Description Usage Arguments Details Value Note Author(s) References See Also Examples
The function extracts the main parameters of the pilot data using basic R functions as well as functions like specpool
and dispweight
1 |
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 (edlinguerra@gmail.com), Juan Carlos Cajas, Juan Jose Cruz-Motta, Nuno Simoes and Maite Mascaro (mmm@ciencias.unam.mx).
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ##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
|
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