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Alpine tree-line forests are particularly suitable to study such complex interaction patterns as they are usually simple systems composed of only a few, ecologically similar and often taxonomically related species. Moreover, these forests are considered especially sensitive to climate change (e.g. Theurillat & Guisan 2001; Kullman 2002; Dullinger et al. 2004). Taking the north-eastern Calcareous Alps of Austria as an example, the current tree line is dominated by extensive mono-dominant stands of the obligatory shrubby pine species Pinus mugo Turra. Below this pine–krummholz belt, which covers about 100–200 altitudinal metres, subalpine forests of Norway spruce (Picea abies (L.) Karsten) and European larch (Larix decidua Mill.) prevail. Predicted climate warming is likely to shift range margins of all three conifer species upslope, but only if populations of spruce and larch are able to invade the resident pine belt. Whereas it is well established that the shade-intolerant pine is not able to recruit or even to maintain viable clonal populations in the understorey of spruce or larch forests (Michiels 1993), the effects of P. mugo on Picea abies and L. decidua are much less well known. Based on anecdotal evidence it has been suggested that pines may serve as ‘nurse plants’ for spruce and larch, improving local site conditions by accumulating humus and conveying shelter against frost, strong winds and browsing herbivores (Michiels 1993). However, these facilitative effects will probably be restricted to the early life-history stages of the two tree species, i.e. until they overtop the shrub layer. Additionally, low light conditions and raw humus accumulation below P. mugo have been documented severely to reduce germination and establishment rates of the pines themselves (Hafenscherer & Mayer 1986; Michiels 1993) and may have similar effects on spruce and larch. Moreover, L. decidua is a light-demanding tree the recruitment of which is sensitive to thick litter layers whereas Picea abies is comparatively shade-tolerant and its regeneration benefits from raw humus accumulation (Zukrigl 1973; Mayer 1976). It is thus likely that potentially negative effects of pines on the recruitment of the two trees will affect larch more severely than spruce. As a consequence, pine shrubland may represent a differentially permeable barrier if environmental changes trigger simultaneous range dynamics of Picea abies and L. decidua.
In the current study we focus on the effects pines may have on recruitment, growth rates, fecundity and browsing damage of spruce and larch. Based on the above considerations we derived three hypotheses. (i) Effects of P. mugo on Picea abies and L. decidua will involve both positive and negative components: browsing is likely to be reduced within dense pine shrublands whereas the effects on recruitment, growth and fecundity of the two tree species are difficult to predict a priori. (2) Effects of pines on growth rates of the two tree species will be restricted to the juvenile phase of the trees, i.e. while they are growing within the pine canopy. (3) Effects of pines will vary between the two tree species, at least for growth rates and recruitment. We tested these hypotheses by analysing observational data from 250 sampling plots that cover both the environmental variability of the studied tree-line system and a gradient of pine cover.
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Overall, we collected data on 1130 Norway spruce (1074 alive, 66 dead) and 624 European larch (588 alive, 36 dead) individuals, 144 (spruce) and 94 (larch) of them saplings (i.e. between 0.3 and 1.3 m in height) and 48 seedlings (< 0.3 m) of each species on the central crosses of the plots. Sample parameters for the analysed response variables are given in Table 1. Owing to the sampling design, the frequency distribution of percentage pine cover across the plots has two distinct peaks at the very ends of the gradient, i.e. between 0% and 10% and between 90% and 100%.
In general, recruitment is scarce for both spruce and larch. Maxima of pooled seedlings and saplings are 15 (spruce) and 16 (larch) per plot, means are below one per plot for both species and many plots do not have a seedling or sapling of either species (Table 1). Basic regression models are similar for Picea abies and L. decidua in terms of predictors included as well as of goodness of fit (Table 2). Recruitment increases with seed input and with the number of degree days per year, with some additional effects of solar radiation income and topography.
Table 2. Models for analysing effects of Pinus mugo on recruitment, growth, fecundity and browsing damage of Picea abies and Larix decidua. Basic models comprise site conditions together with descriptors either of life-history stage and damage status due to reasons other than browsing, or of seed input. Pine models are the same as basic models except for the inclusion of percentage pine cover as an additional predictor. TM – type of regression model (PGLM – Generalized Linear Model with log-link for Poisson distributed data; OLS – Ordinary Least-Squares model; POR – Proportional Odds model). d.f. – degrees of freedom in the data set (with those spent for the regression model in parentheses). Deviations from overall sums of sampled individuals listed in Table 1 are due to missing values in single predictors. ‘R2’ is (NullDeviance − ResidualDeviance)/NullDeviance for PGLM, least-squares R2 for OLS, and Nagelkerke's R2 for POR. Somers’Dxy is a measure of a logistic model's predictive discrimination based on the rank correlation between predicted probabilities of response and actually observed responses. Basic and pine models were compared by analysis of deviance for nested models with degrees of freedom equal to the additional parameters to be estimated. For abbreviations of predictors see text and Fig. 1
|Basic model||Pine model||Comparison|
|Recruitment Picea abies||PGLM|| 249 (9)||DD, SRJ, SLOPE, DBHSumPic, DISTPic||0.52||–||0.55||–||1||< 0.0001|
|Recruitment Larix decidua||PGLM|| 249 (9)||DD, SRJ, WBA, EROS, DBHSumLar, DISTLar||0.5||–||0.57||–||1||< 0.0001|
|Growth||OLS|| 411 (12)||Tree d.b.h., CLD, UND, DD, WET, soil type ||0.38||–||0.51||–||1||< 0.0001|
|Fecundity||POR||1625 (11)||Tree height, DD, SRJ, WSP, WBA, SLOPE, soil type||0.53||0.88||0.53||0.88||1|| 0.63|
|Browsing damage||POR||1645 (7)||Tree height, DD, SRS, WSP, SLOPE||0.36||0.67||0.46||0.77||2||< 0.0001|
Introducing percentage pine cover as an additional predictor significantly improves goodness of fit for both species (Table 2). Increasing pine cover decreases recruitment of spruce and, to a greater degree, that of larch (Fig. 1). Given a reasonable amount of seed input, larch recruits more intensively than spruce at low pine cover whereas seedling numbers are about the same in dense pine stands (Fig. 2).
Figure 1. Relative importance of predictors in regression models of recruitment, growth, fecundity and browsing damage of Picea abies and Larix decidua. Effect of cover of Pinus mugo is given in black, species identity in light grey and their interaction term in dark grey. The x-axis represents the Wald chi-squares statistic minus degrees of freedom of the respective predictor within the multiple regression. Predictors significant (P < 0.05) in the final model that includes pine cover, species identity and their interaction term are marked by an asterisk. CLD/UND – damage due to climatic constraints/undefined reasons, DBHSumPic/DBHSumLar – summed diameter at breast height of Picea abies/L. decidua individuals on the sampling plot, DD – degree days, DISTPic/DISTLar – distance of sampling plot to nearest Picea abies/L. decidua stand, EROS – soil erosion index, PPC – percentage pine cover, SLOPE – slope inclination, SOIL – soil type, Species – species identity (Picea abies or L. decidua), SRS/SRJ – solar radiation income in September/July, WBA – water balance in August, WET – soil wetness index, WSP – wind speed. Colons symbolize interactions.
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Figure 2. Partial effects of Pinus mugo cover on recruitment, growth, fecundity and browsing damage of Picea abies and Larix decidua in multiple regression models. Additional predictors were adjusted to the following values: Recruitment – DBHSumPic/DBHSumLar: 50 cm, DD: 225 days, DISTPic/DistLar: 0 m, SLOPE: 20°, SRJ: 26 MJ m−2 day−1; Growth – CLD: 0, d.b.h.: 20 cm, DD: 230 days, SOIL: rendzic leptosol, UND: 0, WET: 5.7; Fecundity – DD: 230 days, SLOPE: 20°, SOIL: rendzic leptosol, SRJ: 25 MJ m−2 day−1, Tree height: 13 m, WBA: 4 mm day−1, WSP: 10 m s−1; Browsing damage – DD: 230 days, SLOPE: 20°, SRS: 15 MJ m−2 day−1, Tree height: 2 m, WSP: 10 m s−1. Dashed lines represent 90% confidence intervals (grey –Picea abies, black –L. decidua). For abbreviations of predictors see text and Fig. 1.
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The predominant effect of d.b.h. present in the basic model indicates a strong impact of life-history stage on growth rates of both spruce and larch. Damage, irrespective of its cause, obviously dampens growth rates, whereas climatic and topographic predictors per se have comparatively minor partial effects (Fig. 1).
Pine cover has a pronounced impact on growth rates. Within closed pine shrubland, height growth of the pooled spruce and larch individuals decreases to only half of the rates in open habitats. Goodness of fit increases considerably for a regression model that includes percentage pine cover as an additional predictor (Table 2) and Wald statistics indicate pine cover to be the second most important predictor after d.b.h. in the growth model (Fig. 1).
Not surprisingly, a significant partial effect of species identity (Fig. 1) demonstrates that growth rates differ, with larch growing faster than spruce on average (Table 1). However, similar to recruitment, percentage pine cover has a more severe partial effect on growth rates of larch than of spruce. Thus, growth rate differences among the two tree species decrease with increasing pine cover (Fig. 2).
Contrary to our prediction of an effect of life-history stage on this response, Wald statistics indicate only a marginally non-significant (P = 0.07) interaction between percentage pine cover and tree d.b.h. for larch and no effect for spruce (P = 0.31). Overall goodness of fit of the model is not significantly improved by integration of this interaction term for either spruce (P = 0.84, d.f. = 2) or larch (P = 0.58, d.f. = 2).
Fecundity is, of course, primarily a function of life-history stage, represented by tree height in the basic model. In combination with various environmental descriptors, this model allows for quite accurate prediction of ordinally scaled fecundity values (Table 2).
Adding pine cover to the suite of predictors does not improve the overall model's goodness of fit (Table 2). Accordingly, Wald statistics suggest that percentage cover of P. mugo does not affect the fecundity of the pooled larch and spruce individuals (Fig. 1). By contrast, species identity is a significant predictor, indicating cone crop to be generally more abundant for larch than for spruce in the studied tree-line environment, at least for the non-mast year investigated (Figs 1 and 2). However, the pine-cover – species interaction has no impact on fecundity (Fig. 1), indicating that pine cover is equally unimportant for fruit set of Picea abies and L. decidua.
The basic model for browsing damage has tree height as the most important predictor, reflecting that herbivores preferentially feed on juvenile trees with terminal buds within their reach. Apart from some temperature- and radiation-related trend environmental descriptors have limited predictive value (Fig. 1).
Of all responses analysed, browsing damage of spruce and larch is the one most sensitive to the presence of pines. Adding percentage pine cover to the basic regression model increases the model's goodness of fit significantly (Table 2) with pine cover being the most important single predictor within the extended model (Fig. 1). By contrast, neither species identity nor its interaction with percentage pine cover are significant. Closed pine shrubland thus protects both spruce and larch rather effectively against browsing herbivores with little difference in the facilitative effect on the two tree species (Fig. 2).
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In line with our general expectations, the results suggest that the interaction among these three tree-line species involves both negative and positive components and that the effects of pines on spruce and larch are species-specific. Whereas recruitment and growth of Picea abies and L. decidua are a negative function of P. mugo cover, fecundity levels are unaffected by shrub layers in both species. Despite this overall similarity between spruce and larch, the intensity of the effects varies considerably, with recruitment and growth of larch repressed by pine–krummholz more severely than that of spruce. By contrast, dense pine cover has a marked, and similar, facilitative effect on Picea abies and L. decidua, providing shelter against browsing herbivores.
Taken together, our results thus corroborate the recent theoretical emphasis on the complex nature of plant–plant interactions (Callaway & Walker 1997; Holmgren et al. 1997; Stachowicz 2001; Bruno et al. 2003): pines obviously compete with spruce and larch for resources such as light and nutrients but, simultaneously, protect the juvenile trees against browsing herbivores. Similar interaction patterns have also been demonstrated in other environments, such as arid subtropical habitats (Callaway et al. 1996). As both growth and recruitment of larch and spruce are a negative function of pine cover but fecundity is unaffected, the balance of effects is presumably negative, with facilitation simply buffering, to some extent, the competitive effects of P. mugo on indicators of individual performance as well as of population dynamics. This finding somewhat contradicts the hypothesis that the net plant–plant interactions tend to shift towards facilitation under harsh abiotic conditions like those prevailing at the tree line (Callaway 1995; Callaway et al. 2002). In fact, facilitation has primarily been demonstrated in extreme environments such as arid ecosystems (Nobel & Franco 1989; Holzapfel & Mahall 1999), salt marshes (Bertness & Shumway 1993; Callaway & Pennings 2000) and high mountain habitats (Callaway 1998; Choler et al. 2001; Callaway et al. 2002). For conifers at the tree line in particular, advantages of both intraspecific (Srutek et al. 2002) and interspecific (Callaway 1998) spatial clumping have been reported. The contrasting results of our study may, at least in part, be due to the fact that we did not analyse interactions among species of the same but of different growth forms. The predominance of facilitation among different tree species in a Rocky Mountains tree-line ecosystem has primarily been attributed to the reciprocal shelter that individuals – irrespective of their species identity – provide against climatic constraints such as snow-ice abrasion (Callaway 1998). However, these constraints affect trees in high mountain environments most strongly when they are growing above the more favourable climate near the ground (Geiger 1965; Körner 1999). Thus, shrubby pines, which themselves hardly grow higher than 2 m in the studied tree-line system, have rather little potential to protect upright larch and spruce against such stress.
Despite their shrubby growth form, and contrary to our expectations, the negative effects of pines on growth rates of spruce and larch are not restricted to the juvenile phase of the trees: the regression models indicate that both tree d.b.h. and percentage pine cover strongly affect growth of spruce as well as of larch, but their interaction term does not. If competition for light were the dominant component of the interaction between P. mugo and the two tree species, we should observe a competitive release, indicated by sharply increasing growth rates, when trees overtop the shrub layer (e.g. Frelich 2002). As a consequence, the dependence of average lifetime growth rates (tree height : tree age ratios) on pine cover should decrease with the size of the trees. However, the data provide no evidence for such a competitive release for Picea abies and only a marginal indication for L. decidua. There are at least two possible, and not mutually exclusive, explanations for this unexpected finding. On the one hand, when trees grow above the shrub layer and escape the low light conditions below pines, they become exposed to climatic constraints that had been ameliorated within the pine canopy. Overtopping the shrub layers thus involves a trade-off between improved light supply and increased risk of damage due to frost, frost desiccation, snow-ice abrasion and other consequences of the harsh climatic conditions at the tree line. On the other hand, dense pine canopies may have negative effects on growth rates of both juvenile and adult spruce and larch individuals, due to shading, and thus depressed soil temperatures. Low soil temperatures limit root activity with a feedback on shoot development and growth, a mechanism that has recently been suggested to be a main reason for tree-line formation (Körner 1999). In fact, measurements of soil temperatures below dense pine thickets and in nearby grasslands within our study area revealed considerable differences. Soils below pine–krummholz are between 0.5 °C (45 cm depth) and 4 °C (5 cm depth) cooler on average than soils below grasslands during the vegetation period (1 May to 31 October 2000; Köck et al. 2003).
As hypothesized, pines affect spruce and larch differently with respect to some, but not all, of the analysed interaction components. Protection against herbivores is most probably due to the reduced visibility of juvenile trees within dense shrub layers (Callaway 1992; Rousset & Lepart 2000; Garcia & Obeso 2003; Rao et al. 2003; Russel & Fowler 2004) and it is therefore not surprising that this effect does not depend on species identity. Concerning fecundity, comparable studies on the impact of shrub understories on seed production of trees are largely lacking. Our results suggest that this impact may generally be negligible, at least within our study system. However, caution should be taken when interpreting fecundity data from only one year (Clark et al. 1999), especially if a masting species like spruce is involved. Additional data that cover a time series involving at least one mast year of spruce will be necessary to confirm our preliminary results.
In contrast to browsing damage and fecundity, larch is more sensitive to pine competition in terms of recruitment and growth. Its superior regeneration and growth in open habitats such as grasslands is reduced in dense pine thickets. This result is in line with the conventional wisdom of foresters that classifies larch as a light-demanding early to mid-successional and spruce as a shade-tolerant mid- to late-successional species in the subalpine forests of the northern Alps (Zukrigl 1973; Mayer 1976). Although not surprising, these findings may have considerable implications for environmental changes such as climate warming that trigger a simultaneous range expansion of pine shrublands as well as of spruce and larch forests at the expense of subalpine and alpine grasslands (Dullinger et al. 2004). The encroachment of grasslands and other non-forest habitats by pine–krummholz involves a reduction of those parts of the landscape where larch recruits more effectively and grows faster than the late-successional spruce. In other words, the invasibility of the resident vegetation cover decreases more sharply for larch than for spruce, if pine shrublands replace grasslands and other non-forest habitats. Hence, the differential effects of pines on spruce and larch will probably bias the rates of climate-change-triggered range shifts of these two tree species, favouring spruce at the expense of larch. We thus conclude that disregarding the complex details of plant–plant interactions will probably result in unrealistic predictions of species responses to environmental changes.