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