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Mass production of unvouchered records fails to represent global biodiversity patterns

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From Nature Ecology & Evolution

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Abstract

The ever-increasing human footprint even in very remote places on Earth has inspired efforts to document biodiversity vigorously in case organisms go extinct. However, the data commonly gathered come from either primary voucher specimens in a natural history collection or from direct field observations that are not traceable to tangible material in a museum or herbarium. Although both datasets are crucial for assessing how anthropogenic drivers affect biodiversity, they have widespread coverage gaps and biases that may render them inefficient in representing patterns of biodiversity. Using a large global dataset of around 1.9 billion occurrence records of terrestrial plants, butterflies, amphibians, birds, reptiles and mammals, we quantify coverage and biases of expected biodiversity patterns by voucher and observation records. We show that the mass production of observation records does not lead to higher coverage of expected biodiversity patterns but is disproportionately biased toward certain regions, clades, functional traits and time periods. Such coverage patterns are driven by the ease of accessibility to air and ground transportation, level of security and extent of human modification at each sampling site. Conversely, voucher records are vastly infrequent in occurrence data but in the few places where they are sampled, showed relative congruence with expected biodiversity patterns for all dimensions. The differences in coverage and bias by voucher and observation records have important implications on the utility of these records for research in ecology, evolution and conservation research.

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Fig. 1: The taxonomic coverage of lineages and grid cells by observation records are more biased and less congruent to expected richness patterns.
Fig. 2: Patterns of geographic coverage of species and grid cells by voucher and observation records of individual taxa.
Fig. 3: Temporal coverage of species and grid cells by voucher and observation records.
Fig. 4: Coverage of functional traits documented by voucher and observation records.
Fig. 5: The estimates and 95% confidence intervals predicted by a spatial autoregressive error model of coverage (taxonomic, geographic and temporal) by voucher and observation records with socioeconomic predictors.

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Data availability

The links to the species occurrence records downloaded from the GBIF are available at Zenodo (https://doi.org/10.5281/zenodo.6834577). The datasets, data tables, grid cell vector polygons and R codes are archived at Zenodo (https://doi.org/10.5281/zenodo.6834577).

Code availability

All scripts, codes and data documentation necessary to repeat our analyses have been made available in the Zenodo database (https://doi.org/10.5281/zenodo.6834577) under the folder ‘SCRIPTS’.

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Acknowledgements

We thank Stanford University and Texas A&M University-Corpus Christi for logistic support. B.H.D. was supported by the US National Science Foundation (awards 2031928 and 2113424). We are grateful to G. Nakamura, L. Ford and S. Pons for comments on earlier drafts of the paper. In addition, we are grateful to Holger Kreft for kindly sharing his data on the expected distribution of plants, which was instrumental in our analysis.

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  1. Department of Biology, Stanford University, Stanford, CA, USA

    Barnabas H. Daru

  2. Department of Biology, Institute of Ecology and Evolution, University of Oregon, Eugene, OR, USA

    Jordan Rodriguez

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The study was conceived and designed by B.H.D. Analyses were carried out by B.H.D. The paper was written by B.H.D and revised by B.H.D. with help from J.R.

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Correspondence to Barnabas H. Daru.

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Extended data

Extended Data Fig. 1 Patterns of expected species richness of terrestrial taxa.

The expected species richness of (a) Plants was derived from a co-kriging interpolation model of 1,032 regional floras worldwide, and (b) Butterflies, derived from a co-kriging interpolation of 543 geographic units covering the known inventory of butterflies, whereas the expected species richness of (c) Amphibians, (d) Birds, (e) Reptiles, and (f) Mammals, were generated by overlaying expert-based extent-of-occurrence range map of each species with equal-area grid cells of 100 km × 100 km. The bamako colour palette is common to all panels, with dark green indicating high coverage and yellow indicating low coverage. The maps are in the Wagner IV projection.

Extended Data Fig. 2 Spatial composition of β-diversity across grid cells by voucher and observation records.

Maps of dissimilarity between record types for: (a, b) Plants (n = 240,377 species), (c, d) Butterflies (n = 9809 species), (e, f) Amphibians (n = 4862 species), (g, h) Birds (n = 9380 species), (i, j) Reptiles (n = 7259 species), and (k, l) Mammals (n = 4508 species). Dissimilarity was assessed by generating pairwise distance matrices of Simpson’s β-diversity between all pairs of grid cells within major biogeographically defined areas recognized by the Biodiversity Information Standards (also known as the Taxonomic Databases Working Group (TDWG)). Values of β vary between 0 (species composition is identical between grid cells) and 1 (high dissimilarity, no shared taxa). Both voucher and observation records of most taxonomic groups showed high dissimilarity in less frequently sampled regions of South America, Africa, and Southeast Asia, and decline in frequently sampled Europe and North America.

Extended Data Fig. 3 Relationship between sampling effort (measured as taxonomic coverage) versus dissimilarity (measured as spatial composition of beta diversity) by voucher and observation records.

Indicated are the relationships between sampling effort and dissimilarity of record types for (a, b) plants, (c, d) butterflies, (e, f) amphibians, (g, h) birds, (i, j) reptiles, and (k, l) mammals. Trend line (in red) computed by evaluating the loess smooth at equally spaced points covering the range of dissimilarity values for each sampling effort.

Extended Data Fig. 4 Patterns of geographic coverage of grid cells by voucher and observation records of plants across spatial grain (50 × 50, 100 × 100, 200 × 200, 400 × 400, 800 × 800 and 1600 km × 1600 km).

Geographic coverage of grid cells was calculated as number of unique collection locales for each grid cell. Evenness or clustering of geographic coverage indicated by Moran’s I (Monte Carlo test, 999 randomizations) with values of 1 indicating clustered/biased coverage and 0 corresponding to geographically even coverage. The bamako colour palette is common to all panels, with darkgreen indicating high coverage and yellow indicating low coverage. The maps are in the Wagner IV projection.

Extended Data Fig. 5 Patterns of geographic coverage of grid cells by voucher and observation records of butterflies across spatial grain (50 × 50, 100 × 100, 200 × 200, 400 × 400, 800 × 800 and 1600 km × 1600 km).

Geographic coverage of grid cells was calculated as number of unique collection locales for each grid cell. Evenness or clustering of geographic coverage indicated by Moran’s I (Monte Carlo test, 999 randomizations) with values of 1 indicating clustered/biased coverage and 0 corresponding to geographically even coverage. The bamako colour palette is common to all panels, with darkgreen indicating high coverage and yellow indicating low coverage. The maps are in the Wagner IV projection.

Extended Data Fig. 6 Patterns of geographic coverage of grid cells by voucher and observation records of amphibians across spatial grain (50 × 50, 100 × 100, 200 × 200, 400 × 400, 800 × 800 and 1600 km × 1600 km).

Geographic coverage of grid cells was calculated as number of unique collection locales for each grid cell. Evenness or clustering of geographic coverage indicated by Moran’s I (Monte Carlo test, 999 randomizations) with values of 1 indicating clustered/biased coverage and 0 corresponding to geographically even coverage. The bamako colour palette is common to all panels, with darkgreen indicating high coverage and yellow indicating low coverage. The maps are in the Wagner IV projection.

Extended Data Fig. 7 Patterns of geographic coverage of grid cells by voucher and observation records of birds across spatial grain (50 × 50, 100 × 100, 200 × 200, 400 × 400, 800 × 800 and 1600 km × 1600 km).

Geographic coverage of grid cells was calculated as number of unique collection locales for each grid cell. Evenness or clustering of geographic coverage indicated by Moran’s I (Monte Carlo test, 999 randomizations) with values of 1 indicating clustered/biased coverage and 0 corresponding to geographically even coverage. The bamako colour palette is common to all panels, with darkgreen indicating high coverage and yellow indicating low coverage. The maps are in the Wagner IV projection.

Extended Data Fig. 8 Patterns of geographic coverage of grid cells by voucher and observation records of reptiles across spatial grain (50 × 50, 100 × 100, 200 × 200, 400 × 400, 800 × 800 and 1600 km × 1600 km).

Geographic coverage of grid cells was calculated as number of unique collection locales for each grid cell. Evenness or clustering of geographic coverage indicated by Moran’s I (Monte Carlo test, 999 randomizations) with values of 1 indicating clustered/biased coverage and 0 corresponding to geographically even coverage. The bamako colour palette is common to all panels, with darkgreen indicating high coverage and yellow indicating low coverage. The maps are in the Wagner IV projection.

Extended Data Fig. 9 Patterns of geographic coverage of grid cells by voucher and observation records of mammals across spatial grain (50 × 50, 100 × 100, 200 × 200, 400 × 400, 800 × 800 and 1600 km × 1600 km).

Geographic coverage of grid cells was calculated as number of unique collection locales for each grid cell. Evenness or clustering of geographic coverage indicated by Moran’s I (Monte Carlo test, 999 randomizations) with values of 1 indicating clustered/biased coverage and 0 corresponding to geographically even coverage. The bamako colour palette is common to all panels, with darkgreen indicating high coverage and yellow indicating low coverage. The maps are in the Wagner IV projection.

Extended Data Fig. 10 Pairwise relationships between 6 socioeconomic and ecological variables.

Correlations based on pairwise Spearman-rank correlations between the variables at spatial grain of 100 km. All variables were log-transformed before analysis. The statistical test used was two-sided. Exact p values are indicated below correlation coefficients.

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Daru, B.H., Rodriguez, J. Mass production of unvouchered records fails to represent global biodiversity patterns. Nat Ecol Evol 7, 816–831 (2023). https://doi.org/10.1038/s41559-023-02047-3

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