Abstract
Anthropogenic activities are leading to changes in the environment at
global scales, and understanding these changes requires rapid,
high-throughput methods of assessment. Pollen DNA metabarcoding and
related methods provide advantages in throughput and efficiency over
traditional methods, such as microscopic identification of pollen and
visual observation of plant-pollinator interactions. Pollen DNA
metabarcoding is currently being applied to assessments of
plant-pollinator interactions and their responses to land-use change
such as increased agricultural intensity and urbanisation, surveillance
of ecosystem change, and monitoring of spatiotemporal distribution of
allergenic pollen. In combination with historical specimens, pollen DNA
metabarcoding can compare contemporary and past ecosystems. Current
technical challenges with pollen DNA metabarcoding include the need to
understand the relationship between sequence read and species abundance,
develop methods for determining confidence limits for detection and
taxonomic classification, increase method standardisation, and improve
of gaps in reference databases. Future research expanding the method to
intraspecific identification, analysis of DNA in ancient pollen samples,
and increased use of museum and herbarium specimens could open further
avenues for research. Ongoing developments in sequencing technologies
can accelerate progress towards these goals. Global ecological change is
happening rapidly, and we anticipate that high-throughput methods such
as pollen DNA metabarcoding are critical for assessing these changes and
providing timely management recommendations to preserve biodiversity and
the evolutionary and ecological processes that support it.
Keywords: pollen; pollination; DNA metabarcoding;
metagenomics; environmental DNA; global change ecology; ecosystem change
Introduction
Anthropogenic activities are leading to global changes in the
environment, including, habitat loss (Ellis, Klein Goldewijk, Siebert,
Lightman, & Ramankutty, 2010), climate change (Hansen, Reudy, Sato, &
Lo, 2010), biodiversity decline (e.g., Bowler et al., 2020; Butchart et
al., 2010), and the spread of invasive species and diseases (Hulme,
2009; Pyšek et al., 2020). Such global changes can act additively or
interactively (Didham, Tylianakis, Gemmell, Rand, & Ewers, 2007; Peters
et al., 2019) to alter species composition through events such as local
introductions and extinctions (Mathiasson & Rehan, 2020; Portman,
Tepedino, Tripodi, Szalanski, & Durham, 2018), shifts in phenology
(Bartomeus et al., 2011; Forrest, 2015), and changes in the dispersal
and connectivity of populations (Damschen et al., 2019). These impacts
can subsequently affect the spatiotemporal overlap of species and their
behaviour, which can alter species interactions, restructure food webs
(Dunn et al., 2018; Kortsch, Primicerio, Fossheim, Dolgov, & Aschan,
2015; Richardson et al., 2021), and create network instability (Brosi &
Briggs, 2013; Revilla, Encinas-Viso, & Loreau, 2015). Ultimately, these
changes can lead to negative impacts on ecosystem services (e.g.,
pollination; Burkle, Marlin, & Knight, 2013; Potts et al., 2010),
economic productivity (e.g., decreasing agricultural production; Lark,
Spawn, Bougie, & Gibbs, 2020; Reilly et al., 2020) and human health and
quality of life (e.g., changing distribution and phenology of allergenic
pollen; Anderegg et al., 2021; or loss of pollinator-dependent crops; M.
R. Smith, Singh, Mozaffarian, & Myers, 2015). Understanding and
managing these changes requires rapid, high-throughput assessments and
responses.
Pollen is a powerful biomarker for detecting spatial and temporal
variation in species assemblages and interspecific interactions, making
it ideal for a high-throughput assessment of global ecological change
(Hornick et al., 2021). For example, the presence of pollen in
environmental samples can be used to determine plant species
composition, which can be helpful for surveying changes in biodiversity
(Leontidou et al., 2021; Matthias et al., 2015), comparing current
ecosystems to historical samples (Gous, Swanevelder, Eardley, &
Willows-Munro, 2019; Simanonok et al., 2021), the early detection of
biological invasions (Tremblay et al., 2019), and monitoring airborne
allergenic pollen impacting human health (Suanno, Aloisi,
Fernández-González, & Del Duca, 2021). Pollen can also be used to
assess changes in phenology (Burkle et al., 2013), detect
plant-pollinator interactions (Bänsch et al., 2020; Gresty et al., 2018;
Kaluza et al., 2017; Lucas, Bodger, Brosi, Ford, Forman, Greig, Hegarty,
Jones, et al., 2018; Richardson et al., 2021; Sponsler, Shump,
Richardson, & Grozinger, 2020), reconstruct pollen transport networks
(Tur, Vigalondo, Trojelsgaard, Olesen, & Traveset, 2014), and
reconstruct past vegetation and, from this, climate (Courtin et al.,
2021; Liu et al., 2021; Niemeyer, Epp, Stoof-Leichsenring, Pestryakova,
& Herzschuh, 2017; Laura Parducci et al., 2019). The identification of
pollen from the bodies of animals is particularly useful for the
reconstruction of plant-pollinator interaction networks because it
increases the temporal scale of information obtained, leading to more
connected networks than those reconstructed through observations of
flower visitation (Arstingstall et al., 2021; Bosch, Gonzalez, Rodrigo,
& Navarro, 2009; de Manincor et al., 2020).
Traditionally, taxonomic identification of pollen is based on the visual
observation of pollen morphology. Pollen grains are stained and mounted
on microscope slides and identified to the lowest possible taxonomic
group using morphological characteristics visualised with light
microscopy. While counting pollen with this method is possible and can
be used to assign species abundance, it requires highly trained
specialists (of whom there is a small and dwindling number) and is
time-consuming, leading to most identifications taking place on small
subsets of pollen samples (Stillman & Flenley, 1996). Moreover, the
lack of variation in distinctive morphological characteristics limits
the microscopic identification of pollen typically to the level of genus
and often only family (Lau et al., 2019; Mander & Punyasena, 2014;
Richardson et al., 2018). Automated taxonomic identification has been
suggested (Stillman & Flenley, 1996) and implemented to overcome these
limitations in three ways. First, image analysis of morphological
characteristics employs methods such as texture analysis (Marcos et al.,
2015), and multiple convolutional neural networks (Bourel et al., 2020;
Olsson et al., 2021; M. Polling et al., 2021; Sevillano, Holt, &
Aznarte, 2020). Second, methods based on chemical characteristics
include Raman spectroscopy (Pereira, Guedes, Abreu, & Ribeiro, 2021),
magnetic resonance spectroscopy (MRS) (Klimczak, Ebner von Eschenbach,
Thompson, Buters, & Meuller, 2020), or Fourier-transform infrared
(FTIR) spectroscopy (Muthreich, Zimmermann, Birks, Vila‐Viçosa, &
Seddon, 2020; Zimmermann, 2018). Spectroscopy has been applied to single
pollen grains (Diehn et al., 2020) and, as far as we know, it has not
been used for mixed-species pollen samples and may be limited in this
capacity. Machine learning (Gonçalves et al., 2016) or deep learning
(Dunker et al., 2021) methods can be used to improve the identification
of taxa from the data obtained with either morphological or chemical
methods. In addition, flow cytometry has been successfully used to count
pollen and sort grains for downstream analyses (Kron, Loureiro, Castro,
& Čertner, 2021). Combining chemical and image analyses with flow
cytometry and deep learning methods can yield fast and accurate taxon
identification and quantification (Dunker et al., 2021). However,
although these methods work in some contexts, only a small number of
different pollen types are often included, and most studies have not
tested multi-species pollen samples. Moreover, most advanced techniques
require specialised equipment and extensive training datasets to
calibrate the taxonomic assignments. Research on a third method for
pollen identification, molecular genetics (i.e., the use of DNA
sequences), has recently gained attention for its high-throughput
capabilities and ubiquitous techniques and equipment and is the focus of
this paper.
The DNA present in the cells of pollen can be used for the taxonomic
identification of plant species by using standard DNA barcoding (for
identifying a single-species sample), metabarcoding (mixed-species
samples), or single-pollen genotyping (direct amplification and
sequencing of individual pollen grains; Isagi & Suyama, 2011). Standard
DNA barcoding approaches for plants typically use chloroplast DNA
(cpDNA; e.g., rbc L, mat K, trn L; CBOL Plant Working
Group et al., 2009) and/or nuclear ribosomal DNA (e.g., ITS1, ITS2; Chen
et al., 2010). This approach has been extended to the identification of
pollen, where DNA metabarcoding and high-throughput sequencing (HTS) are
used to sequence PCR-amplified DNA of all the species in a mixed-species
pollen sample (Bell et al., 2016; Cristescu, 2014; Taberlet, Coissac,
Pompanon, Brochmann, & Willerslev, 2012). This approach can be applied
in any standard molecular laboratory (assuming PCR product is
subsequently sent off for HTS sequencing) and has been used extensively
on bulk pollen extract for detecting plant-pollinator interactions
through freshly collected pollinator specimens (e.g., Keller et al.,
2015; Lucas et al., 2018) and museum specimens (e.g., Gous et al.,
2019), identifying the floral composition of honey (e.g., Hawkins et
al., 2015), and monitoring allergenic pollen (e.g., Kraaijeveld et al.,
2015).
Compared to morphology-based identifications, DNA-based identifications
of pollen provide several advantages: there are more people with the
required laboratory and bioinformatics expertise; greater taxonomic
resolution can be obtained (Lau et al., 2019); there is potential for
higher throughput (Sickel et al., 2015); and it is possible to identify
the entire pollen composition of a sample (e.g., pollen load carried by
an individual pollinator), rather than being limited to a subsample that
is tractable by microscopic identification. DNA metabarcoding still has
some challenges, mainly in respect to assigning abundances (Bell et al.,
2019) and species resolution, which depends on the gene region used for
metabarcoding and the quality and completeness of the relevant reference
database (Jones, Twyford, et al., 2021). In most cases, however, pollen
DNA metabarcoding resolves taxonomy equally well or better than
traditional morphological based methods (Keller et al., 2015; Leontidou
et al., 2018; Macgregor et al., 2019). Pollen DNA metabarcoding data is
generally considered to be semi-quantitative (i.e., sequence read counts
are correlated with pollen grain numbers in a sample) (Baksay et al.,
2020; Hawkins et al., 2015; Alexander Keller et al., 2015; Kraaijeveld
et al., 2014; Marcel Polling et al., 2022; Richardson et al., 2018;
Richardson et al., 2021), particularly for the most abundant taxa in a
sample (Bänsch et al., 2020), however, quantification is dependent on
factors such as study system and choice of gene region, as well as
laboratory and bioinformatic methods (Berry, Mahfoudh, Wagner, & Loy,
2011; O’Donnell, Kelly, Lowell, & Port, 2016; Piñol, Senar, &
Symondson, 2019; Richardson et al., 2018; Richardson et al., 2015).
Ongoing method development in pollen DNA metabarcoding is likely to
improve taxonomic resolution and quantification accuracy, and could
eventually include intraspecific identifications (i.e., distinct
subspecies or populations within a species). These developments could
include using more of the genome (Bell, Petit, et al., 2021; Lang, Tang,
Hu, & Zhou, 2019), using longer reads (Peel et al., 2019), and
correcting for copy number of barcode gene regions (L. Garrido-Sanz,
Senar, & Pinol, 2021).
While methods for pollen DNA metabarcoding are still evolving, it is
evident that a molecular approach to pollen identification represents an
important tool for understanding and monitoring ecosystems under global
change. This paper will review the history of pollen DNA metabarcoding
and related methods, discuss current applications of these methods,
outline the basic requirements for a DNA-based pollen identification
study, and provide a current assessment of progress on technical issues
and future research directions.
History of pollen DNA metabarcoding
Research identifying species or genotypes of plants using DNA from
pollen began in the 1990s and was based on Sanger sequencing of
individual pollen grains. Given that conifers typically have male
inheritance of plastids, early studies indicated that it should be
easier to genotype conifer pollen using the ‘standard’ DNA barcoding
gene regions (rbc L and mat K; CBOL Plant Working Group et
al., 2009) given that they are on the plastid genome. However, in most
angiosperm species, plastids are typically inherited maternally, and
plastid DNA is less abundant after pollen maturation (Nagata, 1996).
Thus, in the early days of pollen barcoding, researchers expected this
method to work effectively on gymnosperm pollen, but not necessarily on
angiosperm pollen. Suyama et al. (1996) were the first to amplify and
sequence cpDNA of Abies (fir, a gymnosperm) pollen collected from
Quaternary peat at Kurota Lowland, Fukui, Japan. Petersen, Johansen, and
Seberg (1996) were the first to amplify short regions of cpDNA from
single pollen grains of angiosperms (Hordeum and Secalegrasses). The technique of Suyama et al. (1996) was later used to
analyse cpDNA from ancient pollen of conifers (Pinus sylvestris; L.
Parducci, Suyama, Lascoux, & Bennett, 2005) and angiosperms
(Fagus orientalis ; Paffetti et al., 2007) extracted from ancient
sediments, Pinus pollen grains collected from a glacier (Nakazawa
et al., 2013), and airborne pollen grains of Pinus (Ito, Suyama,
Ohsawa, & Watano, 2008).
Today, we know cpDNA is present in angiosperm pollen since cpDNA gene
regions (e.g., rbc L, trn L) have been successfully
amplified from single pollen grains and bulk pollen samples in many
studies. Single-pollen genotyping (MATSUKI, ISAGI, & SUYAMA, 2007;
Suyama, 2011) remains labour intensive and a challenge for the research
community. However, Isagi and Suyama (2011) have successfully used
multiplex PCR and a single-pollen genotyping method on fresh pollen to
conduct paternity analysis and to infer the pattern and distance of
pollen dispersal in modern plant populations. The same technique was
successively used in several studies by their groups (Hasegawa, Suyama,
& Seiwa, 2009; Hirota et al., 2013; MATSUKI et al., 2007; Matsuki,
Tateno, Shibata, & Isagi, 2008). Sanger sequencing has also been used
with the cloning of PCR products (amplicons) to identify pollen from
bulk samples (Bruni et al., 2015; Galimberti et al., 2014).
With the advent of HTS technology, DNA-based pollen identification is no
longer dependent on the time-consuming isolation and sequencing of
individual pollen grains (Aziz & Sauve, 2008; MATSUKI et al., 2007) or
the cloning of amplicons prior to Sanger sequencing (Bruni et al., 2015;
Galimberti et al., 2014). Instead, with HTS, researchers have been able
to sequence pollen from bulk samples using DNA metabarcoding. This
breakthrough has allowed for rapid, large-scale species identification
of species within mixtures. Early proof-of-concept papers on pollen DNA
metabarcoding demonstrated the feasibility of the method (e.g., Cornman
et al., 2015; Hawkins et al., 2015; Alexander Keller et al., 2015;
Kraaijeveld et al., 2014; Richardson et al., 2015) and it has since been
applied to a range of applications. These include understanding the
foraging behaviour of honeybees (e.g., Jones, Brennan, et al., 2021;
Alexander Keller et al., 2015; Richardson et al., 2021; Richardson et
al., 2015) and other pollinators (e.g., Bell, Batchelor, et al., 2021;
Kratschmer, Petrović, Curto, Meimberg, & Pachinger, 2020; Lucas,
Bodger, Brosi, Ford, Forman, Greig, Hegarty, Neyland, et al., 2018;
MacGregor et al., 2019), examining historical flower visitation (Gous et
al., 2019; Simanonok et al., 2021), monitoring allergenic pollen
(Brennan et al., 2019; Kraaijeveld et al., 2014), biodiversity
assessments (Leontidou et al., 2021; Johnson et al. 2021), determining
the floral origin of honey (Hawkins et al., 2015; Milla et al., 2021;
Khansaritoreh et al., 2020), and monitoring invasive species (Tremblay
et al., 2019).
Advances and cost reductions in HTS and the advent of third generation
sequencing technologies may further improve pollen-based DNA
identifications. As the costs of HTS decrease, researchers are moving
from traditional DNA metabarcoding, based only on a small number of gene
regions, to metagenomics, which is based on whole genomes or
reduced-representation genomes. Methods based on whole-genome shotgun
sequencing of pollen mixtures, either using the plastid reads only (Lang
et al., 2019) or all reads (Bell, Petit, et al., 2021), have shown
improved taxonomic resolution and quantification over DNA metabarcoding,
but still require the presence of suitable reference databases for
identification. This is discussed in more detail in section 5.
Current and potential applications of pollen DNA
metabarcoding and related methods in global change ecology
Since the early pollen DNA metabarcoding papers, several papers have
published methodological improvements and proof-of-concept for a range
of sample types. More recently, these methods have begun to be applied
to ecological questions, including papers addressing questions related
to global ecological change. To assess the current application of pollen
DNA metabarcoding to questions of global change, we completed a Web of
Science search (accessed 11/30/2021) with the terms: “pollen” and
“metabarcoding” or “pollen” and “meta-barcoding”. From a list of
134 results, we excluded irrelevant papers, reviews, and those papers
which focused solely on methods development. We also added several
papers (n=28) to this list based on previous knowledge. Following these
alterations, we examined a reduced list of 80 papers from 2014-2021
(Supplementary Table S1). Generally, we found increasing numbers of
papers with time, and a shift in 2017 from predominantly
proof-of-concept papers to those exclusively focused on answering an
ecological question of interest (Figure 1). Research to date has been
concentrated on pollinator foraging behaviour, with an early focus on
honeybee foraging, likely due to the ease with which mass amounts of
pollen can be collected from hives with pollen traps or from honey
samples. However, applications of pollen DNA metabarcoding are varied
and address, among many other topics, lepidopteran migration, historic
foraging reconstruction, and airborne pollen monitoring.
Many applications of pollen DNA metabarcoding that address ecological
change take advantage of the improved resolution and efficiency that
these methods can provide. One specific area of ecological research is
in the reconstruction of plant-pollinator interaction networks from the
pollinator perspective. An important consideration when creating
plant-pollinator interaction networks is the perspective that a given
methodology provides. Historically, visitation networks have been
plant-focused, with pollinators counted or collected from selected
flowers. These observations can provide a complete interaction network
for the flowers and are thus a good representation of pollinator
visitation. However, such observations incompletely represent the
dietary intake of the flower-visiting animal (Arstingstall et al., 2021;
Popic, Wardle, & Davila, 2013). On the other hand, pollen-based
methods, such as DNA metabarcoding or light microscopy, provide the
animal perspective and allow exploration of the dietary input obtained
from these interactions (Piko et al., 2021; Pornon, Andalo, Burrus, &
Escaravage, 2017; Zhao et al., 2018). Pollen-based methods may also
enable the assessment of plant-pollinator networks in hard-to-observe
places, thus avoiding sampling biases. For example, observation-based
detections are impractical in the forest canopy, but pollen analyses can
capture bee foraging patterns (C. Smith, Weinman, Gibbs, & Winfree,
2019).
Another advantage of pollen-based methods, and an essential factor to be
considered in the study design, is the scalability of the samples that
can be used to infer and contrast preferences of individual foragers
(Casanelles‐Abella et al., 2021; Elliott et al., 2020; Piko et al.,
2021), and hive, colony, nest or species level assessments (Danner,
Keller, Härtel, & Steffan-Dewenter, 2017; Nürnberger, Keller, Härtel,
& Steffan‐Dewenter, 2019; Sickel et al., 2015). Individual-level
assessments allow researchers to address potential intraspecific
variation by having snapshots of foraging and immediate responses to
spatiotemporal or anthropogenic changes (Piko et al., 2021). Longer-term
samples provide comprehensive insights into the complete foraging
spectrum and species’ dietary niche, co-evolution and long-term
responses to changes (Kaluza et al., 2017; Vaudo, Biddinger, Sickel,
Keller, & López-Uribe, 2020; Wilson et al., 2021). Resource
partitioning and specialisation can be analysed throughout the entire
network from the community to the individual (Brosi, 2016; Elliott et
al., 2020; Lucas, Bodger, Brosi, Ford, Forman, Greig, Hegarty, Jones, et
al., 2018). In addition, historical samples can be used as an input
(e.g., from museum specimens or honey samples), which allows a direct
comparison of foraging changes through extended time periods (Gous et
al., 2019; Jones, Brennan, et al., 2021).
This “animal perspective” also directly links to the functional and
nutritional components of pollen provided to animals and their offspring
and can thus, for example, be used to relate impacts of changes in plant
resource diversity to nutritional needs, development, and health
(Donkersley et al., 2017; Trinkl et al., 2020). Recently, the importance
of transmitting microbes between plants and pollinators by hitchhiking
pollen grains, nectar and animal bodies have been implied (A. Keller et
al., 2021; McFrederick & Rehan, 2016; Zemenick, Vannette, & Rosenheim,
2021). This accounts both for microbes with beneficial (Dharampal,
Carlson, Currie, & Steffan, 2019; Vuong & McFrederick, 2019) and
detrimental (A. Keller et al., 2018; Voulgari-Kokota, Steffan-Dewenter,
& Keller, 2020) effects on host ecology, nutrition and health (Engel et
al., 2016; Vannette, 2020; Voulgari-Kokota, Grimmer, Steffan-Dewenter,
& Keller, 2018). Given these advantages of pollen-based networks and
the high-throughput capabilities of genetic methods, a wide range of
broad and fine-scale ecological questions may be answered. Particularly
urgent are studies investigating how interactions change throughout
space (across land-use gradients, resource availability) and time
(short-term, e.g., seasonal, or long-term via the use of historical
museum specimens) and elucidating consequences that accompany climate
change and habitat disruption and other anthropogenic influences.
Understanding pollinator responses to land-use change
In recent decades, there have been broad scale shifts in land use with
subsequent changes in resource availability to pollinators. Overall,
there has been deforestation in the tropics but widespread reforestation
and afforestation in temperate regions, often with monocultures or small
numbers of tree species (Song et al., 2018). In the US, there has been
considerable reforestation and urbanisation (He et al., 2019; Song et
al., 2018), at the expense of grassland and herbaceous areas (Lark,
Meghan Salmon, & Gibbs, 2015; Otto, Roth, Carlson, & Smart, 2016).
Pollen DNA metabarcoding has been used to understand the flexibility of
pollinator species dietary niche in response to such changing
environmental conditions (Vaudo et al., 2020) and to evaluate and
improve conservation efforts (Gresty et al., 2018; Piko et al., 2021).
Land-use change can also alter the diversity of resources available to
pollinators, with decreased floral richness in agricultural monocultures
and context-specific increases or decreases with urbanisation (da
Rocha‐Filho et al., 2021; Jones, Brennan, et al., 2021; Lucek et al.,
2019; Richardson et al., 2021; Samuelson, Gill, & Leadbeater, 2020).
Pollen DNA metabarcoding has been used to understand the response of
pollinators to resource-rich and resource-poor environments
(Casanelles‐Abella et al., 2021; Danner et al., 2017; Kaluza et al.,
2017; Sponsler et al., 2020; Wilson et al., 2021) and to understand the
link between behaviour and pollen intake in resource-poor environments
(Nürnberger et al., 2019).
Expansions in agricultural land cover can have a range of impacts on
pollinators (Potts et al., 2010), including decreasing floral resource
diversity (Grab et al., 2019; Richardson et al., 2021), increasing
pesticide exposure risk (Douglas, Sponsler, Lonsdorf, & Grozinger,
2020; Douglas & Tooker, 2015), and increased parasite loads (Cohen et
al., 2021). Pollen DNA metabarcoding can show how pollinators respond to
this changing agroecosystem and can be used to monitor changes. For
instance, a comparison of modern-day honey with samples collected 65
years prior demonstrated shifts in honeybee forage composition and a
reduction in white clover, Trifolium repens (Jones, Brennan, et
al., 2021). Recent studies, using both molecular and morphological
pollen identification, have also found that honeybees situated in modern
agricultural landscapes tend to collect a lower diversity of forage
relative to nearby non-agricultural landscapes (Richardson et al., 2021;
Samuelson et al., 2020). To date it is unclear how this variation in
forage diversity corresponds to pollinator health. Pollinator diets can
also be affected through the uptake of agricultural environmental
schemes (AES) in which supplemental planting can improve forage
availability for pollinators on farms. Molecular analysis of bee
collected pollen can help evaluate if conservation plantings are
successfully supporting bee populations and identify which resources
bees forage on (Gresty et al. 2018; McMinn-Sauder, 2020). Overall, this
research demonstrates how pollinators can be used to monitor changes
associated with agricultural land use and that there is a need for
continued study. Future research efforts in this area will require
greater spatial replication and comparisons to a wider diversity of
alternate landscape types, two challenges to which molecular
identification of pollen is uniquely suited.
While both agricultural and urban intensification can negatively impact
pollinator communities through habitat loss (Potts et al., 2010), many
urban environments also support rich pollinator diversity (Baldock et
al., 2015; Hall et al., 2017), especially when landscapes exhibit
moderate or intermediate levels of urbanisation (Wenzel, Grass,
Belavadi, & Tscharntke, 2020). In part, pollinators can thrive in
cities due to the intensive cultivation of flowering resources (Baldock
et al., 2015; Sponsler et al., 2020) and spontaneous urban vegetation
(i.e., weeds) (Lowenstein, Matteson, & Minor, 2018; Turo & Gardiner,
2019). However, cities also filter pollinator communities and select for
specific traits such as cavity-nesting and generalist foraging (Wenzel
et al., 2020). Recent studies have focused on the management and
improvement of urban green spaces (e.g., gardens, vacant lots, road
verges, green roofs) to increase the diversity of urban pollinators,
particularly for rare and threatened bee species (Threlfall et al.,
2015; Turo & Gardiner, 2019). Improved characterisation of urban
foraging networks can inform green space development. However,
traditional methods for monitoring plant-pollinator interactions (e.g.,
flower-visitor observations, hand-netting) can be challenging to use in
urban areas due to restrictions on sampling private property and the
high floral diversity present in the urban matrix (Sponsler et al.,
2020). Recently, pollen DNA metabarcoding has been used to characterise
plant-pollinator interactions and pollinator diets in urban environments
(Casanelles‐Abella et al., 2021; Potter et al., 2019; Sponsler et al.,
2020). The findings of this research suggest that pollen DNA
metabarcoding can be a useful tool to investigate how urban insect
pollinators partition their diet from available floral resources
(especially native and non-native forage). Pollen DNA metabarcoding can
also evaluate how urban habitat plantings influence bee foraging and
subsequent population growth (Potter et al., 2019).
Monitoring and surveillance of ecosystem change
Identifying plant biodiversity from pollen provides a valuable tool for
surveillance of changes in the ecosystem, which has been applied to the
early detection of invasive species and diseases (Tremblay et al.,
2019), and could potentially be used for monitoring for the presence of
rare, threatened, or endangered species. Community-level monitoring of
plant biodiversity through pollen DNA metabarcoding could track changes
in species composition, range, and phenology, providing a
high-throughput alternative to botanical surveys (Johnson et al., 2021;
Leontidou et al., 2021; Milla, Bovill, Schmidt-Lebuhn, & Encinas-Viso,
in press). These studies could be particularly beneficial when combined
with other high-throughput methods like remote sensing or unmanned
aerial vehicle surveys (Ancin-Murguzur, Munoz, Monz, & Hausner, 2020).
There is potential for pollen-based monitoring to be closely linked to
management, with unexpected detections or non-detections to trigger a
management response. Methods that would enable this technology need to
be developed to provide probabilistic confidence estimates for
identifications and avoid false positives triggering unnecessary
management responses. The development of methods for the surveillance of
aquatic invasive species via eDNA has been discussed (Darling, Pochon,
Abbott, Inglis, & Zaiko, 2020; Sepulveda, Nelson, Jerde, & Luikart,
2020), and the same issues would apply to pollen-based surveillance
methods.
Comparison to past ecosystems
The ability to assess global ecological change often relies on the
comparison of contemporary data to historical data. Pollen
identification is potentially a particularly useful tool for studying
ecological change based on its presence on historical animal specimens
and its preservation in ancient sediments. This type of sampling enables
the comparison of contemporary to past ecosystems and establishes more
accurate baselines for conservation. Preserved pollinating insects in
museum collections often have pollen on their bodies or corbiculae, and
these pollen grains can be identified through DNA metabarcoding to
provide information on historical insect foraging patterns (Gous et al.,
2019). For example, pollen DNA metabarcoding of a century of foraging by
the endangered rusty patched bumble bee found that decline was unlikely
to be driven by changes in forage (Simanonok et al., 2021). Similar
studies are expected to give insights on the past foraging behaviour of
other pollinating species and inform processes associated with more
recent declines.
Pollen preservation in ancient sediments, in combination with ancient
sedimentary DNA (Capo et al., 2021), also provides a resource for
understanding past ecosystems. Usually, the pollen grains are examined
morphologically, while the sediments are analysed through DNA sequencing
to provide complementary data sources (Liu et al., 2021; Laura Parducci
et al., 2017). This approach has been used to determine conservation
baselines for offshore islands in New Zealand (Wilmshurst et al., 2014).
Pollen retrieved from lake sediments is theoretically an ideal material
for ancient DNA analyses in both conifers and angiosperms: depositional
conditions are fast and reduce physical damage of the grains; burial is
rapid, reducing the exposure of the grains to biotic degradation (L.
Parducci, Nota, & Wood, 2019). By accessing the DNA in ancient pollen,
there is potential for further information to be obtained on past
ecosystems (Niemeyer et al., 2017). This is an area where further method
development could prove useful (see section 5).
Monitoring impacts on human health
Changes in pollen abundance and distribution due to climate change will
likely have severe impacts on human health (Anderegg et al., 2021;
Kurganskiy et al., 2021). Airborne pollen sampling, combined with DNA
metabarcoding, allows allergenic species to be monitored across large
spatiotemporal scales (Brennan et al., 2019; Leontidou et al., 2018) and
can identify seasonal changes in allergenic species (Campbell et al.,
2020; Uetake et al., 2021). Many plant families with highly allergenic
pollen are difficult to identify through pollen morphology, however, the
increased taxonomic resolution provided by DNA metabarcoding can allow
allergenic species to be distinguished from non-allergenic species
(e.g., Poaceae; Brennan et al., 2019; Urticaceae; Marcel Polling et al.,
2022). The relationship between the presence of specific pollen types
and human health responses can be investigated to identify the most
harmful species (Rowney et al., 2021). Airborne pollen monitoring has
been occurring for many years, and successful amplification of DNA from
historical microscope slides shows the potential to monitor long-term
ecological changes (Marcel Polling et al., 2022).