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Title:

Simulated Sampling Procedure for Community Ecology

Version:

1.0.2

Date:

2025-04-23

Maintainer:

Edlin Guerra-Castro <edlinguerra@gmail.com>

Description:

The Simulation-based Sampling Protocol (SSP) is an R package designed to estimate sampling effort in studies of ecological communities. It is based on the concept of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015, <doi:10.1111/ele.12385>) and the simulation of ecological data. The theoretical background is described in Guerra-Castro et al. (2020, <doi:10.1111/ecog.05284>).

Depends:

R (≥ 3.5.0)

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.3.2

Suggests:

knitr, rmarkdown, testthat, roxygen2

VignetteBuilder:

knitr

URL:

https://github.com/edlinguerra/SSP

BugReports:

https://github.com/edlinguerra/SSP/issues

Imports:

vegan, stats, sampling, ggplot2

NeedsCompilation:

Packaged:

2025-04-24 22:53:35 UTC; edlin

Author:

Edlin Guerra-Castro [aut, cre], Maite Mascaro [aut], Nuno Simoes [aut], Juan Cruz-Motta [aut], Juan Cajas [aut]

Repository:

CRAN

Date/Publication:

2025-04-24 23:20:02 UTC

SSP: Simulated Sampling Procedure for Community Ecology

Description

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., 2021).

Details

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/).

Author(s)

The SSP development team is Edlin Guerra-Castro, Maite Mascaro, Nuno Simoes, Juan Cruz-Motta and Juan Cajas

References

-Anderson, M.J., & Santana-Garcon, J. (2015). Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters 18(1), 66-73. doi: doi:10.1111/ele.12385

-Guerra-Castro, E.J., Cajas, J.C., Simões, N., Cruz-Motta, J.J., & Mascaró, M. (2021). SSP: an R package to estimate sampling effort in studies of ecological communities. Ecography 44(4), 561-573. doi: doi:10.1111/ecog.05284

Examples

###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)

Estimation of Ecological Parameters of the Assemblage

Description

This function extracts the main parameters of the pilot data using base R functions, as well as functions like specpool and dispweight.

Usage

assempar(data, type = c("P/A", "counts", "cover"), Sest.method = "average")

Arguments

data

Data frame with species names (columns) and samples (rows). 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 specpool is used. Available methods are "chao", "jack1", "jack2", and "boot". By default, the "average" of the four estimates is used.

Details

The expected number of species in the assemblage is estimated using non-parametric methods (Gotelli et al. 2011). Due to variability in the estimates of each approximation (Reese et al. 2014), we recommend using the average. The probability of detection of each species is estimated among and within sites. Among-site detection is calculated as the frequency of occurrences of each species across sampled sites; within-site detection is calculated as the weighted average of frequencies in sites where the species are present. Spatial aggregation (only for count data) is evaluated using the index of dispersion D (Clarke et al. 2006). Properties of unseen species are approximated using information from observed species, assuming their detection probabilities match those of the rarest observed species. Abundance distributions are simulated using random Poisson values with lambda as the overall mean of observed abundances.

Value

A list (class list) containing the estimated parameters of the assemblage, to be used by simdata.

Note

Important: The first column should indicate the site ID of each sample (as character or numeric), even when only a single site was sampled.

References

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. In A. E. Magurran & B. J. McGill (Eds.), Biological diversity: frontiers in measurement and assessment (pp. 39–54). Oxford University Press.

Guerra-Castro, E.J., Cajas, J.C., Simões, N., Cruz-Motta, J.J., & Mascaró, M. (2021). SSP: an R package to estimate sampling effort in studies of ecological communities. Ecography 44(4), 561-573. doi: doi:10.1111/ecog.05284

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.

Examples

## 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

Diversity Metrics of Simulated and Original Data

Description

Estimates the average number of species and the Simpson diversity index per sampling unit, as well as the total multivariate dispersion of both the original (pilot) and simulated datasets.

Usage

datquality(data, dat.sim, Par, transformation, method)

Arguments

data

Data frame with species as columns and samples as rows. The first column should indicate the site to which the sample belongs, regardless of whether a single site was sampled.

dat.sim

List of simulated data sets generated by simdata.

Par

List of parameters generated by assempar.

transformation

Mathematical transformation to reduce the weight of dominant species: one of "square root", "fourth root", "Log (X+1)", "P/A", or "none".

method

Dissimilarity metric used for multivariate dispersion, passed to vegdist.

Details

The quality of the simulated data sets is evaluated by statistical similarity to the pilot data. This includes: (i) the average number of species per sampling unit, (ii) the average Simpson diversity index, and (iii) the multivariate dispersion (MVD), defined as the average dissimilarity of each sampling unit to the group centroid in the dissimilarity space (Anderson 2006). For simulated datasets, mean and standard deviation are reported for (i) and (ii), and the 0.95 quantile of the MVD distribution is used to describe its variability.

Value

A data frame containing the mean and standard deviation of richness and diversity per sampling unit, and the MVD for original data, as well as the 0.95 quantile of MVD from the simulated data.

Note

It is desirable that simulated data resemble observed data in species richness and diversity per sampling unit.

References

Anderson, M. J. (2006). Distance-based tests for homogeneity of multivariate dispersions. Biometrics, 62, 245–253.

Examples

## Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico)
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 3, N = 10, sites = 1)
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)
par.spo <- assempar(data = sponges, type = "counts", Sest.method = "average")
sim.spo <- simdata(par.spo, cases = 3, N = 10, sites = 3)
qua.spo <- datquality(data = sponges, dat.sim = sim.spo, Par = par.spo,
                      transformation = "square root", method = "bray")
qua.spo

Epibionts on Caribbean mangrove roots

Description

Data corresponds to epibenthic organisms in mangrove roots from Laguna de La Restinga National Park, Venezuela (Guerra-Castro et al. 2016).

Usage

data("epibionts")

Format

A data frame with 96 observations on the following 152 variables.

sector: a factor with levels E I M
site: a numeric vector
Aaptos.sp: a numeric vector
Acanthophora.spicifera: a numeric vector
Acetabularia.crenulata: a numeric vector
Aglaothamnion.sp: a numeric vector
Amathia.sp: a numeric vector
Amorphinopsis.atlantica: a numeric vector
Amphimedon.erina: a numeric vector
Anemonia.sargassensis: a numeric vector
Aplidium.accarense: a numeric vector
Aplysilla.glacialis: a numeric vector
Ascidia.curvata: a numeric vector
Ascidia.sp: a numeric vector
Ascidia.sydneiensis: a numeric vector
Balanus.sp: a numeric vector
Bartholomea.annulata: a numeric vector
Biemna.caribea: a numeric vector
Bostrychia.tenella: a numeric vector
Botrylloides.nigrum: a numeric vector
Botrylloides.sp.1: a numeric vector
Botrylloides.sp.2: a numeric vector
Brachidontes.exustus: a numeric vector
Branchiomma.conspersum: a numeric vector
Branchiomma.nigromaculatum: a numeric vector
Bryopsis.sp: a numeric vector
Bugula.neritina: a numeric vector
Bugula.sp: a numeric vector
Calliactis.tricolor: a numeric vector
Callyspongia..Callyspongia..pallida: a numeric vector
Carijoa.riisei: a numeric vector
Caulerpa.racemosa: a numeric vector
Caulerpa.racemosa.var.peltata: a numeric vector
Caulerpa.sertularioides: a numeric vector
Caulerpa.verticillata: a numeric vector
Caulibugula.sp: a numeric vector
Celleporaria.sp: a numeric vector
Ceramium.diaphanum: a numeric vector
Chaetomorpha.sp.1: a numeric vector
Chaetomorpha.sp.2: a numeric vector
Chalinula.molitba: a numeric vector
Chelonaplysilla.erecta: a numeric vector
Chondrilla.nucula: a numeric vector
Chthamalus.sp: a numeric vector
Clathria..Clathria..microchela: a numeric vector
Clathria.sp: a numeric vector
Clavelina.oblonga: a numeric vector
Clavelina.picta: a numeric vector
Complejo.Cliona.celata: a numeric vector
Crassostrea.rhizophorae: a numeric vector
Dictyota.sp: a numeric vector
Didemnum.cineraceum: a numeric vector
Didemnum.perlucidum: a numeric vector
Didemnum.sp: a numeric vector
Diplosoma.listerianum: a numeric vector
Distaplia.bermudensis: a numeric vector
Distaplia.stylifera: a numeric vector
Dynamena.sp: a numeric vector
Dysidea.etheria: a numeric vector
Dysidea.sp: a numeric vector
Ecteinascidia.sp: a numeric vector
Ecteinascidia.styeloides: a numeric vector
Ecteinascidia.turbinata: a numeric vector
Eudistoma.olivaceum: a numeric vector
Eusynstyela.tincta: a numeric vector
Exaiptasia.pallida: a numeric vector
Ficopomatus.sp: a numeric vector
Geodia.papyracea: a numeric vector
Halichondria..Halichondria..magniconulosa: a numeric vector
Halichondria..Halichondria..melanadocia: a numeric vector
Haliclona..Halichoclona..magnifica: a numeric vector
Haliclona..Reniera..implexiformis: a numeric vector
Haliclona..Reniera..manglaris: a numeric vector
Haliclona..Reniera..ruetzleri: a numeric vector
Haliclona..Reniera..tubifera: a numeric vector
Haliclona..Rhizoniera..curacaoensis: a numeric vector
Haliclona..Soestella..caerulea: a numeric vector
Haliclona..Soestella..smithae: a numeric vector
Haliclona..Soestella..twincayensis: a numeric vector
Halimeda.sp: a numeric vector
Halisarca.sp: a numeric vector
Halopteris.sp: a numeric vector
Herdmania.pallida: a numeric vector
Hippopodina.feegeensis: a numeric vector
Hydroides.sp: a numeric vector
Hyrtios.proteus: a numeric vector
Iotrochota.birotulata: a numeric vector
Ircinia.felix: a numeric vector
Ircinia.sp: a numeric vector
Isognomon.alatus: a numeric vector
Kirchenpaueria.sp: a numeric vector
Lissoclinum.sp: a numeric vector
Lissodendoryx..Lissodendoryx..isodictyalis: a numeric vector
Lithophyllum.pustulatum: a numeric vector
Microcosmus.exasperatus: a numeric vector
Molgula.occidentalis: a numeric vector
Murrayella.periclados: a numeric vector
Mycale..Aegogropila..carmigropila: a numeric vector
Mycale..Aegogropila..citrina: a numeric vector
Mycale..Carmia..magnirhaphidifera: a numeric vector
Mycale..Carmia..microsigmatosa: a numeric vector
Mycale..Mycale..laevis: a numeric vector
Mycale..Zygomycale..angulosa: a numeric vector
Mycale.sp: a numeric vector
Nemalecium.sp: a numeric vector
Notaulax.nudicollis: a numeric vector
Obelia.sp: a numeric vector
Oceanapia.nodosa: a numeric vector
Padina.sp: a numeric vector
Perna.viridis: a numeric vector
Perophora.viridis: a numeric vector
Phaeophyceae: a numeric vector
Phallusia.nigra: a numeric vector
Phyllangia.americana: a numeric vector
Pinctada.imbricata: a numeric vector
Plakortis.angulospiculatus: a numeric vector
Polyclinum.constellatum: a numeric vector
Polysiphonia.sp.1: a numeric vector
Polysiphonia.sp.3: a numeric vector
Polysiphonia.subtilissima: a numeric vector
Pteria.colymbus: a numeric vector
Pyura.sp..1: a numeric vector
Pyura.sp..2: a numeric vector
Pyura.vittata: a numeric vector
Rhizoclonium.sp: a numeric vector
Rhodosoma.turcicum: a numeric vector
Sabella.sp: a numeric vector
Sabellastarte.magnifica: a numeric vector
Schizoporella.pungens: a numeric vector
Scopalina.ruetzleri: a numeric vector
Scopalina.sp: a numeric vector
Scrupocellaria.sp: a numeric vector
Sphacelaria.rigidula: a numeric vector
Spongia..Spongia..pertusa: a numeric vector
Spongia..Spongia..tubulifera: a numeric vector
Sporolithon.episporum: a numeric vector
Spyridia.hypnoides: a numeric vector
Styela.canopus: a numeric vector
Styela.sp.1: a numeric vector
Styela.sp.2: a numeric vector
Suberites.aurantiacus: a numeric vector
Symplegma.brakenhielmi: a numeric vector
Symplegma.rubra: a numeric vector
Synnotum.circinatum: a numeric vector
Tedania..Tedania..ignis: a numeric vector
Terpios.manglaris: a numeric vector
Tethya.actinia: a numeric vector
Tethya.sp: a numeric vector
Trididemnum.orbiculatum: a numeric vector
Ulva.sp: a numeric vector
Viatrix.globulifera: a numeric vector
Zoobotryon.verticillatum: a numeric vector

Details

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.

Source

https://doi.org/10.3354/meps11693

References

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.

Examples

data(epibionts)
str(epibionts)

Identification of the Optimal Sampling Effort

Description

Estimates the sampling effort at which the improvement in precision (MultSE) per additional sampling unit becomes sub-optimal or redundant, based on predefined cut-off thresholds.

Usage

ioptimum(xx, multi.site = TRUE, c1 = 10, c2 = 5, c3 = 2.5)

Arguments

xx

A data frame generated by summary_ssp.

multi.site

Logical. Indicates whether multiple sites were simulated.

c1

First cut threshold. Default is 10% improvement over the highest MultSE.

c2

Second cut threshold. Default is 5% improvement over the highest MultSE.

c3

Third cut threshold. Default is 2.5% improvement over the highest MultSE.

Details

Sampling efforts between the minimum (e.g. 2 samples) and c1 represent the necessary effort to achieve acceptable precision. Efforts between c1 and c2 reflect sub-optimal gains, and those between c2 and c3 are considered optimal. Beyond c3, any additional effort results in marginal improvements in MultSE and may be considered redundant. This classification helps support cost-benefit decisions in ecological survey design (see Underwood, 1990). If c3 is not reached within the simulated range, the maximum available effort is returned with a warning.

Value

A vector or matrix indicating the sampling sizes corresponding to each cut-off point.

Note

The cut-off thresholds are arbitrary and should be adjusted based on the ecological question and resource availability. In some cases, c3 may not be reached within the range of simulated sampling efforts.

References

Underwood, A. J. (1990). Experiments in ecology and management: Their logics, functions and interpretations. Australian Journal of Ecology, 15, 365–389.

Examples

## Single site example
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 3, N = 20, sites = 1)
sam.mic <- sampsd(dat.sim = sim.mic,
                  Par = par.mic,
                  transformation = "P/A",
                  method = "jaccard",
                  n = 10,
                  m = 1,
                  k = 3)
summ.mic <- summary_ssp(results = sam.mic, multi.site = FALSE)
opt.mic <- ioptimum(xx = summ.mic, multi.site = FALSE)

## Multiple sites example
data(sponges)
par.spo <- assempar(data = sponges, type = "counts", Sest.method = "average")
sim.spo <- simdata(par.spo, cases = 3, N = 10, sites = 3)
sam.spo <- sampsd(dat.sim = sim.spo,
                  Par = par.spo,
                  transformation = "square root",
                  method = "bray",
                  n = 10,
                  m = 3,
                  k = 3)
summ.spo <- summary_ssp(results = sam.spo, multi.site = TRUE)
opt.spo <- ioptimum(xx = summ.spo, multi.site = TRUE)

Micromollusks of marine shallow sandy bottoms around Cayo Nuevo, Gulf of Mexico, Mexico

Description

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

Usage

data("micromollusk")

Format

A data frame with 6 observations on the following 69 variables.

site: a numeric vector
Leptochiton.sp.: a numeric vector
Ischnochiton..Ischnochiton..erythronotus: a numeric vector
Arcidae.sp.: a numeric vector
Arca.imbricata: a numeric vector
Barbatia.domingensis: a numeric vector
Bentharca.sp.: a numeric vector
Arcopsis.adamsi: a numeric vector
Crenella.sp.: a numeric vector
Anomia.sp..: a numeric vector
Carditopsis.smithii: a numeric vector
Lucinidae..: a numeric vector
Chama.sinuosa: a numeric vector
Chama.sp.: a numeric vector
Galeommatidae.sp.: a numeric vector
Chione.elevata: a numeric vector
Semele.bellastriata: a numeric vector
Gastropoda.sp..1..: a numeric vector
Gastropoda.sp..2..: a numeric vector
Gastropoda.sp..3..: a numeric vector
Diodora.minuta: a numeric vector
Diodora.sp...: a numeric vector
Scissurella.redferni: a numeric vector
Synaptocochlea.picta: a numeric vector
Lodderena.ornata: a numeric vector
Cerithium.sp...: a numeric vector
Sansonia.tuberculata: a numeric vector
Iniforis.turristhomae: a numeric vector
Metaxia.rugulosa: a numeric vector
Cerithiopsis.cf..iuxtafuniculata: a numeric vector
Cerithiopsis.sp.: a numeric vector
Vermetidae.incertae.sedis.irregularis: a numeric vector
Dendropoma.corrodens: a numeric vector
Vermetid.sp..C: a numeric vector
Petaloconchus.mcgintyi: a numeric vector
Thylacodes.sp.: a numeric vector
Alvania.auberiana: a numeric vector
Alvania.colombiana: a numeric vector
Alvania.sp.: a numeric vector
Simulamerelina.caribaea: a numeric vector
Schwartziella.fischeri: a numeric vector
Zebina.browniana: a numeric vector
Zebina.sp.: a numeric vector
Caecum.circumvolutum: a numeric vector
Caecum.donmoorei: a numeric vector
Caecum.floridanum: a numeric vector
Caecum.johnsoni: a numeric vector
Caecum.pulchellum: a numeric vector
Caecum.textile: a numeric vector
Caecum.sp..B: a numeric vector
Meioceras.nitidum: a numeric vector
Cochliolepis.striata: a numeric vector
Parviturboides.interruptus: a numeric vector
Vitrinella.sp.: a numeric vector
Gibberula.lavalleeana: a numeric vector
Prunum.apicinum: a numeric vector
Volvarina.avena: a numeric vector
Astyris.lunata: a numeric vector
Phrontis.albus: a numeric vector
Phrontis.sp.: a numeric vector
Trachypollia.sp...: a numeric vector
Turridae.sp..1: a numeric vector
Turridae.sp..2..: a numeric vector
Turridae.sp..3..: a numeric vector
Ammonicera.lineofuscata: a numeric vector
Ammonicera.minortalis: a numeric vector
Rissoella.galba: a numeric vector
Pyramidellidae.sp.: a numeric vector
Pseudoscilla.babylonia: a numeric vector

Details

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).

Source

https://doi.org/10.3897/zookeys.779.24562

References

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

Examples

data(micromollusk)

Epibionts on Caribbean mangrove roots: pilot data

Description

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).

Usage

data("pilot")

Format

A data frame with 180 observations on the following 118 variables.

Sector: a factor with levels E I M
Site: a numeric vector
sp1: a numeric vector
sp2: a numeric vector
sp3: a numeric vector
sp4: a numeric vector
sp5: a numeric vector
sp6: a numeric vector
sp7: a numeric vector
sp8: a numeric vector
sp9: a numeric vector
sp10: a numeric vector
sp11: a numeric vector
sp12: a numeric vector
sp13: a numeric vector
sp14: a numeric vector
sp15: a numeric vector
sp16: a numeric vector
sp17: a numeric vector
sp18: a numeric vector
sp19: a numeric vector
sp20: a numeric vector
sp21: a numeric vector
sp22: a numeric vector
sp23: a numeric vector
sp24: a numeric vector
sp25: a numeric vector
sp26: a numeric vector
sp27: a numeric vector
sp28: a numeric vector
sp29: a numeric vector
sp30: a numeric vector
sp31: a numeric vector
sp32: a numeric vector
sp33: a numeric vector
sp34: a numeric vector
sp35: a numeric vector
sp36: a numeric vector
sp37: a numeric vector
sp38: a numeric vector
sp39: a numeric vector
sp40: a numeric vector
sp41: a numeric vector
sp42: a numeric vector
sp43: a numeric vector
sp44: a numeric vector
sp45: a numeric vector
sp46: a numeric vector
sp47: a numeric vector
sp48: a numeric vector
sp49: a numeric vector
sp50: a numeric vector
sp51: a numeric vector
sp52: a numeric vector
sp53: a numeric vector
sp54: a numeric vector
sp55: a numeric vector
sp56: a numeric vector
sp57: a numeric vector
sp58: a numeric vector
sp59: a numeric vector
sp60: a numeric vector
sp61: a numeric vector
sp62: a numeric vector
sp63: a numeric vector
sp64: a numeric vector
sp65: a numeric vector
sp66: a numeric vector
sp67: a numeric vector
sp68: a numeric vector
sp69: a numeric vector
sp70: a numeric vector
sp71: a numeric vector
sp72: a numeric vector
sp73: a numeric vector
sp74: a numeric vector
sp75: a numeric vector
sp76: a numeric vector
sp77: a numeric vector
sp78: a numeric vector
sp79: a numeric vector
sp80: a numeric vector
sp81: a numeric vector
sp82: a numeric vector
sp83: a numeric vector
sp84: a numeric vector
sp85: a numeric vector
sp86: a numeric vector
sp87: a numeric vector
sp88: a numeric vector
sp89: a numeric vector
sp90: a numeric vector
sp91: a numeric vector
sp92: a numeric vector
sp93: a numeric vector
sp94: a numeric vector
sp95: a numeric vector
sp96: a numeric vector
sp97: a numeric vector
sp98: a numeric vector
sp99: a numeric vector
sp100: a numeric vector
sp101: a numeric vector
sp102: a numeric vector
sp103: a numeric vector
sp104: a numeric vector
sp105: a numeric vector
sp106: a numeric vector
sp107: a numeric vector
sp108: a numeric vector
sp109: a numeric vector
sp110: a numeric vector
sp111: a numeric vector
sp112: a numeric vector
sp113: a numeric vector
sp114: a numeric vector
sp115: a numeric vector
sp116: a numeric vector

Details

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).

Source

https://www.interciencia.net/wp-content/uploads/2018/01/923-GUERRA-8.pdf

References

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.

Examples

data(pilot)
str(pilot)

SSP Plot: Visualization of MultSE and Sampling Effort

Description

Plots the relationship between MultSE and sampling effort using results from SSP simulations.

Usage

plot_ssp(xx, opt, multi.site)

Arguments

xx

A data frame generated by summary_ssp.

opt

A vector or data matrix generated by ioptimum.

multi.site

Logical. Indicates whether several sites were simulated.

Details

This function visualizes the behavior of MultSE (pseudo-multivariate standard error) as sampling effort increases. If simulations involve two sampling scales (e.g., sites and samples), separate graphs are generated. Two shaded bands highlight sub-optimal (light grey) and optimal (dark grey) improvements in precision. The graph also displays the relative gain in precision (as cumulative percentage) for each level of sampling effort, compared to the lowest.

This visualization helps identify when additional sampling effort results in diminishing returns. The plot is generated using ggplot2 and can be further customized.

Value

A ggplot2 object.

Note

This is an exploratory plot and can be edited or extended using standard ggplot2 functions.

References

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer.

Examples

## Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico)
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 3, N = 20, sites = 1)
sam.mic <- sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A",
                  method = "jaccard", n = 10, m = 1, k = 3)
summ.mic <- summary_ssp(results = sam.mic, multi.site = FALSE)
opt.mic <- ioptimum(xx = summ.mic, multi.site = FALSE)
plot_ssp(xx = summ.mic, opt = opt.mic, multi.site = FALSE)

## Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico)
data(sponges)
par.spo <- assempar(data = sponges, type = "counts", Sest.method = "average")
sim.spo <- simdata(par.spo, cases = 3, N = 10, sites = 3)
sam.spo <- sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root",
                  method = "bray", n = 10, m = 3, k = 3)
summ.spo <- summary_ssp(results = sam.spo, multi.site = TRUE)
opt.spo <- ioptimum(xx = summ.spo, multi.site = TRUE)
plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)

Sampling Simulated Data and Estimation of Multivariate Standard Errors

Description

For each simulated data set, this function performs repeated sampling across a range of effort levels and estimates the corresponding MultSE (pseudo-multivariate standard error) using dissimilarity-based methods.

Usage

sampsd(dat.sim, Par, transformation, method, n, m, k)

Arguments

dat.sim

A list of simulated data sets generated by simdata.

Par

A list of parameters estimated by assempar.

transformation

Mathematical transformation to reduce the influence of dominant species: one of "square root", "fourth root", "Log (X+1)", "P/A", or "none".

method

Dissimilarity metric to use, passed to vegdist (e.g., "bray", "jaccard", "gower").

n

Maximum number of sampling units per site (must be <= total units available).

m

Maximum number of sites to sample per data set (must be <= total number of sites).

k

Number of repetitions of each sampling configuration (samples × sites) for each data set.

Details

For multi-site simulations, the function selects subsets of sites (from 2 to m) and then draws n samples per site using a two-stage sampling method with inclusion probabilities (Tillé, 2006). For single-site simulations, repeated samples of size 2 to n are taken without replacement.

Each sample undergoes the selected transformation and a dissimilarity matrix is computed. MultSE is estimated using:

Single site: pseudo-variance, with MultSE = \sqrt(V/n)
Multiple sites: mean squares from a PERMANOVA model (residual and site effects)

This procedure is computationally intensive, especially with large k. Start with low values for exploration.

Value

A matrix containing the estimated MultSE values for each simulated data set, sampling effort combination, and repetition. This matrix is used by summary_ssp.

Note

For quick exploratory analysis, use small k. Once optimal sampling effort is explored, rerun with larger k (e.g. 100). Computation time will increase accordingly.

References

Anderson, M. J., & Santana-Garcon, J. (2015). Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters, 18(1), 66–73.

Guerra-Castro, E. J., Cajas, J. C., Simoes, N., Cruz-Motta, J. J., & Mascaro, M. (2021). SSP: An R package to estimate sampling effort in studies of ecological communities. Ecography, 44(4), 561–573. doi:10.1111/ecog.05284

Tillé, Y. (2006). Sampling Algorithms. Springer, New York.

Examples

## Single site example
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 3, N = 20, sites = 1)
sam.mic <- sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A",
                  method = "jaccard", n = 10, m = 1, k = 3)

## Multiple site example
data(sponges)
par.spo <- assempar(data = sponges, type = "counts", Sest.method = "average")
sim.spo <- simdata(par.spo, cases = 3, N = 20, sites = 3)
sam.spo <- sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root",
                  method = "bray", n = 10, m = 3, k = 3)

Simulation of Ecological Data Sets

Description

Simulates multiple ecological data sets using parameters estimated from a pilot study. The output can be used in downstream SSP functions for quality evaluation and sampling effort estimation.

Usage

simdata(Par, cases, N, sites)

Arguments

Par

A list of parameters estimated by assempar.

cases

Number of data sets to simulate.

N

Number of samples to simulate in each site.

sites

Number of sites to simulate in each data set.

Details

Presence/absence data are simulated using Bernoulli trials based on empirical frequencies of occurrence among sites (for site-level presence) and within sites (for local occurrence patterns). These matrices are then converted into abundance matrices using values drawn from Poisson or negative binomial distributions (for count data), or from log-normal distributions (for continuous data like coverage or biomass), depending on the aggregation properties estimated in the pilot data.

This process is repeated cases times, producing a list of simulated data sets that reflect the statistical properties of the original assemblage, but without incorporating environmental constraints or species co-occurrence structures.

Value

A list of simulated community data sets, to be used by datquality and sampsd.

Note

This simulation assumes that differences in composition or abundance are due to spatial aggregation, as captured by the pilot data. It does not incorporate environmental gradients or species associations. For more advanced modeling of species associations, copula-based approaches as suggested by Anderson et al. (2019) may be integrated in future versions of SSP.

References

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., de Valpine, P., Punnett, A., & Miller, A. E. (2019). A pathway for multivariate analysis of ecological communities using copulas. Ecology and Evolution, 9, 3276–3294.

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.

Examples

## Single site simulation
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 3, N = 10, sites = 1)

## Multiple site simulation
data(sponges)
par.spo <- assempar(data = sponges, type = "counts", Sest.method = "average")
sim.spo <- simdata(par.spo, cases = 3, N = 10, sites = 3)

Sponges in Alacranes Reef National Park (ARNP), Gulf of Mexico, Mexico

Description

Counts of 41 species of sponges in 36 transects of 20 m * 1 m across 8 sites around ARNP

Usage

data("sponges")

Format

A data frame with 36 observations on the following 42 variables.

site: Factor w/ 6 levels
Agelas.clathrodes: a numeric vector
Agelas.dispar: a numeric vector
Agelas.tubulata: a numeric vector
Agelas.wiedenmayeri: a numeric vector
Aiolocroia.crassa: a numeric vector
Amphimedon.copressa: a numeric vector
Aplysina.archeri: a numeric vector
Aplysina.cauliformis: a numeric vector
Aplysina.fistularis: a numeric vector
Aplysina.fulva: a numeric vector
Aplysina.insularis: a numeric vector
Aplysina.lacunosa: a numeric vector
Callyspongia.plicifera: a numeric vector
Callyspongia.vaginalis: a numeric vector
Callispongia.fallax: a numeric vector
Callispongia.armigera: a numeric vector
Cliona.delitrix: a numeric vector
Cliona.varians: a numeric vector
Cribochalina.vascolum: a numeric vector
Dragmacidon.sp.: a numeric vector
Dysidea.variabilis: a numeric vector
Ectyoplasia.ferox: a numeric vector
Geodia.neptuni: a numeric vector
Hymeniacidon.caerulea: a numeric vector
Iotrochota.birotulata: a numeric vector
Igernella.notabilis: a numeric vector
Ircinia.felix: a numeric vector
Ircinia.strobilina: a numeric vector
Monanchora.arbuscula: a numeric vector
Mycale.laxissima: a numeric vector
Mycale.laevis: a numeric vector
Nipahtes.amorpha: a numeric vector
Niphates.erecta: a numeric vector
Niphathes.digitalis: a numeric vector
Phorbas.amaranthus: a numeric vector
Scopalina.rutzleri: a numeric vector
Svenezea.flava: a numeric vector
Spirastrella.coccinea: a numeric vector
Verongula.reswigui: a numeric vector
Verongula.rigida: a numeric vector
Xestospongia.muta: a numeric vector

Details

This data corresponds to a pilot study about sponge biodiversity in reef habitats in the Yucatán shelf (Ugalde et al., 2015)

Source

https://biotaxa.org/Zootaxa/article/view/zootaxa.3911.2.1

References

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.

Examples

data(sponges)
str(sponges)

Summary of MultSE for Each Sampling Effort in Simulated Data Sets

Description

Computes the average MultSE (pseudo-multivariate standard error) for each sampling effort across simulated datasets, and estimates associated variation and rate of change.

Usage

summary_ssp(results, multi.site)

Arguments

results

A matrix generated by sampsd containing MultSE values for each simulation and sampling configuration.

multi.site

Logical. Indicates whether multiple sites were simulated.

Details

For each sampling effort in each simulated data set, the average MultSE is computed (Anderson & Santana-Garcon, 2015). The function then calculates the overall mean and associated lower and upper quantiles of these averages. To evaluate how precision improves with effort, the average MultSE values are relativized to the maximum (typically at the lowest effort), and a numerical forward finite difference derivative is calculated to approximate the rate of change.

This output is used to support the identification of optimal and redundant sampling efforts based on precision gain.

Value

A data frame summarizing MultSE for each sampling effort, including the mean, quantiles, relativized values, and estimated derivative.

Note

This data frame can be used to plot MultSE versus sampling effort and to apply cutoff rules using ioptimum.

References

Anderson, M. J., & Santana-Garcon, J. (2015). Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters, 18(1), 66–73.

Examples

## Single site example
data(micromollusk)
par.mic <- assempar(data = micromollusk, type = "P/A", Sest.method = "average")
sim.mic <- simdata(par.mic, cases = 3, N = 10, sites = 1)
sam.mic <- sampsd(dat.sim = sim.mic, Par = par.mic, transformation = "P/A",
                  method = "jaccard", n = 10, m = 1, k = 3)
summ.mic <- summary_ssp(results = sam.mic, multi.site = FALSE)

## Multiple site example
data(sponges)
par.spo <- assempar(data = sponges, type = "counts", Sest.method = "average")
sim.spo <- simdata(par.spo, cases = 3, N = 20, sites = 3)
sam.spo <- sampsd(dat.sim = sim.spo, Par = par.spo, transformation = "square root",
                  method = "bray", n = 10, m = 3, k = 3)
summ.spo <- summary_ssp(results = sam.spo, multi.site = TRUE)

SSP: Simulated Sampling Procedure for Community Ecology

Description

Details

Author(s)

References

Examples

Estimation of Ecological Parameters of the Assemblage

Description

Usage

Arguments

Details

Value

Note

References

See Also

Examples

Diversity Metrics of Simulated and Original Data

Description

Usage

Arguments

Details

Value

Note

References

See Also

Examples

Epibionts on Caribbean mangrove roots

Description

Usage

Format

Details

Source

References

Examples

Identification of the Optimal Sampling Effort

Description

Usage

Arguments

Details

Value

Note

References

See Also

Examples

Micromollusks of marine shallow sandy bottoms around Cayo Nuevo, Gulf of Mexico, Mexico

Description

Usage

Format

Details

Source

References

Examples

Epibionts on Caribbean mangrove roots: pilot data

Description

Usage

Format

Details

Source

References

Examples

SSP Plot: Visualization of MultSE and Sampling Effort

Description

Usage

Arguments

Details

Value

Note

References

See Also

Examples

Sampling Simulated Data and Estimation of Multivariate Standard Errors

Description

Usage

Arguments

Details

Value

Note

References

See Also

Examples