knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Stability models
The maps used in this example was built for Peruviant plantcutter.
Peruvian plantcutter is a bird species endemic to the northern coast of Peru. It's considerated to be tied closely to dry forests (see Atauchi et al., 2018).
In our case, we used maximum entropy approaches implemented in Maxent 3.3.3k (Phillips et al., 2006) for calibration models. The best models was transferred to 2050 based on three global circulation models: HagGem2-ES, MIROC5 and ACCESS1-0 (RCP 8.5).
Load current and future distribution of species
library(sdStaf) # We read current distribution of Peruvian Plantcutter current_list <- list.files(path=paste(system.file(package="sdStaf"), '/pre', sep=''), pattern='asc', full.names=TRUE) current <- raster::stack(current_list) # We read future distribution of Peruvian Plantcutter future_list <- list.files(path=paste(system.file(package="sdStaf"), '/fut', sep=''), pattern='asc', full.names=TRUE) future <- raster::stack(future_list)
We calculate stability values of Peruvian plantcutter
stabSpecies <- stability(current = current, project = future, thr.value=0.34)
Details of Stability Models. Realize that upper values to 100 show stability and lower values show areas with colonize potential.
Besides, It has built a stability maps that can be plotter with plot(stabSpecies)
print(stabSpecies)
Atauchi et al. (2018). Species distribution models for Peruvian Plantcutter improve with consideration of biotic interactions. \emph{J. avian biology 2018: e01617} https://onlinelibrary.wiley.com/doi/abs/10.1111/jav.01617
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