Introduction
Phenological shifts, changes the timing of species life history events,
have become one of the best-documented signatures of anthropogenic
climate change (Forrest & Miller-Rushing, 2010; Parmesan & Yohe,
2003). Many prominent studies of phenology have focused on the timing of
reproduction, especially in plants (Parmesan & Yohe, 2003). Some have
documented shifts in seasonal migration (Møller et al., 2008). Others
have documented changes in emergence from or entry into dormant life
stages, including hibernation in vertebrate animals (Inouye, 2000),
leaf-out periods for trees (Polgar & Primack, 2011), and diapause in
insects (Bale & Howard, 2010). Although the majority of past studies
have demonstrated advances in phenology associated with climate warming,
there is considerable variation, with some species delaying phenology
(Fric et al., 2020; Forister & Shapiro, 2003; Roy & Sparks, 2000).
Despite widespread documentation of changes in phenology, few empirical
studies have tested whether phenological changes are associated with
long-term trends in population size (Ramula et al., 2015). In
terrestrial warming experiments, plants with earlier phenology in warmed
plots tended to have higher biomass, growth, and/or reproduction,
suggesting that phenological advances represent adaptive responses to
environmental change (Cleland et al., 2012). Advanced phenology was also
positively associated with long-term population trends of plants in
Concord, Massachusetts (Willis et al., 2008) and may be beneficial for
insect populations that can avoid competition for resources or feed on a
wider range of vegetation (Rathcke & Lacey, 1985). Nevertheless,
changing phenology has also been shown to harm species, if changes lead
to phenological mismatches with resources or interacting species (Both
et al., 2006).
For insects, changes in phenology often arise through changes in the
timing of entry into diapause, as well as timing of spring emergence
(Bale et al., 2002). Many insect species exhibit geographically variable
patterns of voltinism (number of broods within a year) based on location
within their range. Under a warming climate, populations which were
formerly thermally restricted in parts of their range may be capable of
producing an additional generation within the extended growing season
(Kozak et al., 2019; Grevstad & Coop, 2015; Mitton & Ferrenberg, 2012;
Altermatt, 2010; Tobin et al., 2008) which could be beneficial or
detrimental for populations. Species that can successfully add a
generation in the extended growing season may benefit from another bout
of reproduction, leading to higher overall population growth rates (Kerr
et al., 2020; Kerr et al., 2019), and the potential for more rapid
evolutionary responses to climate change (Chevin et al., 2010). In spite
of its potential benefits, an increase in voltinism can also be
detrimental to insects. Mismatched phenological cues could cause a
population to start an additional generation that fails to reach its
diapausing life stage before frost (Levy et al., 2015; VanDyck et al., 2014). Such developmental traps can potentially cause populations to
decline, possibly dramatically, as the flight period increases (VanDyck
et al., 2014). Knowing how these different responses affect population
dynamics is necessary to understand the consequences of phenological
shifts, as well as the longer-term effect of increased growing season
length on insect populations.
Butterflies are known to be sensitive to changes in temperature and have
been widely used as models to study phenological change (Bale et al.,
2002). Phenological studies of butterflies have demonstrated advances in
adult emergence, which have been attributed to warming climates
(Forister & Shapiro, 2003; Roy & Sparks, 2000). In contrast to
numerous examples of phenological advances in the onset of butterfly
flight, few studies have investigated potential changes in the end of
flight. In the two studies that have investigated empirical patterns of
late-season phenology in butterfly communities, Zipf et al. (2017) and
Westwood & Blair (2010) both observed later end dates of flight
activity, correlated with increasing temperatures. These studies are
noteworthy in phenological research because much less is known about
late-season phenology than early-season phenology (Gallinat et al.,
2015; Karlsson, 2014). Nonetheless, in spite of the large body of work
on butterfly phenology, we do not know whether phenological changes are
beneficial or deleterious responses to changing environments. One recent
study has demonstrated that advanced emergence in British Lepidoptera
was associated with significantly higher rates of demographic abundance
within multivoltine species (MacGregor et al., 2019). Some studies of
birds have also generally demonstrated associations between delayed
phenology and decreasing abundance trends (Saino et al., 2011; Møller et
al., 2008; Both et al., 2006). Understanding the population dynamics
associated with phenology is imperative to translating climate-related
phenological changes into their impacts for long-term viability.
One common feature of past research on phenology change is analysis
using simple metrics such as first, average, or last observation date.
Inferences from such metrics may be limited because first and last
observation dates have known biases (Miller-Rushing, 2008) and averages
do not capture changes throughout the activity period (Inouye et al.,
2019). In this study, we use quantile regression (Cade and Noon, 2003)
to evaluate phenological change throughout the activity period. Quantile
regression is a statistical modelling technique that enables us to
robustly estimate changes in the onset and end of adult flight. Like
linear regression, quantile regression generates a slope-intercept line
through a specific part of the distribution. Linear regression minimizes
squared deviations in the response variable around a trend, whereas
quantile regression minimizes the absolute deviations from a trend,
subject to the constraint that some proportion of the data be below the
line (e.g. the 0.1 quantile, or 10th percentile, fits
a line that minimizes absolute deviations with 10% of observations
below the line). Quantile regression uses the complete data set to fit
this line, and so it differs from fitting a line to the first or last
x% of data points, an ad hoc technique that has occasionally
been used in ecological studies of phenology (Zipf, et al., 2017; Brooks
et al., 2014; Polgar et al., 2013). To date, a handful of studies have
used quantile regression to study trends in phenology, mostly in the
context of bird migration., These studies have revealed that changes in
phenology are not uniform (e.g., Barton & Sandercock, 2018; Gimesi et
al., 2012; Gordo et al., 2013). Although under-used, quantile regression
represents a formal and robust technique for evaluating changes in
phenology throughout the activity period.
Here, we quantify long-term trends in phenology and abundance of
butterflies using 27 years of citizen science records from the
Massachusetts Butterfly Club (hereafter MBC). Using these observational
data for Massachusetts butterflies, we test the relationship between
year-to-year changes in flight phenology and abundance. For each
species, we estimate abundance trends through time using list length
analysis, updating previous analyses of an earlier subset of the same
data (Breed et al., 2013), and analyses of counts individuals. We test
whether changes in the onset of activity, the end of activity, the
average date, and the flight period are associated with increases or
declines in abundance. We also evaluate relationships between life
history traits, trends in phenology, and trends in abundance using
structural equation modelling to elucidate the potential mechanistic
pathways. We compare our results to a recent study which documented
similar associations between phenology and abundance for British
(MacGregor et al., 2019). Finally, we discuss the implications of our
findings for insect population viability in their phenological response
to global change.