Warming, competition, and Bromus tectorum ... - Wiley Online Library

Warming, competition, and Bromus tectorum population growth across an elevation gradient ALDO COMPAGNONI1,

AND

PETER B. ADLER

Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main, Logan, Utah 84322 USA Citation: Compagnoni, A., and P. B. Adler. 2014. Warming, competition, and Bromus tectorum population growth across an elevation gradient. Ecosphere 5(9):121. http://dx.doi.org/10.1890/ES14-00047.1

Abstract. Cheatgrass (Bromus tectorum) is one of the most problematic invasive plant species in North America and climate change threatens to exacerbate its impacts. We conducted a two-year field experiment to test the effect of warming, competition, and seed source on cheatgrass performance across an elevation gradient in northern Utah. We hypothesized that warming would increase cheatgrass performance, but that warming effects would be limited by competing vegetation and by local adaptation of cheatgrass seed sources. The warming treatment relied on open top chambers, we removed vegetation to assess the effect of competition from neighboring vegetation, and we reciprocally sowed cheatgrass seed sources from the three study sites. We quantified performance with per capita growth rate and its components (emergence, survival, and fecundity). The main effect of the warming treatment was significant and positive only at high elevation, where warming triggered a three- to six-fold increase in population growth rate. At the lower elevation sites, main effects of warming were always positive but not significant. Interactions between warming and seed source or competition were inconsistent across sites and years. As expected, competition from resident vegetation had strong and consistent negative effects on cheatgrass population growth rate, but it significantly limited warming effects only at the mid-elevation site in one year, presumably by decreasing soil water availability. High elevation seed sources performed best at all sites. Our results indicate that in northern Utah, warming has the potential to increase cheatgrass densities in years with normal to high precipitation, especially at high elevations, regardless of cheatgrass seed source. In this region, climate change is likely to increase the impacts associated with high cheatgrass density and biomass, such as shortened fire return intervals, at high elevations where this species is not yet problematic. Key words: biological invasions; climate change; demography; life table response experiment; open top chambers; plant invasions; population growth. Received 11 February 2014; revised 8 July 2014; accepted 10 July 2014; final version received 6 August 2014; published 30 September 2014. Corresponding Editor: N. Barger. Copyright: Ó 2014 Compagnoni and Adler. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/3.0/ 1

Present address: Rice University, 6100 Main Street, MS-170, Houston, Texas 77005 USA.

E-mail: [email protected]

INTRODUCTION

search challenge. While considerable research has shown that increasing CO2 (IPCC 2007) and soil nitrogen (Vitousek et al. 1997a) can favor invasive species (e.g., Huenneke et al. 1990, Smith et al. 2000), climatic change could have either positive or negative effects (Bradley et al. 2010) depending on an invader’s ecological characteristics (Bradley et al. 2009).

Global change poses tremendous challenges to land management. Exotic invasive species have long been recognized as an important component of global change (Vitousek et al. 1997b) and forecasting their interaction with other global change components represents an urgent rev www.esajournals.org

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September 2014 v Volume 5(9) v Article 121

COMPAGNONI AND ADLER

Cheatgrass (Bromus tectorum) is one of the most ecologically disruptive plant invaders in North America and it is the greatest threat to the sagebrush steppe ecosystems of the Intermountain West (Mack 1981, Knapp 1996 ). This invasive, annual species promotes an uncharacteristically short fire return interval (Balch et al. 2013) that impedes the establishment and survival of native perennial vegetation. At present, cheatgrass invasions are more severe in warm, dry, low elevation sites than in colder and wetter high elevation sites (Suring et al. 2005). The temperature increases predicted for the 21st century (IPCC 2007) could increase cheatgrass density in its current range or allow cheatgrass to expand into higher elevations (Chambers et al. 2007, Bradley 2009), impacting additional native communities. The extent and severity of the cheatgrass invasion may be controlled by abiotic conditions, community composition or both. Climate envelope models (Bradley 2009, Bromberg et al. 2011) have been used to address the role of abiotic conditions over large spatial scales and suggest that warming will expand cheatgrass dominance into higher elevations. A field study examining cheatgrass performance along elevation gradients also has provided valuable insights into the roles of temperature and community interactions: Chambers et al. (2007) suggested that cheatgrass abundance at high elevations is limited by both temperature and competition with neighboring vegetation. The importance of community interactions in limiting cheatgrass’ success is consistent with previous literature documenting the plasticity of cheatgrass’ response to soil resources. In particular, both germination and growth are responsive to moisture (Rice et al. 1992, Roundy et al. 2007) and nitrogen levels (Evans and Young 1975, Monaco et al. 2003). In the Intermountain West, soil resources, aboveground productivity and biomass increase with elevation (Chambers et al. 2007), which might increase competitive pressure from perennial natives (Grime 1979). If warmer temperatures have a positive direct effect on cheatgrass, strong competition might be especially important for limiting cheatgrass performance through negative indirect effects (Suttle et al. 2007, Concilio et al. 2013). Genotype can determine plant response to v www.esajournals.org

climate change (Rehfeldt et al. 2002, Banta et al. 2012) and recent studies suggest that many North American cheatgrass’ populations may be locally adapted (Scott et al. 2010), from low elevation xeric sites to high elevation mesic sites (Merrill et al. 2012). If local adaptation were common, short term cheatgrass response to warming could depend on available genotypes. In particular, warming might have little shortterm effect if the cheatgrass populations currently found at high elevation are poorly adapted to warmer temperatures. Therefore, warming might improve cheatgrass performance only for genotypes adapted to warmer temperatures. We designed an experiment that builds on previous experimental and climate envelope model studies in three ways. First, we directly test the effect of warming on cheatgrass with a field experiment. This approach isolates the effect of temperature from other potentially confounding factors such as neighboring vegetation, precipitation, and soil characteristics. Second, we test the role of plant community interactions in limiting cheatgrass, and whether competition changes the effects of warming on cheatgrass. Third, we test whether variation among local cheatgrass seed sources, which we expect to be locally adapted, influences the response to warming. We conducted these manipulations of temperature, competition from resident vegetation, and local cheatgrass seed sources across an elevation gradient in northern Utah. We tested three hypotheses: (1) Experimental warming will increase cheatgrass per capita growth rates more at high than low elevations; (2) Competition will decrease cheatgrass population growth rate the most at high elevation and will reduce the effect of warming, resulting in significant interactions between warming and neighbor-removal treatments; (3) Cheatgrass seed sources at any one location are adapted to local conditions. As a result, each cheatgrass seed source should perform best at its own site. Moreover, the effect of warming will be most positive for seeds from lower, warmer elevations. We focus our analyses on the population growth rate because it is a synthetic measure of cheatgrass impact by integrating biomass through its effect on seed production and plant density. Specifically, we use data on cheatgrass emergence, survival, and fecundity to gain insight into the mechanisms 2


COMPAGNONI AND ADLER Table 1. Environmental variables of the three experimental sites. Site

1340 m (low)

1460 m (mid)

1830 m (high)

Climate station name Latitude Longitude Soil type Available water supply (mm) Average temperature (8C) Average max. temperature (8C) Average min. temperature (8C) Average total precipitation (mm) Average total snow fall (cm) Average snow depth (cm)

Thiokol Plant 41837 0 N 112832 0 W gravelly loam 270.5 8.5 16.7 0.3 356.9 56.1 2.5

Logan USU 41846 0 N 111847 0 W silt loam 83.5 8.9 15.0 2.8 451.9 149.6 2.5

Hardware Ranch 41838 0 N 111833 0 W cobbly silt loam 99.6 5.15 15.1 4.8 433.6 162.1 5.1

Note: Climate data source: Western Regional Climate Center, http://www.wrcc.dri.edu/climate-summaries/; soil data source: Web Soil Survey, http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx

that determine population growth rate. In particular, emergence reflects how germination responds to abiotic conditions (e.g., Meyer et al. 1997), survival reflects cheatgrass’ response to stress during the growing season (e.g., Mack and Pyke 1984), and fecundity co-varies with vegetative growth (Rice et al. 1992).

were crossed with a neighbor removal treatment with two levels (control and total plant removal). We randomly assigned plots to one of these four treatment combinations. We used open-top chambers (Molau and Mølgaard 1996) to increase temperatures of warmed plots. The air temperature in all experimental plots was monitored every 30 minutes in June of 2010 with DS1921G Thermochron iButton data loggers installed 5 cm above the ground surface and shaded by white styrofoam cups. We measured volumetric soil moisture of the upper 5 cm once in May 2010 with an EC-5 Decagon soil moisture sensor, taking four readings per plot. The neighbor removal treatment was carried out by spraying glyphosate (Roundup, Monsanto, St. Louis, Missouri, USA) in the fall 2009 on a 1-m2 area centered on the selected plots. We did not repeat the treatment the following year because we planted cheatgrass into the plots in late August 2010 and spraying glyphosate in the fall of 2010 would have killed emerging cheatgrass. Moreover, there was little recolonization of the perennial grasses after just one growing season. Within each plot we established four 25 3 25 cm quadrats (sub-plots), each containing 100 2.5 3 2.5 cm grid cells. We collected seeds at the end of the 2009 and 2010 growing seasons from cheatgrass populations naturally occurring at or in the immediate vicinity of the three experimental sites. These populations showed clear ecological differences: from low to high elevation their average seed mass was 2.7 6 0.1 mg, 3.6 6 0.1 mg, and 4.0 6 0.1 mg, respectively. Note, however, that we do not have data to verify whether these differences are genetic. Cheatgrass seeds were planted into experimental plots in

METHODS Experimental sites We chose three sites that span an elevation gradient along the foothills of northern Utah’s Wasatch Mountains: Golden Spike National Monument (1340 m, ‘‘low elevation’’), Green Canyon Ecological Station (1460 m, ‘‘mid elevation’’) and Hardware Ranch Wildlife Management Area (1830 m, ‘‘high elevation’’). All sites are located on flat areas dominated by the shrub Artemisia tridentata. Interspaces are dominated by the exotic perennial bunchgrass Agropyron cristatum at low elevation and by the short-lived exotic perennial grass Poa bulbosa at mid and high elevations. Data on the relative cover of the plant species found in shrub interspaces are summarized in Appendix A. The environmental characteristics for each site are summarized in Table 1. Values for the high elevation site might overestimate temperature and underestimate precipitation and snow cover because the reference meteorological station is located ;100 m lower in elevation than the experimental plots.

Experimental design At each site, we established 20 circular plots 85 cm in diameter in shrub interspaces. Two levels of a warming treatment (control and warming) v www.esajournals.org

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October and November 2009 and at the end of August 2010. Planting time should not necessarily influence the percentage of emerging seedlings, because over most of cheatgrass’ Intermountain West range, plants emerge opportunistically anytime during the growing season (Mack and Pyke 1983, Mack and Pyke 1984, Pierson and Mack 1990). However, fall emerging seedlings might experience higher mortality than those emerged in the spring because of frost heaving (Mack and Pyke 1984) and snow cover (Griffith and Loik 2010). In each plot, we set up four quadrats, three of which were randomly assigned to be planted with one of the three seed sources. In planted quadrats, we drove one cheatgrass seed ;1 cm deep at the center of each 6.3 cm2 grid cell. The fourth quadrat was left unplanted to check for emergence from naturally dispersed seeds. Emergence and seedling survival were measured at least twice during the growing season: in 2009–2010, from April to June on a monthly basis and in 2010–2011 in May and June; the mid and high elevation sites were also sampled in October 2010. To estimate seed production, at the time of seed set we harvested the cheatgrass plants grown in each quadrat and counted the number of spikelets produced by each plant. We estimated the average number of seeds per spikelet by sub-sampling five mature plants per quadrat in 2011 and counting the number of seeds per spikelet. ANOVA analyses showed that the number of seeds produced per spikelet varied between sites and treatments, but not between seed sources. To measure cheatgrass performance, we calculated the geometric population growth rate as k ¼ ntþ1/nt, where nt denotes population abundance in year t. k is a density-independent measure of performance. In annual species, population abundance is equal to the number of viable seeds (Rees and Crawley 1989, Weiner et al. 2009); because we planted 100 seeds per quadrat, we calculated the population growth rate of each quadrat as k ¼ seed production/100. We also calculated the three vital rates that determine k: emergence, the probability that a seed germinates and grows to become a seedling, survival, the probability that an emerged seedling survives to seed-set, and fecundity, the average number of seeds produced per surviving individual estimated by using spikelet counts. v www.esajournals.org

Our model for population growth assumes no seed carry-over of seeds from one year to the next. This means that seeds planted at time t have either emerged or are considered dead at time t þ 1. This assumption is supported by work in natural populations showing emergence rates as high as 96% (Smith et al. 2008). In addition, as part of a separate warming experiment at our mid-elevation site, we buried seed bags to test for germination and found that .99% of buried seeds germinated in both warming and control plots (A. Compagnoni, unpublished data). Furthermore, even if our assumption of zero carryover leads to a small bias in our estimates of k, it should not affect our treatment comparisons. Our assumption is appropriate at field sites with relatively low cheatgrass densities. However, where cheatgrass forms monocultures, seed fall can reach 10,000 seeds m2 and even a seed carry-over of 1% has important ecological consequences. Although the seed bank is unlikely to play an important role in the population dynamics of this species, it is possible that dispersal from naturally occurring plants could contribute propagules to our study plots. We accounted for the effect of natural dispersal on our estimate of k by subtracting the estimated average number of seeds produced in the unplanted quadrats in each treatment from the number of seeds harvested in the planted quadrats in each plot. We subtracted the average treatment-specific background seed production because of high variation in the plot-level data. To estimate treatment means, we fit a series of models in which the seed production in unplanted quadrats was a function of the following predictor variables: warming only, removal only, warming plus removal, and warming, removal and the warming 3 removal interaction. We selected the best of these four models by choosing the one with the lowest Akaike information criterion (AIC). We assessed two additional alternative methods for estimating k: subtracting the plotspecific seed production of unplanted quadrats to the seed production of planted quadrats, and estimating k using data from planted quadrats only. A comparison of these three estimations of k and a comparison of seed production and seedling emergence in planted and unplanted quadrats can be found in Appendix C. We also 4



used this method to account for any cheatgrass seed fall that escaped collection at the end of the first growing season (the one exception was the removal treatments at the mid elevation site, in which we removed fallen seed by hand).

mean annual temperature (e.g., Ko¨rner 2007).

Characterization of climatic variables We compared the climatic and soil moisture conditions during the course of the experiment to historical climate. We limited our analyses to the growing season, which goes from September to June. We chose this period because in the Great Basin, cheatgrass germinates as early as late August or early September (e.g., Mack and Pyke 1983) and sets seed by June or early July (Stewart and Hull 1949). The bulk of cheatgrass’ aboveground growth occurs from late winter to seed set. However, we include fall and winter in the growing season for two reasons. First, a key determinant of cheatgrass success is its early root growth, which occurs in the fall and winter (Harris 1967). Second, fall and winter climate can have lagged effects, especially on soil moisture. To quantify inter-annual variability in the climate of the growing season and its anomalies with respect to historical data, we calculated total precipitation, average temperature and average soil moisture values in 2009–2010 and 2010–2011. Temperature means, total precipitation, and snowfall were calculated using data from the Utah State University weather station (source: Utah Climate Center, http://climate.usurf.usu. edu/mapGUI/mapGUI.php), three km away from the experiment’s mid elevation site. We limited this analysis to our mid elevation site, the only one providing a sufficiently long, continuous data set (from 1893 to 2011) to allow comparisons of historical and experimental conditions. To estimate how temperature and precipitation translate into soil water holding capacity, we calculated mean growing season soil moisture values using estimates of monthly soil moisture for the 344 climate divisions defined by the National Climatic Data Center in the conterminous United States (http://www.cpc.ncep.noaa. gov/soilmst/index_jh.html). These estimates refer to the first 2 m of the soil depth and provide a measure of soil water content in mm between 1933 and 2011. The model was parameterized using runoff data from Eastern Oklahoma and the maximum water holding capacity is 760 mm (Huang et al. 1996). We downloaded raw data for the Utah North central climate division. We compared climate and soil moisture measures for

Characterization of climatic range To test where the temperature and precipitation of our sites fall within the range generally experienced by cheatgrass in the Great Basin region of North America, we used the data set compiled by Bradley (2009). In this data set, cheatgrass dominance in the Great Basin ecoregion was estimated using a time series of advanced very high resolution radiometer (AVHRR) Pathfinder data collected from 5/15/ 1988 through 5/11/2001. The response of cheatgrass dominated areas to interannual variability in precipitation is dramatically larger than native perennial vegetation (Bradley and Mustard 2005), so that cheatgrass dominated areas can be identified by measuring interannual variability in greenness using satellite images. Every 4 3 4 km pixel classified as cheatgrass dominated land based on the AVHHR data was associated with monthly maximum and minimum temperature and total precipitation estimated by the PRISM database (Daly et al. 2002). We used monthly PRISM data to calculate annual mean temperature and total precipitation. Plotting the kernel density estimates of these two measures represents the climate conditions where the species is most likely to reach high cover values. Bradley (2009) demonstrated that the best precipitation and temperature predictors of cheatgrass dominated areas presence/absence were June precipitation and January maximum temperature. However, these predictors are correlational and do not necessarily have a direct link to the physiology of cheatgrass. For example, June precipitation is thought to explain the competitive effect of native perennials, not the physiology of cheatgrass (Bradley 2009), because by June most of cheatgrass’ aboveground growth is completed. Given this limitation, we chose to represent cheatgrass range in terms of growing season precipitation and annual mean temperature. We did so because in the Great Basin, cheatgrass habitat is often identified using annual precipitation (e.g., Pyke and Nova 1994) and elevation (Stewart and Hull 1949), which is a proxy for v www.esajournals.org

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the two years of the experiment to historical trends by calculating the 25th, 50th, and 75th quantiles of the observed values. We also quantified the magnitude of projected climate change by comparing the average climate in the period 1961–1990 (henceforth ‘‘climate normals’’) to climate projections. Data on climate normals and climate projections came from downscaled 1-km resolution data calculated with the ClimateWNA software package (Wang et al. 2012). This data is downloadable on the Pacific Northwest Climate Change Vulnerability Assessment web page (http://www.climatevulnerability. org/?page_id¼9). We downloaded climate normals for the 1961–1990 period and four forecasts for the 2070–2099 period generated by global circulation models running the SRES A2 greenhouse gas emission scenario (IPCC 2007). We retrieved values for locations corresponding to our three experimental sites and calculated growing season (September–June) precipitation and mean temperature. We then compared the climate normals to the average of the four projections. Finally, we used the air temperature and soil moisture data collected in the experimental plots to verify whether open top chambers changed these two environmental variables. To quantify the effect of open top chambers on temperature, we used hourly temperature readings in two ways. First, we calculated the average hourly temperature during the course of the day. Second, we calculated average daily temperature during the course of June 2010. To test whether open top chambers had an effect on soil moisture, we fit a mixed effect ANOVA on the volumetric soil water measurements.

fixed factors and plot as a random factor. We fit six models, because our analyses were replicated at three sites and two years. We modeled each site and year separately for two reasons. First, we did not have more than one replicate site per elevation. Second, the two years were not directly comparable because seeds were planted between October and November in 2009 and at the end of August in 2010; planting in August rather than November maximizes fall emergence. Once we fit models, we used their predicted values to calculate the average effect of treatments. We analyzed each vital rate separately. Fecundity data was log transformed to meet normality assumptions and analyzed with a linear mixed model with treatments as fixed effects and plots as a random effect. When Bartlett’s test found a significant difference in variance between removal and non-removal treatments, we modeled variance independently. We analyzed survival and emergence using a generalized linear mixed model with a binomial distribution and logit link. As for the modeling of fecundity, the fixed and random effects of the model were treatments and plots, respectively. All hypotheses were tested using differences in population growth rate observed among treatments and sites. We tested hypothesis 1, concerning the increase in warming effects with elevation, by looking for a significantly positive effect of warming at high elevation and for a lack of significance or for significantly negative effects at mid and low elevations. To address hypothesis 2, which stated that competition would limit the effects of warming especially at high elevation, we verified whether the effect of neighbor removal increased with elevation and looked for significant warming 3 removal interactions. Finally, we tested hypothesis 3 regarding local adaptation of cheatgrass seed sources by looking for significant differences among cheatgrass seed sources and for significant warming 3 seed source effects. To understand which vital rates drove differences in population growth rate among treatments, we carried out a Life Table Response Experiment (LTRE). We conducted LTREs for the warming, removal, and seed source treatments. The LTRE calculates the contributions of our three vital rates (emergence, survival, and fecundity) to the differences in growth rate (k) caused by these three treatments.

Analyses We used linear mixed-effects models to test the effect of treatments on per capita cheatgrass growth rate. Per capita growth rate was logtransformed to meet normality assumptions. The assumption of homogeneous variance across treatments was not always met, because data from removal plots tended to have larger variance. To address this issue, we modeled independent variances when Bartlett’s test found a significant difference between removal and non-removal treatments. We treated temperature, removal, seed source, and all their interactions as v www.esajournals.org

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We performed these analyses on the main treatments, but not on their interactions (e.g., the warming 3 removal interaction). Closely following Caswell (2001), we calculated differences among treatments as:

aðwarmingÞ ¼k b

ðremovalÞ

¼k

warming

removal

vðMid ElevSeedLow ElevSeedÞ ¼k

-k

-k

an estimate of uncertainty. Because differences in k induced by treatments vary widely in magnitude, so do the contributions to these differences. This hinders the graphical comparison of LTRE results among treatments, sites, and years. To solve this problem, we calculated the proportional contribution of each vital rate to k differences. This constrains contributions between 0 and 1 and allows us to plot all figures on the same scale. All analyses were done using R version 2.14.0 (R Foundation for Statistical Computing, Vienna, Austria), and linear mixed models and generalized linear mixed models were fit using the nlme and lme4 packages, respectively.

control

control

Mid ElevSeed

-k

Low ElevSeed

vðHigh ElevSeedMid ElevSeedÞ High ElevSeed Mid ElevSeed ¼k -k

RESULTS

where a, b, and v estimate the effect caused by the warming, removal, and seed source treatments, respectively. Because the seed source treatment had three levels, we used the low elevation seed source as benchmark and analyzed the contrasts with the mid and high elevation seed sources. The contributions of each vital rate to the above differences are:

a˜ ðwarmingÞ ¼

Characterization of climatic range Mean annual precipitation and temperature at our sites (Table 1) are within the climatic range of cheatgrass dominance. However, values are at the high end of the annual precipitation range and, at the high elevation site, close to the low temperature limit of dominance (Fig. 1). Interestingly, the mean annual temperature is slightly higher at mid elevation than at low elevation, a pattern caused by higher minimum temperature at mid elevation (Table 1).

3 X ]k ðawarming acontrol Þ i i ]a i i

ðremovalÞ X ]k b˜ ¼ ðaremoval acontrol Þ i i ]a 3

v˜

Characterization of climatic variables

i

i

The two years of the experiment (Table 2) were cooler than normal at the mid-elevation site near the Utah State University weather station (the weather stations in proximity to the other two sites did not provide continuous, long-term data sets that could be used to compare historical climate to the weather conditions observed during the experiment). At the Utah State University weather station, growing season temperatures were 5.18 and 5.98C in the two years of the experiment, both of which were below 6.28C, the 50th percentile of the series. Moreover precipitation was 357 mm in the first year, which is between the 25% percentile (328.2 mm) and the median value of the series (411 mm), and 644.7 mm in the second year, which is above the 75th percentile of the series (454.7 mm). Estimated soil water content during the first growing season was 198 mm, which is between the 25th (191.3 mm) and 50th (221.5

ðMid ElevSeedLow ElevSeedÞ

¼

3 X

ðaiMid

ElevSeed

aLow i

ElevSeed

Þ

i

]k ]ai

v˜ ðHigh ElevSeedLow ElevSeedÞ ¼

3 X

ðaiHigh

ElevSeed

aLow i

ElevSeed

i

Þ

]k ]ai

where a is a vital rate and the index i refers to one of the three vital rates: emergence, survival or fecundity. The term ]k/]ai is the sensitivity of growth rate to a particular vital rate. The nominator and denominator of this expression refer to a population whose vital rates are the arithmetic mean of the two populations compared in the equation. We performed these analyses on 1000 bootstrap samples to provide v www.esajournals.org

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Fig. 1. Proportion of cheatgrass dominated land in the Great Basin as a function of annual precipitation (A) and temperature (B). Cheatgrass dominance was inferred from NDVI satellite images (data source: Bethany Bradley).

mm) percentile of the series. In the second growing season, a soil water holding capacity of 279.4 mm exceeded the 75th percentile of the series (251.7 mm). By 2100, growing season temperature is expected to increase by about 4.3 6 1.38C at all three sites. Precipitation projections differ among sites, with increases of 6.5 6 6.7 mm, 10.1 6 8.6

mm, and 10.8 6 9.0 mm at the low, mid, and high site, respectively. Across all sites, the warming treatment increased daily average maximum temperatures by two degrees and daily minimum temperatures by one degree in June 2010. Daytime temperature increase was 2.88, 1.88 and 2.78C at high, mid, and low elevation, respectively (Appendix B: Fig. B1). The increase in temperature appears to be largest by the end of the month, presumably because of higher solar radiation. Warming had no effect on soil moisture, but soil moisture did vary among sites (Appendix B: Table B1, Fig. B2): the lowest and highest moisture availabilities occurred at mid and high elevations, respectively.

Table 2. Growing season temperature and precipitation recorded at the Utah State University weather station. Year

Temperature (8C)

Precipitation (mm)

Snow fall (cm)

2009–2010 2010–2011

5.1 5.9

357.0 644.7

167.0 250.1

Seed production The proportion of emerged seedlings (in planted plots 0.59 6 0.02, in unplanted plots

Note: Weather station coordinates are: Latitude 41877 0 N, Longitude 111879 0 W.

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0.22 6 0.03; Appendix C: Fig. C1) and seed production (in planted quadrats 970.2 6 85.9, in unplanted plots 165.7 6 33.8; Appendix C: Fig. C2) were much smaller in unplanted than in planted quadrats in the first year, with the exception of the unplanted quadrats at the mid elevation site. In the second year, seeding emergence and seed production in unplanted quadrats doubled and tripled, respectively. Because an increase in naturally dispersed seeds is unlikely this suggests that substantial seed fall escaped the June 2010 harvest. Despite this, subtracting seed production of unplanted plots from seed production in planted plots had little qualitative effect on the estimates of k in our experimental treatments (Appendix C: Figs. C3– 5).

but this difference was marginally significant (Table 5; Appendix D: Tables D3 and D4, Fig. D2). Emergence differed in three models, but not at the mid and high elevation site in 2010 when its contribution to population growth was almost as large as fecundity (Table 5; Appendix D: Tables D5 and D6, Fig. D3).

Effects of neighbor removals Removal of surrounding vegetation caused, on average, a fivefold increase in cheatgrass population growth rate (k in removal plots: 13.2 6 1.2, k in control plots: 2.7 6 0.3). This increase was significant in four out of six site-by-year combinations (Table 3), and it was larger at mid elevation (Table 4). The positive effects of removal were always driven by an increase in fecundity (Fig. 3). Accordingly, in removal treatments, significant differences in population growth rate and fecundity were detected at the same sites and years (Table 5; Appendix D: Tables D1 and D2, Fig. D1). The removal 3 warming interaction was significant in only one case: at the mid elevation site in 2010. In the same year, the removal 3 warming was marginally significant at the low elevation site. In this latter case, and contrary to expectations, the removal of neighbor plants decreased rather than increased the effects of warming (Table 4). On the other hand, at mid elevation in 2010, the effect of warming was significantly larger in plots where neighbor vegetation had been removed (Table 4), although the main effect of warming was not significant (Table 3). In this case, competition from surrounding vegetation offset the positive effects of experimental warming on cheatgrass population growth rate.

Effects of warming The main effect of experimental warming significantly increased cheatgrass population growth rate in only one of our six models: at the high elevation site in the second year (Table 3, Fig. 2). A marginally significant increase was also observed in the first year at the high elevation site (Table 3). Effects of warming on population growth rate were always positive, even if not statistically significant. At the high elevation site, warming increased population growth rate threefold in year 2010 and sevenfold in year 2011 (Table 4). In 2010 at the low and mid elevation sites, the main effect of warming was not significant, but the warming 3 removal interaction was, indicating that warming significantly increased population growth rate, but only in the presence or absence of resident vegetation (see Effects of neighbor removals below). In five out of six cases warming increased population growth rates primarily by increasing fecundity (Fig. 3). In 2011, survival was the most important driver of population growth rate at the mid elevation site, and its contribution was comparable to fecundity at the high elevation site. In 2010, the contribution of emergence was comparable to fecundity at the mid and high elevations. Few vital rates statistically differed between controls and warming plots. However, in 2011 warming significantly increased fecundity at the high site (Table 5; Appendix D: Tables D1 and D2 and Fig. D1). Warming increased survival only in 2011 at the mid elevation site, v www.esajournals.org

Effects of seed source Seed source significantly affected population growth rates in three out of six site-by-year combinations (Table 3). Seeds collected at mid and high elevation tended to have the largest growth rate, those from low elevation the lowest (Table 4). Differences in population growth rate among seed sources were driven by both emergence and fecundity; survival had a large effect only at the mid elevation site in 2010 (Fig. 4). Vital rates analyses show emergence always differed among seed sources (Table 5; Appendix 9


COMPAGNONI AND ADLER Table 3. Results of mixed-effects ANOVAs for the effects of treatments on cheatgrass population growth rates in 2010 and 2011. Treatment Low elevation site (2010) Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed Mid elevation site (2010) Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed High elevation site (2010) Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed Low elevation site (2011) Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed Mid elevation site (2011) Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed High elevation site (2011) Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed

Numerator df

Denominator df

F

P

source

1 1 2 1 2 2 2

16 16 32 16 32 32 32

9.1802 2.3825 3.1911 3.7914 0.1078 2.5724 0.7210

0.0080** 0.1422 0.0545 0.0693 0.8981 0.0921 0.4940

source

1 1 2 1 2 2 2

16 16 31 16 31 31 31

54.1941 0.0394 1.4980 9.2230 1.6628 0.5592 1.9287

0.0001** 0.8451 0.2393 0.0079** 0.2061 0.5773 0.1624

source

1 1 2 1 2 2 2

16 16 32 16 32 32 32

10.5692 3.3002 13.0106 0.1311 5.6438 1.7387 2.1475

0.0050** 0.0881 0.0001** 0.7220 0.0080** 0.1920 0.1333

source

1 1 2 1 2 2 2

16 16 30 16 30 30 30

0.5599 0.0592 2.7827 0.7046 0.5095 0.2540 0.7082

0.4652 0.8108 0.0779 0.4136 0.6059 0.7774 0.5006

source

1 1 2 1 2 2 2

16 16 31 16 31 31 31

33.0128 0.5858 0.5402 2.4138 0.4430 0.7475 0.2636

,0.0001** 0.4552 0.5880 0.1398 0.6461 0.4819 0.7700

source

1 1 2 1 2 2 2

16 16 32 16 32 32 32

1.2106 11.4747 0.2012 2.8766 0.8064 1.8238 0.4628

0.2875 0.0038** 0.8188 0.1092 0.4553 0.1778 0.6336

0.05 , P , 0.1; *0.01 , P , 0.05; ** P , 0.01.

D: Tables D3 and D4, Fig. D2), but fecundity significantly differed only at the high elevation site in 2010 (Table 5; Appendix D: Tables D1 and D2).

DISCUSSION Effects of warming Our results support the hypothesis that in northern Utah, warming is likely to increase cheatgrass seed densities and impacts, especially at higher elevations. Experimental warming significantly increased cheatgrass population growth rate at the high elevation site by three and seven fold in the first and second year of the

The seed source 3 warming interaction was never significant, but it was marginally significant in the first year at the low site (Table 3). In this case warming increased the performance of seeds collected at low elevation (Table 4). v www.esajournals.org

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COMPAGNONI AND ADLER Table 4. The effect size of treatments on the population growth rate relative to control plots. Values above 1 denote positive effects and values below 1 denote negative effects. Underlined values highlight statistically significant effects. 2010

2011

Factor and hypothesis

Low

Mid

High

Low

Mid

High

Warming (H1) Removal (H2) Removal 3 warming (H2) Mid elevation seed source (H3) High elevation seed source (H3) Mid seed source 3 warming (H3) High seed source 3 warming (H3)

2.84 7.76* 0.07 1.17* 3.64* 0.12 0.10

2.76 18.83* 11.21* 2.67 2.50 0.16 0.25

3.21 7.95* 1.59 1.10* 2.36* 1.54 0.78

1.22 0.69 2.14 1.20* 2.25* 0.26 0.42

1.35 7.43* 2.95 1.27 1.24 1.17 0.57

6.79* 1.81 6.18 1.16 1.16 4.04 5.72

Note: Values are calculated using the predicted values generated by the six linear mixed effects models (one model for each site and year combination). The asterisk denotes significant (P , 0.05) tests.

experiment, respectively. At the lower elevation sites, the main effects of warming were positive but of smaller magnitude and not statistically significant. However, warming interacted with removal in certain cases. At the low elevation site, removal significantly decreased the effect of warming in the driest year, while at mid elevation removal significantly increased the positive effect of warming. Warming increased cheatgrass population growth rates primarily by increasing fecundity, and secondarily by increasing survival. The strong effect of warming on fecundity may reflect the fact that moisture was above average during our experiment and temperature was far from cheatgrass’ upper tolerance. First, we expect soil moisture to favor cheatgrass seed production, because this species is extremely responsive to moisture availability (e. g. Hull and Pechanec 1947, Bradley and Mustard 2005, Concilio et al. 2013). Second, a separate experiment carried out at the mid elevation site in 2011 provides evidence that temperatures were far from cheatgrass’ upper tolerance. In this experiment, increasing November through June average temperature by 4.38C triggered a tenfold increase in cheatgrass population growth rate (Compagnoni and Adler 2014). The LTRE indicated that survival was also important in driving increased growth rate in warming plots at the mid and high elevations in 2011. This effect may result from decreased snow cover, which is known to influence the survival of fall-emerging individuals in this species (Rice and Mack 1991b, Griffith and Loik 2010, Compagnoni and Adler 2014). During the second growing season, snowfall was v www.esajournals.org

50% higher than the previous one at the mid elevation site and seedlings were exposed to winter weather because August planting maximized fall establishment. Open top chambers can cause earlier snowmelt (Marion et al. 1997), and at our site they decreased snow depth and persistence (A. Compagnoni, personal observation). We speculate that smaller average snow cover might have reduced mortality caused by snow-related pathogens (Klemmedson and Smith 1964) such as Chestnut Bunt (Tilletia fusca, Meyer et al. 2008). We had no direct evidence of Chestnut Bunt infection, but in the second growing season we noticed a white mycelium covering the ground of the mid elevation site immediately after snowmelt (A. Compagnoni, personal observation). Alternatively, persistent snow cover could shorten the most favorable portion of cheatgrass’ growing season, which occurs during early spring when growth is not limited by moisture (Zelikova et al. 2013). If these hypotheses hold, future warming might increase cheatgrass growth rate at high elevations by decreasing snow cover. Our findings support the results of previous modeling (Bradley 2009) and field (Chambers et al. 2007) studies suggesting that at high elevation, low temperature is an important factor preventing cheatgrass from attaining the higher densities and impacts observed at lower elevations. However, substantial increases in temperature may be necessary before cheatgrass can successfully invade undisturbed vegetation at high elevation. Although the temperature increase of 1.68C created by our warming treatment improved cheatgrass performance, positive pop11



Fig. 2. Treatment, site and year specific differences in the log of cheatgrass population growth rate (k). Below zero values denote negative population growth rates (untransformed k is below 1). Error bars represent one standard error.

ever, contrary to our expectation, we found little evidence that the effect of competition is larger at high elevation. Rather, the largest effects of competition occurred at mid elevation. Furthermore, the two significant removal 3 warming interactions were observed in 2010 at the low and mid elevation site. These results are in line with previous observational (Anderson and Inouye 2001, Reisner et al. 2013) and experimental field studies (Beckstead and Augspurger 2004, Chambers et al. 2007) which identify perennial herbaceous vegetation as one of the most

ulation growth rates (log(k) . 0) were not common and they were almost exclusively restricted to plots subjected to a combination of warming and removal.

Effects of removal Across elevations, competition from neighboring species had a strong and consistent negative effect on cheatgrass population growth rate. Compared to other treatments, removal of herbaceous vegetation had by far the largest positive effect on cheatgrass’ performance. Howv www.esajournals.org

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Fig. 3. Percent contribution of vital rates to differences in growth rate for the warming and removal treatments. Error bars represent one standard deviation.

Table 5. Statistically significant results (P , 0.05) for the models of population growth rate (k), emergence (e), survival (s), and fecundity (f ). 2010 Factor and hypothesis

Low

Warming (H1) Removal (H2) Removal 3 warming (H2) Mid elevation seed source (H3) High elevation seed source (H3) Mid seed source 3 warming (H3) High seed source 3 warming (H3)

e k,e,f k k,e k,e


Mid k,e,f k,f e,s e,s e s

13

2011 High

Low

k,f k,e,f

e s e k k,e

e

e

k,s,f

Mid

High k,e,f

k,e,f f e e s s

e,f e e s s



Fig. 4. Percent contribution of vital rates to differences in growth rate for the seed source treatment. Error bars represent 0.3 standard deviations.

with intact neighboring vegetation (Table 4, Fig. 2). At mid elevation, data shows that competition from neighboring plants decreased the positive effects of warming on cheatgrass performance to the point that warming had no significant effect in plots with intact vegetation. At the low elevation site, we speculate that results originated from low early-season survival of highelevation seeds in the warming plus removal plots. On the first census in April, we observed many dead seedlings. Because that year we planted seeds in late fall (mid November), emergence most likely occurred in spring. We

important factors limiting cheatgrass’ abundance. It is not surprising that fecundity drives population growth in our removal plots and was sensitive to competition. In cheatgrass, fecundity co-varies with plant growth (Rice et al. 1992). Removal of plant neighbors increases soil resource levels (e.g., Chambers et al. 2007), which should increase growth and fecundity. The significant removal 3 warming interactions at mid and low elevation arose for different reasons. At mid elevation removal increased the effects of warming, while at low elevation warming had a stronger positive effect in plots v www.esajournals.org

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speculate that seedling mortality might have resulted from excessive heat stress in the removal plots, where the exposed bare ground would warm rapidly. Data from the mid elevation site in 2010 suggest that when moisture is limiting, neighboring vegetation has the potential to offset the positive effects of warming on cheatgrass performance. Because in arid environments plants mainly compete for water (Eissenstat and Caldwell 1988), the effects of competition should be stronger when moisture availability is relatively low. Consistent with this expectation, competition offset warming effects when soil moisture conditions where plausibly the most limiting during the course of the study. First, precipitation in the 2010 growing season was almost half of that fallen in 2011. Second, the mid elevation site has lower water holding capacity than the low elevation site, as suggested by its lower available water supply and by our soil moisture data.

The poor performance of the low-elevation seed source is puzzling. There are two ways to explain this pattern. First, the low elevation seed source might be maladapted to its collection site. In the literature there are other instances of local cheatgrass populations performing worse at their own site than seeds transplanted from different locations (Rice and Mack 1991b, Leger et al. 2009). In these cases, maladapted genotypes might persist because their competitive displacement is prevented by limited dispersal (Rice and Mack 1991b). Second, in this study, fitness differences among seed sources might be determined by maternal effects rather than genetics. Cheatgrass seed weight correlates with fitness (Leger at al. 2009) and it is a plastic trait that increases in response to resource availability (Rice and Mack 1991a). Fitness of cheatgrass populations at low sites should be lower, because water and available soil nitrogen increase with elevation (Chambers et al. 2007), resulting in lighter seeds and lower fitness at low elevation sites. Consistent with this line of reasoning, seed weight in our three populations increased with elevation.

Effects of seed source We expected the seeds from low elevations to respond most to the warming treatment and all seeds to perform best at their original site. On the contrary, the effect of seed origin on growth rate was usually independent of treatment and elevation. Seed sources with the lowest growth rates were always the ones from low elevation, while the highest growth rates were observed for either mid or high elevation seed sources. The interaction between warming and seed source was marginally significant only at the low elevation site in 2010 when warming increased the performance of low elevation seeds. This interaction is the only evidence suggesting the low elevation seed source might have an advantage under increased temperature. However, in this specific case the best performing seed source was still the one from high elevation. These differences in seed source performance were mostly driven by emergence rates, which suggest they might have a genetic basis. In North American cheatgrass populations, differences in the ecology of germination are thought to be adaptive (e.g., Meyer et al. 1997). Collectively, these results indicate that at high elevations warming will express its full effects immediately, with no lag before colonization by seed sources adapted to warmer growing seasons. v www.esajournals.org

Caveats The use of open-top chambers and the wet conditions encountered during the course of our experiment constrain our ability to make quantitative predictions about cheatgrass population growth under climate change. Open-top chambers do not perfectly simulate warming: they increase temperatures as a function of solar radiation (Bokhorst et al. 2013), and they simulate only part of the effect that increased temperature has on snow cover. During our experiment, moisture availability was never low compared to the historical mean, because the first growing season had average rainfall and low temperature, while the second had average temperatures and high precipitation. Moreover, open top chambers did not significantly decrease soil moisture. Cheatgrass growth strongly depends on moisture availability (Hull and Pechanec 1947, Bradley and Mustard 2005), so that combining lower precipitation with higher temperature might decrease its competitive ability. In fact, recent studies have shown reduced performance of cheatgrass when strong warming (þ48C) coincides with low moisture availability 15


COMPAGNONI AND ADLER Kyle Young for field work assistance. Bethany Bradley kindly offered the climatic data associated with cheatgrass dominated land in the Great Basin. We thank Susan Durham for statistical assistance. Mevin Hooten, David Koons, Gene Schupp, John Stark, Mary Price, Tamara Zelikova, and one anonymous reviewer provided comments that strongly improved the manuscript. Tammy Benson and Dan Christensen guided the selection of the experimental sites located at, respectively, Golden Spike National Historic Site and Hardware Ranch Wildlife Management Area. This research was funded by the Quinney Foundation, the Utah State University Ecology Center, by a New Faculty Research Grant to PBA from Utah State University, and by the Utah Agricultural Experiment Station, which approves this paper as journal article 8481.

(Zelikova et al. 2013, Compagnoni and Adler 2014), a situation most likely to occur at sites with lower average total precipitation (e.g., at the low elevation site of this experiment) or at sites that have lower available water supply due to their soil physical properties (e.g., at the mid elevation site). At these lower elevation sites, it is plausible that strong warming could decrease cheatgrass’ population growth rate (e.g., Bradley 2009). With just two years of data we cannot quantify the effect of interannual climate variability on cheatgrass population growth rate. However, our data suggest precipitation and, secondarily, snow have important effects on cheatgrass. When compared to the first year, cheatgrass population growth in the second year increased at the low elevation site and decreased at the mid and high elevation site. We speculate that the increase in population growth at the low elevation site was driven by increased precipitation. On the other hand, lower growth rates at the mid and high elevation sites could have been driven by higher snow cover. This hypothesis is consistent with the LTRE results, which show that at these sites warming increased survival, possibly by decreasing snow cover.

LITERATURE CITED Anderson, J. E., and R. S. Inouye. 2001. Landscapescale changes in plant species abundance and biodiversity of a sagebrush steppe over 45 years. Ecological Monographs 71:531–556. Balch, J. K., B. A. Bradley, C. M. D’Antonio, and J. Gomez-Dans. 2013. Introduced annual grass increases regional fire activity across the arid western USA (1980–2009). Global Change Biology 19:173– 183. Banta, J. A., I. M. Ehrenreich, S. Gerard, L. Chou, A. Wilczek, J. Schmitt, P. X. Kover, and M. D. Purugganan. 2012. Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecology Letters 15:769–777. Beckstead, J., and C. K. Augspurger. 2004. An experimental test of resistance to cheatgrass invasion: limiting resources at different life stages. Biological Invasions 6:417–432. Bokhorst, S. A., et al. 2013. Variable temperature effects of open top chambers at polar and alpine sites explained by irradiance and snow depth. Global Change Biology 19:64–74. Bradley, B. A. 2009. Regional analysis of the impacts of climate change on cheatgrass invasion shows potential risk and opportunity. Global Change Biology 15:196–208. Bradley, B. A., D. M. Blumenthal, D. S. Wilcove, and L. H. Ziska. 2010. Predicting plant invasions in an era of global change. Trends in Ecology and Evolution 25:310–318. Bradley, B. A., and J. F. Mustard. 2005. Identifying land cover variability distinct from land cover change: Cheatgrass in the Great Basin. Remote Sensing of Environment 94:204–213. Bradley, B. A., M. Oppenheimer, and D. S. Wilcove. 2009. Climate change and plant invasions: restora-

Summary Our results suggest that warming has the potential to allow cheatgrass to extend its dominance into higher elevations that have yet to experience the brunt of its invasion. The fact that high elevation cheatgrass populations show the highest fitness indicates that dispersal of low elevation seed sources is not needed for warming to express its full effect on cheatgrass performance. Our results also highlight the important role of resident vegetation in limiting cheatgrass abundance. As temperatures increase, land managers and private land owners responsible for high elevation sagebrush habitats should anticipate allocating more resources to cheatgrass eradication and fire suppression. Conserving a continuous cover of perennial herbaceous species will be the best strategy to minimize the risk of cheatgrass invasion.

ACKNOWLEDGMENTS We thank Amber Kacherian, Mindi Lundberg, and


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SUPPLEMENTAL MATERIAL APPENDIX A Neighbor species’ relative cover at the three experimental sites

Table A1. Relative cover estimation of the plant species observed at each site. Species

Relative abundance (%)

1340 m Agropyron cristatum Bromus tectorum Artemisia tridentata Sysimbrium altissimum Lactuca serriola 1460 m Poa bulbosa Convulvulus arvensis Bromus tectorum Tragopogon dubius Medicago sativa Bromus japonicus Artemisia tridentata Erodium cicutarium 1830 m Poa bulbosa Wyethia amplexicaulis Myosotis spp. Poa glaucifolia Melica bulbosa Achillea millefolium Danthonia californica Taraxacum officinale


41 28 13 6 3 43 19 10 9 6 6 6 2 39 19 15 6 6 4 4 3

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APPENDIX B Effects of experimental warming on plot temperature and moisture

Fig. B1. Average hourly (A–C) and daily (D–F) temperatures for the month of June 2010 in warmed and nonwarmed plots.


20


COMPAGNONI AND ADLER Table B1. Mixed effect ANOVA of May 2010 soil moisture. Factor Intercept Elevation Warming Elevation: warming

Numerator df

Denominator df

F

P

1 2 1

180 54 54

1248.62 106.33 0.64

,0.0001 ,0.0001 0.4255

2

54

2.37

0.1027

Fig. B2. Volumetric soil moisture in the upper 5 cm of soil in control and warming plots at the three sites in May 2010. Error bars represent 6SE.


21



APPENDIX C Seed production of unplanted quadrats and three alternative estimations of cheatgrass k

Fig. C1. Comparison of seed emergence in planted and unplanted quadrats. Error bars represent 6SE.


22



Fig. C2. Comparison of seed production in planted and unplanted quadrats. Error bars represent 6SE.


23



Fig. C3. k calculated subtracting the treatment-specific means of seed production in unplanted quadrats to the seed production of planted quadrats. Model results refer to the three main treatments (NS, P . 0.1; 0.05 , P , 0.1; *0.01 , P , 0.05; **0.001 , P , 0.01; *** P , 0.001). Error bars represent 6SE.


24



Fig. C4. k calculated subtracting the plot-specific seed production in unplanted quadrats to the seed production of planted quadrats. Model results refer to the three main treatments (NS, P . 0.1; 0.05 , P , 0.1; *0.01 , P , 0.05; **0.001 , P , 0.01; *** P , 0.001). Error bars represent 6SE.


25



Fig. C5. k calculated using the seed production of planted quadrats only. Model results refer to the three main treatments (NS, P . 0.1; 0.05 , P , 0.1; *0.01 , P , 0.05; **0.001 , P , 0.01; *** P , 0.001). Error bars represent 6SE.


26



APPENDIX D Effect of treatments, site and year on cheatgrass’ vital rates

Fig. D1. Seeds per individual by site, year, and treatment. Error bars represent 6SE.


27



Fig. D2. Proportion of individuals surviving to seed set by site, year, and treatment. Error bars represent 6SE.


28



Fig. D3. Proportion of cells where cheatgrass emerged during the growing season by site, year, and treatment. Error bars represent 6SE.


29



Table D1. Results of linear mixed-models fit on 2010 fecundity data. Treatment Low elevation site Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed source Mid elevation site Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed source High elevation site Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed source

Numerator df

Denominator df

F

P

1 1 2 1 2 2 2

16 16 32 16 32 32 32

8.8249 1.3276 0.3760 1.8523 0.2248 2.9745 0.1468

0.0090 0.2661 0.6896 0.1924 0.7999 0.0653 0.8641

1 1 2 1 2 2 2

16 16 31 16 31 31 31

64.5880 2.8865 0.4993 6.1357 2.7450 0.6407 2.7514

,0.0001 0.1087 0.6117 0.0248 0.0799 0.5338 0.0794

1 1 2 1 2 2 2

16 16 32 16 32 32 32

18.0331 2.4592 4.1626 1.6289 1.3768 1.4237 0.4524

0.0006 0.1364 0.0247 0.2201 0.2669 0.2557 0.6401

Numerator df

Denominator df

F

P

1 1 2 1 2 2 2

16 16 32 16 32 32 32

0.8634 0.6094 2.2801 0.8645 0.5250 0.4096 0.7982

0.3666 0.4464 0.1197 0.3663 0.5969 0.6676 0.4594

1 1 2 1 2 2 2

16 16 32 16 32 32 32

71.7467 0.6920 0.1144 7.3407 0.9262 1.8777 0.1820

,0.0001 0.4177 0.8923 0.0155 0.4067 0.1699 0.8345

1 1 2 1 2 2 2

16 16 32 16 32 32 32

2.2325 4.7542 0.0067 4.7035 0.8828 0.9199 2.6864

0.1546 0.0445 0.9934 0.0455 0.4235 0.4088 0.0835

Table D2. Results of linear mixed-models fit on 2011 fecundity data. Treatment Low elevation site Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed source Mid elevation site Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed source High elevation site Removal Warming Seed source Removal 3 warming Removal 3 seed source Warming 3 seed source Removal 3 warming 3 seed source


30



Table D3. Results of generalized linear mixed-models fit on 2010 survival data. Treatment Low elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed Mid elevation site Removal Warming Seedsource2 Seedsource3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed High elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed

Estimate

SE

Z

P

source2 source3

0.3146 0.3692 0.8001 0.3118 0.0533 1.2688 0.0500 0.8499 0.2649 2.0951 0.2907

0.6319 0.5434 0.4222 0.3006 0.8673 0.6032 0.4969 0.5012 0.3846 0.7856 0.6883

0.4979 0.6795 1.8950 1.0374 0.0615 2.1034 0.1006 1.6956 0.6888 2.6669 0.4224

0.6186 0.4968 0.0581 0.2996 0.9510 0.0354 0.9199 0.0900 0.4910 0.0077 0.6727

source2 source3

0.2210 0.4383 0.5936 1.3742 0.8312 0.3571 0.8386 0.1330 1.1022 0.8770 0.5796

0.3908 0.3261 0.2087 0.2598 0.5628 0.3785 0.4138 0.3053 0.3364 0.5577 0.6038

0.5655 1.3440 2.8446 5.2904 1.4769 0.9436 2.0266 0.4357 3.2768 1.5726 0.9599

0.5717 0.1790 0.0044 ,0.0001 0.1397 0.3454 0.0427 0.6630 0.0010 0.1158 0.3371

source2 source3

1.3236 0.1821 0.3545 0.6073 0.9264 0.2636 1.1632 0.5095 0.4376 1.0262 0.5049

0.6445 0.5589 0.3347 0.3111 0.8447 0.5660 0.5319 0.4408 0.4261 0.7194 0.6768

2.0536 0.3258 1.0594 1.9524 1.0967 0.4657 2.1869 1.1560 1.0270 1.4263 0.7459

0.0400 0.7445 0.2894 0.0509 0.2728 0.6415 0.0288 0.2477 0.3044 0.1538 0.4557

Note: These are not ANOVA results. ‘‘Seed source2’’ and ‘‘seed source3’’ refer to the mid and high elevation seed sources, respectively, and associated statistics refer to contrasts with the low elevation seed source.


31


COMPAGNONI AND ADLER Table D4. Results of generalized linear mixed-models fit on 2011 survival data. Treatment Low elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed Mid elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed High elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed

Estimate

SE

Z

P

source2 source3

0.9385 0.3470 0.1209 0.1688 0.9299 0.7125 0.2543 0.5345 0.2963 0.8095 0.9126

0.4493 0.3992 0.2924 0.3026 0.6188 0.4727 0.5230 0.4605 0.4336 0.7056 0.7248

2.0889 0.8694 0.4137 0.5578 1.5028 1.5073 0.4862 1.1607 0.6833 1.1473 1.2591

0.0367 0.3846 0.6791 0.5770 0.1329 0.1317 0.6268 0.2457 0.4944 0.2513 0.2080

source2 source3

0.9020 0.8701 0.1648 0.1705 0.8458 0.7367 1.0129 0.7515 0.7451 1.4193 1.1681

0.4706 0.4586 0.1450 0.1450 0.6779 0.2641 0.2660 0.2230 0.2162 0.4259 0.4237

1.9166 1.8973 1.1365 1.1758 1.2478 2.7898 3.8082 3.3693 3.4464 3.3324 2.7571

0.0553 0.0578 0.2558 0.2397 0.2121 0.0053 0.0001 0.0008 0.0006 0.0009 0.0058

source2 source3

0.0374 0.5945 0.2451 0.1410 0.2938 0.5560 1.0338 1.0322 0.8282 0.0841 0.2589

0.7471 0.7237 0.2659 0.2651 1.0279 0.4274 0.4375 0.3292 0.3286 0.5049 0.5148

0.0501 0.8214 0.9215 0.5316 0.2858 1.3010 2.3630 3.1359 2.5205 0.1665 0.5029

0.9600 0.4114 0.3568 0.5950 0.7750 0.1933 0.0181 0.0017 0.0117 0.8678 0.6150



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COMPAGNONI AND ADLER Table D5. Results of generalized linear mixed-models fit on 2010 emergence data. Treatment Low elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed Mid elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed High elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed

Estimate

SE

Z

P

source2 source3

0.9601 0.8528 0.7233 0.8471 0.8244 1.8933 1.5084 0.3462 0.3246 0.6614 0.7556

0.4314 0.4048 0.2033 0.1595 0.6037 0.2937 0.2571 0.2510 0.2097 0.3906 0.3556

2.2256 2.1066 3.5586 5.3112 1.3655 6.4463 5.8673 1.3792 1.5478 1.6931 2.1247

0.0260 0.0352 0.0004 ,0.0001 0.1721 ,0.0001 ,0.0001 0.1678 0.1217 0.0904 0.0336

source2 source3

2.2425 0.6725 0.8388 0.5208 0.8919 0.1665 0.4365 0.6961 0.2814 0.2059 0.3184

0.7273 0.7244 0.1590 0.1531 1.0257 0.2307 0.2279 0.2262 0.2237 0.3135 0.3130

3.0835 0.9284 5.2773 3.4019 0.8696 0.7219 1.9148 3.0777 1.2579 0.6568 1.0172

0.0020 0.3532 ,0.0001 0.0007 0.3845 0.4704 0.0555 0.0021 0.2084 0.5113 0.3091

source2 source3

0.0386 0.6734 0.1321 0.7672 0.2861 0.7375 0.4374 0.2101 0.4847 0.7240 1.5350

0.4154 0.4116 0.1713 0.1543 0.5804 0.2329 0.2167 0.2271 0.2138 0.3077 0.2949

0.0930 1.6358 0.7710 4.9720 0.4929 3.1672 2.0186 0.9250 2.2675 2.3526 5.2057

0.9259 0.1019 0.4407 ,0.0001 0.6221 0.0015 0.0435 0.3550 0.0234 0.0186 ,0.0001



33


COMPAGNONI AND ADLER Table D6. Results of generalized linear mixed-models fit on 2011 emergence data. Treatment Low elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed Mid elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed High elevation site Removal Warming Seed source2 Seed source3 Removal: warming Removal: seed source2 Removal: seed source3 Warming: seed source2 Warming: seed source3 Removal: warming: seed Removal: warming: seed

Estimate

SE

Z

P

source2 source3

0.4625 0.7243 0.0545 0.5907 1.3807 0.0755 0.4905 0.2289 0.5246 1.2777 1.3712

0.3040 0.3087 0.1475 0.1607 0.4323 0.2250 0.2332 0.2396 0.2425 0.3535 0.3378

1.5212 2.3461 0.3697 3.6766 3.1939 0.3357 2.1031 0.9553 2.1637 3.6146 4.0593

0.1282 0.0190 0.7116 0.0002 0.0014 0.7371 0.0355 0.3394 0.0305 0.0003 ,0.0001

source2 source3

1.5748 0.4987 1.5637 1.0318 0.9477 0.4776 0.2698 0.0529 0.2183 1.0997 0.4554

0.5665 0.5814 0.2435 0.2077 0.8101 0.2846 0.2519 0.3690 0.3284 0.4293 0.4009

2.7800 0.8578 6.4217 4.9672 1.1697 1.6784 1.0710 0.1432 0.6647 2.5614 1.1358

0.0054 0.3910 ,0.0001 ,0.0001 0.2421 0.0933 0.2842 0.8861 0.5062 0.0104 0.2560

source2 source3

0.2494 0.4850 2.2307 1.7306 0.4983 1.0141 0.0822 0.3087 0.0146 0.5877 0.5473

0.1431 0.1448 0.1777 0.1552 0.2066 0.2233 0.2157 0.2596 0.2371 0.3442 0.3282

1.7425 3.3498 12.556 11.150 2.4113 4.5411 0.3809 1.1892 0.0618 1.7074 1.6673

0.0814 0.0008 ,0.0001 ,0.0001 0.0159 0.0000 0.7033 0.2344 0.9508 0.0878 0.0954


SUPPLEMENT Cheatgrass demographic data and the R code to perform analyses (Ecological Archives C005-012-S1).


34
