Climate Change and Land Management in the Rangelands of Central Oregon

Climate change and land management in the rangelands of central Oregon

Supplementary Material: Description of state-and-transition models

State-and-transition models (STMs) are a tool used in land management to conceptualize vegetation dynamics and project future vegetation condition. Each STM represents a potential vegetation type (PVT), which describes the potential of a site based on vegetation, soils, and climate. Within each PVT, STMs divide the landscape into distinct combinations of vegetation structure and cover (state classes), linked together by dynamic processes (transitions) such as succession, disturbance, and management activities. This appendix describes the structure and assumptions of the STMs used in this study, focusing primarily on sagebrush steppe PVTs, as they are the most important for rangeland management in central Oregon and the STMs are the most complex.

Model structure

State classes represent combinations of vegetation structure (percent cover of grass, shrub or tree growth forms) and composition (e.g., plant functional types and dominant species). STMs varied in their complexity from 5-15 state classes, depending on the PVT being represented.

Structural categories were based on Karl and Sadowski (2005) and divided into shrub cover categories of 0-5%, >5-15%, >15-25% and >25% cover. In the cool-moist sagebrush steppe PVT, state classes were allocated to three juniper encroachment phases (Miller et al. 2005), defined using a relative tree dominance index (tree cover / (tree cover + shrub cover + native grass cover)). Using this index, we defined phase I woodlands as a relative tree dominance index

<0.33, phase II woodlands between 0.33 and 0.67, and phase III woodlands as >0.67 (Robin

Tausch, personal communication).

Vegetation composition was defined primarily based on herbaceous composition, and included up to four categories: native grasses, exotic annual grasses, seeded non-native grasses, and a mixture of native and exotic grasses (i.e., semi-degraded sites). Native state classes were defined by a minimum absolute and relative cover of grass species sensitive to disturbance

(decreasers such as P. spicata, F. idahoensis, H. comata, some Achnatherum and Elymus species, and others). Exotic grass state classes were defined by a minimum absolute and relative cover of exotic annual grasses (primarily B. tectorum and other invasive bromes,

Ventenata species, T. caput-medusae, Vulpia bromoides (L.) Gray, and others). In PVTs where seeding of non-native species occurs, seeded state classes were defined by Agropyron species and others that are commonly seeded in rangelands. Anything that did not meet the minimum threshold for these indicator species was considered to be in a semi-degraded state class.

Absolute and relative cover thresholds for each group are variable among STMs.

Transitions

Transitions represent processes that cause vegetation change, and can be deterministic (i.e., automatically occur after a specified number of years), or probabilistic, where the user defines an annual probability of occurrence. In all STMs succession was modeled as a deterministic transition, causing automatic change between state classes at a specified stand age in the absence of disturbance. Vegetation age classes for deterministic transitions were based on literature and published models (Evers et al. 2013). The major probabilistic disturbance transitions included wildfire, livestock grazing, drought, insects and disease, natural regeneration of native herbs, and juniper seed dispersal. Management transitions were also modeled to simulate juniper treatments and post-fire seeding. Note that dynamics in each STM are different. For instance, in the warm-dry sagebrush steppe model there were higher probabilities of transitioning to exotic grass state classes and no pathways back to native conditions excepting management transitions, but the cool-moist sagebrush model included automatic recovery from exotic grass state classes in the absence of excessive grazing.

Conversely, the cool-moist sagebrush steppe model included juniper woodland state classes, whereas the warm-dry sagebrush PVT was assumed to be too dry to support juniper woodlands.

Wildfire Transitions

We used data from the Monitoring Trends in Burn Severity (MTBS) data set (Eidenshink et al.

2007; www.mtbs.gov) to derive wildfire probabilities and wildfire transition multipliers for rangeland STMs (Table A1). The MTBS data set records fire perimeters and severity for all fires over 1000 acres that burned between 1984 and 2008. We completed a spatial analysis of fire perimeters based on general wildfire regime characteristics of groups of PVTs and by the level of exotic grass dominance in the herbaceous layer, since exotic grasses are known to increase fire frequency (Whisenant 1990; Brooks et al. 2004). PVTs were allocated into burn groups based on site productivity and fuel potential, ranging from sparse (salt desert shrub) to semi- desert (warm-dry sagebrush steppe) to mesic (cool-moist sagebrush steppe and grasslands).

Exotic grass groups were specified by cover of exotic annual grasses in our current vegetation map, and were combined into groups of 0-10% cover, >10-25% cover, and >25% cover. We assume that the 0-10% exotic grass group is likely to consist of scattered individuals that will not affect ecological integrity or burn conditions, whereas 10-25% exotic grass cover will represent larger and more continuous clumps that may affect fine fuel continuity. At greater than 25% exotic grass cover, fuel continuity is likely to alter the burn pattern and severity. The average annual number of acres burned in each combination of PVT/exotic grass group was calculated in ArcGIS and divided by the total area of the PVT/exotic grass group to derive an annual wildfire probability. Wildfire rotation interval was calculated as the inverse of the annual wildfire probability. Wildfire probabilities were allocated into state classes within a STM based on the exotic grass cover class (0-10%, >10-25%, or >25%). Once wildfire probabilities were allocated to each state class, wildfire severity was divided into mosaic (patchy) fire and stand- replacing fire based on Evers et al (2013).

To account for variability in wildfire weather from year to year, we used transition multipliers, which adjust the overall average probability of an event upward or downward each year in a random sequence based on the value of the multiplier relative to 1. Transition multipliers for wildfire transitions were derived from the proportion of the landscape burned in the MTBS data that was considered to be normal (defined as the 0-85th percentile), high (defined as the 86-95th percentile) and severe (>95th percentile) wildfire years. Multipliers were normalized to the overall average wildfire probability so the overall average and the year-to-year variability matched the wildfire seasons captured in the MTBS data.

Livestock Grazing

Livestock grazing was divided into two types, including managed grazing and excessive grazing.

Managed grazing did not cause any degradation in condition and reduced the probability of transitioning to closed shrub and depleted shrub (high shrub cover and low cover of forbs and grasses), conditions with greater potential for stand-replacing wildfire and exotic grass invasion.

Therefore managed grazing reduced the occurrence of stand-replacing wildfire and subsequent exotic invasion (Davies et al. 2010). Excessive grazing had two possible effects: it could cause degradation toward greater exotic presence in the understory, particularly when it immediately followed disturbance (Pickford 1932; Curtin 2002; Evers 2010); and it increased the probability of transitioning to depleted shrub (high shrub cover with depleted grasses and forbs). For this paper we made the simple assumption that all of the rangeland landscape in the study area was grazed with livestock, and that half of the grazing was managed grazing and half was excessive grazing. This assumption was meant to simplify the very complex process of modeling livestock grazing, recognizing that both proper, well-managed grazing and excessive grazing occur on the landscape but that we do not know how much of each is occurring. Transition probabilities were based on consultation with experts, as little information is available to quantify grazing impacts.

Insects, Drought and Disease In the warm-dry and cool-moist sagebrush steppe models, sagebrush defoliating insects such as the Aroga moth (Aroga websteri Clark) were simulated as a cyclical disturbance based on Evers et al (2013). The sagebrush steppe models also included drought and severe drought transitions. Drought frequencies were derived from Knapp et al. (2004) and mimicked a multi- year drought that had little direct effect on vegetation, but when combined with excessive grazing could result in exotic grass invasion. Severe drought was modeled as a cyclical event that is rare but can result in shrub mortality when it occurs (Evers et al. 2013).

Other Natural Transitions

Juniper seeding probabilities in the cool-moist sagebrush steppe PVT were derived from climatic factors related to juniper establishment from Evers et al. (2013). Other PVTs did not include juniper encroachment. The cool-moist sagebrush STM included recovery transitions from semi-degraded and exotic grass state classes to native grass, simulating re-establishment of native species. These transition rates were largely based on expert judgment. Warm-dry sagebrush steppe and most other models did not include recovery of the herbaceous layer without management intervention.

Management Transitions

Management transitions were modeled in the cool-moist and warm-dry sagebrush steppe PVTs only, as most management is focused on restoring sagebrush habitat. In the cool-moist sagebrush steppe PVT, juniper treatments included prescribed burning in phase I juniper and both prescribed burning and juniper cutting in phase II juniper. Prescribed burning was assumed to burn trees and most shrubs and return treated areas to early-successional shrub steppe with shrub cover <5%, whereas cutting left shrub communities intact and transitioned to open shrub steppe (>5-25%, depending on the model). No management treatments were modeled in phase III woodlands, since mature woodlands are generally difficult and expensive to restore (Miller et al. 2005). Post-fire seeding occurred in the warm-dry sagebrush steppe PVT within one year following wildfire events; therefore treatment levels were highly variable from year-to-year depending on the amount of wildfire simulated in each year. We assumed that seeding mixtures contained both native and non-native species, and that seeding had a success rate of 20% for all seeded species. The success rate was based on Pyke et al (2013), who found that post-fire seeding effectiveness across the Great Basin was 26% across multiple site types.

We adjusted this estimate downward to 20% due to the observed lower seeding success in warm-dry sites compared to cool-moist sites, as we modeled post-fire seeding only in warm-dry sagebrush steppe. Therefore, in the warm-dry sagebrush STM, 80% of the area treated does not transition between state classes but instead remains in an exotic grass state. We assumed that post-fire seeding is not necessary in cool-moist sagebrush steppe, as exotic grasses are less dominant and native communities are more competitive in mesic environments. Table A1. Annual wildfire probabilities and rotation intervals derived from Monitoring Trends in

Burn Severity data for each major rangeland potential vegetation type (PVT) under three levels of exotic grass cover. Numbers reflect current levels of fire suppression.

Fire Exotic Annual PVT Group Rotation Grass Cover Probability Interval Cool-moist 0-10% 0.0068 148 sagebrush 10-25% 0.0089 112 steppe & grasslands >25% 0.0173 58 Warm-dry 0-10% 0.0063 160 sagebrush 10-25% 0.0114 88 steppe >25% 0.0179 56 0-10% 0.0037 269 Salt desert 10-25% 0.0075 134 shrub >25% 0.0085 117

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