Climate – grazing interactions in Mongolian rangelands: Effects of grazing change along a large-scale environmental gradient

Journal of Arid Environments 173 (2020) 104043

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Climate – grazing interactions in Mongolian rangelands: Effects of grazing change along a large-scale environmental gradient

T

Julian Ahlborna,b,c,∗, Henrik von Wehrdenb, Birgit Langd, Christine Römermannd,e, Munkhzul Oyunbilegg, Batlai Oyuntsetsegh, Karsten Weschec,e,f a

Leibniz-Centre for Agricultural Landscape Research, Sustainable Grassland Systems, Gutshof 7, D-14641, Paulinenaue, Germany Leuphana University Lueneburg, Institute of Ecology, Faculty of Sustainability, Scharnhorststr. 1, D-21335, Lueneburg, Germany c Senckenberg Museum of Natural History, Goerlitz, PO Box 300 154, D-02806, Goerlitz, Germany d Friedrich Schiller University, Institute of Ecology and Evolution, Philosophenweg 16, D-07743, Jena, Germany e German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany f International Institute Zittau, Technische Universität Dresden, Markt 23, 02763, D-Zittau, Germany g Mongolian Academy of Science, Institute of General and Experimental Biology, 3600, Ulaanbaatar, Mongolia h National University of Mongolia, Botany Department, School of Biology and Biotechnology, 14200, Ulaanbaatar, Mongolia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Gradients Grazing DCA Species diversity Indicator species analysis Non-equilibrium rangelands Xerophytization

There are still major gaps in our understanding of rangeland degradation. Assessing the interactions between climate and grazing effects could help to explain what unifies and separates rangelands, and may therefore promote a more sustainable management of livestock. We studied 15 local land-use transects along a 600 km long climatic gradient in Central Asia to test the hypothesis that grazing effects differ between relatively moist equilibrium (EQ) and dry non-equilibrium (NEQ) rangeland systems. We analysed plant community composition, species diversity and indicator species for different grazing intensities. We found pronounced differences in community composition along our climate gradient, revealed climate-related grazing effects on richness, responses of Simpson's diversity, and also found different grazing indicator species along the larger transect. We conclude that in NEQ rangelands, grazing effects are limited to sacrifice zones and environmental filtering dominates vegetation composition. With increasing precipitation, resource availability gains in importance leading to more complex communities dominated by grazing-tolerant species under EQ dynamics. Hints for xerophytization in the transition zone between EQ and NEQ highlight the vulnerability of rangelands that temporally shift from one state to the other. This calls for extra care in the management of livestock numbers in these transition areas.

1. Introduction The interplay of climate and grazing is of great importance for the future management of the world's rangelands. Both traditional and modern grazing systems have already been affected by changing landuse and climate, often resulting in irreversible transformations of these ecosystems (Lambin and Meyfroidt, 2011). There is substantial literature addressing the effects of grazing on rangelands across the world, but the relative importance of grazing and climate, and their interaction, on observed degradation patterns is still debated (Vetter, 2005; Addison et al., 2012; Bestelmeyer et al., 2015). This partly reflects the diversity of rangelands, which may differ strongly with respect to e.g. productivity and evolutionary history (Cingolani et al., 2005). A potentially unifying approach for rangeland management lies in

∗

understanding climate as a control of the biotic impacts on rangeland ecosystems. The low and extremely variable precipitation patterns in drylands moderate livestock numbers regardless of the management technique (Ellis and Swift, 1988; Wehrden et al., 2012). Aridity limits plant growth in dry grasslands and therefore constrains density of livestock. Droughts, as the most extreme form of rainfall variability, affect biomass availability with often devastating effects on livestock herds (Mohammat et al., 2013; Sternberg et al., 2011a,b). In contrast, mesic grasslands provide high and reliable forage, allowing herd sizes to accumulate and placing greater pressure on rangeland structure and composition. Differences in climate variability therefore determine different rangeland dynamics, and this is reflected in the development of two theoretical concepts in rangeland ecology. The traditional concept of equilibrium dynamics (EQ) posits that biotic interactions are

Corresponding author. Leibniz-Centre for Agricultural Landscape Research, Sustainable Grassland Systems, Gutshof 7, D-14641, Paulinenaue, Germany. E-mail address: [email protected] (J. Ahlborn).

https://doi.org/10.1016/j.jaridenv.2019.104043 Received 19 March 2018; Received in revised form 8 May 2019; Accepted 18 October 2019 Available online 31 October 2019 0140-1963/ © 2019 Elsevier Ltd. All rights reserved.

Journal of Arid Environments 173 (2020) 104043

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Diaz, 1999), or sampling of fence-line contrasts across dry northern Tibet (Wu et al., 2014). None of these studies paid specific attention to the transition zone. In this paper, we present our findings from a 600 km long multi-site gradient study covering a range of climatic conditions across EQ and NEQ environments and their transition zone in Mongolia (Supplement A). We sampled vegetation plots along grazing intensity transects on 15 sites with the aim to study community composition, species diversity and occurrence of indicator species. The research questions addressed the differences in vegetation responses to grazing under different climate conditions: 1.) According to the NEQ theory, grazing should induce lower local-scale heterogeneity in dry NEQ environments than in moister EQ environments. We therefore hypothesized that plant communities under NEQ conditions are more similar within sites than in EQ environments. 2.) This ties to the question how species richness and diversity responds to grazing along the climate gradient. In concordance with existing theory (Cingolani et al., 2005), we hypothesized that grazing has stronger effects on richness and abundance of species under higher water availability. 3.) We were interested how individual species are associated with specific grazing intensities. We hypothesize that grazing indicators cannot be detected within NEQ systems; while under more mesic conditions clear grazing indicators can be found.

most important under relatively high precipitation. Management in EQ rangelands should focus on density-dependent control of stocking rates, on inter- or intra-specific competition patterns between plants, and on landscape heterogeneity (Illius and O'Connor, 1999). In contrast, the concept of non-equilibrium dynamics (NEQ) states that limited, spatially fractionated resources (Cingolani et al., 2005) or natural disasters are the main controls for livestock numbers (Ellis et al., 1991). Thus, the NEQ concept highlights aridity-induced abiotic controls as the main drivers in rangeland ecosystems. Considering these differences, livestock management must be adapted to the local climatic context. Controlling livestock numbers with a view on carrying capacity is reasonable in EQ systems, while the management in NEQ systems need to be more dynamic, emphasising the interplay between rainfall events, fodder availability and herd size fluctuations (Vetter, 2005; Illius and O'Connor, 1999). Ellis and Swift (1988) defined the threshold between these systems with respect to the coefficient of interannual rainfall variability (CV) of 33%. This threshold was later supported by a global literature review (Wehrden et al., 2012). However, recent theoretical considerations challenged the idea of a clearly defined threshold between EQ and NEQ rangelands (Easdale and Bruzzone, 2015), pointing at the critical gap in our understanding of the dynamics in transition zones among rangeland systems of different climate (Briske et al., 2005). Drylands can be dynamic in their spatial extend over time (Sternberg et al., 2015), and so are grazing degradation effects (Miehe et al., 2010). Transition zones with relatively low yet still reasonably stable precipitation may be particularly sensitive to livestock impact, as plants growing under water stress may still be subjected to constantly high grazing pressure. Multi-site assessments provide a way to study the interacting effect of climate and grazing intensity (Bello et al., 2006). However, so far grazing effects have been evaluated either for NEQ (Wesche and Wehrden, 2011; Wehrden and Wesche, 2007; Sasaki et al., 2005) or for EQ conditions (Takatsuki et al., 2018). Hardly ever have effects been studied across long climate gradients incorporating both major types of climate and therefor rangeland dynamics, and also considering the transition zone. With respect to the choice of response variables considered, simple biodiversity measures such as plant species richness or species diversity have become a standard for the assessment of rangeland health (Cingolani et al., 2005). In addition, analysis on community composition have proven relevant, and studies on the responses of characteristic species to climate, grazing and their interacting effects are also promising, as shown by a recent study from rangelands in Tibet which unraveled livestock effects using indicator species analyses (Wang et al., 2017). Central Asia offers particularly ideal conditions for a comparative study on EQ and NEQ dynamics along a climate gradient, including their transition zone. Rangelands occur from dry deserts to moist forest steppes. Traditional grazing systems that do not use external inputs – particularly additional forage during periods of low productivity – are still the main forms of livestock production. Overstocking, declining precipitation and decreasing forage biomass are commonly quoted as potential causes of degradation, which has been reported at least since the mid-nineties (Johnson et al., 2006). However, an increasing number of studies have challenged the assumption that grazing degradation is omnipresent in Central Asia (Addison et al., 2012; Harris, 2010; Wang et al., 2008). Remote sensing studies demonstrated spatial heterogeneity in the relative importance of abiotic vs. biotic controls (Sternberg et al., 2011b; Lehnert et al., 2016), and field studies tried to quantify differences in grazing effects among EQ and NEQ systems (Fernández-Giménez and Allen-Diaz, 1999; Wesche et al., 2010). Still, the reasons for degradation in Central Asia are debated (Wehrden et al., 2015), partly because approaches that jointly address grazing and climate effects along large climatic gradients are still rare. Exceptions include a resurvey based on the classic paper by Fernandez-Gimenz and Allen-Diaz (Khishigbayar et al., 2015; Fernández-Giménez and Allen-

2. Material & methods 2.1. Study area & design Mongolia's climate is continental with a broadly latitudinal rainfall distribution, causing a steep climatic gradient from semi-arid conditions in the north with 400 mm mean annual precipitation (MAP) to arid conditions in the south at a mere 100 mm (Wesche and Treiber, 2012). The climate supports three main rangeland types: The meadow steppe or forest steppe in the north, the true steppe in the centre and the desert steppe in the south. All types are dominated by Poaceae, with Stipa, Leymus and Agropyron being the most prominent genera on our study sites. The dominant form of land-use is nomadic animal husbandry. Herders mainly raise sheep, goats and horses; cows occur in more humid and camels in more arid areas (Fernández-Giménez, 2006). Small livestock, i.e. sheep and goat are the dominant grazers along the entire gradient. We established a 600 km long sampling gradient by selecting 13 study sites between Mongolia's capital city Ulaanbaatar in the north and Dalandzadgad in the south, and two additional sites in the Gurvan Saykhan Mountains (Fig. 1, Table 1). Along this gradient, mean annual precipitation (MAP) ranges from 100 mm in the south to 250 mm in the north (Hijmans et al., 2005) with a CV of interannual precipitation variability (which is directly related to MAP with r2 = 0.81, Supplement B) of 38% in the south and 30% in the north (Fig. 1). Based on previous studies in the region (Fernández-Giménez and Allen-Diaz, 1999) and elsewhere (Le Houerou et al, 1988; Wehrden et al., 2012), we assumed that the desert steppe represents a NEQ system, while forest steppes in the north are EQ systems. Following the idea that the border between EQ and NEQ is diffuse and shifts over time (Easdale and Bruzzone, 2015; Miehe et al., 2010; Sternberg et al., 2015), we assumed that the sites from Mandalgov northwards to a CV of 33% are part of a transition zone (Fig. 1). We split the precipitation gradient in equal intervals, and for each of these looked out for a grazing hotspot such as a camp or well. Sites were then deliberately selected with regard to representative vegetation (e.g. no disturbance through roads, no azonal vegetation etc.). At each study site, local land-use gradients were surveyed along fixed distances of 50 m, 150 m, 350 m, 750 m and 1500 m (hereafter referred to as A, B, C, D and E) along a straight homogenous transect away from grazing hotspots like camps, wells and stables. Choice of distances was based on previous successful applications in the region and elsewhere (Stumpp et al., 2005; Manthey and Peper, 2010). 2

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Fig. 1. Study area with study sites, annual precipitation and coefficient of rainfall variance (CV). Precipitation data derived from Hijmans et al. (2005), values for coefficient of interannual rainfall variation from von Wehrden et al. (2012). The white points indicate putative NEQ regions in the south and EQ in the north, while grey points to the supposed fluent transition zone. Modified after (Lang et al., 2018).

10 m × 10 m plots with five replicates per distance class from grazing hotspots, resulting in a total of 375 vegetation samples (15 sites x 5 distances x 5 replicates). Within a distance class, we deliberately chose plots with representative steppe vegetation and avoided azonal vegetation or disturbances that were not related to grazing (i.e. car tracks). We identified all species and recorded their percentage cover. Since several indicators for grazing pressure on vegetation have been questioned (Dorji et al., 2013), we counted all animal faeces on every 10 m

Hotspots included camps with small livestock held very close nearby, wells as public water sources for livestock and stables that were not used during time of sampling.

2.2. Sampling Fieldwork was done during the main vegetation period from June to late August in summer 2014. We took vegetation samples on

Table 1 Basic information about the 15 study sites. Mean annual precipitation and temperature were derived from Hijmans et al. (2005); coefficient of interannual rainfall variance (CV) from von Wehrden et al. (2012). Sites are ordered according to mean annual precipitation. All values represent the arithmetic mean for each site. Site

Lat

Long

Altitude

Hotspot Type

Rangeland type

Ø Temp [°C/a]

Ø Prec [mm/a]

CV [%]

Ø Species richness

Ø Veg. Cover [%]

Ø Biomass kg/ha

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

44.50 44.99 43.97 43.52 45.63 43.73 45.91 46.19 46.36 46.57 46.79 47.06 47.34 47.60 48.15

105.33 105.61 105.14 104.22 105.84 103.60 106.29 106.45 106.54 106.65 106.64 106.57 106.63 106.82 106.71

1310 1200 1520 1875 1415 2160 1460 1330 1395 1360 1390 1425 1615 1385 1250

Camp Well Well Camp Well Well Stable Stable Well Well Camp Well Well Camp Camp

Desert Desert Desert Desert Meadow Meadow Meadow Meadow Meadow Meadow True Steppe True Steppe True Steppe Mountain Mountain

3.7 3.6 3.3 2.8 1.7 1.3 1.2 1.2 0.6 0.4 0.0 −0.6 −1.8 −1.0 −1.1

104 110 120 149 151 168 170 172 185 188 201 204 238 244 248

37.7 36.8 38.1 32.5 35.8 30.8 37.2 36.4 36.1 34.6 34.2 33.6 32.7 31.7 31.0

9 13 13 13 17 17 17 15 10 14 18 22 27 25 22

16 21 26 33 31 56 50 45 45 61 65 58 79 74 67

117 132 187 206 257 500 560 702 450 735 503 NA 715 630 764

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Caceres and Legendre, 2009). ISA measures the association between a species and a group via an indicator value index. This index is the product of two components, specificity and fidelity (Dufrene and Legendre, 1997). The specificity of a species reflects the probability that the presence of the given species indicates a target site group. The fidelity of a species mirrors the probability to find that species on any plot belong to the target group. The confidence interval of the ISA was set to 0.05 at 9999 permutations. To find diagnostic species for different grazing intensities, a set of reasonable distance-combinations were chosen as grouping factors. We were interested in the single grazing intensities (A, B, C, D, E), their transitions (A + B, B + C, C + D, D + E) and larger zones (A + B + C, B + C + D, C + D + E). Unusually high groundwater level overrode effects on one site due to an open water source as the grazing hotspot, prompting us to exclude this site from ISA. All statistical analyses were done using R version 3.3.2 (R Core Team, 2018). Models were calculated with the “lme4” package (Bates et al.). Model selection was done using “bbmle” (Bolker and R Development Core Team, 2016) and ranking with “AICcmodavg” (Mazerolle, 2016). Averaged parameter estimates were calculated using the “MuMIn” package (Bartoń, 2015). The “vegan” package (Oksanen et al., 2017) was used to calculate the DCA and indicator species were calculated using “indicspecies” package (Caceres and Legendre, 2009).

by 10 m plot in order to double-check for reliability of distance to grazing hotspots. Despite some criticism, this has been proven successful in previous studies (Marques et al., 2001). As a first step, we differentiated sizes and types of faeces from sheep, goats, horses, cows and camels with the help of local herders. We then recorded the complete defecation event of a single animal as one dung count (now further called “defecation”) and transformed these into Mongolian livestock units (MLU) to get a relative proxy for livestock presence at each study site. This transformation takes into account the presence of different livestock types on the one hand and the approximate proportions of biomass intake on the other hand by using the local standard index used for calculation of livestock density in rangeland management units (Suttie and Reynolds, 2003). A summary of the defecations and MLU per site can be found in Supplement C. 2.3. Data analysis Plant community composition was analysed using Detrended Correspondence Analyses (DCA (Hill and Gauch, 1980), on the vegetation matrix (241 species x 375 plots). Along large gradients, many rare species are to be expected that can seriously distort a DCA. Consequently, rare species were down-weighted prior to the analysis (Leyer and Wesche, 2007; Jongman, ter Braak and van Tongeren, 2007). Precipitation, MLU and distance were fitted to the ordination axes by post hoc correlation. The significances of the correlations were tested using a randomization test with 999 permutations. An additional DCA was calculated excluding the 50m distance class (375–75 = 300 plots) to unravel the importance of the “sacrifice zones”. We used species richness, species evenness and Simpson's D index as metrics for determining the responses of vegetation to grazing intensity. Species evenness was calculated using Pielou's evenness index, which is Shannon's H index divided by the logarithm of the species number. For Simpson's D, we used the most common form of 1 - D to obtain an index from 0 to 1. Broadly similar to evenness, Simpson's D is high if the abundances of species of a community are equal, but also incorporates the species richness of a community. We used generalized linear mixed models to estimate the effects of climate and grazing, and their interaction on species diversity indexes. We assumed Poisson (richness) and binomial (evenness & Simpson's D) error distributions for the model residuals based on the observed distribution of the data. We selected annual mean precipitation derived from (Hijmans et al., 2005) as the main climate predictor because of its widely approved applicability for ecological questions, and its high correlation with CV in drylands (Wehrden et al., 2012). For the random effects components in the models, we specified distance classes nested within sites. The transformed dung counts (MLU) and distance to grazing hotspots (as factors to detect effects on specific distances) were used as predictors for grazing. For model selection, we constructed nine candidate models arising from combinations of the three predictor variables. The null model included only an intercept term and was used in the model set. Each candidate model represented one plausible hypothesis explaining species diversity. We used either distance or MLU to assess which predictor better describes changes in our response variables. The information is potentially important because MLU includes not only the expected decrease of grazing pressure with increasing distance, but also the expected increase of livestock with increasing MAP. Prior to modelling, all predictor variables were centred and scaled to improve model convergence. We used an information-theoretic model selection approach based on Akaike's Information Criterion (AIC). Models considered best had a delta AIC higher than two compared to the next second ranked model. We calculated the conditional average from those models that were within this difference (Burnham and Anderson, 2002) to finally check for effect sizes of grazing, precipitation and livestock presence on species diversity. We used indicator species analysis (ISA) to assess the preferences of individual species to levels of grazing intensity (Cáceres et al., 2010;

3. Results The two main axes of the ordination of plots based on their species composition corresponded surprisingly well to the two main study gradients (Fig. 2). Study sites separated mainly along the first axis, which had a length of > 5 units pointing to more than one full species turnover. Plots representing different distance classes at a given site were mainly spread along the second axis. In correspondence, post hoc correlations of available secondary variables showed that the first axis was associated with precipitation, while the second axis was correlated with distance. MLU was associated with both axes, increasing with precipitation and decreasing with distance which is in line with our overall design. Post-hoc correlations with ordination axes were significant for all three variables at p < 0.05. Spread of plots along the axes differed between sites. While plots from wetter sites on the left side of the ordination were rather homogeneous within a site (as represented by small polygons, i.e. short distances in ordination space) and also between sites (as represented by overlapping polygons), dry sites on the right side were more scattered. Dry sites thus showed a more pronounced turnover between each other (separated polygons), and they also varied within (stretched polygons). The latter effect results mainly from sacrifice zones around wells in dry areas, as shown by a second DCA where 50m plots were excluded resulting in much smaller spread along axis 2 (Fig. 3). The model selection process for species richness favoured two models that both included precipitation and distance as predictors (Table 2). While the first was an additive model (AIC weight = 0.5), the second had an interaction term (AIC weight = 0.2). The conditional average for the two winner models revealed strong effects of precipitation, distance and two interactions between precipitation and distance. The estimates for the additive terms indicate that richness increased with both precipitation and distance to grazing hotspots. The interaction estimates show that the effect size decreased especially for the middle distances 350m and 750m with increasing precipitation (Table 3). The ranking for models of Simpson's D resulted in two highest ranking models with a second order polynomial on distance (AIC weight = 0.33) and precipitation as an additive factor (AIC weight = 0.15), respectively (Table 2). The weighting of the models indicated a less clear selection than that for richness (models 3&4 with 0.13 and 0.11). The conditional average model included a significant unimodal term but a very weak precipitation effect (Table 3). The three best fitting models within a range of δ AIC = 2 for evenness (Table 2) 4

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Fig. 2. Detrended correspondence analysis. The grey polygons indicate the study sites; with darker shades indicating higher precipitation. A, B, C, D and E are the distances 50m, 150m, 350, 750m and 1500m respectively. In blue, vectors for the significantly correlated environmental variables precipitation, distance and transformed livestock units (MLU, all at p < 0.05. Note that distance classes are also displayed as one continuous variable for a better visualization of the environmental variables. Axis lengths were 5.83 and 4.73, eigenvalues 0.71 and 0.54. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

intermediate plots (BCD, n = 8). 35 species were selected as indicators for less intensively grazed plots (1500m). The combined end of the grazing gradients (groups DE, n = 4 and CDE, n = 16) included common grasses like Koeleria cristata, Cleistogenes songorica and Stipa krylovii as well as shrubs Caragana leucophloea and Caragana microphylla. Species commonly associated with dry areas such as Allium mongolicum, Artemisia frigida, and Asparagus gobicus were selected for less moderately grazed plots (CDE), and species more associated with wetter conditions such as Galium verum, Aster alpinus or Veronica incarna occurred in group E (Table 4).

included MLU (AIC weight = 0.33), precipitation (0.17), and also the null model (0.26). Consequently, none of the effects in the average model were significant (Table 3). However, the model selection process indicated at least a trend for positive effects of both MLU and precipitation on evenness. Analysis of grazing indicator species selected 85 out of a total of 241 species (full summary of the ISA in Supplement D). The majority of these species (46) were associated with one grazing level, fewer with combinations of two or three groups (10 and 29). Specificity was generally very high for all selected species (min = 0.68, max = 1, mean 0.9) indicating that presence of these species is informative. Fidelity was, however, quite low in both single grazing groups (min 0.04, max 0.17, mean 0.07) and combinations of classes (min 0.06, max 0.68, Ø 0.24) reflecting that few indicators are widespread and can be employed across a large range of sites. Plants for 50 m distance (n = 5) were ruderal species such as Atriplex sibirica, Chenopodium aristatum, Astragalus mongholicus, Micropeplis arachnoidea and Artemisia anethifolia (Table 4). Grazing or disturbance resistant species such as Achnatherum splendens, Peganum nigellastrum or Artemisia macrocephala were selected for the first 50–350 m (groups AB, n = 3 and ABC, n = 5). Very abundant and grazing tolerant species such as Convolvulus ammannii, Heteropappus altaicus or Artemisia adamsii are indicator species for

4. Discussion 4.1. Community composition Contrary to our initial hypothesis that grazing would lead to higher heterogeneity under EQ conditions, we found evidence for a homogeneous community structure in EQ systems, and a more pronounced difference among distance classes under NEQ conditions. Plant communities under NEQ dynamics varied strongly along both precipitation and grazing gradients (Fig. 2). Wang et al. (2017) have recently shown that grazing can affect vegetation and soils under NEQ dynamics, yet Fig. 3. Detrended Correspondence Analysis without plot "A" (or distance class 50m; “sacrifice zone”) for comparison with Fig. 2. Note the location and smaller spread of polygons for dry sites in the ordination compared to the intermediate or wet sites. Note that distance classes are displayed as one continuous variable for better visualization of the environmental variables.

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would explain the species turnover within NEQ sites. The distance between sites along the first DCA axis illustrates the responses of plant community composition to the regional conditions. In dry areas, water scarcity and climatic variability act as environmental filters causing high species turnover along the respective climate gradients (Keddy, 1992). With decreasing environmental stress under EQ conditions (or increasing precipitation) species cover and richness (Tables 1 and 3) increases, and compositional similarity between and within sites also becomes higher. The core of the EQ communities therefore consists of the same set of abundant species, while new species, as indicated by increasing richness, have low abundances. This partly contrasts with earlier assumptions that dissimilarity of communities increases with productivity of rangelands (Bai et al., 2007). The similarity in EQ communities can also be seen as an increase in multifunctionality (Maestre et al., 2012). Niche theory states that functional space increases with available resources while at the same time the competition for resources increases (Tilman, 2001). Following this theory, it seems possible that our NEQ systems lack grazing-induced community changes (except at sacrifice zones), and are dominated by the abiotic constraints, while in EQ systems grazing tolerance (or avoidance) dominates community assembly. Considering that with environmental filtering and grazing, two contrasting factors dominate community composition under NEQ and EQ, processes that affect the community assembly in the transition zone become important. If grazing effects depend on climatic conditions, EQbased livestock management may overuse the resources of rangelands that are not yet subject to strong abiotic controls yet still have a small carrying capacity (Vetter, 2005). A possible facet of grazing-induced community changes under EQ conditions is the xerophytization of the vegetation. For instance, a study found that grazing across an altitudinal gradient in Mongolia led to the occurrence of more drought adapted species in higher elevations (Zemmrich et al., 2010). Similar processes were visible in our communities of the transition zone. Some plots in the intermediate sites 7 and especially 8 were more similar to the NEQ sites than the drier site 6 was (see Fig. 2). Overgrazing of intermediate sites in times where these sites received less precipitation or experienced higher variability might have led to the immigration of drought-adapted species or a shift towards their dominance. This xerophytization is often accompanied by the deterioration of the soil (Huang et al., 2007), and confirms reports about degradation in Mongolian rangelands of intermediate precipitation levels (Eckert et al., 2015).

Table 2 Summaries of the candidate models for species richness, Simpson's D and evenness against the main predictors. The models are ranked by AIC. Relevant models are highlighted in bold and were later averaged based on their model weights. Abbreviations: P = mean annual precipitation, MLU = transformed dung counts, D = distance to grazing hotspots. K = number of model parameters (including 5 distance classes as well as site and plot as random effects), AIC = Akaike's Information Criterion, δ_AIC = difference between models, AICwt = model weights (Burnham and Anderson, 2002). Richness

Simpsons D

Evenness

Model

K

AIC

δ_AIC(c)

AIC(c)Wt

P+D P*D P*D2 P + D2 P D D2 NULL MLU

8 12 8 6 4 7 5 3 4

2074.12 2075.99 2076.29 2076.77 2086.08 2088.34 2090.94 2100.24 2102.23

0.00 1.88 2.17 2.65 11.96 14.23 16.82 26.13 28.11

0.50 0.20 0.17 0.13 0.00 0.00 0.00 0.00 0.00

Model

K

AIC

δ_AIC(c)

AIC(c)Wt

D2 P + D2 D MLU NULL P+D P*D2 P P*D

5 6 7 4 3 8 8 4 12

2537.34 2539.22 2539.49 2539.91 2540.44 2541.39 2541.84 2542.30 2548.18

0.00 1.88 2.15 2.57 3.10 4.05 4.50 4.96 10.84

0.39 0.15 0.13 0.11 0.08 0.05 0.04 0.03 0.00

Model

K

AIC

δ_AIC(c)

AIC(c)Wt

MLU NULL P D2 P + D2 P*D2 D P+D P*D

4 3 4 5 6 8 7 8 12

2479.98 2480.45 2481.32 2482.23 2483.13 2484.35 2485.71 2486.64 2492.05

0.00 0.47 1.35 2.26 3.16 4.37 5.74 6.66 12.07

0.33 0.26 0.17 0.11 0.07 0.04 0.02 0.01 0.00

effects are often restricted to the sacrifice zones around grazing hotspots. This corresponds to our results, where grazing effects largely disappeared when plot A was removed (Fig. 3). Trampling and high nutrient intake at grazing hotspots put constraints on plant growth that differ fundamentally from the conditions in the open steppe and can thus be considered as azonal (Grime, 1977; James et al., 1999). This

Table 3 Model parameters for the averaged winner models of species richness, Simpson's D and evenness as depending of mean annual precipitation, Mongolian livestock units (MLU) as well as distance as a measure for grazing intensity. For each model, model estimates of predictors included in the winner models are given (compare with Table 2). Estimates are represented by beta values, “Std. Er.” are standard errors, “x” marks interactions, MLU are transformed dung counts. Note that distance was both tested as continuous variable to test unimodal distributions (“Distance global” & “Distance2”) as well as a factor (classes 50–1500m). Significance levels with codes 0 = ***, 0.001 = **, 0.01 = * are given to illustrate effect sizes, but were not part of the model selection process (Burnham and Anderson, 2002). Predictor

Precipitation MLU Distance global Distance2 Distance 150m Distance 350m Distance 750m Distance 1500m Distance 150m x Prec. Distance 350m x Prec. Distance 750m x Prec. Distance1500m x Prec.

Model for Richness β 0.27

***

Model for Simpson's D Std. Er 0.07

β −0.01 −0.53 0.87

0.22 0.30 0.29 0.34 −0.10 −0.16 −0.16 −0.06

** *** *** *** * *

0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08

6

Model for Evenness Std. Er 0.03

*

0.37 0.37

β 0.03 0.02

Std. Er 0.01 0.03

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Table 4 Frequency table with results of Indicator Species Analysis overlaid. The values are the frequency of indicator species in % and embody the sensitivity of the respective species as indicator for a group (Cáceres et al., 2010). Displayed are only significant indicator species with a p-value < 0.05 and a frequency > 7% in their respective distance group. Allowed group combinations were single groups (A, B, C, D, and E), neighbouring groups (e.g. A + B, D + E) and neighbouring triplets (e.g. C + D + E). The colouring shows the affiliation of a species to a group (e.g. first column coloured: group A, first two columns coloured: group A + B), the colours itself are for a better comparability with the graphics.

Because presence-absence data such as richness can't track differences in the abundance of species, we used evenness to unravel possible effects of precipitation and grazing on dominance structures. However, candidate modelling and model averaging revealed just very minor effects (Table 3), implying stable evenness across all gradients. The Simpson index on the other hand, with a stronger focus on dominant species, was best modelled by a hump-shaped curve. Our model shows that communities close to and far away from a grazing hotspot contain few but dominant species whereas on medium grazed plots communities are rather balanced (Table 3). The model selection and the estimates of the final model for Simpson’ D show that precipitation effects are very low, pointing to independence from climatic conditions. The effect for distance is in line with previous results and shows that grazing can lead to a greater dominance of few species (Huang et al., 2007).

4.2. Patterns in species diversity The observed increase of species richness with precipitation in Central Asia is already well documented (Bai et al., 2007) and is most likely a result of increasing resource availability (Wesche and Treiber, 2012). In moister grasslands, species richness is often highest under moderate grazing. This leads to a hump-shaped relationship between richness and grazing; often summarized under the term intermediate disturbance hypothesis (Roxburgh et al., 2004). Most rangeland models predict that this relationship is strong in productive rangelands and weak in unproductive rangelands (Cingolani et al., 2005). Surprisingly, our data showed the opposite. According to the model estimates, intermediate grazing had an increasingly negative effect on richness with increasing precipitation. In other words, the hump-shaped pattern was less pronounced under EQ than expected. Against the background of evidences for the intermediate disturbance hypothesis in other rangelands, it is likely that the observed Mongolian rangelands under EQ conditions face species decline through overgrazing (Milchunas et al., 1989; Cingolani et al., 2005; Sasaki et al., 2009).

4.3. Evidences on species level One third of all species were selected as indicator species, which indicates the broad range of optima for plant species across our 7

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Fig. 4. Same DCA as Fig. 2 but displayed are only significant species from Indicator Species Analysis. In blue again the significant environmental variables precipitation, MLU (transformed dung counts) and distance. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

our transect where harsh environmental filtering overrides grazing effects (apart from sacrifice zones). Thus, our data imply that grazing results in some species attaining dominance. Evidence is, however, indirect and more research would be needed. In addition, some droughtadapted species such as Artemisia frigida, Caragana leucophloea or Cleistogenes songorica showed high fidelity to their ISA groups, indicating a wide occurrence along the gradient. This could be a hint on xerophytization of communities through overgrazing (Zemmrich et al., 2010).

precipitation gradient. Moreover, species are confined to certain climate levels as shown by the generally high specificity and low fidelity. While the high specificity shows that the selected species are good indicators for the respective grazing intensity, the low fidelity indicates that the selected species are locally restricted. Thus, our indicator species display a high degree of specialization to precipitation. Regarding the specialization of species to grazing intensity, results were different. Although relevant literature states that grazed communities in Mongolia are often dominated by graminoids such as Carex duriuscula and Leymus chinensis or by forbs such as Artemisia adamsii or Salsola pestifera (Bazha et al., 2012; Hilbig, 1995), none of these species were associated with heavy grazing (groups A & AB & ABC) in our study. Instead, the species associated with high grazing intensity were almost exclusively ruderal species, such as the fast-reproducing annuals Chenopodium acuminatum and Artiplex sibirica. Among these was also Peganum nigellastrum, a drought-adapted stress tolerating species that was associated with sacrifice zones (Fig. 4). While this contrasts our hypothesis that NEQ-species would be absent among grazing indicators, it shows that the ruderal plant strategy (Grime, 1977) dominates heavily disturbed areas in rangelands. The other part of our hypothesis stated that grazing would filter out species under EQ conditions. Both ISA and DCAs implied that a number of species become dominant under EQ conditions, especially at intermediate levels of grazing along the local gradients. There are fewer clearly dominant species in sacrifice zones and under relatively low grazing, i.e. at the ends of the local gradients. In fact, group E shows that less grazing resistant species prefer at the weakly grazed end of the grazing gradient while the dominant species of the wetter steppe types (Hilbig, 1995), such as Stipa krylovii or Heteropappus altaicus, occur more often on medium grazed plots (e.g. groups BCD or CDE). Under NEQ conditions, we do not find clear differences between intermediate levels and the extremes, most likely reflecting the overall limited impact of grazing under NEQ conditions. Drought-adapted species such as Asparagus gobicus, Lagochilus ilicifolius or Krascheninnikovia ceratoides are only associated with medium grazed plots. These findings are also reflected in the DCA biplot (Fig. 4). Supposed generalists occur in the centre of the DCA, dominant EQ species are packed together at the wet end of the first axis and the drought-adapted species are widespread along the drier parts of

4.3.1. Synthesis and management implications Our study on grazing effects in Mongolian rangelands yielded three key findings that have important implications for the management of global rangelands. First, community composition in a Mongolian NEQ system was determined by abiotic filtering of species and grazing effects were limited to sacrifice zones. In contrast to assisted livestock management, such as in China (Sneath, 1998), or in African farming systems (Vetter, 2005), Mongolian herds are not supported by additional fodder during harsh times, and the recovery of herds after drought go along with the recovery of the vegetation under NEQ conditions (FernándezGiménez, 2006). Outside sacrifice zones, abiotic constraints effectively control grazing effects in NEQ systems. Second, community composition of our EQ system was determined by grazing, which might lead to some species become more dominant and others become more marginalized. While literature on the management of NEQ systems is relatively sparse, EQ systems are well understood and various strategy guides are available to rangeland managers (Briske et al., 2005; Milchunas et al., 1989). A stricter management of Mongolian EQ systems could probably tackle our observed grazing-induced decrease of richness (which might go along with a decrease of multifunctionality, Maestre et al., 2012) more effectively than traditional livestock management does. Third, xerophytization occurred in our transition zone between EQ and NEQ systems. As pointed out before, NEQ and EQ rangelands are not separated by stable borders. Temporally varying rainfall trigger shifts of their margins (Briske et al., 2005). In regions with the majority of dominant plant species being perennials, such as Central Asia, xerophytization indicates an immigration of droughtadapted species into former moister rangelands due to grazing, changed 8

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climate or soil-induced changes (Wang et al., 2008; Zemmrich et al., 2010). Sensitive transition zones should therefore receive more attention to avoid overgrazing and risk irreversible changes in the community composition. This is even more important in regions with a high amount of ephemerals because already a few years of mismanagement can shift the communities towards unpalatable forbs and foster a lasting loss in rangeland quality. This is captured by classical state-and-transition models (Westoby et al., 1989). Admittedly, our data only present a snapshot from one vegetation period. We argue that our findings are nonetheless representative since rainfall was average compared to other years, and since the majority of species are perennials which do not change quickly in presence or absence (Wesche et al., 2010). However, it would be useful to have data from additional years and from rangelands with different community composition to view the full range of community responses to grazing under MAP changes.

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Statement of authors’ contribution Julian Ahlborn collected data, did the analysis and wrote most of the paper. Birgit Lang and Munkhzul Oyunbileg are associated PhD students and were substantially involved in collecting the data during field work. Batlai Oyuntsetseg organised field work, collected data and provided substantial help with determination of species. Henrik von Wehrden, Karsten Wesche and Christine Römermann designed the study, supervised field- and lab work and made leading contributions to the manuscript. Declaration of competing interest None. Acknowledgements The authors wish to thank Ganbud Yeruultkhuyag for his precious commitment as a field assistant. Neil Collier made valuable comments to methodology and helped improving the writing quality. We are thankful for the two anonymous reviewers for thoughtful and very detailed comments. This work was funded by the German science foundation Deutsche Forschungsgemeinschaft (DFG) – Project Nr. 239358027 (RO 3842/3-1 | WE 5297/3-1 | WE 2601/8-1). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jaridenv.2019.104043. References Addison, J., Friedel, M., Brown, C., Davies, J., Waldron, S., 2012. A critical review of degradation assumptions applied to Mongolia's Gobi Desert. Rangel. J. 34, 125. Bai, Y., Wu, J., Pan, Q., Huang, J., Wang, Q., LI, F., Buyantuyev, A., Han, X., 2007. Positive linear relationship between productivity and diversity: evidence from the Eurasian Steppe. J. Appl. Ecol. 44, 1023–1034. Bartoń, K., 2015. MuMIn: Multi-Model Inference. R Package. Bates, D., Mächler, M., Bolker, B., Walker, S. Fitting Linear Mixed-Effects Models Using Lme4. R Package. Bazha, S.N., Gunin, P.D., Danzhalova, E.V., Drobyshev, Y.I., Prishcepa, A.V., 2012. Pastoral degradation of steppe ecosystems in Central Mongolia. In: Werger, M.J.A., van Staalduinen, M.A. (Eds.), Eurasian Steppes. Ecological Problems and Livelihoods in a Changing World. Springer Netherlands, Dordrecht, pp. 289–319. Bello, F. de, Lepš, J., Sebastià, M.-T., 2006. Variations in species and functional plant diversity along climatic and grazing gradients. Ecography 29, 801–810. Bestelmeyer, B.T., Okin, G.S., Duniway, M.C., Archer, S.R., Sayre, N.F., Williamson, J.C., Herrick, J.E., 2015. Desertification, land use, and the transformation of global drylands. Front. Ecol. Environ. 13, 28–36. Bolker, B., R Development Core Team, 2016. Bbmle: Tools for General Maximum Likelihood Estimation. Briske, D.D., Fuhlendorf, S.D., Smeins, F.E., 2005. State-and-transition models, thresholds, and rangeland health. A synthesis of ecological concepts and perspectives. Rangel. Ecol. Manag. 58, 1–10.

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