DOI: 10.5061/dryad.rxwdbrvm8
title: Fluctuating selection among years in a wild insect (Gryllus campestris)

description: Temporal or spatial variation in selection has the potential to explain
 long standing evolutionary problems such as evolutionary stasis and the
 maintenance of genetic variation. Long-term field studies of plants and
 wild vertebrates have provided some insights, but multigenerational
 measures of selection in wild invertebrates remain scarce. Short-lived
 ectothermic animals are likely to experience more pronounced environmental
 variation across generations than longer-lived and endothermic species. As
 a result, variation in selection may be particularly significant in these
 groups. Over ten years, we have monitored an individually tagged
 population of wild crickets (Gryllus campestris) using a network of up to
 133 day-night video cameras. The over a million hours of video that we
 watched allowed us to capture detailed information about naturally and
 sexually selected traits and life-history parameters. Over ten discrete
 generations, population size ranged from 51 to 546 adults. There were also
 substantial differences among years in the average values of traits
 including adult emergence date, body size, lifespan, and several
 behavioral traits. We combined measurements of these traits with
 individual fitness, measured as the number of adult offspring inferred
 from genetic-marker-based parentage assignments. This revealed substantial
 variation in selection gradients across years in several traits, with
 evidence that in one trait, adult emergence date, selection switched from
 positive to negative among years. Our findings suggest that fluctuations
 in selection gradients are common but complete reversals in the direction
 of selection may not be very frequent.
description: Study system Our data are the product of
 WildCrickets, a long-term project monitoring of a wild population of field
 crickets <em>G. campestris</em> in a meadow in northern Spain
 (Rodríguez-Muñoz et al. 2019d). This species has a single generation each
 year, with the first adults emerging in mid to late April and the last
 adults dying in mid-July. Individuals of both sexes build burrows as a
 refuge from predation and bad weather. Most interesting events occur at
 burrow mouths (Rost and Honegger 1987) with individuals spending only
 short periods moving between them. This lifestyle allows us to record the
 adult lives of the entire population in great detail, by attaching unique
 tags to individuals as they become adult and monitoring the population
 through daily surveys and a network of up to 133 day/night video cameras.
 During the adult season, males call from their burrows to attract females
 and both sexes move around the meadow, displacing members of the same sex
 from burrows and sharing burrows with a single member of the opposite sex
 (Fisher, Rodríguez-Muñoz, & Tregenza, 2016). We take a DNA sample
 at adult emergence, allowing us to assign parentage of each generation
 from amongst the adults sampled in the previous breeding season. Details
 on how the meadow is managed every year, our monitoring protocol and
 parentage assignment to estimate fitness, are available in Rodriguez-Muñoz
 <em>et al.</em> (2010) and Rodriguez-Muñoz <em>et
 al.</em> (2019d). The data included in this study cover eight
 generations within the period 2006-2014, and comprise 364,902 hours of
 video where a cricket was present under the camera. We did not include the
 years 2009, 2015 and 2016 as we do not have parentage assignments for
 offspring from those years. Parentage
 assignment Genetic profiling with microsatellite loci
 was performed to conduct parentage analysis. Details of this procedure are
 provided elsewhere (Bretman et al. 2011; Rodríguez-Muñoz et al. 2019d).
 Briefly, we used between 14 and 21 autosomal loci. Genotyping was
 performed on an ABI3730 capillary sequencer, using standard protocols
 (Ball et al. 2010) and scoring was performed using GeneMapper v3.7
 software. Parentage analysis was performed using genotype data combined
 with spatial and mating information in a Bayesian framework using the
 MasterBayes package (Hadfield et al. 2006; Koch et al. 2008). We estimated
 the pedigree on a year-by-year basis rather than as a single run, as field
 crickets are annual and thus generations are not overlapping. Using the
 modal parentage assignment for each individual, maternity was assigned to
 a sampled individual in the population for 1,326 out of 1,568 individuals
 (0.85 of the population) and paternity to a sampled individual for 1,441
 individuals (0.92 of the population). These figures, and our observation
 that during the breeding season, new adults (that we have not observed
 having overwintered there) occasionally appear in the meadow, indicates
 that there is limited immigration into our meadow (see also Bretman et al.
 2011). The median confidence of maternity assignments, to known
 individuals, was 0.985 and in paternity assignments was 0.987.
 Description of traits We explored variation
 in selection by analyzing the relationship between fitness (response
 variable) and eight morphological, behavioral and life history traits
 (predictors) that could affect reproductive success. We quantified fitness
 as the number of offspring produced per adult cricket in year
 <em>t</em> that survived to adulthood in year<em> t+1.
 </em>This fitness measure has the weakness that it is affected by
 both parent and offspring traits (as offspring traits will affect their
 survival to adulthood). This means there is the potential for our
 selection metrics to be affected by direct and indirect effects of traits
 on juvenile survival. However, our metric does provide a clear measure of
 each parent’s count of offspring that have the potential to pass their
 genes on to the next generation. In common with most invertebrates,
 counting the eggs laid by female crickets living in the wild is an
 impossible task, as each female injects hundreds of eggs into the soil in
 multiple locations. Our predictors included adult size, timing of adult
 emergence and longevity, and traits that quantified the intensity of
 polygamy, mobility (in relation to both time and space), and effort in
 attracting mates (for males only). In our study
 population, sexual activity does not start until about five days after
 adult emergence (Rodríguez-Muñoz et al. 2019a). We therefore only included
 data from events that happened at least five days post adult emergence.
 Adult crickets move frequently between burrows, leaving one burrow and
 arriving at another a few minutes later (median duration of each visit to
 a burrow is 1.2 h). Because we often have more burrows than cameras to
 monitor them, there are some crickets where we only have a small amount of
 observational data, perhaps from a single period during the individual’s
 life. Cricket behavior is dependent on the weather (for instance crickets
 avoid leaving their burrows when it is raining). It is also affected by
 the seasonal changes that occur as spring progresses and there are
 systematic changes in behavior with age (Makai et al. 2020). These effects
 mean that parameter estimates from crickets where we only have a few hours
 of observational data are likely to be very unreliable. To avoid the noise
 from these poorly sampled, but otherwise random individuals masking
 biologically relevant patterns, we excluded crickets observed for less
 than 96 h over their reproductive lives. For those living more than 35 d,
 at least 24 h out of the total observation time had to happen after that
 age, this meant that we had between 787 and 1374 unique individuals for
 each trait that we studied. This data exclusion is not based on any
 characteristics of the crickets, but on chance in relation to their visits
 to our cameras, so we do not expect it to cause any bias. The number of
 individuals per year and sex before removal of poorly sampled crickets is
 shown in Table S2. We quantified our predictors as follows:
 <em>Thorax width:</em> Measured from a digital
 picture using imageJ analysis software (Schneider et al. 2012).
 <em>Emergence date:</em> Day of the year when the
 cricket reached adulthood (counted from day 1 on 1 Jan).
 <em>Lifespan:</em> Number of days alive as an adult.
 For individuals where death date was not observed, we assume they died on
 the day after the last available observation.
 <em>Mates per day:</em> This is the mean number of
 unique partners a cricket mated with per day, over its whole life. We
 calculated it by dividing the total number of different mates an
 individual was observed mating with by the total number of days of video
 observation we had of that cricket. <em>Burrows
 per day:</em> Mean number of different burrows visited per day. We
 calculated it by dividing the total number of unique burrows visited when
 under observation by the total number of days we observed the cricket with
 a camera. <em>Distance among burrows:</em>
 Maximum distance between any two pairs of burrows from among all the
 burrows where we observed the cricket during its adult life. Moving
 exposes crickets to risk of predation and reflects investment in
 reproduction, as food resources are hyper-abundant through the meadow. To
 control for the effect of time under observation, we divided the maximum
 distance by the total number of days the target individual was
 observed. <em>Time at burrow:</em> Median
 duration of time spent on each visit to any burrow, calculated from all
 the visits recorded for this individual over its life. Usually, sexual
 activity starts a few days after adult emergence. Hence, for crickets with
 known emergence date, we excluded all the movements happening before 5
 days of age. <em>Calling effort (males
 only):</em> We recorded point samples every two minutes during the
 first 10 minutes of every hour where we observed the target male with a
 camera. If we saw the male calling at any of those point samples, we
 recorded him as calling. We then calculated calling effort as the
 proportion of hourly observations when the male was calling. As for the
 previous trait and for the same reason, we excluded the first five days
 after adult emergence. We also excluded all individuals with less than 10
 samples in total to avoid very unreliable estimates due to small sample
 size. Statistical analyses To
 compare selection among years we need a common model to describe the
 relationship between traits and fitness. Inspection of graphs for
 individual years indicated that the dominant patterns we observed were
 either no effect or directional selection within years, we therefore
 estimated linear selection gradients. To test for variation in selection
 gradients, we followed the common approach of running generalized linear
 mixed models independently for each trait. To simplify the analyses, we
 ran separate models for each sex. This approach is also appropriate
 because it is likely that the same traits are under different selection
 regimes in males and females; for instance, movements around the meadow
 have a completely different function in mate-searching males than they do
 in females (who are surrounded by the singing of potential mates). We did
 not include age in our analyses because our species has discrete
 generations; adults emerge in early to mid-spring and die between late
 spring and early summer within the same year. We ran analyses in R (R
 Development Core Team 2020, v. 4.0.3) and R Studio (R Studio Team 2020, v.
 1.3.1093 ) using the lme4 package (Bates et al. 2015) with a Negative
 binomial family distribution to cope with overdispersion. This approach
 uses a log-link function by default, which puts fitness on a relative
 scale equivalent to the traditional approach of dividing by mean fitness
 and using linear regression (e.g. Lande and Arnold 1983; Bonnet and Postma
 2018). This means that our fitness gradients can be directly compared with
 those estimated using earlier methods. We checked whether the output of
 the models showed overdispersion by using the method proposed by Harrison
 (2014). Before the analyses, we centered all traits within years (by
 subtracting the mean of the year) and standardized them across years by
 dividing by standard deviation of the centered values. The phenotypic
 traits of small ectotherms like these crickets are extremely dependent on
 weather conditions which vary substantially from year to year. A cold
 winter will affect the size and emergence date of the entire cohort and
 failing to control for this means that variation is dominated by
 environmental effects. In view of this, we centered traits within years.
 For those traits where means are similar across years, centering within
 years will not make a relevant difference. However, in the supplementary
 information, we have also included the same analyses based on data
 centered among years, so that it is possible to compare results between
 both approaches. We estimated selection gradients
 separately for each trait using mixed models with Fitness as the response
 variable, focal Trait (as described above) as a fixed effect, and Year as
 a random effect. In each case, our main ‘random slopes’ model allowed both
 the average Fitness and the effect of the focal Trait on Fitness to vary
 across each Year in the random effects, i.e., (Fitness ~ 1 + Trait + (1 +
 Trait|Year)). We then ran a second model whereby the effect of the Trait
 on Fitness was constrained to be the same across all Years, with the
 ‘random intercepts’ allowing Years to vary only in their average Fitness,
 i.e., (Fitness ~ 1 + Trait + (1|Year)). We then compared these models
 using a likelihood ratio test  to assess whether the contribution of the
 random slopes term for Year was significant. We followed  Visscher (2006)
 and assumed the difference in loglikelihood between the models was
 distributed as a 50:50 mix of  and . This contribution indicates whether
 the selection gradient varies among years. When a gradient varies, it can
 do so without changing the direction of selection, so that the differences
 are just in the intensity of selection. Alternatively, variation can
 involve changes in the direction of selection (reversals); i.e. a trait
 can have a positive or negative effect on fitness depending on the year.
 To provide insights into how selection gradients vary among years, we
 extracted the coefficients per year from the random effects, and estimated
 their confidence intervals by bootstrapping using the bootMer function in
 <em>lme4</em>.
description: # Fluctuating selection among years in a wild insect
 [https://doi.org/10.5061/dryad.rxwdbrvm8](https://doi.org/10.5061/dryad.rxwdbrvm8) ## Description of the data and file structure This README explains the meaning of each of the variables included in the "VariationSelection" data file. Each row represents an individual. The study is based on the monitoring of a wild population of the field cricket (*Gryllus campestris*), a univoltine species, over eight consecutive generations.  ### Files and variables #### File: VariationSelection.txt **Description:** Each row represents an individual. This species is univoltine. Missing data are shown as n/a. ##### Variables * Fitness: Number of adult offspring left for the next generation * Year: Year of the breeding season for that adult * Sex: Female (F) or male (M) * TWc: Thorax width (mm) centered within year * TWaY: Thorax width (mm) centered within year and standardised across years * EDc: Julian emergence date (days from 1st January) centered within year * EDaY: Julian emergence date (days from 1st January) centered within year and standardised across years * LSc: Lifespan (d) centered within year * LSaY: Lifespan (d) centered within year and standardised across years * MDc: Mates per day centered within year * MDaY: Mates per day centered within year and standardised across years * BDc: Burrows per day centered within year * BDaY: Burrows per day centered within year and standardised across years * DBc: Distance among burrows (m) centered within year * DBaY: Distance among burrows (m) centered within year and standardised across years * TBc: Time at burrow (h) centered within year * TBaY: Time at burrow (h) centered within year and standardised across years * CEc: Calling effort (proportion of time spent calling) centered within year * CEaY: Calling effort (proportion of time spent calling) centered within year and standardised across years ## Code/software Any software able to open a text file should work (e.g. Notepad from Windows)