Methods associated with modeling the evolution of plant populations and community
changes in response to plant evolution
Confidence intervals around matrix projections
fff
To place 95% confidence intervals on our projections for  c , we used a newly
developed parametric bootstrapping procedure (Stinchcombe unpublished MS). For each of
the elements of α and β, we considered the point estimates obtained from multiple regression
to be the mean of a normal distribution, with standard deviations equal to the standard error
of the regression coefficient. We then drew 999 values from each of these normal
distributions independently, assembled them into their appropriate vectors at random, and
fff
multiplied these values by the G matrix to obtain a distribution of  c . We used the 25th and
fff
975th values of each  c as estimates of the lower and upper 95% confidence limits. It should
be noted that this approach does not capture uncertainty in the estimation of G and it
therefore provides lower limit to the confidence limits of how insect species abundance will
change due to an evolutionary response in the plant.
Simulations of O. biennis evolution and community change
We used our empirical data to simulate evolution in O. biennis populations and changes
in the associated arthropod community. To understand whether our results varied across
different ecological conditions we conducted simulations in large and small O. biennis
populations, and populations that experienced either contstant size, or exponential growth
with periodic population crashes.
Simulations with constant population size K approximate large stable populations of O.
biennis at carrying capacity, which are most common in sandy soils near lakes and oceans.
Simulations began with all genotypes at equal frequency, and the associated life history, fruit
production, and arthropod community of each individual plant was determined by randomly
selecting plants within genotypes. Dead plants were replaced by a random draw from the pool
of seeds produced by the population in the previous time step. For a new plant of a given
genotype, its life history, fruit production, and community data were again determined by a
random draw from the field data. Simulations allowing only for genetic drift were identical
except that fitness was held constant across all plants. We ran 1000 replicate simulations
with K = 2800 (200 plants per genotype) and K = 28 (two plants per genotype initially), for
both the selection and drift scenarios.
Allowing seed production to determine the population dynamics produces exponential
population growth, which is expected to provide a relative fitness advantage to annual plants
when mortality is low (Hart 1977). These simulations were run exactly as above, but with the
following changes. We specified minimum and maximum Oenothera population sizes (Nmin
and K, respectively), with the initial population established at Nmin. Population growth was
determined by seed production (specified in the empirical data), and a recruitment parameter
specifying the proportion of seeds that produce new plants. Once the population exceeded K,
the population declined instantly to Nmin. The aim here was to approximate an extreme
scenario of repeated disturbances followed by periods of exponential growth, which are
frequently observed in Oenothera (M. Johnson pers. obs.). In the results reported here, we
used Nmin = 420, K = 5600 in large populations, and Nmin = 14, K = 280 in large populations;
recruitment was set at 0.03.