| Title: | Simulated Sampling Procedure for Community Ecology |
|---|---|
| Description: | Simulation-based sampling protocol (SSP) is an R package design to estimate sampling effort in studies of ecological communities based on the definition of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015) <doi:10.1111/ele.12385> and simulation of ecological data. The theoretical background is described in Guerra-Castro et al. (2020) <doi:10.1101/2020.03.19.996991>. |
| Authors: | Edlin Guerra-Castro [aut, cre], Maite Mascaro [aut], Nuno Simoes [aut], Juan Cruz-Motta [aut], Juan Cajas [aut] |
| Maintainer: | Edlin Guerra-Castro <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.1 |
| Built: | 2025-02-20 04:15:27 UTC |
| Source: | https://github.com/edlinguerra/ssp |
SSP is an R package designed to estimate sampling effort in studies of ecological communities based on the definition of pseudo multivariate standard error (MultSE) (Anderson & Santana-Garcon 2015) and simulation of data (Guerra-Castro et al., 2020).
The protocol in SSP consists in simulating several extensive data matrices that mimic some of the relevant ecological features of the community of interest using a pilot data set. For each simulated data, several sampling efforts are repeatedly executed and MultSE is calculated to each one. The mean value, 0.025 and 0.975 quantiles of MultSE for each sampling effort across all simulated data are then estimated and plotted. The mean values are standardized in relation to the lowest sampling effort (consequently, the worst precision), and an optimal sampling effort can be identified as that in which the increase in sample size do not improve the precision beyond a threshold value (e.g. 2.5%).
SSP includes seven functions: assempar for extrapolation of assemblage parameters using pilot data; simdata for simulation of several data sets based on extrapolated parameters; datquality for evaluation of plausibility of simulated data; sampsd for repeated estimations of MultSE for different sampling designs in simulated data sets; summary_ssp for summarizing the behavior of MultSE for each sampling design across all simulated data sets, ioptimum for identification of the optimal sampling effort, and plot_ssp to plot sampling effort vs MultSE of simulated data.
The SSP package is developed at GitHub (https://github.com/edlinguerra/SSP/).
The SSP development team is Edlin Guerra-Castro, Maite Mascaro, Nuno Simoes, Juan Cruz-Motta and Juan Cajas
-Anderson, M. J., & J. Santana-Garcon. (2015). Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters 18:66-73.
-Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) #Cut-off points to identify optimal sampling effort opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE) #Plot plot_ssp(xx = summ.mic, opt = opt.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE) #Cut-off points to identify optimal sampling effort opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE) #Plot plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) #Cut-off points to identify optimal sampling effort opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE) #Plot plot_ssp(xx = summ.mic, opt = opt.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE) #Cut-off points to identify optimal sampling effort opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE) #Plot plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)
The function extracts the main parameters of the pilot data using basic R functions as well as functions like specpool and dispweight
assempar(data, type, Sest.method)assempar(data, type, Sest.method)
data |
Data frame with species names (columns) and samples (rows) information. The first column should indicate the site to which the sample belongs, regardless of whether a single site has been sampled. |
type |
Nature of the data to be processed. It may be presence / absence ("P/A"), counts of individuals ("counts"), or coverage ("cover") |
Sest.method |
Method for estimating species richness. The function |
The expected number of species in the assemblage is estimated using non-parametric methods (Gotelli et al. 2011). Due to the variability in the estimates of each approximation (Reese et al. 2014), we recommend using an average of these. The probability detection of each species is estimated among and within sites. The former is calculated as the frequency of occurrences of each species against the number of sites sampled, the second as the weighted average frequencies in sites where the species were present. Also, the degree of spatial aggregation of species (only for real counts of individuals), is identified with the index of dispersion D (Clarke et al. 2006). The corresponding properties of unseen species are approximated using the information of observed species. Specifically, the probabilities of detection are assumed to be equal to the rarest species in the pilot data. The mean and variance of the abundances are defined using random Poisson values with lambda as the overall mean of species abundances.
Par |
The function returns an object of class list, to be used by |
Important: the first column should indicate the site ID of each sample (as character or numeric), even when a single site was sampled.
Edlin Guerra-Castro ([email protected]), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro ([email protected]).
Clarke, K. R., Chapman, M. G., Somerfield, P. J., & Needham, H. R. (2006). Dispersion-based weighting of species counts in assemblage analyses. Journal of Experimental Marine Biology and Ecology, 320, 11-27.
Gotelli, N. J., & Colwell, R. K. (2011). Estimating species richness. Pages 39-54, in A. E. Magurran and B. J. McGill (editors). Biological diversity: frontiers in measurement and assessment. Oxford University Press, Oxford, UK.
Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
Reese, G. C., Wilson, K. R., & Flather, C. H. (2014). Performance of species richness estimators across assemblage types and survey parameters. Global Ecology and Biogeography, 23(5), 585-594.
##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) par.mic<-assempar(data = micromollusk, type= "P/A", Sest.method = "average") par.mic ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") par.spo##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) par.mic<-assempar(data = micromollusk, type= "P/A", Sest.method = "average") par.mic ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") par.spo
The function estimates the average number of species, and the Simpson diversity index per sampling unit, as well as the total multivariate dispersion of pilot data and simulated data
datquality(data, dat.sim, Par, transformation, method)datquality(data, dat.sim, Par, transformation, method)
data |
Data frame with species names (columns) and samples (rows) information. The first column should indicate the site to which the sample belongs, regardless of whether a single site has been sampled or not |
dat.sim |
List of simulated data generated by simdata |
Par |
List of parameters generated by assempar |
transformation |
Mathematical function to reduce the weight of dominant species: 'square root', 'fourth root', 'Log (X+1)', 'P/A', 'none' |
method |
The appropriate distance/dissimilarity metric. The function |
The quality of the simulated data sets is quantified through the statistical similarity with respect to the pilot data using the following estimators: (i) average number of species per sampling unit, (ii) diversity, defined as the average Simpson diversity index per sampling unit, and (iii) the multivariate dispersion (MVD), measured as the average dissimilarity from all sampling units to the main centroid in the space of the dissimilarity measure used (Anderson 2006). For the simulated data, the overall mean and standard deviation for (i) and (ii) are presented. However, to assess the magnitude of variability in the simulated data, 0.95 quantiles of the MVD for all simulated data sets are also presented.
divmetrics |
A data frame that includes the mean and standard deviation of richness and diversity per sampling unit, and the MVD for original and 0.95 quantiles of MVD of simulated data. |
It is desirable that the simulated data would be similar to the data observed in terms of species richness and diversity per sampling unit.
Edlin Guerra-Castro ([email protected]), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro ([email protected]).
Anderson, M.J. (2006) Distance-based tests for homogeneity of multivariate dispersions. Biometrics, 62, 245-253
Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
###To speed up the simulation of these examples, the cases, sites and n were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 10, sites = 1) #Estimation of diversity metrics of original and simulated data qua.mic<-datquality(data = micromollusk, dat.sim = sim.mic, Par = par.mic, transformation = "none", method = "jaccard" ) qua.mic ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Estimation of diversity metrics of original and simulated data qua.spo<-datquality(data = sponges, dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray") qua.spo###To speed up the simulation of these examples, the cases, sites and n were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 10, sites = 1) #Estimation of diversity metrics of original and simulated data qua.mic<-datquality(data = micromollusk, dat.sim = sim.mic, Par = par.mic, transformation = "none", method = "jaccard" ) qua.mic ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Estimation of diversity metrics of original and simulated data qua.spo<-datquality(data = sponges, dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray") qua.spo
Data corresponds to epibenthic organisms in mangrove roots from Laguna de La Restinga National Park, Venezuela (Guerra-Castro et al. 2016).
data("epibionts")data("epibionts")
A data frame with 96 observations on the following 152 variables.
sectora factor with levels E I M
sitea numeric vector
Aaptos.spa numeric vector
Acanthophora.spiciferaa numeric vector
Acetabularia.crenulataa numeric vector
Aglaothamnion.spa numeric vector
Amathia.spa numeric vector
Amorphinopsis.atlanticaa numeric vector
Amphimedon.erinaa numeric vector
Anemonia.sargassensisa numeric vector
Aplidium.accarensea numeric vector
Aplysilla.glacialisa numeric vector
Ascidia.curvataa numeric vector
Ascidia.spa numeric vector
Ascidia.sydneiensisa numeric vector
Balanus.spa numeric vector
Bartholomea.annulataa numeric vector
Biemna.caribeaa numeric vector
Bostrychia.tenellaa numeric vector
Botrylloides.nigruma numeric vector
Botrylloides.sp.1a numeric vector
Botrylloides.sp.2a numeric vector
Brachidontes.exustusa numeric vector
Branchiomma.conspersuma numeric vector
Branchiomma.nigromaculatuma numeric vector
Bryopsis.spa numeric vector
Bugula.neritinaa numeric vector
Bugula.spa numeric vector
Calliactis.tricolora numeric vector
Callyspongia..Callyspongia..pallidaa numeric vector
Carijoa.riiseia numeric vector
Caulerpa.racemosaa numeric vector
Caulerpa.racemosa.var.peltataa numeric vector
Caulerpa.sertularioidesa numeric vector
Caulerpa.verticillataa numeric vector
Caulibugula.spa numeric vector
Celleporaria.spa numeric vector
Ceramium.diaphanuma numeric vector
Chaetomorpha.sp.1a numeric vector
Chaetomorpha.sp.2a numeric vector
Chalinula.molitbaa numeric vector
Chelonaplysilla.erectaa numeric vector
Chondrilla.nuculaa numeric vector
Chthamalus.spa numeric vector
Clathria..Clathria..microchelaa numeric vector
Clathria.spa numeric vector
Clavelina.oblongaa numeric vector
Clavelina.pictaa numeric vector
Complejo.Cliona.celataa numeric vector
Crassostrea.rhizophoraea numeric vector
Dictyota.spa numeric vector
Didemnum.cineraceuma numeric vector
Didemnum.perluciduma numeric vector
Didemnum.spa numeric vector
Diplosoma.listerianuma numeric vector
Distaplia.bermudensisa numeric vector
Distaplia.styliferaa numeric vector
Dynamena.spa numeric vector
Dysidea.etheriaa numeric vector
Dysidea.spa numeric vector
Ecteinascidia.spa numeric vector
Ecteinascidia.styeloidesa numeric vector
Ecteinascidia.turbinataa numeric vector
Eudistoma.olivaceuma numeric vector
Eusynstyela.tinctaa numeric vector
Exaiptasia.pallidaa numeric vector
Ficopomatus.spa numeric vector
Geodia.papyraceaa numeric vector
Halichondria..Halichondria..magniconulosaa numeric vector
Halichondria..Halichondria..melanadociaa numeric vector
Haliclona..Halichoclona..magnificaa numeric vector
Haliclona..Reniera..implexiformisa numeric vector
Haliclona..Reniera..manglarisa numeric vector
Haliclona..Reniera..ruetzleria numeric vector
Haliclona..Reniera..tubiferaa numeric vector
Haliclona..Rhizoniera..curacaoensisa numeric vector
Haliclona..Soestella..caeruleaa numeric vector
Haliclona..Soestella..smithaea numeric vector
Haliclona..Soestella..twincayensisa numeric vector
Halimeda.spa numeric vector
Halisarca.spa numeric vector
Halopteris.spa numeric vector
Herdmania.pallidaa numeric vector
Hippopodina.feegeensisa numeric vector
Hydroides.spa numeric vector
Hyrtios.proteusa numeric vector
Iotrochota.birotulataa numeric vector
Ircinia.felixa numeric vector
Ircinia.spa numeric vector
Isognomon.alatusa numeric vector
Kirchenpaueria.spa numeric vector
Lissoclinum.spa numeric vector
Lissodendoryx..Lissodendoryx..isodictyalisa numeric vector
Lithophyllum.pustulatuma numeric vector
Microcosmus.exasperatusa numeric vector
Molgula.occidentalisa numeric vector
Murrayella.pericladosa numeric vector
Mycale..Aegogropila..carmigropilaa numeric vector
Mycale..Aegogropila..citrinaa numeric vector
Mycale..Carmia..magnirhaphidiferaa numeric vector
Mycale..Carmia..microsigmatosaa numeric vector
Mycale..Mycale..laevisa numeric vector
Mycale..Zygomycale..angulosaa numeric vector
Mycale.spa numeric vector
Nemalecium.spa numeric vector
Notaulax.nudicollisa numeric vector
Obelia.spa numeric vector
Oceanapia.nodosaa numeric vector
Padina.spa numeric vector
Perna.viridisa numeric vector
Perophora.viridisa numeric vector
Phaeophyceaea numeric vector
Phallusia.nigraa numeric vector
Phyllangia.americanaa numeric vector
Pinctada.imbricataa numeric vector
Plakortis.angulospiculatusa numeric vector
Polyclinum.constellatuma numeric vector
Polysiphonia.sp.1a numeric vector
Polysiphonia.sp.3a numeric vector
Polysiphonia.subtilissimaa numeric vector
Pteria.colymbusa numeric vector
Pyura.sp..1a numeric vector
Pyura.sp..2a numeric vector
Pyura.vittataa numeric vector
Rhizoclonium.spa numeric vector
Rhodosoma.turcicuma numeric vector
Sabella.spa numeric vector
Sabellastarte.magnificaa numeric vector
Schizoporella.pungensa numeric vector
Scopalina.ruetzleria numeric vector
Scopalina.spa numeric vector
Scrupocellaria.spa numeric vector
Sphacelaria.rigidulaa numeric vector
Spongia..Spongia..pertusaa numeric vector
Spongia..Spongia..tubuliferaa numeric vector
Sporolithon.episporuma numeric vector
Spyridia.hypnoidesa numeric vector
Styela.canopusa numeric vector
Styela.sp.1a numeric vector
Styela.sp.2a numeric vector
Suberites.aurantiacusa numeric vector
Symplegma.brakenhielmia numeric vector
Symplegma.rubraa numeric vector
Synnotum.circinatuma numeric vector
Tedania..Tedania..ignisa numeric vector
Terpios.manglarisa numeric vector
Tethya.actiniaa numeric vector
Tethya.spa numeric vector
Trididemnum.orbiculatuma numeric vector
Ulva.spa numeric vector
Viatrix.globuliferaa numeric vector
Zoobotryon.verticillatuma numeric vector
Data consists of the coverage (by point-intercept) of 110 taxa identified in 240 mangrove roots, sampled under a hierarchically nested spatial design that included four random sites within each of three sectors of the lagoon system corresponding to a strong environmental gradient: external (E), intermediate (M), and internal (I). The abundance of epibenthic organisms of 8 roots were described within each site, producing a total of 32 roots in each sector. This spatial protocol was repeated five times over a period of 14 months. For demonstrative purpose, data from the 4th sampling period was randomly chosen as data for this package.
https://doi.org/10.3354/meps11693
Guerra-Castro, E. J., J. E. Conde, and J. J. Cruz-Motta. (2016). Scales of spatial variation in tropical benthic assemblages and their ecological relevance: epibionts on Caribbean mangrove roots as a model system. Marine Ecology Progress Series 548:97-110.
data(epibionts) str(epibionts)data(epibionts) str(epibionts)
The function estimates the sampling effort in which the rate of change for each additional sampling unit can be considered optimal.
ioptimum(xx, multi.site = TRUE, c1 = 10, c2 = 5, c3 = 2.5)ioptimum(xx, multi.site = TRUE, c1 = 10, c2 = 5, c3 = 2.5)
xx |
A data frame generated by |
multi.site |
Logical argument indicating if several sites were simulated |
c1 |
First cut. By default 10% improvement for each sample with respect to the highest MultSE. |
c2 |
Second cut. By default 5% improvement for each sample with respect to the highest MultSE. |
c3 |
Third cut. By default 2.5% improvement for each sample with respect to the highest MultSE. |
Sampling efforts between the minimum (i.e. 2) and c1, can be considered the necessary efforts to improve the precision. The number of samples between c1 and c2 reflects the sub-optimal sampling efforts. The number of samples between c2 and c3 indicate the optimal sampling effort. A cost / benefit criterion (e.g. Underwood, 1990) can be used to set the final sample size within this range. The sampling effort beyond c3 would imply a marginal improvement of the MultSE for each increase in sample size, which would result in an unnecessary sampling effort due to redundancy. The relationship between MultSe, sampling effort, and optimal sampling can be visualized with plot_ssp
sample.cut |
A vector or matrix with the sampling size for each cut point |
The cuts that define the sampling effort as necessary, sub-optimal, optimal or redundant are arbitrary and can be modified according to each research problem. In particular, it is possible that c3 as 2.5% is not generated because this would be achieved with a sample size larger than the maximum simulated. In this case, the maximum effort generated with sampsd will be returned with a warning message.
Edlin Guerra-Castro ([email protected]), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro ([email protected]).
Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
Underwood, A. J. (1990). Experiments in ecology and management: Their logics, functions and interpretations. Australian Journal of Ecology, 15, 365-389.
###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) #Cut-off points to identify optimal sampling effort opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE) #Cut-off points to identify optimal sampling effort opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE)###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) #Cut-off points to identify optimal sampling effort opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE) #Cut-off points to identify optimal sampling effort opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE)
Presence/absence of 68 species registered in six cores of 4 cm diameter and 10 cm depth taken in sandy bottoms around Cayo Nuevo, Gulf of Mexico, Mexico
data("micromollusk")data("micromollusk")
A data frame with 6 observations on the following 69 variables.
sitea numeric vector
Leptochiton.sp.a numeric vector
Ischnochiton..Ischnochiton..erythronotusa numeric vector
Arcidae.sp.a numeric vector
Arca.imbricataa numeric vector
Barbatia.domingensisa numeric vector
Bentharca.sp.a numeric vector
Arcopsis.adamsia numeric vector
Crenella.sp.a numeric vector
Anomia.sp..a numeric vector
Carditopsis.smithiia numeric vector
Lucinidae..a numeric vector
Chama.sinuosaa numeric vector
Chama.sp.a numeric vector
Galeommatidae.sp.a numeric vector
Chione.elevataa numeric vector
Semele.bellastriataa numeric vector
Gastropoda.sp..1..a numeric vector
Gastropoda.sp..2..a numeric vector
Gastropoda.sp..3..a numeric vector
Diodora.minutaa numeric vector
Diodora.sp...a numeric vector
Scissurella.redfernia numeric vector
Synaptocochlea.pictaa numeric vector
Lodderena.ornataa numeric vector
Cerithium.sp...a numeric vector
Sansonia.tuberculataa numeric vector
Iniforis.turristhomaea numeric vector
Metaxia.rugulosaa numeric vector
Cerithiopsis.cf..iuxtafuniculataa numeric vector
Cerithiopsis.sp.a numeric vector
Vermetidae.incertae.sedis.irregularisa numeric vector
Dendropoma.corrodensa numeric vector
Vermetid.sp..Ca numeric vector
Petaloconchus.mcgintyia numeric vector
Thylacodes.sp.a numeric vector
Alvania.auberianaa numeric vector
Alvania.colombianaa numeric vector
Alvania.sp.a numeric vector
Simulamerelina.caribaeaa numeric vector
Schwartziella.fischeria numeric vector
Zebina.brownianaa numeric vector
Zebina.sp.a numeric vector
Caecum.circumvolutuma numeric vector
Caecum.donmooreia numeric vector
Caecum.floridanuma numeric vector
Caecum.johnsonia numeric vector
Caecum.pulchelluma numeric vector
Caecum.textilea numeric vector
Caecum.sp..Ba numeric vector
Meioceras.nitiduma numeric vector
Cochliolepis.striataa numeric vector
Parviturboides.interruptusa numeric vector
Vitrinella.sp.a numeric vector
Gibberula.lavalleeanaa numeric vector
Prunum.apicinuma numeric vector
Volvarina.avenaa numeric vector
Astyris.lunataa numeric vector
Phrontis.albusa numeric vector
Phrontis.sp.a numeric vector
Trachypollia.sp...a numeric vector
Turridae.sp..1a numeric vector
Turridae.sp..2..a numeric vector
Turridae.sp..3..a numeric vector
Ammonicera.lineofuscataa numeric vector
Ammonicera.minortalisa numeric vector
Rissoella.galbaa numeric vector
Pyramidellidae.sp.a numeric vector
Pseudoscilla.babyloniaa numeric vector
Cayo Nuevo is a small reef cay located 240 km off the North-Western coast of Yucatan. Data correspond to a study about the biodiversity of marine benthic reef habitats off the Yucatan shelf (Ortigosa, Suarez-Mozo, Barrera et al. 2018).
https://doi.org/10.3897/zookeys.779.24562
Ortigosa, D., Suarez-Mozo, N. Y., Barrera, N. C., & Simoes, N. (2018). First survey of Interstitial molluscs from Cayo Nuevo, Campeche Bank, Gulf of Mexico. Zookeys, 779. doi:10.3897/zookeys.779.24562
data(micromollusk)data(micromollusk)
Data corresponds to a pilot study abput epibenthic organisms in mangrove roots from Laguna de La Restinga National Park, Venezuela (Guerra-Castro et al. 2011).
data("pilot")data("pilot")
A data frame with 180 observations on the following 118 variables.
Sectora factor with levels E I M
Sitea numeric vector
sp1a numeric vector
sp2a numeric vector
sp3a numeric vector
sp4a numeric vector
sp5a numeric vector
sp6a numeric vector
sp7a numeric vector
sp8a numeric vector
sp9a numeric vector
sp10a numeric vector
sp11a numeric vector
sp12a numeric vector
sp13a numeric vector
sp14a numeric vector
sp15a numeric vector
sp16a numeric vector
sp17a numeric vector
sp18a numeric vector
sp19a numeric vector
sp20a numeric vector
sp21a numeric vector
sp22a numeric vector
sp23a numeric vector
sp24a numeric vector
sp25a numeric vector
sp26a numeric vector
sp27a numeric vector
sp28a numeric vector
sp29a numeric vector
sp30a numeric vector
sp31a numeric vector
sp32a numeric vector
sp33a numeric vector
sp34a numeric vector
sp35a numeric vector
sp36a numeric vector
sp37a numeric vector
sp38a numeric vector
sp39a numeric vector
sp40a numeric vector
sp41a numeric vector
sp42a numeric vector
sp43a numeric vector
sp44a numeric vector
sp45a numeric vector
sp46a numeric vector
sp47a numeric vector
sp48a numeric vector
sp49a numeric vector
sp50a numeric vector
sp51a numeric vector
sp52a numeric vector
sp53a numeric vector
sp54a numeric vector
sp55a numeric vector
sp56a numeric vector
sp57a numeric vector
sp58a numeric vector
sp59a numeric vector
sp60a numeric vector
sp61a numeric vector
sp62a numeric vector
sp63a numeric vector
sp64a numeric vector
sp65a numeric vector
sp66a numeric vector
sp67a numeric vector
sp68a numeric vector
sp69a numeric vector
sp70a numeric vector
sp71a numeric vector
sp72a numeric vector
sp73a numeric vector
sp74a numeric vector
sp75a numeric vector
sp76a numeric vector
sp77a numeric vector
sp78a numeric vector
sp79a numeric vector
sp80a numeric vector
sp81a numeric vector
sp82a numeric vector
sp83a numeric vector
sp84a numeric vector
sp85a numeric vector
sp86a numeric vector
sp87a numeric vector
sp88a numeric vector
sp89a numeric vector
sp90a numeric vector
sp91a numeric vector
sp92a numeric vector
sp93a numeric vector
sp94a numeric vector
sp95a numeric vector
sp96a numeric vector
sp97a numeric vector
sp98a numeric vector
sp99a numeric vector
sp100a numeric vector
sp101a numeric vector
sp102a numeric vector
sp103a numeric vector
sp104a numeric vector
sp105a numeric vector
sp106a numeric vector
sp107a numeric vector
sp108a numeric vector
sp109a numeric vector
sp110a numeric vector
sp111a numeric vector
sp112a numeric vector
sp113a numeric vector
sp114a numeric vector
sp115a numeric vector
sp116a numeric vector
Data consists of the coverage (by point-intercept) of 116 taxa identified in 180 mangrove roots, sampled under a hierarchically nested spatial design that included six random sites within each of three sectors of the lagoon system corresponding to a strong environmental gradient: external (E), intermediate (M), and internal (I). The abundance of epibenthic organisms of 10 roots were described within each site, producing a total of 60 roots in each sector. The analysis of these pilot data defined the sampling design used by Guerra-Castro et al. (2016).
https://www.interciencia.net/wp-content/uploads/2018/01/923-GUERRA-8.pdf
Guerra-Castro, E., J. J. Cruz-Motta, and J. E. Conde. 2011. Cuantificación de la diversidad de especies incrustantes asociadas a las raíces de Rhizophora mangle L. en el Parque Nacional Laguna de La Restinga. Interciencia 36:923-930.
Guerra-Castro, E. J., J. E. Conde, and J. J. Cruz-Motta. (2016). Scales of spatial variation in tropical benthic assemblages and their ecological relevance: epibionts on Caribbean mangrove roots as a model system. Marine Ecology Progress Series 548:97-110.
data(pilot) str(pilot)data(pilot) str(pilot)
Plotting MultSE and sampling effort relationships of simulated data
plot_ssp(xx, opt, multi.site)plot_ssp(xx, opt, multi.site)
xx |
A data frame generated by |
opt |
A vector or data matrix generated by |
multi.site |
Logical argument indicating whether several sites were simulated |
This function allows to visualize the behavior of the MultSE as sampling effort increases. When the simulation involves two sampling scales, a graph for samples and one for sites will be generated. Above the MultSE~Sampling effort projection, two shaded areas are drawn, highlighting: sub-optimal improvement (light grey), and optimal improvement (dark gray). Both reflect the sampling effort that improves the precision at acceptable (light gray) or desirable levels (dark gray), but beyond the later, any gain could be considered unnecessary. In addition, for each sampling effort, the relativized improvement (in relation to the MultSE estimated with the lower sampling effort) is presented cumulatively (as percentages). This is very useful because it indicates exactly how much the precision is improved for each sampling effort.The plot is generated using ggplot2.
A ggplot2 object
This is an exploratory plot that can be edited using ggplot2 functions.
Edlin Guerra-Castro ([email protected]), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro ([email protected])
Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
Wickham, H. 2016. ggplot2: elegant graphics for data analysis. Springer.
###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) #Cut-off points to identify optimal sampling effort opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE) #Plot plot_ssp(xx = summ.mic, opt = opt.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE) #Cut-off points to identify optimal sampling effort opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE) #Plot plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) #Cut-off points to identify optimal sampling effort opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE) #Plot plot_ssp(xx = summ.mic, opt = opt.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE) #Cut-off points to identify optimal sampling effort opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE) #Plot plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)
Each set of simulated data is sampled many times for each sampling effort, from 2 replicates to those defined as an argument in the function. Then, distance-based multivariate standard errors are estimated using pseudo-variance (for single site evaluation) or Mean Squares Estimates in a linear model (for multisite evaluation).
sampsd(dat.sim, Par, transformation, method, n, m, k)sampsd(dat.sim, Par, transformation, method, n, m, k)
dat.sim |
A list of data sets generated by |
Par |
A list of parameters estimated by |
transformation |
Mathematical function to reduce the weight of very dominant species: 'square root', 'fourth root', 'Log (X+1)', 'P/A', 'none' |
method |
The appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc). The function |
n |
Maximum number of samples to take at each site. Can be equal or less than N |
m |
Maximum number of sites to sample at each data set. Can be equal or less than sites |
k |
Number of repetitions of each sampling effort (samples and sites) for each data set |
If several virtual sites have been generated, subsets of sites of size 2 to m are sampled, followed by the selection of sampling units (from 2 to n) using inclusion probabilities and self-weighted two-stage sampling (Tille, 2006). Each combination of sampling effort (number of sample units and sites), are repeated several times (e.g. k = 100) for all simulated matrices. If simulated data correspond to a single site, sampling without replacement is performed several times (e.g. k = 100) for each sample size (from 2 to n) within each simulated matrix. This approach is computationally intensive, especially when k is high (> 10). Keep this in mind as it will affect the time to get results. For each sample, suitable pre-treatments are applied and distance/similarity matrices constructed using the appropriate coefficient. When simulations are done for a single site, the MultSE is estimated as , being V the pseudo variance measured at each sample of size n (Anderson & Santana-Garcon, 2015). When several sites were generated, MultSE are estimated using the residual mean squares and the sites mean squares from a PERMANOVA model (Anderson & Santana-Garcon, 2015).
mse.results |
A matrix including all estimated MultSE for each simulated data, combination of sample replicates and sites for each k repetition. This matrix will be used by |
For quick exploratory analyzes, keep the number of repetitions small. Once you have explored the behavior of the MultSE, you can repeat the process keeping k-values large (e.g. 100). This process will take some time and it will depend on the power of your computer.
Edlin Guerra-Castro ([email protected]), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro ([email protected]).
Anderson, M.J. & Santana-Garcon, J. (2015) Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters, 18, 66-73
Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
Tillé, Y. (2006). Sampling algorithms. Springer, New York, NY.
assempar, simdata, summary_ssp, vegdist
###To speed up the simulation of these examples, the cases, sites and n were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases = 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases = 3, N = 20, sites = 3) #Sampling and estimation of MultSE for each sampling design (few #repetitions to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3)###To speed up the simulation of these examples, the cases, sites and n were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases = 3, N = 20, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases = 3, N = 20, sites = 3) #Sampling and estimation of MultSE for each sampling design (few #repetitions to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3)
The function simulates data sets (as many as requested) using estimated parameters from the list generated by assempar. The function returns an object of class list that includes all the simulated data to be used by datquality and sampsd.
simdata(Par, cases, N, sites)simdata(Par, cases, N, sites)
Par |
A list of parameters estimated by |
cases |
Number of data sets to be simulated |
N |
Total number of samples to be simulated in each site |
sites |
Total number of sites to be simulated in each data set |
The presence/absence of each species at each site are simulated with Bernoulli trials and probability of success equals to the empirical frequency of occurrence of each species among sites in the pilot data. For sites with the presence of a particular species, Bernoulli trials are used (with a probability of success equal to the estimated empirical frequency within the sites where it appears), to simulate the distribution of the species at that site. Once created, the P/A matrices are converted to matrices of abundances replacing presences by random values from an adequate statistical distribution and parameters equal to those estimated in the pilot data. Simulations of counts of individuals are generated using Poisson or negative binomial distributions, depending on the degree of aggregation of each species in the pilot data (McArdle & Anderson 2004; Anderson & Walsh 2013). Simulations of continuous variables (i.e. coverage, biomass), are generated using the log-normal distribution. The simulation procedure is repeated to generate as many simulated data matrices as needed.
simulated.data |
The function returns an object of class List, that includes all simulated data. This object will be used by |
This approach is not free from assumptions. Simulations do not consider any environmental constraint, neither co-occurrence structure of species. It is assumed that potential differences in species composition/abundance among samples and sites are mainly due to spatial aggregation of species, as estimated from the pilot data. Hence, any ecological property of the assemblage that was not captured by the pilot data, will not be reflected in the simulated data. Associations among species can be modeled using copulas, as suggested by Anderson et al (2019), which could be included in an upcoming version of SSP.
Edlin Guerra-Castro ([email protected]), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro ([email protected]).
Anderson, M. J., & Walsh, D. C. I. (2013). PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? Ecological Monographs, 83(4), 557-574.
Anderson, M. J., P. de Valpine, A. Punnett, & Miller, A. E. (2019). A pathway for multivariate analysis of ecological communities using copulas. Ecology and Evolution 9:3276-3294.
Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
McArdle, B. H., & Anderson, M. J. (2004). Variance heterogeneity, transformations, and models of species abundance: a cautionary tale. Canadian Journal of Fisheries and Aquatic Sciences, 61, 1294-1302.
###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar(data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units from a single site sim.mic<-simdata(par.mic, cases = 3, N = 10, sites = 1) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar (data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases = 3, N = 10, sites = 3)###To speed up the simulation of these examples, the cases, sites and N were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar(data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units from a single site sim.mic<-simdata(par.mic, cases = 3, N = 10, sites = 1) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar (data = sponges, type= "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases = 3, N = 10, sites = 3)
Counts of 41 species of sponges in 36 transects of 20 m * 1 m across 8 sites around ARNP
data("sponges")data("sponges")
A data frame with 36 observations on the following 42 variables.
siteFactor w/ 6 levels
Agelas.clathrodesa numeric vector
Agelas.dispara numeric vector
Agelas.tubulataa numeric vector
Agelas.wiedenmayeria numeric vector
Aiolocroia.crassaa numeric vector
Amphimedon.copressaa numeric vector
Aplysina.archeria numeric vector
Aplysina.cauliformisa numeric vector
Aplysina.fistularisa numeric vector
Aplysina.fulvaa numeric vector
Aplysina.insularisa numeric vector
Aplysina.lacunosaa numeric vector
Callyspongia.pliciferaa numeric vector
Callyspongia.vaginalisa numeric vector
Callispongia.fallaxa numeric vector
Callispongia.armigeraa numeric vector
Cliona.delitrixa numeric vector
Cliona.variansa numeric vector
Cribochalina.vascoluma numeric vector
Dragmacidon.sp.a numeric vector
Dysidea.variabilisa numeric vector
Ectyoplasia.feroxa numeric vector
Geodia.neptunia numeric vector
Hymeniacidon.caeruleaa numeric vector
Iotrochota.birotulataa numeric vector
Igernella.notabilisa numeric vector
Ircinia.felixa numeric vector
Ircinia.strobilinaa numeric vector
Monanchora.arbusculaa numeric vector
Mycale.laxissimaa numeric vector
Mycale.laevisa numeric vector
Nipahtes.amorphaa numeric vector
Niphates.erectaa numeric vector
Niphathes.digitalisa numeric vector
Phorbas.amaranthusa numeric vector
Scopalina.rutzleria numeric vector
Svenezea.flavaa numeric vector
Spirastrella.coccineaa numeric vector
Verongula.reswiguia numeric vector
Verongula.rigidaa numeric vector
Xestospongia.mutaa numeric vector
This data corresponds to a pilot study about sponge biodiversity in reef habitats in the Yucatán shelf (Ugalde et al., 2015)
https://biotaxa.org/Zootaxa/article/view/zootaxa.3911.2.1
Ugalde, D., Gomez, P., & Simoes, N. (2015). Marine sponges (Porifera: Demospongiae) from the Gulf of Mexico, new records and redescription of Erylus trisphaerus (de Laubenfels, 1953). Zootaxa, 3911(2), 151-183.
data(sponges) str(sponges)data(sponges) str(sponges)
For each simulated data set, averages of MultSE are estimated for each sampling size. Then an overall mean, as well as lower and upper intervals of means for each sample size are tabulated. A relativization to the maximum is applied to the average MultSE and a numerical derivative, using a forward finite difference, of the resulting quantity is obtained.
summary_ssp(results, multi.site)summary_ssp(results, multi.site)
results |
A matrix generated by |
multi.site |
Logical argument indicating whether several sites were simulated |
For each set of simulated data, the average of the MultSE in each sampling effort is estimated (Anderson & Santana-Garcon 2015). Then, an overall mean, lower and upper quantiles of means are tabulated for each sampling effort among all simulated data. In order to have a general and comparable criteria to evaluate the rate of change of the average MultSE with respect to the sampling effort, a relativization to the maximum MultSE (obtained with the lower sampling effort) is calculated; then, a standard forward finite derivation is calculated.
mse.results |
A data frame including the summary of multivariate standard error for each sampling effort. |
This data frame can then be used to plot MultSE with respect to the sampling effort
Edlin Guerra-Castro ([email protected]), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro ([email protected]).
Anderson, M.J. & Santana-Garcon, J. (2015) Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters, 18, 66-73
Guerra-Castro, E. J., J. C. Cajas, F. N. Dias Marques Simoes, J. J. Cruz-Motta, and M. Mascaro. (2020). SSP: An R package to estimate sampling effort in studies of ecological communities. bioRxiv:2020.2003.2019.996991.
###To speed up the simulation of these examples, the cases, sites and n were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 10, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type = "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 20, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE)###To speed up the simulation of these examples, the cases, sites and n were set small. ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico) data(micromollusk) #Estimation of parameters of pilot data par.mic<-assempar (data = micromollusk, type= "P/A", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units from a single site sim.mic<-simdata(par.mic, cases= 3, N = 10, sites = 1) #Sampling and estimation of MultSE for each sample size (few repetitions #to speed up the example) sam.mic<-sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A", method = "jaccard", n = 10, m = 1, k = 3) #Summary of MultSE for each sampling effort summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE) ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico). data(sponges) #Estimation of parameters of pilot data par.spo<-assempar(data = sponges, type = "counts", Sest.method = "average") #Simulation of 3 data sets, each one with 20 potential sampling units in 3 sites. sim.spo<-simdata(par.spo, cases= 3, N = 20, sites = 3) #Sampling and estimation of MultSE for each sampling design (few repetitions #to speed up the example) sam.spo<-sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root", method = "bray", n = 10, m = 3, k = 3) #Summary of MultSE for each sampling effort summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE)