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The net outcome of interactions between plants varies through space and time, ranging from facilitation and mutualism (i.e. positive) to competition and parasitism (i.e. negative). Spatial variation in the net outcome of plant interactions is strongly linked to environmental conditions, with facilitative interactions generally dominating under conditions of abiotic extremes, low resource availability, high herbivory or intense disturbance (i.e. high environmental severity; sensu Brooker & Callaghan, 1998), and competition being more common in milder environments (Bertness & Callaway, 1994; although see also e.g. Maestre et al., 2009). The outcome of plant–plant interactions can also vary within and between years as environmental conditions fluctuate, with the strength of positive interactions increasing relative to negative interactions during more stressful periods (Tielbörger & Kadmon, 2000; Kikvidze et al., 2006; Sthultz et al., 2007). This spatial and temporal variation in the balance of positive and negative interactions is predicted to be related to environmental severity by the stress-gradient hypothesis (SGH; Bertness & Callaway, 1994; Brooker & Callaghan, 1998), an assumption that is well supported by the majority of studies that have tested the hypothesis (He et al., 2013).
Changes in individuals' size, age or life stage may also influence the outcome of interactions, although this source of temporal variation is less frequently studied (Callaway & Walker, 1997; Soliveres et al., 2010). As plants germinate, establish and grow, their physiological tolerances and resource requirements change, as does their influence on the surrounding environment (Parish & Bazzaz, 1985; Miriti, 2006). In consequence, the balance between the positive and negative components of plant–plant interactions often shifts as plants age, giving rise to ontogenetic shifts in the outcome of interactions (i.e. a change in the nature and/or strength of an interaction related to an individual's ontogeny). The majority of studies show a transition from facilitation during establishment (i.e. neighbouring plants benefit seedling survival) to the inhibition of, or a neutral effect on, adult plant growth and reproduction (Miriti, 2006; Reisman-Berman, 2007; Lortie & Turkington, 2008; Valiente-Banuet & Verdú, 2008; Armas & Pugnaire, 2009). This probably reflects the fact that larger plants often have greater resource requirements which increase their competitive impacts, and also usually have lower sensitivity to climatic extremes, reducing the benefits of environmental amelioration by neighbouring plants.
Schiffers & Tielbörger (2006) hypothesized that the timing of ontogenetic shifts could vary with environmental severity, with the net outcome of interactions remaining positive for longer under more stressful conditions. Thus, under greater environmental severity an ontogenetic shift in the interaction (from facilitation to competition) should be delayed. Sthultz et al. (2007) supported this hypothesis by demonstrating that at low altitudes Fallugia paradoxa facilitates the survival of Pinus edulis seedlings but increases the mortality of adult P. edulis (i.e. a negative ontogenetic shift). By contrast, at a more stressful high-altitude site, all life stages of P. edulis were facilitated by F. paradoxa, illustrating a marked change in the nature of the ontogenetic shift in this interaction with increasing environmental severity. Few other studies have determined whether ontogenetic shifts in plant interactions are affected by environmental conditions (Eränen & Kozlov, 2008; Soliveres et al., 2010), with none explicitly testing Schiffers & Tielbörger's (2006) hypothesis or examining any consequences of the ontogenetic shifts. Ignoring ontogenetic shifts in interactions could lead to incorrect interpretation of variation in the outcome of plant–plant interactions and to inaccurate broad generalizations, which may be especially critical for areas showing rapid changes in environmental severity (see e.g. Hansen et al., 2012). Specifically, the SGH's failure to incorporate ontogenetic shifts in interactions may account for some of the discrepancies between the model's predictions and observed patterns (He et al., 2013). Ontogenetic shifts therefore need to be examined more critically and incorporated more explicitly into plant–plant interaction models.
One potential impact of plant interactions and their associated ontogenetic shifts may be on species population structure, acting through altered survival and reproduction rates. However, studies of plant–plant interactions have generally focused either on the impact of neighbouring individuals on the performance of focal plants (e.g. survival, growth rate or photosynthetic efficiency; Cavieres et al., 2005; Sthultz et al., 2007; Armas & Pugnaire, 2009) or on the composition of the entire flora associated with benefactor species (including biomass, species richness and diversity; Tewksbury & Lloyd, 2001; Holzapfel et al., 2006; see also Gross et al., 2009). Use of these methods has yielded important insights into the effect of plant interactions at the individual and community levels (Brooker et al., 2008; Butterfield et al., 2013). However, effects on individuals result in variation at the community level only insofar as the former alter population-level parameters such as stage-specific survival or age-specific reproduction. The balance between mortality (including success of immigration), reproduction and emigration for each species largely determines community diversity (richness, abundance structure and size structure; e.g. Andrewartha & Birch, 1954; Ricklefs, 2008). In consequence, investigations at the population level are essential for understanding how the outcome of individual plant–plant interactions scales up to affect communities.
In this study, we investigated whether there was an ontogenetic shift in the impact of the sub-Antarctic cushion plant Azorella selago (benefactor) on the grass Agrostis magellanica (beneficiary), whether the nature and timing of the ontogenetic shift varied with environmental severity (examined along two different stress gradients), and the extent to which the interaction affected the population structure and reproductive output of A. magellanica.
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The impact of A. selago on A. magellanica was generally positive, increasing the grass's abundance and inflorescence production relative to individuals growing on the adjacent soil in most size classes (i.e. 85% of all RIIabund values > 0 and 90% of RIIinflor values > 0; Table 1). Furthermore, A. selago's effect on A. magellanica abundance was significantly related to the size class of grasses considered (Table 1). Along the wind exposure gradient, the relationship between RIIabund and A. magellanica size was best described at all stress levels by quadratic functions (all with negative quadratic coefficients; Fig. 1a, Table 1). Therefore, A. selago increased the abundance of intermediate-sized A. magellanica most, relative to the abundance of the same A. magellanica size classes on the adjacent soil. By contrast, along the altitudinal gradient the form of the relationship differed according to stress level (Fig. 1b; Table 1); at low elevations the RIIabund was not related to A. magellanica size, while at intermediate altitudes the relationship was quadratic, with A. selago increasing the abundance of medium-sized grasses most. At high altitudes (i.e. under more stressful conditions) A. selago had the most positive effect on the abundance of the largest grasses (i.e. a positive linear relationship; Fig. 1b, Table 1).
Table 1. Results from regression of interaction intensity (relative interaction index (RII)) against Agrostis magellanica size class, for both types of stress gradient (wind exposure and altitude) and all stress levels (low, mid and high; relationships illustrated in Fig. 1)
| || n ||Proportion RII values positive||Beta regression|| P ||Quantile regression|| F || P ||Turning point ± SE |
|Minimum adequate model||χ2||Turning point ± SE||Minimum adequate model|
|Abundance||Low||99||0.81||Quadratic||6.94||0.031||0.48 ± 0.04aa||Quadratic||29.83||<0.001||1.10 ± 0.06a|
|Mid||99||0.92||Quadratic||10.35||0.006||0.67 ± 0.05b||Quadratic||5.10||0.008||1.99 ± 0.16b|
|High||102||0.97||Quadratic||27.33||< 0.001||0.74 ± 0.04b||Quadratic||9.84||< 0.001||2.60 ± 0.15c|
|Inflorescences||Low||36||0.78||None|| || || ||Null|| || || |
|Mid||47||0.96||None|| || || ||Quadratic||3.18||0.051||2.13 ± 0.40|
|High||54||0.98||None|| || || ||Null|| || || |
|Abundance||Low||136||0.57||None|| || || ||Quadratic||2.57||0.080||1.54 ± 0.13a|
|Mid||206||0.93||Quadratic||24.70||< 0.001||0.65 ± 0.02||Quadratic||12.08||< 0.001||2.29 ± 0.16b|
|High||126||0.88||Linear||4.74||0.029|| ||Linear||10.99||0.001||2.88 ± 0.23bc|
|Inflorescences||Low||49||0.69||None|| || || ||Linear||8.43||0.006|| |
|Mid||96||0.95||None|| || || ||Null|| || || |
|High||54||0.96||None|| || || ||Null|| || || |
Figure 1. Relationship between interaction intensity (relative interaction index for the Agrostis megellanica abundance (RIIabund); i.e. the impact of Azorella selago on A. magellanica abundance) and A. magellanica mass. (a) Wind exposure gradient; (b) altitudinal gradient. The size of symbols reflects the number of overlapping data points. Dashed lines show the best beta regression fit to the data, and dotted lines the best quantile regression fit (details in text and statistics in Table 1). Where a quadratic function gave the best fit, error bars above the panel indicate 1 SE on either side of the turning point.[Correction added after online publication 6 June 2013: in the preceding sentence the definition of RIIabund has been corrected.]
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Quantile regression revealed that the impact of A. selago on A. magellanica abundance was constrained by the size of A. magellanica individuals (Table 1). In five of the six stress gradient–stress level combinations, quadratic models provided a better fit than linear models to the lower bound of the RIIabund–A. magellanica size relationship. Along the exposure gradient, the location of the turning points of the RIIabund–size relationship occurred at significantly greater size under higher environmental stress (Table 1, Fig. 1a). Similarly, along the altitudinal gradient the turning point in the quadratic curves was at increasingly larger A. magellanica sizes under progressively greater environmental severity (with no turning point at the highest elevation; Table 1, Fig. 1b). By contrast, the impact of A. selago on the number of A. magellanica inflorescences did not consistently vary with the size of grasses along the exposure or altitudinal gradient when using either beta or quantile regression (Table 1, Fig. S3).
Comparison of the population structures of A. magellanica growing on A. selago and on the soil revealed a positive effect of A. selago on the abundance of A. magellanica (Table 2), and particularly on the relative abundance of intermediate and large individuals (Figs 2, S4). The largest impact of A. selago on A. magellanica abundance was in the intermediate size classes (e.g. 100.75–102.75 mg; Figs 2, S1), with A. magellanica abundance on A. selago three to 17 times higher than on the soil (Table 2). Along the altitudinal gradient, A. magellanica population structure differed significantly between substrates (i.e. comparing grasses on A. selago and on soil at the same stress level; Table 3). By contrast, population structure did not differ between altitudinal bands when comparing grasses growing on the same substrate (i.e. size-class distribution was not different between low, mid and high altitudes for grasses growing on the same substrate; Table 3, see e.g. Fig. 2). The same trend was evident for A. magellanica population structure on the exposure gradient (i.e. higher KS statistics when comparing population structure between substrates than when comparing between wind exposure levels; Fig. S4, Table S1).
Table 2. The abundance and reproductive effort of Agrostis magellanica growing on the soil and on Azorella selago cushion plants at three stress levels (low, mid and high) along two types of stress gradient (altitude and wind exposure)
| ||Number of A. magellanica per sample (mean ± SE)||Number of A. magellanica inflorescence stalks per sample||Minimum mass of flowering A. magellanica (mg)||Agrostis magellanica root:shoot ratio (mean ± SE)|
|Soil|| A. selago ||Soil|| A. selago ||Soil|| A. selago ||A. selago (rarefied; mean ± SE)||Soil|| A. selago |
|Low||68.4 ± 17.1||174.8 ± 63.0||11.5 ± 6.2||14.4 ± 5.1||114||19||53.2 ± 2.5a||0.32 ± 0.03||0.19 ± 0.04a|
|Mid||23.6 ± 7.8||178.5 ± 27.6a||0.7 ± 0.3||24.3 ± 5.5a||428||8||54.6 ± 3.7a||0.38 ± 0.05||0.25 ± 0.02a|
|High||6.8 ± 2.3||52.2 ± 11.4a||0.3 ± 0.3||21.8 ± 5.9a||30||7||66.0 ± 4.9a||0.37 ± 0.07||0.29 ± 0.03|
|Low||67.0 ± 21.9||225.8 ± 35.5a||4.3 ± 2.2||13.3 ± 5.2||43||29||43.1 ± 1.3||0.40 ± 0.08||0.25 ± 0.01a|
|Mid||18.9 ± 5.2||158.1 ± 45.5a||3.3 ± 1.4||24.8 ± 4.7a||116||17||60.0 ± 3.1a||0.37 ± 0.08||0.22 ± 0.02|
|High||8.5 ± 1.0||146.1 ± 21.1a||0.5 ± 0.4||33.9 ± 6.4a||121||12||134.8 ± 15.8||0.42 ± 0.07||0.19 ± 0.01a|
Table 3. Results of Kolmogorov–Smirnov tests comparing Agrostis magellanica size-class distributions across stress levels (high, mid and low altitude) and substrate types (growing on Azorella selago versus growing on the adjacent soil) along the altitudinal gradient
|Stress level||Substrate comparison||D statistic||P value|
|Low||Soil versus A. selago||0.320||< 0.001a|
|Mid||Soil versus A. selago||0.353||< 0.001a|
|High||Soil versus A. selago||0.474||< 0.001a|
|Substrate||Stress level comparison|| || |
|Soil||Low versus mid||0.078||0.093|
|Soil||High versus mid||0.091||0.650|
|Soil||Low versus high||0.170||0.033|
| A. selago ||Low versus mid||0.017||0.862|
| A. selago ||High versus mid||0.078||0.005|
| A. selago ||Low versus high||0.076||0.010|
Figure 2. Size-class distribution of Agrostis magellanica rooted in Azorella selago or in the adjacent soil, in three altitudinal bands (low, < 150 m above sea level (asl); mid, 150–300 m asl; high, > 300 m asl). Black bars indicate the size-class distribution of flowering individuals, while light and dark grey bars indicate all individuals greater than the observed or rarefied size threshold for flowering, respectively. Note the differences in the scaling of the y-axis between panels.
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The minimum flowering size (i.e. reproductive threshold) of A. magellanica differed between individuals growing on the soil and on A. selago, with the grass flowering at a smaller minimum size on A. selago (Table 2; see also Figs 2, S4). Rarefied estimates of A. magellanica's minimum flowering size on A. selago were considerably higher than the observed values, but were still significantly smaller than for grasses growing on the soil in three comparisons (Table 2). Moreover, more inflorescences were produced by grasses growing on A. selago than by those growing on the adjacent soil at all stress levels, with > 99% of inflorescence stalks at high altitudes and wind exposure being carried by A. magellanica individuals growing on A. selago (Table 2). Root:shoot ratios were consistently higher in soil-rooted A. magellanica than in individuals growing on A. selago, with the differences being significant in four of the six comparisons, indicating a greater proportion of biomass allocated to below-ground growth in soil-rooted individuals (Table 2).
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The impact of A. selago on A. magellanica was related to A. magellanica size, but not consistently so, with the form of the relationship varying with A. magellanica performance measure and stress gradient type. Moreover, none of the significant ontogenetic shifts documented were of the expected form (i.e. monotonically negative), with the most positive impact of A. selago on the abundance of intermediate-sized grasses. However, despite the variability in the ontogenetic shifts, there was a clear trend for the shift towards more negative interactions to be delayed under greater environmental severity. As a result, the hypothesis that ontogenetic shifts in plant interactions are delayed under more stressful conditions could not be rejected.
Effects of ontogenetic stage
The nonmonotonic relationship between RIIabund and A. magellanica size was unexpected, as seedlings are generally the most strongly facilitated life stage, while the largest individuals usually have neutral or negative interactions with other plants (although more complicated ontogenetic shifts have been described; e.g. Rousset & Lepart, 2000). The A. selago–A. magellanica interaction contrasts with this expected pattern, as the abundance of the smallest A. magellanica individuals was not most strongly increased by A. selago (Fig. 1). This pattern was more pronounced in the quantile regression, suggesting that, while other factors also influence the impact of A. selago on A. magellanica abundance, the occurrence of strong negative interactions are least likely for grasses of intermediate size. The ontogenetic shift observed suggests that there may be multiple facilitative and competitive components to the A. selago–A. magellanica interactions. Indeed, it is likely that with increasing size A. magellanica individuals probably compete more strongly with A. selago for space, nutrients and water, while the benefit of environmental amelioration by the cushion plant probably remains similar (or declines slightly) for larger grasses. However, an additional mechanism that exerts a strong negative effect on the smallest grasses growing on A. selago must also be important to produce a unimodal RIIabund–A. magellanica size relationship. One potential mechanism is the overgrowing of small A. magellanica grasses by A. selago, thereby reducing their survival. Indeed, this is quite possible as A. selago shows rapid shoot elongation under shading (le Roux et al., 2005; although other mechanisms may also contribute, including inhibited germination; Olofsson et al., 1999). Therefore, intermediate-sized grasses may benefit most from the interaction with A. selago by being large enough that A. selago cannot overgrow them, but still small enough to avoid strong competition with A. selago and to benefit from environmental amelioration by the cushion plant. By contrast, A. magellanica individuals growing in the open soil probably have a consistently lower probability of mortality with increasing size, as the more extensive root systems of larger individuals would reduce their vulnerability to soil moisture deficits and the chance of frost-heaving (Kleier & Rundel, 2004; Haussmann et al., 2010).
The difference between the shape and significance of the RII–A. magellanica size relationship for the abundance of individuals and of inflorescences fit with the current understanding that the impact of plant interactions differs between performance measures (Brooker et al., 2008). Previous studies have shown that plant survival generally responds strongly to environmental amelioration by neighbouring plants, but that reproduction is less affected by changes in environmental severity caused by the presence or absence of facilitators (Goldberg et al., 1999; Maestre et al., 2005). Thus, a similar pattern may exist in the size dependence of an interaction, with ontogenetic shifts in the benefactor's impact being more pronounced on the beneficiary's abundance than on its reproductive output.
Impacts on population structure
The A. selago–A. magellanica interaction altered the population structure of A. magellanica, with a disproportionately strong increase in medium-size grasses. The shape of the A. magellanica size-class distribution was more strongly affected by the occurrence of A. selago than by differences in altitude, suggesting that the plant–plant interaction has a stronger impact than variation in environmental severity, at least along one of the stress gradients. Differences in the population structure of A. magellanica growing on the soil and on A. selago are probably a result of improved growth and/or survival of individuals on A. selago, with the lower root:shoot ratio observed for the grasses growing on the cushion plant suggesting one possible mechanism. The lower root:shoot ratio probably reflects a reduced requirement for resource allocation to the production of roots when growing on A. selago as a result of the more stable substrate (especially in contrast to the frequent freeze–thaw cycles in the soil; Boelhouwers et al., 2003) and increased availability of water and nutrients that the cushion plant offers (McGeoch et al., 2008; Anthelme et al., 2012). Therefore, through altering the fine-scale environmental conditions experienced by A. magellanica, A. selago also affects the expression of a functional trait in A. magellanica (Cavieres et al., 2005), which may contribute to the interaction's impact on A. magellanica population structure.
The presence of A. selago also strongly impacted A. magellanica's reproductive output, increasing the grasses' inflorescence production greatly. Our results identified three A. selago-driven changes in the biology and population structure of A. magellanica which contribute to the 1.3- to 73-fold difference in reproductive output between soil-rooted and A. selago-associated A. magellanica populations. First, A. magellanica individuals growing on A. selago tended to flower at smaller sizes than individuals growing on the soil, possibly as a result of the altered resource allocation associated with changes in the root:shoot ratio. The observation that grasses growing on the soil initiate reproduction at a larger size is in agreement with previous studies that demonstrated that flowering is increasingly delayed under progressively more negative interactions (Weiner, 1988). Secondly, the A. selago–A. magellanica interaction disproportionately increased the relative abundance of medium-sized, and thus potentially reproductive, grasses. Finally, the total abundance of A. magellanica individuals of all sizes was increased by A. selago. As a result, a larger number (in absolute and relative terms) of A. magellanica grasses exceed the grasses' minimum flowering size when growing on A. selago, thereby increasing the abundance of potentially reproductive individuals. Thus, the population's reproductive effort is positively affected by A. selago via changes in the grass's abundance, population structure and size threshold for reproduction, highlighting the diverse mechanisms through which this facilitative interaction operates.
Three important conclusions are evident from this study. First, there is a strong ontogenetic shift in the effect of A. selago on A. magellanica, with this size-dependent interaction showing a previously undocumented form (i.e. strongest facilitation for intermediate-size individuals). Secondly, our results provide support for Schiffers & Tielbörger's (2006) hypothesis that ontogenetic shifts may be delayed under greater environmental severity, illustrating that the nature of ontogenetic shifts can be dependent on environmental conditions. Finally, we show for the first time that the relative abundance structure of a beneficiary species is more strongly affected by its interaction with the benefactor species than by variation in abiotic conditions, demonstrating that biotic interactions can be more important than environmental severity in some situations.
As a consequence of the potential for ontogenetic shifts in plant–plant interactions, studies examining interactions need to consider facilitative (or competitive) effects on both the abundance and population structure of beneficiary species, as focusing on the former alone may fail to capture important aspects of the latter. Thus, following the recent calls for the refinement of the stress-gradient hypothesis to reflect improved understanding of competition and facilitation (Maestre et al., 2009; Malkinson & Tielbörger, 2010), we argue that ontogenetic shifts in plant–plant interactions also need to be included in this framework. More generally, by examining the changes in the A. selago–A. magellanica interaction along two environmental gradients, these results highlight the potential for climate change to affect ontogenetic shifts in species interactions. Because shifts in temperature and/or precipitation patterns may affect both the phenology and ontogeny of species (Parmesan, 2006; Barton, 2010), this is a mechanism through which changing climatic conditions could alter species interactions (Klanderud, 2005; Cavieres & Sierra-Almeida, 2012), a key challenge for climate change impact forecasting (Wisz et al., 2013). Therefore, models aiming to accurately predict species- and community-level responses to changing environmental conditions need to consider how shifts in species' ontogenies (via changes in development rates and/or phenology) may affect their interactions with co-occurring species (Barton, 2010; Yang & Rudolf, 2010).