1.1 The Human Impulse to Quantify

Ethnobotany is fundamentally about people’s relationships with plants. That’s qualitative, complex, embedded in culture and history.

But in academic and policy contexts, you face pressure to measure, rank, compare. Numbers feel objective. A Use Value of 0.67 sounds more scientific than “most people know about this plant, but what that means varies.”

Indices exist because: - Funders and policymakers want numbers. “Biodiversity is important” is vague; “this ecosystem provides value to 1.2 million people” is actionable. - Comparison requires a common scale. If you’re studying 30 plant species, you need a way to ask: which are most important to the community? - Quantification looks like rigor. When you report “Use Value = 0.63 ± 0.12,” you appear scientific and defensible. (You’re not, but you appear to be.) - Methods feel replicable. “Calculate UV as (number of citations) / (number of respondents)” is clear and cookbook-style. Qualitative description seems subjective.

These are reasonable impulses. But they collide with reality.

2.1 False Precision

The core problem: An index compresses multidimensional, context-dependent knowledge into a single number.

2.2 Incommensurability Collapsed into Commensurability

Different “uses” of a plant are not commensurate categories, yet indices treat them as if they are.

2.3 What Gets Counted vs. What Matters

An index counts citations. But citation frequency ≠ use frequency ≠ importance ≠ ecosystem impact ≠ livelihood dependence.

2.4 Power and Access Invisible

An index assumes all knowledge is equally valid and all people have equal access to plants.

They don’t.

2.5 Assumes Stable Preferences in Unstable Times

Indices are calculated at a moment in time. But knowledge, use, and preferences change:

• Market shocks: A traditionally used crop becomes too expensive; people switch to cheaper alternatives
• Climate change: A plant that thrived in traditional climate becomes unreliable; knowledge about it becomes outdated
• Policy changes: A medicinal plant is banned; knowledge is suppressed (even if practice continues underground)
• Social change: Younger generations have different knowledge and values than elders; practices are forgotten

An index says: “At this moment, this is what people know and use.”

But within months or years, that snapshot may be obsolete. Indices create false stability.

2.6 Almost Zero Predictive Power

Here’s the critical failure: Quantitative ethnobotany indices don’t predict conservation outcomes, livelihood impacts, or policy success.

3.1 Confidence Intervals Are Usually Wide

With typical ethnobotany sample sizes (n = 20-50 informants), confidence intervals around indices are wide.

3.2 Power to Detect Differences Is Low

Most ethnobotany papers don’t report statistical power. If they did, they’d find:

• To reliably detect a difference of UV = 0.60 vs. 0.40 with α = 0.05 and 80% power, you need n ≈ 56 per group
• Most ethnobotany studies have smaller samples
• Conclusion: Many published “differences” in UV between groups are not statistically reliable

3.3 Multiple Comparisons Problem

If you calculate indices for 30 plant species across 10 categories of use, that’s 300 different statistics. Even if there’s no real pattern, random noise will produce some “significant” differences by chance.

Few ethnobotany papers correct for multiple comparisons. They report the apparent findings without acknowledging the multiple testing inflation.

3.4 Representativeness Is Usually Unknown

Who did you interview? - Gender balance? (Usually weighted toward older males, who are assumed “elders”) - Wealth representation? (Wealthier people often more accessible; poorer farmers harder to reach) - Age range? (Youth often underrepresented; dying knowledge is often overrepresented) - Geographic coverage? (Easy to over-sample in villages near roads; remote areas undersampled)

Yet indices are reported as if they represent “the community.” They usually represent “the people we could conveniently access.”

4.1 Are You Measuring Use or Knowledge?

Use Value is based on citation frequency in interviews. But citations measure knowledge, not actual use.

• Someone might cite a use they heard about but never practice
• Someone might practice a use but forget to mention it (salience bias)
• Someone might hide use due to stigma or fear (social desirability bias)

4.2 Are You Measuring Frequency or Salience?

People cite plants they think about frequently, not necessarily plants they use most.

• A medicinal plant used once in emergencies might be highly salient (memorable) and get many citations
• A staple food eaten every day might be so routine people forget to mention it
• A ceremonial plant might be cited for its cultural weight, not its use frequency

Use Value captures salience, not objective use.

4.3 Are You Measuring Importance or Accessibility?

A plant might be cited by many people not because it’s important but because it’s available.

• If everyone lives near a particular tree, they all know about it and cite it
• That creates high UV, but it doesn’t mean the tree is uniquely important
• It means it’s locally abundant

Yet the index doesn’t distinguish between “important” and “available.”

5.1 Describing Knowledge Distribution (Not Predicting Outcomes)

What indices can do: Show which plants/uses are widely known vs. specialized knowledge.

5.2 Surfacing Disagreement and Variation

The real value of indices is not the single number but what variation around it reveals.

If Use Value for a medicinal plant averages 0.60 across the community, but disaggregating shows: - Older women = 0.85 - Younger women = 0.40 - Men = 0.50

You’ve just discovered something important: Knowledge is not evenly distributed. Gender and age matter. This variation is actionable for program design.

Indices are useful for forcing yourself to disaggregate and show variation, not for producing a comparable single metric.

5.3 Facilitating Dialogue (Not Decision-Making)

In a workshop, if you present “Use Value of 0.67,” people hear: “This plant is 67% important—it’s scientific.”

That’s misleading. But if you present: “14 of 20 community members cited this plant; 11 of 14 women cited it as nutritious, but only 5 of 9 men did,” you’ve opened a conversation.

The index becomes a prompt for dialogue, not the answer itself.

This is honest use of indices: - Calculate them - Report why different groups cite differently - Let people discuss what that variation means - Make decisions about the variation, not based on the index

5.4 Communicating to Non-Expert Audiences

Sometimes you need to explain complex knowledge to people unfamiliar with ethnobotany. A simple number can be a communication tool.

“68% of interviewed farmers recognize this species’ nutritional value” is more accessible than a 20-page qualitative description.

But add the caveats: Sample size, what “recognize” means, which farmers, what changed afterward.

6.1 Always Report Confidence Intervals

Don’t:

Use Value of Digitaria exilis = 0.73

Do:

Use Value of Digitaria exilis = 0.73 (95% CI: 0.51–0.88, n = 20 informants)

The confidence interval shows you’re aware that the point estimate is uncertain. It invites readers to see the margin of error.

# Suppose 12 citations out of 20 informants
k <- 12  # citations
n <- 20  # informants

# Posterior parameters for Beta(1,1) prior
alpha_post <- k + 1
beta_post <- n - k + 1

# Posterior mean
mean_uv <- alpha_post / (alpha_post + beta_post)
# mean
mean_uv

## [1] 0.5909091

# 95% credible interval
ci_uv <- qbeta(c(0.025, 0.975), alpha_post, beta_post)

# upper 
ci_uv[1]

## [1] 0.3843544

# lower 
ci_uv[2]

## [1] 0.7818031

6.2 Disaggregate by Stakeholder Type, Gender, Age, Wealth (At Minimum)

Don’t:

Use Value of medicinal plants ranges from 0.42 to 0.89

Do:

Use Value of medicinal plants differs markedly by informant type:
- Healers: 0.85 (7 of 8; 95% CI: 0.55–0.96)
- Farmers: 0.62 (8 of 13; 95% CI: 0.40–0.79)
- Traders: 0.40 (2 of 5; 95% CI: 0.11–0.74)

This variation suggests specialized knowledge among healers. Conservation efforts targeting medicinal plants should engage healers directly, as they hold knowledge others may not have.

Now the index becomes a springboard for insight, not a false consensus.

6.3 Report Actual Frequencies, Not Just Percentages

Don’t:

85% of respondents cited nutritional use

Do:

17 of 20 respondents cited nutritional use (85%; 95% CI: 67–95%)

Raw numbers force readers to see sample sizes. They’re less misleading than percentages alone.

6.4 Avoid Causal or Predictive Language

Don’t:

High Use Value (0.78) indicates this plant is critical for community nutrition.
Therefore, conservation should prioritize this species.

Do:

16 of 20 respondents cited nutritional uses of this plant (Use Value = 0.78; 95% CI: 0.62–0.88).
However, actual consumption depends on market prices, land access, and dietary preferences—all of which vary. 
To assess conservation priority, we would need to document: (1) actual consumption patterns, 
(2) population trends of the wild species, (3) sustainability of current harvest levels, and 
(4) substitutability if the species becomes unavailable.
This index contributes one piece of that picture; it is not sufficient for strategy alone.

This frames the index as what it is: one input among many, not the decision itself.

6.5 Name What You Don’t Know

Don’t hide the limitations. Name them:

Limitations of this analysis:
- Sample size (n = 20) produces wide confidence intervals; differences between groups 
  may not be statistically reliable.
- Participants were selected through convenience sampling; results may overrepresent 
  accessible community members and underrepresent those unable to attend interviews.
- Data collection was cross-sectional (single point in time); seasonal, annual, or 
  multi-year variation in use is not captured.
- The analysis describes what informants said they know and use; actual consumption 
  patterns may differ.
- Citation frequency reflects salience, not necessarily frequency of use.

Being honest about limitations is not weakness; it’s credibility.

7.1 Your Sample Size Is Less Than ~15 Per Group

With n = 10, confidence intervals are so wide that rankings are meaningless. You’re reporting noise as signal.

Instead: Describe patterns qualitatively. Quote informants. Show the range of knowledge and practice.

7.2 You’re Making High-Stakes Decisions

If you’re allocating millions in conservation funding, designing policy, or determining land use, don’t rest that on indices.

Use indices as one input in a larger decision analysis that accounts for: - Actual conservation outcomes (species population trends, ecosystem health) - Livelihood impacts (income, food security, gender equity) - Feasibility (cost, institutional capacity, market conditions) - Values (whose priorities are you serving?)

7.3 You Lack Important Context

If you don’t understand: - Why communities use plants the way they do (social, economic, historical context) - What constraints shape actual practice (poverty, policy, climate) - Power dynamics in the community (who influenced knowledge circulation, whose views dominant)

Don’t pretend an index captures that. Indices will mislead you without context.

7.4 Different Interpretations of What’s Being Cited

If people in your study have fundamentally different ideas about what they’re using a plant for, aggregating citations is misleading.

Example: - Farmer A: “I use this plant as a nitrogen-fixing crop.” - Farmer B: “I use this plant as fodder.” - Farmer C: “I use it for ceremonies; it’s not really food.”

Same plant; different uses; different meanings. Aggregating into a single citation obscures this diversity.

Better approach: Show the different uses separately. Let the variation speak for itself.

8.1 The Attraction

ethnobotanyR makes it easy to calculate indices. That’s useful: - Accessibility: Non-statisticians can compute complex measures - Standardization: Everyone uses the same formula; results are comparable - Transparency: Code is visible; calculations are reproducible

These are genuine goods.

8.2 The Danger

Easy computation becomes incentive to compute without thought: - Researchers calculate UV for 50 species without understanding what variation means - Results get published as fact (“Use Value = 0.62”) without caveats - Policy makers read the number and make decisions - Communities are affected by decisions resting on incomplete understanding

The package’s accessibility paradoxically increases the risk of misuse.

8.3 Our Responsibility

As maintainers and users of ethnobotanyR, we bear responsibility:

• We should not encourage false precision. Every function in the package should include documentation of limitations.
• We should make disaggregation the default, not an afterthought. If you calculate an index, calculate it by subgroup, not just overall.
• We should teach via examples that show responsible and irresponsible use. Make the consequences of misuse clear.
• We should steer users toward robust methods (Bayesian modeling, decision analysis, qualitative synthesis) rather than stopping at indices.

9.1 Quantify, but With Caveats

• Calculate Use Value, RI, etc.
• Always report confidence intervals
• Always disaggregate by stakeholder type, gender, age, wealth, geography
• Never report an index without the raw frequency data
• Name study limitations (sample size, representativeness, temporal snapshot)

9.2 Triangulate: Connect Knowledge to Actual Practice

• Interview: What do people say they know and use?
• Observe: What do they actually do? (Market data, harvesting patterns, food diaries)
• Compare: Where do reported and observed diverge? Why?
• Explain: What barriers or incentives explain the gap?

Knowledge is not practice. You must document both.

9.3 Model Under Uncertainty: Use Bayesian Methods

Instead of presenting a single point estimate (Use Value = 0.73), use Bayesian networks to show: - What we know about plant use (elicited from interviews) - What we’re uncertain about - How decisions change with different assumptions - What would convince us to change our minds

See the TEK Modeling vignette in ethnobotanyR for practical examples.

9.4 Frame Decisions Explicitly

Use ethnobotanical knowledge to inform decisions, not determine them.

Run structured decision-framing exercises that: - Surface stakeholder values and priorities - Name structural constraints (what’s possible given tenure, markets, policy, climate) - Acknowledge tradeoffs (you can’t optimize all values simultaneously) - Build in learning and adaptation (plans will change; that’s okay)

See the Decision-Framing vignette in ethnobotanyR for methods.

9.5 Follow Up: Document Implementation and Outcomes

The real test of ethnobotanical research is impact: - Did documented knowledge actually inform decisions? - Were interventions implemented as planned? - Did they achieve intended outcomes? - What unexpected consequences emerged? - What would you do differently?

Most ethnobotany papers stop at the index or workshop. Research that stops there doesn’t really know whether it mattered.