Correspondence with Resilience Theory
The statistical protocol identified unique communities and revealed temporal transitions within the long-term vegetation records (Figs 1, 4 and 5). Community transitions were not evenly distributed through time, their frequency was not correlated with precipitation, and they could not be readily attributed to fire or grazing regimes. Previous studies have also found that cheatgrass invasion can proceed in the sagebrush-steppe in the absence of fire (Bangert & Huntly 2010), and that fire has weak and transient effects on the native species in absence of cheatgrass (Seefeldt, Germino & DiCristina 2007). While infrequent fires can promote cheatgrass establishment, cheatgrass can eventually be displaced by native perennial vegetation over decadal timescales (West & Yorks 2002; Mata-González et al. 2007). But, in areas where cheatgrass invasion has changed fire return intervals (Baker 2011; Balch et al. 2013), it can suppress fire intolerant native species (Davies et al. 2012). Instead, in both of these records without major fires, the majority of transitions occurred within an 8–10 year window coincident with increasing cheatgrass density, and then became infrequent after cheatgrass density peaked and thereafter transition frequency declined. Cheatgrass invasion at INEEL was associated with a reduction in the densities of several resident species (Fig. 2f–i), as well as with an increase in another invasive annual (desert madwort, Fig. 2j). In contrast, increasing cheatgrass densities were not associated with concomitant responses in the resident species at USSES (Fig. 2a–e). Cheatgrass invasion at USSES appears to be a case where an invasive species was simply added to the ecosystem, at low or moderate density, perhaps without modifying the pre-existing species interactions (Besaw et al. 2011). These contrasting responses may represent variation in susceptibility among eco-regions featuring different species and subspecies of Artemisia (Davies & Bates 2010).
Only about half of the potential transition pathways were recorded (nine of potential 20 at USSES, and 17 of 30 at INEEL); some transition pathways were clearly more prevalent than others (Figs 4 and 5), suggesting that certain types of dynamics are either exceedingly rare, or implausible (Bagchi et al. 2012). The absence of specific transition pathways may indicate the presence of strong negative feedbacks that increase resilience of the current states. While transitions between a pair of communities could be bidirectional, the frequency of transitions in one direction was not necessarily matched in the reverse direction. For example, communities, [H] and [I] at INEEL, were characterized by a large number of transitions into cheatgrass communities (Fig. 6). Asymmetric transitions were less pronounced at USSES (community [A], Fig. 4), which is consistent with the more transient nature of compositional change and the partial recovery of pre-invasion status after cheatgrass declined (Fig. 3a). Expectedly, if communities shared similar physiognomy and dominant species (Fig. 3b–e), they also exhibited frequent transitions (Figs 4 and 5). Otherwise, transitions were either infrequent or nonexistent among communities that differed greatly in species composition.
Greater dissimilarity of community composition following cheatgrass invasion at INEEL compared to USSES corresponds with higher cheatgrass density (Fig. 1b–c). A maximum relative density of 30% (maximum absolute density 3200 individuals m−2) at INEEL, appeared to have been sufficient to modify composition of the resident species, even in the absence of an accelerated fire regime. This reiterates the point that cheatgrass invasion can occur successfully in the absence of fire (Bangert & Huntly 2010) to establish conditions that are, at least partially, consistent with prevalent interpretations of thresholds, as evident at INEEL. In comparison, a maximum relative density of 24% (and maximum absolute density 700 individuals m−2) at USSES may have been insufficient to modify composition of the resident species and cheatgrass density declined after 8–10 years, which is inconsistent with the occurrence of thresholds.
If removal or reduction in livestock grazing at the two research sites had been a key driver of vegetation dynamics, we would have anticipated that most transitions would have occurred early in the vegetation record, but this was not the case. Neither was the incidence of community transitions correlated with precipitation, at either site, indicating that fluctuations in precipitation, at this sampling scale, either had a weak influence on the observed dynamics, or involved more complex time-lags not included in our analysis (Fig. 6). Previous studies have also noted a similar absence of simple correlation between precipitation and the dynamics of common plant species (Anderson & Inouye 2001; Adler, HilleRisLambers & Levine 2009).
Cheatgrass establishment at both sites (Fig. 1b–c) appears to coincide with periods of average to below-average annual precipitation (Fig. 6a). At USSES, precipitation during the 1930s was about 250 mm yr−1, compared with 300 mm yr−1 in the subsequent two decades (Fig. 6a). Similarly, Anderson & Inouye (2001) have also noted that precipitation at INEEL during the 1950s and 1960s was about 190 mm yr−1, compared with 220 mm yr−1 in the 1970s (Fig. 6a). The initial period of cheatgrass establishment appears to coincide with a period of average to below-average precipitation, and cheatgrass subsequently attained peak density during years of average to above-average precipitation, at both sites (Fig. 1b,c); the invasion may have been influenced by subtle climate variation at both sites. Also, frequency of precipitation events, especially in the fall and early spring, are likely to be related to cheatgrass growth and fecundity (Concilio, Loik & Belnap 2013), rather than annual total precipitation. Feedback mechanisms involving plant–soil interactions, seed banks and soil-resource acquisition may also be relevant to ecosystem resilience following cheatgrass invasion, in addition to fire, climate and grazing (Humphrey & Schupp 2001; Boxell & Drohan 2009; Leffler, Monaco & James 2011).
Community composition at USSES appeared to recover towards the initial conditions that were prevalent during the 1920s and 1930s following peak cheatgrass density during the 1940s (Fig. 3a), but there was no evidence for a similar recovery at INEEL (Fig. 3d). This inconsistency between sites may reflect: (i) a time-lag in relation to the continued existence of moderate cheatgrass densities at INEEL (Fig. 1) and/or (ii) the alteration of prevalent feedback mechanisms, or development of novel feedbacks, in response to occurrence of high cheatgrass density at INEEL. The decreasing number of community transitions following peak cheatgrass density can be interpreted as recovery of the former stable state at USSES (Bradley & Wilcove 2009), but as strengthening of the alternative state containing cheatgrass at INEEL where communities [F] and [J] could be separated from the others by a threshold (Fig. 5).
Assessment of Expert STMs
Communities identified in the vegetation record correspond, in large part, with those identified by the expert STMs. For USSES, communities [A], [B], [D] and [E] appear to correspond with community phases in the sagebrush and perennial grass state of the expert models, and community [C] matches the alternative state containing cheatgrass (Figs 1a and 4). However, there were frequent, and often bidirectional, transitions between these communities (Fig. 4), which are inconsistent with the interpretation of thresholds in expert models. Frequency and directionality of transitions between communities at USSES indicate that cheatgrass invasion may not represent a distinct alternative state defined by an irreversible threshold. But, for INEEL, communities [G], [H], [I] and [K] correspond well with different community phases comprising perennial grasses and sagebrush, while communities [F] and [J] correspond with a cheatgrass state (Figs 1a and 5). Transitions involving communities [G] and [K] were bidirectional and mostly symmetrical, which, once again, is inconsistent with the interpretation of thresholds. However, transitions to the cheatgrass communities were asymmetrical for communities [I] and [H] and indicate that a biophysical threshold may separate them from other states. These historical records highlight complex and varied dynamics, and clarify some practical challenges associated with threshold identification. The STM framework accounts for only broad approximations of these complexities and in so doing may overlook valuable information related to resilience and threshold conditions.
The ecological consequences of cheatgrass invasion were expressed within unexpectedly short temporal scales and with different effects on resident plant communities. Evidence for large temporal fluctuations in cheatgrass density is consistent with bioclimatic projections that cheatgrass dominance may last only a few decades at specific sites, although invasion may simultaneously expand into previously unoccupied areas (Bradley & Wilcove 2009). These temporal patterns emphasize a recurring dilemma with threshold interpretation, in that cheatgrass densities at USSES were ecologically reversible, but only in time frames that may seriously constrain management options (West & Yorks 2002; Mata-González et al. 2007). Distinctions between ‘ecological’ and ‘managerial’ thresholds have previously be recognized (Brown, Herrick & Price 1999) and may represent a viable solution to the recurring dilemma over temporal scale in threshold identification for STMs.
Implication for Resilience-Based Ecosystem Management
Long-term vegetation records, when analysed to represent unique communities and temporal transitions between them (Figs 4 and 5), provide a valuable source of information for construction and interpretation of STMs that is not accessible from other sources (Knapp et al. 2011). Specifically, these historical records quantitatively define four criteria – frequency, magnitude, directionality and temporal scale of community transitions – that may increase insight into resilience theory and its application to ecosystem management. Such quantitative information can inform the STM framework to refine procedures and guidelines to identify triggers, feedback mechanisms, temporal scales, at-risk communities and restoration pathways. For example, these records suggest that communities containing a moderate proportion of Montana wheatgrass (10–17% average relative density) may be ‘at-risk’ for cheatgrass invasion, as it had a high number of unidirectional transitions to the cheatgrass state. Recognition of ‘at-risk’ communities may be especially relevant because lack of clear early-warning signals constrain the ability to respond to ecological indicators of imminent dynamics. As a corollary, a high frequency of bidirectional transitions between communities [E] and [D] could be further investigated as potential restoration pathways which represent communities that have a high probability for recovery to a pre-invasion state (Stringham, Krueger & Shaver 2003; Ray Mukherjee et al. 2011).
In conclusion, analyses of historical vegetation records promise to enrich the STM framework with empirical patterns and relationships that can refine their construction rules and management value. These records clarify that community transitions can be induced by natural events and autogenic drivers, in addition to management actions that are frequently emphasized in STMs (Bagchi et al. 2012). Despite similar population trends, cheatgrass invasion yielded different outcomes at the two sites; it invaded specific communities, but not others; concentration of transitions within an 8–10 year window, collectively show the complexity of thresholds. Practical assessment and interpretation of ecological thresholds will benefit from consideration of a set of criteria describing community transitions. These criteria are likely to vary across biogeographic regions susceptible to cheatgrass invasion that are characterized by various Artemisia species and subspecies (Davies & Bates 2010), and necessitate more in-depth assessments of thresholds and alternative stable states.