stim.M: Build buffer zone to M

In patauchi/sdStaf: Species Distribution and Stability Future Models

Description

Returns buffer zone based on ocurrence data

Usage

stim.M(
  occs,
  radio = NULL,
  bgeo = NULL,
  method = "user",
  env = NULL,
  Vrc = 1,
  ncal = 1,
  ...
)

Arguments

occs	data.frame of occurrence data (longitude/latitude).
radio	radio of buffer.
bgeo	Biogeographical layer. Categorical values.
method	default = 'user'. Another option is calculate the mean of all points 'mean'.
env	if True. Environmental dataset used to build M. Only method = 'Tol.pca'
Vrc	Integer. sd(IQR) * value, used to increase range tolerance of dataset env
ncal	Integer. Dataset using to define IQR. Only method = 'Tol.pca'
...	Optional features of buffer

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 difficult, in these cases is appropiate 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).

Value

SpatialPolygons* object

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. 222:1810–1819.

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): 667–674.

Examples

# Phytotoma ocurrence data
data(phytotoma)

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

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

patauchi/sdStaf documentation built on May 10, 2020, 9:34 a.m.