- Email: [email protected]
Geomorphology, soil and vegetation patterns in an arid ecotone
Catena 174 (2019) 353–361
Contents lists available at ScienceDirect
Catena journal homepage: www.elsevier.com/locate/catena
Geomorphology, soil and vegetation patterns in an arid ecotone a,⁎
a
Ana I. Casalini , Pablo J. Bouza , Alejandro J. Bisigato a b
T
a,b
IPEEC, CONICET, Blvd. Brown 2915, 9120 Puerto Madryn, Argentina Universidad Nacional de la Patagonia San Juan Bosco, Blvd. Brown 3500, 9120 Puerto Madryn, Argentina
A R T I C LE I N FO
A B S T R A C T
Keywords: Geomorphology Arid environments Spatial data Soil heterogeneity
Ecotonal boundaries are often arranged by following topoedaphic gradients. Despite its relevance, the interrelationships between vegetation and geomorphology have not been adequately documented in most arid regions. The Southern Monte – Patagonia ecotone, defined by anastomosing systems of paleochannels and paleobars, presents an opportunity to study these interrelationships. Our aim was to determine whether topography and/or soil characteristics explained the distribution of communities which are typical of both Phytogeographic Provinces. Vegetation and soil surveys were performed along topographical gradients found between several paleochannels and paleobars. Surface soil texture, organic carbon, total nitrogen, carbonate content and electric conductivity were measured at each site. Digital elevation models and moisture index maps were derived from topographical field data. Soil and plant cover differences between landforms were inspected. Afterwards, species distribution, communities' attributes and its relation with topographical variables were explored. Paleochannels exhibited higher shrub and total cover, as well as higher organic carbon and lower carbonate content than the paleobars. Chuquiraga avellanedae (Asteraceae) dominated the plant community in the paleochannels, while on the contrary Larrea divaricata (Zygophyllaceae) was the most abundant species found on the paleobars. These communities are related to Patagonia and Monte Phytogeographic Provinces, respectively. Species distribution was mainly explained by elevation and secondly by surface water redistribution. Current vegetation patterns strongly mirror the paleolandscape and as a consequence of that, plant communities are interspersed throughout the landscape. Our results emphasize the importance of geomorphology as a factor influencing community distribution along arid ecotones.
1. Introduction Vegetation distribution is closely related to environmental heterogeneity (Arellano et al., 2017). Ecological processes operating at both local and regional scales control the distribution of plant communities within the landscape (Catorci et al., 2014; Münzbergová, 2004). Environmental gradients arising from the combination of geomorphology and hydrology within an area often explain most of the observed plant spatial pattern (Chaneton, 2005; Ding et al., 2018; Poulos and Camp, 2010). Despite the fact that a geo-ecological approach has been recognized as being fundamental in the understanding of rangeland dynamics as well as necessary to accomplish a better land management (Pringle and Tinley, 2003; Zhu et al., 2014), the interrelationships between vegetation and geomorphology that could define species' spatial arrangement have not been adequately documented in most arid regions (Gutierrez-Jurado et al., 2006; Marfo et al., 2018; Moeslund et al.,
2013; but see Mohseni et al., 2017). Since water is the most limiting resource in deserts (Fernández, 2007; Noy-Meir, 1973), the influence of geomorphology on vegetation is commonly ascribed to its effects on water availability (Acebes et al., 2010; McAuliffe, 1994; Wondzell et al., 1996). Low relief positions are moister and cooler than high relief positions (Bromley et al., 1997; Cowles et al., 2018; Wondzell et al., 1996). These differences in water availability, frequently accompanied by changes in physical and chemical soil attributes (Wysocki et al., 2011), make different species occupy different landforms (El-Keblawy et al., 2015; Holtmeier and Broll, 2012; Solon et al., 2007). Geomorphology effects on vegetation are especially relevant when considering ecotones (Gonçalves and Souza, 2014). At these places, environmental constrains restrain species to small fragmented areas topoedaphically similar to their biome core (Buxbaum and Vanderbilt, 2007; Neilson, 1993; Reed et al., 2009). This way, topoedaphic
Abbreviations: GLMM, generalized linear mixed models; IV, indicator values; PCNM, principal coordinates of neighbour matrices; RDA, Redundancy Analysis; SWI, SAGA wetness index ⁎ Corresponding author. E-mail addresses: [email protected] (A.I. Casalini), [email protected] (P.J. Bouza), [email protected] (A.J. Bisigato). https://doi.org/10.1016/j.catena.2018.11.026 Received 24 January 2018; Received in revised form 13 November 2018; Accepted 18 November 2018 0341-8162/ © 2018 Elsevier B.V. All rights reserved.
Catena 174 (2019) 353–361
A.I. Casalini et al.
of rhyolitic and sedimentary rocks (Inselbergs) and endorheic basins. Oldest fluvial terraces correspond to a landscape consisting of wide plateau-like plains make up of Pliocene–Pleistocene fluvial terraces of the Rodados Patagónicos (RP) lithostratigraphic unit (Fidalgo and Riggi, 1970). Due to base level changes, presumably occurred during Upper Pleistocene, younger fluvial terraces (T1-T3), named as Bajo Simpson Formation, were formed (Haller et al., 2005) (Fig. 1). This Formation is constituted by gravelly deposits with a sandy matrix. The Simpson paleo-valley represents the proto-Chubut river and shows a braided stream as can be appreciated in a detailed aerial photograph of the study site (Fig. 2a–b). Fig. 1 shows the geomorphological sketch where the study site is located within the Bajo Simpson terrace (T1). This Bajo Simpson terrace (T1) stopped being functional towards the Pleistocene - Holocene when a new base level change formed a new terrace (T2) (González Díaz and Di Tommaso, 2011). Later, paleochannels developed into small and narrow endorheic basins (pans) receiving fine particles of silt, clay and salts by superficial runoff from the paleobars and by aeolian influx. This way, on the T1 geomorphic surface, an association of soils with contrasting parent materials would have developed (González Díaz and Di Tommaso, 2011). These processes would continue to occur in the present, although with less intensity due to more arid conditions. Recently, paleochannel's soils were classified as Vertic Natrigypsids, while paleobar's soils were classified as Typic Haplocalcids (Casalini, 2016). Climate at the study area is arid, temperate, and windy. Mean annual temperature is 13.5 °C and mean annual precipitation is 233.8 mm, with high inter-annual variation (series 1984–2013) (Laboratory of
heterogeneity at ecotones, which could explain the coexistence of different communities at these landscapes, represents an opportunity to evaluate the effects of geomorphology on species distribution. The general purpose of this study was to determine whether topography and/or soil characteristics influence community distribution along an arid ecotone. We hypothesized that at the ecotone, where the species are under high environmental stress, community distribution will be affected by topography and soils, which are determinant in the competitive ability of plants. Particularly, we expect that different landforms (i.e. different elements of the landscape, paleochannels and paleobars in our case) will be occupied by distinct communities and soils. Particularly in this research, we propose that in Northeastern Patagonia the Larrea divaricata (Zygophyllaceae) community, typical of the Southern Monte (Bisigato and Bertiller, 1997), and the Chuquiraga avellanedae (Asteraceae) community, which is more characteristic of those areas related to the Patagonia Phytogeographic Provinces (Beeskow et al., 1995) coexist by means of the underlying geomorphological heterogeneity. 2. Methods 2.1. Study area Field work was carried out in the northeast of the Chubut Province, in Argentina (W 65°05′, S 42°55′). The landscape of this region is mainly characterized by a succession of old river terrace levels of different ages (Fig. 1). In sectors, these plains are interrupted by outcrops
Fig. 1. SRTM relief map and geomorphological sketch of the study area. RP1-RP6: Terrace levels of Rodados Patagónicos lithoestrafigraphic unit. T1–T5: Terraces levels of the Chubut River, T1–T3: Bajo Simpson Formation (BS Fm.), T4 intermediate terrace (IT), T5: lower valley (LV). 354
Catena 174 (2019) 353–361
A.I. Casalini et al.
b)
a) PB PB
PC
PC PB PC
600m
c)
d)
e)
Fig. 2. Study site's images: a) Black and white aerial photograph of the study site showing the anastomosing systems of paleochannels (PC) and paleobars (PB), b) example of the position of transects (lines) and plots (rectangle). The rectangle in a) indicates the area shown in b). Photographs of c) community of C. avellanedae, d) community of L. divaricata and e) terrain unevenness from a paleochannel with C. avellanedae dominance to a paleobar with L. divaricata dominance. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
method measures plant cover and species composition of herbaceous vegetation and shrubs and it's suitable for large areas sampling. A measuring tape is extended to create transects across the site while an observer proceeds along the line-transect identifying plants intercepted by the tape and recording the intercepted distance. Vegetation cover is computed by adding all intercepted distances by species and expressing each sum as a proportion of tape length.
Climatology, CENPAT-CONICET, Puerto Madryn). Floristically the study area corresponds to the ecotonal area between the Monte and Patagonia Phytogeographic Provinces. Plant cover is organized in a mosaic of plant patches associated to soil mounds dispersed in a matrix of bare soil (Bisigato and Bertiller, 1997; Rostagno and del Valle, 1988). Most patches are dominated by Larrea divaricata or Chuquiraga avellanedae which are accompanied by other less frequent shrub species, grasses and forbs.
2.2.2. Community attributes Some community attributes were also estimated in order to characterize each site. Species richness (S) was determined as the number of species sampled in each site. Species diversity was estimated using Shannon index of species diversity (Nagendra, 2002; Spellerberg and Fedor, 2003)
2.2. Field sampling 2.2.1. Vegetation surveys Nine pairs of sites encompassing a paleochannel and the closest paleobar were selected in such a way that they were dispersed in a paddock located within the paleodrainage, avoiding sites evidencing natural or anthropogenic disturbances. In each landform (i.e. paleochannel and paleobar) three parallel transects 50 m in length were set (Fig. 2b). Total cover and cover by species along each transect was evaluated following the line intercept method (Canfield, 1941). This
5
H = − ∑ pi ln pi i=1
(1)
where pi is the proportion of individuals for species i. The Shannon index increases as both community richness and evenness increase. 355
Catena 174 (2019) 353–361
A.I. Casalini et al.
2.2.3. Soil sampling Since topsoil exhibits the highest organic matter and root biomass accumulation throughout the soil profile, it is also the soil layer of greater nutrient uptake by plants (Jobbágy and Jackson, 2001; Muñoz Vallés et al., 2015). In each site, the proportion intercepted by soil mounds or bare soil areas (hereafter microsites) was calculated for each transect and three superficial (0–10 cm) soil samples were randomly taken from each microsite with a metallic tube (5 cm diameter). Samples were transported to the laboratory in plastic bags where they were air dried. Afterwards, microsite samples were combined in a unique pooled sample according to the proportion of each microsite in the site.
Table 1 Plot attributes. Plot area, number of individuals of C. avellanedae and L. divaricata, and topographic characteristics of each plot. Plots are sorted according to their area. Plot
I 2
Area (m ) C. avellanedae L. divaricata Dominant species density (n/m2) Elevation range (m) SWI (min-max)
2.2.4. Soil characteristics The soil texture of the pooled samples was determined in the laboratory by the hydrometer method of particle-size analysis (Day, 1965). Organic carbon content was analysed with the Walkley and Black (1934) method and total nitrogen with the semi-micro Kjeldahl procedure (Bremner and Mulvaney, 1982). The carbonate content was calculated from the amount of CO2 released by reaction with HCl (Loeppert and Suarez, 1996). Electric conductivity was measured in saturation extract following Rhoades (1982).
II
III
IV
1000 180 108 0,288
1200 282 183 0.387
1200 182 146 0.273
1600 388 155 0.339
1.27 (−0.78)-9.35
0.93 (−0.01)-9.28
2.01 1.28–9.25
2.70 0.65–9.23
Table 1. 2.3. Terrain modelling Digital elevation models (DEM) with grid cells of 0.5 m wide were built by interpolation (Kriging) of all registered points (i.e. those belonging to shrubs of both species and those recorded among shrubs) using ‘geoR’ (Ribeiro and Diggle, 2001) and ‘raster’ (Hijmans and van Etten, 2012) packages in R statistical computing software (v. 3.1.3, R Core Team, 2015). SAGA wetness index was derived from each DEM using SAGA external application on QGIS 2.8.1 (QGIS Development Team 2016). This index is a variation of the Topographic Wetness Index (TWI) based on a modified catchment area calculation, which does not treat the flow as a thin film as done in the calculation of catchment areas in conventional algorithms. As a result, the SWI tends to assign higher moisture potentials to grid cells situated in valley floors, what would better represent reality in the field (Hedley et al., 2013). High SWI values indicate flow accumulation at a given point as a product of topographic control over hydrological processes (Franklin, 2010).
2.2.5. Species spatial pattern and topography Four locations where communities dominated either by Larrea divaricata or Chuquiraga avellanedae coexisted were selected in order to determine the relationship between topography and the distribution of both species. A ten meter width plot of varying length was set in each location. Plot length was defined by the distance between the centre of a paleochannel and the top of the closest paleobar and ranged between 100 and 160 m (Figs. 2b and 3). The centre of each individual of the dominant species was recorded in each plot with a Pentax V-227 total station. This electronic theodolite registers the coordinates (i.e. x, y) and the soil level (z) keeping a precision of ± 3.6 mm for distances below 100 m between the total station and the sampled points, as those handled in this sampling. To obtain a more realistic digital terrain model the coordinates and soil level of the points among shrubs were also recorded. Details of the topographic characteristics and the dominant species abundance corresponding to each plot are provided at
2.4. Data analysis 2.4.1. Vegetation surveys and soil characteristics Soil characteristics (texture, electrical conductivity, organic carbon,
Fig. 3. Maps of elevation (a) and SWI (b) for each plot (I-IV). Paleobars are on the left side and paleochannels on the right side. Map scales presented on top are valid for all plots. Crosses (x) and circles (○) at elevation maps indicate individual positions of L. divaricata and C. avellanedae, respectively. 356
Catena 174 (2019) 353–361
A.I. Casalini et al.
total nitrogen and carbonate content), specific plant cover, and community attributes (species richness, Shannon diversity index and total vegetation cover) differences between landforms (1 factor, 2 levels: 9 paleobars and their closest paleochannels) were inspected by one-way ANOVA. When ANOVA assumptions were not fulfilled, the nonparametric Mann-Whitney test was performed. Both analyses were performed using the R software (R Development Core Team, 2015). Indicator species analysis (Dufrêne and Legendre, 1997) was used to find the indicator species of plant communities occupying each landform. Indicator values (IV) of each species were calculated using the Rpackage ‘labdsv’ (Roberts, 2010). Species cover values were organized in a matrix of p species by n sites. Species by sites matrix was submitted to a direct gradient analysis (Redundancy Analysis-RDA) which assesses the influence of spatial and soil variables on species composition and abundance. When two or more soil variables were correlated only one was included in the RDA. Prior to analysis, data were Hellinger-transformed (Legendre and Gallagher, 2001). This transformation is particularly suited to analyse species abundance data, giving low weights to variables with low counts and many zeros (Borcard et al., 2011; Legendre and Legendre, 1998). Principal coordinates of neighbour matrices (PCNM) method was used to introduce space as an explanatory variable in the analysis (Borcard and Legendre, 2002). By using this method complex spatial patterns can be modelled at different spatial scales (Jones et al., 2008). We computed PCNM variables with the ‘vegan’ package (Oksanen et al., 2013) of the R-Project. A permutation procedure was performed to test the significance of the RDA models including soil and PCNM variables. When a model was significant, the significance of each variable was evaluated by Monte Carlo simulations using ‘vegan’ package. The relationship between community attributes and soil characteristics was evaluated by correlation and regression analysis. Finally we assessed the relationship among community attributes and site scores on RDA axes 1 and 2 by means of Pearson's correlation coefficients.
Table 2 Species composition of plant communities. Specific plant cover (%, mean ± standard error, n = 9) of paleochannels and paleobars. +: Specific plant cover < 0.01%. Different superscript letters indicate differences in plant cover between landforms. Numbers in bold indicate species with significant indicator values.
2.4.2. Species distribution and topographic characteristics Species identity was treated as a binary response variable and it was related to topographical variables (elevation and SWI) through generalized linear mixed models (GLMM; binomial errors, logistic link). Model adjustment was performed in R software using the ‘lme4’ package (Bates et al., 2008) determining the probability of a shrub of being L. divaricata (Gómez Rubio and López-Quílez, 2013). Elevation and SWI were considered fixed effects, while location (plot) was treated as a random effect. The regression is given by the equation
⎛ pij ⎞ log it (pij ) = ln ⎜ = β0 + 1 − pij ⎟ ⎝ ⎠
n=1
Paleochannel (%)
Paleobar (%)
Perennial dicots Acantholippia seriphioides Baccharis spp. Boopis anthemoides Bougainvillea spinosa Chuquiraga aurea Chuquiraga avellanedae Chuquiraga erinacea Cyclolepis genistoides Ephedra ochreata Hoffmannseggia erecta Hoffmannseggia trifoliata Junellia seriphioides Larrea divaricata Larrea nitida Lycium ameghinoi Lycium chilense Mulguraea ligustrina Nassauvia fuegiana Perezia recurvata Prosopidastrum striatum Prosopis alpataco Schinus johnstonii Grasses Bromus catharticus Elymu ssp. Jarava neaei Nassella tenuis Pappostipa humilis Pappostipa speciosa Poa lanuginosa Poa ligularis Total
20.42 ± 1.61a +
14.72 ± 1.68b 0.17 ± 0.07 + + +
+ 0.64 ± 0.30 + 14.58 ± 0.82
2.67 ± 0.51 2.33 ± 0.63
+ + 0.30 ± 0.07 + + 0.49 ± 0.21 2.04 ± 0.67 0.67 ± 0.19 +
0.70 ± 0.27 0.74 ± 0.37 + 0.66 ± 0.15a + + 0.07 ± 0.03 0.19 ± 0.07 + 0.26 ± 0.07 0.01 ± 0.01 0.12 ± 0.06 21.09 ± 1.66a
0.01 ± 0.01 0.10 ± 0.05 + 5.05 ± 0.66 + 0.36 ± 0.13 0.53 ± 0.22 + 0.03 ± 0.03 2.21 ± 0.82 0.66 ± 0.23 0.33 ± 0.22 1.20 ± 0.24a 0.04 ± 0.03 + 0.03 ± 0.02 0.52 ± 0.18 0.02 ± 0.01 0.40 ± 0.13 0.08 ± 0.03 0.12 ± 0.03 15.93 ± 1.80b
and e) which was found on paleobars (Table 2). Indicator species of paleochannel community were Lycium ameghinoi (IV = 0.89), Chuquiraga avellanedae (IV = 0.84), Larrea nitida (IV = 0.84), Hoffmannseggia trifoliata (IV = 0.74), and Bougainvillea spinosa (IV = 0.62). Likewise, Chuquiraga erinacea spp. hystrix (IV = 1.00), Larrea divaricata (IV = 0.99), and Mulguraea ligustrina (IV = 0.88) were the indicator species of the paleobar community. Richness was higher at paleobars (p < 0.01, F(1;16) = 11.8) and although diversity showed the same tendency the difference between landforms was only marginally significant (p = 0.06, F(1;16) = 4.11). In contrast, total vegetation cover showed the opposite trend and it was higher at paleochannels (p = 0.05, F(1;16) = 4.45). In general, community attributes and soil characteristics were uncorrelated at the site level. We only found a significant positive relationship between soil carbonate and Shannon diversity index (r = 0.59, p = 0.01, t(16) = 2.93, y = (0.13 ± 0.04) x + (1.73 ± 0.12), R2 = 0.35, p = 0.01, F(1;16) = 2.92). Other relationships, such as those between carbonate and both richness (p = 0.07, t(16) = 1.97) and total vegetation cover (p = 0.07, t(16) = 1.94), were marginally significant. The analysis of the relationship between species and soil characteristics (RDA) was statistically significant, indicating that environmental variables explain 44.3% of total variance in species composition. In contrast, the PCNM model was not significant. Plant communities belonging to each landform were segregated along the first RDA-axis (RDA1) (Fig. 4). Clay, silt and electrical conductivity were excluded from the RDA because they were correlated with sand concentration, while the exclusion of nitrogen concentration was due to its correlation with organic carbon. Carbonate and sand were
k
∑ βn Zn,i + (μ0j + εij)
Species
(2)
where pij is the probability of an event occurring (in this case the shrub being L. divaricata) associated with a given observation i (each shrub) at plot j and n are covariables (elevation and SWI). In this model, the random effect μ0j represents the deviation of the j-th higher-level unit's average (plot) from the overall intercept β0 and ε represents the unknown random residuals (Manning, 2007; Cayuela, 2012). Prior to this analysis covariables where tested for correlation by means of Pearson's correlation coefficients. 3. Results 3.1. Vegetation and soil characteristics Chuquiraga avellanedae dominated the community associated to paleochannels (Fig. 2c and e), which exhibited higher shrub and total cover compared to the community dominated by L. divaricata (Fig. 2d 357
Catena 174 (2019) 353–361
A.I. Casalini et al.
Fig. 4. Redundancy analysis (RDA) of the 18 vegetation relevés. The percentage of the variance explained by each axis is indicated in parenthesis. Filled circles indicate paleochannels and empty circles paleobars. Significant environmental variables are showed by thin arrows. The thick arrow indicates the Pearson's correlation coefficient between species richness and the axes (rRDA1 and rRDA2).
Table 3 Soil attributes. Physical and chemical characteristics of soils of paleochannels and paleobars (n = 9, mean ± standard error). Different lowercase letters indicate significant (p ≤ 0.05) differences between landforms. Soil characteristic
Paleochannel
Paleobar
Soil texture (%) Sand Silt Clay Electrical conductivity (μS·cm−1) Total nitrogen (%) Organic carbon (%) Carbonate (%)
77.2 ± 1.7a 12.3 ± 1.1a 10.5 ± 1.3a 2056 ± 661a 0.057 ± 0.004a 0.717 ± 0.101a 1.90 ± 0.22b
80.6 ± 0.8a 10.6 ± 1.6a 8.8 ± 1.2a 580 ± 66a 0.056 ± 0.003a 0.449 ± 0.018b 3.34 ± 0.30a
Table 4 Larrea divaricata density models. Component's coefficients obtained by adjustment to logistic regressions for L. divaricata density, χ2 statistics and its p-values for the change in deviance in relation to the null model of GLMMs with location as a random factor and elevation and SWI as fixed factors. Coefficients values for C. avellanedae are opposite and equal in magnitude. Variable
χ2
p-Value
Linear term (β1 ± EE)
Random effect (μ ± EE)
Elevation SWI
441.56 47.83
< 0.001 < 0.001
2.43 ± 0.14 −0.31 ± 0.05
1.40 ± 0.29 0.07 ± 0.06
4. Discussion Our results support the hypothesis of geomorphology and soils affecting community distribution. Paleochannels, with Vertic Natrigypsids soils that are moister and richer in organic matter, are dominated by C. avellanedae. In contrast, paleobars with Typic Haplocalcids soils and higher carbonate concentration are drier and occupied by richer and more diverse communities dominated by L. divaricata (Tables 2 and 3, Fig. 2c–e). Soil influence over plant distribution in this ecotonal area agrees with previous work in Central Monte Desert (Flores et al., 2015; Roig and Rossi, 2001) and many arid systems around the world (Buxbaum and Vanderbilt, 2007; El-Keblawy et al., 2015; Fernandez-Gimenez and Allen-Díaz, 2001; Grellier et al., 2014; Parker, 1991; Schenk et al., 2003; Zare et al., 2011). The greater diversity under drier conditions at paleobars compared to paleochannels agrees with Rueter and Bertolami (2010) for other environments of Patagonia, where dense shrublands occupying moister soils showed lower diversity than open shrublands under more xeric conditions. Moreover, higher organic carbon concentration in paleochannels is in accordance with greater organic matter levels for concavities, furrows, washes and surface depressions (Cantón et al., 2004; Chi et al., 2009; Sebastiá, 2004; Wondzell et al., 1996) and is consistent with the trend towards greater humidity in that landform (Pei et al., 2010). Conversely, Larrea dominance over highly calcic soils was also reported in North America (Hamerlynck et al., 2000; Laity,
negatively related to RDA1, while organic carbon was positively related to this axis. Consistently, soils from paleobars exhibited significantly higher carbonate and lower organic carbon concentration than soil from paleochannels, though no difference was observed in texture composition between both soil types (Table 3). Site scores on RDA1 axis were negatively and significantly related with richness (t(16) = −3.19, p = 0.01, r = −0.62). Although Shannon diversity index showed the same tendency correlation was only marginally significant (t(16) = −1.79, p = 0.09, r = 0.40). On the contrary, total vegetation cover showed a marginally significant positive correlation with site scores on RDA1 (t(16) = 1.76, p = 0.10, r = 0.20).
3.2. Species distribution and topographic characteristics Since elevation and SWI were significantly and negatively correlated (r = −0.32, p < 0.01, t = −13.63) separated GLMM were adjusted. The association of elevation with L. divaricata was positive (Fig. 2e), contrary to SWI (Table 4). These results suggest that L. divaricata is concentrated on higher elevations and dryer areas compared to C. avellanedae. The model considering elevation explained a greater part of species distribution respect to SWI (greater χ2, Table 4).
358
Catena 174 (2019) 353–361
A.I. Casalini et al.
variables as a valuable tool to predict vegetation responses to climate change at arid ecotones. Specifically, elevation was the best explanatory variable, although modelling with SWI also showed a good fit to the data. The explanatory power of SWI could be improved by performing topographic surveys at greater extents, comprising the two paleobars adjoining each paleochannel. In summary, in the study area, characterized by anastomosing systems of paleochannels and paleobars, the coexistence of plant communities related to different Phytogeographic Provinces can be ascribed to geomorphology. Specifically, as a result of soil and topographical properties that vary between landforms, a partial replacement of C. avellanedae by L. divaricata occurs along the paleochannel/paleobar gradient. As a consequence, both communities are interspersed at a landscape scale which makes it impossible to locate a net limit between the Monte and Patagonia Phytogeographic Provinces at a regional scale. In this manner, constraints imposed by geomorphological factors (e.g. topography, water redistribution in the landscape and soil type) give rise to vegetation patterns that currently mirror the paleolandscape.
2008) and has been linked to its tolerance for high soil osmotic potential (Shreve and Hinckley, 1937). In addition, it has been seen that boundaries of these Larrea communities are related to changes in soilparent material (Barbour, 1969). Due to the pedogenic processes (Section 2.1) in which paleochannels would receive fine particles by superficial runoff from the paleobars, a coarser texture would be expected in the latter. Although soil texture differences between landforms were not statistically significant, a greater predominance of fine particles in paleochannels was observed (Table 3). It is possible that high variability among samples prevented us from finding a statistical significance between landforms. This variability could be related to the pooled nature of the soil samples. Although this technique allowed us to obtain a unique value for each landform in each site, enabling a multivariate analysis that integrates environmental and community data, it may have masked differences between soils. The same would apply to the electrical conductivity, which would be higher in the paleochannels due to the superficial runoff of water towards these landforms that would function as endorheic basins (Section 2.1, Fig. 3). Elevation was the strongest topographical variable explaining species distribution in this investigation (Table 4). This is in agreement with findings by several authors in different biomes. Elevation has been one of the environmental factors more frequently related to species distribution in desert landscapes (Bisigato et al., 2009; Cantón et al., 2004; Florinsky and Kuryakova, 1996; Miller and Franklin, 2002; Reynolds and Wu, 1999), as well as in forests (Anic et al., 2010; Piraino et al., 2015), grasslands (Peco et al., 1998), and mires (Luoto and Seppälä, 2002). Since topography effects over vegetation are indirect (Lin et al., 2011), plants would not be responding to elevation directly but rather to a suite of covarying biotic and abiotic factors. Mostly hydrology, but also resource availability, and biotic interactions may be controlled by topography even in relatively flat lowland areas (Moeslund et al., 2013). Our results are in agreement with this assertion. The negative correlation between elevation and SWI as well as the GLMM considering SWI as a fixed factor (Table 4) indicate higher water availability at paleochannels. This is consistent with higher plant cover and organic carbon content at that landform. In fact, previous work carried out in the area at a coarser scale show lower humidity (NDWI) and vegetation cover (NDVI) indices in paleobars respect to paleochannels (Casalini, 2016). This study's findings should be considered when evaluating vegetation changes in disturbed landscapes. For example, most artificial watering points in the area are built at paleochannels and/or at other places where water accumulates after rainfall. As well, a space for time substitution approach was frequently employed to assess grazing effects on vegetation and soil (e.g. Bisigato and Bertiller, 1997). Since watering points are not randomly located throughout the landscape, there is a greater probability of the highly grazed areas (which are very close to watering points) to be often located at paleochannels and other topographically lower landforms. In consequence, an inexperienced or distracted observer could wrongly conclude that high grazing pressure is associated to certain vegetation and/or soil characteristics which are in fact related to geomorphology. Thus, overlooking geomorphology can lead to erroneous conclusions which in turn can conduce to inaccurate management practices. Recently, ecotones have received increasing interest as it is expected that future climate changes could strongly affect vegetation (Camarero et al., 2017; Gebrekirstos et al., 2014; King et al., 2013), because species living there are at the limit of their distribution area (Hansen et al., 1988). Recent studies carried out at other arid ecotones throughout the world have also emphasized the importance of geomorphology as a control of species distribution (Bestelmeyer et al., 2006; Buxbaum and Vanderbilt, 2007; Corenblit et al., 2011; Silva and Souza, 2018) and recruitment (Peters et al., 2010). The results of this work support these evidences and the suitability of modelling species distribution from spatial data as well as their correlation with geomorphological
5. Conclusion Species distribution modelling from topographic variables together with the analysis of edaphic variables contributed with valuable information for understanding influence of geomorphology over species abundance at this ecotone. These results could be further improved including simultaneous measurements of soil moisture along the topographic gradient. A thorough geomorphological and edaphological analysis is needed prior to any vegetation study at a landscape scale. Species nomenclature Flora Argentina Plantas vasculares de la República Argentina (http://www.floraargentina.edu.ar/, accessed on 1 Aug 2015). Acknowledgements We thank Lucas Bandieri, Romina Palacio, Ileana Rios, Verónica Soñez, Marina Muñoz, Cristian Barrionuevo, Claudia Saín, Lina Videla and Estela Cortez for their assistance during field sampling and laboratory work. Julio Lancelotti, Gustavo Pazos, Laura Lamuedra and Germán Cheli helped us with data analysis. Recognition is also given to Mr. Valentín Simpson who allowed the access to the study area in Estancia “Bajo Simpson”. This research was supported by PICT 20122436, Agencia Nacional de Promoción Científica y Tecnológica. AI Casalini fellowship was supported by CONICET (National Research Council of Argentina). References Acebes, P., Traba, J., Peco, B., Reus, M.L., Giannoni, S.M., Malo, J.E., 2010. Abiotic gradients drive floristic composition and structure of plant communities in the Monte Desert. Rev. Chil. Hist. Nat. 83, 395–407. Anic, V., Hinojosa, L.F., Díaz-Forester, J., Bustamante, E., de la Fuente, L.M., Casale, J.F., de la Harpe, J.P., Montenegro, G., Ginocchio, R., 2010. Influence of soil chemical variables and altitude on the distribution of high-alpine plants: the case of the Andes of central Chile. Arct. Antarct. Alp. Res. 42, 152–163. Arellano, G., Umaña, M.N., Macía, M.J., Loza, M.I., Fuentes, A., Cala, V., Jørgensen, P.M., 2017. The role of niche overlap, environmental heterogeneity, landscape roughness and productivity in shaping species abundance distributions along the Amazon–Andes gradient. Glob. Ecol. Biogeogr. 26, 191–202. Barbour, M.G., 1969. Age and space distribution of the desert shrub Larrea divaricata. Ecology 55, 245–261. Bates, D., Maechler, M., Dai, B., 2008. The lme4 Package. http://lme4.r-forge.r-project. org/. Beeskow, A.M., Elissalde, N.O., Rostagno, C.M., 1995. Ecosystem changes associated with grazing intensity on the Punta Ninfas rangelands of Patagonia, Argentina. J. Range Manag. 48, 517–522. Bestelmeyer, B.T., Ward, J.P., Havstad, K.M., 2006. Soil-geomorphic heterogeneity governs patchy vegetation dynamics at an arid ecotone. Ecology 87, 963–973. Bisigato, A.J., Bertiller, M.B., 1997. Grazing effects on patchy dryland vegetation in northern Patagonia. J. Arid Environ. 36, 639–653.
359
Catena 174 (2019) 353–361
A.I. Casalini et al.
D., Lorentz, S., 2014. Soil clay influences Acacia encroachment in a South African grassland. Ecohydrology 7, 1474–1484. Gutierrez-Jurado, H.A., Vivoni, E.R., Harrison, J.B.J., Guan, H., 2006. Ecohydrology of root zone water fluxes and soil development in complex semiarid rangelands. Hydrol. Process. 20, 3289–3316. Haller, M.J., Meister, C.M., Monti, A.J., Weiler, N., 2005. Hoja Geológica 4366-II Puerto Madryn. Programa Nacional de Cartas Geológicas 1:250.000, Buenos Aires. Hamerlynck, E.P., Mcauliffe, J.R., Smith, S.D., 2000. Effects of surface and sub-surface soil horizons on the seasonal performance of Larrea tridentata (creosotebush). Funct. Ecol. 14, 596–606. Hansen, A.J., di Castri, F., Naiman, R.J., 1988. Ecotones: what and why. Biol. Int. 17, 9–46. Hedley, C.B., Roudier, P., Yule, I.J., Ekanayake, J., Bradbury, S., 2013. Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling. Geoderma 199, 22–29. Hijmans, R.J., van Etten, J., 2012. raster: Geographic Analysis and Modeling With Raster Data. R Package Version 2.0–12. R Found. Stat. Comput., Vienna. http://cran.rproject.org/package=raster. Holtmeier, F.K., Broll, G., 2012. Landform influences on treeline patchiness and dynamics in a changing climate. Phys. Geogr. 33, 403–437. Jobbágy, E.G., Jackson, R.B., 2001. The distribution of soil nutrients with depth: global patterns and the imprint of plants. Biogeochemistry 53, 51–77. Jones, M.M., Tuomisto, H., Borcard, D., Legendre, P., Clark, D.B., Olivas, P.C., 2008. Explaining variation in tropical plant community composition: influence of environmental and spatial data quality. Oecologia 155, 593–604. King, D.A., Bachelet, D.M., Symstad, A.J., 2013. Climate change and fire effects on a prairie–woodland ecotone: projecting species range shifts with a dynamic global vegetation model. Ecol. Evol. 3, 5076–5097. Laity, J., 2008. Deserts and Desert Environments. Wiley-Blackwell, UK. Legendre, P., Gallagher, E.D., 2001. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280. Legendre, P., Legendre, L., 1998. Numerical ecology. In: Developments in Environmental Modelling, Second English edition. 20 Elsevier, Amsterdam (853 pp.). Lin, Y., Chang, L., Yang, K., Wang, H., Sun, I., 2011. Point patterns of tree distribution determined by habitat heterogeneity and dispersal limitation. Oecologia 165, 175–184. Loeppert, R.H., Suarez, D.L., 1996. Carbonate and gypsum. In: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3—SSSA Book Series n.5 ASA and SSSA, Madison, WI, USA. Luoto, M., Seppälä, M., 2002. Modelling the distribution of palsas in Finnish Lapland with logistic regression and GIS. Permafr. Periglac. Process. 13, 17–28. Manning, C., 2007. Generalized Linear Mixed Models (illustrated with R on Bresnan et al.'s datives data). https://nlp.stanford.edu/manning/courses/ling289/GLMM.pdf. Marfo, T.D., Resjek, K., Vranova, V., 2018. Spatial variations in soil properties across ecotones: a short review. Bull. Geogr. Phys. Geogr. Ser. 14, 71–77. McAuliffe, J.R., 1994. Landscape evolution, soil formation, and ecological patterns and processes in Sonoran Desert bajadas. Ecol. Monogr. 64, 111–148. Miller, J., Franklin, J., 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol. Model. 157, 227–247. Moeslund, J.E., Arge, L., Bøcher, P.K., Dalgaard, T., Svenning, J.C., 2013. Topography as a driver of local terrestrial vascular plant diversity pattern. Nord. J. Bot. 31, 129–144. Mohseni, N., Sepehr, A., Hosseinzadeh, S.R., Golzarian, M.R., Shabani, F., 2017. Variations in spatial patterns of soil–vegetation properties over subsidence-related ground fissures at an arid ecotone in northeastern Iran. Environ. Earth Sci. 76, 234. Muñoz Vallés, S.M., Cambrollé, J., Gallego-Fernández, J.B., 2015. Effect of soil characteristics on plant distribution in coastal ecosystems of SW Iberian Peninsula sand spits. Plant Ecol. 216, 1551–1570. Münzbergová, Z., 2004. Effect of spatial scale on factors limiting species distributions in dry grassland fragments. J. Ecol. 92, 854–867. Nagendra, H., 2002. Opposite trends in response for the Shannon and Simpson indices of landscape diversity. Appl. Geogr. 22, 175–186. Neilson, R.P., 1993. Transient ecotone response to climatic change: some conceptual and modelling approaches. Ecol. Appl. 3, 385–395. Noy-Meir, I., 1973. Desert ecosystems: environment and producers. Annu. Rev. Ecol. Syst. 4, 25–51. Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., O'Hara, R.B., Simpson, G.L., Solymos, P., Henry, M., Stevens, H., Wagner, H., 2013. vegan: community ecology package. R package version 2.3-2. http://cran.r-project.org/package=vegan. Parker, K.C., 1991. Topography, substrate, and vegetation patterns in the northern Sonoran Desert. J. Biogeogr. 18, 151–163. Peco, B., Ortega, M., Levassor, C., 1998. Similarity between seed bank and vegetation in Mediterranean grassland: a predictive model. J. Veg. Sci. 9, 815–828. Pei, T., Qin, C.Z., Zhu, A.X., Yang, L., Luo, M., Li, B., Zhou, C., 2010. Mapping soil organic matter using the topographic wetness index: a comparative study based on different flow-direction algorithms and kriging methods. Ecol. Indic. 10, 610–619. Peters, D.P., Herrick, J.E., Monger, H.C., Huang, H., 2010. Soil-vegetation-climate interactions in arid landscapes: effects of the North American monsoon on grass recruitment. J. Arid Environ. 74, 618–623. Piraino, S., Abraham, M.E., Diblasi, A., Juñent, F.A.R., 2015. Geomorphological-related heterogeneity as reflected in tree growth and its relationships with climate of Monte Desert Prosopis flexuosa DC woodlands. Trees 29, 903–916. Poulos, H.M., Camp, A.E., 2010. Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands. Ecology 91, 1140–1151. Pringle, H., Tinley, K., 2003. Are we overlooking critical geomorphic determinants of landscape change in Australian rangelands? Ecol. Manag. Restor. 4, 180–186. R Development Core Team, 2015. R: A Language and Environment for Statistical
Bisigato, A.J., Villagra, P., Ares, J.O., Rossi, B., 2009. Vegetation heterogeneity in Monte Desert ecosystems: a multi-scale approach linking patterns and processes. J. Arid Environ. 73, 182–191. Borcard, D., Legendre, P., 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153, 51–68. Borcard, D., Gillet, F., Legendre, P., 2011. Unconstrained ordination. In: Borcard, D., Gillet, F., Legendre, P. (Eds.), Numerical Ecology With R. Springer, New York, pp. 115–151. Bremner, J.M., Mulvaney, C.S., 1982. Nitrogen-total regular Kjeldhal method. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis—Chemical and Microbiological Properties. Series Agronomy No. 9 American Society of Agronomy, Wisconsin, pp. 595–624. Bromley, J., Brouwer, J., Barker, A.P., Gaze, S.R., Valentine, C., 1997. The role of surface water redistribution in an area of patterned vegetation in a semi-arid environment, south-west Niger. J. Hydrol. 198, 1–29. Buxbaum, C., Vanderbilt, K., 2007. Soil heterogeneity and the distribution of desert and steppe plant species across a desert-grassland ecotone. J. Arid Environ. 69, 617–632. Camarero, J.J., Linares, J.C., García-Cervigón, A.I., Batllori, E., Martínez, I., Gutiérrez, E., 2017. Back to the future: the responses of alpine treelines to climate warming are constrained by the current ecotone structure. Ecosystems 20, 683–700. Canfield, R., 1941. Application of the line interception method in sampling range vegetation. J. For. 39, 388–394. Cantón, Y., Del Barrio, G., Solé-Benet, A., Lázaro, R., 2004. Topographic controls on the spatial distribution of ground cover in the Tabernas badlands of SE Spain. Catena 55, 341–365. Casalini, A.I., 2016. Heterogeneidad de la vegetación en el ecotono entre las provincias fitogeográficas del Monte y Patagónica. Descripción y factores asociados. Universidad Nacional del Sur, Bahía Blanca, Argentina (87 pp.). Catorci, A., Velasquez, J.L., Cesaretti, S., Malatesta, L., Tardella, F.M., Zeballos, H., 2014. How environment and grazing influence floristic composition of dry Puna in the southern Peruvian Andes. Phytocoenologia 44, 103–122. Cayuela, L., 2012. Modelos lineales mixtos (LMM) y modelos lineales generalizados mixtos (GLMM) en R. Universidad Rey Juan Carlos, España (65 pp.). Chaneton, E.J., 2005. Factores que determinan la heterogeneidad de la comunidad vegetal en diferentes escalas espaciales. In: Oesterheld, M., Aguiar, M., Ghersa, C.M., Paruelo, J.M. (Eds.), La heterogeneidad de la vegetación de los agroecosistemas. Un homenaje a Rolando J.C. León. Facultad de Agronomía, Universidad de Buenos Aires, pp. 19–42. Chi, B.L., Bing, C.S., Walley, F., Yates, T., 2009. Topographic indices and yield variability in a rolling landscape of Western Canada. Pedosphere 19, 362–370. Corenblit, D., Baas, A.C., Bornette, G., Darrozes, J., Delmotte, S., Francis, R.A., Steiger, J., 2011. Feedbacks between geomorphology and biota controlling Earth surface processes and landforms: a review of foundation concepts and current understandings. Earth-Sci. Rev. 106, 307–331. Cowles, J., Boldgiv, B., Liancourt, P., Petraitis, P.S., Casper, B.B., 2018. Effects of increased temperature on plant communities depend on landscape location and precipitation. Ecol. Evol. 8, 5267–5278. Day, P.R., 1965. Particle fractionation and particle-size analysis. In: Black, C.A., Evans, D.D., White, J.L., Ensminger, L.E., Clark, F.E. (Eds.), Methods of Soil Analysis, Part 1, Physical and Mineralogical Properties. Including Statistics of Measurement and Sampling. ASA-SSSA, Madison, pp. 545–567. Ding, J., Johnson, E.A., Martin, Y.E., 2018. Linking soil moisture variation and abundance of plants to geomorphic processes: a generalized model for erosion-uplifting landscapes. J. Geophys. Res. Biogeosci. 123, 960–975. Dufrêne, M., Legendre, P., 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366. El-Keblawy, A., Abdelfattah, M.A., Khedr, A., 2015. Relationships between landforms, soil characteristics and dominant xerophytes in the hyper-arid northern United Arab Emirates. J. Arid Environ. 117, 28–36. Fernández, R.J., 2007. On the frequent lack of response of plants to rainfall events in arid areas. J. Arid Environ. 68, 688–691. Fernandez-Gimenez, M., Allen-Díaz, B., 2001. Vegetation change along gradients from water sources in three grazed Mongolian ecosystems. Plant Ecol. 157, 101–118. Fidalgo, F., Riggi, J.C., 1970. Consideraciones geomórficas y sedimentológicas sobre los Rodados Patagónicos. Rev. Asoc. Geol. Argent. 25, 430–443. Flores, D.G., Suvires, G., Dalmasso, A., 2015. Distribución de la vegetación nativa en ambientes geomorfológicos cuaternarios del Monte Árido Central de Argentina. Rev. Mex. Biodiversidad 86, 72–79. Florinsky, I.V., Kuryakova, G.A., 1996. Influence of topography on some vegetation cover properties. Catena 27, 123–141. Franklin, J., 2010. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press. Gebrekirstos, A., Teketay, D., Mitlöhner, R., 2014. Responses of Doberaglabra and eight co-occurring species to drought and salinity stress at a savanna-scrub ecotone: implications in the face of climate change. Open J. For. 4, 327–337. Gómez Rubio, V., López-Quílez, A., 2013. Análisis conjunto de dos patrones puntuales en ecología. In: de la Cruz, M., Maestre, F.T. (Eds.), Avances en el Análisis Espacial de Datos ecológicos: Aspectos Metodológicos y Aplicados. ECESPA-Asociación Española de Ecología Terrestre, Móstoles, pp. 125–146. Gonçalves, E.T., Souza, A.F., 2014. Floristic variation in ecotonal areas: patterns, determinants and biogeographic origins of subtropical forests in South America. Austral Ecol. 39, 122–134. González Díaz, E., Di Tommaso, I., 2011. Evolución geomorfológica y cronología relativa de los niveles aterrazados del área adyacente a la desembocadura del río Chubut al Atlántico (Provincia del Chubut). Rev. Asoc. Geol. Argent. 68, 507–525. Grellier, S., Florsch, N., Janeau, J.L., Podwojewski, P., Camerlynck, C., Barot, S., Ward,
360
Catena 174 (2019) 353–361
A.I. Casalini et al.
landscape and community scales. Basic Appl. Ecol. 5, 331–346. Shreve, F., Hinckley, A.L., 1937. Thirty years of change in desert vegetation. Ecology 18, 463–478. Silva, A.C., Souza, A.F., 2018. Aridity drives plant biogeographical sub regions in the Caatinga, the largest tropical dry forest and woodland block in South America. PLoS One 13, e0196130. Solon, J., Degórski, M., Roo-Zielińska, E., 2007. Vegetation response to a topographicalsoil gradient. Catena 71, 309–320. Spellerberg, I.F., Fedor, P.J., 2003. A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’ Index. Glob. Ecol. Biogeogr. 12, 177–179. Walkley, A., Black, I.A., 1934. An examination of Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–37. Wondzell, S.M., Cunningham, G.L., Bachelet, D., 1996. Relationships between landforms, geomorphic processes, and plant communities on a watershed in the northern Chihuahuan Desert. Landsc. Ecol. 11, 351–362. Wysocki, D.A., Schoeneberger, P.J., Hirmas, D.R., LaGarry, H.E., 2011. Geomorphology of soil landscapes. In: Ming-Huang, P., Li, Y., Sumner, M.E. (Eds.), Handbook of Soil Science. CRC Press (Chapter 29; 1443 pp.). Zare, S., Jafari, M., Tavili, A., Abbasi, H., Rostampour, M., 2011. Relationship between environmental factors and plant distribution in arid and semiarid area (case study: Shahriyar Rangelands, Iran). American-Eurasian J. Agric. Environ. Sci. 10, 97–105. Zhu, Q., Nie, X., Zhou, X., Liao, K., Li, H., 2014. Soil moisture response to rainfall at different topographic positions along a mixed land-use hillslope. Catena 119, 61–70.
Computing. R Foundation for Statistical Computing, Vienna. Reed, D.N., Anderson, T.M., Dempewolf, J., Metzger, K., Serneels, S., 2009. The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics. J. Biogeogr. 36, 770–782. Reynolds, J.F., Wu, J., 1999. Do landscape structural and functional units exists? In: Tenhunen, J.D., Kabat, P. (Eds.), Integrating Hydrology. Ecosystem Dynamics and Biogeochemistry in Complex Landscapes. John Wiley & Sons Ltd, Berlin, pp. 272–296. Rhoades, J.D., 1982. Soluble salts. In: Page, A.L. (Ed.), Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties, 2nd edn. American Society of Agronomy, Madison, Wisconsin, pp. 167–179. Ribeiro Jr., P.J., Diggle, P.J., 2001. “geoR: a package for geostatistical analysis”, R package version 1.7–4.1. https://CRAN.R-project.org/package=geoR. Roberts, D.W., 2010. “labdsv: ordination and multivariate analysis for ecology”. R package version 1.6-1. http://CRAN.R-project.org/package=labds. Roig, F.A., Rossi, B.E., 2001. Flora y vegetación. In: Claver, S., Roig-Juñent, S.A. (Eds.), El desierto del Monte: la reserva de biósfera de Ñacuñán. IADIZA–MAB–UNESCO, Mendoza, pp. 41–70. Rostagno, C.M., del Valle, H.F., 1988. Mounds associated with shrubs in aridic soils of northeastern Patagonia. Catena 15, 347–359. Rueter, B., Bertolami, M., 2010. Comunidades vegetales y factores ambientales en los cañadones costeros de Patagonia. Ecol. Austral 20, 19–32. Schenk, H.J., Holzapfel, C., Hamilton, J.G., Mahall, B.E., 2003. Spatial ecology of a small desert shrub on adjacent geological substrates. J. Ecol. 91, 383–395. Sebastiá, M.T., 2004. Role of topography and soils in grassland structuring at the
361
Contents lists available at ScienceDirect
Catena journal homepage: www.elsevier.com/locate/catena
Geomorphology, soil and vegetation patterns in an arid ecotone a,⁎
a
Ana I. Casalini , Pablo J. Bouza , Alejandro J. Bisigato a b
T
a,b
IPEEC, CONICET, Blvd. Brown 2915, 9120 Puerto Madryn, Argentina Universidad Nacional de la Patagonia San Juan Bosco, Blvd. Brown 3500, 9120 Puerto Madryn, Argentina
A R T I C LE I N FO
A B S T R A C T
Keywords: Geomorphology Arid environments Spatial data Soil heterogeneity
Ecotonal boundaries are often arranged by following topoedaphic gradients. Despite its relevance, the interrelationships between vegetation and geomorphology have not been adequately documented in most arid regions. The Southern Monte – Patagonia ecotone, defined by anastomosing systems of paleochannels and paleobars, presents an opportunity to study these interrelationships. Our aim was to determine whether topography and/or soil characteristics explained the distribution of communities which are typical of both Phytogeographic Provinces. Vegetation and soil surveys were performed along topographical gradients found between several paleochannels and paleobars. Surface soil texture, organic carbon, total nitrogen, carbonate content and electric conductivity were measured at each site. Digital elevation models and moisture index maps were derived from topographical field data. Soil and plant cover differences between landforms were inspected. Afterwards, species distribution, communities' attributes and its relation with topographical variables were explored. Paleochannels exhibited higher shrub and total cover, as well as higher organic carbon and lower carbonate content than the paleobars. Chuquiraga avellanedae (Asteraceae) dominated the plant community in the paleochannels, while on the contrary Larrea divaricata (Zygophyllaceae) was the most abundant species found on the paleobars. These communities are related to Patagonia and Monte Phytogeographic Provinces, respectively. Species distribution was mainly explained by elevation and secondly by surface water redistribution. Current vegetation patterns strongly mirror the paleolandscape and as a consequence of that, plant communities are interspersed throughout the landscape. Our results emphasize the importance of geomorphology as a factor influencing community distribution along arid ecotones.
1. Introduction Vegetation distribution is closely related to environmental heterogeneity (Arellano et al., 2017). Ecological processes operating at both local and regional scales control the distribution of plant communities within the landscape (Catorci et al., 2014; Münzbergová, 2004). Environmental gradients arising from the combination of geomorphology and hydrology within an area often explain most of the observed plant spatial pattern (Chaneton, 2005; Ding et al., 2018; Poulos and Camp, 2010). Despite the fact that a geo-ecological approach has been recognized as being fundamental in the understanding of rangeland dynamics as well as necessary to accomplish a better land management (Pringle and Tinley, 2003; Zhu et al., 2014), the interrelationships between vegetation and geomorphology that could define species' spatial arrangement have not been adequately documented in most arid regions (Gutierrez-Jurado et al., 2006; Marfo et al., 2018; Moeslund et al.,
2013; but see Mohseni et al., 2017). Since water is the most limiting resource in deserts (Fernández, 2007; Noy-Meir, 1973), the influence of geomorphology on vegetation is commonly ascribed to its effects on water availability (Acebes et al., 2010; McAuliffe, 1994; Wondzell et al., 1996). Low relief positions are moister and cooler than high relief positions (Bromley et al., 1997; Cowles et al., 2018; Wondzell et al., 1996). These differences in water availability, frequently accompanied by changes in physical and chemical soil attributes (Wysocki et al., 2011), make different species occupy different landforms (El-Keblawy et al., 2015; Holtmeier and Broll, 2012; Solon et al., 2007). Geomorphology effects on vegetation are especially relevant when considering ecotones (Gonçalves and Souza, 2014). At these places, environmental constrains restrain species to small fragmented areas topoedaphically similar to their biome core (Buxbaum and Vanderbilt, 2007; Neilson, 1993; Reed et al., 2009). This way, topoedaphic
Abbreviations: GLMM, generalized linear mixed models; IV, indicator values; PCNM, principal coordinates of neighbour matrices; RDA, Redundancy Analysis; SWI, SAGA wetness index ⁎ Corresponding author. E-mail addresses: [email protected] (A.I. Casalini), [email protected] (P.J. Bouza), [email protected] (A.J. Bisigato). https://doi.org/10.1016/j.catena.2018.11.026 Received 24 January 2018; Received in revised form 13 November 2018; Accepted 18 November 2018 0341-8162/ © 2018 Elsevier B.V. All rights reserved.
Catena 174 (2019) 353–361
A.I. Casalini et al.
of rhyolitic and sedimentary rocks (Inselbergs) and endorheic basins. Oldest fluvial terraces correspond to a landscape consisting of wide plateau-like plains make up of Pliocene–Pleistocene fluvial terraces of the Rodados Patagónicos (RP) lithostratigraphic unit (Fidalgo and Riggi, 1970). Due to base level changes, presumably occurred during Upper Pleistocene, younger fluvial terraces (T1-T3), named as Bajo Simpson Formation, were formed (Haller et al., 2005) (Fig. 1). This Formation is constituted by gravelly deposits with a sandy matrix. The Simpson paleo-valley represents the proto-Chubut river and shows a braided stream as can be appreciated in a detailed aerial photograph of the study site (Fig. 2a–b). Fig. 1 shows the geomorphological sketch where the study site is located within the Bajo Simpson terrace (T1). This Bajo Simpson terrace (T1) stopped being functional towards the Pleistocene - Holocene when a new base level change formed a new terrace (T2) (González Díaz and Di Tommaso, 2011). Later, paleochannels developed into small and narrow endorheic basins (pans) receiving fine particles of silt, clay and salts by superficial runoff from the paleobars and by aeolian influx. This way, on the T1 geomorphic surface, an association of soils with contrasting parent materials would have developed (González Díaz and Di Tommaso, 2011). These processes would continue to occur in the present, although with less intensity due to more arid conditions. Recently, paleochannel's soils were classified as Vertic Natrigypsids, while paleobar's soils were classified as Typic Haplocalcids (Casalini, 2016). Climate at the study area is arid, temperate, and windy. Mean annual temperature is 13.5 °C and mean annual precipitation is 233.8 mm, with high inter-annual variation (series 1984–2013) (Laboratory of
heterogeneity at ecotones, which could explain the coexistence of different communities at these landscapes, represents an opportunity to evaluate the effects of geomorphology on species distribution. The general purpose of this study was to determine whether topography and/or soil characteristics influence community distribution along an arid ecotone. We hypothesized that at the ecotone, where the species are under high environmental stress, community distribution will be affected by topography and soils, which are determinant in the competitive ability of plants. Particularly, we expect that different landforms (i.e. different elements of the landscape, paleochannels and paleobars in our case) will be occupied by distinct communities and soils. Particularly in this research, we propose that in Northeastern Patagonia the Larrea divaricata (Zygophyllaceae) community, typical of the Southern Monte (Bisigato and Bertiller, 1997), and the Chuquiraga avellanedae (Asteraceae) community, which is more characteristic of those areas related to the Patagonia Phytogeographic Provinces (Beeskow et al., 1995) coexist by means of the underlying geomorphological heterogeneity. 2. Methods 2.1. Study area Field work was carried out in the northeast of the Chubut Province, in Argentina (W 65°05′, S 42°55′). The landscape of this region is mainly characterized by a succession of old river terrace levels of different ages (Fig. 1). In sectors, these plains are interrupted by outcrops
Fig. 1. SRTM relief map and geomorphological sketch of the study area. RP1-RP6: Terrace levels of Rodados Patagónicos lithoestrafigraphic unit. T1–T5: Terraces levels of the Chubut River, T1–T3: Bajo Simpson Formation (BS Fm.), T4 intermediate terrace (IT), T5: lower valley (LV). 354
Catena 174 (2019) 353–361
A.I. Casalini et al.
b)
a) PB PB
PC
PC PB PC
600m
c)
d)
e)
Fig. 2. Study site's images: a) Black and white aerial photograph of the study site showing the anastomosing systems of paleochannels (PC) and paleobars (PB), b) example of the position of transects (lines) and plots (rectangle). The rectangle in a) indicates the area shown in b). Photographs of c) community of C. avellanedae, d) community of L. divaricata and e) terrain unevenness from a paleochannel with C. avellanedae dominance to a paleobar with L. divaricata dominance. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
method measures plant cover and species composition of herbaceous vegetation and shrubs and it's suitable for large areas sampling. A measuring tape is extended to create transects across the site while an observer proceeds along the line-transect identifying plants intercepted by the tape and recording the intercepted distance. Vegetation cover is computed by adding all intercepted distances by species and expressing each sum as a proportion of tape length.
Climatology, CENPAT-CONICET, Puerto Madryn). Floristically the study area corresponds to the ecotonal area between the Monte and Patagonia Phytogeographic Provinces. Plant cover is organized in a mosaic of plant patches associated to soil mounds dispersed in a matrix of bare soil (Bisigato and Bertiller, 1997; Rostagno and del Valle, 1988). Most patches are dominated by Larrea divaricata or Chuquiraga avellanedae which are accompanied by other less frequent shrub species, grasses and forbs.
2.2.2. Community attributes Some community attributes were also estimated in order to characterize each site. Species richness (S) was determined as the number of species sampled in each site. Species diversity was estimated using Shannon index of species diversity (Nagendra, 2002; Spellerberg and Fedor, 2003)
2.2. Field sampling 2.2.1. Vegetation surveys Nine pairs of sites encompassing a paleochannel and the closest paleobar were selected in such a way that they were dispersed in a paddock located within the paleodrainage, avoiding sites evidencing natural or anthropogenic disturbances. In each landform (i.e. paleochannel and paleobar) three parallel transects 50 m in length were set (Fig. 2b). Total cover and cover by species along each transect was evaluated following the line intercept method (Canfield, 1941). This
5
H = − ∑ pi ln pi i=1
(1)
where pi is the proportion of individuals for species i. The Shannon index increases as both community richness and evenness increase. 355
Catena 174 (2019) 353–361
A.I. Casalini et al.
2.2.3. Soil sampling Since topsoil exhibits the highest organic matter and root biomass accumulation throughout the soil profile, it is also the soil layer of greater nutrient uptake by plants (Jobbágy and Jackson, 2001; Muñoz Vallés et al., 2015). In each site, the proportion intercepted by soil mounds or bare soil areas (hereafter microsites) was calculated for each transect and three superficial (0–10 cm) soil samples were randomly taken from each microsite with a metallic tube (5 cm diameter). Samples were transported to the laboratory in plastic bags where they were air dried. Afterwards, microsite samples were combined in a unique pooled sample according to the proportion of each microsite in the site.
Table 1 Plot attributes. Plot area, number of individuals of C. avellanedae and L. divaricata, and topographic characteristics of each plot. Plots are sorted according to their area. Plot
I 2
Area (m ) C. avellanedae L. divaricata Dominant species density (n/m2) Elevation range (m) SWI (min-max)
2.2.4. Soil characteristics The soil texture of the pooled samples was determined in the laboratory by the hydrometer method of particle-size analysis (Day, 1965). Organic carbon content was analysed with the Walkley and Black (1934) method and total nitrogen with the semi-micro Kjeldahl procedure (Bremner and Mulvaney, 1982). The carbonate content was calculated from the amount of CO2 released by reaction with HCl (Loeppert and Suarez, 1996). Electric conductivity was measured in saturation extract following Rhoades (1982).
II
III
IV
1000 180 108 0,288
1200 282 183 0.387
1200 182 146 0.273
1600 388 155 0.339
1.27 (−0.78)-9.35
0.93 (−0.01)-9.28
2.01 1.28–9.25
2.70 0.65–9.23
Table 1. 2.3. Terrain modelling Digital elevation models (DEM) with grid cells of 0.5 m wide were built by interpolation (Kriging) of all registered points (i.e. those belonging to shrubs of both species and those recorded among shrubs) using ‘geoR’ (Ribeiro and Diggle, 2001) and ‘raster’ (Hijmans and van Etten, 2012) packages in R statistical computing software (v. 3.1.3, R Core Team, 2015). SAGA wetness index was derived from each DEM using SAGA external application on QGIS 2.8.1 (QGIS Development Team 2016). This index is a variation of the Topographic Wetness Index (TWI) based on a modified catchment area calculation, which does not treat the flow as a thin film as done in the calculation of catchment areas in conventional algorithms. As a result, the SWI tends to assign higher moisture potentials to grid cells situated in valley floors, what would better represent reality in the field (Hedley et al., 2013). High SWI values indicate flow accumulation at a given point as a product of topographic control over hydrological processes (Franklin, 2010).
2.2.5. Species spatial pattern and topography Four locations where communities dominated either by Larrea divaricata or Chuquiraga avellanedae coexisted were selected in order to determine the relationship between topography and the distribution of both species. A ten meter width plot of varying length was set in each location. Plot length was defined by the distance between the centre of a paleochannel and the top of the closest paleobar and ranged between 100 and 160 m (Figs. 2b and 3). The centre of each individual of the dominant species was recorded in each plot with a Pentax V-227 total station. This electronic theodolite registers the coordinates (i.e. x, y) and the soil level (z) keeping a precision of ± 3.6 mm for distances below 100 m between the total station and the sampled points, as those handled in this sampling. To obtain a more realistic digital terrain model the coordinates and soil level of the points among shrubs were also recorded. Details of the topographic characteristics and the dominant species abundance corresponding to each plot are provided at
2.4. Data analysis 2.4.1. Vegetation surveys and soil characteristics Soil characteristics (texture, electrical conductivity, organic carbon,
Fig. 3. Maps of elevation (a) and SWI (b) for each plot (I-IV). Paleobars are on the left side and paleochannels on the right side. Map scales presented on top are valid for all plots. Crosses (x) and circles (○) at elevation maps indicate individual positions of L. divaricata and C. avellanedae, respectively. 356
Catena 174 (2019) 353–361
A.I. Casalini et al.
total nitrogen and carbonate content), specific plant cover, and community attributes (species richness, Shannon diversity index and total vegetation cover) differences between landforms (1 factor, 2 levels: 9 paleobars and their closest paleochannels) were inspected by one-way ANOVA. When ANOVA assumptions were not fulfilled, the nonparametric Mann-Whitney test was performed. Both analyses were performed using the R software (R Development Core Team, 2015). Indicator species analysis (Dufrêne and Legendre, 1997) was used to find the indicator species of plant communities occupying each landform. Indicator values (IV) of each species were calculated using the Rpackage ‘labdsv’ (Roberts, 2010). Species cover values were organized in a matrix of p species by n sites. Species by sites matrix was submitted to a direct gradient analysis (Redundancy Analysis-RDA) which assesses the influence of spatial and soil variables on species composition and abundance. When two or more soil variables were correlated only one was included in the RDA. Prior to analysis, data were Hellinger-transformed (Legendre and Gallagher, 2001). This transformation is particularly suited to analyse species abundance data, giving low weights to variables with low counts and many zeros (Borcard et al., 2011; Legendre and Legendre, 1998). Principal coordinates of neighbour matrices (PCNM) method was used to introduce space as an explanatory variable in the analysis (Borcard and Legendre, 2002). By using this method complex spatial patterns can be modelled at different spatial scales (Jones et al., 2008). We computed PCNM variables with the ‘vegan’ package (Oksanen et al., 2013) of the R-Project. A permutation procedure was performed to test the significance of the RDA models including soil and PCNM variables. When a model was significant, the significance of each variable was evaluated by Monte Carlo simulations using ‘vegan’ package. The relationship between community attributes and soil characteristics was evaluated by correlation and regression analysis. Finally we assessed the relationship among community attributes and site scores on RDA axes 1 and 2 by means of Pearson's correlation coefficients.
Table 2 Species composition of plant communities. Specific plant cover (%, mean ± standard error, n = 9) of paleochannels and paleobars. +: Specific plant cover < 0.01%. Different superscript letters indicate differences in plant cover between landforms. Numbers in bold indicate species with significant indicator values.
2.4.2. Species distribution and topographic characteristics Species identity was treated as a binary response variable and it was related to topographical variables (elevation and SWI) through generalized linear mixed models (GLMM; binomial errors, logistic link). Model adjustment was performed in R software using the ‘lme4’ package (Bates et al., 2008) determining the probability of a shrub of being L. divaricata (Gómez Rubio and López-Quílez, 2013). Elevation and SWI were considered fixed effects, while location (plot) was treated as a random effect. The regression is given by the equation
⎛ pij ⎞ log it (pij ) = ln ⎜ = β0 + 1 − pij ⎟ ⎝ ⎠
n=1
Paleochannel (%)
Paleobar (%)
Perennial dicots Acantholippia seriphioides Baccharis spp. Boopis anthemoides Bougainvillea spinosa Chuquiraga aurea Chuquiraga avellanedae Chuquiraga erinacea Cyclolepis genistoides Ephedra ochreata Hoffmannseggia erecta Hoffmannseggia trifoliata Junellia seriphioides Larrea divaricata Larrea nitida Lycium ameghinoi Lycium chilense Mulguraea ligustrina Nassauvia fuegiana Perezia recurvata Prosopidastrum striatum Prosopis alpataco Schinus johnstonii Grasses Bromus catharticus Elymu ssp. Jarava neaei Nassella tenuis Pappostipa humilis Pappostipa speciosa Poa lanuginosa Poa ligularis Total
20.42 ± 1.61a +
14.72 ± 1.68b 0.17 ± 0.07 + + +
+ 0.64 ± 0.30 + 14.58 ± 0.82
2.67 ± 0.51 2.33 ± 0.63
+ + 0.30 ± 0.07 + + 0.49 ± 0.21 2.04 ± 0.67 0.67 ± 0.19 +
0.70 ± 0.27 0.74 ± 0.37 + 0.66 ± 0.15a + + 0.07 ± 0.03 0.19 ± 0.07 + 0.26 ± 0.07 0.01 ± 0.01 0.12 ± 0.06 21.09 ± 1.66a
0.01 ± 0.01 0.10 ± 0.05 + 5.05 ± 0.66 + 0.36 ± 0.13 0.53 ± 0.22 + 0.03 ± 0.03 2.21 ± 0.82 0.66 ± 0.23 0.33 ± 0.22 1.20 ± 0.24a 0.04 ± 0.03 + 0.03 ± 0.02 0.52 ± 0.18 0.02 ± 0.01 0.40 ± 0.13 0.08 ± 0.03 0.12 ± 0.03 15.93 ± 1.80b
and e) which was found on paleobars (Table 2). Indicator species of paleochannel community were Lycium ameghinoi (IV = 0.89), Chuquiraga avellanedae (IV = 0.84), Larrea nitida (IV = 0.84), Hoffmannseggia trifoliata (IV = 0.74), and Bougainvillea spinosa (IV = 0.62). Likewise, Chuquiraga erinacea spp. hystrix (IV = 1.00), Larrea divaricata (IV = 0.99), and Mulguraea ligustrina (IV = 0.88) were the indicator species of the paleobar community. Richness was higher at paleobars (p < 0.01, F(1;16) = 11.8) and although diversity showed the same tendency the difference between landforms was only marginally significant (p = 0.06, F(1;16) = 4.11). In contrast, total vegetation cover showed the opposite trend and it was higher at paleochannels (p = 0.05, F(1;16) = 4.45). In general, community attributes and soil characteristics were uncorrelated at the site level. We only found a significant positive relationship between soil carbonate and Shannon diversity index (r = 0.59, p = 0.01, t(16) = 2.93, y = (0.13 ± 0.04) x + (1.73 ± 0.12), R2 = 0.35, p = 0.01, F(1;16) = 2.92). Other relationships, such as those between carbonate and both richness (p = 0.07, t(16) = 1.97) and total vegetation cover (p = 0.07, t(16) = 1.94), were marginally significant. The analysis of the relationship between species and soil characteristics (RDA) was statistically significant, indicating that environmental variables explain 44.3% of total variance in species composition. In contrast, the PCNM model was not significant. Plant communities belonging to each landform were segregated along the first RDA-axis (RDA1) (Fig. 4). Clay, silt and electrical conductivity were excluded from the RDA because they were correlated with sand concentration, while the exclusion of nitrogen concentration was due to its correlation with organic carbon. Carbonate and sand were
k
∑ βn Zn,i + (μ0j + εij)
Species
(2)
where pij is the probability of an event occurring (in this case the shrub being L. divaricata) associated with a given observation i (each shrub) at plot j and n are covariables (elevation and SWI). In this model, the random effect μ0j represents the deviation of the j-th higher-level unit's average (plot) from the overall intercept β0 and ε represents the unknown random residuals (Manning, 2007; Cayuela, 2012). Prior to this analysis covariables where tested for correlation by means of Pearson's correlation coefficients. 3. Results 3.1. Vegetation and soil characteristics Chuquiraga avellanedae dominated the community associated to paleochannels (Fig. 2c and e), which exhibited higher shrub and total cover compared to the community dominated by L. divaricata (Fig. 2d 357
Catena 174 (2019) 353–361
A.I. Casalini et al.
Fig. 4. Redundancy analysis (RDA) of the 18 vegetation relevés. The percentage of the variance explained by each axis is indicated in parenthesis. Filled circles indicate paleochannels and empty circles paleobars. Significant environmental variables are showed by thin arrows. The thick arrow indicates the Pearson's correlation coefficient between species richness and the axes (rRDA1 and rRDA2).
Table 3 Soil attributes. Physical and chemical characteristics of soils of paleochannels and paleobars (n = 9, mean ± standard error). Different lowercase letters indicate significant (p ≤ 0.05) differences between landforms. Soil characteristic
Paleochannel
Paleobar
Soil texture (%) Sand Silt Clay Electrical conductivity (μS·cm−1) Total nitrogen (%) Organic carbon (%) Carbonate (%)
77.2 ± 1.7a 12.3 ± 1.1a 10.5 ± 1.3a 2056 ± 661a 0.057 ± 0.004a 0.717 ± 0.101a 1.90 ± 0.22b
80.6 ± 0.8a 10.6 ± 1.6a 8.8 ± 1.2a 580 ± 66a 0.056 ± 0.003a 0.449 ± 0.018b 3.34 ± 0.30a
Table 4 Larrea divaricata density models. Component's coefficients obtained by adjustment to logistic regressions for L. divaricata density, χ2 statistics and its p-values for the change in deviance in relation to the null model of GLMMs with location as a random factor and elevation and SWI as fixed factors. Coefficients values for C. avellanedae are opposite and equal in magnitude. Variable
χ2
p-Value
Linear term (β1 ± EE)
Random effect (μ ± EE)
Elevation SWI
441.56 47.83
< 0.001 < 0.001
2.43 ± 0.14 −0.31 ± 0.05
1.40 ± 0.29 0.07 ± 0.06
4. Discussion Our results support the hypothesis of geomorphology and soils affecting community distribution. Paleochannels, with Vertic Natrigypsids soils that are moister and richer in organic matter, are dominated by C. avellanedae. In contrast, paleobars with Typic Haplocalcids soils and higher carbonate concentration are drier and occupied by richer and more diverse communities dominated by L. divaricata (Tables 2 and 3, Fig. 2c–e). Soil influence over plant distribution in this ecotonal area agrees with previous work in Central Monte Desert (Flores et al., 2015; Roig and Rossi, 2001) and many arid systems around the world (Buxbaum and Vanderbilt, 2007; El-Keblawy et al., 2015; Fernandez-Gimenez and Allen-Díaz, 2001; Grellier et al., 2014; Parker, 1991; Schenk et al., 2003; Zare et al., 2011). The greater diversity under drier conditions at paleobars compared to paleochannels agrees with Rueter and Bertolami (2010) for other environments of Patagonia, where dense shrublands occupying moister soils showed lower diversity than open shrublands under more xeric conditions. Moreover, higher organic carbon concentration in paleochannels is in accordance with greater organic matter levels for concavities, furrows, washes and surface depressions (Cantón et al., 2004; Chi et al., 2009; Sebastiá, 2004; Wondzell et al., 1996) and is consistent with the trend towards greater humidity in that landform (Pei et al., 2010). Conversely, Larrea dominance over highly calcic soils was also reported in North America (Hamerlynck et al., 2000; Laity,
negatively related to RDA1, while organic carbon was positively related to this axis. Consistently, soils from paleobars exhibited significantly higher carbonate and lower organic carbon concentration than soil from paleochannels, though no difference was observed in texture composition between both soil types (Table 3). Site scores on RDA1 axis were negatively and significantly related with richness (t(16) = −3.19, p = 0.01, r = −0.62). Although Shannon diversity index showed the same tendency correlation was only marginally significant (t(16) = −1.79, p = 0.09, r = 0.40). On the contrary, total vegetation cover showed a marginally significant positive correlation with site scores on RDA1 (t(16) = 1.76, p = 0.10, r = 0.20).
3.2. Species distribution and topographic characteristics Since elevation and SWI were significantly and negatively correlated (r = −0.32, p < 0.01, t = −13.63) separated GLMM were adjusted. The association of elevation with L. divaricata was positive (Fig. 2e), contrary to SWI (Table 4). These results suggest that L. divaricata is concentrated on higher elevations and dryer areas compared to C. avellanedae. The model considering elevation explained a greater part of species distribution respect to SWI (greater χ2, Table 4).
358
Catena 174 (2019) 353–361
A.I. Casalini et al.
variables as a valuable tool to predict vegetation responses to climate change at arid ecotones. Specifically, elevation was the best explanatory variable, although modelling with SWI also showed a good fit to the data. The explanatory power of SWI could be improved by performing topographic surveys at greater extents, comprising the two paleobars adjoining each paleochannel. In summary, in the study area, characterized by anastomosing systems of paleochannels and paleobars, the coexistence of plant communities related to different Phytogeographic Provinces can be ascribed to geomorphology. Specifically, as a result of soil and topographical properties that vary between landforms, a partial replacement of C. avellanedae by L. divaricata occurs along the paleochannel/paleobar gradient. As a consequence, both communities are interspersed at a landscape scale which makes it impossible to locate a net limit between the Monte and Patagonia Phytogeographic Provinces at a regional scale. In this manner, constraints imposed by geomorphological factors (e.g. topography, water redistribution in the landscape and soil type) give rise to vegetation patterns that currently mirror the paleolandscape.
2008) and has been linked to its tolerance for high soil osmotic potential (Shreve and Hinckley, 1937). In addition, it has been seen that boundaries of these Larrea communities are related to changes in soilparent material (Barbour, 1969). Due to the pedogenic processes (Section 2.1) in which paleochannels would receive fine particles by superficial runoff from the paleobars, a coarser texture would be expected in the latter. Although soil texture differences between landforms were not statistically significant, a greater predominance of fine particles in paleochannels was observed (Table 3). It is possible that high variability among samples prevented us from finding a statistical significance between landforms. This variability could be related to the pooled nature of the soil samples. Although this technique allowed us to obtain a unique value for each landform in each site, enabling a multivariate analysis that integrates environmental and community data, it may have masked differences between soils. The same would apply to the electrical conductivity, which would be higher in the paleochannels due to the superficial runoff of water towards these landforms that would function as endorheic basins (Section 2.1, Fig. 3). Elevation was the strongest topographical variable explaining species distribution in this investigation (Table 4). This is in agreement with findings by several authors in different biomes. Elevation has been one of the environmental factors more frequently related to species distribution in desert landscapes (Bisigato et al., 2009; Cantón et al., 2004; Florinsky and Kuryakova, 1996; Miller and Franklin, 2002; Reynolds and Wu, 1999), as well as in forests (Anic et al., 2010; Piraino et al., 2015), grasslands (Peco et al., 1998), and mires (Luoto and Seppälä, 2002). Since topography effects over vegetation are indirect (Lin et al., 2011), plants would not be responding to elevation directly but rather to a suite of covarying biotic and abiotic factors. Mostly hydrology, but also resource availability, and biotic interactions may be controlled by topography even in relatively flat lowland areas (Moeslund et al., 2013). Our results are in agreement with this assertion. The negative correlation between elevation and SWI as well as the GLMM considering SWI as a fixed factor (Table 4) indicate higher water availability at paleochannels. This is consistent with higher plant cover and organic carbon content at that landform. In fact, previous work carried out in the area at a coarser scale show lower humidity (NDWI) and vegetation cover (NDVI) indices in paleobars respect to paleochannels (Casalini, 2016). This study's findings should be considered when evaluating vegetation changes in disturbed landscapes. For example, most artificial watering points in the area are built at paleochannels and/or at other places where water accumulates after rainfall. As well, a space for time substitution approach was frequently employed to assess grazing effects on vegetation and soil (e.g. Bisigato and Bertiller, 1997). Since watering points are not randomly located throughout the landscape, there is a greater probability of the highly grazed areas (which are very close to watering points) to be often located at paleochannels and other topographically lower landforms. In consequence, an inexperienced or distracted observer could wrongly conclude that high grazing pressure is associated to certain vegetation and/or soil characteristics which are in fact related to geomorphology. Thus, overlooking geomorphology can lead to erroneous conclusions which in turn can conduce to inaccurate management practices. Recently, ecotones have received increasing interest as it is expected that future climate changes could strongly affect vegetation (Camarero et al., 2017; Gebrekirstos et al., 2014; King et al., 2013), because species living there are at the limit of their distribution area (Hansen et al., 1988). Recent studies carried out at other arid ecotones throughout the world have also emphasized the importance of geomorphology as a control of species distribution (Bestelmeyer et al., 2006; Buxbaum and Vanderbilt, 2007; Corenblit et al., 2011; Silva and Souza, 2018) and recruitment (Peters et al., 2010). The results of this work support these evidences and the suitability of modelling species distribution from spatial data as well as their correlation with geomorphological
5. Conclusion Species distribution modelling from topographic variables together with the analysis of edaphic variables contributed with valuable information for understanding influence of geomorphology over species abundance at this ecotone. These results could be further improved including simultaneous measurements of soil moisture along the topographic gradient. A thorough geomorphological and edaphological analysis is needed prior to any vegetation study at a landscape scale. Species nomenclature Flora Argentina Plantas vasculares de la República Argentina (http://www.floraargentina.edu.ar/, accessed on 1 Aug 2015). Acknowledgements We thank Lucas Bandieri, Romina Palacio, Ileana Rios, Verónica Soñez, Marina Muñoz, Cristian Barrionuevo, Claudia Saín, Lina Videla and Estela Cortez for their assistance during field sampling and laboratory work. Julio Lancelotti, Gustavo Pazos, Laura Lamuedra and Germán Cheli helped us with data analysis. Recognition is also given to Mr. Valentín Simpson who allowed the access to the study area in Estancia “Bajo Simpson”. This research was supported by PICT 20122436, Agencia Nacional de Promoción Científica y Tecnológica. AI Casalini fellowship was supported by CONICET (National Research Council of Argentina). References Acebes, P., Traba, J., Peco, B., Reus, M.L., Giannoni, S.M., Malo, J.E., 2010. Abiotic gradients drive floristic composition and structure of plant communities in the Monte Desert. Rev. Chil. Hist. Nat. 83, 395–407. Anic, V., Hinojosa, L.F., Díaz-Forester, J., Bustamante, E., de la Fuente, L.M., Casale, J.F., de la Harpe, J.P., Montenegro, G., Ginocchio, R., 2010. Influence of soil chemical variables and altitude on the distribution of high-alpine plants: the case of the Andes of central Chile. Arct. Antarct. Alp. Res. 42, 152–163. Arellano, G., Umaña, M.N., Macía, M.J., Loza, M.I., Fuentes, A., Cala, V., Jørgensen, P.M., 2017. The role of niche overlap, environmental heterogeneity, landscape roughness and productivity in shaping species abundance distributions along the Amazon–Andes gradient. Glob. Ecol. Biogeogr. 26, 191–202. Barbour, M.G., 1969. Age and space distribution of the desert shrub Larrea divaricata. Ecology 55, 245–261. Bates, D., Maechler, M., Dai, B., 2008. The lme4 Package. http://lme4.r-forge.r-project. org/. Beeskow, A.M., Elissalde, N.O., Rostagno, C.M., 1995. Ecosystem changes associated with grazing intensity on the Punta Ninfas rangelands of Patagonia, Argentina. J. Range Manag. 48, 517–522. Bestelmeyer, B.T., Ward, J.P., Havstad, K.M., 2006. Soil-geomorphic heterogeneity governs patchy vegetation dynamics at an arid ecotone. Ecology 87, 963–973. Bisigato, A.J., Bertiller, M.B., 1997. Grazing effects on patchy dryland vegetation in northern Patagonia. J. Arid Environ. 36, 639–653.
359
Catena 174 (2019) 353–361
A.I. Casalini et al.
D., Lorentz, S., 2014. Soil clay influences Acacia encroachment in a South African grassland. Ecohydrology 7, 1474–1484. Gutierrez-Jurado, H.A., Vivoni, E.R., Harrison, J.B.J., Guan, H., 2006. Ecohydrology of root zone water fluxes and soil development in complex semiarid rangelands. Hydrol. Process. 20, 3289–3316. Haller, M.J., Meister, C.M., Monti, A.J., Weiler, N., 2005. Hoja Geológica 4366-II Puerto Madryn. Programa Nacional de Cartas Geológicas 1:250.000, Buenos Aires. Hamerlynck, E.P., Mcauliffe, J.R., Smith, S.D., 2000. Effects of surface and sub-surface soil horizons on the seasonal performance of Larrea tridentata (creosotebush). Funct. Ecol. 14, 596–606. Hansen, A.J., di Castri, F., Naiman, R.J., 1988. Ecotones: what and why. Biol. Int. 17, 9–46. Hedley, C.B., Roudier, P., Yule, I.J., Ekanayake, J., Bradbury, S., 2013. Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling. Geoderma 199, 22–29. Hijmans, R.J., van Etten, J., 2012. raster: Geographic Analysis and Modeling With Raster Data. R Package Version 2.0–12. R Found. Stat. Comput., Vienna. http://cran.rproject.org/package=raster. Holtmeier, F.K., Broll, G., 2012. Landform influences on treeline patchiness and dynamics in a changing climate. Phys. Geogr. 33, 403–437. Jobbágy, E.G., Jackson, R.B., 2001. The distribution of soil nutrients with depth: global patterns and the imprint of plants. Biogeochemistry 53, 51–77. Jones, M.M., Tuomisto, H., Borcard, D., Legendre, P., Clark, D.B., Olivas, P.C., 2008. Explaining variation in tropical plant community composition: influence of environmental and spatial data quality. Oecologia 155, 593–604. King, D.A., Bachelet, D.M., Symstad, A.J., 2013. Climate change and fire effects on a prairie–woodland ecotone: projecting species range shifts with a dynamic global vegetation model. Ecol. Evol. 3, 5076–5097. Laity, J., 2008. Deserts and Desert Environments. Wiley-Blackwell, UK. Legendre, P., Gallagher, E.D., 2001. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280. Legendre, P., Legendre, L., 1998. Numerical ecology. In: Developments in Environmental Modelling, Second English edition. 20 Elsevier, Amsterdam (853 pp.). Lin, Y., Chang, L., Yang, K., Wang, H., Sun, I., 2011. Point patterns of tree distribution determined by habitat heterogeneity and dispersal limitation. Oecologia 165, 175–184. Loeppert, R.H., Suarez, D.L., 1996. Carbonate and gypsum. In: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3—SSSA Book Series n.5 ASA and SSSA, Madison, WI, USA. Luoto, M., Seppälä, M., 2002. Modelling the distribution of palsas in Finnish Lapland with logistic regression and GIS. Permafr. Periglac. Process. 13, 17–28. Manning, C., 2007. Generalized Linear Mixed Models (illustrated with R on Bresnan et al.'s datives data). https://nlp.stanford.edu/manning/courses/ling289/GLMM.pdf. Marfo, T.D., Resjek, K., Vranova, V., 2018. Spatial variations in soil properties across ecotones: a short review. Bull. Geogr. Phys. Geogr. Ser. 14, 71–77. McAuliffe, J.R., 1994. Landscape evolution, soil formation, and ecological patterns and processes in Sonoran Desert bajadas. Ecol. Monogr. 64, 111–148. Miller, J., Franklin, J., 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol. Model. 157, 227–247. Moeslund, J.E., Arge, L., Bøcher, P.K., Dalgaard, T., Svenning, J.C., 2013. Topography as a driver of local terrestrial vascular plant diversity pattern. Nord. J. Bot. 31, 129–144. Mohseni, N., Sepehr, A., Hosseinzadeh, S.R., Golzarian, M.R., Shabani, F., 2017. Variations in spatial patterns of soil–vegetation properties over subsidence-related ground fissures at an arid ecotone in northeastern Iran. Environ. Earth Sci. 76, 234. Muñoz Vallés, S.M., Cambrollé, J., Gallego-Fernández, J.B., 2015. Effect of soil characteristics on plant distribution in coastal ecosystems of SW Iberian Peninsula sand spits. Plant Ecol. 216, 1551–1570. Münzbergová, Z., 2004. Effect of spatial scale on factors limiting species distributions in dry grassland fragments. J. Ecol. 92, 854–867. Nagendra, H., 2002. Opposite trends in response for the Shannon and Simpson indices of landscape diversity. Appl. Geogr. 22, 175–186. Neilson, R.P., 1993. Transient ecotone response to climatic change: some conceptual and modelling approaches. Ecol. Appl. 3, 385–395. Noy-Meir, I., 1973. Desert ecosystems: environment and producers. Annu. Rev. Ecol. Syst. 4, 25–51. Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., O'Hara, R.B., Simpson, G.L., Solymos, P., Henry, M., Stevens, H., Wagner, H., 2013. vegan: community ecology package. R package version 2.3-2. http://cran.r-project.org/package=vegan. Parker, K.C., 1991. Topography, substrate, and vegetation patterns in the northern Sonoran Desert. J. Biogeogr. 18, 151–163. Peco, B., Ortega, M., Levassor, C., 1998. Similarity between seed bank and vegetation in Mediterranean grassland: a predictive model. J. Veg. Sci. 9, 815–828. Pei, T., Qin, C.Z., Zhu, A.X., Yang, L., Luo, M., Li, B., Zhou, C., 2010. Mapping soil organic matter using the topographic wetness index: a comparative study based on different flow-direction algorithms and kriging methods. Ecol. Indic. 10, 610–619. Peters, D.P., Herrick, J.E., Monger, H.C., Huang, H., 2010. Soil-vegetation-climate interactions in arid landscapes: effects of the North American monsoon on grass recruitment. J. Arid Environ. 74, 618–623. Piraino, S., Abraham, M.E., Diblasi, A., Juñent, F.A.R., 2015. Geomorphological-related heterogeneity as reflected in tree growth and its relationships with climate of Monte Desert Prosopis flexuosa DC woodlands. Trees 29, 903–916. Poulos, H.M., Camp, A.E., 2010. Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands. Ecology 91, 1140–1151. Pringle, H., Tinley, K., 2003. Are we overlooking critical geomorphic determinants of landscape change in Australian rangelands? Ecol. Manag. Restor. 4, 180–186. R Development Core Team, 2015. R: A Language and Environment for Statistical
Bisigato, A.J., Villagra, P., Ares, J.O., Rossi, B., 2009. Vegetation heterogeneity in Monte Desert ecosystems: a multi-scale approach linking patterns and processes. J. Arid Environ. 73, 182–191. Borcard, D., Legendre, P., 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153, 51–68. Borcard, D., Gillet, F., Legendre, P., 2011. Unconstrained ordination. In: Borcard, D., Gillet, F., Legendre, P. (Eds.), Numerical Ecology With R. Springer, New York, pp. 115–151. Bremner, J.M., Mulvaney, C.S., 1982. Nitrogen-total regular Kjeldhal method. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis—Chemical and Microbiological Properties. Series Agronomy No. 9 American Society of Agronomy, Wisconsin, pp. 595–624. Bromley, J., Brouwer, J., Barker, A.P., Gaze, S.R., Valentine, C., 1997. The role of surface water redistribution in an area of patterned vegetation in a semi-arid environment, south-west Niger. J. Hydrol. 198, 1–29. Buxbaum, C., Vanderbilt, K., 2007. Soil heterogeneity and the distribution of desert and steppe plant species across a desert-grassland ecotone. J. Arid Environ. 69, 617–632. Camarero, J.J., Linares, J.C., García-Cervigón, A.I., Batllori, E., Martínez, I., Gutiérrez, E., 2017. Back to the future: the responses of alpine treelines to climate warming are constrained by the current ecotone structure. Ecosystems 20, 683–700. Canfield, R., 1941. Application of the line interception method in sampling range vegetation. J. For. 39, 388–394. Cantón, Y., Del Barrio, G., Solé-Benet, A., Lázaro, R., 2004. Topographic controls on the spatial distribution of ground cover in the Tabernas badlands of SE Spain. Catena 55, 341–365. Casalini, A.I., 2016. Heterogeneidad de la vegetación en el ecotono entre las provincias fitogeográficas del Monte y Patagónica. Descripción y factores asociados. Universidad Nacional del Sur, Bahía Blanca, Argentina (87 pp.). Catorci, A., Velasquez, J.L., Cesaretti, S., Malatesta, L., Tardella, F.M., Zeballos, H., 2014. How environment and grazing influence floristic composition of dry Puna in the southern Peruvian Andes. Phytocoenologia 44, 103–122. Cayuela, L., 2012. Modelos lineales mixtos (LMM) y modelos lineales generalizados mixtos (GLMM) en R. Universidad Rey Juan Carlos, España (65 pp.). Chaneton, E.J., 2005. Factores que determinan la heterogeneidad de la comunidad vegetal en diferentes escalas espaciales. In: Oesterheld, M., Aguiar, M., Ghersa, C.M., Paruelo, J.M. (Eds.), La heterogeneidad de la vegetación de los agroecosistemas. Un homenaje a Rolando J.C. León. Facultad de Agronomía, Universidad de Buenos Aires, pp. 19–42. Chi, B.L., Bing, C.S., Walley, F., Yates, T., 2009. Topographic indices and yield variability in a rolling landscape of Western Canada. Pedosphere 19, 362–370. Corenblit, D., Baas, A.C., Bornette, G., Darrozes, J., Delmotte, S., Francis, R.A., Steiger, J., 2011. Feedbacks between geomorphology and biota controlling Earth surface processes and landforms: a review of foundation concepts and current understandings. Earth-Sci. Rev. 106, 307–331. Cowles, J., Boldgiv, B., Liancourt, P., Petraitis, P.S., Casper, B.B., 2018. Effects of increased temperature on plant communities depend on landscape location and precipitation. Ecol. Evol. 8, 5267–5278. Day, P.R., 1965. Particle fractionation and particle-size analysis. In: Black, C.A., Evans, D.D., White, J.L., Ensminger, L.E., Clark, F.E. (Eds.), Methods of Soil Analysis, Part 1, Physical and Mineralogical Properties. Including Statistics of Measurement and Sampling. ASA-SSSA, Madison, pp. 545–567. Ding, J., Johnson, E.A., Martin, Y.E., 2018. Linking soil moisture variation and abundance of plants to geomorphic processes: a generalized model for erosion-uplifting landscapes. J. Geophys. Res. Biogeosci. 123, 960–975. Dufrêne, M., Legendre, P., 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366. El-Keblawy, A., Abdelfattah, M.A., Khedr, A., 2015. Relationships between landforms, soil characteristics and dominant xerophytes in the hyper-arid northern United Arab Emirates. J. Arid Environ. 117, 28–36. Fernández, R.J., 2007. On the frequent lack of response of plants to rainfall events in arid areas. J. Arid Environ. 68, 688–691. Fernandez-Gimenez, M., Allen-Díaz, B., 2001. Vegetation change along gradients from water sources in three grazed Mongolian ecosystems. Plant Ecol. 157, 101–118. Fidalgo, F., Riggi, J.C., 1970. Consideraciones geomórficas y sedimentológicas sobre los Rodados Patagónicos. Rev. Asoc. Geol. Argent. 25, 430–443. Flores, D.G., Suvires, G., Dalmasso, A., 2015. Distribución de la vegetación nativa en ambientes geomorfológicos cuaternarios del Monte Árido Central de Argentina. Rev. Mex. Biodiversidad 86, 72–79. Florinsky, I.V., Kuryakova, G.A., 1996. Influence of topography on some vegetation cover properties. Catena 27, 123–141. Franklin, J., 2010. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press. Gebrekirstos, A., Teketay, D., Mitlöhner, R., 2014. Responses of Doberaglabra and eight co-occurring species to drought and salinity stress at a savanna-scrub ecotone: implications in the face of climate change. Open J. For. 4, 327–337. Gómez Rubio, V., López-Quílez, A., 2013. Análisis conjunto de dos patrones puntuales en ecología. In: de la Cruz, M., Maestre, F.T. (Eds.), Avances en el Análisis Espacial de Datos ecológicos: Aspectos Metodológicos y Aplicados. ECESPA-Asociación Española de Ecología Terrestre, Móstoles, pp. 125–146. Gonçalves, E.T., Souza, A.F., 2014. Floristic variation in ecotonal areas: patterns, determinants and biogeographic origins of subtropical forests in South America. Austral Ecol. 39, 122–134. González Díaz, E., Di Tommaso, I., 2011. Evolución geomorfológica y cronología relativa de los niveles aterrazados del área adyacente a la desembocadura del río Chubut al Atlántico (Provincia del Chubut). Rev. Asoc. Geol. Argent. 68, 507–525. Grellier, S., Florsch, N., Janeau, J.L., Podwojewski, P., Camerlynck, C., Barot, S., Ward,
360
Catena 174 (2019) 353–361
A.I. Casalini et al.
landscape and community scales. Basic Appl. Ecol. 5, 331–346. Shreve, F., Hinckley, A.L., 1937. Thirty years of change in desert vegetation. Ecology 18, 463–478. Silva, A.C., Souza, A.F., 2018. Aridity drives plant biogeographical sub regions in the Caatinga, the largest tropical dry forest and woodland block in South America. PLoS One 13, e0196130. Solon, J., Degórski, M., Roo-Zielińska, E., 2007. Vegetation response to a topographicalsoil gradient. Catena 71, 309–320. Spellerberg, I.F., Fedor, P.J., 2003. A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’ Index. Glob. Ecol. Biogeogr. 12, 177–179. Walkley, A., Black, I.A., 1934. An examination of Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–37. Wondzell, S.M., Cunningham, G.L., Bachelet, D., 1996. Relationships between landforms, geomorphic processes, and plant communities on a watershed in the northern Chihuahuan Desert. Landsc. Ecol. 11, 351–362. Wysocki, D.A., Schoeneberger, P.J., Hirmas, D.R., LaGarry, H.E., 2011. Geomorphology of soil landscapes. In: Ming-Huang, P., Li, Y., Sumner, M.E. (Eds.), Handbook of Soil Science. CRC Press (Chapter 29; 1443 pp.). Zare, S., Jafari, M., Tavili, A., Abbasi, H., Rostampour, M., 2011. Relationship between environmental factors and plant distribution in arid and semiarid area (case study: Shahriyar Rangelands, Iran). American-Eurasian J. Agric. Environ. Sci. 10, 97–105. Zhu, Q., Nie, X., Zhou, X., Liao, K., Li, H., 2014. Soil moisture response to rainfall at different topographic positions along a mixed land-use hillslope. Catena 119, 61–70.
Computing. R Foundation for Statistical Computing, Vienna. Reed, D.N., Anderson, T.M., Dempewolf, J., Metzger, K., Serneels, S., 2009. The spatial distribution of vegetation types in the Serengeti ecosystem: the influence of rainfall and topographic relief on vegetation patch characteristics. J. Biogeogr. 36, 770–782. Reynolds, J.F., Wu, J., 1999. Do landscape structural and functional units exists? In: Tenhunen, J.D., Kabat, P. (Eds.), Integrating Hydrology. Ecosystem Dynamics and Biogeochemistry in Complex Landscapes. John Wiley & Sons Ltd, Berlin, pp. 272–296. Rhoades, J.D., 1982. Soluble salts. In: Page, A.L. (Ed.), Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties, 2nd edn. American Society of Agronomy, Madison, Wisconsin, pp. 167–179. Ribeiro Jr., P.J., Diggle, P.J., 2001. “geoR: a package for geostatistical analysis”, R package version 1.7–4.1. https://CRAN.R-project.org/package=geoR. Roberts, D.W., 2010. “labdsv: ordination and multivariate analysis for ecology”. R package version 1.6-1. http://CRAN.R-project.org/package=labds. Roig, F.A., Rossi, B.E., 2001. Flora y vegetación. In: Claver, S., Roig-Juñent, S.A. (Eds.), El desierto del Monte: la reserva de biósfera de Ñacuñán. IADIZA–MAB–UNESCO, Mendoza, pp. 41–70. Rostagno, C.M., del Valle, H.F., 1988. Mounds associated with shrubs in aridic soils of northeastern Patagonia. Catena 15, 347–359. Rueter, B., Bertolami, M., 2010. Comunidades vegetales y factores ambientales en los cañadones costeros de Patagonia. Ecol. Austral 20, 19–32. Schenk, H.J., Holzapfel, C., Hamilton, J.G., Mahall, B.E., 2003. Spatial ecology of a small desert shrub on adjacent geological substrates. J. Ecol. 91, 383–395. Sebastiá, M.T., 2004. Role of topography and soils in grassland structuring at the
361