This vignette is heavily based on the paper that accompanies the package (Matthews et al. 2019).To cite this vignette, please cite the corresponding paper:
Matthews, T.J., Triantis, K., Whittaker, R.J. and Guilhaumon, F. (2019) sars: an R package for fitting, evaluating and comparing species–area relationship models. Ecography, 42, 14461455.
Version 1.1.1 of the package has been archived on the Zenodo research data repository (DOI: 10.5281/zenodo.2573067).
The species–area relationship (SAR) describes the near universally observed pattern whereby the number of species increases with the area sampled, and it has been described as one of ecology’s few laws (Rosenzweig (1995)). The SAR is a fundamental component of numerous ecological and biogeographical theories, such as the equilibrium theory of island biogeography (MacArthur and Wilson (1967)). In addition, SAR models have been widely used in applied ecology and conservation biogeography: for example, to predict the number of extinctions due to habitat loss. Numerous types of SAR have been described, and one primary dichotomy employed is the split of SARs into island SARs (ISARs), whereby each data point is an individual island or isolated sample, and species accumulation curves (SACs) that represent cumulative counts of increased species number with sampling area (Gray, Ugland, and Lambshead (2004)). Whilst the remainder of the paper and the described R package are focused on ISARs, the models and the model fitting procedure can equally be applied to SACs (see Matthews, Triantis, et al. (2016)), although it should be noted that in SACs the data points are not independent of one another.
Over 20 SAR models have been described in the literature (Dengler (2009), Tjørve (2003), Tjørve (2009), Triantis, Guilhaumon, and Whittaker (2012)). However, despite this wide range of models, the majority of SAR studies are still based exclusively on the power model (Arrhenius (1921)), which if fitted in its nonlinear (untransformed) form generally takes a convex form. Often, the log–log representation of the power model is used as it can be fitted using standard linear regression, and its parameters are more easily interpretable (Rosenzweig (1995)). However, whilst the power model has been found to provide a reasonable fit to a wide range of datasets (Dengler (2009), Matthews, Guilhaumon, et al. (2016)), it is not universally the best model, and a number of studies have reported other models to provide better fits to empirical data (e.g. Triantis, Guilhaumon, and Whittaker (2012), Matthews, Guilhaumon, et al. (2016)). The possibility of scale dependency of the form of the SAR has long been of interest, with, for example, a theoretical case being made for SARs at intermediate spatial scales being approximated by a power model, whilst at larger spatial scales the form of the SAR has been theorised to be sigmoidal. Additionally, it is only recently that the SAR for archipelagos as units of analysis and not just islands has started to be studied, and thus we know little about the form of archipelago SARs.
Due to the increased recognition of model uncertainty in SAR research, a number of recent studies have employed a multimodel inference approach (Burnham and Anderson (2002)) in the analysis of SARs, whereby either (1) multiple SAR models are compared using various criteria (e.g. AIC) and a best model is chosen (e.g. Dengler (2009)), or (2) multiple SAR models are fitted and a multimodel averaged curve is calculated using, for example, AIC weights (e.g. Guilhaumon et al. (2008)). We are not aware of any published software package that enables users to fit, and create multimodel averaged curves using more than eight SAR models. Considering currently available software, the BAT R package provides functions to fit three SAR models (linear, power and logarithmic); however, this package is focused on general biodiversity assessment and thus does not provide any additional SAR functionality. The mmSAR R package (Guilhaumon, Mouillot, and Gimenez (2010)) is focussed on SARs and while it allows users to fit eight SAR models using an information theoretic framework, it does not include several models that have been found to provide the best fits to several empirical datasets. To provide a set of tools to fill these gaps, we have developed the R package ‘sars’. The package provides functions to fit 20 SAR models using nonlinear and linear regression, calculate multimodel averaged curves using various information criteria, and generate confidence intervals using bootstrapping. Novel features compared with mmSAR include (i) userfriendly functions for plotting (the user can now plot weighted multimodel SAR curves along with the individual SAR model curves) and (ii) determining the observed shape of the model fit (i.e. linear, convex up, convex down or sigmoidal) and (iii) presence or not of an asymptote, and (iv) functions to fit, plot and evaluate Coleman (1981)’s random placement model using a speciessite abundance matrix, and (v) to fit the general dynamic model (GDM) of island biogeography (Whittaker, Triantis, and Ladle (2008)). In addition, the mmSAR package (which has been deprecated) no longer complies with recognised programming good practice , is not on CRAN (the main repository of R packages), and is not user friendly (e.g. it requires the user to load individual models prior to fitting). There was therefore a need to design a new package from scratch.
The ‘sars’ (species–area relationships) package has been programmed using standard S3 methods and is available on CRAN (version 1.2.1) and the development version on GitHub (txm676/sars), meaning researchers can easily add in their own models and functions and integrate these into the multimodel inference framework. Thus, the package represents a resource for future SAR work that can be built on and expanded by workers in the field.
Fitting individual SAR models and a set of SAR models The package provides functions to fit each of the 20 SAR models (see also Triantis, Guilhaumon, and Whittaker (2012)). The ‘sar_models’ function can be used to bring up a list of the 20 model names and a Table can be generated in the package using the ‘display_sars_models’ function. With the exception of the linear model (which is fitted using standard linear regression), all models are fitted using nonlinear regression and the model parameters are estimated by minimizing the residual sum of squares with an unconstrained NelderMead optimization algorithm and the ‘optim’ R function.
Choosing starting parameter values for nonlinear regression optimisation algorithms is not always straight forward, depending on the data at hand. The starting values for the parameter estimates used as defaults in the package are carefully chosen to avoid numerical problems and to speed up the convergence process. Custom starting values can be provided for any of the 20 models using the ‘start’ argument in the model fit functions. In addition, we also use a grid search / brute force process (see Archontoulis & Miguez, 2014). ‘grid_start’ creates 100 starting values for each parameter within sensible bounds and then creates a large matrix storing all possible combinations of starting values across the n parameters in the model. For example, for a three parameter model this matrix contains one million rows. A random number of these rows are then selected and used as starting parameter values. There are three options for the grid_start argument to control this process. The default (grid_start = “partial”) randomly samples 500 different sets of starting parameter values for each model, adds these to the model’s default starting values and tests all of these. A more comprehensive set of starting parameter estimates can be used (grid_start = “exhaustive”)  this option allows the user to choose the number of starting parameter sets to be tested (using the grid_n argument) and includes a range of additional starting parameter estimates, e.g. very small values and particular values we have found to be useful for individual models. A large ‘grid_n’ will ensure a more extensive search of starting parameter value space, but will increase the time taken to fit the model. More generally, using grid_start = “exhaustive” in combination with a large grid_n can be very time consuming; however, we would recommend it as it makes it more likely that the optimal model fit will be found, particularly for the more complex models. This is particularly true if any of the model fits does not converge, returns a singular gradient at parameter estimates, or the plot of the model fit does not look optimum. The grid start procedure can also be turned off (grid_start = “none”), meaning just the default starting parameter estimates are used. It should be noted that for certain models (e.g. Weibull 3 par.) ‘grid_start’ has been found to occasionally push model fits into strange parameter space (e.g. step curves) and so for these models ‘grid_start’ just reverts back to our provided parameter starting values. However, for these models the ‘start’ argument can still be used. For certain models, there are also checks to ensure that the returned parameters are logical. For example, the optimisation procedure can sometimes return a negative zvalue for the asymptotic model, meaning in the term z^A, the base is negative. Thus, if a fitting process returns a negative zvalue we return that the model could not be fitted. Remember that, as grid_start has a random component, when grid_start != “none”, you can get slightly different results each time you fit a model or run sar_average().
Each individual model fit returns an object of class ‘sars’, which is a list of 22 elements containing relevant model fit information, such as the model parameter estimates, the fitted values, the residuals, model fit statistics (e.g. AIC, R2), the observed model shape (linear, convex or sigmoidal), whether or not the fit is asymptotic, and convergence information. The returned object can easily be plotted using the ‘plot.sars’ generic function; as this function is based on the base R plotting framework, the plot aesthetics can be edited using standard plotting arguments (see the ‘plot.sars’ documentation in the package). Summary and print generic functions are also provided for class ‘sars’; these functions follow the output of the standard ‘lm’ function in the ‘stats’ R package. Multiple SAR models can be fitted to the same dataset using the ‘sar_multi’ function and the resultant n model fit objects stored together as a ‘fit_collection’ object. This object is a list of class ‘sars’, where each of the n elements contains an individual SAR model fit. Using the ‘plot.sars’ generic function on a ‘fit_collection’ object generates a grid of the n individual model fit plots (Fig. 1).
#load an example dataset (Preston, 1962), fit the logarithmic SAR model using
#the grid_start method of selecting starting parameter values, return a model
#fit summary and plot the model fit.
data(galap)
< sar_loga(data = galap, grid_start = "partial")
fit summary(fit)
##
## Model:
## Logarithmic
##
## Call:
## S == c + z * log(A)
##
## Did the model converge: TRUE
##
## Residuals:
## 0% 25% 50% 75% 100%
## 115.68753 38.74990 11.36383 25.67070 163.95994
##
## Parameters:
## Estimate Std. Error t value Pr(>t) 2.5% 97.5%
## c 0.2915341 34.5985767 0.0084262 0.9933958 68.9056193 69.489
## z 30.2802630 8.3203931 3.6392827 0.0026812 13.6394768 46.921
##
## Rsquared: 0.49, Adjusted Rsquared: 0.41
## AIC: 187.19, AICc: 189.19, BIC: 189.51
## Observed shape: convex up, Asymptote: FALSE
##
##
## Warning: The fitted values of the model contain negative values (i.e. negative species richness values)
plot(fit)
#Create a fit_collection object containing multiple SAR model fits, and
#plot all fits.
< sar_multi(data = galap, obj = c("power", "loga", "monod")) fitC
##
## Now attempting to fit the 3 SAR models:
##
##  multi_sars  multimodel SAR 
## > power : v
## > loga : v
## > monod : v
plot(fitC) #see Fig.1
Model fits can be evaluated through tests of the normality and homoscedasticity of the residuals. In certain cases this is advised, given that the maximum likelihood parameter estimates and leastsquares estimates are equivalent only under the assumption of normal errors with constant variance (see the Potential issues section, below). Any of three tests can be selected to test the normality of the residuals: 1) the Lilliefors extension of the Kolmogorov normality test (normaTest = “lillie”), 2) the ShapiroWilk test of normality (to be preferred when sample size is small; “shapiro”), and 3) the KolmogorovSmirnov test (“kolmo”). As a default, the option to omit a residuals normality test (“none”) is used  thus it is up to the user to select a test if appropriate. Three options are provided to check for the homogeneity of the residuals: 1) a correlation of the squared residuals with the model fitted values (“cor.fitted”), 2) a correlation of the squared residuals with the area values (“cor.area”), or 3) no homogeneity test (“none”; the default). If a test is selected and is significant at the 5% level a warning is provided in the model summary; alternatively, the full results of the three tests can be accessed in the model fit output. A third model validation check for negative predicted richness values (i.e. when at least one of the fitted values is negative) can be undertaken and a warning is provided in the model summary if negative values are predicted. It should be noted that in earlier versions of the package (pre Version 1.3.1) there was a bug in the test checking for homogeneity of the residual variance  the correlation used the raw residuals rather than the squared residuals. This has now been corrected. Finally, the convergence code from the optim algorithm can be checked manually. Occasionally the optim algorithm may not fully converge (e.g. due to degeneracy of the Nelder–Mead simplex) but still return a fitted model. As such, for each model fit we provide the optim model convergence code (anything other than 0 can indicate an issue) and a logical value (verge) specifying whether or not the code was 0.
#load an example dataset, fit the linear SAR model whilst running residual
#normality and homogeneity tests, and return the results of the residual
#normality test
data(galap)
< sar_linear(data = galap, normaTest ="lillie", homoTest = "cor.fitted")
fit summary(fit) #a warning is provided indicating the normality test failed
##
## Model:
## Linear model
##
## Call:
## S == c + m*A
##
## Did the model converge: TRUE
##
## Residuals:
## 0% 25% 50% 75% 100%
## 107.94802 52.11499 24.16226 31.72842 217.95711
##
## Parameters:
## Estimate Std. Error t value Pr(>t) 2.5 % 97.5 %
## c 70.314003 25.743130 2.731370 0.016228 15.100481 125.5275
## m 0.185382 0.072884 2.543504 0.023410 0.029060 0.3417
##
## Rsquared: 0.32, Adjusted Rsquared: 0.21
## AIC: 191.77, AICc: 193.77, BIC: 194.08
## Observed shape: linear, Asymptote: FALSE
##
##
## Warning: The normality test selected indicated the model residuals are not normally distributed (i.e. P < 0.05)
##
## Warning: The homogeneity test selected indicated a signifiant correlation between the residuals and the fitted values (i.e. P < 0.05)
$normaTest fit
## $test
## [1] "lillie"
##
## [[2]]
##
## Lilliefors (KolmogorovSmirnov) normality test
##
## data: res
## D = 0.2146, pvalue = 0.04725
Whilst each of the 20 models has a general shape (Triantis, Guilhaumon, and Whittaker (2012)), the actual observed shape of the model fit can be different, for some models, depending on the parameter estimates. This is important as the shape of the curve has significant implications for conservation applications and the testing of macroecological theory (Rosenzweig (1995)). In ‘sars’, the observed shape of a model fit is determined using the sequential algorithm outlined in Triantis, Guilhaumon, and Whittaker (2012). The shape is calculated using the model fit within the observed range of area values. Briefly, the algorithm works by first determining whether the fit is a straight line. Then, if the fit is classified as not being linear, the observed shape is classified as either convex (upwards or downwards) or sigmoidal by analysis of the second derivative (with respect to area) of the model fit (the full algorithm is detailed in Triantis, Guilhaumon, and Whittaker (2012), p. 220). It should be noted that a small number of models (e.g. Extended Power 2) are listed in previous papers (e.g. Tjørve (2009)) as being sigmoidal without an upper asymptote (and we have followed these classifications). However, a recent study (Godeau et al. (2020)) has argued that sigmoidal models must have an upper asymptote by definition; thus, the classification of these models is currently uncertain.
There has also been considerable debate in the SAR literature as to whether or not the SAR is asymptotic. In the ‘sars’ package, to determine whether a fit is asymptotic, for the relevant models the fitted model parameters are analysed to check whether the estimated asymptote is within the range of the sample data (Triantis, Guilhaumon, and Whittaker (2012)).
As well as fitting individual models, the package provides a function (‘sar_average’) to fit up to 20 models, compare the resultant fits using information criteria, and construct a multimodelaveraged SAR curve based on information criteria weight (see Guilhaumon, Mouillot, and Gimenez (2010)). The multimodel average curve is constructed as a linear combination of individual model fits by multiplying the predicted richness values of each of the successfully fitted models by the model’s information criterion weight, and then summing the resultant values across all models (Burnham and Anderson (2002)). Three information criteria are available in the package: AIC, AICc and BIC. Confidence intervals around the multimodel averaged curve can be calculated using a nonparametric bootstrap algorithm described in Guilhaumon, Mouillot, and Gimenez (2010). Briefly, each of the SAR models used in the ‘sar_average’ function is fitted to the data, and the fitted values and residuals stored. The residuals are then transformed using the approach in Davison and Hinkley (1997) (p.259). For each bootstrap sample, an individual model fit is selected with the probability of selection being equal to that model’s information criterion weight. The transformed residuals from this fit are then sampled with replacement and added to the model’s fitted richness values. The ‘sar_average’ function is then used to fit all candidate SAR models to this bootstrapped set of response values, and the multimodel averaged fitted values stored. Percentile confidence intervals are then calculated using all bootstrapped fitted values.
The ‘sar_average’ function can be used without specifying any models, in which case the package attempts to fit each of the 20 models in Table 1; alternatively, a vector of model names or a ‘fit_collection’ object (generated using the ‘sar_multi’ function) can be provided using the ‘obj’ argument. The three model validation tests listed above (normality and homogeneity of residuals, and negative predicted values) can be selected (by default none are selected, so it is up to the user to select any that are appropriate); if any model fails one or more of the tests during the fitting process it is removed from the resultant multimodel SAR curve. The output of the ‘sar_average’ function is a list of class ‘multi’ and class ‘sars’, with two elements. The first element (‘mmi’) contains the multi model inference (fitted values of the multimodel SAR curve), and the second element (‘details’) contains a range of information regarding the fitting process, including the successfully fitted models, the models removed due to failing any of the validation tests, and the information criterion values, delta values and weights for the successfully fitted models. The returned object can easily be plotted using the ‘plot.multi’ generic function, and multiple plot options are available (using the ‘type’ argument; see Fig. 2). The fits of all the successfully fitted models and the multimodel SAR curve (with or without confidence intervals) can be plotted together (‘type’ = “multi”), and a barplot of the information criterion weights of each model can also be produced (‘type’ = “bar”).
#load an example dataset (Niering, 1963), run the ‘sar_average’ function
#using a vector of model names and with no model validation tests, and
#produce the plots in Figure 2 of the paper
data(niering)
#run the ‘sar_average’ function using a vector of model names, and with simply
#using the default model starting parameter estimates (grid_start = "none")
< sar_average(data= niering, obj =c("power","loga","koba","logistic","monod",
fit "negexpo","chapman","weibull3","asymp"),
grid_start = "none", normaTest = "none", homoTest = "none", neg_check = FALSE,
confInt = TRUE, ciN = 50) #a message is provided indicating that one model
##
## Now attempting to fit the 9 SAR models:
##
##  multi_sars  multimodel SAR 
## > power : v
## > loga : v
## > koba : v
## > logistic : v
## > monod : v
## > negexpo : v
## > chapman : v
## > weibull3 : v
## > asymp : v
##
## No model validation checks selected
##
## 9 remaining models used to construct the multi SAR:
## Power, Logarithmic, Kobayashi, Logistic(Standard), Monod, Negative exponential, Chapman Richards, Cumulative Weibull 3 par., Asymptotic regression
## 
##
## Calculating sar_multi confidence intervals  this may take some time:
## Warning in sar_conf_int(fit = res, n = ciN, crit = IC, obj_all = obj_all, : The following model(s) has been removed from the confidence interval calculations as NAs/Infs are
## present in the transformed residuals: asymp
##

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#(asymp) could not be used in the confidence interval calculation
par(mfrow = c(3,1)) #plot all model fits and the multimodel SAR curve as a separate curve on top
plot(fit, ModTitle = "a) Multimodel SAR", mmSep = TRUE)
#plot the multimodel SAR curve (with confidence intervals; see explanation
#in the main text, above) on its own
plot(fit, allCurves = FALSE, ModTitle =
"c) Multimodel SAR with confidence intervals", confInt = TRUE)
#Barplot of the information criterion weights of each model
plot(fit, type = "bar", ModTitle = "b) Model weights", cex.lab = 1.3)
One thing to note is how the sar_average() function deals with model nonconvergence. There are different types of nonconvergence and these are dealt with differently. If the optimisation algorithm fails to return any solution, the model fit is defined as NA and is then removed, and so does not appear in the model summary table or multimodel curve etc. However, the optimisation algorithm (e.g. NelderMead) can also return nonNA model fits but where the solution is potentially nonoptimal (e.g. degeneracy of the Nelder–Mead simplex)  these cases are identified by any optim convergence code that is not zero. We have decided not to remove these fits (i.e. they are kept in the model summary table and multimodel curve), but any instances can be checked using the returned ‘details$converged’ vector and then the model fitting rerun without these models, if preferred. Increasing the starting parameters grid search (see above) may also help avoid this issue.
In addition to the main functions used to fit and compare the 20 SAR models, the sars package provides additional functions for specific SARbased analyses. First, a function is provided to fit the log–log version of the power model (a function that is often fitted in SAR studies; Rosenzweig 1995) and compare parameter values with those generated using the nonlinear power model. The log–log version of the power model is not equivalent to its nonlinear counterpart because of nonequivalence in the study of the variation in a variable and in its transformation, and bias of backtransformed results obtained on a logarithmic scale. Second, a function has been added that enables the fitting of Coleman (1981)’s random placement model to a species/sites abundance matrix. According to this model, the number of species occurring on an island depends on the relative area of the island and the regional relative species abundances. The fit of the random placement model can be determined through use of a diagnostic plot (which can be generated from the function output) of island area (log transformed) against species richness, alongside the model’s predicted values (see Wang et al. (2010)). Following Wang et al. (2010), the model is rejected if more than a third of the observed data points fall beyond one standard deviation from the expected curve. Finally, a function is provided to fit the general dynamic model of island biogeography (Whittaker, Triantis, and Ladle (2008)) using five different SAR models (linear, logarithmic, power(area), power(area and time), linear power).
#load an example dataset, fit the loglog power model, return a model fit
#summary and plot the model fit. When ‘compare’ == TRUE, the nonlinear
#power model is also fitted and the resultant parameter values compared.
#If any islands have zero species, a constant (‘con’) is added to all
#species richness values.
data(galap)
< lin_pow(dat = galap, compare = TRUE, con = 1)
fit summary(fit)
## Model = Loglog power
##
## Logtransformation function used: log()
##
## Call:
## lm(formula = logT(S) ~ logT(A), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## 1.3591 0.7584 0.1177 0.6009 1.0739
##
## Coefficients:
## Estimate Std. Error t value Pr(>t)
## LogC 3.01865 0.35442 8.517 6.56e07 ***
## z 0.33854 0.08523 3.972 0.00139 **
## 
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7626 on 14 degrees of freedom
## Multiple Rsquared: 0.5298, Adjusted Rsquared: 0.4962
## Fstatistic: 15.78 on 1 and 14 DF, pvalue: 0.001391
##
## Power (nonlinear) parameters:
## c = 33.18
## z = 0.28
plot(fit)
#load an example dataset, fit the random placement model and plot the
#model fit and standard deviation. The ‘data’ argument requires a species
#site abundance matrix: rows are species and columns are sites. The area
#argument requires a vector of site (island) area values.
data(cole_sim)
< coleman(data = cole_sim[[1]], area = cole_sim[[2]])
fit plot(fit, ModTitle = "Hetfield")
#load an example dataset, fit the GDM using the logarithmic SAR model, and
#compare the GDM with three alternative (nested) models: area and time
#(age of each island), area only, and intercept only.
data(galap)
$t < rgamma(16, 5, scale = 2)#add a random time variable
galapgdm(data = galap, model = "loga", mod_sel = TRUE)
##
## GDM fit using the logarithmic SAR model
##
## GDM model summary:
##
## Nonlinear regression model
## model: SR ~ Int + A * log(Area) + Ti * Time + Ti2 * Time^2
## data: data
## Int A Ti Ti2
## 78.198 27.711 20.644 1.161
## residual sumofsquares: 48578
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 4.048e08
##
## All model summaries:
##
## RSE R2 AIC AICc Delta.AIC Delta.AICc
## GDM 63.62533 0.678 183.6995 189.6995 0.000000 0.6710548
## A + T 68.60624 0.595 185.3921 189.0284 1.692582 0.0000000
## A 74.44350 0.486 187.1908 189.1908 3.491330 0.1623849
## Intercept 100.32744 0.000 195.8435 196.7666 12.143978 7.7381097
Extrapolation (e.g. predicting the richness of areas too large to be sampled) is one of the primary uses of the SAR. Using an individual SAR model fit, extrapolation simply involves using the model function in combination with the parameter values estimated using the observed area / richness data to predict the richness on a larger area. An alternative extrapolation approach to simply using an individual model l is to use multimodel inference and model averaging, whereby a larger number of n models is fitted to a set of islands, the models ranked according to some criterion (e.g. Akaike’s information criterion, AIC) and the criterion values converted into model weights (i.e. the conditional probabilities for each of the n models). 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 multimodel averaged prediction. Both of these options are available inside the package.
We would recommend using grid_start = “exhaustive” when fitting models for use with sar_pred(), as this is more likely to find the optimum fit for a given model. However, remember that, as grid_start has a random component, when grid_start != “none”, you can get slightly different results each time you fit a model or run sar_average, and then run sar_pred().
#fit the power model and predict richness on an island of area = 5000
data(galap)
< sar_power(data = galap)
p sar_pred(p, area = 5000)
##
## This is a sar_pred object:
##
## Model Area Prediction
## 1 Power 5000 370.1208
#fit three SAR models and predict richness on islands of area = 5000 & 10000
< sar_multi(galap, obj = c("power", "loga", "koba")) p2
##
## Now attempting to fit the 3 SAR models:
##
##  multi_sars  multimodel SAR 
## > power : v
## > loga : v
## > koba : v
sar_pred(p2, area = c(5000, 10000))
##
## This is a sar_pred object:
##
## Model Area Prediction
## 1 Power 5000 370.1389
## 2 Power 10000 450.4146
## 3 Logarithmic 5000 258.1944
## 4 Logarithmic 10000 279.1831
## 5 Kobayashi 5000 290.6980
## 6 Kobayashi 10000 318.4351
#calculate a multimodel curve and predict richness on islands of area = 5000 & 10000
#grid_start set to "none" for speed
< sar_average(data = galap, grid_start = "none") p3
##
## Now attempting to fit the 20 SAR models:
##
##  multi_sars  multimodel SAR 
## > power : v
## > powerR : v
## > epm1 : v
## > epm2 : v
## > p1 : v
## > p2 : v
## > loga : v
## > koba : v
## > monod : v
## > negexpo : v
## > chapman : v
## > weibull3 : v
## > asymp : v
## > ratio : v
## > gompertz : v
## > weibull4 : v
## > betap : v
## > logistic : v
## > heleg : v
## > linear : v
##
## No model validation checks selected
##
## 20 remaining models used to construct the multi SAR:
## Power, PowerR, Extended Power model 1, Extended Power model 2, Persistence function 1, Persistence function 2, Logarithmic, Kobayashi, Monod, Negative exponential, Chapman Richards, Cumulative Weibull 3 par., Asymptotic regression, Rational function, Gompertz, Cumulative Weibull 4 par., BetaP cumulative, Logistic(Standard), Heleg(Logistic), Linear model
## 
sar_pred(p3, area = c(5000, 10000))
##
## This is a sar_pred object:
##
## Model Area Prediction
## 1 Multi 5000 227.2078
## 2 Multi 10000 243.0279
An increasing number of studies have focused on identifying thresholds in the ISAR, including studies i) focused on identifying the small island effect, ii) determining whether the ISAR has an upper asymptote and iii) identifying thresholds in habitat ISARs, which are often systems of conservation concern. The most common approach in such studies is to use piecewise regression. As such, the package provide a set of functions for fitting six piecewise regression models to ISAR data, calculating confidence intervals around the breakpoint estimates (for certain models), comparing the models using various information criteria, and plotting the resultant model fits. The six piecewise models are a selection of 6 models out of the 14 listed by Gao et al. (2019) and comprise the continuous onethreshold and twothreshold, discontinuous onethreshold and twothreshold, and lefthorizontal onethreshold and twothreshold models (see Gao et al. (2019) for further information).
The main function is ‘sar_threshold’, which fits the six piecewise models, in addition to a linear model and an intercept only model for comparison (using the ‘non_th_models’ argument). Summary and plot generic functions are available which return userfriendly output. As the coefficients in the fitted breakpoint regression models do not all represent the intercepts and slopes of the different segments (for these it is necessary to add different coefficients together), a separate function (‘get_coef’) can be used to calculate these. A separate function (‘threshold_ci’) generates the confidence intervals around the breakpoints of the onethreshold continuous and lefthorizontal models.
Detailed information on fitting and plotting the threshold models, as well as the additional functions for calculating model slopes and intercepts, and generating confidence intervals around the threshold estimates, can be found in Matthews and Rigal (2021).
#load an example dataset, and fit the continuous twothreshold model
#to the data (with area transformed using log to the base 10), using an
#interval of 0.1 (for speed)
data(aegean2)
< sar_threshold(data = aegean2, mod = c("ContTwo"), interval = 0.1,
fit non_th_models = FALSE, logAxes = "area", con = 1,
logT = log10, nisl = NULL)
#generate model fitting summary table (generally more useful when fitting multiple models)
summary(fit)
##
## Sar_threshold object summary:
##
## 1 models fitted
##
## Models fitted with area logtransformed
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## Models ranked using BIC
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## LL Pars AIC AICc BIC R2 R2a Th1 Th2 seg1 seg2
## ContTwo 1002.74 7 2019.48 2020.16 2041.55 0.95 0.95 0.122 1.478 86 37
## seg3
## ContTwo 50
#Plot the resultant model fit
plot(fit, cex = 0.8, cex.main = 1, cex.lab = 0.8, pcol = "grey")
There are different ways to calculate the various information criteria (IC) used for model comparison (e.g. AIC, BIC). One difference relates to whether the residual sum of squares (rss) or the loglikelihood (ll) is used. Under standard assumptions (e.g. independence of data points, homoscedasticity and normality of the residuals), the two approaches produce identical parameter estimates for regression models. However, the formulas are different and thus can produce different IC values for the same model. For example, historically in the ‘sars’ package we have calculated IC values using formulas based on the rss (Burnham & Anderson, 2002; Guilhaumon et al. 2008). This meant that the IC values generated in ‘sars’ were not comparable with values generated in packages using different formulas. For example, in the nls (the main function for nonlinear regression in R) and lm functions in the stats R package, a ll approach is used, meaning IC values from models fitted using nls could not be compared with IC values from sars models. To reiterate, the parameter estimates are comparable, it is simply that the IC values will differ. In version of ‘sars’ (version 1.2.2) we changed our IC formulas to match those in nls and lm. Thus, if users have built their own models using nls (or lm) and wish to compare IC values with models fitted in ‘sars’, this is now straightforward. To recreate IC values from previous studies (i.e. those using a version of sars pre 1.2.2), it will be necessary to download sars Version 1.2.1 or earlier (either from CRAN or GitHub; version 1.1.1 was published as a release on GitHub). It is important to note that these are not the only two ways of calculating ICs for regression models, and other formulas exits. Thus, if building models using other functions and packages (i.e. other than nls or lm), users should make sure to check how these packages calculate IC values before comparing with models fitted in sars. In sars, as in nls and lm, we include an additional parameter for estimation of the variance. For example, the power model is considered to have three parameters when calculating IC values. Finally, if users are comparing models fitted in sars with their own models fitted using other packages, it is essential that IC values are all calculated using the same dependent variable (i.e. nontransformed richness values and not logged richness, as sars only uses the former currently).
Before starting this section, it is worth pointing out that we are not statisticians and the following discussion should be interpreted with this point in mind. We have however reviewed a lot of the literature (including a number of statistics forums) on the use of information criteria (e.g. AIC) in the context of comparing nonlinear regression models. Based on this review, we have concluded that there are three potential issues with this approach.
First, many information criteria (including AIC) are theoretically applicable only when a model is fitted through the approach of maximum likelihood. In the case of linear and nonlinear regression (e.g. the nls function in R, and the approach we use in ‘sars’), a leastsquares estimator (minimising the residual sum of squares) is generally used rather than likelihood maximisation, for various reasons, such as analytical tractability. This is not necessarily a problem, as the leastsquares parameter estimates and the maximum likelihood parameter estimates are equivalent in the case of normal and independent errors with a mean of zero and constant variance (Bates and Watts (1988)). This holds for both linear and nonlinear regression models (Bates and Watts (1988); Forum1 (n.d.)). However, if these assumptions are not met this equivalence may break down and thus the use of AIC etc would not necessarily be appropriate. The use of the normality and homogeneity of variance checks within the package may be useful in this regard, but note that it is now up to the user to manually turn these checks on.
Second, most of the commonly used information criteria (e.g. AIC) include a term that penalises for the number of parameters. In the case of linear regression models, counting the number of parameters is straight forward. However, nonlinear models are so called because they are nonlinear in regard to their parameters (e.g. a quadratic model is a linear model but with a nonlinear shaped curve). As such, it is not always as straight forward to count parameters in the same way, particularly in the case of very complex nonlinear and machine learning type models (i.e. not those used in ‘sars’). “That is, a single parameter in the model may count as more or less than one parameter, in some sense” (Forum2 (n.d.)). However, in the absence of any other information, or indeed alternative model comparison approaches, all one can do is use the number of estimable parameters (sensu Burnham & Anderson) in a model, which is what we follow in the ‘sars’ package, and is what a large number of studies do (and advise) in the ecological and wider literature (e.g. Archontoulis and Miguez (2015); Banks and Joyner (2017); see also Forum3 (n.d.)’s response to Forum2 (n.d.)).
Finally, some have argued that you should only use AIC etc to compare models within the same ‘model complex’, and even only in the case of nested models (e.g. Ripley (no date)). From this it could be concluded that we should not compare the suite of 19 nonlinear models with the linear model. However, Burnham and Anderson (2002) and Anderson and Burnham (2006) have argued that this is not the case and AIC for example can be used to compare nonnested models. In addition, again many studies use AIC to compare nonnested models from different ‘complexes’. We have worked on the assumption that, as long as the response variable is the same and models are comparable in terms of parameter number calculation (e.g. all include or not a parameter for the variance), it is appropriate to compare different types of model (at least within the limited selection used in the package).
The SAR has been a cornerstone of ecological and biogeographical science for almost a century and its form and fit are still of great significance in both theoretical and applied contexts. The development of the ‘sars’ R package should aid future SAR research by providing a comprehensive set of tools that enable indepth exploration of SARs and SARrelated patterns. In addition, the package has been designed in such a way as to allow other SAR researchers to add (e.g. via GitHub) new functions and models in the future to ensure the package is of lasting value. For example, future additions to the package could include the suite of countryside biogeography SAR models that have recently been published, standard SAR functions not so far incorporated, or functions specifically intended for analysis of species accumulation curves or endemics–area relationships. Finally, whilst the focus of this paper has been on classic SARs, there is no reason that the functionality in the ‘sars’ package cannot be used to analyse other diversity–area relationships (e.g. functional or phylogenetic diversity–area relationships). Application of the full set of 20 models, in addition to the multimodel SAR framework, included in the ‘sars’ package to a wider range and type of data (e.g. trait and phylogenetic data) will likely be revealing and will help in improving our understanding of SARs, and diversity–area relationships more generally.