What is R and RStudio

• Know the GUI of R and Rstudio softwares

• Create a project R

• Install and load packages

• Import data into the RStudio software

• Use basic functions of a package

What is ethnobotanyR

The ethnobotanyR package can be used to calculate common quantitative ethnobotany indices to assess the cultural significance of plant species based on informant consensus.

Why this training

The goal is to provide an easy-to-use platform for ethnobotanists to calculate quantitative ethnobotany indices ;

To be More productive ;

To be opened to current research methodologies and skills.

The author

References (1)

The package closely follows two papers, one by Tardio and Pardo-de-Santayana (2008) and another by Whitney et al. (2018).

Users are highly encouraged to read the theory papers of Albuquerque et al. (2019) and Gaoue et al. (2017).

References (2)

Tsioutsiou E E, Giordani P, Hanlidou E, Biagi M, De Feo V, and Cornara L (2019), Ethnobotanical Study of Medicinal Plants Used in Central Macedonia, Greece. Evidence-Based Complementary and Alternative Medicine. 2019: 1-22.

Table of Content

The Package

Data format

Package functions

Visualize ethnobotanyR results

Demo

Definition

The field of study that analyzes the results of indigenous manipulations of plants material together with the cultural context in which the plants are used, is called Ethnobotany (Balick et Cox, 1996)

Why carry out an ethnobotay study

• Cultural knowledge ;

• Anthropology ;

• Archeology ;

• Economic ;

• New Drug discovery ;

_ …

Why carry out an ethnobotay study

What are the conservation and livelihood implications of plant use?

Biodiversity conservation (Gaoue et al., 2017)

Open and semi-structured interviews Vs Structure

• walk-in-the-woods ;

• Show and tell

Use categories

“construction”, “food”, “medicine”, “technology”, “firewood” and “other”.

Use categories for medicinal plants 1/4

• gastrointestinal disorders (GI): (diarrhea, nausea, vomiting, stomach ache, gastric problems, loss of appetite, colic, flatulence, dysentery);

• dermatological diseases (DO): (skin burns, skin spots, skin rashes, boils, cut, wounds, hair problems, ectoparasites);

• urogenital and gynecological problems (UGP): (sexual problems including frigidity, lack of libido, infertility, gonorrhea, diuretic, aphrodisiac, menstrual disorders);

Use categories for medicinal plants 2/4

• skeletomuscular disorders (SD);

• internal medical diseases (IM): (diabetes, cancers, and tumors, hypertension, piles/hemorrhoids);

• respiratory-nose, ear, oral/dental, throat problems (RT): (asthma, nose bleeding, sinusitis, earache, throat shore, dental problems);

• and others (OT) (motion sickness). (Jadid et al., 2020)

Cultural Diseases and Disorders, and Ritual/Magical Uses (Gruca et al., 2014)

Use categories for medicinal plants 3/4

13 disease categories

• Gastrointestinal diseases ;

• Malaria ;

• General pathology symptoms (Headache, tiredness, cold, insomnia, fatigue) ;

• Dermatosis (Skin disorders, wound healing) ;

• Respiratory diseases (Respiratory disorder, sinusitis, cough) ;

• Hemorrhoids ;

Use categories for medicinal plants 4/4

• Internal organ diseases (Liver diseases, kidney diseases) ;

• Cardiovascular diseass (Heart aches, control of heartbeat, hypertension) ;

• Infections (excl. malaria) (Dental infections, eye infections) ;

• Child diseases (Umbilical cord treatment, children fears, growth retardation) ;

• Urogenital diseases (Syphilis,prostate, sexual impotence) ;

• Arthritis (Rheumatism and gout)

• Blood diseases (Sickle cell disease, anemia) (Ouédraogo1 et al, 2020)

Quantitative ethnobotany

Quantitative ethnobotany may be defined as “the application of quantitative techniques to the direct analysis of contemporary plant use data”.

Data collection, processing and interpretation.

Such quantitative approaches aim to describe the variables quantitatively and analyse the observed patterns in the study (Höft et al. 1999).

Installation of the Package

Script Mode :

Documentation & Vignette of the Package

Charge the package and the data set

library(ethnobotanyR)
# View(ethnobotany_cat)
library(readxl)
ethnobotany_cat <- read_excel("/Volumes/USER-1/Projets/rworkshop/data/ethnobotany_cat.xlsx")
View(ethnobotany_cat)

Definition

• This is an ethnobotany data set including one column of knowledge holder identifiers informant and one of species names sp_name.

• The rest of the columns are the identified ethnobotany use categories (Tsioutsiou et al., 2019).

• The data in the use categories is populated with counts of uses per person (should be 0 or 1 values).

dim(ethnobotany_cat)
unique(ethnobotany_cat$sp_name)
length(unique(ethnobotany_cat$informant))

Comment the dataset: EDA

Use Report (UR) per species (URs)

• Definiton :

URs() calculates the total uses for the species by all informants (from \(i_1\) to \(^iN\)) within each use-category for that species \((s)\).

It is a count of the number of informants who mention each use-category \(NC\) for the species and the sum of all uses in each use-category (from \(u_1\) to \(^uNC\)) (Prance et al. 1987).

Use Report (UR) per species (URs)

• Formular :

\[\begin{equation} UR_{s} = \sum_{u=u_1}^{^uNC} \sum_{i=i_1}^{^iN} UR_{ui} \end{equation}\]

• Function :

ethnobotanyR::URs(ethnobotany_cat)

Use Report (URsum) per species

The URsum() function calculates the sum of all ethnobotany use reports (UR) for all species in the data set.

• Function :

ethnobotanyR::URsum(ethnobotany_cat)

Cultural Importance (CI) index

• Definition

The CIs() function calculates the cultural importance index (CI) for each species in the data set.

The CIs() is essentially URs() divided by the number of informants to account for the diversity of uses for the species.

Cultural Importance (CI) index

• Formular

\[\begin{equation} CI_{s} = \sum_{u=u_1}^{^uNC} \sum_{i=i_1}^{^iN} UR_{ui/N}. \end{equation}\]

• Function

ethnobotanyR::CIs(ethnobotany_cat)

Frequency of Citation (FC) per species

• Definition

The FCs() function calculates the frequency of citation (FC) for each species in the data set.

The FCs() is the sum of informants that cite a use for the species.

Frequency of Citation (FC) per species

• Formular

\[\begin{equation} FC_s = \sum_{i=i_1}^{^iN} UR_i \end{equation}\]

• Function

ethnobotanyR::FCs(ethnobotany_cat)

Number of Uses (NU) per species

• Definition

The NUs() function calculates the number of uses (NU) for each species in the data set.\(NC\) are the number of use categories.

The NUs() is the sum of all categories for which a species is considered useful (Prance et al. 1987).

• Formular

\[\begin{equation} NU_s = \sum_{u=u_1}^{^uNC} \end{equation}\]

Number of Uses (NU) per species

• Function

ethnobotanyR::NUs(ethnobotany_cat)

Relative Frequency of Citation (RFC) index

• Definition

The RFCs() function calculates the relative frequency of citation (RFC) for each species in the data set.

\(FC_s\) is the frequency of citation for each species \(s\), \(UR_i\) are the use reports for all informants \(i\) and \(N\) is the total number of informants interviewed in the survey.

Relative Frequency of Citation (RFC) index

• Formular

\[\begin{equation} RFC_s = \frac{FC_s}{N} = \frac{\sum_{i=i_1}^{^iN} UR_i}{N} \end{equation}\]

• Function

ethnobotanyR::RFCs(ethnobotany_cat)

Relative Importance (RI) index

• Definition

The RIs() function calculates the relative importance index (RI) for each species in the data set.

\(RFC_{s(max)}\) is the relative frequency of citation for the species \(s\) over the maximum, \(RNU_{s(max)}\) is the relative number of uses for \(s\) over the maximum (see Tardio and Pardo-de-Santayana 2008).

• Formular

\[\begin{equation} RI_s = \frac{RFC_{s(max)} + RNU_{s(max)}}{2} \end{equation}\]

Relative Importance (RI) index

• Function

ethnobotanyR::RIs(ethnobotany_cat)

Use Value (UV) index

• Definition

The UVs() function calculates the use value (UV) index for each species in the data set.

\(U_i\) is the number of different uses mentioned by each informant \(i\) and \(N\) is the total number of informants interviewed in the survey (Albuquerque et al. 2006).

• Formular

\[\begin{equation} UV_{s} = \sum U_i/N \end{equation}\]

Use Value (UV) index

• Function

ethnobotanyR::UVs(ethnobotany_cat)

Fidelity Level (FL) per species

• Definition

The FLs() function calculates the fidelity level (FL) per species in the study.

It is a way of calculating the percentage of informants who use a plant for the same purpose as compared to all uses of all plants.

• Formular \[\begin{equation} FL_{s} = \frac {N_{s}}{UR_{s}} \end{equation}\]

where \(N_s\) is the number of informants that use a particular plant for a specific purpose, and \(UR_s\) is the total number of use reports for the species.

Fidelity Level (FL) per species

• Function

ethnobotanyR::FLs(ethnobotany_cat)

Divide FLs by 100 to get the percent FL, as it is reported in some studies.

Radial plots

URs_plot <- ethnobotanyR::Radial_plot(ethnobotany_cat, ethnobotanyR::URs)
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.

NUs_plot <- ethnobotanyR::Radial_plot(ethnobotany_cat, ethnobotanyR::NUs)
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.

FCs_plot <- ethnobotanyR::Radial_plot(ethnobotany_cat, ethnobotanyR::FCs)
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.

CIs_plot <- ethnobotanyR::Radial_plot(ethnobotany_cat, ethnobotanyR::CIs)
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.

cowplot::plot_grid(URs_plot, NUs_plot, FCs_plot, CIs_plot, 
    labels = c('URs', 'NUs', 'FCs', 'CIs'), 
    nrow = 2, 
    align="hv",
    label_size = 12)

Chord diagrams

Chord_sp <- ethnobotanyR::ethnoChord(ethnobotany_cat, by = "sp_name")

Chord_informant <- ethnobotanyR::ethnoChord(ethnobotany_cat, by = "informant")

Flow diagrams

ethnobotanyR::ethno_alluvial(ethnobotany_cat)

Labels for use, species and Expert

# correct internal assignment for stat = "stratum" 
  StatStratum <- ggalluvial::StatStratum

ggalu<-ethnobotanyR::ethno_alluvial(ethnobotany_cat, alpha = 0.2) + 
  ggplot2::theme(legend.position = "none") +  
             ggplot2::geom_label(stat = "stratum", 
                      ggplot2::aes(label = ggplot2::after_stat(stratum)))
plot(ggalu)

Confidence in responses

library(dplyr)
sp_an_mu_data <- ethnobotany_cat %>% filter(sp_name == "anona_muricata") 

sp_an_mu_use <- ethno_boot(sp_an_mu_data$food, statistic = mean, n1 = 1000)

sp_alc_cor_data <- ethnobotany_cat %>% filter(sp_name == "alchornea_cordifolia") 

sp_alc_cor_use<- ethno_boot(sp_alc_cor_data$food , statistic = mean, n1 = 1000)

quantile(sp_an_mu_use, c(0.05, 0.95))

quantile(sp_alc_cor_use, c(0.05, 0.95))

boot_data <- data.frame(sp_an_mu_use, sp_alc_cor_use)

ethno_boot_melt <- reshape2::melt(boot_data)

library(ggplot2)
ggplot(ethno_boot_melt, aes(x = value, 
                y = variable, fill = variable)) +
                ggridges::geom_density_ridges() +
                ggridges::theme_ridges() + 
                theme(legend.position = "none") +
                labs(y= "", x = "Example Bayesian bootstraps of food use category")

ethno_an_mu <- dplyr::filter(ethnobotany_cat, sp_name == "anona_muricata")

answers <- 2

ethno_compet_an_mu <- dplyr::recode(ethno_an_mu$informant, 
                                    aime = 0.9,rutho = 0.5,evan = 0.5,
                                    konan = 0.9, lidie = 0.9, rosine = 0.5,
                                    larrisa = 0.7,jean = 0.5, paul = 0.9, ib_chab = 0.9,
                                    claud= 0.9, kouadio = 0.9)

ethno_an_mu_bayes <- ethnobotanyR::ethno_bayes_consensus(ethno_an_mu, 
                    answers = 2,
                    #here we keep the default normal distribution with `prior = -1`
                    prior_for_answers = ethno_compet_an_mu)

heatmap(ethno_an_mu_bayes)

Here the ‘1’ and ‘2’ represent ‘use’ and ‘no use’ (y-axis)

Richer response data

set.seed(123) #make random number reproducible
ethno_sp_a_rich <- data.frame(replicate(3,sample(0:10,20,rep=TRUE)))
  names(ethno_sp_a_rich) <- 
  gsub(x = names(ethno_sp_a_rich), 
  pattern = "X", replacement = "Use_")  
  ethno_sp_a_rich$informant <- sample(c('User_1', 'User_2'), 
  20, replace=TRUE)
  ethno_sp_a_rich$sp_name <- sample(c('sp_a'), 
  20, replace=TRUE)

ethno_compet_sp_a_rich <- 
          dplyr::recode(ethno_sp_a_rich$informant,
          User_1 = 0.9, User_2 = 0.5)

ethno_sp_a_bayes <- ethnobotanyR::ethno_bayes_consensus(ethno_sp_a_rich,
       answers = 10, 
       prior_for_answers = ethno_compet_sp_a_rich, 
       prior=-1) #keep a normal prior in this example with -1

ethno_sp_a_bayes_melt <-  ethno_sp_a_bayes %>%
  as.data.frame() %>%
  reshape2::melt()

#> No id variables; using all as measure variables

ggplot2::ggplot(ethno_sp_a_bayes_melt, aes(x = value, 
                y = variable, fill = variable)) +
                ggridges::geom_density_ridges() +
                ggridges::theme_ridges() + 
                theme(legend.position = "none")+
                labs(y= "", x = "Example ethno_bayes_consensus of use categories for sp_a")
#> Picking joint bandwidth of 0.00853

Summary

1. Install Rand Rstudio
2. Importing the dataset
3. Run functions
4. Use Ethnobotary R
5. Exploratory Data Analysis

Thank you for your coming!