sar_pred: Use SAR model fits to predict richness on islands of a given...

In txm676/mmSAR2: Fit and Compare Species-Area Relationship Models Using Multimodel Inference

Use SAR model fits to predict richness on islands of a given size

Description

Predict the richness on an island of a given size using either individual SAR model fits, a fit_collection of model fits, or a multi-model SAR curve.

Usage

sar_pred(fit, area)

Arguments

fit	Either a model fit object, a fit_collection object (generated using sar_multi), or a sar_multi object (generated using sar_average).
area	A numeric vector of area values (length >= 1).

Details

Extrapolation (e.g. predicting the richness of areas too large to be sampled) is one of the primary uses of the SAR. The sar_pred function provides an easy method for undertaking such an exercise. The function works by taking an already fitted SAR model, extacting the parameter values and then using these values and the model function to predict the richness for any value of area provided.

If a multi-model SAR curve is used for prediction (i.e. using sar_average), the model information criterion weight (i.e. the conditional probabilities for each of the n models) for each of the individual model fits that were used to generate the curve are stored. The n models are then each used to predict the richness of a larger area and these predictions are multiplied by the respective model weights and summed to provide a multi-model averaged prediction.

Value

A data.frame of class 'sars' with three columns: 1) the name of the model, 2) the area value for which a prediction has been generated, and 3) the prediction from the model extrapolation.

Note

This function is used in the ISAR extrapolation paper of Matthews & Aspin (2019).

Code to calculate confidence intervals around the predictions using bootstrapping will be added in a later version of the package.

As grid_start has a random component, when grid_start != "none" in your model fitting, you can get slightly different results each time you fit a model or run sar_average and then run sar_pred on it. We would recommend using grid_start = "exhaustive" as this is more likely to find the optimum fit for a given model.

References

Matthews, T.J. & Aspin, T.W.H. (2019) Model averaging fails to improve the extrapolation capability of the island species–area relationship. Journal of Biogeography, 46, 1558-1568.

Examples

data(galap)
#fit the power model and predict richness on an island of area = 5000
fit <- sar_power(data = galap)
p <- sar_pred(fit, area = 5000)

#fit three SAR models and predict richness on islands of area = 5000 & 10000
#using no grid_start for speed
fit2 <- sar_multi(galap, obj = c("power", "loga", "koba"), grid_start = "none")
p2 <- sar_pred(fit2, area = c(5000, 10000))

#calculate a multi-model curve and predict richness on islands of area = 5000 & 10000
#using no grid_start for speed
fit3 <- sar_average(data = galap, grid_start = "none")
p3 <- sar_pred(fit3, area = c(5000, 10000))

txm676/mmSAR2 documentation built on Nov. 21, 2024, 5:03 a.m.