
==== Front
Nat Commun
Nat Commun
Nature Communications
2041-1723
Nature Publishing Group UK London

51899
10.1038/s41467-024-51899-1
Article
Herbarium collections remain essential in the age of community science
http://orcid.org/0000-0002-4384-5041
Eckert Isaac isaac.eckert@mail.mcgill.ca

12
http://orcid.org/0000-0001-5547-0796
Bruneau Anne 23
Metsger Deborah A. 4
http://orcid.org/0000-0002-0865-8267
Joly Simon 35
http://orcid.org/0000-0003-1366-145X
Dickinson T. A. 46
http://orcid.org/0000-0002-6004-4027
Pollock Laura J. 12
1 https://ror.org/01pxwe438 grid.14709.3b 0000 0004 1936 8649 Department of Biology, McGill University, Montréal, QC Canada
2 Québec Centre for Biodiversity Science, Montréal, QC Canada
3 https://ror.org/0161xgx34 grid.14848.31 0000 0001 2104 2136 Institut de recherche en biologie végétale & Département de Sciences biologiques, Université de Montréal, Montréal, QC Canada
4 https://ror.org/00vcj2z66 grid.421647.2 0000 0001 2197 9375 Green Plant Herbarium, Department of Natural History, Royal Ontario Museum, Toronto, ON Canada
5 Montreal Botanical Garden, Montréal, QC Canada
6 https://ror.org/03dbr7087 grid.17063.33 0000 0001 2157 2938 Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, ON Canada
31 8 2024
31 8 2024
2024
15 758613 2 2024
8 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The past decade has yielded more biodiversity observations from community science than the past century of traditional scientific collection. This rapid influx of data is promising for overcoming critical biodiversity data shortfalls, but we also have vast untapped resources held in undigitized natural history collections. Yet, the ability of these undigitized collections to fill data gaps, especially compared against the constant accumulation of community science data, remains unclear. Here, we compare how well community science (iNaturalist) observations and digitized herbarium specimens represent the diversity, distributions, and modeling needs of vascular plants in Canada. We find that, despite having only a third as many records, herbarium specimens capture more taxonomic, phylogenetic, and functional diversity and more efficiently capture species’ environmental niches. As such, the digitization of Canada’s 7.3M remaining specimens has the potential to more than quintuple our ability to model biodiversity. In contrast, it would require over 27M more iNaturalist observations to produce similar benefits. Our findings indicate that digitizing Earth’s remaining herbarium specimens is likely an efficient, feasible, and potentially critical investment when it comes to improving our ability to predict and protect biodiversity into the future.

Here, the authors compare the diversity of vascular plants found in community science observations and digitized herbarium specimens, finding that with only one-third the records, herbaria still capture more data by several metrics.

Subject terms

Biogeography
Biodiversity
Macroecology
Data publication and archiving
Data integration
https://doi.org/10.13039/501100000038 Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (Conseil de Recherches en Sciences Naturelles et en Génie du Canada) CGS-D RGPIN-2019-05771 Eckert Isaac Pollock Laura J. issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

What was once the purview of trained scientists, collecting biodiversity data is rapidly becoming an endeavor of community (citizen) scientists logging species sightings into their phones rather than collecting physical specimens (Fig. 1). The Global Biodiversity Information Facility (GBIF) now has over 2.3 billion occurrence records, 50% of which were collected by community scientists since 20101. But despite this data windfall, existing information on biodiversity remains biased and largely incomplete2, limiting our ability to consider all aspects of biodiversity in conservation planning3. Overcoming these critical data shortfalls will rely on obtaining additional biodiversity data that captures the diversity and distribution of life on Earth. To that end, the digitization of Earth’s remaining natural history collections is a potentially feasible and efficient option.Fig. 1 Herbarium specimens and community science observations differ in terms of how they are collected and what biodiversity data they record.

Definitions (a) and examples (b, c) of community science observations, herbarium specimens, and the digitization process.

Natural history collections like herbaria might be less prone to some of the biases that pervade community science observations. While community science platforms like iNaturalist favor common and larger species and urban areas4,5, herbarium collections, despite sharing some biases6,7, better represent rare species and rural areas8 and contain a wealth of irreplaceable data9. For example, recent work by Daru & Rodriguez showed that natural history collections like herbaria outperform community science records in terms of spatial and taxonomic bias and better match expected biodiversity patterns8. However, the sheer rate at which we are accumulating community science observations may eventually negate its biases and so it remains unclear whether the digitization of Earth’s remaining 314M herbarium specimens is needed to overcome existing data shortfalls10,11.

Alongside data coverage, it is also important to understand whether we have enough data to build reliable models, which are often essential for converting biodiversity data into a form useable for conservation needs. The recent Kunming-Montreal Global Biodiversity Framework reinvigorated humanity’s effort to protect Earth’s biodiversity over the next few decades12, but to inform conservation policy amid climate change, nations around the world need a basic understanding of current and future species distributions13–15. Species distribution models (SDMs), which predict species occurrence across geographic space and time based on abiotic and biotic predictors, are increasingly used for this purpose. But SDM performance is limited by incomplete and biased biodiversity data that poorly capture the full extent of species’ environmental niches16–18. The ability of herbarium specimens versus community science observations to represent species’ niches is untested and so it remains unclear how the accumulation of additional iNaturalist observations versus the digitization of existing herbarium specimens might improve our ability to describe, quantify, and model biodiversity. To fill this gap, we assess the ability of both data types to capture the taxonomic, phylogenetic, and functional diversity and environmental niches of Canada’s vascular plants. Additionally, we leverage the coordinated network of herbaria and detailed information on the extent of undigitized herbarium collections in Canada to predict the potential gains in biodiversity knowledge and ability to model species distributions that could be accrue from digitizing remaining specimens.

We report that, compared to iNaturalist observations, herbarium records exhibit less bias and more efficiently represent both the diversity and distributions of Canadian plants. Moving forward, we estimate that the digitization of Canada’s remaining herbarium specimens could greatly benefit our knowledge of plant biodiversity, potentially quintupling our ability to model species’ spatial distributions. Finally, despite the growing rate at which we are accumulating new observations, it is unlikely that community science alone can match the benefits of herbarium digitization in the near future, pointing to the likely critical importance of herbaria and their collections for informing conservation planning to reach our 2030 goals and beyond.

Results

For all of Canada’s 4392 vascular plant species, we downloaded observation data from GBIF for Canada and the United States from the year 1900 to present (January 2024), which resulted in a total of 12,293,856 records across 3968 species. After removing those with high spatial uncertainty (n = 4,774,596) we were left with 7,519,260 records, of which 23% were identified as herbarium specimens and 72% were identified as iNaturalist observations.

Biases

We find that herbarium records are less temporally and spatially biased than iNaturalist observations. Herbarium data exhibit a more even temporal coverage over the past twelve decades compared to iNaturalist data which were largely gathered in the past five years (Fig. 2a). Spatially, iNaturalist observations are more clustered compared to herbarium records (Fig. 2b, Table S1) and their distribution is strongly dependent on human population density (Fig. 2c, Tables S2 and S3). That said, the distribution of both herbarium and iNaturalist data across space is highly uneven (Fig. 3a) and of the pixels (25 km by 25 km) with at least 1 record of either data type, 55% contain more iNaturalist records, 44% contain more herbarium records, and roughly 1% have an equal number of records (Fig. 3b). Across Canada and the US, around 37% of land area does not have a single record of either data type, the vast majority of which is in northern Canada.Fig. 2 Herbarium records exhibit less temporal and spatial bias than iNaturalist observations.

Temporal bias (a) is illustrated as the accumulation of new records over time. Spatial bias (b) is quantified using nearest neighbor index (NNI) which is a measure of spatial autocorrelation. When log transformed, a negative NNI indicates clustering, a positive NNI indicates dispersion, with an NNI of 0 indicating points are randomly distributed in space. NNI was calculated at the species level and only significant (p < 0.05) estimates of NNI were retained for this visualization (n = 2823). Herbarium records exhibited a mean log NNI of −0.88 while the mean for iNaturalist records was significantly (p = 0.006) lower at −0.93 (Table S1). Boxes show the quantiles (Q1-3) with the horizontal line representing the median and the whiskers representing the minima and maxima (calculated as 1.5 times the difference between Q1-3). We also tested for spatial bias (c) by estimating the relationship between density of records and human population density using negative binomial generalized linear models for which we report R2 (Kullback–Leibler) and p values (Tables S2-S3).

Fig. 3 The spatial distribution of herbarium and iNaturalist records is highly uneven and varies across jurisdictions.

To visualize the spatial distribution, we produced maps of the log density of herbarium and iNaturalist records (a) at 25 km2 resolution for Canadian vascular plants across Canada and the United States. These maps were combined to generate a map of sampling imbalance (b) where orange pixels indicate more herbarium records and green pixels indicate more iNaturalist records. The distribution of sampling imbalance is visualized next to the legend.

When it comes to representing plant diversity, we find that herbarium records are less taxonomically, phylogenetically, and functionally biased. On average, while a single plant species is represented by more iNaturalist observations (µ = 1234) compared to herbarium records (µ = 396), the variance is 4.8 times higher for iNaturalist data (F = 4.776, df = 4391, p < 0.001), which leads to half of all iNaturalist observations (2.7M) representing only 4% (178) of plant species. As a result, 47% of Canadian plants are better represented by herbarium records despite there being considerably fewer compared to iNaturalist observations (Fig. 4a). Pagel’s λ, which measures the strength of signal in the distribution of a trait at the tips of a phylogenetic tree or of a functional dendrogram, is higher for iNaturalist observations compared to herbarium records, indicating stronger phylogenetic and functional bias (Fig. 4b, c).Fig. 4 Herbarium records exhibit less taxonomic, phylogenetic, and functional bias than iNaturalist observations.

Taxonomic bias (a) is represented as the ratio of herbarium to iNaturalist records for each species of plant, accompanied by bar plots above and below which correspond to the number of herbarium and iNaturalist records for each species respectively. Phylogenetic (b) and functional (c) bias is represented as the number of herbarium and iNaturalist records per plant arranged around the phylogenetic tree or functional dendrogram. To enhance visualization, we took the square root of the number of records. Finally, we tested for bias in the distribution of the number of records per species at the tips of both the phylogenetic tree and functional dendrogram using Pagel’s λ, which varies between 0 and 1 with 0 indicating no bias (number of records per sample randomly distributed across the tree) signal and 1 indicating high bias (number of records per sample are highly correlated with the phylogenetic or functional structures). We assessed significance using likelihood ratio tests and reported p-values.

Taxonomic, phylogenetic, and functional coverage

Despite only having a third as many records in GBIF, herbarium specimens better capture the taxonomic, phylogenetic, and functional diversity of Canadian plants (Fig. 5a–c). Of the 4392 terrestrial vascular plant species in Canada, digitized herbarium specimens represent 3662 (83.4%) compared to the 3504 (79.8%) represented by iNaturalist observations. Species at risk are also better represented, with Herbarium records capturing 93.2% compared to 88.7% for iNaturalist data. Surprisingly, 714 (16.3%) species were unrepresented by either data type. When it comes to phylogenetic and functional coverage, herbarium records capture 3.4% more phylogenetic diversity and 1.8% more functional diversity. Based on the rate at which iNaturalist data accumulate taxonomic, phylogenetic, and functional diversity, we estimate it would take over 4.2M additional iNaturalist observation to capture the diversity currently represented by digitized herbarium specimens.Fig. 5 Herbarium records capture more taxonomic, phylogenetic, and functional diversity compared to iNaturalist observations.

Based on 1000 randomizations, we show that despite only having one-third as many records, herbarium specimens accumulate more taxonomic (a), phylogenetic (b), and functional (c) diversity than iNaturalist observations.

Capturing species’ environmental niches

Using species range maps and current climate normals, we report that the environmental niches of Canadian plants are surprisingly poorly captured by both herbarium and iNaturalist data. Together, both data types capture an average 9.2% of species’ environmental niches. Separately, iNaturalist observations capture an average 5.7% of species environmental niches while herbarium records capture around 5.1%. However, when adjusted for the fact that there are over three times as many iNaturalist records, herbarium records captured an average 1.8 times more environmental niche space per record. This difference is reflected in accumulation curves (Fig. 6a), where herbarium records accumulate environmental niche coverage 9% quicker than iNaturalist data for the average plant species. Interestingly, the proportion of species’ niches captured by both data types was on average very small (1.6%) indicating that the different data types usually capture very different areas within a species’ geographic range.Fig. 6 Because herbarium records better capture species’ environmental niches, the digitization of Canada’s remaining herbarium specimens could have disproportionate benefits for our ability to describe and model plant biodiversity.

Average niche accumulation curves (a) for herbarium and iNaturalist data show that herbarium records more efficiently capture species’ environmental niches compared to iNaturalist observations. By extrapolating these curves for each species of plant, we estimated the potential niche coverage (b) achievable by digitizing Canada’s remaining 7.3M herbarium records. The large points and thick joining line reflect the existing and potential averages across all species. Using existing niche coverage, we (c) modeled the relationship with our ability to predict species ranges using species distribution models (SDMs), referred to as range filling score (e.g., proportion of species Canadian range predicted by SDMs), using an inflated beta regression (Table S4). Along with the fitted relationship, we also visualized the 95% confidence interval of the curve (darker band) and the prediction interval of the model (lighter band). The fitted curve was used to predict how each species range filling score would increase with the potential increase in niche coverage conferred by digitizing Canada’s remaining herbarium specimens. The potential increase in environmental niche coverage was plotted against the increase in range filling score (d) to illustrate that increasing niche coverage disproportionately increases our ability to model species ranges. To illustrate this further, we fit a simple linear regression (orange line) on the log transformed data (Table S5) to assess whether the slope was significantly greater than 1 (represented by the dashed black line). The shaded area around the line is the 95% confidence interval of the slope based on 2547 degrees of freedom. Points, representing species, are colored based on sampling imbalance between data types.

Benefits of digitizing Canada’s remaining herbarium specimens

An estimated 7.3M specimens remain undigitized in herbaria across Canada19. Assuming that these remaining specimens can be adequately georeferenced and that the taxonomic representation remains like that of digitized specimens, we estimate that the digitization of the remaining records could capture around 156 additional species (3.6%), 5.3% more phylogenetic diversity, and 2.3% more functional diversity. In contrast, it would take an estimated 42M, 41M, and 74M additional iNaturalist records, to capture the same amount of taxonomic, phylogenetic, and functional diversity, respectively.

Based on our analysis of how well herbarium specimens capture species’ environmental niches, we estimate that digitizing Canada’s remaining specimens could almost quadruple existing coverage. While the existing 1.74M herbarium records capture an average 5.1% of species environmental niches, an additional 7.3M records could increase this coverage to 19.8%, which when added to the coverage conferred by existing iNaturalist data, equates to an average niche coverage of around 23.8% across all Canadian vascular plants (Fig. 6b).

Benefits of digitization for modeling the distribution of Canadian flora

The power and usefulness of SDMs to predict species ranges today and into the future under climate change depends on how well the biodiversity observations we use to fit models represent species’ niches. Based on SDMs for Canadian vascular plants, we found a strong positive relationship between niche coverage (the amount of environmental niche space captured in biodiversity data) and our ability to predict the extent of species geographic ranges (range filling score) (Fig. 6c, Table S4). Currently, when using both data types to model Canadian flora, we are only able to predict the occurrence of species across 20.8% of their Canadian range on average, which means we are likely underestimating the true distribution of plant biodiversity across Canada. However, we predict that by digitizing Canada’s remaining herbarium collections, we could increase our ability to model Canadian plant distributions by over 5 times per species on average. Furthermore, our results show that the relationship between increasing the ability of our data to capture species’ niches and the resulting increase in SDM power is likely non-linear, indicating that small improvements in niche coverage translate to disproportionate increases in our ability to model species distributions (Fig. 6d, Table S5).

In contrast, we estimate it would take an additional 27.3M iNaturalist observations to match these benefits of digitization. To put this in perspective, despite the rapid increase in the amount of community science data over the past 5 years, GBIF only contains 78M iNaturalist observations across the entire tree of life (as of January 2024). As such, accumulating this many additional iNaturalist observations for Canadian plants would likely take decades.

Discussion

Our ability to protect biodiversity today and into the future under climate change is underpinned by our ability to predict species distributions, which depends on the availability, amount, and coverage of biodiversity data. Over the past decade, the rise of community science platforms like iNaturalist have changed the way humans interact with nature and provided a windfall of biodiversity observations. But large data gaps persist8, and the extent to which community science has helped overcome the Wallacean shortfall (lack of knowledge of species distributions20) is unclear. Here, we report that iNaturalist observations have significantly increased the diversity and distribution of species captured in online inventories like GBIF. However, in line with past work8, we find that despite having over three times the number of records, iNaturalist data exhibit more bias and capture less taxonomic, phylogenetic, and functional diversity than digitized herbarium specimens. Record-to-record, herbarium specimens also more efficiently describe both plant biodiversity at large as well as the environmental niches of individual species. As such, the digitization of Earth’s remaining natural history collections has the potential to substantially improve both our knowledge of biodiversity as well as our ability to build SDMs that better predict biodiversity today and into the future.

As nations around the world embark on their path to protecting 30% of land by 203012, our ability to integrate climate change planning into the expansion of protected areas may determine the future of Earth’s biodiversity13–15. Given the value of already digitized herbarium specimens, our results suggest that the digitization of remaining collections would likely greatly improve our ability to model biodiversity today and into a climatically uncertain future. Moreover, alongside offering valuable (and more scientifically reliable21) species occurrence data, the physical specimen behind the digital record holds a wealth of additional information. In fact, the concept of digitization has recently been extended beyond simple digital images to include layers of additional morphological and genetic data22 that have been used to estimate evolutionary lineages, sample historical genomes, quantify changes in functional traits, uncover new taxa, and re-evaluate what we thought were extinct species23–29. In the context of global change, herbaria, and their collections are increasingly relied on to understand biodiversity trends in the Anthropocene and provide critical baseline data for future assessments of climate change30–34. This has sparked calls for an open-access global metaherbarium to facilitate the use of herbarium data and realize the immense scientific potential of fully digitized collections25. So why then have only ~21% of Earth’s more than 396M herbarium specimens been digitized10,11?

It is not because herbaria lack the methodological knowledge to do so. Around the world, herbaria are undertaking mass-digitization projects that have resulted in millions of specimens now being digitally available to researchers35–37. In Canada, many of the 88 currently active herbaria have digitization experience, and standardized and reproducible digitization workflows are publicly available38,39. Attempts since 2007 to aggregate data under the Canadensys40 network have contributed to the digitization of over 900,000 specimens and have produced a trove of digitization knowledge, resources, and technology. Unfortunately, in Canada and around the world, funding remains a major hurdle to the digitization of remaining specimens10. We estimate that the digitization of Canada’s remaining 7.3M specimens would cost around $22M (~$3 per specimen using traditional workflows35,41,42), which is relatively low when viewed against the biodiversity benefits. For context, in 2023, $22M represented only 0.14% of Canada’s annual spending on science and technology43.

The other major hurdle to mass digitization is the time it takes for specimens to undergo full digitization workflows. While estimates vary greatly, a single herbarium worker can digitize anywhere between 6 and 500 specimens per hour depending on the workflow, complexity of specimen data, level of automation, and capacity of herbarium35,44–47. Fortunately, the rise of computer vision, artificial intelligence, and high-throughput workflows has the potential to greatly reduce both the cost and time it takes to digitize, while also increasing data standards44,46,48–50. For example, the Smithsonian’s US National Herbarium, which houses roughly 3.8M specimens, was recently completely digitized and the use of high-throughput workflows reduced the cost of digitization from $3.32 down to $1.85 per specimen and allowed for the digitization of 3000–4000 specimens daily35. As technologies continue to advance, the digitization of remaining records is not only a potentially effective way to generate valuable biodiversity data but is also increasingly feasible and cost-efficient.

Alongside the digitization of existing records, targeted collection of new herbarium specimens combined with modern geolocating technology, has the potential to rapidly fill gaps in our understanding of biodiversity. For example, targeting species that have been described but are underrepresented in GBIF could help fill Canada’s taxonomic, phylogenetic, and functional data gaps. Likewise, because iNaturalist data poorly capture species niches due to the high spatial bias in sampling location and the taxonomic preference towards common species4,5, targeted collection of rare species in under-sampled regions could rapidly produce disproportionately valuable data for modeling species distributions. One example of such a program is the Canadian Museum of Nature’s Arctic Flora Biodiversity Project51 which aims to increase our knowledge of Arctic plant and lichen biodiversity through systematic collection. The concept of targeted collection can also harness the usefulness of community science platforms52 and initiatives like British Columbia Parks Biodiversity Program53 offer a good example of how informed data collection can leverage public engagement to benefit conservation, research, and biodiversity.

Fundamentally, while we can try to predict the importance of undigitized herbarium collections, we ultimately do not know what hidden value they hold. Until we bring these specimens out of their cabinets and into the digital light, their significance to our understanding of biodiversity will remain unclear30. What is clear is that community science observations are limited in their ability to capture the diversity and distribution of plants and the ongoing rapid accumulation of new observations is unlikely to fill existing data gaps—at least for the next few decades. For this reason, the funding of either large-scale targeted sampling programs or mass digitization initiatives is likely necessary to rapidly improve our understanding of plant biodiversity. And while targeted programs are almost certainly more effective54–56, funding Earth’s remaining herbaria offers more than just georeferenced point data. Herbaria operate as critical bridges between the scientific community and the public36,57,58, and represent one of the last refuges for the study of plant taxonomy and systematics25, fields that have been hemorrhaging funding, positions, and representation at universities and even museums worldwide59. As such, empowering our remaining herbaria would not only preserve irreplaceable knowledge, skills, and specimens but might also hold the key to producing the critical biodiversity data we need to predict and protect Earth’s biodiversity now and into the future.

Methods

Ethics and inclusion

The lands that many now call Canada, are the stolen traditional territories of many diverse First Nations, Métis, and Inuit Peoples. We recognize that the 88 remaining herbaria in Canada (and aspects of herbaria globally60) are a product of the colonization and exploitation of land long stewarded by Indigenous Peoples. Many of the specimens housed in herbarium collections were gathered by botanists who used local Indigenous Knowledge often without appropriate recognition. As a result, despite their critical influence, Indigenous voices are largely absent from Canadian herbarium collections. However, some herbaria are taking steps to amplify Indigenous voices, and ongoing initiatives such as Plenty Canada’s Greenbelt Indigenous Botanical Survey61, Canadian Museum of Nature’s Capture the Collections62, and McGill University Herbarium’s Recovering Lost Voices63, demonstrate ways that herbaria can work towards truth and reconciliation in Canada. Moving forward, as herbarium specimens are digitized and made accessible, investigating and communicating the colonial and Indigenous legacies of these collections is critical for both their scientific and societal value.

Species lists and occurrence data

Starting with a full list of Canada’s vascular plants64 we downloaded all the GBIF observations in both Canada and the United States from 1900 to present (January 2024) to enable a contemporary comparison of both iNaturalist and herbarium records65,66. First, GBIF observations with a coordinate uncertainty of over 25 km were removed. Then data were divided into two groups representing iNaturalist records (institution code “iNaturalist”) and herbarium records (basis of record “PRESERVED_SPECIMEN”). This resulted in 5,423,637 research grade iNaturalist observations and 1,742,166 Herbarium records.

Temporal bias

To illustrate temporal bias, we simple plotted the accumulation of herbarium and iNaturalist records annually from 1900 till present (January 2024).

Spatial bias

To test for spatial biases in each data type we used Nearest Neighbor Index (NNI) which is a measure of spatial autocorrelation67. When log transformed, negative NNI indicates that the geographic locations (latitude, longitude) of records are more clustered than expected and positive NNI indicates records are more spread than expected, with an NNI of 0 indicating points are randomly distributed in space. After calculating NNI for each point for each data type, we used a two-sided t-test to test for significant differences between the means. NNI was calculated using the nni() function in the “spatialEco” package for R68. To visualize spatial bias in number of records, we mapped the total number of records per 25 km2 grid cells across Canada and the United States. To visualize the imbalance between herbarium and iNaturalist sampling effort per cell, we mapped the difference between the relative portion of records of each data type. This produced a map that highlights areas of higher iNaturalist observation density compared to herbarium record density and vice versa. Finally, because past work has demonstrated that iNaturalist observations tend to occur in areas of high human population density, we modeled the relationship between number of records and population density across North America for both data types. To do this we used the Gridded Population of the World (v4) raster for the year 202069, downloaded at 2.5 arc-minute resolution (roughly 5 km2). We then resampled this raster to our 25 km2 grid for North America using bilinear resampling in the terra package for R70. Using the rasterized layers of herbarium and iNaturalist record counts, we then assembled a data frame for all cells with at least 1 record of either data type and extracted the corresponding population density value for each cell. Because we are dealing with integer count data, we fit negative-binomial generalized linear models using the MASS package for R71. We log-transformed population density to improve model fit. We report both model parameters, AIC, and pseudo R2 (Kullback–Leibler) calculated with the performance package for R72.

To visualize the spatial distribution of herbarium and iNaturalist records, we rasterized record data at 25 km2 resolution across Canada and the United States. To view the balance between data types across space, we took the difference in number of records per pixel. We overlayed national, provincial, territorial, and state borders73 to aid visualization.

Taxonomic bias

To quantify taxonomic bias, we assessed whether the variance across the number of records per species differed between herbarium and iNaturalist data using an F test. To control for the different number of total records per data type, we used relative number of records to perform the F test.

Phylogenetic and functional bias

To quantify phylogenetic and functional bias we first needed phylogenetic and functional data. We built a phylogenetic tree of Canadian vascular plants using the “rtrees” package for R74. To visualize and assess bias we built a functional dendrogram to match our phylogenetic tree. First, we downloaded the following plant functional traits from the TRY database75: Seed dry mass, Plant height vegetative, Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA): undefined if petiole is in- or excluded, Plant lifespan (longevity), Plant nitrogen(N) fixation capacity, Plant growth form, Leaf photosynthesis pathway, Dispersal syndrome, Plant reproductive phenology timing, Leaf compoundness, Plant woodiness, Leaf type. These traits were selected based on taxonomic coverage and have been used in the past to capture and represent the functional diversity of Canada’s plants15. Using these traits, we calculated an average trait value for each species of plant and phylogenetically imputed missing values using phylogenetic vector regressions in the “PVR” package for R76 and random forest regression trees in the “missForest” package for R77. Plants with no functional trait data were dropped from the analysis since imputing these species would be solely based on PVR values. This left us with a remaining 4147 (94%) species with complete functional and phylogenetic data. From there, functional traits were used to calculate a Gower’s distance matrix using the “FD” package for R78 which was used to construct a functional dendrogram using UPGMA clustering achieved with the hclust() function in “stats” package included in base R79. Once we had both our phylogenetic tree and functional dendrogram for the remaining 4147 plant species, we used Pagel’s λ which estimates the degree to which shared branch lengths influence the distribution of trait values at the tips of a phylogenetic tree or functional dendrogram80. Values of Pagel’s λ range between zero and one, with zero representing phylogenetic independence and one representing perfect Brownian motion (strong phylogenetic bias). Pagel’s λ has been shown in the past to outperform other estimates of phylogenetic signal81. We chose to use Pagel’s λ to test for both phylogenetic and functional bias to standardize our approach and to allow us to compare the strength in both phylogenetic and functional bias in our data. In our data, bias can be thought of as the evenness of the number of records per species across the phylogenetic tree or functional dendrogram. In this case, a high λ (close to 1) would indicate that the number of records is highly correlated to the phylogenetic or function structure of the tree/dendrogram, meaning that, for example, if one species is represented by a high number of records, more phylogenetically related or functionally similar species are more likely to also be represented by a high number of records compared to a distantly related species. On the other hand, a λ close to 0 would suggest that the number of records is randomly distributed across the phylogenetic tree or functional dendrogram, suggesting that little bias is present in that data type. Pagel’s λ was calculated using the phylosig() function in the “phytools” package for R82 based on the log-transformed (to achieve a normal distribution) number of herbarium and iNaturalist records for each species of plant. Phylogenetic and functional bias were visualized by plotting the square root (instead of log) of these values for each tip in the phylogenetic and functional trees. This was done to enable better visualization of the variation in record number across species.

Taxonomic coverage

Once we had assessed bias, we were interested in quantifying the degree to which herbarium and iNaturalist records captured the full diversity of Canada’s vascular plants. Starting with taxonomic coverage, we first quantified the number and proportion of all Canadian vascular plants represented in each data type. Then, to understand how efficiently each data type captures the taxonomic diversity of Canada’s plants, we built modified species accumulation curves. First, each species in the VASCAN list was assigned a value representing its contribution to the total taxonomic diversity. For taxonomic diversity, this was simply one divided by the number of species. Then, working with iNaturalist and herbarium records separately, we then randomly accumulated records, and for each record added, recorded both the index number (e.g., the 5th record added), the species (e.g., Cypripedium parviflorum), and the proportion of taxonomic diversity represented by that species (e.g., one divided by the total number of species in VASCAN). When the added record (e.g., the 5th randomly selected record) corresponded to a species that was already captured in the accumulation by an earlier record (e.g., the 3rd randomly selected record), the proportion of taxonomic diversity represented by that record was recorded as zero, since that species had already been accounted for. This process was repeated until all records had been added, and the entire process was then repeated 1000 times. From these 1000 runs, we calculated the average proportion of taxonomic diversity captured for each index position for each data type. Using these averages and the corresponding index position, we then fit logarithmic beta regressions83 to these curves to allow us to estimate the increase in niche coverage with the addition of novel records. Traditional species accumulation curves are usually used to quantify sampling completeness or estimate the true number of species in a study region (usually by identifying the asymptote of the accumulation curve). In our case, we know the total number of plant species in Canada (based on the VASCAN list). Because of this, we needed to be able to fit a curve that reflected the fact that eventually, if we accumulate an infinite number of herbarium or iNaturalist records, we should be able to reach (at least asymptotically) the total number of plants in the VASCAN list. Logistic Beta regressions allowed us to do this and by log-transforming the independent variable (number of records) we achieved a logarithmic-shaped curve that reaches an asymptote at 1 as expected under sampling theory. Beta regressions operate with a response variable distributed between 0 and 1 and in this study, we used the proportion of all species as the response instead of the total number of species. The slope of this regression indicates how quickly different datatypes accumulate taxonomic diversity. We fit beta regressions using the “betareg” package for R84.

Phylogenetic coverage

We used phylogenetic coverage to assess how well each data type captures the full phylogeny, representing millions of years of plant evolution in Canada. Using the phylogenetic tree described earlier, we used the evol_distinct() command in the “phyloregion” package for R85 to estimate the relative phylogenetic distinctiveness of each species. Total coverage then, was calculated as the proportion of all phylogenetic distinctiveness captured by each data type. We then built accumulation curves but instead of taxonomic diversity, we accumulated phylogenetic diversity with the addition of new records. To do so we used the same approach as described above and fit the same beta regressions.

Functional coverage

We used functional coverage to assess how well each data type captures Canadian functional diversity, representing the variation in ecological roles played by different species of Canadian plants. Starting with the functional dendrogram described above, we calculated the relative functional distinctiveness of each species again using the evol_distinct() command in the “phyloregion” package for R85. To estimate total coverage, we calculated the proportion of all functional distinctiveness captured by each data type. We then built accumulation curves but instead of accumulating taxonomic diversity, we accumulated functional diversity with the addition of new records. Again, using the same approach and beta regressions as described above.

Niche coverage

To estimate how herbarium and iNaturalist records represent the spatial and environmental niches of plants we first had to calculate the extent of their niche space. To do so we relied on the expertly estimated plant ranges provided as polygons in the BIEN dataset, accessed through the “BIEN” package for R86. Of the 4392 vascular plants in Canada, only 3269 (74%) have estimated range maps, of which only 3174 also had GBIF records. We acknowledge that areas within species range polygons do not always indicate species presence. If analyzed at a fine grain size (e.g., 1 km2), one would expect there to be cells within a species range that are not occupied by that species due to variation in suitable habitat across species ranges. To account for this, we chose a coarse grain size (25 km2) to try and maximize the probability that suitable habitat occurred in each grid cell. Furthermore, the range polygons used in our analysis were not simple convex hulls but contained holes to account for regions within the spatial extent of the range where species are likely not present.

To estimate spatial coverage, we rasterized range maps at the 25 km2 resolution and simply calculated the proportion of raster cells in the species range that had herbarium/iNaturalist records in them. To go from spatial coverage to environmental niche coverage, we needed to estimate the “proportion” of the species environmental niche present in each raster grid cell. Starting with 5 climate normal layers (Mean Annual Precipitation, Precipitation as Snow, Humidity, Degree days above 0°, and Degree days above 18°) downloaded from AdaptWest Project87 spanning the past 30 years, we first aggregated the climate layers up to 25 km2 resolution to match the spatial grid of already rasterized plant ranges. These climate layers were used to match the climate variables used to construct the SDMs used later in the analysis. For each species of plant, we extracted the climate values for all cells within its rasterized range polygon to assemble a cell by climate matrix. To account for differences in measurement scale, we standardized climate variables using decostand() in the “vegan” package for R88. From there we computed a Euclidean distance matrix, which was clustered to form a dendrogram, like our functional dendrogram but clustering cells by climate similarity instead of by species functional traits. Using this climatic dendrogram, we calculated the individual contribution of each cell in said species range to the total climatic niche of that species using the same evol_distinct() command in the “phyloregion” package for R85. Finally, these values, representing the climatic distinctiveness of each 25 km2 cell in a species range were made relative so that the total across all cells in a species ranges summed to 1. This process was repeated for all species, recalculating the climatic distance matrix and climatic dendrogram each time to allow for differences between species based on differences between their realized climatic niches. Another possible approach would have been to decompose our climate variables into 2-dimensional principal component space, to identify geographic areas with similar environmental conditions. However, at coarse grain sizes such as the one we used, we believe it is safe to assume that each geographic pixel represents a unique combination of environmental variables. As such, we chose to instead assign a proportion of environmental niche space to each geographic pixel to give weight to pixels with highly dissimilar combinations of environmental conditions compared to pixels with more similar conditions.

Using these estimates of spatial and environmental coverage per cell, we then built modified accumulation curves to model how rapidly herbarium and iNaturalist records were capturing species niches. First, we removed all herbarium and iNaturalist records found outside of the species rasterized range. Then we assigned each record a cell number corresponding to the 25 km2 grid cell it occurred in. Working with herbarium and iNaturalist records separately, we then randomly accumulated records, and for each record added, recorded both the index number (e.g., the 5th record added), the spatial contribution of that cell (e.g., 1 divided by the number of cells in that species range), and the climatic contribution of that cell (e.g., the relative climatic distinctiveness). When the added record (e.g., the 5th randomly selected record) corresponded to a cell that was already captured in the accumulation by an earlier record (e.g., the 3rd randomly selected record), the spatial and climatic contribution of that record was recorded as zero, since that cell had already been accounted for. This process was repeated until all records had been added, and the entire process was then repeated 1000 times. From these 1000 runs, we calculated the average spatial and environmental contribution of each indexed record for each data type. Using these averages and the corresponding index position, we then fit logarithmic beta regressions to these curves to allow us to estimate the increase in niche coverage with the addition of novel records. While we used both spatial and environmental niches in our analysis, the results were virtually identical, so we chose to report environmental niche results in the main text and figures.

Beta regression fit

For the 3174 plants for which we attempted to fit beta regressions, model fit was very good. Goodness of fit (pseudo-R2) ranged from an average 0.996 (min = 0.898, max = 1) for herbarium regressions to an average 0.991 (min = 0.772, max = 1) for iNaturalist regressions. Because some regressions did not converge and some species were excluded due to too few data points, we chose to report community averages instead of individual species results. Regressions that did not converge were removed from the analysis.

Extrapolating beta regressions

There is an estimated 7.3 million undigitized herbarium specimens housed in active Canadian herbaria19. While the exact number is unknown, this estimate is probably on the lower side, since it only reflects information from herbaria for which metadata are available, including herbarium specimens that have at least been counted and catalogued and does not include the potentially millions of other records that remain hidden away in collections that lack resources to make even their metadata accessible. As such, our use of this number to reflect the potential benefits incurred by the digitization of Canada’s remaining herbarium specimens is likely conservative.

To estimate how the digitization of herbarium records would add taxonomic, phylogenetic, functional, and niche coverage, we used regression coefficients. Then we used curves fit to iNaturalist data to estimate how many additional iNaturalist records would be required to match the added coverage conferred by herbarium digitization. For niche coverage, since there is taxonomic bias in herbarium representation, we first calculated the relative incidence of each species in existing herbarium records on GBIF. This was used to divide the undigitized 7.3M records in Canada into an expected number of undigitized records per species, which was used to estimate the increase in niche coverage using beta regressions. While this approach assumes that no additional species will be present in the undigitized 7.3M records, our other results suggest that an additional 156 species could be found in the undigitized records. However, these species, not represented in already digitized specimens, are likely rare and represented by only a few of the remaining 7.3M undigitized records. One limitation of this approach is that past digitization may have focused on specific clades (e.g., to understand trait variation in a single genus across space or time) and so it is possible that the taxonomic representation in undigitized collections is different than that of digitized specimens. To estimate the number of iNaturalist records required to match potential coverage given digitization, we simply extrapolated the iNaturalist beta regressions.

Translating current and potential niche coverage into benefits for species distribution modelling

We relied on SDMs detailed in Eckert et al. (2023). These models were built using GBIF data downloaded on June 5th, 202189,90 for all Canadian vascular plants. We first thinned observation records down to a single observation per 1 km grid cell in North America. To further clean data points unlikely to represent the native distribution, we removed any points in core urban areas (e.g., areas designated ‘urban or built-up’ in the land-use/land cover data described below), which often included clusters of data points in botanical gardens/zoos/sanctuaries.

We used the following set of climatic variables that were biologically meaningful and had low correlation: mean annual precipitation (mm), chilling degree days (Degree-days below 0 °C), precipitation as snow (mm), Hargreave’s climatic moisture index and warming degree-days above 18 °C. We used current climate models from AdaptWest Project (2021). Current climate data is based on PRISM and WorldClim and spans 1991–2020. We also included topographic wetness index (calculated based on the 1-km) digital elevation model using package “dynatopmodel” in R91, topographic ruggedness index (from AdaptWest), and an aggregated land cover layer based on MODIS land cover data and reprojected to our grid and reclassified to: unvegetated, hardwood forests, evergreen forest, mixed forests, shrubs, and grasslands92. Finally, we used three variables to represent soil properties (topsoil silt fraction, subsoil pH, and topsoil organic C content) from the Unified North American Soil Map93 (0.25 degree resolution) that were projected to match the 1-km2 climate raster.

We fit Boosted Regression Trees (BRTs)94–96 with all environmental, topographic, and soil predictors in the “dismo” package for R97. All presences were used in the models unless they exceeded 5000, in which case 5000 presences were randomly drawn along with 10,000 absences. BRTs were projected across all North America, although only the Canadian ranges were used in subsequent analyses. Model outputs were used to estimate the degree to which our SDMs can “fill” expertly estimated species range maps from the BIEN package86. To do so, we first aggregated projections (keeping the maximum value) to 25 km and thresholded at 0.5 to identify areas where presence is highly likely. While it is unrealistic to expect any single species to occupy the entire extent of its spatial range, once aggregated to a coarse 25 km2 resolution, we operated under the assumption that within a species range boundary, there is likely suitable habitat in each 25 km2 subdivision, which we accounted for by retaining the maximum predicted probability of occurrence during aggregation. We then calculated the number of grid cells in the BIEN range that predicted a presence versus the number of grid cells in the entire range to estimate range filling. For example, if a species was predicted to be present in 10 grid cells in its BIEN range of 100 grid cells, then its range-filling score is 0.1.

To estimate how increased niche coverage might impact the ability of our SDMs to fill species ranges, we needed to model the relationship between current niche coverage and range filling. Since range-filling scores were distributed between 0 and 1 (representing the proportion of the species range filled by our SDMs) we again used a beta regression. Because some species had either none or all of their range filled by our models, and traditional beta distributions do not handle 0 s and 1 s, we used an inflated beta distribution (family=BEINF) and logit link functions to fit a Generalized Additive Model for Location Scale and Shape (GAMLSS) in the “gamlss” package for R98 (Table S4). To improve model fit we used the square root of niche coverage as our predictor variable. Model estimates are provided in Table S1. Instead of using the fitted µ curve to generate a single mean estimate of potential gain in predictive power, we used the curve to estimate potential gain for each species of plant, predicting values using potential niche coverage. The average across all plants (including species represented by 0 or 1) is reported in the main text and visualized in Fig. 6b. Finally, to understand how increasing the niche coverage translates to increases in range-filling scores we fit a simple linear regression on log-transformed data (Table S5).

Estimating the cost of digitization

To estimate how much it costs herbaria to digitize a single specimen, we consulted the curators of major herbaria in Canada along with past work and reports from other herbaria around the world. This estimate of $3 per specimen represents the use of traditional workflows involving cameras and humans and does not account for new high-throughput workflows that are largely automated but initially costly to install.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information

Peer Review File

Reporting Summary

Source data

Source data

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-024-51899-1.

Acknowledgements

The authors would like to thank the Canadian Society for Ecology and Evolution and the Canadian Botanical Association for hosting a symposium on the future of herbarium collections from which this work was inspired. The authors would also like to acknowledge members of the Canadensys network for their tireless efforts coordinating the digitization of Canadian herbarium collections. This work was supported by NSERC Discovery Grant RGPIN-2019-05771 (L.J.P.) and by a NSERC CGS-D award (I.E.).

Author contributions

All authors conceived the idea for the manuscript and helped developed the methodology which was executed by I.E. Results were generated, interpreted with the help of S.J., and visualized by I.E. I.E. wrote the first draft of the manuscript and all authors provided critical feedback. L.J.P. supervised the project and acquired the funding. A.B., D.A.M., and T.A.D. provided expertise and resources relating to the extent of Canada’s undigitized collections as well as the cost of digitization.

Peer review

Peer review information

Nature Communications thanks Lorenzo Peruzzi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Data availability

All publicly accessible data is cited and can be downloaded from their respective repositories. The data used and generated in this study have been deposited in Figshare under accession code 10.6084/m9.figshare.25180595.v2. This includes all data needed to reproduce the analysis and figures. Sources of raw data used in this study are available in Table S6 in the Supplementary Information. Source data are provided with this paper.

Code availability

Annotated code to reproduce the analyses and figures is available in the same Figshare repository (10.6084/m9.figshare.25180595.v2).

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Waller, J. Will citizen science take over? data blog. https://data-blog.gbif.org/post/gbif-citizen-science-data/ (2019).
2. García-Roselló E González-Dacosta J Lobo JM The biased distribution of existing information on biodiversity hinders its use in conservation, and we need an integrative approach to act urgently Biol. Conserv. 2023 283 110118 10.1016/j.biocon.2023.110118
García-Roselló, E., González-Dacosta, J. & Lobo, J. M. The biased distribution of existing information on biodiversity hinders its use in conservation, and we need an integrative approach to act urgently. Biol. Conserv. 283, 110118 (2023).10.1016/j.biocon.2023.110118
3. Grand J Cummings MP Rebelo TG Ricketts TH Neel MC Biased data reduce efficiency and effectiveness of conservation reserve networks Ecol. Lett. 2007 10 364 374 10.1111/j.1461-0248.2007.01025.x 17498135
Grand, J., Cummings, M. P., Rebelo, T. G., Ricketts, T. H. & Neel, M. C. Biased data reduce efficiency and effectiveness of conservation reserve networks. Ecol. Lett. 10, 364–374 (2007).17498135 10.1111/j.1461-0248.2007.01025.x
4. Callaghan CT Poore AGB Hofmann M Roberts CJ Pereira HM Large-bodied birds are over-represented in unstructured citizen science data Sci. Rep. 2021 11 19073 10.1038/s41598-021-98584-7 34561517
Callaghan, C. T., Poore, A. G. B., Hofmann, M., Roberts, C. J. & Pereira, H. M. Large-bodied birds are over-represented in unstructured citizen science data. Sci. Rep. 11, 19073 (2021).34561517 10.1038/s41598-021-98584-7
5. Geurts EM Reynolds JD Starzomski BM Turning observations into biodiversity data: broadscale spatial biases in community science Ecosphere 2023 14 e4582 10.1002/ecs2.4582
Geurts, E. M., Reynolds, J. D. & Starzomski, B. M. Turning observations into biodiversity data: broadscale spatial biases in community science. Ecosphere 14, e4582 (2023).10.1002/ecs2.4582
6. James SA Herbarium data: global biodiversity and societal botanical needs for novel research Appl. Plant Sci. 2018 6 e1024 10.1002/aps3.1024 29732255
James, S. A. et al. Herbarium data: global biodiversity and societal botanical needs for novel research. Appl. Plant Sci. 6, e1024 (2018).29732255 10.1002/aps3.1024
7. Panchen ZA Doubt J Kharouba HM Johnston MO Patterns and biases in an Arctic herbarium specimen collection: Implications for phenological research Appl. Plant Sci. 2019 7 e01229 10.1002/aps3.1229 30937221
Panchen, Z. A., Doubt, J., Kharouba, H. M. & Johnston, M. O. Patterns and biases in an Arctic herbarium specimen collection: Implications for phenological research. Appl. Plant Sci. 7, e01229 (2019).30937221 10.1002/aps3.1229
8. Daru BH Rodriguez J Mass production of unvouchered records fails to represent global biodiversity patterns Nat. Ecol. Evol. 2023 7 816 831 10.1038/s41559-023-02047-3 37127769
Daru, B. H. & Rodriguez, J. Mass production of unvouchered records fails to represent global biodiversity patterns. Nat. Ecol. Evol. 7, 816–831 (2023).37127769 10.1038/s41559-023-02047-3
9. Greve M Realising the potential of herbarium records for conservation biology S. Afr. J. Bot. 2016 105 317 323 10.1016/j.sajb.2016.03.017
Greve, M. et al. Realising the potential of herbarium records for conservation biology. S. Afr. J. Bot. 105, 317–323 (2016).10.1016/j.sajb.2016.03.017
10. Paton A Plant and fungal collections: current status, future perspectives Plants People Planet 2020 2 499 514 10.1002/ppp3.10141
Paton, A. et al. Plant and fungal collections: current status, future perspectives. Plants People Planet 2, 499–514 (2020).10.1002/ppp3.10141
11. Thiers, B. Index herbariorum (The New York Botanical Garden, 2020).
12. United Nations Convention on Biological Diversity, Kunming-Montreal Global Biodiversity Framework (CBD/COP/15/L.25) (Convention on Biological Diversity, 2022).
13. Dobrowski SZ Protected-area targets could be undermined by climate change-driven shifts in ecoregions and biomes Commun. Earth Environ. 2021 2 198 10.1038/s43247-021-00270-z
Dobrowski, S. Z. et al. Protected-area targets could be undermined by climate change-driven shifts in ecoregions and biomes. Commun. Earth Environ. 2, 198 (2021).10.1038/s43247-021-00270-z
14. Schuster, R. et al. Protected area planning to conserve biodiversity in an uncertain future. Conserv. Biol.10.1101/2022.11.18.517054 (2022).
15. Eckert I Brown A Caron D Riva F Pollock LJ 30×30 biodiversity gains rely on national coordination Nat. Commun. 2023 14 7113 10.1038/s41467-023-42737-x 37932316
Eckert, I., Brown, A., Caron, D., Riva, F. & Pollock, L. J. 30×30 biodiversity gains rely on national coordination. Nat. Commun. 14, 7113 (2023).37932316 10.1038/s41467-023-42737-x
16. Araújo MB Guisan A Five (or so) challenges for species distribution modelling J. Biogeogr. 2006 33 1677 1688 10.1111/j.1365-2699.2006.01584.x
Araújo, M. B. & Guisan, A. Five (or so) challenges for species distribution modelling. J. Biogeogr. 33, 1677–1688 (2006).10.1111/j.1365-2699.2006.01584.x
17. Kramer‐Schadt S The importance of correcting for sampling bias in MaxEnt species distribution models Divers. Distrib. 2013 19 1366 1379 10.1111/ddi.12096
Kramer‐Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).10.1111/ddi.12096
18. Meynard CN Leroy B Kaplan DM Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing? Ecography 2019 42 2021 2036 10.1111/ecog.04385
Meynard, C. N., Leroy, B. & Kaplan, D. M. Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing? Ecography 42, 2021–2036 (2019).10.1111/ecog.04385
19. Bruneau A Sinou S Tudor S Herbarium digitisation in Canada Can. Bot. Assoc. Bull. 2020 35 37
Bruneau, A., Sinou, S. & Tudor, S. Herbarium digitisation in Canada. Can. Bot. Assoc. Bull. 35, 37 (2020).
20. Lomolino, M. V. Conservation biogeography. in Frontiers of Biogeography: New Directions in the Geography of Nature, vol. 293 (Sinauer Associates, 2004).
21. Aceves‐Bueno E The accuracy of citizen science data: a quantitative review Bull. Ecol. Soc. Am. 2017 98 278 290 10.1002/bes2.1336
Aceves‐Bueno, E. et al. The accuracy of citizen science data: a quantitative review. Bull. Ecol. Soc. Am. 98, 278–290 (2017).10.1002/bes2.1336
22. Hedrick BP Digitization and the future of natural history collections BioScience 2020 70 243 251 10.1093/biosci/biz163
Hedrick, B. P. et al. Digitization and the future of natural history collections. BioScience 70, 243–251 (2020).10.1093/biosci/biz163
23. Albani Rocchetti G Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species N. Phytologist 2021 230 433 450 10.1111/nph.17133
Albani Rocchetti, G. et al. Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species. N. Phytologist 230, 433–450 (2021).10.1111/nph.17133
24. Bebber DP Herbaria are a major frontier for species discovery Proc. Natl Acad. Sci. US. 2010 107 22169 22171 10.1073/pnas.1011841108
Bebber, D. P. et al. Herbaria are a major frontier for species discovery. Proc. Natl Acad. Sci. US. 107, 22169–22171 (2010).10.1073/pnas.1011841108
25. Davis CC The herbarium of the future Trends Ecol. Evol. 2023 38 412 423 10.1016/j.tree.2022.11.015 36549958
Davis, C. C. The herbarium of the future. Trends Ecol. Evol. 38, 412–423 (2023).36549958 10.1016/j.tree.2022.11.015
26. Folk, R. A. et al. High‐throughput methods for efficiently building massive phylogenies from natural history collections. Appl. Plant Sci. 9, e11410 (2021).
27. Kothari S Beauchamp‐Rioux R Laliberté E Cavender‐Bares J Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves Methods Ecol. Evol. 2023 14 385 401 10.1111/2041-210X.13958
Kothari, S., Beauchamp‐Rioux, R., Laliberté, E. & Cavender‐Bares, J. Reflectance spectroscopy allows rapid, accurate and non‐destructive estimates of functional traits from pressed leaves. Methods Ecol. Evol. 14, 385–401 (2023).10.1111/2041-210X.13958
28. Nic Lughadha E The use and misuse of herbarium specimens in evaluating plant extinction risks Philos. Trans. R. Soc. B 2019 374 20170402 10.1098/rstb.2017.0402
Nic Lughadha, E. et al. The use and misuse of herbarium specimens in evaluating plant extinction risks. Philos. Trans. R. Soc. B 374, 20170402 (2019).10.1098/rstb.2017.0402
29. Ramirez‐Parada TH Park IW Mazer SJ Herbarium specimens provide reliable estimates of phenological responses to climate at unparalleled taxonomic and spatiotemporal scales Ecography 2022 2022 e06173 10.1111/ecog.06173
Ramirez‐Parada, T. H., Park, I. W. & Mazer, S. J. Herbarium specimens provide reliable estimates of phenological responses to climate at unparalleled taxonomic and spatiotemporal scales. Ecography 2022, e06173 (2022).10.1111/ecog.06173
30. Johnson KG Climate change and biosphere response: unlocking the collections vault BioScience 2011 61 147 153 10.1525/bio.2011.61.2.10
Johnson, K. G. et al. Climate change and biosphere response: unlocking the collections vault. BioScience 61, 147–153 (2011).10.1525/bio.2011.61.2.10
31. Lang PLM Willems FM Scheepens JF Burbano HA Bossdorf O Using herbaria to study global environmental change N. Phytologist 2019 221 110 122 10.1111/nph.15401
Lang, P. L. M., Willems, F. M., Scheepens, J. F., Burbano, H. A. & Bossdorf, O. Using herbaria to study global environmental change. N. Phytologist 221, 110–122 (2019).10.1111/nph.15401
32. Lister AM Natural history collections as sources of long-term datasets Trends Ecol. Evol. 2011 26 153 154 10.1016/j.tree.2010.12.009 21255862
Lister, A. M. Natural history collections as sources of long-term datasets. Trends Ecol. Evol. 26, 153–154 (2011).21255862 10.1016/j.tree.2010.12.009
33. Meineke EK Davis CC Davies TJ The unrealized potential of herbaria for global change biology Ecol. Monogr. 2018 88 505 525 10.1002/ecm.1307
Meineke, E. K., Davis, C. C. & Davies, T. J. The unrealized potential of herbaria for global change biology. Ecol. Monogr. 88, 505–525 (2018).10.1002/ecm.1307
34. Meineke EK Davies TJ Daru BH Davis CC Biological collections for understanding biodiversity in the Anthropocene Philos. Trans. R. Soc. B 2019 374 20170386 10.1098/rstb.2017.0386
Meineke, E. K., Davies, T. J., Daru, B. H. & Davis, C. C. Biological collections for understanding biodiversity in the Anthropocene. Philos. Trans. R. Soc. B 374, 20170386 (2019).10.1098/rstb.2017.0386
35. Zorich, D. The Digitization of the US National Herbarium - Done! | Digitization Program Office. SI Digi Blog https://dpo.si.edu/blog/digitization-us-national-herbarium-done (2022).
36. Borsch T A complete digitization of German herbaria is possible, sensible and should be started now RIO 2020 6 e50675 10.3897/rio.6.e50675
Borsch, T. et al. A complete digitization of German herbaria is possible, sensible and should be started now. RIO 6, e50675 (2020).10.3897/rio.6.e50675
37. Mandrioli M From dormant collections to repositories for the study of habitat changes: the importance of herbaria in modern life sciences Life 2023 13 2310 10.3390/life13122310 38137911
Mandrioli, M. From dormant collections to repositories for the study of habitat changes: the importance of herbaria in modern life sciences. Life 13, 2310 (2023).38137911 10.3390/life13122310
38. Canadensys. Digitization and imaging https://www.canadensys.net/resources/documents/#digitization-and-imaging (2024).
39. iDigBio. Digitization resources https://www.idigbio.org/wiki/index.php/Digitization_Resources (2021).
40. Canadensys. https://www.canadensys.net/.
41. FLAS. Estimated cost of flas services and supplies. University of Florida - Florida Museum https://web.archive.org/web/20240403174322/https://www.floridamuseum.ufl.edu/herbarium/policies/cost-of-services/ (2022).
42. Granzow-de La Cerda Í Beach JH Semi‐automated workflows for acquiring specimen data from label images in herbarium collections TAXON 2010 59 1830 1842 10.1002/tax.596014
Granzow-de La Cerda, Í. & Beach, J. H. Semi‐automated workflows for acquiring specimen data from label images in herbarium collections. TAXON 59, 1830–1842 (2010).10.1002/tax.596014
43. Statistics Canada. Federal Science Expenditures and Personnel, Activities in the Social Sciences and Natural Sciences (2023).
44. Harris KM Marsico TD Digitizing specimens in a small herbarium: a viable workflow for collections working with limited resources Appl. Plant Sci. 2017 5 1600125 10.3732/apps.1600125
Harris, K. M. & Marsico, T. D. Digitizing specimens in a small herbarium: a viable workflow for collections working with limited resources. Appl. Plant Sci. 5, 1600125 (2017).10.3732/apps.1600125
45. Tulig M Tarnowsky N Bevans M Kirchgessner A Thiers B Increasing the efficiency of digitization workflows for herbarium specimens Zookeys 2012 209 103 113 10.3897/zookeys.209.3125
Tulig, M., Tarnowsky, N., Bevans, M., Kirchgessner, A. & Thiers, B. Increasing the efficiency of digitization workflows for herbarium specimens. Zookeys 209, 103–113 (2012).10.3897/zookeys.209.3125
46. Sweeney PW Large–scale digitization of herbarium specimens: development and usage of an automated, high–throughput conveyor system TAXON 2018 67 165 178 10.12705/671.10
Sweeney, P. W. et al. Large–scale digitization of herbarium specimens: development and usage of an automated, high–throughput conveyor system. TAXON 67, 165–178 (2018).10.12705/671.10
47. Lohonya K Livermore L Penn M Georeferencing the Natural History Museum’s Chinese type collection: of plateaus, pagodas and plants BDJ 2020 8 e50503 10.3897/BDJ.8.e50503 32431560
Lohonya, K., Livermore, L. & Penn, M. Georeferencing the Natural History Museum’s Chinese type collection: of plateaus, pagodas and plants. BDJ 8, e50503 (2020).32431560 10.3897/BDJ.8.e50503
48. Hussein BR Malik OA Ong W-H Slik JWF Applications of computer vision and machine learning techniques for digitized herbarium specimens: a systematic literature review Ecol. Inform. 2022 69 101641 10.1016/j.ecoinf.2022.101641
Hussein, B. R., Malik, O. A., Ong, W.-H. & Slik, J. W. F. Applications of computer vision and machine learning techniques for digitized herbarium specimens: a systematic literature review. Ecol. Inform. 69, 101641 (2022).10.1016/j.ecoinf.2022.101641
49. Guralnick R Humans in the loop: community science and machine learning synergies for overcoming herbarium digitization bottlenecks Appl. Plant Sci. 2024 12 e11560 10.1002/aps3.11560 38369981
Guralnick, R. et al. Humans in the loop: community science and machine learning synergies for overcoming herbarium digitization bottlenecks. Appl. Plant Sci. 12, e11560 (2024).38369981 10.1002/aps3.11560
50. Thompson KM Turnbull R Fitzgerald E Birch JL Identification of herbarium specimen sheet components from high‐resolution images using deep learning Ecol. Evol. 2023 13 e10395 10.1002/ece3.10395 37589042
Thompson, K. M., Turnbull, R., Fitzgerald, E. & Birch, J. L. Identification of herbarium specimen sheet components from high‐resolution images using deep learning. Ecol. Evol. 13, e10395 (2023).37589042 10.1002/ece3.10395
51. Arctic Flora Biodiversity - Canadian Museum of Nature. Canadian Museum of Nature https://nature.ca/en/our-science/research-projects/arctic-flora-biodiversity/ (2024).
52. Tulloch AIT Possingham HP Joseph LN Szabo J Martin TG Realising the full potential of citizen science monitoring programs Biol. Conserv. 2013 165 128 138 10.1016/j.biocon.2013.05.025
Tulloch, A. I. T., Possingham, H. P., Joseph, L. N., Szabo, J. & Martin, T. G. Realising the full potential of citizen science monitoring programs. Biol. Conserv. 165, 128–138 (2013).10.1016/j.biocon.2013.05.025
53. BC Parks Biodiversity Program. BC Parks Biodiversity Program https://www.bcinat.com/ (2021).
54. Navarro, L., Fernández, N. & Pereira, H. The GEO BON approach to globally coordinated biodiversity monitoring. in Proc. 5th European Congress of Conservation Biology (Jyvaskyla University Open Science Centre, Jyväskylä, Finland, 2018). 10.17011/conference/eccb2018/108135.
55. Nuñez‐Penichet C Selection of sampling sites for biodiversity inventory: Effects of environmental and geographical considerations Methods Ecol. Evol. 2022 13 1595 1607 10.1111/2041-210X.13869
Nuñez‐Penichet, C. et al. Selection of sampling sites for biodiversity inventory: Effects of environmental and geographical considerations. Methods Ecol. Evol. 13, 1595–1607 (2022).10.1111/2041-210X.13869
56. Valdez JW The undetectability of global biodiversity trends using local species richness Ecography 2023 10.1111/ecog.06604
Valdez, J. W. et al. The undetectability of global biodiversity trends using local species richness. Ecography10.1111/ecog.06604 (2023).10.1111/ecog.06604
57. Soteropoulos, D. L. & Marsico, T. D. Community science success for herbarium transcription in Arkansas: building a network of students and volunteers for notes from nature. Castanea 87, 54–74 (2022).
58. Wen J Ickert‐Bond SM Appelhans MS Dorr LJ Funk VA Collections‐based systematics: Opportunities and outlook for 2050 J. Syts. Evol. 2015 53 477 488 10.1111/jse.12181
Wen, J., Ickert‐Bond, S. M., Appelhans, M. S., Dorr, L. J. & Funk, V. A. Collections‐based systematics: Opportunities and outlook for 2050. J. Syts. Evol. 53, 477–488 (2015).10.1111/jse.12181
59. Wheeler Q Are reports of the death of taxonomy an exaggeration? N. Phytologist 2014 201 370 371 10.1111/nph.12612
Wheeler, Q. Are reports of the death of taxonomy an exaggeration? N. Phytologist 201, 370–371 (2014).10.1111/nph.12612
60. Park DS The colonial legacy of herbaria Nat. Hum. Behav. 2023 7 1059 1068 10.1038/s41562-023-01616-7 37308536
Park, D. S. et al. The colonial legacy of herbaria. Nat. Hum. Behav. 7, 1059–1068 (2023).37308536 10.1038/s41562-023-01616-7
61. Greenbelt Indigenous Botanical Survey Launches! Plenty Canada http://www.plentycanada.com/1/post/2023/12/greenbelt-indigenous-botanical-survey-launches.html
62. Capture the Collections. Canadian Museum of Nature https://nature.ca/en/learn-explore/activities/capture-collections/.
63. Recovering Lost Voices. Hidden Hands in Colonial Natural Histories https://hiddenhands.ca/canada/ (2022).
64. Brouillet, L. et al. Database of vascular plants of Canada (VASCAN). Online. (2010).
65. GBIF. Occurrence Download. 1380345621 The Global Biodiversity Information Facility 10.15468/DL.6FJT8Y (2024).
66. GBIF. Occurrence Download. 396343141 The Global Biodiversity Information Facility 10.15468/DL.TAKS2V (2024).
67. Clark PJ Evans FC Distance to nearest neighbor as a measure of spatial relationships in populations Ecology 1954 35 445 453 10.2307/1931034
Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).10.2307/1931034
68. Evans, J. & Murphy, M. spatialEco. (2023).
69. Center For International Earth Science Information Network-CIESIN-Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: Socioeconomic Data and Applications Center (SEDAC) 10.7927/H49C6VHW (2017).
70. Hijmans, R. J. et al. Package ‘terra’ (Maintainer: Vienna, Austria, 2022).
71. Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, New York, NY, 2002). 10.1007/978-0-387-21706-2.
72. Lüdecke D Ben-Shachar M Patil I Waggoner P Makowski D performance: an R package for assessment, comparison and testing of statistical models JOSS 2021 6 3139 10.21105/joss.03139
Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. performance: an R package for assessment, comparison and testing of statistical models. JOSS 6, 3139 (2021).10.21105/joss.03139
73. Commission for Environmental Cooperation. Political boundaries (2021).
74. Li D rtrees: an R package to assemble phylogenetic trees from megatrees Ecography 2023 2023 e06643 10.1111/ecog.06643
Li, D. rtrees: an R package to assemble phylogenetic trees from megatrees. Ecography 2023, e06643 (2023).10.1111/ecog.06643
75. Fraser LH TRY—A plant trait database of databases Glob. Change Biol. 2020 26 189 190 10.1111/gcb.14869
Fraser, L. H. TRY—A plant trait database of databases. Glob. Change Biol. 26, 189–190 (2020).10.1111/gcb.14869
76. Santos, T. PVR: Phylogenetic eigenvectors regression and phylogentic signal-representation curve (2018).
77. Stekhoven DJ Buhlmann P MissForest-non-parametric missing value imputation for mixed-type data Bioinformatics 2012 28 112 118 10.1093/bioinformatics/btr597 22039212
Stekhoven, D. J. & Buhlmann, P. MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).22039212 10.1093/bioinformatics/btr597
78. Laliberté, E., Legendre, P. & Shipley, B. Package ‘FD’ for R. (2014).
79. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2023).
80. Pagel M Inferring the historical patterns of biological evolution Nature 1999 401 877 884 10.1038/44766 10553904
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).10553904 10.1038/44766
81. Münkemüller T How to measure and test phylogenetic signal Methods Ecol. Evol. 2012 3 743 756 10.1111/j.2041-210X.2012.00196.x
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).10.1111/j.2041-210X.2012.00196.x
82. Revell LJ phytools: an R package for phylogenetic comparative biology (and other things) Methods Ecol. Evol. 2012 3 217 223 10.1111/j.2041-210X.2011.00169.x
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).10.1111/j.2041-210X.2011.00169.x
83. Geissinger EA Khoo CLL Richmond IC Faulkner SJM Schneider DC A case for beta regression in the natural sciences Ecosphere 2022 13 e3940 10.1002/ecs2.3940
Geissinger, E. A., Khoo, C. L. L., Richmond, I. C., Faulkner, S. J. M. & Schneider, D. C. A case for beta regression in the natural sciences. Ecosphere 13, e3940 (2022).10.1002/ecs2.3940
84. Cribari-Neto, F. & Zeileis, A. Beta Rregression in R. J. Stat. Soft. 34, 1–24 (2010).
85. Daru BH Karunarathne P Schliep K phyloregion: R package for biogeographical regionalization and macroecology Methods Ecol. Evol. 2020 11 1483 1491 10.1111/2041-210X.13478
Daru, B. H., Karunarathne, P. & Schliep, K. phyloregion: R package for biogeographical regionalization and macroecology. Methods Ecol. Evol. 11, 1483–1491 (2020).10.1111/2041-210X.13478
86. Maitner BS The BIEN R package: a tool to access the Botanical Information and Ecology Network (BIEN) database Methods Ecol. Evol. 2018 9 373 379 10.1111/2041-210X.12861
Maitner, B. S. et al. The BIEN R package: a tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).10.1111/2041-210X.12861
87. AdaptWest Project. Gridded Current and Projected Climate Data for North America at 1km Resolution, Generated Using the ClimateNA v7.01 Software (T. Wang et al., 2021). adaptwest.databasin.org (2021).
88. Oksanen, A. J. et al. Package ‘ vegan’. CRAN Repository 0–291 (2013).
89. GBIF.org. Occurrence Download. 525916669 The Global Biodiversity Information Facility 10.15468/DL.897YAH (2021).
90. GBIF.org. Occurrence Download. 165004383 The Global Biodiversity Information Facility 10.15468/DL.G424JV (2021).
91. Metcalfe P Beven K Freer J Dynamic TOPMODEL: a new implementation in R and its sensitivity to time and space steps Environ. Model. Softw. 2015 72 155 172 10.1016/j.envsoft.2015.06.010
Metcalfe, P., Beven, K. & Freer, J. Dynamic TOPMODEL: a new implementation in R and its sensitivity to time and space steps. Environ. Model. Softw. 72, 155–172 (2015).10.1016/j.envsoft.2015.06.010
92. Friedl MA MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets Remote Sens. Environ. 2010 114 168 182 10.1016/j.rse.2009.08.016
Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).10.1016/j.rse.2009.08.016
93. Liu S The Unified North American Soil Map and its implication on the soil organic carbon stock in North America Biogeosciences 2013 10 2915 2930 10.5194/bg-10-2915-2013
Liu, S. et al. The Unified North American Soil Map and its implication on the soil organic carbon stock in North America. Biogeosciences 10, 2915–2930 (2013).10.5194/bg-10-2915-2013
94. Elith J Leathwick JR Hastie T A working guide to boosted regression trees J. Anim. Ecol. 2008 77 802 813 10.1111/j.1365-2656.2008.01390.x 18397250
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).18397250 10.1111/j.1365-2656.2008.01390.x
95. Hijmans, R. J. & Elith, J. Species Distribution Modeling with R (R Cran Project, 2013).
96. Valavi R Elith J Lahoz‐Monfort JJ Guillera‐Arroita G Modelling species presence‐only data with random forests Ecography 2021 44 1731 1742 10.1111/ecog.05615
Valavi, R., Elith, J., Lahoz‐Monfort, J. J. & Guillera‐Arroita, G. Modelling species presence‐only data with random forests. Ecography 44, 1731–1742 (2021).10.1111/ecog.05615
97. Hijmans RJ Phillips S Leathwick J Elith J Hijmans MRJ Package ‘dismo’ Circles 2017 9 1 68
Hijmans, R. J., Phillips, S., Leathwick, J., Elith, J. & Hijmans, M. R. J. Package ‘dismo’. Circles 9, 1–68 (2017).
98. Rigby RA Stasinopoulos DM Generalized additive models for location, scale and shape J. R. Stat. Soc. Ser. C Appl. Stat. 2005 54 507 554 10.1111/j.1467-9876.2005.00510.x
Rigby, R. A. & Stasinopoulos, D. M. Generalized additive models for location, scale and shape. J. R. Stat. Soc. Ser. C Appl. Stat. 54, 507–554 (2005).10.1111/j.1467-9876.2005.00510.x
