ArticlePDF Available

Abstract and Figures

2018. STEPWAT2: an individual-based model for exploring the impact of climate and disturbance on dryland plant communities. Ecosphere 9(8): Abstract. The combination of climate change and altered disturbance regimes is directly and indirectly affecting plant communities by mediating competitive interactions, resulting in shifts in species composition and abundance. Dryland plant communities, defined by low soil water availability and highly variable climatic regimes, are particularly vulnerable to climatic changes that exceed their historical range of variability. Individual-based simulation models can be important tools to quantify the impacts of climate change, altered disturbance regimes, and their interaction on demographic and community-level responses because they represent competitive interactions between individuals and individual responses to fluctuating environmental conditions. Here, we introduce STEPWAT2, an individual plant-based simulation model for exploring the joint influence of climate change and disturbance regimes on dryland ecohydrology and plant community composition. STEPWAT2 utilizes a process-based soil water model (SOILWAT2) to simulate available soil water in multiple soil layers, which plant individuals compete for based on the temporal matching of water and active root distributions with depth. This representation of resource utilization makes STEPWAT2 particularly useful for understanding how changes in soil moisture and altered disturbance regimes will concurrently impact demographic and community-level responses in drylands. Our goals are threefold: (1) to describe the core modules and functions within STEPWAT2 (model description), (2) to validate STEPWAT2 model output using field data from big sagebrush plant communities (model validation), and (3) to highlight the usefulness of STEPWAT2 as a modeling framework for examining the impacts of climate change and disturbance regimes on dryland plant communities under future conditions (model application). To address goals 2 and 3, we focus on 15 sites that span the spatial extent of big sage-brush plant communities in the western United States. For goal 3, we quantify how climate change, fire, and grazing can interact to influence plant functional type biomass and composition. We use big sage-brush-dominated plant communities to demonstrate the functionality of STEPWAT2, as these communities are among the most widespread dryland ecosystems in North America.
Content may be subject to copyright.
EMERGING TECHNOLOGIES
STEPWAT2: an individual-based model for exploring the impact of
climate and disturbance on dryland plant communities
KYLE A. PALMQUIST ,
1,
JOHN B. BRADFORD ,
2
TRACE E. MARTYN ,
3
DANIEL R. SCHLAEPFER ,
4
AND WILLIAM K. LAUENROTH
1,4
1
Department of Botany, University of Wyoming, Laramie, Wyoming 82071 USA
2
U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, Arizona 86001 USA
3
School of Biological Sciences, The University of Queensland, St. Lucia, Queensland 4072 Australia
4
School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut 06511 USA
Citation: Palmquist, K. A., J. B. Bradford, T. E. Martyn, D. R. Schlaepfer, and W. K. Lauenroth. 2018. STEPWAT2: an
individual-based model for exploring the impact of climate and disturbance on dryland plant communities. Ecosphere
9(8):e02394. 10.1002/ecs2.2394
Abstract. The combination of climate change and altered disturbance regimes is directly and indirectly
affecting plant communities by mediating competitive interactions, resulting in shifts in species composi-
tion and abundance. Dryland plant communities, dened by low soil water availability and highly variable
climatic regimes, are particularly vulnerable to climatic changes that exceed their historical range of vari-
ability. Individual-based simulation models can be important tools to quantify the impacts of climate
change, altered disturbance regimes, and their interaction on demographic and community-level responses
because they represent competitive interactions between individuals and individual responses to uctuat-
ing environmental conditions. Here, we introduce STEPWAT2, an individual plant-based simulation model
for exploring the joint inuence of climate change and disturbance regimes on dryland ecohydrology and
plant community composition. STEPWAT2 utilizes a process-based soil water model (SOILWAT2) to simu-
late available soil water in multiple soil layers, which plant individuals compete for based on the temporal
matching of water and active root distributions with depth. This representation of resource utilization
makes STEPWAT2 particularly useful for understanding how changes in soil moisture and altered distur-
bance regimes will concurrently impact demographic and community-level responses in drylands. Our
goals are threefold: (1) to describe the core modules and functions within STEPWAT2 (model description),
(2) to validate STEPWAT2 model output using eld data from big sagebrush plant communities (model
validation), and (3) to highlight the usefulness of STEPWAT2 as a modeling framework for examining the
impacts of climate change and disturbance regimes on dryland plant communities under future conditions
(model application). To address goals 2 and 3, we focus on 15 sites that span the spatial extent of big sage-
brush plant communities in the western United States. For goal 3, we quantify how climate change, re,
and grazing can interact to inuence plant functional type biomass and composition. We use big sage-
brush-dominated plant communities to demonstrate the functionality of STEPWAT2, as these communities
are among the most widespread dryland ecosystems in North America.
Key words: Artemisia tridentata; climate change; disturbance; dryland; ecohydrology; re; gap dynamics; grazing;
individual-based model; plant simulation model; sagebrush; semiarid.
Received 29 January 2018; revised 15 May 2018; accepted 25 June 2018. Corresponding Editor: Robert A.
Washington-Allen.
Copyright: ©2018 The Authors. This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
E-mail: kpalmqu1@uwyo.edu
www.esajournals.org 1August 2018 Volume 9(8) Article e02394
INTRODUCTION
Climate change has important consequences
for plant communities from the leaf to ecosystem
scale (Gornish and Prather 2014). Shifting precipi-
tation and temperature regimes can result in
changes in functional type or species composition
and abundance and cause shifts in species distri-
butions (Lin et al. 2010, Chen et al. 2011, Thom
et al. 2017). Changes in disturbance regimes are
accelerating (Seidl et al. 2014) and act simultane-
ously with climate to impact plant community
structure. The direct effects of climate change and
altered disturbance precipitate indirect effects on
demographic processes and community-level
responses by altering intraspecic and interspeci-
c competition between plant individuals (Tylia-
nakis et al. 2008). As such, quantifying individual
plant responses and changes in competitive inter-
actions in response to climate change and altered
disturbance regimes is a key challenge.
These issues are particularly pertinent for dry-
land ecosystems, as soil water availability and
temperature are often key limiting factors for
plant growth, inuence plant species composi-
tion and richness (Buttereld and Munson 2016,
Pennington et al. 2017), and are expected to
change in the coming century (Palmquist et al.
2016a, b, Schlaepfer et al. 2017). Several lines of
evidence suggest dryland ecosystems are already
being exposed to increasingly extreme climate
conditions, including increases in the frequency
of extreme events (Trenberth et al. 2014, Wu
et al. 2015), large-scale drought (Seager et al.
2007, Cook et al. 2015), and drought-related mor-
tality (Fensham et al. 2015, Meddens et al. 2015).
In addition, many dryland ecosystems have
experienced substantial changes in disturbance
regimes, most notably increases in the size, fre-
quency, and severity of re (Westerling et al.
2006, Balch et al. 2013) and heavy grazing by
livestock (Bai et al. 2012). When these distur-
bances interact with climatic change, the impacts
can exacerbate. Climate change-induced drought
and warming will likely continue to increase re
frequency in the future at least in areas where
there is enough fuel to carry re (McKenzie and
Littell 2017). Grazing impacts are also likely to
vary with soil moisture, as drought-adapted spe-
cies are often more resistant to grazing (Adler
et al. 2004, 2005), especially in ecosystems that
have a long evolutionary history of grazing
(Milchunas et al. 1988).
Simulation modeling, particularly individual-
based modeling, is an important tool to under-
stand how plant communities will respond to
changing environmental conditions and distur-
bance regimes. In particular, it is useful for explor-
ing questions over long time periods and under
future conditions for a range of environmental set-
tings, which are not feasible using experiments
(Smith and Huston 1990). The strengths of
individual-based models are that they simu-
late intraspecic and interspecic competition
between plant individuals and individual plant
responses (establishment, growth, and mortality)
to uctuating environmental conditions, from
which population- and community-level proper-
ties emerge (Huston et al. 1988, DeAngelis and
Grimm 2014, Grimm et al. 2017). Thus, they can
be utilized to understand hierarchical (individual,
population, community, ecosystem) responses to
environmental variability and trajectories between
dynamic equilibrium (Huston et al. 1988). Because
individual-based simulation modeling explicitly
represents the key demographic stages of plants,
it can provide a more process-based representa-
tion of plant responses to climate change and dis-
turbance than bioclimatic envelope modeling,
albeit at a smaller scale.
Gap dynamics models are a subset of individ-
ual-based models, which have been used primar-
ily as a tool for understanding the dynamics in
forest stands after individual trees die, where
light is the limiting resource plants compete for
(Botkin et al. 1972, Shugart 1984, Seidl et al. 2012,
Morin et al. 2018). This approach has been
applied in non-forested ecosystems, but less fre-
quently (shortgrass steppe, Cofn and Lauenroth
1990, Cofn et al. 1993, big sagebrush ecosys-
tems, Bradford and Lauenroth 2006, desert grass-
lands, Peters 2000, 2002). In non-forested
drylands, gap dynamics models are similar to
gap dynamics models implemented in forests,
except that the primary resource for which plants
compete is soil water, which is the most frequent
limiting resource in drylands (Sala et al. 1992,
Loik et al. 2004, Gremer et al. 2015).
Here, we present STEPWAT2, an individual-
based, gap dynamics plant simulation model, as
a tool for exploring the impacts of climate, cli-
mate change, and disturbance regimes on
www.esajournals.org 2August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
dryland plant communities under current and
future conditions. STEPWAT2 can be applied to
any dryland ecosystem where soil water controls
plant growth and plant community structure.
Specically, this paper has three goals: (1) to
describe the core modules and functions within
STEPWAT2 (model description), (2) to use eld
data from big sagebrush plant communities to
validate STEPWAT2 model output (model vali-
dation), and (3) to highlight the usefulness of
STEPWAT2 as a framework for examining the
joint impacts of climate change and disturbance
regimes on future dryland plant communities
(model application). To pursue goal 2 and to
highlight the types of research questions that can
be addressed and results that can be produced
by STEPWAT2 (goal 3), we focus on 15 sites that
span the spatial extent of big sagebrush plant
communities in the western United States. For
these sites, we ask: How will climate change and
disturbances (re and grazing) interact to inu-
ence plant functional type biomass and composi-
tion? We focus on big sagebrush ecosystems
because they are one of the most widespread
dryland ecosystems in North America and pro-
vide critical habitat for ~350 species of conserva-
tion concern (Davies et al. 2011), while providing
important ecosystem services (e.g., recreation,
energy resources; Knick et al. 2003, Rowland
et al. 2006).
STEPWAT2 MODEL DESCRIPTION
Overview of STEPWAT2 and model revisions
STEPWAT2 is a simulation framework that
combines a stochastic, individual-based plant
simulation model (based on STEPPE, Cofn and
Lauenroth 1990, Cofn et al. 1993, Bradford and
Lauenroth 2006) and a deterministic, process-
based soil water balance model (SOILWAT2, Sch-
laepfer et al. 2012a). STEPWAT2 simulates plant
demographics on patches that correspond to the
belowground resource space utilized by a full-
sized individual of the dominant species (Cofn
and Lauenroth 1990). Resources (soil water avail-
able for transpiration) are generated each year in
SOILWAT2, passed to STEPPE, and end-of-year
plant aboveground biomass is then passed back
to SOILWAT2 to update the vegetation parame-
ters before the next years SOILWAT2 run
(Fig. 1).
STEPPE, on which STEPWAT2 depends, was
originally conceptualized for the shortgrass
steppe (Cofn and Lauenroth 1990, Cofnetal.
1993), a semiarid grassland ecosystem in the cen-
tral United States, but has since been modied for
big sagebrush ecosystems (Bradford and Lauen-
roth 2006). The model was easily translatable to
big sagebrush ecosystems as soil water is the key
limiting resource in both ecosystems. STEPPE
simulates establishment, growth, and death of
SOILWAT2
Resource
availability
Soil
properties growth
Daily
weather
STEPPE
mortality
Establishment
Plant available
soil water
STEPWAT2
Biomass
Root depth
End-of-year biomass
Fig. 1. STEPWAT2 conceptual model diagram. STEPWAT2 is a coupled simulation model framework that links
SOILWAT2 and STEPPE. Each year, plant available soil water for transpiration is generated by SOILWAT2 and is
passed to STEPPE and serves as resource availability for plant growth. After establishment, growth, and mortal-
ity in STEPPE, the end-of-year biomass for multiple plant functional types is calculated and passed to SOILWAT2
to serve as input for the next year. Variables that are passed between SOILWAT2 and STEPPE are shown in bold.
www.esajournals.org 3August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
individual plants, whose life-history traits (phe-
nology, root distribution, growth rate, maximum
size, life span) are dictated by the species and
functional type to which the individual belongs
(Bradford and Lauenroth 2006). Individuals, spe-
cies, and functional types are all tracked within
STEPPE. Annual output from STEPPE includes
the number of individuals established for each
species (density), the total aboveground biomass
for each species and functional type (g/m
2
,here-
after biomass), and the number of individuals
that died according to their age.
SOILWAT2 is a daily time step, process-based
soil water model that simulates water balance
uxes and pools for multiple soil layers (Sch-
laepfer et al. 2012a, Bradford et al. 2014). Output
from SOILWAT2 includes daily water uxes and
pools. Estimates of transpiration, derived from
SOILWAT2 output, are used within STEPPE to
represent abiotic inuences on resource alloca-
tion, plant growth, and mortality. Resource avail-
ability is determined by the ability of each
functional type to extract water through time
and with depth in the soil. This representation of
resource utilization with depth and across each
month for multiple plant functional types makes
STEPWAT2 particularly useful for understanding
how climate-driven changes in soil moisture will
impact demographic and community-level
responses in drylands. Resource availability from
SOILWAT2 and demand for resources in STEPPE
are captured by the parameter PR: the ratio of
resources required to resources available. PR
greater than one indicates resource limitation
(resources required exceed resources available),
while PR of one indicates there are sufcient
resources.
STEPWAT2 is run via the Monte Carlo method
to simulate vegetation dynamics in a single cell
at each site (non-gridded mode) or for a grid of
cells that are linked by dispersal (gridded mode)
to model spatially explicit vegetation dynamics
across a site (based on ideas presented by Cofn
et al. 1993). In either case, the size of each cell is
based on the belowground resource space of a
full-sized individual of the dominant species and
can be adjusted based on the dryland ecosystem
and dominant species in question. Both single
cell and gridded simulations use the same core
modules. However, when run in gridded mode,
seed dispersal among cells is enabled. The size
and arrangement of the gridded landscape are
specied by providing the number of cells within
the landscape and how those cells are congured
in 2D space. Each cell within the grid can have
its own soil properties, disturbance regime, and
species composition, but climate is held constant
for all cells within a site (Cofn et al. 1993).
Because soil characteristics and species composi-
tion can vary, resource availability for plant
establishment, growth, and mortality can differ
among cells in a gridded landscape. This can
result in divergence of species composition
among cells in the grid in response to spatial
heterogeneity. In addition, gridded mode allows
for examination of how disturbances that vary in
their spatial extent (e.g., wildre, an oil or gas
well-pad, road network) and spatial patterns
(e.g., conguration, patch size, and number) may
impact dryland plant communities in the face of
climatic change. Two features in gridded mode
allow for vegetation to become established
before simulations begin. The rst is a module in
which seed dispersal for the specied species is
allowed for a set number of years before the rst
year of simulation. The second option, called
spin-up, runs the full model for a number of
years until steady-state vegetation is reached,
after which the experimental simulations begin.
In contrast, simulations in the non-gridded mode
begin with no established plant community and
assume unlimited seed rain of every species.
We have substantially revised STEPWAT2
since its original presentation in Cofn and
Lauenroth (1990) and Bradford and Lauenroth
(2006), which include changes to SOILWAT2 and
STEPPE. Within STEPPE, we added functionality
to simulate re and grazing in both non-gridded
and gridded mode (see Disturbances), along with
addition of spin-up and seed dispersal options in
the gridded mode to aid in establishment of veg-
etation prior to simulation experiments. Annual
plant functional types are now fully represented
by individual plants within the model: Establish-
ment of annuals now occurs at the same time as
perennials and annual individuals are tracked
and grow throughout the year (previously indi-
viduals of annual species were not tracked and
had no capacity for growth). The most substan-
tial changes to SOILWAT2 include addition of
the following: a snow module, hydraulic redistri-
bution, plant functional types, an option to limit
www.esajournals.org 4August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
percolation and allow for saturated soils and
ponding of water, and an overhaul of soil water
that is available for transpiration (see Appendix S1
for more detail on SOILWAT2).
Below we briey describe the main modules of
STEPWAT2 in the order of their execution: plant
establishment, generation of soil water resources
from SOILWAT2, partitioning of soil water
resources, growth, mortality, and implementation
of disturbances. A more detailed description of
each module is in Appendix S2. STEPWAT2 is writ-
teninC++ and is publicly available on Zenodo:
http://doi.org/10.5281/zenodo.1306924 (Palmquist
et al. 2018).
Plant establishment
The rst process in STEPWAT2 is plant estab-
lishment for perennial and annual species. Estab-
lishment is stochastic, but also is a function of the
number of individuals that can establish each
year and the probability of establishment (P
estab
)
for each species (Table 1). P
estab
reects the aver-
age probability of suitable environmental condi-
tions for germination and survival and the
species-specic requirements for regeneration. If
the establishment conditions for perennial spe-
cies are met, the number of individuals that
establish in that year is determined based on the
maximum number of seedlings that can establish
per year (maxindivs; Table 1; Appendix S2). For
species of annual functional types, the number of
individuals that establish is based on the number
of viable seeds in the seedbank (see Appendix S2
for further description of the annual seedbank)
and P
estab
: More seedlings are added as the
viable seedbank increases and as P
estab
increases.
Seedlings are added at the size of a 1-yr-old indi-
vidual for perennials and at the size of a recent
germinate for annuals. The biomass of added
individuals is specied for each species (minbio,
Table 1).
Each time establishment, growth, or mortality
occurs, the relative sizes of individuals, species,
and functional types are updated. The relative
size of each individual (relsize
ndv
) is a function of
its size relative to a full-sized individual of that
species (range =01). The species relative size
(relsize
sp
) is the summation of the relsize
ndv
and
reects both the number and size of established
individuals belonging to that species. The mean
relative size of the functional type (relsize
ft
) is the
summed relsize
sp
divided by the number of
species in the functional type.
SOILWAT2: generation of soil water resources for
plants to use
After establishment, resource availability for
the year is generated by SOILWAT2. Key
processes within SOILWAT2 are inuenced by
three types of inputs: weather and climate, vege-
tation parameters, and physical properties for
each soil layer (Schlaepfer et al. 2012a). Here, we
Table 1. STEPWAT2 model parameters for species.
Species FT age rP
estab
maxindivs minbio maxbio clonal vegindv slow
Artemisia tridentata 1 1500.02 0.30|| 2 2# 400.0 N NA 4
Cryptantha sp. 2 1 0.947 0.03 5 0.035 4.7 N NA NA
Phlox hoodii 3 10 0.426# 0.15 10 0.035# 5.0 N NA 2
Bromus tectorum 4 1 0.947# varied 20 0.025.0 N NA NA
Pseudoroegnaria spicata 5 35 0.474# 0.15 10 0.5# 15.096 Y 3 2
Sporobolus curtissii 629§0.474 0.05 3 0.605# 6†† Y32
Chrysothamnus viscidiorus 7170.4740.15 2 0.98670.726NNA2
Opuntia polyacantha 8 30 0.2890.051 2.2515Y3NA
Notes: FT, STEPPE functional types listed in Table 2; age, maximum age (years); r, intrinsic rate of growth; P
estab
, probability
of establishment; maxindivs, the maximum number of individuals for each species that can establish in each year; minbio,
biomass (g/m
2
) of a 1-yr-old individual for perennials and of a recent germinate for annuals; maxbio, the biomass at maturity;
clonal, whether the species can reproduce clonally; vegindv, the maximum number or ramets added during clonal growth; and
slow, the number of continuous slow-growth years.
Ferguson (1964).
Cofn and Lauenroth (1990).
§Lauenroth and Adler (2008).
P. B. Adler, unpublished data.
# Bradford and Lauenroth (2006).
|| Schlaepfer et al. (2014).
†† Lucero et al. (2008).
www.esajournals.org 5August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
describe the differences in how weather and veg-
etation inputs are calculated when SOILWAT2 is
running within the STEPWAT2 framework.
Additional detail on how these inputs inuence
water balance uxes and pools is provided in
Appendix S1.
Weather data include daily precipitation and
daily minimum and maximum temperature,
while climate data consist of monthly mean rela-
tive humidity, wind speed, and cloud cover. His-
torical weather data or future weather data
extracted from global circulation models (GCMs)
and downscaled to the relevant temporal and
spatial scales can be used as input to SOILWAT2.
Because the establishment of a steady-state plant
community within STEPWAT2 often takes 50
100 yr and because daily weather data are most
often not available for 100 yr or more, additional
weather data must be generated to run STEP-
WAT2. We use a rst-order Markov weather gen-
erator within SOILWAT2, which is a modied
WGEN weather generator (Richardson and
Wright 1984), to produce daily weather data for
the required number of years. The Markov pro-
cess uses 30-yr daily precipitation averages and
wet- and dry-day probabilities and weekly tem-
perature averages and covariances calculated
from historical or predicted future weather to
generate daily weather data with statistical char-
acteristics that are equivalent to the input data
(Appendix S3).
There are four plant functional types in SOIL-
WAT2 (trees, shrubs, grasses, and forbs), whose
monthly parameters (estimates of total above-
ground biomass, litter, living aboveground bio-
mass, and root depth prole) inuence a number
of ecohydrological processes (Appendix S1).
Each year before SOILWAT2 runs within the
STEPWAT2 framework, vegetation parameters
are updated based on the biomass (g/m
2
) of each
established functional type in STEPPE. STEPPE
functional type biomass is tracked on an annual
basis and must be converted to monthly biomass,
along with monthly values of litter (g/m
2
) and
percent live (Appendix S4: Table S1). In addition,
STEPPE functional type biomass must be con-
verted to biomass for each of the SOILWAT2
functional types, as multiple STEPPE functional
types can make up a single SOILWAT2 functional
type. Appendix S2 provides additional detail on
the conversion of annual STEPPE functional type
biomass to monthly SOILWAT functional type
biomass.
In addition to updating biomass and litter each
year, the relative proportion of active roots for
each STEPPE functional type in each soil layer is
updated before SOILWAT2 runs (Appendix S2).
Thereafter, transpiration coefcients are updated
for each established functional type in SOIL-
WAT2 based on the relative proportion of active
roots for each STEPPE functional type in each
soil layer. Finally, the relative fractional composi-
tion of each established SOILWAT2 functional
type is updated.
Available SOILWAT2 output within the STEP-
WAT2 framework include the following: precipi-
tation (cm), snowpack (SWE), air temperature
(°C), soil temperature (°C), potential evapotran-
spiration (cm), annual evapotranspiration (cm),
interception by vegetation and litter (cm), bare-
soil evaporation (cm), evaporation of intercepted
water (cm), surface water (cm), transpiration
(cm), inltration (cm), percolation (cm), ground-
water recharge (cm), hydraulic redistribution
(cm), available soil water (cm), soil water content
(cm), soil water potential (MPa), volumetric water
content (m
3
/m
3
), and the number of wet days
(>1.5 MPa). These variables are available on
daily, weekly, monthly, and annual time frames.
The most important SOILWAT2 variable within
the STEPWAT2 framework is monthly transpira-
tion (cm) from each soil layer, which determines
resource availability for plant growth. Monthly
transpiration is calculated by summing daily tran-
spiration for each month for each soil layer for
each SOILWAT2 functional type. Since transpira-
tion is strongly coupled to leaf area index (LAI),
when plant biomass and LAI are low, so is tran-
spiration. For example, in a year after a distur-
banceevent,suchasare, STEPPE passes low
biomass to SOILWAT2, which simulates low tran-
spiration for that year. However, some plant spe-
cies and functional types can recover quickly after
disturbance and produce a substantial amount of
new biomass within a year in response to low bio-
mass and high available soil moisture. To approxi-
mate this within-year new growth, we calculate
the amount of water that could be available to be
transpired and augment resource availability for
plant functional types in below-average transpira-
tion years. For each year, we compare the annual
transpiration/annual precipitation ratio to a
www.esajournals.org 6August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
moving average of the ratio of annual transpira-
tion/annual precipitation across simulation years.
If the current years ratio is within one standard
deviation of the moving average, then no adjust-
ments are made and resource availability is based
solely on actual transpired water. Otherwise,
additional soil water is added to actual transpira-
tion to augment resource availability (hereafter
added transpired water,Appendix S2). These
additional soil water resources would otherwise
not be utilized when plant biomass is low (e.g.,
disturbance years) if resource availability were
based solely on actual transpiration.
Resource partitioning
Resource partitioning to functional types in
STEPPE is determined by their relative proportion
of active roots in each soil layer in each month
and the matching of those temporal and spatial
root distributions to transpiration from SOIL-
WAT2 in each month and in each soil layer. Thus,
resource partitioning is sensitive to each functional
types phenology and root distribution. If added
transpired water is available, it is partitioned to
each STEPPE functional type based on the propor-
tion of actual transpired water each functional
type received and then is added to actual tran-
spired water (Appendix S2). After resource parti-
tioning to functional types, resources are then
divided among the individuals of each species in
each functional type. Individuals are sorted by
size and resources are allocated beginning with
the largest individuals and partitioned until none
remain (Appendix S2). In some years, extra
resources remain after partitioning to individuals.
These extra resources are divided proportionally
to functional types that can utilize other types
resources according to each functional typescon-
tribution to total biomass (types with larger bio-
mass receive a greater share of extra resources).
Thereafter, extra resources are partitioned to indi-
viduals based on size, where larger individuals
receive extra resources rst, along with a greater
share of extra resources.
Plant growth
There are two components of growth within
STEPWAT2: annual increase in size and annual bio-
mass increment. Annual increase in size is the
increase in the size of the perennial parts of the
plant, and biomass increment is the increase in
biomass that will senesce at the end of the growing
season. The increase in size of each plant individual
is a function of its optimum growth rate, resource
availability and resource competition with other
plants, and mean annual temperature (MAT). The
optimum growth rate (dR/dt)iscalculatedas:
dR=dt ¼rð1:0relsizendvðtÞÞgmod (MAT) (1)
where ris the intrinsic rate of growth,relsize
ndv
(t)
is the relative size of an individual at time t,and
gmod is the growth year modier based on tem-
perature and resource availability in the current
year (Appendix S2). The intrinsic rates of growth
were calculated from years to reach full size
under optimal growing conditions (Cofnand
Lauenroth 1990, Bradford and Lauenroth 2006,
Table 1). Temperature effects on growth rates via
gmod give C
3
functional types an advantage
under cool conditions and C
4
functional types an
advantage under warm conditions (Appendix S2).
For succulents, increase in size does not occur in
wet years, due to the negative impact of wet
conditions on this functional types growth and
persistence (see Plant mortality).
Interspecic competition is represented in three
ways. First, functional types (and ultimately indi-
viduals of different species) receive different
amounts of resources each year based on the
matching of each functional types active root dis-
tribution to transpiration (see Resource partition-
ing). Second, species-specicrvalues simulate
differences in competitive ability between species
such that some species grow more rapidly in
response to available resources. Third, optimum
growth rates for each individual are modied by
gmod based on PR
ndv
(the ratio of resources
required/resources available for each individual)
such that higher PR
ndv
results in lower gmod val-
ues (see Appendix S2). Gmod values are also
modied based on whether that individual
belongs to a species that is C
3
or C
4
. Intraspecic
competition is represented by resource allocation
by size, in which larger individuals receive
resources rst and a larger share of the resources.
Since larger individuals receive resources rst,
the PR
ndv
for these individuals is more likely to
be near 1, resulting in higher gmod values.
Clonal species are handled differently from
unitary species in that clonal species can either
increase in size (add ramets) or decrease in size
www.esajournals.org 7August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
(lose ramets) at each time step. Increase in size is
handled identically to unitary species. However,
clonal species can recover following loss of ram-
ets by a disturbance. Each species is identied as
having or lacking the ability to recover from such
losses. For clonal species, we specify a probabil-
ity of regrowth after slow-growth mortality or
age-independent mortality and a probability of
recovery after mortality due to resource limita-
tion (Cofn and Lauenroth 1990).
Increase in biomass (that will senesce at the
end of the year) occurs when there are extra
resources available after the resource needs of
each individual in a functional type have been
met (Appendix S2).
Plant mortality
Mortality occurs through multiple mecha-
nisms: resource limitation, age-independent mor-
tality, slow-growth mortality, and succulent
mortality in wet years. Annual functional types
are not subject to these sources of mortality and
instead die at the end of each year.
The rst type of mortality occurs when there
are not enough resources available in a given year
to support the current biomass of each individual
in a functional type (e.g., resource limitation). This
algorithm is controlled by PR
ft
, the ratio of
resources required to resources available for each
functional type, and the number of continuous
years low resources can be tolerated before low-
resource mortality occurs (stretch, Table 2;
Appendix S2). Low-resource mortality occurs
when PR
ft
>1. Individuals in each functional type
are sorted according to their relsize
ndv,
and then,
mortality occurs beginning with the smallest indi-
vidual. As resource limitation increases (e.g.,
higher PR
ft
values), the number of individuals
experiencing mortality increases (Appendix S2).
Clonal functional types are subject to an alter-
native source of mortality when resources are
limited (PR
ft
>1) and when the number of years
where PR
ft
>1 exceeds stretch. In cases where an
entire clonal individual is not eliminated from
resource limitation, its size can be reduced by
partial mortality. Smaller clonal individuals are
most susceptible to reduction (Appendix S2).
After low-resource mortality, killing occurs
for functional types that are susceptible to age-
independent mortality (mort, Table 2). Age-
independent mortality simulates the probability
that only a small percentage of individuals within
a cohort will reach their maximum age (age;
Table 1, Shugart 1984, Cofn and Lauenroth
1990) and represents a type II survivorship curve
(here, P
age-indep
=1%; Pearl 1928). However, in
combination with all other sources of mortality,
most species simulated within STEPWAT2 follow
type III survivorship curves. To implement age-
independent mortality, the age of each individual
is divided by the age for that species (ageratio).
The age, ageratio, and proportion of the cohort
surviving to maturity are used to calculate the
probability of mortality by year n(Appendix S2).
Thereafter, the slow-growth mortality routine
is implemented. Individuals that are growing
slowly may have a higher risk of mortality due
to greater vulnerability to stressors than individ-
uals with average or above average growth rates
(Cofn and Lauenroth 1990). A slow-growth rate
is 5% of the maximum growth rate (90% of the r)
and can result in mortality if the number of con-
tinuous slow-growth years (slow) exceeds the
number that is allowed (Table 2; Appendix S2).
An additional source of mortality in STEP-
WAT2 is succulent mortality in wet years. Several
lines of evidence suggest succulents are vulnera-
ble in wet years (Cook 1942, Lauenroth et al.
2009). First, the cactus moth and other herbivores
have greater larval survivorship in wet years,
which results in greater mortality of succulents
due to insect damage (Cook 1942). Second, when
water resources are abundant, grass, forb and/or
shrub biomass increases. This can lead to
decreases in succulents (Lauenroth et al. 2009),
due to shading and greater humidity surrounding
Table 2. STEPWAT2 model parameters for STEPPE
functional types.
Functional type FT Space Stretch
Big sagebrush 1 0.32 8
Annual C3 forbs 2 0.05 NA
Perennial C3 forbs 3 0.18 2
Annual C3 grasses 4 0.05 NA
Perennial C3 grasses 5 0.22 2
Perennial C4 grasses 6 0.04 2
Non-sagebrush shrubs 7 0.09 2
Succulents 8 0.05 8
Note: Space is the proportion of total resources each func-
tional type receives and stretch is the number of continuous
years low resources can be tolerated before low-resource
mortality occurs.
www.esajournals.org 8August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
cladodes leading to mortality from fungal patho-
gens. To implement wet-year mortality, a proba-
bility of succulent mortality is determined for
each year as a function of precipitation during the
growing season (AprilSeptember; see Eq. 16 in
Cofn and Lauenroth 1990). If it is a wet year,
partial mortality can occur, which increases with
growing-season precipitation (see Eq. 10 in Cofn
and Lauenroth 1990). At the end of the year, mor-
tality of annuals occurs, along with senescence of
biomass produced due to extra resources.
Disturbances
There are several types of disturbances within
STEPWAT2, including plant mortality in a partic-
ular year, extirpation of plants (and the seed-
bank) in a year after which they do not return,
prescribed re, wildre, and grazing. Prescribed
re, wildre, and grazing can be turned on or
off. Prescribed re is implemented for each func-
tional type by specifying: the re-return interval
(FRI, the xed number of years between res),
the proportion of biomass removed by re
(which combines re severity and a speciessen-
sitivity to re), and the proportion of biomass
recovered after re via resprouting (01). In con-
trast, wildre occurs probabilistically, but bio-
mass removed and recovered after re is
implemented identically to prescribed re. Both
prescribed re and wildre can result in plant
mortality if all plant biomass is removed by re.
If grazing is implemented, grazing frequency
and intensity must be provided for each func-
tional type, which represents the proportion of
biomass removed by grazers at the frequency that
is specied. The impacts of different grazing ani-
mals (e.g., cattle vs. native herbivores) and forag-
ing preference for particular plant functional
types (e.g., perennial grasses) can be manipulated
by adjusting the proportion of biomass removed
for each plant functional type. Grazing can
directly kill plants if all biomass is removed by
grazers or indirectly through low-resource mor-
tality, as larger plant individuals receive resources
before smaller (grazed) individuals.
FIELD DATA FOR MODEL VALIDATION
We validated our model with vegetation data
collected in 2013 and 2014 from 15 big sagebrush
plant communities (Fig. 2; Pennington et al.
2017). These 15 sites were distributed across the
spatial extent of big sagebrush ecosystems in the
western United States and were selected based on
several criteria. First, sites were chosen to capture
areas in ve of the seven Greater Sage-grouse
Management Zones (MZs, Manier et al. 2013)
with high relative density of greater sage-grouse
(Centrocercus urophasianus) breeding populations
(Appendix S5; Fig. 10 in Doherty et al. 2016):
Great Plains (MZ I), Wyoming Basins (MZ II),
Southern Great Basin (MZ III), Snake River Plain
(MZ IV), and Northern Great Basin (MZ V). Sec-
ond, all sites had few signs of disturbance from
oil and gas development, invasive species (e.g.,
cheatgrass, Bromus tectorum), re, and heavy graz-
ing (Pennington et al. 2017). Finally, sites had
level topography (range of slope, 01.3°).
Within each site, we established three 100 m
2
plots. Within each 100-m
2
plot, we randomly sam-
pled 30 20 950 cm quadrats and recorded per-
cent canopy cover for each plant species, using
cover classes (1 =0.15%, 2 =515%, 3 =15
25%, 4 =2540%, 5 =4060%, 6 =60100%;
MZ 1
MZ 2
MZ 7
MZ 3
MZ 4
MZ 5
MZ 6
Field sites
Fig. 2. Map of the 15 big sagebrush eld sites in the
western United States on a background of sagebrush
ecosystem occurrence (green shading) from Schlaepfer
et al. (2012a). Sagebrush ecosystem occurrence was
summarized for 10 910 km
2
pixels from 30 930 m
2
GAP grid cells. Solid black lines delineate Sage-grouse
Management Zones (MZ).
www.esajournals.org 9August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
Daubenmire 1959). We assigned each taxon found
in each of the 15 eld sites to a functional type:
annual C
3
forbs, perennial C
3
forbs, annual C
3
grasses, perennial C
3
grasses, perennial C
4
grasses, big sagebrush, non-sagebrush shrubs,
and succulents (Appendix S6: Table S1). To calcu-
late absolute cover for functional types in each of
the 90 quadrats in each site, we summed cover
class midpoints for each functional type for each
quadrat. We then averaged the quadrat-level
cover values to determine a site level mean cover
value for each functional type. To calculate func-
tional type percent relative cover at the site level,
we divided the mean functional type cover by the
total cover and multiplied by 100.
To characterize big sagebrush stand structure,
we counted the number of big sagebrush individu-
als in each 100-m
2
plot and assigned each individ-
ual a canopy volume size class, which captures
both average canopy diameter and height: class
1=diameter 05cm,2=515 cm, 3 =1530 cm,
4=3050 cm, 5 =5075 cm, 6 =75100 cm, 7 =
100150 cm, and 8 =>150 cm. For each big sage-
brush individual, we calculated canopy volume
(m
3
)usingthemidpointofthecanopydiameter
class that was assigned to that individual. There-
after, we calculated mean canopy volume (m
3
)for
each site. For 10% of the big sagebrush individuals
within each plot, we also measured the length,
weight, and height of the canopy in cm. We then
calculated big sagebrush biomass (g/m
2
)foreach
of the 15 sites using length, width, and height
measurements of individual shrubs, the allometric
equation provided in Cleary et al. (2008), and big
sagebrush density (individuals/m
2
). We collected
soil samples from three locations in each plot at
depths of 010 cm, 1020 cm, and 2030 cm, for a
total of nine samples per plot and determined soil
texture using a method modied from Bouyoucos
(1962).
MODEL PARAMETERS FOR BIG SAGEBRUSH
PLANT COMMUNITIES
We simulated plant community dynamics
using the non-gridded mode for the 15 sites on
patches the size of a full-sized big sagebrush indi-
vidual (1 m
2
), which was determined from esti-
mates of average rooting characteristics of
A. tridentata (~1m
2
; Sturges 1977) and average
density of individuals/m
2
from our 15 sites
(mean =1.5 individuals/m
2
,range=0.63.5).
Eight plant functional types were included in our
simulations: annual C
3
forbs, perennial C
3
forbs,
annual C
3
grasses, perennial C
3
grasses, perennial
C
4
grasses, big sagebrush, non-sagebrush shrubs,
and succulents. C
4
forbs are uncommon in big
sagebrush ecosystems, and only one C
4
forb
genus (Amaranthus; Sage 2004) occurred in one of
our sites. We removed this taxon and represented
all forbs as C
3
. For simplicity, we represented each
functional type by a single plant species (Table 1),
which are both common and widely distributed
in big sagebrush ecosystems in our data sets (Mar-
tyn et al. 2016, Pennington et al. 2017). For each
of the 15 sites, we turned off functional types that
were not detected in the eld.
We specied several key parameters for each
functional type including space, stretch, rooting
depth distribution, and phenological activity
(Table 2; Appendix S7: Tables S1, S2). Space is an
estimate of the resource space each functional
type can access and is represented as the propor-
tion of total resources each functional type
receives and is a function of the temporal match-
ing of available soil water with depth and active
root distribution with depth (Cofn and Lauen-
roth 1990, Bradford and Lauenroth 2006). We
modied the space parameters for C
3
grasses, C
4
grasses, and shrubs across our 15 eld sites to
represent differences in the relative abundance
and distribution of these functional types in the
western United States (Paruelo and Lauenroth
1996, Epstein et al. 2002). First, we extracted the
specicC
3
grass, C
4
grass, and shrub potential
relative abundance values from Epstein et al.
(2002) for each eld site. We rescaled those val-
ues so they summed to 1, summed the previous
space parameters for those functional types, and
then multiplied the summed value by the
rescaled relative abundance values from Epstein
et al. (2002). For shrubs, we partitioned the new
space parameter proportionally between big
sagebrush and non-sagebrush shrubs based on
the previous space parameters. For C
3
grasses,
we partitioned the new space parameter
proportionally between perennial C
3
grasses and
annual C
3
grasses based on the previous space
parameters.
For functional types that are drought-tolerant
(big sagebrush, succulents), we increased the
stretch parameters (the number of years a
www.esajournals.org 10 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
functional type can survive low-resource condi-
tions) relative to other functional types (Table 2).
Rooting depth distributions were based on
parameters identied in the literature (App-
endix S7: Table S1). Phenological activity was
based on published literature whenever possible,
but otherwise was estimated from life-history
strategies for each functional type (Appendix S7:
Table S2). Big sagebrush is phenologically active
throughout the year, but the bulk of activity is
concentrated in MarchAugust (Lauenroth et al.
2014). Phenological activity for C
3
forbs and C
3
grasses begins earlier in the year than for C
4
grasses, as C
3
species begin growth earlier to
exploit moist conditions in spring and early sum-
mer (Paruelo and Lauenroth 1996). Annual C
3
species have most of their phenological activity
concentrated in May and June and complete
their life cycle by mid-summer before their peren-
nial counterparts (Campbell and Harris 1977,
Appendix S7: Table S2). The one exception is
annual C
3
grasses, represented by the winter
annual Bromus tectorum, which germinate in fall
(Jones et al. 2015), and are phenologically active
from October to June. All functional types were
subject to age-independent and slow-growth
mortality except annuals. For clonal species, we
specied a 90% probability of regrowth after
slow-growth mortality or age-independent mor-
tality and a 75% probability of regrowth after
low-resource mortality.
Species-specic parameters were obtained
from the literature for age, r,P
estab
, minbio, and
maxbio (see Table 1). In cases where we could
not nd specic parameters, we estimated values
for each species based on functional type infor-
mation provided in Cofn and Lauenroth (1990)
and Bradford and Lauenroth (2006). The pres-
ence and abundance of cheatgrass (Bromus tecto-
rum) varies considerably across the range of big
sagebrush ecosystems, with greater abundance
in the Great Basin, Columbia River Plateau, Col-
orado Plateau, and parts of the Snake River Plain
(Brummer et al. 2016). To accurately represent
differences in the presence and abundance of
cheatgrass across our 15 sites, we allowed the
P
estab
for cheatgrass to vary. We assigned one of
three p
estab
values to sites (0.015, 0.15, 0.25) based
on whether sites occur in a region with high
cheatgrass abundance, as documented by Brum-
mer et al. (2016). All sites in the Great Plains (MZ
I) and Wyoming Basins (MZ II) were assigned
P
estab
values of 0.015, sites in the Southern Great
Basin (MZ III) were assigned values of 0.15, and
sites in the Snake River Plain (MZ IV) and North-
ern Great Basin (MZ V) were assigned values of
0.25.
We specied maxindivs, the maximum num-
ber of individuals for each species that can estab-
lish in each year, to reect average density for
each species in 1 m
2
(Table 1). We set vegindv,
the number of seedling-sized units added when
clonal growth occurs, to three for all clonal spe-
cies: Pseudoroegneria spicata,Sporobolus curtissii,
and Opuntia polyacantha. Finally, slow was set to
four for Artemisia tridentata and two for all other
perennial species.
STEPWAT2 SIMULATIONS
We ran STEPWAT2 for our 15 sites for 300 yr
and 100 iterations under current climatic condi-
tions (19802010) and future climatic conditions
derived from 13 GCMs (Appendix S8: Table S1)
for RCP8.5 for end of century (20702100). We
simulated conditions for 300 yr because it
typically takes 50100 yr for the vegetation to
reach steady-state conditions in non-gridded
mode. We chose the 13 GCMs to capture models
that perform well in the western United States
(Rupp et al. 2013) and span the families of
GCMS (Knutti et al. 2013), according to the rec-
ommendations of Baker et al. (2017). Current and
future monthly gridded climate data for each
GCM were extracted from the Downscaled
CMIP3 and CMIP5 Climate and Hydrology Pro-
jects archive at http://gdo-dcp.ucllnl.org/downsca
led_cmip_projections/ (data accessed on February
3, 2014; Maurer et al. 2007). Hybrid-delta down-
scaling was implemented to obtain future daily
forcing (Hamlet et al. 2010, Tohver et al. 2014).
We used rSFSTEP2, an R program that runs
STEPWAT2 for multiple sites, climate scenarios,
and disturbance regimes (Appendix S9), to create
300 yr of weather data for each site. Specically,
we used rSFSTEP2 to create site-specic versions
of the required les for the Markov weather gen-
erator (Appendix S9). We created these les
using 30 yr of weather data for each site for cur-
rent conditions and all GCMs and then utilized
the Markov weather generator in tandem with
the site-specicles to run STEPWAT2. To
www.esajournals.org 11 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
estimate parameters for soil properties for each
site, we calculated mean soil texture across all
nine soil samples collected in the eld in each site
and specied those values as inputs.
Using rSFSTEP2, we ran simulations for all
combinations of three FRIs (no re, 50-yr, and 10-
yr return intervals) and three grazing intensities
(light, moderate, and heavy) under all of the cli-
matic conditions described above. We utilize
these re and grazing treatments as potential sce-
narios that represent the range of re and grazing
regimes that big sagebrush ecosystems are cur-
rently experiencing. Big sagebrush is re-intoler-
ant and does not re-sprout after re (Schultz
2006); thus, there was no recovery of big sage-
brush biomass the year after a re. We also
assumed no recovery for non-sagebrush shrubs
and succulents. We implemented a 40% recovery
for perennial C
3
forbs, and a 70% recovery for
perennial C
3
and C
4
grasses in the year after re,
reecting evidence that perennial grasses are typ-
ically not killed by re and can respond rapidly
post-re (Ellsworth and Kauffman 2017). Grazing
occurred every year. Under light grazing, we
assumed only grasses and forbs were grazed, as
those functional types are preferred by native
ungulates and cattle (Davies et al. 2014). As graz-
ing intensity increased, more biomass was
removed for each functional type. For grasses
and forbs, the proportion of biomass removed for
light, moderate, and heavy grazing treatments
was 0.24, 0.41, and 0.58, respectively. For big
sagebrush and non-sagebrush shrubs, the propor-
tion of biomass removed was 0, 0.02, and 0.10 for
light, moderate, and heavy grazing intensities,
respectively. We assumed succulents were never
grazed. Light, moderate, and heavy grazing
intensities were based on estimated consumption
of aboveground net primary production data
under light, moderate, and heavy grazing in
shrublands around the globe, including big sage-
brush ecosystems (see Fig. 1 in Milchunas and
Lauenroth 1993).
MODEL VALIDATION
We compared model output from STEPWAT2
under current climate conditions, light grazing,
and no re to eld data from the 15 sites for the
dominant functional types: big sagebrush, peren-
nial C
3
grasses, and perennial C
4
grasses, along
with total herbaceous and total shrub abun-
dance. We utilized simulated values under light
grazing and no re because the eld sites we
sampled exhibited no signs of re or heavy graz-
ing. The values presented here are means across
the 100 iterations and the last 100 yr. We also
compared simulated density values for big sage-
brush and forb and perennial grass species to
density values recorded in big sagebrush sites in
Wyoming during 2017. Finally, we compared
simulated biomass to published values from big
sagebrush plant communities.
Shrub functional types
For big sagebrush, we compared mean canopy
volume (m
3
) for each site to mean simulated bio-
mass (g/m
2
), with the expectation that sites with
larger mean canopy volume would also have
larger mean biomass. We also compared mean
big sagebrush biomass (g/m
2
) calculated from
eld measurements to mean simulated bio-
mass (g/m
2
). In addition, we examined differ-
ences in total shrub relative abundance between
simulated data and eld data.
Big sagebrush eld-measured canopy volume
was positively correlated with simulated big sage-
brush biomass (r=0.47): Sites with the largest
canopy volume also had the largest simulated
biomass (Fig. 3A). In addition, eld-derived esti-
mates of big sagebrush biomass were positively
correlated (r=0.50) with simulated big sagebrush
biomass (Fig. 3B). Our mean simulated biomass
values for big sagebrush (113332 g/m
2
)arewithin
the range reported in other big sagebrush eld
studies (Rickard 1985, 100148 g/m
2
; Vora 1988,
9.81534 g/m
2
; Perfors et al. 2003, 86365 g/m
2
).
Mean big sagebrush density from the 15 eld sites
ranged from 0.6 to 3.5 stems/m
2
(V. Pennington,
unpublished data). Our simulated density for big
sagebrush closely overlap those values (simulated
range across all sites =1.64.1 stems/m
2
).
For most sites, total shrub relative biomass
from simulations was greater than total shrub
relative cover from eld data (Fig. 4B). However,
simulated relative biomass for most sites was
within one standard deviation (SD) of relative
cover documented in the eld (Fig. 4B).
Herbaceous functional types
We compared the mean simulated relative bio-
mass of perennial C
3
and C
4
grasses to the mean
www.esajournals.org 12 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
relative cover of perennial C
3
and C
4
grasses
from eld data. We also examined differences in
the total herbaceous relative abundance from
simulations and from eld data.
Perennial C
3
grass simulated mean relative
biomass was greater than perennial C
3
grass
mean relative cover recorded in eld sites, except
for the three sites in the Great Plains (MZ I,
Fig. 5A). Despite this, seven sites had simulated
mean relative biomass that was within 10% of
mean relative cover values and all of the simu-
lated values were within one SD of relative cover
recorded in the eld (Fig. 5A). In our simula-
tions, perennial C
3
grasses were represented by
Pseudoroegneria spicata (bluebunch wheatgrass).
Mean simulated densities for P. spicata varied
from 9 to 17 individuals/m
2
across the 15 eld
sites. These are very similar to density values we
have for P. spicata from 2017 in three different big
sagebrush eld sites in Wyoming: 117.5 indi-
viduals/m
2
(L. Smith, unpublished data).
For perennial C
4
grasses, our simulated mean
relative biomass was nearly identical to relative
cover for two of the sites in the Great Plains
(Fig. 5B). For the remaining two sites with peren-
nial C
4
grasses, one site in the Great Plains had
slightly greater simulated relative biomass (5.4%)
than relative cover, while the second site in the
Southern Great Basin (MZ III) had slightly lower
simulated relative biomass (10.7%) than relative
cover. However, in both cases, simulated relative
biomass was within one SD of relative cover val-
ues from the eld (Fig. 5B).
In our simulations, Phlox hoodii, a common and
ubiquitous species in big sagebrush plant com-
munities (Pennington et al. 2017), represented
the perennial C
3
forb functional type. Mean sim-
ulated density for P. hoodii across our sites was
3.9 individuals/m
2
, which fell within the range of
densities (122 individuals/m
2
) that we recorded
in 2017 across 19 big sagebrush sites in Wyoming
(L. Smith, unpublished data). In addition, our
mean simulated biomass of 7.2 g/m
2
for peren-
nial forbs is similar to values reported for peren-
nial forbs in other big sagebrush studies (Rickard
1985, 3.2 g/m
2
; Bates et al. 2006, 313 g/m
2
).
Total herbaceous simulated relative biomass
was in most cases slightly lower than total herba-
ceous relative cover from our eld data (Fig. 4A).
At most, these differences in mean relative abun-
dance were 27.8%, and 9 of 15 sites have differ-
ences of 10% or less. In all cases, the values we
100 150 200 250 300 350
0.1 0.2 0.3 0.4 0.5 0.6
Simulated biomass (g/m2)
Field-measured canopy volume (m3)
r = 0.47
A)
MZ I
MZ II
MZ III
MZ IV
MZ V
100 150 200 250 300 350
100 200 300 400 500 600
Simulated biomass (g/m2)
Field-derived biomass (g/m2)
r = 0.5
B)
MZ I
MZ II
MZ III
MZ IV
MZ V
Fig. 3. Comparisons of mean big sagebrush canopy volume (m
3
) from eld data to simulated mean big sage-
brush biomass (g/m
2
) (A), and mean big sagebrush biomass (g/m
2
) estimated from allometric equations and eld
measurements to simulated mean big sagebrush biomass (g/m
2
) (B). Simulated biomass values represent means
over 100 iterations and the last 100 yr of the simulations for current conditions, light grazing, and no re. Sites
are colored according to their Sage-grouse Management Zone (MZ).
www.esajournals.org 13 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
simulated for relative biomass were within one SD
of relative cover measured in the eld (Fig. 4A).
MODEL APPLICATIONIMPACTS OF CLIMATE
CHANGE AND DISTURBANCE ON BIG
SAGEBRUSH PLANT COMMUNITIES
We summarized simulated biomass (g/m
2
)for
each site for current and future conditions (across
13 GCMs for RCP8.5 by 20702100) and for dif-
ferent re-return intervals and grazing intensi-
ties. We focus on presenting results for the two
dominant functional types in big sagebrush plant
communities: big sagebrush and perennial C
3
grasses. However, we calculated absolute
changes in biomass from current conditions to
20702100 for all functional types. Results for
each site are presented as the median biomass
across simulations forced by the 13 GCMs and as
the mean biomass across the last 100 yr of the
simulations, unless otherwise noted. All values
represent the mean across 100 iterations.
Under the no re and light grazing treatment,
most sites were expected to have increases in big
sagebrush biomass by 2100 (Fig. 6A), with the
largest increases in our sites in the Northeast
(MZs I and II: Great Plains and Wyoming Basins)
and one high elevation (2115 m) site in the
Southwest (MZ III: Southern Great Basin,
Fig. 7B). In contrast, the two sites farthest west
Total herb relative abundance
020 40 60 80
Blak Malt Winn Cowd Jeff Pine Rock Loa Wells Yomb Gras Jack Laid MCD Adel
A)
MZ I MZ II MZ III MZ IV MZ V
Field relative cover Sim relative biomass
Total shrub relative abundance
Site
0 20406080
100
Blak Malt Winn Cowd Jeff Pine Rock Loa Wells Yomb Gras Jack Laid MCD Adel
B)
MZ I MZ II MZ III MZ IV MZ V
Fig. 4. (A) Total herbaceous percent relative cover from eld sites (light blue) vs. total herbaceous percent rela-
tive biomass from simulations (green) for each site. (B) Total shrub percent relative cover from eld sites (light
blue) vs. total shrub percent relative biomass from simulations (green) for each site. Simulated biomass values
represent means over 100 iterations and the last 100 yr of the simulations for current conditions, light grazing,
and no re. Error bars for eld data represent 1 SD of relative cover across 90 Daubenmire quadrats in each site.
Error bars for simulated biomass represent 1 SD of relative biomass across 300 simulation years. Sites are ordered
according to their Sage-grouse Management Zone (MZ).
www.esajournals.org 14 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
will likely have decreases in big sagebrush bio-
mass in the future (14.4 and 1.4 g/m
2
, respec-
tively; Figs. 6A, 7B). For perennial C
3
grasses,
both increases (N =5) and decreases of biomass
(N =10) were projected (Figs. 6A, 7E). The lar-
gest predicted increases in perennial C
3
grass
biomass (510 g/m
2
) were for the three sites in
Wyoming (Fig. 7E). Biomass for almost all of the
remaining functional types will likely decrease or
not change by 2100, with the exception of peren-
nial C
4
grasses, for which increases in biomass
were projected in all four sites where perennial
C
4
grasses currently occur (Fig. 6B).
Grazing interacted with climate to inuence
functional type biomass by 2100. Under heavy
grazing, increases in big sagebrush biomass
were smaller for almost all sites, relative to
light grazing (light grazing mean absolute
change across all sites =15.2 g/m
2
, heavy graz-
ing mean absolute change across all sites =
12.7 g/m
2
; Fig. 7B, C). The impact of heavy
grazing on perennial C
3
grass biomass was con-
siderable: 14 sites were expected to decrease in
biomass under heavy grazing, relative to 10
sites under light grazing. Furthermore, there
were large declines in perennial C
3
grass bio-
mass under heavy grazing (heavy grazing
mean absolute change across all sites =9.8 g/
m
2
, light grazing mean absolute change across
all sites =0.1 g/m
2
; Fig. 7E, F).
C3 grass relative abundance
010
20 30 40 50 60
Blak Malt Winn Cowd Jeff Pine Rock Loa Wells Yomb Gras Jack Laid MCD Adel
A)
MZ I MZ II MZ III MZ IV MZ V
Field relative cover Sim relative biomass
C4 relative abundance
Site
010 20 30 40 50
Blak Malt Winn Cowd Jeff Pine Rock Loa Wells Yomb Gras Jack Laid MCD Adel
B)
MZ I MZ II MZ III MZ IV MZ V
Fig. 5. (A) Perennial C3 grass percent relative cover from eld sites (light blue) vs. perennial C3 grass percent
relative biomass from simulations (green) for each site. (B) Perennial C4 grass percent relative cover from eld
sites (light blue) vs. perennial C4 grass percent relative simulated biomass from simulations (green) for each site.
Simulated biomass values represent means over 100 iterations and the last 100 yr of the simulations for current
conditions, light grazing, and no re. Error bars for eld data represent 1 SD of relative cover across 90 Dauben-
mire quadrats in each site. Error bars for simulated biomass represent 1 SD of relative biomass across 300 simula-
tion years. Sites are ordered according to their Sage-grouse Management Zone (MZ).
www.esajournals.org 15 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
Fire substantially reduced the mean future bio-
mass of big sagebrush across all sites: 38.3 g/m
2
under light grazing and FRI of 10 yr vs. 266.8
g/m
2
under light grazing and no re (Fig. 8A, B).
Frequent re had less of an impact on future
perennial C
3
grass biomass: 68 g/m
2
for FRI of
10 yr and light grazing versus 81 g/m
2
for no re
and light grazing (Fig. 8C, D). Furthermore,
perennial C
3
grass biomass recovered more
rapidly after each re event than big sagebrush
(Fig. 8B, D).
DISCUSSION
Climate change and altered disturbance
regimes are expected to impact drylands in the
future (Palmquist et al. 2016a, b, Schlaepfer et al.
2017) and alter competitive hierarchies. Thus,
quantifying the direct and indirect effects of cli-
mate change will be critical for understanding
future plant community responses. STEPWAT2 is
a useful tool for understanding demographic
and community-level outcomes as emergent phe-
nomena of individual plant responses to chang-
ing climate and disturbance regimes, as it
simulates soil water availability in multiple soil
layers and competitive interactions in response
to uctuating soil water resources. Furthermore,
STEPWAT2 can elucidate how climate change,
re, and grazing will interact to inuence plant
community structure for multiple future time
periods throughout the 21st century in any dry-
land ecosystem. The STEPWAT2 modeling
framework has the capacity to identify specic
disturbance regimes that will push dryland
ecosystems across thresholds and to inform con-
servation planning and land management deci-
sions in the face of environmental change.
Model validation
Our model output for big sagebrush biomass
was positively correlated with eld-derived big
sagebrush canopy volume and biomass, indicat-
ing that STEPWAT2 is adequately representing
variability in big sagebrush abundance across
sites. Furthermore, simulated big sagebrush
density was similar to eld-derived density,
despite the fact that density varies considerably
with time since disturbance, grazing history,
and land-use history (Miller and Eddleman
Sagebrush C3PG
–10
0
10
20
30
40
Functional group
Change in Biomass (g/m2)
A)
C3AF C3PF C3AG C4PG Shrub Succ
–4
–2
0
2
Functional group
Change in Biomass (g/m2)
B)
Fig. 6. Change in functional type biomass from current conditions (19802010) to end of century (20702100)
with no re and light grazing. Biomass values represent means over 100 iterations and the last 100 yr of the sim-
ulations for the established functional types at each site. Functional types include big sagebrush, perennial C
3
grasses (C3PG), annual C
3
forbs (C3AF), perennial C
3
forbs (C3PF), annual C
3
grasses (C3AG), perennial C
4
grasses (C4PG), shrubs, and succulents (succ). Values shown are the median (black horizontal line), rst and
third quartiles (ends of the box), range (ends of the whiskers), and outliers (circles) across all sites for the median
biomass across simulations forced by 13 GCMs for RCP8.5.
www.esajournals.org 16 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
2001), factors we did not explicitly control for or
simulate. For total shrub abundance, simulated
mean relative biomass was almost always
greater than mean relative cover estimated in
the eld, despite being within one standard
deviation of relative cover. We did not expect a
one-to-one relationship between total shrub rela-
tive biomass and total shrub relative cover, as
there is greater biomass per unit cover for
shrubs than for other functional types (e.g.,
forbs, grasses). Instead, we expected greater rel-
ative biomass than relative cover and that is
what we documented.
Simulated mean relative biomass and mean
density values for perennial C
3
and C
4
grasses
were similar and in all cases within one standard
deviation of mean relative cover and mean den-
sity documented in the eld. In addition, mean
density for Phlox hoodii, which represented
perennial C
3
forbs, was within the range of den-
sity values recorded for Phlox hoodii in a different
set of big sagebrush sites in Wyoming (L. Smith,
unpublished data). However, total herbaceous
mean simulated relative biomass was often less
than total herbaceous mean relative cover from
eld sites. This difference occurs because there is
less biomass per unit cover for grasses and forbs,
relative to shrubs.
A challenge in validating STEPWAT2 was
relating the most frequently used metric of
MZ 1
MZ 2
MZ 7
MZ 3
MZ 4
MZ 5
MZ 6
120 –180
180 – 240
240 – 320
320 – 380
Biomass
A) Current Sagebrush Biomass, light grazing
MZ 1
MZ 2
MZ 7
MZ 3
MZ 4
MZ 5
MZ 6
Biomass
B) Change in Sagebrush Biomass, light grazing
MZ 1
MZ 2
MZ 7
MZ 3
MZ 4
MZ 5
MZ 6
Biomass
C) Change in Sagebrush Biomass, heavy grazing
MZ 1
MZ 2
MZ 7
MZ 3
MZ 4
MZ 5
MZ 6
63 – 73
73 – 83
83 – 93
93 – 103
Biomass
D) Current C3Pgrass Biomass, light grazing
MZ 1
MZ 2
MZ 7
MZ 3
MZ 4
MZ 5
MZ 6
–15 to –10
–10 to 0
0 – 5
5 – 10
Biomass
–15 to 0
0 – 10
10 – 20
20 – 30
30 – 45
E) Change in C3Pgrass Biomass, light grazing
MZ 1
MZ 2
MZ 7
MZ 3
MZ 4
MZ 5
MZ 6
–15 to –10
–10 to 0
0 – 5
5 – 10
Biomass
–15 to 0
0 – 10
10 – 20
20 – 30
30 – 45
F)Change in C3Pgrass Biomass, heavy grazing
Fig. 7. Maps of big sagebrush and perennial C
3
grass (C3Pgrass) biomass for the 15 eld sites. Panels show
current (19802010) biomass (A, D), change in biomass from current to end of century (20702100) with no re
and light grazing (B, E), and change in biomass from current to end of century with no re and heavy grazing
(C, F). Biomass values represent means over 100 iterations and the last 100 years of the simulations for the med-
ian biomass across simulations forced by 13 GCMs for RCP8.5. Maps are color-coded according to current bio-
mass or change in future biomass: Warm colors indicate decreases in biomass, while cool colors indicate
increases in biomass in the future.
www.esajournals.org 17 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
abundance in plant community eld studies
(percent cover) to the metric of abundance avail-
able from STEPWAT2 (biomass). Whenever pos-
sible, we included additional biomass estimates
from other big sagebrush studies to validate
simulated biomass from STEPWAT2. There is a
clear need for studies that collect both biomass
and percent cover data in the eld to build
relationships between these two metrics of
abundance.
While our modeling approach lacks some of
the detailed ecophysiological complexity of
plant growth implemented in other simulation
models (e.g., photosynthesis, nutrient cycling),
STEPWAT2 is designed to be sensitive to water
availability, the key resource for plants in dry-
land ecosystems, by utilizing a process-based
ecosystem water balance model to determine
soil water availability coupled with classic
logistic growth equations. STEPWAT2 follows a
long tradition of gap dynamic models that are
conceptualized and implemented at the individ-
ual patch or stand level (Botkin et al. 1972, Shu-
gart 1984), which are utilized extensively for
forests and other ecosystems (Morin et al.
2018). The advantages of gap dynamics models
Fig. 8. End-of-century big sagebrush and perennial C
3
grass (C3Pgrass) biomass (g/m
2
) across the 300-yr simu-
lation with light grazing and no re (A, C) and light grazing and re every 10 yr (B, D). The colored lines are the
response of the 15 sites, while the black line is the average response for all sites over the nal 100 yr. Biomass
values represent means over 100 iterations for the median biomass across simulations forced by 13 GCMs for
RCP8.5.
www.esajournals.org 18 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
are that they simulate both interspecicand
intraspecic competition for limiting resources
at the scales at which plants compete for them
(e.g., ne spatial scales or patch scales) and rep-
resent individual plant responses to disturbance
and uctuating environmental conditions.
Thus, while representing plant growth simplis-
tically, gap dynamics models like STEPWAT2
have some advantages over other approaches
that explicitly represent detailed ecophysiologi-
cal aspects of plant growth, albeit at larger spa-
tial scales.
Model application
Simulations for our 15 eld sites under future
climatic conditions and different disturbance
regimes highlight the utility of this model in
understanding how climate change, re, and
grazing will interact to inuence plant functional
type biomass and composition. Under no re
and light grazing, for most of our sites, big sage-
brush biomass is expected to increase in the
future, except for the two sites farthest west,
which will have decreases in big sagebrush bio-
mass due to declines in available soil water in the
future. These results are consistent with other
studies that have documented drying leading to
declines in big sagebrush in warmer sites in the
Desert Southwest or along the western edge of
the big sagebrush distribution (Neilson et al.
2005, Schlaepfer et al. 2012b, Renwick et al.
2018). Most sites were projected to have
decreased biomass of C
3
grasses and forbs, while
perennial C
4
grasses were expected to increase
by 2100. These results suggest that plant func-
tional types in big sagebrush plant communities
will respond differently to climatic change, with
potential increases in perennial C
4
grasses due to
warming and decreases in C
3
herbaceous species.
Thus, STEPWAT2 can be utilized to explore spe-
cies-specic, functional type-specic, and com-
munity-level responses to climate change and
identify sites or geographic regions that would
continue to support the dominant functional
types in dryland plant communities in the
future.
Grazing intensity and re-return interval inu-
enced changes in functional type biomass by
2100: Heavy grazing signicantly reduced peren-
nial C
3
grass biomass, while frequent re caused
a large reduction in big sagebrush biomass. Our
simulation results indicate that re-return inter-
val (FRI) has large implications for functional
type biomass under climatic change, especially
for re-intolerant species, such as big sagebrush.
These results demonstrate that frequent re can
shift these shrub-dominated communities to
grass-dominated states. STEPWAT2 can be uti-
lized to understand how specic management
strategies (reduced grazing intensity or pre-
scribed re) will impact dryland plant communi-
ties and guide management action now and in
the future.
ACKNOWLEDGMENTS
Funding for this work came from the U.S. Geologi-
cal Survey, North Central Climate Science Center
(Grant G12AC 20504), the U.S. Fish and Wildlife Ser-
vice (F13AC00865 and Interagency Agreement
#4500054042 to USGS), Yale University, and the
University of Wyoming. We would like to thank sev-
eral ecologists and computer programmers who have
contributed to the development of STEPWAT2: Debra
Peters, Ruihua Liu, Donovan Miller, Ryan Murphy,
Eric Murphy, Ashish Tiwari, Brenden Bernal, Karan
Sodhi, and Caitlin Andrews. We would also like to
acknowledge Victoria Pennington, Lukas Lindquist,
Lexi Smith, Ashley Wildeman, Jonathan Paklaian,
Margarita Reza, Vincent Irizarry, and Kim Hill, who
collected eld data that was used in the Model Valida-
tion section. Any use of trade, product, or rm names
is for descriptive purposes only and does not imply
endorsement by the U.S. Government.
LITERATURE CITED
Adler, P. B., D. G. Milchunas, W. K. Lauenroth, O. E.
Sala, and I. C. Burke. 2004. Functional traits of
graminoids in semi-arid steppes: a test of graz-
ing histories. Journal of Applied Ecology 41:653
663.
Adler, P. B., D. G. Milchunas, O. E. Sala, I. C. Burke,
and W. K. Lauenroth. 2005. Plant traits and ecosys-
tem grazing effects: comparison of U.S. sagebrush
steppe and Patagonian steppe. Ecological Applica-
tions 15:774792.
Bai, Y., J. Wu, C. M. Clark, Q. Pan, L. Zhang, S. Chen,
Q. Wang, and X. Han. 2012. Grazing alters ecosys-
tem functioning and C:N: P stoichiometry of grass-
lands along a regional precipitation gradient.
Journal of Applied Ecology 49:12041215.
Baker, D. J., A. J. Hartley, J. W. Pearce-Higgins, R. G.
Jones, and S. G. Willis. 2017. Neglected issues in
www.esajournals.org 19 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
using weather and climate information in ecology
and biogeography. Diversity and Distributions
23:329340.
Balch, J. K., B. A. Bradley, C. M. DAntonio, and J.
G
omez-Dans. 2013. Introduced annual grass
increases regional re activity across the arid west-
ern USA (19802009). Global Change Biology
19:173183.
Bates, J. D., T. Svejcar, R. F. Miller, and R. A. Angell.
2006. The effects of precipitation timing on sage-
brush steppe vegetation. Journal of Arid Environ-
ments 64:670697.
Botkin, D. B., J. F. Janak, and J. R. Wallis. 1972. Some
ecological consequences of a computer model of
forest growth. Journal of Ecology 60:849872.
Bouyoucos, G. J. 1962. Hydrometer method improved
for making particle size analyses of soils 1. Agron-
omy Journal 54:464465.
Bradford, J. B., and W. K. Lauenroth. 2006. Controls
over invasion of Bromus tectorum: the importance
of climate, soil, disturbance and seed availability.
Journal of Vegetation Science 17:693704.
Bradford, J., D. Schlaepfer, and W. Lauenroth. 2014.
Ecohydrology of adjacent sagebrush and lodgepole
pine ecosystems: the consequences of climate
change and disturbance. Ecosystems 17:590605.
Brummer, T. J., K. T. Taylor, J. Rotella, B. D. Maxwell,
L. J. Rew, and M. Lavin. 2016. Drivers of Bromus
tectorum abundance in the western North
American sagebrush steppe. Ecosystems 19:986
1000.
Buttereld, B. J., and S. M. Munson. 2016. Temperature
is better than precipitation as a predictor of plant
community assembly across a dryland region.
Journal of Vegetation Science 27:938947.
Campbell, G. S., and G. A. Harris. 1977. Water
relations and water use patterns for Artemisia tri-
dentata Nutt. in wet and dry years. Ecology 58:652
659.
Chen, I.-C., J. K. Hill, R. Ohlem
uller, D. B. Roy, and C.
D. Thomas. 2011. Rapid range shifts of species
associated with high levels of climate warming.
Science 333:1024.
Cleary, M. B., E. Pendall, and B. E. Ewers. 2008. Testing
sagebrush allometric relationships across three re
chronosequences in Wyoming, USA. Journal of
Arid Environments 72:285301.
Cofn, D. P., and W. K. Lauenroth. 1990. A gap
dynamics simulation model of succession in a
semiarid grassland. Ecological Modelling 49:229
266.
Cofn, D. P., W. K. Lauenroth, and I. C. Burke. 1993.
Spatial dynamics in recovery of shortgrass steppe
ecosystems. Lectures on Mathematics in the Life
Sciences 23:75108.
Cook, C. W. 1942. Insects and weather as they inu-
ence growth of cactus on the central Great Plains.
Ecology 23:209214.
Cook, B. I., T. R. Ault, and J. E. Smerdon. 2015.
Unprecedented 21st century drought risk in the
American Southwest and Central Plains. Science
Advances 1:e1400082.
Daubenmire, R. 1959. A canopy-coverage method of
vegetational analysis. Northwest Science 33:4364.
Davies, K. W., C. S. Boyd, J. L. Beck, J. D. Bates, T. J.
Svejcar, and M. A. Gregg. 2011. Saving the sage-
brush sea: an ecosystem conservation plan for big
sagebrush plant communities. Biological Conserva-
tion 144:25732584.
Davies, K. W., M. Vavra, B. W. Schultz, and N. R. Rim-
bey. 2014. Implications of longer term grazing rest
in the sagebrush steppe. Journal of Rangeland
Applications 1:1434.
DeAngelis, D. L., and V. Grimm. 2014. Individual-
based models in ecology after four decades.
F1000Prime Reports 6:39.
Doherty, K. E., J. S. Evans, P. S. Coates, L. M. Juliusson,
and B. C. Fedy. 2016. Importance of regional varia-
tion in conservation planning: a rangewide exam-
ple of the Greater Sage-Grouse. Ecosphere 7:
e01462.
Ellsworth, L. M., and J. B. Kauffman. 2017. Plant com-
munity response to prescribed re varies by pre-
re condition and season of burn in mountain big
sagebrush ecosystems. Journal of Arid Environ-
ments 144:7480.
Epstein, H. E., R. A. Gill, J. M. Paruelo, W. K. Lauen-
roth, G. J. Jia, and I. C. Burke. 2002. The relative
abundance of three plant functional types in tem-
perate grasslands and shrublands of North and
South America: effects of projected climate change.
Journal of Biogeography 29:875888.
Fensham, R. J., J. Fraser, H. J. MacDermott, and J. Firn.
2015. Dominant tree species are at risk from exag-
gerated drought under climate change. Global
Change Biology 21:37773785.
Ferguson, C. W. 1964. Annual rings in big sagebrush:
Artemisia tridentata. University of Arizona Press,
Tucson, Arizona, USA.
Gornish, E. S., and C. M. Prather. 2014. Foliar
functional traits that predict plant biomass
response to warming. Journal of Vegetation Science
25:919927.
Gremer, J. R., J. B. Bradford, S. M. Munson, and M. C.
Duniway. 2015. Desert grassland responses to cli-
mate and soil moisture suggest divergent vulnera-
bilities across the southwestern United States.
Global Change Biology 21:40494062.
Grimm, V., D. Ayll
on, and S. F. Railsback. 2017. Next-
generation individual-based models integrate
www.esajournals.org 20 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
biodiversity and ecosystems: Yes we can, and yes
we must. Ecosystems 20:229236.
Hamlet, A. F., E. P. Salath
e, and P. Carrasco. 2010. Sta-
tistical downscaling techniques for global climate
model simulations of temperature and precipita-
tion with application to water resources planning
studies. Chapter 4 in Final Report for the Columbia
Basin Climate Change Scenarios Project, Climate
Impacts Group, Center for Science in the Earth Sys-
tem, Joint Institute for the Study of the Atmosphere
and Ocean, University of Washington, Seattle,
USA.
Huston, M., D. DeAngelis, and W. Post. 1988. New
computer models unify ecological theory. BioS-
cience 38:682691.
Jones, R. O., J. C. Chambers, D. I. Board, D. W. John-
son, and R. R. Blank. 2015. The role of resource lim-
itation in restoration of sagebrush ecosystems
dominated by cheatgrass (Bromus tectorum). Eco-
sphere 6:121.
Knick, S. T., D. S. Dobkin, J. T. Rotenberry, M. A.
Schroeder, W. M. Vander Haegen, and C. van
Riper. 2003. Teetering on the edge or too late? Con-
servation and research issues for avifauna of sage-
brush habitats. Condor 105:611634.
Knutti, R., D. Masson, and A. Gettelman. 2013. Cli-
mate model genealogy: generation CMIP5 and
how we got there. Geophysical Research Letters
40:11941199.
Lauenroth, W. K., and P. B. Adler. 2008. Demography
of perennial grassland plants: survival, life expec-
tancy and life span. Journal of Ecology 96:1023
1032.
Lauenroth, W. K., R. L. Dougherty, and J. S. Singh.
2009. Precipitation event size controls on long-term
abundance of Opuntia polyacantha (plains prickly-
pear) in Great Plains grasslands. Great Plains
Research 19:5564.
Lauenroth, W. K., D. R. Schlaepfer, and J. B. Bradford.
2014. Ecohydrology of dry regions: storage versus
pulse soil water dynamics. Ecosystems 17:1469
1479.
Lin, D., J. Xia, and S. Wan. 2010. Climate warming and
biomass accumulation of terrestrial plants: a meta-
analysis. New Phytologist 188:187198.
Loik, M. E., D. D. Breshears, W. K. Lauenroth, and J.
Belnap. 2004. A multi-scale perspective of water
pulses in dryland ecosystems: climatology and eco-
hydrology of the western USA. Oecologia 141:269
281.
Lucero, M. E., J. R. Barrow, P. Osuna, I. Reyes, and S.
E. Duke. 2008. Enhancing native grass productivity
by cocultivating with endophyte-laden calli. Ran-
geland Ecology & Management 61:124130.
Manier, D. J., et al. 2013. Summary of science, activi-
ties, programs, and policies that inuence the ran-
gewide conservation of greater sage-grouse
(Centrocercus urophasianus). US Geological Survey,
Reston, Virginia, USA.
Martyn, T. E., J. B. Bradford, D. R. Schlaepfer, I. C.
Burke, and W. K. Lauenroth. 2016. Seed bank and
big sagebrush plant community composition in a
range margin for big sagebrush. Ecosphere 7:
e01453.
Maurer, E. P., L. Brekke, T. Pruitt, and P. B. Duffy. 2007.
Fine-resolution climate projections enhance regio-
nal climate change impact studies. Eos, Transac-
tions American Geophysical Union 88:504504.
McKenzie, D., and J. S. Littell. 2017. Climate change
and the eco-hydrology of re: Will area burned
increase in a warming western USA? Ecological
Applications 27:2636.
Meddens, A. J. H., J. A. Hicke, A. K. Macalady, P. C.
Buotte, T. R. Cowles, and C. D. Allen. 2015. Pat-
terns and causes of observed pi~
non pine mortality
in the southwestern United States. New Phytolo-
gist 206:9197.
Milchunas, D. G., and W. K. Lauenroth. 1993. Quanti-
tative effects of grazing on vegetation and soils
over a global range of environments. Ecological
Monographs 63:328366.
Milchunas, D. G., O. E. Sala, and W. K. Lauenroth.
1988. A generalized model of the effects of grazing
by large herbivores on grassland community struc-
ture. American Naturalist 132:87106.
Miller, R. F., and L. L. Eddleman. 2001. Spatial and
temporal changes of sage grouse habitat in the
sagebrush biome. Technical Bulletin 151, Oregon
State University Agricultural Experiment Station,
Corvallis, Oregon, USA.
Morin, X., L. Fahse, H. Jactel, M. Scherer-Lorenzen, R.
Garc
ıa-Vald
es, and H. Bugmann. 2018. Long-term
response of forest productivity to climate change is
mostly driven by change in tree species composi-
tion. Scientic Reports 8:5627.
Neilson, R. P., J. M. Lenihan, D. Bachelet, and R. J. Dra-
pek. 2005. Climate change implications for sage-
brush ecosystems. Transactions of the 70yh North
American Wildlife and Natural Resources Confer-
ence 70:145159.
Palmquist, K. A., D. R. Schlaepfer, J. B. Bradford,
and W. K. Lauenroth. 2016a. Mid-latitude shrub
steppe plant communities: climate change conse-
quences for soil water resources. Ecology 97:
23422354.
Palmquist, K. A., D. R. Schlaepfer, J. B. Bradford, and
W. K. Lauenroth. 2016b. Spatial and ecological
variation in dryland ecohydrological responses to
www.esajournals.org 21 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
climate change: implications for management.
Ecosphere 7:e01590.
Palmquist, K., D. Schlaepfer, T. Martyn, J. Bradford, and
W. Lauenroth. 2018. DrylandEcology/STEPWAT2:
STEPWAT2 Model Description (Palmquist et al.
2018 Ecosphere) (Version v1.2.0). Zenodo. http://
doi.org/10.5281/zenodo.1306924
Paruelo, J. M., and W. K. Lauenroth. 1996. Relative
abundance of plant functional types in grasslands
and shrublands of North America. Ecological
Applications 6:12121224.
Pearl, R. 1928. The rate of living. A. E. Kopf, New York,
New York, USA.
Pennington, V. E., K. A. Palmquist, J. B. Bradford, and
W. K. Lauenroth. 2017. Climate and soil texture
inuence patterns of forb species richness and
composition in big sagebrush plant communities
across their spatial extent in the western U.S. Plant
Ecology 218:957970.
Perfors, T., J. Harte, and S. E. Alter. 2003. Enhanced
growth of sagebrush (Artemisia tridentata)in
response to manipulated ecosystem warming. Glo-
bal Change Biology 9:736742.
Peters, D. P. C. 2000. Climatic variation and simulated
patterns in seedling establishment of two dominant
grasses at a semi-arid-arid grassland ecotone. Jour-
nal of Vegetation Science 11:493504.
Peters, D. P. 2002. Plant species dominance at a grass-
landshrubland ecotone: an individual-based gap
dynamics model of herbaceous and woody species.
Ecological Modelling 152:532.
Renwick, K. M., C. Caroline, A. R. Kleinhesselink, D.
Schlaepfer, B. A. Bradley, C. L. Aldridge, B. Poulter,
and P. B. Adler. 2018. Multi-model comparison
highlights consistency in predicted effect of warm-
ing on a semi-arid shrub. Global Change Biology
24:424438.
Richardson, C. A., and D. A. Wright. 1984. A model
for generating daily weather variables. Pages 186.
United States Department of Agriculture, Agricul-
ture Research Service, Washington, D.C., USA.
Rickard, W. H. 1985. Biomass and shoot production in
an undisturbed sagebrush-bunchgrass community.
Northwest Science 59:126133.
Rowland, M. M., M. J. Wisdom, L. H. Suring, and C.
W. Meinke. 2006. Greater sage-grouse as an
umbrella species for sagebrush-associated verte-
brates. Biological Conservation 129:323335.
Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P.
W. Mote. 2013. Evaluation of CMIP5 20th century
climate simulations for the Pacic Northwest USA.
Journal of Geophysical Research: Atmospheres
118:2013JD020085.
Sage, R. F. 2004. The evolution of C4 photosynthesis.
New Phytologist 161:341370.
Sala, O. E., W. K. Lauenroth, and W. J. Parton. 1992.
Long-term soil water dynamics in the shortgrass
steppe. Ecology 73:11751181.
Schlaepfer, D. R., W. K. Lauenroth, and J. B. Bradford.
2012a. Ecohydrological niche of sagebrush ecosys-
tems. Ecohydrology 5:453466.
Schlaepfer, D. R., W. K. Lauenroth, and J. B. Bradford.
2012b. Effects of ecohydrological variables on current
and future ranges, local suitability patterns, and model
accuracy in big sagebrush. Ecography 35:374384.
Schlaepfer, D. R., W. K. Lauenroth, and J. B. Bradford.
2014. Modeling regeneration responses of big sage-
brush (Artemisia tridentata) to abiotic conditions.
Ecological Modelling 286:6677.
Schlaepfer, D. R., et al. 2017. Climate change reduces
extent of temperate drylands and intensies drought
in deep soils. Nature Communications 8:14196.
Schultz, L. M. 2006. Artemisia. Pages 503534 in Flora
of North America Editorial Committee, editor.
Flora of North America North of Mexico. Oxford
University Press, Oxford, UK.
Seager, R., et al. 2007. Model projections of an immi-
nent transition to a more arid climate in southwest-
ern North America. Science 316:11811184.
Seidl, R., W. Rammer, R. M. Scheller, and T. A. Spies.
2012. An individual-based process model to simu-
late landscape-scale forest ecosystem dynamics.
Ecological Modelling 231:87100.
Seidl, R., M.-J. Schelhaas, W. Rammer, and P. J. Verk-
erk. 2014. Increasing forest disturbances in Europe
and their impact on carbon storage. Nature Cli-
mate Change 4:806810.
Shugart, H. H. 1984. A theory of forest dynamics: the
ecological implications of forest succession models.
Springer-Verlag, New York, New York, USA.
Smith, T., and M. Huston. 1990. A theory of the spatial
and temporal dynamics of plant communities.
Pages 4969 in G. Grabherr, L. Mucina, M. B. Dale,
and C. J. F. Ter Braak, editors. Progress in theoreti-
cal vegetation science. Springer, Dordrecht, The
Netherlands.
Sturges, D. L. 1977. Soil water withdrawal and root
characteristics of big sagebrush. American Midland
Naturalist 98:257274.
Thom, D., W. Rammer, and R. Seidl. 2017. Distur-
bances catalyze the adaptation of forest ecosystems
to changing climate conditions. Global Change
Biology 23:269282.
Tohver, I. M., A. F. Hamlet, and S.-Y. Lee. 2014.
Impacts of 21st-century climate change on hydro-
logic extremes in the Pacic Northwest Region of
North America. JAWRA Journal of the American
Water Resources Association 50:14611476.
Trenberth, K. E., A. Dai, G. van der Schrier, P. D. Jones,
J. Barichivich, K. R. Briffa, and J. Shefeld. 2014.
www.esajournals.org 22 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
Global warming and changes in drought. Nature
Climate Change 4:1722.
Tylianakis, J. M., R. K. Didham, J. Bascompte, and D.
A. Wardle. 2008. Global change and species inter-
actions in terrestrial ecosystems. Ecology Letters
11:13511363.
Vora, R. S. 1988. Predicting biomass of ve shrub spe-
cies in northeastern California. Journal of Range
Management 41:6365.
Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T.
W. Swetnam. 2006. Warming and earlier spring
increase western U.S. forest wildre activity.
Science 313:940943.
Wu, W., C. Lan, M. Lo, J. T. Reager, and J. S. Famiglietti.
2015. Increases in the annual range of soil water
storage at northern middle and high latitudes
under global warming. Geophysical Research
Letters 42:39033910.
SUPPORTING INFORMATION
Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2.
2394/full
www.esajournals.org 23 August 2018 Volume 9(8) Article e02394
EMERGING TECHNOLOGIES PALMQUIST ET AL.
... These models use energy budgets and soil physics to balance water budgets. As such, they provide a mechanistic link between climate and plant water uptake (Palmquist et al., 2018). Because they are founded on physical models, the strength of these mechanistic ecohydrological models is that they may perform better at predicting out-of-range phenomena than observational and correlative approaches such as climate envelope modeling (Schlaepfer et al., 2017). ...
... The spatial resolution of this study was of the order of 10 2 m, whereas dynamic global vegetation models (DGVMs) are often run at 1 degree (roughly 110 km 2 ) or larger spatial resolutions. Therefore, linking vegetation responses to climate change across scales will be a critical future advance (Palmquist et al., 2018;Fan et al., 2019). One recent example of this type of work is the FATES model from Massoud et al. (2019). ...
Article
Shrub encroachment, forest decline, and wildfires have caused large‐scale changes in semi‐arid vegetation in the past 50 years. Climate is a primary determinant of plant growth in semi‐arid ecosystems, yet it remains difficult to forecast large‐scale vegetation shifts (i.e., biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi‐arid biome shifts. Moving from small to large scales, first, tree‐level models that simulate the carbon costs of drought‐induced plant hydraulic failure are improving predictions of delayed‐mortality responses to drought. Second, tracer‐informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small‐scale water movement processes at large scales. Fourth, remotely‐sensed measurements of plant traits such as relative canopy moisture are providing early‐warning signals that predict prediction forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g., machine‐learning) that can integrate these perspectives, will rapidly improve forecasts of semi‐arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high‐risk areas for management.
... Model development continues, but Hydrus is essentially a soil physics model and not a physiological or plant competition model. Future modelling efforts that incorporate root competition and physiology can be expected to provide improved estimates of water uptake in communities that may be more or less well correlated with plant landscape abundance (Mackay et al., 2019;Palmquist, Bradford, Martyn, Schlaepfer, & Lauenroth, 2018). ...
Article
Differences in vertical root distributions are often assumed to create resource uptake tradeoffs that determine plant growth and coexistence. Yet, most plant roots are in shallow soils, and data linking root distributions with resource uptake and plant abundances remain elusive. Here we used a tracer experiment to describe the vertical distribution of absorptive roots of dominant species in a shrub‐steppe ecosystem. To describe how these different rooting distributions affected water uptake in wet and dry soils across a growing season, we used a soil water movement model. Root traits were then correlated with plant landscape abundances. Deeper root distributions extracted more soil water, had larger unique hydrological niches and were more abundant on the landscape. Though most (> 50%) root biomass and tracer uptake occurred in shallow soils (0–32 cm), the depth of 50% of tracer uptake varied from 11 to 32 cm across species and species with deeper rooting distributions were more abundant on the landscape (R2 = 0.95). The water flow model revealed that deeper rooting distributions should extract more soil water (i.e., a range of 60 to 113 mm of soil water) because shallow roots were often in dry soils. These potential water uptake values were tightly correlated with species’ abundances on the landscape (R2 = 0.90). Finally, each species’ rooting distribution demonstrated a depth and time at which it could extract more soil water than any other rooting distribution, and the size of these unique hydrological niches indices was also well correlated with species’ abundances (R2 = 0.89). Synthesis. Our results demonstrate not only a correlation between root distributions and species abundance, but also the mechanism through which differences in rooting distributions can determine resource uptake and niche partitioning, even when most roots are found in shallow soils.
Article
Full-text available
Regeneration is an essential demographic step that affects plant population persistence, recovery after disturbances, and potential migration to track suitable climate conditions. Challenges of restoring big sagebrush (Artemisia tridentata) after disturbances including fire‐invasive annual grass interactions exemplify the need to understand the complex regeneration processes of this long‐lived, woody species that is widespread across the semiarid western U.S. Projected 21st century climate change is expected to increase drought risks and intensify restoration challenges. A detailed understanding of regeneration will be crucial for developing management frameworks for the big sagebrush region in the 21st century. Here, we used two complementary models to explore spatial and temporal relationships in the potential of big sagebrush regeneration representing (1) range‐wide big sagebrush regeneration responses in natural vegetation (process‐based model) and (2) big sagebrush restoration seeding outcomes following fire in the Great Basin and the Snake River Plains (regression‐based model). The process‐based model suggested substantial geographic variation in long‐term regeneration trajectories with central and northern areas of the big sagebrush region remaining climatically suitable, whereas marginal and southern areas are becoming less suitable. The regression‐based model suggested, however, that restoration seeding may become increasingly more difficult, illustrating the particularly difficult challenge of promoting sagebrush establishment after wildfire in invaded landscapes. These results suggest that sustaining big sagebrush on the landscape throughout the 21st century may climatically be feasible for many areas and that uncertainty about the long‐term sustainability of big sagebrush may be driven more by dynamics of biological invasions and wildfire than by uncertainty in climate change projections. Divergent projections of the two models under 21st century climate conditions encourage further study to evaluate potential benefits of re‐creating conditions of uninvaded, unburned natural big sagebrush vegetation for post‐fire restoration seeding, such as seeding in multiple years and, for at least much of the northern Great Basin and Snake River Plains, the control of the fire‐invasive annual grass cycle.
Article
Actual evapotranspiration (AET) is the main component of water balance and comprises evaporation (E) and transpiration (T). Despite widespread recognition of the importance of water in terrestrial ecosystems, knowledge about key processes in the water balance is surprisingly limited, especially for how water is partitioned between E and T. We used a daily time step soil water model (SOILWAT2) and 10 years of data to describe AET partitioning and water balance of a degraded meadow and an artificial pasture in the Three‐River Source Region on the Qinghai‐Tibetan Plateau. Our results showed that there are two important environmental factors influencing water balance processes: evaporative demand of the atmosphere and precipitation. Due to the same evaporative demand and precipitation regime, the AET between our two sites were similar in seasonal trends and annual amounts. On an annual basis, AET and deep drainage accounted for almost 100% of the precipitation received by both the two sites. Despite the similarity of annual AET between the degraded meadow and the artificial pasture, the AET partitioning between the two sites was substantially different. Mean annual transpiration/actual evapotranspiration (T/AET) was 27% in the degraded meadow and 52% in the artificial pasture. The key controls on AET partitioning were vegetation and soil, and compared to soil, our study emphasized the influence of vegetation, high amounts of biomass resulted in the highest values of T/AET in the artificial pasture. Additionally, we identified deep drainage as an important and previously overlooked component of water balance accounting for an average of approximately 21% of the annual precipitation in both the two study sites.
Article
Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush (Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual‐based plant simulation model that represents intra‐ and inter‐specific competition for water availability, which is represented by a process‐based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine‐scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of ‐20% to +27%), declines in perennial C3 grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C4 grasses. The largest declines in big sagebrush, perennial C3 grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture‐limited vs. temperature‐limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.
Chapter
Full-text available
Amphibians and reptiles are vertebrates that are often overlooked in assessments of the importance of sagebrush (Artemisia spp.) ecosystems for wildlife. Given their dependence on water, few amphibians are strongly associated with sagebrush habitats, although several use these uplands for foraging, shelter, or dispersal. Of the 60 amphibian species that are predicted to occur within the sagebrush biome, the Great Basin spadefoot (Spea intermontana) is probably the only species that occupies enough of the biome and lives predominantly in terrestrial habitats (mostly in burrows) to be considered sagebrush associated. Of the 116 reptiles that are predicted to occur within the sagebrush biome, about 5 lizards and 5 snakes were identified as both strongly associated with sagebrush habitats and occupied areas likely to be managed for sage-grouse (Centrocercus spp). However, this list could be lower or higher depending on the specific location within the biome, and there remains considerable uncertainty regarding potential threats to reptiles, as well as basic information on distribution and abundance of most reptile species.
Chapter
Full-text available
Adaptive management and monitoring efforts focused on vegetation, habitat, and wildlife in the sagebrush (Artemisia spp.) biome help inform management of species and habitats, predict ecological responses to conservation practices, and adapt management to improve conservation outcomes. This chapter emphasizes the adaptive resource management framework with its four stages: (1) problem definition, (2) outcomes, (3) decision analysis, and (4) implementation and monitoring. Adaptive resource management is an evolving process involving a sequential cycle of learning (the accumulation of understanding over time) and adaptation (the adjustment of management over time). This framework operationalizes monitoring a necessary component of decision making in the sagebrush biome. Several national and regional monitoring efforts are underway across the sagebrush biome for both vegetation and wildlife. Sustaining these efforts and using the information effectively is an important step towards realizing the full potential of the adaptive management framework in sagebrush ecosystems. Furthermore, coordinating monitoring efforts and information across stakeholders (for example, Federal, State, nongovernmental organizations) will be necessary given the limited resources, diverse ownership/management, and sagebrush biome size.
Technical Report
Full-text available
USGS sage-grouse and sagebrush ecosystem research is aligned with priority needs outlined in the “Integrated Rangeland Fire Management Strategy Actionable Science Plan” (Integrated Rangeland Fire Management Strategy Actionable Science Plan Team, 2016). The list of 116 research projects is organized into five thematic areas: fire (16 projects); invasive species (7 projects); restoration (23 projects); sagebrush, sage-grouse, and other sagebrush-associated species (60 projects); and weather and climate (10 projects). Individual projects often overlap multiple themes (for example, effects of wildfire and invasive annual grasses on greater sage-grouse habitat); therefore, project descriptions are organized according to the main focal theme.
Technical Report
Full-text available
USGS sage-grouse and sagebrush ecosystem research is aligned with priority needs outlined in the “Integrated Rangeland Fire Management Strategy Actionable Science Plan” (Integrated Rangeland Fire Management Strategy Actionable Science Plan Team, 2016). The list of 116 research projects is organized into five thematic areas: fire (16 projects); invasive species (7 projects); restoration (23 projects); sagebrush, sage-grouse, and other sagebrush-associated species (60 projects); and weather and climate (10 projects). Individual projects often overlap multiple themes (for example, effects of wildfire and invasive annual grasses on greater sage-grouse habitat); therefore, project descriptions are organized according to the main focal theme.
Article
Full-text available
Although natural resource managers are concerned about climate change, many are unable to adequately incorporate climate change science into their adaptation strategies or management plans, and are not always aware of or do not always employ the most current scientific knowledge. One of the most prominent natural resource management agencies in the United States is the Bureau of Land Management (BLM), which is tasked with managing over 248 million acres (>1million km2) of public lands for multiple, often conflicting, uses. Climate change will affect the sustainability of many of these land uses and could further increase conflicts between them. As such, the purpose of our study was to determine the extent to which climate change will affect public land uses, and whether the BLM is managing for such predicted effects. To do so, we first conducted a systematic review of peer-reviewed literature that discussed potential impacts of climate change on the multiple land uses the BLM manages in the IntermountainWest, USA, and then expanded these results with a synthesis of projected vegetation changes. Finally, we conducted a content analysis of BLM Resource Management Plans in order to determine how climate change is explicitly addressed by BLM managers, and whether such plans reflect changes predicted by the scientific literature. We found that active resource use generally threatens intrinsic values such as conservation and ecosystem services on BLM land, and climate change is expected to exacerbate these threats in numerous ways. Additionally, our synthesis of vegetation modeling suggests substantial changes in vegetation due to climate change. However, BLM plans rarely referred to climate change explicitly and did not reflect the results of the literature review or vegetation model synthesis. Our results suggest there is a disconnect between management of BLM lands and the best available science on climate change. We recommend that the BLM actively integrates such research into on-the-ground management plans and activities, and that researchers studying the effects of climate change make a more robust effort to understand the practices and policies of public land management in order to effectively communicate the management significance of their findings.
Article
Full-text available
Degradation, fragmentation, and loss of native sagebrush (Artemisia spp.) landscapes have imperiled these habitats and their associated avifauna. Historically, this vast piece of the Western landscape has been undervalued: even though more than 70% of all remaining sagebrush habitat in the United States is publicly owned, <3% of it is protected as federal reserves or national parks. We review the threats facing birds in sagebrush habitats to emphasize the urgency for conservation and research actions, and synthesize existing information that forms the foundation for recommended research directions. Management and conservation of birds in sagebrush habitats will require more research into four major topics: (1) identification of primary land-use practices and their influence on sagebrush habitats and birds, (2) better understanding of bird responses to habitat components and disturbance processes of sagebrush ecosystems, (3) improved hierarchical designs for surveying and monitoring programs, and (4) linking bird movements and population changes during migration and wintering periods to dynamics on the sagebrush breeding grounds. This research is essential because we already have seen that sagebrush habitats can be altered by land use, spread of invasive plants, and disrupted disturbance regimes beyond a threshold at which natural recovery is unlikely. Research on these issues should be instituted on lands managed by state or federal agencies because most lands still dominated by sagebrush are owned publicly. In addition to the challenge of understanding shrubsteppe bird-habitat dynamics, conservation of sagebrush landscapes depends on our ability to recognize and communicate their intrinsic value and on our resolve to conserve them. ¿Tambaleando en el Borde o Demasiado Tarde? Asuntos de Conservación e Investigación para la Avifauna de Ambientes de Matorral de Artemisia spp Resumen. La degradación, fragmentación y pérdida de paisajes nativos de matorrales de Artemisia spp. han puesto en peligro a estos ambientes y su avifauna asociada. Históricamente, esta vasta porción del paisaje occidental ha sido subvalorada: aunque más del 70% de todo el hábitat de matorral de Artemisia de los Estados Unidos es de propiedad pública, <3% de éste es protegido por reservas federales o parques nacionales. En este artículo revisamos las amenazas a las que se enfrentan las aves de los matorrales de Artemisia para enfatizar la urgencia de emprender acciones de conservación e investigación, y sintetizamos la información existente que constituye la base para una serie de directrices de investigación recomendadas. El manejo y conservación de las aves de los matorrales de Artemisia necesitará más investigación en cuatro tópicos principales: (1) la identificación de prácticas primarias de uso del suelo y su influencia sobre los ambientes y las aves de Artemisia, (2) un mejor entendimiento de las respuestas de las aves a componentes del hábitat y a procesos de disturbio de los ecosistemas de Artemisia, (3) el mejoramiento de diseños jerárquicos para programas de censos y monitoreos y (4) la conexión de los movimientos de las aves y los cambios poblacionales durante la migración y en los períodos de invernada con la dinámica en las áreas reproductivas de matorrales de Artemisia. Estas investigaciones son esenciales porque ya hemos visto que los ambientes de Artemisia pueden ser alterados por el uso del suelo, la diseminación de plantas invasoras y la disrupción de los regímenes de disturbio más allá de un umbral en el que la recuperación natural es poco probable. La investigación en estos asuntos debe instituirse en tierras manejadas por agencias estatales o federales porque la mayoría de las tierras aún dominadas por Artemisia son de propiedad pública. Además del desafío de entender la dinámica aves-hábitat en las estepas arbustivas, la conservación de los paisajes de matorral de Artemisia depende de nuestra habilidad de reconocer y comunicar su valor intrínseco y de nuestra decisión para conservarlos.
Article
Full-text available
Climate change affects ecosystem functioning directly through impacts on plant physiology, resulting in changes of global productivity. However, climate change has also an indirect impact on ecosystems, through changes in the composition and diversity of plant communities. The relative importance of these direct and indirect effects has not been evaluated within a same generic approach yet. Here we took advantage of a novel approach for disentangling these two effects in European temperate forests across a large climatic gradient, through a large simulation-based study using a forest succession model. We first showed that if productivity positively correlates with realized tree species richness under a changed climate, indirect effects appear pivotal to understand the magnitude of climate change impacts on forest productivity. We further detailed how warmer and drier conditions may affect the diversity-productivity relationships (DPRs) of temperate forests in the long term, mostly through effects on species recruitment, ultimately enhancing or preventing complementarity in resource use. Furthermore, losing key species reduced the strength of DPRs more severely in environments that are becoming climatically harsher. By disentangling direct and indirect effects of climate change on ecosystem functioning, these findings explain why high-diversity forests are expected to be more resilient to climate change.
Article
Full-text available
A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically-derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties, and should help land managers to maximize the value of conservation investments.
Article
Full-text available
Big sagebrush (Artemisia tridentata Nutt.) plant communities are widespread in western North America and, similar to all shrub steppe ecosystems worldwide, are composed of a shrub overstory layer and a forb and graminoid understory layer. Forbs account for the majority of plant species diversity in big sagebrush plant communities and are important for ecosystem function. Few studies have explored geographic patterns of forb species richness and composition and their relationships with environmental variables in these communities. Our objectives were to examine the fine and broad-scale spatial patterns in forb species richness and composition and the influence of environmental variables. We sampled forb species richness and composition along transects at 15 field sites in Colorado, Idaho, Montana, Nevada, Oregon, Utah, and Wyoming, built species-area relationships to quantify differences in forb species richness at sites, and used Principal Components Analysis, non-metric multidimensional scaling, and redundancy analysis to identify relationships among environmental variables and forb species richness and composition. We found that species richness was most strongly correlated with soil texture, while species composition was most related to climate. The combination of climate and soil texture influences water availability, which our results indicate has important consequences for forb species richness and composition, and suggests that climate change-induced modification of soil water availability may have important implications for plant species diversity in the future. [Springer Nature SharedIt Initiative Full-Text Article Available : http://rdcu.be/tFJu].
Article
Full-text available
Drylands cover 40% of the global terrestrial surface and provide important ecosystem services. While drylands as a whole are expected to increase in extent and aridity in coming decades, temperature and precipitation forecasts vary by latitude and geographic region suggesting different trajectories for tropical, subtropical, and temperate drylands. Uncertainty in the future of tropical and subtropical drylands is well constrained, whereas soil moisture and ecological droughts, which drive vegetation productivity and composition, remain poorly understood in temperate drylands. Here we show that, over the twenty first century, temperate drylands may contract by a third, primarily converting to subtropical drylands, and that deep soil layers could be increasingly dry during the growing season. These changes imply major shifts in vegetation and ecosystem service delivery. Our results illustrate the importance of appropriate drought measures and, as a global study that focuses on temperate drylands, highlight a distinct fate for these highly populated areas.
Article
Full-text available
Ecohydrological responses to climate change will exhibit spatial variability and understanding the spatial pattern of ecological impacts is critical from a land management perspective. To quantify climate change impacts on spatial patterns of ecohydrology across shrub steppe ecosystems in North America, we asked the following question: How will climate change impacts on ecohydrology differ in magnitude and variability across climatic gradients, among three big sagebrush ecosystems (SB-Shrubland, SB-Steppe, SB-Montane), and among Sage-grouse Management Zones? We explored these potential changes for mid-century for RCP8.5 using a process-based water balance model (SOILWAT) for 898 big sagebrush sites using site-and scenario-specific inputs. We summarize changes in available soil water (ASW) and dry days, as these ecohydrological variables may be helpful in guiding land management decisions about where to geographically concentrate climate change mitigation and adaptation resources. Our results suggest that during spring, soils will be wetter in the future across the western United States, while soils will be drier in the summer. The magnitude of those predictions differed depending on geographic position and the ecosystem in question: Larger increases in mean daily spring ASW were expected for high-elevation SB-Montane sites and the eastern and central portions of our study area. The largest decreases in mean daily summer ASW were projected for warm, dry, mid-elevation SB-Montane sites in the central and west-central portions of our study area (decreases of up to 50%). Consistent with declining summer ASW, the number of dry days was projected to increase rangewide, but particularly for SB-Mon-tane and SB-Steppe sites in the eastern and northern regions. Collectively, these results suggest that most sites will be drier in the future during the summer, but changes were especially large for mid-to high-elevation sites in the northern half of our study area. Drier summer conditions in high-elevation, SB-Montane sites may result in increased habitat suitability for big sagebrush, while those same changes will likely reduce habitat suitability for drier ecosystems. Our work has important implications for where land managers should prioritize resources for the conservation of North American shrub steppe plant communities and the species that depend on them.
Article
Full-text available
Ecosystem and community ecology have evolved along different pathways, with little overlap. However, to meet societal demands for predicting changes in ecosystem services, the functional and structural view dominating these two branches of ecology, respectively, must be integrated. Biodiversity–ecosystem function research has addressed this integration for two decades, but full integration that makes predictions relevant to practical problems is still lacking. We argue that full integration requires going, in both branches, deeper by taking into account individual organisms and the evolutionary and physico-chemical principles that drive their behavior. Individual-based models are a major tool for this integration. They have matured by using individual-level mechanism to replace the demographic thinking which dominates classical theoretical ecology. Existing individual-based ecosystem models already have proven useful both for theory and application. Still, next-generation individual-based models will increasingly use standardized and re-usable submodels to represent behaviors and mechanisms such as growth, uptake of nutrients, foraging, and home range behavior. The strategy of pattern-oriented modeling then helps make such ecosystem models structurally realistic by developing theory for individual behaviors just detailed enough to reproduce and explain patterns observed at the system level. Next-generation ecosystem scientists should include the individual-based approach in their toolkit and focus on addressing real systems because theory development and solving applied problems go hand-in-hand in individual-based ecology.
Article
Full-text available
We developed rangewide population and habitat models for Greater Sage-Grouse (Centrocercus urophasianus) that account for regional variation in habitat selection and relative densities of birds for use in conservation planning and risk assessments. We developed a probabilistic model of occupied breeding habitat by statistically linking habitat characteristics within 4 miles of an occupied lek using a nonlinear machine learning technique (Random Forests). Habitat characteristics used were quantified in GIS and represent standard abiotic and biotic variables related to sage-grouse biology. Statistical model fit was high (mean correctly classified = 82.0%, range = 75.4–88.0%) as were cross-validation statistics (mean = 80.9%, range = 75.1–85.8%). We also developed a spatially explicit model to quantify the relative density of breeding birds across each Greater Sage-Grouse management zone. The models demonstrate distinct clustering of relative abundance of sage-grouse populations across all management zones. On average, approximately half of the breeding population is predicted to be within 10% of the occupied range. We also found that 80% of sage-grouse populations were contained in 25–34% of the occupied range within each management zone. Our rangewide population and habitat models account for regional variation in habitat selection and the relative densities of birds, and thus, they can serve as a consistent and common currency to assess how sage-grouse habitat and populations overlap with conservation actions or threats over the entire sage-grouse range. We also quantified differences in functional habitat responses and disturbance thresholds across the Western Association of Fish and Wildlife Agencies (WAFWA) management zones using statistical relationships identified during habitat modeling. Even for a species as specialized as Greater Sage-Grouse, our results show that ecological context matters in both the strength of habitat selection (i.e., functional response curves) and response to disturbance.
Article
Artemisia tridentata ssp. vaseyana ecosystems evolved with periodic fire, but invasive grasses, conifer encroachment, fire suppression, and climate change have resulted in altered fire regimes and plant communities. Post-fire increases in invasive annual grasses such as Bromus tectorum and reductions in native vegetation are common across the sagebrush steppe. Where fire has been excluded though, there are increases in the native tree Juniperus occidentalis, which outcompetes the native understory. We applied prescribed fire in spring and fall at three sites (native-dominated, B. tectorum-dominated, and J. occidentalis-dominated). We documented 65% survival of A. tridentata following fall burns and 33% survival following spring burns in native-dominated plots, with evidence of post-fire sprouting in Purshia tridentata and Tetradymia canescens. At the B. tectorum-dominated site, shrub cover was reduced to <1%. Fires at the J. occidentalis site were discontinuous, resulting in ∼50% mortality of trees and shrubs, with little resprouting. Native herbaceous vegetation persisted following fires, with no increases in B. tectorum. There were higher plant survival rates following fall fires and native-dominated sites than in spring burns or where exotics dominated. These results show that burn season and prefire condition are important considerations when evaluating management alternatives in A. tridentata ssp. vaseyana ecosystems.
Article
Anthropogenic climate change is already impacting ecological systems. Understanding how organisms respond to weather (short-term) and climate (long-term) variability, and the population and ecosystem-wide consequences of climate change, is a research priority. The appropriate use of information on past and potential future weather and climate conditions is crucial for such research, but uncertainties and biases in this information are seldom given full consideration, with important consequences for assessing the potential impacts of climate change on the conservation of biodiversity. Here, we highlight three important neglected issues pertaining to the major applications of weather and climate information in ecological and biogeographical studies. These are as follows: (1) the uncertainty associated with historical weather and climate information; (2) the selection of ensembles of simulated future climate conditions derived from general circulation models (GCM); and (3) the uncertainty and assumptions associated with downscaling GCM simulations to ecologically relevant spatial scales. Broadly, in order to improve the use of weather and climate information in ecological studies, we propose that ecologists must: (1) use weather and climate products that are appropriate for their intended purpose; (2) explore the consequences of uncertainty in these products for ecological conclusions; and (3) seek greater integration of ecological and climate research to create products that reflect both the requirements of ecologists and the limits of climatology. This will enable more effective research into the likely responses of ecological systems to future climate change.