Temporal patterns in plant communities and the factors that regulate those dynamics have been fundamental concerns of biologists for some time. Many models have been developed to understand and predict population dynamics, but with the exception of several models of mutualisms (e.g. Holland, DeAngelis & Schultz 2004) most have focused on negative interactions such as predation (e.g. Polis & Strong 1996) and competition (e.g. Tilman 1985). While we have gained much insight into the relevant parameters and behaviours in predatory and competitive systems through both theoretical and empirical research (Deangelis & Waterhouse 1987), dynamics generated by positive interactions within the same trophic level (i.e. facilitation) have not been thoroughly addressed theoretically (but see Travis, Brooker & Dytham 2005), nor have many empirical studies focused explicitly on temporal patterns in systems where facilitation is a predominant form of interaction (but see Armas & Pugnaire 2005; Miriti 2007; Butterfield et al., in press). To advance our understanding of the effects of facilitation on population and community dynamics, existing information must be synthesized and predictive models developed to guide future research (Brooker et al. 2008).
The outcome of biotic interactions is often strongly influenced by variation in the abiotic environment, and within a given habitat environmental conditions can vary dramatically through time, influencing the outcome of biotic interactions and driving community dynamics (Ernest, Brown & Parmenter 2000; Tilman, Reich & Knops 2006). Likewise, the intensity and importance of biotic interactions vary spatially among habitats along gradients of environmental stress or resource availability (Callaway & Walker 1997; Goldberg & Novoplansky 1997), hereafter referred to as ‘environmental severity’sensuBrooker et al. (2008). The importance of competitive interactions generally declines with environmental severity, whereas facilitation is most apparent and influential in moderate to high-severity environments (Bertness & Callaway 1994; Callaway et al. 2002; Travis et al. 2006; Callaway 2007). Assessing the relative effects of competition and facilitation on community dynamics is not, however, a simple matter of comparing observed temporal patterns in sites with different levels of environmental severity. Empirical assessment of facilitation-driven dynamics is impossible without corresponding spatial data of seedling establishment to determine the strength and temporal variation in facilitation (Miriti 2007; Butterfield et al., in press). An additional complication is that potential dynamics generated by positive feedbacks via facilitation are difficult to differentiate from abiotically driven fluctuations in abundance observed in chronically low-productivity environments (Holmgren et al. 2006). Likewise, temporal interactions between facilitative effects and environmental conditions can be significant, such that static spatial predictions of biotic interactions do not reveal the true variation in competitive and facilitative outcomes (Tielbörger & Kadmon 2000; but see Holzapfel et al. 2006). These two factors – differentiating temporal pattern from process, and modulation of biotic interactions by environmental conditions – are the keys to assessing controls on plant community dynamics in severe environments.
A sufficient number of empirical experiments and observations now exist to develop predictive models of facilitation-driven dynamics. While few in number, studies that measure the sign and magnitude of biotic interactions through multiple years have provided insightful results. Increasing environmental severity in some communities causes a shift from competition toward facilitation (Greenlee & Callaway 1996), whereas the opposite occurs in other communities (Tielbörger & Kadmon 2000). This suggests that the relationship between environmental severity and facilitation is unimodal (hump-shaped) and is supported by temporally static studies across spatial gradients in environmental severity (Maestre & Cortina 2004; Michalet et al. 2006). This congruence of spatial and temporal variation in interaction outcomes is an important step in unifying theories of facilitation and points out the importance of understanding the range of spatial and temporal variation in environmental conditions within a study. While the unimodal relationship between facilitation and environmental severity is relatively consistent at certain scales (M. Holmgren & M. Scheffer, unpublished data), environmental severity is relative to the stress tolerance and resource use adaptations of species, such that some species within a community may exhibit different facilitative responses to environmental variation (Maestre et al. 2009) and entire communities may be restricted to a narrow range of the unimodal facilitation–severity curve (Butterfield et al., in press). In addition to the complexity introduced by local adaptation and species specificity in short-term experiments (Maestre, Vallardes & Reynolds 2005), studies conducted at broader temporal scales have revealed other factors that alter the effects of interaction intensity and environmental fluctuations on dynamics. Many interactions are highly dependent upon the ontogenetic stage of the interacting individuals (Miriti 2006), which can generate time lags in facilitative effects and responses. Facilitation may also be a function of the size or quality of benefactors rather than simply their abundance, and therefore be regulated by both demographic and environmental factors. For example, plant cover is influenced by both variation in environmental conditions and number of individuals, and this dual regulation of facilitation can lead to unique dynamics that either buffer or amplify environmental variation (Butterfield et al., in press). While complex, the basic patterns generated by facilitation can be explained by the manner in which plants modulate environmental fluctuations within the microenvironment that they occupy, with additional variation relatable to particular facilitative mechanisms and species-specific characteristics.
The goal of this paper is to present a general model of facilitation-driven community dynamics that is generally applicable across facilitative mechanisms and the biomes in which facilitation is a persistent form of interaction. By synthesizing existing small-scale experiments and observations, the model presented here can be extrapolated to population and community-level dynamics. Empirical research conducted at broader temporal scales is also used to develop slightly more complex models that incorporate developmental lags and demographic regulation of facilitative mechanisms, accompanied with a discussion of the circumstances under which these models may be more or less relevant. Finally, I discuss the effects of interspecific variation in facilitative and competitive effect and response traits, and how these characteristics might influence overall community stability and dynamics. The focus of this study is on ecosystems in which facilitation is a persistent and important form of biotic interaction (e.g. arid, alpine and salt marsh ecosystems, etc.), in contrast to successional processes (Gómez Aparicio 2009). The community dynamics of these low productivity, severe environments are not well understood relative to more productive systems, and must be assessed within a unique framework that incorporates facilitation.