ORIGINAL PAPER

Impact of Prunus serotina invasion on understory functionaldiversity in a European temperate forest

Olivier Chabrerie • Jerome Loinard •

Sidonie Perrin • Robert Saguez •

Guillaume Decocq

Received: 21 October 2008 / Accepted: 2 October 2009 / Published online: 21 October 2009

� Springer Science+Business Media B.V. 2009

Abstract We investigated the relationships between

the overstory dominance of Prunus serotina, and the

functional diversity of the understory plant commu-

nities, in a managed European forest. Vegetation,

habitat characteristics and disturbance history were

surveyed in 32 invaded stands vs. 32 paired uninvaded

stands, after a random stratification. Community

specialization and functional diversity indices were

compared and a RLQ analysis was run to link species

traits to environmental variables. The herb layer of

invaded stands exhibited significantly more specialist

species and a lower trait diversity compared to

uninvaded stands, with respect of species richness

and vegetation cover. Light arrival to the forest floor

and soil properties explained most of the variation in

the RLQ analysis, but 20% of the variation strongly

correlated with P. serotina dominance and associated

disturbances. Traits characterizing shade-tolerant,

short-living ruderals and shade-avoiders (vernal geo-

phytes) were significantly associated to invaded

stands, while those associated to light-demanding

graminoids characterized uninvaded stands. The

establishment of functionally close species (seedlings

and saplings of native woody species) tended to be

lower in invaded stands. We concluded that the

invader was becoming the new ecosystem engineer,

first by inducing trait convergence and community

specialization, thus promoting traits that enable spe-

cies to capture resources in the new environment it

was creating, and second by reducing the grain of

local heterogeneities.

Keywords Exotic plant invasions � Life-history

traits � Functional diversity � Disturbance �Forest management � RLQ analysis

Introduction

Although biological invasions are widely accepted as

one of the leading causes of biodiversity loss (Chapin

et al. 2000; Cronk and Fuller 1995; Sax and Gaines

2003), evidence is accumulating suggesting that

often, alien invaders are primarily ‘passengers’ to

disturbance, together with diversity and environmen-

tal changes, and can naturalize with little impact on

the recipient ecosystem (Gurevitch and Padilla 2004;

MacDougall and Turkington 2005; Seabloom et al.

2003). Non-natives would establish and subsequently

spread into the recipient community, not because

they are better competitors than natives, but simply

because they are less dispersal-limited and thus, able

to pre-empt space and resource before natives can

O. Chabrerie � J. Loinard � S. Perrin � R. Saguez �G. Decocq (&)

Dynamiques des Systemes Anthropises (JE 2532),

Universite de Picardie Jules Verne, 1 rue des Louvels,

80037 Amiens Cedex 1, France

e-mail: guillaume.decocq@u-picardie.fr

123

Biol Invasions (2010) 12:1891–1907

DOI 10.1007/s10530-009-9599-9

reestablish (Chabrerie et al. 2008; MacDougall

and Turkington 2005). However, when an invader

becomes durably incorporated into a recipient com-

munity, long-term effects on the diversity and

ecosystem functioning are expected (Strayer et al.

2006). Some ecosystem properties are even likely to

correlate with the invader’s population density, since

any space and resource it controls constitutes space

and resource no longer available to other species

(Cuddington and Hastings 2004; Ortega and Pearson

2005; Parker et al. 1999). Moreover, as an important

component of the community, the invasive species

may change the abiotic characteristics of the ecosys-

tem, by altering biogeochemical cycles, water avail-

ability and soil properties, which in turn will shift the

composition of the resident plant community (Ehren-

feld 2003; Strayer et al. 2006).

The main difficulty encountered when assessing

invasion impacts is that changes can be cumulative

and slow, taking many years to play out, especially in

ecosystems with a low turnover rate such as forests

(Crooks 2005; Strayer et al. 2006). For example, it

may take decades or even centuries to see species

composition or richness changes in a recently invaded

forest. Since functional attributes of diversity relate to

life-history traits that are specifically sorted by the

environment, they may respond more quickly to

invasion-induced environmental changes and inde-

pendently from species richness change (Chapin et al.

2000; Dıaz and Cabido 2001; Lavorel et al. 1997).

Hence, analyzing the relationships between invasion

and the life-history traits of species within the

recipient community, might help detect early vege-

tation responses and/or ecosystem function altera-

tions, not revealed by variations in species richness.

Also, compared to woody layers, the plant commu-

nities in the more species-rich understory are

expected to respond earlier, due to the often shorter

life-spans of the component species (Decocq 2000;

Halpern and Spies 1995). However, any composi-

tional change is likely to vary among habitats,

depending on soil nutrient (Stevens et al. 2004) or

water (Verheyen et al. 2007) content.

Surprisingly, despite the growing interest into

species classifications that relate directly to response

or function based on shared life-history traits

(Lavorel and Garnier 2002; Lavorel et al. 1997;

Stearns 1989; Violle et al. 2007; Weiher et al. 1999),

very few studies have investigated the functional

response of plant communities to invasion (Levine

et al. 2003). Most have done this by using a priori

functional groups, based on taxonomy (grasses,

legumes, other forbs; e.g. Fargione et al. 2003),

and/or target physiological attributes (photosynthetic

pathway, N-fixing; e.g. Baruch and Goldstein 2004).

The most common procedure for linking species

traits to environment is to group species into plant

functional types (PFTs) on the basis of their shared

traits, using clustering algorithms, and subsequently

relate these groups to environmental variables using

ordination techniques (Dıaz and Cabido 1997;

Lavorel and Garnier 2002). However, such tech-

niques can only analyse data matrices two by two,

and thus, indirectly relate species traits to environ-

ment (Legendre et al. 1997). Moreover, as the size of

the species x traits matrices is usually large, it is

sometimes difficult to derive unambiguous groups

(Simberloff and Dayan 1991), and within-groups

differences are neglected (Mason et al. 2003).

Alternatively, multivariate statistical methods have

recently emerged, that can simultaneously treat

several large data matrices, such as the three-table

method of ‘‘RLQ analysis’’ (Doledec et al. 1996;

Ribera et al. 2001), which can directly link species

traits to environmental variables.

In this paper, we investigate the relationships

between an invasive tree species, environmental

factors, understory plant communities, and species

life-history traits. We combine a paired-site sampling

design (invaded vs. uninvaded) along a soil nutrient

gradient to a RLQ analysis to identify plant traits

responding to overstory dominance of the American

black cherry (Prunus serotina Ehrh.), one of the most

important invaders in European temperate forests

(Starfinger 1997). More specifically we tackle the

three following research questions: (1) Does the

dominance of overstories by Prunus serotina affect

the functional diversity of the understory plant

community? (2) Which plant traits are filtered out?

(3) Can we derive mechanisms from the observed

patterns?

In its exotic range, Prunus serotina promptly

invades forest openings to form a low, closed carpet

of small trees, which impedes natural regeneration of

other species (Starfinger 1997; Chabrerie et al. 2007).

Once established in the canopy, it produces numerous

seeds that are bird- and mammal-dispersed; and

following germination, a long-living seedling/sapling

1892 O. Chabrerie et al.

123

bank is established in the understories (Closset-Kopp

et al. 2007). Due to fast decomposition and high

phosphorous content, the litter has soil amelioration

properties (Lorenz et al. 2004, but see Starfinger

et al. 2003), especially on nutrient-poor, acidic soils

(Dassonville et al. 2008). Because Prunus serotina

often becomes the dominant species in the canopy of

invaded forest stands, we hypothesized that it acts

as an ecosystem engineer (sensu Cuddington and

Hastings 2004) by imposing new environmental

filters to understory plant communities and thus

selecting for specific life-history traits of the compo-

nent species. Given the above mentioned character-

istics, this hypothesis makes three specific predictions

about the impacts of Prunus serotina on an under-

story community: (1) due to reduced light arrival at

the forest floor level, shade-adapted traits should be

promoted and shade-intolerant generalist species

displaced; (2) due to soil amelioration properties,

traits associated to nutrient-poor soils should decline;

(3) due to high seed production, available germina-

tion sites should be taken up by the invader, whereas

the establishment of functionally similar species

should be inhibited.

Methods

Study species and site

Prunus serotina is a gap-dependent tree species

native to North America which has been introduced

in France in ca. 1623–1629, and subsequently

planted through European forests for various pur-

poses (Starfinger 1997; Starfinger et al. 2003). Since

at least the early 1960s, it has largely spread

throughout the temperate forests of Western and

Central Europe, particularly on well-drained, nutri-

ent-poor soils, and has been recognized as one of the

most prominent forest invaders (Chabrerie et al.

2007; Starfinger 1997). It is able to overcompete

native species in openings, to form a low, closed

carpet of small trees which impedes natural regen-

eration (Closset-Kopp et al. 2007; Starfinger 1997).

Recent studies have suggested that its strong inva-

siveness in European forests may be explained by the

‘‘enemy release hypothesis’’ (Reinhart et al. 2003)

and/or the ‘‘empty niche hypothesis’’ (Closset-Kopp

et al. 2007). Paradoxically, although P. serotina can

become the dominant species of the canopy in

invaded stands, few changes have been observed in

the species composition and richness of the herb

layer, suggesting that the species would naturalize

with little impact on the recipient community, at

least in the first decades following canopy invasion

(Chabrerie et al. 2008; Godefroid et al. 2005;

Verheyen et al. 2007).

The study was carried out in the temperate, mixed

forest of Compiegne, located in northern France

(49�220N; 2�540E; 32–148 m altitude; 14,417 ha

area). This forest was chosen because it is the most

invaded by Prunus serotina in France and contains a

wide range of habitat conditions. The climate is of

oceanic type, with a mean annual temperature and

annual rainfall of 10.3�C and 677 mm, respectively.

The geological substrate mainly consists of paleo-

geneous sands and cretaceous chalks (ca. 60 and

20% of the total area, respectively). The forest of

Compiegne is characterized by a high diversity of

soil conditions from acidic and nutrient poor soils

(podzols, alisols) to basic and hydromorphic soils

(rendzinic leptosols, regosols, fluvisols). Forest man-

agement currently represents the main disturbance

regime. The study area contains even-aged planta-

tions of three ‘target’ species: the common beech

(Fagus sylvatica), oaks (Quercus robur, Quercus

petraea) and Scots pine (Pinus sylvestris), with a

silvicultural cycle, always starting with a large clear-

cut, lasting for 110, 180, and 100 years respectively.

During this time interval, thinnings are conducted

every 4–10 years. The major source of natural

disturbances are windthrows. For example, in the

past 30 years, two strong storms affected the forest,

in 1984 (364 m3/ha, i.e. 129,075 trunks over 933 ha)

and in 1990 (227 m3/ha, i.e. 134,451 trunks over 1

199 ha). P. serotina was first reported as an invader

by a local forester in 1970, but the oldest tree which

has been observed during field surveys in 2000 was

aged ca. 150 years. In 2004, P. serotina was present

in over ca. 70% of the total forest area, at least as

seedlings. It was the dominant canopy species in

over ca. 20% of this area (i.e. at least half of the total

canopy cover was made of P. serotina at the stand

scale). Few control operations are conducted in

young plantations; in mature stands, shrubby indi-

viduals are usually removed at the same time as the

coppice during thinning operations.

Impact of Prunus serotina invasion 1893

123

Vegetation sampling

We used a random stratified paired sampling strategy.

Stratification of forest stands was done on the basis of

soil type (haplic podzols, ferric and carbic podzols,

alisols, luvisols, cambic umbrisols, cambisols, fluvi-

sols) so as to represent a gradient of soil pH and

nutrient content, dominant target tree species (beech,

oaks or Scots pine), and dominance/absence of

P. serotina. Only closed-canopy forest stands were

included. We randomly selected 32 invaded stands,

where P. serotina had become the dominant canopy

species (i.e. when P. serotina was the highest ranked

tree species in terms of canopy cover; mean cover ±

standard error = 30 ± 5%). Often, P. serotina was

also the dominant species in the shrub (30 ± 3%) and

undershrub (32 ± 4%) layers, even sometimes in the

herb layer (41 ± 5%). We paired each invaded stand,

with a uninvaded stand nearby (no P. serotina in any

vegetation layer) which shared the same soil and

target tree conditions. This paired-site sampling

approach reduced the impact of possibly confounding

environmental variables on the observed differ-

ences in response variables between invaded and

uninvaded stands, while representing the entire range

of P. serotina in the study forest.

In each stand, a temporary 25 9 25 m plot

(625 m2) was marked out attempting to retain uniform

soil type, stand structure, and relief throughout. Total

vegetation cover was estimated for each vegetation

layer: tree layer ‘[6 m’; shrub layer ‘2–6 m’; under-

shrub layer ‘[0.5 m’; and herb layer ‘\0.5 m’.

Vascular plant species of the herb layer (including

seedlings and saplings of woody species) were

recorded in June-July 2004, and their cover-abundance

was scored using Braun-Blanquet (1964)’s scale. A

total of 170 species were recorded in the 64 plots.

Species nomenclature follows Lambinon et al. (2004).

Environmental variables

Forty-seven environmental variables, distributed

among three sets (i.e. habitat, invasion, and distur-

bance), were collected (see Appendix 1 for details):

(1) Habitat variables Within each 625 m2 plot we

recorded: cumulated species cover (excluding

P. serotina) in the different vegetation layers,

relative proportion of the main tree species,

litter thickness (L) and necromass, organic

horizon thickness (OH) and L/OH ratio, number

of Fe3? spots in the topsoil first 15 cm (used as

an indicator of temporary waterlogging). A

composite soil sample was also taken for further

lab analyses, following the recommendations of

Aubert (1978): plant available phosphorus

(P2O5; Olsen method), pHwater, organic matter

(loss on ignition at 850�C for 15 h), total

nitrogen, N–NH4?, N–NO3

- (Kjeldahl method),

organic carbon (Anne’s method), and C/N ratio.

(2) Invasion variables For invaded stands only, we

described the P. serotina population in a

40 9 40 m quadrat which was centred on the

smaller 625 m2 one: canopy height, diameter at

breast height (dbh), and canopy cover of all

individuals (dbh [ 5 cm) were recorded. A

wood core was also taken at breast height from

the three largest individuals per stand to count

tree rings and determine the population age.

(3) Disturbance variables For each stand, informa-

tion about natural (i.e. storm-induced tree falls)

and management-related disturbances were

extracted from the local forest service database

for the last 50 years (1955–2005). The volume

of tree falls (TF) which has been collected after

each storm event or cut down (Cut trees, CT)

was cumulated into 10 year-classes, from 1955

to 2005. Within each 625 m2 plot, we also

recorded number of canopy gaps and stumps,

dead tree and bare soil cover, and wild boar-

disturbed soil area.

Species traits

Following the recommendations of Weiher et al.

(1999), we targeted functions that are associated to

the three fundamental challenges faced by plants, i.e.

dispersal, establishment, and persistence. A set of 15

biological traits was retained (Appendix 2). The

information was extracted from six plant databases:

‘‘Biological traits of vascular plants’’ (Kleyer 1995),

‘‘Electronic comparative plant ecology’’ (Hodgson

et al. 1995), the soil seed bank database of North

West Europe (Thompson et al. 1997), BIOLFLOR

(Klotz et al. 2002), BIOPOP (Poschlod et al. 2003)

and the LEDA Traitbase (Knevel et al. 2003), and

completed with local floras (Lambinon et al. 2004;

1894 O. Chabrerie et al.

123

Provost 1998; Rameau et al. 1989). Traits were

treated as categorical variables; the optimal number

of categories was defined to be ecologically mean-

ingful and have balanced sizes (Lavorel et al. 1998).

Species which appeared in only one releve as well as

those for which less than 50% of plant trait informa-

tion was available were excluded from the analysis.

We finally obtained a 121 species 9 15 traits matrix.

Data analysis

To test our first research question, whether the

functional diversity of understory plant assemblages

differed between invaded and uninvaded stands, we

used two different measures. First, we calculated the

generalist-specialist measure h of Fridley et al. (2007)

for each of the 170 species occurring in the herb layer.

This measure, which is derived from beta diversity

statistics, quantifies the relative niche width using

species co-occurrence data instead of environmental

factors that control plant species distribution. More-

over, no assumption is required regarding niche shape,

distribution and configuration of habitats, or how

species are distributed along gradients. Given equal

plot occurrences, generalist species should co-occur

with many species, whereas specialists should co-

occur with relatively few species. We then derived a

Community Specialization Index (CSI) by averaging

species’ h values for each vegetation releve, with and

without weighting by species abundance, and with

and without deleting rare species (i.e. species occur-

ring in less than 5% of the releves). The higher the CSI

value, the more generalist the species assemblage.

Second, we computed Rao’s quadratic entropy

FDQ, which has been found to surpass all other

available measures of functional diversity, by incor-

porating both the relative abundances of species and a

measure of the pairwise functional differences

between species (Botta-Dukat 2005). It has also the

advantage to be relatively independent from species

richness. For a S-species community characterized by

the relative abundance vector p = (p1, p2,…, pS), the

quadratic entropy is given by

FDQ ¼XS

i¼1

XS

j¼1

dij pi pj

where dij is a distance measure accounting for

functional difference between the i-th and j-th

species. We used Gower’s dissimilarity index as a

measure of dij, which allows to cope simultaneously

with binary, nominal, ordinal and quantitative data

(Gower 1971). The higher the FDQ value, the more

diverse the species traits within the community. This

index was calculated using the 121 species 9 15

traits matrix and the Macro Excel� file from Leps

et al. (2006).

Species richness, number of rare species (i.e.

species occurring in less than 5% of the plots), herb

layer cover, unweighted and weighted CSI, and FDQ

values between invaded and uninvaded stands were

compared using Wilcoxon rank tests for paired

observations and Statview� software. Results are

expressed as means ± standard errors.

Then, to address our second and third research

questions, we wanted to identify which life-history

traits were associated to Prunus serotina invasion and

search for potential mechanisms behind these pat-

terns. Therefore we conducted a RLQ co-inertia

analysis (Doledec et al. 1996) of our three matrices:

the 64 plots 9 47 environmental variables matrix

describing the habitat template (matrix R); the 64

plots 9 121 species matrix, giving the abundance of

the various species in each plot (matrix L); and the

121 species 9 15 life traits matrix, qualifying bio-

logical features of species (matrix Q). As a three-way

ordination method, RLQ analysis allows for the

exploration of the joint structure between the three

data matrices R, L, and Q, by running simultaneously

a Principal Component Analysis (PCA), a Correspon-

dence Analysis (CA), and a Multiple Correspondence

Analysis (MCA), respectively. These three analyses

are combined so as to maximize the covariation

between environmental variables and species traits,

i.e. plot scores in matrix R constrain plot scores in

matrix L, whereas species scores in matrix Q constrain

species scores in matrix L (Doledec et al. 1996; Dray

et al. 2003; Legendre et al. 1997; Ribera et al. 2001).

Therefore, matrix L is used as a link between matrices

R and Q, to directly relate environmental variables to

species traits. The significance of the relationship

between environmental variables (R) and species

traits (Q) was tested with a Monte Carlo permutation

test: the results of 1000 random permutations between

matrices R and Q were compared to the total inertia in

the RLQ analysis, following Doledec et al. (1996).

RLQ analysis and graphs used ADE-4 software

(Thioulouse et al. 1997).

Impact of Prunus serotina invasion 1895

123

Fig. 1 RLQ diagram as defined by the first two axes with

projection of a environmental variables; b plots; dot size is

proportional to P. serotina cover in the overstories, c species;

d plant traits. Environmental variables. HABITAT VARI-

ABLES: Cover of the tree layer (Cov-Tree), shrub layer (Cov-

Shrub), the undershrub layer (Cov-UShrub) and the herb layer

(Cov-Herb), excluding Prunus serotina. Canopy cover value

for beech (Fagus), oaks (Quercus), hornbeam (Carpinus) and

Scots pine (Pinus). Litter thickness (S-L), litter necromass

(S-LM), soil organic horizon thickness (S-OH), L/OH ratio

(S-L/HO), number of Fe3? spots in upper soil horizons (S-Fe),

plant available soil phosphorus content (S-P), soil pH (S-pH),

soil organic matter content (S-OM), soil nitrogen content

(S-N), soil ammonia content (S-NH4), soil nitrate content

(S-NO3), organic carbon content (S-C), C/N ratio (S-C/N).

INVASION VARIABLES: Prunus serotina cover in the tree

layer (Pr-Tree), shrub layer (Pr-Shrub), undershrub layer

(Pr-UShrub), herb layer (Pr-Herb), all layers (Pr-Tot). Prunusserotina basal area (Pr-BA), stem density (Pr-Dens), mean

diameter (Pr-Diam), Mean height (Pr-Height), basal area/stem

density ratio (Pr-Ratio), age of the oldest individual (Pr-Age).

DISTURBANCE VARIABLES: volume of logs collected after

storms in the different decades (TF-1995–2004, TF-1985–

1994, TF-1975–1984, TF-1965–1974, TF-1955–1964). Vol-

ume of logs cut by foresters in the different decades (TC-

1995–2004, TC-1985–1994, TC-1975–1984, TC-1965–1974,

TC-1955–1964). Number of canopy gaps (D-gap). Fallen tree

cover (D-chablis). Bare soil area (D-baregr). Stump number

(D-stump). Wild boar-disturbed area (D-boar). Species.1 = Acer campestre; 2 = Acer platanoides; 3 = Acer pseudo-platanus; 4 = Adoxa moschatellina; 5 = Agrostis capillaris;

6 = Agrostis stolonifera; 7 = Ajuga reptans; 8 = Alliariapetiolata; 9 = Anemone nemorosa; 10 = Anthriscus sylves-tris; 11 = Arum maculatum; 12 = Athyrium filix-femina; 13 =

Betula pendula; 14 = Betula pubescens; 15 = Brachypodiumsylvaticum; 16 = Calystegia sepium; 17 = Cardamine hirsuta;

18 = Carex flacca; 19 = Carex pallescens; 20 =

Carex pendula; 21 = Carex pilulifera; 22 = Carex remota;

23 = Carex sylvatica; 24 = Carpinus betulus; 25 = Circaealutetiana; 26 = Clematis vitalba; 27 = Convallaria maialis;

28 = Cornus sanguinea; 29 = Corylus avellana; 30 = Cra-taegus laevigata; 31 = Crataegus monogyna; 32 = Cytisusscoparius; 33 = Dactylis glomerata; 34 = Deschampsiacespitosa; 35 = Deschampsia flexuosa; 36 = Dryopteris car-thusiana; 37 = Dryopteris dilatata; 38 = Dryopteris filix-mas;

39 = Epilobium hirsutum; 40 = Epilobium montanum; 41 =

Epilobium parviflorum; 42 = Epipactis helleborine; 43 =

Euphorbia amygdaloides; 44 = Euonymus europaeus;

45 = Fagus sylvatica; 46 = Festuca gigantea; 47 = Ranun-culus ficaria; 48 = Fragaria vesca; 49 = Frangula alnus;

50 = Fraxinus excelsior; 51 = Galeopsis tetrahit; 52 = Gali-um aparine; 53 = Galium odoratum; 54 = Geranium rober-tianum; 55 = Geum urbanum; 56 = Glechoma hederacea;

57 = Glyceria declinata; 58 = Hedera helix; 59 = Holcuslanatus; 60 = Holcus mollis; 61 = Hyacinthoides non-scripta;

62 = Hypericum hirsutum; 63 = Ilex aquifolium; 64 = Jun-cus conglomeratus; 65 = Juncus effusus; 66 = Lamium gale-obdolon; 67 = Lapsana communis; 68 = Ligustrum vulgare;

69 = Lonicera periclymenum; 70 = Luzula forsteri; 71 =

Luzula multiflora; 72 = Luzula pilosa; 73 = Malus sylvestris;

74 = Melica uniflora; 75 = Mercurialis perennis; 76 = Mes-pilus germanica; 77 = Milium effusum; 78 = Moehringiatrinervia; 79 = Molinia caerulea; 80 = Mycelis muralis;

81 = Oxalis acetosella; 82 = Picea abies; 83 = Pinus sylves-tris; 84 = Poa annua; 85 = Poa pratensis; 86 = Poa trivialis;

87 = Polygonatum multiflorum; 88 = Polygonum hydropiper;

89 = Potentilla sterilis; 90 = Primula veris; 91 = Prunusavium; 92 = Prunus spinosa; 93 = Pteridium aquilinum;

94 = Quercus robur; 95 = Ranunculus acris; 96 = Ranuncu-lus repens; 97 = Rosa arvensis; 98 = Rubus caesius; 99 =

Rubus fruticosus; 100 = Rubus idaeus; 101 = Rumex crispus;

102 = Rumex obtusifolius; 103 = Rumex sanguineus; 104 =

Sambucus nigra; 105 = Sanicula europaea; 106 = Scrophu-laria nodosa; 107 = Sonchus oleraceus; 108 = Sorbus aucu-paria; 109 = Stachys sylvatica; 110 = Stellaria holostea;

111 = Stellaria media; 112 = Tamus communis; 113 =

Taraxacum officinale; 114 = Teucrium scorodonia; 115 =

Tilia cordata; 116 = Urtica dioica; 117 = Veronica cha-maedrys; 118 = Veronica montana; 119 = Vicia sepium;

120 = Viola reichenbachiana; 121 = Viola riviniana. Planttraits. Life form: LF1: therophyte, LF2: hemicryptophyte, LF3:

geophyte, LF4: chamaephyte, LF5: climbing phanerophyte,

LF6: phanerophyte. Canopy structure: CS1: basal, CS2: semi-

basal, CS3: leafy, CS4: basal or semi-basal but large leaves,

CS5: small leaves, reduced to scales or spines. Canopy height

at maturity: CH1: \ 100 mm, CH2: 101–299 mm, CH3: 300–

999 mm, CH4: 1.0–3.0 m, CH5: 3.1–4.0 m, CH6: 4.1–15.0 m,

CH7: [ 15.0 m. Lateral spread: LS1: of exceedingly limited

extent and duration (mainly therophytes), LS2: perennials with

compact unbranched rhizomes or tussocks \ 100 mm in

diameter, LS3: perennials with rhizomatous systems or

tussocks attaining 100–250 mm, LS4: perennials with diameter

of 251–1000 mm, LS5: perennials attaining a diame-

ter [ 1000 mm. Climbing: C1: climbing using adhesive roots,

tendrils, trailing, coiling up, C2: spread climbing, C3: no

climbing. Leaf anatomy: LA1: succulent/scleromorphic, LA2:

mesomorphic, LA3: hygromorphic, LA4: helomorphic/hydro-

morphic. Leaf phenology: LP1: aestival, LP2: hivernal, LP3:

vernal, LP4: always evergreen, LP5: partially evergreen.

Woodiness: W1: herbs, W2: grasses, W3: woody at base,

W4: woody. Life history: LH1: annual/biennal, LH2: peren-

nial. Type of reproduction: TR1: by seeds, TR2: mostly by

seeds, rarely vegetatively, TR3: by seeds and vegetatively,

TR4: mostly vegetatively, rarely by seeds, TR5: by spores.

Start of flowering period: SF1: January to April, SF2: May,

SF3: June, SF4: July to December. Duration of flowering

period: DF1: 1 month, DF2: 2 months, DF3: 3 months, DF4:

4 months, DF5: 5–12 months. Dispersule weight: DW1: too

small to be measured easily, DW2: B 0.20 mg, DW3:

0.21–0.50 mg, DW4: 0.51–1.00 mg, DW5: 1.01–2.00 mg,

DW6: 2.01–10.00 mg, DW7: [ 10 mg. Dispersal mode:

AD1: endozoochory, AD2: ectozoochory, AD3: myrmecoch-

ory, AD4: hydrochory, AD5: unspecialized, AD6: anemochory.

Seed bank type: SB1: transient, SB2: short-term persistent,

SB3: long-term persistent, SB4: spores

c

1896 O. Chabrerie et al.

123

Results

Functional diversity metrics

Paired comparisons revealed significant differences

in functional diversity between invaded and unin-

vaded stands despite similar values of species rich-

ness (29.6 ± 2.2 vs. 34.5 ± 3.0 species respectively;

Z = -1.019, P = 0.308) and herb layer cover

(65.2 ± 4.3 vs. 59.8 ± 5.7% respectively; Z =

-0.912, P = 0.362). The mean number of rare

species was lower in invaded than in uninvaded

stands (0.8 ± 0.4 vs. 2.6 ± 0.8, respectively; Z =

-2.126, P \ 0.05). Invaded stands exhibited a higher

number of generalist species compared to uninvaded

stands (CSI = 107.3 ± 1.1 and 101.5 ± 1.9 respec-

tively; Z = -2.393, P \ 0.05). However, the reverse

trend was observed when h values were weighted by

species abundances: weighted CSI = 88.6 ± 3.4 vs.

112.4 ± 1.6 (Z = -4.394, P \ 0.0001), indicating

that generalist species were dominant in uninvaded

stands. Excluding rare species from the analysis

did not alter this pattern: CSI = 109.5 ± 0.9 vs.

106.3 ± 1.0 (Z = -2.244, P \ 0.05) and weighted

CSI = 88.5 ± 3.4 vs. 111.8 ± 1.8 (Z = -4.338,

P \ 0.0001) for invaded and uninvaded stands,

respectively. Rao’s quadratic entropy was signifi-

cantly lower in invaded stands than in uninvaded

stands (0.33 ± 0.03 and 0.49 ± 0.02 respectively;

Z = -4.114, P \ 0.0001), revealing a convergence

of species traits.

RLQ analysis

The RLQ analysis revealed a significant association

between environmental variables and species traits

(P = 0.015, i.e. only 1.5% of the simulated values

were superior to the observed total inertia). The first

two axes extracted 78.9% of the total variance

from the matrix that crosses the plot environmental

-1

-1

1

1

2

3

4

5

6

7

8

9

10

11

12

1314

15

16

17

18

19

20

21

22

2324

25

26

27

28

29

30

31

32

33

34

35

363738

39

40

41

4243

4445

46

47

48

49

50

51

52

53

54

55

56

5758

59

60

61

62

63

6465

66

67

68

69

707172

73 74

75

76

77

78

79

80

81

8283

84

8586

87

88

89

90

9192

93

94

95

96

97

98

99

100

101102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120121

-6 -4 -2 2 4

-4

-2

2

4

6

-2 -1 1

-2

-1

1 LF1

LF2

LF3

LF4

LF5

LF6

CS1

CS2CS3

CS4

CS5

CH1

CH2

CH3

CH4

CH5

CH6

CH7

LS1

LS2

LS3

LS4

LS5

C1

C2

C3LA1

LA2

LA3

LA4

LP1

LP2

LP3

LP4

LP5

W1

W2

W3

W4

LH1

LH2TR1

TR2TR3

TR4TR5

SF1SF2

SF3

SF4

DF1

DF2

DF3

DF4

DF5

DW1

DW2

DW3

DW4

DW5

DW6

DW7

AD1AD2

AD3

AD4

AD5

AD6SB1

SB2

SB3

SB4

(a) (b)

-0.3 -0.2 -0.1 0.1

0.1

Pr-Tree

Pr-Shrub

Pr-UShrubPr-Herb

Pr-Tot

Pr-BA

Pr-Dens

Pr-DiamPr-Height

Pr-Ratio

Pr-Age

Cov-Tree

Cov-Shrub

Cov-UShrub

Cov-Herb

Fagus

Quercus

Carpinus

Pinus

S-LS-LM

S-OH

S-L/HO

S-Fe

S-P

S-pH

S-OM

S-N

S-NH4

S-NO3

S-CS-C/N

TF-1995-2004

TF-1985-1994

TF-1975-1984

TF-1965-1974

TF-1955-1964

TC-1995-2004

TC-1985-1994

TC-1975-1984

TC-1965-1974

TC-1955-1964D-gap

D-chablis

D-baregrD-stumpD-boar

(d)(c)

Impact of Prunus serotina invasion 1897

123

characteristics and the species traits. The results were

best summarized by projecting environment variables

(Fig. 1a), plots (Fig. 1b), species (Fig. 1c) and traits

(Fig. 1d) in the RLQ diagram.

Axis 1 (58.9%): the negative part was mainly

explained by soil carbon, litter and organic horizon

thickness, ammonium content (S-C, S-L, S-OH,

S-LM, S-NH4); it corresponded to Scots pine planta-

tions (Pinus) with a discontinued tree layer due to

canopy gaps (D-gap) and fallen trees (D-chablis), but

a high undershrub cover (Cov-UShrub). Species with

negative positions were mainly ferns (Athyrium filix-

femina, Dryopteris carthusiana, D. dilatata, D. filix-

mas, Pteridium aquilinum), with their specific set of

traits (SB4, DW1, CS4, TR5). To a lesser degree,

several climbers, creepers and blackberries (Lonicera

periclymenum, Rosa arvensis, Rubus species) also

characterized this negative part, together with their

related traits (C2, CH4, CH5, LA1, SF4, W3, AD6).

Conversely, the positive part of axis 1 was rather

explained by soil pH (S-pH), deciduous tree stands

(Carpinus, Fagus) with a shrub layer (Cov-Shrub),

and disturbances at the forest floor level (D-baregr,

D-stump). Corresponding traits were vegetative repro-

duction (TR4), water- or ant-dispersed seeds (AD3,

AD4) and a semi-basal canopy (CS2). Species with

the most positive positions are mainly graminoids

(Geranium robertianum, Luzula forsteri, L. multiflora,

L. pilosa, C. pilulifera, Carex remota, Poa annua,

P. pratensis). Hence, axis 1 reflected the increasing

soil nutrient gradient, which was also a gradient of

decreasing light availability in the understories.

Axis 2 (20.0%): this axis strongly separated invaded

(positive scores) from uninvaded (negative scores)

plots (Fig. 1b). It correlated positively with all

variables describing the degree of invasion (e.g. cover

values in the different layers, basal area and stem

density of P. serotina). The negative part was mainly

explained by soil pH (S-pH), soil moisture (S-Fe), tree

cover (Cov-Tree), beech cover (Fagus) and distur-

bance history (all TC and TF variables except

TF-1995–2004). Associated traits were helo/hydro-

morphic (LA4) or scaly (CS5) leaves, short flowering

period (DF1), basal woodiness (W3), mid-sized seeds

(DW4), water- or ant-dispersed diaspores (AD4, AD3)

with long-term persistent seed banks (SB3). Species

associated to those traits were mostly graminoids

typical of compacted, moist soils (Carex remota,

C. pendula, Juncus conglomeratus, J. effusus, Agrostis

stolonifera, Molinia caerulea) and shrub saplings and

blackberries (Cytisus scoparius, Rosa arvensis, Rubus

fruticosus, R. idaeus). The positive part of axis 2 was

characterized by all P. serotina variables (Pr-Diam,

Pr-Height, Pr-Age, etc.), together with soil phospho-

rus content (S-P). Invaded plots were clearly associ-

ated to a set of traits, like a basal canopy (CS1, CH1), a

wintergreen (LP2) or vernal (LP3) leaf phenology,

hygromorphic leaves (LA3), a short longevity (LH1),

no lateral expansion (LS1), therophyte and geophyte

life forms (LF1, LF3), and small seeds (DW3),

unspecialized for dispersal (AD5), with a short-term

seed bank (SB2). Corresponding species were ruderal,

usually short-living species (Alliaria petiolata, Card-

amine hirsuta, Moehringia trinervia, Stellaria media,

Galium aparine, Galeopsis tetrahit, Poa annua,

Geranium robertianum), geophytic, mostly vernal

species (Hyacinthoides non-scripta, Convallaria maj-

alis, Arum maculatum, Adoxa moschatellina, Ranun-

culus ficaria), and, to a lesser degree, several

chamaephytes (Oxalis acetosella, Fragaria vesca,

Anthriscus sylvestris).

It is noteworthy that traits associated with tree/

shrub seedlings and saplings, such as the phanero-

phyte life form (LF6), a high canopy (CH6, CH7),

woody stems (W4), and a transient seed bank (SB1)

were all located towards the centre of the diagram,

indicating that they were shared by species from both

invaded and uninvaded stands. The species diagram

(Fig. 1c) confirms that all woody species tended to

group close to the origin, but in the negative part of

axis 2. This indicates that native species that are

functionally similar to Prunus serotina in terms of

traits that are relevant for establishment, weakly

responded to the invasion gradient.

Discussion

After pairing sites to take into consideration a wide

range of soil and canopy conditions, except those

related to the dominance of P. serotina, the functional

diversity attributes of the herb layer significantly

differed between invaded and uninvaded stands.

Species richness tended to be higher in uninvaded

stands but due to its high variability along the soil

gradient the difference was not significant. In terms of

measures for the proportion of specialist and gener-

alist species within the communities, the average

1898 O. Chabrerie et al.

123

absolute number of specialist species was signifi-

cantly lower in invaded stands, however we observed

the reverse trend with the weighted CSI score, even

when rare species were removed from the calculation.

In the uninvaded stands, regular disturbance to

the community caused by forest management prac-

tices, appears to prevent specialist species becoming

dominant. High values of CSI and FDQ but a low

weighted CSI value, suggests that the uninvaded

communities are relatively ‘neutral’ communities,

where both generalist and specialist species coexist, as

any potential dominance is only short-lived. Con-

versely, above the understories of the rapidly growing

P. serotina a canopy frequently forms in between

managed disturbance events. Under the invader, even

though there was a significantly fewer number of

specialist species, more co-occurrences involved

specialists (high weighted CSI score), indicating that

the understory environment of P. serotina selected for

these particular species and increased their abundance

to the detriment of the more numerous generalists.

Consistent with this, we also found a lower aver-

age value of the functional diversity measure FDQ

within invaded stands, indicating trait convergence

among herb species of the community formed under

P. serotina.

One possible explanation for the functional diver-

sity responses of the community to P. serotina

dominance is the grain of disturbance-induced heter-

ogeneity. In forest stands, heterogeneity results from

various disturbance types acting at different scales,

from large-scale clearcuts and windthrows, through

mid-scale thinnings and tree falls, to small-scale soil

erosion by mammals. These disturbances can gener-

ate many different ecological niches, allowing for the

coexistence of a large number of both specialists and

generalists. However the invading Prunus serotina

forms a lower canopy than most native species and so

in a mixed forest is less affected by natural

windthrows. P. serotina also grows more rapidly

and therefore restores the canopy after a disturbance

event to provide a ‘‘true’’ forest environment. These

species characteristics appear to erase the effects of

large- and mid-scale disturbances. The subsequent

homogenization of the environment benefits the

specialists, whilst generalists are restricted to micro-

habitats, such as wild boar-disturbed areas, which are

themselves part of the new environment created by

the invader (see below).

The RLQ analysis revealed a highly significant

relationship between the species traits and the

environmental characteristics of the forest stands.

The first axis, which accounted for the largest fraction

of the explained variance for the environmental and

trait data sets (ca. 60%), was explained by light

arrival at the forest floor and upper soil horizon

properties. Canopy-dominant tree species correlated

with this first gradient, since they are primarily

planted according to soil conditions. For example,

Scots pines, whose canopies intercept a low fraction

of sun light, are usually restricted to the most

nutrient-poor soils. Hence, the strongest underlying

environmental gradient structuring the characteristics

of the plots and the species occurring in them was

independent from P. serotina invasion. Fern species

and their related traits were clearly associated with

this first axis. This is consistent with earlier studies

(Decocq et al. 2004a; Griffis et al. 2001; Roberts and

Zhu 2002), which have shown that ferns are associ-

ated with low light interception at the canopy level.

For Pteridium aquilinum, which was the most

widespread fern species in our study, this relationship

was found to be particularly strong on nutrient-poor

and acidic soils (Marrs and Watt 2006).

The second RLQ axis, which explained only 20%

of the total inertia, was strongly associated with

P. serotina invasion (positive correlation), but also

with past episodes of disturbance (negative correla-

tion). Uninvaded stands, which were also the most

heavily disturbed in the last decades, were primarily

characterized by graminoids, especially sedges,

rushes and hygrophilous grasses. All are potentially

agestochoric species (sensu Bossuyt et al. 2002, i.e.

species that are dispersed by vehicles used for agri- or

silviculture operations), which have been found to be

good indicators of forest management intensity

(Decocq et al. 2004b). Traits associated with the

negative part of axis 2 may have been filtered out by

soil erosion (e.g. small seeds, persistent seed bank),

soil compaction and poor drainage conditions (e.g.

hydrochory, helo/hydromorphic leaves), which are

local conditions typically created by the use of heavy

forestry vehicles for log extraction (Decocq et al.

2005). Graminoids as a functional group have often

been reported to rapidly colonize wheel tracks and

trampled zones in forest, following harvesting oper-

ations (Decocq et al. 2004a; Naumburg and DeWald

1999; Thomas et al. 1999).

Impact of Prunus serotina invasion 1899

123

Invaded stands were characterized by two sets of

traits. The first type was typically associated to the

therophyte life form (e.g. short life cycle, no lateral

expansion). These were represented by species, such

as Alliaria petiolata, Moehringia trinervia or Card-

amine hirsuta, which are indicators of disturbed,

shaded sites. Their occurrence may be related to

recent small-scale disturbances at the ground level,

such as tree falls and wild boar-disturbed areas (see

Fig. 1a: variables TF-1995–2004 and D-boar, respec-

tively), which were significantly more important in

invaded stands. A recent study has suggested that

P. serotina stands were very attractive for wild boars

(Sus scrofa L.) and that the boars plough the upper soil

horizons to forage for fruits, creating large zones of

bare ground in invaded stands (Chabrerie et al. 2008).

In such a way, the invader alters natural disturbance

regimes (Mack and D’Antonio 1998) and provides

poor-competitors with micro-habitats that are favour-

able to their germination and local self-replacement.

A second set of traits to be positively related to

invaded stands included species that have a basal

canopy, vernal phenology, hygromorphic leaves, and

geophyte life form. These are traits adapted to a true

forest microclimate (shade, high hygrometry, low

thermal variations; Packham et al. 1992; Decocq and

Hermy 2003), and also to seasonal variation in litter

depth (Decocq and Hermy 2003; Sydes and Grime

1981). As a result of invasion, the forest vertical

structure resembles the ‘coppice-with-standards’ type

with dense populations of P. serotina in both tree and

shrub layers. This is strongly contrasting to the high

forest structure observed in uninvaded stands. Hence,

‘coppice-woodland species’, such as Hyacinthoides

non-scripta and Adoxa moschatellina identified by

the RLQ analysis, might be promoted by the new

forest understory environment created by the invader

(Decocq et al. 2004a; Packham et al. 1992). It should

also be noted that most of the species associated with

the invaded stands are characteristic of nutrient-rich

soils, suggesting soil amelioration beneath Prunus

serotina canopies, as expected from the literature

(Dassonville et al. 2008; Lorenz et al. 2004).

Consistently, we found a higher phosphorus topsoil

content and, to a lesser degree, a lower litter depth in

invaded stands (see Fig. 1a: variables S-P and S-L,

respectively). These soil factors can further act as

another filter on the composition of the vegetation

beneath a P. serotina canopy.

Species exhibiting similar functional traits as

Prunus serotina were not clearly sorted by RLQ axis

2, but instead showed intermediate although consis-

tently negative scores. We suspect an unimodal

response of tree and shrub saplings along this

gradient. Towards the most negative part, soil

compaction and the associated graminoid-dominated

community may physically impair seedling estab-

lishment (Donnelly and Shane 1986; Reisinger et al.

1988), irrespectively of their native or exotic status.

At the opposite, positive part, in the most heavily

invaded stands, Prunus serotina may reduce the

establishment rates of native seedlings by competi-

tively excluding them, due to its faster growth rate

(Closset-Kopp et al. 2007) and/or by saturating

germination sites with its own propagules (Brown

and Fridley 2003; Yurkonis et al. 2005).

In conclusion, although P. serotina had become

the dominant canopy tree species in invaded

stands, the environmental variables which most

explained the structure of plant communities found

in the understory was independent of the invasion

itself. Instead, forest landscape planning and silvi-

culture explained most of the variation in the RLQ

analysis, by having an impact on the light availability

and soil properties. The invasion gradient was

negatively correlated with a disturbance gradient

(including natural and management-associated dis-

turbances). This correlation potentially acts as a

confounding factor in interpreting the associated

plant traits. Our results do support our first prediction

showing that shade-adapted traits (e.g. vernal leaf

phenology, hygromorphic leaves, geophyte life form)

were significantly associated to invaded stands, in

which the overstories were very dense compared to

uninvaded stands. In invaded stands generalist spe-

cies characterizing species assemblages of managed

forests were displaced, and trait convergence

occurred as the dominance of specialists increased.

Conversely, we found little support for our second

prediction since no trait typically associated to

nutrient-poor soil (e.g. chamaephyte life form, meso-

morphic leaves) was significantly associated with

RLQ axis 2. The traits typically associated to

nutrient-rich soils (e.g. hygromorphic leaves) are

the same as those expressed by most shade-tolerant

species (e.g. vernal geophytes). Finally, our third

prediction was partially confirmed. Although not

disproportionately affected, the establishment of

1900 O. Chabrerie et al.

123

functionally similar native species (seedlings and

saplings of native tree and shrub species) was lower

in the invaded than in the uninvaded stands. The

study provided an unexpected result, showing that

under a canopy consisting mainly of P. serotina,

short-living ruderals are more likely to be found,

possibly because the invader promotes wild boar-

induced soil erosion. In this way Prunus serotina is

thus becoming the new ecosystem engineer, acting as

an overstory-driven stabilizing force, promoting

community specialization and thus, niche partitioning

(Gravel et al. 2006; Holt 2006; Tilman 2004).

In reference to our research questions, we con-

clude that the dominance of overstories by Prunus

serotina does impact upon the functional diversity of

understory plant communities in invaded forest

stands. Only species exhibiting specific traits that

enable them to capture resources in the new envi-

ronment created by Prunus serotina are increasing in

their abundance, in accordance with the classic trade-

off between species generality and competitive ability

(Chesson and Neuhauser 2002; Rosenzweig 1987).

Beyond this case study, our results demonstrate

that a functional approach of species assemblages

within the biological invasion framework is a suitable

way to detect early responses of the recipient

community (ca. a decade in our study). Especially

in ecosystems with a slow turnover rate, changing

patterns of functional diversity and community

specialization can be recognized much earlier than

composition or richness changes. Moreover, by

explicitly linking species traits to environmental

factors, the RLQ analysis allows for the inference

of processes from the observed patterns.

Acknowledgments We are indebted to the French ‘Office

National des Forets’ for their facilities during field work. We

thank Francois Rabain for his help in data collection, Mark

Bilton for the language revision, and an anonymous referee for

his very helpful comments on an earlier version of this paper.

This study was financially supported by the French ‘Ministere

de l’Ecologie et du Developpement Durable’ (INVABIO II

program, CR n� 09-D/2003).

Appendix 1

See Table 1

Table 1 The 47

environmental variables

used in the study (matrix R)

with their codes

Variables Codes

Habitat variables

Sum of species covers (%) without P. serotina in each vegetation layer

Tree layer Cov-Tree

Shrub layer Cov-Shrub

Under-shrub layer Cov-UShrub

Herb layer Cov-Herb

Tree species cover in canopy (%)

Fagus sylvatica Fagus

Quercus robur Quercus

Carpinus betulus Carpinus

Pinus sylvestris Pinus

Soil properties

Litter (L) thickness (cm) S-L

Litter necromass (kg/m2) S-LM

Organic horizon (OH) thickness (cm) S-OH

L/OH ratio S-L/HO

Number of Fe3? spots S–Fe

Impact of Prunus serotina invasion 1901

123

Table 1 continuedVariables Codes

P2O5 (g/1,000) S–P

pH-H2O S-pH

Organic matter (g/100) S-OM

Total nitrogen (g/1,000) S–N

NH4? (g/1,000) S-NH4

NO3- (g/1,000) S-NO3

Organic C (g/100) S–C

Ratio C/N S–C/N

Invasion variables

Prunus serotina cover in each layer (%)

Tree layer Pr-Tree

Shrub layer Pr-Shrub

Under-shrub layer Pr-UShrub

Herb layer Pr-Herb

All layers Pr-Tot

Basal area (m2/ha) Pr-BA

Stem density (no. tree/ha) Pr-Dens

Mean diameter Pr-Diam

Mean height Pr-Height

Basal area/density ratio Pr-Ratio

Age of the oldest tree Pr-Age

Disturbance variables

Volume of tree fall logs (m3/ha) collected after storms per decade (TF)

1995–2004 TF-1995–2004

1985–1994 TF-1985–1994

1975–1984 TF-1975–1984

1965–1974 TF-1965–1974

1955–1964 TF-1955–1964

Volume of cut logs (m3/ha) extracted per decade (TC)

1995–2004 TC-1995–2004

1985–1994 TC-1985–1994

1975–1984 TC-1975–1984

1965–1974 TC-1965–1974

1955–1964 TC-1955–1964

Number of canopy gaps D-gap

Fallen tree cover (%) D-chablis

Bare soil cover (%) D-baregr

Number of stumps D-stump

Wild boar-disturbed area (%) D-boar

1902 O. Chabrerie et al.

123

Appendix 2

See Table 2

Table 2 The 15 plant traits used in the study (matrix Q) and their categories

Plant trait

variables

Codes Categories Functions

Life form LF1 Therophyte Holding space and time

LF2 Hemicryptophyte

LF3 Geophyte

LF4 Chamaephyte

LF5 Climbing phanerophyte

LF6 Phanerophyte

Canopy structure CS1 Basal Competitive ability, stress

and disturbance

avoidanceCS2 Semi-basal

CS3 Leafy

CS4 Basal or semi-basal but leaves large

CS5 Leaves small, reduced to scales or spines

Canopy height at maturity CH1 Foliage less than 100 mm in height Competitive ability, plant

longevity, carbon storageCH2 101–299 mm

CH3 300–999 mm

CH4 1.0–3.0 m

CH5 3.1–4.0 m

CH6 4.1–15.0 m

CH7 Greater than 15.0 m

Lateral spread LS1 Lateral spread of exceedingly limited extent

Acquiring and duration (mainly therophytes)

Acquiring space

LS2 Perennials with compact unbranched rhizomes or

tussocks \ 100 mm in diameter

LS3 Perennials with rhizomatous systems or tussocks

attaining 100–250 mm in diameter

LS4 Perennials with diameter of 251–1,000 mm

LS5 Perennials attaining a diameter [ 1,000 mm

Climbing C1 Climbing using adhesive roots, tendrils, trailing,

coiling up

Acquiring light, plasticity,

response to disturbance

C2 Spread climbing

C3 No climbing

Leaf anatomy LA1 Succulent and scleromorphic Hygrometry, growth rate,

plasticity, stress

avoidanceLA2 Mesomorphic

LA3 Hygromorphic

LA4 Helomorphic and hydromorphic

Leaf phenology LP1 Aestival Response to disturbance,

stress and disturbance

avoidanceLP2 Hivernal

LP3 Vernal

LP4 Always evergreen

LP5 Partially evergreen

Impact of Prunus serotina invasion 1903

123

Table 2 continued

Plant trait

variables

Codes Categories Functions

Woodiness W1 Herbs Competitive ability, plant

longevity, carbon storageW2 Grasses

W3 Woody at base

W4 Woody

Life history LH1 Annual and biennal Plant longevity,

space holding ability,

disturbanceLH2 Perennial

Type of reproduction TR1 By seed Dispersal distance,

disturbance tolerance,

acquiring space, plant

longevity

TR2 Mostly by seed, rarely vegetatively

TR3 By seed and vegetatively

TR4 Mostly vegetatively, rarely by seed

TR5 Spores

Start of flowering period SF1 January–April Stress and disturbance

avoidanceSF2 May

SF3 June

SF4 July–December

Duration of flowering period DF1 1 month Stress and disturbance

avoidanceDF2 2 months

DF3 3 months

DF4 4 months

DF5 5–12 months

Dispersule weight DW1 Too small to be measured easily Dispersal distance,

propagule longevity,

longevity in seed bank,

establishment success,

fecundity

DW2 Weight less than or equal to 0.20 mg

DW3 0.21–0.50 mg

DW4 0.51–1.00 mg

DW5 1.01–2.00 mg

DW6 2.01–10.00 mg

DW7 Weight greater than 10 mg

Dispersal agent AD1 Endozoochory Dispersal distance

AD2 Ectozoochory

AD3 Myrmecochory

AD4 Hydrochory

AD5 Unspecialized

AD6 Anemochory

Seed bank type SB1 Transient Longevity in seed bank

SB2 Short-term persistent

SB3 Long-term persistent

SB4 Spores

1904 O. Chabrerie et al.

123

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