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).
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, Andrena and Osmia species emerged
early in the year and had narrow breadth. Second, species in the tribe
Eucerini, (Melissodes and 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 Bombus, Apis ,Xylocopa and 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 (Bombus ) and sweat bees
(Halictidae). In another case, 2 closely related longhorn bees (genusMelissodes ) 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.
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.
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).