The occurrence of alternative states and thresholds has become a central issue at the interface of basic and applied ecology (Beisner, Haydon & Cuddington 2003; Hobbs & Cramer 2008). Alternative states (or regimes) represent major shifts in ecosystem function. The shifts are due to changes in the abundance and composition of dominant species and associated biological and physical processes. Alternative states tend to be recognized when ecosystem changes have societal significance and are persistent with regard to management timeframes (Suding, Gross & Housman 2004). Thresholds describe the changes in drivers and their interactions with local conditions that result in alternative states (Scheffer & Carpenter 2003; Suding & Hobbs 2009).
State-and-transition models (STMs) describe states, thresholds and management conditions leading to the formation of alternative states (state transitions). Although such models were first formalized for rangeland management (Westoby, Walker & Noy-Meir 1989), STMs (and similar conceptual models) have become a common means to synthesize information about state transitions in a variety of terrestrial ecosystems (see examples in Archer 1989; Milton et al. 1994; Bestelmeyer et al. 2004; Chartier & Rostagno 2006; Hobbs & Suding 2009; Zweig & Kitchens 2009). In south-western Australia, for example, land managers use STMs to assist with the restoration of jarrah forest in areas mined for bauxite (Grant 2006). In the United States, federal land management and assistance agencies have formally adopted STMs as a means to set management benchmarks and recommend practices to achieve desired conditions in rangelands and forests (http://www.fs.fed.us/biology/soil/Signed_RIESM_2010.pdf).
State-and-transition models for terrestrial ecosystems are typically developed using some combination of informal historical observations, expert knowledge, inventories of states with space-for-time substitution assumptions, monitoring of state transitions and controlled experiments (Briske, Fuhlendorf & Smeins 2003; Bestelmeyer et al. 2009). Reference (e.g. historical, desired or non-degraded) states and alternative (e.g. degraded) states are defined based on persistent differences in plant community productivity, composition and soil function. Narrative descriptions of transitions describe how management actions and environmental conditions interact to produce alternative states. The transition narratives are often a basis for on-the-ground actions. Observational or experimental data supporting the STMs are usually gathered at a limited number of points without thorough consideration of scale and spatial heterogeneity. Such non-spatial approaches yield valuable information about the possible alternative states and the mechanisms underlying transitions in broad ecosystem types (e.g. Brown & Archer 1999; Beckage & Ellingwood 2008; Okin, D’Odorico & Archer 2009).
Non-spatial approaches, however, miss important information about state transition processes. Transitions are often patchy and asynchronous (Van Nes & Scheffer 2005; Bestelmeyer, Ward & Havstad 2006). For example, within a landscape of 50 000 ha, state transitions may be localized to certain areas. This spatial pattern in state transitions might be partly a reflection of spatial variation in historical driver intensity including grazing pressure, deforestation rates, fire ignitions or localized drought (Pickup, Bastin & Chewings 1998; Foster et al. 2003; Jasinski & Payette 2005). Variation in soils and landforms filter the effects of drivers, compounding the spatial variation in state transitions (McAuliffe 1994; Fensham & Holman 1999). Spatiotemporal patterning in transitions may be evident at various scales. Localized state transitions may occur as fine-scale changes at the level of plant patches that gradually accumulate or transitions may radiate to broad areas from points of initial impact. These effects can be caused by feedbacks between patch spatial patterns at different scales and disturbance, resource redistribution or even climate (Peters et al. 2004; Rietkerk & Van De Koppel 2008). The combination of scale dependent and spatially overlapping processes produces the complex spatial patterns in alternative states typically observed in managed landscapes. Investigations of these spatial patterns could provide insights to improve monitoring and management that would not arise from simpler, non-spatial models (Pringle, Watson & Tinley 2006).
The importance of scale and pattern-process linkages in land management has long been recognized and general theory is well developed (e.g. Wu & Loucks 1995; Liu & Taylor 2002 and chapters therein). Nonetheless, multi-scale spatial perspectives have not been widely incorporated into STMs and managers do not always appreciate the significance of spatial patterns. We propose that STMs can be improved by including data-supported information on spatial processes in STMs. We suggest that three classes of spatial processes should be recognized in studies of state transitions: (i) spatial variation of land use drivers, (ii) spatial dependence in response to drivers imposed by soils and landforms, and (iii) spatial contagion in responses to drivers due to vegetation-environment feedbacks. Consideration of each class is required to robustly translate STMs into land-management applications.
We begin our review with a hierarchical perspective on STMs that can facilitate consideration of multi-scale spatial processes. Each class of spatial processes is then reviewed and illustrated with empirical examples from the rangelands we study. Simulation and mathematical modelling applications derived from such empirical examples are described elsewhere (e.g. Wiegand et al. 2003). The review asks: What governs the distribution and abundance of alternative states across a landscape or region? How are spatial patterns related to transition mechanisms within a site?
To conclude, we discuss how traditional STMs could be improved with spatial perspectives and we explore the implications of such changes for management and monitoring. Our assessment suggests that spatially informed STMs would enhance ecosystem management while simultaneously providing a framework within which to interpret basic ecological studies of state transitions.
A spatial hierarchy for state-and-transition models
A practical scheme to account for multiple scales must be devised to integrate spatial processes with STMs. Associating spatial processes with fixed spatial scales is problematic because different ecosystems (or even the same ecosystem) may experience a particular process at different measured scales. For example, the scale of important soil variations might occur over tens of metres or hundreds of metres. ‘Domains of scale’ or scale domains focus attention on characteristic pattern–process interactions within certain ranges of measured scale (Wiens 1989). Although the specific scales may vary among ecosystems, the hierarchical relationships among domains should be general. Qualitatively different pattern–process relationships occur in different scale domains.
We suggest that three scale domains (hereafter ‘scales’) usefully characterize the way scientists and managers perceive vegetation dynamics in terrestrial (especially rangeland) ecosystems (Fig. 1). We start with the middle scale of sites based on soils and landforms within an area of uniform climate that support similar ecosystems at their potential (i.e. the reference state). The term ‘site’ is used operationally throughout the world to recognize how variations in soils and topographic position create differences in plant community production/composition across a landscape (e.g. Illius & O’Connor 1999; Sasaki et al. 2008) via differences in water and nutrient availability and rooting substrate (McAuliffe 1994; Fensham & Holman 1999). Grazing pastures often subdivide or encompass one or more distinct sites, whereas a property or grazing area usually contains a variety of sites. Site has been formally recognized by US federal land management agencies as units called ‘ecological sites’. These are subdivisions of the landscape based on soil map unit components (i.e. soil series phases of US soil classification; Bestelmeyer et al. 2009). STMs are usually developed for specific ecological sites and define the reference and alternative states for each site. Thus, a mapped delineation of an ecological site (e.g. at a 1 : 5000 scale) could be observed in one or more states.
At the finer patch scale, a given state can feature distinct types, abundances and spatial arrangements of patches. Depending upon the system and processes involved, a patch might be a grass tussock or group of tussocks, a clump of shrubs or trees, or a vegetation band characterized by both plant cover and surface soil properties (Ludwig & Tongway 1995). State changes that managers recognize at the site scale are built upon the rapid, dynamic processes of change in vegetation and surface soils occurring at the patch scale. For example, managers often recognize incipient state change in a site via the expansion of bare ground or invasion of shrubs occurring at the scale of centimetres to metres.
At the broadest scale of the landscape, sites occur in an area of similar local climate and co-occur with other sites to create a mosaic structured by slow geomorphic processes and long-term patterns of land use. Subtle variations in meteorology and the potential for lateral hydrological/eolian interactions among sites influence the net flux of resources and disturbances to and from a site. These fluxes influence the states that a particular site exhibits, all else being equal. For example, changes in the states surrounding a site can affect the magnitude of erosive sheet flow or likelihood of fire spread experienced by the site (Okin et al. 2009). Thus, managers sometimes recognize the landscape context of sites with the axiom ‘look across the fence to see what is coming at you’.
We now illustrate how spatial patterns within these scale domains interact with three classes of spatial processes to produce spatial heterogeneity in alternative states. We review these processes in relation to traditional STMs.
Spatial variation of drivers
Management-related drivers of state transitions in STMs, especially grazing intensity, are usually described in general terms (e.g. ‘overgrazing’) without reference to spatial variation. While it has been common to use gradients in grazing intensity from water points to estimate the magnitude of livestock activity needed to induce state transitions (Pickup, Bastin & Chewings 1998; Sasaki et al. 2008), STMs have seldom addressed how varying land-use histories in discrete management units (e.g. pastures or properties) influence variations in state transitions across a landscape (Turner, Wear & Flamm 1996). Different land users can vary in their application of drivers for social and economic reasons (e.g. degree of dependence on ranch income; Gentner & Tanaka 2002). Sequences of historical events (both natural and cultural) interact with these drivers to amplify or attenuate their effects (Lunt & Spooner 2005). Thus, spatially referenced, historical reconstructions of the motivations and events giving rise to variation in state transitions can provide great explanatory power in STMs (Foster 1992; Todd & Hoffman 2009).
Spatial variation in driver histories can be expressed at several scales. Contrasting policies between countries, such as the implementation of grazing regulations in the USA and not in Mexico in 1934, can create different distributions of states at landscape scales (Bryant et al. 1990). For STMs aimed at management, however, it is especially useful to map and reconstruct management histories that have varied within specific ecological sites. For example, fence-line contrasts between grassland and shrub-dominated, sparse grass states are commonly observed in broad areas of the sandy ecological site in the Chihuahuan Desert, New Mexico, USA that includes several pastures and landowners (Fig. 2). Historical investigation of these units reveal that the mosaic of alternative states originated in 1915–1922 when the New Mexico State University College Ranch (CR) and the Jornada Experimental Range (JER) were isolated from public land now administered by the U.S. Bureau of Land Management (BLM). Subsequently, average utilization (an estimate of the percentage of plant biomass removed by livestock grazing) was 15–55% lower on CR than BLM through the drought period of the 1950s and afterwards (Holechek et al. 1994). The heavier livestock grazing on BLM landscapes during this drought period triggered the extirpation of grasses, erosion and transition to a shrub-dominated state.
The imagery (Fig. 2) also reveals how subtle differences in pasture management within JER influenced state transitions. Pasture 9, in which extensive grasslands have been maintained, was fenced in 1928 and has been managed as a reserve pasture with light summer stocking rates, whereas Pasture 2 experienced higher (but normal for the time period) stocking rates (Jornada Experimental Range Document Archives, unpublished data). The historical reconstruction suggests that relatively minor differences in long-term grazing management can be responsible for the patchy pattern of alternative states among management units that typifies the sandy site. This result highlights the sensitivity of this site to management variations and how difficult it can be to manage stocking rates to avoid state transitions.
State-and-transition models could readily include (i) a characterization of the typical patterning of states at site and landscape scales, and (ii) descriptions of the historical circumstances giving rise to the pattern of states (Swetnam, Allen & Betancourt 1999; Briggs et al. 2006). Information on specific decisions and motivations underlying those decisions would provide an even deeper understanding of state transitions (Brunson & Shindler 2004; Fensham, Fairfax & Archer 2005).
Spatial dependence in response to drivers
Whether or not a driver causes a state transition depends on how inherent (or slow changing) geophysical properties filter the effects of the driver (Swanson et al. 1988). Accounting for this filter has been accomplished in many areas of the US through the development of STMs for different ecological sites. Interpretation of such STMs, however, usually assumes that ecological sites are internally homogeneous with respect to soils and that mapped ecological site delineations do not vary in climate across the region for which they are developed. In arid and semi-arid rangelands, this is often not the case. For example, the spatial pattern of shrub infilling (Archer 1995) and alternative states (Bestelmeyer, Ward & Havstad 2006; Browning et al. 2008) can be patchy or exhibit gradients at scales of metres to hundred of metres. These patterns are controlled by subtle variations in soil properties such as subsurface clay content. High subsurface clay content simultaneously favours perennial grasses by retaining water and nutrients near the soil surface while limiting deep root penetration by shrubs, thereby negating their advantage (McAuliffe 1994). Historical aerial photographs indicate that some grass-dominated patches in an area mapped as the loamy ecological site in the Chihuahuan Desert have been highly stable while other patches on the same landscape and under similar management have undergone transitions between vegetated and non-vegetated states. The patches that are now vegetated or bare are aggregated in certain portions of the site, forming an ecotone in vegetation (Fig. 3a). Soil sampling revealed a limiting–factor relationship between persistent grass cover and subsoil clay content, wherein increasing clay content placed an increasing upper bound (90th quantile) on the amount of grass cover (Fig. 3b). Thus, while relatively high subsoil clay content did not guarantee grassland resilience, it permitted it to occur in discrete areas.
Subtle spatial variations in static soil properties (such as depth) can locally filter drivers that are uniform at site scales and help to produce patchy or gradient patterns of state transitions (Fuhlendorf & Smeins 1998). Similarly, landscape-scale gradients in climate can alter the likelihood of transition in otherwise similar soils (Jasinski & Payette 2005). Thus, local measurements of soil profiles and climate are often needed to properly contextualize individual measurements of state transitions in STMs (Didham, Watts & Norton 2005), even in areas assigned to a single ecological site. Such data can reveal that the likelihood of transitions within STMs is a combined function of driver intensity and gradual variations in geophysical properties.
Spatial contagion and feedbacks
Spatial contagion here refers to the spread of localized state-transitions to adjacent areas due to feedbacks between plant growth, survival and dispersal with local environmental conditions, independent of driver intensity in the adjacent areas (Watt 1947; Peters et al. 2004). Traditional STMs tend to treat mechanisms of state transitions as due to point-based, vertical processes involving an external driver interacting with specific plant patches and local soil/topographic properties. Horizontal processes (contagion), however, can be important components of state transition mechanisms at patch, site and landscape scales.
Positive feedbacks between plant patches and resource availability are postulated to underlie transitions between highly and sparsely vegetated states in a variety of ecosystems (Rietkerk et al. 2004). In arid ecosystems, plants within vegetated patches (e.g. several metres across) facilitate one another by collectively harvesting and retaining water via improved infiltration and capture of overland flow. Plant mortality caused by drought or patchy grazing disturbance (e.g. Adler, Raff & Lauenroth 2001) leads to patch disintegration, the breakdown of positive feedbacks and ultimately a cascade of patch loss that is perceived as a transition between highly vegetated and sparsely vegetated states at the site scale (Davenport et al. 1998; Ludwig et al. 2005; Okin, D’Odorico & Archer 2009). Consequently, vegetation patch metrics are increasingly proposed to describe the structural changes and loss of resilience forewarning of state transitions (Ludwig et al. 2002; Bisigato et al. 2005; Kefi et al. 2007; Guttal & Jayaprakash 2009).
Patch-scale changes in vegetation can initiate contagious processes of state transition at the site scale via cross-scale interactions between large bare patches and broad-scale wind or water erosion (Peters et al. 2004; Pringle, Watson & Tinley 2006). For example, areas of shrub-dominated coppice dunes in the Chihuahuan Desert have been documented to expand, converting adjacent grasslands into coppice dunes (Fig. 4a,b). Coppice dune states result when long-lived stoloniferous grasses are selectively killed by drought and heavy livestock grazing (e.g. the resulting ‘bunchgrass’ and ‘bare’ classes in Fig. 2). The contagion of savanna- or grassland-coppice dune transition is mediated by mesquite recruitment coupled to sand burial of grasses in the prevailing direction of erosive winds (Okin et al. 2009).
Examples of contagious soil degradation are not restricted to warm, arid ecosystems, as is commonly assumed. A similar pattern of spreading soil erosion has also been observed in cold, humid environments such as those of Iceland (Fig. 5a), where grazing disrupts the vegetation thermal barrier. This, in turn, amplifies freeze–thaw dynamics that destabilize highly erodible Andisol soils, making them more prone to frequent, small-scale disturbances associated with frost boils, frost heaving and needle-ice formation (Fig. 5b; Archer & Stokes 2000; Thorsson 2008). These geophysical forces help create and reinforce the persistence of small bare patches, which expose the friable, thick (50–200 cm) mantle of volcanic soil to removal by wind and water (Arnalds 1998). As small eroded patches increase in density and gradually enlarge, coalescence occurs and the length of exposed perimeter increases dramatically. This results in the creation of erosion fronts whose vertical faces are fully exposed to wind and water (Arnalds 2000). These elongated, wind-driven fronts can now advance rapidly across the landscape, leaving glacial till in their wake. As with arid coppice dunes, management practices to preserve the remaining vegetated zones do little to prevent the advance of these fronts.
Spatial contagion can also interact with static soil variation in complex ways across a landscape. Spatial variation in transitions from grass- to shrub-dominated states in the southern Great Plains occurs at a landscape scale, with the rates and patterns of these transitions dependent on subsurface variation in the development of argillic (clay pan) horizons (Fig. 6a,b). Spatial variation in runoff from these upland ecological sites, in turn, influences the patterns of grassland-to-woodland transition in the adjoining lowland ecological site (Fig. 6c). A similar example is that patterns of fire spread can be determined by where the spread was initiated relative to the spatial arrangement of ridges and valley bottoms (Swanson et al. 1988) as well as the local connectivity of fuel loads relative to the direction of spread (Allen 2007).
When state transitions have occurred over a sufficient spatial extent, a distinct set of cross-scale interactions can be initiated at the landscape scale that link even distant sites together. For example, vegetation clearing in upland areas of southern Australia has led to rising water tables and salinization in lower-lying ecological sites within the watershed that were not cleared (Yates & Hobbs 1997). Similarly, changes in land surface conditions can have a pronounced effect on weather, climate and local meteorology (Bryant et al. 1990; Pielke et al. 1998). The landscape-scale cover of highly vegetated- vs. poorly vegetated states can influence meso-scale climate via the influence of vegetation on dust aerosols and soil surface temperatures that intensify local drought and vegetation loss (Balling et al. 1998; Rosenfeld, Rudich & Lahav 2001; Cook, Miller & Seager 2009). Dust deposition can decrease snowpack albedo regionally, thus accelerating melt and potentially increasing summer drought stress in lower elevation ecosystems (Painter et al. 2007). There is little work to indicate the areal extent, continuity or nature of state change needed to initiate feedbacks at landscape and larger scales. Hodgson, Hatton & Salama (2004) provide an example predicting vegetation cover–salinization relationships using hydrological models in southwestern Australia.
Several features of STMs have precluded the representation of contagious processes. With regard to patch- and site-scale contagion, STMs have typically relied exclusively on measurements of surface cover and have not used measures of patch size, arrangement, or spatiotemporal patterns of spread to characterize state transitions. With regard to landscape-scale contagion, STMs are typically developed for specific ecological sites rather than landscapes and are therefore incapable of linking state-transitions occurring in one place (or across an extent) to those occurring in another.