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\begin{document}

\title{Pollen DNA metabarcoding and related methods in global change ecology:
prospects, challenges, and progress}



\author[1]{Karen Bell}%
\author[2]{Katherine Turo}%
\author[3]{Abigail Lowe}%
\author[4]{Kevin Nota}%
\author[5]{Alexander Keller}%
\author[6]{Francisco Encinas-Viso}%
\author[7]{Laura Parducci}%
\author[8]{Rodney Richardson}%
\author[9]{Richard Leggett}%
\author[10]{Berry Brosi}%
\author[11]{Kevin Burgess}%
\author[12]{Yoshihisa Suyama}%
\author[13]{Natasha de Vere}%
\affil[1]{University of Western Australia}%
\affil[2]{Rutgers The State University of New Jersey}%
\affil[3]{National Botanic Garden of Wales}%
\affil[4]{Uppsala universitet Evolutionsbiologiskt centrum}%
\affil[5]{Ludwig-Maximilians-Universität München}%
\affil[6]{CSIRO}%
\affil[7]{Uppsala University}%
\affil[8]{University of Maryland Center for Environmental Science}%
\affil[9]{Earlham Institute}%
\affil[10]{Emory University}%
\affil[11]{Affiliation not available}%
\affil[12]{Tohoku University}%
\affil[13]{University of Copenhagen}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{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.%
\end{abstract}%



\sloppy


Manuscript type: Special Issue

\textbf{Pollen~DNA metabarcoding and related methods~in global change
ecology: prospects, challenges, and progress}

Karen L. Bell\textsuperscript{1,a}, Katherine J.
Turo\textsuperscript{2}, Abigail Lowe\textsuperscript{3,b}, Kevin
Nota\textsuperscript{4,b}, Alexander Keller\textsuperscript{5,b},
Francisco Encinas-Viso\textsuperscript{6,b}, Laura
Parducci\textsuperscript{3,7,b}, Rodney Richardson\textsuperscript{8,b},
Richard M. Leggett\textsuperscript{9}, Berry J.
Brosi\textsuperscript{10}, Kevin S. Burgess\textsuperscript{11},
Yoshihisa Suyama\textsuperscript{12}, Natasha de
Vere\textsuperscript{13}

\textsuperscript{1} CSIRO Health \& Biosecurity and CSIRO Land \& Water,
147 Underwood Avenue, Floreat, WA, Australia; School of Biological
Sciences, University of Western Australia, Crawley, WA 6009, Australia.

\textsuperscript{2} Department of Ecology, Evolution, and Natural
Resources, Rutgers University, New Brunswick, NJ, 08901, United States.

\textsuperscript{3} National Botanic Garden of Wales, Llanarthne, United
Kingdom.

\textsuperscript{4} Department of Ecology and Genetics, Evolutionary
Biology Centre, Uppsala University, Norbyv\selectlanguage{ngerman}ägen 18D, 75236 Uppsala,
Sweden.

\textsuperscript{5} Organismic and Cellular Interactions, Biocenter,
Ludwig-Maximilians-Universität München, Großhaderner Str. 2, 82152
Planegg, Germany.

\textsuperscript{6}Centre for Australian National Biodiversity Research,
CSIRO, Black Mountain, ACT, Australia

\textsuperscript{7}Department of Environmental Biology, Sapienza
University of Rome, Rome, Piazzale Aldo Moro 5, 00185, Italy.

\textsuperscript{8} Appalachian Laboratory, University of Maryland
Center for Environmental Science, 301 Braddock Road, Frostburg, MD
21532, United States.

\textsuperscript{9} Earlham Institute, Norwich Research Park, Norwich,
Norfolk, NR4 7UZ, United Kingdom.

\textsuperscript{10} Department of Biology, University of Washington,
USA

\textsuperscript{11} Department of Biology, College of Letters and
Sciences, Columbus State University, University System of Georgia,
United States.

\textsuperscript{12} Field Science Center, Graduate School of
Agricultural Science, Tohoku University, 232-3 Yomogida, Naruko-onsen,
Osaki, Miyagi 989-6711, JAPAN

\textsuperscript{13} Natural History Museum of Denmark, University of
Copenhagen, Denmark

\textsuperscript{a} Corresponding author: karen.bell@csiro.au

\textsuperscript{b} Authors AL, KN, AK, FEV, LP, and RR all contributed
equally.

ORCID:

KLB: 0000-0002-4445-4026

KJT: 0000-0003-1578-4592

AL: 0000-0001-5431-8936

KN: 0000-0002-4744-5205

AK: 0000-0001-5716-3634

FEV: 0000-0003-0426-2342

LP: 0000-0003-1956-4757

LP: 0000-0003-1956-4757

YS: 0000-0002-3136-5489

NdV: 0000-0001-9593-6925

Short running title: Pollen metabarcoding in global change ecology

\emph{}

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; Py\selectlanguage{polish}š\selectlanguage{english}ek 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,
Fern\selectlanguage{ngerman}ández-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\selectlanguage{english}-Vicosa, \&
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 (Goncalves 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,
\& \selectlanguage{polish}Č\selectlanguage{english}ertner, 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., \emph{rbc} L, \emph{mat} K, \emph{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 (B\selectlanguage{ngerman}änsch 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 (\emph{rbc} L and \emph{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 \emph{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 (\emph{Hordeum} and
\emph{Secale}grasses). 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
(\emph{Fagus orientalis} ; Paffetti et al., 2007) extracted from ancient
sediments, \emph{Pinus} pollen grains collected from a glacier (Nakazawa
et al., 2013), and airborne pollen grains of \emph{Pinus} (Ito, Suyama,
Ohsawa, \& Watano, 2008).

Today, we know cpDNA is present in angiosperm pollen since cpDNA gene
regions (e.g., \emph{rbc} L\emph{, 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, Petrovi\selectlanguage{polish}ć, \selectlanguage{english}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.

\emph{\textbf{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, H\selectlanguage{ngerman}ärtel, \& Steffan-Dewenter, 2017; Nürnberger, Keller, Härtel,
\& Steffan\selectlanguage{english}-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, \& Lopez-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\selectlanguage{english}-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\selectlanguage{english}-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
(Nurnberger 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, \emph{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\selectlanguage{english}-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).

Box 1: Designing a study using pollen DNA metabarcoding and related
methods

There is a broad range of applications for pollen DNA metabarcoding and
related methods. The experimental design will depend on the questions
being addressed in the study and the time, budget, and skillsets
available. There are four main steps in identifying plant species from
pollen: (1) obtaining the pollen sample; (2) selecting appropriate gene
region(s) and creating a reference database; (3) isolating and
sequencing DNA; (4) bioinformatic analysis to compare sequencing reads
to reference databases (Fig. 2). These four steps are outlined below.

Step 1: Obtaining the pollen sample

Pollen can be obtained from a range of environmental samples. Most often
it is sampled from insects, either directly from the body (Chang et al.,
2018; Pornon et al., 2016; Suchan, Talavera, Saez, Ronikier, \& Vila,
2019), or through provisions in nests (Gresty et al., 2018; McFrederick
\& Rehan, 2016), honey (de Vere et al., 2017; Jones, Brennan, et al.,
2021), or honeycomb (Tanaka, Nozaki, Nakadai, Shiwa, \& Shimizu-Kadota,
2020). Alternatively, pollen may be sampled from the air (Brennan et
al., 2019; Leontidou et al., 2018). How, where, and when a sample is
collected affects the number and diversity of target species sampled and
the plant richness and diversity captured. For example, when sampling
directly from a pollinator's body, researchers should consider the
influence of body size and the pilosity of insects on the number and
diversity of pollen retained (Cullen et al., 2021). Likewise, capturing
foraging insects by hand-netting may be limited by surveyor bias. Some
sampling methods are specific to particular taxonomic groups (e.g.,
honey sampling or pollen trapping; Judd, Huntzinger, Ramirez, \&
Strange, 2020). The temporal scale of a pollen sample should also be
considered. Pollen from an individual represents a single foraging trip,
whilst pollen trapping usually entails pooling samples so multiple trips
from multiple bees can be sampled over a short period. Pollen provisions
in solitary bee nests represent multiple trips by one individual. The
temporal scale of airborne samplers is dependent on study design. The
sampling period defines the knowledge which can be gained (e.g.,
sampling pollen from social bees with a long flight period during one
month of the year does not give a global picture of resource use). The
sampling period should also consider floral phenology, as pollen will
only be present when the plants are in flower.

The quantity of pollen required for the selected sequencing method, and
the quantity of pollen available per sample will determine whether
samples need to be pooled. Pooling of samples should be done with
caution, as it could decrease the accuracy of diet estimations relative
to the separate analysis of samples, especially for detecting species
that occur at relatively low abundance (Mata et al., 2019). The number
of pollen grains present in a sample will vary according to the sample
type. For pollen collected directly from the bodies of pollinators, this
will vary according to pollinator species. Estimates show that
Hymenoptera can carry relatively high numbers of pollen grains
(\emph{Apis mellifera} and \emph{Bombus lucorum} carry 2100-2500 pollen
grains; \emph{Bombus pascuorum} , \textasciitilde{}800 grains), Diptera
carry fewer (Syrphidae \textasciitilde{} 500 grains; Empididae
\textasciitilde{}80 grains), and Lepidoptera carry fewer again
(\textasciitilde{}20 grains), while the few Coleoptera observed carried
up to 200 grains (Pornon et al., 2016). The number of samples collected,
whether they are pooled, the number of sampling events across time and
space, and the replication level within each of these samples will
depend on the questions being addressed. Once the pollen samples are
obtained, they seem to be relatively resilient and easily storable over
extended periods under different conditions without introducing biases
(e.g., frozen, room temperature, silica gel dried; Quaresma et al.,
2021).

Control samples should be used to validate the methodology for each
study system. These include negative controls, ideally including
field-collected and laboratory blanks, to assess for contaminating DNA
from external sources, and positive controls or mock communities to
demonstrate that the sequencing method is effective, and if relevant,
quantitative, for the species in the study system. Contamination should
also be minimised through good laboratory practice. Extra precautions
can be taken for specific sample types, such as using separate
collection materials (i.e., nets, vials) to avoid cross-contamination
between specimens during collection (Pornon et al., 2016). For some
sample types, contamination will be unavoidable. For example, some types
of nest pollen from solitary bees may be contaminated by other plant DNA
during nest construction (Gresty et al., 2018; Alexander Keller et al.,
2015; McFrederick \& Rehan, 2016). Finally, analysis of negative
controls will allow for detection and removal of contaminating sequences
during the bioinformatics step (e.g., Davis, Proctor, Holmes, Relman, \&
Callahan, 2018).

Step 2: Selecting appropriate gene region(s) and creating a reference
database

The standard DNA barcode for plants is \emph{rbcL} and \emph{matK} (CBOL
Plant Working Group et al., 2009), but other gene regions such
as\emph{trnL} , ITS1 and ITS2 are often used, especially for DNA
metabarcoding as they have shown deeper resolution for several taxonomic
groups (Wilson et al., 2021). Increasing the number and length of gene
regions is expected to improve taxonomic resolution, and some studies on
pollen mixtures have used whole plastid genomes (Lang et al., 2019) or
whole genomes (Bell, Petit, et al., 2021). The barcode gene region(s)
must be suitable for differentiating the taxa in the study system to the
taxonomic level required and have primers universal enough to amplify
all taxa. Recent research has shown a combination of \emph{rbcL} and
ITS2 to work better than other combinations for detecting the highest
number of species at the lowest level of taxonomic discrimination
(Jones, Twyford, et al., 2021). Several studies have worked towards
developing more universal primers for ITS2 (e.g., Kolter \&
Gemeinholzer, 2021; Moorhouse-Gann et al., 2018) and primers to amplify
shorter regions to account for pollen degradation in historical samples
(Simanonok et al., 2021). More gene regions (and partial or whole
genomes) lead to improved taxonomic resolution but also require more
work in assembling the reference library. Using multiple gene regions
has the additional challenge of determining the best method of combining
results from different markers, with differing taxonomic resolution, for
downstream analysis.

It is essential for the gene regions to have a comprehensive reference
library for the species in the study system. Using a custom, relevant
reference library reduces misidentifications and increases the accuracy
of taxonomic assignments (Arstingstall et al., 2021; A. Keller et al.,
2020). Software such as the BCdatabaser (A. Keller et al., 2020) for
creating custom databases from species lists can be helpful where there
is no national database, different gene regions are being used, or a
more local database is desired. In addition, the recent software
MetaCurator of Richardson, Sponsler, McMinn\selectlanguage{english}-Sauder, and Johnson (2020)
has two advantageous features to curate existing or generated reference
datasets: 1) identifying the exact amplicon of interest and trimming
away extraneous sequence to avoid non-overlapping amplicons of the same
gene, and 2) dereplicating sequences by taxonomy so that barcodes are
retained for multiple species even when there is no barcode gap (i.e., a
higher and non-overlapping range of sequence divergence between species
than among species). A consideration of this and many other methods is
that they create a static database that needs to be updated frequently
as new sequences become available.

Step 3: Isolating and sequencing DNA

Isolating DNA from pollen is relatively straightforward. First, the
pollen is lysed, usually through bead-beating. There are off-the-shelf
kits that work well, along with cheaper extraction methods based on
phenol and chloroform; acetone-based DNA isolation has also been
effective (Gouker, Guo, \& Pooler, 2020). Some studies have shown that
it is possible to extract and sequence DNA from pollen without a lysing
step (Swenson \& Gemeinholzer, 2021). The high-throughput capabilities
of DNA metabarcoding are reliant on the addition of sample-specific
nucleotides which allow the identification of hundreds of individual
samples following pooling into a single sample sent for sequencing
(multiplexing). Three multiplexing methods are commonly used, and the
most appropriate method should be chosen based on an assessment of the
risk of cross-contamination, PCR efficiency and overall cost (reviewed
in Bohmann et al., in press). For sequencing, most amplicon-based
studies use the Illumina MiSeq platform with v3 chemistry (300
paired-end sequencing, 600 cycle) to sequence 300-550 base pair reads.
New long-read sequencing technologies could add improved taxonomic
resolution. These methods are discussed further in section 5.

Step 4: Bioinformatic analysis to compare sequencing reads to reference
databases

Bioinformatic procedures are required to assign taxonomic
identifications to the sequence reads. There are numerous bioinformatic
pipelines for analysing amplicon sequencing data including but not
limited to QIIME2 (Bolyen et al., 2019), dada2 (Callahan et al., 2016),
VSEARCH (Rognes, Flouri, Nichols, Quince, \& Mahe, 2016), USEARCH
(Edgar, 2010), and OBItools (Boyer et al., 2016), as well as custom
pipelines developed specifically for pollen metabarcoding (e.g., Ford
and Jones, 2020). All these pipelines share a few main steps, which are
not necessarily performed in the same order as described here. 1) In
most cases paired-end data is obtained for Illumina sequencing runs. In
many pipelines, the forward and reverse reads are merged, low-quality
reads removed, and low-quality bases are trimmed from reads. Some
paired-end read mergers such as PEAR (Zhang, Kobert, Flouri, \&
Stamatakis, 2014) also remove adapter sequences (technical sequences
added during HTS library preparation). 2) Reads are demultiplexed based
on tags used in the metabarcoding primer, or tags that are added to each
sample during HTS library preparations. 3) Reads are dereplicated and
denoised to cluster sequences together which likely differ only by PCR
errors. There are some approaches such as the Amplicon Sequence Variant
(ASV) (Callahan et al., 2016) which have been applied to pollen
(Casanelles\selectlanguage{english}-Abella et al., 2021; Elliott et al., 2020; Wilson et al.,
2021). 4) Taxonomic assignment using reference databases. Finally, 5)
the creation of a Taxon table or mOTU table containing the number of
reads assigned to each taxon per sample. The Taxon table can be further
filtered to remove noise and contaminants based on extraction and PCR
controls.

Limitations, technical issues, and progress

One of the ongoing challenges associated with DNA-based identification
of species mixtures is quantifying the relative abundance of species in
the mixture. The issue of quantification is likely to be relevant for
many applications in assessing global ecological change. Many ecosystem
changes initially present as changes in species abundances rather than
changes in species composition. Solutions to the technical problem of
quantification could differ for pollen relative to other sample types,
although the issues are similar in many ways. DNA metabarcoding is
considered semi-quantitative. Species in high proportions are usually
represented by many sequencing reads, although the relationship often
deviates from the expected 1:1 ratio (Bell et al., 2019; Marcel Polling
et al., 2022). There are several reasons for this deviation. Different
pollen types can have different DNA extraction efficiencies, which can
be improved with method optimisation. Variation occurs among species in
the copy numbers of the DNA barcodes, and this has been well-studied for
microbes, with the possibility to correct for these biases (Kembel, Wu,
Eisen, \& Green, 2012; Lamb et al., 2019; Pawluczyk et al., 2015) and a
recent study on mitogenomics of insects has applied corrections for copy
number (L. Garrido-Sanz et al., 2021). An additional source of bias
comes from differences among species in primer binding efficiency
(Pompanon et al., 2012) and biases in DNA polymerase binding
efficiencies towards different nucleotide compositions (Nichols et al.,
2018). These biases can be reduced with careful primer design and PCR
optimisation and can be corrected with a good understanding of the
biases.

Amplification-free methods eliminate the PCR biases and have been shown
in a handful of studies to be more quantitative than DNA metabarcoding.
Whole-genome shotgun (WGS) sequencing has been shown to have improved
quantification for pollen (Bell, Petit, et al., 2021) and other mixtures
of eukaryote species (Lidia Garrido-Sanz, Senar, \& Pinol, 2020).
Genome-skimming of organellar DNA from WGS has been shown to be
quantitative for pollen (Lang et al., 2019) and other eukaryote mixtures
(Bista et al., 2018), and quantification can be improved by correcting
for organelle copy number (L. Garrido-Sanz et al., 2021).
Reduced-representation sequencing using endonucleases
(genotyping-by-sequencing) of plant roots has shown within- and
across-species abundances strongly correlate with biomass-based species
abundance (Wagemaker et al., 2021). Reverse Metagenomics, the sequencing
of samples using MinION long reads while reference sequences come from
short read skims, has also been found to be semi-quantitative (Peel et
al., 2019). While most DNA-based detection and identification methods
are semi-quantitative, there is considerable value in the relative read
abundances, which are lost by treating data as presence-absence (Deagle
et al., 2019). Improved quantification is expected to become possible in
the future with an improved understanding of biases.

A related problem is understanding the sensitivity of DNA metabarcoding
and the expected detection limits for species of interest, and the rates
of false positives and false negatives. This issue may be particularly
relevant to biosurveillance and ecosystem monitoring applications, where
researchers and managers may be interested in changes in the presence or
absence of low abundance species, such as a rare species becoming
extinct or early detections of new incursions of non-native invasive
species. Acceptable levels of false positives and false negatives will
differ among applications. For example, for a risk-averse strategy
detecting invasive species, it is important to avoid false negatives. In
contrast, for detecting threatened species, a more risk-averse approach
would avoid false positives. In both cases, a level of confidence is
needed for the detection of a target species. These issues have been
addressed with methods for the eDNA of water samples. For example, eDNA
has been combined with site occupancy models to determine the confidence
of presence/absence results (Dorazio \& Erickson, 2018; Schmidt et al.,
2013), and similar methods would be applicable to pollen.

Improved confidence in the presence of a species in a sample can be
obtained by understanding the overall rate of false positives and false
negatives for the study system and method. Researchers can increase
confidence by using field-based and laboratory-based negative controls
and positive controls or mock communities and the use of no-library
negative controls to quantify sequencing mistag rates (Esling,
Lejzerowicz, \& Pawlowski, 2015). However, confidence estimates are also
lacking for the classification steps in pollen DNA metabarcoding. There
is an additional need for developing classification programs with more
accurate probabilistic confidence estimates. While this has been
attempted several times, available methods do not provide consistent
results depending on the gene regions and databases used (Edgar, 2018).

Another challenge for pollen DNA metabarcoding and related methods is
the development of reference databases. There are an estimated 450,000
angiosperm species (Pimm \& Joppa, 2015), and currently, around 25\% of
these have publicly available sequences for standard DNA barcodes (Bell,
Petit, et al., 2021). Reference libraries have been compiled for
standard DNA barcodes for all flowering plants in the UK (Jones,
Twyford, et al., 2021) and Canada (Kuzmina et al., 2017). There is
ongoing work in other countries to develop national databases. There are
fewer references available for non-standard DNA barcodes, plastomes,
genome skims, and assembled genomes (Bell, Petit, et al., 2021; Lang et
al., 2019). Several large-scale projects are in progress to sequence DNA
barcodes, organellar genomes and whole genomes for a large proportion of
global biodiversity (Lewin et al., 2018), and therefore, the
availability of reference sequences is continually improving. Additional
problems occur with the quality of publicly available sequences. While
quality control standards for databases such as BOLD are high, databases
such as GenBank depend on researchers depositing data to conduct their
quality control, and only minimal checks are performed, and many
erroneous sequences have been found (Breitwieser, Pertea, Zimin, \&
Salzberg, 2019). For most studies, it will be necessary to develop a
custom, curated database, including sequencing any species in the study
system that do not already have reliable sequences on public databases.
Filtering and subsetting public reference databases to species of
interest (e.g., the regional species pool) can be a helpful step in
classification to help avoid misclassifications to closely related
species (A. Keller et al., 2020). This step is likely to remain useful,
even as reliable public databases become more complete.

The ability to assess global ecological change often relies on the
comparison of contemporary data to historical data. Pollen DNA
metabarcoding methods have only been developed recently, so there are no
equivalent historical datasets. Baseline data from earlier studies based
on other methods may not be directly comparable to pollen DNA
metabarcoding. For example, studies have shown that the networks
assembled through pollen DNA metabarcoding are more connected than those
assembled through observations (Arstingstall et al., 2021; Pornon et
al., 2017), although networks assembled through traditional
identification methods of pollinator-collected pollen may be more like
those assembled through DNA metabarcoding, i.e., more connected (Bosch
et al., 2009). There is potential to obtain equivalent historical data
by analysing historical specimens using DNA sequencing technologies
(Gous et al., 2019; Simanonok et al., 2021). Still, assessments are
needed to see if there are any biases due to degradation over time.
Likewise, pollen in sediments could provide a source of material for
comparison with modern pollen DNA metabarcoding or other DNA-based
methods (Niemeyer et al., 2017). Finally, as we move into the future, it
will be essential to retain and archive specimens for optimum
preservation to be reanalysed and compared to future samples.

To date, there has been little standardisation in the methods used for
sequence-based characterisation of pollen communities in terms of
databases, classifiers, gene regions, or other options. There is also
inconsistency in how the methods are described and results reported in
the literature making comparisons of techniques difficult. While this
allows for greater flexibility and more scope for further method
development, standardisation would facilitate consistent benchmarking of
procedures to improve confidence. Once again, the fields of aquatic eDNA
and microbiomics are more advanced in this regard, with several
recommendations and standardised methods available (Loeza\selectlanguage{english}-Quintana,
Abbott, Heath, Bernatchez, \& Hanner, 2020; Yilmaz et al., 2011). There
are many choices on methodology at all stages of a research project,
including: gene region(s), DNA extraction, PCR, sequencing technologies,
and data analysis. Standardisation becomes possible once available
methods have been compared, assessed, and optimised. From this, minimum
sets of standards can be determined for replication, negative and
positive controls, and optimal choices across sampling, sequencing, and
data analysis steps. These standards can then be applied when designing
a project and when assessing papers during peer-review. Some methods for
the various steps involved in pollen DNA metabarcoding have been
compared (Swenson \& Gemeinholzer, 2021; Tommasi, Ferrari, Labra,
Galimberti, \& Biella, 2021), however, there remains a lot of work to be
done before any general recommendations can be made.

\emph{\textbf{Future research directions}}

In addition to the work currently in progress to solve the technical
issues discussed in section 4, we have identified several areas where
method development on pollen DNA metabarcoding and related methods could
open new avenues of research. These include method developments for
intraspecific identification, analysis of DNA from ancient pollen,
increased use of specimens available in museums and herbaria, and
increased use of newer sequencing technologies.

Intraspecific identifications (e.g., populations, individuals) using DNA
sequencing of pollen could open new research areas on the role of
pollinators in gene flow, and the effects of habitat fragmentation on
plant and pollinator health and adaptive potential. Recent developments
with eDNA suggest that this is plausible for pollen. For example, eDNA
has been used to investigate intraspecific variation in the
mitochondrial control region of whale sharks in sea water (Sigsgaard et
al., 2016), cytb variation in harbour porpoises (Parsons, Everett,
Dahlheim, \& Park, 2018), and microsatellite allele frequencies in
artificial mesocosms of round gobies (\emph{Neogobius melanostomus} )
(Andres, Sethi, Lodge, \& Andres, 2021). Genomic DNA from Late
Pleistocene bears was retrieved from shotgun data showing that it might
be possible to use ancient eDNA for intra-species analysis (Pedersen et
al., 2021). Applying target capture of mitochondrial and nuclear DNA in
eDNA is also a promising avenue of research (Jensen et al., 2021). All
the previous investigations into intraspecific variation from eDNA have
been attempted in aquatic study systems and cave sediments, and, as far
as we know, have not been attempted on plant communities or pollen.
Pollen mixtures might be the ideal candidate system to investigate
intraspecific variation. Because all individual pollen grains in a
mixture represent an individual gametophyte, any method focusing on the
genetic content of a single pollen grain can be used to investigate the
mixture on a population level. While population level inferences might
be possible without sequencing single pollen grains, many population
genomics tools require sequence data of individuals. To overcome
problems with single pollen grain sequencing, such as low template
abundance and uncertainties about whether DNA is coming from inside the
pollen or fragments sticking to the outside, it might be beneficial to
germinate the pollen to make accessing the DNA easier (Jayaprakash,
2018). Current advances in the read output and length in HTS platforms
coupled with the potential to sequence individual pollen grains might
advance molecular pollen analysis beyond species identification. As soon
as the DNA inside individual pollen grains can be accessed, amplified,
and sequenced, the road to a whole new set of research questions can be
opened. For example, applying population genomics to not only count the
number of species, but also quantify how many individual plants a
pollinator has visited.

The high level of preservation of pollen morphology in ancient
sedimentary records, means that pollen is often used to understand past
ecosystems. Coupling these methods with ancient DNA technology could
provide narrower taxonomic identification and more fine scale
understanding of past ecosystems. Although it has been demonstrated that
DNA is present in ancient pollen and can be sequenced (Bennett \&
Parducci, 2006; L. Parducci et al., 2005), ancient pollen samples are
difficult to process and there is a high risk of contamination with
exogenous DNA. There are many different approaches for isolating and
cleaning single pollen grains from the abundant pollen usually present
in sediments. These include hand pipetting under a microscope, serial
dilution, flow-assisted cell sorting (flow cytometry), microfluidic
manipulation (Wang \& Navin, 2015), flow sorting or micro-manipulation
(Kron \& Husband, 2012). Potential method development in this area could
focus on improved efficiency and contamination control. Once these
issues are addressed, there is great promise for ancient pollen analysis
to reveal ecosystem change over large time scales.

Likewise, there is currently an unrealised potential to use herbarium
and museum specimens to answer questions in global change ecology.
Currently, a handful of studies have shown that preserved specimens can
be used to study plant-pollinator interactions. Methods have been
developed for identifying historical pollen samples from bees using
metabarcoding (Gous et al., 2019). Museum specimens can be used to
assess resource use over huge temporal scales. For example, Simanonok et
al. (2021) identified resource use by \emph{Bombus affinis} over 100
years, and found that pollen richness and diversity had not changed over
time and neither had the abundance of native nor introduced species.
This work helps understand drivers behind the decline of native
pollinator species. Historical specimens can also be used not only for
temporal analysis of forage use but can help increase sample size for
spatial analysis. For example, Gous, Eardley, Johnson, Swanevelder, and
Willows-Munro (2021) sampled pollen from museum specimens of multiple
species from varying regions in South Africa across large temporal scale
to assess the relationship between resource use and geographic range of
species. There is similar potential to use herbarium specimens to look
at past distributions and morphology of plants to check for
plant-pollinator mismatches.

Finally, future advances in sequencing technology could influence how
pollen DNA metabarcoding and related methods are used for studying
ecological change. New fast, portable, sequencing technologies, such as
Oxford Nanopore Technologies' MinION, could allow for analysis while in
the field. Increased throughput of short read sequencing technologies
(e.g., Illumina NovaSeq), could make high-throughput analysis of pollen
samples faster and more cost-effective. Improvements in accuracy of
PacBio HiFi long-read sequencing could make it suitable for DNA
metabarcoding and related methods. However, many of these sequencing
platforms currently require a very high quality and quantity of DNA. As
sequencing technologies improve, they may require lower quantities of
DNA making them increasingly useful for pollen DNA analysis, especially
of pollen from small insects, single pollen grains, or ancient pollen.

Metagenomics methods depend on reference databases of whole genomes or
whole plastid genomes. However, new initiatives, such as the Earth
BioGenome Project, which aims to sequence the genomes of most eukaryote
species within a decade (Lewin et al., 2018), have the potential to
alleviate this limitation in the future. In addition, there are
metagenomics methods based on long-read sequencing that could improve
the resolution and quantification of pollen identification, without the
need for whole genome reference databases. For example, the RevMet
(reverse metagenomics) method of Peel et al. (2019) uses long-read
nanopore sequences of samples, while the reference database contains low
coverage short-read genome skims. This strategy could potentially
increase resolution and quantification relative to DNA metabarcoding and
could have cost advantages over other metagenomics methods as it does
not require assembled genomes for the reference database. An alternative
approach is provided by reduced-representation sequencing for
metagenomics. For example, the sequencing of restriction fragments
(ddRAD) has been used to identify plant community composition from roots
in soil samples (Wagemaker et al., 2021), and the same methods could
easily be applied to pollen mixtures. Given the rapid advancements in
sequencing technology and the increasing availability of reference
sequences and genomes, method development for DNA-based identification
of pollen should be considered a work in progress; each new study will
need to carefully consider the strengths and weaknesses of the methods
available or likely to become available during the study.

Conclusions

In recent years, there have been increasing numbers of studies applying
pollen DNA metabarcoding and related methods to research questions on
global change ecology (Fig. 1). Pollen DNA metabarcoding has provided
advantages over other methods in throughput and requiring less taxonomic
expertise. The potential applications of pollen DNA metabarcoding and
related methods are likely to increase as reference databases improve,
methods are assessed against traditional approaches and standardised,
and as multi-year datasets are accumulated.

Currently, pollen DNA metabarcoding is complementary to traditional
methods, such as microscopic identification of pollen and direct
observation of plant-pollinator interactions. This is particularly the
case where baseline data exists and has been collected through
traditional methods. However, there are cases where samples have been
captured and preserved: e.g., museum specimens of pollinators (Gous et
al., 2021; Simanonok et al., 2021), slides of pollen collections (Marcel
Polling et al., 2022), and these have been analysed through DNA
metabarcoding, providing scope to analyse contemporary and historical
samples, and assess changes from baseline ecological conditions. As more
long-term studies are completed using DNA metabarcoding, these methods
could be used to understand recent change. Under these scenarios, pollen
DNA metabarcoding and related methods could eventually become more
predominant, especially given the benefits in terms of high-throughput
for large sample sizes, and in combination with other newer
technologies, such as flow cytometry (Kron et al., 2021) and
machine-learning classifications (Goncalves et al., 2016) .

Global ecological change is happening rapidly, and high-throughput
methods are essential for getting timely data on changes so that
management practices can be assessed and changed as required. Pollen DNA
metabarcoding and related methods are important tools for rapid,
high-throughput assessment of ecosystem change, providing real-time
management recommendations to preserve biodiversity and the evolutionary
and ecological process that support it before it is too late.

Acknowledgements

The manuscript was improved with comments from Olly Berry and Liz Milla.
RML is supported by the Biotechnology and Biological Sciences Research
Council (BBSRC), part of UK Research and Innovation, through Core
Strategic Programme Grant BB/CSP1720/1.

Author contributions

Determining the scope of the paper: KLB (lead), in discussion with all
authors. Compiling literature: KLB, FE-V, AK, KN, RR, KT, AL, RML, NdV.
Analysis of literature and creating figures and tables: KT. Writing
first draft: KLB (lead), BB, KSB. Editing and reviewing the manuscript:
all authors. All authors approved of the final submitted manuscript.

References

Ancin-Murguzur, F. J., Munoz, L., Monz, C., \& Hausner, V. H. (2020).
Drones as a tool to monitor human impacts and vegetation changes in
parks and protected areas. \emph{Remote Sensing in Ecology and
Conservation, 6} , 105-113. doi:10.1002/rse2.127

Anderegg, W. R. L., Abatzoglou, J. T., Anderegg, L. D. L., Bielory, L.,
Kinney, P. L., \& Ziska, L. (2021). Anthropogenic climate change is
worsening North American pollen seasons. \emph{Proc Natl Acad Sci U S A,
118} (7). doi:10.1073/pnas.2013284118

Andres, K. J., Sethi, S. A., Lodge, D. M., \& Andres, J. (2021). Nuclear
eDNA estimates population allele frequencies and abundance in
experimental mesocosms and field samples. \emph{Molecular Ecology, 30}
(3), 685-697. doi:10.1111/mec.15765

Arstingstall, K. A., DeBano, S. J., Li, X., Wooster, D. E., Rowland, M.
M., Burrows, S., \& Frost, K. (2021). Capabilities and limitations of
using DNA metabarcoding to study plant-pollinator interactions.\emph{Mol
Ecol} . doi:10.1111/mec.16112

Aziz, A. N., \& Sauve, R. J. (2008). Genetic mapping of Echinacea
purpurea via individual pollen DNA fingerprinting. \emph{Molecular
Breeding, 21} (2), 227-232. doi:10.1007/s11032-007-9123-9

Baksay, S., Pornon, A., Burrus, M., Mariette, J., Andalo, C., \&
Escaravage, N. (2020). Experimental quantification of pollen with DNA
metabarcoding using ITS1 and trnL. \emph{Scientific Reports, 10} (1),
4020. doi:10.1038/s41598-020-61198-6

Baldock, K. C., Goddard, M. A., Hicks, D. M., Kunin, W. E., Mitschunas,
N., Osgathorpe, L. M., . . . Memmott, J. (2015). Where is the UK's
pollinator biodiversity? The importance of urban areas for
flower-visiting insects. \emph{Proc Biol Sci, 282} (1803), 20142849.
doi:10.1098/rspb.2014.2849

Bansch, S., Tscharntke, T., Wunschiers, R., Netter, L., Brenig, B.,
Gabriel, D., \& Westphal, C. (2020). Using ITS2 metabarcoding and
microscopy to analyze shifts in pollen diets of honey bees and bumble
bees along a mass-flowering crop gradient. \emph{Molecular Ecology, 29}
(24), 5003-5018.

Bartomeus, I., Ascher, J. S., Wagner, D., Danforth, B. N., Colla, S.,
Kornbluth, S., \& Winfree, R. (2011). Climate-associated phenological
advances in bee pollinators and bee-pollinated plants. \emph{Proceedings
of the National Academy of Sciences, 108} (51), 20645-20649.
doi:10.1073/pnas.1115559108

Bell, K. L., Batchelor, K. L., Bradford, M., McKeown, A., Macdonald, S.
L., \& Westcott, D. (2021). Optimisation of a pollen DNA metabarcoding
method for diet analysis of flying-foxes
(\textless{}i\textgreater{}Pteropus\textless{}/i\textgreater{}
spp.).\emph{Australian Journal of Zoology} , -.
doi:https://doi.org/10.1071/ZO20085

Bell, K. L., Burgess, K. S., Botsch, J. C., Dobbs, E. K., Read, T. D.,
\& Brosi, B. J. (2019). Quantitative and qualitative assessment of
pollen DNA metabarcoding using constructed species mixtures.
In\emph{Molecular Ecology} (Vol. 28, pp. 431-455): Wiley/Blackwell
(10.1111).

Bell, K. L., de Vere, N., Keller, A., Richardson, R. T., Gous, A.,
Burgess, K. S., \& Brosi, B. J. (2016). Pollen DNA barcoding: current
applications and future prospects. \emph{Genome, 59} (9), 629-640.
doi:10.1139/gen-2015-0200

Bell, K. L., Petit, R. A., Cutler, A., Dobbs, E. K., Macpherson, J. M.,
Read, T. D., . . . Brosi, B. J. (2021). Comparing whole\selectlanguage{english}-genome shotgun
sequencing and DNA metabarcoding approaches for species identification
and quantification of pollen species mixtures. \emph{Ecology and
Evolution} . doi:10.1002/ece3.8281

Bennett, K., \& Parducci, Y. (2006). DNA from pollen: principles and
potential. In \emph{The Holocene} (Vol. 16, pp. 1031-1034).

Berry, D., Mahfoudh, K. B., Wagner, M., \& Loy, A. (2011). Barcoded
Primers Used in Multiplex Amplicon Pyrosequencing Bias
Amplification.\emph{Applied and Environmental Microbiology, 77} (21),
7846-7849. doi:doi:10.1128/AEM.05220-11

Bista, I., Carvalho, G. R., Tang, M., Walsh, K., Zhou, X., Hajibabaei,
M., . . . Creer, S. (2018). Performance of amplicon and shotgun
sequencing for accurate biomass estimation in invertebrate community
samples. In \emph{Molecular Ecology Resources} (Vol. 18, pp. 1020-1034).

Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C.
C., Al-Ghalith, G. A., . . . Caporaso, J. G. (2019). Reproducible,
interactive, scalable and extensible microbiome data science using QIIME
2. \emph{Nature Biotechnology, 37} (8), 852-857.
doi:10.1038/s41587-019-0209-9

Bosch, J., Gonzalez, A. M., Rodrigo, A., \& Navarro, D. (2009).
Plant-pollinator networks: adding the pollinator's
perspective.\emph{Ecol Lett, 12} (5), 409-419.
doi:10.1111/j.1461-0248.2009.01296.x

Bourel, B., Marchant, R., de Garidel-Thoron, T., Tetard, M., Barboni,
D., Gally, Y., \& Beaufort, L. (2020). Automated recognition by multiple
convolutional neural networks of modern, fossil, intact and damaged
pollen grains. \emph{Computers \& Geosciences, 140} , 104498.
doi:10.1016/j.cageo.2020.104498

Bowler, D. E., Bjorkman, A. D., Dornelas, M., Myers\selectlanguage{english}-Smith, I. H.,
Navarro, L. M., Niamir, A., . . . Fish, R. (2020). Mapping human
pressures on biodiversity across the planet uncovers anthropogenic
threat complexes. \emph{People and Nature, 2} (2), 380-394.
doi:10.1002/pan3.10071

Boyer, F., Mercier, C., Bonin, A., Le Bras, Y., Taberlet, P., \&
Coissac, E. (2016). obitools: a unix-inspired software package for DNA
metabarcoding. \emph{Molecular Ecology Resources, 16} (1), 176-182.
doi:10.1111/1755-0998.12428

Breitwieser, F. P., Pertea, M., Zimin, A. V., \& Salzberg, S. L. (2019).
Human contamination in bacterial genomes has created thousands of
spurious proteins. \emph{Genome Research, 29} (954-960).
doi:10.1101/gr.245373.118

Brennan, G. L., Potter, C., de Vere, N., Griffith, G. W., Skjoth, C. A.,
Osborne, N. J., . . . Creer, S. (2019). Temperate airborne grass pollen
defined by spatio-temporal shifts in community composition. \emph{Nature
Ecology \& Evolution, 3} (5), 750-754. doi:10.1038/s41559-019-0849-7

Brosi, B. J. (2016). Pollinator specialization: from the individual to
the community. \emph{New Phytologist, 210} (4), 1190-1194.
doi:10.1111/nph.13951

Brosi, B. J., \& Briggs, H. M. (2013). Single pollinator species losses
reduce floral fidelity and plant reproductive function.\emph{Proceedings
of the National Academy of Sciences, 110} (32), 13044-13048.
doi:10.1073/pnas.1307438110

Bruni, I., Galimberti, A., Caridi, L., Scaccabarozzi, D., De Mattia, F.,
Casiraghi, M., \& Labra, M. (2015). A DNA barcoding approach to identify
plant species in multiflower honey. In \emph{Food chemistry} (Vol. 170,
pp. 308-315).

Burkle, L. A., Marlin, J. C., \& Knight, T. M. (2013). Plant-pollinator
interactions over 120 years: Loss of species, co-occurrence, and
function. \emph{Science, 339} (6127), 1611-1615.

Butchart, S. H. M., Walpole, M., Collen, B., van Strien, A.,
Scharlemann, J. P. W., Almond, R. E. A., . . . Watson, R. (2010). Global
biodiversity: Indicators of recent declines. \emph{Science, 328} (5982),
1164-1168.

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A.
J., \& Holmes, S. P. (2016). DADA2: High-resolution sample inference
from Illumina amplicon data. \emph{Nature Methods, 13} (7), 581-583.
doi:10.1038/nmeth.3869

Campbell, B. C., Al Kouba, J., Timbrell, V., Noor, M. J., Massel, K.,
Gilding, E. K., . . . Davies, J. M. (2020). Tracking seasonal changes in
diversity of pollen allergen exposure: Targeted metabarcoding of a
subtropical aerobiome. \emph{Science of The Total Environment, 747} ,
141189. doi:10.1016/j.scitotenv.2020.141189

Capo, E., Giguet-Covex, C., Rouillard, A., Nota, K., Heintzman, P. D.,
Vuillemin, A., . . . Parducci, L. (2021). Lake Sedimentary DNA Research
on Past Terrestrial and Aquatic Biodiversity: Overview and
Recommendations. \emph{Quaternary, 4} (1), 6. doi:10.3390/quat4010006

Casanelles\selectlanguage{english}-Abella, J., Muller, S., Keller, A., Aleixo, C., Alos Orti,
M., Chiron, F., . . . Moretti, M. (2021). How wild bees find a way in
European cities: Pollen metabarcoding unravels multiple feeding
strategies and their effects on distribution patterns in four wild bee
species. \emph{Journal of Applied Ecology} . doi:10.1111/1365-2664.14063

CBOL Plant Working Group, Hollingsworth, P. M., Forrest, L. L., Spouge,
J. L., Hajibabaei, M., Ratnasingham, S., . . . Little, D. P. (2009). A
DNA barcode for land plants. \emph{Proceedings of the National Academy
of Sciences, 106} (31), 12794-12797. doi:10.1073/pnas.0905845106

Chang, H., Guo, J., Fu, X., Liu, Y., Wyckhuys, K., Hou, Y., \& Wu, K.
(2018). Molecular-Assisted Pollen Grain Analysis Reveals Spatiotemporal
Origin of Long-Distance Migrants of a Noctuid Moth.
In\emph{International Journal of Molecular Sciences} (Vol. 19, pp. 567):
Multidisciplinary Digital Publishing Institute.

Cohen, H., Smith, G. P., Sardinas, H., Zorn, J. F., McFrederick, Q. S.,
Woodard, S. H., \& Ponisio, L. C. (2021). Mass-flowering monoculture
attracts bees, amplifying parasite prevalence. \emph{Proceedings of the
Royal Society B: Biological Sciences, 288} (1960), 20211369.
doi:doi:10.1098/rspb.2021.1369

Courtin, J., Andreev, A. A., Raschke, E., Bala, S., Biskaborn, B. K.,
Liu, S., . . . Herzschuh, U. (2021). Vegetation Changes in Southeastern
Siberia During the Late Pleistocene and the Holocene. \emph{Frontiers in
Ecology and Evolution, 9} . doi:10.3389/fevo.2021.625096

Cristescu, M. E. (2014). From barcoding single individuals to
metabarcoding biological communities: towards an integrative approach to
the study of global biodiversity. In \emph{Trends in Ecology \&
Evolution} (Vol. 29, pp. 566-571): Elsevier Ltd.

Cullen, N., Xia, J., Wei, N., Kaczorowski, R., Arceo-Gomez, G., O'Neill,
E., . . . Ashman, T.-L. (2021). Diversity and composition of pollen
loads carried by pollinators are primarily driven by insect traits, not
floral community characteristics. \emph{Oecologia, 196} (1), 131-143.
doi:10.1007/s00442-021-04911-0

da Rocha\selectlanguage{english}-Filho, L. C., Montagnana, P. C., Araujo, T. N., Moure\selectlanguage{english}-Oliveira,
D., Boscolo, D., \& Garofalo, C. A. (2021). Pollen analysis of
cavity\selectlanguage{english}-nesting bees (Hymenoptera: Anthophila) and their food webs in a
city. \emph{Ecological Entomology} . doi:10.1111/een.13097

Damschen, E. I., Brudvig, L. A., Burt, M. A., Fletcher, R. J., Haddad,
N. M., Levey, D. J., . . . Tewksbury, J. J. (2019). Ongoing accumulation
of plant diversity through habitat connectivity in an 18-year
experiment. \emph{Science, 365} (6460), 1478-1480.
doi:doi:10.1126/science.aax8992

Danner, N., Keller, A., Hartel, S., \& Steffan-Dewenter, I. (2017).
Honey bee foraging ecology: Season but not landscape diversity shapes
the amount and diversity of collected pollen. In W. Blenau
(Ed.),\emph{PLoS one} (Vol. 12, pp. e0183716): Springer New York.

Darling, J. A., Pochon, X., Abbott, C. L., Inglis, G. J., \& Zaiko, A.
(2020). The risks of using molecular biodiversity data for incidental
detection of species of concern. \emph{Diversity and Distributions, 26}
(9), 1116-1121. doi:10.1111/ddi.13108

Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., \& Callahan,
B. J. (2018). Simple statistical identification and removal of
contaminant sequences in marker-gene and metagenomics data.
In\emph{Microbiome} (Vol. 6, pp. 226): BioMed Central.

de Manincor, N., Hautekeete, N., Mazoyer, C., Moreau, P., Piquot, Y.,
Schatz, B., . . . Massol, F. (2020). How biased is our perception of
plant-pollinator networks? A comparison of visit- and pollen-based
representations of the same networks. \emph{Acta Oecologica, 105} ,
103551. doi:10.1016/j.actao.2020.103551

de Vere, N., Jones, L. E., Gilmore, T., Moscrop, J., Lowe, A., Smith,
D., . . . Ford, C. R. (2017). Using DNA metabarcoding to investigate
honey bee foraging reveals limited flower use despite high floral
availability. In \emph{Scientific Reports} (Vol. 7, pp. 42838).

Deagle, B. E., Thomas, A. C., McInnes, J. C., Clarke, L. J., Vesterinen,
E. J., Clare, E. L., . . . Eveson, J. P. (2019). Counting with DNA in
metabarcoding studies: How should we convert sequence reads to dietary
data? \emph{Molecular Ecology, 28} (2), 391-406. doi:10.1111/mec.14734

Dharampal, P. S., Carlson, C., Currie, C. R., \& Steffan, S. A. (2019).
Pollen-borne microbes shape bee fitness. \emph{Proceedings of the Royal
Society B: Biological Sciences, 286} (1904), 20182894.
doi:10.1098/rspb.2018.2894

Didham, R. K., Tylianakis, J. M., Gemmell, N. J., Rand, T. A., \& Ewers,
R. M. (2007). Interactive effects of habitat modification and species
invasion on native species decline. \emph{Trends Ecol Evol, 22} (9),
489-496. doi:10.1016/j.tree.2007.07.001

Diehn, S., Zimmermann, B., Tafintseva, V., Ba\selectlanguage{polish}ğ\selectlanguage{english}c\selectlanguage{polish}ı\selectlanguage{english}o\selectlanguage{polish}ğ\selectlanguage{english}lu, M., Kohler, A.,
Ohlson, M., . . . Kneipp, J. (2020). Discrimination of grass pollen of
different species by FTIR spectroscopy of individual pollen
grains.\emph{Analytical and Bioanalytical Chemistry, 412} (24),
6459-6474. doi:10.1007/s00216-020-02628-2

Donkersley, P., Rhodes, G., Pickup, R. W., Jones, K. C., Power, E. F.,
Wright, G. A., \& Wilson, K. (2017). Nutritional composition of honey
bee food stores vary with floral composition. \emph{Oecologia, 185} (4),
749-761. doi:10.1007/s00442-017-3968-3

Dorazio, R. M., \& Erickson, R. A. (2018). eDNAoccupancy: An R package
for multiscale occupancy modelling of environmental DNA
data.\emph{Molecular Ecology Resources, 18} (2), 368-380.
doi:10.1111/1755-0998.12735

Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V., \& Grozinger, C. M.
(2020). County-level analysis reveals a rapidly shifting landscape of
insecticide hazard to honey bees (\emph{Apis mellifera} ) on US
farmland.\emph{Scientific Reports, 10} (1), 797.
doi:10.1038/s41598-019-57225-w

Douglas, M. R., \& Tooker, J. F. (2015). Large-Scale Deployment of Seed
Treatments Has Driven Rapid Increase in Use of Neonicotinoid
Insecticides and Preemptive Pest Management in U.S. Field
Crops.\emph{Environmental Science \& Technology, 49} (8), 5088-5097.
doi:10.1021/es506141g

Dunker, S., Motivans, E., Rakosy, D., Boho, D., M\selectlanguage{ngerman}äder, P., Hornick, T.,
\& Knight, T. M. (2021). Pollen analysis using multispectral imaging
flow cytometry and deep learning. \emph{New Phytologist, 229} (1),
593-606. doi:10.1111/nph.16882

Dunn, J. C., Stockdale, J. E., Moorhouse-Gann, R. J., McCubbin, A.,
Hipperson, H., Morris, A. J., . . . Symondson, W. O. C. (2018). The
decline of the Turtle Dove: Dietary associations with body condition and
competition with other columbids analysed using high-throughput
sequencing. \emph{Molecular Ecology, 27} , 3386-3407.
doi:10.1111/mec.14766

Edgar, R. C. (2010). Search and clustering orders of magnitude faster
than BLAST. \emph{Bioinformatics, 26} (19), 2460-2461.
doi:10.1093/bioinformatics/btq461

Edgar, R. C. (2018). Accuracy of taxonomy prediction for 16S rRNA and
fungal ITS sequences. \emph{PeerJ, 6} , e4652. doi:10.7717/peerj.4652

Elliott, B., Wilson, R., Shapcott, A., Keller, A., Newis, R.,
Cannizzaro, C., . . . Wallace, H. M. (2020). Pollen diets and niche
overlap of honey bees and native bees in protected areas. In \emph{Basic
and Applied Ecology} .

Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D., \&
Ramankutty, N. (2010). Anthropogenic transformation of the biomes, 1700
to 2000. \emph{Global Ecology and Biogeography, 19} , 589-606.
doi:10.1111/j.1466-8238.2010.00540.x

Engel, P., Kwong, W. K., McFrederick, Q., Anderson, K. E., Barribeau, S.
M., Chandler, J. A., . . . Dainat, B. (2016). The Bee Microbiome: Impact
on Bee Health and Model for Evolution and Ecology of Host-Microbe
Interactions. \emph{mBio, 7} (2), e02164-02115.
doi:10.1128/mBio.02164-15

Esling, P., Lejzerowicz, F., \& Pawlowski, J. (2015). Accurate
multiplexing and filtering for high-throughput
amplicon-sequencing.\emph{Nucleic Acids Research, 43} (5), 2513-2524.
doi:10.1093/nar/gkv107

Forrest, J. R. K. (2015). Plant-pollinator interactions and phenological
change: What can we learn about climate impacts from experiments and
observations? \emph{Oikos} (4-13).

Galimberti, A., De Mattia, F., Bruni, I., Scaccabarozzi, D., Sandionigi,
A., Barbuto, M., . . . Labra, M. (2014). A DNA barcoding approach to
characterize pollen collected by honeybees. In \emph{PLoS one} (Vol. 9,
pp. e109363).

Garrido-Sanz, L., Senar, M. A., \& Pinol, J. (2021). Relative species
abundance estimation in artificial mixtures of insects using
mito-metagenomics and a correction factor for the mitochondrial DNA copy
number. \emph{Mol Ecol Resour} . doi:10.1111/1755-0998.13464

Garrido-Sanz, L., Senar, M. À., \& Piñol, J. (2020). Estimation of the
relative abundance of species in artificial mixtures of insects using
low-coverage shotgun metagenomics. In \emph{Metabarcoding and
Metagenomics} (Vol. 4, pp. e48281).

Gonçalves, A. B., Souza, J. S., da Silva, G. G., Cereda, M. P., Pott,
A., Naka, M. H., \& Pistori, H. (2016). Feature extraction and machine
learning for the classification of Brazilian savannah pollen
grains.\emph{PLoS one, 11} (6), e0157044.
doi:10.1371/journal.pone.0157044

Gouker, F. E., Guo, Y., \& Pooler, M. R. (2020). Using acetone for rapid
PCR-amplifiable DNA extraction from recalcitrant woody plant
taxa.\emph{Applications in Plant Sciences, 8} (12), e11403.

Gous, A., Eardley, C. D., Johnson, S. D., Swanevelder, D. Z. H., \&
Willows-Munro, S. (2021). Floral hosts of leaf-cutter bees
(Megachilidae) in a biodiversity hotspot revealed by pollen DNA
metabarcoding of historic specimens. \emph{PLoS one, 16} (1), e0244973.
doi:10.1371/journal.pone.0244973

Gous, A., Swanevelder, D. Z. H., Eardley, C. D., \& Willows-Munro, S.
(2019). Plant-pollinator interactions over time: Pollen metabarcoding
from bees in a historic collection. In \emph{Evolutionary
Applications}(Vol. 12, pp. 187-197): Wiley/Blackwell (10.1111).

Grab, H., Brokaw, J., Anderson, E., Gedlinske, L., Gibbs, J., Wilson,
J., . . . Diamond, S. (2019). Habitat enhancements rescue bee body size
from the negative effects of landscape simplification. \emph{Journal of
Applied Ecology, 56} (9), 2144-2154. doi:10.1111/1365-2664.13456

Gresty, C. E. A., Clare, E., Devey, D. S., Cowan, R. S., Csiba, L.,
Malakasi, P., . . . Willis, K. J. (2018). Flower preferences and pollen
transport networks for cavity-nesting solitary bees: Implications for
the design of agri-environment schemes. \emph{Ecology and Evolution, 8}
(15), 7574-7587. doi:10.1002/ece3.4234

Hall, D. M., Camilo, G. R., Tonietto, R. K., Ollerton, J., Ahrné, K.,
Arduser, M., . . . Threlfall, C. G. (2017). The city as a refuge for
insect pollinators. \emph{Conserv Biol, 31} (1), 24-29.
doi:10.1111/cobi.12840

Hansen, J., Reudy, R., Sato, M., \& Lo, K. (2010). Global surface
temperature change. \emph{Reviews of Geophysics, 48} (RG4004),
2010RG000345.

Hasegawa, Y., Suyama, Y., \& Seiwa, K. (2009). Pollen donor composition
during the early phases of reproduction revealed by DNA genotyping of
pollen grains and seeds of Castanea crenata. \emph{New Phytologist, 182}
(4), 994-1002. doi:10.1111/j.1469-8137.2009.02806.x

Hawkins, J., de Vere, N., Griffith, A., Ford, C. R., Allainguillaume,
J., Hegarty, M. J., . . . Adams-Groom, B. (2015). Using DNA
Metabarcoding to Identify the Floral Composition of Honey: A New Tool
for Investigating Honey Bee Foraging Preferences. In \emph{PLoS
one}(Vol. 10, pp. e0134735): Public Library of Science.

He, C., Liu, Z., Gou, S., Zhang, Q., Zhang, J., \& Xu, L. (2019).
Detecting global urban expansion over the last three decades using a
fully convolutional network. \emph{Environmental Research Letters, 14}
(3), 034008. doi:10.1088/1748-9326/aaf936

Hirota, S. K., Nitta, K., Suyama, Y., Kawakubo, N., Yasumoto, A. A., \&
Yahara, T. (2013). Pollinator-Mediated Selection on Flower Color, Flower
Scent and Flower Morphology of Hemerocallis: Evidence from Genotyping
Individual Pollen Grains On the Stigma. \emph{PLoS one, 8} (12), e85601.
doi:10.1371/journal.pone.0085601

Hornick, T., Richter, A., Harpole, W. S., Bastl, M., Bohlmann, S., Bonn,
A., . . . Dunker, S. (2021). An integrative environmental pollen
diversity assessment and its importance for the Sustainable Development
Goals. \emph{Plants, People, Planet} . doi:10.1002/ppp3.10234

Hulme, P. E. (2009). Trade, transport and trouble: managing invasive
species pathways in an era of globalization. \emph{Journal of Applied
Ecology, 46} (1), 10-18.
doi:https://doi.org/10.1111/j.1365-2664.2008.01600.x

Isagi, Y., \& Suyama, Y. (2011). \emph{Single-Pollen Genotyping} .
Tokyo: Springer.

Ito, M., Suyama, Y., Ohsawa, T. A., \& Watano, Y. (2008).
Airborne-pollen pool and mating pattern in a hybrid zone
between\emph{Pinus pumila} and \emph{P. parviflora} var.
\emph{pentaphylla} .\emph{Molecular Ecology, 17} (23), 5092-5103.
doi:10.1111/j.1365-294X.2008.03966.x

Jayaprakash, P. (2018). Pollen Germination in vitro. In P. W. Mokwala
(Ed.), \emph{Pollination in Plants} : IntechOpen.

Jensen, M. R., Sigsgaard, E. E., Liu, S., Manica, A., Bach, S. S.,
Hansen, M. M., . . . Thomsen, P. F. (2021). Genome-scale target capture
of mitochondrial and nuclear environmental DNA from water
samples.\emph{Molecular Ecology Resources, 21} (3), 690-702.
doi:10.1111/1755-0998.13293

Jones, L., Brennan, G. L., Lowe, A., Creer, S., Ford, C. R., \& de Vere,
N. (2021). Shifts in honeybee foraging reveal historical changes in
floral resources. \emph{Communications Biology, 4} (1), 37.
doi:10.1038/s42003-020-01562-4

Jones, L., Twyford, A. D., Ford, C. R., Rich, T. C. G., Davies, H.,
Forrest, L. L., . . . de Vere, N. (2021). Barcode UK: A complete DNA
barcoding resource for the flowering plants and conifers of the United
Kingdom. \emph{Mol Ecol Resour, 21} (6), 2050-2062.
doi:10.1111/1755-0998.13388

Judd, H. J., Huntzinger, C., Ramirez, R., \& Strange, J. P. (2020). A 3D
Printed Pollen Trap for Bumble Bee (\emph{Bombus} ) Hive
Entrances.\emph{JoVE} (161), e61500. doi:10.3791/61500

Kaluza, B. F., Wallace, H., Keller, A., Heard, T. A., Jeffers, B.,
Drescher, N., . . . Leonhardt, S. D. (2017). Generalist social bees
maximize diversity intake in plant species-rich and resource-abundant
environments. In \emph{Ecosphere} (Vol. 8, pp. e01758).

Keller, A., Brandel, A., Becker, M. C., Balles, R., Abdelmohsen, U. R.,
Ankenbrand, M. J., \& Sickel, W. (2018). Wild bees and their nests host
Paenibacillus bacteria with functional potential of
avail.\emph{Microbiome, 6} (1), 229. doi:10.1186/s40168-018-0614-1

Keller, A., Danner, N., Grimmer, G., Ankenbrand, M. J., von der Ohe, K.,
von der Ohe, W., . . . Steffan-Dewenter, I. (2015). Evaluating
multiplexed next-generation sequencing as a method in palynology for
mixed pollen samples. \emph{Plant Biology, 17} (2), 558-566.
doi:10.1111/plb.12251

Keller, A., Hohlfeld, S., Kolter, A., Schultz, J., Gemeinholzer, B., \&
Ankenbrand, M. J. (2020). BCdatabaser: on-the-fly reference database
creation for (meta-)barcoding. \emph{Bioinformatics, 36} (8), 2630-2631.
doi:10.1093/bioinformatics/btz960

Keller, A., McFrederick, Q. S., Dharampal, P., Steffan, S., Danforth, B.
N., \& Leonhardt, S. D. (2021). (More than) Hitchhikers through the
network: the shared microbiome of bees and flowers. \emph{Current
Opinion in Insect Science, 44} , 8-15. doi:10.1016/j.cois.2020.09.007

Kembel, S. W., Wu, M., Eisen, J. A., \& Green, J. L. (2012).
Incorporating 16S Gene Copy Number Information Improves Estimates of
Microbial Diversity and Abundance. \emph{PLOS Computational Biology, 8}
(10), e1002743. doi:10.1371/journal.pcbi.1002743

Khansaritoreh, E., Salmaki, Y., Ramezani, E., Akbari Azirani, T.,
Keller, A., Neumann, K., . . . Behling, H. (2020). Employing DNA
metabarcoding to determine the geographical origin of
honey.\emph{Heliyon, 6} (11), e05596. doi:10.1016/j.heliyon.2020.e05596

Klimczak, L. J., Ebner von Eschenbach, C., Thompson, P. M., Buters, J.
T. M., \& Meuller, G. A. (2020). Mixture analyses of air-sampled pollen
extracts can accurately differentiate pollen taxa. \emph{Atmospheric
Environment, 243} , 117746. doi:10.1016/j.atmosenv.2020.117746

Kolter, A., \& Gemeinholzer, B. (2021). Internal transcribed spacer
primer evaluation for vascular plant metabarcoding. \emph{Metabarcoding
and Metagenomics, 5} , e68155. doi:10.3897/mbmg.5.68155

Kortsch, S., Primicerio, R., Fossheim, M., Dolgov, A. V., \& Aschan, M.
(2015). Climate change alters the structure of arctic marine food webs
due to poleward shifts of boreal generalists. \emph{Proceedings of the
Royal Society B: Biological Sciences, 282} , 20151546.
doi:10.1098/rspb.2015.1546

Kraaijeveld, K., de Weger, L. A., Ventayol Garcia, M., Buermans, H.,
Frank, J., Hiemstra, P. S., \& den Dunnen, J. T. (2014). Efficient and
sensitive identification and quantification of airborne pollen using
next-generation DNA sequencing. \emph{Molecular Ecology Resources, 15}
(1), 8-16. doi:10.1111/1755-0998.12288

Kratschmer, S., Petrovi\selectlanguage{polish}ć, \selectlanguage{english}B., Curto, M., Meimberg, H., \& Pachinger, B.
(2020). Pollen availability for the Horned mason bee (

Osmia cornuta

) in regions of different land use and landscape
structures.\emph{Ecological Entomology, 45} (3), 525-537.
doi:10.1111/een.12823

Kron, P., Loureiro, J., Castro, S., \& \selectlanguage{polish}Č\selectlanguage{english}ertner, M. (2021). Flow
cytometric analysis of pollen and spores: An overview of applications
and methodology. \emph{Cytometry Part A, 99} (4), 348-358.
doi:10.1002/cyto.a.24330

Kurganskiy, A., Creer, S., Vere, N. d., Griffith, G. W., Osborne, N. J.,
Wheeler, B. W., . . . Skj\selectlanguage{ngerman}øth, C. A. (2021). Predicting the severity of
the grass pollen season and the effect of climate change in Northwest
Europe. \emph{Science Advances, 7} (13), eabd7658.
doi:10.1126/sciadv.abd7658

Kuzmina, M. L., Braukmann, T. W. A., Fazekas, A. J., Graham, S. W.,
Dewaard, S. L., Rodrigues, A., . . . Hebert, P. D. N. (2017). Using
herbarium-derived DNAs to assemble a large-scale DNA barcode library for
the vascular plants of Canada. \emph{Applications in Plant Sciences, 5}
(12), apps.1700079. doi:10.3732/apps.1700079

Lamb, P. D., Hunter, E., Pinnegar, J. K., Creer, S., Davies, R. G., \&
Taylor, M. I. (2019). How quantitative is metabarcoding: A
meta-analytical approach. \emph{Molecular Ecology, 28} (2), 420-430.
doi:10.1111/mec.14920

Lang, D., Tang, M., Hu, J., \& Zhou, X. (2019). Genome\selectlanguage{english}-skimming provides
accurate quantification for pollen mixtures. In \emph{Molecular Ecology
Resources} (Vol. 19, pp. 1433-1446): John Wiley \& Sons, Ltd (10.1111).

Lark, T. J., Meghan Salmon, J., \& Gibbs, H. K. (2015). Cropland
expansion outpaces agricultural and biofuel policies in the United
States. \emph{Environmental Research Letters, 10} (4), 044003.
doi:10.1088/1748-9326/10/4/044003

Lark, T. J., Spawn, S. A., Bougie, M., \& Gibbs, H. K. (2020). Cropland
expansion in the United States produces marginal yields at high costs to
wildlife. \emph{Nature Communications, 11} (1), 4295.
doi:10.1038/s41467-020-18045-z

Lau, P., Bryant, V., Ellis, J. D., Huang, Z. Y., Sullivan, J., Schmehl,
D. R., . . . Rangel, J. (2019). Seasonal variation of pollen collected
by honey bees (Apis mellifera) in developed areas across four regions in
the United States. \emph{PLoS one, 14} (6), e0217294.
doi:10.1371/journal.pone.0217294

Leontidou, K., Vernesi, C., De Groeve, J., Cristofolini, F., Vokou, D.,
\& Cristofori, A. (2018). DNA metabarcoding of airborne pollen: new
protocols for improved taxonomic identification of environmental
samples. In \emph{Aerobiologia} (Vol. 34, pp. 63-74): Springer
Netherlands.

Leontidou, K., Vokou, D., Sandionigi, A., Bruno, A., Lazarina, M., De
Groeve, J., . . . Cristofori, A. (2021). Plant biodiversity assessment
through pollen DNA metabarcoding in Natura 2000 habitats (Italian
Alps).\emph{Scientific Reports, 11} , 18226.

Lewin, H. A., Robinson, G. E., Kress, W. J., Baker, W. J., Coddington,
J., Crandall, K. A., . . . Zhang, G. (2018). Earth BioGenome Project:
Sequencing life for the future of life. \emph{Proceedings of the
National Academy of Sciences, 115} (17), 4325-4333.
doi:10.1073/pnas.1720115115

Liu, S., Li, K., Jia, W., Stoof-Leichsenring, K. R., Liu, X., Cao, X.,
\& Herzschuh, U. (2021). Vegetation Reconstruction From Siberia and the
Tibetan Plateau Using Modern Analogue Technique--Comparing Sedimentary
(Ancient) DNA and Pollen Data. \emph{Frontiers in Ecology and Evolution,
9} , 668611. doi:10.3389/fevo.2021.668611

Loeza\selectlanguage{english}-Quintana, T., Abbott, C. L., Heath, D. D., Bernatchez, L., \&
Hanner, R. H. (2020). Pathway to Increase Standards and Competency of
eDNA Surveys (PISCeS)---Advancing collaboration and standardization
efforts in the field of eDNA. \emph{Environmental DNA, 2} (3), 255-260.
doi:10.1002/edn3.112

Lowenstein, D. M., Matteson, K. C., \& Minor, E. S. (2018). Evaluating
the dependence of urban pollinators on ornamental, non-native, and
`weedy' floral resources. \emph{Urban Ecosystems, 22} (2), 293-302.
doi:10.1007/s11252-018-0817-z

Lucas, A., Bodger, O., Brosi, B. J., Ford, C. R., Forman, D. W., Greig,
C., . . . Vere, N. d. (2018). Floral resource partitioning by
individuals within generalised hoverfly pollination networks revealed by
DNA metabarcoding. In \emph{Scientific Reports} (pp. 1-11): Springer US.

Lucas, A., Bodger, O., Brosi, B. J., Ford, C. R., Forman, D. W., Greig,
C., . . . de Vere, N. (2018). Generalisation and specialisation in
hoverfly (Syrphidae) grassland pollen transport networks revealed by DNA
metabarcoding. In N. Sanders (Ed.), \emph{Journal of Animal Ecology} .

Lucek, K., Galli, A., Gurten, S., Hohmann, N., Maccagni, A., Patsiou,
T., \& Willi, Y. (2019). Metabarcoding of honey to assess differences in
plant-pollinator interactions between urban and non-urban
sites.\emph{Apidologie, 50} (3), 317-329. doi:10.1007/s13592-019-00646-3

MacGregor, C. J., Kitson, J. J. N., Fox, R., Hahn, C., Lunt, D. H.,
Pocock, M. J. O., \& Evans, D. M. (2019). Construction, validation, and
application of nocturnal pollen transport networks in an agro-ecosystem:
a comparison using light microscopy amd DNA
metabarcoding.\emph{Ecological Entomology, 44} (1), 17-29.
doi:10.1111/een.12674

Mander, L., \& Punyasena, S. W. (2014). On the taxonomic resolution of
pollen and spore records of earth's vegetation. \emph{International
Journal of Plant Sciences, 175} (8), 931-945. doi:10.1086/677680

Marcos, J. V., Nava, R., Cristobal, G., Redondo, R., Escalante-Ramirez,
B., Bueno, G., . . . Rodriguez, T. (2015). Automated pollen
identification using microscopic imaging and texture
analysis.\emph{Micron, 68} , 36-46. doi:10.1016/j.micron.2014.09.002

Mata, V. A., Rebelo, H., Amorim, F., McCracken, G. F., Jarman, S., \&
Beja, P. (2019). How much is enough? Effects of technical and biological
replication on metabarcoding dietary analysis. In \emph{Molecular
Ecology} (Vol. 28, pp. 165-175).

Mathiasson, M. E., \& Rehan, S. M. (2020). Wild bee declines linked to
plant\selectlanguage{english}-pollinator network changes and plant species
introductions.\emph{Insect Conservation and Diversity, 13} (6), 595-605.
doi:10.1111/icad.12429

MATSUKI, Y., ISAGI, Y., \& SUYAMA, Y. (2007). The determination of
multiple microsatellite genotypes and DNA sequences from a single pollen
grain. In \emph{Molecular Ecology Notes} (Vol. 7, pp. 194-198).

Matsuki, Y., Tateno, R., Shibata, M., \& Isagi, Y. (2008). Pollination
efficiencies of flower-visiting insects as determined by direct genetic
analysis of pollen origin. \emph{American Journal of Botany, 95} (8),
925-930. doi:10.3732/ajb.0800036

McFrederick, Q. S., \& Rehan, S. M. (2016). Characterization of pollen
and bacterial community composition in brood provisions of a small
carpenter bee. In \emph{Molecular Ecology} (Vol. 25, pp. 2302-2311).

Milla, E., Bovill, J., Schmidt-Lebuhn, A. N., \& Encinas-Viso, F. (in
press). Monitoring of honey bee floral resources with pollen DNA
metabarcoding as a complementary tool to vegetation
surveys.\emph{Ecological Solutions and Evidence, in press} .

Moorhouse-Gann, R. J., Dunn, J. C., de Vere, N., Goder, M., Cole, N.,
Hipperson, H., \& Symondson, W. O. C. (2018). New universal ITS2 primers
for high-resolution herbivory analyses using DNA metabarcoding in both
tropical and temperate zones. In \emph{Scientific Reports} (Vol. 8, pp.
8542).

Muthreich, F., Zimmermann, B., Birks, H. J. B., Vila\selectlanguage{english}-Vicosa, C. M., \&
Seddon, A. W. R. (2020). Chemical variations in \emph{Quercus} pollen as
a tool for taxonomic identification: Implications for long-term
ecological and biogeographical research. \emph{Journal of Biogeography,
47} (6), 1298-1309. doi:10.1111/jbi.13817

Nakazawa, F., Uetake, J., Suyama, Y., Kaneko, R., Takeuchi, N., Fujita,
K., . . . Kanda, H. (2013). DNA analysis for section identification of
individual \emph{Pinus} pollen grains from Belukha glacier, Altai
Mountains, Russia. \emph{Environmental Research Letters, 8} , 014032.
doi:10.1088/1748-9326/8/1/014032

Nichols, R. V., Vollmers, C., Newsom, L. A., Wang, Y., Heintzman, P. D.,
Leighton, M., . . . Shapiro, B. (2018). Minimizing polymerase biases in
metabarcoding. In \emph{Molecular Ecology Resources} : Wiley/Blackwell
(10.1111).

Niemeyer, B., Epp, L. S., Stoof-Leichsenring, K. R., Pestryakova, L. A.,
\& Herzschuh, U. (2017). A comparison of sedimentary DNA and pollen from
lake sediments in recording vegetation composition at the Siberian
treeline. In \emph{Molecular Ecology Resources} (Vol. 17, pp. e46-e62).

Nurnberger, F., Keller, A., Hartel, S., \& Steffan\selectlanguage{english}-Dewenter, I. (2019).
Honey bee waggle dance communication increases diversity of pollen diets
in intensively managed agricultural landscapes. \emph{Molecular Ecology,
28} (15), 3602-3611. doi:10.1111/mec.15156

O'Donnell, J. L., Kelly, R. P., Lowell, N. C., \& Port, J. A. (2016).
Indexed PCR Primers Induce Template-Specific Bias in Large-Scale DNA
Sequencing Studies. \emph{PLoS one, 11} (3), e0148698.
doi:10.1371/journal.pone.0148698

Olsson, O., Karlsson, M., Persson, A. S., Smith, H. G., Varadarajan, V.,
Yourstone, J., . . . Freckleton, R. (2021). Efficient, automated and
robust pollen analysis using deep learning. \emph{Methods in Ecology and
Evolution, 12} (5), 850-862. doi:10.1111/2041-210x.13575

Otto, C. R. V., Roth, C. L., Carlson, B. L., \& Smart, M. D. (2016).
Land-use change reduces habitat suitability for supporting managed honey
bee colonies in the Northern Great Plains. \emph{Proceedings of the
National Academy of Sciences, 113} (37), 10430-10435.
doi:10.1073/pnas.1603481113

Paffetti, D., Vettori, C., Caramelli, D., Vernesi, C., Lari, M.,
Paganelli, A., . . . Giannini, R. (2007). Unexpected presence of Fagus
orientalis complex in Italy as inferred from 45,000-year-old DNA pollen
samples from Venice lagoon. \emph{BMC Evolutionary Biology, 7} (2), S6.
doi:10.1186/1471-2148-7-S2-S6

Parducci, L., Alsos, I. G., Unneberg, P., Pedersen, M. W., Han, L.,
Lammers, Y., . . . Wohlfarth, B. (2019). Shotgun Environmental DNA,
Pollen, and Macrofossil Analysis of Lateglacial Lake Sediments From
Southern Sweden. In \emph{Frontiers in Ecology and Evolution} (Vol. 7,
pp. 189): Frontiers.

Parducci, L., Bennett, K. D., Ficetola, G. F., Alsos, I. G., Suyama, Y.,
Wood, J. R., \& Pedersen, M. W. (2017). Ancient plant DNA in lake
sediments. In \emph{New Phytologist} (Vol. 214, pp. 924-942).

Parducci, L., Nota, K., \& Wood, J. R. (2019). Reconstructing Past
Vegetation Communities Using Ancient DNA from Lake Sediments. In C.
Lindqvist \& O. P. Rajora (Eds.), \emph{Paleogenomics: Genome-Scale
Analysis of Ancient DNA} (pp. 163-187). Cham: Springer International
Publishing.

Parducci, L., Suyama, Y., Lascoux, M., \& Bennett, K. D. (2005). Ancient
DNA from pollen: a genetic record of population history in Scots pine.
In \emph{Molecular Ecology} (Vol. 14, pp. 2873-2882).

Parsons, K. M., Everett, M., Dahlheim, M., \& Park, L. (2018). Water,
water everywhere: environmental DNA can unlock population structure in
elusive marine species. \emph{Royal Society Open Science, 5} (8),
180537. doi:10.1098/rsos.180537

Pawluczyk, M., Weiss, J., Links, M. G., Egana Aranguren, M., Wilkinson,
M. D., \& Egea-Cortines, M. (2015). Quantitative evaluation of bias in
PCR amplification and next-generation sequencing derived from
metabarcoding samples. \emph{Analytical and Bioanalytical Chemistry,
407} (7), 1841-1848. doi:10.1007/s00216-014-8435-y

Pedersen, M. W., De Sanctis, B., Saremi, N. F., Sikora, M., Puckett, E.
E., Gu, Z., . . . Willerslev, E. (2021). Environmental genomics of Late
Pleistocene black bears and giant short-faced bears. \emph{Current
Biology, 31} (12), 2728-2736.e2728. doi:10.1016/j.cub.2021.04.027

Peel, N., Dicks, L. V., Clark, M. D., Heavens, D., Percival-Alwyn, L.,
Cooper, C., . . . Yu, D. W. (2019). Semi-quantitative characterisation
of mixed pollen samples using MinION sequencing and Reverse Metagenomics
(RevMet). \emph{Methods in Ecology and Evolution, 10} (10), 1690-1701.
doi:10.1111/2041-210X.13265

Pereira, S. G., Guedes, A., Abreu, I., \& Ribeiro, H. (2021). Testing
the Raman parameters of pollen spectra in automatic
identification.\emph{Aerobiologia, 37} (1), 15-28.
doi:10.1007/s10453-020-09669-1

Peters, M. K., Hemp, A., Appelhans, T., Becker, J. N., Behler, C.,
Classen, A., . . . Steffan-Dewenter, I. (2019). Climate-land-use
interactions shape tropical mountain biodiversity and ecosystem
functions. \emph{Nature, 568} (7750), 88-92.
doi:10.1038/s41586-019-1048-z

Petersen, G., Johansen, B., \& Seberg, O. (1996). PCR and sequencing
from a single pollen grain. In \emph{Plant Molecular Biology} (Vol. 31,
pp. 189-191).

Piko, J., Keller, A., Geppert, C., Batary, P., Tscharntke, T., Westphal,
C., \& Hass, A. L. (2021). Effects of three flower field types on
bumblebees and their pollen diets. \emph{Basic and Applied Ecology, 52}
, 95-108. doi:10.1016/j.baae.2021.02.005

Pimm, S. L., \& Joppa, L. N. (2015). How many plant species are there,
where are they, and what rate are they going extinct? \emph{Annals of
the Missouri Botanical Garden, 100} (170-176).

Pinol, J., Senar, M. A., \& Symondson, W. O. C. (2019). The choice of
universal primers and the characteristics of the species mixture
determine when DNA metabarcoding can be quantitative. In \emph{Molecular
Ecology} (Vol. 28, pp. 407-419): Wiley/Blackwell (10.1111).

Polling, M., Li, C., Cao, L., Verbeek, F., de Weger, L. A., Belmonte,
J., . . . Gravendeel, B. (2021). Neural networks for increased accuracy
of allergenic pollen monitoring. \emph{Scientific Reports, 11} (1),
11357. doi:10.1038/s41598-021-90433-x

Polling, M., Sin, M., de Weger, L. A., Speksnijder, A. G. C. L.,
Koenders, M. J. F., de Boer, H., \& Gravendeel, B. (2022). DNA
metabarcoding using nrITS2 provides highly qualitative and quantitative
results for airborne pollen monitoring. \emph{Science of The Total
Environment, 806} , 150468.
doi:https://doi.org/10.1016/j.scitotenv.2021.150468

Pompanon, F., Deagle, B. E., Symondson, W. O., Brown, D. S., Jarman, S.
N., \& Taberlet, P. (2012). Who is eating what: diet assessment using
next generation sequencing. \emph{Molecular Ecology, 21} (8), 1931-1950.
doi:10.1111/j.1365-294X.2011.05403.x

Popic, T. J., Wardle, G. M., \& Davila, Y. C. (2013). Flower-visitor
networks only partially predict the function of pollen transport by
bees. \emph{Austral Ecology, 38} (1), 76-86.
doi:10.1111/j.1442-9993.2012.02377.x

Pornon, A., Andalo, C., Burrus, M., \& Escaravage, N. (2017). DNA
metabarcoding data unveils invisible pollination networks.
In\emph{Scientific Reports} (Vol. 7, pp. 16828).

Pornon, A., Escaravage, N., Burrus, M., Holota, H., Khimoun, A.,
Mariette, J., . . . Andalo, C. (2016). Using metabarcoding to reveal and
quantify plant-pollinator interactions. In \emph{Scientific
Reports}(Vol. 6, pp. 27282).

Portman, Z. M., Tepedino, V. J., Tripodi, A. D., Szalanski, A. L., \&
Durham, S. L. (2018). Local extinction of a rare plant pollinator in
Southern Utah (USA) associated with invasion by Africanized honey
bees.\emph{Biological Invasions, 20} (3), 593-606.
doi:10.1007/s10530-017-1559-1

Potter, C., de Vere, N., Jones, L. E., Ford, C. R., Hegarty, M. J.,
Hodder, K. H., . . . Franklin, E. L. (2019). Pollen metabarcoding
reveals broad and species-specific resource use by urban
bees.\emph{PeerJ, 7} , e5999. doi:10.7717/peerj.5999

Potts, S. G., Biesmeijer, J. C., Kremen, C., Neumann, P., Schweiger, O.,
\& Kunin, W. E. (2010). Global pollinator declines: trends, impacts and
drivers. \emph{Trends Ecol Evol, 25} (6), 345-353.
doi:10.1016/j.tree.2010.01.007

Py\selectlanguage{polish}š\selectlanguage{english}ek, P., Hulme, P. E., Simberloff, D., Bacher, S., Blackburn, T. M.,
Carlton, J. T., . . . Richardson, D. M. (2020). Scientists' warning on
invasive alien species. \emph{Biological Reviews, 95} (6), 1511-1534.
doi:https://doi.org/10.1111/brv.12627

Quaresma, A., Brodschneider, R., Gratzer, K., Gray, A., Keller, A.,
Kilpinen, O., . . . Pinto, M. A. (2021). Preservation methods of honey
bee-collected pollen are not a source of bias in ITS2
metabarcoding.\emph{Environmental Monitoring and Assessment, 193} (12),
785. doi:10.1007/s10661-021-09563-4

Reilly, J. R., Artz, D. R., Biddinger, D., Bobiwash, K., Boyle, N. K.,
Brittain, C., . . . Winfree, R. (2020). Crop production in the USA is
frequently limited by a lack of pollinators. \emph{Proceedings of the
Royal Society B: Biological Sciences, 287} (1931), 20200922.
doi:10.1098/rspb.2020.0922

Revilla, T. A., Encinas-Viso, F., \& Loreau, M. (2015). Robustness of
mutualistic networks under phenological change and habitat
destruction.\emph{Oikos, 124} , 22-32. doi:10.1111/OIK.01532

Richardson, R. T., Curtis, H. R., Matcham, E. G., Hua Lin, C., Suresh,
S., Sponsler, D. B., . . . Johnson, R. M. (2018). Quantitative
multi-locus metabarcoding and waggle dance interpretation reveal honey
bee spring foraging patterns in Midwest agroecosystems. \emph{Molecular
Ecology, 28} (3), 686-697. doi:10.1111/mec.14975

Richardson, R. T., Eaton, T. D., Lin, C. H., Cherry, G., Johnson, R. M.,
\& Sponsler, D. B. (2021). Application of plant metabarcoding to
identify diverse honeybee pollen forage along an urban--agricultural
gradient. In \emph{Molecular Ecology} (Vol. 30, pp. 310-323).

Richardson, R. T., Lin, C.-H., Quijia, J. O., Riusech, N. S., Goodell,
K., \& Johnson, R. M. (2015). Rank-Based Characterization of Pollen
Assemblages Collected by Honey Bees Using a Multi-Locus Metabarcoding
Approach. In \emph{Applications in Plant Sciences} (Vol. 3, pp.
1500043).

Richardson, R. T., Sponsler, D. B., McMinn-Sauder, H., \& Johnson, R. M.
(2020). MetaCurator: A hidden Markov model-based toolkit for extracting
and curating sequences from taxonomically-informative genetic
markers.\emph{Methods in Ecology and Evolution, 11} (1), 181-186.
doi:10.1111/2041-210x.13314

Rognes, T., Flouri, T., Nichols, B., Quince, C., \& Mahe, F. (2016).
VSEARCH: a versatile open source tool for metagenomics. \emph{PeerJ, 4}
, e2584. doi:10.7717/peerj.2584

Rowney, F. M., Brennan, G. L., Skjoth, C. A., Griffith, G. W., McInnes,
R. N., Clewlow, Y., . . . Creer, S. (2021). Environmental DNA reveals
links between abundance and composition of airborne grass pollen and
respiratory health. \emph{Current Biology, 31} (9), 1995-2003 e1994.
doi:10.1016/j.cub.2021.02.019

Samuelson, A. E., Gill, R. J., \& Leadbeater, E. (2020). Urbanisation is
associated with reduced Nosema sp. infection, higher colony strength and
higher richness of foraged pollen in honeybees. \emph{Apidologie, 51}
(5), 746-762. doi:10.1007/s13592-020-00758-1

Schmidt, B. R., K\selectlanguage{ngerman}éry, M., Ursenbacher, S., Hyman, O. J., Collins, J. P.,
\& Yoccoz, N. (2013). Site occupancy models in the analysis of
environmental DNA presence/absence surveys: a case study of an emerging
amphibian pathogen. \emph{Methods in Ecology and Evolution, 4} (7),
646-653. doi:10.1111/2041-210x.12052

Sepulveda, A. J., Nelson, N. M., Jerde, C. L., \& Luikart, G. (2020).
Are Environmental DNA Methods Ready for Aquatic Invasive Species
Management? \emph{Trends Ecol Evol, 35} (8), 668-678.
doi:10.1016/j.tree.2020.03.011

Sevillano, V., Holt, K., \& Aznarte, J. L. (2020). Precise automatic
classification of 46 different pollen types with convolutional neural
networks. \emph{PLoS one, 15} (6), e0229751.
doi:10.1371/journal.pone.0229751

Sickel, W., Ankenbrand, M. J., Grimmer, G., Holzschuh, A., Härtel, S.,
Lanzen, J., . . . Keller, A. (2015). Increased efficiency in identifying
mixed pollen samples by meta-barcoding with a dual-indexing approach.
In\emph{BMC Ecology} (Vol. 15, pp. 20).

Sigsgaard, E. E., Nielsen, I. B., Bach, S. S., Lorenzen, E. D.,
Robinson, D. P., Knudsen, S. W., . . . Thomsen, P. F. (2016). Population
characteristics of a large whale shark aggregation inferred from
seawater environmental DNA. \emph{Nature Ecology \& Evolution, 1} (1),
0004. doi:10.1038/s41559-016-0004

Simanonok, M. P., Otto, C. R. V., Cornman, R. S., Iwanowicz, D. D.,
Strange, J. P., \& Smith, T. A. (2021). A century of pollen foraging by
the endangered rusty patched bumble bee (Bombus affinis): inferences
from molecular sequencing of museum specimens. \emph{Biodiversity and
Conservation, 30} , 123-137. doi:10.1007/s10531-020-02081-8

Smith, C., Weinman, L., Gibbs, J., \& Winfree, R. (2019). Specialist
foragers in forest bee communities are small, social or emerge
early.\emph{Journal of Animal Ecology, 88} (8), 1158-1167.
doi:10.1111/1365-2656.13003

Smith, M. R., Singh, G. M., Mozaffarian, D., \& Myers, S. S. (2015).
Effects of decreases of animal pollinators on human nutrition and global
health: a modelling analysis. \emph{The Lancet, 386} (10007), 1964-1972.
doi:10.1016/s0140-6736(15)61085-6

Song, X.-P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina,
A., Vermote, E. F., \& Townshend, J. R. (2018). Global land change from
1982 to 2016. \emph{Nature, 560} (7720), 639-643.
doi:10.1038/s41586-018-0411-9

Sponsler, D. B., Shump, D., Richardson, R. T., \& Grozinger, C. M.
(2020). Characterizing the floral resources of a North American
metropolis using a honey bee foraging assay. \emph{Ecosphere, 11} (4),
e03102.

Stillman, E. C., \& Flenley, J. R. (1996). The needs and prospects for
automation in palynology. \emph{Quaternary Science Reviews, 15} (1),
1-5. doi:10.1016/0277-3791(95)00076-3

Suanno, C., Aloisi, I., Fernández-González, D., \& Del Duca, S. (2021).
Monitoring techniques for pollen allergy risk
assessment.\emph{Environmental Research, 197} , 111109.
doi:https://doi.org/10.1016/j.envres.2021.111109

Suchan, T., Talavera, G., Saez, L., Ronikier, M., \& Vila, R. (2019).
Pollen metabarcoding as a tool for tracking long-distance insect
migrations. \emph{Molecular Ecology Resources, 19} (1), 149-162.
doi:10.1111/1755-0998.12948

Suyama, Y. (2011). Procedure for Single-Pollen Genotyping. In Y. Isagi
\& Y. Suyama (Eds.), \emph{Single-Pollen Genotyping} (pp. 7-15). Tokyo:
Springer Japan.

Suyama, Y., Kawamuro, K., Kinoshita, I., Yoshimura, K., Tsumura, Y., \&
Takahara, H. (1996). DNA sequence from a fossil pollen of
\textless{}i\textgreater{}Abies\textless{}/i\textgreater{} spp. from
Pleistocene peat. \emph{Genes \& Genetic Systems, 71} (3), 145-149.
doi:10.1266/ggs.71.145

Swenson, S. J., \& Gemeinholzer, B. (2021). Testing the effect of pollen
exine rupture on metabarcoding with Illumina sequencing. In B. Heinze
(Ed.), \emph{PLoS one} (Vol. 16, pp. e0245611).

Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C., \& Willerslev,
E. (2012). Towards next-generation biodiversity assessment using DNA
metabarcoding. In \emph{Molecular Ecology} (Vol. 21, pp. 2045-2050).

Tanaka, K., Nozaki, A., Nakadai, H., Shiwa, Y., \& Shimizu-Kadota, M.
(2020). Using pollen DNA metabarcoding to profile nectar sources of
urban beekeeping in K\selectlanguage{polish}ō\selectlanguage{english}t\selectlanguage{polish}ō-\selectlanguage{english}ku, Tokyo. In.

Threlfall, C. G., Walker, K., Williams, N. S. G., Hahs, A. K., Mata, L.,
Stork, N., \& Livesley, S. J. (2015). The conservation value of urban
green space habitats for Australian native bee
communities.\emph{Biological Conservation, 187} , 240-248.
doi:10.1016/j.biocon.2015.05.003

Tommasi, N., Ferrari, A., Labra, M., Galimberti, A., \& Biella, P.
(2021). Harnessing the Power of Metabarcoding in the Ecological
Interpretation of Plant-Pollinator DNA Data: Strategies and Consequences
of Filtering Approaches. \emph{Diversity, 13} (9), 437.

Tremblay, \selectlanguage{ngerman}É. D., Duceppe, M.-O., Thurston, G. B., Gagnon, M.-C., Côté,
M.-J., \& Bilodeau, G. J. (2019). High-resolution biomonitoring of plant
pathogens and plant species using metabarcoding of pollen pellet
contents collected from a honey bee hive. In \emph{Environmental DNA} :
John Wiley \& Sons, Ltd.

Trinkl, M., Kaluza, B. F., Wallace, H., Heard, T. A., Keller, A., \&
Leonhardt, S. D. (2020). Floral Species Richness Correlates with Changes
in the Nutritional Quality of Larval Diets in a Stingless
Bee.\emph{Insects, 11} (2). doi:10.3390/insects11020125

Tur, C., Vigalondo, B., Trojelsgaard, K., Olesen, J. M., \& Traveset, A.
(2014). Downscaling pollen-transport networks to the level of
individuals. \emph{Journal of Animal Ecology, 83} (1), 306-317.
doi:10.1111/1365-2656.12130

Turo, K. J., \& Gardiner, M. M. (2019). From potential to practical:
conserving bees in urban public green spaces. \emph{Frontiers in Ecology
and the Environment, 17} (3), 167-175. doi:10.1002/fee.2015

Uetake, J., Tobo, Y., Kobayashi, S., Tanaka, K., Watanabe, S., DeMott,
P. J., \& Kreidenweis, S. M. (2021). Visualization of the seasonal shift
of a variety of airborne pollens in western Tokyo. In \emph{Science of
The Total Environment} (Vol. 788, pp. 147623).

Vannette, R. L. (2020). The Floral Microbiome: Plant, Pollinator, and
Microbial Perspectives. \emph{Annual Review of Ecology, Evolution, and
Systematics, 51} (1), 363-386. doi:10.1146/annurev-ecolsys-011720-013401

Vaudo, A. D., Biddinger, D. J., Sickel, W., Keller, A., \& López-Uribe,
M. M. (2020). Introduced bees (\emph{Osmia cornifrons} ) collect pollen
from both coevolved and novel host-plant species within their
family-level phylogenetic preferences. \emph{Royal Society Open Science,
7} , 200225. doi:10.1098/rsos.200225

10.6084/m9.figshare.c

Voulgari-Kokota, A., Grimmer, G., Steffan-Dewenter, I., \& Keller, A.
(2018). Bacterial community structure and succession in nests of two
megachilid bee genera. \emph{FEMS Microbiology Ecology, 95} (1).
doi:10.1093/femsec/fiy218

Voulgari-Kokota, A., Steffan-Dewenter, I., \& Keller, A. (2020).
Susceptibility of Red Mason Bee Larvae to Bacterial Threats Due to
Microbiome Exchange with Imported Pollen Provisions. \emph{Insects, 11}
(6), 373. doi:10.3390/insects11060373

Vuong, H. Q., \& McFrederick, Q. S. (2019). Comparative Genomics of Wild
Bee and Flower Isolated Lactobacillus Reveals Potential Adaptation to
the Bee Host. \emph{Genome Biology and Evolution, 11} (8), 2151-2161.
doi:10.1093/gbe/evz136

Wagemaker, C. A. M., Mommer, L., Visser, E. J. W., Weigelt, A., van
Gurp, T. P., Postuma, M., . . . de Kroon, H. (2021). msGBS: A new
high-throughput approach to quantify the relative species abundance in
root samples of multispecies plant communities. \emph{Mol Ecol Resour,
21} (4), 1021-1036. doi:10.1111/1755-0998.13278

Wenzel, A., Grass, I., Belavadi, V. V., \& Tscharntke, T. (2020). How
urbanization is driving pollinator diversity and pollination - A
systematic review. \emph{Biological Conservation, 241} , 108321.
doi:10.1016/j.biocon.2019.108321

Wilmshurst, J. M., Moar, N. T., Wood, J. R., Bellingham, P. J.,
Findlater, A. M., Robinson, J. J., \& Stone, C. (2014). Use of pollen
and ancient DNA as conservation baselines for offshore islands in New
Zealand. \emph{Conservation Biology, 28} (1), 202-212.
doi:10.1111/cobi.12150

Wilson, R. S., Keller, A., Shapcott, A., Leonhardt, S. D., Sickel, W.,
Hardwick, J. L., . . . Wallace, H. M. (2021). Many small rather than few
large sources identified in long-term bee pollen diets in
agroecosystems. In \emph{Agriculture, Ecosystems \& Environment} (Vol.
310, pp. 107296).

Yilmaz, P., Kottmann, R., Field, D., Knight, R., Cole, J. R.,
Amaral-Zettler, L., . . . Glockner, F. O. (2011). Minimum information
about a marker gene sequence (MIMARKS) and minimum information about any
(x) sequence (MIxS) specifications. \emph{Nature Biotechnology, 29} (5),
415-420. doi:10.1038/nbt.1823

Zemenick, A. T., Vannette, R. L., \& Rosenheim, J. A. (2021). Linked
networks reveal dual roles of insect dispersal and species sorting for
bacterial communities in flowers. \emph{Oikos, 130} , 697-707.
doi:10.1101/847376

Zhang, J., Kobert, K., Flouri, T., \& Stamatakis, A. (2014). PEAR: a
fast and accurate Illumina Paired-End reAd mergeR. \emph{Bioinformatics,
30} (5), 614-620. doi:10.1093/bioinformatics/btt593

Zhao, Y. H., Lázaro, A., Ren, Z. X., Zhou, W., Li, H. D., Tao, Z. B., .
. . Wang, H. (2018). The topological differences between visitation and
pollen transport networks: a comparison in species rich communities of
the Himalaya--Hengduan Mountains. \emph{Oikos, 128} (4), 551-562.
doi:10.1111/oik.05262

Zimmermann, B. (2018). Chemical characterization and identification of
Pinaceae pollen by infrared microspectroscopy. \emph{Planta, 247} (1),
171-180. doi:10.1007/s00425-017-2774-9

Data Accessibility and Benefit Sharing

The data that supports the findings of this study are available in the
supplementary material of this article. No biological samples were
collected or analyzed in this article. \emph{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. (A) Studies using pollen DNA metabarcoding or related methods
to demonstrate proof-of-concept or applied questions in global change
ecology (GCE) (n=80). (B) Studies characterized by pollen source (n=80).
Bee nests included pollen collected from honeybee hives with a pollen
trap (n=20) and pollen provisions taken from solitary cavity nesting
bees (n=15). Insects were predominately net or hand collected foraging
bees (n=16) but some flies (Syrphidae) (n=7) and moths (n= 3) were also
focal subjects. Pollen sourced from bats (n=1), soil (n=1), a glacial
core (n=1), and a commercially sourced pollen diet supplement (n=1) were
categorized as Other.\emph{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: Graphical depiction of the workflow of a study involving
DNA-based pollen identification. Prior to starting the study, decisions
need to be made on: (1) the method to obtain pollen from the
environment, e.g., from (a) a pollinator's body, (b) nest, or (c) the
air; (2) the gene regions and methods to create the reference database;
(3) the appropriate method for the isolation and sequencing of DNA; and
(4) the bioinformatics methods to be used. These decisions will depend
on the questions, the study system, the availability of existing
reference sequences, and the funding.

\selectlanguage{english}
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