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- Materials and methods
- Supporting Information
1. The ornamental plant trade, forestry, and agriculture have been responsible for the initial introduction of over 60% of invasive alien plant species. Screening tools to test potentially new horticultural species should help curtail the continued introduction of new invaders.
2. Using two methods for analysing phylogenetically independent contrasts (PICs) of known invasive and non-invasive, exotic woody horticultural species, we tested the potential of relative growth rate (RGR) and related traits including net assimilation rate (NAR), leaf area ratio (LAR), and specific leaf area (SLA) as predictors of invasiveness. These 29 PICs include 65 species and broadly cover angiosperms.
3. Without accounting for phylogeny, no significant differences were found in seedling RGR or related traits between invasive and non-invasive woody species. Using PICs, invasive species’ RGRs were significantly higher. RGR was considerably more significant using our extensive dataset than in previous smaller studies, while SLA and LAR remained marginally significant. NAR was significantly higher for invasives for the 10–20 days interval.
4. Analysis of this broad data set confirms that RGR is significantly higher for invasive woody species than their non-invasive counterparts, and may serve as a useful biological predictor of invasiveness for woody angiosperms. This expanded study shows that plant species use different physiological and biomass allocation patterns to achieve higher RGR; therefore individual components of RGR, such as SLA, do not consistently predict potential invasiveness of species.
5. Synthesis and applications. Comparative seedling RGR studies show that this measure has potential as a screening tool for new exotic plant species. Unfortunately, more easily measurable components of RGR do not consistently predict invasiveness, as previously thought. Using seedling RGR analysis as an invasive species’ screening tool requires growing a species proposed for introduction with related invasive and non-invasive species. If the tested species’ RGR is higher or not significantly different from its known invasive counterpart, it should be considered highly likely to become invasive, and excluded from further consideration as a potential horticultural species. Seedling RGR could potentially produce a useful, straightforward screening tool when phylogenetically related species or cultivars are available.
- Top of page
- Materials and methods
- Supporting Information
Woody plant invaders have been introduced through the ornamental plant trade, forestry, agriculture, and/or as accidental introductions (Pemberton & Liu 2009). Escaped ornamental and other horticultural species account for 52% of naturalised species in Europe (Lambdon et al. 2008), more than half of wildland invasive plants globally, and more than 80% of woody invaders in the USA (Reichard in press).
Horticultural organisations, governmental agencies, and academic institutions recognise the link between the ornamental plant trade and invasion and have introduced a number of voluntary and mandatory regulations to curtail the continued introduction of invasive species (Pheloung, Williams & Halloy 1999; Daehler et al. 2004; Burt et al. 2007). While engagement and outreach with industry members and the public can help decrease the planting of readily available invasive species, screening tools are needed to help prevent further introductions of potential invaders. Biologists have been researching fundamental differences between invasive and non-invasive species since Baker (1965). Biological traits common in invasive species, many of which are also favoured by horticulture and gardeners, include longer flowering and fruiting periods (Lloret et al. 2005; Pyšek & Richardson 2007), faster germination (Schlaepfer et al. 2010), shorter minimum generation times (Rejmánek & Richardson 1996; Widrlechner et al. 2004), higher specific leaf area (SLA) and relative growth rate (RGR; Grotkopp, Rejmánek & Rost 2002; Burns 2004; Hamilton et al. 2005), smaller seeds (Rejmánek & Richardson 1996; Hamilton et al. 2005), greater seed production (Mason et al. 2008), animal dispersal (Widrlechner et al. 2004; Lloret et al. 2005), vegetative growth (Lloret et al. 2005), and self-fertilisation (van Kleunen et al. 2008).
Risk assessment tools have been used to screen potentially invasive species, but they require a large amount of biological and geographical information for each species tested, some of which is difficult to obtain (Pyšek & Richardson 2007). Rejmánek & Richardson (1996) developed successful predictive models for woody plant invasiveness, and their work has been elaborated upon by others (Grotkopp, Rejmánek & Rost 2002; Simberloff, Relva & Nuñez 2002; Jaryan et al. 2007). Minimum generation time, an integral trait in the model, is very difficult to obtain for non-forestry species, making their work inapplicable for general screening purposes. Additionally, complexity of trait interactions (Küster et al. 2008; Bucharova & van Kleunen 2009) and confounding effects such as phylogeny, residence time, and propagule pressure (Pyšek & Richardson 2007; Pemberton & Liu 2009) make invasion predictions difficult. Several new frameworks have been developed to predict species’ potential invasiveness into a particular habitat using biological traits as well as climate information, native range, and plant community traits (Widrlechner et al. 2004; Herron et al. 2007; Moles, Gruber & Bonser 2008).
Among the more promising invasive screening tools are comparative growth rate studies that capitalise on the ability of invasive species to exploit excess resources for growth and reproduction in high resource environments (Burns 2004; Blumenthal 2006; Grotkopp & Rejmánek 2007; Leishman et al. 2007). High-resource environments, often the initial sites of naturalisation and the first front for invasion of new species (Blumenthal 2006), can be simulated in a greenhouse. The RGR of a species determined under such conditions often is comparable in rank to that performed in field studies despite large differences in resources, environmental conditions, and competition (Vile, Shipley & Garnier 2006). Although seedling RGR generally decreases under lower light and/or nutrient levels, woody species’ RGR rankings across different light and nutrient conditions are strongly correlated with those obtained under more optimal conditions (reviewed by Cornelissen, Castro-Díez & Carnelli 1998). Additionally, species’ RGR rankings are consistent over time; seedling RGR is strongly correlated with both RGR of older seedlings as well as G, the long-term growth constant of mature trees (Cornelissen, Castro-Díez & Carnelli 1998). Therefore the results from comparative seedling growth studies reflect, at least in relative terms, the potential growth patterns of species in many natural habitats. Exceptions are those habitats with temperature, light, and/or moisture extremes, and those with strong resident competitors (Rejmánek in press, and references therein).
RGR is a robust tool for understanding life history traits because it integrates a species’ anatomy, physiology, and morphology (Grotkopp, Rejmánek & Rost 2002). RGR can be broken into two components: net assimilation rate (NAR) and leaf area ratio (LAR). Furthermore, LAR can be decomposed into leaf mass ratio (LMR) and SLA (Causton & Venus 1981; Hunt 1982):
These growth traits are influential at the physiological and ecological levels. High SLA is physiologically related to lower leaf construction costs, higher leaf nitrogen levels, higher nitrogen allocation to photosynthesis, and therefore higher photosynthetic nitrogen use efficiency (Meziane & Shipley 2001; Feng, Fu & Zheng 2008). These traits may allow invasive species to exploit resources opportunistically for fast growth and early reproduction (Davis, Grime & Thompson 2000; Blumenthal 2006; Leishman et al. 2007), especially when exotics are released from their natural enemies (Herms & Mattson 1992). Invasive species tend to have smaller seed mass (but see Pyšek & Richardson 2007), which is often correlated with larger seed production per unit canopy per year (Moles & Westoby 2006) and with higher SLA and RGR (Grotkopp, Rejmánek & Rost 2002; Hamilton et al. 2005). Non-invasive species tend to be those with lower SLA (leaves that are more expensive, better defended, and have longer leaf lifespans), lower LAR (more investment into storage and support tissues), and therefore lower RGR (more conservative growth strategies), all strategies well suited for long-term survival under less favourable conditions (Herms & Mattson 1992; Westoby et al. 2002). Understanding these fundamental differences in strategies in terms of the trade-offs in plant growth and biomass allocation patterns can potentially form the basis of successful screening procedures.
Since many traits, including RGR and SLA, are dependent on evolutionary history, differences in these traits between groups of plants often are only apparent when treated as phylogenetically independent contrasts (PICs) and not across species (Saverimuttu & Westoby 1996). PICs account for shared phylogeny by analysing attributes of closely phylogenetically related species that differ in one important trait, in this case invasiveness. Several studies using PICs have found that under optimal conditions and no competition, invasive species have higher RGR, LAR, and/or SLA than their phylogenetically related non-invasive congeners (Grotkopp, Rejmánek & Rost 2002; Burns 2004; Hamilton et al. 2005; Grotkopp & Rejmánek 2007; Feng, Fu & Zheng 2008).
Our goal was to assess the use of RGR and its components as predictors of invasiveness by testing a broad range of phylogenetically related sets of known invasive and non-invasive, horticultural, non-native, woody species. Our species selection avoids the comparison of invasive exotics with natives that may be invasive in other geographic locations (Rejmánek 1999) and reduces the differences in human introduction effort that can confound analyses of invasive and non-invasive species (Pyšek & Richardson 2007). We chose to perform this study under near optimal resources since Cornelissen, Castro-Díez & Carnelli (1998) found that RGR rankings for woody seedlings were similar under higher- and lower-resource levels and Burns (2004) found that differences between invasive and non-invasive species were more apparent at higher nutrient levels. Seedlings grow faster under more optimal conditions, which also better represent our generally resource-rich disturbed habitats. We expand on earlier work (10 intrafamilial contrasts from Grotkopp & Rejmánek 2007) by adding data from 43 species to form a total of 29 PICs that cover a broad spectrum of the angiosperm phylogeny (Table 1).
Table 1. Contrasts, species, year grown, and relative growth rate (RGR) for the 10–20 and 10–30 days intervals. All growth analysis components are means ± SE (standard error). Invasive species within a contrast are in bold
|Contrast||Species||Year||RGR 10–20 days (mg g−1 day−1)||RGR 10–30 days (mg g−1 day−1)|
|Acacia 1||Acacia dealbata Linka||2004||114·1 (13·0)||79·3 (6·6)|
|Acacia pendula A. Cunn ex G. Dona||2004||52·5 (9·5)||80·2 (6·6)|
|Acer||Acer tataricum subsp. ginnala (Maxim.) Wesm.a||2004||98·9 (18·3)||103·4 (7·4)|
|Acer truncatum Bungeaa||2004||128·6 (13·7)||94·7 (5·7)|
|Broom 1||Cytisus scoparius (L.) Linka||2004||85·7 (11·7)||69·5 (6·4)|
|Genista monspessulana (L.) L.A.S. Johnsonb||2004||89·9 (16·1)||65·3 (8·0)|
|Genista aetnensis (Raf. Ex Biv.) DC.b||2004||85·3 (10·3)||76·7 (4·9)|
|Eucalyptus 1||Eucalyptus camaldulensis Dehnh.a||2004||281·4 (14·4)||214·9 (9·2)|
|Eucalyptus leucoxylon F. Muell.a||2004||163·6 (13·4)||142·6 (7·7)|
|Fabaceae 1||Albizia julibrissin Durazz.b||2004||79·7 (11·8)||78·4 (5·2)|
|Ceratonia siliqua L.b||2004||26·0 (6·0)||38·7 (3·3)|
|Fabaceae 2||Robinia pseudoacacia L.b||2004||176·1 (46·9)||101·2 (15·6)|
|Sesbania punicea (Cav.) Benth.a||2004||102·9 (24·7)||93·2 (14·4)|
|Cercis canadensis L.a||2004||107·5 (5·9)||82·3 (3·4)|
|Moraceae||Ficus carica L.b||2004||183·9 (13·0)||154·8 (7·1)|
|Maclura pomifera (Raf.) C.K. Schneid.b||2004||126·9 (7·6)||84·4 (5·4)|
|Oleaceae||Fraxinus velutina Torr.a||2004||120·4 (22·5)||87·8 (10·4)|
|Syringa vulgaris L.a||2004||58·3 (13·9)||51·4 (7·3)|
|Rosaceae||Cotoneaster lacteus W.W. Sm.a||2004||133·3 (6·1)||125·5 (3·1)|
|Photinia serratifolia (Desf.) Kalkmanb||2004||85·9 (14·9)||101·8 (5·8)|
|Rubus||Rubus armeniacus Fockeb||2004||176·4 (17·8)||160·4 (9·9)|
|Rubus idaeus L. a||2004||207·8 (28·9)||186·6 (13·5)|
|Acacia 2||Acacia cyclops A. Cunn. Ex G. Dona||2006||–||56·1 (13·7)|
|Acacia melanoxylon R. Br.a||2006||–||101·2 (11·1)|
|Acacia pendula A. Cunn. Ex G. Dona||2006||–||71·4 (10·3)|
|Acacia 3||Acacia saligna (Labill.) H. L. Wendl.a||2006||97·9 (12·4)||73·0 (6·5)|
|Acacia cultriformis A. Cunn. Ex G. Dona||2006||70·7 (16·8)||74·6 (7·9)|
|Apocynaceae||Nerium oleander L.b||2006||110·5 (9·4)||109·9 (4·3)|
|Thevetia peruviana (Pers.) K. Schum.a||2006||39·8 (8·9)||42·6 (3·7)|
|Broom 2||Genista monspessulana (L.) L.A.S. Johnsonb||2006||95·9 (8·2)||91·7 (3·0)|
|Genista tinctoria L.a||2006||108·2 (15·3)||106·9 (6·2)|
|Broom 3||Retama monosperma (L.) Boiss.a||2006||63·8 (13·9)||61·5 (6·9)|
|Genista aetnensis (Raf. Ex Biv.) DC.b||2006||64·4 (11·3)||61·2 (7·0)|
|Broom 4||Spartium junceum L.a||2006||84·5 (7·7)||58·0 (4·3)|
|Ulex europaeus L.a||2006||88·3 (9·7)||86·4 (5·6)|
|Genista hispanica L.a||2006||85·7 (6·5)||63·9 (4·2)|
|Buddleja||Buddleja davidii Franch.a||2006||214·3 (13·9)||216·1 (11·4)|
|Buddleja globosa Hopea||2006||–||258·7 (24·0)|
|Eucalyptus 2||Eucalyptus camaldulensis Dehnh.a||2006||147·5 (18·1)||126·2 (11·1)|
|Eucalyptus pulverulenta Simsa||2006||138·9 (14·5)||118·7 (7·4)|
|Eucalyptus 3||Eucalyptus cladocalyx F. Muell.a||2006||–||149·0 (13·2)|
|Eucalyptus conferruminata D. Carr & S. Carra,c||2006||101·9 (15·5)||103·3 (8·2)|
|Eucalyptus nicholii Maiden & Blakelya||2006||111·6 (14·9)||106·0 (7·8)|
|Morus||Morus alba L.a||2006||148·7 (9·8)||129·5 (4·4)|
|Morus rubra L.a||2006||115·4 (17·3)||120·7 (5·1)|
|Anacardiaceae||Schinus molle L.a||2007||206·1 (17·6)||167·4 (8·6)|
|Schinus terebinthifolia Raddia||2007||93·4 (16·2)||113·4 (6·8)|
|Searsia lancea (L. f.) F.A. Barkleya||2007||102·7 (22·5)||113·8 (8·7)|
|Berberis||Berberis thunbergii DC.a||2007||125·1 (19·5)||108·5 (10·2)|
|Berberis koreana Palib.a||2007||125·5 (23·7)||74·4 (1·3)|
|Caesalpinia||Caesalpinia gilliesii (Hook.) D. Dietr.a||2007||80·3 (6·3)||68·8 (5·3)|
|Caesalpinia cacalaco Humb. & Bonpl.a||2007||42·6 (16·7)||78·8 (4·1)|
|Eriobotrya||Eriobotrya japonica (Thunb.) Lindl.a||2007||–||98·5 (8·2)|
|Eriobotrya deflexa (Hemsl.) Nakaia||2007||–||62·0 (8·5)|
|Erythrina||Erythrina crista-galli L.a||2007||88·1 (16·3)||71·2 (7·2)|
|Erythrina coralloides DC.a||2007||57·6 (17·7)||55·3 (5·7)|
|Eucalyptus 4||Eucalyptus globulus Labill.a||2007||199·9 (15·1)||159·3 (6·2)|
|Eucalyptus pauciflora Sieber ex Spreng.a||2007||220·6 (50·3)||196·5 (14·8)|
|Eucalyptus 5||Eucalyptus conferruminata D. Carr & S. Carra,c||2007||217·9 (20·3)||150·8 (8·0)|
|Eucalyptus baueriana Schauera||2007||336·7 (28·7)||221·7 (18·5)|
|Eucalyptus macrocarpa Hook.a||2007||136·3 (16·3)||138·5 (11·3)|
|Lavandula||Lavandula stoechas L.a||2007||–||159·3 (9·7)|
|Lavandula angustifolia Mill.a||2007||168·2 (13·6)||149·5 (13·0)|
|Leptospermum||Leptospermum laevigatum (Gaertn.) F. Muell.a||2007||83·6 (28·3)||105·3 (14·0)|
|Leptospermum lanigerum (Sol. ex Aiton) Sm.a||2007||55·3 (45·8)||135·4 (22·8)|
Specifically, we asked the following questions:
Do invasive species have higher seedling RGR, LAR, SLA, and other growth-related variables and/or smaller seed mass, than phylogenetically related non-invasive species?
Are comparative seedling RGR studies useful as invasive species screening tools?
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- Materials and methods
- Supporting Information
Taking a multivariate problem, such as the invasive potential of a plant, and whittling it down to a univariate trait, is a difficult, if not impossible task. We conclude that, overall, RGR is a strong single biological predictor of invasiveness in woody species using PICs. It is unfortunate that some more easily measurable and less labour- and time-intensive attribute, such as SLA, did not emerge as the best trait for separating invasive species from their non-invasive counterparts. This probably reflects the fact that different species or clades have different ways of increasing RGR, whether by increasing NAR or by increasing allocation to leaf area (SLA) or overall leaf mass (LAR). RGR is a good candidate for inclusion in screening procedures because it encompasses many traits related to morphology, physiology, and ecology, but it should not be used on its own for risk assessment purposes.
The use of seedling RGR studies as invasive screening tools still needs to be fine-tuned including standardising greenhouse conditions (including resource levels) and determining the appropriate growth intervals for varying growth forms of species. From our study, it appears that the 10–20 days interval is better suited to woody angiosperms than the 10–30 days interval, although the 10–20 days interval tends to have a higher SE because of its proximity to maximum RGR. Minimally, related species should be within family (Grotkopp & Rejmánek 2007), but for large families such as Fabaceae, they should be within closely related genera. If combined with easily obtainable climatic and geographic information, seedling RGR could produce a useful, straightforward screening tool when phylogenetically related species or cultivars are available.
While the initial escape and naturalisation of a species may occur following disturbance or other increases in nutrients/resources, the survival and further spread of a species in the wild will more likely occur under less than ideal conditions, such as drought. To gain a better understanding of the underlying mechanisms that influence both RGR and the invasive potential of a species, further basic research should include examining how invasive and non-invasive species respond to stresses and increases in resources.
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- Materials and methods
- Supporting Information
Appendix S1. Details of methods, soil and nutrient solution composition, and references used for species selection.
Appendix S2. Contrasts, species, invasive status (based on number of invasive reports; see footnote), year grown, relative growth rate (RGR) for the 10-20 day interval, RGR and net assimilation rate (NAR) for the 10-30 day interval, leaf area ratio (LAR), leaf mass ratio (LMR), and specific leaf area (SLA) at 30 days after emergence, and mean seed mass. All growth analysis components are means +/- SE (standard error). Invasive species are in bold.
Appendix S3. Phylogeny of species analysed using the program CAIC (Purvis & Rambaut 1995). Branch lengths are set as equal. Relative invasiveness of species within a contrast are designated as “+” (more invasive) and “–” (less invasive). This phylogeny is based on the most recent angiosperm phylogeny (APG III 2009) with further resolution from the Tree of Life website (Maddison & Schulz 2007 and pages therein).
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