} <- occs_{{spAbr}}[!duplicated(occs_vals_{{spAbr}}[, 1]), ] occs_vals_{{spAbr}} <- occs_vals_{{spAbr}}[!duplicated(occs_vals_{{spAbr}}[, 1]), -1] # remove occurrence records with NA environmental values occs_{{spAbr}} <- occs_{{spAbr}}[!(rowSums(is.na(occs_vals_{{spAbr}})) >= 1), ] # also remove variable value rows with NA environmental values occs_vals_{{spAbr}} <- na.omit(occs_vals_{{spAbr}}) # add columns for env variable values for each occurrence record occs_{{spAbr}} <- cbind(occs_{{spAbr}}, occs_vals_{{spAbr}})
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