SEARCH

SEARCH BY CITATION

Keywords:

  • Acer rubrum;
  • Acer saccharum;
  • activated carbon;
  • Ailanthus altissima;
  • allelopathy;
  • field experiment;
  • invasive species;
  • neighbourhood models;
  • plant interactions;
  • Quercus rubra;
  • seedling performance

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • 1
    Allelopathic interactions between invasive and native species have been suggested to be an important mechanism for the success of some of the most aggressive plant invaders. However, field experiments that test the effects of natural levels of allelopathic compounds on coexisting native species are exceptionally rare.
  • 2
    In this study, we analyzed the allelopathic effects of the invasive tree Ailanthus altissima on seedling emergence, survival and growth of three native tree species (Acer rubrum, A. saccharum and Quercus rubra) in temperate forests of the northeastern United States. We used activated carbon (AC) to reduce potential allelopathic interference, and developed neighbourhood models that explain the observed spatial variation in the effects of the AC treatments on seedling performance as a function of the size, abundance and distribution of Ailanthus trees in the neighbourhood.
  • 3
    Our results showed that the addition of AC to the soil did not affect seedling emergence or survival, but caused a significant increase in seedling growth of all three species. Moreover, the AC shifted the overall interaction between Ailanthus and maple seedlings from neutral or slightly positive to very positive for A. rubrum, and from negative to positive for A. saccharum, whereas the net interaction between Ailanthus and Q. rubra was always negative. As Ailanthus has the ability to increase soil fertility, these species-specific responses are presumably influenced by among-species differences in the net effects of both allelopathy and changes in resource availability caused by the presence of Ailanthus.
  • 4
    The cumulative allelopathic effects of Ailanthus were proportional to the density of Ailanthus in the neighbourhood, regardless of their size. In contrast, Ailanthus effects were strongly influenced by distance from a tree, generally dropping to zero within 5 m from the trunk.
  • 5
    Synthesis. Taken together, our results indicate that allelopathy is an important mechanism to take into account when trying to understand the causes and consequences of plant invasions. However, this study also strongly suggests that the real significance of the allelopathic effects of an invasive species cannot be assessed independently of its target community, or in isolation of other resource interactions involving the invader and the native community.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Biological invasions are recognized as one of the most important causes of ecosystem degradation and biodiversity loss worldwide (Vitousek et al. 1996; Mack et al. 2000). As a result, there has been extensive research to unravel the causes and consequences of the introduction of new species into natural systems. Ironically, by serving as research tools, invasive species have contributed enormously to advances in our understanding of the mechanisms by which individual species can alter community structure and ecosystem processes (Vitousek 1986; Lodge 1993; Bruno et al. 2005). A notable example of how invasions have stimulated ecological research is in the revitalization of the interest in biochemical interactions among plants (Callaway & Maron 2006). The ecological consequences of allelopathy, or the negative effects of one plant on another by the release of chemical compounds into the environment (sensu Muller 1966), has been greeted with scepticism for decades (Harper 1977; Keeley 1988). As a result, resource-based interactions such as competition have been usually invoked as the explanation for the effects of invasive plant species on community structure (Levine et al. 2003). However, recent studies present compelling evidence that allelopathic interactions between invasive and native species can be one of the mechanisms underlying the remarkable success of some of the most aggressive plant invaders (Bais et al. 2003; Callaway & Ridenour 2004; Prati & Bossdorf 2004; Stinson et al. 2006).

The scepticism that has characterized the assessment of the role of allelopathy in nature is at least partially the consequence of the methodological difficulties inherent in demonstrating the effects of allelopathy under field conditions. Most studies have used experimental additions of plant extracts under laboratory conditions, tested on Petri dishes or sterilized soils on weed species particularly sensitive to chemicals (Hierro & Callaway 2003). Although there has been significant progress in the use of more realistic and sophisticated techniques in recent years (mainly in research with invasive species), tests for allelopathic effects under field conditions are, apart from some remarkable exceptions (e.g. Nilsson 1994; Nilsson et al. 2000), still exceptionally rare (Inderjit & Callaway 2003). Field experiments offer the possibility to overcome three main drawbacks of laboratory and glasshouse tests. First, they test if toxic substances accumulate at sufficient concentrations or if they persist long enough in natural soils to inhibit the growth of other plants. Second, toxic effects are tested on coexisting species that can vary in their sensitivity to allelochemicals. Third, they permit exploration of spatio-temporal patterns of variation in allelochemical effects (e.g. Zackrisson & Nilsson 1992; Jose & Gillespie 1998). The ecological importance of any given allelochemical compound is likely to be highly dependent on its spatio-temporal variability in natural communities.

In this paper, we test for allelopathic effects under natural conditions of one of the most important invasive tree species of temperate forests in the northeastern United States, Ailanthus altissima (Mill.) Swingle (Simaroubaceae). Members of the family Simaroubaceae are known to produce quassinoid compounds with a wide range of effects on insects, fungi, protozoa, viruses and cancer cells (Polonsky 1973, 1985). Several studies have shown that extracts of all parts of Ailanthus (i.e. roots, leaves, trunk) inhibit germination and growth of several plant species in bioassays or glasshouse experiments (Mergen 1959; Voigt & Mergen 1962; Heisey 1990a,b; De Feo et al. 2003). At least 10 quassinoids, together with alkaloids and other secondary products, have been isolated from different parts of the plant (see references in Heisey & Heisey 2003). One quassinoids, ailanthone, has been identified as the major compound responsible for phytotoxic effects (Lin et al. 1995; Heisey 1996). However, ailanthone is rapidly inactivated in non-sterile soils (Heisey 1990b, 1996), so its actual allelopathic effect under field conditions may be less (if any) than what laboratory experiments indicate. Ailanthus thus represents an example of a successful invader for which presumed allelopathical effects are invoked when explaining its invasive success (e.g. Kowarik 1995; Viláet al. 2006; Webster et al. 2006), but for which no evidence of allelopathic effects under natural conditions is currently available.

We investigated the importance of allelochemical interference by Ailanthus on the emergence, survival and growth of native tree seedlings by using activated carbon (AC) to reduce potential allelopathic effects in a 2-year field experiment. We used a spatially-explicit, neighbourhood analysis to develop models that explain the observed spatial variation in the effects of the AC on seedling performance as a function of the size, abundance and distribution of Ailanthus trees in the immediate neighbourhood. A similar neighbourhood approach was successfully used in a parallel study to analyze the impacts of Ailanthus on nutrient cycling in the same study sites (Gómez-Aparicio & Canham 2008). Results from that study showed that Ailanthus increases nutrient availability (i.e. pH, exchangeable Ca, exchangeable K, total N) and cycling rates (i.e. net nitrification) in surface soil, presumably due to the extraordinarily high nutrient concentration in its leaf litter (as much as four times higher than in native tree species). For example, soil Ca and N pools were twofold higher in neighbourhoods dominated by Ailanthus than in neighbourhoods dominated by native species such as Acer saccharum, Fraxinus americana or Quercur rubra. These results imply that Ailanthus could have positive effects on the native seedling community, due to increases in soil fertility. This highlights the need for an analysis of the relative importance of allelopathy in a natural setting where other interactions (i.e. facilitation) may also take place. Specifically, we asked the following questions: (i) Does Ailanthus interfere with native tree seedlings via allelopathic exudates? (ii) Are allelopathic effects species-specific? (iii) How does the magnitude of the allelopathic effects vary with the size and spatial distribution of Ailanthus in the neighbourhood? (iv) What role does allelopathy play in the net effect of Ailanthus on native seedlings?

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

study-species and sites

Ailanthus was introduced from China in 1784, and is now widely naturalized throughout much of the United States (Hu 1979). It produces very large numbers of wind-dispersed seeds (Hu 1979), grows quickly in high light (Bazzaz 1979; Feret 1985), and can reproduce asexually via root sprouts (Hu 1979). Although early studies considered it shade-intolerant and unable to invade closed forests, recent studies show that it can establish in intact forests when canopy gaps open (Kowarik 1995; Knapp & Canham 2000). It can reach mature size during a single period of release in a treefall gap, while most native species need several periods (Canham 1989; Knapp & Canham 2000). Although rapid growth and prolific reproduction (both sexually and vegetatively) undoubtedly contribute to its success as an invader, the arsenal of biochemical defences found in all Ailanthus tissues suggests allelopathy as another potential mechanism of invasion.

The experiment was conducted in three forest stands in northwestern Connecticut (USA), at elevations from 300 to 500 m. All sites had patchy distributions of Ailanthus within the stands. The three sites are approximately 20 km apart, one located near the village of Amesville (41°58′N, 73°27′W), the second located in the Dark Entry Forest (DEF) near the village of Kent (41°49′N, 73°23′W), and the third located in the village of New Milford (41°36′N, 73°25′W). Soils at Amesville (AM) and New Milford (NM) are Dystric Eutrochrepts derived from calcareous glacial outwash over limestone bedrocks, whereas soils at DEF are Typic Dystrochrepts derived from glacial till over mica-schist bedrocks (Hill et al. 1980). All sites are in second-growth stands (80–130 years) with a history of logging but no history of agriculture. The species composition of the stands included elements of the oak forests of southern New England and the northern hardwood forests of the northeastern United States and Canada. The dominant native tree species were A. saccharum Marsh. (sugar maple), F. americana L. (white ash), Q. rubra L. (northern red oak), Prunus serotina Ehrh. (black cherry), and Betula lenta L. (black birch). The relative basal area of Ailanthus was 31.2% in AM, 28.4% in DEF and 32.3% in NM. The mean DBH of Ailanthus trees was approximately 20 cm in the three sites (19.9 cm in AM, 21.6 cm in DEF and 24.6 cm in NM), and the maximum DBH ranged from 37.9 cm in DEF to 54.8 cm in AM.

seedling transplant experiment

In June 2005, seedlings of A. rubrum L., A. saccharum and Q. rubra were collected from the surrounding forests and planted at each of the three study sites. For each species, we selected seedlings of similar age (3–5 years) and height without evident signs of herbivory damage. Twenty planting locations were selected per site, stratified along a gradient of distance from and abundance of Ailanthus in the immediate neighbourhood (defined as a 25-m radius circle around each planting location). We identified and mapped every Ailanthus tree with a DBH ≥ 2 cm within each of the 60 neighbourhoods (n = 337 trees), using a laser rangefinder with a digital compass (Laser Technology Inc., Centennial, CO). Relative basal area of Ailanthus in the neighbourhoods varied between 0% and 70%.

At each planting location, two 20 × 60 cm plots were established, separated by a distance of 50 cm. The soil of one of the plots was dug to a depth of 20 cm, moved to a bucket where it was hand-mixed with AC (GC Powdered Activated Carbon, General Carbon Corp., Paterson, NJ) at a rate of 20 mL/L soil, and put back in the ground (AC treatment). The soil from the second plot was dug to the same depth and moved to a different bucket (to reproduce the disturbance caused to the soil in the AC treatment), and then put back into the ground without adding any chemicals (Control treatment). An additional amount of AC (0.125 L) was applied in May 2006 onto the soil surface of each AC treatment. AC is frequently used in allelopathy experiments because it acts as an adsorbant for many large organic compounds, therefore minimizing allelopathic effects while having minor impacts on nutrient dynamics (Cheremisinoff & Ellerbusch 1978). AC has been successfully used to test for allelopathic interactions in a large number of studies (see review in Hierro & Callaway 2003), and has been recommended as an effective approach for allelopathy studies in the field (Inderjit & Callaway 2003).

Three seedlings (one of each of the three native tree species) were planted in each plot (n = 360 seedlings). We measured the initial stem height, extension growth of the stem, basal diameter, number of leaves and diameter of each expanded leaf at the time of planting. In order to facilitate accurate remeasurement of stem height, a small mark was made on the stem of each seedling at the ground level. Within the initial pool of seedlings collected for transplanting, a random subsample (n = 30 per species) was taken to the laboratory at the beginning of the experiment for measurement of initial stem height, stem dry biomass and dry biomass, area and diameter of all leaves. The objective was to generate, for each species, regressions of (i) stem dry biomass as a function of stem height, (ii) leaf area as a function of leaf diameter (measured from the base to the tip of the leaves), and (iii) leaf dry biomass as a function of leaf area. These regressions (R2 > 0.9 in all cases, data not shown) allowed non-destructive estimations of initial stem biomass, initial leaf area and initial leaf biomass for each experimental seedling using field measurements (see below).

For each experimental seedling, survival, stem height, stem extension growth, number of leaves and diameter of each individual leaf were sampled twice, once at the end of the first growing season (September 2005) and once at the end of the second growing season (September 2006). Seedlings alive in September 2006 were harvested and taken to the laboratory where leaves were removed and measured for total area using a leaf area meter (LI-COR Inc., Superior St. Lincoln, NE). Roots were rinsed by hand, and the root and shoot systems were separated at ground level (using the marks on the stems). Roots, stems and leaves were dried for 48 h at 60 °C and weighed. The extension growth of the stem for the second growing season was separated from the rest of the stem and weighed separately. We considered the biomass of the extension growth as a more accurate estimation of the effects of AC on biomass allocation to stems than the total stem biomass. As some seedlings had finished their annual growth by the time the experiment started (June 2005), we decided to conduct statistical analyses using growth data only for the second growing season. Specifically, we considered six response variables: (i) survival after the two years of the experiment; (ii) extension growth in 2006; (iii) dry biomass of the 2006 extension growth; (iv) root dry biomass; (v) leaf dry biomass in 2006; and (6) leaf area in 2006. Due to the difficulties of excavating Q. rubra roots without losing a significant part of the root system, we decided not to consider root biomass as a response variable in the analyses for this species.

Because variation in light availability was expected to affect seedling growth and survival, we used fisheye photography to estimate a gap light index (GLI, Canham 1988) for each seedling plot. GLI is the percentage of ‘gap’ light (Canham 1988; i.e. photosynthetically active radiation transmitted through discrete openings in the canopy) that reaches a point in the understorey over the course of a defined growing season. Photographs were taken in the middle of each plot by placing the camera (with a fisheye lens) at approximately 30 cm over the ground. All pictures were taken on cloudy days during August 2005.

The transplant experiment was initially designed including Ailanthus as a fourth seedling species. Given the difficulty of finding natural seedlings of Ailanthus in our study sites, seeds were germinated in the glasshouse in May 2005 and Ailanthus seedlings transplanted to the field at the same time as the native species. Ailanthus seedlings were transplanted to individual 20 × 20 cm plots 30 cm away from the native plots (in order to avoid potential allelochemical interference among seedlings), using the same soil treatments used for the plots containing native tree seedlings. However, even though dead Ailanthus seedlings were replaced during the first 3 weeks of the experiment, no Ailanthus seedlings were alive at the end of the first growing season, and the species had to be excluded from the study.

seed sowing experiment

We conducted a seed sowing experiment at the same locations as the seedling transplant experiment (n = 20 locations per site). Two 30 × 30 cm quadrats were established at each location, one (AC quadrat) next to the seedling plot with AC and the second one (Control quadrat) next to the Control seedling plot. In the AC quadrat, AC was added at a rate of 20 mg/L soil to the first 5 cm of the soil. In the Control quadrat the soil was mixed by hand but no chemicals were added. In each quadrat, 10 seeds of A. rubrum and 10 seeds of Q. rubra were sown at 1 cm depth in four lines of five seeds, each line were 2 cm from each other and 5 cm from the border of the quadrat. Acer saccharum was not included in the experiment due to the unavailability of seeds during the years of the study. Seeds of A. rubrum were obtained commercially from regional seed sources (lot with 98% viability). Seeds of Q. rubra were collected in the surrounding forests, and non-viable acorns (empty or depredated by insects), identified by flotation in water, were excluded. To exclude seed predators, we built cages around each quadrat using 24-gauge, 1.5 cm mesh hardware cloth buried to a depth of 5 cm and extending 25 cm above-ground. Seeds were sown in November 2005, and emergence was monitored every 2 weeks during April–June 2006.

In September 2006, all live seedlings in the seed sowing experiment were harvested and taken to the laboratory, where they were measured using the same procedures for seedlings in the transplant experiment (see above). Response variables from the sowing experiment were: (i) emergence, estimated as the percentage of seeds with shoots growing beyond the ground surface by the time of the last emergence census (June 2006); (ii) survival, estimated as number of emerged seedlings in June that were alive at the end of the experiment in each quadrat; (iii) stem dry biomass, estimated as the mean stem biomass of all alive seedlings in each quadrat; (iv) leaf dry biomass, estimated as a mean per quadrat; and (v) leaf area, also estimated as a mean per quadrat. As for the transplant experiment, we decided not to consider root biomass as a response variable in Q. rubra.

neighbourhood analyses of seedling emergence, survival and growth

We used a neighbourhood approach to the study of allelopathy in which seed emergence, seedling survival and seedling growth were analyzed as a function of: (i) the study site; (ii) the initial size of the seedling (only in the case of transplanted seedlings); and (iii) the size, abundance and spatial distribution of Ailanthus in the neighbourhood. The models were run separately for each of the study species and response variables in each of the two experiments. For each response variable (Y), our basic allelopathy model takes the form:

  • image( eqn 1)

The first term in the model, Sitej, is an estimated parameter that represents the average potential seedling performance (i.e. survival, root biomass, leaf area, etc., per unit effect of plant size) in the absence of neighbouring Ailanthus for j = 1 ... 3 study sites. The second term, Sizeλ, controls for the effects of initial plant size on seedling performance in the transplant experiment, as a function of the parameter λ, which scales the response variable to size as a power function. We used different measures of plant size depending on the response variable analyzed in the model: (i) initial stem height was used as the size estimator for survival and extension growth; (ii) initial stem biomass (calculated indirectly using regressions, see above) as the size estimator for extension biomass and root biomass; (iii) initial leaf biomass (calculated using regressions) as the size estimator for (final) leaf biomass; and (iv) initial leaf area (calculated using regressions) as the size estimator for (final) leaf area. The range of variation among individuals was relatively small because of their similar age.

The third term in the model X, captures the neighbourhood effects of Ailanthus on individual seedling performance. If Ailanthus has no effect on seedling performance then X = 1, if the effect is negative then 0 ≤ X < 1, and if the effect is positive then X > 1. We assumed that the neighbourhood effects vary monotonically as a function of an Ailanthus neighbourhood index (ANI):

  • image( eqn 2)

ANIi is the Ailanthus neighbourhood index for seedling i of the target species (equation below), and ANImax is the maximum value of ANI for all seedlings of the target species. The use of ANImax standardizes the neighbourhood effects term (0 ≤ ANIi/ANImax ≤ 1) and facilitates comparisons across seedling species. To compute ANI we used a simple additive index of the abundance of Ailanthus within the immediate neighbourhood, as a function of the size and the distance to Ailanthus neighbours. Thus, for i = 1 ... n Ailanthus trees ≥ 2 cm DBH within a 25-m radius around the target seedling, the ANI is given by:

  • image( eqn 3)

In order to keep the number of parameters manageable and to avoid parameter trade-offs, we allowed β to vary and be estimated by the analyses but tried alternative models setting the value of α either to α = 2 or α = 0. A value of α = 2 indicates that the influence of Ailanthus scales approximately with tree biomass (i.e. DBH2), whereas a value of α = 0 means that the influence of Ailanthus varies as a function of density, regardless of size.

We estimated a separate γ parameter in eqn 2 for each of the two treatments (i.e. for AC vs. the control). The parameter γ is an exponential decay coefficient, and defines the sign and steepness of the variation in the neighbourhood effects (X), and therefore in seedling performance (Y), due to an increment in ANI. Positive values of γ would indicate a positive effect of the presence of Ailanthus neighbours relative to the mean effects of the native neighbours, whereas negative γ values would indicate a negative effect of the presence of Ailanthus relative to the presence of native neighbours. The difference in the γ values among the AC and control treatments measures the magnitude of the allelopathic effects of Ailanthus on seedling performance.

In order to test for the possibility of any AC effects, independent of the presence of Ailanthus, we ran a modified version of the basic allelopathy model in which the average potential seedling performance in the absence of neighbouring Ailanthus at each site (i.e. term Sitej in eqn 1) was estimated separately for AC and Control seedlings. Different Sitej terms for the two groups of seedlings would indicate that AC affected seedling performance even in the absence of Ailanthus. This could reflect either the presence of other allelopatic species or some other unintended effect of the addition of AC. However, the modified basic allelopathy model was never a better fit to the data than the simpler basic allelopathy model (see Appendix S1 in Supplementary Material), indicating that effects in AC treatments were directly linked to the presence of Ailanthus. The absence of side-effects (i.e. not related to the presence of Ailanthus) of the AC is also supported by the lack of significant differences in seedling performance among treatments (AC vs. Control) when only seedlings without Ailanthus neighbours were considered in the analyses (see Appendix S2).

We also explored the effect of light on seedling performance by adding a fourth term (GLIδ) to the basic allelopathy model (eqn 1). However, as the resulting models were never a better fit to the data, this term was dropped from the analyses (results not shown for simplicity). The absence of a light effect on seedling performance was probably a consequence of the limited variation in light levels experienced by seedlings in the understorey (GLI = 3–6% in 90% of the cases).

In order to analyze whether the allelopathic effects of Ailanthus varied among sites, we tested a modified version of the basic allelopathy model in which the value of γ (eqn 2) for the AC treatment was allowed to vary as a function of the site (Site-specific allelopathy model). The value of γ for the Control treatment was not allowed to vary among sites due to limitations in the number of parameters permitted by our sample size. The basic and site-specific allelopathy models were compared to a null model in which seedling performance was predicted just as a function of the site and the seedling initial size (i.e. setting the multiplier X to 1 in eqn 1). By doing this, we assessed whether including the neighbouring effects of Ailanthus into a model significantly improved its explanatory power.

parameter estimation and comparison of alternate models

We used simulated annealing, a global optimization procedure, to determine the most likely parameters (i.e. the parameters that maximize the log-likelihood) given our observed data (Goffe et al. 1994). We used three different error structures depending on the nature of the response variables. We analyzed survival of individual transplanted seedlings using a logistic regression in which the probabilistic scientific model provided the likelihood function. For emergence and survival in the sowing experiment we assumed a binomial error structure. For growth variables we used a normal error structure with the variance as a power function of the mean. This required estimating an additional parameter to determine the scaling of the variance to the mean. Details on the likelihood functions and the software code used for the simulated annealing algorithm are provided in Appendix S3. Alternate models were compared using the Akaike Information Criterion (AICc) corrected for small sample sizes (Burnham & Anderson 2002). Models with a difference in AICc < 2 units are considered to have equivalent empirical support. When the difference in AICc between two models is > 2, the model with the lowest AICc is considered to have larger empirical support. We used asymptotic two-unit support intervals to assess the strength of evidence for individual maximum likelihood parameter estimates (Edwards 1992). These are simply the range of parameter estimates for which ‘support’ (log-likelihood) is within two units of the maximum log-likelihood, and were determined by incrementally varying parameter estimates above and below the maximum likelihood estimate until log-likelihood had dropped by two units. The R2 of the regression (1 – SSE/SST, sum of squares error (SSE); sum of squares total (SST)) of observed vs. predicted was used as a measure of goodness-of-fit. All analyses were done using software written specifically for this study using Delphi for Windows (Version 7, Borland Software Corp., Cupertino, CA).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

seedling transplant experiment

The null model (ignoring the neighbourhood effects of Ailanthus) was the best fit for survival of the three study species (Table 1), indicating that Ailanthus did not influence seedling survival during the course of the experiment. However, the basic allelopathy model (including the influence of Ailanthus) was the best fit for 11 out of 14 species-specific models for growth variables (Table 1). Thus, Ailanthus had an effect on extension growth, extension biomass, leaf biomass and leaf area of A. rubrum; on extension growth, extension biomass, root biomass, leaf biomass and leaf area of A. saccharum; and of extension growth and extension biomass of Q. rubra (Table 1). The site-specific allelopathy model (assuming different allelopathic effects of Ailanthus among sites) was never the best model. Model R2 for the best models ranged between 0.10 and 0.33 for A. rubrum, 0.28 and 0.35 for A. saccharum, and 0.17 and 0.49 for Q. rubra (Table 1).

Table 1.  Comparison of alternate models (using AICc) for performance variables of transplanted seedlings of the three native species
SpeciesAICcNPnR2
Null modelBasic allelopathy modelSite-specific allelopathy model
  1. The null model predicts seedling performance only as a function of the site and the seedling size. The other two models include a third term to account for the neighbourhood effects of Ailanthus on seedlings. This term is either maintained constant for the three study sites (basic allelopathy model) or is allowed to vary among sites (site-specific allelopathy model). The best model (lowest AICc) is indicated in boldface type. Also reported are the number of parameters (NP), the sample size (n), and the R2 of the model.

Acer rubrum
 Survival162.63162.76175.244116 
 Extension growth 630.22626.42638.068 700.33
 Extension biomass687.48681.25695.488 700.26
 Root biomass846.38850.53860.274 710.10
 Leaf biomass934.54874.64880.128 690.26
 Leaf area601.15592.98608.588 690.28
Acer saccharum
 Survival159.31160.15173.564118 
 Extension growth554.46551.84554.698 730.31
 Extension biomass470.82467.62473.258 720.35
 Root biomass790.26785.85791.168 710.30
 Leaf biomass756.45748.05755.398 600.28
 Leaf area521.69510.89525.668 590.33
Quercus rubra
 Survival167.85167.95181.564117 
 Extension growth443.29441.16450.788 530.49
 Extension biomass459.28457.27469.258 530.36
 Leaf biomass 659.44666.92674.394 510.20
 Leaf area499.86504.77512.204 510.17

Initial seedling size had a minor influence on performance of A. rubrum seedlings as indicated by small values of the λ parameter (λ = 0–0.25), and a larger influence on performance of Q. rubra seedlings (λ = 0.47–0.91; Table 2). The λ-values for A. saccharum varied considerably depending on the response variable (λ = 0–0.94; Table 2). The exponent α (eqn 3) controls the scaling of the influence of Ailanthus size on ANI, and hence on seedling performance. In 9 of the 11 cases where the basic allelopathy model was the best model, α = 0 offered a better fit to the data than α = 2 (Table 2), indicating that the influence of Ailanthus was simply proportional to the density of Ailanthus (DBH ≥ 2 cm) in the neighbourhood, regardless of their size. In contrast, Ailanthus influence was strongly influenced by distance, as shown by large values of the parameter β (i.e. β ≥ 1; Table 2). In most cases the influence of Ailanthus dropped to effectively zero within the first 5 m from the trunk of a neighbouring tree (Fig. 1). Among species, distance decay in the effect of Ailanthus was somewhat steeper for the two maples than for Q. rubra (Fig. 1).

Table 2.  Parameter estimates and two-unit support intervals (in brackets) for the most parsimonious models for performance variables of transplanted seedlings of the three native species
SpeciesSiteAMSiteDEFSiteNMλαβγ ACγ Control
Acer rubrum
Survival0.590.690.540.25    
[0.51–0.64][0.61–0.75][0.49–0.64][0.21–0.40]    
Extension growth (mm)36.0119.3123.12000.901.110.41
[33.08–40.22][18.13–22.51][20.36–24.98][0–0.09] [0.84–0.96][1.02–1.45][0.34–0.52]
Extension biomass (mg)18.297.039.140.0901.110.990.43
[16.45–20.22][5.39–8.51][7.67–11.02][0–0.19] [1.05–1.18][0.79–1.11][0.33–0.51]
Root biomass (mg)186.40112.44149.730.15    
[146.23–217.45][89.14–126.73][131.56–157.68][0.06–0.26]    
Leaf biomass (mg)70.7853.8068.540.1202.541.080.05
[61.18–80.38][40.98–65.79][56.29–81.49][0.03–0.23] [2.46–2.64][0.99–1.14][–0.14 to 0.16]
Leaf area (cm2)29.4721.3828.790.2101.451.05–0.02
[22.16–37.29][13.34–29.84][19.31–37.78][0.13–0.31] [1.44–1.46][0.86–1.18][–0.17 to 0.11]
Acer saccharum
Survival0.460.680.650.42    
[0.34–0.53][0.54–0.76][0.59–0.71][0.24–0.51]    
Extension growth (mm)31.5213.3213.88021.140.10–1.02
[29.78–33.45][12.26–14.56][12.78–15.76][0–0.14] [1.01–1.21][0.05–0.18][–1.27 to –0.79]
Extension biomass (mg)22.814.658.330.1201.210.03–0.47
[17.72–30.35][3.58–5.47][7.65–9.24][0.01–0.23] [1.14–1.36][0.01–0.15][–0.61 to –0.34]
 Root biomass (mg)263.02189.04212.230.5400.820.55−0.21
[243.89–309.64][167.87–228.42][198.45–248.03][0.42–0.65] [0.70–1.19][0.38–0.72][−0.28 to −0.17]
 Leaf biomass (mg)155.8082.42134.040.9402.820.02−0.62
[132.96–172.89][68.43–97.16][114.47–152.56][0.71–1.20] [2.65–3.01][0.01–0.04][−0.73 to −0.53]
Leaf area (cm2)77.6559.8996.270.8001.19−0.09−0.42
[47.24–109.32][36.59–84.14][68.45–119.83][0.69–0.93] [1.01–1.32][−0.12 to −0.04][−0.51 to −0.34]
Quercus rubra
  Survival0.500.450.340.52    
[0.33–0.67][0.29–0.56][0.20–0.45][0.34–0.67]    
 Extension growth (mm)54.016.8126.890.9120.46−0.48−1.57
[48.10–59.9][6.23–7.39][22.23–29.49][0.39–1.42] [0.34–0.79][−0.54 to −0.36][−1.66 to −1.49]
 Extension biomass (mg)21.534.2111.210.8100.67−0.45−0.96
[20.42–22.41][3.22–4.79][10.15–11.95][0.65–0.99] [0.44–0.89][−0.61 to −0.29][−1.31 to −0.64]
 Leaf biomass (mg)158.10100.53131.190.47    
[151.23–165.23][94.27–107.29][119.25–143.12][0.36–0.60]    
 Leaf area (cm2)66.2146.7665.740.64    
[60.50–71.66][41.82–51.20][61.34–71.15][0.59–0.69]    
image

Figure 1. Predicted decrease of the influence of Ailanthus (estimated as ANI, eqn 3) on seedling performance as a function of the distance to Ailanthus trees. For simplicity of the presentation of results, the parameter α was set to 0 in eqn 3.

Download figure to PowerPoint

For A. rubrum, the values of the γ parameter in the Control treatment varied between almost neutral for leaf biomass (γ = 0.05) and area (γ = −0.02) to positive for extension growth (γ = 0.41) and biomass (γ = 0.43). When AC was added to the soil, the γ values increased significantly for the four variables (γ = 0.99–1.11; Table 2), indicating much larger positive effects of Ailanthus on A. rubrum seedling performance when potential allelochemical effects were reduced by the AC. Thus, the extension growth, extension biomass, leaf biomass, and leaf area of A. rubrum seedlings in the AC treatment were as much as 2.5 to 3-times larger at high values of the ANI than at low values (Fig. 2a–d). For A. saccharum, the γ values were always highly negative in the Control treatment (γ = −1.02 to –0.21; Table 2), indicating a decrease in seedling performance as ANI increases. However, the addition of AC shifted the sign of the γ parameter to almost neutral or positive (γ = −0.09 to 0.55), indicating a shift in the net interaction among A. saccharum seedlings and Ailanthus from negative to positive when allelochemical effects were ameliorated. The largest positive effect of AC on A. saccharum seedlings was on root biomass, which increased almost twofold under the maximum influence of Ailanthus (Fig. 2g). For Q. rubra, γ values were negative in all cases, with both extension growth and biomass diminishing under the influence of Ailanthus (Fig. 2j–k). However, the magnitude of the negative net interaction with Ailanthus was much larger in the Control (γ = –1.57 and γ = –0.96) than in the AC treatment (γ = –0.48 and γ = –0.45; Table 2 and Fig. 2).

image

Figure 2. Predicted variation in the neighbourhood effects (X), and therefore in seedling performance, as a function of the Ailanthus influence index (ANI) using eqn 2 and γ values reported in Table 2. Values of X > 1 indicate positive neighbourhood effects, whereas values of 0 ≤ X < 1 indicate negative neighbourhood effects. Only response variables for which the basic allelopathy model was the best fit (Table 1) are shown. ACRU, Acer rubrum; ACSA, Acer saccharum; QURU, Quercus rubra.

Download figure to PowerPoint

seed sowing experiment

The goodness-of-fit of the models for the sowing experiment were in general lower than for the transplant experiment (R2 = 0.04–0.28; Table 3). The null model was the best fit for both emergence and survival of A. rubrum seedlings (Table 3), indicating that there was no detectable neighbourhood effect of Ailanthus on these variables. However, we have to qualify this result by noting that there were very low percentages of seed emergence (i.e. 2–13%) and survival (i.e. 0–28%) of A. rubrum seedlings in all the three sites. Due to the low rates of A. rubrum emergence and survival, there were not enough live seedlings at the end of the experiment for the analyses of growth variables. In the case of Q. rubra, the basic allelopathy model (including the effect of Ailanthus) was the best fit for most response variables (Table 3). However, as the difference in AIC between the basic allelopathy model and the null model (assuming no effect of Ailanthus) was never larger than two units, there is not strong support for the effect of Ailanthus on Q. rubra emergence and 1-year stem biomass, leaf biomass and leaf area. As for the transplant experiment, the site-specific allelopathy model was never the best fit (Table 3).

Table 3.  Comparison of alternate models (using AICc) for performance variables of Acer rubrum and Quercus rubra seedlings in the sowing experiment
SpeciesAICcNPnR2
Null modelBasic allelopathy modelSite-specific allelopathy model
  1. The best model (lower AICc) is indicated in boldface type. Also reported are the number of parameters (NP), the sample size (n), and the R2 of the model.

Acer rubrum
 Emergence 257.53 262.98 270.2131200.20
 Survival  79.85  81.98  86.653 480.04
Quercus rubra
 Emergence 632.14 631.59 637.7471030.11
 Survival 401.33 401.76 406.7631010.03
 Stem biomass1034.241033.521040.257 840.23
 Leaf biomass1119.861118.161123.277 770.28
 Leaf area 658.28 656.98 665.987 760.25

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Our results suggest that the production of allelochemical compounds by the invasive tree A. altissima has important negative effects on seedling growth of dominant native tree species in these forests. To our knowledge, this is the first study showing allelopathic effects of an invasive species on coexisting native species exposed to real spatio-temporal variation of allelochemical production and activity. We have shown in a separate study that Ailanthus increases the availability of key soil resources, including N and Ca, within its neighbourhood (Gómez-Aparicio & Canham 2008). Depending on how sensitive a native species is to the allelochemical effects of Ailanthus, the net effect (i.e. the combined effects of allelopathy and changes in soil resources) of the presence of this invasive tree in the vicinity of a seedling varied from strongly positive to strongly negative, relative to the average effect of native species.

species-specific response of native tree seedlings to allelochemicals

Results from this study show a strong species-specific response of native seedlings to both the presence of Ailanthus in the neighbourhood and the reduction of allelochemical interference by AC. In the absence of AC, the net interaction between Ailanthus and A. rubrum was generally positive (relative to the average effects of the native neighbours), with seedlings growing taller and having more biomass with increasing exotic tree influence (Fig. 2a–d). Moreover, the addition of AC to the soil multiplied this positive effect threefold. These two results together suggest that, even though an important chemical interference exists between the two species, Ailanthus have other, positive effects on A. rubrum seedlings that allow the seedlings to benefit from proximity to the exotic even in the presence of allelochemicals. A potential mechanism of facilitation would be the increase in soil fertility reported for Ailanthus trees in the same study sites (Gómez-Aparicio & Canham 2008). In fact, previous studies have suggested that A. rubrum is N-limited in northeastern forests (Finzi & Canham 2000; Catovsky et al. 2002), and that it responds to increases in soil nutrients even at low-light levels (Canham et al. 1996).

In contrast to the response of A. rubrum, the net influence of Ailanthus on A. saccharum seedlings in the absence of AC was negative, with seedling extension growth, biomass and leaf area decreasing due to the influence of the exotic tree (Fig. 2e–i). However, the addition of AC to the soil shifted the net interaction from negative to neutral or positive. The largest positive effect of AC was on the root system of A. saccharum seedlings, with root biomass increasing almost twofold under the maximum influence of Ailanthus. This result suggests that when allelochemicals are reduced, A. saccharum also benefits from the presence of Ailanthus, presumably due to its positive effect on soil fertility. In fact, there are several studies showing A. saccharum to be Ca-limited in these forests (Kobe et al. 2002; Juice et al. 2006). However, in contrast to A. rubrum, A. saccharum invested in root biomass instead of above-ground biomass. These differences between species agree with the observation that shade-tolerant species such as A. saccharum usually have short periods of above-ground growth and high levels of stored carbon in the roots, whereas more shade-intolerant species such as A. rubrum usually exhibit shoot growth throughout the growing season and maximize above-ground biomass at the expenses of root allocation (Marks 1975; Canham et al. 1996; Kobe 1997).

Quercus rubra was the only one of the three native seedlings that never benefited from the presence of Ailanthus in its neighbourhood under either treatment. However, the magnitude of the net negative interaction decreased significantly when allelochemicals were reduced in the soil using AC, once again showing the importance of allelochemical interference as a depressor of growth in this native tree species. For example, while extension growth and biomass were reduced by 60–80% in the Control treatment due to the presence of Ailanthus in the neighbourhood, they only decreased by 35–40% in the AC treatment. The fact that the influence of Ailanthus was negative even when allelochemicals were reduced with AC could indicate a higher sensibility of Q. rubra than the two maples to the secondary compounds produce by the exotic. On the other hand, the lack of a positive effect of the proximity to Ailanthus in the AC treatment is consistent with the fact that Q. rubra is more conservative in the use of resources than the two maples, usually being insensitive to increases in soil fertility (Canham et al. 1996; Tripler et al. 2002; Zaccherio & Finzi 2007).

Our experimental design does not allow us to discern whether the allopathic effects were due to direct toxic effects on the seedlings, indirect effects on nutrient uptake (i.e. reduced root or mycorrhizal activity, see Nilsson et al. 1993), or both. Accordingly, we cannot determine to what extent the differential species response to the presence of Ailanthus and to the AC were due to differences in sensitivity to allelochemicals, in resource use, or both. Regardless of the specific mode of action, our results clearly showed that the allelopathic effects of an invasive species could generate strongly species-specific responses within the native tree community.

what a neighbourhood approach tells us about allelopathy: the importance of size and distance to ailanthus

The use of neighbourhood models for the study of allelopathy allowed us to gain useful insights on how the allelopathic effects of an invasive species vary as a function of the size and spatial distribution of the invading trees. These aspects of allelopathic interactions between plants have very rarely been explored, presumably because most studies have been conducted under controlled conditions in the laboratory or the glasshouse. To explore the importance of Ailanthus size in its interaction with native seedlings, we fit models assuming different scaling factors (i.e. parameter α in eqn 3) for the effect of DBH on the ANI, and therefore on seedling performance. We found that models in which the effect of Ailanthus did not increase with DBH (i.e. α = 0) were in most cases a better fit than models where the effect was assumed to scale with tree biomass (i.e. α = 2). This result is in accordance with the results of Lawrence et al. (1991), who found young Ailanthus saplings to produce higher concentrations of inhibitory compounds than mature trees. These authors suggested that once an individual is established, the advantages of producing secondary metabolites for competitive or anti-herbivore purposes could be lower, and their biosynthesis may be interrupted. In the field of secondary chemistry and plant defence, this line of reasoning is known as the ‘plant age hypothesis’ (Bryant et al. 1992), and predicts a decrease in plant secondary chemistry with ontogeny. If this hypothesis applies to Ailanthus and other invasive plant species, then there is a reason to expect allelopathy to be a more important interaction during the early stages of invasion (i.e. colonization or expansion phase; Shigesada et al. 1995; Radosevich et al. 2003), when the invader population is characterized by a young age structure and exponential growth, than in advance stages (i.e. saturation phase) once growth rates stabilize and the age structure of the population consolidates (but see Dietz & Edwards 2006).

Our approach also allowed us to explore the variation in the influence of Ailanthus on seedling performance as a function of the distance to the invader (i.e. parameter β in eqn 3). The influence of Ailanthus showed a sharp decrease with distance, tending to zero within 5 m from the invader (Fig. 1). For some of the response variables (i.e. leaf area) the effect of Ailanthus was restricted to the first 2 m from the trunk of a neighbouring tree. As toxins have been identified in all tissues of Ailanthus (i.e. leaves, trunk, stems, roots), this localized spatial pattern could be supported by several non-exclusive processes. First, it could be the result of Ailanthus toxins moving in stemflow and throughfall (Lawrence et al. 1991). Also, we have analyzed litterfall dispersal functions for Ailanthus and found that most of the litter was concentrated within 5 m from the trunk of adult trees (Gómez-Aparicio & Canham 2008). Finally, the influence of plants below-ground is often presumed to decrease exponentially with distance to the stem (Casper et al. 2003), suggesting that root exudation of allochemicals should also suffer a sharp decrease with distance. In fact, in one of the few available studies of spatial patterns of allelochemicals in the soil, Jose & Gillespie (1998) found that levels of the phenolic compound juglone (release by roots into the soil) diminished exponentially within a distance of 5 m from Juglans nigra L. (black walnut) trees. We suggest that as ailanthone (the main allelochemical compound identified in Ailanthus) quickly loses its toxicity in the soil, biological activity is probably restricted to areas of relatively high and continuous inputs (e.g. close to the trunk). However, our results indicate that the shape of the ‘chemical footprint’ of an invader could vary depending on the sensitivity of different native species. Thus, the effect of Ailanthus on Q. rubra decreased more slowly with distance than for the two maples, a result in agreement with the higher sensitivity to allelochemicals suggested for this species in our study.

implications for forest composition

It has long been recognized that patterns of growth and survival in the understorey influence successional dynamics and community composition in forests (Spurr & Barnes 1980; Pacala et al. 1996). By inducing changes in these key demographic traits, invasive tree species can have more pervasive effects on the composition of the native forest community than simply by co-opting space. Our results showed that, by the release of allelochemical compounds, Ailanthus had a strongly differential potential to suppress seedling growth of different native dominant tree species in our study sites. Although we did not detect any effect of allelopathy in terms of survival after 2 years, it is not unlikely that the observed reductions in growth could affect seedling survival in the long term (Kobe et al. 1995). Within species, the probability of survival of tree seedlings and saplings under the low-light conditions typical of forest understoreys has been generally reported to be a positive function of their growth rate (Kobe et al. 1995; Kobe 1996; Walters & Reich 1996, 2000). Enhanced growth rates would allow seedlings to out-compete slower growing neighbours and, as a result of an improved overall carbohydrate status, to better cope with pathogens, drought episodes, and other mortality causes. Therefore, the negative effects of Ailanthus on seedling growth during the last year of the experiment might have depressed survival in the following year (e.g. Walters & Reich 1996), as well as in the long-term due to a continuous exposure to allelochemicals (e.g. Jose et al. 2006). Long-term studies would be necessary to fully understand the consequences of allelopathic interactions with exotics for the survival and growth of native species, and the resulting impacts on community composition.

Because both the overall interaction with Ailanthus and the effects of allelopathy were highly species-specific, there are reasons to believe that Ailanthus invasion will alter competitive interactions and relative abundances of native species in these forests. Interestingly, of the three native species considered in this study, A. rubrum was the only species that consistently showed a net positive response to the presence of Ailanthus, even in the presence of allelochemicals. One of the most widespread changes in the forest composition of the eastern United States in the last century has been the increase in the abundance of A. rubrum (Larsen 1953; Abrams 1998). This increase has been explained on the basis of the ability of this ‘super generalist’ to act as both an early and late successional species, and to thrive on sites with contrasting soil conditions. Our results suggest that Ailanthus invasion might constitute another factor promoting the dominance of A. rubrum at the expense of coexisting species such as A. saccharum or Q. rubra. Identifying the underlying mechanisms for the positive response of A. rubrum to the presence of Ailanthus (i.e. higher tolerance to allelochemicals, greater ability to capitalize on high fertility patches created by Ailanthus) deserves further study.

Unfortunately, the death of all Ailanthus seedlings at the beginning of the experiment precluded us from obtaining conclusions about autotoxicity and the potential role of allelopathy as a mechanism providing competitive advantage to conspecific seedlings and saplings during the process of invasion. However, if results from laboratory experiments are applicable in the field, then we could expect Ailanthus seedlings to be highly resistant to their own allelochemicals (Heisey 1996). Ailanthus growth rates are reported to be much higher than in any native species the invader has been compared to (Knapp & Canham 2000; P. H. Martin & C. D. Canham, unpubl. data). The production of allelochemicals that suppress growth in coexisting species but not in their own seedlings and saplings might further magnify the differences between the invader and natives in their ability to overtop competitors and rapidly reach canopy, contributing to the success of Ailanthus invasion in northeastern temperate forests. Taken together, our results indicate that allelopathy is an important mechanism to take into account when trying to understand the causes and consequences of plant invasions. However, this study also strongly suggests that the real significance of the allelopathic effects of an invasive species cannot be assessed independently of its target community, or in isolation of other resource interactions involving the invader and the native community.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We thank the Dark Entry Forest Association and Nancy Nichols for permission to do research on their properties. Thanks to Star Child and James McCardle for their help in the field. We are grateful to Patrick H. Martin for his intellectual input during the design of this study. This work was supported by a Postdoctoral Fulbright Fellowship (FU2004-1288) to L.G.A. and a grant from U.S.D.A. (2003-35320-13583) to C.D. Canham and P.L. Marks. This study is a contribution to the program of the Institute of Ecosystem Studies.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • Abrams, M.D. (1998) The red maple paradox. Bioscience, 48, 355364.
  • Bais, H.P., Vepachedu, R., Gilroy, S., Callaway, R.M. & Vivanco, J.M. (2003) Allelopathy and exotic plant invasion: from molecules and genes to species interactions. Science, 301, 13771380.
  • Bazzaz, F.A. (1979) The physiological ecology of plant succession. Annual Review of Ecology and Systematics, 10, 351371.
  • Bruno, J.F., Fridley, J.D., Bromberg, K.D. & Bertness, M.D. (2005) Insights into biotic interactions from studies of species invasions. Species Invasions: Insights into Ecology, Evolution and Biogeography (eds D.F.Sax, J.J.Stachowicz & S.D.Gaines), pp. 1340. Sinauer Associates Inc., Sunderland, Massachusetts.
  • Bryant, J.P., Reichardt, P.B., Clausen, T.P., Provenza, F.D. & Kuropat, P.J. (1992) Woody plant-mammal interactions. Herbivores: their Interactions with Plant Secondary Metabolites, Vol. 2, 2nd edn (eds G.A.Rosenthal & M.R.Berenbaum), pp. 344371. Academic Press, San Diego, California.
  • Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd edn. Springer-Verlag, New York.
  • Callaway, R.M. & Maron, J.L. (2006) What have exotic plant invasions taught us over the past 20 years? Trends in Ecology and Evolution, 21, 369374.
  • Callaway, R.M. & Ridenour, W.M. (2004) Novel weapons: invasive success and the evolution of increased competitive ability. Frontiers in Ecology and the Environment, 2, 436443.
  • Canham, C.D. (1988) An index for understory light levels in and around canopy gaps. Ecology, 69, 16341638.
  • Canham, C.D. (1989) Different responses to gaps among shade-tolerant tree species. Ecology, 70, 548550.
  • Canham, C.D., Berkowitz, A.R., Kelly, V.R., Lovett, G.M., Ollinger, S.V. & Schnurr, J. (1996) Biomass allocation and multiple resource limitation in tree seedlings. Canadian Journal of Forest Research, 26, 15211530.
  • Casper, B.B., Schenk, H.J. & Jackson, R.B. (2003) Defining a plant's belowground zone of influence. Ecology, 84, 23132321.
  • Catovsky, S., Kobe, R.K. & Bazzaz, F.A. (2002) Nitrogen-induced changes in seedling regeneration and dynamics of mixed conifer-broad-leaved forests. Ecological Applications, 12, 16111625.
  • Cheremisinoff, P.N. & Ellerbusch, F. (1978) Carbon Adsorption Handbook. Ann Arbor Science Publishers, Ann Arbor, Michigan.
  • De Feo, V., De Martino, L., Quaranta, E. & Pizza, C. (2003) Isolation of phytotoxic compounds from tree of heaven (Ailanthus altissima Swingle). Journal of Agricultural and Food Chemistry, 51, 11771180.
  • Dietz, H. & Edwards, P.J. (2006) Recognition that causal processes change during plant invasion helps explain conflicts in evidence. Ecology, 87, 13591367.
  • Edwards, A.W.F. (1992) Likelihood-Expanded Edition. Johns Hopkins University Press, Baltimore, Maryland.
  • Feret, P.P. (1985) Ailanthus: variation, cultivation, and frustration. Journal of Arboriculture, 11, 361368.
  • Finzi, A.C. & Canham, C.D. (2000) Sapling growth in response to light and nitrogen availability in a southern New England forest. Forest Ecology and Management, 131, 153165.
  • Goffe, W.L., Ferrier, G.D. & Rogers, J. (1994) Global optimization of statistical functions with simulated annealing. Journal of Econometrics, 60, 6599.
  • Gómez-Aparicio, L. & Canham, C.D. (2008) Neighborhood models of the effects of invasive tree species on ecosystem processes. Ecological Monographs, 78, in press.
  • Harper, J.L. (1977) Population Biology of Plants. Academic Press, London.
  • Heisey, R.M. (1990a) Allelopathic and herbicidal effects of extracts from tree of heaven (Ailanthus altissima). American Journal of Botany, 77, 662670.
  • Heisey, R.M. (1990b) Evidence for allelopathy by tree of heaven. Journal of Chemical Ecology, 16, 20392055.
  • Heisey, R.M. (1996) Identification of an allelopathic compund from Ailanthus altissima (Simaroubaceae) and characterization of its herbicidal activity. American Journal of Botany, 83, 192200.
  • Heisey, R.M. & Heisey, T.K. (2003) Herbicidal effects under field conditions of Ailanthus altissima bark extract, which contains ailanthone. Plant and Soil, 256, 8599.
  • Hierro, J.L. & Callaway, R.M. (2003) Allelopathy and exotic plant invasion. Plant and Soil, 256, 2939.
  • Hill, D.E., Sautter, E.H. & Gunick, W.N. (1980) Soils of Connecticut. Bulletin Number 787. Connecticut Agricultural Experiment Station, New Haven, Connecticut.
  • Hu, S.Y. (1979) Ailanthus. Arnoldia, 39, 2950.
  • Inderjit & Callaway, R.M. (2003) Experimental designs for the study of allelopathy. Plant and Soil, 256, 111.
  • Jose, S. & Gillespie, A.R. (1998) Allelopathy in black walnut (Juglans nigra L.) alley cropping. I. Spatio-temporal variation in soil juglone in a black walnut-corn (Zea mays L.) alley cropping system in the Midwestern USA. Plant and Soil, 203, 191197.
  • Jose, S., Williams, R. & Zamora, D. (2006) Belowground ecological interactions in mixed-species forest plantations. Forest Ecology and Management, 233, 231239.
  • Juice, S.M., Fahey, T.J., Siccama, T.G., Driscoll, C.T., Denny, E.G., Eagar, C., Cleavitt, N.L., Minocha, R. & Richardson, A.D. (2006) Response of sugar maple to calcium addition to northern hardwood forest. Ecology, 87, 12671280.
  • Keeley, J.E. (1988) Allelopathy. Ecology, 69, 292293.
  • Knapp, L.B. & Canham, C.D. (2000) Invasion of an old-growth forest in New York by Ailanthus altissima: sapling growth and recruitment in canopy gaps. Journal of the Torrey Botanical Society, 127, 307315.
  • Kobe, R.K. (1996) Intraspecific variation in sapling mortality and growth predicts geographic variation in forest composition. Ecological Monographs, 66, 181201.
  • Kobe, R.K. (1997) Carbohydrate allocation to storage as a basis of interspecific variation in sapling survivorship and growth. Oikos, 80, 226233.
  • Kobe, R.K., Likens, G.E. & Eagar, C. (2002) Tree seedling growth and mortality responses to manipulations of calcium and aluminium in a northern hardwood forest. Canadian Journal of Forest Research, 32, 954966.
  • Kobe, R.K., Pacala, S.W., Silander, J.A. & Canham, C.D. (1995) Juvenile tree survivorship as a component of shade tolerance. Ecological Applications, 5, 517532.
  • Kowarik, I. (1995) Clonal growth in Ailanthus altissima on a natural site in West Virginia. Journal of Vegetation Science, 6, 853856.
  • Larsen, J.A. (1953) A study of an invasion by red maple of an oak woods in southern Wisconsin. American Midland Naturalist, 49, 908914.
  • Lawrence, J.G., Colwell, A. & Sexton, O.J. (1991) The ecological impact of allelopathy in Ailanthus altissima (Simaroubaceae). American Journal of Botany, 78, 948958.
  • Levine, J.M., Vilá, M., D’Antonio, C.M., Dukes, J.S., Grigulis, K. & Lavorel, S. (2003) Mechanisms underlying the impacts of exotic plant invasions. Proceedings of the Royal Society of London B, 270, 775781.
  • Lin, L.J., Peiser, G., Ying, B.P., Mathias, K., Karasina, F., Wang, Z., Itatani, J., Green, L. & Hwang, S. (1995) Identification of plant growth inhibitory principles in Ailanthus altissima and Castela tortuosa. Journal of Agricultural and Food Chemistry, 43, 17081711.
  • Lodge, D.M. (1993) Biological invasions: lessons for ecology. Trends in Ecology and Evolution, 8, 133137.
  • Mack, R.N., Simberloff, D., Lonsdale, W.M., Evans, H., Clout, M. & Bazzaz, F.A. (2000) Biotic invasions: causes, epidemiology, global consequences and control. Ecology, 10, 689710.
  • Marks, P.L. (1975) On the relation between extension growth and successional status of deciduous trees of the northeastern United States. Bulletin of the Torrey Botanical Club, 102, 172177.
  • Mergen, F. (1959) A toxic principle in the leaves of Ailanthus. Botanical Gazette, 121, 3236.
  • Muller, C.H. (1966) The role of chemical inhibition (allelopathy) in vegetational composition. Bulletin of the Torrey Botanical Club, 93, 332351.
  • Nilsson, M.C. (1994) Separation of allelopathy and resource competition by the boreal dwarf shrub Empetrum hermaphroditum Hagerup. Oecologia, 98, 17.
  • Nilsson, M.C., Högberg, P., Zackrisson, O. & Fengyou, W. (1993) Allelopathic effects by Empetrum hermaphroditum on development and nitrogen uptake by roots and mycorrhizae of Pinus sylvestris. Canadian Journal of Botany, 71, 620628.
  • Nilsson, M.C., Zackrisson, O., Sterner, O. & Wallstedt, A. (2000) Characterization of the differential interference effects of two boreal dwarf shrub species. Oecologia, 123, 122128.
  • Pacala, S.W., Canham, C.D., Saponara, J., Silander, J.A., Kobe, R.K. & Ribbens, E. (1996) Forest models defined by field measurements: estimation, error analyses and dynamics. Ecological Monographs, 66, 143.
  • Polonsky, J. (1973) Quassinoid bitter principles. Fortschritte der Chemie Organischer Naturstoffe, 30, 101150.
  • Polonsky, J. (1985) Quassinoid bitter principles II. Fortschritte der Chemie Organischer Naturstoffe, 47, 221264.
  • Prati, D. & Bossdorf, O. (2004) Allelopathic inhibition of germination by Alliaria petiolata (Brassicaceae). American Journal of Botany, 91, 285288.
  • Radosevich, S.R., Stubbs, M.M. & Ghersa, C.M. (2003) Plant invasions: process and patterns. Weed Science, 51, 254259.
  • Shigesada, N., Kawasaki, K. & Takeda, Y. (1995) Modeling stratified diffusion in biological invasions. American Naturalist, 146, 229251.
  • Spurr, S.H. & Barnes, B.V. (1980) Forest Ecology, 3rd edn. John Wiley and Sons, New York.
  • Stinson, K.A., Campbell, S.A., Powell, J.R., Wolfe, B.E., Callaway, R.M., Thelen, G.C., Hallett, S.G., Prati, D. & Klironomos, J.N. (2006) Invasive plant suppresses the growth of native tree seedlings by disrupting belowground mutualisms. Public Library of Science Biology, 4, 727731.
  • Tripler, C.E., Canham, C.D., Inouye, R.S. & Schnurr, J.L. (2002) Soil nitrogen availability, plant luxury consumption, and herbivory by white-tail deer. Oecologia, 133, 517524.
  • Vilá, M., Tessier, M., Suehs, C.M., Brundu, G., Carta, L., Galanidis, A., et al . (2006) Local and regional assesment of the impacts of plant invaders on vegetation structure and soil properties of Mediterranean islands. Journal of Biogeography, 33, 853861.
  • Vitousek, P.M. (1986) Biological invasions and ecosystem properties: can species make a difference? Ecology of Biological Invasions of North America and Hawaii (eds H.A.Mooney & J.A.Drake), pp. 163176. Springer-Verlag, New York.
  • Vitousek, P.M., D’Antonio, C.M., Loope, L.L. & Westbrooks, R. (1996) Biological invasions as global environmental change. American Scientist, 84, 468478.
  • Voigt, G.K. & Mergen, F. (1962) Seasonal variation in toxicity of Ailanthus leaves to pine seedlings. Botanical Gazette, 123, 262265.
  • Walters, M.B. & Reich, P.B. (1996) Are shade tolerance, survival, and growth linked? Low light and, nitrogen effects on hardwood seedlings. Ecology, 77, 841853.
  • Walters, M.B. & Reich, P.B. (2000) Seed size, nitrogen supply, and growth rate affect tree seedling survival in deep shade. Ecology, 81, 18871901.
  • Webster, C.R., Jenkins, M.A. & Jose, S. (2006) Woody invaders and the challenges they pose to forest ecosystems in the eastern United States. Journal of Forestry, 104, 366374.
  • Zaccherio, M.T. & Finzi, A.C. (2007) Atmospheric deposition may affect northern hardwood forest composition by altering soil nutrient supply. Ecological Applications, 17, 19291941.
  • Zackrisson, O. & Nilsson, M.C. (1992) Allelopathic effects by Empetrum hermaphroditum on seed germination of two boreal tree species. Canadian Journal of Forest Research, 22, 13101319.

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Appendix S1. Results of the modified basic allelopathy models.

Appendix S2. Summary of among-treatment differences (AC vs. Control) in performance for seedlings with and without Ailanthus trees in the neighbourhood.

Appendix S3. Pascal source code for the likelihood functions and the simulated annealing algorithm used in this study.

Please note: Blackwell Publishing is not responsible for the content or functionality of any supplementary materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

FilenameFormatSizeDescription
JEC_1352_sm_AppendixS1.doc89KSupporting info item
JEC_1352_sm_AppendixS2.doc65KSupporting info item
JEC_1352_sm_AppendixS3.txt18KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.