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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
Seed dispersal distances: a typology based on dispersal modes and plant traits
Pascal Vittoz
1
and Robin Engler
2
1
Faculty of Geosciences and Environment, University of Lausanne, Bâtiment Biophore, CH-1015
Lausanne, Switzerland; e-mail: pascal.vittoz@unil.ch (corresponding author)
2
Department of Ecology and Evolution, University of Lausanne, Bâtiment Biophore, CH-1015
Lausanne, Switzerland; e-mail: robin.engler@unil.ch
Vittoz P. and Engler R. 2007. Seed dispersal distances: a typology based on dispersal modes
and plant traits. Botanica Helvetica, 117 (2), 109–124. DOI: 10.1007/s00035-007-0797-8
Abstract
The ability of plants to disperse seeds may be critical for their survival under the current constraints
of landscape fragmentation and climate change. Seed dispersal distance would therefore be an
important variable to include in species distribution models. Unfortunately, data on dispersal
distances are scarce, and seed dispersion models only exist for some species with particular
dispersal modes. To overcome this lack of knowledge, we propose a simple approach to estimate
seed dispersal distances for a whole regional flora. We reviewed literature about seed dispersal in
temperate regions and compiled data for dispersal distances together with information about the
dispersal mode and plant traits. Based on this information, we identified seven "dispersal types"
with similar dispersal distances. For each type, upper limits for the distance within which 50% and
99% of a species' seeds will disperse were estimated with the 80
th
percentile of the available values.
These distances varied 5000-fold among the seven dispersal types, but generally less than 50-fold
within the types. Thus, our dispersal types represented a large part of the variation in observed
dispersal distances. The attribution of a dispersal type to a particular species only requires
information that is already available in databases for most Central European species, i.e. dispersal
vector (e.g. wind, animals), the precise mode of dispersal (e.g. dyszoochory, epizoochory), and
species traits influencing the efficiency of dispersal (e.g. plant height, typical habitats). This
typology could be extended to other regions and will make it possible to include seed dispersal in
species distribution models.
Key words: Anemochory, anthropochory, autochory, hydrochory, plant migration, zoochory.
Introduction
Plant dispersal has attracted scientists since long ago (Darwin 1859; Schmidt 1918; Ridley
1930; Müller-Schneider 1983) and is particularly relevant with relation to human-driven
environmental changes. For example, the survival of plant metapopulations in fragmented
landscapes strongly depends on their dispersal potential (Fischer et al. 1996; Couvreur et al. 2004;
Soons and Ozinga 2005), and the predicted global warming will require considerable migration
rates for plant species to remain under similar climatic conditions (Malcolm et al. 2002).
Nevertheless, most models attempting to predict future plant distributions did not include dispersal,
considering it as unlimited (Guisan and Theurillat 2000; Thuiller et al. 2005). Even without
constraints on seed dispersal, these models already predict local extinctions, e.g. for isolated
populations in mountains (Guisan and Theurillat 2000; Dirnbock et al. 2003; Thuiller et al. 2005).
The actual extinction rates might be even higher if plant species cannot keep pace with rapid climate
change due to limited dispersal. A more precise assessment of plant species extinction risk thus
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
calls for the incorporation of plant dispersal potential (Pitelka et al. 1997; Davis et al. 1998; Ronce
2001).
Many studies have measured or estimated dispersal distances of plants in the field (Schneider
1935; Stöcklin and Bäumler 1996; Jongejans and Telenius 2001), and several mathematical models
have been developed to estimate these distances (Tackenberg et al. 2003; Mouissie et al. 2005a;
Nathan et al. 2005; Soons and Ozinga 2005). However, all of these studies have considered only a
limited number of species or dispersal vectors. No dispersal distance data exist for a complete
regional flora. Müller-Schneider (1986) reviewed dispersal vectors for the entire flora of
Graubünden (East of Switzerland), but his work includes only few dispersal distances, most of
which stem from anecdotal observations. Likewise, Bonn and Poschlod (1998) and Bonn (2004)
wrote important syntheses on seed dispersal in Central Europe, but dispersal distances were only
provided for a few dispersal vectors, mainly from anecdotal observations. It is thus currently
impossible to conduct an assessment of the extinction risk of plant species under landscape
fragmentation or global warming that would take dispersal into account.
The distance over which plants disperse seeds depends on plant traits as well as environmental
conditions and varies strongly in time and space. This variability can be represented by a dispersal
curve (dispersal kernel), which gives the proportion of seeds reaching a given distance (Mouissie et
al. 2005a). However, it would be highly time consuming, if not impossible, to determine dispersal
kernels for each species of a region. Thus, a simplified approach is needed to estimate dispersal
distances for a whole regional flora. For example, if dispersal curves could be classified into a
limited number of types with similar dispersal distances, and if plant species could be attributed to
these "dispersal types" based on generally available plant traits, it would be possible to estimate
dispersal kernels for all of them.
In this paper, we develop such an approach for the Swiss flora based on an extensive review of
seed dispersal literature. We propose a typology of dispersal curves that can be applied to most
Swiss and Central European plants. This typology could be extended to other regions and could be
used to account for dispersal distances in species distribution models, enabling refined extinction
risk assessments to be made for large numbers of species.
Methods
Plant dispersal is generally achieved through seeds. These can be enclosed in fruits or larger
structures (usually called "diaspores"), but for the sake of simplification, the term “seed” will be
used here as a general denomination.
Data for seed dispersal distances were compiled by reviewing a large proportion of available
literature from Switzerland and other European countries, including monographies (Müller-
Schneider 1983, 1986), reviews and research articles. Swiss species or close relatives were
considered first priority, since our aim was to develop a typology for this region. However, other
species were included when data available for Swiss species were insufficient to assess dispersal
distances for certain dispersal modes (see below). The complete data set (ca. 300 values) is
presented in Appendix 1. Species nomenclature follows Aeschimann et al. (1996) for the Swiss
species.
The data set proved to be very heterogeneous. A small proportion of the distances had been
determined through experiments, detailed field observations of seed or seedling distributions, or
mathematical models. In such cases, it is often possible to calculate a dispersal kernel. However,
most of the available data represent isolated and often anecdotal observations, from which a precise
dispersal kernel cannot be derived. Some of these isolated observations clearly represented long-
distance dispersal events (LDD), i.e. extreme values reached only by a very small minority of seeds.
We therefore classified the data into three categories: (1) mean, mode or median values, (2)
maximum values (99th percentiles of distribution kernels) and (3) values for LDD (clearly above the
potential dispersal of 99% of the seeds). LDD values were excluded from the further analysis of the
data.
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
Our typology of dispersal curves was based on the dispersal modes recognised by Müller-
Schneider (1983). The English translation of Müller-Schneider's German terminology generally
follows Bonn et al. (2000). Müller-Schneider's (1983) classification of dispersal modes is primarily
based on the dispersal vector (wind, water, animals, etc.), with additional subdivisions for the
differing ways in which seeds are released and transported (e.g. on the fur or after ingestion by
animals). Additional subdivisions were made for dispersal modes whose efficiency clearly depends
on supplementary factors: plant height, pappus efficiency and environing vegetation structure for
anemochory, and vector size for zoochory. Of the numerous possible subdivisions, only those
considered most relevant were retained for our classification, as explained in the next section. This
yielded a total of 21 refined dispersal modes (Tab. 1).
Tab. 1. Dispersal distances for seven dispersal types, estimated as the upper limits of the distances within which 50%
and 99% of the seeds of a plant population are dispersed. Note that actual dispersal distances will usually be lower than
those given here (cf. Fig. 1). The dispersal distances were estimated from the 80
th
percentile of the data compiled in Fig.
1 as well as additional qualitative information as explained in the text ('Dispersal modes and evaluation of published
dispersal distances'). The dispersal modes included in each dispersal type are indicated; they are based on dispersal
vectors (categories in parentheses) and plants traits that influence the efficiency of dispersal.
Type Corresponding dispersal modes
50% 99%
1 0.1 1 Blastochory (autochory)
Boleochory (anemochory) for species < 30 cm
Ombrochory (hydrochory)
2 1 5 Ballochory (autochory)
Cystometeorochory (anemochory)
Chamaechory (anemochory) for fruits in grassland
Boleochory (anemochory) for species > 30 cm
3 2 15 Pterometeorochory (anemochory) for herbs
Myrmecochory (zoochory)
Cystometeorochory (anemochory) ferns, Orchidaceae, Pyrolaceae, Orobanchaceae in forest
Trichometeorochory (anemochory) in forest or little efficient plumes
Epizoochory (zoochory) for small mammals
4 40 150 Chamaechory (anemochory) for seeds on snow or dry inflorescence
Pterometeorochory (anemochory) for trees
Dyszoochory (zoochory) for seeds not stocked and dispersed by small animals
5 10 500 Trichometeorochory (anemochory) in openland with efficient plumes
Cystometeorochory (anemochory) ferns, Orchidaceae, Pyrolaceae, Orobanchaceae in openland
6 400 1500 Dyszoochory (zoochory) for seeds stocked by large animals
Endozoochory (zoochory) for seeds eaten by birds and large vertebrates
Epizoochory (zoochory) by large mammals
7 500 5000 Agochory (anthropochory)
Dispersal distances [m]
Each dispersal distance in our data set was attributed to a dispersal mode, which was either the
mode for which the distance had been determined (if mentioned in the original study) or the main
dispersal mode of the species according to Müller-Schneider (1986). For species with more than one
dispersal mode, distances that could not be clearly related to one of the modes were excluded from
further data analysis. Dispersal types were then defined by grouping together dispersal modes with
similar dispersal distances. This was done graphically by plotting the mean and maximal distances
for each dispersal mode and identifying modes for which distances were in the same order of
magnitude (Fig. 1).
Finally, we estimated upper limits of the distances, within which 50% and 99% of the seeds
would disperse, by using the 80
th
percentiles of the available mean, mode or median values and of
the maximum values. Results were rounded to one significant digit to reflect their approximate
nature. Our aim was to provide a conservative estimate of the dispersal constraint experienced by
most species belonging to a dispersal type. Therefore we did not take the average of the published
values (Fig. 1), but rather the 80th percentile of the distribution, as this allowed us to exclude the
most extreme values. In some cases, a comparison of the results with qualitative information from
the literature or with the authors' experience indicated that the available data were not quite
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
representative for a certain dispersal type; values were then adjusted to obtain more realistic
estimates. Such decisions are explained in the next section of the text for the individual dispersal
modes.
Fig. 1. Distribution of the dispersal distances found in literature (Appendix 1) for each dispersal mode,
and subdivision of the data set into seven dispersal types. Diamonds are for mean, median or mode
values, and crosses for 99% or maximum values (without long-distance dispersal). Four retained
maximum values of type 5 are outside of the graph: 1714 m, 2112 m, 2194 m and 3673 m. See Table 1
for definitions of the dispersal modes.
Dispersal modes and evaluation of published dispersal distances
Autochory
Autochorous plants disperse seeds without the help of an external vector. As a result, dispersal is
limited to very short distances.
In blastochory, the stem of the plant grows or crawls on the ground to deposit the seeds as far as
possible from the mother plant (e.g. Cymbalaria muralis, Polygonum aviculare, Veronica
hederifolia; Müller-Schneider 1983). No data were found in the literature, but since the dispersal
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
distance corresponds to the length of the stem, although species-specific, it is mostly very short and
blastochory can hence be classified as type 1 (Tab. 1). This dispersal mode is, however, frequently
completed by another one (Müller-Schneider 1986).
In ballochory, the explosion of the fruit ejects the seeds (ballistichory, ballistic dispersal). This
explosion may be due to the turgescence of tissues (Impatiens sp., Cardamine sp.) or the tension
between cells or different cell layers when the fruit is drying (Viola sp., Vicia sp., Lotus sp.).
Published values are scattered and very variable (maximum 0.89-6.2 m; Fig. 1). Ballochory is
classified in dispersal type 2 (Tab. 1).
Two further dispersal modes are barochory (seeds fall from the plant) and herpochory (seeds
creep on the soil by the movement of organs in a succession of dry and wet conditions). However,
since these strategies are not very efficient and always combined with other dispersal modes
(Müller-Schneider 1986), they were not retained here.
Anemochory
Anemochorous seeds are dispersed by wind, often with the help of specific organs. This
dispersal vector is the most studied as it is easily observable and measurable, at least over short
distances (e.g. Bullock and Clarke 2000; Jongejans and Telenius 2001). Moreover, it relies on
physical processes that can be translated into models (Tackenberg et al. 2003; Nathan et al. 2005;
Soons and Ozinga 2005). Anemochory is subdivided according to the organs used to slow down the
falling of seeds.
An air filled structure lightens small seeds in cystometeorochory (balloon-like). This dispersal
mode is little studied. Maximum calculated distances are below 2 m (Soons and Ozinga 2005), but
extreme values were measured up to 80 m for Calluna vulgaris (Bullock and Clarke 2000). This
mode is certainly less efficient in forests, as wind is weaker, but it seemed useless to subdivide these
already low values and thus cystometeorochory as a whole was attributed to type 2.
The tiny seeds of Orchidaceae, Pyrolaceae and Orobanchaceae also have a low falling velocity
(0.2-0.31 m/s for Orchidaceae; Müller-Schneider 1986). But only a calculated dispersion distance is
available (median 0.95 m and 99-percentile 14.7 m for Cephalanthera damasonium, Soons and
Ozinga 2005). However, because it is thought that very light seeds (<0.05 mg), even without
corresponding adaptation for anemochory, are as efficient in wind as plumed seeds (Bonn and
Poschlod 1998; Greene and Calogeropoulos 2002), we decided to classify these plant families with
trichometeorochory in type 5 in openland but decreased to type 3 for forest species (Tab. 1). Fern
spores can be included in cystometeorochory as well, but no data exist on their dispersal capacity
except a calculated distance of 330 km for Lycopodium sp. based on its very low falling velocity
(1.8 cm/s; Schmidt 1918). This value seems exaggerated and in the absence of a more precise value,
we attributed the ferns to the same types as orchids.
Plumed seeds are more efficient for wind dispersal. In trichometeorochory, seeds are
completed with a hairy structure (e.g. pappus) to reduce falling velocity. These organs have very
variable efficiency, however, with falling velocity varying from 8 cm/s for Epilobium angustifolium
to 165 cm/s for Pulsatilla alpina (Müller-Schneider 1986). With an arbitrary separation at 30 cm/s,
on the basis of our own observations, we distinguished species with less efficient plumes from those
with efficient plumes (long plumes for small seeds). The first group has maximum distances
between 1-15.7 (36) m, corresponding to type 3, and the second mainly between 20 and 179 m (Fig.
1). However, because some species have much higher calculated potentiality (e.g. up to 3600 m for
Typha latifolia; Soons and Ozinga 2005), we retained intermediate values and assigned
trichometeorochory to type 5. Forest species were classified with trichometeorochory for less
efficient plumes (type 3).
In pterometeorochory (or pterochory), seed dispersal is improved through wings. Trees are
frequent in this category, but herbs are present as well, with a generally higher falling velocity.
Because tree seeds are often large and easy to find, many available maximum dispersal distances are
to be classified as LDD (e.g. Müller-Schneider 1983). Reviewed maximum distances ranged mainly
between 80-314 m for trees and 1-12 m for herbs (Fig. 1). Pterometeorochory was thus classified as
dispersal type 3 for herbs and type 4 for trees (Tab. 1).
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
A much less studied dispersal mode is chamaechory, with diaspores rolling on the ground
pushed by the wind. This diaspore can be either a circular-shaped fruit (Colutea arborescens,
Astragalus alpinus), the fruit with calyx (Anthyllis vulneraria) or the complete, dry, inflorescence
(synaptospermie of Eryngium campestre, Carlina acaulis). Chamaechory is especially common and
efficient in steppes where nothing hampers dispersal (Müller-Schneider 1983), but it also occurs in
mountains, with small seeds on snow (e.g. Saxifraga bryoides, S. exarata, Sempervivum
montanum). The only available data are from Greene and Johnson (1997), who observed Betula
alleghaniensis seeds and calculated a possible dispersion of 38 m for spherical 1mg-seeds on snow.
Dispersal is usually restricted because seeds get stuck in irregularities. For chamaechory, we
retained dispersal distance type 2 for fruits in grassland and type 3 for seeds on snow or carried by
dry inflorescences (Tab. 1), but supplementary data would be necessary to get more precise values.
Boleochory (semachory) is another mode used by anemochorous plants. The small seeds
without particular features are spread when the fruit is shaken by wind. At maturity, the stem of
such plants is often rigid but elastic and sways in the wind, acting like a catapult. As animals or
others may shake the capsules as well, some classify this mode independently (semachory; Bonn et
al. 2000). Although small, the seeds are dense and have a high falling velocity (1.2-5 m/s; Müller-
Schneider 1983; Tackenberg 2001). Consequently, Soons and Ozinga (2005) calculated very short
dispersal distances, generally <0.5 m, but without considering the catapult effect. Yet, this effect is
certainly important, as measured distances sometimes exceed 10 m and are always higher than
calculations for the same or close species. Since the catapult effect strongly depends on the stem
size, we distinguished small species (<30 cm) whose seeds rarely go beyond 1 m (type 1) from taller
species (>30 cm), whose seeds may reach up to 3-5 m (type 2).
Hydrochory
Water can disperse seeds in various ways. In wetland plants, seeds are often light enough to
float and move on rivers, lakes or ponds (nautochory of Alisma plantago-aquatica, Carex flava, C.
elata, Iris pseudacorus, Sparganium sp.). Some seeds can float and survive for one year or more
(Müller-Schneider 1983). Similarly, running water may carry many different types of seeds with
heavy rains (bythisochory), sometimes to rivers and down to lowland areas. Bythisochory is
complementary to other dispersal modes and randomly affects many different species dwelling on
slopes. It is through this vector that high mountain species are frequently observed on gravel areas
along rivers (Bill et al. 1999). Although the dispersal distances may be important, we did not
attribute dispersal types to hydrochorous dispersal modes because distances are highly unpredictable
and never documented. Moreover, nautochory is geographically limited and the bythisochory
downslope restricted.
Rain may contribute to disperse seeds through the shock generated by the rain droplets hitting
the fruits (ombrochory). Some species (e.g. Caltha palustris, Veronica serpyllifolia, Prunella
vulgaris, Thlaspi perfoliatum) have developed fruit shapes and elastic fruitstalks in order to use this
energy to eject seeds. Very few measurements are published for ombrochory, but they are all below
or close to 1 m (type 1).
Zoochory
Animals are frequent and efficient vectors of dispersal, either voluntary when foraging or
involuntary when carrying seeds on their fur or in their guts. Even though zoochory has often been
observed and studied, estimating dispersal distances nevertheless remains difficult, as they highly
depend on the disperser's behaviour. Zoochory can be split into four subcategories.
Many seeds are foraged as food by animals, which sometimes hide them as stock for the winter
and forget about them, or lose them during transport (dyszoochory or dysochory). Vectors are
mainly rodents or birds, and the dispersal distance is thus strongly dependent on the vector size.
Small rodents, like voles or mice (Clethrionomys sp., Microtus sp., Apodemus sp.), generally
disperse seeds less than 30 m (Cain et al. 1998; Xiao et al. 2004), and squirrels (Sciurus sp.) a little
farther. In most cases, small birds disperse seeds by chance when feeding, for example when tits or
woodpeckers are looking for a convenient place to break a nut. The rare available data do not
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
exceed 60 m. However, some larger species are more efficient dispersers by hiding fruits for winter
stocks. The most famous examples are the nutcracker (Nucifraga carcyocatactes; Müller-Schneider
1986; Mattes 1992) and the jays (Garrulus glandarius; Müller-Schneider 1949; Kollmann and
Schill 1996). The literature contains different data, but those are unfortunately too often extreme
values (Mattes 1992), and most of the seeds are probably hidden within a few hundred meters. We
thus retained type 4 when the vector of dyszoochory is a small animal and type 6 when seeds are
stocked by a large animal (Tab. 1).
A particular case of dyszoochory is myrmecochory, or dispersal by ants. Generally interested
by the elaiosome, a fatty appendix of the seeds, ants transport the seeds before eating the elaisome
but leaving the rest of seed untouched and still able to germinate. Seeds may be used as building
material for their nest as well, without loosing their germination potential (Müller-Schneider 1963;
Cherix 1981). This dispersal mode has been extensively studied in the world, but only rarely are
distances available for European plants, and they rarely exceed 10 m. Some exceptional
observations nevertheless give values up to 70 m (Müller-Schneider 1983; Bonn and Poschlod
1998), and myrmecochory was hence classified as type 3.
Animals are important dispersal vectors when eating fruit or even the complete plant (Janzen
1984), and seeds go undamaged through their gut (endozoochory). Many authors have studied the
survival of the seeds through vertebrate guts and the importance of this vector (see Janzen 1984;
Pakeman 2001). As the consumer can be anything from a worm to a snail, mammal or bird,
dispersal distance is very dependent on its size and mobility. No data exist for small animals and
they are scarce and mostly anecdotal for larger ones such as birds or foxes. Models based on seed-
retention time is a possibility for getting dispersal distance estimates, but they are still rare (e.g.
Hickey et al. 1999; Vellend et al. 2003), and seed-retention time depends on seed and animal
species (Bonn 2004; Mouissie et al. 2005b). Moreover, these models usually calculate linear
distance, but animals generally live in a limited territory and do not move linearly. We chose type 6
to translate potential dispersal by large mammals or birds (Tab. 1).
Seeds are also frequently transported by animals in fur (epizoochory). This is partly the result
of specific structures, with seeds or fruits bearing hooks or glandulous hairs (Galium aparine,
Arctium sp., Saxifraga tridactylites,...) but seeds without an appendix can attach to fur as well (e.g.
Fischer et al. 1996; Mouissie et al. 2005a; Römermann et al. 2005). Observations in natural
conditions are rare and most of the data are from retention time measurements with sheep, cattle or
dummies (e.g. Fischer et al. 1996; Mouissie et al. 2005a). Although small rodents may disperse
seeds as well (Kiviniemi and Telenius 1998), the most efficient epizoochory is obtained with taller
animals. The maximum distance calculated by models based on seed retention time in fur is
between 435-1242 m, but can be longer with sheep whose long and curled wool is particularly
efficient at retaining seeds (Fischer et al. 1996; Mouissie et al. 2005a). The habitual dispersal
distance is thus estimated as type 6 for epizoochory with large animals, but much longer distances
can occasionally be achieved by sheep during transhumance (Fischer et al. 1996).
Anthropochory
Seed dispersal by humans certainly always occurred, but it strongly increased during the last
centuries, and became particularly important a few decades ago with the market globalisation and
the intercontinental transport of goods (e.g. Hodkinson and Thompson 1997; Tinner and
Schumacher 2004).
Müller-Schneider (1983, 1986) distinguished three modes of anthropochory: plants or seeds
being sold for agriculture and gardening (ethelochory), seeds being involuntarily mixed with the
previous ones (speirochory), or seeds travelling hidden in goods, cars, soil under soles, with hay,
etc. (agochory). All three means can potentially lead to very long dispersal distances and are, for
example, responsible for the advent of neophytes in Switzerland and Europe. But while ethelochory
and speirochory mostly concern urban and cultivated areas, agochory is probably more important in
natural or semi-natural ecosystems. Seed dispersal distance through anthropochory is strongly
dependent on the type of human activity but, in general, agricultural activities are the most
susceptible to spreading seeds in semi-natural ecosystems due to movements between fields or
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
meadows (McCanny and Cavers 1988). We can thus limit most of the dispersal distance to the
approximate size of a farming property (type 7).
Dispersal types and estimated dispersal distances
Despite the heterogeneous origin of the data compiled here, dispersal distances for individual
dispersal modes proved to be rather consistent, mostly belonging to the same order of magnitude.
Across the entire data set, maximal dispersal distances ranged between 0.09 and 6300 m (LDD
excluded), corresponding to a factor of 70'000 between the highest and lowest value. After
classification into dispersal types, this variation was reduced to a factor of 10 for type 1, 40 for type
2, 70 for type 3, 20 for type 4, 1700 for type 5, 200 for type 6, and 1 for type 7 (very few data). This
variability within types may still seem considerable, but it is small compared to the 5000-fold
difference in dispersal distances between types 1 and 7. Furthermore, the high value for type 5
(trichometeorochory with efficient plumes) reflects the high variability of pappus efficiency in this
category and the high variability found within species (e.g. Taraxacum officinale). The typology
presented here thus expresses a large part of the variation in seed dispersal distances. Accordingly,
attributing species to dispersal types makes it possible to describe interspecific variation in dispersal
capacity.
The estimated distances in Table 1 do of course not represent the dispersal kernel of one single
plant, nor even the mean pluri-annual dispersal kernel of a particular plant population. They were
estimated as the upper limits (80
th
percentile) of the dispersal distance values (Fig. 1), meaning that
they represent the dispersal potential of the plant species grouped into a dispersal type. Most plant
populations will disperse over smaller distances than those indicated in Table 1, but data and
models indicate that they could potentially disperse 50% or 99% of their seeds inside the retained
distances. Estimating upper limits to dispersal, rather than average distances, is justified when
dispersal is included as a possible constraint to species survival in predictive models of species
distributions. In this case, upper dispersal limits yield a constraint that holds for all species of a
certain dispersal type. This ensures that dispersal constraints will not be overestimated.
Alternative dispersal modes
Multiple dispersal vectors
About 40% of the species considered by Müller-Schneider (1986) have two or more dispersal
modes. The species can either use them alternatively depending on the available vector (Picea abies
is anemochorous or dyszoochorous with the red squirrel or some birds) or on its phenology (Urtica
dioica is anemochorous and avoided by animals when green but grazed and endozoochorous once
dry), or it can rely on them successively to improve dispersal (Leucojum vernum is firstly
blastochorous and lately myrmecochorous; Müller-Schneider 1983).
If the most obvious dispersal mode can often be inferred from the seed or fruit morphology,
finding out what the alternative dispersal modes of a species are generally requires precise
observations. For example, Campanula rotundifolia and Primula elatior are considered
endozoochorous by Müller-Schneider (1986) but not Campanula scheuchzeri or Primula veris,
which are only described as boleochor species. This difference, probably incorrect, strongly affects
their dispersal potential, as endozoochory is much more efficient than boleochory (Tab. 1), and
shows the gaps in our attainments.
Recent results showed that this problem appears with other dispersal modes too. Tackenberg et
al. (2003) modelled wind dispersion of seeds on the basis of their falling velocity and release height.
They concluded that some species normally not considered as anemochorous could be as efficient as
species traditionally thought-of as wind dispersed. Another example is given by Higgins et al.
(2003), who demonstrated that a 7.8 g Carya glabra nut is able to disperse 647 m if uplifted by
strong winds. Similarly, epizoochory concerns more species than what diaspore morphology
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Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
indicates, and many plumed seeds for anemochory or smooth seeds are transported as well (Fischer
et al. 1996; Couvreur et al. 2004; Mouissie et al. 2005a; Römermann et al. 2005).
When multiple vectors are recognized, it is logical to classify the species into the dispersal
distance type corresponding to the most efficient one (e.g. dyszoochory for Picea abies or
endzoochory for Campanula rotundifolia). But this can not consider the unsuspected supplementary
vectors.
Long-distance dispersal (LDD) and Reid's paradox
The inadequacy between the dispersal potential of plants and their post-glacial recolonisation,
also known as “Reid’s paradox” (Clark et al. 1998), is an issue that has been recognized for a long
time (Reid 1899; Skellam 1951; Cain et al. 1998). Lang (1994) calculated the migration rate of
anemochorous trees through Europe and found per-generation travel distances of 0.5-5 km for Tilia
sp, 1.2-9 km for Abies alba, 10-20 km for Acer sp. or 15-60 km for Pinus sylvestris. This is much
higher than the 200 m considered in table 1 for 99
th
percentile. Similarly, dyszoochorous species
with an estimated potential dispersal of 1 km (Tab. 1) showed post-glacial colonisation rate of 2.2-
15 km per generation for Quercus sp. or 7-14 km for Fagus sylvatica (Lang 1994). However, as was
recently found for Fagus sylvatica (Magri et al. 2006), it is possible that those recolonisation rates
are overestimated because some glacial refugia remain yet unknown (Clark et al. 1998; Stewart and
Lister 2001; Pearson 2006).
Recent data for invasive species show similar high rates of spread for many species. Pyšek and
Hulme (2005) listed 16 species with colonisation superior to 1 km/y for long-distance dispersal,
with a maximum of 167 km yr
-1
. They showed that the rate of spread may be similarly high for
wind, water or animal dispersed plants. But the landscape structure and human activity influence
this spreading, with higher rates found in densely inhabited or particularly economically active
regions (Williamson et al. 2005).
A solution to resolve this discrepancy between estimated dispersal distances and observed
migration rates is to consider that dispersal vectors indicated by seed morphology mainly explain
the short dispersal distances, with the rare events responsible for LDD relying on other vectors
(Cain et al. 1998; Higgins et al. 2003). For example, 78% of the plants that arrived on Surtsey island
(Iceland) were transported by water when only one quarter of those taxa were morphologically
adapted for water dispersal (Higgins et al. 2003). Birds can transport seeds in mud sticking to their
feet (Carlquist 1967), ingest some anemochorous seeds (Wilkinson 1997) or use them to build their
nest (Salix sp. or Clematis vitalba; Müller-Schneider 1983; Dean et al. 1990). Seed plumes or
pappus are not only very efficient for wind dispersal (anemochory), but also for fixing on animal fur
(Fischer et al. 1996; Couvreur et al. 2004). Finally, humans also are efficient involuntary dispersal
vectors nowadays (e.g. Hodkinson and Thompson 1997), but were also vectors during the post-
glacial recolonisation, like for Corylus avellana or agricultural weeds (Braun-Blanquet 1970; Lang
1994; Clark et al. 1998).
Taking into account the influence of LDD on plant migration in a better manner can possibly be
achieved by improving the models fitted on dispersal observations (Kot et al. 1996; Clark et al.
1998; Higgins and Richardson 1999). These improved models would help to propose dispersal
distance values for the remaining 1% of the seeds (Tab. 1). But up to now, the necessary values for
this improvement are missing for most species and dispersal modes, and hence we cannot propose
realistic values for our dispersal distance types. Yet, even though this improvement in LDD
estimation would be achieved, it could explain only a part of LDD, as the randomness of
unconventional dispersal vectors cannot be standardised for all species. The importance of these
accidental dispersions is not known in nature. It may be an important factor for colonising large,
new areas (Higgins et al. 2003) or disturbed areas (Bergelson et al. 1993; Williamson et al. 2005),
but it is certainly less frequent in closed, natural vegetation. Takahashi and Kamitani (2004)
observed the colonisation of native herbaceous species in an artificial pine forest. They found that
the distances dispersed by species using various dispersal vectors were similar to what we proposed
for our dispersal types (Tab. 1), thus indicating that the unconventional dispersal vectors were
certainly not predominant.
10
Vittoz and Engler (2007) Botanica Helvetica 117: 109-124
Conclusions
Although the data compiled in this paper are certainly incomplete, they are the most
comprehensive data set currently available for the Central European flora. Our method for
estimating dispersal distances based on dispersal types is less precise than the calculation of species-
specific dispersal models. On the other hand, our typology can be applied to almost all European
plant species, which is not the case of a species specific model. As discussed above, our typology is
able to represent a large fraction of the interspecies variation in dispersal distances as long as long-
distance dispersal is ignored.
Future research on dispersal mechanisms as well as the inclusion of our estimates in species
distribution models will show whether the use of this typology leads to predicted migration rates
that are close to the observed ones. If differences prove to be important for some of the dispersal
types, our typology could be improved by adjusting the corresponding dispersal distances.
Alternatively, if observed migration rates are consistently underestimated by the use of our
typology, this would suggest that long-distance dispersal is much more important for long-term
plant displacements than the dispersal modes presented here. Our typology is therefore certainly not
the final one, but an important basis for improving predictive models of species distributions.
Résumé
La capacité des plantes à disperser est un facteur important à leur survie dans un paysage
fragmenté ou sous l'influence des changements climatiques. Il est donc important de pouvoir tenir
compte des distances de dispersion dans les modèles de répartition des espèces, mais les valeurs
existantes, mesurées ou calculées, sont rares. Nous proposons donc une approche simple permettant
d'estimer ces distances pour l'ensemble d'une flore régionale. Nous avons recherché dans la
littérature les données disponibles pour la flore des régions tempérées (avant tout pour les espèces
suisses) et associé les distances de dispersion trouvées avec le mode de dispersion et des traits
biologiques. Sept types de dispersion ont pu être identifiés sur la base de ces informations, chaque
type regroupant des espèces avec des distances de dispersion proches. Les distances à l'intérieur
desquelles 50 % et 99 % des graines sont dispersées ont été estimées sur la base du 80
e
percentile
des valeurs disponibles au sein de chaque type. Ces distances varient d'un facteur 5000 entre les sept
types de dispersion, alors que les valeurs à disposition pour chaque type ne dépassent généralement
pas un facteur de 50. Nos types de dispersion conservent donc une large part de la variation
existante dans la dispersion des graines. L'attribution d'une espèce à un type de dispersion ne
nécessite que des informations couramment disponibles, comme le vecteur de dispersion (vent,
animaux, …), le mode précis de la dispersion (dyszoochorie, épizoochorie, …) et des traits
biologiques influençant la dispersion (hauteur de la plante, habitat, …). Cette typologie pourrait être
étendue à d'autres régions et permet d'inclure la dispersion des graines dans les modèles de
répartition des espèces.
This research has been partly supported by the Federal Office for the Environment. We are grateful to Christophe
Randin and Antoine Guisan for useful advice and discussion. We thank S. Güsewell, J. Kollmann and an anonymous
reviewer for their useful comments on an earlier version of the manuscript.
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Appendix
App. 1. Literature data on seed dispersal distances. The examples are mainly from the Swiss flora,
except when data were insufficient for certain dispersal modes. Some supplementary species were
thus added, mostly from temperate regions. Asterisks indicate values that were considered to
represent long-distance dispersal and were therefore excluded from data analysis.
This Appendix can be downloaded freely from http://www.birkhauser.ch/BH, "Electronic
supplementary material".
