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\begin{document}

\title{Simultaneous selection on vegetative and reproductive phenology in a
perennial herb}



\author[1]{Elsa Fogelström}%
\author[2]{Giulia Zacchello}%
\author[1]{Johan Ehrlen}%
\affil[1]{Stockholm University}%
\affil[2]{Uppsala Universitet}%


\vspace{-1em}



  
  \date{May 28, 2024}


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\selectlanguage{english}
\begin{abstract}
The timing of different life history events are often correlated, and
selection might only rarely be exerted independently on the timing of a
single event. In plants, phenotypic selection has often been shown to
favour earlier flowering. However, little is known about to what extent
this selection acts directly vs. indirectly via vegetative phenology,
and if selection on the two traits is correlational. We estimated
direct, indirect and correlational phenotypic selection on vegetative
and reproductive phenology over three years for the perennial herb
Lathyrus vernus. Direct selection favoured earlier flowering and shorter
timespans between leaf-out and flowering in all years. However, early
flowering was associated with early leaf-out, and the direction of
selection on leaf-out day varied among years. As a result, selection on
leaf-out weakened selection for early flowering in one of the study
years. We found no evidence of correlational selection. Our results
highlight the importance of including temporally correlated traits when
exploring selection on the phenology of seasonal events.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

In seasonal environments, timing of events such as emergence, growth,
reproduction and seasonal senescence is often temporally and
developmentally correlated (Rathcke and Lacey 1985, Kelly 1992, O'Neil
1997, Aizen 2003, Sola and Ehrl\selectlanguage{ngerman}én 2007, Keenan and Richardson 2015).
Such correlations imply that selection might only rarely be exerted
independently on the timing of a single event. Instead, selection on a
focal phenological trait is often a combination of direct selection, and
indirect selection acting via other phenological traits (Rathcke and
Lacey 1985, Kelly 1992, Ehrlén 2015, Galloway et al. 2018). Selection
could also target the relative timing of phenological traits. Such
selection has been found for development time to maturity and
reproduction in both plants and animals (Kingsolver and Pfennig 2004).
Lastly, the strength or direction of selection on a focal trait might
often depend on the level of other phenological traits (correlational
selection; Lande and Arnold 1983, Phillips and Arnold 1989, Kelly 1992).
Taken together, this implies that to accurately estimate selection
acting on a focal phenological trait, it is necessary to account for
indirect selection via temporally correlated traits, as well as for
correlational selection.

In temperate plants, the timing of reproduction is a particularly
important life-history trait, as it influences interactions with the
biotic and abiotic environment and is often strongly linked to fitness
(e.g. Elzinga et al. 2007, Ehrlén 2015, Austen et al. 2017). Phenotypic
selection has been found to favour early flowering in many temperate
plant species (Harder and Johnson 2009, Munguía-Rosas et al. 2011), and
the consistency of this pattern has raised questions as to why plants do
not appear to respond to this selection (Austen et al. 2017). One
suggested explanation for the observed pattern is that selection on
flowering time is exerted indirectly via correlated life-history traits
and that this indirect selection is not accounted for in analyses (cf.
Rathcke and Lacey 1985, Austen et al. 2017). For example, several
studies have documented correlations between flowering time and
vegetative phenology (e.g. Kelly 1992, Diggle 1999, Yao and Mehlenbacher
2000, Sola and Ehrlén 2007, Brachi et al. 2012). If vegetative and
reproductive spring phenology are correlated, evolution of flowering
time is influenced also by selection on timing of vegetative phenology,
and selection will act simultaneously on both traits. In these cases, we
expect direct and indirect selection on both traits, as well as on their
relationship. Yet, very few studies have simultaneously examined
selection on vegetative and reproductive phenology, and quantified
direct, indirect and correlational selection (but see Kelly 1992).

In this study, we explored the relationship between vegetative spring
phenology and flowering phenology in a natural population of the
perennial understorey herb \emph{Lathyrus vernus} in Sweden, and used
multiple regressions to estimate phenotypic selection on these two
traits. Previous studies with this system have found that selection
favours earlier flowering in most years (Ehrlén and Münzbergová 2009,
Ehrlén and Valdés 2020), but also that flowering time is correlated with
start of shoot growth and leaf development (Sola and Ehrlén 2007). It is
thus possible that some of the observed selection on flowering time is
exerted indirectly, via selection on vegetative phenology. We expected
that most of the among-year differences in the distributions of
phenological traits in \emph{L. vernus} are driven by climatic
conditions (Ehrlén and Valdés 2020), while within-year differences among
individuals in these traits are caused by other factors, including
genetic differences. We also expected the causes of selection to both
depend on the density and frequency of phenological trait values within
years (soft selection, e.g. in terms of among-individual competition for
pollinators, and avoidance of seed-predation and vertebrate grazing),
and to be density-independent (hard selection, e.g. in terms of effects
of weather conditions) (cf. De Lisle and Svensson 2017). To assess
selection on vegetative and reproductive spring phenology, we monitored
leaf-out day and first flowering day, and recorded individual fitness in
terms of seed production, during three years. We addressed four specific
questions: 1) How closely are vegetative and reproductive phenology
correlated? 2) Is there phenotypic selection acting on vegetative and
reproductive phenology? 3) If so, to what extent is selection on
vegetative and reproductive phenology direct vs. indirect? 4) Is there
selection on the relationship between vegetative and reproductive
phenology, in terms of selection for time of development between
leaf-out and flowering or correlational selection?

\section*{Materials and methods}

{\label{materials-and-methods}}

\subsection*{Study system}

{\label{study-system}}

\emph{Lathyrus vernus} (L. Bernh) is a perennial herb that is
distributed over Europe and North-West Asia (POWO 2019). In the study
area, \emph{L. vernus} is mainly found in the understorey of deciduous
or mixed-deciduous forests. Each plant individual produces one to
several shoots that emerge early in spring and that can reach up to 40
cm in height (Ehrlén 2002). The leaves are pinnate and consist of 2-4
pairs of leaflets that unfold starting with the basalmost leaf on the
shoot and the pair of leaflets closest to the stem. Each flowering
individual produces 1 to 5 racemes, each having 1 to 9 flowers with the
basalmost flowers opening first (Ehrlén 2002). The racemes emerge from
leaf axes and start development when the neighbouring leaf unfolds.
Racemes and their adjacent leaf develop simultaneously, and although
flowers normally open after that leaflets have unfolded, the first
flower might sometimes open before the first leaflet has unfolded. A
previous study with this species showed that plants with an earlier leaf
development initiate flowering earlier than plants with a later leaf
development (Sola and Ehrlén 2007). In South-East Sweden, where this
study was carried out, flowering usually starts in late April to early
May. Fruits contain up to 18 ovules, and the mature seeds are dispersed
ballistically about 2 months after flowering (Ehrlén 1992). \emph{L.
vernus} reproduces only sexually.

The reproductive performance of \emph{L. vernus} is influenced by
several abiotic and biotic factors, some of which have been linked to
selection on flowering time. Cold April temperatures are associated with
weaker selection for early flowering, possibly because frost damage
reduces the benefit of flowering early (Ehrlén and Valdés 2020). Grazing
by roe deer (\emph{Capreolus capreolus} ) favours later flowering
because early-flowering individuals experience the highest levels of
damage (Fogelström and Ehrlén 2019). \emph{L. vernus} individuals rely
on pollinators (\emph{Bombus} sp.) for their reproduction and seed
production is sometimes pollen limited (Ehrlén 1992), suggesting that
there are fitness benefits of synchronising flowering with a high
availability of bumblebees. \emph{Bruchus atomarius} (Chrysomelidae)
larvae can damage a large proportion of the seeds before dispersal in
the study area. \emph{B. atomarius} seed predation is sometimes linked
to the timing of flowering in \emph{L. vernus,} but the relationship is
generally weak and its direction varies among years (Ehrlén 1996, Ehrlén
and Münzbergová 2009).

\subsection*{Data collection}

{\label{data-collection}}

This study was conducted in a mixed deciduous forest at Kålsö, Sweden
(58°56'N, 17°39'E), in the years 2013 to 2015. We recorded all
individuals in the population from shoots emergence in spring each year.
We only included individuals that had not been damaged (e.g. by
trampling, falling tree branches or by molluscs) before the first
recording. Just before the first leaves in the population started to
unfold, we increased the frequency of recordings to once or twice per
week and continued recordings until both leaf-unfolding and flowering
were terminated. During the study, individuals started to leaf-out 23
April -- 20 May 2013, 2 April -- 14 May 2014, and 26 March -- 5 May
2015. Individuals initiated flowering 10 -- 28 May in 2013, 18 April --
25 May in 2014, and 20 April -- 23 May in 2015, respectively. The
average time between leaf-out and first flowering day was 10 days in
2013, 18 days in 2014 and 20 days in 2015.

We used the estimated day of year when the first leaflet unfolded as a
measure of vegetative spring phenology. Leaflets open sequentially
in\emph{L. vernus} , and we recorded the number of unfolded leaflets at
each visit. Each individual was assigned a leaf-out day within the
interval between the recording when the first leaflet was observed to be
unfolded and the recording prior. We then used the proportion of open
leaflets at the first recording to estimate the most probable first day
with any open leaflets within that interval. We assumed that individuals
with a larger proportion of open leaflets had started to leaf out
earlier than individuals with a smaller proportion, and used a linear
model including the total number of leaflets and its squared term to
predict the number of unfolded leaflets at the first recording (Appendix
S1, Table S1). Based on the deviation of the observed proportion of
unfolded leaflets from this predicted proportion, we assigned
individuals to a most likely leaf-out day within each recording
interval, i.e. plants with larger deviations from the predicted
proportion of unfolded leaflets were assigned earlier leaf-out dates
than individuals with lower values (Appendix S1, Figure S1). We assigned
a leaf-out day to all individuals that were undamaged at leaf-out and
that had been recorded on at least one occasion before leaf-out.

We used the day of year when the first flower opened (first flowering
day) as a measure of flowering phenology. We recorded the size of the
largest bud for each flowering individual at each visit up to flowering.
The size of the largest bud at the recording before the recording of the
first open flower was then used to assign a first flowering day value
when first flowering day occurred between two recordings (Appendix S2).
First flowering day for individuals grazed before the first flower was
observed was estimated using information about the relationship between
first flowering day and bud size, day of year for the bud size
observation, and aboveground volume of intact individuals (Appendix S2).
We used the number of days from of leaf-out to first flowering day as a
measure of relative timing. We refer to this timespan as ``development
time'' henceforward.

The total number of shoots and the height and diameter of the focal
shoot was measured in early July in each year. We then calculated the
size of each individual in terms of the aboveground volume, using the
formula for the volume of a cylinder, applying this to a focal shoot,
and multiplying the resulting volume with the number of shoots
(aboveground volume = (0.5 × shoot diameter)\textsuperscript{2} × shoot
height × \selectlanguage{greek}π \selectlanguage{ngerman}× number of shoots). During each visit in spring, we recorded
incidences of roe-deer grazing. The final height of individuals grazed
before the last recording in spring was estimated from the shoot
height-diameter relationship of non-grazed plants (Appendix S3). For
individuals that had been grazed between the last recording in spring
and the final shoot recording in early July, we used the maximum
recorded height in spring as an estimate of final shoot height.

We used the number of developed seeds that had not been damaged
by\emph{B. atomarius} larvae, as a measure of plant fitness. We
collected all fruits in the population in early July each year. The
number of seeds in fruits that had not yet opened was counted in the
field, and each seed was checked for \emph{B. atomarius} entry holes.
The number of seeds in fruits that had opened prior to collection in the
field was estimated at the lab at Stockholm University. We counted the
total number of seeds from clearly visible indentations made by the
seeds in the pod walls. We also registered the number of \emph{B.
atomarius}entry holes in the pod wall, and estimated the proportion of
seeds that had been preyed upon from the relationship between the
proportions of seeds preyed upon and the number of \emph{B.}
\emph{atomarius} entrance holes in the pod wall of intact fruits. We
used the relationship between these two variables established in a
previous study with \emph{L. vernus} to estimate seed predation
(proportion of seeds preyed upon = 1-e\textsuperscript{-1.218}
\textsuperscript{\emph{B. atomarius} entry holes per seed}; Supporting
Information Appendix S3 in Fogelström and Ehrlén 2019). We were able to
get estimates of size, vegetative phenology, reproductive phenology and
fitness from 198, 207 and 207 individuals in 2013, 2014 and 2015,
respectively.

\subsection*{Statistical analyses}

{\label{statistical-analyses}}

All statistical analyses were carried out in R version 4.0.5 (R Core
Team 2021). Analyses were carried out separately for each year because a
large proportion of individuals (46.1\%) were recorded in only one year,
and including individual as a random effect in mixed effects models
could render the models unstable (Harrison et al. 2018). Summary
statistics for the variables used in the analyses are presented in
Appendix S4.

To estimate the strength of the correlation between leaf-out day and
first flowering day, we calculated Pearson's \emph{r} for the
untransformed variables We estimated phenotypic selection on leaf-out
and first flowering day using linear models (Lande and Arnold 1983).
Before analysis, we standardized leaf-out day, first flowering day, and
the development time between leaf-out and first flowering day
(\((x-\overset{\overline{}}{x})/\sigma\)) to a mean of 0 and unit standard deviation.
Aboveground volume was transformed to its natural logarithm before
standardization. Fitness was relativized\((x/\overset{\overline{}}{x})\) to a mean of
1. For soft selection, we expect the selection surface to be related to
the flowering time of a focal individual relative to other individuals
within a given year, rather than to day number. For hard selection, it
is possible that the selective surface is similar across years, e.g. in
terms of the probability of cold weather conditions, but that
individuals experience different parts of this surface in different
years. To examine selection corresponding to these two scenarios, we ran
two sets of selection models that were based on relativizing fitness and
standardizing traits within and across years, respectively (De Lisle and
Svensson 2017). These two sets of models yielded similar results, and we
present the results based on local within-year relativizations and
standardizations in the main text, and the results for global across
years relativizations and standardizations in Appendix S5. A relatively
large proportion of the flowering individuals produced no seeds (67\% in
2013, 31\% in 2014 and 48\% in 2015), and thus the residuals from the
linear models did not meet the assumption of normality. We therefore
evaluated the significance for the estimates from all linear models by
estimating 95\% bias-corrected and accelerated (BCa) bootstrap intervals
using the function `Boot' from the package `car' version 3.0-10 with
10000 replications (Fox and Weisberg 2019) and `boot.ci' in the package
`boot' version 1.3-27 (Davison and Hinkley 1997, Canty and Ripley 2021)
in R. Estimates for which the 95\% BCa interval does not overlap 0 are
considered significant at \selectlanguage{greek}α \selectlanguage{english}= 0.05.

To estimate total selection on leaf-out and first flowering day, we
estimated linear selection differentials from simple linear regression
models for the two traits, with fitness as a response variable and the
trait as the predictor variable. We estimated nonlinear selection by
adding a squared term to each model. To reduce bias of the selection
estimates caused by differences plant condition, we included plant size
as a covariate in these regression models (cf. Rausher 1992). Below, we
report the selection estimates from these models including size as
``total selection''. The results of corresponding models without size
are presented in Appendix S6.

To investigate to what extent selection on first flowering day was
direct vs. indirect via leaf-out day, we estimated direct selection in
terms of linear selection gradients for leaf-out and first flowering day
from a multiple linear regression with fitness as a response variable,
and aboveground volume as a covariate. We then examined whether these
estimates of direct selection differed from the estimates of total
selection on leaf-out and first flowering day. We considered estimates
to differ if the estimate of direct selection was outside the BCa
intervals for the estimate of total selection on each trait. Differences
between the estimate of direct selection and the estimate of total
selection on leaf-out day or first flowering day were interpreted as
that selection was exerted indirectly, via the other phenological trait.

We investigated selection on the relationship between leaf-out and first
flowering day in two ways: First, we estimated selection on the
development time from leaf-out to first flowering day, using linear
models with relative fitness as a response variable, development time as
a predictor variable, and plant size as a covariate. Second, we
estimated correlational selection by including the interaction leaf-out
day \selectlanguage{ngerman}× first flowering day in the multiple linear regression model with
relative fitness as a response variable, leaf-out and first flowering
day as predictor variables and with plant size as a covariate (cf.
Brodie 1992). A statistically supported effect of the leaf-out day ×
first flowering day interaction indicates that there is correlational
selection on the two traits, i.e. that the strength or direction of
selection on flowering time and timing of leaf-out is dependent on the
level of the other trait. Selection on development time, on the other
hand, could occur regardless of whether leaf-out and flowering time are
correlated or not, and regardless of whether selection on flowering time
is dependent on the timing of leaf-out or not.

Lastly, we ran models with leaf-out day\textsuperscript{2} and first
flowering day\textsuperscript{2} added to the model estimating
correlational selection, to estimate the nonlinear selection gradients.
All nonlinear selection estimates were doubled to obtain more accurate
measures of the magnitude of nonlinear selection (Stinchcombe et al.
2008).

\section*{Results}

{\label{results}}

Leaf-out and first flowering day were significantly positively
correlated in all years, and correlation coefficients ranged from 0.32
to 0.46 (Pearson's correlation; 2013: r = 0.46, t\textsubscript{196} =
7.29, p \textless{} 0.001; 2014: r = 0.41, t\textsubscript{201} = 6.44,
p \textless{} 0.001; 2015: r = 0.32, t\textsubscript{205} = 4.77, p
\textless{} 0.001; Fig. 1).

There was total selection for earlier leaf-out in all years (Table 1a).
Selection on leaf-out day in 2015 was nonlinear, fitness being highest
in individuals with an early to intermediate leaf-out day (Table 1a;
Fig. 2a). There was total selection for earlier flowering in 2013 and
2014, but not in 2015 (Table 1b; Fig. 2b). We found no evidence of
nonlinear selection on first flowering day in any of the study years
(Table 1b).

There was direct selection for earlier leaf-out in 2013, but in 2015
selection favoured later leaf-out instead (Table 1c; Fig. 2a). There was
no direct selection on leaf-out in 2014. Direct phenotypic selection
favoured earlier flowering in all three years (Table 1c; Fig. 2b). We
found no support for nonlinear selection on leaf-out or first flowering
day in any of the study years.

The estimate of direct selection on leaf-out in 2014 was just outside
the BCa interval for the corresponding estimate of total selection,
suggesting that selection for earlier leaf-out 2014 acted largely via
first flowering day (Table 1a,c). The estimates of total and direct
selection on leaf-out day in 2013 and 2015, and on first flowering day
in all years, did not differ significantly (Table 1b; Fig. 2).

Individuals with shorter development time between leaf-out and first
flowering day had higher fitness than individuals with longer
development time in 2014 and 2015, but not in 2013 (Appendix S7, Table
S1; Fig. 2c). We found no evidence of correlational selection on
leaf-out and first flowering day in any of the three study years (Table
1c).

\section*{Discussion}

{\label{discussion}}

For understorey plants in temperate regions, leafing out and flowering
before canopy closure should be important for fitness (Ida and Kudo
2008, Augspurger 2008). Because the timing of these two events are often
developmentally and temporally correlated, selection on either event
cannot be accurately estimated independently (Lande and Arnold 1983,
Kelly 1992, Diggle 1999). We showed that early leaf-out was consistently
associated with early flowering in a population of \emph{L. vernus} ,
and that phenotypic selection was acting on both traits. The direction
of direct selection on leaf-out day differed among years, whereas direct
selection consistently favoured earlier flowering. Selection on leaf-out
day acted indirectly, via flowering time, in one of the three years.
Selection favoured shorter development times in two of three years.
There was no evidence for correlational selection on leaf-out and first
flowering day. Taken together, our results show that flowering phenology
is correlated with vegetative phenology during spring, and that
selection on vegetative phenology can affect selection on flowering
time. Still, in our study selection favoured early onset of flowering in
all three years study, also when accounting for indirect selection via
leaf-out day.

Leaf-out and first flowering day were significantly positively
correlated in all three years, implying that flowering time is
constrained by the timing of leaf-out in \emph{L. vernus} , i.e.
individuals must start their vegetative development early in order to
flower early (cf. Diggle 1999, Sola and Ehrlén 2007). At the same time,
there was also considerable independent variation in the two traits.
Some of this variation might be attributed to differences in shoot
architecture, in terms of the placement of the first inflorescence on
the shoot relative to the first leaf (Diggle 1999, Sola and Ehrlén
2007). The moderately strong association between timing leaf-out and
flowering initiation found in this study is in accordance with the
results for other herb species (e.g. Kelly 1992, Dahlgren et al. 2007),
and suggests that selection can act independently on each trait, as well
as on relative timing.

We found direct phenotypic selection on leaf-out day in two of three
years. Interestingly, selection was in opposite directions in these two
years. Such among-year differences in the direction of selection might
be related changes in trait means due to plastic responses to
inter-annual variation in spring temperature. In our study, selection
favoured earlier leaf-out in the year when development in spring was on
average latest, but favoured later leaf-out in the year when average
spring development was fastest. This pattern could be the result of that
early leaf-out, relative to the population mean, implies a larger risk
in years when temperatures during early spring are higher and
development on average starts earlier, and that the benefits of an early
development are larger in years when development on average starts
later. Little is known about the agents of selection on leaf-out time in
plants, but for plants where shoot development starts in early spring it
is likely that weather conditions, e.g. in terms of the timing of
snowmelt or late frosts events, constitute important agents of selection
(cf. Inouye 2008, Augspurger 2013). In the alpine shrub \emph{Salix
herbacea} , selection favoured intermediate leaf-out time in sites with
late snowmelt, and early leaf-out in sites with early snowmelt (Sedlacek
et al. 2015), suggesting that variation in the direction of selection
was mediated by the local climate. Late spring frosts likely often
mediate selection for later spring development, as suggested by a study
reporting that late frost events in spring primarily damaged plants in
later developmental stages (Augspurger 2013). Selection on leaf-out
could also be mediated by seasonal variation in light availability, and
individuals that leaf-out early in spring before canopy closure are
likely to have a fitness advantage due to a longer period of resource
acquisition. Leafing out before canopy closure has been shown to be
important for the growth and survival of understorey tree saplings
(Augspurger 2008). Early spring phenology might also infer costs in
terms of increased herbivory (Roy et al. 2004, Sedlacek et al. 2015). In
\emph{L. vernus} , grazing is likely to be the most intense early in the
season when there are fewer alternative food sources, and it is possible
that also variation in grazing intensity among years contributed to the
observed variation in selection on vegetative phenology in our study
(7\% of individuals were grazed in 2013, 26\% in 2014 and 38\% in 2015).

The consistent selection for early flowering found in this study is in
accordance with the results of previous studies with \emph{L. vernus} ,
as well as with several other species (Harder and Johnson 2009,
Munguía-Rosas et al. 2011, Ehrlén and Valdés 2020). In our study, this
pattern persisted also after taking variation in vegetative phenology
into account. In addition, the results were similar for analyses that
relativized fitness and standardized phenology traits within vs. across
years (Table 1, Appendix S5). Selection for early flowering in \emph{L.
vernus} has been found to be mediated by warm April temperatures (Ehrlén
and Valdés 2020). In many understorey species, early flowering is likely
advantageous because light availability. and possibly pollinator
activity, decrease rapidly as the canopy develops (Bertin and Sholes
1993, Ida and Kudo 2008, McKinney and Goodell 2010). This advantage is
likely to be particularly large under warm spring conditions. Taken
together, selection on both leaf-out and first flowering day in \emph{L.
vernus} is likely driven by multiple biotic and abiotic factors that
vary in intensity and relative importance among years.

Although it has been suggested that selection on flowering time can be
mediated by correlated life-history traits, for example via correlations
between flowering initiation and flowering duration or between flowering
time and emergence time (Rathcke and Lacey 1985, Austen et al. 2017), we
are not aware of any previous study simultaneously estimating indirect
selection on flowering time and leaf-out day. In our study, there was
indirect selection for early leaf-out via start of flowering in 2014,
suggesting that early flowering initiation was a main benefit of early
leaf-out in that year. We found no statistical support for indirect
selection on flowering time acting via leaf-out day, and overall our
results suggest that observed consistent selection for early flowering
in \emph{L. vernus} is not driven by indirect selection via the timing
of leaf-out. Still, selection for later leaf-out might to some extent
have counteracted and weakened selection for early flowering in one of
our study years; selection for early flowering in 2015 was less than
half the size of selection in the two other years. In that year,
selection for early flowering was not detectable without accounting for
effects of leaf-out day. This suggests that accounting for differences
in vegetative phenology can affect the ability to correctly estimate
selection on flowering time. However, rather than overestimating the
frequency of selection for early flowering, which we might have expected
given the previous literature (e.g. Rathcke and Lacey 1985, Austen et
al. 2017), neglecting variation in leaf-out time in our analyses would
have led to underestimation of the strength of selection for early
flowering.

In our study, phenotypic selection favoured short development times
between leaf-out and first flowering day in two of three study years. We
are unaware of any previous studies estimating selection on the time
period between leaf-out and flowering. However, it has been hypothesised
that rapid development to reproduction should be favoured since it
decreases the likelihood of damage before reproduction (Williams 1966,
Post et al. 2008). Still, the fitness effects of a short development
time between leaf-out day and first flowering day are difficult to
separate from the effects of early flowering, and a short development
time might simply be favoured because it allows for early reproduction.
It is also possible that selection for short development time is the
result of that the optimal leaf-out time is close to the optimal timing
of flowering initiation, and that the fitness benefits of a short
development time reflect independent benefits of leafing out and
flowering during a particular period.

While we found selection on the relative timing of leaf-out and first
flowering day in terms of development time, we found no evidence of
correlational selection on the two traits. That is, the benefits of a
shorter development time were independent of the timing of leaf-out and
flowering initiation. Estimates of correlational selection are overall
rare (Kingsolver et al. 2001), and for plants, we are aware of only one
study reporting significant correlational selection on combinations of
phenological traits (flowering time and fruit maturation, Kelly 1992).
To better understand how correlations among seasonal events affect the
selection on a focal phenological trait, we therefore need studies
estimating selection on combinations of seasonal events, as well as
correlations and indirect selection for such events.

Our analyses of phenotypic selection provide important information about
how selection can act on correlated life history traits. However, it is
important to remember that our findings were based on female
reproductive success and not on lifetime fitness, and it is possible
that selection acting via male fitness differs from selection via female
fitness. However, we consider it unlikely that including also male
fitness would substantially alter our estimates of selection on
phenological traits. Pollen is transferred between male and female parts
of flowers that have a similar phenology in \emph{L. vernus} , and it is
likely that donating and recipient plants on average have a similar
phenology. It is also true that trade-offs between current and future
reproduction implies that some of the advantages of early flowering
observed in this study might be offset by reduced fitness in subsequent
years. Still, such costs of reproduction appear to be relatively modest
in \emph{L. vernus} , and the probability of flowering is higher in
individuals that flowered in the previous year than individuals that
were non-reproductive (Ehrlén and Van Groenendael 2001). Lastly, to
predict evolutionary responses to observed selection, it is essential to
know to what extent observed trait variation correspond to genetically
based variation vs. plasticity. A previous study with \emph{L.
vernus}showed that much of the variation in timing of flowering was
plastic (Fogelström and Ehrlén 2019), suggesting that observed
phenotypic selection might not correspond to genotypic selection and
lead to evolutionary responses.

\emph{Concluding remarks}

To obtain accurate estimates of phenotypic selection, it is essential to
consider also selection via correlated traits (Lande and Arnold 1983).
Yet, selection on the timing of life-cycle events is rarely estimated
comprehensively enough to detect indirect selection via other
phenological traits or correlational selection on trait combinations.
Our results show that correlations between reproductive and vegetative
phenology, in combination with contrasting selection on these two
traits, can affect selection on flowering phenology, although the
direction of selection on flowering time remained the same in our study.
More generally, the results illustrate that selection on phenological
traits can only be fully understood from the perspective of the seasonal
development cycle. Yet, we still know little about the relationships
among temporally correlated life history events, and the importance of
these events for fitness. Insights into the extent to which selection on
the timing of seasonal events is influenced by selection on temporally
correlated events are also essential to understand long-term responses
to anthropogenic climate change, as climate is likely to influence the
phenotypic expression of sequential seasonal events as well as the
optimal timing of these events (Li et al. 2016, Zohner et al. 2018,
Mohan 2019).

\textbf{Data accessibility statement}

The datasets and code supporting this article are available
at\emph{https://fogelstrom.github.io/Elsa/data\_files/Data\_SimultaneousSelection.zip}during
the review process, and will be uploaded to Dryad data repository once
the paper is accepted for publication.

\textbf{Competing Interests}

The authors declare no conflict of interests.

\textbf{Authors' Contributions}

\textbf{Elsa Fogelström} : Conceptualization (supporting); Data curation
(lead); Formal analysis (lead); Investigation (equal); Methodology
(supporting); Writing-original draft (lead); Writing-review \& editing
(lead). \textbf{Giulia Zacchello} : Investigation (equal). \textbf{Johan
Ehrlén} : Conceptualization (lead); Data curation (supporting); Formal
analysis (supporting); Funding acquisition (lead); Investigation
(supporting); Methodology (lead); Resources (lead); Writing-original
draft (supporting); Writing-review \& editing (supporting).

\textbf{Acknowledgments}

We thank Tove von Euler, Anna Herrström, Sara Jeschke\selectlanguage{english}-Sjoqvist, Annelie
Jorgensen, Malin Konig, and Jessica Oremus for assistance in the field.
We are grateful to Alicia Valdes and Ayco Tack who provided useful
comments on previous versions of the manuscript. This work was supported
by the Swedish Research Council (grant number 2019-04610 VR).

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\section*{Figure legends}

{\label{figure-legends}}

\textbf{Fig. 1.} Relationship between leaf-out day and first flowering
day for \emph{Lathyrus vernus} individuals in the years 2013, 2014 and
2015 (n\textsubscript{2013} = 198, n\textsubscript{2014} = 207,
n\textsubscript{2015} = 207).

\textbf{Fig. 2.} Relationships between fitness and (a) leaf-out day, (b)
first flowering day, and (c) the development time between leaf-out and
first flowering day in \emph{Lathyrus vernus} in the years 2013 (left
panels), 2014 (mid-panels) and 2015 (right panels). Each point
represents the raw trait-fitness values for one individual. Dashed lines
represent total selection predicted from multiple regression models with
fitness (the number of intact seeds, relativized within years) as a
response variable, and (a) leaf-out day, (b) first flowering day, or (c)
the development time between leaf-out and first flowering day as the
predictor variable, and plant size (aboveground volume) included as a
covariate. Solid lines represent direct selection, predicted from
multiple linear regression models as above, but with both leaf-out day
and first flowering day included as predictors. All predictor variables
were standardized within years to 0 mean and unit variance before
analysis. Plant size was ln-transformed before standardization.

\section*{Figures:}

{\label{figures}}

Figure 1\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 2\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\section*{Tables}

{\label{tables}}

\textbf{Table 1:} Total selection on (a) leaf-out day and (b) first
flowering day, and direct linear, nonlinear and correlational selection
for both traits (c). Model estimates with 95\% bias-corrected and
accelerated (BCa) bootstrap intervals. Estimates with BCa intervals that
do not overlap zero are in\textbf{bold} . Fitness (the response
variable, the number of intact seeds) was relativized and leaf-out day,
first flowering day, and plant size (above-ground volume) were
standardized to 0 mean and unit standard deviation before analysis.
Plant size was ln-transformed before standardization. The nonlinear
(quadratic) model estimates represent \selectlanguage{ngerman}½ of the magnitude of nonlinear
selection (Stinchcombe et al. 2008)\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
& 2013 & 2013 & 2013 & & 2014 & 2014 & 2014 & & 2015 & 2015 &
2015\tabularnewline
& Estimate & BCa interval & BCa interval & & Estimate & BCa interval &
BCa interval & & Estimate & BCa interval & BCa interval\tabularnewline
& & Lower & Upper & & & Lower & Upper & & & Lower & Upper\tabularnewline
Total selection: Leaf-out day & Total selection: Leaf-out day & Total
selection: Leaf-out day & Total selection: Leaf-out day & Total
selection: Leaf-out day & Total selection: Leaf-out day & Total
selection: Leaf-out day & Total selection: Leaf-out day & Total
selection: Leaf-out day & Total selection: Leaf-out day & Total
selection: Leaf-out day & Total selection: Leaf-out day\tabularnewline
Leaf-out day & \textbf{-0.584} & \textbf{-0.816} & \textbf{-0.347} & &
\textbf{-0.262} & \textbf{-0.388} & \textbf{-0.135} & & 0.096 & -0.066 &
0.255\tabularnewline
Plant size & \textbf{0.767} & \textbf{0.520} & \textbf{1.111} & &
\textbf{0.260} & \textbf{0.134} & \textbf{0.384} & & \textbf{0.278} &
\textbf{0.074} & \textbf{0.554}\tabularnewline
Leaf-out day\textsuperscript{2} & 0.096 & -0.073 & 0.227 & & 0.078 &
-0.003 & 0.166 & & \textbf{-0.173} & \textbf{-0.347} &
\textbf{-0.021}\tabularnewline
Total selection: First flowering day & Total selection: First flowering
day & Total selection: First flowering day & Total selection: First
flowering day & Total selection: First flowering day & Total selection:
First flowering day & Total selection: First flowering day & Total
selection: First flowering day & Total selection: First flowering day &
Total selection: First flowering day & Total selection: First flowering
day & Total selection: First flowering day\tabularnewline
First flowering day & \textbf{-0.608} & \textbf{-0.895} &
\textbf{-0.385} & & \textbf{-0.502} & \textbf{-0.622} & \textbf{-0.373}
& & -0.155 & -0.383 & 0.025\tabularnewline
Plant size & \textbf{0.691} & \textbf{0.435} & \textbf{1.024} & &
\textbf{0.122} & \textbf{0.001} & \textbf{0.243} & & 0.213 & -0.003 &
0.484\tabularnewline
First flowering day\textsuperscript{2} & 0.109 & -0.070 & 0.276 & &
0.056 & -0.064 & 0.161 & & -0.110 & -0.242 & 0.157\tabularnewline
Direct selection & Direct selection & Direct selection & Direct
selection & Direct selection & Direct selection & Direct selection &
Direct selection & Direct selection & Direct selection & Direct
selection & Direct selection\tabularnewline
Leaf-out day & \textbf{-0.396} & \textbf{-0.659} & \textbf{-0.127} & &
-0.100 & -0.220 & 0.029 & & \textbf{0.180} & \textbf{0.009} &
\textbf{0.359}\tabularnewline
First flowering day & \textbf{-0.425} & \textbf{-0.752} &
\textbf{-0.175} & & \textbf{-0.463} & \textbf{-0.589} & \textbf{-0.326}
& & \textbf{-0.229} & \textbf{-0.482} & \textbf{-0.046}\tabularnewline
Plant size & \textbf{0.685} & \textbf{0.433} & \textbf{1.013} & & 0.112
& -0.009 & 0.240 & & 0.205 & -0.006 & 0.475\tabularnewline
Leaf-out day\textsuperscript{2} & 0.107 & -0.051 & 0.295 & & 0.013 &
-0.072 & 0.110 & & -0.144 & -0.342 & 0.020\tabularnewline
First flowering day\textsuperscript{2} & 0.055 & -0.159 & 0.308 & &
-0.006 & -0.148 & 0.133 & & -0.113 & -0.256 & 0.160\tabularnewline
Leaf-out day \selectlanguage{ngerman}× First flowering day & 0.024 & -0.322 & 0.320 & & 0.129 &
-0.031 & 0.282 & & 0.032 & -0.129 & 0.234\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
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