Please remember to cite this package if you use it in your publications.
citation("ethnobotanyR")
#>
#> To cite package 'ethnobotanyR' in publications use:
#>
#> Whitney C (2022). _ethnobotanyR: Calculate Quantitative Ethnobotany
#> Indices_. R package version 0.1.9,
#> <https://CRAN.R-project.org/package=ethnobotanyR>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {ethnobotanyR: Calculate Quantitative Ethnobotany Indices},
#> author = {Cory Whitney},
#> year = {2022},
#> note = {R package version 0.1.9},
#> url = {https://CRAN.R-project.org/package=ethnobotanyR},
#> }The ethnobotanyR package offers quantitative tools to
assess the cultural significance of plant species based on informant
consensus. The package closely follows two papers, one on cultural
importance indices (Tardio and Pardo-de-Santayana
2008) and another on agrobiodiversity valuation (Whitney, Bahati, and Gebauer 2018). The goal is
to provide an easy-to-use platform for ethnobotanists to perform
quantitative ethnobotany assessments. Users are highly encouraged to
familiarize themselves with ethnobotany theory (Albuquerque and Hurrell 2010) and social
ecological theory (Albuquerque et al.
2019). An overview of this theoretical background will be helpful
in understanding approaches in ethnobotany and formulating useful
research questions.
The standard quantitative ethnobotany indices are probably too narrow a tool for a proper assessment of human and ecological interactions of interest. Still, they can be a useful entry way into understanding some aspects of human populations and how they interact with nature. The steps required to calculate these indices offer a way to quantify intangible factors of how human communities interact with the world. They can come in handy as additive pieces for more holistic assessments and analyses.
An example data set called ethnobotanydata is provided
to show how 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
Many of the functions in ethnobotanyR make use of
select() and filter_all() functions of the
dplyr package (Wickham et al.
2019) and pipe functions %>% from the
magrittr package (Bache and Wickham
2014). These are easy to use and understand and allow users the
chance to pull the code for these functions and change anything they see
fit.
| 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 indices functionsThe use report URs() is the most basic ethnobotany
calculation. The function calculates the use report (UR) for each
species in the data set.
\[\begin{equation} UR_{s} = \sum_{u=u_1}^{^uNC} \sum_{i=i_1}^{^iN} UR_{ui} \end{equation}\]
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).
ethnobotanyR::URs(ethnobotanydata)
#> sp_name URs
#> 1 sp_c 52
#> 2 sp_a 43
#> 3 sp_d 43
#> 4 sp_b 36The URsum() function calculates the sum of all
ethnobotany use reports (UR) for all species in the data set (see Prance et al. 1987).
ethnobotanyR::URsum(ethnobotanydata)
#> [1] 174The 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).
ethnobotanyR::CIs(ethnobotanydata)
#> sp_name CI
#> 1 sp_c 2.60
#> 2 sp_a 2.15
#> 3 sp_d 2.15
#> 4 sp_b 1.80The 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).
ethnobotanyR::FCs(ethnobotanydata)
#> sp_name FCs
#> 1 sp_c 17
#> 2 sp_a 15
#> 3 sp_b 12
#> 4 sp_d 12The 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).
ethnobotanyR::NUs(ethnobotanydata)
#> sp_name NUs
#> 1 sp_c 8
#> 2 sp_d 8
#> 3 sp_a 7
#> 4 sp_b 7The 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).
ethnobotanyR::RFCs(ethnobotanydata)
#> sp_name RFCs
#> 1 sp_c 0.85
#> 2 sp_a 0.75
#> 3 sp_b 0.60
#> 4 sp_d 0.60The 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).
ethnobotanyR::RIs(ethnobotanydata)
#> sp_name RIs
#> 1 sp_c 1.000
#> 2 sp_a 0.879
#> 3 sp_d 0.853
#> 4 sp_b 0.790The 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).
ethnobotanyR::UVs(ethnobotanydata)
#> sp_name UV
#> 1 sp_c 2.60
#> 2 sp_a 2.15
#> 3 sp_d 2.15
#> 4 sp_b 1.80The 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).
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 (Reyes-Garcia et al. 2006).
\[\begin{equation} CV_{e} = {Uc_{e}} \cdot{IC_{e}} \cdot \sum {IUc_{e}} \end{equation}\]
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).
ethnobotanyR::CVe(ethnobotanydata)
#> Joining, by = "sp_name"
#> Joining, by = "sp_name"
#> sp_name CVe
#> 1 sp_c 1.768
#> 2 sp_a 1.129
#> 3 sp_d 1.032
#> 4 sp_b 0.756The 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 the plant for any purpose.
\[\begin{equation} FL_{s} = \frac {N_{s}*100}{FC_{s}} \end{equation}\]
where \(N_s\) is the number of informants that use a particular plant for a specific purpose, and \(FC_s\) is the frequency of citation for the species (see Friedman et al. 1986).
ethnobotanyR::FLs(ethnobotanydata)
#> sp_name Primary.use FLs
#> 1 sp_a Use_2 53.33
#> 2 sp_a Use_3 60.00
#> 3 sp_a Use_5 33.33
#> 4 sp_a Use_6 46.67
#> 5 sp_a Use_8 40.00
#> 6 sp_a Use_9 33.33
#> 7 sp_a Use_10 20.00
#> 8 sp_b Use_1 50.00
#> 9 sp_b Use_3 41.67
#> 10 sp_b Use_4 33.33
#> 11 sp_b Use_5 33.33
#> 12 sp_b Use_6 41.67
#> 13 sp_b Use_7 58.33
#> 14 sp_b Use_9 41.67
#> 15 sp_c Use_1 52.94
#> 16 sp_c Use_2 41.18
#> 17 sp_c Use_4 35.29
#> 18 sp_c Use_5 17.65
#> 19 sp_c Use_6 23.53
#> 20 sp_c Use_7 29.41
#> 21 sp_c Use_8 70.59
#> 22 sp_c Use_10 35.29
#> 23 sp_d Use_1 41.67
#> 24 sp_d Use_2 25.00
#> 25 sp_d Use_3 58.33
#> 26 sp_d Use_5 8.33
#> 27 sp_d Use_6 41.67
#> 28 sp_d Use_7 66.67
#> 29 sp_d Use_8 75.00
#> 30 sp_d Use_9 41.67Divide FLs by 100 to get the percent FL, as it is reported in some studies.
ethnobotanyR resultsFor quick assessments of differences between indices use the
Radial_plot function to show ethnobotanyR results as a
radial bar plot using the ggplot2 library. The
cowplot package (Wilke 2019)
can be useful for comparing several Radial_plot results for
easy comparison across indices.
URs_plot <- ethnobotanyR::Radial_plot(ethnobotanydata, ethnobotanyR::URs)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
NUs_plot <- ethnobotanyR::Radial_plot(ethnobotanydata, ethnobotanyR::NUs)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
FCs_plot <- ethnobotanyR::Radial_plot(ethnobotanydata, ethnobotanyR::FCs)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
CIs_plot <- ethnobotanyR::Radial_plot(ethnobotanydata, 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)
circlizeThe following chord plots are made using functions from the
circlize package (Gu et al.
2014). An example of the application of chord plots in
ethnobotany is described in a study on agrobiodiversity in Uganda (Whitney, Bahati, and Gebauer 2018).
The ethnoChord() function creates a chord diagram of
ethnobotany uses and species.
Chord_sp <- ethnobotanyR::ethnoChord(ethnobotanydata, by = "sp_name")
The ethnoChord() function can also be used to create a
chord diagram of ethnobotany uses and informants.
Chord_informant <- ethnobotanyR::ethnoChord(ethnobotanydata, by = "informant")
ggalluvialThe ethno_alluvial() function uses the
ggplot2 extension ggalluvial to make flow
diagrams. This may be a useful way to visualize frequency distributions
across uses, experts and use categories.
ethnobotanyR::ethno_alluvial(ethnobotanydata)
Generate the same plot with labels on the strata and without the legend.
# correct internal assignment for stat = "stratum"
StatStratum <- ggalluvial::StatStratum
ethnobotanyR::ethno_alluvial(ethnobotanydata, alpha = 0.2) +
ggplot2::theme(legend.position = "none") +
ggplot2::geom_label(stat = "stratum",
ggplot2::aes(label = ggplot2::after_stat(stratum)))
The example ethnobotanydata is included
with the ethnobotanyR package but can also be downloaded
from GitHub https://github.com/CWWhitney/ethnobotanyR/tree/master/data.↩︎