Spatial and Temporal Functional Changes in Alpine Summit Vegetation Are Driven by Increases in Shrubs and Graminoids

Research Article

Spatial and temporal functional changes in alpine summit vegetation are driven by increases in shrubs and graminoids

Susanna Venn1,2*, Catherine Pickering1 and Ken Green3 1 School of Environment, Environmental Futures Centre, Grifﬁth University, Gold Coast, QLD 4222, Australia 2 Department of Botany, Research Centre for Applied Alpine Ecology, La Trobe University, Bundoora, VIC 3086, Australia 3 New South Wales National Parks and Wildlife Service, Snowy Mountains Region, PO Box 2228, Jindabyne, NSW 2627, Australia Received: 14 October 2013; Accepted: 24 January 2014; Published: 21 February 2014 Citation: Venn S, Pickering C, Green K. 2014. Spatial and temporal functional changes in alpine summit vegetation are driven by increases in shrubs and graminoids. AoB PLANTS 6: plu008; doi:10.1093/aobpla/plu008

Abstract. Classical approaches to investigating temporal and spatial changes in community composition offer only partial insight into the ecology that drives species distribution, community patterns and processes, whereas a functional approach can help to determine many of the underlying mechanisms that drive such patterns. Here, we aim to bring these two approaches together to understand such drivers, using an elevation gradient of sites, a repeat species survey and species functional traits. We used data from a repeat vegetation survey on five alpine summits and measured plant height, leaf area, leaf dry matter content and specific leaf area (SLA) for every species recorded in the surveys. We combined species abundances with trait values to produce a community trait-weighted mean (CTWM) for each trait, and then combined survey results with the CTWMs. Across the gradient of summits, more favourable conditions for plant growth (warmer, longer growing season) occurred at the lower elevations. Vegetation composition changes between 2004 and 2011 (according to non-metric multi-dimensional scaling ordination) were strongly affected by the high and increasing abundance of species with high SLA at high elevations. Species life-form categories strongly affected compositional changes and functional composition, with increasing dominance of tall shrubs and graminoids at the lower-elevation summits, and an overall increase in graminoids across the gradient. The CTWM for plant height and leaf dry matter content significantly decreased with elevation, whereas for leaf area and SLA it significantly increased. The significant relationships between CTWM and elevation may suggest specific ecological processes, namely plant competition and local productivity, influencing vegetation preferentially across the elevation gradient, with the dominance of shrubs and graminoids driving the patterns in the CTWMs.

Keywords: Alpine vegetation; community composition; functional composition; functional traits; GLORIA; Snowy Mountains.

composition (Dı´az and Cabido 1997; Tilman et al. 2001; Introduction Lavorel and Garnier 2002; McGill et al. 2006). The inter- In the context of environmental change, linking quantita- action among functional groups of species with climatic tive measures of plant species physical characteristics, changes undoubtedly affects community composition for example their functional traits or life form, with spe- and underlying ecosystem functioning (Hooper and cies distributions and local environmental factors can Vitousek 1997; Westoby and Wright 2006; Spasojevic reveal the processes that drive patterns in vegetation and Suding 2012). Hence, measures of functional traits,

* Corresponding author’s e-mail address: [email protected]

Published by Oxford University Press on behalf of the Annals of Botany Company. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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functional diversity or functional composition, in combin- change in vegetation composition across an elevation ation with measures of species composition and diversity, gradient of alpine summits. We used four easily mea- may provide greater insights into the assembly processes sured morphological traits: plant height at maturity, leaf that drive community composition and for gauging area, leaf dry matter content (LDMC) and specific leaf ecosystem stability in changing environments (Tilman area (SLA); some of these are interrelated, but all repre- et al. 1997). sent important dimensions of functional and strategic Using environmental gradients is a simple way to variation among plant species (Westoby 1998; Weiher examine natural variation in vegetation and community et al. 1999) and are responsible for some of the most strik- responses to environmental changes (McGill et al. 2006). ing and important patterns in species distributions in the Intraspecific variation and species turnover can cause field (Westoby et al. 2002; Choler 2005). In addition, shifts in trait values differentially across gradients, and large-scale comparisons among biomes provide evidence thus functional diversity across environmental gradients that these traits may be viewed as relevant functional is a function of biotic and abiotic interactions (Venn markers suitable for predicting species performance et al. 2011; Scho¨b et al. 2012; Spasojevic and Suding along gradients (Reich et al. 1997). While we did not 2012), disturbance regimes (Choler 2005), population dy- measure community assembly processes directly, these namics (Pollock et al. 2012) and biogeochemical cycling chosen traits are important for various assembly pro- (Mason et al. 2012) at local, regional and biome scales cesses and population dynamics such as competition, (Reich et al. 1997). Mountainous regions are therefore facilitation, productivity, and stress tolerance and longev- ideal for testing the role of plant functional traits across ity (Grime 1977; Westoby 1998; Cornelissen et al. 2003; environmental gradients in determining community Scho¨b et al. 2012; Michalet et al. 2014). We can therefore composition and shifts therein; over relatively short spa- infer the mechanisms behind changes in community tial scales, rapid changes in elevation interact with local composition based on life forms, individual traits and topography to create steep gradients in temperature patterns in the CTWMs. The elevation gradient of sites and precipitation (Ko¨rner 1999). Thus, the variation in represents a strong gradient of temperatures, snowmelt species abundance across an environmental or elevation date and growing season length, and therefore repre- gradient will impact on community composition and sents an important gradient across which community ecosystem functioning (Tilman et al. 1997), as different composition and functional composition are expected processes are affected by different species via changes to vary preferentially, according to position along the in the representation of species functional traits (Chapin gradient. Specifically we ask: what is the spatial and et al. 1996). The interrelation between a species life form temporal variation in species composition across the and its functional traits is exemplified across elevation gradient of sites; and where are various combinations gradients in alpine regions; for example, shrubs (taller, of traits and life forms most prevalent? We then discuss long-lived, woody plants) dominate in favourable envir- how these composition patterns and functional traits onments. Through their morphological and physiological interact with species’ ecology preferentially across the traits, shrub species can modify a wide range of ecosys- gradient of sites. tem processes, including alteration of local snow depths and associated hydrological dynamics, nutrient exchange Methods and associated net carbon balance (Myers-Smith et al. 2011). In addition, shrub species are often taller than Study sites neighbouring forbs and they can be competitively super- We used five alpine summits in this study that represent ior, forming dense thickets with closed canopies. In- the Australian contribution to the Global Observation Re- creases in shrub cover and height can also potentially search Initiative in Alpine Environments (GLORIA): an on- restrict the growth of other plant species by limiting going, empirical study with the specific aim of detecting light availability (Myers-Smith et al. 2011). Graminoids, alpine vegetation change on summits in relation to cli- namely grasses and sedges, however, may compete mate change. In January 2011, we re-surveyed the sum- with shrubs in the more favourable environments, recruit mits that were originally marked and surveyed in January within the canopy of senescing shrubs (Williams and 2004 that exist along a continuous ridge from close to the Ashton 1988) and are well adapted to a range of environ- valley floor to the summit of Mt Clarke (Pickering et al. mental conditions; many C3 grasses and sedges can also 2008; Pickering and Green 2009)(Fig.1). The summits rapidly increase in abundance after sufficient rainfall and cover an elevation range of 301 m from the lowest at improved abiotic conditions (Jarrad et al. 2009). 1813 m (Clarke 5) through to the highest at 2114 m Here, we use community trait-weighted means (Clarke 1) (Table 1) and cover a horizontal distance of (CTWMs) to explore the driving mechanisms of temporal 1600 m. The summits were selected for long-term

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Figure 1. Location of the Snowy Mountains in Australia and study site locations at Mt Clarke 1–5 (CL1, CL2, CL3, CL4, CL5) representing the five summits. monitoring under the GLORIA sampling protocols (Pauli temperature loggers (Tinytag Plus—Gemini Data Loggers, et al. 2004), as they experience similar effects of exposure Chichester, UK) buried 10 cm below the ground surface. and differences in climate that are most likely due to the From January 2004 to the present, loggers have been in elevation gradient. They are all relatively flat, rather than position at the ‘corner’ of each aspect of each summit. cone-shaped peaks, and the vegetation is characteristic Temperatures were recorded every 2 h. Temperature of nearby summits. The soils are around 350 + 110 mm data from 2004 to 2011 were used to calculate annual va- in depth (K. Green, unpubl. res.), well-formed alpine lues of absolute minimum soil temperature, annual daily humus soils (Costin 1954). The highest summits are domi- mean soil temperature, absolute maximum soil tempera- nated by tall alpine herbfield (a mixture of forbs and gra- ture, temperature sums (.5 8C), growing degree days minoids), whereas the lower summits are characterized and the length of the growing season across the years by shrubs. As a result of the continuous, mostly perennial sampled. Several climate parameters were derived from vegetation cover, biomass is high compared with some these data and used in previously published analyses other alpine regions (Costin 1954). There are some rock (Pickering and Green 2009; Venn et al. 2012a). outcrops, but these are not a defining feature of the Precipitation data were collected from 2003 to 2011 summits. Disturbance is minimal as cattle grazing ceased from a Bureau of Meteorology automated weather sta- .60 years ago, the historical stock travelling route tion about 8 km to the south at Thredbo (1957 m), and avoided these summits and there are few native and from Pengilley Bog (1730 m, during the growing season no exotic burrowing mammals at these elevations. only), 13 km to the north-east. No walking tracks cross the summits, resulting in low Soil samples were collected in 2003 at each summit, visitation rates. on each aspect close to the 1-m2 quadrat clusters, giv- ing a total of four individual soil samples per summit. Variation in abiotic variables across the gradient A minimum of 500 g was collected in a single sample, of summits taking the top 10 cm of soil through a 75-mm core. The variation in soil temperatures, nutrients and minerals Samples were air-dried, sieved through a 2-mm mesh across the elevation gradient has been described previ- and analysed by the CSIRO Division of Soil and Water, ously for the summits (Pickering et al. 2008; Pickering Canberra, Australia. Samples were analysed for 17 and Green 2009; Venn et al. 2012a). Temperature record- minerals/nutrients (see Pickering and Green 2009 for ing began at the summits in January 2002 using full details).

AoB PLANTS www.aobplants.oxfordjournals.org & The Authors 2014 3 4 o PLANTS AoB Venn tal. et Table 1. Summit details including locations, elevation (metres above sea level (m.a.s.l.)) and area (m2); the mean species richness and overlapping vegetation cover for each of the upper and +

lower SASs divided into three life-form groups (graminoids, forbs and shrubs) for 2011 and the change in mean species richness since 2004 (increase or decrease 2) at each summit; and vegetation summit alpine in change Functional — www.aobplants.oxfordjournals.org the CTWM for four traits (height, leaf area, LDMC and SLA) for each of the sampling units for 2011 and the change in overlapping cover (%) since 2004 at each summit.

Clarke 1 Clarke 2 Clarke 3 Clarke 4 Clarke 5 ...... Location E 148.2875, S 36.4328 E 148.2911, S 36.4328 E 148.2961, S 36.4347 E 148.3000, S 36.4356 E 148.3078, S 36.4356 Elevation (m.a.s.l.) 2114 2079 1992 1948 1813 5mSASarea(m2) 4722 3373 2860 3435 2212 10 m SAS area (m2) 7581 8196 8551 8920 8664 Mean species richness per area 2011 Change +/2 2011 Change +/2 2011 Change +/2 2011 Change +/2 2011 Change +/2 Upper mountain SAS (mean of aspects) 21.2 20.5 23 +0.3 31.8 +0.5 31 +2.3 30.3 21.3 Lower mountain SAS (mean of aspects) 25 +4 28.5 +2.8 34.5 +4 34.7 +11.5 32.3 +4.5 Vegetation (overlapping cover %) 2011 Change (%) 2011 Change (%) 2011 Change (%) 2011 Change (%) 2011 Change (%) Upper mountain SAS (sum for summit) Graminoids 72.6 +22.4 50.9 +30.4 82.6 +90.7 67.3 +115 79.1 +324.7 Forbs 25.6 25.0 14.2 +10.2 13.7 233.8 18.9 +11.3 12.3 +85 Shrubs 9.5 +29.1 42.6 +23.9 14.2 +12.5 50.5 +34 92.7 +38.4 Lower mountain SAS (sum for summit) Graminoids 62.0 +25.7 68.6 +46.3 81.1 +75.4 63.1 +96.1 81.6 +150.3 Forbs 34.3 +10.8 25.5 217.6 18.4 226.3 28.8 +46.5 22.2 +349.4 Shrubs 18.1 +18.1 30.4 +53.5 14.6 28.0 47.7 +12.6 65.4 +12.7 Community trait-weighted mean 2011 Change (%) 2011 Change (%) 2011 Change (%) 2011 Change (%) 2011 Change (%) Upper mountain SAS (mean of aspects) Height (mm) 128.0 23.3 116.3 +4.5 154.1 210.1 180.9 25.1 227.5 221.2 Leaf area (mm2) 396.2 28.9 237.5 +3.7 291.7 21.6 208.7 +10.3 138.3 +104.4 LDMC (mg g21 ) 192.0 +4.8 299.5 27.5 183.3 214.3 335.2 27.8 382.3 216.1 SLA (mm2 mg21 ) 38.4 +6.8 30.2 +4.7 40.6 +21.6 28.5 +19.6 20.6 +92.8

& Lower mountain SAS (mean of aspects) h uhr 2014 Authors The Height (mm) 121.6 +0.5 123.9 +0.8 144.6 26.7 187.9 212.9 226.5 227.4 Leaf area (mm2) 354.9 +2.5 297.5 215.9 307.1 +13.7 258.3 +16.1 163.4 +110.3 LDMC (mg g21 ) 225.2 23.9 248.7 +6.3 184.3 218.8 317.7 26.2 346.8 216.6 SLA (mm2 mg21 ) 32.5 +6.8 35.2 +14.7 41.1 +26.8 28.0 +18.4 22.2 +84.6 Venn et al. — Functional change in alpine summit vegetation

Vegetation sampling ensure that changes in species between 2004 and 2011 The top section of each summit was divided into eight were accurate. All species names follow Costin et al. summit area sections (SASs), four covering the area (2000) to be consistent with the initial 2004 survey. down to 5 m below the summit, the 5-m isoline, for We selected four important, character-based plant each of the four cardinal compass bearings (hereafter re- traits (plant height, leaf area, LDMC and SLA) (Table 2) ferred to as the upper or 5-m SAS), and another four cov- to use in the functional composition analyses. Trait ering the four compass bearings down to the 10-m isoline data, including destructive sampling, were not directly (hereafter referred to as the lower or 10-m SAS) (Pauli measured from plants within the study sites; rather all et al. 2011). Where the summit was exceptionally flat, traits were collected from adjacent areas ,200 m from the upper sampling area extended 50 m from the summit the study sites, from within the Kosciuszko National and the lower area extended 100 m. In each of the eight Park alpine area in growing seasons of 2010, 2011 and SASs the percentage cover of each vascular plant species 2012, using 10 adult individuals for each measurement. was estimated using a random step-pointing method We chose these traits based on the individual plant and (200 points per area). In this method, vegetation is ecosystem functions in which they are involved (Lepsˇ sampled at randomly generated points across the site, et al. 2006; Petchey and Gaston 2006), in the context of and hits are summed for each species in order to provide alpine plant community dynamics (Table 2). a measure of species abundance. Any species not sampled by the step-point method were visually assessed Data analysis for cover (Pauli et al. 2011). Vegetation composition. In order to determine shifts in Sampling in 2011 was conducted ‘blind’ without refer- species composition and abundance between 2004 and ring to the 2004 data, and was performed by the same 2011 across the gradient of sites, we used non-metric people to ensure consistency in the data over the days multi-dimensional scaling (NMDS) and ordination. of fieldwork (Vittoz et al. 2010). Postsampling, a rigorous Species abundance data from the upper and lower species identification checking procedure, was used to SASs were log + 1 transformed to down-weight very

Table 2. Plant traits measured for all species recorded in the upper and lower SASs. Traits were measured in the ﬁeld and laboratory based on protocols outlined by Cornelissen et al. (2003).

Trait (unit) Description Functional indicator ...... Plant height (mm) Shortest distance between the upper boundary of the A measure of species overall competitive ability at plant main photosynthetic material (usually the canopy) and maturity. Species that are relatively taller will be more ground level competitive, usually for light. Indirect measurement for biomass, lateral spread, rooting depth and leaf size Leaf area (mm2) One-sided projected surface area of an average leaf A measure of stress tolerance. Small leaves tend to be favoured under heat stress, cold stress, drought stress and high-radiation stress. Within a climate zone, leaf size tends to increase with plant height and soil nutrients, but decreases with disturbance. Larger leaves are expected in more productive landscapes LDMC (mg g21) The ratio of the oven-dry mass of a leaf to the fresh Indirectly represents the mean density of leaf tissue, relates weight of the leaf to the inverse of SLA. Low LDMC can indicate fast resource acquisition. Leaves tend to be more resistant to physical stress such as wind and hail. Species with low LDMC tend to be associated with highly disturbed environments and high productivity SLA (mm2 mg21) Ratio of one-sided area of a fresh leaf to its oven-dry Low values correspond to relatively high investments in mass defences to harsh conditions including long life spans and structural adaptations. Reﬂects the expected return on previously captured resources such as light and nutrients. A good positive correlate of potential growth rate

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common species, and dissimilarities between all pairs of CTWMs were calculated by the method proposed by aspects from the upper and lower SASs were calculated Mason et al. (2003), interpreted by Mason et al. (2005) using the Bray–Curtis dissimilarity coefficient (after 999 and modified by Lepsˇ et al. (2006). This method allowed iterations), previously noted as a robust measure in us to calculate the relative abundance of individual trait recovering ecological distance over a range of models responses to the gradient of sites and over time, and stochastic variations in the data (Bray and Curtis weighted by the absolute individual species abundances, 1957; Quinn and Keough 2003). How good the rather than provide an aggregate index based on dissimilarity matrix is was determined by the stress multi-trait space (Villeger et al. 2008). We utilized the value (Kruskal 1964; McCune and Mefford 1999). Stress software of Lepsˇ and de Bello (2008) to assist in scaling values ,0.2 are recommended (Clarke 1993)asvalues our trait and abundance values for the data from the above this threshold may mislead interpretations. three spatial scales and to calculate the CTWM values. The difference in vegetation composition between Temporal change across the summits in CTWM was 2004 and 2011 was analysed by an analysis of similarities measured as the per cent difference between mean (ANOSIM) procedure (Clarke 1993; Quinn and Keough values between 2004 and 2011, and by examining the 2003). This procedure is analogous to an analysis of vari- overlap of 95 % confidence intervals from a normal ance comparing between-group and within-group vari- distribution of the data across the three spatial scales ation. The ANOSIM procedure tests the null hypothesis (Cumming and Finch 2005). Combining all sites, the dif- that there are no differences between aprioridefined ference between the 2004 and 2011 values of functional groups (in this case years), or that the average rank of composition for each trait was compared with paired dissimilarities between all possible pairs of objects in dif- t-tests using the quadrants of each site (aspects) as repli- ferent groups is the same as the average rank of dissimi- cates. The relationships between site elevation and the larities between pairs of objects in the same groups abundance-weighted trait means were assessed with (Quinn and Keough 2003). All ANOSIM procedures for simple linear regression. Additionally, the relationships abundance data from the three spatial scales used per- between the vegetation composition across sites and mutation/randomization methods on a similarity matrix trends in the CTWMs at those sites were illustrated by to allocate objects randomly to groups and then generate overlaying vectors on each of the two-dimensional ordi- the distribution of R under the null hypothesis that all ran- nations, for both the upper and lower SASs, in order to il- dom allocations are equally likely (Clarke 1993; Quinn and lustrate which traits are more abundant and display any Keough 2003). The R distribution is scaled between pairs trends across the ordination, as well as their direction and of objects in the same group with values between 21and1. influence in relation to the compositional floristic data. Differences between groups would be suggested by Only the community-weighted trait means were used R values greater than zero where objects are more dis- as vectors, and only those with correlations to each ordin- similar between groups than within groups. R values of ation where R2 . 0.5 are displayed. zero indicate that the null hypothesis is true. R ¼ 1 indi- Ordinations, ANOSIM and SIMPER routines were per- cates that all samples within groups are more similar to formed using the PRIMER 6 package (Plymouth Routines each other than to those in different groups (Quinn and in Multivariate Ecological Research 2010). SYSTAT ver. 10 Keough 2003). (Copyright SPSS Inc., 2000) was used for all other statistical The dominant character species, those that are useful analyses. in discriminating between years at each of the spatial scales, were explored using the similarity percentages Results (SIMPER) procedure (Clarke 1993), performed using the PRIMER 6 package (Plymouth Routines in Multivariate Variation in abiotic variables across the elevation Ecological Research 5.1.2. 2010). This procedure utilizes gradient the similarity and dissimilarity indices between all pairs Across the gradient of sites, soil temperature and growing of samples to identify typical species within a site/year, season length have been shown in previous analyses to as well as important species that distinguish between vary predictably with elevation. Repeatedly, more favour- years. able conditions for plant growth have been found at the lower-elevation summits, with cooler, shorter growing Community trait-weighted means. In order to illustrate seasons with (often) less available nutrients at the higher functional changes between 2004 and 2011 across the summits (Pickering and Green 2009; Venn et al. 2012a). gradient of summits, we calculated the CTWM for the As previously described in Venn et al. (2012a), there assemblages of plants recorded in the upper and lower were no consistent summit-specific trends (increases SASs at every summit, for each of the four traits. The or decreases) in climatic variables across the summits

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between 2004 and 2011, although relationships between upper and lower SASs, with per cent change decreasing summit elevation and climate between 2004 and 2011 significantly with site elevation (upper SAS: R2 ¼ 0.92, remained consistent; minimum temperatures (linear P ¼ 0.008; lower SAS: R2 ¼ 0.99, P ¼ ,0.001). regression: R2 ¼ 0.28, P ¼ 0.016), mean temperatures Compositional changes between 2004 and 2011, illu- (linear regression: R2 ¼ 0.44, P ¼ 0.005) and number of strated by the ordination diagrams, point to similar direc- growing season (snow-free) days (linear regression: tional changes across all summits and sampling areas. R2 ¼ 0.23, P ¼ 0.032) all significantly decreased with in- In general, the vegetation composition in 2011 has chan- creasing elevation but not maximum temperatures ged in a consistent manner within and among sites in (using the data from four temperature loggers at each ordination space (Fig. 2), tending towards groupings of site as replicates) (Venn et al. 2012a). species with similarly high SLA. This also indicates that Although precipitation was not specifically measured the turnover and abundances of key species have been at each summit, data from the nearby rain gauges re- similar across sites. This is most pronounced in the dia- vealed substantial increases in annual and growing sea- gram illustrating the combined summit data for the son precipitation over the 2010/2011 growing season lower SASs, indicating that the vegetation composition (October to April) in comparison with previous years in between 2004 and 2011 changed similarly across sum- which the region experienced low rainfall conditions mits (Fig. 2B). The ANOSIM results point to significant dif- for almost a decade (Venn et al. 2012a, b). At Thredbo, ferences in the vegetation composition between sample there was a 30 % increase in annual precipitation in times, using aspects as replicates across sites (Table 3) 2011 compared with 2004 (mean precipitation between within quadrats, the upper and lower summit areas. 2004 and 2009 was 1136 mm, whereas in 2011 it was The vectors that represent the functional composition 1647 mm). Growing season precipitation at Pengilley in the community-weighted trait means, overlaid on Bog was about 50 % higher in 2011 compared with the ordination diagrams, point clearly to the higher- previous years (mean growing season precipitation be- elevation summits having high abundances of species tween 2003/2004 and 2009/2010 was 575 mm, whereas with higher SLA ratios and larger leaf areas (Fig. 2). Con- in 2010/2011 it was 1182 mm) (Venn et al. 2012a). versely, the lower-elevation summits appear to have Soil nutrient analyses conducted in 2003 revealed few proportionally higher abundances of species that are tal- trends across the elevational gradient of sites (Pickering ler and have larger LDMC values. Graminoid and shrub and Green 2009). Importantly, however, organic carbon abundance tended to contribute to the majority of the (%), available nitrogen (as ammonium) and aluminium differences between 2004 and 2011, revealed through (mg kg21) all decreased with increasing elevation. the SIMPER analyses, using aspects on summits as replicates and with the data combined for each summit Temporal change in vegetation composition (Table 4). The abundances of the top 10 most influential Overall, species richness between 2004 and 2011 tended and discerning species generally increased between to increase at all sites (Table 1), with the largest mean in- sample times (Table 4), leading to the dissimilarity in crease occurring in the lower mountain SAS at Clarke 4 vegetation composition between 2004 and 2011 as (+11.5 species). Vegetation composition changes be- identified in the ANOSIM analyses. At the lower and tween 2004 and 2011 across the gradient of sites were higher SASs across sites, shrub species were identified most pronounced among graminoids and forbs at the as the most influential in determining differences be- lowest site, Clarke 5, with an increase in forb abundance tween years (at least 5/10 of the top species were of up to 350% in the lower mountain SAS (Table 1). Not- shrubs,usingaspectsasreplicatesandwithdatacom- able increases in graminoids were partly made up of the bined for each summit respectively) (Table 4). Graminoid presence of new species in 2011 in many SASs across species were the second most influential group leading all summits, namely Agrostis sp., Deyeuxia crassilisica, to dissimilarity between years. Australopyrum velutinum (Poaceae), Carex breviculmis (Cyperaceae) and Luzula alpestris (Juncaceae). Spatial and temporal patterns in functional traits Changes in abundance across life forms were generally and CTWMs within the same order of magnitude between the upper The most substantial differences in the CTWM occurred at and lower SASs on each summit. Shrub abundance in- the lowest summit, Clarke 5, of up to 110% for leaf area creased substantially in the lower SASs at the two highest and SLA traits between years. At the higher-elevation sites, Clarke 1 and 2 (Table 1). Simple linear regression re- summits, the CTWM for leaf area and SLA traits increased vealed few discernible patterns in the change in abun- by up to 26% in 2011, with the largest increases within dance of particular life forms between years across the the lower SASs (Table 1). The CTWM for SLA increased sig- gradient of sites, except for within graminoids in the nificantly between 2004 and 2011 in both the upper and

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Figure 2. Non-metric multi-dimensional scaling ordinations of the (A) upper and (B) lower SASs at two levels of replication, within the ﬁve Mt Clarke summits using aspects, north, east, south, west, as replicates, and whole summits, to illustrate differences in vegetation composition (abundance) between 2004 (closed shapes) and 2011 (open shapes). Sites (shapes) close to each other in ordination space indicate high levels of similarity in species composition. Vector lines represent the CTWM values for the plant traits (height, leaf area, LDMC, SLA) with a Pearson’s correlation of .50 % with the vegetation data for each year and sampling unit. Vector lines indicate the degree to which those functional traits affect the groupings (similarity) of sites in ordination space.

Table 3. Results from the ANOSIM procedure across summits at the (n ¼ 20 for each test), revealed several significant differ- three sampling units and two levels of replication within sites ences between years; on the upper summit areas the (summits), comparing the similarity in vegetation data between CTWM for plant height and LDMC were significantly 2004 and 2011. lower in 2011 (P ¼ 0.004 and P ¼ 0.003, respectively), Sampling unit/replication n Global RP whereas the CTWM for SLA was significantly higher ...... (P , 0.001). On the lower summit areas, the significant Upper SAS/summit aspects 10 0.04 0.01 differences trended in the same direction (plant height, Upper SAS/summits 5 0.09 0.19 P ¼ 0.019; LDMC, P ¼ 0.011; SLA, P , 0.001). Lower SAS/summit aspects 10 0.07 0.05 Across the gradient of summits there were several sig- Lower SAS/summits 5 0.06 0.25 nificant linear relationships with good predictive power between the CTWM value for each of the measured traits and site elevation. Notably, plant height (decrease with lower SASs of Clarke 5 (1813 m) and Clarke 3 (1992 m) elevation), leaf area (increase with elevation), LDMC (de- (Fig. 3). The CTWM for leaf area was significantly greater crease with elevation) and SLA (increase with elevation) at Clarke 5 in the upper SASs between 2004 and 2011 and were all significantly related to elevation in 2004, where- the CTWM for plant height decreased significantly as only plant height and leaf area were significantly re- between 2004 and 2011 at Clarke 5 in the upper and lated to elevation in 2011 (Fig. 3). Considering species lower SASs (Fig. 3). preferentially organize themselves across the gradient Paired t-tests between the CTWM for each trait, com- in relation to environmental variables (Pickering and bining all sites and using summit aspects as replicates Green 2009; Venn et al. 2012a), we are confident that

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any intraspecific trait variation will be overridden by the of short-statured species as found at these sites, but also strong patterns in species distributions and life forms at many of the shrub species dominant at the lower eleva- these sites. tions, which is typical of many Australian shrubs (Groves The relationships between site elevation and the CTWM 1994). This is consistent with studies relating canopy values for each trait (Fig. 3) demonstrate how the CTWM architecture and plant height to wind in exposed sites of each site is determined floristically (by abundance) (Caldwell et al. 1974; Smith et al. 1995; Ko¨rner 1999)or across the gradient of sites. Additionally, the significant possibly phylogenetic conservatism of habitat (Cornwell increases in the CTWM for SLA between 2004 and 2011 and Ackerly 2009). In the context of this study, high SLA can be attributed to the changes in mean abundance of and leaf areas at the higher, supposedly more stressful key shrub species (Table 4) with small, tough leaves such sites appear counterintuitive and are inconsistent with as Kunzea muelleri which increased in abundance. The similar studies of leaf traits across growing season and significant decreases in the CTWM for plant height at snowmelt gradients (Kudo et al. 1999; Spasojevic and Clarke 5 (Fig. 3), however, can mostly be attributed to Suding 2012). In the Snowy Mountains, fewer shrubs are the relative increases of the short/prostrate shrub Eparcis present at higher elevations and forbs at high elevations microphylla (Ericaceae) on this summit. are generally selected to have smaller leaves, although their leaves are generally fleshy and larger than those Discussion of the shrubs, hence the patterns in SLA and leaf area across the gradient. However, at the highest wind-swept Linking changes in vegetation composition areas of feldmark, several tough, dwarf, prostrate shrub with functional composition species are present (such as Epacris and Chionohebe Our exploratory analyses have demonstrated how a spp.) in an environment where long-lived species are se- short-term change in vegetation composition affects lected for. Large-scale interspecific comparisons have the CTWM and how it varies preferentially across the strengthened the idea that SLA is related negatively gradient of summits. Not surprisingly, the patterns in with leaf life span and positively with relative growth life forms across sites strongly reinforce the patterns in rate (Reich et al. 1999; Wright et al. 2004), a potentially the CTWM, as do broad functional groups of species advantageous strategy in harsh climates with short grow- across other environmental gradients (Reich et al. 1997; ing seasons. However, a longer snow season at the higher Choler 2005). summits in the Snowy Mountains may actually provide In general, with increasing elevation within both the more favourable conditions in early spring when plants lower and upper SASs, shrub species appear less abun- are protected from early-season frosts and winds, where- dant and forb species more so, whereas the proportion as they would be exposed earlier at the lower summits of graminoid species tends to increase in abundance (Inouye et al. 2002; Venn et al. 2012b), which may have with elevation, a trend similarly reported in other local caused these unexpected patterns in the CTWM for SLA studies (Venn and Morgan 2005; Venn 2007). Across the across the gradient. This is certainly the case around gradient, the CTWM for plant height decreases signifi- the edges of late-lying snowpatches in the Snowy cantly with elevation as the proportion of tall shrub spe- Mountains, which melt out earlier than the centre of the cies (particularly Phebalium ovatifolium) decreases with snowpatch (Venn et al. 2011), and where tough, high longer snow seasons and lower temperatures at higher fibrous (low SLA) species are more abundant in the elevations. Similarly, the CTWM for the leaf traits corre- early-exposed alpine areas (Choler 2005). Conversely, sponds with the higher proportion of forbs and grami- low LDMC and large leaf areas at higher elevations may noids at the higher elevations; SLA and leaf area be indicative of fast resource acquisition and relatively increase with elevation and LDMC decreases, reflecting high productivity, even in areas where growing seasons the higher abundances of smaller, compact forb and gra- are short and unpredictable (Kudo et al. 1999). minoid species (particularly the sedges Carex hebes, Only a few studies have documented temporal func- C. breviculmis) at the higher elevations, as is found in simi- tional diversity or functional composition change in lar studies (Kudo et al. 1999; Choler 2005; Venn et al. plant communities, with mixed results; but see the review 2012a). These trends were also apparent at the scale of in Dıáz and Cabido (2001). Here, significant differences in SAS within mountains, where directional differences in the CTWM of several traits between years, but some non- life form and the CTWM exist between the upper and significant differences in terms of species composition lower summit areas. (from ANOSIM analyses) point to species abundance Species with small, thick leaves and low SLA are key change, rather than high turnover or increases of new features of high, wind-exposed sites (Tranquillini 1964; species. These changes are likely attributable to the over- Kudo et al. 1999; Choler 2005), as is the high abundance all large increases in shrub and graminoid abundance

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Table 4. The mean overlapping cover (%) in 2004 and 2011 of the 10 most typical species within each sampling unit and level of replication that contribute to the differences seen in the ordinations and ANOSIM analyses and the contribution (%) of each species to the total species pool in each group, using the SIMPER routine (Primer ver. 6).

Sampling unit/replication Species Family Life form 2004 Mean 2011 Mean Contribution (%) abundance (%) abundance (%) ...... Upper SAS/summit aspects Kunzea muelleri Myrtaceae Shrub 3.08 3.44 9.38 Epacris microphylla Ericaceae Shrub 3.76 4.24 9.03 Poa sp. Poaceae Graminoid 7.4 9.76 8.33 Celmisia costiniana Asteraceae Forb 3.72 3.8 5.69 Empodisma minus Restionaceae Graminoid 1.6 2 4.88 Phebalium ovatifolium Rutaceae Shrub 1.6 1.52 4.6 Grevillea australis Proteaceae Shrub 1.8 1.48 4.59 Trisetum spicatum Poaceae Graminoid 2.04 1.88 4.45 Prostanthera cuneata Lamiaceae Shrub 1.32 1.32 3.85 Microseris lanceolata Asteraceae Forb 1.44 1.44 3.75 Upper SAS/summits Kunzea muelleri Myrtaceae Shrub 5.24 5.72 7.13 Phebalium ovatifolium Rutaceae Shrub 3.8 3.72 4.88 Grevillea australis Proteaceae Shrub 4.28 3.48 4.8 Empodisma minus Restionaceae Graminoid 4.6 5.44 4.4 Prostanthera cuneata Lamiaceae Shrub 3.2 3.56 4.22 Epacris microphylla Ericaceae Shrub 8.32 9.68 4.17 Pentachondra pumila Ericaceae Shrub 3.16 2.92 4.08 Agrostis sp. Poaceae Graminoid 0.04 3.8 4.05 Carex breviculmis Cyperaceae Graminoid 0 3.84 4.02 Poa sp. Poaceae Graminoid 12.64 15.68 3.31 Lower SAS/summit aspects Kunzea muelleri Myrtaceae Shrub 2.32 2.72 7.85 Poa sp. Poaceae Graminoid 7.6 9.92 7.6 Celmisia costiniana Asteraceae Forb 4.76 4.6 6.72 Epacris microphylla Ericaceae Shrub 3.52 4.08 6.24 Phebalium ovatifolium Rutaceae Shrub 2.04 1.8 5.57 Empodisma minus Restionaceae Graminoid 1.76 1.8 5.01 Grevillea australis Proteaceae Shrub 1.92 1.6 4.5 Trisetum spicatum Poaceae Graminoid 2.12 2.2 4.31 Pentachondra pumila Ericaceae Shrub 1.4 1.6 3.91 Prostanthera cuneata Lamiaceae Shrub 1.12 0.96 3.04 Lower SAS/summits Kunzea muelleri Myrtaceae Shrub 4.72 5.28 5.82 Phebalium ovatifolium Rutaceae Shrub 4.52 3.96 5.25 Empodisma minus Restionaceae Graminoid 4.72 4.32 4.62 Grevillea australis Proteaceae Shrub 4.56 3.72 4.21 Agrostis sp. Poaceae Graminoid 0 3.64 3.76 Carex breviculmis Cyperaceae Graminoid 0.12 3.72 3.59

Continued

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Table 4. Continued

Sampling unit/replication Species Family Life form 2004 Mean 2011 Mean Contribution (%) abundance (%) abundance (%) ...... Pentachondra pumila Ericaceae Shrub 3.92 4.4 3.51 Epacris microphylla Ericaceae Shrub 7.72 9.04 3.02 Luzula alpestris Juncaceae Graminoid 0.04 2.92 2.89 Acetosella vulgaris Polygonaceae Forb 1.16 3.56 2.87

across the gradient, which in turn has consequences for CTWM. Across gradients such as this one, there is an ex- driving overall community assembly processes given the pectation that the CTWM of each trait will be a reflection trait-space and life-history strategies of these broad life- of a gradient of abiotic stress: higher at the higher- form groups (Dıáz and Cabido 2001; Westoby et al. 2002). elevation summits and lower at the lower-elevation sum- Although temperatures did not change significantly over mits. Conversely, the CTWM for certain traits might reflect the study period, precipitation did. The recent spikes in some overriding biotic interactions which ameliorate precipitation, however, are unlikely to have caused the stressful abiotic conditions as per the stress-gradient patterns observed in the functional composition in this hypothesis (Bertness and Callaway 1994; Kikvidze et al. study; instead these may be realized in the subsequent 2006): in areas where abiotic stress is low and negative 1–2 growing seasons as the herbaceous vegetation re- biotic interactions (competition) are expected (at the sponds to the additional rainfall after many years of lower-elevation summits: higher temperatures, longer drought conditions. growing seasons) (Callaway et al. 2002; Cornwell and Ackerly 2009; Venn et al. 2009). This may explain the pat- Community composition and ecology across terns in two of the four traits considered (the high CTWM the environmental gradient values for SLA and leaf area), indicating that biotic filter- Our analysis of functional patterns from leaf traits may be ing likely due to facilitation may be operating at the used to help describe the mechanisms driving commu- higher-elevation summits, allowing for relatively high nity composition across this elevation gradient of sum- productivity. Further experimental work in situ including mits (McGill et al. 2006). Overall, species have sorted measures of below-ground plant interactions will be ne- themselves preferentially across the gradient, most likely cessary to unravel the interacting effects of biotic and as a result of abiotic filtering (Venn et al. 2011), the harsh abiotic filtering (Wilson 1993) at these sites. At the conditions at the higher summits compared with the lower summits, we observed relatively higher values in lower summits, and possibly through the additional impli- the CTWM for plant height and LDMC, strongly driven by cations of biotic interactions. Both interaction types are the high proportion of shrub and graminoid species. ultimately predetermined by species’ inherent functional A high proportion of these species can indicate fast and traits and the functional trait space that each species oc- high biomass accumulation, lateral spread and competi- cupies (Dıáz and Cabido 2001). Whereas a significant tive superiority (Westoby et al. 2002; Cornelissen et al. change in the CTWM across the gradient for a specific 2003), as well as leading to increases in soil organic trait could indicate a threshold for an abiotic filter, it matter and available nitrogen (Myers-Smith et al. 2011; could also be due in part to biotic filtering, where positive Vankoughnett and Grogan 2013). Here, plants may also or negative interactions between plants interact with en- be less constrained by (relatively) harsh conditions, but vironmental factors preferentially across a gradient to ei- biotic filtering such as competition may override other ther constrain or facilitate species with certain traits or assembly processes. trait combinations (Scho¨b et al. 2012; Spasojevic and Trade-offs between an energy-efficient strategy for Suding 2012). nutrient conservation, with a high capacity for resource Although we did not formally address the issue of intra- acquisition under harsh conditions, will likely drive future specific trait variation in this study, we noted that there is shifts in functional traits and species composition across little variation in the traits measured within species the gradient (Choler 2005). However, the interaction be- across the gradient of sites; rather most of the variation tweenabioticfilters(suchasgrowingseasonlength in plant traits across the gradient comes from the vari- and variability) and biotic filters will ultimately determine ation in species abundances, as demonstrated by the whether short-term temporal shifts in species

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Figure 3. The CTWM + 95 % conﬁdence intervals for the four functional traits, plant height, leaf area, LDMC and SLA, based on species abundance in the upper and lower SASs. 2004 data ¼ closed circles, 2011 data ¼ open circles. Simple linear relationships (using all site aspect data) between site elevation and CTWM values are only displayed for R2 ≥ 0.5 and P , 0.05.

composition will eventually manifest as signiﬁcant herbaceous species with high SLA and high productivity changes to observational trait patterns and ecosystem at high elevations must continue to ‘hold their ground’. functioning in the long term. In the short term, the However, the relatively tall graminoids and shrubs, with

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thick, tough leaves (low SLA) that dominate the lower Chapin FS III, Bret-Harte MS, Hobbie SE, Zhong H. 1996. Plant func- summit areas and at lower elevations more generally, tional traits as predictors of transient responsers of Arctic vege- are likely to maintain their dominance, increase in tation to global change. Journal of Vegetation Science 7: 347–358. abundance and out-compete other life forms in the Choler P. 2005. Consistent shifts in alpine plant traits along a meso- local species pool. topographical gradient. Arctic, Antarctic and Alpine Research 37: 444–453. Sources of Funding Clarke KR. 1993. Non-parametric multivariate analysis of changes in community structure. Australian Journal of Ecology 18:117–143. 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