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Global Ecology and Conservation 50 (2024) e02848
Available online 15 February 2024
2351-9894/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Original research article
Declining pronghorn (Antilocapra americana) population
productivity caused by woody encroachment and oil and
gas development
Victoria M. Donovan
a
,
b
,
*
, Jeffrey L. Beck
c
, Carissa L. Wonkka
d
, Caleb P. Roberts
e
,
Craig R. Allen
f
,
g
, Dirac Twidwell
b
a
School of Forest, Fisheries, and Geomatics Sciences, West Florida Research and Education Center, University of Florida, Milton, FL 32583, USA
b
Department of Agronomy & Horticulture, University of Nebraska, Lincoln, NE 66583-0915, USA
c
Department of Ecosystem Science and Management, University of Wyoming, 1000 E University Ave, Laramie, WY 82071, USA
d
United States Department of Agriculture-Agricultural Research Service, Northern Plains Agricultural Research Laboratory, 1500 N Central Avenue,
Sidney, MT 59270, USA
e
Arkansas Cooperative Fish and Wildlife Research Unit, University of Arkansas, SCEN 522, Fayetteville, AR 72701, USA
f
University of Nebraska, School of Natural Resources, Lincoln, NE 68583-0961, USA
g
Center for Resilience in Agricultural Working Landscapes, University of Nebraska-Lincoln, Lincoln, NE 68583-0961, USA
ARTICLE INFO
Keywords:
Antilocapra americana
Climate
Oil and gas
Population resilience
Precipitation
Pronghorn
Tree encroachment
Woody encroachment
ABSTRACT
Conservation is increasingly focused on preventing losses in species’ populations before they
occur. Tracking changes in demographic parameters that can impact a population’s resilience in
response to drivers of global change can support early conservation efforts. We assessed trends in
population productivity (late summer juveniles per 100 females) relative to drivers of global
change in 40 pronghorn (Antilocapra americana) herds across sagebrush (Artemisia spp.) steppe in
Wyoming. Pronghorn are an iconic rangeland species that have been exposed to increasing levels
of anthropogenic, climatic, and land-use change. Using data collected across the state of
Wyoming, we (1) assessed long-term trends in population productivity, (2) identied patterns in
large-scale drivers of global change (i.e., climate, land cover change) across pronghorn habitat,
and (3) determined the relationship between drivers of global change and population productivity
over a 35-year (1984–2019) period. While Wyoming hosts some of the most abundant pop-
ulations of pronghorn in North America that have been largely stable in recent years, we found
many herds are experiencing long-term declines in productivity. Long-term declines in produc-
tivity were associated with increases in oil and gas development and woody encroachment.
Although increasing across almost all herd units, woody vegetation cover remains at low levels,
suggesting that pre-emptive management may help to prevent losses in pronghorn populations.
1. Introduction
Global species diversity continues to decline in response to factors such as anthropogenic development and resource use, changing
* Corresponding author at: School of Forest, Fisheries, and Geomatics Sciences, West Florida Research and Education Center, University of
Florida, Milton, FL 32583, USA.
E-mail address: victoria.donovan@u.edu (V.M. Donovan).
Contents lists available at ScienceDirect
Global Ecology and Conservation
journal homepage: www.elsevier.com/locate/gecco
https://doi.org/10.1016/j.gecco.2024.e02848
Received 11 December 2023; Received in revised form 9 February 2024; Accepted 13 February 2024
Global Ecology and Conservation 50 (2024) e02848
2
climate, and biological invasions (Butchart et al., 2010; Pimm et al., 2014; Pimm and Raven, 2000). Conservation efforts often focus on
species that are most endangered or at risk, leading to a conservation triage approach to balance limited resources (Bottrill et al., 2008;
Gerber, 2016; McCarthy et al., 2008; Scott et al., 2010). However, it is often more economical and effective to focus on long-term
preventative conservation rather than short-term ‘crisis management’ to optimize conservation and keep common species common
(Joseph et al., 2009; Schneider et al., 2010; Wilson et al., 2011). Thus, there is a need to understand species’ responses to external
changes before drastic declines in populations occur (Capdevila et al., 2021, 2020).
Changes in demographic structure within a population are increasingly being recognised as an indicator of stress on populations in
response to global change (Capdevila et al., 2021, 2020). Environment-driven demographic rates such as reproduction and survival are
highly sensitive to environmental deterioration that can drive changes in populations (Drake and Griffen, 2010). For instance, Gas-
away et al., (1983) found that high juvenile mortality in moose (Alces alces) driven by wolf (Canis lupus) predation in Alaska helped to
sustain population declines initiated by hunting and severe winter, leading to heightened risk for species extirpation. Tracking trends
in components of population demographics can be used to signal potential changes in population resilience to disturbances within their
range (Clements and Ozgul, 2018).
Productivity, the number of surviving offspring produced during a given year, is highly responsive to environmental changes
(Britten et al., 2016; Gaillard et al., 1998). It can be one of the rst population parameters to indicate resource limitation (Bishop et al.,
2005; Eberhardt, 1977) and is sensitive to changes in predator abundance (Brown and Conover, 2011). Declines in population pro-
ductivity have been associated with lowered population resilience in a number of species (Fujiwara et al., 2014; Graham et al., 2007;
Kruszynski et al., 2021) and can lead to declines in population numbers, particularly when paired with disturbances that drive adult
mortality (Bender et al., 2013; Bonnot et al., 2017; Hatter and Janz, 1994). Because annual productivity tends to be easily quantied
compared to other demographic rates like survival, they can be used as a broad indicator of the impacts of changing environment on
populations (Gaillard et al., 1998).
We assessed long-term trends in productivity for pronghorn (Antilocapra americana) relative to drivers of global change across the
state of Wyoming. Pronghorn are an iconic species of western North American rangelands. The Wyoming Basin shrub steppe, one of the
most intact rangeland ecoregions on the planet (Scholtz and Twidwell, 2022), provides habitat to approximately half of the worldwide
pronghorn population (Yoakum and O’Gara, 2000). However, environmental stressors are increasing across the region. Oil and gas
development and associated roads and fencing have expanded rapidly in recent decades leading to loss and fragmentation of sagebrush
habitat (Finn and Knick, 2011; Walston et al., 2009). Neighbouring regions are experiencing rapid encroachment of woody species that
lead to the conversion of rangeland to woodland (Fogarty et al., 2020; Roberts et al., 2021; Twidwell et al., 2013) and invasion by
non-native annuals that can increase re frequency and damage some sagebrush ecosystems (Balch et al., 2013; Knapp, 1996). It is not
clear how rapid large-scale changes to pronghorn habitat (Allred et al., 2015; Christie et al., 2015; D’Antonio and Vitousek, 1992;
Fogarty et al., 2020) are impacting population demographic trends across Wyoming.
We tracked long-term trends in productivity (juveniles per 100 females collected during late summer) in 40 pronghorn herds in
relation to vegetation change, climate, and anthropogenic development across their range. Low productivity in pronghorn can lead to
population decline, particularly when paired with disturbances that impact other vital rates like adult survival (Bender et al., 2013).
Pronghorn productivity is also highly sensitive to environmental change, with reproductive success tracking dietary income (Bar-
nowe-Meyer et al., 2011; Bender et al., 2013; Parker et al., 2009) and shifts in predation pressure (Brown and Conover, 2011). We use
almost 40 years of pronghorn herd productivity data within and surrounding the Wyoming Basin Shrub Steppe ecoregion in relation to
oil and gas well development, roads, re, annual forb and grass invasion, woody encroachment, and changing precipitation patterns to
evaluate (1) if and where there were long-term signals of declining pronghorn productivity, (2) where there were signals of long-term
change in pronghorn habitat associated with large-scale drivers of global change (climate, anthropogenic development, vegetation
change), and (3) what drivers of global change were associated with changes in pronghorn productivity. We predicted that large-scale
global change drivers have increasingly degraded pronghorn habitat during our study period, resulting in long-term declines in
pronghorn productivity.
2. Materials and methods
2.1. Study region
The state of Wyoming is an arid region surrounded by mountains in the west, south, and north and prairies to the north and east.
Elevations range from 940 to 4200 m. Annual average precipitation ranges from 160 mm to 762 mm (Frankson et al., 2017). The state
is dominated by the Wyoming Basin Steppe ecoregion (Dinerstein et al., 2017). Big sagebrush (Artemisia tridentata ssp.) dominates the
Wyoming Basin Steppe rangelands while mixed grass prairie interspersed with sagebrush patches characterizes rangelands of
northeastern Wyoming. Large-scale energy and mineral development along with associated road networks, invasion of exotic annual
grasses, increasing wildre frequency and extent, conifer encroachment, and the conversion of sagebrush to grassland for grazing are
substantial threats to habitat across much of the region (Bradley, 2010; Finn and Knick, 2011). Pronghorn herds are distributed across
the majority of Wyoming, and represent >50% of the range-wide population (Yoakum and O’Gara, 2000). Wyoming’s pronghorn
population numbers have been largely stable in recent years, while harvest rates have declined (Jason Carlisle, Wyoming Game and
Fish Department, Personal Communication).
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
3
2.2. Data collection and summary
We quantied pronghorn productivity by calculating annual ratios of juveniles to 100 females using data collected by the Wyoming
Game and Fish Department between 1984 and 2019. Data were collected across 40 herd units that spanned pronghorn range in
Wyoming using aerial and ground surveys following established routes within each herd unit (Emmerich et al., 2007; Grant Frost,
Wyoming Game and Fish Department, Personal Communication). Surveys were conducted in the late summer (typically August
1–31st) before hunting season. Females typically give birth during late May and early June, meaning juveniles are ~2–3 months old at
the time of surveys. Herd units were delineated by Wyoming Game and Fish Department based on geographic or man-made barriers
that restrict interchange among populations (Emmerich et al., 2007). In some cases, herds that were originally monitored separately
were aggregated to form one larger herd by the Wyoming Game and Fish Department. In these instances, we took the average number
of juveniles per 100 females across both herd units before the time they were merged to obtain the longest possible data sets for each
unit.
We assessed large-scale changes in vegetation across each herd unit using annual, Landsat-derived, 30-m resolution percent cover
(1984–2019) and herbaceous biomass estimates (1986–2019) for multiple vegetation functional groups using the Rangeland Analysis
Platform (Jones et al., 2018). Within each herd unit we summarized tree cover, shrub cover, the cover and biomass of perennial and
annual forbs and grasses, and total herbaceous biomass inclusive of all herbaceous functional groups.
We assessed the impacts of climate on pronghorn productivity using changes in winter and spring precipitation. We evaluated
changes in annual winter and spring total precipitation per herd unit using PRISM 4-km resolution monthly precipitation data from
1984 to 2019 (https://prism.oregonstate.edu/). We dened winter from November-March (Reinking et al., 2019; Smith et al., 2020;
Taylor et al., 2016) and spring from April to June (Canon and Bryant, 1997; Collins, 2016) based on past assessments of pronghorn in
Wyoming.
We quantied anthropogenic disturbance patterns by calculating the length of roads in Wyoming and the cumulative number of oil
and gas wells through time. We collected road data mapped across Wyoming in 2009 from the U.S. Geological Survey (O’Donnell et al.,
2014). We calculated the total road length within each herd unit, and then standardised the measure by dividing it by total herd unit
area. While there was no temporal component to road data within Wyoming, we included this variable to indicate the general level of
fragmentation that occurred among herd units. We obtained oil and gas well data from 1980–2019 from the Wyoming Oil and Gas
Conservation Commission (Data) for each herd unit. We used spud date, the date when drilling activity began, to determine the year a
new well was added to each herd unit. If spud date was not available, we used the date of the rst completion report ling. We
calculated the cumulative number of oil and gas wells added each year from 1980 onward to determine the increase in oil well
development within each herd unit.
Wildres are increasing across the western and central U.S. (Dennison et al., 2014; Donovan et al., 2017) linked to changing climate
(Dennison et al., 2014; Westerling et al., 2006), shifting vegetation cover (Balch et al., 2013; Donovan et al., 2020c; Pilliod et al.,
2017), and anthropogenic ignitions (Balch et al., 2017). Thus, we also assessed the impacts of re on pronghorn by calculating the
annual total area burned within each herd unit using burn perimeters from 1984–2019 from the Monitoring Trends in Burn Severity
Database (MTBS Project, 2022).
Table 1
Environmental variables assessed across Wyoming and their predicted impacts on pronghorn.
Environmental
Variable
Predicted Impact on
Pronghorn
Rationale
Perennial forbs and
grasses
Positive Pronghorn select for perennial forbs and grasses found in native rangelands (Jakes et al., 2020; Milligan
et al., 2021)
Shrubs Positive Pronghorn select for shrubs associated with sagebrush habitat (Christie et al., 2017; Smith et al., 2020).
Trees Negative Trees in rangeland systems are associated with woody encroachment and afforestation that can drive losses
in forage and rangeland habitat, eventually leading to a regime shift to an alternative woodland state (Coates
et al., 2017; Twidwell et al., 2013). In addition, trees decrease visibility, potentially increasing predation risk
(Goldsmith, 1990).
Annual forbs and
grasses
Negative Recent increases in annual forbs and grasses are generally associated with the large-scale invasion of non-
native species such as cheatgrass (Bromus tectorum), which are degrading sagebrush habitat across much of
western North America (Balch et al., 2013; Mahood and Balch, 2019).
Winter precipitation Negative Winter precipitation is associated with snow cover and depth, which can decrease pronghorn mobility and
increase foraging energy expenditure leading to higher mortality (Barrett, 1982; Reinking et al., 2019; Smith
et al., 2020; Taylor et al., 2016).
Spring precipitation Positive Increased spring precipitation results in higher quality and quantity of forage and higher pronghorn densities
(Brown et al., 2006; Gedir et al., 2015)
Roads Negative Roads fragment habitat and facilitate movement of predators and hunters (Gamo et al., 2017; Hebblewhite
et al., 2009; Popp and Donovan, 2016; Seidler et al., 2015).
Oil and gas
development
Negative Oil and gas development is associated with habitat loss both directly and through fragmentation which can
lead to habitat abandonment and lower pronghorn abundance (Christie et al., 2015; Sawyer et al., 2019)
Fire Mixed Fires can degrade sagebrush habitat (Balch et al., 2013; Mahood and Balch, 2019); however, they also reduce
tree cover to restore rangeland habitat impacted by woody encroachment (Bielski et al., 2021; Donovan
et al., 2020b). Pronghorn have been shown to utilize recently burned areas (Augustine and Derner, 2015;
Courtney, 1989).
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
4
2.3. Analysis
We assessed long-term trends in pronghorn productivity through time within each herd unit using Mann Kendall monotonic trend
tests (McLeod, 2011). Mann Kendall trend tests are non-parametric tests often used to detect signicant upward or downward trends in
long-term time series data. Numerous abiotic and biotic factors can inuence population demographics and these factors vary from one
population to the next, making it unlikely that there is a single unifying threshold in population productivity that can be used to
indicate when populations will experience declines in size. Instead, we used Mann Kendall’s test statistic Tau, which focuses on
capturing patterning in data rather than specic threshold values (McLeod, 2011). Unlike slope, which is a measure of the magnitude
of decline or increase through time, Tau represents the degree to which a trend is monotonic, i.e., the degree with which it consistently
increases or decreases. Tau ranges from −1 to 1, where a greater positive Tau indicates a stronger positive monotonic trend (a value of
1 indicates a perfect positive monotonic trend), and a greater negative Tau indicates a stronger negative monotonic trend (a value of
−1 indicates a perfect negative monotonic trend). We used Auto Correlation Function (ACF) plots to identify serial autocorrelation
within time series data. When autocorrelation was indicated, we used a block bootstrapping procedure and modied Mann Kendall
tests (modied to adjust of serial correlation) to improve signicance tests (McLeod, 2011; Patakamuri and O’Brien, 2021). We
similarly used Mann Kendall trend tests to assess long-term trends in vegetation (perennial forb and grass cover and biomass, annual
forb and grass cover and biomass, tree cover, shrub cover), climate (spring precipitation, winter precipitation), and anthropogenic
disturbances (oil and gas wells) through time.
We modelled the responses of pronghorn productivity to the environment using two modelling approaches. First, we used linear
mixed effects models to determine the relationship between annual pronghorn productivity and annual variability in environmental
variables (Table 1; excluding roads), using herd unit as a random intercept (Pinheiro et al., 2021). Following the top-down strategy
recommended in Zuur et al., (2009), we used Akaike’s Information Criterion adjusted for small sample sizes (AICc; Akaike, 1973) to
select among random intercept versus random intercept and slope models that contained our global model structure within the xed
component. A random intercept model was preferred over a random intercept and slope model. Second, we used linear models to
determine relationships between long-term trends in productivity in response to patterns in environmental variables over our study
period. The Tau of productivity, calculated from Mann Kendall monotonic trend tests, was used to represent long-term trends in
productivity for each herd unit (n=40). There were multiple instances where we had similar measures (e.g., perennial forb and grass
cover versus biomass) or, in the case of our long-term models, multiple long-term summary statistics for the same variable (Tau of tree
cover versus average tree cover of each herd unit). In such cases, we used AICc to determine which measure or summary statistic was
better at predicting our dependent variable (Appendix 1: Section S2). Preferred forms of each variable as indicated by AICc were
Table 2
Candidate models used to predict (1) the change in annual pronghorn juveniles per 100 females and (2) long-term trends in
pronghorn juveniles per 100 females represented by Tau, calculated using Mann Kendall monotonic trend tests.
Model Dependent Variables
Annual candidate models
1 ~1
2 ~Year
3 ~Year +Shrub cover
4 ~Year +Perennial forb and grass cover
5 ~Year +Herbaceous biomass +Spring precipitation
6 ~Year +Herbaceous biomass +Area burned +Spring precipitation
7 ~Year +Shrub cover +Annual forb and grass cover +Area burned
8 ~Year +Tree cover
9 ~Year +Oil and gas wells
10 ~Year +Winter precipitation
11 ~Year +Tree cover +Oil and gas wells
12 ~Year +Tree cover +Oil and gas wells +Winter precipitation
13 ~Year +Annual forb and grass cover +Tree cover +Oil and gas wells
14 ~Year +Shrub cover +Tree cover +Oil and gas wells
15 ~Year +Herbaceous biomass +Tree cover +Oil and gas wells
Long-term trend candidate models
1 ~1
2 Tau of shrub cover
3 Tau of perennial forb and grass biomass
4 Tau of herbaceous biomass +Average spring precipitation
5 Tau of herbaceous biomass +Tau of burned area +Average spring precipitation
6 Tau of shrub cover +Tau of annual forb and grass biomass +Tau of burned area
7 Tau of tree cover
8 Total oil and gas wells
9 Tau of winter precipitation
10 Tau of tree cover +Total oil and gas wells
11 Tau of tree cover +Total oil and gas wells +Road Density +Tau of winter precipitation
12 Tau of annual forb and grass biomass +Tau of tree cover +Total oil and gas wells +Road Density
13 Tau of shrub cover +Tau of tree cover +Total oil and gas wells
14 Tau of herbaceous biomass +Tau of tree cover +Total oil and gas wells
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
5
included within our models (Table 2). Because biomass data started in 1986, modeled data spanned 1986–2019.
Using a multi-model inference approach, we generated candidate model sets composed of variables that we predicted would
positively impact pronghorn productivity (e.g., shrubs, spring precipitation), variables we predicted would negatively impact
pronghorn productivity (e.g., trees, oil and gas well development), and a combination of the two (Tables 1, 2). Year was also included
in models as a discrete numeric variable. We screened independent variables for collinearity by using pairwise correlations. When
variables had a Pearson’s correlation coefcient >0.65, we did not include them within the same candidate model. We used global
models to test for violation of model assumptions before candidate model sets were input into AICc to determine the most parsimo-
nious model (Burnham and Anderson, 2002). Burnham and Anderson, (2002) suggest that models with a ΔAICc <2 have substantial
support. When multiple models fell within this range, we classied them as our condence set, and applied model averaging using the
zero average method to determine model averaged coefcients that could be used to predict the relationships between pronghorn
productivity and patterns in environmental change (Barton, 2020; Burnham and Anderson, 2002; Grueber et al., 2011). We completed
all analyses using R software v. 4.0.4 (R Core Team, 2021).
3. Results
3.1. Patterns in pronghorn productivity
Pronghorn productivity declined signicantly across 43% (17 of 40) of pronghorn herds in Wyoming between 1984 and 2019
(Fig. 1b; Appendix 1: Tables S1–1; Appendix 1: Figs. S3–1). Average juveniles per 100 females ranged from 40 ±12 SD (Badger Basin
Herd Unit 207) to 83 ±11 SD (Crazy Woman Herd Unit 318; Fig. 1a). Declines in productivity were not limited to populations with low
overall productivity numbers. Populations with some of the greatest average productivity over our study period also experienced
strong declines in productivity (Fig. 1).
3.2. Patterns in global change drivers
All herd units experienced signicant increases in one or more drivers of global change identied as threats to pronghorn pop-
ulations during our study period. Woody encroachment, annual forb and grass invasion, and oil and gas wells all signicantly increased
across the majority of herd units in Wyoming (Table 3; Fig. 2; Appendix 1: Tables S1–2). Seventy percent (28 of 40) of herd units had
signicant positive monotonic trends in tree cover (Fig. 2; Appendix 1: Tables S1–2), though there was high variation in tree cover
across herds, ranging from an average of less than 1% in Pumpkin Buttes (Herd Unit 309) to 18% ±4 SD in Elk Mountain (Herd Unit
528). Annual forb and grass cover (ranging from an average of 3% ±0.7 SD in the Red Desert [Herd Unit 615] to 15% ±5 SD cover in
Leiter [Herd Unit 321]) signicantly increased across 58% of herd units, while annual forb and grass biomass (ranging from an average
Fig. 1. Maps of Wyoming, USA pronghorn herd units colour coded to represent (a) the average number of juveniles per 100 females and (b)
signicant monotonic trends in juveniles per 100 females, from 1984–2019. Monotonic trends are represented by a Tau value (ranging from −1 to
1), calculated using Mann-Kendall monotonic trend tests. Numbers in each polygon represent herd unit number used by the Wyoming Game and
Fish Department.
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
6
of 11 kg/ha ±3 SD Wind River to 206 kg/ha ±121 SD in Leiter) signicantly increased across 90% of herd units (Fig. 2; Appendix 1:
Tables S1–2). Oil and gas well numbers increased across all herd units except two, where no oil and gas wells were recorded (Big Creek
[Herd Unit 529] and North Ferris [Herd Unit 636]; Fig. 2). The greatest average.
number of wells was in the Pumpkin Buttes Herd Unit (6283 ±5186 SD wells). Annual total winter precipitation also increased to a
small extent over our study period, but only in 3 herd units at the center of the state (averages ranging from 3667 mm ±1316 SD in
Chalk Bluffs [Herd Unit 520] to 309,387 mm ±84,508 SD in Sublette [Herd Unit 401]; Fig. 2).
Although global change threats to pronghorn were increasing across the majority of herd units, perennial forb and grass cover
(averages ranging from 19% ±3 SD in Bitter Creek [Herd Unit 414] to 61% ±6 SD in Chalk Bluffs) and shrub cover (averages ranging
from 6% ±2 SD in Chalk Bluffs to 24% ±2 SD in Carter Lease [Herd Unit 419]), important components of pronghorn habitat,
remained relatively stable and only declined in a few herd units (5 and 1 herd unit respectively; Fig. 2). Perennial forb and grass
biomass signicantly increased in 3 herd units (averages ranging from 218 kg/ha ±46 SD in Bitter Creek to 1254 kg/ha±180 SD in
Beckton [Herd Unit 355]) and herbaceous biomass signicantly increased across 63% of herd units (averages ranging from 218 kg/ha
±59 SD in Bitter Creek to 1311 kg/ha ±131 SD in Beckton; Fig. 2). No herd units demonstrated declines in herbaceous biomass or
perennial forb and grass biomass. Increases in total spring precipitation did not align strongly with increases in herbaceous and
perennial forb and grass biomass and only signicantly increased in 4 herd units (averages ranging from 6857 mm ±2429 SD in Big
Creek to 211,564 mm ±71,047 SD in Sublette; Fig. 2).
The annual total area burned by re, which was predicted to have both positive (reduce trees, increase perennial forb and grass
cover and biomass) and negative (reduce shrubs, increase annual forb and grass cover and biomass) impacts on pronghorn, signi-
cantly increased in 4 herd units (Appendix 1: Figure S2-13). A signicant increase in burned area in the Wind River herd unit aligned
with the only herd unit that demonstrated a signicant decline in tree cover (Fig. 2). There were several herd units that did not
experience any re over our study period, while the Sublette herd unit had the greatest average annual burned area at 3623.42 ha ±
6097 SD.
3.3. Drivers of global change impact on pronghorn productivity
The most parsimonious model for annual changes in productivity was the model containing shrub cover and year (Tables 2 and 4).
Model coefcients indicated that approximately every 4 years, the number of juveniles per 100 females decreased by 1 over our study
period (estimate = − 0.28, SE =0.04, p <0.01; Fig. 3; Appendix 1: S4–1). There was a positive relationship between productivity and
shrub cover, where with every 1% increase in shrub cover within a herd unit, the number of juveniles per 100 females increased by 1
(estimate =1.05, SE =0.21, p <0.01; Fig. 3). Shrub cover did not change drastically across most herd units (Fig. 2), and thus, could not
explain long-term trends in declining pronghorn productivity we observed.
Model selection among candidate models tracking long-term productivity trends across herds indicated that the cumulative
number of oil and gas wells within herd units was the best predictor of declining productivity. However, two additional models fell
within 2 AIC of the top model: one including the cumulative number of oil and gas wells and the Tau of tree cover, and the other
containing the cumulative number of oil and gas wells, the Tau of tree cover, and the Tau of total herbaceous biomass (Table 4). More
consistent declines in productivity through time were associated with a greater cumulative number of oil and gas wells within a herd
unit (estimate = − 1.83×10
−5
, SE =8.28×10
−6
, p =0.03) and greater tree cover (estimate = − 0.08, SE =0.09, p =0.40; Fig. 4). This
aligns with increases in oil and gas development and tree cover recorded across several herds in the Wyoming Basin Steppe (Fig. 2). In
contrast, more consistent increases in productivity through time were associated with increased herbaceous.
biomass within herd units (estimate =0.08, SE =0.19, p =0.68; Fig. 4), again aligned with increases in herbaceous biomass
identied across herd units (Fig. 2).
Table 3
A summary of trends in productivity (juveniles per 100 females), environmental variables predicted to have positive or negative outcomes for
pronghorn across 40 herd units in Wyoming, USA, as indicated by Mann Kendall monotonic trend tests.
Variable Percent increasing Percent decreasing Percent signicantly increasing Percent signicantly decreasing
Productivity 20 80 0 42.5
Variables predicted to have negative outcomes
Annual forb and grass cover 92.5 7.5 57.5 0
Annual forb and grass biomass 100 0 90 0
Tree cover 90 10 70 2.5
Winter precipitation 82.5 17.5 7.5 0
Oil and gas wells 95 NA 95 NA
Variables predicted to have positive outcomes
Perennial forb and grass cover 60 40 7.5 12.5
Perennial forb and grass biomass 90 10 27.5 0
Shrub cover 52.5 47.5 5 2.5
Herbaceous biomass 95 5 25 0
Spring precipitation 100 0 10 0
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
7
4. Discussion
Pronghorn productivity is declining in the center of their distribution in response to large-scale drivers of global change. While the
Wyoming Basin is viewed as a stronghold for pronghorn populations, increasing oil and gas development and tree encroachment may
Fig. 2. The strength of signicant temporal trends in climate, vegetation, and anthropogenic change variables within pronghorn herd units in
Wyoming, USA between 1984 and 2019 (1986–2019 for biomass data) as indicated by Tau (ranging from −1 to 1) from Mann Kendall monotonic
trend tests. Signicant trends in environmental variables that were predicted to positively impact pronghorn within each herd unit are coloured in
blue, while signicant trends predicted to negatively impact pronghorn within each herd unit are coloured in red. Darkening colour indicates a
greater positive or negative Tau value. Herd units with non-signicant trends are coloured grey.
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
8
be increasing pronghorn population susceptibility to decline. Increases in pronghorn productivity are associated with higher shrub
cover and long-term increases in herbaceous biomass, suggesting that declines in rangeland habitat quality associated with oil and gas
development and tree encroachment (e.g., Avirmed et al., 2015; Miller et al., 2000) are helping to drive long-term losses in pronghorn
productivity. This follows trends of declining pronghorn productivity found across a number of jurisdictions in past research, suggested
to be in part, due to density-dependent responses related to decreased forage conditions and habitat fragmentation in pronghorn
habitat (Jones and Yoakum, 2010).
We found that both tree cover and oil and gas development have increased substantially across most herd units in Wyoming over
the last ~40 years. Wyoming ranked eighth nationally in crude oil production in the U.S. in 2020 and saw natural gas production
increase 7-fold from 1978 to its peak in 2010 (Wyoming State Geological Survey, 2021). Expansive increases in oil and gas devel-
opment are a well-known threat to rangeland ecosystems in Wyoming, driving declines in iconic rangeland species like the greater
sage-grouse (Centrocercus urophasianus; Gregory and Beck, 2014; Hess and Beck, 2012). For pronghorn, fragmentation caused by oil
and gas development has been suggested to drive declines in population productivity by increasing pronghorn numbers within smaller,
more isolated patches of habitat (Jones and Yoakum, 2010). In contrast, tree encroachment is not a widely recognised threat to
Wyoming sagebrush ecosystems. This may be tied to relatively low levels of tree encroachment in the state; we identied a maximum
average tree cover of 18% within herd units. However, low levels of tree encroachment have been shown to have drastic impacts on
sagebrush-dependent wildlife. For instance, pinyon-juniper cover as low as 2–5 trees per ha has been shown to cause greater
Table 4
The top 5 AIC model rankings for annual models, used to assess relationships between annual pronghorn herd unit productivity (juveniles per 100
females) and annual variability in environmental variables, and for long-term trend models, used to assess the relationship between the Tau of
productivity through time and patterns in environmental variables in Wyoming, USA. Annual models were linear mixed effects models with herd unit
as the random effect while long-term models were linear models. Tau values used in long-term models ranged from −1 to 1 and are calculated using
Mann Kendall monotonic trend tests.
Model Df LogL AICc ΔAICc Weight
Annual models
~Year +Shrub cover 5 -4867.99 9746.03 0.00 0.93
~Year +Shrub cover +Annual forb and grass cover +Area burned 8 -4868.56 9751.22 5.19 0.07
~Year +Shrub cover +Tree cover +Oil and gas wells 7 -4871.37 9756.84 10.80 0.00
~Year +Tree cover 7 -4871.37 9763.20 17.17 0.00
~Year 5 -4876.58 9766.32 20.29 0.00
Long-term trend models
~Total oil and gas wells 3 16.40 -26.14 0 0.21
~Tau of tree cover +Total oil and gas wells 4 17.61 -26.07 0.07 0.20
~Tau of herbaceous biomass +Tau of tree cover +Total oil and gas wells 5 18.47 -25.18 0.96 0.13
~ Tau of shrub cover +Tau of annual forb and grass biomass +Tau of burned area 4 16.54 -23.93 2.21 0.07
~Tau of shrub cover +Tau of tree cover +Total oil and gas wells 5 17.74 -23.71 2.43 0.06
Fig. 3. Predicted relationships in annual changes in productivity (juveniles per 100 females) relative to (a) shrub cover and (b) year in Wyoming,
USA. Shaded area represents 95% condence intervals. Grey points represent raw data.
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
9
sage-grouse to abandon otherwise suitable habitat (Baruch-Mordo et al., 2013; Coates et al., 2017). Low levels of tree cover may drive
declines in pronghorn productivity through increased predation rates by providing cover for predators (Goldsmith, 1990). Moreover,
tree cover can drive loss of forage associated with sagebrush and grassland cover (Bielski et al., 2021; Miller et al., 2000) and drive
behavioural avoidance (Milligan et al., 2021; Reinking et al., 2018; Sawyer et al., 2019).
Our results contribute to the overwhelming evidence that early management of invading trees within sagebrush habitat will help to
protect iconic rangeland species like pronghorn. Conifer encroachment has already been recognised as a threat within sagebrush
ecosystems (Maestas et al., 2021; NRCS, 2021). The impacts of conifer encroachment rapidly increase as tree cover increases (Roberts
et al., 2018). Preventative management and management applied in the early phases of encroachment is thus the most impactful and
cost-effective approach. Manual removal of trees in early stages of invasion along with infrequent moderate intensity res have been
recommended in recent management frameworks for controlling conifer encroachment (Maestas et al., 2021; NRCS, 2021).
Other drivers of global change viewed as threats to pronghorn, including non-native annual grass invasions, wildre, roads, and
increased winter precipitation, were not prominent drivers of long-term declines in pronghorn productivity. Annual forb and grass
biomass and cover were relatively low across herd units. Wildres were similarly only increasing in a few herd units. It is possible that
cheatgrass is not yet well enough established across the region for the re-annual invasion cycle to have strong impacts on pronghorn’s
sagebrush habitat (Balch et al., 2013; D’Antonio and Vitousek, 1992). However, the re-invasive annual grass cycle prominent in the
Great Basin where perennial grasses are less adapted to re has not been shown to occur in the western Great Plains (Archer et al.,
2023; Porensky and Blumenthal, 2016). Roads were also not found to have a signicant impact on pronghorn productivity. This may
be an artifact of our data set, as time-series data for road cover were not available at the time of our study. However, the impacts of oil
and gas development likely indirectly represent road development impacts on pronghorn. In addition to fencing, each well pad is
associated with on average 2 km of roads (BLM, 2003) which can fragment pronghorn habitat and increase access for hunters and
predators (Gamo et al., 2017; Jakes et al., 2020). Predation was not directly included in our assessment due to data limitations
associated with large spatial and temporal scales of our analysis, but likely plays an important role in population dynamics through
both direct and indirect effects on adult and juvenile survival (Gosselin et al., 2015; Hatter and Janz, 1994). Predators are often a
leading proximate cause of fawn mortality in pronghorn (Barrett, 1982; Beale and Smith, 1973; Linnell et al., 1995; Panting et al.,
2021). However, because maternal nutritional status and body mass index for fawns typically determine the fate of a fawn regardless of
proximate cause of mortality (Panting et al., 2021), links to predation and fawn mortality are likely partially captured by the
large-scale changes in habitat quality we assessed. We did not assess hunting pressure in our models. In Wyoming, over 15,000 adult
female pronghorn were harvested in 2018 alone (Wyoming Game and Fish Department, 2022). However, annual harvest quotas are
dictated by annual fawn:doe ratios from the previous year and thus are not independent from pronghorn productivity (Christie et al.,
2015).
Because roads, along with drivers of global change like winter precipitation (associated with snow cover) commonly impact
pronghorn movement and migration patterns (Jacques et al., 2009; White et al., 2007), it is possible that these factors are more likely
to directly impact immigration patterns among populations than population productivity. Assessing combined patterns in immigration
and productivity would likely better indicate the impacts of drivers of global change on pronghorn populations. However, assessments
of immigration are notoriously difcult. Assessing immigration requires exhaustive population monitoring to quantify movements to
and from a population, which are rare (Millon et al., 2019; Williams et al., 2002). While dispersal can be tracked using telemetry
movement data, large-mammal movement data are often collected over 2–3-year time scales and on a small subset of a population (e.g.
Reinking et al., 2019; Taylor et al., 2016). Low delity to winter seasonal ranges also makes immigration challenging to track for
pronghorn (Morrison et al., 2021). Increases in the number of collared individuals and the longevity of movement studies may allow
Fig. 4. Predicted relationships for the top three models predicting long-term trends in productivity (juveniles per 100 females) relative to (a) the
number of oil and gas wells, (b) change in tree cover (Tau), and (c) the change in herbaceous biomass (Tau) from model averaged coefcients (black
solid line). Grey shaded area represents a 95% condence interval. Grey points represent the raw data distribution.
V.M. Donovan et al.
Global Ecology and Conservation 50 (2024) e02848
10
for better assessments of changing immigration patterns to determine the cumulative impacts of drivers of global change on population
resilience. Alternatively, genetic assessments of gene ow, which have been used to assess connectivity among pronghorn populations,
could be used to determine relationships between drivers of global change and pronghorn immigration (LaCava et al., 2020). This
becomes increasingly important as large declines in habitat connectivity are predicted for pronghorn over the next century (Zeller
et al., 2021). Our assessment should assist managers in identifying where these more detailed assessments are needed in Wyoming.
Focusing research on assessing changing population demographics in species experiencing shifting conditions from drivers of
global change may help support preventative conservation for more effective long-term conservation outcomes and decrease the need
for short-term reactive approaches (Wilson et al., 2011). Population monitoring efforts in recent decades have generated large amounts
of population-level data that span multiple jurisdictions, providing readily available information that can be used to help identify the
impacts of drivers of global change on populations across regions. Our results can be used to target populations for smaller-scale
population modelling to generate population-level management approaches. Because of the larger ranges associated with big game
species, management focused on these species may help apportion protection to other smaller ranging species at risk (Branton and
Richardson, 2011; Caro, 2003; Tack et al., 2019), though outcomes can be species- and scale-dependent (e.g., Carlisle et al., 2018).
Large-scale population data sets that occur across multiple jurisdictions like those utilised in this study allow us to track trends that
may not be detectable from a more local perspective (e.g., Donovan, Roberts, et al., 2020a) and assess the impacts of drivers of global
change that function well beyond the local scale (e.g. Johnson et al., 2017). Such research is fundamental for keeping common species
common.
CRediT authorship contribution statement
Twidwell Dirac: Writing – review & editing, Conceptualization. Allen Craig R.: Writing – review & editing, Writing – original
draft, Conceptualization. Roberts Caleb P.: Writing – review & editing, Writing – original draft, Methodology, Conceptualization.
Wonkka Carissa L.: Writing – review & editing, Writing – original draft, Methodology, Conceptualization. Beck Jeffrey L.: Writing –
review & editing, Writing – original draft, Conceptualization. Donovan Victoria M.: Writing – review & editing, Writing – original
draft, Visualization, Methodology, Formal analysis, Conceptualization.
Declaration of Competing Interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
Data Availability
Data will be made available on request.
Acknowledgments
Our work was supported by the University of Nebraska’s Institute of Agriculture and Natural Resources, the University of Wyoming,
the University of Florida IFAS, the US Department of Agriculture–National Institute of Food and Agriculture award
2019–68012–29819, and the Arkansas Game and Fish Commission through cooperative agreement 1434–04HQRU1567. We thank the
Wyoming Game and Fish Department for collecting and sharing their pronghorn population data sets, with special thanks to Tod
Larson and Grant Frost who compiled and explained the data. Any use of trade, rm, or product names is for descriptive purposes only
and does not imply endorsement by the U.S. Government.
Open research statement
Data will be provided upon acceptance of the manuscript. The authors need to conrm data sharing policies for the data provided
by the Wyoming Game and Fish Department. Data will be permanently archived with Dryad.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.gecco.2024.e02848.
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