Quantitative ethnobotany analysis with ethnobotanyR

Cory Whitney

2019-08-06

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. The package closely follows two papers, one by Tardio and Pardo-de-Santayana (2008) and another by Whitney et al. (2018). The goal is to provide an easy-to-use platform for ethnobotanists to calculate quantitative ethnobotany indices. Users are highly encouraged to read the theory papers of Albuquerque et al. (2019) and Gaoue et al. (2017). Both papers offer helpful guidance to understanding theoretical approaches in ethnobotany and developing meaningful hypotheses.

An example data set called ethnobotanydata is provided to show what the layout for standard ethnobotany data should be formatted to interface with the ethnobotanyR package. This is an ethnobotany data set including one column of 20 knowledge holder identifiers informant and one of 4 species names sp_name. The rest of the columns are the identified ethnobotany use categories. The data in the use categories is populated with counts of uses per person (should be 0 or 1 values). 1

First six rows of the example ethnobotany data included with ethnobotanyR
informant sp_name Use_1 Use_2 Use_3 Use_4 Use_5 Use_6 Use_7 Use_8 Use_9 Use_10
inform_a sp_a 0 0 1 0 0 0 0 1 1 0
inform_a sp_b 0 0 0 0 0 0 0 0 0 0
inform_a sp_c 0 0 0 0 0 1 1 0 0 0
inform_a sp_d 0 0 0 0 0 0 0 0 0 0
inform_b sp_a 0 1 1 0 0 1 0 0 1 0
inform_b sp_b 1 0 0 0 0 0 1 0 0 0

ethnobotanyR package functions

The use report URs() function calculates the use report (UR) for each species in the data set.

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

Use Report (UR) per species

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 \(^uNC\)) (see Prance et al. 1987).

The URsum() function calculates the sum of all ethnobotany use reports (UR) for all species in the data set (see Prance et al. 1987).

Cultural Importance (CI) index

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

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

CIs() is essentially URs() divided by the number of informants to account for the diversity of uses for the species (see Tardio and Pardo-de-Santayana 2008).

Frequency of Citation (FC) per species

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

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

FCs() is the sum of informants that cite a use for the species (see Prance et al. 1987).

Number of Uses (NU) per species

The NUs() function calculates the number of uses (NU) for each species in the data set.

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

\(NC\) are the number of use categories. NUs() is the sum of all categories for which a species is considered useful (see Prance et al. 1987).

Relative Frequency of Citation (RFC) index

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

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

\(FC_s\) 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 (see Tardio and Pardo-de-Santayana 2008).

Relative Importance (RI) index

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

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

\(RFC_{s(max)}\) 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).

Use Value (UV) index

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

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

UVs() is essentially the same as CIs() except that it starts with the sum of UR groupings by informants. \(U_i\) is the number of different uses mentioned by each informant \(i\) and \(N\) is the total number of informants interviewed in the survey (see Tardio and Pardo-de-Santayana 2008).

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

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

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

Cultural Value (CVe) for ethnospecies

The CVe() function calculates the cultural value (CVe) for ethnospecies. The index is one of three proposed for assessing the cultural, practical and economic dimensions (ethno) species importance. Reyes-Garcia et al. (2006) suggest several more indices but \(CV_e\) is the most commonly used from that study.

\[\begin{equation} CV_{e} = {Uc_{e}} \cdot{IC_{e}} \cdot \sum {IUc_{e}} \end{equation}\]

Where \(UC_e\) is the number of uses reported for ethnospecies \(e\) divided by all potential uses of an ethnospecies considered in the study. \(Ic_e\) expresses the number of informants who listed the ethnospecies \(e\) as useful divided by the total number of informants. \(IUc_e\) expresses the number of informants who mentioned each use of the ethnospecies \(e\) divided by the total number of informants (see Reyes-Garcia et al. 2006).

Fidelity Level (FL) per species

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.

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

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 (see Friedman et al. 1986).

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

Visualize ethnobotanyR results

The Radial_plot function shows these results as a radial bar plot for quick assessment of the result using the ggplot2 library.

Radial_plot can also be used to show several indices together.

ethnobotanyR chord diagrams with circlize

The following chord plots are made using functions from the circlize package. An example of the application of chord plots in ethnobotany is described by Whitney et al. (2018).

The ethnoChord() function creates a chord diagram of ethnobotany uses and species.

#> [1] "Chord diagram for each use (top half) related to each species (bottom half) in the data set"

The ethnoChordUser() function creates a chord diagram of informants and species uses for ethnobotany studies.

#> [1] "Chord diagram for each use (top half) related to each informant (bottom half) in the data set"

References

Albuquerque, Ulysses Paulino, Patricia Muniz de Medeiros, Washington Soares Ferreira Junior, Taline Cristina da Silva, Rafael Ricardo Vasconcelos da Silva, and Thiago Goncalves-Souza. “Social-Ecological Theory of Maximization: Basic Concepts and Two Initial Models.” Biological Theory, February 11, 2019. https://doi.org/10.1007/s13752-019-00316-8.

Albuquerque, Ulysses P., Reinaldo FP Lucena, Julio M. Monteiro, Alissandra TN Florentino, and Cecília de Fatima CBR Almeida. “Evaluating Two Quantitative Ethnobotanical Techniques.” Ethnobotany Research and Applications 4 (2006): 51–60. http://hdl.handle.net/10125/237.

Friedman, J., Z. Yaniv, A. Dafni, and D. Palewitch. “A Preliminary Classification of the Healing Potential of Medicinal Plants, Based on a Rational Analysis of an Ethnopharmacological Field Survey among Bedouins in the Negev Desert, Israel.” Journal of Ethnopharmacology 16, no. 2–3 (June 1986): 275–87.

Gaoue, Orou G., Michael A. Coe, Matthew Bond, Georgia Hart, Barnabas C. Seyler, and Heather McMillen. “Theories and Major Hypotheses in Ethnobotany.” Economic Botany 71, no. 3 (September 2017): 269–87. https://doi.org/10.1007/s12231-017-9389-8.

Prance, G. T., W. Baleé, B. M. Boom, and R. L. Carneiro. “Quantitative Ethnobotany and the Case for Conservation in Amazonia.” Conservation Biology 1, no. 4 (1987): 296–310.

Reyes-Garcia, V., T. Huanca, V. Vadez, and W. Leonard “Cultural, Practical, and Economic Value of Wild Plants: A Quantitative Study in the Bolivian Amazon.” Economic Botany, 2006.

Tardio, Javier, and Manuel Pardo-de-Santayana. “Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain)1.” Economic Botany 62, no. 1 (May 2008): 24–39. https://doi.org/10.1007/s12231-007-9004-5.

Whitney, Cory W., Joseph Bahati, and J. Gebauer. “Ethnobotany and Agrobiodiversity; Valuation of Plants in the Homegardens of Southwestern Uganda.” Ethnobiology Letters 9, no. 2 (2018): 90–100. https://doi.org/10.14237/ebl.9.2.2018.503.

  1. The example ethnobotanydata is included with the ethnobotanyR package but can also be downloaded from GitHub https://github.com/CWWhitney/ethnobotanyR/tree/master/data.↩