get_maxent_predict: get_maxent_predict

In elaineliu5/DVM: Disease Vector Modeling

Description

get_maxent_predict uses the Maxent model to predict species distribution in a given month. The model for a species is determined from a set of environmental or climate layers for a set of grid cells in a landscape, together with a set of sample locations where the species has been observed. The computed model is a probability distribution (indicating the level of suitability for species' living) over all the grid cells.

Usage

get_maxent_predict(month, species_presence, climate, species_name)

Arguments

month	an integer representing the month of the year (from 1 to 12)
species_presence	a dataframe containing longitudes and latitudes of species observation occurred in the given month
climate	a raster stack of environmental layers in the corresponding month
species_name	a vector containing genus and species name

Value

raw: raw probability raster

logis: logistic probability raster

Examples

# Example: predict mosquito distribution in June
month <- 6
mosquito_month <- mosquito_presence[mosquito_presence$month==month, c("lon", "lat")]
mosquito_month <- subset(mosquito_month, !is.na(lon) & !is.na(lat))
mosquito_predictions_6 <- get_maxent_predict(month, mosquito_month, environment_data[[6]], c("aedes", "aegypti"))
par(mfrow=c(1,2))
plot(mosquito_predictions_6$raw)
title("Aedes Aegypti Distribution Raw Predictions")
plot(mosquito_predictions_6$logis)
title("Aedes Aegypti Distribution Logistic Predictions")

elaineliu5/DVM documentation built on May 16, 2019, 2:57 a.m.