DOI: 10.5061/dryad.2547d7x2j
title: Seasonal evolution and its effects on competition

description: Rapid evolution can influence competitive dynamics. While theory and past
 work have focused on the competitive consequences of rapid evolution in
 response to competitors (e.g., character displacement), abiotic
 environmental factors are also responsible for much of the rapid evolution
 observed in nature. Yet, we lack knowledge of how evolution in response to
 a variable abiotic environment shapes competition between evolving
 species. Our knowledge of these effects is limited not only by the absence
 of direct quantification of environment-driven evolution’s effects on
 competition, but also by the rarity of systems in which environment-driven
 evolution itself has been measured across multiple competitors. Here, we
 used an outdoor mesocosm experiment with three species of naturally
 co-occurring Drosophilid fly competitors to quantify the seasonal
 evolution of stress tolerance and reproductive traits and the effects of
 this evolution on demographic performance under competition. We found that
 rapid seasonal evolution occurred across all three species, across all
 measured traits, and across every sampling interval. Throughout the course
 of their growing season, species first converged on similar phenotypic
 profiles (e.g., high heat tolerance and low fecundity), then evolved in
 parallel. This seasonal evolution had clear impacts on pairwise
 competition between species. In competition trials in a common garden
 greenhouse environment, seasonal evolution had the potential to alter
 demographic performance by over 30%. Under seasonally varying field
 conditions over the summer, in one species pair, seasonal evolution had 7x
 stronger effects on competitive performance than did the changing
 environmental conditions themselves. Our work highlights that the rapid,
 environment-driven evolution increasingly documented by evolutionary
 biologists can have a major, underappreciated influence on ecological
 competitive dynamics.
description: # Seasonal evolution and its effects on competition Here, we present data
 and code for the paper: Seasonal evolution and its effects on competition.
 In this study, we maintained outdoor mesocosms of three species of
 drosophilid flies, tracked the evolution of these populations over the
 summer and fall, and conducted competition trials in the lab and field to
 quantify the effects of seasonal evolution on competition. The repository
 contains datasets on phenotypic traits (body mass, fecundity, starvation
 tolerance, chill coma recovery, heat tolerance); competition trials;
 population dynamics in outdoor mesocosms; temperature data from ibuttons;
 and quantities derived from models using some of these data. The
 repository also contains code in R to analyze these datasets.
 Specifically, "pheno_analyze_dryad.Rmd" contains models
 analyzing phenotypic evolution; "pheno_pca_dryad.Rmd" analyzes
 this phenotypic evolution using principle components analysis;
 "haldanes_dryad.Rmd" computes rates of phenotypic evolution;
 "pcva_dryad.Rmd" conducts phenotypic change vector analysis,
 used to test parallelism and convergence of phenotypic evolution across
 species; "census_temp_dryad.Rmd" plots mesocosm census (i.e.,
 population size) measurements throughout the experiment in addition to
 temperature measurements; and "compet_analyze_dryad.Rmd"
 analyzes data from competition trials. Datasets for phenotypic traits all
 contain the following columns: treatment (for the month of the measurement
 or founder); species (hydei, melanogaster, or zaprionus); and cage (for
 the mesocosm cage). Below, we detail dataset-specific columns besides
 these. "body_mass.csv" contains data on male and female body
 mass. Other columns are: replicate (within cage and timepoint); sex; mass
 (mg), num (the number of flies in the pool being weighed); and mass.per
 (mass divided by number of flies). "fecundity.csv" contains data
 on fecundity assays. Pools of five females were placed into a bottle with
 a small petri dish of food slotted in at the top of the bottle. Every 24
 hours, for 72 hours, the dish was replaced with a fresh dish. All eggs
 laid were counted. Some flies occasionally escaped during the trial, so we
 also record how many flies were laying eggs per day. The other columns
 are: replicate, bottle (within each cage and timepoint), and eggs.1,
 eggs.2, and eggs.3 (eggs laid on specific days); fem.1, fem.2, and fem.3
 (the number of egg-laying females per day); avg.day1, avg.day2, and avg.3
 (eggs per female in each day); and eggs.per.fem (the average of the
 previous three columns). "starvation_tolerance.csv" contains
 data on starvation tolerance assays. Other columns include: replicate.vial
 (within cage and timepoint); replicate.female (within each vial);
 death.time; and time.median, which is the median time between the last
 time the fly was observed alive and the first time it was observed dead.
 "chill_coma.csv" contains data on chill coma recovery assays.
 Other columns include: replicate.vial (within cage and timepoint);
 replicate.female (within vial); and recovery.time (time in mins of
 recovery from chill coma). "heat_tolerance.csv" contains data on
 heat tolerance assays. These were mortality assays (for *D. melanogaster*)
 or heat knockdown assays (for *D. hydei* and *Z. indianus*). Further
 details for each species can be found in the manuscript. Other columns
 are: replicate.vial (within cage and timepoint); replicate.female (within
 vial); dead (for mortality assays); alive (for mortality assays; inverse
 of dead); knockdown.day (1 if the fly was knocked down at the end of the
 trial); and up_day (the inverse of knockdown.day). "census.csv"
 contains data on population dynamics in mesocosm cages. Columns are: cage;
 species; date (of measurement); sq1, sq2, sq3, sq4, sq5, sq6, sq7, and sq8
 (for the number of flies in each square at the top of the cage);
 total.count (summed across all squares); and total.estimate (total.count x
 20.125, a scaling factor which extrapolates from the squares at the top to
 the full top of the cage and the top 0.4 m of the sides of the cage, where
 flies aggregate at dusk, when these censuses were conducted.
 "ibuttons.csv" contains data on temperature from ibuttons placed
 in nine random cages. The columns are: date; time; temp (in C); and cage
 (a unique identifier but not the same as the actual cage ID's used in
 other datasets, as we never match this data to any other data; we only
 average across caged) "competition.csv" contains data on
 competition trials. Columns include: date; location (of the trial, either
 field or greenhouse); treatment (either stock or evolving for field
 trials, always evolving for greenhouse trials); id (either of the
 replicate for stock trials, or of the two cages from which evolving flies
 were sourced); cage; species pair (with just one letter from each species:
 M for melanogaster, H for hydei, or Z for zaprionus); replicate (just for
 stock trials); species (as each row has data on only one species); and
 count (for the number of adult offspring that ultimately emerged for that
 species and trial) "haldanes_errors.csv" has data on estimates
 of rates of evolution (in haldanes) calculated from the phenotypic data
 and models constructed in "pheno_analyze.dryad". I store this
 data in a csv because it takes a somewhat long time for the models to run,
 and I didn't want to rerun them every time I wanted to tweak a
 figure. The columns are: species, period (for the period over which
 evolutionary change occurred, either f_1 for founder to first timepoint,
 etc.); unit (for assay); haldanes (estimate); upper (upper 95% credible
 interval); lower (lower 95% credible interval); abs_haldanes (absolute
 value); abs_lower (lower 95% CI); abs_upper (upper 95% CI); raw (estimate
 of the number of SD's of change, not standardized by number of gens);
 raw_lower (lower CI); raw_upper (upper CI); and gens (estimated number of
 gens over this period). Finally, "all_effs.csv" contains
 estimates from the models in "pheno_analyze.dryad". Again, I
 save these estimates so I don't have to recalculate them every time I
 close R without saving the workspace and then want to change figures. The
 columns are: treatment; species; recovery (just named recovery from the
 chill coma recovery column, but is actually the median estimate for any
 assay); lower (lower 95% CI); upper (upper 95% CI); unit (for the assay);
 and date.