Assemblages of Macroinvertebrates in the Loire River (France)

Description

A total of 38 sites were surveyed along 800 km of the Loire River yielding 40 species of Trichoptera and Coleoptera sampled from riffle habitats. The river was divided into three regions according to geology: granitic highlands (Region#1), limestone lowlands (Region#2) and granitic lowlands (Region#3). This data set has been collected for analyzing changes in macroinvertebrate assemblages along the course of a large river. Four criterias are given here: variation in 1/ species composition and relative abundance, 2/ taxonomic composition, 3/ Body Sizes, 4/ Feeding habits.

Usage

data(macroloire)

Format

macroloire is a list of 5 components.

fau

is a data frame containing the abundance of each species in each station.

traits

is a data frame describes two traits : the maximal sizes and feeding habits for each species. Each trait is divided into categories. The maximal size achieved by the species is divided into four length categories: <= 5mm ; >5-10mm ; >10-20mm ; >20-40mm. Feeding habits comprise seven categories: engulfers, shredders, scrapers, deposit-feeders, active filter-feeders, passive filter-feeders and piercers, in this order. The affinity of each species to each trait category is quantified using a fuzzy coding approach. A score is assigned to each species for describing its affinity for a given trait category from "0" which indicates no affinity to "3" which indicates high affinity. These affinities are further transformed into percentage per trait per species.

taxo

is a data frame with species and 3 factors: Genus, Family and Order. It is a data frame of class "taxo": the variables are factors giving nested classifications.

envir

is a data frame giving for each station, its name (variable "SamplingSite"), its distance from the source (km, variable "Distance"), its altitude (m, variable "Altitude"), its position regarding the dams [1: before the first dam; 2: after the first dam; 3: after the second dam] (variable "Dam"), its position in one of the three regions defined according to geology: granitic highlands, limestone lowlands and granitic lowlands (variable "Morphoregion"), presence of confluence (variable "Confluence")

labels

is a data frame containing the latin names of the species.

Source

Ivol, J.M., Guinand, B., Richoux, P. and Tachet, H. (1997) Longitudinal changes in Trichoptera and Coleoptera assemblages and environmental conditions in the Loire River (France). Archiv für Hydrobiologie, 138, 525–557.

Pavoine S. and Dolédec S. (2005) The apportionment of quadratic entropy: a useful alternative for partitioning diversity in ecological data. Environmental and Ecological Statistics, 12, 125–138.

Examples

    data(macroloire)
    apqe.Equi <- apqe(macroloire$fau, , macroloire$morphoregions)
    apqe.Equi
    #test.Equi <- randtest.apqe(apqe.Equi, method = "aggregated", 99)
    #plot(test.Equi)

    ## Not run:  

    m.phy <- taxo2phylog(macroloire$taxo)
    apqe.Tax <- apqe(macroloire$fau, m.phy$Wdist, macroloire$morphoregions)
    apqe.Tax
    #test.Tax <- randtest.apqe(apqe.Tax, method = "aggregated", 99)
    #plot(test.Tax)

    dSize <- sqrt(dist.prop(macroloire$traits[ ,1:4], method = 2))
    apqe.Size <- apqe(macroloire$fau, dSize, macroloire$morphoregions)
    apqe.Size
    #test.Size <- randtest.apqe(apqe.Size, method = "aggregated", 99)
    #plot(test.Size)

    dFeed <- sqrt(dist.prop(macroloire$traits[ ,-(1:4)], method = 2))
    apqe.Feed <- apqe(macroloire$fau, dFeed, macroloire$morphoregions)
    apqe.Feed
    #test.Feed <- randtest.apqe(apqe.Feed, method = "aggregated", 99)
    #plot(test.Size)

    
## End(Not run)

Worked out examples

> library(ade4)
> ### Name: macroloire
> ### Title: Assemblages of Macroinvertebrates in the Loire River (France)
> ### Aliases: macroloire
> ### Keywords: datasets
> 
> ### ** Examples
> 
>     data(macroloire)
>     apqe.Equi <- apqe(macroloire$fau, , macroloire$morphoregions)
>     apqe.Equi
$call
apqe(samples = macroloire$fau, structures = macroloire$morphoregions)

$results
                diversity
Between samples 0.2701165
Within samples  0.5035630
Total           0.7736795

>     #test.Equi <- randtest.apqe(apqe.Equi, method = "aggregated", 99)
>     #plot(test.Equi)
> 
> 
>     m.phy <- taxo2phylog(macroloire$taxo)
>     apqe.Tax <- apqe(macroloire$fau, m.phy$Wdist, macroloire$morphoregions)
>     apqe.Tax
$call
apqe(samples = macroloire$fau, dis = m.phy$Wdist, structures = macroloire$morphoregions)

$results
                diversity
Between samples 0.2701165
Within samples  0.5035630
Total           0.7736795

>     #test.Tax <- randtest.apqe(apqe.Tax, method = "aggregated", 99)
>     #plot(test.Tax)
> 
>     dSize <- sqrt(dist.prop(macroloire$traits[ ,1:4], method = 2))
>     apqe.Size <- apqe(macroloire$fau, dSize, macroloire$morphoregions)
>     apqe.Size
$call
apqe(samples = macroloire$fau, dis = dSize, structures = macroloire$morphoregions)

$results
                 diversity
Between samples 0.04896350
Within samples  0.08965492
Total           0.13861842

>     #test.Size <- randtest.apqe(apqe.Size, method = "aggregated", 99)
>     #plot(test.Size)
> 
>     dFeed <- sqrt(dist.prop(macroloire$traits[ ,-(1:4)], method = 2))
>     apqe.Feed <- apqe(macroloire$fau, dFeed, macroloire$morphoregions)
>     apqe.Feed
$call
apqe(samples = macroloire$fau, dis = dFeed, structures = macroloire$morphoregions)

$results
                 diversity
Between samples 0.07075216
Within samples  0.07035300
Total           0.14110516

>     #test.Feed <- randtest.apqe(apqe.Feed, method = "aggregated", 99)
>     #plot(test.Size)
> 
> 
> 
> 
> 
> 
>