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We have no competing interests to report.

Contributed to conception and design: PBA, AC

Contributed to acquisition of data: AC

Contributed to analysis and interpretation of data: AC, PBA

Drafted and/or revised the article: AC, PBA

Approved the submitted version for publication: AC, PBA

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.

Climate change threatens to exacerbate the impacts of invasive species. In temperate ecosystems, direct effects of warming may be compounded by dramatic reductions in winter snow cover. Cheatgrass (

Climate change (

In many temperate zones, winter snow cover plays a key role in regulating the function (

Cheatgrass (

Understanding limitations on cheatgrass performance at high elevations, and predicting how these limitations may be altered by climate change, requires disentangling the effect of abiotic factors that co-vary with elevation, such as temperature, precipitation, and snow pack. If temperature has a direct, limiting effect on cheatgrass performance, then warming should favor cheatgrass at high elevations regardless of snow cover. However, because a small temperature increase can trigger large reductions in snow cover (

Our objective was to experimentally test the effect of warming and loss of snow on cheatgrass population growth rates. We manipulated temperature and snow cover to evaluate the following hypotheses: (1) warming will improve cheatgrass performance, and (2) melting of snow will contribute to the positive effect of warming. We tested these hypotheses by monitoring population growth rate, a proxy for cheatgrass impact, and the vital rates that determine it: emergence, survival and fecundity. Emergence reflects how abiotic factors affect seed germination, survival reflects the effect of stress on established plants, and fecundity is a proxy for plant growth. Thus, the vital rate data provide inference about the mechanisms driving responses to the warming and snowmelt treatments.

The Green Canyon ecological station is located in Logan, Utah, USA, at 41°76′ N, 111°79′ W and at 1460m above sea level. The site is located on a flat alluvial fan and soil is a silt loam (

Growing season | Temperature (°C) | Precipitation (mm) | Snow fall (cm) | Mean snow depth (cm) |
---|---|---|---|---|

2010–2011 | 5.9 | 645 | 250 | 4.2 |

2011–2012 | 7.3 | 327 | 130 | 1.2 |

Average | 6.2 | 438 | 164 | 3.7 |

In September 2010, we implemented an experiment with three treatments replicated eight times: control, snowmelt and warming. Snowmelt and warming treatments were imposed with infrared heat lamps (Model HS-2420, Kalgo Electronics Co.; e.g. ^{-2}, much lower than cheatgrass dominated sites in the Great Basin where seed rain can range from 6000 (^{-2}. We planted cheatgrass at relatively low density to estimate population growth rate in the absence of intense intraspecific competition.

Three plots in each of the control, snowmelt, and warming treatments were randomly chosen for soil moisture and temperature monitoring. We used Decagon Devices EC-5 and 5TM soil moisture sensors and ECT temperature sensors connected to Em50 digital/analog and Em5b analog data loggers to measure soil moisture at 5 cm and 20 cm depth, soil temperature at 5 cm depth, and air temperature at 5 cm above the ground surface. Data loggers operated from December through the end of June and recorded data every hour by saving the average of the values observed by sensors in the previous 60 minutes.

We estimated the geometric population growth rate (λ) of cheatgrass in each quadrat as λ=n_{t+1}/n_{t}, where n_{t} is the number of seeds in the population at year t. n_{t} = 100 because every quadrat is planted with exactly 100 seeds at the beginning of the growing season. Therefore, λ = quadrat seed production/100. Quadrat seed production was estimated by multiplying the number of cheatgrass spikelets harvested in each quadrat by the average number of seeds per spikelet. The latter was estimated by subsampling five individuals per quadrat. ANOVA tests showed that the number of seeds per spikelet varied with treatment but not with seed provenance.

We followed the fate of seeds planted in each cell of the plastic grids to estimate the three vital rates that determine cheatgrass population growth rate:

Our estimate of λ assumes no carryover of seeds from one year to the next. Therefore, we consider dead those seeds that did not emerge the year we planted them. This assumption is supported by a buried bag experiment carried out in control and warming plots during the first growing season which showed that more than 99% of seeds germinated regardless of treatment. Even if the seed bank has little effect on population dynamics, natural dispersal could add seeds to our study plots. To account for contributions from naturally dispersed seeds, we subtracted the seed production in unplanted quadrats from the seed production of planted quadrats. We did this in two ways. First, we subtracted treatment-specific averages of seed production in unplanted plots. These averages were the predicted values of a linear model explaining seed production in unplanted quadrats as a function of treatment. Second, we subtracted the plot-specific seed production in unplanted quadrats. To check for differences between these two ways of accounting for naturally dispersed seeds, we fit models of λ using both of these estimations.

Our two estimates of λ, based on different methods of correcting for background emergence, yielded qualitatively identical figures (

Contrast | Estimate | z value | p value |
---|---|---|---|

2011 | |||

Snowmelt - Control | 1.2830 | 3.6198 | 0.0009 |

Warming - Control | 2.2198 | 6.2068 | <0.0001 |

Warming - Snowmelt | 0.9368 | 2.7490 | 0.0166 |

2012 | |||

Snowmelt - Control | 0.4984 | 3.0119 | 0.0069 |

Warming - Control | 0.1324 | 0.4658 | 0.8841 |

Warming - Snowmelt | −0.3661 | −1.2912 | 0.3905 |

Growing season (September through June) temperature, snow cover, precipitation and snow fall were measured at the Utah State University weather station, located 4 km from the experimental site (source: Utah Climate Center

Because we tested results averaged across the three ecotypes planted in each plot, before testing for treatment differences in population growth rate we verified that no ecotype x treatment interactions were significant. We did this because ignoring significant interactions could confound treatment effects. We tested for interactions with ecotype by fitting two year-specific models where we log-transformed λ and modeled it as a function of treatment, ecotype and their interactions, assuming normally-distributed errors.

We tested treatment differences in population growth rate and vital rates using linear mixed-models and Tukey’s Honestly Significant Difference (HSD) test for post-hoc comparisons. We log-transformed λ and fecundity and modeled them as normally-distributed variables. We modeled emergence, survival, winter survival, and spring survival with a binomial distribution. We fit λ and fecundity data with a linear mixed model and emergence and survival data with a generalized linear mixed-model with a logit link function. We modeled unequal variance for λ and fecundity because Bartlett homogeneity of variance tests for these variables were significant among treatments. All models were fit using treatment as a fixed factor and plot as a random factor. Note that the plot effect was estimated with data from all three planted quadrats located within each plot.

We employed a Life Table Response Experiment (LTRE) to estimate the contribution of each vital rate to the differences in λ among treatments. Following _{i} is one of the three vital rates and

Weather varied dramatically between the two growing seasons. The first year was cold and wet, and the second year was hot and dry (^{th} and 50^{th} percentile in the first year and above the 75^{th} percentile in the second year. Precipitation was above the 75^{th }percentile the first year and below the 25^{th} percentile the second year. Snow cover is common, but intermittent, at our site between the end of December and the end of February (

Panels (a) and (b) refer to the first and second growing seasons, respectively. Bars show the size of cheatgrass population growth rate (λ).

Panels (a) and (b) refer to probability of snow presence and average snow depth, respectively. Data comes from the Utah State University weather station, which is located 4 km from the experimental site.

The warming treatment increased air temperatures an average of 4.3 °C the first year and by 7 °C the second. The increase in the effect of infrared heaters in warming plots during the second year likely resulted from lower soil moisture decreasing evaporative cooling. Soil volumetric water content at 5 cm depth in the second year was on average 0.06 % lower than in the previous year. We found a negative correlation between soil moisture and the increase in temperature caused by warming treatments (

The effect of the snowmelt treatment on abiotic conditions was generally smaller. Loss of snow increased surface and soil temperature by ∼1 °C and had little effect on average soil moisture. Soil temperatures in snowmelt treatments were lower than in control plots only for a week, during January 2011 (

Cheatgrass’ population growth rate, λ, responded significantly to warming and/or snowmelt treatments in both the first (F_{2,21} = 18.5039, P = <0.0001) and second year (F_{2,21} = 4.6343, P = 0.0215) of the experiment. Tukey’s HSD contrasts show that the warming and snowmelt treatments significantly increased cheatgrass population growth rates relative to controls in all but one case. Warming plots had significantly higher population growth rates than all other treatments in the first year, but not in the second (

Vital rates also responded significantly to warming and snowmelt treatments. Relative to controls, warming increased fecundity and survival in the first year (Z = 4.1809, P = 0.0001 for fecundity; Z = 5.4699, P = <0.0001 for survival) but not in the second (Z = 1.4737, P = 0.2992 for fecundity; Z = -0.0552, P = 0.9983 for survival). Snowmelt increased survival in both years (Z = 4.4753, P = <0.0001 in 2011; Z = 3.7157, P = <0.0006 in 2012) and caused a marginally significant increase in emergence in the first year (

Vital rates from the first (a–c) and second (d–f) growing seasons. Letters denote statistically significant Tukey’s HSD contrasts. In particular, snowmelt vs. control (x), warming vs. control (y), and warming vs. snowmelt (z). The y-axis in each graph reports the vital rate’s unit of measure in brackets.

Panels (a) and (b) refer to the first and second growing seasons, respectively. Bars show the effect of treatments on vital rates by using controls plots as a reference point.

Including ecotype in the models of λ did not change the effect of treatments. Ecotype x treatment interactions were not significant (F_{4,42 }= 1.3186, P = 0.2788 in 2011; F_{4,42 }= 0.2022, P = 0.9357 in 2012) because ecotype effects were generally equal across treatments: high elevation ecotypes had higher λ regardless of treatment (

We found that warming increased cheatgrass population growth rate and that the magnitude of this increase depended on soil moisture. Warming effects were much stronger in the first year when soil moisture was high. In this year, warming increased cheatgrass population growth rate ten-fold. During the second year, low soil moisture likely limited growth and also amplified the effect of infrared heaters much more than we expected: December through June average temperature in warmed plots was up to ∼4.5 °C higher than in the same plots the previous year. Despite such high temperatures, cheatgrass still increased its population growth by 50% compared to controls, almost the same increase observed in snowmelt treatments (

In the first year, warming increased per capita growth rates mostly through its effect on fecundity. Previous work suggests that warming may increase fecundity by increasing both photosynthetic rate and nitrogen uptake. First, cheatgrass net photosynthesis increases with temperature to a peak at 25–30 °C (

An interaction between temperature and cheatgrass pathogens might offer an alternative explanation to differences in soil moisture for the interannual variability in the effects of warming. However, we are doubtful that our manipulations increased the prevalence or virulence of

Our data show that reduced snow cover contributes to the direct effects of warming by increasing cheatgrass seedling survival. In the first year, survival in both snowmelt and warming plots was two-fold higher than survival in control plots (

Our results imply that snow should limit cheatgrass population growth rate at sites where a significant proportion of plants overwinter as seedlings rather than seeds. This should be common across cheatgrass’ range, as indicated by several observational studies that found the bulk of cheatgrass seedlings generally emerges between fall and winter (

In portions of its range where cheatgrass overwinters as seed and emerges after snowmelt, changes in snow cover should have smaller ecological effects. Consistent with this interpretation, a recent study carried out at high elevations found that the effect of snow on cheatgrass is neutral (

We were surprised that survival was lower in warming than snowmelt plots in the second year (

Our data strongly support the prediction that warming will exacerbate cheatgrass impacts in sites and years when moisture is not limiting. First, warming has a positive direct effect on cheatgrass fecundity, consistent with the assumption that temperature limits this species’ performance at high elevations (

Population growth rate was estimated by subtracting seed production in unplanted quadrats to the seed production in planted quadrats. “Mean correction” shows population growth rates calculated by subtracting the treatment-specific mean of seed production in unplanted quadrats. “Plot correction” shows population growth rates calculated by subtracting the plot-specific values of seed production in unplanted quadrats.

The daily air temperature increase is the difference between temperatures in warming and control treatments. Temperatures were pooled across the three replicates per treatment which were equipped with a data logger.

Data are for the period from mid December to mid June.

Data are for the period from mid December to mid June.

Letters denote statistically significant Tukey’s HSD contrasts. In particular, snowmelt vs. control (x), warming vs. control (y), and warming vs. snowmelt (z). Bars represent survival rate.

Results from a model fit with population growth rate data calculated by subtracting the plot-specific values of seed production in unplanted quadrats to the seed production in planted quadrats.

The following dataset was generated:

Plant demographic data: Data available from the Dryad Digital Repository:

© 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.

We thank Mindi Lundberg, Kyle Young, Kevin Smith, Chelsea DeMarco and Molly Olson for providing field help. We thank John Stark, David Koons, Gene Schupp, Mevin Hooten, and two anonymous reviewers for insightful suggestions that greatly improved the quality of the original manuscript.

_{2}and climate change: dynamic global vegetation models