Data and code from:Global diversity estimates need to acknowledge species–area relationships
Data files
Sep 09, 2026 version files 418.11 KB
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Data1_Island_herptiles.txt
157 B
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Data2_Plant_and_insects.txt
965 B
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Data3_Tree_abundance.txt
362.70 KB
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Data4_SAR_Curves.txt
40.83 KB
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README.md
4.42 KB
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Script.txt
9.04 KB
Abstract
This submission is related to the paper "Global diversity estimates need to acknowledge species–area relationships" published in Global Ecology ad Biogeography.
This submission contains all codes and raw data needed to reproduce the figures for that paper.
Background
Many studies have tried to estimate the number of undescribed species based on the known diversity. These estimates often rely on extrapolation based on data from a limited number of species. Although statistical methods provide accurate inference when generalizing from a random sample, their predictions will be biased when based on a non-random sample unless the sampling process is explicitly accounted for.
Problem
In this paper, we argue that this is a fundamental issue in many estimates of unrecorded biodiversity. We show that the sample of species used in biodiversity extrapolation represents disproportionately common and abundant taxa, which leads to an overestimation of extrapolated diversity. We discuss this issue in the context of three specific cases: estimates of plant-associated insect diversity, estimates of parasite diversity and estimates of cryptic species diversity.
Implications
For the example of plant-associated insects, we provide an estimate of the magnitude of the error, but insufficient data are currently available to estimate the magnitude of the problem for the other examples. Our findings cast doubt over previous attempts to estimate the number of undescribed species, suggesting that they provide consistent overestimations.
Description of the data and file structure
The code relies on four input datasets.
These are added here for reproducibility of analyses but we stress that any anyone wanting to use them for anything other than recreating our results should cite the original data providers rather than this paper.
File: Data1_Island_herptiles.txt
Description: The data from this file comes from Caribherp (2025)
This contains number of endemic amphibians and reptiles in seven Caribbean islands.
Variables:
- Island: Island name
- Species: Number of endemic herptiles
- Area: Area of the island in square kilometers
File: Data2_Plant_and_insects.txt
Description: The data from this file comes from Brändle, M. and Brandl, R. (2002), Species richness of insects and mites on trees: expanding Southwood. Journal of Animal Ecology, 70: 491-504. https://doi.org/10.1046/j.1365-2656.2001.00506.x
This contains the number of insects associated with tree species in Germany.
Variables:
- Genus: Genus name of tree
- Insects: Total number of associated insects
- Specialists: Number of specialist insects
- Present_abundance: Metric of abundance, for details see Brändle and Brandl (2002).
- Pollen_abundance: Metric of abundance, for details see Brändle and Brandl (2002).
- COngenerics: Numher of other species in genus in Germany
- Postglacial_occurrence: Metric of abundance, for details see Brändle and Brandl (2002).
- Tree_height: Height in meters
File: Data3_Tree_abundance.txt
Description: The data from this file comes from Ter Steege et al. (2020).
ter Steege, H., Prado, P.I., Lima, R.A.F.d. et al. Biased-corrected richness estimates for the Amazonian tree flora. Sci Rep 10, 10130 (2020). https://doi.org/10.1038/s41598-020-66686-3
This contains information on abundance of trees in the Amazon.
Variables:
- Accepted.species: Name of species
- n.ind: Metric of abundance, for details see Ter Steege et al. (2020).
- n.plots: Metric of abundance, for details see Ter Steege et al. (2020).
- est.ind: Metric of abundance, for details see Ter Steege et al. (2020).
- population.mean: Metric of abundance, for details see Ter Steege et al. (2020).
- population.sd: Metric of abundance, for details see Ter Steege et al. (2020).
- log10A: Metric of abundance, for details see Ter Steege et al. (2020).
File: Data4_SAR_Curves.txt
Description: The data from this file comes from Triantis, K.A., Guilhaumon, F. and Whittaker, R.J. (2012), The island species–area relationship: biology and statistics. Journal of Biogeography, 39: 215-231. https://doi.org/10.1111/j.1365-2699.2011.02652.x
This contains estimates of a number of empirical SAR relationships:
- c_log: Metric of model performance, for details see Triantis et al 2012
- z_log: Metric of model performance, for details see Triantis et al 2012
- P_log: Metric of model performance, for details see Triantis et al 2012
- c_log10: Metric of model performance, for details see Triantis et al 2012
- z_log10: Metric of model performance, for details see Triantis et al 2012
- c_lin10: Metric of model performance, for details see Triantis et al 2012
- z_lin10: Metric of model performance, for details see Triantis et al 2012
- P_lin10: Metric of model performance, for details see Triantis et al 2012
- AIC_log: Metric of model performance, for details see Triantis et al 2012
- AIC_log10: Metric of model performance, for details see Triantis et al 2012
- AIC_lin10: Metric of model performance, for details see Triantis et al 2012
- Max_SR: Metric of model performance, for details see Triantis et al 2012
All data are provided here under a CC0 license
Code/software
All analyses were run in R version 4.5.1 and rely on the package scales_1.4.0
File: Script.txt
Description: includes all code to reproduce the figures in the paper. Minor visual modification of the figures were done in illustrator. The code will therefore recreate all results but not the full visual appearance of figures. Explanation of code is added before each step in the script file.