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In common garden experiments, a number of genotypes are raised in a common environment in order to quantify the genetic component of phenotypic variation. Common gardens are thus ideally suited for disentangling how genetic and environmental factors contribute to the success of invasive species in their new non-native range. Although common garden experiments are increasingly employed in the study of invasive species, there has been little discussion about how these experiments should be designed for greatest utility. We argue that this has delayed progress in developing a general theory of invasion biology. We suggest a minimum optimal design (MOD) for common garden studies that target the ecological and evolutionary processes leading to phenotypic differentiation between native and invasive ranges. This involves four elements: (A) multiple, strategically sited garden locations, involving at the very least four gardens (2 in the native range and 2 in the invaded range); (B) careful consideration of the genetic design of the experiment; (C) standardization of experimental protocols across all gardens; and (D) care to ensure the biosafety of the experiment. Our understanding of the evolutionary ecology of biological invasions will be greatly enhanced by common garden studies, if and only if they are designed in a more systematic fashion, incorporating at the very least the MOD suggested here.
Plant population modelling has been around since the 1970s, providing a valuable approach to understanding plant ecology from a mechanistic standpoint. It is surprising then that this area of research has not grown in prominence with respect to other approaches employed in modelling plant systems. In this review, we provide an analysis of the development and role of modelling in the field of plant population biology through an exploration of where it has been, where it is now and, in our opinion, where it should be headed. We focus, in particular, on the role plant population modelling could play in ecological forecasting, an urgent need given current rates of regional and global environmental change. We suggest that a critical element limiting the current application of plant population modelling in environmental research is the trade-off between the necessary resolution and detail required to accurately characterize ecological dynamics pitted against the goal of generality, particularly at broad spatial scales. In addition to suggestions how to overcome the current shortcoming of data on the process-level we discuss two emerging strategies that may offer a way to overcome the described limitation: (1) application of a modern approach to spatial scaling from local processes to broader levels of interaction and (2) plant functional-type modelling. Finally we outline what we believe to be needed in developing these approaches towards a 'science of forecasting'.