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\begin{document}

\title{Climate drivers of adult insect activity are conditioned by life history
traits}



\author[1]{Michael Belitz}%
\author[1]{Vijay Barve}%
\author[1]{Joshua Doby}%
\author[1]{Maggie Hantak}%
\author[2]{Elise Larsen}%
\author[1]{Daijiang Li}%
\author[1]{Jessica Oswald}%
\author[1]{Neeka Sewnath}%
\author[1]{Mitchell Walters}%
\author[1]{Narayani Barve}%
\author[1]{Kamala Earl}%
\author[1]{Nicholas Gardner}%
\author[3]{Robert Guralnick}%
\author[1]{Brian Stucky}%
\affil[1]{Florida Museum of Natural History}%
\affil[2]{Georgetown University}%
\affil[3]{University of Florida}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Insect phenological lability is key for determining which species will
adapt under environmental change. However, little is known about when
adult insect activity terminates, and overall activity duration. We used
community-science and museum specimen data to investigate the effects of
climate and urbanization on timing of adult insect activity for 101
species varying in life history traits. We found detritivores and
species with aquatic larval stages extend activity periods most rapidly
in response to increasing regional temperature. Conversely, species with
subterranean larval stages have relatively constant durations regardless
of regional temperature. Multivoltine and univoltine species both
extended their period of adult activity similarly in warmer conditions.
Longer adult durations may represent a general response to warming, but
voltinism data in subtropical environments is likely underreported. This
effort provides a framework to address drivers of adult insect phenology
at continental scales, and a basis for predicting species response to
environmental change.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Shifts in phenology, i.e., the timing of seasonal biological events, are
among the most noticeable impacts of human-caused global change (Cleland
et al., 2007; Parmesan, 2007). Numerous studies have shown that recent
climate warming advances the timing of spring events, e.g., budburst,
breeding of amphibians, and arrival of migrating birds (Parmesan, 2007;
While and Uller, 2014; Cohen et al., 2018). Beyond global climate
change, large-scale landscape modification can also impact phenology.
For example, urbanization has been shown to advance the timing of
seasonal events such as plant flowering, in part through the urban heat
island effect (reviewed in Neil and Wu, 2006). However, more recent work
has revealed that urbanization can also delay phenological events,
especially in warmer regional climates (Li et al., 2019), although the
mechanisms driving these delays have yet to be determined. Overall,
there is considerable variation in phenological responses to global
change both within and among species (Edwards and Richardson, 2004;
Thompson and Clark, 2006; Park et al., 2019). If interdependent species
differentially respond to human-mediated changes, phenological
mismatches may occur with potentially significant, negative demographic
consequences (Miller-Rushing et al., 2010; Renner and Zohner, 2018).

Better understanding insect phenological responses is critical given
their vast diversity, temperature-dependent developmental timing, and
critical role in ecosystems and the services they provide. These
services include dung burial, pest control, pollination, and wildlife
nutrition and are valued at over \$57 billion annually in the United
States (Losey and Vaughan, 2006). Further, several recent papers have
reported dramatic declines in insect populations (Hallmann et al., 2020;
Wagner, 2020), potentially due to human-caused land-use change, climate
change, introduced species, and pollution (Wagner et al., 2021).
Phenological shifts may exacerbate losses due to mismatches, but might
provide a means to adapt to warmer temperatures and could even lead to
overall population growth rates, particularly in species that can
successfully add a generation due to extended growing seasons (Kerr et
al., 2020).

Most insect phenological studies focus on how climate drives
the\emph{emergence} of insects. In general, warmer-than-average years
cause adult insects to emerge earlier (Bartomeus et al., 2011; Roy et
al., 2015; Villalobos-Jim\selectlanguage{ngerman}énez and Hassall, 2017). Much less is known
about what determines termination or total duration of insect activity
(Forrest, 2016). Given extended growing seasons for many plant species
(Steltzer and Post, 2009), it might be expected that insects also delay
termination of adult insect activities in warmer regions. Longer growing
seasons are increasing the number of generations per year (voltinism) of
some insects (Altermatt, 2010a; Pöyry et al., 2011), but many species
are obligate univoltine across their entire range, including warm
regions (Forrest, 2016). For these reasons, life history traits, known
to be important in determining insect activity (Diamond et al., 2011;
Zografou et al., 2021), may strongly determine adult insect termination.
For example, Stemkovski et al. (2020) found that timing of bee emergence
was most influenced by climatic variation, but termination of adult bee
foraging was better explained by life history traits. Specifically, bee
species that nest below ground ended foraging earlier than species
nesting above, but bee species that overwinter as prepupae ended
foraging later than those that overwinter as pupae (Stemkovski et al.,
2020).

Interannual regional climate variation is not the only driver of insect
phenology. Urbanization is in general leading to earlier flowering in
many plant species due to the urban heat island effect, although these
responses may be complex and context dependent (Jochner and Menzel,
2015). Less is known about insects' phenological response to
urbanization. The emergence of some insect species appears to be
advancing in urbanized areas (Diamond et al., 2015; Chick et al., 2019),
but other studies have found no change in phenology across urbanization
gradients, despite phenological advances in co-occurring plant species
(Seress et al., 2018; Fisogni et al., 2020). The interaction between
urbanization and regional temperature can be an important driver of
spatial phenology patterns, as urbanization appears to advance plant
phenology in cold areas but causes delays in warm areas (Li et al.,
2019). Diamond et al. (2014) also demonstrated that the urbanization
effect on phenological responses of butterflies depends on regional
temperature. This expected interaction between urbanization and
temperature remains untested at larger spatial scales and across a
broader range of insect groups.

Establishing generalities about determinants of emergence, termination,
and duration of adult insect activity is challenging because most
phenological research relies on surveys that provide much needed
information on species' population abundance but are limited spatially
and taxonomically. Recent enormous growth in open and freely accessible
and curated community science photographs, such as those available via
the iNaturalist platform, are allowing researchers to ask novel
phenological questions at greater spatial and taxonomic scales (Li et
al., 2021). However, careful data curation and specialized analytical
methods must be used to generate biologically meaningful results (Larsen
and Shirey, 2021).

Here we use community science generated digital vouchers and digitized
museum specimens to investigate how the emergence, termination, and
total duration of adult insects varies spatially in response to climate
and urbanization. We also examine how such responses differ across
species-specific life history traits. We predict strong interactions
between traits and key climate predictors. For example, we expect the
seasonal activity of insects with thermally buffered larval stages to be
less sensitive to variation in temperature than species without
thermally buffered larval stages, aligning with a recent study on bees
(Stemkovski et al., 2020). We further predict adult duration to vary
based on voltinism, with multivoltine species showing stronger responses
to temperature than univoltine species. Lastly, we predict adult insect
termination to be later and adult duration longer in warmer and
urbanized areas, consistent with recent studies in plants (Li et al.,
2021).

\textbf{Methods}

\emph{Insect species selection and data acquisition}

We focused on the five most speciose orders with highly distinct adult
forms: Coleoptera (beetles), Diptera (flies), Hymenoptera (bees, wasps,
ants, and sawflies), Lepidoptera (butterflies and moths), and
Trichoptera (caddisflies). We also include the order Odonata
(dragonflies and damselflies) and Cicadidae (cicadas), for which nymphs
or larvae are visually distinct from adults and occupy
infrequently-surveyed habitats (aquatic and subterranean, respectively).
A detailed explanation on the species selection protocol can be found in
the Supporting Information.

Within these seven higher-level taxonomic groups, we used overall record
counts in iNaturalist to select an initial set of candidate species. We
limited searching to research-grade observations (verifiable
observations with at least two independent species identifications) and
included all insect species in focal groups that had at least 1,000
observations within Canada, the United States, and Mexico as of April 8,
2020. Four-hundred and seventy insect species met our search criteria,
representing all our focal higher taxonomic groups except for
Trichoptera.

We then further filtered to species primarily observed as adults and
maintained those for which at least 99.5\% of annotated observations had
the life stage annotated as ``Adult'' by iNaturalist users. Because
Lepidoptera are commonly observed as juveniles, we only kept records
annotated as adults in our final dataset. Using our final species list,
we collected data from GBIF and iDigBio, which aggregate iNaturalist
records along with other biological collections. In total, we collected
records for 435 species across six insect orders. We limited the
temporal scope of our study to the recent years of 2015-2019.

\emph{Phenology estimates}

We gridded our study area into 25 x 25-km equal area cells using the
North America Albers Equal Area Conic projection. Community science
observations can be biased by organized, public observation events
(e.g., iNaturalist's City Nature Challenge) generating an unusually high
number of observations that do not reflect the actual seasonal abundance
of a species. We therefore filtered our dataset to include only one
observation per day of each species in a year, given each cell. Next,
the number of observations for each cell-by-year-by-species combination
was counted and deemed usable if at least 10 observations were
documented. For each unique cell-year-species combination, we estimated
the 0.05 and 0.95 sample quantiles using the quantile() function within
the \emph{stats} R package (R Core Team, 2020) to represent the
emergence (first appearance) and termination (last evidence) of adult
insect activity. These quantiles are demonstrated to be more robust
estimates of phenology than estimating the absolute bounds of a
phenophase (Belitz et al. 2020). We calculated the duration of adult
insect activity as the difference between the termination and emergence.
In total, we used 228,423 records to generate 5,469 emergence,
termination, and duration estimates across 626 unique grid cells for 284
species. Over 97\% had a basis of record listed as human observation,
indicating the vast majority of the data were generated by community
scientists.

\emph{Trait data}

We collected trait information from literature and web sources for the
284 included insect species. We focused on traits thought to be relevant
to insect phenology, including: 1) voltinism, 2) development type, 3)
habitat of immature life stage, 4) diapause stage, and 5) larval diet.
We also estimated a categorical trait for the season of adult emergence
by calculating the mean emergence for each species. Species that on
average emerge as adults prior to the 126th day of the year (DOY) were
categorized as spring species, those between the 126th and 176th
(\textasciitilde{} end of June) DOY were categorized as summer species,
and the remaining insects were categorized as fall species. Voltinism
(broods per year) was categorized as either obligate univoltine, or not
obligate univoltine (including species that are only univoltine in part
of their range and semi-, parti-, and merovoltine species). Development
type was categorized as either hemimetabolous or holometabolous. Habitat
of immature life stage was categorized as above ground, freshwater, or
underground. Diapause stage was categorized as egg, larva, pupa, adult,
none, or migratory. The larval diet was categorized as carnivorous,
detritivorous, or herbivorous.

Many of the species that passed initial filters for numbers of
observations were missing trait data and were excluded from further
analyses (Figure 1). We also removed species that do not diapause or are
migratory, as these species likely have different physiological
responses to temperature and urbanization than the other species.
Additionally, eusocial insects were removed, because many can
thermoregulate in unique ways (Heinrich, 1972; Jones and Oldroyd, 2006;
Menzel and Feldmeyer, 2021). Finally, we removed species that had less
than 5 unique year-by-grid cell combinations. After filtering our
dataset to match these requirements, we were left with 2,643 estimates
of emergence, termination, and duration from 101 species across 412
unique grid cells (Figure 2).

\emph{Climate and urbanization data}

We included four climate and one urbanization variable in our modeling
framework. The climate variables were year-specific and included mean
annual temperature, annual precipitation, temperature seasonality, and
precipitation seasonality. We obtained annual mean values of the maximum
2-m air temperature data and annual precipitation values for North
America at a 1-km spatial resolution for the five years of our study
from Daymet (Thornton et al., 2016a). We generated annual temperature
and precipitation seasonality using the monthly maximum temperature and
precipitation summaries provided by Daymet at a 1-km spatial resolution
(Thornton et al., 2016b). Temperature seasonality was calculated as the
standard deviation of the monthly maximum temperature values for the
corresponding year, and precipitation seasonality was the coefficient of
variation of the monthly precipitation values for the corresponding
year.

We used estimated human population density for the year 2020 as a proxy
for urbanization and obtained this data from the Center for
International Earth Science Information Network, which provides global
estimates of population density at a 0.25 degree resolution
(\textasciitilde{}27-km; CIESIN, 2017). Global estimates of human
population density are only available on five-year intervals, so we
choose to only use the 2020 estimates as a proxy for urbanization given
our sampling temporal extent. Year-specific changes in human population
density are minimal compared to the variation across space, and
therefore should have minimal impact on statistical models.

\emph{Statistical analysis}

We used linear mixed models (LMM) to examine the effects of climate,
urbanization, and life history traits on the emergence, termination, and
duration of adult insect activity across North America. Estimates of
emergence, termination, and duration were the response variables, and we
included human population density, mean annual temperature, annual
precipitation, temperature seasonality, precipitation seasonality as
predictor variables along with two, two-way interactions based on
previous biological knowledge. The two-way interaction terms tested
were: 1) whether the effects of annual temperature changed along a
precipitation gradient and 2) whether the impacts of human population
density changed along a temperature gradient. Areas with higher human
population densities may have more incidental data records, biasing
phenological metrics and we controlled for this by including the number
of observations as a fixed effect in our initial model. We scaled
variables to have a mean of zero and standard deviation (s.d.) of one to
ensure comparable model effect sizes across variables. Cell identity and
insect species were included as random terms for intercepts, and insect
species were included as a random term for the slope of each predictor
variable. Next, we used backward model selection using the step function
from the R package \emph{lmerTest}(Kuznetsova et al., 2017). If any
variance inflation factor (VIF) was greater than five for a variable in
the best model, we removed the correlated variable with lower
coefficient and reran our backward selection process.

After selecting the best model with only climate, human population
density, and number of observation variables, we added insect traits to
our models. We added two-way interaction terms between each trait
variable with the climate and human population density variables to
examine if adult insect phenology along climate and population density
gradients changed for each trait. Backward model selection was performed
as described above to reduce model variables and select a best model.
Again, if any VIF was greater than five for the variables in the best
model, we removed the correlated variable with the lowest coefficient
and reran our backward selection process.

LMM can lead to false conclusions and inflated type I error rates if
phylogenetic relationships are ignored (Li and Ives, 2017). We therefore
generated a subtree from the Open Tree of Life for the 101 insects in
our analysis using the R package \emph{rotl}(Michonneau et al., 2016).
Branch lengths were generated by searching the TimeTree of Life database
(Kumar et al., 2017) to get the estimated divergence time of each
internal node. The branch lengths were then scaled from these ages using
the ph\_bladj() function from the R package \emph{phylocomr}(Ooms and
Chamberlain, 2019). We used this synthesis phylogeny to fit phylogenetic
linear mixed models (PLMM) using a Bayesian framework with the default
uninformative INLA priors (Rue et al., 2009). We used the R package
\emph{phyr}(Li et al., 2020) to fit our top LMM as PLMM. Our results
differed slightly between the PLMM and LMM, and therefore, we present
the results based on PLMM in the main text. Results of linear mixed
models are included in the supporting information (Supporting
Information Tables 1-3). We checked residuals of the top models to
ensure models did not have any obvious deviation from model assumptions,
including spatial autocorrelations (See Supporting Information for more
details and Figure S1 for residuals of the top models). We measured the
goodness of fit of our PLMM using the package\emph{rr2} (Ives and Li,
2018) to generate partial R\textsuperscript{2} values (see supporting
information for additional details).

\textbf{Results}

The emergence of adult insect activity is impacted by temperature,
precipitation, temperature seasonality, life history traits, and the
interactions among these variables (Table 1). Species that diapause as
an egg have later emergence dates (approximately 45.7 days) than species
that overwinter as adults (Table 1). A one standard deviation (s.d.)
increase in temperature (4.77 °C) led to earlier emergence of
approximately 12.8 days (95\% Bayesian credible intervals (CI) 3.6 -
22.0), but the effects of temperature varied with respect to regional
precipitation and human population density values. Compared to areas
with high human population density, emergence values are earlier in cool
regions with low population density, but later in warm regions with low
population density (Figure 3A). Insects in warmer areas emerge earlier
than in cool areas, and these phenology shifts are more extreme in wet
areas compared to drier areas (Figure 3B). The interaction between
temperature and diapause stage, and the interaction between
precipitation and voltinism also impacted the emergence of adult insect
activity. The emergence of species that diapause as larva or pupa were
more sensitive to temperature than species that diapause as adults or
eggs (Figure 3C). Univoltine species had earlier emergence in areas with
more precipitation, while species that are not univoltine had later
emergence in areas with more precipitation (Figure 3D). There was a
phylogenetic signal of the random species-specific intercept and the
random species-specific slopes of temperature seasonality and
precipitation (Supporting Information Figure S2). The partial
R\textsuperscript{2} of our best emergence model was 0.75 (Supporting
Information Figure S3).

The top termination model consisted of a set of predictors that included
precipitation, temperature seasonality, life-history traits, and the
interaction between temperature seasonality and diapause stage (Table
1). A one s.d. increase in precipitation (324 mm) led to a delay in
termination of approximately 3 days (95\% CI 1.1 - 4.8). Termination of
activity was earlier for species that are first observed in spring or
summer compared to species that are first observed as adults in fall
(Table 1). Additionally, insect species that diapause as larvae
terminate adult activity earlier than insects that diapause in other
life stages (-18.4, 95\% CI -35.8 - -0.7; Table 1). The termination of
species that diapause as eggs were the most sensitive to temperature
seasonality (Figure 4). Species that spend their immature life stage
underground had earlier termination than species that spend their
immature life stage in freshwater or aboveground. Detritivores had later
terminations than herbivores or carnivores. Phylogenetic signal was
again apparent for the species-specific random intercept and
species-specific random slope of temperature seasonality and
precipitation (Supporting Information Figure S2). The partial
R\textsuperscript{2} of our best termination model was 0.58 (Supporting
Information Figure S3).

The top model of adult insect duration was predicted by climate, life
history traits, and the interaction between temperature and these other
variables. Areas with greater temperature seasonality had shorter
durations (Table 1). Additionally, areas with high precipitation had
longer durations in warm regions but shorter durations in cool regions
(Figure 5A). In contrast to species whose immature habitat is above
ground or in freshwater, species whose immature habitat is underground
were found to have consistent durations, regardless of whether they were
in a warm or cool region (Figure 5B). Diapause stage was again an
important trait, with insects that diapause as adults having the longest
durations. Conversely, the durations of species that diapause as larvae
were the shortest, 44.8 days shorter than species that diapause as
adults (95\% CI 26.1 - 62.7; Table 1). The duration of detritivores was
strongly tied to regional temperature, with duration being much longer
in warm regions compared to cool regions (Figure 5C). In addition to
there being a phylogenetic signal in species-specific intercept and the
species-specific slope of temperature, variation in duration was also
partially explained by the phylogenetic signal in the species-specific
precipitation slope (Supporting Information Figure 2). The partial
R\textsuperscript{2} of our best duration model was 0.75 (Supporting
Information Figure 3). The number of observations used to estimate the
phenometrics was included as a predictor variable in the top duration
model, as well as in the top emergence and termination model.

\textbf{Discussion}

Most studies of insect phenology have focused on regional scales, and
the few studies at larger spatial scales have been constrained to
species easily identified by trained volunteers such as butterflies in
the United Kingdom (e.g., Hodgson et al., 2011; Roy et al., 2015). Here,
we leverage rapidly increasing, openly available observation records to
achieve the broad phylogenetic sampling necessary for finding
commonalities in phenological sensitivity related to climate and life
history traits, both important predictors of insect phenology. Our key
finding is that timing of adult insect activity is temperature-driven
but strongly conditioned by species traits and less-so by phylogenetic
relatedness. As we discuss below, these findings provide a basis for
prediction of phenological responses in the face of environmental
change.

\emph{Drivers of adult insect emergence, termination, and duration}

In comparison to other aspects of insect phenology, the drivers of adult
emergence are relatively well understood. The emergence of many insect
species is controlled by temperature because the growth rate of immature
stages increases at warmer temperatures (Gilbert and Raworth, 1996;
Hodgson et al., 2011). Life history traits, particularly diapause stage,
are also known to be an important predictor of when insects emerge
(Scott and Epstein, 1987; Altermatt, 2010b). Our results confirm earlier
emergence in warmer areas and in species that diapause as adults.

Less is known about drivers of termination and duration of adult
insects. For many insects, photoperiod is likely predictive of activity
termination since it is a primary cue in many insects to initiate
diapause (Tauber and Tauber, 1976; Denlinger, 2002). Although
photoperiod is often the main driver, diapause induction has also been
related to temperature, food availability, moisture, and chemical cues
(Danks, 2007). Our results also suggest that many factors contribute to
the termination of adult activity and indicate that these processes may
be linked to temperature seasonality, timing of the first fall freezes,
or resource depletion caused for example by drought. In multivoltine
insects, we expected longer activity durations in warmer regions, due to
the production of additional adult generations in areas with longer
growing seasons (Altermatt, 2010a; Zeuss et al., 2017). It is less clear
how temperature influences the duration of univoltine insects. Because
obligate univoltine species cannot produce additional generations in
warmer regions, we expected more consistent activity durations across
temperature gradients, regardless of emergence dates. However, we did
not find an interaction between temperature and voltinism, suggesting
similar temperature-driven increases in duration regardless of
voltinism. This surprising result could be explained by multiple
mechanisms for extending adult activity, including lengthening timing of
activity of cohorts of adults, more generations per season, and reduced
adult synchrony of univoltine populations in warm areas. However, this
result may be due to our simplistic trait coding system labeling semi-,
parti-, and merovoltine species as ``not univoltine''. While this does
not impact our finding of longer activity periods for univoltine species
in warmer regions, more finely scored voltinism states may help
elucidate trait-mediated phenology responses to temperature. We also
note that our understanding of life history traits, particularly
voltinism and migratory behavior, is incomplete across broad temperature
gradients. Species that are documented in the literature as
non-migratory and univoltine may in fact have undescribed migratory
patterns (Robinson et al., 2009) or potentially multiple generations in
warmer regions that remain unreported.

One of the challenges with understanding phenological sensitivities is
that regional contexts may impact not only the strength of sensitivity
but also its direction. Li et al. (2019) showed, for example, that
urbanization shifts from advancing flowering in cold regions to delaying
in warmer ones. Here we also find strong evidence of these
region-specific contextual effects. One of the clearest examples is that
the influence of precipitation and life history traits on insect
duration varies with regional temperature. Warm, wet areas demonstrated
longer adult insect duration than cooler, wetter regions. This
interaction is expected, considering that snow contributes to annual
precipitation in cool regions and snowmelt date delays the beginning of
activity more than the end (Stemkovski et al., 2020). These regional
effects also included life history interactions with temperature. We
found that detritivores terminated activity later and had longer
durations than carnivores, potentially due to detritus being more
available later into the fall compared to green leaf materials or prey
items. This effect is especially strong in warm environments, where
detritus can accumulate year round.

We also expected a complex interaction between temperature and
urbanization, similar to the results of Diamond et al. (2014) for
butterflies in Ohio, which documented earlier emergence in cold areas
with high human population density and delayed emergence in warmer, more
urbanized areas. While we found such an interaction, it was in the
opposite direction than expected: insects generally emerged later in
cool, urbanized areas and earlier in warm, urbanized areas, in
comparison to corresponding rural areas. Due to our species selection
protocol, our species list likely overrepresented exploitative species
since these are commonly observed. Exploitative butterflies showed
smaller delays in first appearance in warm and urbanized areas (Diamond
et al., 2014), potentially, in part, explaining our differing results.
We encourage future work exploring how urbanization impacts insect
phenology and at what spatial scales urbanization influences are most
apparent.

\emph{Ecological implications}

Our results suggest that adult insects will emerge earlier under climate
change scenarios due to warming temperatures, but the termination of
insect activity will be less sensitive to changing climates. This may
lead to an overall lengthening of insect duration in response to global
warming, particularly in areas with high precipitation. Longer insect
activity periods may buffer against phenological mismatch of insects
interacting with other trophic levels, as long as insect abundance is
sufficiently high. However, mounting evidence suggests widespread
terrestrial insect declines (van Klink et al., 2020; Wagner et al.,
2021; Warren et al., 2021), which raises the threat of reduced
ecological services regardless of how much synchrony occurs between
interacting species.

One of the fundamental unanswered questions in understanding insect
response to global change is which species will thrive --- the winners,
and which will be most negatively impacted --- the losers. Phenology may
be a key indicator of winners versus losers given recent work
demonstrating that changes in insect population sizes correlate with
phenological lability. Some insects may be able to adjust and thrive in
warmer environments if additional land use changes are not occurring
(Michielini et al., 2021). For example, multivoltine Lepidoptera with
early adult emergence in warm years showed increased within- and
between-year population growth in Britain (Macgregor et al., 2019).
Elongated adult activity periods were also the best predictor of
increases in relative abundance of Massachusetts butterflies (Michielini
et al., 2021).

Our results point to two life history traits that may predict winners
and losers in the face of future climate change. Detritivores and
insects with larval habitats in freshwater exhibit a stronger response
of activity period to temperature than do other insects, indicating
these species may be relatively better suited to persist in novel
climate scenarios. Conversely, species that have underground larval
habitats may be more at risk, as duration for these species remains
relatively fixed across temperature gradients. We note that these
conclusions must be interpreted with care, as our models are primarily
fit across a spatial gradient. Space-for-time models of ecological
change are controversial because ecological processes are often
nonstationary (Damgaard, 2019). Still, some empirical studies support
these inferences. A recent meta-analysis found broad declines in
terrestrial insects but increases in freshwater insect populations (van
Klink et al., 2020). No net declines in detritivores were detected
across five long term ecological research sites (Crossley et al., 2020),
although these results have been questioned (Welti et al., 2020).

\emph{Future work and caveats}

Our study is unique in its spatial and taxonomic extent, yet the
majority of the species used are in Odonata and Lepidoptera. This is not
surprising, since these clades have relatively large body sizes and are
easy to see and photograph. In addition to the taxonomic biases in our
dataset, the 25 x 25-km grid cells that were included in our analyses
were biased geographically, with most cells occurring in metropolitan
areas in the Eastern Temperate Forests ecoregion and along the
California Coast. There are noticeable sampling gaps in the Great
Plains, North American deserts, and the complex and diverse ecoregions
of southern Mexico. Finally, we note that increasing observer effort,
measured as the number of observations, did show a significant, albeit
relatively weak effect on phenology estimates. Although our modeling
framework attempted to control for these effects, care must be taken
when fitting estimations using community science datasets. As the amount
of available incidental data continues to grow, we expect better
spatial, and phylogenetic resolution, albeit with biases still likely,
unless concerted efforts are made by community scientists to survey more
rural locations (Shirey et al., 2021).

Even if more species-specific phenology estimates can be generated for
insects, our full ability to understand drivers of insect phenology will
be hampered by the lack of available trait data. We had enough
incidental intra-annual observations to generate phenological estimates
for 154 species with at least five year-by-cell combinations but could
only use 101 species because of missing trait data. This is particularly
problematic because our results highlight the importance of life history
traits in predicting all aspects of adult insect phenology. Therefore,
to better understand how insects will respond to climate change and
urbanization, it is imperative that continued effort goes into
generating, compiling, and archiving openly available insect life
history information (e.g., Middleton-Welling et al., 2020). Improving
insect trait knowledge and access will allow researchers to better
understand ecological processes using the accelerating accumulation of
occurrence and other natural history records. This research showcases a
framework to gather and use these resources to answer fundamental
questions about the duration of adult activity across broad spatial and
phylogenetic scales.

\textbf{Acknowledgements}

We thank the many iNaturalist users who are recording and identifying
insect biodiversity. We also thank those who have collected, curated,
and digitized insect specimen records. MWB was partially supported by a
University of Florida Biodiversity Institute (UFBI) fellowship. CJ
Campbell provided valuable feedback. No direct funding supported this
study, but MWB, EAL, and RPG developed phenological tools and concepts
used in this study that were supported by the National Science
Foundation DEB \# 1703048 and DEB \# 1702664.

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\textbf{Tables}

Table 1. Fixed effects coefficients for top emergence, termination, and
duration models. Bolding denotes coefficients whose 95\% Bayesian
credible interval does not include zero.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Term & Emergence & Termination & Duration\tabularnewline
\midrule
\endhead
(Intercept) & \textbf{109.0 (94.1 - 123.8)} & \textbf{277.3 (247.9 -
306.4)} & \textbf{147.6 (119.6 - 174.8)}\tabularnewline
temp & \textbf{-12.8 (-22.0 - -3.6)} & & 9.3 (-8.0 -
26.6)\tabularnewline
pop & -1.02 (-3.4 - 1.4) & &\tabularnewline
prec & \textbf{3.3 (0.9 - 5.7)} & \textbf{2.9 (1.1 - 4.8)} & 0.6 (-1.8 -
3.1)\tabularnewline
temp\_seas & \textbf{6.0 (2.5 - 9.4)} & -5.2 (-13.1 - 2.7) &
\textbf{-17.3 (-21.5 - -13.2)}\tabularnewline
diapause.stageEgg & \textbf{45.7 (24.8 - 66.2)} & -16.4 (-37.5 - 5.0) &
\textbf{-44.6 (-65.6 - 22.7)}\tabularnewline
diapause.stageLarvae & \textbf{31.2 (14.0 - 48.3)} & \textbf{-18.4
(-35.8 - -0.7)} & \textbf{-44.8 (-62.7 - 26.1)}\tabularnewline
diapause.stagePupae & \textbf{38.2 (20.0 - 56.7)} & -2.2 (-21.0 - 17.0)
& \textbf{-33.2 (-51.8 - -14.1)}\tabularnewline
flightsUnivoltine & 6.9 - (-3.7 - 17.5) & &\tabularnewline
seasSpring & & \textbf{-37.1 (-50.7 - .23.3)} &\tabularnewline
seasSummer & & \textbf{-24.4 (-35.5 - -13.3)} &\tabularnewline
immature.habitatFreshwater & & 15.3 (-20.1 - 50.7) & 4.6 (-29.0 -
38.2)\tabularnewline
immature.habitatUnderground & & \textbf{-26.9 (-47.0 - -6.6)} &
\textbf{-31.5 (-52.2 - -10.5)}\tabularnewline
larval.dietDetritivorous & & \textbf{37.8 (6.02 - 69.0)} & \textbf{35.4
(2.1 - 68.1)}\tabularnewline
larval.dietHerbivorous & & 14.6 (-8.6 - 37.6) & -1.8 (-25.6 -
21.8)\tabularnewline
temp:prec & \textbf{-2.6 (-4.5 - -0.7)} & & \textbf{3.1 (0.6 -
5.6)}\tabularnewline
temp:pop & \textbf{-2.2 (-4.3 - -0.1)} & &\tabularnewline
temp:diapause.stageEgg & -5.1 (-16.6 - 6.2) & &\tabularnewline
temp:diapause.stageLarvae & -9.3 (-18.7 - 0.2) & &\tabularnewline
temp:diapause.stagePupae & \textbf{-12.7 (-23.0 - -2.5)} &
&\tabularnewline
prec:flightsUnivoltine & \textbf{-5.0 (-9.8 - -0.2)} & &\tabularnewline
temp\_seas:diapause.stageEgg & & \textbf{-15.1 (-27.0 - 3.2)}
&\tabularnewline
temp\_seas:diapause.stageLarvae & & -7.8 (-16.6 - 0.8) &\tabularnewline
temp\_seas:diapause.stagePupae & & -2.6 (-12.1 - 6.8) &\tabularnewline
temp:larval.dietDetritivorous & & & \textbf{29.0 (6.3 -
51.5)}\tabularnewline
temp:larval.dietHerbivorous & & & 6.1 (-11.0 - 23.1)\tabularnewline
temp:immature.habitatFreshwater & & & 12.0 (-10.4 - 34.5)\tabularnewline
temp:immature.habitatUnderground & & & -13.1 (-26.8 -
0.9)\tabularnewline
numObs & \textbf{-3.6 (-4.5 - -2.8)} & \textbf{4.5 (3.5 - 5.4)} &
\textbf{8.1 (7.0 - 9.3)}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Number of study species (A) and number of phenology estimates
(B) included in or excluded from our modeling framework per taxonomic
group (Cicadidae, Coleoptera, Diptera, Hymenoptera, Odonata, and
Lepidoptera in top to bottom order shown above). The same species could
contribute multiple phenology estimates to the model by having enough
records to generate estimates across multiple cells and years. Dark
purple indicates the number of species or phenology estimates included
in the modeling framework. Light purple indicates species or phenology
estimates with enough phenology observations (\textgreater{} 1,000
iNaturalist observations and available in at least five year-by-cell
combinations) that were removed from the analyses because of missing
trait data. Counts do not include species that were eusocial, migratory,
or do not diapause, as these were not included in our analyses.
Silhouettes of Coleoptera (vectorization by T. Michael Keesey;
photography by Thorsten Assmann, J\selectlanguage{ngerman}örn Buse, Claudia Drees,
Ariel-Leib-Leonid Friedman, Tal Levanony, Andrea Matern, Anika Timm, and
David W. Wrase), Hymenoptera (vectorization by Melissa Broussard),
Odonata (vectorization by Maxime Dahirel), and Lepidoptera
(vectorization by Mali'o Kodis, photograph by Jim Vargo) were downloaded
from www.phylopic.org. NS generated the Diptera and cicada silhouettes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Locations of 25 x 25-km cells with at least one
species-by-year combination with enough data to produce phenology
estimates of emergence, termination, and duration. Number of
species-year combinations are represented by the color of the cell.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure. 3 Two-way interactions included in the top model predicting
emergence of adult insect activity. Overall, adult emergence of adult
insect activity was earlier in warmer areas and was even earlier in warm
areas with high human population densities (A). High precipitation led
to earlier emergence in warm areas, but delayed emergence in cool areas
(B). The sensitivity of insect emergence to temperature differs
depending on what life stage an insect enters diapauses (C). Emergence
was earlier for univoltine species in areas with high precipitation
compared to species that are not univoltine (D). Low, mid, and high
population density and precipitation values represent values that are
one s.d. above the mean, at the mean, or one s.d. below the mean.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4. Two-way interaction included in the top model predicting
termination of adult insect activity. Termination of adult insect
activity was more sensitive in detritivorous insects.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5. Two-way interactions included in the top model predicting
duration of adult insect activity. Insects were active longer in warm
areas, especially warm areas with high (one s.d. above the mean)
precipitation (A). Species with immature habitats that are underground
had relatively constant durations regardless of the regional temperature
(B). Detritivores had durations that were most sensitive to temperature
(C).

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