Plant Soil (2008) 304:103–115 DOI 10.1007/s11104-007-9525-9
REGULAR ARTICLE
Contrasting effects of resource availability and plant mortality on plant community invasion by Bromus tectorum L. E. Carol Adair & Ingrid C. Burke & William K. Lauenroth
Received: 23 August 2007 / Accepted: 12 December 2007 / Published online: 29 December 2007 # Springer Science + Business Media B.V. 2007
Abstract The positive effect of disturbance on plant community invasibility is one of the more consistent results in invasion ecology. It is generally attributed to a coincident increase in available resources (due to the disturbance) that allows non-resident plant species to establish (Davis MA, Grime JP Thompson K, J Ecol 88:528–534, 2000). However, most research addressing this issue has been in artificial or highly modified plant communities. Our goal in this study
Responsible Editor: Tibor Kalapos. E. C. Adair (*) Department of Ecology, Evolution, and Behavior, University of Minnesota, 1987 Upper Buford Circle, Saint Paul, MN 55108, USA e-mail:
[email protected] I. C. Burke : W. K. Lauenroth Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA I. C. Burke : W. K. Lauenroth Department of Forest, Rangeland, and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523, USA I. C. Burke : W. K. Lauenroth Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
was to investigate the interactive effects of resource availability and plant mortality disturbance on the invasion of natural plant communities. We conducted a series of experiments that examined the response of Bromus tectorum L., a highly invasive annual grass, to experimentally created gradients of resource availability [nitrogen (N) and water] and resident plant species mortality. We found that B. tectorum biomass was co-limited by N and water. Biomass at the end of the growing season was a saturating function (i.e., increased to a maximum) of water, which determined maximum biomass, and N, which determined the rate at which maximum biomass was attained. Despite that fact that plant mortality increased N availability, it had a negative impact on invasion success. Plant mortality also decreased foliar cover, standing dead biomass, and soil cover by litter. In harsh environments, removing foliar and soil cover may increase germination and seedling stress by increasing soil temperatures and water loss. Across all treatments, B. tectorum success decreased with decreasing foliar cover and standing dead biomass. This, in combination with the strong limitation of B. tectorum biomass by water in this experiment, suggests that our plant mortality disturbance removed soil cover that may have otherwise aided B. tectorum invasion into this semi-arid plant community by reducing water stress. Keywords Cheatgrass . Exotic species . Invasive plants . Nitrogen . Disturbance . Water
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Introduction Disturbance and resource availability have both been linked to plant community invasibility (Levin and D’Antonio 1999; Lonsdale 1999; Stohlgren et al. 2001; Thompson et al. 2001), but most recent theories have focused primarily on the role of resource availability, hypothesizing that community invasion is determined by spatial and temporal variation in limiting resources (Davis et al. 2000; Foster and Dickson 2004). In this framework, the role of disturbance is to increase resource availability, which, in turn, facilitates community invasion. Disturbance may increase resource availability through a number of pathways: soil disturbances may unlock nutrients stored in soil organic matter by changing soil microclimate, microbial community structure, or by making new substrate available for decomposition by breaking up soil aggregates (Elliot 1986; Kay 1990; Davis et al. 2000; Jackson et al. 2003; Kristensen et al. 2000; Norton et al. 1990, 2003, 2004, 2007); plant mortality may decrease plant uptake and release nutrients previously stored in perennial plant biomass (Greenlee and Callaway 1996; Kitzberger et al. 2000; Norton et al. 2007); and anthropogenic activities may increase external inputs of a limiting resource (e.g., N deposition). Although disturbance types are often coincident in natural plant communities, individual disturbance types may differentially influence invasion (e.g., Beckstead and Augspurger 2004). Thus, disturbances, or particular elements of disturbances, may interact, obscuring the specific influence of each, and ultimately hampering our understanding of the mechanisms that drive successful exotic species invasions. The invasion of the annual grass Bromus tectorum into the western United States is one of the bestdocumented and most damaging plant invasions in human history (Mack 1981). At least 40 million hectares of original sagebrush-perennial bunchgrass vegetation in the Intermountain West has been replaced by monocultures of B. tectorum (D‘Antonio and Vitousek 1992), with often drastic consequences for fire regimes and successional pathways (West 1979; Whisenant 1990; Peters and Bunting 1994). The spread and success of B. tectorum in this region is thought to be due largely to disturbances in the form of grazing and increased fire frequency (Mack 1986; D’Antonio and Vitousek 1992; Mack et al.
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2000). In some cases, B. tectorum success has been linked to increases in nitrogen (N) (NPS 2000; Lowe et al. 2003; Monaco et al. 2003; Beckstead and Augspurger 2004) and water (Link et al. 1995; Chambers et al. 2007), but the nature of the response of B. tectorum to increasing levels of these resources is not known (e.g., linear, nonlinear or threshold dynamics). Despite somewhat different climate patterns and plant community characteristics, B. tectorum has recently made inroads into montane ecosystems, including the foothills of Colorado’s Front Range (Adair 2005; Brown and Rowe 2004). Most arid and semi-arid locations where B. tectorum invasion is successful receive precipitation primarily during the winter and early spring (Knapp 1996; Bradford and Lauenroth 2006). Colorado’s low elevation foothills also receive a large proportion of yearly rainfall in the spring, but in contrast, Colorado’s foothills continue to receive moderate amounts of precipitation throughout the summer and fall and are driest during the winter months (see methods for further detail; NCDC 2005). Like the native vegetation of the sagebrush steppe, which consists primarily of perennial C3 bunchgrasses and shrubs, B. tectorum invaded foothills communities are also dominated by C3 perennial bunchgrasses, but may have a smaller shrub component and may also contain C4 grasses (Rickard and Vaughn 1988; Adair 2005). In spite of these differences, B. tectorum has become relatively successful in Colorado’s foothills (Adair 2005; Brown and Rowe 2004), perhaps because this area has been subjected to high levels of disturbance through grazing, land use change, recreational activities, and altered resource availability due to increased N deposition (Baron et al. 2000) and/or changes in precipitation and soil water availability (Stohlgren et al. 1998; Chase et al. 1999). Given the historical role of disturbance in facilitating B. tectorum invasion, determining the response of B. tectorum to various levels of these factors is essential for understanding how to effectively manage or determine the susceptibility of un-invaded montane ecosystems, such as Colorado’s low elevation forests, to extensive invasion. Our goal in this study was to investigate the individual and interactive effects of resource availability (water and N) and disturbance on B. tectorum success in the foothill meadow communities of
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Colorado’s Front Range. While most experimental disturbance treatments allow the effects of plant mortality and physical soil disruption to interact (e.g., Burke and Grime 1996), we made an attempt to disentangle the potentially different effects of these two disturbance types, and focused on only plant mortality. Our overall hypothesis was that increasing resource availability, whether through resource additions or reduced plant uptake, would increase the success of B. tectorum invasion either linearly or with a saturating response (increasing with resource additions or plant mortality disturbance to a maximum biomass) due to limitation by other resources or inherent plant characteristics (e.g., maximum growth rates; Table 1; Chapin et al. 1986). Specifically, we predicted that: (1) B. tectorum establishment and growth in this ecosystem would be co-limited by water and nitrogen and that (2) plant mortality disturbance would increase N availability by reducing plant uptake. Given the truth of these predictions, we predicted that (3) increasing levels of plant mortality disturbance alone would increase the success of B. tectorum invasion due to the associated release of resources, and that (4) plant mortality disturbance would amplify the effect of increasing resource availability by reducing resource competition.
Methods Site locations and experimental design We established four sites in low elevation ponderosa pine forests/woodlands (2,029–2,063 m) in the ArapahoRoosevelt National Forest in Buckhorn Canyon,
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Larimer County, Colorado, USA (40.57°N, 105.36°W). In this ecosystem, mean annual temperature decreases and mean annual precipitation increases with elevation, ranging from 8.9°C and 380 mm in Fort Collins (at the base of the foothills, 1,530 m), to 7.9°C and 559 mm at Buckhorn Mountain (2,295 m), a weather station located near but higher than our sites (Western Regional Climate Center 2006). In Colorado’s northern foothills, winter months are the driest, receiving approximately 10 mm of precipitation per month (December through February), while spring and early summer months are the wettest (approximately 55– 70 mm per month in May through July; long term climate data from eight sites located between 1,500– 2,295 m in along the Front Range; NCDC 2005). The remaining months receive between 20–50 mm per month. June through August are the warmest months (18–20°C) while November through March are the coldest (−1–5°C; NCDC 2005). Sites were located in grassy openings within ponderosa pine forests, on south facing slopes, and were located within a 5 km2 area. While periodic grazing has occurred in this area, we found no evidence of recent cattle grazing in our sites. Bromus tectorum has invaded this canyon, but all of our plots were un-invaded at the beginning of our study. Sites were chosen randomly from a larger group of potential sites located in the area. All sites had similar vegetation and soils. Sites were dominated by the C3 grasses Achnatherum robustum, Hesperostipa comata, Poa pratensis, and Koeleria macrantha with lesser amounts of the C4 grasses Bouteloua gracilis and Muhlenbergia montana. At each site, C3 and C4 grasses made up approximately 20–55 and 1–8% of total foliar cover at
Table 1 Mathematical expressions for hypotheses of linear and saturating increases in B. tectorum biomass (g dry weight at the end of its growing season) with resource availability or plant mortality disturbance (linear and natural growth equations). Plant mortality disturbance (% of plant cover removed) may also affect B. tectorum biomass negatively in a linear equation Linear increase with resource availability or plant mortality disturbance (plant mortality term may be negative)
B ¼ b0 þ b1 R1 B ¼ b0 þ b1 R1 þ b2 R2 B ¼ b0 þ b1 R1 þ b2 R2 þ b3 R1 R2
Saturating increase with resource availability or plant mortality disturbance; natural growth equation B ¼ b0 þ b1 1 eðb2 R1 Þ ðb2 R1 Þ B ¼ b0 þ b1 R2 1 e B ¼ b0 þ b1 1 eðb2 R1 R2 Þ
In the nonlinear saturating function (or natural growth equation), b1 describes the maximum biomass, and b2 describes the rate at which the curve approaches the biomass maximum, (i.e., how quickly B. tectorum approaches its maximum biomass in response to increasing levels of resources). B=log(0.001+dry weight (g) of B. tectorum), R1 and R2 =N, water or plant mortality. AIC model selection was used to determine the inclusion of site effects in the linear and nonlinear models, and was also used to determine placement of site effects within the nonlinear models (e.g., affecting b0, b1, or b2, in the saturating function)
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each site, respectively (data from June 2004 foliar cover measurements of undisturbed plots, see below for further details regarding cover estimation methods). Common forbs at all sites included Artemisia frigida, Artemisia ludoviciana, and Heterotheca villosa. Soils at these sites are well-drained cobbly and stony sandy loam, with low available water capacity (0.08 cm water cm−1 soil across all layers; Soil Survey Staff 2007). Our texture measurements agreed with these classifications: surface soils from our sites were sandy loams, containing 64% sand, 18% silt, and 18% clay on average (0–15 cm; hydrometer method). Although we did not measure soil concentrations of N or organic C in these soils, similar sites across the foothills of Colorado contain low levels of total N (average of 0.18% or 230 g N m−2 in 0– 15 cm) and mineral N (0.07 g N m−2, 0–5 cm; Adair 2005), and the Larimer County Soil Survey estimates these soils to contain approximately 1.3% organic matter (across all layers; Soil Survey Staff 2007). At each of the four sites, we set up experiments in which we manipulated N, water, and disturbance via plant mortality; each factor was implemented at four levels including the control (zero or ambient). We designed the experiments as three full factorial experiments: additions of N and/or water (NxW), plant mortality and/or N additions (PMxN), and plant mortality and/or water additions (PMxW). Each experiment consisted of all combinations of the four levels (including zero) of two of the three variables (water, N, and/or plant mortality disturbance). The space and material requirements for these experiments did not allow us to replicate the design within individual sites, nor complete a full 3-way factorial. We established 16 1 m2 plots at each site for each factorial experiment (a total of 48 plots per site) in early August of 2003. The experiment was initiated by creating the plant mortality gradient in late August and planting locally collected B. tectorum seeds in late September 2003. Water and N treatments were applied for the remainder of the fall 2003 growing season and the following spring 2004 growing season. We created a gradient of plant mortality disturbances by removing 0, 20, 60, or 100% of the total plant cover measured approximately 5 cm above the soil (i.e., vertical projection of the innermost perimeter of foliage) in each plot, which we recorded in each plot 2–4 days prior to creating this gradient. At this time, we also recorded the percent cover of litter, duff, bare
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soil, and rock for each plot. Our plant mortality disturbance was designed to free plant resources (used by resident plants) without adding resources or confounding effects from other disturbance types (e.g., soil disruption). Resident plants were removed by randomly selecting a 10×10 cm square in a 1 m2 frame placed over the plot. The plant occupying this section of the plot was removed by clipping it to approximately one inch above the soil surface. If more cover removal was required to attain the desired plant mortality level (i.e., 20 or 60% of total plant cover), we randomly selected another square (and associated plant). If the chosen plant would remove more cover than was required, another square was randomly selected instead. The vegetation we removed consisted mainly of native bunchgrasses (e.g., Hesperostipa comata, Muhlenbergia montana, Achnatherum robustum, Bouteloua gracilis) and large native forbs (e.g., Heterotheca villosa and Artemisia frigidia). Following clipping, we sprayed what remained of the clipped plant with a small amount of the herbicide glyphosate, N-(phosphono-methyl)-glycine, trade name Roundup® (glyphosate; Monsanto Co., St. Louis Missouri, USA), being careful not to spray adjacent plants or the soil. However, glyphosate residue may remain in soil or be exuded from the roots of treated plants and thus impact non-target plants (Coupland and Caseley 1979; Rodrigues et al. 1982; Evans and Dexter 2006). Coupland and Lutman (1982) found no effect on non-target plants 3 weeks after treatment of neighboring plants, and Cornish and Burgin (2005) found that transplanting seedlings into glyphosate-treated loamy sand soils (as our sites were) after two or more weeks had no effect on seedling growth. Therefore, we waited 4 weeks before planting B. tectorum seeds in our three experiments: the plant mortality gradient was established in late August, at least 4 weeks prior to seeding the plots with the non-native B. tectorum on September 25–27. During these 4 weeks, our sites received approximately 45 mm of rain (Buckhorn Mountain 1E, NCDC 2005). Approximately 1 month after creating the plant mortality gradient, we planted locally collected B. tectorum seeds at a depth of 5 mm in a 50×50 cm area located in the center each plot, using a grid of the same size divided into 25 10×10 cm squares. We planted two seeds at the corners of each square
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(36 corners for a total of 72 seeds per plot). Seeds were planted in each plot coincident with the onset of B. tectorum germination, from September 25–27, 2003. Nitrogen and water treatments began shortly after planting. To minimize edge effects, we applied all treatments to the entire 1 m2 plot. We added a total of 0, 1, 3, or 5 g N m−2 in three additions from September 2003 to May 2004 (September 30, 2003, March 3, 2004, and May 5, 2004) as ammonium nitrate. Ambient levels of wet plus dry N deposition for the Colorado Front Range are approximately 6 kg ha−1 year −1 (Baron et al. 2000; Fenn et al. 2003), which amounted to about 0.45 g N m−2 over the duration of our experiment. We used this value to represent the level of N received by our 0 N addition plots. We added water at rates of 0, 2, 5, or 10 mm week−1. The 10 mm week−1 treatment doubled the amount of water in precipitation alone (288 mm from October to June), as measured by rain gauges we placed at two of the sites. We added water weekly for 5 weeks in the fall of 2003 (beginning September 30) and 10 weeks in the spring (beginning March 15, 2004). Water was transported to each site via foot or water pump and carefully applied with a watering can, to prevent soil compaction and runoff. Precipitation was measured weekly at sites 1 and 2 using rain gauges. These data correlated well with data from the closest weather station located at Buckhorn Mountain (NCDC 2005). Precipitation measured over the duration of the experiment (October 2003–June 2004) was 288 mm. We used this value as the amount of water received by 0 addition plots. To characterize B. tectorum success in each plot, we harvested all aboveground biomass of B. tectorum on June 30–July 2, 2004 (after seed set when the plants began to cure). Biomass was air dried and weighed to determine dry B. tectorum biomass in each plot. Prior to and immediately after B. tectorum harvest, we measured foliar cover (vertical projection of all exposed leaf area) by species, litter cover, and percent bare soil in each plot (June 6–9 and July 7– 15). For the June sampling, foliar cover data do not include percent cover of B. tectorum. We monitored available mineral N in each treatment plot using Plant Root Simulator (PRS) probe– ion exchange membranes (Western Ag Innovations, Inc., Saskatoon, Canada). The PRS probes were sent to Western Ag Innovations, Saskatoon, Canada for
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extraction. At Western Ag, the probes were extracted with 17.5 ml of 0.5 M HCl for 1 h in a zip lock bag, and the extract was analyzed for NH4+ and NO3− using a Technicon autoanalyzer (Bran and Lubbe, Inc., Buffalo, NY). This allowed us to investigate not only how well N additions increased available N, but also how plant mortality disturbance changed available N. We installed a pair of probes (one anion and one cation probe) in each plot on March 9, 2004 and left them in place until May 26, 2004, after B. tectorum had set seed. Results are reported as μg N 10 cm−2 probe surface area over the 79-day burial period. Data analysis Using the N probe data, we used linear regression to test our hypothesis that N availability increases with plant mortality disturbance level (proc REG, SAS Version 9.1). Aboveground B. tectorum biomass was linearly correlated with both the number of individuals in a plot (R2 =0.78) and seed production (R2 = 0.97; approximately 1/2 of dry biomass). We therefore used biomass to describe invasion success because it was an indicator of current year success as well as the potential future success of B. tectorum in our plots. B. tectorum biomass was log(0.001+x) transformed to improve normality, where x was the dry biomass per plot in grams. Because several of our hypotheses included nonlinear models with multiplicative effects among treatments, we chose to characterize the zero level of each treatment as either a naturally occurring amount (detailed above for N and water treatments) or a very low level (the case for plant mortality disturbance only; we used a value of 1%, insignificant in comparison to the treatment levels that are detailed above). If we had used zero, many of the equations would = 0 due to multiplication effects. We used Akaike’s Information Criterion model selection modified for small sample sizes (AICc) to choose the model (linear or saturating) and combination of variables (N, water, and/or plant mortality) that best explained the success of B. tectorum in each experiment. AICc is used to rank a set of a priori models based on the support for each in the data (Burnham and Anderson 2002), and thus allowed us to determine if our treatments had significant effects on B. tectorum invasion (as in a classic ANOVA), and
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also to select which of our hypothesized curves (linear or saturating) best described how the success of B. tectorum invasion changed along our gradients of resource availability and/or plant mortality disturbance. We ranked our models (Table 1) by comparing the AICc values; the model with the lowest AICc value had the most support in the data. The difference between the AICc value of the best model and the values of the models ranked below it (Δr), provides information about whether the other models in our set are close competitors to the best model: the Δr of the best model is zero; models with a Δr10 have essentially no support in the data when compared to the better models (Burnham and Anderson 2002). This methodology also provides us with information on model selection uncertainty by calculating Akaike weights (wr) for each model. Akaike weights are the probability that the best model would again be selected as the best model, given the same set of models and a new set of similar but independent data (Burnham and Anderson 2002). We included site as a fixed effect and model selection was used to determine inclusion and/or placement of the site effect within the nonlinear models (e.g., affecting the intercept, b0, maximum, b1, or slope term, b2, in the saturating natural growth equation; proc NLIN, SAS). We selected models from each set that met the SAS default convergence criterion (proc NLIN, SAS), had a Δr of less than 3, and an R2 of 0.2 or greater. These analyses allowed us to determine if mineral N increased with plant mortality, which (if either) resource (water or N) was more limiting to B. tectorum success and examine how B. tectorum biomass changed with increasing water and nitrogen availability, as well as how plant mortality disturbance influenced B. tectorum invasion alone or in combination with N or water.
Results Co-limitation of B. tectorum by N and water As we predicted, B. tectorum was most successful in treatments that included both water (W) and nitrogen
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additions, indicating that B. tectorum biomass was limited by a combination of N and water availability. In the NxW experiment, two saturating equation models and one linear equation model had good support (Δr
