Package: ethnobotanyR 0.2.0

ethnobotanyR modeling functions | The non-parametric Bayesian bootstrap | Richer response data | References

Introduction | 1. The Appeal: Why Indices Exist and Why They're Seductive | 1.1 The Human Impulse to Quantify | 2. The Problems: A Harsh Critique | 2.1 False Precision | Example: | Worse: Confidence intervals hide the problem. | 2.2 Incommensurability Collapsed into Commensurability | 2.3 What Gets Counted vs. What Matters | 2.4 Power and Access Invisible | 2.5 Assumes Stable Preferences in Unstable Times | 2.6 Almost Zero Predictive Power | Evidence: | 3. Statistical Limitations (The Numbers Behind the Numbers) | 3.1 Confidence Intervals Are Usually Wide | 3.2 Power to Detect Differences Is Low | 3.3 Multiple Comparisons Problem | 3.4 Representativeness Is Usually Unknown | 4. Validity Questions: What Do Indices Actually Measure? | 4.1 Are You Measuring Use or Knowledge? | 4.2 Are You Measuring Frequency or Salience? | 4.3 Are You Measuring Importance or Accessibility? | 5. When Indices Are Actually Useful | 5.1 Describing Knowledge Distribution (Not Predicting Outcomes) | 5.2 Surfacing Disagreement and Variation | 5.3 Facilitating Dialogue (Not Decision-Making) | 5.4 Communicating to Non-Expert Audiences | 6. Responsible Reporting: How to Use Indices if You Use Them | 6.1 Always Report Confidence Intervals | Example: Calculating a Bayesian Credible Interval for Use Value in R | 6.2 Disaggregate by Stakeholder Type, Gender, Age, Wealth (At Minimum) | 6.3 Report Actual Frequencies, Not Just Percentages | 6.4 Avoid Causal or Predictive Language | 6.5 Name What You Don't Know | 7. When NOT to Use Indices | 7.1 Your Sample Size Is Less Than ~15 Per Group | 7.2 You're Making High-Stakes Decisions | 7.3 You Lack Important Context | 7.4 Different Interpretations of What's Being Cited | 8. The Paradox: Why ethnobotanyR Exists, and Why It's Critical | 8.1 The Attraction | 8.2 The Danger | 8.3 Our Responsibility | 9. Forward: What Robust Ethnobotanical Research Looks Like | 9.1 Quantify, but With Caveats | 9.2 Triangulate: Connect Knowledge to Actual Practice | 9.3 Model Under Uncertainty: Use Bayesian Methods | 9.4 Frame Decisions Explicitly | 9.5 Follow Up: Document Implementation and Outcomes | 10. Summary: The Principles | 11. References

Overview | Required packages | 1. Short motivation and data | 2. Example: encode TEK as Beta priors and sample CPTs | Simulated informant dataset (multivariate) | 3. Build a small BN and run Monte Carlo parameter sampling | 4. Decision rules and visualization | 5. Practical notes on elicitation and aggregation | 5.1 Realistic aggregation examples | Appendix: TEK Elicitation Form and Simulated Dataset | A. TEK Elicitation Form Template | Informant Metadata | Question 1: Management Impact (Binary) | Question 2: Current Resource Status (Categorical) | Question 3: Management Recommendation (Categorical) | Question 4: Informant Confidence (Rating) | Question 5: Contextual Notes | B. Complete Simulated Dataset for Reproducibility | Generate the simulated dataset | Using the simulated dataset with the aggregation methods | C. Guidance for Adapting Form and Data | D. Example: Loading Your Own Data

Introduction | 1. Setting Up: Load Libraries and Data | 2. Generate Simulated TEK Data for Fonio | 3. Calculate Use Values (UV) and Relative Importance (RI) | 4. Visualize Knowledge Distribution | 5. Identify Areas of Agreement and Disagreement | 6. Use TEK Data in a Decision-Framing Workshop | 6.1 Present Findings Honestly | 6.2 Facilitate Reflection, Not Extraction | 7. From Knowledge to Priorities: The Explicit Step | 8. Communicate Uncertainty and Limitations | 9. Disaggregate Results for Different Audiences | 10. Integrate ethnobotanyR Quantification into a Full Workflow | 11. Summary: ethnobotanyR as a Tool in Decision-Framing | What ethnobotanyR functions help with: | What ethnobotanyR functions do NOT do: | Best practice: | 12. References

Overview | 1. The Core Problem: Why Framing Matters | 2. Theoretical Foundations | 2.1 Decision Theory and Its Limits | 2.2 Participatory Ethics: Whose Process? Whose Framing? | 2.3 What Ethnobotany Contributes | 3. Epistemological Questions You Must Answer First | 3.1 Who Are "the Community"? | 3.2 Whose Decision Is It, Really? | 3.3 What Counts as a Better Decision? | 3.4 What Happens After the Workshop? | 4. When Does Decision-Framing Help? When Do They Obscure? | 4.1 Conditions Where Structured Framing Helps | 4.2 Conditions Where Decision-Framing Obscures | 5. Case Illustrations: Success, Partial Success, and Failure | 5.1 Benin: Fonio Value-Chain Framing (Partial Success) | 5.2 Parallel Case: Cocoa Agroforestry in Ghana (Partial Failure) | 5.3 Contrasting Case: Internally Driven Adaptation in Madagascar (Clearer Success) | 5.4 Case of Near-Total Failure: "Participatory" Conservation That Ignored Local Property Claims (Central Africa) | 6. Structural Constraints: What No Framing Can Solve | 6.1 Economic Constraints | 6.2 Institutional Constraints | 6.3 Ecological and Climate Constraints | 6.4 Political Constraints | The Core Truth | 7. Principles for Defensible Decision-Framing in Community-Based Ethnobotany | 7.1 Be Radically Honest About What You're Doing | 7.2 Distinguish Process from Outcomes | 7.3 Check Your Assumption About Knowledge | 7.4 Leave Room for Partial Failure | 7.5 Commit to Follow-Up | 7.6 Make Power Visible | 8. When Should You NOT Conduct a Decision-Framing Exercise? | 9. Pedagogical Framework: Using Ethnobotany Data in Decisions | 9.1 Elicit TEK Systematically | 9.2 Translate TEK into Decision-Relevant Information | 9.3 Communicate Uncertainty Explicitly | Extended Example: Ethnobotany in Benin Fonio Case | 10. Summary: Decision-Framing as Tool, Not Solution | What decision-framing can do: | What it cannot do: | The ethical stance: | 11. References and Further Reading | Appendix: Benin Case Study Details | Interventions Prioritized (from workshop voting) | Major Objectives Endorsed | Key Observation from Disaggregation

Introduction | 1. Context: Fonio in Benin | 1.1 The Crop and Its Significance | 1.2 Development Context | 2. The Participatory Prioritization Workshop | 2.1 Design and Participants | 2.2 Outputs: Interventions and Objectives | Prioritized Interventions (in order of stakeholder consensus votes): | Endorsed Objectives (what these interventions should achieve): | 3. What the Workshop Achieved: Disaggregating the Data | 3.1 Geographic Variation | 3.2 Gender Differentiation | 3.3 Stakeholder-Role Variation | 4. What the Workshop Does NOT Tell Us | 4.1 Post-Workshop Implementation: What Actually Happened? | 4.2 Representativeness of Participants | 4.3 Quality of Voting Data | 4.4 Why Did These Objectives Matter? | 5. Structural Constraints: What the Workshop Didn't Address | 5.1 Land Tenure and Access | 5.2 Credit and Capital | 5.3 Markets and Prices | 5.4 Climate Uncertainty | 5.5 Policy Environment | 6. What Went Well: Genuine Insights | 6.1 Brought Diverse Voices to the Same Table | 6.2 Made Implicit Knowledge Explicit | 6.3 Created Space for Strategic Thinking | 6.4 Produced a Concrete Priority List for Decision-Makers | 7. Lessons for Other Contexts: Transferable Principles vs. Context-Specific Details | 7.1 What Transfers (Methods & Principles) | 7.2 What Doesn't Transfer | 8. Follow-Up Questions for a Stronger Case Study | 9. Benin Within the Broader Decision-Framing Framework | 10. How to Use the Benin Case in Practice | 10.1 Use Benin as Inspiration, Not Template | 10.2 Clarify Your Own Framing Choices | 10.3 Plan for Follow-Up | 10.4 Disaggregate Data | 11. References

A Critical Perspective on Ethnobotany Indices | using the ethnobotanyR package | Working with figures | Chord diagrams with circlize | Flow diagrams with ggalluvial | ethnobotanyR indices functions | Use Report (UR) per species | Cultural Importance (CI) index | Frequency of Citation (FC) per species | Number of Uses (NU) per species | Relative Frequency of Citation (RFC) index | Relative Importance (RI) index | Use Value (UV) index | Cultural Value (CVe) for ethnospecies | Fidelity Level (FL) per species | Plot all indices | Exploratory analyses | Ordination methods | Detrended correspondence analysis (DCA) | Principal component analysis (PCA) | Conclusions | Acknowledgements | References