Quantitative ethnobotany is the study of the use and importance of plants in human cultures, using statistical and analytical methods to quantify and interpret data. This approach has emerged as a key tool for understanding the cultural and ecological significance of plant species, and for evaluating the impact of human activities on plant communities and ecosystems.
The field of ethnobotany has a long history, with roots dating back to the early 20th century when researchers began to systematically study the relationships between humans and plants. In recent decades, advances in statistical and computational techniques have enabled researchers to more rigorously and systematically analyze and interpret ethnobotanical data, leading to a greater understanding of the complex and diverse ways in which plants are used and valued in different cultural and ecological contexts.
Today, quantitative ethnobotany is an interdisciplinary field that draws on a range of methods and approaches from the natural and social sciences, including anthropology, sociology, biology, and ecology. It is a rapidly growing field that is making significant contributions to our understanding of human-plant relationships, and to the conservation and management of plant biodiversity.
This manual offers an overview of the basic functions of the
ethnobotanyR package for generating figures and performing
some very basic quantitative ethnobotany analysis. It offers a general
overview and some arguments for and against the use of indices and
offers some guidance toward other options for quantitative ethnobotany
analysis. It also offers some handy and useful tools for exploratory
analysis of ethnobotany data. All analyses are done using the
ethnobotanyR package (Whitney
2022) in the R programming language (R
Core Team 2022).
ethnobotanyR packageThe ethnobotanyR package offers quantitative tools to
assess the cultural significance of plant species based on informant
consensus (Whitney 2022) 1. The package closely
follows two papers, one on cultural importance indices (Tardío 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 (U. de Albuquerque and Hurrell
2010) and social ecological theory (Ulysses Paulino Albuquerque et al. 2019). An
overview of this theoretical background will be helpful in understanding
approaches in ethnobotany and formulating useful research questions.
An example data set called ethnobotanydata is provided
to show how standard quantitative 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) 2.
| 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 |
Many of the functions in ethnobotanyR make use of
select() and filter_all() functions of the
dplyr package (Wickham et al.
2022) and pipe functions %>% from the
magrittr package (Bache and Wickham
2022). The intention is that these should be easy to understand
and use, and that they should allow users the chance to pull the code
for these functions and change anything they see fit.
Figures can help to visually represent the main ideas or key results
of ethnobotany work. A figure is a graphical representation of data or
concepts, and it can be an effective tool for conveying complex
information in a clear and concise manner. Figures are often used in
ethnobotany research to illustrate important points, highlight trends or
patterns in the data, or demonstrating results of statistical analyses.
There are several benefits to working on making good figures. A
meaningful and well formulated figure can help to make ethnobotany work
engaging, visually appealing and accessible to a wide audience. Overall,
generating a figure can be a valuable tool for effectively communicating
the key ideas and results of ethnobotany research. Here we walk through
a few of the functions within the ethnobotanyR package for
generating figures (Whitney 2022).
circlizeThe following chord plots are made using functions from the
circlize package (Gu 2022).
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")
For an example of the application of a chord diagram in ethnobotany research see Tabuti et al. (2022) on values and challenges for woody species in Uganda and Mon et al. (2020) on fig species use in Myanmar.
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)))
For an exaple of the alluvial plot in ethnobotany paper see Mon et al. (2020) on the traditional knowledge of edible fig tree species in Myanmar.
ethnobotanyR indices functionsEthnobotany indices are numerical measures that are used to quantify the importance of plant species in different cultural or ecological contexts. Some common ethnobotany indices are included here. These indices are often used to analyze ethnobotanical data and to compare the relative importance of different plant species within a particular culture or ecosystem.
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.
The 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 Tardío 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 Tardío 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 Tardío 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 Tardío 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 Ulysses P. 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-García 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-García 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.
For 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 2020)
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)
Despite their popularity, ethnobotany indices have been criticized for not accurately capturing the complexity and diversity of human-plant relationships. They may oversimplify or overlook important cultural or ecological factors. Additionally, ethnobotany indices are often based on subjective judgments and may be influenced by cultural biases or other factors that can affect the results of the analysis. Still, it is important to have an overview of ethnobotany indices, as they can provide useful insights into the role of plants in different cultural and ecological contexts. Additionally, understanding the background theory and limitations of these indices can help researchers to critically evaluate and interpret ethnobotanical data, and to design more accurate and comprehensive studies of human-plant interactions. Further exploratory methods may be helpful in understanding important relationships.
For the following examples we create some ethnobotany data for use in
ethnobotanyR.
eb_data <- data.frame(replicate(10,sample(0:1,20,rep=TRUE)))
names(eb_data) <- gsub(x = names(eb_data), pattern = "X", replacement = "Use_")
eb_data$informant <- sample(c('User_1', 'User_2', 'User_3'), 20, replace=TRUE)
eb_data$sp_name <- sample(c('sp_1', 'sp_2', 'sp_3', 'sp_4'), 20, replace=TRUE)Detrended correspondence analysis (DCA) and principal component analysis (PCA) are statistical techniques that can be useful for exploring patterns and trends in ethnobotany data. DCA is a multivariate statistical technique that is used to analyze patterns of species abundance or occurrence in relation to environmental variables, and to identify species groups or communities that are associated with different environmental conditions. PCA, on the other hand, is a statistical method that is used to identify the underlying structure of a dataset by reducing the number of variables and identifying the most important or influential factors.
Both DCA and PCA can be useful tools for exploratory analysis of ethnobotany data, as they can help to identify patterns and trends in the data that may not be immediately apparent. However, it is important for researchers to be aware of the limitations of these techniques and to use them with caution, as they can be sensitive to certain types of data and may not always produce reliable results.
In order to use these techniques effectively, it is important for
researchers to have access to programming tools that allow them to run
the analyses. The ethnobptanyR package offers a few handy
tools to make these work with standard quantitative ethnobotany data. By
having access to these tools, researchers can more easily and
efficiently run these analyses and incorporate the results into their
research projects.
Overall, it is important for researchers to be aware of tools like DCA and PCA and to have access to programming tools that allow them to run these analyses. These tools can be valuable tools for exploring and interpreting ethnobotany data, but they should be used with caution and within the confines of the predetermined research aims and objectives.
Detrended correspondence analysis (DCA) can be used to find the main
factors or ‘gradients’ in large and sparse community data sets. This can
be a useful tool for assessing important informant-species-use
associations in ethnobotany. Here we use the data set presented in
eb_data to perform DCA using the decorana()
function in the vegan package (Oksanen et al. 2022).
The first step is to remove zero value row sums. The DCA can only
work with data that is complete (i.e. we remove all NA cases as well as
those where the species is listed by an informant but no specific use is
recorded). This can be done quickly using the select() and
filter_all() functions of the dplyr package
(Wickham et al. 2022) and pipe
%>% functions of the magrittr package (Bache and Wickham 2022).
#remove rows with zero rowsums
ethno_data_complete <- eb_data %>%
dplyr::select(-informant, -sp_name) %>%
dplyr::filter_all(any_vars(!is.na(.)))%>%
dplyr::filter_all(any_vars(. != 0))Use the decorana function of the vegan
package (Oksanen et al. 2022) to get the
ordination results.
#Save ordination results
ethno_ordination <- vegan::decorana(ethno_data_complete) Ordination results can be shown with the command
summary() and the argument for the named ordination,
i.e. summary(ethno_ordination) in our case. Likewise,
ordination scores can be extracted with scores(). Calling
the plot() function on the ordination results will plot the
scores from the ordination.
#Plot ordination results
plot(ethno_ordination)
Principal component analysis (PCA) can be used to create sets of
linearly uncorrelated variables from cases of informant-species-use
associations. The resulting principal components are linear combinations
of variables that are fed in and each contain all observations. We
perform PCA using the broom (Robinson, Hayes, and Couch 2022),
tidyr (Wickham and Girlich
2022) and purrr (Henry and
Wickham 2022) packages using the nest() function
along with the mutate() and map() functions to
operate on the nested columns. This gives a tibble with one
row and three columns: 1. our original data set, 2. the
prcomp object and 3. a data frame of principal component
values for each observation.
nested_ethno_pca <- eb_data %>%
tidyr::nest(., data = everything()) %>%
dplyr::mutate(pca = purrr::map(data, ~ stats::prcomp(.x %>%
dplyr::select(-informant, -sp_name),
center = TRUE, scale = TRUE)),
pca_augmented = map2(pca, data, ~broom::augment(.x, data = .y)))For PCA model evaluation we can check to see how much variance is
explained by each principal component. This tells us how many of the
components would be reasonable to assess when analyzing the results. To
do this, we can use the data in the pca_augmented column of
our nested_ethno_pca tibble along with dplyr
(Wickham et al. 2022) and
magrittr (Bache and Wickham
2022) functions.
var_exp_tidy <- nested_ethno_pca %>%
tidyr::unnest(pca_augmented) %>%
dplyr::summarize_at(.vars = dplyr::vars(contains("PC", ignore.case = FALSE)), .funs = tibble::lst(var)) %>%
tidyr::gather(key = pc, value = variance) %>%
dplyr::mutate(var_exp = variance/sum(variance),
cum_var_exp = cumsum(var_exp),
pc = stringr::str_replace(pc, ".fitted", ""))The 10 principle components selected explain 1% of variance. As a general rule the first few principal components explain much of the variation. To keep the analysis within reasonable bounds it makes sense to select the first four principle components (this choice may change according to the analysis).
var_exp <- var_exp_tidy %>%
head(4)Plot the variance explained and the cumulative variance explained to see trends across components.
var_exp %>%
dplyr::rename(
`Variance Explained` = var_exp,
`Cumulative Variance Explained` = cum_var_exp
) %>%
tidyr::gather(key = key, value = value, `Variance Explained`:`Cumulative Variance Explained`) %>%
ggplot2::ggplot(ggplot2::aes(pc, value, group = key)) +
ggplot2::geom_point() +
ggplot2::geom_line() +
ggplot2::facet_wrap(~key, scales = "free_y") +
ggplot2::theme_minimal() +
ggplot2::lims(y = c(0, 1)) +
ggplot2::labs(y = "Variance",
title = "Variance explained by each principal component")
Quantitative ethnobotany offers a way to study the use and importance of plants in human cultures, using statistical and analytical methods to quantify and interpret data. This approach can be useful in a variety of contexts, including understanding the cultural and ecological significance of plant species, evaluating the impact of human activities on plant communities, and conserving plant biodiversity.
One of the main benefits of quantitative ethnobotany is that it allows researchers to rigorously and systematically analyze and interpret data, providing more reliable and objective insights into the role of plants in different cultural and ecological settings. By using standardized methods and statistical analyses, researchers can more accurately compare and contrast the importance of different plant species and evaluate the significance of their findings.
In addition, the development of a set of standard practices and guidelines for quantitative ethnobotany can be helpful for the field in several ways. First, standard practices can help to ensure the reliability and validity of research findings, by providing a consistent and transparent approach to data collection, analysis, and interpretation. Second, standard practices can facilitate the sharing and dissemination of research findings, by providing a common language and framework for communication and collaboration among researchers. Finally, standard practices can help to advance the field of ethnobotany by promoting the use of rigorous and transparent methods, and by encouraging the development of new and innovative approaches to understanding the role of plants in human cultures.
This work was written and formatted using the handy tools provided by
the knitr package (Xie
2022).
Please remember to cite this package if you use it in
your publications. Use the R command
citation("ethnobotanyR") to get the citation to the your
loaded version of the package.↩︎
The example ethnobotanydata is included
with the ethnobotanyR package but can also be downloaded
from GitHub https://github.com/CWWhitney/ethnobotanyR/tree/master/data.↩︎