Biological Control of Plant Invaders: Regional Patterns, Field Experiments, and Structured Population Models

Population demographics and trade-offs to reproduction of an invasive and noninvasive species of Rubus

“…For example, they can be used to identify the most important life-history stage or process to the population growth of a species, information which can be used to guide decisions with the objective of sustaining or increasing the population size of rare species (e.g., Charron and Gagnon 1991;Maschinski et al 1997;Kaye et al 2001) or to target and decrease the population size of invasive species (e.g., Maxwell et al 1988;Shea and Kelly 1998;McEvoy and Coombs 1999;Parker 2000). A further application of demographic analyses to invasive plant study is they can provide valuable, but often ignored, insights into the connection between theories of plant invasions and quantitative field data, such as determining whether population growth remains constant throughout the stages of invasion (Parker 2000).…”

Theoretical contributions to biological control success

“…We found that the ragwort flea beetle was by far the more effective regulator of ragwort abundance (Dauer et al 2012;McEvoy and Coombs 1999); the flea beetle epitomizes the 'search and destroy' strategy (Dauer et al 2012;McEvoy and Coombs 1999), offered as an alternative to creating a low, stable equilibrium on a local spatial scale (Murdoch et al 1985). We found that the cinnabar moth makes a relatively small but detectable contribution to ragwort suppression (Dauer et al 2012;McEvoy and Coombs 1999), thereby providing marginal support for the 'complementary enemies' model (Murdoch et al 1985). The ragwort flea beetle attacks perennial rosettes that live up to five years.…”

“…Our linear, deterministic model best represents a population growing exponentially following disturbance, and it permits elasticity values to be easily obtained analytically, while elasticity methods for non-linear models are more difficult. This simple model performed remarkably well for projecting the speed of control, the time taken to eliminate an incipient outbreak (Dauer et al 2012;McEvoy and Coombs 1999), a valuable metric for management. Perturbation analysis (including elasticity, sensitivity, decomposition of effects) provided a framework for 'targeted life cycle disruption' as a control strategy, identifying which life cycle transitions are potentially most influential on ragwort's population growth rate, and which of these transitions are actually the most variable and amenable to management manipulations (Dauer et al 2012;McEvoy and Coombs 1999).…”

“…This simple model performed remarkably well for projecting the speed of control, the time taken to eliminate an incipient outbreak (Dauer et al 2012;McEvoy and Coombs 1999), a valuable metric for management. Perturbation analysis (including elasticity, sensitivity, decomposition of effects) provided a framework for 'targeted life cycle disruption' as a control strategy, identifying which life cycle transitions are potentially most influential on ragwort's population growth rate, and which of these transitions are actually the most variable and amenable to management manipulations (Dauer et al 2012;McEvoy and Coombs 1999). The standard approach in biological weed control is to target plant parts like roots, shoots, leaves, and seeds (a way to kill individuals), but targeting life cycle transitions is now generally recognized as a more reliable way to control populations (Shea et al 2010).…”

“…The ragwort flea beetle reduces all transitions in the ragwort life cycle graph used to develop the model. The cinnabar moth reduces only of one (from flowering plants to rosettes) of the two transitions (the 'biennial' transitions) identified as potentially most influential for ragwort population growth (Dauer et al 2012;McEvoy and Coombs 1999). Third, we asked: is plant competition necessary to augment the impact of natural enemies on the dynamics of weed populations?…”