sar_countryside: Fit the countryside SAR model

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

Fit the countryside SAR model

Description

Fit the countryside biogeography SAR model in either power or logarithmic form.

Usage

sar_countryside(data, modType = "power", gridStart =
  "partial", startPar = NULL, zLower = 0, habNam = NULL, spNam
  = NULL)

Arguments

data	A dataset in the form of a dataframe, with columns for habitat area values and species richness values – requires a specific column order (see 'Details' below).
modType	Fit the power ("power") or logarithmic ("logarithmic") form of the countryside model.
gridStart	The type of grid search procedure to be implemented to test multiple starting parameter values: can be one of partial (default), exhaustive or none. If startPar is provided, this argument is ignored. Note that exhaustive can be quite time consuming to run. In contrast, none is much quicker but only checks a very small number of starting paramter values (technically not "none").
startPar	Optional (default = NULL) starting parameter estimates for the constituent models. Must be a numeric matrix (see 'Details' below).
zLower	The lower bound to be used for the z-parameter in the nlsLM function. Default is set to zero, but can be changed to any numeric value (e.g., -Inf to allow for a full search of parameter space).
habNam	Either a vector of habitat names (must be the same length as the number of habitat area columns in data, and in the same order as the area columns), or the habitat area column numbers in data.
spNam	Either a vector of species group names (must be the same length as the number of species richness columns in data, and in the same order as the richness columns), or the species richness column numbers in data.

Details

The provided input dataset (data) will typically relate to a series of landscapes (sites) with differing areas of N habitats (e.g., forest and grassland), and for each landscape the number of species in a priori defined groups.

To work, the countryside SAR model requires that all species in the study system have been classified into groups. This is typically done based on the habitats present in the study system. For example, in a study system with two habitats (forest and grassland), the species can be a priori classified as either forest species or grassland species. Optionally, species can also be classified as ubiquitous species (i.e., species that do not have strong affinity for a specific habitat – true habitat generalists). However, the model is flexible and species can technically be grouped into any groups. For example, in a study system with three habitats (forest, grassland, wetlands), species could be grouped into two groups: forest species and other species. Note that species must be classified prior to fitting the model, but the data can still be used to help guide these classifications.

It is important that the column orders in data are correct. The first set of columns should be all the habitat area columns, followed by all the group species richness columns. Within these two sets (i.e., area and richness columns), the order of columns is not important. The user must make clear which columns are the area columns and which the richness columns, using the habNam and spNam arguments. These can either provide habitat and species group names (e.g., habNam = c("Forest", "Other")) or the column numbers in data (e.g., spNam = 4:6). If names are provided, note that these names can be different to the column names in data, but they need to be in the same order as their respective columns in data.

No columns should be present in data before the area columns (i.e., the first column must be an area column) and all columns after the last species richness column are excluded by the function. And do not use the arguments to re-order columns as this will not be undertaken, e.g. use 4:6 or c(4,5,6), and not c(4,6,5). If habNam and spNam are numeric (i.e., column numbers), the habitats and species groups are named Habitat1, Habitat2, and Sp_grp1, Sp_grp2, and so on, in the output.

The countryside SAR model works by fitting individual component models of a particular form (e.g., power), one for each of the species groups (e.g., one model for forest species, one for grassland species, and so on). The predictions from these component models are then combined to generate a total predicted richness for each site / landscape. The output of the model fitting includes the individual component model fits, the total predicted (fitted) richness values for each site, and the habitat affinity values for each species group. The latter vary from zero to one, and equal 1 for a given species group's affinity to its preferred habitat (e.g., forest species for forest).

Note that the logarithmic model can generate negative fitted richness values for small areas in some cases.

If you find some or all of your component models are not fitting / converging, you can try using gridStart = "exhaustive} to undertake a wider search of parameter space. If that still doesn't work you will need to provide a wide range of starting parameter values manually using the \code{startPar} argument. To speed up, you can try \code{gridStart = "none"}, which typically runs in seconds, but does not provide much of a search of starting parameter values. For \code{startPar}, if not NULL, it needs to be a numeric matrix, where number of rows = number of species groups, and number of columns equals number of habitats + 1. Matrix row order matches the order of species group columns in \code{data}, and matrix column order matches the order of habitat columns in \code{data} + 1 extra final column for the z-parameter estimate. Three different types of plot can be generated with the output, using \code{plot.habitat}. The \code{countryside_extrap} function can be used with the output of \code{sar_countryside} to predict the species richness of landscapes with varying areas of the analysed habitats. See Matthews et al. (2025) for further details. } \note{ The model fits in (i) are objects of class ‘nls’, meaning that all the basic non-linear regression R methods can be applied (e.g., generating model summary tables or plotting the model residuals). This also means that information criteria values can be returned for each component model, simply by using, for example, \code{AIC}. This can then be compared with equivalent values from, for example, the power model (see Examples, below). However, importantly note that while the values returned from \code{AIC} and \code{sar_power} are comparable, these values are not comparable with the AIC / AICc values presented in Proença & Pereira (2013) and related studies, due to the different information criteria equations used (although the delta values (calculated using a given equation) are comparable across equations). For more information, see the package vignette. } \examples{ data(countryside) \dontrun{ #Fit the countryside SAR model (power form) to the data, #which contrains 3 habitat types and 4 species groups. #Use the function’s starting parameter value selection procedure. #Abbreviations: AG = agricultural land, SH = shrubland, F = #oak forest, UB = ubiquitous species. s3 <- sar_countryside(data = countryside, modType = "power", gridStart = "partial", habNam = c("AG", "SH", "F"), spNam = c("AG_Sp", "SH_Sp", "F_Sp", "UB_Sp")) #Predict the richness of a site which comprises 1000 area units #of agricultural land, 1000 of shrubland and 1000 of forest. countryside_extrap(s3, area = c(1000, 1000, 1000)) #Generate a plot of the countryside model’s predicted total #richness vs. the observed total richness, and include the #predictions of the Arrhenius power model plot(s3, type = 1, powFit = TRUE) #Generate Type 2 & 3 plots providing set line colours, plot #titles, and modifying other aspects of the plot using the #standard #ase R plotting commands. See ?plot.habitat for more #info plot(s3, type = 2, lcol = c("black", "aquamarine4", "#CC661AB3" , "darkblue"), pLeg = TRUE, lwd = 1.5, ModTitle = c("Agricultural land", "Shrubland", "Forest")) plot(s3, type = 3, lcol = c("black", "aquamarine4", "#CC661AB3" , "darkblue"), pLeg = TRUE, lwd = 1.5, ModTitle = c("Agricultural land", "Shrubland", "Forest")) #Calculate AIC for a component model and compare with the #power model AIC(s3$fits$AG_Sp) SA <- rowSums(countryside[,1:3])#total site area SR <- countryside[,4] #agriculture column SP <- sar_power(data.frame(SA, SR)) SP$AIC #Provide starting parameter estimates for the component models #instead of using gridStart. M2 <- matrix(c(3.061e+08, 2.105e-01, 1.075e+00, 1.224e-01, 3.354e-08, 5.770e+05, 1.225e+01, 1.090e-01, 6.848e-01, 1.054e-01, 4.628e+05, 1.378e-01, 0.20747, 0.05259, 0.49393, 0.18725), nrow = 4, byrow = TRUE) #Provide column numbers rather than names s4 <- sar_countryside(data = countryside, modType = "power", startPar = M2, habNam = 1:3, spNam = 4:7) #Speed up by trying gridStart = "none" s5 <- sar_countryside(data = countryside, modType = "power", gridStart = "none", habNam = c("AG", "SH", "F"), spNam = c("AG_Sp", "SH_Sp", "F_Sp", "UB_Sp"))

Value

A list (of class ‘habitat’ and ‘sars’; and with a ‘type’ attribute of ‘countryside’) with eight elements:

• i. A list of the non-linear regression model fits for each of the species groups. In the model output, the h coefficients follow the order of the habitat area columns in data (e.g., h1 = column 1).

• ii. The habitat affinity values for each of the models in (i).

• iii. The c-parameter values for each of the models in (i).

• iv. The predicted total richness values (calculated by summing the predictions for each constituent countryside model) for each site in the dataset.

• v. The residual sum of squares – calculated using the predicted and observed total richness values – for both the countryside model and the Arrhenius power SAR model (or logarithmic model) to enable model comparison.

• vi. The dataset used for fitting (i.e., data).

• vii. The power (or logarithmic) model fit object.

• viii. The habNam and spNam vectors.

Author(s)

Thomas J. Matthews, Inês Santos Martins, Vânia Proença and Henrique Pereira

References

Matthews et al. (2025) In prep.

Pereira, H.M. & Daily, G.C. (2006) Modelling biodiversity dynamics in countryside landscapes. Ecology, 87, 1877–1885.

Proença, V. & Pereira, H.M. (2013) Species–area models to assess biodiversity change in multi-habitat landscapes: the importance of species habitat affinity. Basic and Applied Ecology, 14, 102–114.

txm676/mmSAR2 documentation built on Jan. 26, 2025, 3:47 p.m.