sdStaf (version 1.0.2)

stim.M: Build buffer zone to M

Description

Returns buffer zone based on ocurrence data

Usage

stim.M(occs, radio, bgeo = NULL, ...)

Arguments

occs

data.frame of ocurrence data (longitude/latitude).

radio

radio of buffer.

bgeo

Biogeographical layer. Categorical values.

...

Optional features of buffer

Value

SpatialPolygons* object

Details

To define calibration area is crucial step (Barve et al., 2011), even more with incomplete sample data sometime is complicated, because to get complete sample within geography space is dificult, in these cases is apropiate define M with buffer zone (Peterson et al., 2017); and in other cases it helps to cut the ends of the calibration area based on the maximum dispersion capacity (Atauchi et al., 2018).

References

Atauchi et al. (2018). Species distribution models for Peruvian Plantcutter improve with consideration of biotic interactions. J. avian biology 2018: e01617. <doi:http://10.1111/jav.01617.>

Barve et al. (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Mod. 222:1810<U+2013>1819.

Peterson et al.(2017) Influences of climate change on the potential distribution of Lutzomyia longipalpis sensu lato (Psychodidae: Phlebotominae). International journal for parasitology. 45(10-11): 667<U+2013>674.

Examples

# NOT RUN {
# Phytotoma ocurrence data
data(phytotoma)

# Build buffer zone
buf_M <- stim.M(occs=phytotoma[,2:3], 100)

# Add points
points(phytotoma[,2:3])

# }