Estimating global bee species richness and taxonomic gaps

Domain: chemistry
Published: 2026-02-24
Editorial outcome: major_revision

Abstract
Robust species richness estimates are critical for meaningful conservation prioritisation, understanding ecosystem resilience, and studying evolutionary processes. Yet, they remain elusive even for some of the best-studied groups and regions. As keystone pollinators, bees are crucial for the maintenance of healthy ecosystems, yet rigorous estimates of their species richness are lacking. Here, we statistically estimate global, continental, and country lower bounds of bee species richness. Globally, we estimate 24,705–26,164 bee species, an 18–25% increase, representing at least 32–45 years of taxonomic research. We estimate particularly high undescribed biodiversity from Asia, Africa, and the Americas. We find that taxonomic gaps are correlated with gross domestic product per capita (GDPc), observed species richness, number of occurrence records, and completeness of occurrence databases. Our statistical R-package framework will progress our understanding of lesser-known groups, downstream consensus, and mobilise existing occurrence datasets to quantitatively estimate species richness on global scales.

Introduction
Quantifying species richness is a fundamental aim of biology. Species richness influences ecosystem resilience1, conservation2, policy3, and our understanding of evolutionary processes4. Reliable richness quantification is difficult, and our inability to do so represents ‘a colossal failure of science’5, with global species richness estimates varying wildly from 2 million6 to 1 trillion7. Currently there are ~2.2 million described species on Earth8 with global rates of eukaryotic species description at about 18,000 species/year; 75% of which are invertebrates9. However, rates of species description have varied across time and taxa10. Understanding species richness is non-trivial and has considerable real-world implications for species conservation in the face of anthropogenic threats11, placing taxonomy as the cornerstone of conservation assessments and biological research12. There is a bias in species description to large and charismatic organisms. For instance, it is generally accepted that relatively few new bird species remain to be discovered13, and this holds for most other vertebrate groups as well (however, see bats14). But outside of these well-studied groups, even critical ecosystem engineers, such as trees, have an estimated 9000 undescribed species15, in addition to the described 64,088 species (+14%)15. Estimates of undescribed species, such as this, can influence and inform the allocation of funding, research, management, and conservation efforts.

The vast majority of described animal species are insects16. Insects are so overwhelmingly diverse that May17 noted, ‘To a good approximation, all species are insects’. Estimations of this diversity range from ‘guesstimates’ to more realistic data-driven estimates; there is much uncertainty across the diversity of applied methods, but reliable approximates range from 2.6 to 7.8 million insect species16. The lack of consensus on the number of insect species is especially concerning given the reported widespread decline in insects18,19 and the low number of species currently described (one million or 13–38% of expected diversity)20. Advances in molecular techniques allow cryptic taxon discovery and robust taxonomic hypotheses; however, validating names and integrating molecular and morphological evidence (via integrative systematics workflows) remains challenging21. Even for some of the best-understood insect taxa, global or regional richness estimates are rare. For example, a recent large-scale collaborative work on ants focused on discovering the distribution of unknown species; rather than explicit quantification of richness19. However, this method provided a useful ‘treasure map’ for targeted species discovery.

Despite their status as the pre-eminent animal pollinators22, to our knowledge, there are no statistical estimates of bee (Anthophila) species richness, with haphazard species richness estimates mis-cited and proliferated in literature and science communication. In 2007, Michener23 estimated that there were ~18,000 described species with >20,000 total in the world. The Discover Life bee taxonomy contains ~21,000 valid species24,25, globally; yet many new species are hypothesised to exist, especially in Australia, China, and Argentina and Chile26. Recent bee revisions have seen increases of up to 96%27 and, through synonyms, reductions of 57%28 of species in other groups. However, most bee groups have not been subjected to in-depth taxonomic assessment, and thus, we have no clear idea of the total number of bee species.

Here, we generate statistically derived and quantitative global-, continental-, and country-level estimates of bee species richness. We combine a global bee occurrence dataset (8.3 million cleaned records), taxonomy (~21k valid names and ~24k synonyms), country checklist (~66k species-country pairs, including almost all valid names)24,29, and literature records (497 species), with statistical estimators of species richness (iChao1 and Hill numbers). We additionally propose a major hypothesis and several predictions to explain patterns in our estimated taxonomic gaps.

We hypothesise that contemporary and historical differences in socioeconomic and scientific investment as well as physical regional characteristics have generated uneven rates of bee species description across regions. We propose several predictions in relation to physical and social diversity gap correlates and our response variables, predicted number and percentage of new species, to address this hypothesis. First, due to increased niche diversity and potential difficulty in sampling, we predict that our response variables will be positively correlated with elevational range and country area. Second, because increased median distance to nearest road could make sampling for bees more difficult, our response variables will be positively correlated with median distance to nearest road. Third, we predict that our response variables will be positively correlated with observed species richness. This is because we expect that the taxonomic load might be higher; however, we also recognised that it could indicate that a larger portion of the bee diversity was already described. Fourth, we predict that our response variables will be negatively correlated with gross domestic product per capita (GDPc) and education. This is because we expected that GDPc and education to be indicators of a country’s investment into research and a greater capacity to undertake taxonomic works locally or to host collaborating taxonomists. Finally, we predict that, due to greater inventory and taxonomic efforts, our response variables would be negatively correlated with the number of clean occurrence records and the proportion of occurrence-based species per country.

The framework and R package (BeeBDC ≥v1.3.125) toolset we provide can be used for estimating actual taxonomic diversity across larger taxonomic and geographic scales.

Results
Global, continental, and country bee diversity
We statistically quantified the number of bee species at the global-, continental-, and country-levels for 186 countries and provided custom statistical tools for use with other taxa (BeeBDC v1.3.125; Supp. Materials). Over 100 iterations, we estimated the lower bound of bee species richness globally to be between 24,705 and 26,164 species (lower and upper 95% confidence intervals; Fig. 1A). This equates to an estimated taxonomic gap of 3771–5230 species, or an 18–25% increase on the 20,934 currently recognised bee species24,25. With the exception of our global (iNEXT, richness rarefaction and extrapolation, 24,985; 24,705–25,266; +16% and iChao, non-parametric richness estimation, 25,929; 25,705–26,164; +19%) and Asian (iNEXT 8275; 8057–8473; +34% and iChao 8708; 8528–8902; +41%) median estimates, all 95% confidence intervals of both estimate methods (iNEXT and iChao) overlapped, providing greater confidence in our estimates (Figs. 1A–C and S1–S4). Because of this, and for simplicity, we report on iChao below.

At the continental level, Asia, including the Middle East, had the largest taxonomic gap of 2525 (+40%) species; although, in terms of percentages, Africa (1668; +34%), Europe (568; +27%), Oceania (472; +23%), and South America (1262; +29%) also exhibit large gaps (Figs. 1B, 2, and S5, S6). The gap for North America (1010; +18%), while lower as a percentage, is still large and represents years of taxonomic research at present global discovery rates (Figs. 1B, 2, and S5, S6; see below). Furthermore, at the continent and country levels where land boundaries are shared, estimates will include described species that could occur in neighbouring regions.

Our estimates provide a powerful guide and useful hypotheses for where to concentrate species discovery and revisionary research. For example, we estimated a taxonomic gap of 843 (+46%) species for Turkey, which is larger than estimates for the entirety of continental Europe. Of the remaining top ten most species rich countries (Figs. 1C and S7, S8), China (637; +47%) and Israel (553; +52%) have particularly high taxonomic gaps. Many regions identified here with high taxonomic gaps have been modelled, based on environmental factors (especially xeric areas, solar radiation and non-forest plant productivity), to have high species richness, including Turkey, China, and Argentina26. However, we also found discordance with countries like Australia, Chile, and Madagascar showing relatively low estimated undescribed species diversity (Fig. 2C) but otherwise high modelled richness26. This discordance doesn’t appear related to recent descriptions in those regions and should be the topic of further, potentially molecular, research.

Our estimates are likely conservative as they estimate the lower bound of richness and taxonomic revisions are frequently undertaken without the inclusion of molecular techniques. For example, even in a country with high GDPc like Australia, only 12% of new species described since 2000 were named in articles that formally used phylogenetic analyses (Table S1). In a recent and contrasting example, the morphologically cryptic Fijian Lasioglossum bees went from four morphologically described species to 13 when systematic techniques were included30, and recent molecular evidence suggests at least 28 total species31; a 3.3- and 7-fold increase, respectively. Hence, we should expect that these estimates will increase overall as molecular sampling techniques are implemented more broadly, sequencing costs fall, and data availability increases32. However, it is important that these techniques are used reliably and without over-interpretation32,33. Additionally, sequencing efforts can, and do, result in the discovery of synonyms (rejections of existing species hypotheses)34. Large barcoding efforts may elucidate, by region, the extent to which existing taxonomies have over- or under-described species.

Wild bee conservation, research, and taxonomy around the world faces challenges, including regionally poor data, extinctions, poaching, rare conservation assessments, political and cultural barriers, expert deficits, museum backlogs, and national restrictions to sharing and collaboration26,35–39. These challenges are clear in Asia, which has the highest observed and predicted species richness and taxonomic gap (Fig. 2 and S5), but major issues with taxonomy and species discovery capacity40. Issues are also apparent for Africa, which potentially lacks ample data (Figs. 2 and S6) when compared to Orr et al.26, who suggested high relative richness in North, East, and South Africa. Chad is the closest African country to asymptote with a predicted 17% taxonomic gap (globally 20th smallest gap for n≥300; while 14/20 are European). Many Central and South American countries are also far from asymptote (Fig. S4). However, continentally, there are trends towards asymptote (Fig. S3). In contrast, Europe has relatively few remaining species to describe with countries like Sweden and Switzerland nearly at asymptote (Fig. S4). The low predicted taxonomic gap in Oceania was unexpected and disagrees with recent qualitative analyses26. However, Australia holds 94% of Oceania’s clean—filtered and reliable29—bee occurrence records with few integrative systematic descriptions (e.g. Table S1). This likely results in cryptic diversity going unrecognised and leading to a scarcity of singleton and doubleton species, which would drive the lower regional diversity estimates. This, in combination with a lack of recent and thorough integrative systematic research in Oceania, could explain the lower estimates where we otherwise expected higher richness. A concurrently published paper, not included in our analyses, highlighted this issue by describing 71 new species of Australian Megachile (Austrochile) (28% of the estimated taxonomic gap of 250 species) using systematic techniques41. It might also be a broader issue for countries where molecular techniques are inadequately implemented in taxonomic research; such countries are likely the majority, and most new arthropods are described using morphology alone42. However, non-statistical estimates of species richness in Australia—‘as high as 2000’43, 1947–204744, ~260045, 2–300046—do not overlap with, but are close to, our estimate of the lower bound for Australia, 1759–1919 (Fig. S4).

Our work also provides insights into how bee species richness varies between continental and island countries. Previous modelling could not effectively estimate even relative island species richness26. Our approach better incorporates biogeographic limitations to estimate that species richness for island nations is significantly higher (p=5.0×10−8), per unit area, than continental nations (Fig. S9). Isolated islands are known to host large numbers of endemic species and simultaneously be disproportionately threatened by anthropogenic changes47. Hence, we highlight that islands should be a priority for taxonomic and conservation research.

The development of integrated, novel, and accessible workflows, like ours, to pinpoint discovery and collaboration opportunities, help allocate efforts and prioritise funding accordingly, is a major advance. Our workflow quantitatively, rather than relatively, estimates richness at multiple macro-scales and is applicable to any taxon and region with sufficient data. Our taxonomic gap analysis quantifies global biodiversity patterns and highlights the urgent need for investment in the global south and parts of the global north.

Species accumulation and discovery rate
Since 1960, new bee species names that remain valid today have accumulated at the relatively constant rate of 117 species/year (Fig. 1D). This likely indicates that the rate of description is limited by a taxonomic bottleneck rather than availability of new species. Applying this rate directly, we might expect to close the taxonomic gap in 32–45 years. However, given that our richness estimates are a lower bound, the integration of phylogenetic analyses can be very limited (Table S1), research funding and capacity are geographically biased40, reduced support for taxonomy, and the likely diminishing returns of species revision toward the tail end of discovery, we expect this estimate to be quite conservative.

Synonymy of more-recent names has slowed since about 2000, while the rate of new valid names has not (Fig. 1D). This could indicate that the reliability of bee taxonomy is increasing overall but that we are not starting to exhaust the number of new species left to describe. Additionally, from the 497 species for which we collected sample size data from the most-recently published revisions, 226 (45%) were described from a single specimen (Fig. S10), further supporting the notion that there are many remaining bee species to describe. In a similar timeframe, the number of first digitised occurrence records for each species has also fallen behind the rate of new valid names (Fig. 1D). This, along with our literature search (Supp. Materials), indicates issues in the open sharing of occurrences with taxonomic works and a shortfall in museum digitisation efforts. Even in developed countries, occurrence record shortfalls are a major issue for conservation assessments as well as species discovery32,38. These issues are likely broadly applicable to many other taxa. Removing barriers to uploading research data and enforcing open science practices will be critical to addressing these issues in the future. Furthermore, standardised approaches to digitising and sharing such data in ways that are easy to aggregate (e.g. using the Darwin Core occurrence data standard—dwc.tdwg.org—which is required by some journals such as the European Journal of Taxonomy) would greatly facilitate our ability to collate these data and track species descriptions.

Diversity gap correlates
To examine the factors contributing to taxonomic shortfalls, we used linear mixed-effects models with countries as samples and continents as the random effect. We analysed correlates of the number and percentage of new species. Our correlates included GDPc48, tertiary education49, the observed species richness, the number of BeeBDC-cleaned bee occurrence records29, elevational range50, country area51, and country-wide median distance from roads52, and the proportion of occurrence-based species versus species drawn from the literature curve. We consistently found no significant relationship between our response variables and the percentage of tertiary education, median distance to roads, elevational range, or country area (Tables S2 and S3). In agreement with our prediction, increasing GDPc was negatively associated with the estimated number and percentage of undescribed species (Figs. S11A and S12A). This was expected because richer countries, in general, should have described a greater number and proportion of their bee fauna due to greater investment in science and research capacity. However, for Africa, increasing GDPc appeared to be associated with an increase in the number of undescribed species (Fig. S11A). This likely reflects an overall larger taxonomic gap for Africa and an inability to reliably estimate trends due to a lack of data (Fig. S11F, G). In such cases, it seems likely that even initial efforts at collections and taxonomic research in the region will uncover many new species and greatly enhance our ability to work with and understand the fauna.

Observed species richness had a very strong, consistent, and positive impact on the estimated number of new species in every continent (Fig. S11F). This likely indicates that bee-diverse regions are expected to have a larger number of species remaining undescribed, supporting our prediction for this variable. Even in the best studied regions, such as North America, many species may persist undescribed in larger groups (such as Andrena, Lasioglosoglossum, or Osmia in the USA) that are best treated altogether to enhance identifiability and ensure accurate taxonomic actions. In contrast, the positive correlation between the percentage of new species and observed species richness appears to mostly be driven by Asia, with wide confidence intervals for other continents (Fig. S12F). This reinforces the suggestion that Asia, including the Middle East, is a standout priority in terms of addressing the taxonomic gap for bees40.

Both the number of clean occurrence records and the proportion of occurrence-based species appeared to have a negative association with both the number and percent of undescribed species (Figs. S11G, H and S12G, H) and both had a weak but significant interaction (Tables S2 and S3). However, our model indicated that the proportion of occurrence-based species had a positive association with both response variables (Tables S2 and S3). Without log-transformation, we supported our prediction that the number of clean occurrence records and the proportion of occurrence-based species were negatively associated with our response variables because both variables indicate greater sample completeness associated with more taxonomic work and material (Supplementary Materials). This makes intuitive sense, as areas with more data should have undergone more taxonomic research and identification work necessary to generate such data.

Across regions, we see great variability in taxonomic completeness and gaps. There are opportunities for foundational and traditional taxonomy in areas such as Africa to more coordinated efforts on larger or more challenging groups in places such as North America. For the former, integrative systematics will build strong taxonomic scaffolds early, and for the latter, it will allow robust testing of existing species hypotheses.

Hence, GDPc, observed species richness, the number of clean records, and the taxonomic completeness of digitised occurrence records are good indicators of the taxonomic gap. Not only are the latter three variables the strongest correlates, but they are also the ones that can be improved through building capacity and collaborations. Our results support the socioeconomic component of our hypothesis in generating uneven rates of bee species description across regions. However, we reject the physical regional characteristic component for the variables that we examined.

To determine the potential impact of sampling species counts from the empirically derived literature curve, we further sampled two curves based on the empirical country- and global-level occurrence datasets. Most estimates were robust to changes in the chosen curve, with the literature-sampled occurrence curve sometimes producing higher estimates than the global or country curves (Figs. S10 and S13). Except for GDPc, and—for the global dataset—the interaction term between the number of clean records and the proportion of occurrence-based species, the significance and sign of the remaining fixed effects were consistent (Table S3). This suggests that our use of the empirical literature curve is a robust choice and hence useful for implementation with other taxa.

We applied several tests of the data and found that the significant patterns and directions of fixed effects in our linear mixed-effects model were mostly robust to changes in the models; however, changing the data transformations could alter the results. Importantly, when the number of clean records and the proportion of occurrence-based species were not log-transformed, the significance values were identical; however, both fixed effect variables exhibited negative associations with the response variables.

Further diversity gap correlates could be explored if those data were reliably compiled. Phylogenetic signal is one possibility, and a global super-matrix bee phylogeny exists53; however, the available data for some groups are insufficient to make reliable inferences. While we try to understand taxonomic capacity through variables such as GDPc and tertiary education, datasets that describe the origin and employment locality for taxonomists might also explain some of our patterns. However, taxonomists do not solely work on taxa from those places. Verification of our results could also be achieved by surveying the number of undescribed species in museums. However, this would require a colossal digitisation effort, which has not been adequately completed even for described materials or taxa.

Broader implementation
For decades, we have been unable to estimate species richness or its uncertainty, at least partly because easily-implemented methods did not exist54. This explains the rarity of global quantitative species richness estimates. However, global occurrence, taxonomic, and checklist datasets are quickly growing in size and completeness. For example, the Global Biodiversity Information Facility has over three billion occurrence records currently listed55. Other repositories29, data-providers (e.g. iNaturalist)56, and taxonomic/checklist providers57 similarly continue to appear and grow. However, different datasets should be judged and used based on their relative merits and reliability for each taxon of interest58,59. The tools for reliably dealing with these occurrence data, identifying synonyms, and cleaning data are also becoming rapidly more available and increasingly comprehensive25,60,61. Bees are not the only understudied taxon for which sizeable datasets exist. Given the availability of data and processing pipelines—e.g. integrated species occurrence data cleaning and species richness estimation functions now in BeeBDC25—these kinds of analyses can be implemented for other taxa with relative ease. Such research will provide quantitative, statistical, and geographically explicit roadmaps for prioritising the description of whole taxonomic groups.

Such analyses should be made and explained with caveats kept in mind. For example, while iChao and Hill numbers are relatively robust estimators of species richness within assemblages62–64, our ‘assemblages’ might be considered very large. Global species occurrence datasets, while relatively extensive, have inherent biases29; however, biases abound for perhaps all sampling methods65. We expect that taxonomic concepts will also impact our estimates, particularly where rare or cryptic species are left undescribed or lumped. While we state species richness estimates here at several levels, we highlight that they are estimates of the lower bound of diversity, biases are likely to further decrease estimates62–64, and that estimates can be somewhat sensitive to input data (e.g. Figs. S10 and S13). We suggest that our estimates, while far better than haphazard estimates, are appropriately used as statistically derived hypotheses and roadmaps for future work and management.

Summary
Understanding the number of species on Earth remains an enduring and unanswered question in biology. We provide quantitative global-, continental-, and country-level estimates for all bee species. Our prediction of bee richness globally is 24,705–26,164 species, which represents a lower-bound. We conservatively estimate 32–45 years of taxonomic work, which is certainly an underestimate and unevenly distributed across the globe, with most new bee richness expected in Asia, followed by Africa, South America, North America, Europe, and then Oceania. The relatively low estimate for Oceania was unanticipated. However, we caution that this might be driven by relatively high data availability in Australia, but poor regional availability, and insufficient use of molecular techniques, even in recent taxonomic research. Insufficient implementation of integrative systematics is an issue for many countries and taxa. Our analyses also highlight that, for bees and per unit area, island nations are more species-rich than continental countries, indicating the evolutionary and conservation importance of islands beyond simple endemism.

We provide the methods needed to prioritise bee species discovery and taxonomic investment (see vignettes https://jbdorey.github.io/BeeBDC/). We suggest that countries with lower research capacity in taxonomy (e.g. fewer taxonomists) need to be prioritised, and that wealthy countries should aim to build regional capacity and collaborations to address shortfalls. We further suggest that molecular techniques should be used whenever possible in combination with morphometric data (integrative systematics) to correctly identify and describe cryptic diversity, build robust species concepts66, identify potential synonyms, and generate better estimations of taxonomic gaps. While there are challenges in building reliable estimates of species richness, particularly for data-poor countries, our analyses enable powerful conclusions to be drawn with real-world applications for taxonomic and conservation prioritisation. With ever-growing occurrence datasets, checklists, and accessible programming tools, our research also provides a statistical and computational template to estimate species richness across different taxa. With broader adoption and expansion of these methods, we hope to address the ‘colossal failure of science’5 that is our inability to robustly quantify species richness at large geographic and taxonomic scales.

Methods
Data acquisition and manipulation
We used the BeeBDC species occurrence data and the Discover Life taxonomy and country checklist downloaded in BeeBDC version 1.2.024,25,29. We filtered the uncleaned version of this dataset to remove records with (i) invalid binomials, (ii) improper basis of record, (iii) unmatched coordinates and country name, (iv) absences, (v) invalid license, or (vi) outside of their country checklist using BeeBDC25 in R version 4.4.167. We manipulated data mostly using tidyverse68 packages and created plots using ggplot2 version 3.5.069 and iNEXT version 3.0.163. All code for data management, manipulation, analyses, and figures is provided on our GitHub (https://github.com/jbdorey/BDE_R_wofklow) and core functions are available with BeeBDC ≥v1.3.1 (https://jbdorey.github.io/BeeBDC/).

For valid species names that lacked any occurrence records, we applied a distribution from empirical sample sizes using the most recent revisions as follows. For the 4819 species that had no occurrence records, we found sample sizes using one of two methods. Firstly, we sought complete occurrence datasets from the literature. On the 12th of April 2024, to find data from taxonomic revisions, we searched Scopus with the term ‘bee AND revision AND taxonomy’ and looked at all multiple species bee papers with citations >2 (n=53; citations available from https://github.com/jbdorey/BDE_R_wofklow SuppMaterials/ScopusSearch). Of these papers, only one provided data in a tabular format70. We also extracted occurrence datasets from papers where the present authors knew that datasets were provided with the publication4,27,38,71–75 and almost 100 additional records that were digitised (see https://github.com/jbdorey/BDE_R_wofklow/blob/main/SuppMaterials/DiversityLiteratureRecords.xlsx). Secondly, we randomly extracted 600 of the 4819 species names that lacked any occurrence records and extracted sample sizes from the most recent taxonomic revision that covered each taxon (see https://github.com/jbdorey/BDE_R_wofklow SuppMaterials/1.6c_randomLitTogether.csv). Across these literature records, we got sample sizes for 497 or >10% of all missing species, that lacked any occurrence points in the BeeBDC dataset (see https://github.com/jbdorey/BDE_R_wofklow/blob/main/SuppMaterials/1.6c_randomLitTogether.csv).

We estimated the curve of best fit to these literature data using Mosaic version 1.9.176. We used the formula y = (226.6 * x) * x^(log(10.7)), which had the lowest residual standard error (6.1) that we could achieve (Fig. S10). For our global species richness analysis, we used this distribution to randomly generate sample sizes for the remaining 4322 no-occurrence species. For the global-, continent-, and country-level analyses, and for those species that occur in the Discover Life country checklist but not in the occurrence dataset, we applied the above formula and capped the distribution to the maximum empirical species sample size for each country.

To compare the rate of new species descriptions, first occurrences, and synonyms, we needed to filter orthographic variants out of the greater synonym list. To do this, we grouped our data by accepted id (i.e. accepted name) and author year (without brackets). We then removed numbers from names, and compared all within-group combinations using tidystringdist version 0.1.477 and igraph version 2.1.478 and accepted only the first of each orthographic variant. This resulted in a total of 24,166 synonyms from a starting list of 30,831. We used the year of description to build the synonym accumulation curve; hence, the rate of accumulation does not equate to the year when names were synonymized but their initial description.

Estimates and visualisations of species richness
To examine patterns of species richness and discovery, we analysed several datasets in statistically independent ways. Firstly, we used the bee taxonomy and the year of first description to plot species accumulation (Fig. 1D). Secondly, we used the combined species occurrence and literature datasets (globally, by continent, and by country) and estimated (i) the lower bound of species richness using iChao162 with SpadeR version 0.1.179 and (ii) Hill numbers using iNext 3.0.163. In order to run SpadeR and iNEXT analyses, we built functions, ChaoWrapper, iNEXTwrapper, and ggRichnessWrapper, that allow multiple levels to be analysed at once in parallel (https://github.com/jbdorey/BeeBDC). The iChao estimate is known to perform well when species abundances in ‘communities’ are homogenous and can be negatively biased where they are heterogenious62. Hill numbers, as estimated in iNEXT, are implemented to make fairer comparisons between assemblages that may vary in size63,64. We further built a function, richnessEstimateR, that allowed us to iteratively sample the literature curve 100 times at the global-, continent-, and country-levels and extract the estimates, upper, and lower confidence intervals for both statistics. Especially at the global level, these analyses took up to a couple of weeks, even using a 20-core Mac Pro. These functions are deployed in BeeBDC ≥v1.3.1.

Country-level exclusions
We excluded countries from analyses for several data-related reasons. Firstly, we entirely excluded all countries with a sample size (number of species occurrence records) of less than 30. Secondly, we excluded countries where the species estimates were excessively different (by an order of magnitude) from the observed species richness. This was usually because the number of occurrences approximately equalled the number of species, because the maximum species-level sample size was one.

Diversity gap correlates
We looked to determine the potential drivers of diversity gap estimates; i.e. predictors of the number or percentage of species remaining to be described per species. We correlated our iChao estimates against GDPc48, tertiary education49, the observed species richness, the number of clean BeeBDC records, elevational range50, country area51,80, median distance from roads52, and the proportion of species derived from the literature curve. We analysed these variables using a linear mixed-effects model in R package, lmerTest version 3.1-381 and iteratively removed interaction terms. Linear mixed-effects models are versatile models that are also reasonably robust to mild violations of assumptions, such as skewed data and correlated predictors82. When trying to explain the (i) number of new species predicted and (ii) the predicted percentage increase of species from iChao, we took the log of all variables and included an interaction term between the number of clean records and the proportion of species derived from the literature curve. We took the same approach to examine the relationship between the number and percentage of predicted new species.

Furthermore, and in order to examine the interaction between terms, we extracted data from a curve based off of the (a) global and (b) per-country species occurrence data frequencies as we did with the literature data (Fig. S10). These data were normalised with the literature curve to work from the same scale during formula production and plotting. We ran these analyses ten times at the country level for iChao and iNEXT to examine how changing empirical data that fills in the no-occurrence species impacts the diversity gap analyses. We ran further tests by removing combinations of fixed effects, applying different transformation factors, iteratively removing interaction factors, applying different transformations (none, log, exponential, or square root), and applying iChao- or iNext-derived sample sizes rather than the raw number of clean records.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "How many bee species are there? A quantitative global estimate\nIn this manuscript the authors estimate the number of bee species in the world. They estimate an 18–25% increase on the\nknown diversity, and suggest that this will require at least 32–45 years of taxonomic research in order to describe the new\nspecies..\nThe authors take novel approaches to estimating bee diversity and it is helpful to have such a well-known group intensively\nstudied in this way since it provides ways for others to replicate with other groups. However, in my view the authors have\nattempted to include too much in this manuscript and would be better served by separating it out into possibly three\nmanuscripts. For example the socio-economic factors contributing to lack of taxonomic effort on bees should be in a\nseparate paper. In addition, we don’t need to see all the graphs for all the different countries.\nI had some problems with the way the material is laid out. For example, in lines 193 and onwards it refers to hypotheses\nwhich are in Supp Materials. I think this is a mistake and the hypotheses should be upfront and not hidden away in\nsupporting information. Similarly line 117 refers to GDPc which is explained later in the manuscript. What is Darwin Core\n(line 181) – may be that is explained elsewhere? Looks like this manuscript was submitted to Nature first and then passed\non to Nature Communications – I suggest some consideration of expanding the main part of the article so that the reader is\nnot asked to go between a plethora of valuable supporting documents.\nFig 1 No need for ‘The’ in the title", "reviewer_2": "Thank you for the opportunity to review this manuscript. While it is not always apparent to readers, the amount of data\ncompilation and effort involved in assembling such country-level species lists—with associated metadata such as year of\ndescription and bibliographic references—is considerable. The integration of this foundation with openly available resources\nlike GBIF, alongside comprehensive data analysis, constitutes a valuable and credible contribution to the field. I find the\nmethodology both sound and appropriate.\nThe comments below are minor and relate primarily to data interpretation. These could be addressed either in the\nintroduction or the discussion:\nIn ecology, the notion of a shifting baseline can obscure emerging patterns. In the context of this study, it may be worth\nreflecting on what the baseline actually represents. Given the evolving nature of species concepts—both historically and\nprospectively—can the authors comment on how taxonomic practices have changed over time (then and now, only)? Are\nrecent discoveries more likely to involve the splitting of previously singular species/detection of cryptic taxa? A shift is\ncertainly reflected in increasingly complex identification keys.\nThe Fiji Lasioglossum is used as an example on multiple occasions (from 4 to 13), but at the same time also serves as a\nreminder of the difficulties. Several of those species are known from very few specimens. There might be additional still\nundescribed, and they were not revised previously. Is it then really an example of how phylogeny can inform, or simply yet\nanother group that had not previous taxonomic revisionary work conducted?\nThe projected 32–45 years of work needed to fully describe the fauna raises further questions: Is this projection based on a\ncontinuation of current levels of effort, or is a tapering expected as the species pool becomes more thoroughly sampled?\nHave taxonomists systematically prioritized the more accessible taxa first, leaving more challenging groups for future work?\nDifficulties may stem from either poor sampling efficiency or a lack of distinguishing morphological/molecular characters in\ncertain hyperdiverse clades.\nIn summary, I have no major concerns and am eager to cite this work and apply its methodologies to other groups, across\ndifferent spatial and taxonomic scales, in support of biodiversity research.", "reviewer_3": "In this manuscript, Dorey and colleagues present an analysis of a dataset published by some of the authors in 2023 (DOI:\n10.1038/s41597-023-02626-w) combined with a taxonomical framework and country-level checklist (from the Discover Life\nguide, also generated by some of the authors), and an additional literature survey to find additional species missing from\nthose data sources. Then, they used two alternative methods (both developed by Anne Chao and collaborators) to generate\nestimates of species richness (order 0 Hill number) – or its lower bound. By pooling all data, or binning by continent or\ncountry, global, continental or country-wise estimates are given. These estimates are then compared to the actual count of\ntotal species from the data at each of those levels, to generate what they call “taxonomic gaps”, meaning the “minimum\nnumber of species remaining to be discovered/described” at each of those levels. Then, they use linear models to try\ncorrelating the size of these gaps to several correlates like per capita GDP and continent- or country-specific traits of the\ndataset. The authors focused on bees (the group of hymenopteran families known as Anthophila), but their approach is\nstraightforward to apply to any other taxon. Although the methodology for the analysis is rather scantly described in the\nMethods, the authors make available a set of R-based resources that allow reproduction of their results, detailed insight in\ntheir methodological approach, and re-usability for other taxa.\nThe authors claim that their “framework and R package toolset we provide here represents the first of its kind for any insect\ntaxon and can be used for estimating actual taxonomic diversity across larger taxonomic and geographic scales”. They take\ntheir global estimate at face value and compare the continental and country level estimate to available checklists, discussing\ntaxonomic gaps as a fact. Honestly, I do not think this claim can be made. Other people have used Chao’s approximation on\ndatasets built from occurrence data to estimate richness (at regional and global scales, even for bees), or else tried using\ndata from checklists. There is even a previous 2021 article (10.1016/j.cub.2020.10.053) in which the authors already use the\nsame kind of data to estimate worldwide distribution patterns of bee richness.\nThe main issue with this analysis is that Chao’s estimates were mainly aimed at comparing diversity at the community level\nacross samples or sites and assume a level of homogeneity that is not really fulfilled by global datasets. If we are to accept\nthat these richness estimates are biologically valid as absolute quantities, at least some grounding evidence should be\npresented. However, at no point in the text is the validity of evidence ever questioned. The authors mentioned “haphazard\nspecies richness estimates mis-cited and proliferated in literature and science communication” without providing examples,\nbut the 20-21k number that is most often thrown (even here by the authors) comes from Michener’s book (though the authors\ncite their own online sources). Their estimate (between ~24k and ~26k species) looks like it could be right, but in the\nabsence of any measurement of the actual accuracy of the approach, these numbers are still (statistically informed) guesses.\nThis is not to say there is no value in this work – in fact, data curation and literature search made here is invaluable and\npraise-worthy. But it is crucial to make explicit the fact that the patterns detected in the analysis are data patterns, not\nbiological patterns. This is evidenced by the fact that correlated with taxonomic gaps were GDPc (negatively, marginally\nsignificant compared to other significant correlates), the number of species already known (positively, largest effect), the\nnumber of clean records (negatively), the proportion of species added from literature (positively), and the interaction between\nthe last two. All these correlates are directly related to data quality/distribution, while things like elevation range, road\ndistance or local education had no effect.\nIn summary, in my humble opinion, this manuscript represents a remarkable data wrangling and curation effort, combined with\ngeneration of very useful resources and tools usable way beyond the bee-based case study presented here. However, it\nsuffers from a high stakes claim presented without independent or grounding supporting evidence: that the estimators are\naccurate reflections of biological richness. Unless the authors can provide a measurement of the accuracy of their\nestimation approach, I think they should refrain from stating that their approach yields “actual taxonomic diversity”.", "reviewer_4": "This is an important paper in providing a replicable workflow for addressing the gap in estimates of species richness at\nglobal and country scales.\nI have made comments on the attached manuscript on a number of additional considerations to be made, which would\nstrengthen the interpretation of the results, and add discussion for further studies."}, "author_response": "Response: We really appreciate the reviewer’s comments here and we’re\nglad that they see a use in our current paper. We understand the concern about the\nbreadth of the paper and the wish to split it into multiple manuscripts. We will try to\naddress this comment by expanding the writing of the present paper to better\naccommodate the Nature Communications format. We generally prefer to keep the\nanalyses together here to tell a more-complete story, spanning from patterns to\nprocesses underlying them. A similar approach was taken with some of the authors’\n2021 paper on global bee distribution and we feel that this makes the paper here a\nsimilarly complete and highly citable treatment. We think that Nature\nCommunications is an ideal platform for such an expansive treatment. We are also\nhappier to leave all of the per-country graphs in the supplement as we believe that\nthis is an open and accessible way for those estimates to be accessed and\nassessed.\nI had some problems with the way the material is laid out. For example, in lines 193\nand onwards it refers to hypotheses which are in Supp Materials. I think this is a\nmistake and the hypotheses should be upfront and not hidden away in supporting\ninformation.\nResponse: We agree with the reviewer and are taking advantage of the\nincreased manuscript length to do just that.\nSimilarly line 117 refers to GDPc which is explained later in the manuscript.\nResponse: Many thanks for picking up that error, it is now defined at first use.\nWhat is Darwin Core (line 181) – may be that is explained elsewhere?\nResponse: Good point, this is maybe not common knowledge. We have now\nbriefly described it and provided a link to the data standard website.\n“… aggregate (e.g., using the Darwin Core occurrence data standard —\ndwc.tdwg.org) would …”\nLooks like this manuscript was submitted to Nature first and then passed on to\nNature Communications – I suggest some consideration of expanding the main part\nof the article so that the reader is not asked to go between a plethora of valuable\nsupporting documents.\nResponse: We thank the reviewer for this comment and note that we have\nnow made efforts to do just that in response to your review.\nFig 1 No need for ‘The’ in the title\nResponse: This has now been removed.\nReviewer #1 (Remarks on code availability):\nNA\nReviewer #2 (Remarks to the Author):\nThank you for the opportunity to review this manuscript. While it is not always\napparent to readers, the amount of data compilation and effort involved in\nassembling such country-level species lists—with associated metadata such as year\nof description and bibliographic references—is considerable. The integration of this\nfoundation with openly available resources like GBIF, alongside comprehensive data\nanalysis, constitutes a valuable and credible contribution to the field. I find the\nmethodology both sound and appropriate.\nResponse: Many thanks and much appreciated. Many hands make light\nwork!\nThe comments below are minor and relate primarily to data interpretation. These\ncould be addressed either in the introduction or the discussion:\nIn ecology, the notion of a shifting baseline can obscure emerging patterns. In the\ncontext of this study, it may be worth reflecting on what the baseline actually\nrepresents. Given the evolving nature of species concepts—both historically and\nprospectively—can the authors comment on how taxonomic practices have changed\nover time (then and now, only)? Are recent discoveries more likely to involve the\nsplitting of previously singular species/detection of cryptic taxa? A shift is certainly\nreflected in increasingly complex identification keys.\nResponse: This is a complex question and not one we can fully interrogate in\nthe current paper, although we have made efforts to give some increased\nexplanation. Essentially, the type of taxonomic work left to be done depends on the\narea, in line with our findings on underdescription. For those areas with more species\nleft to describe, typically even the most basic accounting and taxonomic descriptions\nof relatively obvious species will make up a substantial portion of the necessary\nwork. However, even the best studied countries like North America retain hundreds\nof species to be described in places like the species-rich SW USA deserts. In many\ncases, these remaining species belong to large groups that are more complex to\nrevise, which inhibits quick description because such groups are best treated in total\nwith comprehensive descriptions and keys.\nIn regards to Africa, as an example where much work is to be done, we write:\n“In such cases, it seems likely that even initial efforts at taxonomic description and\nrevision in the region will uncover many new species and greatly enhance our ability\nto work with and understand the fauna.”\nConversely, we explain the situation for better known regions as “Even in the\nbest studied regions such as North America, many species may persist undescribed\nin larger groups (such as Andrena or Osmia in the USA) that are best treated\naltogether to enhance identifiability and ensure accurate taxonomic actions.”\nWe then return later when discussing overall drivers to reinforce the\noverarching situation: “There are opportunities for foundational and traditional\ntaxonomy in areas such as Africa to more coordinated efforts on larger or more\nchallenging groups in places such as North America. For the former, integrative\nsystematics will build strong taxonomic scaffolds early and for the latter it will allow\nrobust testing of existing species hypotheses.”\nWe also add more nuance and raise the possibility of empirically testing this\nissue “Additionally, sequencing efforts can, and do, result in the discovery of\nsynonyms (rejections of existing species hypotheses)33. Large barcoding efforts may\nelucidate, by region, the extent to which existing taxonomies have over- or under-\ndescribed species.”\nFinally, our valid name accumulation curve actually represents the number of\nspecies that remain valid today. And so, at least since 1960, the amount of “good”\nnames described per year has remained stable. As for the changes in rate prior to\nthat further research might uncover the degree to which the number of active\ntaxonomists and the taxonomic gaps might have contributed.\nThe Fiji Lasioglossum is used as an example on multiple occasions (from 4 to 13),\nbut at the same time also serves as a reminder of the difficulties. Several of those\nspecies are known from very few specimens. There might be additional still\nundescribed, and they were not revised previously. Is it then really an example of\nhow phylogeny can inform, or simply yet another group that had not previous\ntaxonomic revisionary work conducted?\nResponse: This example was used only once in the manuscript. However, I\nsee the reviewer’s point that some context is missing here. We have added in that\nthe group is morphologically cryptic. Additionally, the initial descriptions around the\n1920’s included only two species, and the subsequent revision around the1970’s\nincluded four species, but we know that samples from several more species were\nincluded under the current name “Lasioglossum fijiense”. Hence, these went\nundescribed using purely morphological analyses.\n“In a recent and contrasting example, the morphologically cryptic Fijian\nLasioglossum bees went from four morphologically described species to 13 when\nsystematic techniques were included28, and recent molecular evidence suggests at\nleast 28 total species29; a 3.3- and 7-fold increase, respectively”\nThe projected 32–45 years of work needed to fully describe the fauna raises further\nquestions: Is this projection based on a continuation of current levels of effort, or is a\ntapering expected as the species pool becomes more thoroughly sampled?\nResponse: This is a wonderful question that we would like a good answer to\nourselves. We state that this is at the current rate of descriptions, but also already\nnote that we expect a tapering of discovery. Again, this is a fascinating line of\nquestioning, but we don’t see a robust way to test that at present, especially\nconsidering that the rate of description has been stable for some time.\n“Applying this rate directly we might expect to close the taxonomic gap in\n32–45 years. However, given that our richness estimates are a lower bound, the\nintegration of phylogenetic analyses can be very limited (Table S1), research funding\nand capacity is geographically biased37, reduced support for taxonomy, and the\nlikely diminishing returns of species revision towards the tail end of discovery,\nwe expect this estimate to be quite conservative. ”\nHave taxonomists systematically prioritized the more accessible taxa first, leaving\nmore challenging groups for future work? Difficulties may stem from either poor\nsampling efficiency or a lack of distinguishing morphological/molecular characters in\ncertain hyperdiverse clades.\nResponse: This is a good question. We don’t have a universal response for\nthat, as taxonomic discovery and description are a process at different stages\ndepending on the region. We can point out that, in Australia, some of the more-\nrecent taxonomic works, some very difficult groups have been tackled — several\nrevisions of Lasioglossum groups for example. But also a recent revision of\nMegachile (Austrochile) which could be considered either easy or difficult depending\non how you look at it. Bombus is a great example of a group that might be assumed\nto be easy but in fact is very challenging due to mimicry etc. Anthophorini could be\nan example of a charismatic and long-studied group that is actually very difficult and\nunresolved.\nWe can’t comment too much and aren’t sure how to get at that question easily\nor at a global scale, as this requires different methods beyond our scope and\npossibly even expert opinion surveys, but we hope that the added text from above\nwhere discussing the process helps to suffice and explain the current state of things\n(especially in regards to many new species clustered in bigger, tougher groups).\nIn summary, I have no major concerns and am eager to cite this work and apply its\nmethodologies to other groups, across different spatial and taxonomic scales, in\nsupport of biodiversity research.\nResponse: Once again, many thanks for your positive and thoughtful\ncomments. There is room to think more and look for ways to answer more questions\nabout this area of research.\nReviewer #3 (Remarks to the Author):\nIn this manuscript, Dorey and colleagues present an analysis of a dataset published\nby some of the authors in 2023 (DOI: 10.1038/s41597-023-02626-w) combined with\na taxonomical framework and country-level checklist (from the Discover Life\nguide, also generated by some of the authors), and an additional literature survey to find\nadditional species missing from those data sources. Then, they used two alternative\nmethods (both developed by Anne Chao and collaborators) to generate\nestimates of species richness (order 0 Hill number) – or its lower bound. By pooling all data, or\nbinning by continent or country, global, continental or country-wise estimates are\ngiven. These estimates are then compared to the actual count of total species from\nthe data at each of those levels, to generate what they call “taxonomic gaps”,\nmeaning the “minimum number of species remaining to be discovered/described” at\neach of those levels. Then, they use linear models to try correlating the size of these\ngaps to several correlates like per capita GDP and continent- or country-specific\ntraits of the dataset. The authors focused on bees (the group of hymenopteran\nfamilies known as Anthophila), but their approach is straightforward to apply to any\nother taxon. Although the methodology for the analysis is rather scantly described in\nthe Methods, the authors make available a set of R-based resources that allow\nreproduction of their results, detailed insight in their methodological approach, and\nre-usability for other taxa.\nResponse: Thank you for your constructive feedback. We do describe our\nmethods relatively concisely, although we hope with enough information to be\nreproducible. However, our results are truly reproducible through making all of our\ncode and data publicly available on GitHub. We are happy to add additional detail\nwherever it is suggested as necessary.\nThe authors claim that their “framework and R package toolset we provide here\nrepresents the first of its kind for any insect taxon and can be used for estimating\nactual taxonomic diversity across larger taxonomic and geographic scales”. They\ntake their global estimate at face value and compare the continental and country\nlevel estimate to available checklists, discussing taxonomic gaps as a fact. Honestly,\nI do not think this claim can be made. Other people have used Chao’s approximation\non datasets built from occurrence data to estimate richness (at regional and global\nscales, even for bees), or else tried using data from checklists. There is even a\nprevious 2021 article (10.1016/j.cub.2020.10.053) in which the authors already use\nthe same kind of data to estimate worldwide distribution patterns of bee richness.\nThe main issue with this analysis is that Chao’s estimates were mainly aimed at\ncomparing diversity at the community level across samples or sites and assume a\nlevel of homogeneity that is not really fulfilled by global datasets.\nResponse: We think that this is an interesting point to raise and it does have\nsome merit. However, we don’t agree that the Chao’s estimates, particularly iChao,\nhas such a narrow definition. For example, Chao described statistical estimates of\n“richness” very broadly to include “They may be biological species, bugs in software\nprograms, words in a book, genes or alleles in genetic code, or other discrete\nentities”. Additionally, iChao is a non-parametric estimator in the sense that “In this\narticle, we focus on a nonparametric approach in the sense that no assumptions are\nmade about the underlying distribution of species abundances”.\nHeterogeneity — in species-level abundances — can indeed negatively bias\nChao’s estimators. However, this is likely to be the case in any community.\nAdditionally, see: “In the homogeneous model (Figure 1a and Web Table C1), the\nChao1 and the iChao1 estimators are close to each other (the latter has slightly\nhigher positive bias), and both are nearly unbiased. For heterogeneous models\n(CVformula0), both estimators have negative biases because they are derived\nas lower bounds. Nevertheless, they follow the intuitive pattern: The magnitude of\nbias and RMSE decrease as sample size increases. The iChao1 estimator has\nsmaller bias (Figure 1), but larger variance (in all tables) than the Chao1 estimator\ndue to its use of more frequencies to estimate the number of undetected species. In\nterms of bias, the proposed iChao1 estimator always outperforms the Chao1\nestimator (Figure 1).” — https://doi.org/10.1111/biom.12200\nIn comparison, hill numbers (iNEXT), “When q = 0, 0D is simply species\nrichness, which counts species equally without regard to their relative\nabundances.” — https://doi.org/10.1111/2041-210X.12613\nI have added “The iChao estimate is known to perform well when species\nabundances in “communities” are homogenous and can be negatively biased where\nthey are heterogenious69. Hill numbers, as estimated in iNEXT, are implemented to\nmake fairer comparisons between assemblages that may vary in size59,71.” To the\nmethods and have added another reference (71). We think that our set of estimators\ntogether provides a relatively less biased and highly reliable estimate of bee species\nrichness at the global scale.\nNotably, we should also add that the 2021 paper by some of the authors was\nunable to use absolute richness, only relative expected species richness. This paper\nis a very big step forward from the methods of the prior one, in terms of both the data\navailable and the efforts to ascertain true species richness values for bees globally.\nIf we are to accept that these richness estimates are biologically valid as\nabsolute quantities, at least some grounding evidence should be presented.\nHowever, at no point in the text is the validity of evidence ever questioned.\nResponse: We argue that we do question the validity of our evidence\nthroughout the text. However, we also add further caveats to try and make our\nuncertainties clearer. For example:\n“Our estimates provide a powerful guide and useful hypotheses for where to\nconcentrate species discovery and revisionary research”\n“However, for Africa, increasing GDPc appeared to be associated with an\nincrease in the number of undescribed species (Fig. S11A). This likely reflects an\noverall larger taxonomic gap for Africa and an inability to reliably estimate\ntrends due to a lack of data (Figs S11F-G).”\n“Without log-transformation we maintained our hypotheses that the number of\nclean occurrence records and the proportion of occurrence-based species were\nnegatively associated with our response variables because both variables indicate\ngreater sample completeness associated with more taxonomic work and material\n(Supp. Materials). This makes intuitive sense, as areas with more data should\nhave undergone more taxonomic research and identification work necessary\nto generate such data.”\n“To determine the potential impact of sampling species counts from the\nempirically derived literature curve, we further sampled the two curves based\non the empirical country- and global-level occurrence datasets. Most estimates\nwere robust to changes in the chosen curve, with the country-sampled\noccurrence curve sometimes producing higher estimates than the literature or global\ncurves (Figs. S10 and S13). This suggests that our use of the empirical literature\ncurve is a conservative and a robust choice and hence useful for implementation\nwith other taxa.”\n“Verification of our results could also be achieved by surveying the\nnumber of undescribed species in museums. However, this would require a\ncolossal digitization effort which has not even been adequately done for described\nmaterials.”\n“The relatively low estimate for Oceania was unanticipated. However, we\ncaution that this might be driven by relatively high data availability in Australia,\nbut poor regional availability, and insufficient use of molecular techniques even in\nrecent taxonomic research. Insufficient implementation of molecular techniques in\ntaxonomy is an issue for many countries and taxa.”\n“We applied several tests of the data and found that the significant\npatterns and directions of fixed effects in our model were mostly robust to\nchanges in the models; however, changing the transformations could alter the\nresults. Importantly, without log-transforming the number of clean records and\nthe proportion of occurrence-based species the significance values were\nidentical but both fixed effects variables were negatively associated with the\nresponse variables. We additionally sampled curves derived from the global- and\ncountry-level species occurrence data (Fig. S10). Except for GDPc, and — for the\nglobal dataset — the interaction term between the number of clean records and the\nproportion of occurrence-based species, the significance and sign of the remaining\nfixed effects were consistent (Table S3).”\n“Furthermore, and in order to examine the interaction between terms, we\nextracted data from a curve based off of the (a) global and (b) per-country\nspecies occurrence data frequencies as we did with the literature data (Fig.\nS10). These data were normalized with the literature curve to work from the same\nscale during formula production and plotting. We ran these analyses ten times at\nthe country level for iChao and iNEXT to examine how changing empirical data\nthat fills in the no-occurrence species impacts the diversity gap analyses.”\nThe authors mentioned “haphazard species richness estimates mis-cited and\nproliferated in literature and science communication” without providing examples, but\nthe 20-21k number that is most often thrown (even here by the authors) comes from\nMichener’s book (though the authors cite their own online sources). Their estimate\n(between ~24k and ~26k species) looks like it could be right, but in the\nabsence of any measurement of the actual accuracy of the approach, these numbers are still\n(statistically informed) guesses.\nResponse: This is a good comment and we have added reference to\nMichener’s book in our manuscript “In 2007, Michener 23 estimated that there were\n~18,000 described species with >20,000 total in the world.”. We think that this\nimproves the manuscript and the context.\nHowever, Michener estimated that there were 20k+ species in the world and\nalso references conversations with John Ascher (an included author) and on an early\ndraft of the Discover Life checklist in coming up with these estimates. The numbers\ncited in the manuscript actually refer to the current empirical number of described\nbee species in the world. We still argue that this is a haphazard estimate “A guess\nas to the total number of bee species in the world is therefore near or above the\noften-mentioned figure of 20,000.” — Michener 2007\nThis is not to say there is no value in this work – in fact, data curation and literature\nsearch made here is invaluable and praise-worthy. But it is crucial to make explicit\nthe fact that the patterns detected in the analysis are data patterns, not\nbiological patterns. This is evidenced by the fact that correlated with taxonomic gaps were\nGDPc (negatively, marginally significant compared to other significant correlates),\nthe number of species already known (positively, largest effect), the number of clean\nrecords (negatively), the proportion of species added from literature (positively),\nand the interaction between the last two. All these correlates are directly related to\ndata quality/distribution, while things like elevation range, road distance or local education\nhad no effect.\nIn summary, in my humble opinion, this manuscript represents a remarkable data\nwrangling and curation effort, combined with generation of very useful resources and\ntools usable way beyond the bee-based case study presented here. However, it\nsuffers from a high stakes claim presented without independent or grounding\nsupporting evidence: that the estimators are accurate reflections of biological\nrichness. Unless the authors can provide a measurement of the accuracy of their\nestimation approach, I think they should refrain from stating that their approach\nyields “actual taxonomic diversity”.\nResponse: We understand the reviewers concern here. While we also point\nout that we state that we are “estimating actual taxonomic diversity”.\nSadly, no census for bee species richness exists for any country. However,\nwe did have some assumptions about countries that might asymptote prior to\nbeginning our analyses like Sweden and Singapore and these countries did indeed\n[nearly] asymptote. We did not formalise these hypotheses and so did not consider it\nfair to present as evidence.\nWe do additionally provide some tests of the impact of how we generate the\nliterature curve and believe that we have made a conservative estimate. Additionally,\nfrom estimates made by Chao et al. we believe that we have conservative estimates\nof species richness\n(https://figshare.com/articles/dataset/Appendix_F_Using_simulation_to_test_the_pro\nposed_analytic_estimators_/3568410?backTo=%2Fcollections%2FRarefaction_and\n_extrapolation_with_Hill_numbers_a_framework_for_sampling_and_estimation_in_s\\pecies_diversity_studies%2F3309822&file=5645115).\nI also feel that similar concerns could be raised around most large-scale\necological modelling techniques. But, we maintain that our estimates are more\nreliable than any haphazardly-derived estimates to date and they provide estimates\nof error around which to test. However, we do want to make this clear and so have\nadded the following paragraph:\n“Such analyses should be made and explained with caveats kept in mind. For\nexample, while iChao and Hill numbers are relatively robust estimators of species\nrichness within assemblages55-57, our “assemblages” are large. Global species\noccurrence datasets, while relatively extensive, have inherent biases28; however,\nbiases abound for perhaps all sampling methods58. We expect that taxonomic\nconcepts will also impact our estimates, particularly where rare or cryptic species are\nleft undescribed or lumped. While we state species richness estimates here at\nseveral levels, we highlight that they are estimates of the lower bound of diversity\n(biases are likely to further decrease estimates55-57) and that they can be\nsomewhat sensitive to input data (e.g., Figs S10 and S13). We suggest that\nestimates, while better than haphazard estimates, are appropriately used as\nstatistically derived hypotheses and roadmaps for future work and management.”\nWe hope that you find the added explanation and caveats sufficient to make\nclear the potential limits of the study and the need for future work to provide\nvalidations of our estimates provided here.\nReviewer #3 (Remarks on code availability):\nI did a very quick review of what is available, including the vignette and the contents\nof the GitHub repo. I did not try to clone or replicate the analysis locally (considering\nthe reported runtimes) nor took the time to start digging into the code to try to\nfigure out how exactly it was done. I found that the description given in the\nMethods section was not enough to figure out the structure of the input data; I also\nonly partially followed how they \"generated\" missing data out of literature-retrieved\nspecies with no occurrence data. I am quite certain the code is complete and runs\ncorrectly; however, I am under the impression that a reader should not have to get\ninto the nitty-gritty details of the R code and scripts to figure out how results were\ngenerated.\nResponse: We will undertake a check of the methods to see if things can be\nmade clearer and if the editor believes further clarification is required in the available\nspace, we will seek to further simplify. We do agree that readers should not\nnecessarily need to understand the R code to understand the analysis. However, R\ncode of course provides an opportunity for any reader to get into the nitty-gritty and\nsee exactly what we have done. Both our annotated workflow and our vignette are\nintended to serve this purpose as a supplement to understanding what’s presented\nin the manuscript.\nReviewer #4 (Remarks to the Author):\nThis is an important paper in providing a replicable workflow for addressing the gap\nin estimates of species richness at global and country scales.\nResponse: We greatly appreciate your supportive words and hope that the\nmanuscript will have a positive impact more broadly.\nI have made comments on the attached manuscript on a number of additional\nconsiderations to be made, which would strengthen the interpretation of the results,\nand add discussion for further studies.\nNotes from the annotated document:\n“I think it might be prudent to look at a phylogenetic signal” … also notes on\nthe number of taxonomists and undescribed specimens in museums.\nWe appreciate this comment and we had considered attempting to include\nphylogenetic information. However, we consider the existing data on bees to be\ninsufficient to make many meaningful assertions. An alternative might be to run\nthese analyses at higher-level groupings like family, subfamily, or tribe. However, we\nconsidered that this would take an already massive manuscript and blow it up too\nfar. It could be a good topic for a further manuscript.\nWe also agree that correlating the number of taxonomists and undescribed\nspecies in museums would be a fantastic way to attempt to examine our results.\nHowever, many problems arise, particularly in compiling those data (especially the\nlatter would be impossible without a huge funding and digitization effort for each\ncountry). Similarly, people often do different quantities of taxonomic work at different\nstages in their career these days, depending on the career expectations and other\nconsiderations in their fields and areas. Regardless, we have noted all of these in the\nmanuscript because we do agree with your assertions, and perhaps there are taxa\nfor which those data will be easier to compile.\n“But the Austrochile just had 71 new species described … with previously\n1700 described species , and generally estimates by taxonomists eg Terry Houston\nthere’s at least 500 undescribed species, that suggests a taxonomic gap of about\n30%”\nResponse: Yes, you are absolutely correct and we have been unsure if we\nshould include these new papers in a revision of the work. The first author is also a\nco-author on that work but the timing of publications was not ideal. However, he is\nhoping to analyse those data separately and undertake a deeper dive into the\nAustralian data using datasets like that made available by Remko et al. Notably,\nAustralia was also a major challenge for earlier efforts at mapping bee species\nrichness (Orr et al. 2021), given the unusual colletid-heavy fauna and the distribution\nof most data records along the coast rather than the interior. As such, it will be useful\nto perform additional studies specifically on Australia.\n“Interesting to see the relative contribution of new species based on revisions\nof a group, vs new species based on new collection records etc.”\nResponse: That is a very interesting idea. There are some new R packages\nin development to digitise collection data from taxonomic works (amazing!). Such a\npackage will make answering such a question feasible — the lead author started to\ndigitise records from taxonomic works and the time input was too considerable to do\nthis at scale. We suspect that this could be done soon though and we’d be interested\nin trying to help out.\n“Is there a way to quantify this? E.g. number of authors on bee taxonomic\npublications per country over the last 20 years?”\nResponse: Another very good question. We had been getting at taxonomic\ncapacity through sociopolitical data and this would require building a new dataset of\nall of the people who have worked on bees and where they were from… or if they\nwere from multiple countries… maybe where they have worked throughout their life.\nWe thought that this was interesting but another large and detective-intensive\ndataset to collate. But, again, we think that this could be an interesting exercise! We\ndo at least have the last names of all bee taxonomists and the years that they were\nactive! Then you have to separate the Schwarz’s and the Schwarz’s, etc.\nAdditionally, we’d need to think about the probable increase in the number of co-\nauthors on more recent taxonomic works and how to measure those. For the present\nmanuscript, we feel like this is outside of the scope, given that we use multiple\nmetrics that should correlate relatively well with numerical taxonomic capacity.\n…The remaining minor line comments have all been integrated in the new\nmanuscript\nReviewer #4 (Remarks on code availability):\nThe vignette is extremely useful and I wish more papers included one.\nResponse: We greatly appreciate your kind comments in this regard. We\nagree with you in this instance that the best way to have method actually\nimplemented by the research community is to remove as many barriers to use as\npossible."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I am happy that the authors have addressed my main concern that there was too much information in the main document. I\nthink the manuscript is ready to be accepted", "reviewer_2": "Dear authors and editor,\nI have checked the concerns raised by myself in the first review and the responses now provided by the authors. I think they\nhave explained and addressed all well and have no further observations.", "reviewer_3": "After reading replies to comments made by myself and the other reviewers, I am under the impression that while they are\nmostly correct and acceptable, we will still get to the point of \"agreeing to disagree\". I believe the issue is not really about the\nactual work (which is laudable), but about how it is portrayed in the title and abstract... and how it will be used by the press\nand superficial readers. The main point in this work deals with estimating the gap between described diversity and\nexpected diversity: it is a valid approach with important implications, as stated throughout the manuscript. However, the title and\nabstract stand out as selling something else, that is, the absolute number of bee species - the term \"taxonomic gap\" is buried\nin the abstract -, and most uncritical readers (that is, most people looking for something to cite when introducing their bee\nwork) will just read the title and cite the article even if their work has nothing to do with the taxonomical gap. I believe even a\nsubtle change in the title could make it clear what the work is really about, e.g., \"How many bee species are still\nundescribed?... \".\nI have one additional criticism about the new paragraph stating hypotheses. At least within the framework of the hypothetico-\ndeductive method, a hypothesis is a broad, testable explanation for an observed phenomenon. It proposes a potential\ncause-and-effect relationship or a general principle. It's the underlying reason you think something happens. It is not the\nexpected result of a specific experiment or analysis. Yet that is exactly what is presented as a hypothesis in statements like \"\n(w)e hypothesised that elevational range and country area would be positively correlated with our\n86 response variables due to increased niche diversity and potential difficulty in sampling\". I am very aware that this misuse\nof the term \"hypothesis\" is widespread... however, two wrongs (or too many, in this\ncase) do not make a right. The hypothesis should be a tentative, testable explanation to an observed pattern, and there is a general one here related to the\nspatial (regional) variance in taxonomic gap across the world, and that is that current and historical differences in\nsocioeconomic and scientific investment generate uneven rates of species description. Based on that general hypothesis,\nseveral predictions about the outcomes of specific regression analyses can be stated (similar to the presented\n\"hypotheses\"). I encourage the authors to revise this paragraph.", "reviewer_4": "The authors have largely addressed many of my concerns thank you.\nWhat I think could strengthen the paper, given the many unknowns is to have a section specifically stating these e.g.\n- Needing to better ascertain new findings via collections vs revisions\n- New species based on surveying unsurveyed areas vs surveyed areas\n- The number of taxonomists / outputs per taxonomist\n- Number of new species that result from molecular techniques and number that are synonymised as a result (average)\n- Phylogenetic signal\n(and some of the other comments from other reviewers) as a 'call to action'\n- Needing to conduct greater censuses, ESPECIALLY in places where records were made decades ago and none since -\nare these species even still extant? It may be that many species are now extinct...\nIn the section on molecular techniques, it might be worth emphasising that such methods alone are notn necessarily reliable\n(e.g. describing species based on a barcode alone, without morphological analysis)\nI also agree with one of the other reviewers in that the authors should be more cautious in stating it yields “estimated actual\ntaxonomic diversity”. Even as estimates, I don't think you can say 'actual taxonomic diversity'; with the many unknowns, this\nis an estimate of taxonomic diversity. I don't know why it is 'actual' when there's no census for many countries.\nI recommend minor revisions.\nSincerely,\nDr Kit Prendergast"}, "author_response": "REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nI am happy that the authors have addressed my main concern that there was too\nmuch information in the main document. I think the manuscript is ready to be\naccepted\nResponse: We greatly appreciate your efforts in improving our work!\nReviewer #2 (Remarks to the Author):\nDear authors and editor,\nI have checked the concerns raised by myself in the first review and the responses\nnow provided by the authors. I think they have explained and addressed all well and\nhave no further observations.\nResponse: We thank you for your effort and your help!\nReviewer #3 (Remarks to the Author):\nAfter reading replies to comments made by myself and the other reviewers, I am\nunder the impression that while they are mostly correct and acceptable, we will still\nget to the point of \"agreeing to disagree\". I believe the issue is not really about the\nactual work (which is laudable), but about how it is portrayed in the title and\nabstract... and how it will be used by the press and superficial readers. The main\npoint in this work deals with estimating the gap between described diversity and\nexpected diversity: it is a valid approach with important implications, as stated\nthroughout the manuscript.\nHowever, the title and abstract stand out as selling something else, that is, the\nabsolute number of bee species - the term \"taxonomic gap\" is buried in the\nabstract -, and most uncritical readers (that is, most people looking for something to\ncite when introducing their bee work) will just read the title and cite the article even if\ntheir work has nothing to do with the taxonomical gap. I believe even a subtle\nchange in the title could make it clear what the work is really about, e.g., \"How many\nbee species are still undescribed?... \".\nResponse: We have adopted the title suggested by the journal\nI have one additional criticism about the new paragraph stating hypotheses. At least\nwithin the framework of the hypothetico-deductive method, a hypothesis is a broad,\ntestable explanation for an observed phenomenon. It proposes a potential cause-\nand-effect relationship or a general principle. It's the underlying reason you think\nsomething happens. It is not the expected result of a specific experiment or\nanalysis. Yet that is exactly what is presented as a hypothesis in statements like\n\"(w)e hypothesised that elevational range and country area would be\npositively correlated with our response variables due to increased niche\ndiversity and potential difficulty in sampling\". I am very aware that this misuse of\nthe term \"hypothesis\" is widespread... however, two wrongs (or too many, in this\ncase) do not make a right. The hypothesis should be a tentative, testable explanation\nto an observed pattern, and there is a general one here related to the spatial\n(regional) variance in taxonomic gap across the world, and that is that current and\nhistorical differences in socioeconomic and scientific investment generate uneven\nrates of species description. Based on that general hypothesis, several predictions\nabout the outcomes of specific regression analyses can be stated (similar to the\npresented \"hypotheses\"). I encourage the authors to revise this paragraph.\nResponse: We have revised this paragraph and believe that it reads better\nnow. The paragraph is also in line with the reviewer’s suggestions.\n“We hypothesize that contemporary and historical differences in\nsocioeconomic and scientific investment as well as physical regional characteristics\nhave generated uneven rates of bee species description across regions. We propose\nseveral predictions in relation to our response variables, predicted number and\npercentage of new species, to address this hypothesis. Firstly, due to increased\nniche diversity and potential difficulty in sampling, we predict that our response\nvariables will be positively correlated with elevational range and country area.\nSecondly, because increased median distance to nearest road could make sampling\nfor bees more difficult, our response variables will be positively correlated with\nmedian distance to nearest road. Thirdly, we predict that our response variables will\nbe positively correlated with observed species richness. This is because we expect\nthat the taxonomic load might be higher; however, we also recognized that it could\nindicate that a larger portion of the bee diversity was already described. Fourthly, we\npredict that our response variables will be negatively correlated with gross domestic\ncapital per capita (GDPc) and education. This is because we expected that GDPc\nand education would be indicators of a country’s investment into research and a\ngreater capacity to undertake taxonomic works locally or to host collaborating\ntaxonomists. Finally, we predicted that, due to greater inventory and taxonomic\nefforts, our response variables would be negatively correlated with the number of\nclean occurrence records and the proportion of occurrence-based species per\ncountry.”\nReviewer #4 (Remarks to the Author):\nThe authors have largely addressed many of my concerns thank you.\nWhat I think could strengthen the paper, given the many unknowns is to have a\nsection specifically stating these e.g.\n- Needing to better ascertain new findings via collections vs revisions\nResponse: I’m not certain exactly what the reviewer wants here. However, in\nrelation to collections vs revisions, we think that these should go hand-in-hand. We\nalso believe that this will be regionally specific but that most places with a taxonomic\ngap likely require both more collection effort and more revisionary works.\nRegardless, we have added some of this context here “However, for Africa,\nincreasing GDPc appeared to be associated with an increase in the number of\nundescribed species (Fig. S11A). This likely reflects an overall larger taxonomic gap\nfor Africa and an inability to reliably estimate trends due to a lack of data (Figs S11F-\nG). In such cases, it seems likely that even initial efforts at collections and\ntaxonomic research in the region will uncover many new species and greatly\nenhance our ability to work with and understand the fauna.”\n- New species based on surveying unsurveyed areas vs surveyed areas\nResponse: This is perhaps too specific a recommendation to give at a global,\ncontinental, or country level. We highlight the country level regions that require more\nwork, but most countries are large and have diverse regions that are sampled better\nor worse. Other works highlight at a more granular level where we might expect\nmore diversity to be found at a relative level (e.g., Orr et al 2021), even if they don’t\nmake quantitative estimates. Our workflow could be used very easily at lower spatial\nresolutions by treating Bioregions, Biomes, Vegetations classifications, etc. as\nhabitats for which to estimate species richness. But, it’s too granular for this specific\nwork. We think that this work should certainly be used to prioritise those granular\nefforts and that those efforts likely would strongly benefit from regional expertise to\ninterpret.\n- The number of taxonomists / outputs per taxonomist\nResponse: As in the last revision this isn’t as simple a project as you might\nthink. Particularly as authors with the same name need to be split per species. That\nwould represent a whole new digitisation and quality assurance phase. this requires\na very specific type of dataset on species, their types, and their authors, that we did\nnot have access to for this paper. I think that interesting work could be done based\naround this idea as a whole, rather than doing a poor job of it in this manuscript\nwhen it is tangentially related. I am also told that such an effort might already be\nunderway.\n- Number of new species that result from molecular techniques and number that are\nsynonymised as a result (average)\nResponse: This would again represent a major review process and would\nhave to form its own work. To be presented in this work would require a global\nreview of all bee taxonomy papers. This should be done systematically if it’s to be\ndone.\n- Phylogenetic signal\n(and some of the other comments from other reviewers) as a 'call to action'\nResponse: We’re not sure what the reviewer is suggesting here. Phylogenetic\nsignal — as the tendency of closely related species to resemble one another —\ndoesn’t immediately seem to fit into our work. I agree that using phylogenetics at\nsome large scale would be interesting. A large global bee phylogeny exists, but this\nmatrix-based phylogeny is not well-enough resolved to be of global use. We also\nthink that such a work should be done with great care and attention to detail and\nwould be worthy of its own hypothesis-driven science. Our present statistics are,\nby-and-large, taxon-agnostic.\n- Needing to conduct greater censuses, ESPECIALLY in places where records were\nmade decades ago and none since - are these species even still extant? It may be\nthat many species are now extinct...\nResponse: We hope that our work leads to great taking of census information\nand targeted research. To make broad claims at a global level would be too much for\nthis current paper — which has already been called too large for a single work by\none of the reviewers. We appreciate your enthusiasm and believe that there are\ngood ideas here. For us, we would like to see these ideas addressed fairly, with\nfocussed and data-driven research.\nIn the section on molecular techniques, it might be worth emphasising that such\nmethods alone are notn necessarily reliable (e.g. describing species based on a\nbarcode alone, without morphological analysis)\nResponse: See the section with “Hence, we should expect that these\nestimates will increase overall as molecular sampling techniques are implemented\nmore-broadly, sequencing costs fall, and data availability increases31. However, it is\nimportant that these techniques are used reliably and without over-\ninterpretation31,32.” We do not advocate for barcode-only taxonomy. We strongly\nadvocate for a robust and systematic approach to taxonomy. See also “This, in\ncombination with a lack of recent and thorough integrative systematic research in\nOceania, could explain the lower estimates where we otherwise expected higher\nrichness. A concurrently published paper, not included in our analyses, highlighted\nthis issue by describing 71 new species of Australian Megachile (Austrochile) (28%\nof the estimated taxonomic gap of 250 species) using systematic techniques40.”\nI also agree with one of the other reviewers in that the authors should be more\ncautious in stating it yields “estimated actual taxonomic diversity”. Even as\nestimates, I don't think you can say 'actual taxonomic diversity'; with the many\nunknowns, this is an estimate of taxonomic diversity. I don't know why it is 'actual'\nwhen there's no census for many countries.\nResponse: That line has already been removed. Perhaps the issue is with the\nword “Actual” in this instance? We can see that this might strengthen the sentence\nand its meaning and so we were happy to remove it.\nI recommend minor revisions.\nSincerely,\nDr Kit Prendergast"}]

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