Cited by 248Nutritional Studies and DietChild Nutrition and Water AccessAgriculture Sustainability and Environmental Impact

The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index; none of the 24 sources its claims cite is checked for support yet.

Dietary species richness as a measure of food biodiversity and nutritional quality of diets

Open access

Abstract

Significance: Current research linking biodiversity and human diets has used metrics without justification from a nutritional point of view. Diet species richness, or a count of the number of different species consumed per day, assesses both nutritional adequacy and food biodiversity of diets for women and children in rural areas. The positive association of food species richness with dietary quality was observed in both the wet and the dry season. Food biodiversity contributes to diet quality in vulnerable populations in areas with high biodiversity. Reporting the number of species consumed during dietary assessment provides a unique opportunity to cut across two critical dimensions of sustainable development—human and environmental health—and complements existing indicators for healthy and sustainable diets.

Overview

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TL;DR

Dietary species richness (DSR), a count of species consumed per day, was positively associated with micronutrient adequacy in women and children in seven low- and middle-income countries. The authors recommend DSR as a simple cross-cutting indicator linking food biodiversity and diet quality.

Key findings

  • All three food biodiversity indicators were positively associated with mean adequacy ratio (MAR): per z-score increase, MAR rose by 0.03 for species richness (SR), 0.70 for Simpson’s index (D), and 0.52 for functional diversity (FD); standardized coefficients were 0.10, 0.08, and 0.06, respectively (all P < 0.001).
  • SR showed stronger and more consistent associations with diet quality than D or FD; mean SR was lower in children than women (8.24 ± 1.17 vs. 10.19 ± 3.52; P < 0.001).
  • A total of 234 species were consumed; 94% of energy intake was identified at species level. In the wet season, nine species consumed in all countries provided 61% of energy, 10% of vitamin A, 24% of vitamin C, 42% of folate, 51% of iron, 65% of zinc, and 35% of calcium.
  • Adding SR to dietary diversity score (DDS) improved detection of higher nutrient adequacy in women: DDS × SR ≥24 gave sensitivity 81% and specificity 60%, versus DDS ≥5 with sensitivity 42% and specificity 96%.

Methods

  • Existing dietary datasets were identified through a systematic literature review and selected if they used quantitative, comparable 24-h recalls, identified foods/drinks to at least species level, and assessed women and/or children. Wet-season data came from rural Benin, Cameroon, Democratic Republic of Congo, Ecuador, Kenya, Sri Lanka, and Vietnam; dry-season data also came from Vietnam, Kenya, and Benin; data were collected July 2009–April 2015.
  • Dietary intake data were obtained for n = 3,449 and n = 2,777 participants in wet and dry seasons, respectively; the sample included n = 2,188 women and n = 4,038 children.
  • The study calculated SR, Simpson’s D, and FD from species consumed; MAR from vitamin A, vitamin C, folate, calcium, iron, and zinc; and DDS. Associations were modeled with random-effects linear regression (season fixed, country random), with energy adjustment by the residual method, and cutoffs assessed with ROC curves.

Limitations

  • Stated: nutrient-composition data were missing for some foods, species, and varieties, so values from similar foods were substituted; a single 24-h recall per subject does not capture within-person variability or usual intake; data came from rural areas of middle-income countries where locally produced food dominates, so validity in urban, high-income, or highly processed-food diets is uncertain; food-source information (own production, market, wild) was not included, so the relative contribution of agricultural versus wild biodiversity could not be assessed.
  • Stated: previous studies have misidentified 6–10% of species, and species-level identification can be challenging.
  • Evident from the text: the analysis is observational and cross-sectional, so the positive associations cannot establish causality.

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Integrity signals

Registry

Crossref · 10.1073/pnas.1709194115

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1. Association of biodiversity indicators with MAR for 6,226 women and children in seven countries (wet and dry season combined).
Fig. 1. Association of biodiversity indicators with MAR for 6,226 women and children in seven countries (wet and dry season combined).CC BY-NC-ND · PubMed Central
Fig. 2. ROC curves of standardized biodiversity indicators with micronutrient adequacy in women and children. MAR50, diet with 50% mean adequacy of vitamin A, vitamin C, folate, calcium, iron, and zinc; zFD, standardized FD; zD, standardized D; zSR, standardized SR.
Fig. 2. ROC curves of standardized biodiversity indicators with micronutrient adequacy in women and children. MAR50, diet with 50% mean adequacy of vitamin A, vitamin C, folate, calcium, iron, and zinc; zFD, standardized FD; zD, standardized D; zSR, standardized SR.CC BY-NC-ND · PubMed Central
Fig. 3. Association of MAR with SR for 6,226 women and children in seven countries (wet and dry season combined). DR, Democratic Republic.
Fig. 3. Association of MAR with SR for 6,226 women and children in seven countries (wet and dry season combined). DR, Democratic Republic.CC BY-NC-ND · PubMed Central
Fig. 4. MAR against SR and DDS (Left) and ROC curves for SR × DDS, SR, and DDS (Right) for 6,226 women and children in seven countries. MAR50, diet with 50% mean adequacy of vitamin A, vitamin C, folate, calcium, iron, and zinc.
Fig. 4. MAR against SR and DDS (Left) and ROC curves for SR × DDS, SR, and DDS (Right) for 6,226 women and children in seven countries. MAR50, diet with 50% mean adequacy of vitamin A, vitamin C, folate, calcium, iron, and zinc.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

41 Own result · 22 Not checked yet · 12 Own method · 12 Own interpretation · 7 No basis given

Verify all 24 references — free account
  • Probably not a substantial claim. 31
  • It may state two separate claims as one. 24
49 of 49
  • The paper relies on this without support.

    Every sample was considered equally representative.

    “Because every sample was considered equally representative, the overall summary statistics were calculated averages for women and children separately, per country and across countries.”
  • The authors state this as an assumption.

    No previous studies have applied common measures of biodiversity to measure levels of food biodiversity in the diet.

    “To our knowledge, no previous studies have applied common measures of biodiversity to measure levels of food biodiversity in the diet.”
  • The paper relies on this without support.

    SR can more easily be calculated in comparison with D and FD.

    “Given that SR can more easily be calculated in comparison with D and FD, we recommend Dietary Species Richness (DSR) as the most appropriate measure of food biodiversity in diets.”
  • The paper relies on this without support.

    Communities are not entirely composed of subsistence farmers.

    “Communities are not entirely composed of subsistence farmers.”
  • The paper relies on this without support.

    The availability of legumes, vitamin A-rich fruits and vegetables, and dark-green leafy vegetables is highly seasonal.

    “The availability of these foods is highly seasonal.”
  • The paper relies on this without support.

    Food systems in (peri)urban areas and high-income countries have a higher degree of complexity than in rural areas and middle-income countries.

    “Food systems in (peri)urban areas and high-income countries have a higher degree of complexity than in rural areas and middle-income countries.”
  • The paper relies on this without support.

    This complexity is mainly caused by the consumption of processed foods that have often not been locally produced but have been obtained from retail outlets or urban markets.

    “This complexity is mainly caused by the consumption of processed foods that have often not been locally produced but have been obtained from retail outlets or urban markets.”
  • The paper relies on this without support.

    Global datasets such as FAOSTAT identify general food items or food groups and do not facilitate valorization of the full range of food biodiversity.

    “Global datasets such as FAOSTAT identify general food items or food groups and do not facilitate valorization of the full range of food biodiversity.”
  • The paper relies on this without support.

    DDS is a common measure to assess diversity and diet quality and is widely applied in population surveys.

    “DDS is a common measure to assess diversity and diet quality and is widely applied in population surveys.”
9 of 9

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Citations

Cites

Show all 36 references

Research works printed but not linked: 3

  1. ; FAO The Second International Conference on Nutrition: Committing to a future free of malnutrition. Available at www.fao.org/3/a-ml542e.pdf. Accessed January 24 2017. (2015).
  2. G Kennedy, G Nantel Basic Guidelines for Validation of a Simple Dietary Diversity Score as an Indicator of Dietary Nutrient Adequacy for Non-Breastfeeding Children 2-6 Years (FAO, Rome, 2006).
  3. ; Institute of Medicine Dietary Reference Intakes: The Essential Guide to Nutrient Requirements (Natl Acad Press Washington DC) p 560. Available at nap.edu/11537. Accessed October 27 2016. (2006).

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Metadata

Authors
Lachat, C., Raneri, J. E., Smith, K. W., Kolsteren, P., Van Damme, P., Verzelen, K., Penafiel, D., Vanhove, W., Kennedy, G., Hunter, D., Odhiambo, F. O., Ntandou-Bouzitou, G., De Baets, B., Ratnasekera, D., Ky, H. T., Remans, R., & Termote, C.
Affiliations
  • Ghent University (BE)
  • Alliance Bioversity International - CIAT (IT)
  • Laboratory of Tropical and Subtropical Agronomy and Ethnobotany, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium;
  • KERMIT, Department of Mathematical Modeling, Statistics, and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium;
  • University of Ruhuna (LK)
  • HealthBridge Foundation of Canada (CA)
Corresponding author
Lachat, Carl
Venue
Proceedings of the National Academy of Sciences
Published
2017-12-18
Volume
115
Issue
1
Pages
127-132
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2776954001
Cited by
248
References
36
Citations by year
2026: 22 · 2025: 38 · 2024: 36 · 2023: 32 · 2022: 30 · 2021: 27 · 2020: 29 · 2019: 23 · 2018: 10
FWCI
15.90
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
  • CGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) (submittedVersion, other-oa)
Funders
  • Ministry of Foreign Affairs Finland (FoodAfrica 29891501)
  • Vlaamse Interuniversitaire Raad (ICP PhD 2010-012)
  • Global environmental facility (Biodiversity for food and nutrition project GFL-2328-2715-4B07)
  • Daniel and Nina Carasso Foundation (PG008608)
  • International institute for Tropical Agriculture and Bioversity International (Humidtropics A4NH: CRP4-002)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
  • Health Sciences › Nursing › Nutrition and Dietetics › Child Nutrition and Water Access
  • Physical Sciences › Environmental Science › Ecology › Agriculture Sustainability and Environmental Impact
Keywords
food biodiversity, diet quality, nutritional adequacy, women and children, rural population, seasonal variation, healthy eating, sustainable diets
MeSH terms
Child, Child, Preschool, Eating, Female, Food Preferences, Humans, Infant, Male, Nutritive Value, Rural Population, Micronutrients

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