Key points are not available for this paper at this time.
The scientific aspiration of building causal knowledge has received little explicit discussion in ecology despite its fundamental importance. When methods are described as 'causal', emphasis is increasingly placed on statistical techniques for isolating associations so as to quantify causal effects. In contrast, natural scientists have historically approached the pursuit of causal knowledge through the investigation of mechanisms that interconnect the components of systems. In this paper, we first summarise a recently published multievidence paradigm for causal studies meant to reconcile conflicting viewpoints. We then describe some of the basic principles of causal statistics and the challenge of estimating pure causal effects. We follow that by describing basic principles related to causal mechanistic investigations, which focus on characterising the structures and processes conveying causal effects. While causal statistics focuses on estimating effect sizes, mechanistic investigations focus on characterising the attributes of the underlying structures and processes linking causative agents to responses. There are important differences between how one approaches each endeavour, as well as differences in what is obtained from each type of investigation. Finally, the case is made that an explicit assessment of existing mechanistic knowledge should be an initial step in causal investigations.
Grace et al. (Tue,) studied this question.