Abstract

Background and Aims

Success of invasive plant species is thought to be linked with their higher leaf carbon fixation strategy, enabling them to capture and utilize resources better than native species, and thus pre-empt and maintain space. However, these traits are not well-defined for invasive woody vines.

Methods

In a glass house setting, experiments were conducted to examine how leaf carbon gain strategies differ between non-indigenous invasive and native woody vines of south-eastern Australia, by investigating their biomass gain, leaf structural, nutrient and physiological traits under changing light and moisture regimes.

Key Results

Leaf construction cost (CC), calorific value and carbon : nitrogen (C : N) ratio were lower in the invasive group, while ash content, N, maximum photosynthesis, light-use efficiency, photosynthetic energy-use efficiency (PEUE) and specific leaf area (SLA) were higher in this group relative to the native group. Trait plasticity, relative growth rate (RGR), photosynthetic nitrogen-use efficiency and water-use efficiency did not differ significantly between the groups. However, across light resource, regression analyses indicated that at a common (same) leaf CC and PEUE, a higher biomass RGR resulted for the invasive group; also at a common SLA, a lower CC but higher N resulted for the invasive group. Overall, trait co-ordination (using pair-wise correlation analyses) was better in the invasive group. Ordination using 16 leaf traits indicated that the major axis of invasive-native dichotomy is primarily driven by SLA and CC (including its components and/or derivative of PEUE) and was significantly linked with RGR.

Conclusions

These results demonstrated that while not all measures of leaf resource traits may differ between the two groups, the higher level of trait correlation and higher revenue returned (RGR) per unit of major resource need (CC) and use (PEUE) in the invasive group is in line with their rapid spread where introduced.

INTRODUCTION

In a novel environment, invasive alien organisms may disrupt the delivery of ecosystem services and goods (Batianoff and Butler, 2003; Hulme, 2006). Consequently, for understanding and management of biological invasion, the search for functional traits associated with invasiveness has been the focus of many empirical studies and reviews, especially in plants (e.g. Rejmánek and Richardson, 1996; Daehler, 2003; Funk and Vitousek, 2007; Leishman et al., 2007, 2010; Pyšek and Richardson, 2007; Kleunen et al., 2010). Reported diagnostic leaf traits for invasive plant species when compared with co-occurring native species, include greater morphological and physiological plasticity, higher specific leaf area (SLA), higher maximum photosynthesis (Amax), and higher nitrogen (N) content but decreased allocation to defence and shorter leaf life span (Pattison et al., 1998; Grotkopp et al., 2002; Grotkopp and Rejmánek, 2007; Drenovsky et al., 2008). Most of these traits are believed to be strongly linked with greater nutrient, water or light resource-use efficiency (RUE), consequently resulting in higher relative growth rate (RGR) and rapid biomass gain. However, not all these diagnostic traits have been fully accepted by ecologists as equivocal findings have been reported, notably those relating to greater trait plasticity across resources, higher RGR and higher RUE for the invasive species (see Williams and Black, 1994; Baruch and Goldstein, 1999; Leishman et al., 2007, 2010; McAlpine et al., 2008; Osunkoya et al., 2004, 2010). Richards et al. (2006) argued that plasticity of morphological and physiological traits enhances niche breadth but is unlikely to have any effect on invasiveness unless that plasticity is adaptive and thus contributes to fitness such as reproduction and survival. Examples of plant adaptive traits likely to contribute to fitness include RGR, total biomass and SLA; the first two traits can influence niche pre-emption and occupation while the last drives carbon assimilation per unit energy invested (Reich et al., 2003; Wright et al., 2004). Thus it is expected that plasticity of these traits, amongst many others, is likely to increase fitness in multiple environments, with invasives exhibiting higher values than native or non-invasive exotics.

Expressing plant activities in terms of efficiencies gives good indications of resource capture (i.e. revenue stream) per unit investment (McDowell, 2002; Wright et al., 2004). For example, Amax can be limited by low N or water availability. Therefore maximizing Amax relative to N and moisture cost may be one mechanism of invasive plant success (Baruch and Goldstein, 1999; McDowell, 2002; Funk and Vitousek, 2007; Feng et al., 2008). Also low resource requirements for photosynthesis, as evidenced by low leaf construction cost [CC; defined as grams of glucose (+ minerals) required to synthesize 1 g of carbon skeleton (Poorter et al., 2006)] often increase the competitive ability of a plant species as more of the fixed carbon (C) is left for growth and reproduction. Thus low tissue density (i.e. high SLA) and low CC have been advocated as important traits contributing to the runaway success of introduced plants in their novel environment (see Nagel and Griffin, 2001, 2004; Funk and Vitousek, 2007; Song et al., 2007; Feng et al., 2008). However, there is a dearth of studies linking the two performance measures of SLA and CC to fitness (e.g. seed production) or other traits closely associated with fitness itself such as RGR or biomass accumulation (but see Grotkopp et al., 2002; Poorter and Bongers, 2006; James and Drenovsky, 2007), especially so in woody vines.

Vines are an enigmatic group of plants with greater growth and energy investment in leaves at the expense of other biomass parts (Putz and Mooney, 1991). In coastal Queensland and New South Wales, Australia, many introduced woody vine species have become major environmental weeds affecting riparian habitats, disturbed rainforest communities and remnant natural vegetation, where they displace native tree and vine species (Batianoff and Butler, 2003). In densely infested areas, they smother standing vegetation, including large trees and shubs, causing mechanical damage, reduction of resources available to their hosts and eventually host canopy collapse (Batianoff and Butler, 2003; Osunkoya et al., 2009). In the habitats mentioned above, these exotic vines also deplete water courses especially during periodic and long-term drought events (Osunkoya et al., 2009), but there is no information on their long-term water-use efficiency (WUE) in comparison to co-occurring native species.

In an earlier work in south-east Queensland, Australia (Osunkoya et al., 2010) on comparative ecophysiology of four exotic invasive versus their phylogenetically equivalent native woody vines, it was shown that certain whole plant (e.g. leaf area ratio) and leaf level physiological traits [e.g. quantum use efficiency (AQE), dark respiration, light compensation point and SLA], rather than plasticity of these traits, consistently differed between the two groups. However, the explanatory power of these combined traits on the dichotomy was only moderate (approx. 31 %) as some native vine species were found to possess trait ranking similar to those of the invasives. We then hypothesized that perhaps other leaf traits such as tissue CC, leaf chemistry and RUEs, especially integrated as opposed to instantaneous WUE, might explain the dichotomy better. Herein, data on leaf chemistry, including the stable carbon isotope (a measure of long-term WUE) and CC of the same group of invasive and native vines species responding to changes in light and moisture regimes, are presented. Then these primary data are combined with the groups' leaf structural and physiological responses reported in Osunkoya et al. (2010). We hypothesized the following: (a) the invasive group of plants will exhibit lower resource needs (e.g. tissue density/SLA, N, CC) but higher returns in terms of revenue stream (e.g. Amax, biomass and RGR) due to higher RUE and greater level of trait co-ordination when compared with the native group; and (b) the invasive species, due to niche pre-emption capability, will show greater trait plasticity.

MATERIALS AND METHODS

Four highly invasive (introduced) and four native vines species were investigated (see Table 1). These invasive vines and their phylogenetically paired natives are Macfadyena unguis-cati (L.) A.H.Gentry (cat's claw creeper vine; Bignoniaceae) vs. Pandorea jasminoides (Lindl.) K. Schum. (bower of beauty vine; Bignoniaceae), Anredera cordifolia (Ten.) Steenis (Madeira vine; Bassellaceae) vs. Hibbertia scandens (Willd.) Dryand (climbing guinea vine; Dilleniaceae), Araujia sericifera Brot. (white moth vine; Asclepiadaceae) vs. Parsonsia straminea (R.Br.) F.Muell (monkey rope vine; Apocynaceae) and Cardiospermum grandiflorum Sw. vs. C. halicacabum L. (both with the common name of balloon vine; Sapindaceae). Young plants of these focal species were grown either from seeds (the two Cardiospermum species), field-collected stem cuttings (Anredera and Araujia) and seedlings (Macfadyena) or from young seedlings procured via a nursery (Hibbertia, Pandorea and Parsonsia) in a glasshouse setting during the warmer months of November 2007 to April 2008 at Alan Fletcher Research Station, Brisbane, Australia (27 °31′S, 152 °58'E, 50 m a.s.l.). Here, in a factorial experiment, individual plants were exposed for 14 weeks in 20-L pots to either 30 % (645·72 ± 26·21 µmol m−2 s−1) or 2–5 % (44·93 ± 26·21 µmol m−2 s−1) full sunlight (high and low) and three moisture regimes of either 200 mL of water every other day, twice weekly or once weekly (low, medium and high). At the end of each watering cycle, moisture regimes were assessed using a moisture probe attached to an LI-8100 machine (LI-Cor, Lincoln, NE, USA), and corresponded to 62·5 %, 27·2 % and 11·1 % soil volumetric water content, respectively (O. O. Osunkoya, unpubl. res.). There were eight replicate potted plants of each species in two light × three moisture regimes (i.e. a total of 48 plants per species; for further details of experimental protocols, see Osunkoya et al., 2010).

Table 1.

Mean trait performance of invasive and native vine species of south-east Queensland, Australia. Only data on light treatment effects are presented as moisture effects were minimal and hence data were pooled. The superscript number besides each species name identifies the invasive and ecologically/phylogenetically equivalent native pair

  Leaf chemistry
 
Leaf resource need
 
Leaf resource use efficiency
 
Plant fitness
 
Species Light cond. Nmass Narea C : N Ash HC SLA CCmass CCarea Amax,area Amax,mass AQE WUE δ13PNUE PEUE Biomass RGR 
Invasive (I) 
1Macfadyena High 5·14 0·032 41·19 8·11 11·7 17·65 161·09 1·254 78·17 11·35 0·184 0·079 7·31 −28·86 36·52 146·70 16·25 1·06 
unguis-cati Low 5·52 0·017 52·67 9·53 11·7 16·96 439·12 1·212 32·06 6·68 0·29 0·076 5·69 −29·16 34·23 183·68 0·87 0·21 
2Anredera High 4·79 0·016 37·00 7·77 17·5 16·66 310·55 1·147 37·31 7·54 0·229 0·058 10·79 −28·65 48·65 199·96 34·79 0·84 
cordifolia Low 7·41 0·009 33·00 4·46 30·0 13·02 867·88 1·081 9·94 3·83 0·331 0·055 3·27 −33·22 44·42 383·56 2·35 0·09 
3Araujia High 4·87 0·021 43·62 8·95 11·2 18·89 245·33 1·348 57·40 5·70 0·132 0·049 8·99 −28·09 27·28 97·71 31·23 0·96 
sericifera Low 5·29 0·006 39·02 7·37 14·9 17·30 623·81 1·199 18·51 5·26 0·333 0·045 5·84 −33·11 57·07 283·30 0·90 −0·13 
4Cardiospermum High 2·82 0·011 43·82 16·08 6·6 18·58 203·29 1·361 67·61 9·91 0·201 0·085 9·39 −26·59 74·76 147·63 64·65 1·07 
grandiflorum Low 4·98 0·007 42·40 8·59 13·5 17·11 672·70 1·204 17·89 6·27 0·422 0·088 5·08 −31·76 85·26 351·31 2·90 0·16 
Native (N) 
1Pandorea High 3·17 0·020 46·39 14·85 7·9 20·03 198·40 1·457 112·46 10·34 0·206 0·033 8·69 −26·66 65·47 141·98 26·45 1·25 
jasminoides Low 3·61 0·011 46·22 12·83 9·3 19·64 335·34 1·42 44·79 5·60 0·199 0·051 6·1 −26·87 54·48 139·94 1·46 0·40 
2Hibbertia High 2·91 0·020 43·64 15·04 10·6 18·37 146·74 1·314 89·86 5·28 0·078 0·025 10·36 −29·07 26·79 59·62 33·31 0·66 
scandens Low 3·16 0·009 40·68 12·85 13·5 17·27 336·90 1·215 36·25 7·65 0·259 0·035 3·93 −31·49 81·78 213·42 3·79 −0·08 
3Parsonsia High 5·42 0·033 39·45 7·41 10·3 19·10 167·77 1·369 123·37 6·40 0·107 0·066 8·58 −31·55 20·01 78·48 8·28 1·27 
straminea Low 6·1 0·011 37·76 12·83 10·9 17·76 588·45 1·306 26·05 3·79 0·225 0·058 7·18 −32·16 26·70 113·19 0·94 0·58 
4Cardiospermum High 5·85 0·019 9·6 19·51 315·37 1·405 44·50 
halicacabum Low 5·61 0·007 15·7 18·48 823·76 1·273 15·50 
Summary of ANOVA 
Light  *** NS NS ** *** *** *** *** ** *** NS *** ** *** *** *** 
Moisture  NS NS NS NS NS NS NS NS NS NS NS NS ** NS NS NS 
Group  *** NS NS *** *** *** ** *** ** NS NS NS NS ** NS 
Light xGroup  NS NS NS ** ** NS NS NS NS NS ** NS NS NS 
Light x Moisture  NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS 
Moisture x Group  NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS 
Direction of group difference 
 High I > N   I < N I > N I < N I > N I < N I < N  I > N I > N    I > N I > N NS 
 Low I > N   I < N I > N I < N I > N I < N I < N  I > N I > N    I > N NS I < N 
Overall mean performance 
Invasive High 4·94 0·022 40·60 8·28 13·4 17·73 238·19 1·25 61·02 8·21 0·182 0·06 9·16 −28·53 37·42 148·12 27·42 0·95 
Invasive Low 6·31 0·010 41·74 6·42 19·3 15·53 643·60 1·08 18·46 5·25 0·318 0·058 4·92 −31·61 45·25 272·34 1·37 0·06 
Native High 3·81 0·024 43·16 12·44 9·6 19·17 170·97 1·38 89·35 7·34 0·131 0·038 9·62 −29·09 37·42 93·36 22·68 1·06 
Native Low 4·12 0·012 40·72 11·96 10·9 18·42 420·23 1·31 34·04 5·68 0·228 0·049 6·01 −30·46 62·56 171·22 2·06 0·30 
 LSD 0·05 0·004 2·98 2·65 1·85 0·44 53·56 0·04 8·40 1·1 0·04 0·01 1·04 0·81 12·92 38·90 2·88 0·09 
  Leaf chemistry
 
Leaf resource need
 
Leaf resource use efficiency
 
Plant fitness
 
Species Light cond. Nmass Narea C : N Ash HC SLA CCmass CCarea Amax,area Amax,mass AQE WUE δ13PNUE PEUE Biomass RGR 
Invasive (I) 
1Macfadyena High 5·14 0·032 41·19 8·11 11·7 17·65 161·09 1·254 78·17 11·35 0·184 0·079 7·31 −28·86 36·52 146·70 16·25 1·06 
unguis-cati Low 5·52 0·017 52·67 9·53 11·7 16·96 439·12 1·212 32·06 6·68 0·29 0·076 5·69 −29·16 34·23 183·68 0·87 0·21 
2Anredera High 4·79 0·016 37·00 7·77 17·5 16·66 310·55 1·147 37·31 7·54 0·229 0·058 10·79 −28·65 48·65 199·96 34·79 0·84 
cordifolia Low 7·41 0·009 33·00 4·46 30·0 13·02 867·88 1·081 9·94 3·83 0·331 0·055 3·27 −33·22 44·42 383·56 2·35 0·09 
3Araujia High 4·87 0·021 43·62 8·95 11·2 18·89 245·33 1·348 57·40 5·70 0·132 0·049 8·99 −28·09 27·28 97·71 31·23 0·96 
sericifera Low 5·29 0·006 39·02 7·37 14·9 17·30 623·81 1·199 18·51 5·26 0·333 0·045 5·84 −33·11 57·07 283·30 0·90 −0·13 
4Cardiospermum High 2·82 0·011 43·82 16·08 6·6 18·58 203·29 1·361 67·61 9·91 0·201 0·085 9·39 −26·59 74·76 147·63 64·65 1·07 
grandiflorum Low 4·98 0·007 42·40 8·59 13·5 17·11 672·70 1·204 17·89 6·27 0·422 0·088 5·08 −31·76 85·26 351·31 2·90 0·16 
Native (N) 
1Pandorea High 3·17 0·020 46·39 14·85 7·9 20·03 198·40 1·457 112·46 10·34 0·206 0·033 8·69 −26·66 65·47 141·98 26·45 1·25 
jasminoides Low 3·61 0·011 46·22 12·83 9·3 19·64 335·34 1·42 44·79 5·60 0·199 0·051 6·1 −26·87 54·48 139·94 1·46 0·40 
2Hibbertia High 2·91 0·020 43·64 15·04 10·6 18·37 146·74 1·314 89·86 5·28 0·078 0·025 10·36 −29·07 26·79 59·62 33·31 0·66 
scandens Low 3·16 0·009 40·68 12·85 13·5 17·27 336·90 1·215 36·25 7·65 0·259 0·035 3·93 −31·49 81·78 213·42 3·79 −0·08 
3Parsonsia High 5·42 0·033 39·45 7·41 10·3 19·10 167·77 1·369 123·37 6·40 0·107 0·066 8·58 −31·55 20·01 78·48 8·28 1·27 
straminea Low 6·1 0·011 37·76 12·83 10·9 17·76 588·45 1·306 26·05 3·79 0·225 0·058 7·18 −32·16 26·70 113·19 0·94 0·58 
4Cardiospermum High 5·85 0·019 9·6 19·51 315·37 1·405 44·50 
halicacabum Low 5·61 0·007 15·7 18·48 823·76 1·273 15·50 
Summary of ANOVA 
Light  *** NS NS ** *** *** *** *** ** *** NS *** ** *** *** *** 
Moisture  NS NS NS NS NS NS NS NS NS NS NS NS ** NS NS NS 
Group  *** NS NS *** *** *** ** *** ** NS NS NS NS ** NS 
Light xGroup  NS NS NS ** ** NS NS NS NS NS ** NS NS NS 
Light x Moisture  NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS 
Moisture x Group  NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS 
Direction of group difference 
 High I > N   I < N I > N I < N I > N I < N I < N  I > N I > N    I > N I > N NS 
 Low I > N   I < N I > N I < N I > N I < N I < N  I > N I > N    I > N NS I < N 
Overall mean performance 
Invasive High 4·94 0·022 40·60 8·28 13·4 17·73 238·19 1·25 61·02 8·21 0·182 0·06 9·16 −28·53 37·42 148·12 27·42 0·95 
Invasive Low 6·31 0·010 41·74 6·42 19·3 15·53 643·60 1·08 18·46 5·25 0·318 0·058 4·92 −31·61 45·25 272·34 1·37 0·06 
Native High 3·81 0·024 43·16 12·44 9·6 19·17 170·97 1·38 89·35 7·34 0·131 0·038 9·62 −29·09 37·42 93·36 22·68 1·06 
Native Low 4·12 0·012 40·72 11·96 10·9 18·42 420·23 1·31 34·04 5·68 0·228 0·049 6·01 −30·46 62·56 171·22 2·06 0·30 
 LSD 0·05 0·004 2·98 2·65 1·85 0·44 53·56 0·04 8·40 1·1 0·04 0·01 1·04 0·81 12·92 38·90 2·88 0·09 

*, P < 0·05; **, P < 0·02; ***, P < 0·001; n.s., not significant; LSD, least significant difference.

SLA, Amax, biomass and RGR values were extracted from Osunkoya et al. (2010) and are presented here for ease of comparison of trait–pair relationships.

Abbreviations and units: Nmass = mass-based nitrogen concentration (mg g−1); Narea = area-based nitrogen content (mg cm−2); C = carbon concentration (mg g−1); Ash = mineral ash (mg g−1); SLA = specific leaf area (cm g−1); HC = heat of combustion (Kj g−1); CCmass = mass-based construction cost (g g−1); CCarea = area-based construction cost (g m−2); Amax,area = area-based maximum photosynthesis (μmol m−2 s−1); Amax,mass = mass-based maximum photosynthesis (μmol g−1 s−1); AQE = quantum (light)- use efficiency (μmol CO2 μmol−1 photon); WUE = instantaneous water-use efficiency (μmol CO2 mmol−1 H2O); δ13C = isotopic carbon (%); PNUE = photosynthesis nitrogen use efficiency (μmol CO2 mol N−1); PEUE = photosynthesis energy-use efficiency(μmol CO2 g−1); Biomass = total biomass accumulated by 14 weeks of growth (g); RGR = relative growth rate (g−1 g month−1).

Leaf gas exchange (carbon gain)

At the end of 14 weeks of growth, and for each species × light × moisture treatment, leaf photosynthetic response to increasing and saturated light (PAR) (i.e. 0, 5, 10, 20, 30, 40, 50, 60, 90, 100, 200, 250, 300, 400, 500, 600, 1000, 1500 and 2000 µmol m−2 s−1) was determined with an LI-6400 portable photosynthesis system (LI-Cor) using mature leaves from at least four individuals. From these primary data, various gas exchange parameters were derived, including light-saturated net assimilation rate, maximum photosynthetic rate, leaf conductance and transpiration rates, quantum efficiency, dark respiration, light compensation and saturation points (for details of measurement protocols and associated equations, see Osunkoya et al., 2010).

Leaf structural and total biomass traits

Following gas exchange measurements, plants were harvested and the roots washed free of soil. Plants were separated into leaves, roots and shoots. Leaf samples were scanned and computer image analysis software (ImageJ 1·31v; Rasband, 2004) was then used to estimate leaf area (in cm2). Plant parts were then dried at 70 °C for at least 4 d before weighing. A number of growth parameters were calculated from the primary data collected. These included total biomass, leaf area, specific leaf area (SLA = leaf area/leaf mass) and relative growth rate [RGR = (ln W2 – ln W1)/(t2t1), where ln W is the natural logarithm of biomass, t is the time (in months), and the subscript refers to initial and final biomass]. The initial biomass values were obtained from eight set-aside plants of each species grown at the high light regime prior to treatment applications.

Leaf chemistry and derivation of RUEs

At the end of the growth period, the following analyses were carried out on dried leaf samples collected per plant per treatment (n = 8) per species. Total N and C (% dry mass) were determined by the Dumas micro-combustion technique (Eurovector EA 3000, Milan, Italy). Ash content (g g−1) was determined in triplicate by incinerating 1 g samples of the dried leaves in a muffle furnace at 500 °C until a white-grey residue remained (3–4 h). Heat of combustion (HC) was measured in triplicate per plant using 1 g, with a Gallenkamp bomb calorimeter (model CRA-305, UK) calibrated against benzoic acid pellets of known energy value.

Leaf CC was calculated using a formula based on the growth efficiency of leaf tissue, heat of combustion, ash and nitrogen content according to Williams et al. (1987):  

formula
where HC = ash free heat of combustion (kJ g−1), N = total Kjeldahl nitrogen (g g−1), k is the oxidation state of the nitrogen source (+5 for nitrate, or –3 for ammonium), and Eg is the growth efficiency (the fraction of cost required to provide reductant that is not incorporated into biomass) (see Williams et al., 1987; Poorter et al., 2006). The value of Eg used in this study was 0·87 (see Williams et al., 1987). In the calculation it was assumed that the nitrogen source was both nitrate and ammonium for all species and hence CC is calculated using both k = +5 and –3, and the average values are reported.

Photosynthetic nitrogen-use efficiency (PNUE) was calculated as the ratio of instantaneous Amax to N on area basis. Photosynthetic energy-use efficiency (PEUE) was calculated as the ratio of instantaneous Amax to CC on an area basis. Instantaneous WUE was calculated as the ratio of Amax to transpiration for PAR > 300 µmol m−2 s−1. The carbon isotope ratio (δ13C) of leaves indicates the long-term WUE integrated over the lifetime of a leaf (Farquhar et al., 1989), reflecting both stomatal opening and carboxylation rate. Plants with less negative δ13C values generally display higher time-integrated WUE (Farquhar et al., 1989). The leaf 13C : 12C ratio was analysed by mass spectrometry (GV Isoprime, Manchester, UK) at Griffith University, Brisbane, Australia, and δ13C (‰) calculated as:  

formula
where Rsample is the 13C : 12C ratio of a sample and Rstandard is the 13C : 12C ratio of the international PeeDee Belemnite standard. Analytical precision was ±0·3 %.

Data analysis

Where required, area-based estimates for individual × species × treatment data were obtained by dividing mass-based values by their corresponding SLA data. Excluding the invasive Cardiospermum grandiflorum because of lack of some data for the equivalent native C. halicacabum (see Table 1) did not affect trends or significance of tests carried out and hence their data, where available, were included in all analyses. Leaf photosynthetic response, structural and chemical parameters as well as whole-plant biomass data were analysed using a linear mixed ANOVA model with fixed terms for light (high vs. low), moisture (low, medium and high), and species origin group (invasive vs. native), and random term for individual species pair (taxonomic grouping: four levels) and their two-way interaction effects. Interactions were limited to two-way effects to maintain power of the tests ≥80 %. The last term (i.e. taxonomic group) helps to correct/reduce the influence of phylogenetic membership on the dataset (see Harvey and Pagel, 1991). Moisture effects were minimal (see Results), and hence species mean values at each light treatment were used to carry out correlations and regressions between pairs of measured variables. For correlation, a critical level of P = 0·02 was selected to exert table-wise alpha control yet avoid the unduly harsh effect a full Boniferonni alpha adjustment of P = 0·05/n (where n is the number of pair-wise comparison being made). To examine overall differences, including level of trait co-ordination between invasive and native vine species, as well as to explore which traits were most influential in separating the two groups, ordination following normalization and using principal component analysis (PCA) was carried out on the species mean values of all traits, except RGR and biomass. All the above analyses were done using SPSS ver. 16·0, except where otherwise stated.

Across multiple resources of light and moisture, the plastic response of the invaders relative to the native species was estimated using the point-based plasticity index of Valladares et al. (2006) (see also Funk, 2008), calculated for each species as the difference between the minimum and maximum mean value divided by the maximum mean value (per trait). The index ranges between 0 (no plasticity) and 1 (maximum plasticity). The difference between the two species origin groups in the median for the plasticity index was then determined by the Wilcoxon paired signed rank test. To explore how adaptive traits of RGR, total biomass and SLA varied across the light and/or moisture environments for each group, these traits were regressed against log-transformed measured structural, physiological, nutrient and resource-use traits using standardized major axis (SMA) regression as implemented in SMATR software (Warton et al., 2006), and used the value of the scaling (slope) and intercept parameters as an index of C economy (revenue stream per unit investment; see also Leishman et al., 2010). SMA estimates of lines summarizing the relationship between two variables are superior to ordinary least square linear regression and have been advocated in the literature because residual variance is minimized in both x and y dimensions rather than in the y dimension only (Falster et al., 2003; Warton et al., 2006). When comparing regressions, differences can occur in either the exponent of a (y intercept) and/or b (regression slope). If b (slope) differs amongst species, species with larger b will show greater increase in y per increment of x. If a differs, but b does not, species with larger a will have a consistently larger amount of y at any given value of x (Warton et al., 2006).

RESULTS

General trends

Light had a greater effect on species and group performance than moisture (mixed model ANOVA; Table 1). Light availability had a significant effect on all measured leaf variables, except C, the C : N ratio and AQE, whereas the moisture availability appeared only to influence instantaneous and long-term WUE and biomass accumulated. Light × group interaction effects were significant for many of the traits examined, suggesting that group performance varied depending on the light condition, especially for HC, CCmass and long-term WUE (Table 1). In contrast, the light × moisture and group × moisture interaction effects were minimal, indicating consistency of moisture effect on traits under changing light regimes. Hence in most cases, except where the moisture effect was significant, data on moisture level effects were pooled. Consistent, significant differences were found, irrespective of the light condition, in the following traits (a) Nmass, total ash, SLA, Amax (mass-based), AQE and PEUE, in which invasive > native group values; and (b) the C : N ratio, HC, CCmass and CCarea in which invasive < native group values. In the high light condition only, total biomass but not RGR was significantly higher in the invasive species group; the converse was the case at low light, with the invasive species exhibiting a significantly lower value for RGR but not total biomass.

Resource conservation, usage and efficiency

Of the six resource traits examined, three (WUE, isotopic C and PNUE) did not show significant differences between invasive and native groups of species (Table 1). On both area and mass bases, CC was lower in the invasive group, while PEUE and AQE showed the opposite trend, being higher in the invasive relative to the native species (Table 1). WUE and isotopic C were similar under high light, but differed significantly between the two groups under the low light regime where the natives have a better water conservation strategy as indicated by the group's higher WUE and higher isotopic C signature (i.e. less negative value; Table 1).

Trait plasticity, co-ordination and correlation

There was no evidence of a difference in individual trait plasticity between the two groups (data not shown). However, across light regimes, a significantly higher level of trait correlations existed within the invasive group compared with the native group. Of the 153 leaf trait pairs (from 18 traits) examined, 17 and 29 of the relationships were significant at P < 0·02 and P < 0·05, respectively, for the native group (mean R2 = 0·85, 0·65) compared with 37 and 59 for the invasive group (mean R2 = 0·92, 0·76) (see also Osunkoya et al., 2010). The difference between the two groups in the strength of these relationships was significant (t = 3·77, P = 0·001, n = 17; match-paired t-test). The trends between SLA and leaf resource traits are given in Fig. 1. In general and across light regimes, thinner leaves (i.e. higher SLA) had significantly lower Narea, lower CC and higher PNUE and PEUE but demonstrated low water conservation. SMA regression analyses indicated that the slopes did not differ significantly between the two groups in any of the relationships tested (Table 2). However, assuming slope homogeneity, significant differences existed in the intercepts of the SLA relationships with Narea, with CC (mass- and area-based) and with PEUE (Table 2 and Fig. 1B–D and F). Thus the analyses indicated that at a common (given) SLA value, significantly lower CC but higher N content were achieved by the invasive relative to the native species (Fig. 1 and Table 2). Mean distance along the common slope for PEUE vs. SLA relationship also differed between the two groups; natives were at the lower end while the invasives had a much wider distribution on the spectrum.

Fig. 1.

The relationships across light regimes between SLA and various leaf resource traits for invasive and native vines, as indicated. Each point is a mean of species value from four to eight plants per light (high and low) treatment. Significant trends (R2 value) are in bold and underlined. See Table 2 for tests of differences between the two groups in slope, intercept and distance along a common slope.

Fig. 1.

The relationships across light regimes between SLA and various leaf resource traits for invasive and native vines, as indicated. Each point is a mean of species value from four to eight plants per light (high and low) treatment. Significant trends (R2 value) are in bold and underlined. See Table 2 for tests of differences between the two groups in slope, intercept and distance along a common slope.

Table 2.

Test of differences in coefficients of the scaling (slope), the intercept and shift along a common slope of bivariate relationships between invasive and native species across two light regimes using standardized major axis regression analyses as implemented within SMATR software

 Test of plant group difference in:
 
Trait pair (y vs. xSlope Intercept Distance along a common slope 
Nmass vs. SLA n.s. n.s. n.s. 
Narea vs. SLA n.s. n.s. 
CCmass vs. SLA n.s. n.s. 
CCarea vs. SLA n.s. n.s. 
PNUE vs. SLA n.s. n.s. n.s. 
PEUE vs. SLA n.s. 
WUE vs. SLA n.s. n.s. n.s. 
δ13C vs. SLA n.s. n.s. n.s. 
RGR vs. Nmass n.s. n.s. n.s. 
RGR vs. Narea n.s. n.s. n.s. 
RGR vs. CCmass n.s. n.s. 
RGR vs. CCarea n.s. n.s. 
RGR vs. PNUE n.s. n.s. n.s. 
RGR vs. PEUE n.s. n.s. 
RGR vs. WUE n.s. n.s. n.s. 
RGR vs. δ13n.s. n.s. n.s. 
RGR vs. SLA n.s. n.s. n.s. 
 Test of plant group difference in:
 
Trait pair (y vs. xSlope Intercept Distance along a common slope 
Nmass vs. SLA n.s. n.s. n.s. 
Narea vs. SLA n.s. n.s. 
CCmass vs. SLA n.s. n.s. 
CCarea vs. SLA n.s. n.s. 
PNUE vs. SLA n.s. n.s. n.s. 
PEUE vs. SLA n.s. 
WUE vs. SLA n.s. n.s. n.s. 
δ13C vs. SLA n.s. n.s. n.s. 
RGR vs. Nmass n.s. n.s. n.s. 
RGR vs. Narea n.s. n.s. n.s. 
RGR vs. CCmass n.s. n.s. 
RGR vs. CCarea n.s. n.s. 
RGR vs. PNUE n.s. n.s. n.s. 
RGR vs. PEUE n.s. n.s. 
RGR vs. WUE n.s. n.s. n.s. 
RGR vs. δ13n.s. n.s. n.s. 
RGR vs. SLA n.s. n.s. n.s. 

n.s., non-significant; *, P < 0·05.

See Figs 1 and 2 for equations describing each of the ends and Table 1 for meaning of trait abbreviations.

The trends between RGR and leaf resource traits are given in Fig. 2 and Table 2. Across groups and light regimes, RGR increased significantly with increasing Narea, CCarea and WUE (Fig. 2A, B and D), but decreased with increasing PEUE and SLA (Fig. 2C and F). The RGR relationships with PNUE and Nmass were not clear (i.e. not significant), due to different and often conflicting directions of the trends at each of the two light regimes. Slope values were also homogenous for the two groups for all the RGR and leaf resource traits examined (Fig. 2 and Table 2). However, evidence was found that the intercepts differed significantly between the two groups for RGR relationships with CC (mass- and area-based) and PEUE, such that at a common investment, a higher RGR often resulted for the invaders.

Fig. 2.

The relationships across light regimes between biomass RGR and various leaf resource traits for invasive and native vines, as indicated. Mass-based traits gave weaker trends of the relationships and hence are not presented. Each point is a mean of species value from four to eight plants per light (high and low) treatment. Significant trends (R2 value) are in bold and underlined. See Table 2 for tests of differences between the two groups in slope, intercept and distance along a common slope.

Fig. 2.

The relationships across light regimes between biomass RGR and various leaf resource traits for invasive and native vines, as indicated. Mass-based traits gave weaker trends of the relationships and hence are not presented. Each point is a mean of species value from four to eight plants per light (high and low) treatment. Significant trends (R2 value) are in bold and underlined. See Table 2 for tests of differences between the two groups in slope, intercept and distance along a common slope.

In summary, of the 17 pair relationships between SLA or RGR vs. leaf resource traits examined there was no evidence of slope heterogeneity between invasive and native groups (Table 2). However, there appeared to be some incidence of differences in intercept and/or in distance along a common slope value, mainly with CC or its derivative of PEUE.

Ordination of species

Data on leaf physico-chemical and resource traits were used in multivariate analyses. Ordination of the species based on all traits (except RGR and total biomass, see Table 1) indicated that the first three axes explained close to 90 % of variation in the data set. A plot of species positions on ordination space of axes I and II is shown in Fig. 3. Axis 1, which explained 65·3 % of the variation in the data set, was influenced in decreasing order by CC (mass- and area-based), HC, ash, SLA and PEUE and, to a limited extent, by Nmass. This axis correlated significantly (P < 0·05; n ≥ 14) with RGR (r = 0·74), and especially more so for invasive than native species (Fig. 4). The second axis (with 17 % capture of data variation) was a mixed bag of traits (N, C : N ratio, Amax, PNUE) but was not significantly linked to RGR (P > 0·05). The third axis (with 8 % capture of data variation) was a physiological axis of AQE, Amax and Narea and was also not correlated with RGR. Separate analyses for each group indicated that the vector loading (as % variance) of axis I (which correlated significantly with RGR) was higher for the invasive species (60·7 %) compared with the natives (43·4 %), again confirming greater trait co-ordination in the invasives.

Fig. 3.

Ordination of invasive and native woody vine species grown at two light regimes and based on 16 leaf eco-physiological and chemical traits. The 16 leaf traits are listed in Table 1. The directions and magnitudes of traits that load significantly and hence separate the invasive from the native group are indicated as well as the proportion of variation captured by each axis. The asterisk ‘*’ denotes the low light regime.

Fig. 3.

Ordination of invasive and native woody vine species grown at two light regimes and based on 16 leaf eco-physiological and chemical traits. The 16 leaf traits are listed in Table 1. The directions and magnitudes of traits that load significantly and hence separate the invasive from the native group are indicated as well as the proportion of variation captured by each axis. The asterisk ‘*’ denotes the low light regime.

Fig. 4.

Plot of the axis I score from PCA against RGR for invasive and native species, as indicated. Each trend is significant at P ≤ 0·05.

Fig. 4.

Plot of the axis I score from PCA against RGR for invasive and native species, as indicated. Each trend is significant at P ≤ 0·05.

DISCUSSION

Plant adaptive and fitness traits such as growth, defence or reproduction are expected to be influenced by synthesis costs and resource availability, to the extent that lower CC as found in this study for the invasive group has been associated with plants with higher RGR (Daehler, 2003; Pyšek and Richardson, 2007; Kleunen et al., 2010). Overall, no difference in RGR could be detected between the two groups (see Table 1). Probable reasons for the lack of a difference in RGR between exotic invasive and native species have been fully discussed in Osunkoya et al. (2010), including (a) the fact that some of the native species chosen are common, widespread components of the local flora (especially Pandorea jasminoides and Parsonsia straminea) with invasive potential elsewhere and thus may have RGRs that overlap with those of the invasive exotic species investigated, and (b) RGR is known to decrease with age, being maximal when seedlings are young and then to decrease over time (Hunt, 1982). Nonetheless, the SMA regression analyses indicated that at a common CC, a higher intercept (i.e. higher RGR) value was obtained for the invasive group relative to the native group (Fig. 2 and Table 2). This suggests that the invasives utilize essential growth resources more efficiently than their native counterparts by investing less energy per unit of RGR or leaf structure (SLA). This observed trend is increasingly being reported in invasive plant biology work (Baruch and Goldstein, 1999; Grotkopp and Rejmánek, 2007; Song et al., 2007; Feng et al., 2008).

The cost of constructing leaves in the invasive group was lower and, consequently, one can expect leaf payback time to occur rapidly (in terms of revenue stream through higher carbon fixation potential per investment) and leaf longevity to be shorter (Wright et al., 2004; Poorter et al., 2006; Osunkoya et al., 2008) compared with the natives. Indeed, a shorter duration of leaf retention was observed in members of the invasive group investigated in the course of the experiment, though the trend was not documented. No doubt rapid replacement of older leaves by new ones photosynthesizing at a greater rate will be an adaptive advantage for the invasive over the native species. The invasive species have lower CCmass despite higher Nmass, though N is used in the derivation of the CC currency (Table 1). The relatively higher mass-based leaf N but low mass-based leaf CC of the invasive group (especially Anredera cordifolia) suggests that this group has a low energetic expense per unit N, thus contributing to its greater photosynthetic capacity at minimal costs.

Contrary to one of our hypotheses, a difference in instantaneous PNUE between the two groups, expressed both on a mass and an area basis, was not detected. This is in contrast with most studies (Baruch and Goldstein, 1999; McDowell, 2002; Funk and Vitousek, 2007; Feng et al., 2008), but similar to findings reported by Feng et al. (2007) and Leishman et al. (2007, 2010). Thus, though there was a moderate and significant increase in Amax of invasive compared with native species (approx. 20 %; see Table 1), it seems a higher N content in the invasive group more than offset any expected higher PNUE. Taken in isolation, this observation for the investigated invasive vines will make them no more efficient in N use than the natives. On the other hand, the lower resource need (CC) and shorter leaf life span when combined with the instantaneous PNUE may still lead to a higher long-term PNUE for the invasive group (Nagel and Griffin, 2004), thus contributing to the field-observed runaway success of this group.

Higher N content in leaves of the invasives is in line with the group's preference for disturbed habitats commonly associated with high pulses of nutrient input (Funk et al., 2008; Vasquez et al., 2008). A significantly lower leaf C : N in the invasive group (Table 1) could indicate a reduced level of herbivory in their novel environment (Nagel and Griffin, 2001; Pyšek and Richardson, 2007; Feng et al., 2008), the consequence of which is low CC as a result of reduced need for structural and/or defence carbon compounds, such as cellulose and lignin which have higher energetic costs (Osunkoya et al., 2008). In fact, Onoda et al. (2004) and Feng et al. (2008) have shown that allocation of large proportions of N to structural cell wall toughness and chemical defence may be selected for more strongly in the native range where consumer pressure is intense, but this allocation strategy may be selected against in the absence of strong consumer selective pressure in the introduced range. Thus it would be interesting to explore the C : N composition of the tested invasive vine species in their native range compared with values obtained in the present study.

Across light availabilities, a greater number and higher level of trait–pair correlations were found in the invasive group relative to the natives. This pattern was also supported by the results of the multivariate PCA in which axis I for the invasive group data captured more of the dichotomy as well as correlated more with RGR than did the same axis for the native group data. Though correlation does not necessarily imply causation, traits within and between traits categories are frequently causally related or associated though trade-offs, especially if they contribute to a common adaptive function (Shipley et al., 2000; Grotkopp et al., 2002; Westoby et al., 2002). Thus a greater trait correlation in the invasives may favour a better, more mechanistic exploitation and adaptation to the changing environment as resources that are captured are tightly coupled and are efficiently utilized in some major dimensions (e.g. CC, SLA and PEUE in this work). This may allow excess resources captured to be diverted to major fitness components of carbon balance, reproduction or biomass growth (see Shipley et al., 2000; Reich et al., 2003: Westoby and Wright, 2006).

Management implications

Amongst the 16 leaf physico-chemical traits measured, including six resource requirements, capture and use (Amax, AQE, PNUE, SLA, WUE, CC), through summarizing data using PCA, consistent and diagnostic differences were found between the invasive and native groups in five (SLA, CC, HC, ash, PEUE; Fig. 3). Only axis I of the PCA which captured most of the variation in these traits correlated significantly with adaptive trait of RGR. This is further evidence that these are indeed syndromes of traits that could serve as determinants of plant invasiveness and is in line with the findings of Pattison et al. (1998), Nagel and Griffin (2001) and Funk et al. (2008). Although many of these measures require expensive technical equipment, leaf SLA and ash which correlate significantly with the above-mentioned diagnostic traits (see Fig. 1), can quickly and easily be measured to screen for plant species with invasive potential and possibly to assist in choice of replacement species in restoration work (see Funk et al., 2008; Osunkoya et al., 2010). The latter will involve selecting native species with traits whose magnitudes and direction are similar to the diagnostic traits of the invaders.

Conclusions

As in previous work (Osunkoya et al., 2010), limited evidence of differences in trait plasticity was found between exotic invasive and native vines at singular and trait-pair (relationship) levels. Slope homogeneity in all of the trait pairs examined also confirmed this observation which is in line with the assertion by Leishman et al. (2010) that the two groups may not have fundamentally different carbon-capture strategies. However, this study took a step further and included tissue CC, a functional trait which summarizes inorganic mineral content, calorific value and N need of a leaf carbon economy, but is rarely investigated in studies of trait–pair relationship across multiple habitats in plant invasion work. At the same resource requirement (e.g. at a common SLA), intercept tests indicated that the invasive species have lower CC and higher energy utilization efficiency (PEUE), partly in line with the hypotheses raised in the Introduction. These test results, coupled with the PCA patterns, are indicative that for a given investment (CC or SLA), the return was likely to be much higher in the invasive group than in their native counterparts. Water usage/conservation strategy as quantified both by instantaneous (WUE) and integrated measures (C isotopic ratio) did not differ between the two groups, a pattern consistent with some previous studies (e.g. Leishman and Thomson, 2005; Funk and Vitousek, 2007) but at variant with others (e.g. Daehler, 2003; McAlpine et al., 2008). The inconsistency in WUE difference between the two groups and across studies indicates limited utility of this measure as a diagnostic trait. Equally, the lack of a significant difference between the groups for PNUE is at odds with many published works, but all these studies are based on different life forms to the one investigated herein. Thus more studies using woody vines and integrating leaf lifespan are needed to clarify further the role of PNUE in invasiveness within this life form.

ACKNOWLEDGEMENTS

The research was funded by the Queensland Land Protection Council and the Queensland government. We thank Mr Chandima Fernando for his assistance in plant harvesting physiological measurements, data input and analyses.

LITERATURE CITED

Baruch
Z
Goldstein
G
Leaf construction cost, nutrient concentration and net CO2 assimilation of native and invasive species in Hawaii
Oecologia
 , 
1999
, vol. 
121
 (pg. 
183
-
192
)
Batianoff
GN
Butler
DW
Impact assessment and analysis of sixty-six priority invasive weeds in south-east Queensland
Plant Protection Quarterly
 , 
2003
, vol. 
18
 (pg. 
11
-
17
)
Daehler
CC
Performance comparisons of co-occurring native and alien invasive plants: implications for conservation and restoration
Annual Review of Ecology, Evolution & Systematics
 , 
2003
, vol. 
34
 (pg. 
183
-
211
)
Drenovsky
RE
Martin
CE
Falasco
MR
James
JJ
Variation in resource acquisition and utilization traits between native and invasive perennial forbs
American Journal of Botany
 , 
2008
, vol. 
95
 (pg. 
681
-
687
)
Falster
DS
Warton
DI
Wright
IJ
(S)MATR; standardized major axis tests and routines
 , 
2003
 
Farquhar
GD
Hubick
KT
Condon
AG
Richards
RA
Rundel
PW
Ehleringer
JR
Nagy
KA
Carbon isotope fractionation and plant water-use efficiency
Stable isotopes in ecological research
 , 
1989
New York, NY
Springer Verlag
(pg. 
21
-
40
)
Feng
YL
Auge
H
Ebeling
SK
Invasive Buddleja davidii allocates more nitrogen to its photosynthetic machinery than five native woody species
Oecologia
 , 
2007
, vol. 
153
 (pg. 
501
-
510
)
Feng
YL
Fu
GL
Zheng
YL
Specific leaf area related to differences in leaf construction cost, photosynthesis, nitrogen allocation, and use efficiency between invasive and non-invasive alien congeners
Planta
 , 
2008
, vol. 
228
 (pg. 
383
-
390
)
Funk
JL
Differences in plasticity between invasive and native plants from a low resource environment
Journal of Ecology
 , 
2008
, vol. 
96
 (pg. 
1162
-
1173
)
Funk
JL
Vitousek
PM
Resource-use efficiency and plant invasion in low-resource systems
Nature
 , 
2007
, vol. 
446
 (pg. 
1079
-
1081
)
Funk
JL
Cleland
EE
Suding
KN
Zavaleta
ES
Restoration through assembly: plant traits and invasion resistance
Trends in Ecology & Evolution
 , 
2008
, vol. 
23
 (pg. 
695
-
703
)
Grotkopp
E
Rejmánek
M
High seedling relative growth rate and specific leaf area are traits of invasive species: phylogenetically independent contrasts of woody angiosperms
American Journal of Botany
 , 
2007
, vol. 
94
 (pg. 
526
-
532
)
Grotkopp
E
Rejmánek
Rost
TL
Toward a causal explanation of plant invasiveness: seedling growth and life-history strategies of 29 pine (Pinus) species
American Naturalist
 , 
2002
, vol. 
159
 (pg. 
396
-
419
)
Harvey
PH
Pagel
MD
The comparative method in evolutionary biology
 , 
1991
Oxford
Oxford University Press
Hulme
PE
Beyond control: wider implications for the management of biological invasions
Journal of Applied Ecology
 , 
2006
, vol. 
43
 (pg. 
835
-
847
)
Hunt
R
Plant growth curves: the functional approach to plant growth analysis.
 , 
1982
London
Edward Arnold
James
J
Drenovsky
RE
A basis for relative growth rate differences between native and invasive forb seedlings
Rangeland Ecology & Management
 , 
2007
, vol. 
60
 (pg. 
395
-
400
)
Kleunen
M
Weber
E
Fischer
M
A meta-analysis of trait differences between invasive and non-invasive plant species
Ecology Letters
 , 
2010
, vol. 
13
 (pg. 
235
-
245
)
Leishman
MR
Thomson
VP
Experimental evidence for the effects of additional water, nutrients and physical disturbance on invasive plants in low fertility Hawkesbury Sandstone soils, Sydney, Australia
Journal of Ecology
 , 
2005
, vol. 
93
 (pg. 
38
-
49
)
Leishman
MR
Haslehurst
T
Ares
A
Baruch
Z
Leaf trait relationships of native and invasive plants: community- and global-scale comparisons
New Phytologist
 , 
2007
, vol. 
176
 (pg. 
635
-
643
)
Leishman
MR
Thomson
VP
Cooke
J
Native and exotic invasive plants have fundamentally similar carbon capture strategies
Journal of Ecology
 , 
2010
, vol. 
98
 (pg. 
28
-
42
)
McAlpine
KG
Jesson
LK
Kubien
DS
Photosynthesis and water-use efficiency: a comparison between invasive (exotic) and non-invasive (native) species
Austral Ecology
 , 
2008
, vol. 
33
 (pg. 
10
-
19
)
McDowell
SCL
Photosynthetic characteristics of invasive and non-invasive species of Rubus (Rosaceae)
American Journal of Botany
 , 
2002
, vol. 
89
 (pg. 
1431
-
1438
)
Nagel
JM
Griffin
KL
Construction cost and invasive potential: comparing Lythum salicaria (Lythaceae) with co-occurring native species along pond banks
American Journal of Botany
 , 
2001
, vol. 
88
 (pg. 
2252
-
2258
)
Nagel
JM
Griffin
KL
Can gas-exchange characteristics help explain the invasive success of Lythum salicaria?
Biological Invasions
 , 
2004
, vol. 
6
 (pg. 
101
-
111
)
Onoda
Y
Hikosaka
K
Hirose
T
Allocation of nitrogen to cell walls decreases photosynthetic nitrogen use efficiency
Functional Ecology
 , 
2004
, vol. 
18
 (pg. 
419
-
425
)
Osunkoya
OO
Bujang
D
Moksin
H
Wimmer
FL
Holige
TM
Leaf properties and the construction cost of common, co-occurring plant species of disturbed heath forest in Borneo
Australian Journal of Botany
 , 
2004
, vol. 
52
 (pg. 
499
-
507
)
Osunkoya
OO
Daud
SD
Wimmer
FL
Longevity, lignin content and construction cost of the assimilatory organs of Nepenthes species
Annals of Botany
 , 
2008
, vol. 
102
 (pg. 
845
-
853
)
Osunkoya
OO
Pyle
K
Scharaschkin
T
Dhileepan
K
What lies beneath? A study on the pattern and abundance of subterranean tuber bank of the invasive liana cat's claw creeper (Macfadyena unguis-cati (Bignoniaceae) (L.) Gentry)
Australian Journal of Botany
 , 
2009
, vol. 
57
 (pg. 
132
-
138
)
Osunkoya
OO
Bayliss
D
Panetta
FD
Vivian-Smith
G
Variation in ecophysiology and carbon economy of invasive and native woody vines of riparian zones in south eastern Queensland
Austral Ecology
 , 
2010
 
in press. doi:10.1111/j.1442-9993.2009.02071.x
Pattison
RR
Goldstein
G
Ares
A
Growth, biomass allocation and photosynthesis of invasive and native Hawaiian rainforest species
Oecologia
 , 
1998
, vol. 
117
 (pg. 
449
-
459
)
Poorter
H
Pepin
S
Rijkers
T
De Jong
Y
Evans
JR
Kőrner
C
Construction costs, chemical composition and payback time of high- and low-irradiance leaves
Journal of Experimental Botany
 , 
2006
, vol. 
57
 (pg. 
355
-
371
)
Poorter
L
Bongers
F
Leaf traits are good predictors of plant performance across 53 rainforest species
Ecology
 , 
2006
, vol. 
87
 (pg. 
1733
-
1743
)
Putz
FE
Mooney
HA
The biology of vines.
 , 
1991
Cambridge
Cambridge University Press
Pyšek
P
Richardson
DM
Nentwig
W
Traits associated with invasiveness in alien plants: where do we stand?
Biological invasions
 , 
2007
Berlin
Springer Verlag
(pg. 
97
-
126
)
Rasband
W
ImageJ.
 , 
2004
 
National Institutes of Health, Bethesda, MD. http://rsb.info.nih.gov/ij/
Richards
CL
Bossdorf
O
Muth
NZ
Gurevitch
J
Pigliucci
M
Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions
Ecology Letters.
 , 
2006
, vol. 
9
 (pg. 
981
-
993
)
Reich
PB
Wright
IJ
Cavender-Bares
J
, et al.  . 
The evolution of plant functional variation: traits, spectra and strategies
International Journal of Plant Science
 , 
2003
, vol. 
164
 (pg. 
S143
-
164
)
Rejmánek
M
Richardson
DM
What attributes make some plant species more invasive?
Ecology
 , 
1996
, vol. 
77
 (pg. 
1655
-
1661
)
Shipley
B
Cause and correlation in biology
 , 
2000
Cambridge
Cambridge University Press
Song
LY
Ni
GY
Chen
BM
Peng
SL
Energetic cost of leaf construction in the invasive weed, Mikania micrantha H.B.K. and its co-occurring species: implications for invasiveness
Botanical Studies
 , 
2007
, vol. 
48
 (pg. 
331
-
338
)
Valladares
F
Sanchez-Gomez
D
Zavala
MA
Quantitative estimation of phenotypic plasticity: bridging the gap between the evolutionary concept and its ecological applications
Journal of Ecology
 , 
2006
, vol. 
94
 (pg. 
1103
-
1116
)
Vasquez
E
Sheley
R
Svejcar
T
Creating invasion resistant soils via nitrogen management
Invasive Plant Science and Management
 , 
2008
, vol. 
1
 (pg. 
304
-
314
)
Warton
DI
Wreight
IJ
Falster
DS
Westoby
M
Bivariate line fitting methods for allometry
Biological Review
 , 
2006
, vol. 
81
 (pg. 
259
-
291
)
Westoby
M
Wright
IJ
Land-plant ecology on the basis of functional traits
Trends in Ecology and Evolution
 , 
2006
, vol. 
21
 (pg. 
261
-
268
)
Westoby
M
Falster
DS
Moles
AT
Vesk
PA
Wright
IJ
Plant ecological strategies: some leading dimensions of variation between species
Annual Review of Ecology and Systematics
 , 
2002
, vol. 
33
 (pg. 
125
-
159
)
Williams
DG
Black
RA
Drought response of a native and introduced Hawaiian grass
Oecologia
 , 
1994
, vol. 
97
 (pg. 
512
-
519
)
Williams
K
Percival
F
Merino
J
Mooney
HA
Estimation of tissue construction cost from heat of combustion and organic nitrogen content
Plant, Cell & Environment
 , 
1987
, vol. 
10
 (pg. 
725
-
734
)
Wright
IJ
Reich
PB
Westoby
M
, et al.  . 
The worldwide leaf economic spectrum
Nature
 , 
2004
, vol. 
428
 (pg. 
821
-
827
)

Author notes

Present address: AECOM –Australia, PO Box 1823, Brisbane, QLD 4064, Australia

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