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

Abstract

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’.

1. Introduction

The fungal kingdom is one of the most diverse of the eukaryotic kingdoms, with some 1.5–6 million estimated species [1–4]. Fungi play numerous ecological roles in ecosystems—as saprotrophic nutrient cyclers, mutualistic symbionts and pathogens—making an impact on all ecosystems. Despite the ubiquitous importance of fungi, they have not been simple to study. In part this can be explained by the traditional relegation of mycology—an entire field in its own right—to a subset of botany. Fungi are also a hidden group, spending major parts of their life cycle belowground or within substrates; hence, combined with an unparalleled assortment of ecological functions, there is still only rudimentary knowledge about fungal biology and diversity.

In the subkingdom Dikarya (comprised of the Ascomycota and Basidiomycota), many species produce macroscopic fruit bodies, hereafter referred to as sporocarps, which are reproductive structures for actively spreading spores across space (from millimetres to kilometres) and time (from hours to years). The sporocarps are ‘tips of the icebergs’, aboveground manifestations of the belowground or within-substrate vegetative mycelia that can range from millimetres to hundreds of metres in size. Because of their visible presence, fungal biologists have relied on sporocarps to observe and analyse fungal systematics and ecology. DNA-based inferences, adopted in the early 1990s, next allowed novel insights into fungi living in their substrates, and are central now to most fungal biology research. However, when it comes to information about fungal occurrences, distributions and diversity, collections of sporocarp specimens in fungaria (the mycological equivalent to herbaria) far outnumber DNA-based information in unique sampling locations worldwide.

Sporocarps have been collected and deposited in fungaria by naturalists, amateur mycologists and researchers for several hundred years. Recreational hobby, taxonomic research and documentation of fungal diversity have all motivated collections. The most important fungarium records are type specimens, which serve as reference material tied to a species concept. As with plants, a Linnaean classification system based on reproductive structures was adopted for describing and naming fungal species. Worldwide plant herbaria, with some 350 million estimated specimens [5], represent an additional source of information about plant-associated fungi, such as pathogenic rust and smut fungi [6,7]. Many of the western European fungaria are now digitized, providing easily accessible metadata that even a few years prior was less available [8,9]. Most of this information is available through deposition of collections records in the Global Biodiversity Information Facility (GBIF) database (https://www.gbif.org/; [9]). At times, national-scale initiatives collect data from independent fungaria, such as in North America (United States), where collections are being digitized concertedly through the Macro and Microfungal collections consortium projects [10]. Unfortunately, in many parts of the world, efforts at both collecting and digitizing still lag behind, and especially in areas of conservation concern.

Global change processes such as climate change, habitat fragmentation and destruction, and pollution are affecting most organisms on earth, including fungi. As it is challenging to assess the temporal effects of these perturbations by directly measuring changes in fungal mycelia, fungarium records have enabled pioneering research in this area. Fungarium material and sporocarps may be ‘tips of the icebergs’, but there is considerable knowledge gained through their records: they represent an entirely unique historic record of global fungal diversity, are valuable for documenting existing patterns related to fungal ecology, and provide the data necessary to build models of how fungi will respond to global change processes [11]. Sporocarps may be considered the ‘bellwethers of fungi’, demonstrating visible impacts of global change that are more complex and cryptic to quantify within the substrates that fungi vegetatively reside in [12]. In this review, we discuss modern uses of fungarium material for research, and we put forward examples of emerging questions in fungal biology that leverage fungarium material (table 1). After highlighting which types of data can be extracted from fungarium records, we discuss how this information can be used to analyse fungal diversity patterns, distributions and range shifts, ecological interactions, phenology, demography and population genetics. Many of the opportunities and challenges of using specimens are actually similar between fungi and plants, but we highlight the differences where they emerge. A major distinction should be kept in mind, in that fungi are far more challenging to collect and, thus, to ecologically understand; herbarium collections support vegetative and reproductive components of plants, even roots at times, while fungal collections are almost exclusively relegated to their reproductive structures. But within such collections is a wealth of data and scientific potential.

Table 1.

Research areas and questions in global change biology and beyond that can be addressed with fungarium data paired with other types of data.

research area	topics and questions
phenology	— Which environmental factors control the timing of fungal fruiting? What is the relative importance of climate versus other factors, such as soil type? What is the importance of direct versus indirect effects, such as host plant growth? What are the mechanisms behind extreme and widespread fruiting events (‘mast years’)? — How is climate change altering the fruiting period in different areas, beyond what has already been reported? — Are there ecological or life-history traits that are predictive of how sensitive fungal fruiting is to changing climate? — To what extent do the aboveground phenological changes mirror belowground processes, including mycelial growth? — How is fungal fruiting time synchronized with other organisms' phenology and life-history events? How will changes in fruiting period affect other organisms, including invertebrates residing in or grazing on sporocarps? Can changes in fruiting period lead to mismatches in life-history events between the host sporocarp and the invertebrates?
distribution and range shifts	— Which factors determine fungal distributions? What is the relative importance of climate versus biotic interactions such as plant distributions? What is the effect of host specialization? What is the effect of extreme events versus overall means? — To what extent and how fast are fungi changing their distributions? Can we see any lag effects? — Do fungi with different life-history traits, including dispersal ability, exhibit different rates of range shifts? — How will climate change affect fungal distributions under future climate scenarios? — What is the potential for establishment and spread of invasive species in non-native areas? To what degree can the spread of invasive fungi be tracked using historic fungarium records? — Do fungi exhibit the typical increasing latitudinal gradient of diversity, or is this pattern reversed in some groups? — How are fungal traits distributed and affected by global change factors?
ecology and substrate usage	— What are the ecological preferences/niches of fungal taxa? — What are the nutritional modes of various fungi? — Can shifts in species ecology, such as host/substrate associations, be observed in light of global change? — What proportion of taxa is completely hidden from view and unrepresented in fungaria or culture collections? Which taxa are prevalent in fungaria but not in sequence databases (indicating e.g. primer mismatch)?
conservation biology	— What are the species boundaries and what are the appropriate units for conservation? — Which areas are biodiversity hot spots when it comes to fungal species diversity and phylogenetic diversity? — Where are hotspots for rare and red-listed fungi? Where can rare and red-listed species potentially be found (based on extrapolations)? — How may global change, including habitat and climate change, affect the distributions of red-listed fungi? Which global change drivers are the largest threats to fungi?
demography, population genetics and systematics	— How much genetic variation can be found in rare and red-listed species? Does the genetic structure indicate a population decline? — What is the level of genetic variation in invasive species? Did multiple introductions happen? Where are the source populations and how were the species introduced? — Has rapid evolution in recent decades (such as evolution of fungicide resistance) occurred by selection on existing variants or by novel mutations? — How often are species based on a single record accurate? — How often is hybridization involved in the origins of new species? — Which lineages are diversifying at a faster rate than others?

2. Fungarium data—a resource and a challenge

Diverse types of data can be extracted from fungarium specimens, directly or indirectly (figure 1). Although most of these types of data are similar to what is available with plant specimens in herbaria, some are unique to the fungal life-histories, such as substrate types. Metadata associated with the records are easily accessible in digitized form and represent a rich source of information. Records missing important metadata, such as collection date and location (figure 2), can easily be filtered out, in addition to tentative duplicate records that hold exactly the same metadata information or are the same collection event deposited in multiple fungaria. Further, records with obviously inaccurate metadata, for example georeferences ‘out at sea’ for terrestrial taxa, or with inaccurate countries reported compared to their geographical coordinates (often related to inverted numeric patterns or displacement of a negative sign), or records that behave as extreme outliers in e.g. distribution models or regressions, may be removed or their taxon identity controlled. The physical specimens themselves provide genetic, morphological and biochemical information. New techniques from the field of ancient DNA analyses and high throughput DNA sequencing can be used to obtain genome-scale DNA data from even the oldest specimens available (see below).

Figure 1.

Sketch illustrating various types of information that can be obtained from fungarium records, either directly through the annotated metadata or through analyses of the physical specimens themselves. Blue colour indicates the primary information, red colour external metadata, such as external DNA data from GenBank, that can be linked up for further analyses, and green colour which scientific fields the information can be applied to. The sketch is not exhaustive; there are certainly additional creative ways the information can be used and inter-linked, e.g. to similar information for other organismal groups. The solid lines indicate types of data that can be obtained or extracted from the fungarium specimen itself, while the dashed lines reflect external data that can be linked in or research topics where the data can be used. (Online version in colour.)

Figure 2.

Fungarium records (1960–2010 shown, but data available earlier) from Norway (Artsdatabanken), the United Kingdom (FRDBI), and Slovenia (Slovenian Mycological Association) demonstrate a variety of sampling characteristics and ecological information. Records have accumulated in fungaria across time (a), especially since the 1960s, and for countries with a longer legacy of collecting. The sampling density of records (b) does bias fungaria collections to urban areas (log-scale) and (c) certain fungal orders are collected more. Fungaria records collections are directly related to the number of species catalogued in those fungaria (d). Despite biases, clear phenology patterns are easily demonstrable (e), for example with the average sporulation day of autumnal-sporulating fungi. Human population levels have a positive impact on fungaria records collections (f). (Online version in colour.)

The most basic information included in most metadata is the taxonomic identity of the specimen, which comes with a level of uncertainty, depending on the local taxonomic expertise. For many fungal groups, likely more so than for plants and animals, identification can be very difficult, even for a trained expert using microscopic characters. Although distinguishing between morphologically similar species is therefore not always possible, identification at the genus level is typically achievable. However, DNA barcoding of fungal specimens has now become a routine operation to aid in taxonomy and is commonplace when naming new species [13,14]. The taxonomic identity represents an important link to additional external data, such as independent DNA data or species trait data. Species with macroscopic sporocarps in the orders Agaricales, Boletales, Russulales, Polyporales and Hymenochaetales are heavily represented in fungaria, while species with more inconspicuous sporocarps in Ascomycota are underrepresented (figure 2). Hence, a taxonomic bias exists that must be taken into consideration in some types of analyses, for example by restricting analyses to certain well-sampled taxonomic groups [15].

Georeferences (coordinates) are crucial information for most analyses. Although not all specimens come with high-resolution georeferences, location information (e.g. limited to county or municipality) is usually provided and can be used to assign approximate coordinates for macro-scale analyses. Georeferences form the basis for distribution modelling, phylo-/biogeographic analyses, phenology and studies in macroecology, and serve as a link to external georeferenced data, such as climate, land-use, pollution, vegetation and soil data. There can be a high spatial bias in the distribution of fungarium collections that must be considered during later analyses, typically with more heavily sampled areas in densely populated areas (figure 2). Collection date is a third basic piece of data annotated on most fungarium specimens, providing crucial information for research of e.g. phenology, demography and distributional range-shifts (figure 1). Together with georeferences, collection dates can be used to link the fungarium record to historic land-use or climate conditions using databases such as WorldClim [16]. There is often a major bias in collection dates and years in fungarium records [17], with more records in recent periods (figure 2). The extent of metadata on habitat and substrate, from where the specimens were collected, varies extensively, but when available this can be used to study ecological interactions such as host association patterns [18,19]. Often fungal specimens are collected together with the substrate, which may be the host plant (or parts of it). The combined substrate – fungal specimens can provide unique research opportunities, such as testing for coevolution and cophylogeny [7].

There are several ways to correct for taxonomic, temporal and spatial sampling biases, including rarefication [15], weighting or offsetting the sampling effects in models [20], or focusing on responses not likely influenced by sampling biases [21]. As this topic has been reviewed by many others recently [22,23], we will not go into detail here. The important point is to understand sources of bias, and to structure hypotheses and objectives in manners that either account for biases or prepare the data appropriately to minimize biases, which are inherent to all forms of data and certainly not exclusive to fungaria records.

3. Phenology

Phenology, the study of seasonal timing of life-history events, may reflect how species respond to climate change since the phenological events often are triggered by environmental cues, including temperature. The emergence of macroscopic sporocarps is a crucial step in many fungal life cycles and is used as a phenological marker for fungi. Since many fungi, and especially mushroom-producing agarics of Agaricomycetes, produce short-lived sporocarps, their collection date functions as a good proxy for fruiting time (figure 2). In temperate zones, the main fruiting period is split into a minor spring season and a more extensive autumn season, delimited by the warm and dry summer and the cold and frosty winter. In phenological studies, the two seasons are often analysed separately [20,24–26].

The first fungal phenology studies relied on rigorously assembled sporocarp records from smaller plots or forests [27,28]. In southern England, a dataset assembled during 1955–2005 indicated a general widening of the fruiting season, and found that some typical fall-fruiting fungi began fruiting during both spring and autumn in more recent years [27]. Importantly, a recent study using local and national-scale data in Switzerland and the UK, including fungarium data, suggests that analyses of plot-based and national-scale data estimate similar trends in fruiting, most notably widening of the fruiting seasons [29]. Although further tests using additional citizen-science and fungaria datasets will be important, this result is encouraging for the widespread use of large presence-only fungaria datasets for analysing fungal phenology.

In the first phenology study integrating fungarium records, Kauserud et al. [26] observed a general delay in the autumnal fruiting season in Norway during the period 1940–2006, where especially early fruiting species changed towards later fruiting (up to 40 days). Then analysing four national-scale datasets from Europe [20], an average delay in the autumn fruiting season was observed again but also a general widening of the season, especially for saprotrophic fungi and in oceanic regions like the UK. In another fungarium-based study from North America, Diez et al. [30] observed that the overall fungal community (274 macro-fungi) fruited later in warmer and drier years, which has led to a shift toward later fruiting dates for autumn-fruiting species, as in Europe. Both Kauserud et al. [20] and Diez et al. [30] noted that the effects were highly variable among species and were linked to their basic life-history characteristics. Mirroring phenological changes in plant flowering time and bird migration, a shift of spring fungi towards earlier fruiting has been observed in both UK and Norway from 1960 to 2007 [25]. A recent study covering larger parts of Europe [24] demonstrated large-scale differences in spring and autumn fruiting seasons across Europe and found that temperature is the main driver of fruiting time of autumnal ectomycorrhizal and saprotrophic, as well as spring saprotrophic, fungi, while primary production and precipitation are more major drivers for spring-fruiting ectomycorrhizal fungi. The above-mentioned studies were partly based on national-scale digitized data from fungaria, as well as field records, where date of collection was used as an approximation for fruiting time and, hence, as response in the statistical analyses. Phenology data from fungaria must be analysed and interpreted with great care owing to sampling biases [31]. If not accounted for, the temporal skewness in sampling intensity, typically with more intensive sampling in recent periods, may alone drive what may seem to be a widening of the fruiting season.

Although phenological trends are inferred from aboveground sporocarps, the observed changes likely reflect belowground changes in activity and physiology of the fungal mycelia, with potential effects on ecosystem function. For example, some of the analyses indicate a stronger widening of the fruiting season for saprotrophic compared to ectomycorrhizal fungi [20], which may indicate that decomposition processes associated with increased release of CO₂ will outweigh the carbon sequestration mediated by some ectomycorrhizal fungi [32]. However, it should be noted that mismatch in community composition, particularly of ectomycorrhizal fungi, when analyses focus on sporocarps versus non-reproductive structures (e.g. root tips) are frequently observed [33]. In a large-scale survey of European ectomycorrhizal fungi, it was observed that about two-thirds of all species produced macroscopic sporocarps [34]. For these reasons, it is important to not over-interpret the aboveground observations with belowground processes.

4. Diversity and distribution

The paucity of fungal data worldwide, in combination with their primarily hidden life histories, pose unique challenges for analysing patterns of fungal diversity and conservation priorities. Fungaria are critical resources for quantifying patterns of fungal diversity [4]. Checklists, atlases, foray excursions, museum and institutional records databases are all commonly used when compiling species' presences in a geographical area; these are the primary sources to create red-lists and other conservation prioritizations [4,35–38]. Most countries still lack red-lists and information from fungaria will be important for further development of red-lists. Conservation prioritizations are fractured by borders and sociopolitical boundaries, and most red-lists remain at the country level when they exist at all [35,38]. The overall paucity of information regarding patterns of fungal diversity makes it nearly impossible to prioritize conservation efforts, and makes efforts to unify fungal databases across countries very important [39]. The assembly of national-scale datasets into a broader-scale database [8] enables macroecological-scale analyses of diversity patterns (figure 2) [40].

Georeferenced fungarium records provide basic observational data that can be used to understand, predict and map species distributions (figure 3). With georeferenced location data as input, species distribution models (SDMs) can be used to understand the underlying drivers of species' distributions (i.e. their realized niches). Various SDM methods have been extensively used to study species distributions of plants and animals [41,42], but applications to fungi are more limited [43]. Distribution models are fundamental to understanding basic biogeographic processes and patterns, and also have wide applicability to conservation and management. For example, SDMs can be used to map hotspots of fungal diversity, anticipate how these hotspots may change under changing climate, forecast likelihoods and patterns of non-native species invasions and target conservation efforts for specific red listed taxa. Despite the extensive precedent for using herbarium data to build SDMs for plants and animals, there remain significant statistical issues that must be addressed when using presence-only data to understand species distributions.

Figure 3.

Species' distribution modelling (SDM) is commonly used in macroecology and conservation biology to determine local to large-scale patterns of a species' distribution. Within the field, arguments abound regarding the spatial scale and extent at which information can be extracted. Mechanistic SDMs are increasingly being used to understand the processes underlying the patterns, moving beyond simple correlations with climate. For understanding fungal distributions, and how they may change with climate change, knowledge of plant associations may often help predictions when environmental tolerances are poorly understood. For example, we compared species distribution models of a wood decay specialist species of conservation priority (Phellopilus nigrolimitatus) that included: (a) abiotic variables alone, and (b) abiotic variables and the host tree presence (Picea abies and Pinus sylvestris). Abiotic variables included mean annual temperature, precipitation, soil organic carbon, nitrate(-ite) deposition, NDVI (normalized difference vegetation index) and altitude. The abiotic-only model predicted high probabilities of occurrence in extreme northern Norway, whereas models including host tree presence better capture the observed distribution of the fungus based on fungaria specimens (points within grids). This suggests that including host plants could help overcome the difficulty of identifying suitable climate for organisms like fungi with hidden life-histories and tight couplings to the plant community. (Online version in colour.)

There are many questions about fungal biogeography that fungarium data are uniquely suited for answering. For example, what are the most important climatic and habitat factors that govern fungal species distributions, and how do these constraints vary with species' life-history traits? These are quite basic biogeographic questions that have been investigated for decades with plants and animals, but the datasets to evaluate these questions have not existed for fungi. Recently [43], fungarium data from Norway were used to show that temperature and solar radiation were key predictors of the distributions of nine common fungal species at the national scale. Using models to select from among 75 explanatory variables, the authors found relatively consistent effects of temperature and radiation. Nonetheless, there were also many species-specific environmental responses, as found for plants and animals, highlighting the need for more studies that attempt to link species responses to key life-history traits. In a recent study [44], the distribution of the ectomycorrhizal fungus Suillus lakei was found to be mainly impacted by the tree host distribution (Douglas fir) but they also observed that precipitation of the coldest quarters, isothermality and annual mean temperature were important factors influencing the potential distribution of S. lakei. This example suggests that biotic variables will be important to include when generating fungal SDMs (figure 3).

There are several ways that fungarium collections can be used to anticipate how fungi may respond to a changing climate. First, fungarium and herbarium data are among the best data on historic distributions of fungi, so they are critical for examining how historic range shifts are correlated with changes in global change factors [21]. Models can then couple these climate-distribution relationships to future climate scenarios, in order to forecast likely scenarios of range shifts, as has been done extensively for plants and animals [45]. In one recent study of fungal distributions based on sporocarp records, ectomycorrhizal species were shown to have become more abundant at higher latitudes in the UK in recent decades, consistent with a climate-driven process [21]. Moreover, additional factors such as habitat characteristics and associations with host plant species can be evaluated for their effects on distributions. For example, it has been shown [46,47] that citizen-science observational data could be reliably used to forecast responses of a fungus dependent on dead wood in response to projected forest management. These approaches may be especially important for threatened and red-listed species.

Fungarium data may also be used to assess the colonization potential of non-native fungi, again through the use of distribution modelling analyses. The principle underlying this approach is that the climatic niche estimated for a species in its native range is very often a good predictor of its potential range in invaded territories [47]. For plants, it has been shown that herbarium data can indeed be used to detect expansions of invasive species having accounted for collection effort [48]. Owing to their inconspicuous nature, fungal introductions are often more difficult to detect than plants or animals, but fungi have also been moved around the globe and established in new areas [49]. In addition to saprotrophic [50] and mycorrhizal species [18,49], many plant and animal pathogens are also being moved around the globe via trading routes and tourism, with dire consequences for native flora and fauna. There is interesting untapped potential for using fungarium data to build predictive models of potentially suitable climate areas for fungal pathogens that can affect native plants and animals. For example, distribution models of chytrid pathogens have been used to predict hot spots and safe refuges for amphibians [51]. Models can also use fungarium data to some extent in order to estimate the lag phases of species introductions [52], providing a management tool for anticipating when species may become invasive.

Several well-known challenges are associated with efforts to use fungarium data for understanding species distributions [53]. First, they are presence-only data, meaning that species' absences from a location are not recorded. This inherently biases model estimates of the species' response to environmental variables. One solution is to generate pseudo-absences, in which the background environmental conditions from a region are sampled for a comparison against the observed presences [54]. One can also establish minimum number of observations necessary for building robust SDMs, which will likely be dependent on a species' prevalence [55]. These are details of distribution modelling already well covered in the available literature.

5. Substrate usage and nutritional mode

Fungarium records often contain ecologically-based meta-data that can be highly useful for understanding fungal interactions with hosts and substrates over space and time. For parasitic and saprotrophic fungi collected from living or decaying plants, information on the host plant is often annotated, thereby providing information about fungal host affinities. In a circumboreal fungarium-based phylogeographic survey of the bracket fungus Gloeoporus taxicola, marked differences in host tree ranges were observed between two main genetic lineages [56]. In parts of Europe where the lineages appear in sympatry they seem to hybridize, influencing the host affinity [56]. Substrate affinity has also been of interest in climate change studies, where for example a widening in substrate usage of the wood-decay fungus Auricularia auricula in the UK over 59 years has been observed [57], which they linked to climate change. There is indication that integrating observational data from tree databases [58], even at the simplest level where host tree presences are included as a covariate in a distribution model, increases the accuracy of fungal distribution models (figure 3).

For ectomycorrhizal fungal records, information on putative host tree is often provided (i.e. the most proximal tree to the sporocarp). The host range of Amanita phalloides, which is native to Europe and invasive in North America, was assessed by cataloguing host plant information from North American fungarium records [18]. These data showed dramatic changes in the fungus' host specificity, suggesting that invasive ectomycorrhizal fungi are not necessarily restricted by the absence of their original host plants. This result may not be generalizable and needs further tests, however, as it will likely vary depending on fungal host specificity and trait-similarities between native and non-native trees. Information about host plants also must be cautiously interpreted, since the physical (belowground) connection between fungus and plant roots is not directly observed, and fungi can fruit considerable distances from their host tree(s). Such collector-based biases and mistakes can be minimized by compiling and comparing data from multiple sources.

The physical fungarium specimen can also be used to obtain novel ecological knowledge. Stable carbon and nitrogen isotope analysis is widely used to understand whether fungi are drawing nutrients and energy from dead organic matter, living plants or both. A study of fungarium records of the coral-fungus genus Ramaria [59] observed that archived sporocarps could be used as integrators of patterns of carbon and nitrogen cycling. The authors observed good correspondence between the fungal isotopic profiles and substrate usage, and concluded that archived specimens represent a useful trove of life-history information. Fungarium data have also been used to understand trophic mode of other fungal groups, including in Hygrophoraceae, where the isotopic N and C profiles indicate a biotrophic nutritional mode [60].

6. DNA-based information

Fungal taxonomy has radically changed since the onset of the DNA sequencing revolution brought on by the advent of PCR, and DNA-based phylogenies have become the norm, largely because morphological classifications were generally shown to be erroneous [61]. As more advanced DNA sequencing techniques are becoming commonplace, the era of juxtaposing fungaria specimens with innovative molecular methods has just dawned. Accessing the DNA in fungal specimens provides a vast potential to ask biological questions that span human and geological history; not only can DNA data address taxonomy and systematics, but it can be used for population genetics using representative and longitudinal samples and as a reference material to pin environmental DNA barcodes and metagenomes to morphologically identified material. Fungarium specimens may fulfil their maximal role when the date and location of the specimen are used along with the specimen's DNA. For example, numerous fungal invasions, such as the chestnut blight that completely eliminated a dominant North American tree in the early twentieth century [62], are a part of human history and have led to massive changes in the landscape. Many of these invasions are framed by distinct hypotheses on their origins. However, these introductions mostly occurred before there was a way of testing hypotheses, i.e. using molecular markers. Now, scientists are returning to fungaria to leverage specimens from a temporal slice across these invasions to test these hypotheses. For example, the invasion of A. phalloides from Europe into the western United States was narrowed to a particular earliest date of introduction (1938) and region (Monterey, CA) by DNA-sequencing of fungarium collections spanning nearly 100 years [63]. In order that range expansions can be documented by fungarium specimens, a baseline of community and nationwide collecting must be done, as with support for phenological studies. A well-sampled mycoflora of Denmark was vital for establishing that the emerging ash dieback pathogen Hymenoscyphus pseudoalbidus has recently replaced its closely related saprotrophic species H. albidus in the past 20 years [64].

One problem that must be overcome is that many of the most important specimens (from extinct habitats, difficult to access locales) are becoming increasingly older, and the DNA is becoming increasingly degraded. A major breakthrough in recent years is the appreciation that this highly fragmented DNA may be more readily sequenced using next-generation sequencing approaches. Next-generation sequencing is primarily done using short sequencing read methods that are highly suited for working with ancient and sub-ancient DNA. These methods typically rely on fragmenting DNA by enzymes or sonication into small fragments that are sequenced in 35–150 bp reads. As the older DNA samples are already highly fragmented, short read methods may perform better than PCR based methods which are more sensitive to contamination and require longer DNA templates.

A first assessment of genome analysis from dried fungarium samples of mushrooms showed great promise, with three species of age 20–80 years post collection providing 60–90% coverage of the entire genome regardless of whether the short reads were mapped to a reference assembly or genomes assembled de novo [65]. This implies that use of fungarium samples to grow the tree of life is feasible, and more recently, it was demonstrated [66] by using shotgun genomes from 11 fungarium samples to aid in resolving phylogenetic relationships and revising the taxonomy of the Agaricales (fleshy mushrooms). These methods hold promise to get the most data out of each specimen, which is critical as many named species are represented by few or single specimens despite being critical for taxonomic revision.

Genomic sequence from 100+ year-old specimens has helped reveal the genetic events behind the epidemic related to the Irish potato famine caused by the fungus-like Oomycete, Phytophthora infestans. Early studies of the population genetics of P. infestans had shown that before the 1980s most of Europe was dominated by a clonal genotype known as US-1, and it was hypothesized that the spread of US-1 from Mexico was also the cause for the outbreak associated with the famine in the 1840s [67]. However, because fungarium specimens from the time and location of the plague were available, it became possible to show using PCR amplification of these old specimens that US-1 was not the cause of the epidemic, and a hypothesis developed of repeated emergence of pathogenic genotypes from the Andean region where potato originated [68,69]. However, because the number of bases sampled in these PCR studies was low, the precise geographical origin of the famine-associated genotype and its relationship to globally circulating modern and extinct genotypes were unclear. More recently, shotgun genome sequencing of infected potato specimens from nineteenth century Europe recovered partial pathogen genomes and demonstrated that the genotype behind the epidemic was related to, but distinct from, the US-1, and that the famine-associated genotype (HERB-1) is apparently extinct and no longer circulating [70]. Advances like this highlight the importance of preserving specimens from outbreaks as well as non-outbreak populations for reference.

Modern fungal community ecology is largely done using culture-independent approaches, just like for bacteria, and demands a well-populated reference of DNA barcodes with which to identify species. The barcode region in fungi is typically the ribosomal RNA internal transcribed spacer region, and building a dataset using type specimens and well vouchered material is a priority of the field [71]. To appreciate this need, consider that in the largest community study of soil fungi to date [72] only 34% of hypothesized species were found to have matches with named material in GenBank. Further work in sequencing fungarium is likely to pay off in putting names on existing sequences [73–75]. One problem with generating these barcodes is the degraded DNA problem. Recently, Tedersoo et al. [76] attempted to use next-generation sequencing to assess how well barcode data from the ribosomal RNA region could be recovered by shotgun sequencing fungarium specimens. Data from specimens collected less than about 10 years prior to the study could frequently be assembled into large, nearly complete contigs of the ribosomal DNA operon, but data from most (57%) of the specimens older than this time frame could not be assembled into rDNA contigs greater than 1500 bp. The advent of genomic approaches to developing barcodes of reference material is promising, but additional technical details need to be worked out so that we can more readily sift out the rRNA from the millions of sequence reads per specimen at a higher success rate.

7. Fungaria—future treasure troves

As pinpointed by Meineke et al. [11], fungal research on global change, using fungarium specimens, lags behind research on plants. However, as with plant herbarium material, a wealth of information can be extracted from fungarium records. Globally interconnected networks of scientists now have unprecedented ability to answer a number of basic questions in mycology, such as how will changing climate affect fungal distributions and where are the biodiversity hotspots for fungi (table 1). While we have discussed the applications of fungarium records data for fungal diversity, red-listing and general conservation research, the unfortunate irony is that these sources of unparalleled information, in themselves, are threatened owing to dwindling funds necessary to maintain collections properly [77]. Mycologists need to be supported to continue collecting baseline specimen data and maintain them properly in fungaria, not only for systematics, but to support studies in phenology, epidemiology and community ecology. Free and open databases of species occurrences, such as GBIF, are becoming increasingly important repositories in this regard as data from multiple sources accumulate, although careful quality control becomes even more important when using these open databases [23,78,79]. To our knowledge, few fungaria have so far been photographed in a high throughput manner, and made publicly available as images, which will represent a next important step for easy data accessibility. The millions of specimens already existing in fungaria worldwide provide a treasure trove of genomic resources that should be tapped and maintaining the accessibility of DNA in the samples is a priority. The imagination can barely conceive the technological advances that will occur and change how we address biological questions in future generations by use of fungarium data.

Data accessibility

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Authors' contributions

H.K. was the main, coordinating author for this manuscript. J.D., T.Y.J. and C.A. contributed to sections, edited, and commented on the manuscript. H.K. or C.A. prepared the figures. All authors gave final approval for publication.

Competing interests

We have no competing interests.

Funding

We acknowledge partial funding from the Swiss National Science Foundation, for a postdoctoral grant (for C.A.), ‘Linking European Fungal Ecology with Climate Variability - Euro-FC’. The Research Council of Norway, project ‘Climate change impacts on the fungal ecosystem component (ClimFun)’ provided original support to create the fungal records meta-database from which data were extracted for this project.