SSP (version 1.1.0)

assempar: 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")

Value

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

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.

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: tools:::Rd_expr_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.

See Also

dispweight, specpool, simdata

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