Nonlinear life table response experiment analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers.

Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. We extend regression LTREs to incorporate nonlinear (second-order) terms and compare the accuracy of both approaches for three previously published demographic datasets. We show that the second-order approach equals or outperforms the linear approach for all three case studies, even when all of the underlying vital rate functions are linear. Nonlinear vital rate responses to driver changes contributed most to population growth rate responses, but life history changes also made substantial contributions. Our results suggest that moving from linear to second-order LTRE analyses could improve our understanding of population responses to changing environments.

Mechanisms of prezygotic reproductive isolation between two sympatric species, Gelsemium rankinii and G. sempervirens (Gelsemiaceae), in the southeastern United States.

Natural hybridization plays a critical role in speciation, the maintenance of reproductive isolation, and genetic introgression. While many plant species have hybrid swarms in areas of sympatry, the lack of hybrids among closely related sympatrically distributed species suggests that strong pre- and/or postzygotic barriers exist to hybridization. Gelsemium sempervirens and G. rankinii (Gelsemiaceae) are sympatrically distributed southeastern sister taxa that have strong postzygotic barriers to hybrid formation and high levels of genetic differentiation. In this study, two sympatric populations in Lowndes County, Georgia were surveyed from 1999-2005 to assess the role of temporal and pollinator isolation as potential prezygotic barriers. The populations had mostly non-overlapping flowering periods in 2003-2005, with significant differences in time of peak flowering and length of flowering. Both species shared a similar community of flower visitors, with the apid bee Habropoda laboriosa the dominant visitor to both species. A choice experiment found that H. laboriosa visited both species but preferred G. sempervirens. The primary prezygotic barrier is temporal isolation preventing hybridization in spite of the shared pollinators. This study suggests that reliance on a shared pollinator during speciation may limit opportunity for divergent selection on flowering time.

The many growth rates and elasticities of populations in random environments.

Despite considerable interest in the dynamics of populations subject to temporally varying environments, alternate population growth rates and their sensitivities remain incompletely understood. For a Markovian environment, we compare and contrast the meanings of the stochastic growth rate (lambdaS), the growth rate of average population (lambdaM), the growth rate for average transition rates (lambdaA), and the growth rate of an aggregate represented by a megamatrix (shown here to equal lambdaM). We distinguish these growth rates by the averages that define them. We illustrate our results using data on an understory shrub in a hurricane-disturbed landscape, employing a range of hurricane frequencies. We demonstrate important differences among growth rates: lambdaS lambdaM. We show that stochastic elasticity, ESij, and megamatrix elasticity, EMij, describe a complex perturbation of both means and variances of rates by the same proportion. Megamatrix elasticities respond slightly and stochastic elasticities respond strongly to changing the frequency of disturbance in the habitat (in our example, the frequency of hurricanes). The elasticity EAij of lambdaA does not predict changes in the other elasticities. Because ES, although commonly utilized, is difficult to interpret, we introduce elasticities with a more direct interpretation: ESmu for perturbations of means and ESsigma for variances. We argue that a fundamental tool for studying selection pressures in varying environments is the response of growth rate to vital rates in all habitat states.