Chemical diversity among populations of Mikania micrantha: geographic mosaic structure and herbivory

Abstract

Populations of the same species vary in their secondary metabolite content. This variation has been attributed to biotic and abiotic environmental conditions as well as to historical factors. Some studies have focused on the geographic variation of chemical diversity in plant populations, but whether this structure conforms to a central-marginal model or a mosaic pattern remains unclear. Furthermore, assessing the chemical diversity of invasive plants in their native distribution facilitates the understanding of their relationships with natural enemies. We examined the geographic variation of chemical diversity in Mexican populations of the bittervine weed Mikania micrantha and its relationship to herbivore damage. The foliar volatile terpenoid blend was analyzed in 165 individuals of 14 populations in the Pacific and Gulf of Mexico tropical watersheds. A cluster analysis grouped individuals with similar terpenoid blends into 56 compositional types. Chemical diversity was measured using the number of compounds and their concentration within the blends for individuals, and the number and frequency of compositional types for populations. A stepwise multiple regression analysis performed with geographic, climatic, and chemical diversity variables explained herbivore damage. However, population-level chemical diversity was the only variable found to be significant (β = -0.79, P = 0.042) in the model (R(2) = 0.89). A Mantel test using Euclidean distances did not indicate any separation by geographic origin; however, four barriers were identified using Monmonier's algorithm. We conclude that variation in population-level chemical diversity follows a mosaic pattern in which geographic factors (i.e., natural barriers) have some effect and that variation is also associated with the local intensity of herbivore attack.

Optimal defense theory explains deviations from latitudinal herbivory defense hypothesis

“…If differences in PPG concentrations or arsenal composition are responsible for variation in resistance to different herbivore species, plant populations may adapt to produce higher levels of the PPGs that promote resistance against the most damaging and/or abundant herbivores in a community. The evolution of such geographic mosaics in plant defense driven by herbivore richness or abundance is common (Berenbaum and Zangerl 1998, Zangerl et al 2003, Bravo-Monzón et al 2014.…”

The smell of environmental change: Using floral scent to explain shifts in pollinator attraction

“…For example, one could field sample floral VOCs of plants growing along environmental gradients or use time-for-space substitutions to sample floral volatiles of plants over multiple years that vary in environmental conditions. Research investigating genetic and geographic patterns in plant secondary chemistry also offers methodological insights to those interested in conducting similar studies with compounds related to floral scent (e.g., Keefover-Ring and Linhart, 2010; Holeski et al, 2013; Bravo-Monzón et al, 2014; Keefover-Ring et al, 2014; Pratt et al, 2014). Although taking advantage of naturally occurring environmental conditions offers a broader scale of inference, it is more difficult to attribute any shifts in floral volatiles or pollinator behavior to a particular environmental factor, and any patterns in pollinator attraction may be related to differences in local pollinator communities (e.g., Devoto et al, 2005; Bates et al, 2011).…”

Central-marginal population dynamics in species invasions

“…Leading researchers have called for more proactive approaches to the invasive species crisis that incorporate prediction, monitoring, and early detection. However, there have been few satisfactory attempts to predict future spread of invasive species at both patch (or population) and range (or species) levels (e.g., Figure 1), and even fewer through incorporating broad scale geographic context and state-of-the-art spatial analytic technology (Petrovskii and Li, 2005; but see Alexander and Edwards, 2010;Colautti et al, 2010;Bravo-Monzón et al, 2014).…”