DOI: 10.5061/dryad.w6m905qph
title: Methodological advances for hypothesis‐driven ethnobiology

description: Ethnobiology as a discipline has evolved recently to increasingly embrace
 theory-inspired and hypothesis driven approaches to study why and how
 local people choose plants and animals they interact with and use for
 their livelihood. However, testing complex hypotheses or a network of
 ethnobiological hypotheses is challenging, particularly for datasets with
 non-independent observations due to species phylogenetic relatedness or
 socio-relational links between participants. Further, to fully account for
 the dynamics of local ecological knowledge, it is important to account for
 the spatially explicit distribution of knowledge, the changes in
 knowledge, knowledge transmission and use. To promote the use of advanced
 statistical modeling approaches that address these limitations, we
 synthesize methodological advances for hypothesis-driven research in
 ethnobiology while highlighting the need for more figures than tables and
 more tables than text in ethnobiological literature. We present the
 ethnobiologicalmotivations for conducting generalized linear mixed-effect
 modeling, structural equation modeling, phylogenetic generalized least
 squares, social network analysis, species distribution modeling, and
 predictive modeling. For each element of the proposed ethnobiologists
 quantitative toolbox, we present practical applications along with script
 for a widespread implementation. Because these statistical modeling
 approaches are rarely taught in most ethnobiological programs but are
 essential for careers in academia or industry, it is critical to promote
 specialized workshops organized during annual professional meetings and
 focused on these advanced methods. By embracing these quantitative
 modeling techniques without sacrificing qualitative approaches which
 provide essential context, ethnobiology will further progress toward an
 expansive interaction with other disciplines.
description: These datasets include portions of data from authors'
 research used for illustrative purpose, data from published
 sources. Qian, H. & Y. Jin. (2016) An updated
 megaphylogeny of plants, a tool for generating plant phylogenies and an
 analysis of phylogenetic community structure. Journal of Plant Ecology
 9(2): 233–239 Coe MA and Gaoue OG(2021) Phylogeny
 explains why therapeutically redundant plant species are not necessarily
 facing greater use pressure. People and
 Nature, DOI:10.1002/pan3.10216 Bond, Matthew; Gaoue,
 Orou (2020), Adjacency matrices and nodal attributes for prestige and
 homophily predict network structure for social learning of medicinal plant
 knowledge, Dryad,
 Dataset, https://datadryad.org/stash/dataset/doi:10.5061/dryad.cfxpnvx3q 
description: 1. R Scripts This includes 6 independent R
 scripts developed to show how to develop and implement advanced
 statistical models in ethnobiology.
 <strong>Code R1.</strong> R script for generalized
 linear model and comparison with generalized linear mixed effect
 models. <strong>Code R2.</strong> R script
 for using piecewiseSEM to develop structural equation model of the
 influence of socio-demographic traits and urbanization on local people
 knowledge of plants.
 <strong>Code R3.</strong> R script for phylogenetic
 general least square (PGLS) testing the effect of controlling for species
 phylogenetic relatedness on the effects of species preference and
 therapeutic redundancy on use pressure. This script calls the
 “PhyloMaker.R” function which is included.
 <strong>Code R4.</strong> R script for phylogenetic
 signal test for species use pressure, preference and therapeutic
 redundancy across plant species used by local people.
 <strong>Code R5. </strong>R script for social network
 analysis using exponential random graph modeling (ERGM) to investigating
 the role of homophily in shaping knowledge distribution.
 <strong>Code R6. </strong>R script to implement
 train/test split and cross-validation for ethnobiological data.
 2. Datasets To use these R scripts, you need
 the following datasets <strong>Dataset 1:</strong> Data on the effect of socio-demographic traits and urbanization on local people knowledge of plants (data_1_ethno.csv). This data includes 144 observations for 9 variables including “nb_species”, the number of plants freelisted by an individual. This dataset is used for analyses in Code R1 and R2. <strong>Dataset 2: </strong>This dataset is used for analyses in Code R3 and R4. Data on the list of plant species and their family to build the phylogeny (data_2_phylo.csv) Phylogeny which is pruned from the general phylogeny (data_3_shipibo_phylo.nex) Ethnobiological data for which one which to conduct PGLS (data_4_shipibo_redundancy.csv) To build the phylogeny used for the PGLS source data and function from Qian & Jin (2016). This includes: The general phylogeny (Qian_PhytoPhylo.tre) The nodes (Qian_nodes.csv) The R function (Qian_S.phyloMaker.R) <strong>Dataset 3:</strong> This dataset is used for Code S4 To test for the phylogenetic signal you need: Data on the phylogeny constructed for the plant list, here as an R script (data_5_shipibo_tree.txt) Ethnobiological data that includes a column of the trait(s) or variable(s) for which one wants to test for signal (data_4_shipibo_redundancy.csv) Ethnobiological data for the NRI and NTI tests (data_6_ethno_shipibo.txt) <strong>Dataset 4: </strong> Data available at: Bond, Matthew; Gaoue, Orou (2020), Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge, Dryad, Dataset, https://datadryad.org/stash/dataset/doi:10.5061/dryad.cfxpnvx3q  Directed and asymmetric adjacency matrix of incoming knowledge sharing (data_7_adj_knowledge.csv) Undirected and symmetric adjacency matrix of spouses (data_8_adj_married.csv) Node attributes (data_9_node_attributes.csv) <strong>Dataset 5:</strong> Data available at: https://archive.ics.uci.edu/ml/datasets/census+income Census income data set (adult.csv) used here to illustrate how test/split analysis can be done in ethnobiology.