f_divs: Functional diversity
In alexology/biomonitoR: biomonitoring indices and metrics

f_divs

R Documentation

Functional diversity

Description

\Sexpr[results=rd, stage=render]{ lifecycle::badge("maturing") }

This function calculates the functional diversity.

Usage

f_divs(
  x,
  trait_db = NULL,
  tax_lev = "Taxa",
  type = NULL,
  traitSel = FALSE,
  col_blocks = NULL,
  distance = "gower",
  zerodist_rm = FALSE,
  traceB = FALSE,
  correction = "none",
  set_param = NULL
)

Arguments

`x`	Result of `aggregate_taxa()`.
`trait_db`	A trait dataset. Can be a `data.frame` or a `dist` object. Taxonomic level of the traits dataset must match those of the taxonomic dataset. No automatic check is done.
`tax_lev`	Character string giving the taxonomic level used to retrieve trait information. Possible levels are `Taxa`, `Species`, `Genus`, `Family` as returned by `aggregate_taxa()`.
`type`	The type of variables speciefied in `trait_db`. Must be one of `F`, fuzzy, or `C`, continuous. If more control is needed please consider to provide `trait_db` as a `dist` object. It works only when `trait_db` is a `data.frame`, otherwise ingored.
`traitSel`	Interactively select traits.
`col_blocks`	A vector that contains the number of modalities for each trait. Not needed when `euclidean` distance is used.
`distance`	To be used to compute functional distances, `euclidean` or `gower`. Default to `gower`. See details.
`zerodist_rm`	If `TRUE` aggregates taxa with the same traits.
`traceB`	If `TRUE` returns a list as specified in details.
`correction`	Correction methods for negative eigenvalues, can be one of `none`, `lingoes`, `cailliez`, `sqrt` and `quasi`. Ignored when type is set to `C`.
`set_param`	A list of parameters for fine tuning the calculations. `max_nbdim` set the maximum number of dimension for evaluating the quality of the functional space. `prec` can be `Qt` or `QJ`, please refere to the `convhulln` documentation for more information. Deafault to `QJ`, less accurate but less prone to errors. `tol` a tolerance threshold for zero, see the function `is.euclid`, `lingoes` and `cailliez` from the `ade4` for more details. Default to 1e-07. If `cor.zero` is `TRUE`, zero distances are not modified. see the function `is.euclid`, `lingoes` and `cailliez` from the `ade4` for more details. Default to `TRUE`.

Details

Rao quadratic entropy (Q; Rao, 1982) is used to estimate Functional Diversity because it has been considered more appropriate than other indices (Botta-Dukat, 2005; Ricotta, 2005). In this formula (Q) dij is the dissimilarity (ranging from 0 to 1), between species i and j based on a set of specified functional traits (i.e. effect traits, see below). This index is standardized by the maximum value to constrain the values within the range of 0-1. Rao index is estimated using presence or abundance data and the Euclidean transformed version of the traits-based Gower dissimilarity matrix. For this, Gower's dissimilarity index (which ranges from 0 to 1) is used because it can deal with traits of different nature and measuring scales (continuous, nominal, binary, ordinal, etc.; see Podani 1999 for more information)

Q = \sum_{i=1}^{S} \sum_{j=1}^{S} d_{ij} \ p_i \ p_j

The gower distance refers to the mixed-variables coefficient of distance of Pavoine et al. (2009) as implemented in the ade4 package. This distance is meant to be used with fuzzy data.

Taxa without traits assigned in the trait dataset are removed from both the trait and abundance datasets.

Value

a vector with fuzzy functional richness results.

results Results of f_divs().
traits A data.frame containing the traits used for the calculations.
taxa A data.frame containing the taxa used for the calculations.
correction The type of correction used.
NA_detection A data.frame containing taxa on the first column and the corresponding trais with NAs on the second column.
duplicated_traits If present, list the taxa with the same traits.

References

Biggs, R., Schluter, M., Biggs, D., Bohensky, E. L., BurnSilver, S., Cundill, G., ... & Leitch, A. M. (2012). Toward principles for enhancing the resilience of ecosystem services. Annual Review of Environment and Resources, 37, 421-448.

Botta-Dukat, Z. (2005). Rao's quadratic entropy as a measure of functional diversity based on multiple traits. Journal of Vegetation Science, 16(5), 533-540.

de Bello, F., Leps, J., Lavorel, S., & Moretti, M. (2007). Importance of species abundance for assessment of trait composition: an example based on pollinator communities. Community Ecology, 8(2), 163-170.

Elmqvist, T., Folke, C., Nystrom, M., Peterson, G., Bengtsson, J., Walker, B., & Norberg, J. (2003). Response diversity, ecosystem change, and resilience. Frontiers in Ecology and the Environment, 1(9), 488-494.

Guillemot, N., Kulbicki, M., Chabanet, P., & Vigliola, L. (2011). Functional redundancy patterns reveal non-random assembly rules in a species-rich marine assemblage. PLoS One, 6(10), e26735.

Hevia, V., Martin-Lopez, B., Palomo, S., Garcia-Llorente, M., de Bello, F., & Gonzalez, J. A. (2017). Trait-based approaches to analyze links between the drivers of change and ecosystem services: Synthesizing existing evidence and future challenges. Ecology and evolution, 7(3), 831-844.

Hooper, D. U., Chapin, F. S., Ewel, J. J., Hector, A., Inchausti, P., Lavorel, S., et al. (2005). Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecological Monographs, 75(1), 3-35.

Lawton, J.H. & Brown, V.K. (1993) Redundancy in ecosystems. Biodiversity and Ecosystem Function (eds E.-D. Schulze & H.A. Mooney), pp. 255-270. Springer-Verlag, Berlin.

Pavoine, S., Vallet, J., Dufour, A. B., Gachet, S., & Daniel, H. (2009). On the challenge of treating various types of variables: application for improving the measurement of functional diversity. Oikos, 118(3), 391-402.

Pillar, V. D., Blanco, C. C., Muller, S. C., Sosinski, E. E., Joner, F., & Duarte, L. D. (2013). Functional redundancy and stability in plant communities. Journal of Vegetation Science, 24(5), 963-974.

Podani, J. (1999). Extending Gower's general coefficient of similarity to ordinal characters. Taxon, 331-340.

Rao, C. R. (1982). Diversity and dissimilarity coefficients: a unified approach. Theoretical population biology, 21(1), 24-43.

Ricotta, C. (2005). A note on functional diversity measures. Basic and Applied Ecology, 6(5), 479-486.

Rosenfeld, J. S. (2002). Functional redundancy in ecology and conservation. Oikos, 98(1), 156-162.

Schmera, D., Heino, J., Podani, J., Eros, T., & Doledec, S. (2017). Functional diversity: a review of methodology and current knowledge in freshwater macroinvertebrate research. Hydrobiologia, 787(1), 27-44.

Walker, B. H. (1992). Biodiversity and ecological redundancy. Conservation biology, 6(1), 18-23.

Examples

data(macro_ex)

data_bio <- as_biomonitor(macro_ex)
data_agr <- aggregate_taxa(data_bio)
data_ts <- assign_traits(data_agr)
# averaging
data_ts_av <- average_traits(data_ts)

col_blocks <- c(8, 7, 3, 9, 4, 3, 6, 2, 5, 3, 9, 8, 8, 5, 7, 5, 4, 4, 2, 3, 8)

f_divs(data_agr, trait_db = data_ts_av, type = "F", col_blocks = col_blocks)
f_divs(data_agr,
  trait_db = data_ts_av, type = "F", col_blocks = col_blocks,
  correction = "cailliez"
)

library(ade4)

rownames(data_ts_av) <- data_ts_av$Taxa
traits_prep <- prep.fuzzy(data_ts_av[, -1], col.blocks = col_blocks)

traits_dist <- ktab.list.df(list(traits_prep))
traits_dist <- dist.ktab(traits_dist, type = "F")

f_divs(data_agr, trait_db = traits_dist)

alexology/biomonitoR documentation built on Oct. 10, 2024, 12:02 a.m.