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\begin{document}

\title{Six years of wild bee monitoring shows dramatic seasonal shifts in
biodiversity and species declines}



\author[1]{Nash Turley}%
\author[2]{Neelendra Joshi}%
\author[3]{David Biddinger}%
\author[1]{Margarita López-Uribe}%
\affil[1]{The Pennsylvania State University Department of Entomology}%
\affil[2]{University of Arkansas Department of Entomology}%
\affil[3]{The Pennsylvania State University}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Wild bees form diverse communities that pollinate plants in both native
and agricultural ecosystems making them both ecologically and
economically important. The growing evidence of bee declines has sparked
increased interest in monitoring bee community and population dynamics
using standardized methods. Here, we studied the dynamics of bee
biodiversity within and across years by monitoring wild bees adjacent to
four apple orchard locations in Southern Pennsylvania, USA. We collected
bees using passive Blue Vane traps continuously from April to October
for six years amassing over 26,000 bees representing 144 species. We
quantified total abundance, richness, diversity, composition, and
phylogenetic structure. There were large seasonal changes in all
measures of biodiversity with month explaining an average of 72\% of the
variation in our models. Changes over time were less dramatic with years
explaining an average of 44\% of the variation in biodiversity metrics.
We found declines in all measures of biodiversity especially in the last
3 years. Analyses of population trends over time for the 40 most
abundant species indicate that about one third of species showed at
least some evidence for declines in abundance. Bee family explained
variation in species-level seasonal patterns but we found no consistent
family-level patterns in declines, though bumble bees and sweat bees
were groups that declined the most. Overall, our results show that
season-wide standardized sampling across multiple years can reveal
nuanced patterns in bee biodiversity, phenological patterns of bees, and
population trends over time of many co-occurring species. These datasets
could be used to quantify the relative effects that different aspects of
environmental change have on bee communities and to help prioritize
conservation efforts.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

Pollinators facilitate reproduction for over 80\% of flowering plants
(Ollerton et al. 2011) and increase the yield, to varying extent, of
75\% of crop species (Klein et al., 2007). Bees are the single most
important group of pollinators (Neff and Simpson, 1993) thus detecting
changes in bee biodiversity is important for developing pollinator
management plans to sustain wild plant communities while maximizing crop
yields (Garibaldi et al., 2013, Winfree et al., 2018). A variety of bee
monitoring efforts have found troubling declines among wild bees (Koh et
al., 2016). For example, some species have had substantial range
contractions and declines in abundance, especially bumble bees in North
America and Europe (Beiseijer et al., 2006, Williams and Osborne. 2009,
Cameron et al, 2011, Bartomeus et al., 2013). Overall wild bee abundance
is falling in over 23\% of the United States land area (Koh et al.,
2016). And the number of bee species observed around the world in museum
collections and from community science observations has dropped by 25\%
from 1990 to 2015 (Zattara and Aizen, 2021). Because of their importance
and growing evidence of declines, bee monitoring efforts that build a
better understanding of the dynamics of bee biodiversity are important
for developing plans that can lead to conserving and restoring wild bee
populations (Winfree, 2010, LeBuhn et al., 2013, Woodard et al., 2020).

Bee biodiversity can be measured in a variety of ways, all of which can
give unique insights into the dynamic of populations and communities
within and across years. The simplest component of biodiversity is the
total abundance (e.g. total number of bees) and the abundance of
individual species measured over a given period. Total abundance can
provide information on the times within years that are most favorable
for most species. Data on the abundance of individual species across
years is critical for understanding if species population trends are
stable, increasing, or declining over time. For bees, these types of
abundance data are often not available because of a lack of repeated and
standardized sampling over time (Portman et al., 2020). Richness, or the
number of species, is another metric of interest in biodiversity
studies, particularly from a restoration and conservation perspective.
However, richness can sometimes provide limited unique information
because the detection of species is highly dependent on sample sizes as
more individuals counted tend to lead to higher species detection.
Diversity metrics help solve these limitations by summarizing aspects of
richness and relative abundance among species (evenness) in a single
estimator. For example, measures like inverse Simpson's and rarefied
richness represent the effective number of species and provide
biodiversity measures that are independent of abundance-driven changes
in richness (Jost, 2006). Diversity can also be measured in a way that
incorporates information about the evolutionary distance that is present
among all species in a community using tools from the field of community
phylogenetics (Webb et al., 2002). A community with many closely related
species is more clustered, while a community populated with distantly
related species is more even (also called overdispersed). Finally,
measures of community composition are among the most information-rich
measures of biodiversity because they can incorporate the relative
abundances of all species to determine how similar communities are. The
multivariate nature of these measures means it is possible to detect
changes among communities even if overall richness, abundance, and
diversity are the same. For this reason, community composition measures
can be particularly powerful in detecting changes over time or responses
of communities to environmental degradation or restoration (Nerlekar and
Veldman, 2020).

Adult bee communities are highly dynamic within years making
standardized and season-long sampling necessary to accurately
characterize seasonal variations in biodiversity (Leong et al, 2016). In
temperate climates, bees overwinter as larvae, pupae, or adults and then
emerge in spring or summer in response to a variety of environmental
cues (Cane, 2021). But the time of the year in which bees are active
(seasonality) and the duration of their period of activity (phenological
breadth) vary greatly among species, resulting in ever-shifting
communities within each year (Ogilvie and Forrest, 2017, Kammerer et
al., 2021). Some studies have investigated changes in bee community
composition using continuous standardized sampling across the entire
period of adult bee activity (e.g., Heithaus, 1979, Wilson et al., 2008,
Joshi et al., 2015, Kammerer et al., 2016, Leong et al., 2016, Naeve et
al., 2020, Stemkovski et al. 2020, Roubik et al., 2021) However, for
many bee communities, we still lack a detailed understanding of how
community biodiversity and composition change from month to month.
Relatively fewer studies have compared the phenological patterns, both
seasonality, and phenological breadth, for many co-occurring species
(but see: Stemkovski et al. 2020). One reason for this is that the focus
of many bee community studies are in agricultural settings where the
blooming period of crops is only over a small period of time (e.g. Russo
et al., 2015, Grab et al., 2019, Graham et al., 2021).

Bee abundance and richness can also change greatly across years (Ogilvie
et al., 2017, Graham et al., 2021). One method for studying changes in
bee species over time is to compare historical records with more recent
collections (Cameron et al, 2011, Bartomeus et al., 2013, Burkle et al.,
2013, Mathiasson et al., 2019, Wood et al., 2019). This approach is
typically only able to test for changes in a small number of species
that are popular among collectors (like bumble bees) and is most
informative to detect changes in species geographic distribution
(Cameron et al, 2011, Mathiasson et al., 2019, Wood et al., 2019). The
lack of standardized collection methods between historical and current
records means it is difficult to separate if the changes in abundance
are the result of changes in population size or changes in sampling
efforts (Portman et al., 2020). An alternative approach is to conduct
standardized sampling, often using passive traps, in the same locations
across multiple years (Iserbyt and Rasmont, 2012, Martins et al. 2013,
Gezon et al., 2015, Onuferko et al., 2018, Graham et al., 2021). These
types of long-term longitudinal studies of bee communities are rare but
the existing ones have reported a mix of species that are stable,
increasing, and decreasing over time (e.g. Graham et al., 2021, Roubik
et al., 2021). These sampling approaches can give more explicit insight
into the changes in relative abundance in a wide variety of species.
Jointly, these studies have increased the need and interest in formal
monitoring of bee biodiversity to assess if there are declines and
potential links to decreasing pollination services (Woodard et al.,
2020).

In this study, we conducted standardized bee collections across 6 years
in Southern Pennsylvania, USA to characterize changes in bee community
biodiversity and changes in abundance of specific species. Specifically,
we quantified abundance, richness, diversity, phylogenetic structure,
and composition of bee communities between months and years. We
collected bees continuously from April through October using passive
Blue Vane traps. With this, we asked the following specific research
questions:

\begin{enumerate}
\tightlist
\item
  How does bee biodiversity change within-years?
\item
  How does bee biodiversity change across years?
\item
  How do seasonal patterns differ among bee families and species?
\item
  Do bee families and species differ in their changes in abundance over
  time?
\end{enumerate}

\section*{Methods}

{\label{methods}}

\subsection*{Study site}

{\label{study-site}}

Our study sites were located in-and-around the Pennsylvania State Fruit
Research and Extension Center in Adams County, Pennsylvania, USA
(39.935226, -77.254530). The area has an average yearly rainfall of 112
cm, average summer temperature ranging from 16 \selectlanguage{ngerman}°C to 28 °C, and average
winter temperatures of -5 °C to 5 °C (Biddinger et al., 2018). This
landscape is hilly with well-drained soils and the broader area is
approximately 56\% broadleaf forest fragments, 25\% pastureland, 9\%
developed areas, and 8\% commercial orchards (Biddinger et al., 2018).
All orchards were managed under growers' choice conventional pest
management programs that use pesticide classes including insect growth
regulators, anthranilic diamide, tetramic acid, microbials, and
neonicotinoid insecticides (Biddinger et al., 2018). We sampled bees at
8 locations adjacent to 4 different active apple orchards. Sampling
locations were within 150 m of orchards and 250 m of a forest fragment
(Figure 1) which have diverse plant and pollinator communities (Kammerer
et al., 2016). Often orchards rely, in part, on managed honey bee
colonies for pollination, which have the potential to negatively impact
native bee populations (Mallinger et al., 2017). However, our sampling
sites did not have managed honey bee hives within 2 km and growers
managing the adjacent orchards had not rented honey bees for at least 15
years.

Our bee monitoring traps were located within perennial wildflower strips
approximately 50 m x 10 m in size that were sown between 2-3 years
before the beginning of our study. Wildflower sites used in this study
were established and managed using the specific planting guidelines
developed by the Pennsylvania USDA-NRCS and the Xerces Society for
Invertebrate Conservation (NRCS, 2011). They were sown with 21 species
of native forbs and grasses sourced from a local native seed supplier
(Ernst Conservation Seed, Meadville, PA 16335). All wildflower sites
were mowed once a year and received spot sprays of common selective
herbicides as needed.

\subsection*{Bee collections}

{\label{bee-collections}}

We trapped bees continuously from April to October from 2014 to 2019
using Blue Vane traps (BanfieldBio Inc., Woodinville WA). A previous
study in this region showed that Blue Vane traps collect a higher
abundance and total richness of bees than colored bowl traps, also
called pan traps (Joshi et al., 2015). Although the overall community
composition of bees captured in Blue Vane traps was different from bowl
traps, nearly all species were more likely to be captured in Blue Vane
traps over bowls, except some \emph{Andrena} and
\emph{Lasioglossum}species (Joshi et al., 2015). In our study, Blue Vane
traps were filled with about 7 cm of 60\% ethylene glycol (Supertech®
Wal-Mart Stores, Inc., Bentonville, AR), hung from posts about 1.5 m off
the ground. At each of our 8 locations, we placed 2 traps 25 m apart
(Figure 1). Traps were left outside continuously from April to October
every year and traps were replaced each year in case wear over time
decreases their attractiveness. Each week, all specimens were removed
and the ethylene glycol was replaced. Bee specimens were separated from
other insects collected in the traps and stored in 70\% alcohol until
they were washed, pinned, and labeled. All bees were identified to the
species level except 14 individuals were removed because of uncertain
species-level identification. For bee identification, we used published
dichotomous keys (Mitchell, 1960, 1962; Michener et al., 1994, Michener,
2000) and various interactive bee identification guides available at
Discover Life (http://www.discoverlife.org). Species identifications
were conducted by David Biddinger (Pennsylvania State University),
Robert Jean (Senior Entomologist, Environmental Solutions and
Innovations, Inc.), Jason Gibbs (University of Manitoba), and Sam Droege
(United States Geological Survey). All specimens from this study are
stored at the Pennsylvania State Fruit Research and Extension Center,
Biglerville, PA, or the Frost Insect Museum at Pennsylvania State
University, University Park, PA.

\subsection*{Data and statistical
analyses}

{\label{data-and-statistical-analyses}}

All analyses were conducted with R version 4.0.3. To explore how
thorough our sampling of bee biodiversity was we created a species
accumulation curve based on the number of individuals sampled. We did
this using the `specaccum' function in the vegan package (Oksanen et
al., 2013).

We calculated biodiversity metrics for each of our 8 sampling locations
using the combined data from both traps at each site. For month analyses
(within years) we calculated biodiversity metrics for each month and
then averaged across all years. We then averaged together sites closer
than 900 m resulting in 4 replicates. Therefore, the interpretation of
each replicate is the average biodiversity value per site for a single
month. For year analyses (across years), we first summed data across all
months and then calculated metrics for each year and then averaged to
get 4 replicates per year. The interpretation of these data is then the
total biodiversity value per site for a single year.

Using community abundance data, we measured total abundance, richness,
and diversity (inverse Simpson's) within and across years using the
`specnumber' and `diversity' functions in the vegan package (Oksanen et
al., 2013). We also used community abundance data to measure differences
in community composition using a Bray-Curtis dissimilarity matrix. To
measure phylogenetic structure, we use a genus-level molecular phylogeny
from Hedtke et al. (2013). We made the phylogeny ultrametric with the
`force.ultrametric' function in the phytools package (Revel, 2012) using
the non-Negative Least-squares method. We then amended our species below
the genus-level using the `genus.to.species.tree' function which creates
bifurcating subtrees among species in each genus and then binds them at
a random place along the terminal edge. With this approach, the
relationships among species below the genus level are created at random.
We only had to drop 2 species from these analyses because their genera
(\emph{Cemolobus, Triepeolus} ) were not in the tree, which only had 7
and 2 individuals in the dataset, respectively.

Using the community data and this phylogeny, we calculated mean pairwise
distance (MPD) among all taxa. This metric is a measure of the average
evolutionary distance between all pairs of species in a community and
was calculated with the `mpd.query' function in the PhyloMeasures
package (Tsirogiannis and Sandel, 2016). Because raw values of MPD can
be impacted by species richness of samples, we used a standardized
effect size of MPD that is based on the difference between the observed
measure of MPD and a random null model of a community with the same
number of species (Tsirogiannis and Sandel, 2016). This value is also
standardized by variance making the metric an effect size in standard
deviation units. Negative values of this metric indicate a community is
more clustered (less average evolutionary distance among pairs of
species) than a random community with the same richness, and positive
values indicate a more even community, also known as overdispersed. The
resolution of phylogenies below the species level has little-to-no
impact on MPD calculations. Qian and Jin (2021) found that MPD values
are nearly identical when calculated with a genus-level phylogeny with
species amended (like ours) compared to a fully resolved phylogenetic
tree. And in our case, repeating the process of randomly adding species
below the genus level resulted in nearly perfectly correlated measures
of MPD (r = 0.99). However, trees without species-level resolution do
not provide reliable estimates of phylogenetic signals of species'
traits (Davies and Kraft, 2012), so we did not include those analyses in
the current study.

We modeled the changes in bee biodiversity within and across years by
fitting general additive models using the `gam' function in the mgcv
package (Wood, 2017). These allowed for non-linear fits to the data. For
all tests, we used 5 knots which allowed for sufficient curviness to
represent observed patterns and to produce linear relationships between
observed and fitted values. The amount of variation explained by these
models was typically similar to ANOVA models fit to the same data. We
report percent change effect sizes among extreme values of months and
years as the difference in means between a pair of values (e.g. the mean
from April minus the mean for August) then divided that difference by
the overall mean for that variable. This gives a standardized effect
size that can be compared among variables. We used perMANOVA to test
differences in composition among groups using the `escalc' function and
visualized results with non-metric multidimensional scaling fit with the
`metaMDS' function, both from the vegan package (Oksanen et al., 2013).

To assess how phenological patterns differ among bee families and
species, we calculated a metric for seasonality (time of year when the
species showed the highest abundance) and phenological breadth (a
measure of the amount of time bees are active as adults). Phenology
measures were calculated for species with 30 or more individuals (a
total of 40 species) as this is enough to reliably estimate phenological
breadth (Bartomeus et al., 2013). We measured seasonality as the median
day of year (Julian date) of capture across individuals collected for a
species. Our measure of breadth was the difference between the 10th and
90th percentile of capture dates. To control for the different numbers
of individuals across species, we randomly drew 30 data points for each
species repeated 500 times. For each of these subsamples, we calculated
the seasonality and breadth statistics and then averaged those 500
values to get our final statistics (Bartomeus et al., 2013).

To evaluate how bee families and species differ in their changes in
abundance over time, we calculated a metric of change over time for each
species as the slope of the linear relationship between abundance and
year. We standardized abundance data for each species to have a mean of
0 and a standard deviation of 1 to allow comparisons across species. We
multiplied the model coefficients by five (the change in years in our
sampling) to get a predicted level of change in standard deviation units
over the course of the study.

\section*{Results}

{\label{results}}

\subsection*{Description of
biodiversity}

{\label{description-of-biodiversity}}

Our final dataset included 26,716 individual bees representing 5 bee
families, 30 genera, and 144 species. See {[}CITATION FOR DRYAD
SUBMISSION{]} for the complete dataset and list of species. We collected
33\% of the total number of bee species that have been found in
Pennsylvania (Kilpatrick et al., 2020). The species accumulation curve
for these data shows a leveling off pattern but did not reach an
asymptote (Figure 2A). The abundances among species show a typical rank
abundance curve with a small number of very abundant species and many
rare species (Figure 2B). Ten species had over 1,000 individuals while
over half of the species had 5 or fewer individuals. We captured by far
the most individuals and species from the family Apidae (19,870
individuals and 47 species) followed by Halictidae (5,942 and 33),
Andrenidae (477 and 28), Megachilidae (383 and 30), and Colletidae (44
and 6).

\subsection*{Biodiversity changes within
years}

{\label{biodiversity-changes-within-years}}

We found very strong evidence for seasonal changes in all measures of
biodiversity and on average month explained an average of 74\% of the
variation in our models (Figure 3, Table 1). Abundance and richness
showed a hump-shaped pattern peaking in July. In our models, month
explained nearly 90\% of variation in abundance and richness. The number
of bees captured per site increased by 193\% between April and August,
which were the lowest and highest values we observed (Figure 3A).
Similarly, average richness increased by 89\% between April and July
(Figure 3B). Diversity also increased and decreased across the seasons,
but less sharply than in richness, and peaked in August instead of July
(Figure 3C). Diversity increased by 41\% between May and August and
decreased by 60\% from August to October (Figure 3C). Because diversity
incorporates both richness and evenness, the weaker pattern in diversity
compared to richness is a consequence of evenness having a pattern
nearly opposite that seen in richness (P \textless{} 0.0001, R2 = 0.68),
highest in spring and fall and lowest in July.

Phylogenetic structure also varied between months. Mean pairwise
distance dropped (becoming more clustered) by 1.9 standard deviations
between May and July and then increased (becoming more even) by 1.1
standard deviations between July and October. The months of April, June,
August, and September had intermediate values (Figure 3D). We observed
limited variation in phylogenetic structure between sites resulting in
our model explaining 87\% of the total variation (Table 1). Community
composition varied substantially among months with our multivariate
model explaining 64\% of the variation in bee communities (Figure 4A,
Table 1). Spring months (April-June) all had distinct bee communities.
July through September had similar compositions which were themselves
distinct from spring months and October (Figure 4A).

\subsection*{Biodiversity change across
years}

{\label{biodiversity-change-across-years}}

We found very strong evidence for biodiversity change over time between
2014 and 2019 with year explaining an average of 42\% of variation
across all biodiversity metrics (Figure 3, Table 1). Abundance of bees
captured declined by 48\% between 2014 and 2019 (Figure 3E). Richness
declined by 41\% between the peak in 2016 and the lowest point in 2019
(Figure 3F). Similar to richness, diversity also declined after 2016,
dropping by 59\% between 2016 and 2019 (Figure 3G), though the model for
diversity explained about half as much variation as the model for
richness (Table 1).

Phylogenetic structure increased and decreased over time with the most
clustered communities in 2014 and 2019, and the most even communities in
2016 (Figure 3H). Mean pairwise distance increased (became more even) by
1.8 standard deviations between 2014 and 2016, and then decreased
(becoming more clustered) by 1.6 standard deviations between 2016 and
2019. Bee communities were quite stable across the first three years
though they shifted slightly over time in the last three years (Figure
4B). Year explained about 36\% of the variation in community
composition.

\subsection*{Species patterns in seasonality, phenological breadth, and
change over
time}

{\label{species-patterns-in-seasonality-phenological-breadth-and-change-over-time}}

Looking across the 40 species for which we collected 30 or more
individuals (Table A1), bee families varied significantly in seasonality
(Figure 5, Figure 6, F\textsubscript{3,36} = 10.91, P \textless{}
0.001). Species in the families Megachilidae and Andrenidae were
collected an average of 54 days earlier than species in the families
Apidae and Halictidae (Tukey tests, P \textless{} 0.006). In the family
Megachilidae, \emph{Osmia} species and \emph{Hoplitis pilosifrons} were
among the earliest emerging species, but \emph{Megachile mendica} was
most active in July and August (Figure 5). In the Andrenidae,
all\emph{Andrena} species are most active in April and May,
but\emph{Calliopsis andreniformis} was most abundant in July. Among
species in the family Apidae, \emph{Eucera hamata} was the only species
with peak abundance in May. Species in the genera \emph{Anthophora,
Ceratina,} and \emph{Bombus} were most active in June, though there is
some variation among species within those genera. Other species in the
family Apidae, including \emph{Ptilothrix} , \emph{Melitoma,}
and\emph{Melissodes,} as well as \emph{Eucera} (\emph{Peponapis}
)\emph{pruinosa,} peaked in July and August (Figure 5). Most species in
the family Halictidae were most abundant in July and August, though
two\emph{Agapostemon} species were active earlier (Figure 5, Figure 6).

Phenological breadth varied among families (Figure 5, Figure 6,
F\textsubscript{3,36} = 3.896 , P = 0.02) with two families that were
significantly different. Species in the family Andrenidae had the
narrowest breadth at 35 days on average, and species in the Halictidae
were active the longest at 89 days on average (Tukey test, P = 0.02).
The families Megachilidae (49 days) and Apidae (66 days) showed
intermediate breadth. In the Andrenidae, all \emph{Andrena} species had
a breadth of less than 42 days, but \emph{Calliopsis andreniformis} had
a breadth of 79 days. In the Megachilidae, \emph{Osmia} species'
breadths range from 21 to 58 days while \emph{Megachile mendica} had a
breadth of 71 days. Species in the Halictidae consistently had a wide
phenological breadth, greater than 75 days, though \emph{Halictus
ligatus} had a relatively narrow window of 55 days. We split species in
the Apidae into two groups, each consisting of related clades, that
varied significantly in phenological breadth (t-test, t = -3.7, df =
18.3, P = 0.002). Species in the genera \emph{Bombus, Apis,
Ceratina,}and \emph{Xylocopa} had an average breadth of 85 days, while
species in the genera \emph{Eucera, Mellisodes, Ptilothrix, Melitoma,}
and\emph{Anthophora} had an average breadth of 44 days (Figure 5, Figure
6).

We observed substantial species-level variation in the changes in
abundance across years (Figure 5, Table A1). We detected no little-to no
change in abundance for 26 species (P\textgreater{}0.1), 8 species
showed moderate to strong evidence for decline (P\textless{}0.05), 5
more showed weak evidence for decline (P\textless{}0.1), and we found
strong evidence for increase in 1 species (P\textless{}0.01). While bee
family was not a significant predictor of changes in abundance
(F\textsubscript{3,36} = 1.813, P = 0.16), we observed some patterns
among families and genera. Species in the families Megachilidae and
Andrenidae were all stable. Other families showed mixed trends. For
example, among species in the family Apidae, all carpenter bee species
in the genera \emph{Xylocopa} and \emph{Ceratina} were stable except
for\emph{Ceratina mikmaqi} which declined by 1 standard deviation unit
(SD) between 2014 and 2019. Six out of eight species in the Halictidae
showed evidence for declines, most species by about 1 SD, but
\emph{Agapostemon virescens} declined by 1.7 SD. Four species in the
genus \emph{Bombus}showed pronounced declines ranging between 1.2 and
1.8 SD while two\emph{Bombus} species were stable. Similarly,
\emph{Melitoma taurea}declined by 1.3 SD. We saw radical variation among
species in tribe Eucerini (Apidae): some species stable over time
(\emph{Eucera} and some\emph{Melissodes} ), \emph{Melissodes desponsa}
had the biggest decline we observed (1.8 SD), and \emph{Melissodes
bimaculatus} increased by 2.2 SD, which was the only significant
increase and also the largest magnitude of change we found.

\section*{Discussion}

{\label{discussion}}

With 6 years of continuous sampling with passive traps we collected over
26,000 bees and 144 species. The leveling off of the species
accumulation curve suggests we captured most, but not all, of bee
biodiversity in the system (Figure 2A). The inability to fully document
biodiversity (i.e., reach an asymptote in the species accumulation
curve) is typical for other extensive bee monitoring efforts (Wilson et
al., 2008, Russo et al., 2015) and species-rich invertebrate communities
more generally (Gotelli and Colwell, 2001).

We found that all measures of community biodiversity varied dramatically
within years and across years (Figure 3, Figure 4). Abundance, richness,
and diversity all peaked in summer, though diversity to a lesser extent.
By contrast, phylogenetic structure was most even in spring (May) and
became most clustered in summer. Community composition also varied
greatly within years with communities changing quickly early then
becoming much more stable between July and September (Figure 4). This
high-level of variation in species-level phenological patterns (Figure
6) resulted in the dramatic turnover of communities between seasons.

All measures of biodiversity also changed across years (Figure 3, Figure
4). The magnitude of changes within years was about twice as large for
abundance, richness, and community composition than it was for changes
across years. However, for diversity and phylogenetic structure, the
changes within and across years were of similar magnitude. After 2016,
all measures of biodiversity declined. Community composition also
shifted in that time, but not dramatically (Figure 4). These changes in
diversity metrics and composition were the result of, in part, 13
species that declined in abundance over time, which were dispersed
across the bee evolutionary tree (Figure 5).

\subsection*{Insights from species-level changes in
abundance}

{\label{insights-from-species-level-changes-in-abundance}}

Species across the bee evolutionary tree showed a wide variety of
phenological patterns (changes in abundance within years). Among the 40
species for which we had sufficient data for, we observed three general
patterns, which could be called ``phenological syndromes'' illustrated
in Figure 6. First, \emph{Andrena} and \emph{Osmia} species emerged
early in the year and had narrow breadth. Second, species in the tribe
Eucerini, (\emph{Melissodes} and \emph{Eucera} ), and other sister
clades had narrow breadth but, in the summer, rather than in spring. The
third group was composed of species with wide phenological breadth
including the social and multivoltine species in the genera
\emph{Bombus, Apis} ,\emph{Xylocopa} and \emph{Ceratina} , and nearly
all the sweat bees (family Halictidae). Monitoring of species that
represent these different phenological syndromes is important because
they provide unique ecological functions (Ogilvie and Forrest, 2017).
For example, many of the early emerging bee species are of critical
importance for early flowering plants such as spring ephemeral
wildflowers, and these interactions may be particularly sensitive to
disruptions from climate change (Kudo and Ida, 2013). And many crops
such as apples and blueberries rely on pollination by early emerging
wild bees (Isaacs and Kirk, 2010, Biddinger et al., 2018, Grab et al.,
2019, Reilly et al., 2020).

We found that 33\% of species had at least some evidence of declines
while only 3\% increased, and 65\% percent showed no changes over time.
For comparison, a study using museum records of 187 bee species in
eastern North America found significant decreases in the relative number
of samples in collections for 29\% of species and increases for 27\% of
species (Bartomeus et al., 2013). Similarly, 38\% of non-parasitic
bumble-bee species in the UK show clear signs of decline (Williams and
Osborne, 2009). While it is possible that we could have had more power
to detect changes in rare species with more thorough sampling, we found
significant changes among species with a wide variety of abundance (min
= 37, max = 3774, mean = 894) and there was no correlation between
species' total abundance and amount of predicted change (r = 0.18, P =
0.26). Therefore, our finding of 65\% of species being stable is robust
and comparable to studies in North America and Europe. We did not find
that bee family was a significant predictor of which bees are stable or
declining. But, there were some clades that were more prone to declines
than others, notably bumble bees (\emph{Bombus} ) and sweat bees
(Halictidae). In another case, 2 closely related longhorn bees
(genus\emph{Melissodes} ) showed large changes in abundance in opposite
directions. More generally, this suggests that phylogenetic
relationships are not a good predictor of species changes over time.
Understanding which adaptations or life history traits are associated
with population increases or decreases over time is likely to be a
better approach (Williams et al., 2009). The great variation in species'
changes in abundance is also aligned with van Klink et al. (2022) who
found that, on average, different insect species' population trends are
only weakly correlated.

\subsection*{Insights from multiple measures of community
biodiversity}

{\label{insights-from-multiple-measures-of-community-biodiversity}}

Our thorough collections of bees throughout the seasons, and
measurements of communities using a variety of metrics, highlighted the
unique biodiversity of bee communities in the spring. Measures of
abundance and richness suggested that biodiversity in the spring is low.
However, diversity was similar in April and May as it was in July and
August despite huge differences in richness. This relative elevation of
diversity in the spring was a consequence of greater evenness, or more
equal abundances among species. The total amount of spring species
captured across all sites and years was also high despite the low
abundances. Using rarefaction to standardize the number of individuals
collected, we detected 58 species per 900 individuals in April compared
to 40 in July. The month of May was also the time with the most
phylogenetically even (overdispersed) communities. A non-mechanistic
interpretation is that in May, spring bees (largely from Andrenidae and
Megachilidae) and some summer bees (mostly in Apidae and Halictidae;
Figure 5) were both active resulting in long branch lengths between
pairs of species. This parallels results by Ramirez et al (2015) who
found that orchid bee communities in Colombia were much more
phylogenetically even in the transition period between wet and dry
seasons. Composition of bees also shows great uniqueness of spring bee
communities and the fast turnover communities resulting in totally
unique communities in April, May, and June. These unique aspects of
spring biodiversity would be completely missed by looking at only
abundance and richness measures and not diversity, phylogenetic
structure, and composition. This suggests that studies seeking to
understand the phenological changes of bee communities and the impacts
of environmental change on spring bees, need to have robust sampling and
multiple measure bee biodiversity.

Repeated measures of bee communities across years suggested a loss of
community biodiversity over time, though the patterns of declines depend
on which metric you look at (Figure 3). Total abundance showed a linear
decline over time which mirrors the patterns we observed for many
individual species (Figure 3E, Figure 5). The reasons for these changes
over time are not clear. While habitat loss, land-use changes, and
pesticide use all likely impacts bee communities in this system, these
were all relatively unchanging over the course of this study (Biddinger
et al., 2018). Changes in the floral resources of the flower strips
where we sampled could have been a factor since they likely experience a
reduction of plant diversity over time, as is typical in restored
grasslands (Sluis et al., 2002). Climate could also be a driver of
population declines, though longer-term data would be needed to test the
effect of climate on bee abundance declines (Ogilvie et al., 2017,
Kammerer et al., 2021). Other biodiversity metrics showed similar, but
more nuanced patterns than abundance. Richness, diversity, and
phylogenetic structure were steady or increasing for the first three
years, and then declined for the last three (Figure 3). Similarly,
community composition also shifted but primarily in the last three years
(Figure 4B). Longer-term monitoring is needed to see if these declines
are part of an ongoing trend or a result of year-to-year fluctuations.

From a bee monitoring and conservation perspective, changes in
abundance, richness, and diversity are easy to interpret. In most cases,
decreases in these metrics are problematic and suggest some
environmental degradation is causing losses of biodiversity. However,
metrics like composition, and phylogenetic structure are harder to
interpret without a reference point but can reveal changes not seen in
simpler measures (Tucker et al., 2017, Nerlekar \& Veldman, 2020). For
example, Tonietto et al. (2017) found that old fields, restored
prairies, and remnant prairies all had the same abundance and diversity
of bees but community compositions were different. And similarly,
Frishkoff et al. (2014) found that one type of agricultural land-use did
not change bird richness, but it did lead to more phylogenetically
clustered communities compared to forest reserves. Going forward, more
long-term bee monitoring studies are needed to determine if biodiversity
measures like composition and phylogenetic structure provide unique and
useful information for conservation efforts.

\subsection*{Implications for bee
monitoring}

{\label{implications-for-bee-monitoring}}

There are a variety of bee monitoring approaches that range from
standardized and repeated collections of bees with detailed taxonomic
identification to visual observations of broad taxonomic groups that
involve participation from the public. There are pros and cons to
studies using methods on both ends of this spectrum (Woodard et al.,
2020). Our approach involved continuous collecting using passive Blue
Vane traps and species-level identification of all bees. The sampling
throughout the year gave us the ability to quantify seasonal changes in
biodiversity with fine resolution. The huge number of bees collected
means we had 40 species with sufficient data to characterize
phenological patterns. And the standardized sampling over many years
allowed us to quantify changes in abundance over time, something many
most studies have limitations with (Portman et al., 2020). However, it
is important to highlight that studies using passive trapping need to be
interpreted with caution as the data do not reflect true population
sizes (Portman et al., 2020, Briggs et al., 2022). This is because some
species are more attracted to traps than others and because trapping
results are impacted by context (Kuhlman et al., 2021). While our data
may not reflect the absolute abundance of species in the wild, it does
show that standardized passive trapping is effective at measuring
relative changes within and across years. Overall, the intensive type of
monitoring of our study is a good approach to answer questions about
community biodiversity change and the unique population dynamics across
many co-occurring species.

Our sampling approach and experimental design have several constraints
for its implementation on large-scale monitoring projects that aim to
detect bee declines. First, collecting, processing, and databasing large
numbers of bees is labor-intensive and taxonomic identification requires
specialized skills and expertise. This makes specimen curation and
identification untenable and impractical for large-scale projects.
Second, tens of thousands of bees were killed in the sampling. Concerns
have been raised that sampling many bees with Blue Vane traps could
cause declines in some species (Gibbs et al., 2017). While we did not
estimate how our collections impacted populations, the lack of
correlation between the number of individuals captured and that species
change over time (r = 0.18, P = 0.26) provides at least some evidence
that this was not the case in our study. Third, implementing passive
traps exclusively have inherent biases in the species they collect and
these biases impact biodiversity metrics. While collections with other
techniques would have resulted in different biodiversity measures, we
know from our system that Blue Vane traps provide the most thorough
sample of the whole bee community (Joshi et al., 2015). And finally, our
collections are only from one relatively small area (Figure 1). Given
the local nature of our dataset, the observed changes within and across
years could be unique to our study area. However, similar phenological
patterns and declines have been found in other studies (Bartomeus et
al., 2013, Leong et al., 2016, Graham et al., 2021, Kammerer et al.,
2021).

\section*{Conclusions}

{\label{conclusions}}

Concerns about the ecological consequences of changes in bee
biodiversity are leading to increased recognition of the importance of
wild bee conservation and promoting wild bees in agricultural systems
(Isaacs and Kirk, 2010, Biddinger et al., 2018, Reilly et al. 2020). But
wild bee communities are diverse and dynamic, and little is known about
what species or groups have the greatest conservation needs. Our
intensive sampling across six years shows that bee communities vary
greatly from month to month for all measures of biodiversity. For
monitoring efforts to capture the full breadth of bee biodiversity, it
is important to sample bees across all seasons, especially during spring
when communities turnover rapidly. Biodiversity changes across the six
years of our study were less dramatic than seasonal changes, but we
found evidence of biodiversity loss over time with declines in abundance
in 33\% of the species. Notably, our results indicate that bee family is
not a good predictor of changes over time, and in some cases, closely
related species showed very different patterns. This suggests that
species-specific traits may be more important predictors of long-term
population dynamics than shared evolutionary history. We recommend that
future monitoring efforts that seek to understand species-level dynamics
for multiple co-occurring species, and whole-community patterns in
biodiversity, utilize standardized collection methods repeated over
multiple years. Longer-term and species-specific data on wild bee
dynamics could provide greater insight into which species need targeted
conservation efforts (Woodard et al., 2020).

\subsection*{Acknowledgments}

{\label{acknowledgments}}

We thank the orchard fruit grower Scott Slaybaugh, Jim Lerew, Bill
Pulig, and Barry Rice for allowing us to conduct studies on their land
and to Jim Gillis and Mace Vaughan for their support in establishing the
USDA-NRCS wildflower strips. Thank you to Kathryn Wholaver, Lolita
Miller, Sarah Heller, and many others at the Pennsylvania State Fruit
Research and Extension Center. Jason Gibbs, Robert Jean, and Sam Droege
identified bees and the members of the López-Uribe Lab provided helpful
feedback on the previous versions of the manuscript. This research was
supported by a Pennsylvania Department of Agriculture Grant number
C940000555, and USDA-NIFA-AFRI Specialty Crop Research Initiative,
Project 2012-51181-20105. DJB was funded through the USDA NIFA
Appropriations under Project PEN04620. MML-U was funded through the USDA
NIFA Appropriations under Projects PEN04716 and PEN04620.

\subsection*{Conflicts of interest}

{\label{conflicts-of-interest}}

The authors declare no conflicts of interest.

\subsection*{Data availability
statement}

{\label{data-availability-statement}}

The data used in this study will be posted on Dryad after the paper is
accepted for publication.

\subsection*{Author contributions}

{\label{author-contributions}}

Nash E. Turley: Formal analysis (lead); Visualization (lead); Writing --
original draft (lead); Writing -- review \& editing (equal)

David J. Biddinger: Conceptualization (equal); Funding acquisition
(equal); Investigation (lead); Methodology (equal); Writing -- review \&
editing (equal)

Neelendra K. Joshi: Conceptualization (equal); Investigation (equal);
Methodology (equal); Writing -- review \& editing (equal)

Margarita M. López-Uribe: Conceptualization (equal), Funding acquisition
(lead); Writing -- review \& editing (lead)

\section*{References}

{\label{references}}

Bartomeus, I., Ascher, J.S., Gibbs, J., Danforth, B.N., Wagner, D.L.,
Hedtke, S.M. and Winfree, R., 2013. Historical changes in northeastern
US bee pollinators related to shared ecological traits. Proceedings of
the National Academy of Sciences, 110(12), pp.4656-4660.

Biddinger, D.J., Rajotte, E.G., and Joshi, N.K. 2018. Integrating
pollinator health into tree fruit IPM - A case study of Pennsylvania
apple production, in and Roubik, D.W., (ed). The pollination of
cultivated plants: a compendium for practitioners. Vol. 1. Food and
Agricultural Organization of the United Nations. pp. 69-83.

Biesmeijer, J.C., Roberts, S.P., Reemer, M., Ohlemuller, R., Edwards,
M., Peeters, T., Schaffers, A.P., Potts, S.G., Kleukers, R.J.M.C.,
Thomas, C.D. and Settele, J., 2006. Parallel declines in pollinators and
insect-pollinated plants in Britain and the Netherlands. Science,
313(5785), pp.351-354.

Briggs, E.L., Baranski, C., Münzer Schaetz, O., Garrison, G., Collazo,
J.A. and Youngsteadt, E., 2022. Estimating bee abundance: can
mark-recapture methods validate common sampling protocols? Apidologie,
53(1), pp.1-24.

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

Cameron, S.A., Lozier, J.D., Strange, J.P., Koch, J.B., Cordes, N.,
Solter, L.F. and Griswold, T.L., 2011. Patterns of widespread decline in
North American bumble bees. Proceedings of the National Academy of
Sciences, 108(2), pp.662-667.

Cane, J., 2021. Global warming, advancing bloom and evidence for
pollinator plasticity from long-term bee emergence monitoring. Insects,
12(5), p.457.

Davies, T.J., Kraft, N.J., Salamin, N. and Wolkovich, E.M., 2012.
Incompletely resolved phylogenetic trees inflate estimates of
phylogenetic conservatism. Ecology, 93(2), pp.242-247.

Frishkoff, L.O., Karp, D.S., M'Gonigle, L.K., Mendenhall, C.D., Zook,
J., Kremen, C., Hadly, E.A. and Daily, G.C., 2014. Loss of avian
phylogenetic diversity in neotropical agricultural systems. science,
345(6202), pp.1343-1346.

Gallai, N., Salles, J.M., Settele, J. and Vaissière, B.E., 2009.
Economic valuation of the vulnerability of world agriculture confronted
with pollinator decline. Ecological economics, 68(3), pp.810-821.

Garibaldi, L.A., Steffan-Dewenter, I., Winfree, R., Aizen, M.A.,
Bommarco, R., Cunningham, S.A., Kremen, C., Carvalheiro, L.G., Harder,
L.D., Afik, O. and Bartomeus, I., 2013. Wild pollinators enhance fruit
set of crops regardless of honey bee abundance. science, 339(6127),
pp.1608-1611.

Gezon, Z.J., Wyman, E.S., Ascher, J.S., Inouye, D.W. and Irwin, R.E.,
2015. The effect of repeated, lethal sampling on wild bee abundance and
diversity. Methods in Ecology and Evolution, 6(9), pp.1044-1054.

Gibbs, J., Joshi, N.K., Wilson, J.K., Rothwell, N.L., Powers, K., Haas,
M., Gut, L., Biddinger, D.J. and Isaacs, R., 2017. Does passive sampling
accurately reflect the bee (Apoidea: Anthophila) communities pollinating
apple and sour cherry orchards? Environmental Entomology, 46(3),
pp.579-588.

Gotelli, N.J., and Colwell, R.K., 2001. Quantifying biodiversity:
procedures and pitfalls in the measurement and comparison of species
richness. Ecology letters, 4(4), pp.379-391.

Goulson, D., Nicholls, E., Botías, C. and Rotheray, E.L., 2015. Bee
declines driven by combined stress from parasites, pesticides, and lack
of flowers. Science, 347(6229), p.1255957.

Grab, H., Branstetter, M.G., Amon, N., Urban-Mead, K.R., Park, M.G.,
Gibbs, J., Blitzer, E.J., Poveda, K., Loeb, G. and Danforth, B.N., 2019.
Agriculturally dominated landscapes reduce bee phylogenetic diversity
and pollination services. Science, 363(6424), pp.282-284.

Graham, K.K., Gibbs, J., Wilson, J., May, E. and Isaacs, R., 2021.
Resampling of wild bees across fifteen years reveals variable species
declines and recoveries after extreme weather. Agriculture, Ecosystems
\& Environment, 317, p.107470.

Hedtke, S.M., Patiny, S. and Danforth, B.N., 2013. The bee tree of life:
a supermatrix approach to apoid phylogeny and biogeography. BMC
evolutionary biology, 13(1), pp.1-13.

Heithaus, E.R., 1979. Community structure of neotropical flower visiting
bees and wasps: diversity and phenology. Ecology, 60(1), pp.190-202.

Isaacs, R. and Kirk, A.K., 2010. Pollination services provided to small
and large highbush blueberry fields by wild and managed bees. Journal of
Applied Ecology, 47(4), pp.841-849.

Iserbyt, S. and Rasmont, P., 2012. The effect of climatic variation on
abundance and diversity of bumblebees: a ten-year survey in a mountain
hotspot. Annales de la Société entomologique de France. 48(3-4). pp.
261-273.

Joshi, N.K., Leslie, T., Rajotte, E.G., Kammerer, M.A., Otieno, M. and
Biddinger, D.J., 2015. Comparative trapping efficiency to characterize
bee abundance, diversity, and community composition in apple orchards.
Annals of the Entomological Society of America, 108(5), pp.785-799.

Jost, L., 2006. Entropy and diversity. Oikos, 113(2), pp.363-375.

Kammerer, M., Goslee, S.C., Douglas, M.R., Tooker, J.F. and Grozinger,
C.M., 2021. Wild bees as winners and losers: Relative impacts of
landscape composition, quality, and climate. Global change biology,
27(6), pp.1250-1265.

Kammerer, M.A., Biddinger, D.J., Rajotte, E.G. and Mortensen, D.A.,
2016. Local plant diversity across multiple habitats supports a diverse
wild bee community in Pennsylvania apple orchards. Environmental
Entomology, 45(1), pp.32-38.

Kilpatrick, S.K., Gibbs, J., Mikulas, M.M., Spichiger, S.E., Ostiguy,
N., Biddinger, D.J. and Lopez-Uribe, M.M., 2020. An updated checklist of
the bees (Hymenoptera, Apoidea, Anthophila) of Pennsylvania, United
States of America. Journal of Hymenoptera Research, 77, p.1.

Klein, A.M., Vaissiere, B.E., Cane, J.H., Steffan-Dewenter, I.,
Cunningham, S.A., Kremen, C. and Tscharntke, T., 2007. Importance of
pollinators in changing landscapes for world crops. Proceedings of the
royal society B: biological sciences, 274(1608), pp.303-313.

Koh, I., Lonsdorf, E.V., Williams, N.M., Brittain, C., Isaacs, R.,
Gibbs, J., and Ricketts, T.H., 2016. Modeling the status, trends, and
impacts of wild bee abundance in the United States. Proceedings of the
National Academy of Sciences, 113(1), pp.140-145.

Kudo, G., and Ida, T.Y., 2013. Early onset of spring increases the
phenological mismatch between plants and pollinators. Ecology, 94(10),
pp.2311-2320.

Kuhlman, M.P., Burrows, S., Mummey, D.L., Ramsey, P.W. and Hahn, P.G.,
2021. Relative bee abundance varies by collection method and flowering
richness: Implications for understanding patterns in bee community data.
Ecological Solutions and Evidence, 2(2), p.e12071.

Lebuhn, G., Droege, S., Connor, E.F., Gemmill\selectlanguage{english}-Herren, B., Potts, S.G.,
Minckley, R.L., Griswold, T., Jean, R., Kula, E., Roubik, D.W. and Cane,
J., 2013. Detecting insect pollinator declines on regional and global
scales. Conservation Biology, 27(1), pp.113-120.

Leong, M., Ponisio, L.C., Kremen, C., Thorp, R.W., and Roderick, G.K.,
2016. Temporal dynamics influenced by global change: bee community
phenology in urban, agricultural, and natural landscapes. Global change
biology, 22(3), pp.1046-1053.

Mallinger, R.E., Gaines-Day, H.R. and Gratton, C., 2017. Do managed bees
have negative effects on wild bees? A systematic review of the
literature. PloS one, 12(12), p.e0189268.

Martins, A.C., Goncalves, R.B. and Melo, G.A., 2013. Changes in wild bee
fauna of a grassland in Brazil reveal negative effects associated with
growing urbanization during the last 40 years. Zoologia (Curitiba),
30(2), pp.157-176.

Mathiasson, M.E. and Rehan, S.M., 2019. Status changes in the wild bees
of north\selectlanguage{english}-eastern North America over 125 years revealed through museum
specimens. Insect Conservation and Diversity, 12(4), pp.278-288.

Michener, C.D., 2000. The bees of the world. The John Hopkins University
Press. Baltimore, MD

Michener, C.D., McGinley, R.J., and Danforth, B.N., 1994. The bee genera
of North and Central America (Hymenoptera:Apoidea). Smithsonian
Institution Press, Washington, DC.

Mitchell, T.B., 1960. Bees of the eastern United States Volume 1.
Technical bulletin North Carolina Agricultural Experiment Station. 141,
pp. 1-538.

Mitchell, T.B., 1962. Bees of the eastern United States Volume 2.
Technical bulletin North Carolina Agricultural Experiment Station. 152,
pp. 1-557.

Neave, M.J., Brown, J., Batley, M., Rao, S. and Cunningham, S.A., 2020.
Phenology of a bee (Hymenoptera: Apoidea) community over a 10 year
period in south\selectlanguage{english}-eastern Australia. Austral Entomology, 59(3),
pp.602-611.

Neff, J.L. and Simpson, B.B., 1993. Bees, pollination systems and plant
diversity. In LaSalle, J. and Gauld I.D. (Eds) Hymenoptera and
Biodiversity. CAB International. Wallingford, UK. pp 143--167.

Nerlekar, A.N. and Veldman, J.W., 2020. High plant diversity and slow
assembly of old-growth grasslands. Proceedings of the National Academy
of Sciences, 117(31), pp.18550-18556.

NRCS, 2011. Pollinator Habitat- Conservation Reserve Program Job Sheet,
CP42. P1-5. Available at:
https://www.nrcs.usda.gov/Internet/FSE\_DOCUMENTS/nrcs142p2\_017524.pdf

Ogilvie, J.E. and Forrest, J.R., 2017. Interactions between bee foraging
and floral resource phenology shape bee populations and communities.
Current opinion in insect science, 21, pp.75-82.

Ogilvie, J.E., Griffin, S.R., Gezon, Z.J., Inouye, B.D., Underwood, N.,
Inouye, D.W. and Irwin, R.E., 2017. Interannual bumble bee abundance is
driven by indirect climate effects on floral resource phenology. Ecology
letters, 20(12), pp.1507-1515.

Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R.,
O'hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs,
E., Wagner, H., 2013. Community ecology package. R package version,
2(0).

Ollerton, J., Winfree, R. and Tarrant, S., 2011. How many flowering
plants are pollinated by animals? Oikos, 120(3), pp.321-326.

Onuferko, T.M., Skandalis, D.A., Leon Cordero, R. and Richards, M.H.,
2018. Rapid initial recovery and long\selectlanguage{english}-term persistence of a bee
community in a former landfill. Insect Conservation and Diversity,
11(1), pp.88-99.

Portman, Z.M., Bruninga-Socolar, B. and Cariveau, D.P., 2020. The state
of bee monitoring in the United States: a call to refocus away from bowl
traps and towards more effective methods. Annals of the Entomological
Society of America, 113(5), pp.337-342.

Qian, H. and Jin, Y., 2021. Are phylogenies resolved at the genus level
appropriate for studies on phylogenetic structure of species
assemblages? Plant diversity, 43(4), pp.255-263.

Ramirez, S.R., Hernandez, C., Link, A. and Lopez\selectlanguage{english}-Uribe, M.M., 2015.
Seasonal cycles, phylogenetic assembly, and functional diversity of
orchid bee communities. Ecology and evolution, 5(9), pp.1896-1907.

Reilly, J.R., Artz, D.R., Biddinger, D., Bobiwash, K., Boyle, N.K.,
Brittain, C., Brokaw, J., Campbell, J.W., Daniels, J., Elle, E. and
Ellis, J.D., 2020. Crop production in the USA is frequently limited by a
lack of pollinators. Proceedings of the Royal Society B, 287(1931),
p.20200922.

Revell, L. J., 2012. phytools: An R package for phylogenetic comparative
biology (and other things). Methods Ecology and Evolution. 3,
pp.217-223. doi:10.1111/j.2041-210X.2011.00169.x

Roubik, D.W., Basset, Y., Lopez, Y., Bobadilla, R., Perez, F. and
Ramirez S, J.A., 2021. Long\selectlanguage{english}-term (1979--2019) dynamics of protected
orchid bees in Panama. Conservation Science and Practice, 3(12), p.e543.

Russo, L., Park, M., Gibbs, J., \& Danforth, B. (2015). The challenge of
accurately documenting bee species richness in agroecosystems: bee
diversity in eastern apple orchards. Ecology and Evolution, 5(17),
3531-3540.

Sluis, W.J., 2002. Patterns of species richness and composition in
re\selectlanguage{english}-created grassland. Restoration Ecology, 10(4), pp.677-684.

Stemkovski, M., Pearse, W.D., Griffin, S.R., Pardee, G.L., Gibbs, J.,
Griswold, T., Neff, J.L., Oram, R., Rightmyer, M.G., Sheffield, C.S. and
Wright, K., 2020. Bee phenology is predicted by climatic variation and
functional traits. Ecology Letters, 23(11), pp.1589-1598.

Tonietto, R.K., Ascher, J.S. and Larkin, D.J., 2017. Bee communities
along a prairie restoration chronosequence: similar abundance and
diversity, distinct composition. Ecological Applications, 27(3),
pp.705-717.

Tsirogiannis, C. and Sandel, B., 2016. PhyloMeasures: a package for
computing phylogenetic biodiversity measures and their statistical
moments. Ecography, 39(7), pp.709-714.

Tucker, C.M., Cadotte, M.W., Carvalho, S.B., Davies, T.J., Ferrier, S.,
Fritz, S.A., Grenyer, R., Helmus, M.R., Jin, L.S., Mooers, A.O. and
Pavoine, S., 2017. A guide to phylogenetic metrics for conservation,
community ecology and macroecology. Biological Reviews, 92(2),
pp.698-715.

van Klink, R., Bowler, D.E., Gongalsky, K.B. and Chase, J.M., 2022.
Long-term abundance trends of insect taxa are only weakly correlated.
Biology Letters, 18(2), p.20210554.

Webb, C.O., Ackerly, D.D., McPeek, M.A. and Donoghue, M.J., 2002.
Phylogenies and community ecology. Annual review of ecology and
systematics, 33(1), pp.475-505.

Williams, P., Colla, S. and Xie, Z., 2009. Bumblebee vulnerability:
common correlates of winners and losers across three continents.
Conservation Biology, 23(4), pp.931-940.

Williams, P.H. and Osborne, J.L., 2009. Bumblebee vulnerability and
conservation world-wide. Apidologie, 40(3), pp.367-387.

Wilson, J.S., Griswold, T. and Messinger, O.J., 2008. Sampling bee
communities (Hymenoptera: Apiformes) in a desert landscape: are pan
traps sufficient? Journal of the Kansas Entomological Society, 81(3),
pp.288-300.

Winfree, R., 2010. The conservation and restoration of wild bees. Annals
of the New York academy of sciences, 1195(1), pp.169-197.

Winfree, R., Reilly, J.R., Bartomeus, I., Cariveau, D.P., Williams, N.M.
and Gibbs, J., 2018. Species turnover promotes the importance of bee
diversity for crop pollination at regional scales. Science, 359(6377),
pp.791-793.

Wood, S.N. (2017) Generalized Additive Models: An Introduction with R
(2nd edition). Chapman and Hall/CRC.

Wood, T.J., Gibbs, J., Graham, K.K. and Isaacs, R., 2019. Narrow pollen
diets are associated with declining Midwestern bumble bee species.
Ecology, 100(6), p.e02697.

Woodard, S.H., Federman, S., James, R.R., Danforth, B.N., Griswold,
T.L., Inouye, D., McFrederick, Q.S., Morandin, L., Paul, D.L., Sellers,
E., Strange, J.P., Vaughan, W., Branstetter, M.G., Burns, C.T., Cane,
J., Cariveau, A.B., Cariveau, D.P., Childers, A., Childers, C.,
Cox-Foster, D.L., Evans, E.C., Graham, K.K., Hackett, K., Huntzinger,
K., Irwin, R.E., Jha, S., Lawson, S., Liang, C., Lopez-Uribe, M.M.,
Melathopoulos, A., Moylett, H.M.C., Otto, C.R.V., Ponislo, L.C.,
Richardson, L.L., Rose, R., Singh, R. \& Wehling, W. 2020. Towards a US
national program for monitoring native bees. Biological Conservation,
252, p.108821.

Zattara, E.E. and Aizen, M.A., 2021. Worldwide occurrence records
suggest a global decline in bee species richness. One Earth, 4(1),
pp.114-123.

\section*{Tables}

{\label{tables}}

\textbf{Table 1} . Model results for the effect of month (within year
changes) and year (across year effects) on measures of biodiversity.
Results for abundance, richness, diversity (inverse Simpson's), and
phylogenetic structure (mean pairwise distance) are from generalized
additive models and the test statistics are t-values. Community
composition results are from a perMANOVA and the test statistics are
F-values.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Month effects & Month effects & Month effects & & Year effects & Year
effects & Year effects &\tabularnewline
\midrule
\endhead
\textbf{Response variable} & \textbf{Test stat.} & \textbf{P} &
\textbf{R\textsuperscript{2}} & \textbf{Response variable} &
\textbf{Test stat.} & \textbf{P} &
\textbf{R\textsuperscript{2}}\tabularnewline
Abundance & 20.01 & \textless{}0.001 & 0.88 & Abundance & 20.71 &
\textless{}0.001 & 0.25\tabularnewline
Richness & 38.70 & \textless{}0.001 & 0.89 & Richness & 51.78 &
\textless{}0.001 & 0.61\tabularnewline
Diversity & 22.89 & \textless{}0.001 & 0.34 & Diversity & 18.22 &
\textless{}0.001 & 0.35\tabularnewline
Phylogenetic structure & 14.16 & \textless{}0.001 & 0.87 & Phylogenetic
structure & 4.92 & \textless{}0.001 & 0.62\tabularnewline
Composition & 6.21 & \textless{}0.001 & 0.64 & Composition & 2.03 &
0.002 & 0.36\tabularnewline
\bottomrule
\end{longtable}



\section*{Figure legends}

{\label{figure-legends}}

\textbf{Figure 1.} Map of collection sites in Adams County,
Pennsylvania, USA. Each yellow marker is the location of a single Blue
Vane trap which was left out to capture bees from April to October for 6
years. The four shapes show the collection sites that were closer than
900 m and were lumped together for data analysis. The town of
Biglerville is seen at the bottom right.

\textbf{Figure 2.} A) Species accumulation curve shows how the average
number of species detected increases with the total number of bees
collected. The flattening of the curve suggests that most, but not all,
of bee biodiversity is represented in our collections. B) Rank abundance
curve shows the number of individuals collected for each species ranked
from highest to lowest, note the log y-axis. In our dataset of over
26,000 bees, only 10 species had over 1000 individuals while over half
of the species had 5 or fewer individuals.

\textbf{Figure 3.} Patterns of bee biodiversity across months, and
changes across years. All model relationships were highly significant
(P\textless{}0.002). Abundance (A, E) is the average number of bees
collected per site. Richness (B, F) is the number of bee species per
site. Diversity (C, G) is the inverse Simpson's diversity index.
Phylogenetic structure (D, H) is standardized effect size of mean
pairwise distance (higher values are more even, and lower values are
more clustered). Line fits and adjusted R\textsuperscript{2} values are
from general additive models and the shading represents 95\% confidence
intervals of the models.

\textbf{Figure 4.} Effects of months (A) and years (B) on bee community
composition. Both months and years have significant effects on bee
composition but differences among months are larger than among years.
Data are visualized using non-metric multidimensional scaling on a
Bray-Curtis dissimilarity matrix which includes species abundances. The
R\textsuperscript{2} values are the variance explained from perMANOVA
models.

\textbf{Figure 5.} Species-level phenological patterns and changes in
abundance over time for 40 species with at least 30 individuals
collected. The colored heatmap shows the percentage of individuals
captured for each species, therefore a value of 100\% would mean all
individuals of that species were captured in that one month. The black
and gray points represent the positive or negative change in abundance
over time. The size of the points are from coefficients from linear
models (i.e. slope of the relationship between year and abundance using
standardized data). The phylogeny has our focal species amended (see
methods) to a genus-level tree by Hedtke et al (2013).

\textbf{Figure 6.} The distributions of both seasonality and
phenological breadth among bee genera and families. Seasonality is the
median Julian date in which each genus and species was captured across 6
years of continuous sampling. Phenological breadth is a measure of the
length of time in which bees are active. Error bars show the highest and
lowest values for species in each genus. Red dotted lines illustrate the
conceptual idea of ``phenological syndromes''. The bottom left quadrant
are early emerging species with narrow phenological breadth. Species in
the bottom right quadrant emerge in summer but still have narrow
breadth. Species in the top right have wide phenological breadth and are
most abundant in summer.

\section*{Appendix}

{\label{appendix}}

\textbf{Table A1.} Phenological and change over time data for 40 bee
species which had 30 or more individuals captured. Seasonality
represents the time of year a bee is most active and is calculated as
the median Julian date that each species was captured. Phenological
breadth, or the length of time a species is active, is the difference
between the 10th and 90th percentile of Julian day of capture. Rate of
change is a measure of how much the abundance of each species changed
from 2014-2019. It is measured as the model coefficient for the linear
regression between year and abundance, or the slope of that
relationship. Data were standardized so the coefficient represents the
change in standard deviation units in abundance per year. P-value is
associated with that linear regression. Values with P\textless{}0.1 are
bolded.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Species} & \textbf{Family} & \textbf{Seasonality (median Julian
day)} & \textbf{Phenological breadth (days)} & \textbf{Rate of change
(model coef.)} & \textbf{P-value}\tabularnewline
\midrule
\endhead
\emph{Agapostemon texanus} & Halictidae & 181.9 & 109.6 & \textbf{-0.19}
& \textbf{0.10}\tabularnewline
\emph{Agapostemon virescens} & Halictidae & 178.2 & 93.9 &
\textbf{-0.35} & \textbf{\textless{}0.01}\tabularnewline
\emph{Andrena imitatrix} & Andrenidae & 106.4 & 19.5 & -0.16 &
0.20\tabularnewline
\emph{Andrena miserabilis} & Andrenidae & 105.0 & 17.9 & -0.15 &
0.22\tabularnewline
\emph{Andrena perplexa} & Andrenidae & 117.8 & 42.0 & -0.13 &
0.30\tabularnewline
\emph{Andrena violae} & Andrenidae & 130.5 & 19.9 & 0.14 &
0.25\tabularnewline
\emph{Anthophora bomboides} & Apidae & 169.3 & 25.4 & 0.05 &
0.67\tabularnewline
\emph{Anthophora terminalis} & Apidae & 173.8 & 93.1 & -0.15 &
0.22\tabularnewline
\emph{Apis mellifera} & Apidae & 178.2 & 147.1 & -0.16 &
0.17\tabularnewline
\emph{Augochlora pura} & Halictidae & 208.6 & 97.5 & -0.10 &
0.40\tabularnewline
\emph{Augochlorella aurata} & Halictidae & 193.8 & 73.6 & -0.11 &
0.37\tabularnewline
\emph{Bombus bimaculatus} & Apidae & 168.9 & 45.1 & \textbf{-0.29} &
\textbf{0.01}\tabularnewline
\emph{Bombus fervidus} & Apidae & 176.8 & 116.5 & \textbf{-0.31} &
\textbf{0.01}\tabularnewline
\emph{Bombus griseocollis} & Apidae & 194.4 & 61.7 & 0.16 &
0.19\tabularnewline
\emph{Bombus impatiens} & Apidae & 210.9 & 80.6 & \textbf{-0.23} &
\textbf{0.05}\tabularnewline
\emph{Bombus perplexus} & Apidae & 164.2 & 25.2 & -0.15 &
0.22\tabularnewline
\emph{Bombus vagans} & Apidae & 193.8 & 73.8 & \textbf{-0.35} &
\textbf{\textless{}0.01}\tabularnewline
\emph{Calliopsis andreniformis} & Andrenidae & 200.7 & 79.4 & -0.13 &
0.30\tabularnewline
\emph{Ceratina calcarata} & Apidae & 177.4 & 91.4 & 0.11 &
0.36\tabularnewline
\emph{Ceratina dupla} & Apidae & 180.8 & 110.5 & -0.07 &
0.54\tabularnewline
\emph{Ceratina mikmaqi} & Apidae & 165.9 & 100.6 & \textbf{-0.20} &
\textbf{0.09}\tabularnewline
\emph{Ceratina strenua} & Apidae & 167.3 & 76.1 & 0.12 &
0.34\tabularnewline
\emph{Eucera hamata} & Apidae & 148.4 & 31.1 & -0.12 &
0.34\tabularnewline
\emph{Eucera pruinosa} & Apidae & 202.0 & 36.8 & -0.09 &
0.47\tabularnewline
\emph{Halictus ligatus} & Halictidae & 209.5 & 55.2 & \textbf{-0.25} &
\textbf{0.04}\tabularnewline
\emph{Hoplitis pilosifrons} & Megachilidae & 147.2 & 36.4 & 0.02 &
0.89\tabularnewline
\emph{Lasioglossum hitchensi} & Halictidae & 183.4 & 116.5 &
\textbf{-0.22} & \textbf{0.07}\tabularnewline
\emph{Lasioglossum pilosum} & Halictidae & 204.0 & 68.7 & \textbf{-0.20}
& \textbf{0.09}\tabularnewline
\emph{Lasioglossum tegulare} & Halictidae & 209.5 & 98.3 &
\textbf{-0.22} & \textbf{0.07}\tabularnewline
\emph{Megachile mendica} & Megachilidae & 206.2 & 71.0 & 0.10 &
0.41\tabularnewline
\emph{Melissodes bimaculata} & Apidae & 208.7 & 35.2 & \textbf{0.44} &
\textbf{\textless{}0.01}\tabularnewline
\emph{Melissodes denticulata} & Apidae & 220.7 & 36.7 & -0.02 &
0.86\tabularnewline
\emph{Melissodes desponsa} & Apidae & 230.9 & 47.5 & \textbf{-0.36} &
\textbf{\textless{}0.01}\tabularnewline
\emph{Melissodes trinodis} & Apidae & 212.8 & 43.2 & 0.14 &
0.25\tabularnewline
\emph{Melitoma taurea} & Apidae & 212.4 & 55.1 & \textbf{-0.25} &
\textbf{0.03}\tabularnewline
\emph{Osmia bucephala} & Megachilidae & 144.8 & 58.2 & -0.04 &
0.76\tabularnewline
\emph{Osmia cornifrons} & Megachilidae & 106.3 & 21.1 & -0.10 &
0.39\tabularnewline
\emph{Osmia pumila} & Megachilidae & 109.2 & 56.5 & 0.00 &
0.98\tabularnewline
\emph{Ptilothrix bombiformis} & Apidae & 204.1 & 33.0 & 0.06 &
0.64\tabularnewline
\emph{Xylocopa virginica} & Apidae & 188.9 & 88.8 & 0.16 &
0.18\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/FIgure-1-map/FIgure-1-map}

\end{center}
\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure-2-curves/Figure-2-curves}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/Figure-3-biodiversity-smaller/Figure-3-biodiversity-smaller}

\end{center}
\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure-4-composition/Figure-4-composition}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/Figure-5-phylo-smaller/Figure-5-phylo-smaller}

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\end{figure}\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/Figure-6-scatter/Figure-6-scatter}

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\end{figure}

\selectlanguage{english}
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