The unrealized potential of herbaria for global change biology

Precipitation contributes to plant height, but not reproductive effort, for western prairie fringed orchid (Platanthera praeclara Sheviak & Bowles): Evidence from herbarium records

The western fringed prairie orchid (WFPO) is a rare plant found in mesic to wet tallgrass prairies in the Great Plains and Midwest regions of the United States. The size of WFPO populations varies considerably from year to year, and studies have suggested that population size is dependent on precipitation during critical periods in the plant's annual development. We hypothesized that plant height and reproductive effort would also be controlled by precipitation, either during these periods or over a broader period. We acquired available images of WFPO from 21 herbaria, and of these 141 individual plants had information adequate for analysis, although some population/year combinations were represented multiple times. For each specimen, we measured plant height (cm) and reproductive effort (as measured by total flower and bud count). We used bootstrapped linear regression, randomly selecting one individual from each population/year combination, to compare precipitation models, both during critical periods and the various summaries. We found that precipitation during the phenologically critical periods was a poor predictor of plant height and reproductive effort. Of the broader precipitation variables, accumulated precipitation from January 1 to collection date best described plant height. We also used correlations to detect a relationship among the variables WFPO height, reproductive effort, precipitation, latitude, and year of collection. Year of specimen collection was negatively correlated with WFPO plant height and accumulated precipitation, suggesting that both have declined in more recent years. Negative correlations with latitude also suggest height and precipitation decrease in the northern part of WFPO's range. Reproductive effort was not related to tested precipitation variables; however, it was weakly correlated with plant height. Although the results are limited, this study leverages available data and makes inferences on WFPO biology over broad ranges of time (1894-2012) and latitude (37.5°-49.9°).

Century-long apparent decrease in intrinsic water-use efficiency with no evidence of progressive nutrient limitation in African tropical forests

Forests exhibit leaf- and ecosystem-level responses to environmental changes. Specifically, rising carbon dioxide (CO₂ ) levels over the past century are expected to have increased the intrinsic water-use efficiency (iWUE) of tropical trees while the ecosystem is gradually pushed into progressive nutrient limitation. Due to the long-term character of these changes, however, observational datasets to validate both paradigms are limited in space and time. In this study, we used a unique herbarium record to go back nearly a century and show that despite the rise in CO₂ concentrations, iWUE has decreased in central African tropical trees in the Congo Basin. Although we find evidence that points to leaf-level adaptation to increasing CO₂ -that is, increasing photosynthesis-related nutrients and decreasing maximum stomatal conductance, a decrease in leaf δ¹³ C clearly indicates a decreasing iWUE over time. Additionally, the stoichiometric carbon to nitrogen and nitrogen to phosphorus ratios in the leaves show no sign of progressive nutrient limitation as they have remained constant since 1938, which suggests that nutrients have not increasingly limited productivity in this biome. Altogether, the data suggest that other environmental factors, such as increasing temperature, might have negatively affected net photosynthesis and consequently downregulated the iWUE. Results from this study reveal that the second largest tropical forest on Earth has responded differently to recent environmental changes than expected, highlighting the need for further on-ground monitoring in the Congo Basin.

A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN

Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from >3,000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with >87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with ≥90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant responses to ongoing climatic change.

Biases in estimation of insect herbivory from herbarium specimens

Information regarding plant damage by insects in the past is essential to explore impacts of climate change on herbivory. We asked whether insect herbivory measured from herbarium specimens reflects the levels of herbivory occurring in nature at the time of herbarium sampling. We compared herbivory measurements between herbarium specimens collected by botany students and ecological samples collected simultaneously by the authors by a method that minimized unconscious biases, and asked herbarium curators to select one of two plant specimens, which differed in leaf damage, for their collections. Both collectors and curators generally preferred specimens with lesser leaf damage, but the strength of this preference varied among persons. In addition, the differences in measured leaf damage between ecological samples and herbarium specimens varied among plant species and increased with the increase in field herbivory. Consequently, leaf damage in herbarium specimens did not correlate with the actual level of herbivory. We conclude that studies of herbarium specimens produce biased information on past levels of herbivory, because leaf damage measured from herbarium specimens not only underestimates field herbivory, but it is not proportional to the level of damage occurring in nature due to multiple factors that cannot be controlled in data analysis.

Herbaria macroalgae as a proxy for historical upwelling trends in Central California

Planning for future ocean conditions requires historical data to establish more informed ecological baselines. To date, this process has been largely limited to instrument records and observations that begin around 1950. Here, we show how marine macroalgae specimens from herbaria repositories may document long-term ecosystem processes and extend historical information records into the nineteenth century. We tested the effect of drying and pressing six macroalgae species on amino acid, heavy metal and bulk stable isotope values over 1 year using modern and archived paper. We found historical paper composition did not consistently affect values. Certain species, however, had higher variability in particular metrics while others were more consistent. Multiple herbaria provided Gelidium (Rhodophyta) samples collected in southern Monterey Bay from 1878 to 2018. We examined environmental relationships and found δ15N correlated with the Bakun upwelling index, the productivity regime of this ecosystem, from 1946 to 2018. Then, we hindcasted the Bakun index using its derived relationship with Gelidium δ15N from 1878 to 1945. This hindcast provided new information, observing an upwelling decrease mid-century leading up to the well-known sardine fishery crash. Our case study suggests marine macroalgae from herbaria are an underused resource of the marine environment that precedes modern scientific data streams.

Applying machine learning to investigate long-term insect-plant interactions preserved on digitized herbarium specimens

PREMISE

Despite the economic significance of insect damage to plants (i.e., herbivory), long-term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant-insect interactions during the Anthropocene.

METHODS

We initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens by training an insect damage detector and a damage type classifier on two distantly related plant species (Quercus bicolor and Onoclea sensibilis). We experimented with (1) classifying six types of herbivory and two control categories of undamaged leaf, and (2) detecting two of the damage categories for which several hundred annotations were available.

RESULTS

Damage detection results were mixed, with a mean average precision of 45% in the simultaneous detection and classification of two types of damage. However, damage classification on hand-drawn boxes identified the correct type of herbivory 81.5% of the time in eight categories. The damage classifier was accurate for categories with 100 or more test samples.

DISCUSSION

These tools are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models, allowing researchers to extract similar data and apply them to biological hypotheses.

Evolution of defense and herbivory in introduced plants-Testing enemy release using a known source population, herbivore trials, and time since introduction

The enemy release hypothesis is often cited as a potential explanation for the success of introduced plants; yet, empirical evidence for enemy release is mixed. We aimed to quantify changes in herbivory and defense in introduced plants while controlling for three factors that might have confounded past studies: using a wide native range for comparison with the introduced range, measuring defense traits without determining whether they affect herbivore preferences, and not considering the effect of time since introduction. The first hypothesis we tested was that introduced plants will have evolved lower levels of plant defense compared to their source population. We grew South African (source) and Australian (introduced) beach daisies (Arctotheca populifolia) in a common-environment glasshouse experiment and measured seven defense traits. Introduced plants had more ash, alkaloids, and leaf hairs than source plants, but were also less tough, with a lower C:N ratio and less phenolics. Overall, we found no difference in defense between source and introduced plants. To determine whether the feeding habits of herbivores align with changes in defense traits, we conducted preference feeding trials using five different herbivore species. Herbivores showed no overall preference for leaves from either group. The second hypothesis we tested was that herbivory on introduced plant species will increase through time after introduction to a new range. We recorded leaf damage on herbarium specimens of seven species introduced to eastern Australia and three native control species. We found no change in the overall level of herbivory experienced by introduced plants since arriving in Australia.

CONCLUSION

In the field of invasion ecology, we need to rethink the paradigm that species introduced to a new range undergo simple decreases in defenses against herbivores. Instead, plants are likely to employ a range of defense traits that evolve in both coordinated and opposing ways in response to a plethora of different biotic and abiotic selective pressures.

Generating segmentation masks of herbarium specimens and a data set for training segmentation models using deep learning

PREMISE

Digitized images of herbarium specimens are highly diverse with many potential sources of visual noise and bias. The systematic removal of noise and minimization of bias must be achieved in order to generate biological insights based on the plants rather than the digitization and mounting practices involved. Here, we develop a workflow and data set of high-resolution image masks to segment plant tissues in herbarium specimen images and remove background pixels using deep learning.

METHODS AND RESULTS

We generated 400 curated, high-resolution masks of ferns using a combination of automatic and manual tools for image manipulation. We used those images to train a U-Net-style deep learning model for image segmentation, achieving a final Sørensen-Dice coefficient of 0.96. The resulting model can automatically, efficiently, and accurately segment massive data sets of digitized herbarium specimens, particularly for ferns.

CONCLUSIONS

The application of deep learning in herbarium sciences requires transparent and systematic protocols for generating training data so that these labor-intensive resources can be generalized to other deep learning applications. Segmentation ground-truth masks are hard-won data, and we share these data and the model openly in the hopes of furthering model training and transfer learning opportunities for broader herbarium applications.

Using computer vision on herbarium specimen images to discriminate among closely related horsetails (Equisetum)

PREMISE

Equisetum is a distinctive vascular plant genus with 15 extant species worldwide. Species identification is complicated by morphological plasticity and frequent hybridization events, leading to a disproportionately high number of misidentified specimens. These may be correctly identified by applying appropriate computer vision tools.

METHODS

We hypothesize that aerial stem nodes can provide enough information to distinguish among Equisetum hyemale, E. laevigatum, and E . ×ferrissii, the latter being a hybrid between the other two. An object detector was trained to find nodes on a given image and to distinguish E. hyemale nodes from those of E. laevigatum. A classifier then took statistics from the detection results and classified the given image into one of the three taxa. Both detector and classifier were trained and tested on expert manually annotated images.

RESULTS

In our exploratory test set of 30 images, our detector/classifier combination identified all 10 E. laevigatum images correctly, as well as nine out of 10 E. hyemale images, and eight out of 10 E. ×ferrissii images, for a 90% classification accuracy.

DISCUSSION

Our results support the notion that computer vision may help with the identification of herbarium specimens once enough manual annotations become available.

Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection

The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.

Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

Digitization and the Future of Natural History Collections

Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, the online mobilization of specimens via digitization—the conversion of specimen data into accessible digital content—has greatly expanded the use of NHC collections across a diversity of disciplines. We broaden the current vision of digitization (Digitization 1.0)—whereby specimens are digitized within NHCs—to include new approaches that rely on digitized products rather than the physical specimen (Digitization 2.0). Digitization 2.0 builds on the data, workflows, and infrastructure produced by Digitization 1.0 to create digital-only workflows that facilitate digitization, curation, and data links, thus returning value to physical specimens by creating new layers of annotation, empowering a global community, and developing automated approaches to advance biodiversity discovery and conservation. These efforts will transform large-scale biodiversity assessments to address fundamental questions including those pertaining to critical issues of global change.

A complete digitization of German herbaria is possible, sensible and should be started now

Plants, fungi and algae are important components of global biodiversity and are fundamental to all ecosystems. They are the basis for human well-being, providing food, materials and medicines. Specimens of all three groups of organisms are accommodated in herbaria, where they are commonly referred to as botanical specimens.

The large number of specimens in herbaria provides an ample, permanent and continuously improving knowledge base on these organisms and an indispensable source for the analysis of the distribution of species in space and time critical for current and future research relating to global biodiversity. In order to make full use of this resource, a research infrastructure has to be built that grants comprehensive and free access to the information in herbaria and botanical collections in general. This can be achieved through digitization of the botanical objects and associated data.

The botanical research community can count on a long-standing tradition of collaboration among institutions and individuals. It agreed on data standards and standard services even before the advent of computerization and information networking, an example being the Index Herbariorum as a global registry of herbaria helping towards the unique identification of specimens cited in the literature.

In the spirit of this collaborative history, 51 representatives from 30 institutions advocate to start the digitization of botanical collections with the overall wall-to-wall digitization of the flat objects stored in German herbaria. Germany has 70 herbaria holding almost 23 million specimens according to a national survey carried out in 2019. 87% of these specimens are not yet digitized. Experiences from other countries like France, the Netherlands, Finland, the US and Australia show that herbaria can be comprehensively and cost-efficiently digitized in a relatively short time due to established workflows and protocols for the high-throughput digitization of flat objects.

Most of the herbaria are part of a university (34), fewer belong to municipal museums (10) or state museums (8), six herbaria belong to institutions also supported by federal funds such as Leibniz institutes, and four belong to non-governmental organizations. A common data infrastructure must therefore integrate different kinds of institutions.

Making full use of the data gained by digitization requires the set-up of a digital infrastructure for storage, archiving, content indexing and networking as well as standardized access for the scientific use of digital objects. A standards-based portfolio of technical components has already been developed and successfully tested by the Biodiversity Informatics Community over the last two decades, comprising among others access protocols, collection databases, portals, tools for semantic enrichment and annotation, international networking, storage and archiving in accordance with international standards. This was achieved through the funding by national and international programs and initiatives, which also paved the road for the German contribution to the Global Biodiversity Information Facility (GBIF).

Herbaria constitute a large part of the German botanical collections that also comprise living collections in botanical gardens and seed banks, DNA- and tissue samples, specimens preserved in fluids or on microscope slides and more. Once the herbaria are digitized, these resources can be integrated, adding to the value of the overall research infrastructure. The community has agreed on tasks that are shared between the herbaria, as the German GBIF model already successfully demonstrates.

We have compiled nine scientific use cases of immediate societal relevance for an integrated infrastructure of botanical collections. They address accelerated biodiversity discovery and research, biomonitoring and conservation planning, biodiversity modelling, the generation of trait information, automated image recognition by artificial intelligence, automated pathogen detection, contextualization by interlinking objects, enabling provenance research, as well as education, outreach and citizen science.

We propose to start this initiative now in order to valorize German botanical collections as a vital part of a worldwide biodiversity data pool.

New protocol for successful isolation and amplification of DNA from exiguous fractions of specimens: a tool to overcome the basic obstacle in molecular analyses of myxomycetes

Herbarium collections provide an essential basis for a wide array of biological research and, with development of DNA-based methods, they have become an invaluable material for genetic analyses. Yet, the use of such material is hindered by technical limitations related to DNA degradation and to quantity of biological material. The latter is inherent for some biological groups, as best exemplified by myxomycetes which form minute sporophores. It is estimated that ca. two-thirds of myxomycete taxa are represented by extremely scanty material. As DNA isolation methods applied so far in myxomycete studies require destructive sampling of many sporophores, a large part of described diversity of the group remains unavailable for phylogenetic studies or barcoding. Here, we tested several procedures of DNA isolation and amplification to seek for an efficient and possibly non-destructive method of sampling. Tests were based on herbarium specimens of 19 species representing different taxonomic orders. We assayed several variants of isolation based on silica gel membrane columns, and a newly designed procedure using highly reduced amount of biological material (small portion of spores), based on fine disruption of spores and direct PCR. While the most frequently used column-based method led to PCR success in 89.5% of samples when a large amount of material was used, its performance dropped to 52% when based on single sporophores. Single sporophores provided amplicons in 89.5% of samples when using a kit dedicated to low-amount DNA samples. Our new procedure appeared the most effective (94.7%) while it used only a small fraction of spores, being nearly non-destructive; it was also the most cost-effective. We thus demonstrate that combination of adequate handling of spore micro-disruption coupled with application of direct PCR can be an efficient way to circumvent technical limitations for genetic studies in myxomycetes and thus can substantially improve taxon sampling for phylogeny and barcoding. Additionally, this approach gives a unique possibility to apply both molecular and morphological assays to the same structure (sporophore), which then can be further stored as documentation.

The Changing Uses of Herbarium Data in an Era of Global Change: An Overview Using Automated Content Analysis

Widespread specimen digitization has greatly enhanced the use of herbarium data in scientific research. Publications using herbarium data have increased exponentially over the last century. Here, we review changing uses of herbaria through time with a computational text analysis of 13,702 articles from 1923 to 2017 that quantitatively complements traditional review approaches. Although maintaining its core contribution to taxonomic knowledge, herbarium use has diversified from a few dominant research topics a century ago (e.g., taxonomic notes, botanical history, local observations), with many topics only recently emerging (e.g., biodiversity informatics, global change biology, DNA analyses). Specimens are now appreciated as temporally and spatially extensive sources of genotypic, phenotypic, and biogeographic data. Specimens are increasingly used in ways that influence our ability to steward future biodiversity. As we enter the Anthropocene, herbaria have likewise entered a new era with enhanced scientific, educational, and societal relevance.

Species characteristics affect local extinctions

PREMISE OF THE STUDY

Human activities threaten thousands of species with extinction. However, it remains difficult to predict extinction risk for many vulnerable species. Species traits, species characteristics such as rarity or habitat use, and phylogenetic patterns are associated with responses to anthropogenic environmental change and may help predict likelihood of extinction.

METHODS

We used historical botanical data from Kalamazoo County, Michigan, USA, to examine whether species traits (growth form, life history, nitrogen-fixation, photosynthetic pathway), species characteristics (community association, species origin, range edge, habitat specialization, rarity), or phylogenetic relatedness explain local species loss at the county level.

KEY RESULTS

Across Kalamazoo County, prairie species, species at the edge of their native range, regionally rare species, and habitat specialists were most likely to become locally extinct. Prairie species experienced the highest local extinction rates of any habitat type, and among prairie species, regionally rare and specialist species were most vulnerable to loss. We found no evidence for a phylogenetic pattern in plant extinctions.

CONCLUSIONS

Our study illustrates the value of historical datasets for understanding and potentially predicting biodiversity loss. Not surprisingly, rare, specialist species occupying threatened habitats are most at risk of local extinction. As a result, identifying mechanisms to conserve or restore rare or declining species and preventing further habitat destruction may be the most effective strategies for reducing future extinction.

Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras

PREMISE OF THE STUDY

Phenological annotation models computed on large-scale herbarium data sets were developed and tested in this study.

METHODS

Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time-consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras.

RESULTS

Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer-scale information (84.3% for flower and 80.5% for fruit detection).

DISCUSSION

The method described has the potential to allow fine-grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed.

Temperature controls phenology in continuously flowering Protea species of subtropical Africa

PREMISE OF THE STUDY

Herbarium specimens are increasingly used as records of plant flowering phenology. However, most herbarium-based studies on plant phenology focus on taxa from temperate regions. Here, we explore flowering phenologic responses to climate in the subtropical plant genus Protea (Proteaceae), an iconic group of plants that flower year-round and are endemic to subtropical Africa.

METHODS

We present a novel, circular sliding window approach to investigate phenological patterns developed for species with year-round flowering. We employ our method to evaluate the extent to which site-to-site and year-to-year variation in temperature and precipitation affect flowering dates using a database of 1727 herbarium records of 25 Protea species. We also explore phylogenetic conservatism in flowering phenology.

RESULTS

We show that herbarium data combined with our sliding window approach successfully captured independently reported flowering phenology patterns (r = 0.93). Both warmer sites and warmer years were associated with earlier flowering of 3-5 days/°C, whereas precipitation variation had no significant effect on flowering phenology. Although species vary widely in phenological responsiveness, responses are phylogenetically conserved, with closely related species tending to shift flowering similarly with increasing temperature.

DISCUSSION

Our results point to climate-responsive phenology for this important plant genus and indicate that the subtropical, aseasonally flowering genus Protea has temperature-driven flowering responses that are remarkably similar to those of better-studied northern temperate plant species, suggesting a generality across biomes that has not been described elsewhere.

Using herbaria to study global environmental change

During the last centuries, humans have transformed global ecosystems. With their temporal dimension, herbaria provide the otherwise scarce long-term data crucial for tracking ecological and evolutionary changes over this period of intense global change. The sheer size of herbaria, together with their increasing digitization and the possibility of sequencing DNA from the preserved plant material, makes them invaluable resources for understanding ecological and evolutionary species' responses to global environmental change. Following the chronology of global change, we highlight how herbaria can inform about long-term effects on plants of at least four of the main drivers of global change: pollution, habitat change, climate change and invasive species. We summarize how herbarium specimens so far have been used in global change research, discuss future opportunities and challenges posed by the nature of these data, and advocate for an intensified use of these 'windows into the past' for global change research and beyond.

Biological collections for understanding biodiversity in the Anthropocene

Global change has become a central focus of modern biology. Yet, our knowledge of how anthropogenic drivers affect biodiversity and natural resources is limited by a lack of biological data spanning the Anthropocene. We propose that the hundreds of millions of plant, fungal and animal specimens deposited in natural history museums have the potential to transform the field of global change biology. We suggest that museum specimens are underused, particularly in ecological studies, given their capacity to reveal patterns that are not observable from other data sources. Increasingly, museum specimens are becoming mobilized online, providing unparalleled access to physiological, ecological and evolutionary data spanning decades and sometimes centuries. Here, we describe the diversity of collections data archived in museums and provide an overview of the diverse uses and applications of these data as discussed in the accompanying collection of papers within this theme issue. As these unparalleled resources are under threat owing to budget cuts and other institutional pressures, we aim to shed light on the unique discoveries that are possible in museums and, thus, the singular value of natural history collections in a period of rapid change.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

Fungarium specimens: a largely untapped source in global change biology and beyond

For several hundred years, millions of fungal sporocarps have been collected and deposited in worldwide collections (fungaria) to support fungal taxonomy. Owing to large-scale digitization programs, metadata associated with the records are now becoming publicly available, including information on taxonomy, sampling location, collection date and habitat/substrate information. This metadata, as well as data extracted from the physical fungarium specimens themselves, such as DNA sequences and biochemical characteristics, provide a rich source of information not only for taxonomy but also for other lines of biological inquiry. Here, we highlight and discuss how this information can be used to investigate emerging topics in fungal global change biology and beyond. Fungarium data are a prime source of knowledge on fungal distributions and richness patterns, and for assessing red-listed and invasive species. Information on collection dates has been used to investigate shifts in fungal distributions as well as phenology of sporocarp emergence in response to climate change. In addition to providing material for taxonomy and systematics, DNA sequences derived from the physical specimens provide information about fungal demography, dispersal patterns, and are emerging as a source of genomic data. As DNA analysis technologies develop further, the importance of fungarium specimens as easily accessible sources of information will likely continue to grow.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.

The history and impact of digitization and digital data mobilization on biodiversity research

The first two decades of the twenty-first century have seen a rapid rise in the mobilization of digital biodiversity data. This has thrust natural history museums into the forefront of biodiversity research, underscoring their central role in the modern scientific enterprise. The advent of mobilization initiatives such as the United States National Science Foundation's Advancing Digitization of Biodiversity Collections (ADBC), Australia's Atlas of Living Australia (ALA), Mexico's National Commission for the Knowledge and Use of Biodiversity (CONABIO), Brazil's Centro de Referência em Informação (CRIA) and China's National Specimen Information Infrastructure (NSII) has led to a rapid rise in data aggregators and an exponential increase in digital data for scientific research and arguably provide the best evidence of where species live. The international Global Biodiversity Information Facility (GBIF) now serves about 131 million museum specimen records, and Integrated Digitized Biocollections (iDigBio) in the USA has amassed more than 115 million. These resources expose collections to a wider audience of researchers, provide the best biodiversity data in the modern era outside of nature itself and ensure the primacy of specimen-based research. Here, we provide a brief history of worldwide data mobilization, their impact on biodiversity research, challenges for ensuring data quality, their contribution to scientific publications and evidence of the rising profiles of natural history collections.This article is part of the theme issue 'Biological collections for understanding biodiversity in the Anthropocene'.