Marwan Setiawan, BRIN

Please remember to cite the ethnoecologyR package if you use it in your publications. Use citation("ethnoecologyR") to get a citation for the latest version.

To cite package ‘ethnoecologyR’ in publications use:

Marwan Setiawan (2026). ethnoecologyR: Quantitative Ethnoecology Analysis and Landscape Knowledge Modeling. R package version 0.1.2.

A Critical Perspective on Ethnoecology Indices

Quantitative ethnoecology is the study of the use and importance of landscape units and ecosystems 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 landscape units, and for evaluating the impact of human activities on ecosystems.

While widely used in the literature, traditional ethnoecology indices have significant limitations that researchers should consider:

• Oversimplification: Complex cultural-ecological relationships reduced to single numbers
• Context stripping: Remove cultural, ecological, and historical context
• Methodological issues: Sensitive to sampling design and interview methods
• Theoretical concerns: Often lack strong theoretical foundations

Using the ethnoecologyR package

The ethnoecologyR package offers quantitative tools to assess the cultural significance of landscape units based on informant consensus. The package closely follows several key papers on ethnoecology indices (Tardío and Pardo-de-Santayana 2008; Friedman et al. 1986; Reyes-García et al. 2006; Toledo 2002). The goal is to provide an easy-to-use platform for ethnoecologists to perform quantitative ethnoecology assessments.

Users are highly encouraged to familiarize themselves with ethnoecology theory (Berkes 2012; Toledo 2002) and social-ecological systems theory (Ostrom 2009). An overview of this theoretical background will be helpful in understanding approaches in ethnoecology and formulating useful research questions.

An example data set called ethnoecology_data is provided to show how standard quantitative ethnoecology data should be formatted to interface with the ethnoecologyR package. This is an ethnoecology data set including one column of 15 knowledge holder identifiers informant and one of 6 landscape unit names landscape_unit. The rest of the columns are the identified ethnoecology use categories. The data in the use categories is populated with counts of uses per person (should be 0 or 1 values).

data(ethnoecology_data)
head(ethnoecology_data)
#>   informant landscape_unit use_forest use_water use_agriculture use_hunting
#> 1    Info_1    Landscape_1          1         1               0           0
#> 2    Info_1    Landscape_2          1         0               1           0
#> 3    Info_1    Landscape_3          1         0               1           0
#> 4    Info_1    Landscape_4          1         1               0           1
#> 5    Info_1    Landscape_5          1         0               0           0
#> 6    Info_1    Landscape_6          0         1               1           1
#>   use_fishing use_gathering
#> 1           0             0
#> 2           0             0
#> 3           0             1
#> 4           0             1
#> 5           0             0
#> 6           0             1

Many of the functions in ethnoecologyR make use of select() and filter() functions of the dplyr package (Wickham et al. 2026) and pipe functions %>% from the magrittr package (Bache and Wickham 2025). 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.

Ethnoecology indices functions

Ethnoecology indices are numerical measures that are used to quantify the importance of landscape units in different cultural or ecological contexts. Some common ethnoecology indices are included here. These indices are often used to analyze ethnoecological data and to compare the relative importance of different landscape units within a particular culture or ecosystem.

The standard quantitative ethnoecology 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.

LUs(ethnoecology_data)
#> # A tibble: 6 × 2
#>   landscape_unit    LU
#>   <chr>          <int>
#> 1 Landscape_3       51
#> 2 Landscape_2       49
#> 3 Landscape_1       44
#> 4 Landscape_4       44
#> 5 Landscape_6       43
#> 6 Landscape_5       39

CIEs(ethnoecology_data)
#> # A tibble: 6 × 4
#>   landscape_unit total_informants total_uses   CIE
#>   <chr>                     <int>      <int> <dbl>
#> 1 Landscape_3                  15         51  3.4 
#> 2 Landscape_2                  15         49  3.27
#> 3 Landscape_1                  15         44  2.93
#> 4 Landscape_4                  15         44  2.93
#> 5 Landscape_6                  15         43  2.87
#> 6 Landscape_5                  15         39  2.6

FCs_eco(ethnoecology_data)
#> # A tibble: 6 × 2
#>   landscape_unit    FC
#>   <chr>          <int>
#> 1 Landscape_1       15
#> 2 Landscape_2       15
#> 3 Landscape_3       15
#> 4 Landscape_4       15
#> 5 Landscape_5       15
#> 6 Landscape_6       15

FLs_eco(ethnoecology_data, "use_forest")
#> # A tibble: 6 × 4
#>   landscape_unit total_informants specific_informants    FL
#>   <chr>                     <int>               <int> <dbl>
#> 1 Landscape_1                  15                  13  86.7
#> 2 Landscape_3                  15                  13  86.7
#> 3 Landscape_2                  15                  12  80  
#> 4 Landscape_5                  15                  11  73.3
#> 5 Landscape_6                  15                  11  73.3
#> 6 Landscape_4                  15                   6  40

NUs_eco(ethnoecology_data)
#> # A tibble: 6 × 2
#>   landscape_unit    NU
#>   <chr>          <int>
#> 1 Landscape_1        6
#> 2 Landscape_2        6
#> 3 Landscape_3        6
#> 4 Landscape_4        6
#> 5 Landscape_5        6
#> 6 Landscape_6        6

CVe_eco(ethnoecology_data)
#> # A tibble: 6 × 4
#>   landscape_unit    LU    FC   CVe
#>   <chr>          <int> <int> <dbl>
#> 1 Landscape_3       51    15  3.4 
#> 2 Landscape_2       49    15  3.27
#> 3 Landscape_1       44    15  2.93
#> 4 Landscape_4       44    15  2.93
#> 5 Landscape_6       43    15  2.87
#> 6 Landscape_5       39    15  2.6

Working with figures

Figures can help to visually represent the main ideas or key results of ethnoecology work. Here we walk through a few of the functions within the ethnoecologyR package for generating figures.

Chord diagrams with circlize

The following chord plots are made using functions from the circlize package (Gu 2022). An example of the application of chord plots in ethnoecology is described in a study on agrobiodiversity in Uganda (Whitney, Bahati, and Gebauer 2018).

The ethno_landscape_chord() function creates a chord diagram of ethnoecology uses and landscape units.

ethno_landscape_chord(ethnoecology_data, by = "landscape_unit")

Chord diagram of landscape units and uses

The function can also be used to create a chord diagram of ethnoecology uses and informants.

ethno_landscape_chord(ethnoecology_data, by = "informant")

Chord diagram of informants and uses

Alluvial plots with ggalluvial

The ethno_landscape_alluvial() function creates an alluvial diagram showing flows between informants, landscape units, and ecological uses.

ethno_landscape_alluvial(ethnoecology_data)

Alluvial plot of landscape uses

Radial plot with ggplot2

The landscape_radial_plot() function creates a radial bar plot to visualize multiple ethnoecology indices per landscape unit.

landscape_radial_plot(ethnoecology_data, list(LUs, CIEs, FCs_eco))
#> Warning: Unknown or uninitialised column: `index`.
#> Unknown or uninitialised column: `index`.
#> Unknown or uninitialised column: `index`.
#> Warning: Removed 12 rows containing missing values or values outside the scale range
#> (`geom_col()`).

Radial plot of multiple ethnoecology indices

Bayesian Consensus Modeling

The ethnoecologyR package provides a Bayesian model (ethno_bayes_land) to estimate consensus among informants regarding landscape use and ecological knowledge. The model is fitted using Hamiltonian Monte Carlo via rstan (Stan Development Team 2024).

fit <- ethno_bayes_land(ethnoecology_data, iter = 500, chains = 2)
print(fit, pars = c("theta[1]", "theta[2]", "theta[3]"))

Conclusions

Quantitative ethnoecology offers a way to study the use and importance of landscape units 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 landscape units
• Evaluating the impact of human activities on ecosystems
• Informing conservation and management strategies
• Documenting local ecological knowledge (LEK)

One of the main benefits of quantitative ethnoecology is that it allows researchers to rigorously and systematically analyze and interpret data, providing more reliable and objective insights into the role of landscape units in different cultural and ecological settings.

Overall, the ethnoecologyR package provides a comprehensive set of tools for researchers and practitioners working in ethnoecology and related fields. By using these standardized methods and statistical analyses, researchers can more accurately compare and contrast the importance of different landscape units and evaluate the significance of their findings.

Acknowledgements

This work was inspired by the pioneering contributions of: - Cory Whitney and the ethnobotanyR package - Tardío and Pardo-de-Santayana (2008) on cultural importance indices - Friedman et al. (1986) on fidelity level - Reyes-García et al. (2006) on cultural value - Berkes (2012) on sacred ecology

The vignette was written and formatted using the handy tools provided by the knitr package (Xie 2025).

References

Bache, Stefan Milton, and Hadley Wickham. 2025. Magrittr: A Forward-Pipe Operator for R. https://magrittr.tidyverse.org.

Berkes, F. 2012. Sacred Ecology. 3rd ed. Routledge.

Friedman, J., Z. Yaniv, A. Dafni, and D. Palewitch. 1986. “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(2-3): 275-287.

Gu, Z. 2022. circlize: Circular Visualization. R package version 0.4.15.

Ostrom, E. 2009. “A General Framework for Analyzing Sustainability of Social-Ecological Systems.” Science 325(5939): 419-422.

R Core Team. 2025. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

Reyes-García, V., T. Huanca, V. Vadez, W. Leonard, and D. Wilkie. 2006. “Cultural, Practical, and Economic Value of Wild Plants: A Quantitative Study in the Bolivian Amazon.” Economic Botany 60(1): 62-74.

Stan Development Team. 2024. RStan: The R Interface to Stan. R package version 2.32.6.

Tardío, J., and M. Pardo-de-Santayana. 2008. “Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain).” Economic Botany 62(1): 24-39.

Toledo, V. M. 2002. “Ethnoecology: A Conceptual Framework for the Study of Indigenous Knowledge of Nature.” In Ethnobiology and Biocultural Diversity, edited by J. R. Stepp, F. S. Wyndham, and R. K. Zarger, 511-522. University of Georgia Press.

Whitney, C. W., J. Bahati, and J. Gebauer. 2018. “Ethnobotany and Agrobiodiversity: Valuation of Plants in the Homegardens of Southwestern Uganda.” Ethnobiology Letters 9(2): 1-12.

Wickham, H., et al. 2026. dplyr: A Grammar of Data Manipulation. R package version 1.1.0.

Wickham, H., and L. Henry. 2026. purrr: Functional Programming Tools. R package version 1.0.0.

Wickham, H., D. Vaughan, and M. Girlich. 2025. tidyr: Tidy Messy Data. R package version 1.3.0.

Xie, Y. 2025. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.45.