Northeastern Naturalist 67 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 Grazing and the Coupling of Biodiversity in Vascular Plant and Soil Microbial Communities Caroline B. Girard-Cartier1,* and Gary S. Kleppel1 Abstract - This study sought to ascertain how different grazing management protocols affect the coupling between soil microbial and vascular plant communities. Changes in microbial and plant communities were observed over a grazing season (Spring 2013–Spring 2014) at 2 previously ungrazed agricultural sites—a moist lowland (Longfield Farm) and a drier upland (Normanskill Farm)—near Albany, NY. Each landscape was divided into 6 fenced enclosures (paddocks). One paddock at each farm was managed by a managementintensive grazing (MIG) protocol, which employs high stock density and frequent rotations. A second paddock was managed by continuous grazing (CG) at lower (conventional) stock density. A third paddock at each site was left ungrazed (U). Three “simulation” paddocks were used to explore the underlying dynamics of grazer-microbe-plant interactions. By spring 2014, plant species richness (S) was significantly higher (t-tests: P < 0.05) in the MIG paddocks at both farms. At Normanskill Farm, S was correlated with both microbial diversity and biomass, while at Longfield Farm, S was independent of microbial diversity and biomass but varied directly with soil moisture. Our findings suggest that while MIG leads to increased S relative to CG, different forcing factors may be responsible for the enhancements in upland and lowland systems. Introduction Historically, grassland ecosystems dominated by vast herds of ungulates covered much of the Earth’s surface (Frank et al. 1998). While many of these ecosystems are now gone, grasslands still constitute one third of the Earth’s land mass (Knapp 2001, Suttie et al. 2005) and are, for the most part, being grazed by domesticated livestock (Frank et al. 1998, Suttie et al. 2005). What is now the northeastern United States was forested and not part of the American grassland biome. However, during the 17th–19th centuries, European settlers and their descendants, cleared much of the forest for pasture, cropland, and orchards. Although a great deal of that farmland has been abandoned and has reverted back to forest, it is estimated that approximately 11.4 million acres of land in the Northeast is currently used as farmland, and that more than 4 million acres are maintained as pasture (Osteen et al. 2012). Efforts to understand the modern ecosystems of the northeastern United States should consider agricultural grazing as a significant contributor to the dynamics of these systems. Perceptions of how agricultural activities affect ecosystem functioning are changing in the Northeast and elsewhere. Early studies suggested that grazing by 1Department of Biological Sciences, University at Albany, SUNY, Albany, NY. *Corresponding author - cgirard@albany.edu. Manuscript Editor: Elizabeth N. Hane Natural History of Agricultural Landscapes 2017 Northeastern Naturalist 24(Special Issue 8):67–85 Northeastern Naturalist C.B. Girard-Cartier. and G.S. Kleppel 2017 68 Vol. 24, Special Issue 8 livestock has a negative or, at best, neutral effect on plant communities (Brady et al. 1989, Daubenmire 1940, Reynolds and Trost 1980, Rummell 1951). Increasingly agricultural practices are focusing on creating a more benign relationship with wild systems (Fischer et al. 2008, Kleppel 2014). For example, between 2000 and 2010, the amount of organically certified cropland and pasture in New York State increased by 304% (NASS 2012). Livestock management techniques are increasingly focused on creating positive responses by plant communities to grazing (Collins et al. 1998, Dupre and Deikmann 2001, Hickman et al. 2004, Klimmek et al. 2007, Pavlu et al. 2007). The way land is managed for livestock relative to its natural attributes will determine the structure of the plant community and ultimately the functionality of the ecosystem. The impact of grazing on plant diversity depends on numerous factors (Bruun et al. 2006, Litaor et al. 2008, Roem and Berendse 2000, Wang et al. 2003, Wilson et al. 1990, Zhao et al. 2005). The model of Milchunas et al. (1998) broadly predicts plant species richness (i.e., the number of species; S ) in grassland systems from a knowledge of regional soil moisture, grazing intensity, and the historical connection between the plant community and co-evolving grazers. However, while many studies of the effects of grazing on plant diversity have been conducted on pastures and rangelands in the western United States (Biondini et al. 1998, DiTomaso 2000, Hickman et al. 2004, Kauffman et al. 1983, Reeder and Schuman 2002, Rook et al. 2004, Sanderson et al. 2005, Teague et al. 2011, West 1993), less is known about the effects of grazing on plant communities in northeastern pastures (Tracy and Sanderson 2000). Several studies suggest that the effect of grazing on biodiversity in plant communities depends on the number or mass of livestock per unit of pasture or rangeland area (i.e., stock density). It was thought that as stock density increases, plant diversity would decline (Bakker 1989, Hodgson and Illius 1996, Van Wieren 1995). Thus, at lower stock densities, plant diversity should be relatively high (Hobbs and Huenneke 1992). However, in agricultural systems, the opposite has often been observed (Teague et al. 2009). In most studies, few details are offered about the protocols used to manage the livestock on pasture or rangeland. Many of those that do provide such information indicate that the livestock was allowed to graze continuously, often at relatively low stock densities (less than ~2.5 tons ha-1) over large areas for extended periods of time with little or no rotation of animals to new grazing areas. Such management protocols result in selective grazing, patchy impacts on the forage and, ultimately, overgrazing and loss of soil structure (Savory and Butterfield 1999, Teague et al. 2011). In eastern New York State, Kleppel et al. (2011) used management-intensive grazing (MIG; Gerrish 2004), in which densely stocked animals are moved at high frequency (less than 1–3 days) through a system of partitioned sub-pastures (paddocks), to rapidly restore biodiversity to a variety of landscapes infested with invasive plant species. These studies support long-term observations by farmers and ranchers that MIG improves pasture quality (see Flack 2016, Gerrish 2004, Salatin 1996). Similar studies by Teague et al. (2011) in southwestern rangelands have supported the hypothesis that frequent rotation of Northeastern Naturalist 69 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 livestock results in positive changes in biodiversity within the plant community. A mechanistic understanding of ungulate–plant community dynamics is still emerging, however. The importance of the soil microbes to those dynamics is widely recognized, and grazing affects the biomass and diversity of soil microbial communities (Bardgett and Leemans 1995, Bardgett and Wardle 2003, Bardgett et al. 2001, Clegg 2006, Frank and Groffman 1998, Ingram et al. 2008, Liu et al. 2012, Patra et al. 2005). A large, and still-growing body of research on the plant–microbe coupling in grazed ecosystems suggests that microbial biomass increases with grazing (Frank and Groffman 1998, Liu et al. 2012, Patra et al. 2005, Wang et al 2006), though some studies suggest otherwise (Holt 1997, Kieft 1994, and Tracy and Frank 1998). As with vascular plant communities, the effects of livestock on microbial communities and soil ecosystems seem to depend, in part, on grazing intensity (Holt 1997, Ingram et al. 2008), though exactly how remains unclear (Bardgett and Wardle 2003). Over the past few decades, the understanding of what constitutes “intensity” for grazed ecosystems has been changing. New grazing protocols such as MIG and holistic planned grazing (Flack 2016; Gerrish 2004; Savory and Butterfield 1999, 2016) may allow a clearer picture to emerge of the interactive coupling between grazers, plants, and microbes in the soil. However, to our knowledge, these interactions have not yet been systematically explored in the Northeast. In this paper, we examine the nature of the coupling between the vascular plant and microbial communities as a function of different grazing protocols in an upland and a lowland pasture in eastern New York State. We compared management intensive grazing (MIG), in which sheep, stocked at high density, were rotated frequently; and continuous grazing (CG), in which sheep stocked at lower densities were allowed to graze without rotation until they depleted the resource. We also compared the responses of microbial and plant communities in grasslands that were grazed to those in ungrazed reference paddocks (U). Field-Site Description Two sites were selected for this study. The first was a low-lying, old-field located at Longfield Farm (LFF) in Altamont, NY (42°41'58.67"N, 74° 04'21.05"W) that had not been grazed for >30 years. The soil at LFF is poorly drained Angola silt loam, overlying shallow shale bedrock. The vegetation is a mixture of wet meadow and upland graminoids, particularly sedges (e.g., Carex vulpinoidea Michx. [Fox Sedge], Cyperus esculentus L. [Yellow Nutsedge]) and herbaceous forbs, including several legumes, such as Trifolium pratense L. [Red Clover]) and Lotus corniculatus L. (Birdsfoot Trefoil). Succession to shrub-scrub and, ultimately, lowland conifer and hardwood forest has been controlled by annual mowing. The second study site was located at Normanskill Farm (NKF) in Albany, NY (40°42'46.02"N, 74°00'21.39"W), on a portion of the landscape that had not been grazed in >5 years. The soil is moderately well-drained Hudson silt loam overlying shale bedrock. The vegetation is dominated by pasture grasses (e.g., Dactylis glomerata L. [Orchardgrass]) and native and exotic forbs. A more complete description Northeastern Naturalist C.B. Girard-Cartier. and G.S. Kleppel 2017 70 Vol. 24, Special Issue 8 of the plant communities at the 2 study sites and the changes in these communities over the course of the grazing season are provided in the Resul ts section. Methods Experimental design The study was conducted from June 2013 to July 2014. At each study site, 3 paddocks (~200 m2 each), designated for management intensive grazing (MIG), continuous grazing (CG) and no grazing (U), were delineated using Electronet® temporary fencing (Premier 1 Supplies, Washington, IA) charged at 1 joule with a PRS 100 energizer with solar battery recharge (Premier 1 Supplies). Stock densities (tons of live weight per hectare) were 8 tons ha-1 and 1.5 tons ha-1 in MIG and CG treatments, respectively. We moved sheep in both MIG and CG groups from their paddocks to a barn each evening to reduce the risk of predation, and then in the morning returned the sheep were returned to their paddocks for ~12 hours. We rotated the sheep in the MIG group out of the paddock (into the larger paddock system of the farm) after 1–3 days, and the vegetation was “rested” for 12–28 days before we returned the livestock to the paddock, following the basic protocol described by Gerrish (2004). Three rotations through the MIG paddocks were completed during the 2013 grazing season. CG sheep were allowed to graze in the same paddock until they had depleted forage. To avoid food deprivation and potential long-term pasture damage, we removed the sheep from the CG paddocks when the average vegetation height was less than 2 cm. In addition to paddocks for MIG, CG, and U treatments, we created paddocks for 3 “simulation” treatments to explore the underlying dynamics of grazer– microbe–plant interactions. We sought to segregate the effects of grazing and fertilization (urine and feces) on microbe–plant–grazer coupling. The simulations were: (1) vegetation clipped manually (C), (2) manure in the form of feces and urea added (M), and (3) a combination of clipped and manure added (CM). Clipping (in C and CM paddocks) involved mechanically mowing the vegetation to a height of 4–6 cm on the days that sheep were present in the MIG paddock. The manure (M) treatment involved the manual distribution of urea solution and feces within the paddocks on the days when the sheep were grazing in the MIG paddock. The concentrations of urea and manure deposited in the M and CM paddocks were based on the average daily manure and urea production of livestock in the MIG paddock at a stock density of 8 tons ha-1 (Barker and Walls 2002). Thus, we distributed 4 kg of manure and 2.5 L of 6.5% aqueous urea solution in these paddocks on these days. Sample and data collection We conducted detailed plant community composition surveys in each treatment paddock prior to the deployment of livestock in the spring of 2013 and ~1 year later in the spring of 2014. The pre-experiment surveys were conducted on 6 June 2013 at LFF and on 12 June 2013 at NKF. We reandomly deployed a 0.25-m2 quadrat in each paddock 6 times and identified to lowest taxonomic category (species where possible) all plants within the quadrat. Data were never collected within 1 m of Northeastern Naturalist 71 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 fence lines. The post-experiment surveys were conducted on 27 May 2014 at LFF and 29 May 2014 at NKF. We also measured a suite of environmental variables on the same dates that we conducted plant community surveys. These included: soil temperature, soil pH, light penetration, vegetation height, and plant biomass. We placed a standard laboratory thermometer ~3 cm into the ground to measure soil temperature, and measured soil pH with a waterproof double junction pH Test® 10 (Oakton) using the method of Sikora and Kissel (2014). We took light measurements with an INS DX- 100 Digital Lux Meter (accuracy 2%) (Takemura Electric Works, Ltd., Toshima-ku, Japan), measured vegetation height with a meter stick, and estimated plant biomass using an NRCS/USDA “grazing stick” (Smith et al. 2010). We collected 15 soil cores (~13 cm deep) from each of 3 randomly selected locations in each paddock with a JMC 91 soil corer in the spring of 2013 and the spring of 2014. The cores from each sampling location were mixed, and 3 samples were transferred to plastic freezer bags, returned to the laboratory, and processed to determine soil moisture and microbial community composition as follows: 10 g of soil from each bag was dried at 100 °C for 48 hrs and then reweighed. We calculated soil moisture as M = (1 – Wd / Ww) * 100%, where M is soil moisture, and Wd and Ww are dry and wet weights of the soil sample, respectively. We characterized soil microbial community composition by phospholipid fatty acids (PLFA) analysis. Specific PLFAs are characteristically found in specific groups of bacteria, fungi, and protistans, and hence they represent biomarkers for these groups (Frostegård et al. 2011). PLFAs were extracted from the soil as fatty acid methyl esters (FAMEs) (Bligh and Dyer 1959) and isolated by gas chromatography with a modification of the method of Hamel et al. (2006). Chromatographic peaks representing specific fatty acids were identified by their characteristic retention times and quantified (ng biomass/gram soil) by regression of serially diluted standards. The limitations of the PLFA analysis have been discussed by Frostegård et al. (2011). For instance, modern gene-coding methods for ribosomal RNA provide a greater detail on the structure of the microbial community. However, this method tends to be costly, and while useful for addressing many kinds of problems, would not necessarily provide better answers to questions that we addressed in this study. PLFA analysis is a rapid, relatively inexpensive, and sensitive way of detecting responses by microbial communities, at the level of functional groups, to physiochemical and biological variability in soil ecosystems. We characterized 5 microbial functional groups by the analysis: actinomycetes, Rhizobia, arbuscular mycorrhizae, saprophytes, and Protozoa. Functional group diversity was expressed using the Shannon-Weaver index (Shannon and Weaver 1949): H' = -S (Pi ln Pi), Northeastern Naturalist C.B. Girard-Cartier. and G.S. Kleppel 2017 72 Vol. 24, Special Issue 8 where Pi = an importance value (biomass) associated with functional group i divided by the sum of the importance values (total biomass) of all functional groups. Frostegård et al. (2011) criticized the use of the Shannon-Weaver index as a measure of microbial diversity because it suggests that microbial groups identified with PLFA are treated as species. However, we were careful to use H' to characterize functional group diversity, employing functional group biomass as the importance value. Nowhere do we suggest that PLFA data provide information about the species richness of a particular functional group or groups. Statistical analysis The present experiment involved pseudoreplication, which unfortunately limits the generality of our results and conclusions. This problem is not atypical among small studies, however, and as Patra et al. (2005) demonstrated in a study of grazing, microbes, and nutrient dynamics in a pasture on the north coast of France, useful observations can be obtained so long as the limitations imposed by pseudoreplication are recognized. Statistical analysis was performed with the SPSS 22 package. We used repeated measures ANOVA to identify the influence of site (LFF and NKF), treatment (MIG, CG, U, CM, C, M), and time (2013–2014), and the interaction among these factors, on S, and calculated annual means for each treatment paddock at each site for use in the ANOVA. We conducted student’s t-tests to identify differences in mean S, soil moisture, microbial diversity (H'), and microbial biomass between sites, and also to identify differences in S, ΔH', and change in microbial biomass between grazing protocols (and ungrazed) as well as between MIG and each simulation (CM, C, M) at each site. We used correlation analysis to identify relationships between plant and microbial community structure and the physical environment, and quantified correlated variables by least squares regression. The regression analyses were performed separately for each site individually as well as with data from the sites combined using the mean values of variables for each of the 6 treatment paddocks (MIG, CG, U, CM, C, M) in 2013 and 2014. Results Vascular plant communities at Longfield and Normanskill farms Both sites were dominated by graminoids. At LFF, Orchardgrass, Phleum pratense L. (Timothy), Lolium perenne L. (Perennial Ryegrass), and Poa pratensis L. (Kentucky Bluegrass) were dominant. Several sedge species (Fox Sedge and Yellow Nutsedge) were co-dominant. At NKF, Orchardgrass, Schedonorus arundinaceus (Schreb.) Dumort (Tall Fescue), and Bromus inermis Leyss. (Smooth Bromegrass) were the dominant grasses. Sedges were scarce. Leguminous forbs were also scarce at both farms, though more species were present at LFF. Moisture-tolerant species such as Birdsfoot Trefoil and Vicia cracca L. (Bird Vetch) were abundant legumes at LFF, while Red Clover dominated at NKF. There were many non-leguminous forb species at LFF, with the principal species being Fragaria virginiana Northeastern Naturalist 73 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 Duchesne (Wild Strawberry), Ranunculus acris L. (Buttercup), Galium mollugo L. (Hedge Bedstraw) and Equisetum arvense L. (Horsetail). At NKF, non-leguminous forbs were scarce, with Daucus carota L. (Wild Carrot) and Convolvulus arvensis L. (Field Bindweed) most abundant. Invasive and woody species were rare at both sites. Species richness was higher at LFF than at NKF in both 2013 and 2014 (Table 1). A repeated measures ANOVA (Table 2) revealed that in addition to differences due to location (i.e., site; P < 0.001), S was influenced by treatment (MIG, CG, U; P < 0.01). Prior to the experiment, in 2013, within-site (i.e., LFF; NKF), between-paddock differences in S were not significant (t-tests; P > 0.05). In the spring of 2014, a year after the experiment began, S was 16.0% higher in the MIG paddock than in the U paddock at LFF and 24.5% higher in the MIG than in the U paddock at NKF (P < 0.05; Fig. 1a, b). Similarly, S was 44.8% and 30.1% higher in Table 1. Vascular plant community structure at Longfield (LFF) and Normanskill (NKF) Farms initially (spring 2013) and post grazing (2014). The number of species in each of 5 functional groups and S in management intensive grazing (MIG), continuaous grazing (CG), ungrazed (U), climpping and manure (CM), clipping (C), and manure (M) paddocks are shown + 1 standard deviation. Non- Leguminous leguminous Graminoids forbs forbs Invasives Woody stems S LFF Initial 4.4 ± 0.6 0.7 ± 0.5 6.8 ± 0.8 0.03 ± 0.01 0.7 ± 1.0 12.6 ± 1.5 MIG 4.1 ± 0.5 1.5 ± 0.6 8.1 ± 1.0 0.0 0.5 ± 0.6 14.2 ± 0.9 CG 3.1 ± 0.8 0.8 ± 0.8 4.9 ± 1.8 0.2 ± 0.4 0.0 9.0 ± 2.5 U 3.8 ± 0.6 0.5 ± 1.0 7.8 ± 2.9 0.0 0.0 12.1 ± 2.5 CM 4.3 ± 0.6 1.2 ± 1.0 7.7 ± 1.7 0.0 0.0 13.2 ± 2.0 C 4.2 ± 0.8 0.7 ± 0.6 8.3 ± 1.9 0.0 0.3 ± 0.5 13.5 ± 2.3 M 3.9 ± 0.6 0.8 ± 0.8 5.0 ± 1.3 0.3 ± 0.5 0.2 ± 0.4 10.2 ± 1.7 NKF Initial 2.1 ± 0.2 0.2 ± 0.2 2.0 ± 0.5 0.5 ± 0.4 0.0 4.8 ± 0.9 MIG 2.5 ± 1.0 0.8 ± 0.7 4.6 ± 1.7 0.8 ± 0.9 0.0 8.7 ± 2.1 CG 2.4 ± 0.5 0.3 ± 0.4 3.1 ± 1.8 0.6 ± 0.5 0.0 6.4 ± 2.2 U 1.4 ± 0.5 0.3 ± 0.6 4.1 ± 1.2 1.0 ± 0.0 0.0 6.8 ± 1.6 CM 2.7 ± 0.6 0.0 2.2 ± 1.1 0.2 ± 0.4 0.0 5.1 ± 1.4 C 2.5 ± 0.3 0.1 ± 0.3 3.1 ±1.6 0.7 ± 0.5 0.0 6.4 ± 2.1 M 2.8 ± 0.4 0.1 ± 0.3 2.5 ± 1.2 0.1 ± 0.3 0.0 5.5 ± 1.3 Table 2. Repeated-measures ANOVA comparing the effect of treatment (MIG, CG, U, CM, C, M), site (Longfield Farm and Normanskill Farm), and time (Spring 2013 and 2014) on vascular plant species richness. F P < F Treatment 5.15 0.002 Site 429.13 less than 0.001 Time 0.311 0.579 Treatment * site 3.016 0.027 Treatment * time 3.887 0.008 Time * site 13.157 0.001 Treatment * site * time 1.764 0.153 Northeastern Naturalist C.B. Girard-Cartier. and G.S. Kleppel 2017 74 Vol. 24, Special Issue 8 the MIG paddocks than in the CG paddocks at LFF and NKF, respectively. Although time alone did not affect community structure, the ANOVA cross products of time and treatment, and time and site support our observation that the plant communities at the 2 sites, LFF and NKF, changed significantly over time as a function of treatment (Table 2). Prior to the deployment of sheep in the spring of 2013, differences in S in MIG and simulation paddocks were not significant at either farm. After treatments in 2014, S in the MIG and CM paddocks at LFF were not statistically different from one another, whereas C and M paddocks were (Fig.1c). That is, the combination of clipping and manure deposition appeared to simulate species richness in the MIG paddock, whereas either treatment alone did not. At NKF, S in the MIG paddock was significantly higher than in any of the simulation paddocks in 2014 (Fig. 1d). That is, S was not simulated by clipping, manure addition, or both. Microbial communities at Longfield and Normanskill farms Gram positive bacteria, such as actinomycetes, were dominant at both sites; saprophytes and arbuscular mycorrhizae were subdominant (Table 3). Rhizobial Figure 1. Prior vs. post treatments mean species richness (S) + 1 SD in management-intensive grazing (MIG), continuous grazing (CG), and ungrazed (U) paddocks at (a) Longfield Farm (LFF;n = 12) and (b) Normanskill Farm (NKF; n = 12), and for management-intensive grazing (MIG), clipping and manure (CM), clipping (C), and manure (M) treatment paddocks at (c) LLF and (d) NKF. Letters above bars indicate whether differences among means were different (Student’s t-test). Same letters (e.g., a, a) indicate that the difference is not significant (P > 0.05); different letters (e.g., a, b) indicate that the difference is significant (P < 0.05 or better). Northeastern Naturalist 75 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 Table 3. Soil microbial community structure at Longfield (LFF) and Normanskill (NKF) Farms in the management intensive grazing (MIG), continuous grazing (CG), ungrazed (U) paddocks. Initial (2013) and post-grazing (spring 2014) mean biomass (ng/g) ± 1 standard error and percent total microbial biomass ± 1 standard deviation (%) of major categories of micro bes are provided for each of 5 microbial functional groups. Actinomycetes Rhizobia Arbuscular mycorrhizae Saprophytes Protozoa Total biomass LFF MIG initial 581.54 70.95 244.61 372.04 41.79 1310.93 % 44.4 5.4 18.6 28.4 3.2 2014 702.5 ± 23.4 219.4 ± 155.1 290.8 ± 57.0 827.5 ± 307.5 131.4 ± 86.9 2171.5 ± 605.7 % 35.4 ± 10.8 8.5 ± 6.0 13.8 ± 1.8 36.8 ± 4.9 5.6 ± 2.6 CG initial 501.09 0 153.83 216.39 34.47 905.78 % 55.3 0 17 23.9 3.8 2014 633.1 ± 95.5 146.8 ± 99.5 204.5 ± 43.1 587.6 ± 161.4 72.4 ± 20.5 1644.4 ± 374.4 % 39.8 ± 8.2 8.0 ± 4.6 12.5 ± 0.3 35.3 ± 3.7 4.3 ± 0.3 U initial 553.34 57.18 198.1 294.59 35.24 1138.45 % 48.6 5 17.4 25.9 3.1 2014 903.9 ± 83.0 186.8 ± 102.5 339.9 ± 71.7 695.2 ± 154.0 104.5 ± 22.2 2230.2 ± 386.3 % 41.2 ± 4.7 7.8 ± 3.0 15.3 ± 0.7 30.9 ± 3.0 4.7 ± 0.4 NKF MIG initial 156.28 8.28 58.78 110.81 6.64 340.79 % 45.9 2.5 17.2 32.5 1.9 2014 369.7 ± 42.5 13.2 ± 13.2 168.4 ± 12.6 273.7 ± 33.3 38.1 ± 1.8 853.0 ± 80.9 % 42.8 ± 1.6 1.4 ± 1.4 19.6 ± 1.3 31.8 ± 3.1 4.5 ± 0.6 CG initial 333.25 0 141.78 146.07 10.28 631.38 % 52.8 0 22.5 23.1 1.6 2014 452.7 ± 97.2 14.5 ± 14.0 182.7 ± 35.4 359.4 ± 61.2 51.5 ± 13.9 1060.6 ± 156.9 % 42.4 ± 4.1 1.6 ± 1.7 17.2 ± 1.4 34.0 ± 3.7 4.9 ± 1.0 U initial 280.01 25.98 123.5 310.43 26.62 766.54 % 36.5 3.4 16.1 40.5 3.5 2014 367.9 ± 107.7 37.6 ± 24.2 163.2 ± 60.7 398.8 ± 135.1 40.6 ± 11.8 1008.0 ± 336.6 % 37.1 ± 1.8 3.4 ± 1.2 16.1 ± 0.9 39.4 ± 0.9 4.1 ± 0.2 Northeastern Naturalist C.B. Girard-Cartier. and G.S. Kleppel 2017 76 Vol. 24, Special Issue 8 and protozoan biomasses at both sites were, not unexpectedly, an order of magnitude lower. On average, microbial biomass was 71.4% higher at LFF than at NKF (t-test: P less than 0.01). At LFF, microbial biomass in the grazed and ungrazed paddocks increased by 60%–95% between 2013 and 2014 (Fig. 2a). Differences among grazed and ungrazed paddocks, however, were not significant (t-tests). Conversely, at NKF, the increase in microbial biomass in the MIG paddock was 7 times that in the CG paddock and more than 3 times that in the U paddock (Fig. 2b). While the increased microbial biomass in the MIG paddock was simulated in the C and M paddocks at LFF, the microbial biomass in the MIG paddock was not different from (i.e., was simulated by) that in the M paddock at NKF (Fig. 2c, d). Mean microbial functional diversity, H', was 8.1% higher at LFF than at NKF (t-test: P < 0.01). Over the course of the study, the percent change in microbial functional diversity, %ΔH', among grazed and ungrazed paddocks was not significant at either farm (Fig. 3a, b). At LFF, %ΔH' in the CM, C, and M paddocks appeared to simulate that of the MIG treatment. That is, they were not different from the %ΔH Figure 2. Prior vs. post treatments mean percent change in total microbial biomass + 1 SD in management-intensive grazing (MIG), continuous grazing (CG), and ungrazed (U) paddocks at (a) Longfield Farm (LFF; n = 3) and (b) Normanskill Farm (NKF; n = 3), and for management-intensive grazing (MIG), clipping and manure (CM), clipping (C), and manure (M) treatment paddocks at (c) LFF and (d) NKF. Letters above bars indicate whether differences among means were different (Student’s t-test). Same letters (e.g., a, a) indicate that the difference is not significant (P > 0.05); different letters (e.g., a, b) indicate that the difference is significant (P < 0.05 or better). Northeastern Naturalist 77 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 in the MIG paddock. Conversely, at NKF, the %ΔH in the CM, C, and M paddocks did not appear to simulate the processes observed in the MIG pa ddock (Fig. 3c,d). Coupling of microbial and plant communities and environmental variables Correlation analysis identified relationships between S and microbial biomass, microbial functional diversity, and soil moisture at LFF and NKF. We explored these relationships further by performing least squares regression analysis at 2 spatial scales: the larger, 2-farm scale and the smaller scale represented by the individual farms. At the larger, 2-farm scale, the regressions of S on microbial biomass, H', and soil moisture (M) were all significant (P < 0.01), while at the individual farm scale, the relationships were more complex. The slopes of the regressions of S on microbial community attributes—biomass and diversity—were significant (P less than 0.05) at NKF, but not at LFF (Fig. 4a, b). At NKF, the relationships between S and microbial biomass and between S and H' explained 35.0% and 45.5% of the variability in the data, respectively. Conversely, S was correlated with soil moisture at the wetter LFF, where the regression explained nearly 62% of the scatter in the data, but not at NKF (Fig. 4c). Furthermore, both microbial biomass and diversity varied systematically with soil moisture at the larger scale (Fig. 4d, e). At the scale of the individual farms, Figure 3. Prior vs. post treatments mean percent change in microbial functional diversity (%ΔH') + 1 SD in management-intensive grazing (MIG), continuous grazing (CG), and ungrazed (U) paddocks at (a) Longfield Farm (LFF; n = 3) and (b) Normanskill Farm (NKF; n = 3), and for management-intensive grazing (MIG), clipping and manure (CM), clipping (C), and manure (M) treatment paddocks at (c) LFF and (d) NKF. Letters above bars indicate whether differences among means were different (Student’s t-test). Same letters (e.g., a, a) indicate that the difference is not significant (P > 0.05); different letters (e.g., a, b) indicate that the difference is significant (P < 0.05 or better). Northeastern Naturalist C.B. Girard-Cartier. and G.S. Kleppel 2017 78 Vol. 24, Special Issue 8 however, microbial biomass was correlated with soil moisture at NKF, but was independent of it at LFF. H' was not correlated with soil moisture at either farm individually. Figure 4. Regression of vascular plant species richness (S) on: (a) microbial biomass, (b) microbial diversity (H'), and (c) soil moisture, and regression of (d) microbial diversity (H') on soil moisture and (e) microbial biomass on soil moisture .Triangles represent Longfield Farm (LFF; n = 12). Circles represent Normanskill Farm (NKF; n = 12). Figures show regressions of significant relationships. Arrows indicate the corresponding regressions for the equations, r values, and significance values that are provided. Dotted line represents regression for LFF. Dashed lines represent regressions for NKF. Solid lines represent regressions for LFF and NKF combined. Northeastern Naturalist 79 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 Discussion The key findings of this study are first, that, consistent with findings of studies from other kinds of grassland ecosystems (e.g., in southern Texas rangelands; Teague et al. 2009, 2011), different livestock management protocols can have different outcomes for plant communities. Second, although the coupling of microbial and vascular plant communities has been documented, we propose that the extent and nature of this coupling may be determined by certain environmental variables, such as soil moisture. Finally, while significant interactions occurred between the plant community, the microbial community, and soil moisture at the larger, 2-farm scale, these relationships were not always maintained at the individual-farm scale. Grazing represents a disturbance to terrestrial ecosystems that can affect the structure and biodiversity of plant communities (Collins et al. 1998, Dupre and Deikmann 2001, Hickman et al. 2004, Kleppel et al. 2011, Klimmek et al. 2007, Marion et al. 2010, Pavlu et al. 2007). Whether grazing-induced disturbances result in increases or decreases in S depends in part on how grazing occurs (i.e., continuous at low stock density, short term at high stock density) and the extent to which the plant community has adapted to grazing over time (Bakker 1989, Hobbs and Huenneke 1992, Hodgson and Illius 1996, Milchunas et al. 1998, Patra et al. 2005, Van Wieren 1995). In the northeastern US, pastoral plant communities have had several hundreds to thousands of years’ experience with domesticated ungulates, many of the grasses and forbs having been transported from Europe to the New World with livestock. Thus, the length of experience with grazing would not likely explain the differences in S that we observed. Our observations are among the first to demonstrate that livestock can induce changes in plant and microbial communities in previously ungrazed landscapes within a single season. The changes that we observed varied with grazing management protocol, but are consistent with disturbance theory (Collins et al. 1995, Connell 1978). It is evident from years of observations that continuous grazing at low stock densities represents an intense disturbance to the plant community (Acocks 1966, Flack 2016, Voisin 1959). Continuous exposure to ruminants places constant pressure on the vegetation, especially on desirable forage species (Teague et al. 2011). By comparison, management intensive grazing appears to represent a lower level of grazing pressure despite the high stock densities that are used. Potentially, competition for food created by high stock density reduces the tendency for selective feeding (Gerrish 2004). At Longfield Farm, most of which is managed by a holistic variation of the MIG protocol (Flack 2016, Savory and Butterworth 2016), a paddock will only be occupied for 0.5–2 days and then rested up to 35 days. The extensive rest period (i.e., low to moderate frequency of disturbance events, per Connell [1978]) may act as a grazing-intensity modulator (Kleppel et al. 2011). The rest period represents not only a time of reduced grazing pressure but a period when organic matter—urine and feces—can be processed by the microbial community and cycled back into the plant community. By examining the relationship between the vascular plant and soil microbial communities, we sought to understand what happens to cause S to change in the Northeastern Naturalist C.B. Girard-Cartier. and G.S. Kleppel 2017 80 Vol. 24, Special Issue 8 presence of livestock. The simulation experiment allowed us to explore the coupling between microbial and plant diversity under management intensive grazing. At LFF, where S in the MIG paddock was higher than in the CG and U paddocks, MIG S was simulated when both clipping, i.e., a proxy for grazing, and organic matter deposition occurred together (Figs. 2a). However, neither microbial biomass nor diversity was differentiated by grazing (or non-grazing) management approach (Figs. 2a, 3a). Thus, we do not need to look at the simulations to determine if microbial dynamics were forced by clipping, organic matter or a combination of the two since changes in the microbial community were independent of grazing. It is not surprising then, that S and H' do not vary systematically at this farm (Fig. 4b). The possibility that H' is forced by some other factor(s) is borne out by the positive relationship between soil moisture and S (Fig. 4c). Soil moisture plays a key role in determining the ways that large ungulates influence diversity in plant communities (Milchunas et al. 1998). In addition, the moisture content of the soil affects seed germination and nutrient transformations, and it facilitates the transport of nutrients to the plant (Verchot et al. 2002; Patra et al. 2005). That is, water connects the physical and the biological components of the soil ecosystem. It would appear that in the poorly drained Angola soil of Longfield Farm, plant and microbial communities are decoupled (Fig. 4a, b). Soil moisture appears to be a principal driver of plant diversity. The decoupling of plant and microbial diversity is illustrated further by the observation that microbial diversity varied independently of soil moisture at LFF (Fig. 4e). At NKF, microbial biomass was statistically indistinguishable in MIG and M paddocks (Fig. 2d). This observation, which is consistent with those obtained in a survey of grazing and microbial community structure (G.S. Kleppel, unpubl. data), would lead us to conclude that organic matter provided by livestock influenced microbial production in the MIG paddock. However, neither S nor H' were simulated by clipping and/or manuring (Figs. 1d, 3d). Given the correlation of S on H' at NKF (Fig. 4b), we suspect that one or more intermediate interactions obscures the direct connection between the activities of livestock and diversity in the resident plant and microbial communities. For instance, the link between organic matter (manure) and plant biodiversity is indirect. Mineralization to inorganic nutrients precedes utilization by plants. In their review of microbe-plant interactions, Reynolds et al. (2003) suggested that microbial processing of organic to inorganic nitrogen might permit exploitation of new niches by the vascular plant community, resulting in increased species richness. We are currently examining the nitrogen dynamics of the paddock systems at Longfield and Normanskill Farms. Conversely, Bais et al. (2006) reviewed evidence that root exudates from plants stimulate diversity within the microbial community. In that case, H' would be correlated with S but independent of clipping and/or manuring, as we observed. This study suggests that different kinds of interactions may occur at different spatial scales. Thus, while correlations between S, H', microbial biomass, and soil moisture were detected at the 2-farm scale (Figs. 4), the same relationships may or may not exist at the individual farm scale. In the present study, the plant Northeastern Naturalist 81 C.B. Girard-Cartier. and G.S. Kleppel 2017 Vol. 24, Special Issue 8 communities (S), microbial communities (biomass and H'), and soil moisture contents differed significantly at the 2 sites, due in part to differences in soil-type (Angola at LFF, Hudson at NKF) and drainage characteristics. Soil type affects plant species richness and has been suggested as one of the principal determinants of microbial community structure (Borowik and Wyszkowska 2016, Bosio et al. 1998). The importance of scaling factors, such as spatial variation in soil attributes, would seem to be an area that is rife for further investigation. Our small data set, however, prevents us from exploring this issue further, though we note that changes in forcing functions associated with variations in observational scale are widely reported in many disciplines. For example, Legendre and Demers (1984) demonstrated that at horizontal spatial scales of 1–10 km, biological processes drive plankton distributions in the ocean, but on 100–1000 km spatial scales, physical processes (e.g., winds, currents) appear to drive plankton distributions. In this study, we documented the effects of grazing on the plant and microbial communities in 2 agricultural landscapes, and we suggested some possible mechanisms to explain our observations. We recognize that the biotic and abiotic attributes of pastoral ecosystems are numerous, and that the interactions among them are complex. It is apparent, however, that the protocol by which livestock is managed will likely affect the resident plant and microbial communities and the interactions between them. At the farms and at other locations that we have studied in New York State, the effect of grazing management protocol is becoming clearer (Kleppel et al. 2011). In this sense, we have supported and partially explained why grazing by livestock can be a positive or a negative force in agricultural ecosystems in the Northeast. 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