Long-Distance Seed Dispersal

Chapter 6: Long-Distance Seed Dispersal

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Frank M. Schurr1,2*, Orr Spiegel3, Ofer Steinitz3, Ana Trakhtenbrot3, Asaf Tsoar3 and Ran

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Nathan3

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Institute of Biochemistry and Biology

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University of Potsdam

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Maulbeerallee 2

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14469 Potsdam, Germany

Plant Ecology and Conservation Biology

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Institut des Sciences de l’Evolution de Montpellier, UMR-CNRS 5554

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Université Montpellier II

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34095 Montpellier cedex 5, France

Equipe Génétique et Environnement

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Department of Evolution, Systematics and Ecology

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Alexander Silberman Institute for Life Sciences

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The Hebrew University of Jerusalem

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Edmond J. Safra Campus at Givat Ram

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Jerusalem 91904, Israel

The Movement Ecology Laboratory

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*Corresponding author: Frank Schurr ([email protected])

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Introduction

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In most plant species, the vast majority of seeds is dispersed over short distances that rarely

26

exceed a few dozen metres, and only very few seeds travel over long distances (Willson

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1993). While the long-distance dispersal of seeds (LDD) is thus typically rare, it has

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disproportionately large effects on the long-term and large-scale dynamics of plants.

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In this chapter, we first highlight the importance of LDD for various aspects of plant biology,

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discuss problems with quantifying LDD and advocate a new framework for LDD research

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that may help to overcome some of these problems. We then present six generalizations about

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LDD mechanisms that can be derived using this framework (Nathan et al. submitted), and

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address two fundamental questions about LDD: can we identify all relevant LDD vectors and

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can plant traits influence LDD? We end by discussing the implications of the framework and

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the generalizations for our understanding of LDD evolution and our ability to forecast the

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large-scale dynamics of plants. An overview of the key terms relevant for the study of LDD is

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given in Table 6.1.

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The importance of long-distance seed dispersal

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Because most (terrestrial) plants are mobile only as seeds, the spatial dynamics of plants is

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largely driven by seed dispersal (Nathan & Muller-Landau 2000). Short-distance seed

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dispersal shapes the local dynamics of plant populations and communities and provides a

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template for subsequent local processes such as predation, competition and mating (Nathan &

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Muller-Landau 2000). The long-distance dispersal of seeds (LDD), on the contrary, is of

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primary importance for the large-scale dynamics of plant species and communities. For a

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plant species to persist in a fragmented landscape, local extinction from occupied habitat

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fragments has to be balanced by the colonization of unoccupied fragments, which requires

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LDD to these fragments (e.g. Higgins & Cain 2002, Levin et al. 2003). LDD is also critical

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for the rates at which transgenes spread in plants that are perennial and/or form feral

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populations (Williams & Davis 2005, Kuparinen & Schurr 2007), for the velocity at which

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plant species can migrate in response to climate change (Clark et al. 1998, Higgins et al.

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2003a), and for the speed at which invasive plants expand their range (Hastings et al. 2005).

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Due to its importance for range expansion, LDD is an important determinant of

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biogeographical and macroevolutionary dynamics, a fact that was already recognized by

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Darwin (1859) and has recently received renewed attention (Munoz et al. 2004, de Queiroz

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2005, Alsos et al. 2007). Moreover, LDD is not only important for the dynamics of single

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species, it also shapes community structure by determining migration rates between local

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communities and the regional species pool (or metacommunity) (Hubbell 2001, Levine &

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Murrell 2003). LDD is thus crucial both for fundamental ecological, biogeographical and

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evolutionary processes, as well as for applied questions of biodiversity conservation under

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environmental change (Levin et al. 2003, Levine & Murell 2003, Trakhtenbrot et al. 2005,

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Nathan 2006).

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Defining long-distance seed dispersal

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Two approaches are commonly used to define LDD (Cain et al. 2000, Nathan 2005, Figure

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6.1). The absolute definition considers LDD events as dispersal events that are longer than a

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specified threshold distance (e.g., 1 km). According to the alternative proportional definition,

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LDD events are those that exceed a certain high quantile of dispersal distance (e.g. the 99%

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quantile as the distance exceeded by only 1% of all seeds). It is important to notice that the

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proportional definition actually identifies extreme dispersal events rather than LDD events: in

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a species with highly restricted seed dispersal, the 99% quantile of dispersal distance might

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only be located at a few metres, a distance that typically will not be considered as "long"

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(Figure 6.1a). On the other hand, in a species whose seeds are frequently dispersed over long

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distances, only the most extreme dispersal events will meet the proportional definition, even

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though somewhat less extreme dispersal distances might also be long enough to affect the

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large-scale dynamics of this species (Figure 6.1b). For studies interested in LDD rather than

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extreme dispersal, we thus recommend applying the absolute definition. The threshold

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distance for the absolute definition should be chosen based on the question studied: when

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studying the dynamics of a plant species in a fragmented landscape such a LDD threshold

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could be the typical distance between neighbouring habitat fragments. For certain questions

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(e.g. to predict the spread of populations, Clark et al. 2001), it can be important to quantify

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not only the proportion of dispersal events exceeding this threshold, but to also report the

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frequency distribution of dispersal distances above the threshold. Because this review

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encompasses LDD processes operating at different scales, ranging from landscape (102-104

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m), to regional (103-105 m) and biogeographical (105-107 m) scales, a single all-encompassing

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absolute threshold for LDD cannot be defined. Instead, we identify the range of spatial scales

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over which each LDD mechanism highlighted in this review typically operates (see below).

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The challenge of studying long-distance seed dispersal

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The importance of LDD for the spatial dynamics of plants has long been recognized (e.g.

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Darwin 1859). Nevertheless, LDD research has long been based on the compilation of

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anecdotal observations and has lacked a rigorous theoretical background. LDD has also rarely

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been examined experimentally, and there are very few attempts at quantitatively synthesizing

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observational data on LDD. This paucity in quantitative LDD research can be explained

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through the concurrence of several circumstances: First, direct empirical observations of LDD

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are difficult if not impossible due to the severe methodological problems encountered both in

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tracking seeds during LDD and in finding those seeds that have been long-distance dispersed

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(Cain et al 2000, Nathan et al. 2003). Second, even if LDD events can be observed, the fact

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that these events are typically rare, severely limits our ability to empirically estimate their

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probability of occurrence (and other properties). Third, two different approaches are

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commonly used to define LDD (see section "Defining long-distance seed dispersal" above),

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meaning that there is no general currency to compare different studies. Fourth, the way in

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which seed dispersal has been studied traditionally makes it very difficult to detect and

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quantify LDD mechanisms. In our opinion, a detailed examination of this fourth point is the

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basis for the formulation of a new quantitative framework for LDD research. In the following

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section we thus describe the traditional approaches to dispersal research and examine why

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these approaches fail for LDD.

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The traditional framework of seed dispersal research

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The traditional framework of seed dispersal research can be described as "seed-centred". It

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focuses on a set of seeds (e.g. all seeds produced by a mother plant) and asks by which

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mechanisms and over which distances these seeds are dispersed. This seed-centred framework

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typically assumes that a plant species has a "standard" dispersal vector which disperses most

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seeds and whose identity can be inferred from specific morphological attributes of the

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dispersal unit (e.g. the fact that seeds are equipped with an elaiosome suggests that ants are

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their standard dispersal vector) (van der Pijl 1982, Higgins et al. 2003c). Because seed

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morphology often seems to suggest a particular dispersal vector, the concept of haplochory,

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dispersal mediated by a single (standard) dispersal vector, has long dominated plant dispersal

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research (van der Pijl 1982). To describe this notion of a strong association between a plant

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species and its standard dispersal vector, Berg (1983) coined the term 'specialized dispersal'.

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If haplochory and specialized dispersal prevail, a plant species' potential for LDD should be

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predictable from the identity of its standard dispersal vector. For example, it has commonly

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been assumed that LDD requires morphological adaptations for seed dispersal by birds, water

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or wind, but is extremely unlikely for species exhibiting morphological adaptations for seed

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dispersal by ants, ballistic dispersal (explosive discharge of seeds) or for species lacking any

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obvious dispersal adaptation (Ridley 1930, van der Pijl 1982, Berg 1983).

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While the seed-centred framework with its focus on standard dispersal vectors might produce

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reliable results for short-distance dispersal (which depends on the majority of dispersal

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events), it frequently fails for LDD (Box 6.1, Figure 6.2). The case of the woodland herb

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Asarum canadense provides a good example for such failure (Cain et al. 1998). A. canadense

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has been classified as ant-dispersed because its seeds bear elaiosomes. However, empirically

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parameterized models for range expansion by ant dispersal suggest that since the last glacial

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maximum A. canadense should only have spread by 10-11 km, whereas the species actually

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covered hundreds of kilometres during this time (ca. 16000 years). This suggests that

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occasional LDD by vectors other than ants dominated the postglacial expansion of this

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species.

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The application of the seed-centred framework to LDD is thus problematic because the

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vectors that transport seeds over long distances are typically not those which disperse the

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majority of seeds (Higgins et al. 2003c, Box 6.1). Consequently, a research approach focusing

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on the majority of seeds is likely to miss those seeds that are long-distance dispersed.

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[Insert Box 6.1 here]

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A vector-centred framework for research on long-distance seed dispersal

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Recent years have seen a shift from the traditional seed-centred framework to a vector-centred

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framework for LDD research (e.g. Nathan et al. 2002, Munoz et al. 2004, Levey et al. 2005,

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Jordano et al. 2007). This vector-centred framework focuses on a dispersal vector capable of

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generating LDD and asks how many seeds this vector disperses over which distance. This

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permits to concentrate research efforts on those processes that cause LDD rather than having

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to study a large number of seeds of which at best a few will be long-distance dispersed.

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The vector-centred approach is by no means new: as many other branches of ecology, this

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field was pioneered by Charles Darwin who conducted quantitative experiments on seed

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dispersal by ocean currents (Darwin 1859). More recently, Berg (1983) compellingly

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suggested to shift the focus of dispersal research to polychory (dispersal by multiple vectors)

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and generalized dispersal by nonstandard vectors. Berg's principles, which correspond well

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with recent findings, emphasize that seeds of a given species are usually dispersed by multiple

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vectors (Ozinga et al. 2004, Nathan 2007), including LDD vectors that tend to be different

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from the standard vector which disperses the vast majority of seeds at short distances (Higgins

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et al. 2003c, Box 6.1, Figure 6.2). For example, seeds with morphological adaptations for

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dispersal by ants can be long-distance dispersed by deer (Odocoileus virginianus) (Vellend et

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al. 2003) and emus (Dromaius novaehollandiae) (Calviño-Cancela et al. 2006), and seeds

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with adaptations for bird dispersal can be transported by floods (Hampe et al. 2003). In the

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dandelion (Taraxacum officinale), as the archetype of a wind-dispersed species with a pappus

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promoting aerial transport, the vast majority (99%) of wind-dispersed seeds are predicted to

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travel over less than 2.15 m (Soons & Ozinga 2005). Yet, the dandelion hairy seed has a half-

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time buoyancy of 2.5 days in water (Boedeltje et al. 2003) and a relatively high potential for

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long-term retention in furs (Tackenberg et al. 2006). This implies that hydrochory (dispersal

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by water) and epizoochory (externally by animals) have much greater potential for LDD of

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dandelion seeds than anemochory (dispersal by wind). However, LDD need not be caused by

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non-standard vectors - it can also arise from irregular behaviour of the standard vector. For

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example, strong turbulent vertical updrafts induced by specific atmospheric conditions can

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transport seeds over much greater distances than typical winds (Nathan et al. 2002, Kuparinen

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et al. 2007).

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To identify mechanisms that are capable of generating LDD, we use a general model for

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passive dispersal (Figure 6.3). This model (adapted from Isard & Gage 2001) describes three

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main phases of vector-mediated dispersal (Picture 1): the initiation phase, in which the

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dispersal vector removes seeds from the mother plant; the transport phase, in which the

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vector transports seeds away from the source; and the termination phase, in which seeds are

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deposited. This chain can be repeated, possibly involving different vectors in the transport of

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a single seed (vander Wall & Longland 2004). The key parameter of the initiation phase is the

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vector seed load (Q), the number of seeds taken by a dispersal vector in a certain time period.

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Q depends on plant fecundity, fruiting schedule in relation to the behaviour of the vector, and

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the loading ability of the vector. The mean vector displacement velocity (V) after seed uptake

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is the key parameter of the transport phase. It depends primarily on the movement properties

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of the vector, such as its travel velocity, directionality and intermittence. The key parameter of

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the termination phase is the seed passage time (P), the duration of seed transport by the

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vector, which depends on both seed and vector traits, and their interactions. All three key

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parameters also depend on external factors such as landscape structure and climatic

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conditions.

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Displacement velocity V and seed passage time P together define the shape of the dispersal

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distance kernel, whereas seed load Q defines how many seeds the vector disperses. General

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considerations (Box 6.2, Figure 6.5) show that important LDD vectors are those which

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generate a relatively high per-seed probability of LDD, because retention time (P) and/or

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displacement velocity (V) are at least occasionally high. Such vectors can be important for

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LDD even if they have a small seed load (Q).

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[Insert Box 6.2 here]

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Six generalizations on long-distance dispersal mechanisms

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Using the vector-centred framework outlined above, we reviewed the seed dispersal literature

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in search of mechanisms promoting LDD. This led us to formulate six generalizations on

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LDD mechanisms (Nathan et al. submitted). Arranged by the spatial scale for which they are

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most relevant (Figure 6.6) these generalizations state that LDD is more frequent in open

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terrestrial landscapes (G1), and is likely to be caused by large animals (G2), migratory

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animals (G3), extreme meteorological events (G4), ocean currents (G5) and human

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transportation (G6). The first generalization (G1) thus concerns the environmental conditions

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affecting LDD whereas the remaining generalizations (G2-G6) identify five major LDD

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vectors. In addition to the information given in Nathan et al. submitted, we provide here

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additional evidence, in particular for the importance of large animals (G2, Box 6.3).

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For each generalization, we will in the following summarize the underlying mechanisms,

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review the available evidence, and discuss the scope of the generalization in terms of the plant

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species for which it is most relevant.

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G1) Open landscapes

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Mechanisms: Open landscapes are defined here as regions of no, sparse or very short

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vegetation, such as grassland meadows, arid steppes and arctic tundra, as opposed to more

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closed landscapes such as forests and woodlands. In open landscapes, LDD is facilitated by

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the low density of obstacles to the movement of seeds and their vectors. All else being equal,

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vectors moving through more open landscapes should thus have higher displacement velocity

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V and longer retention time P, but not necessarily larger seed load Q.

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Evidence: Mechanistic models strongly suggest higher LDD in more open landscapes both for

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primary (Nathan & Katul 2005) and secondary (Schurr et al. 2005) seed dispersal by wind.

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Over smooth playa surfaces (Fort & Richards 1998), on snow (Greene & Johnson 1997), and

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in post-fire environments (Bond 1988), dispersal distances of wind-dispersed seeds are much

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longer than in closed landscapes. A lack of vegetation also seems to promote LDD by water

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courses: cleared channels and relatively wide rivers provide highways for the LDD of seeds

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and vegetative fragments of many plant species (Boedeltje et al. 2003, Truscott et al. 2006).

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Furthermore, an analysis of potential LDD vectors for 123 plant communities (mostly

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grasslands) in the Netherlands showed a clear positive correlation between the potential for

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LDD and light availability as an index for landscape openness (Ozinga et al. 2004).

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Clearly, different dispersal vectors may require different degrees of landscape openness: for

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instance, a grassland may lack obstacles for the movement of large mammalian seed

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dispersers, but because wind speed within the grass canopy is low, it holds many obstacles to

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the wind dispersal of herbaceous plants.

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Scope: All plants. It should, however, be noted that LDD is not restricted to open landscapes.

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In closed forests, highly mobile animals such as cassowaries (Casuarius casuarius) and spider

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monkeys (Ateles paniscus) can transport seeds over long distances (Westcott et al. 2005,

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Russo et al. 2006). Explicit analyses of how landscape openness affects the V and P of seed

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dispersing animals are still missing.

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G2) Large animals

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Mechanisms: Animals with larger body mass tend to have larger home range, higher travel

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velocity, larger gut capacity and longer seed retention time, compared to smaller animals

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within the same taxonomic group (Calder 1996, Box 6.3). Larger home ranges and higher

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travel velocities lead to higher V, and longer gut retention times imply longer P. The

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relationship between animal body mass and Q is less clear cut: while the larger intake rate of

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large animals means that they are likely to have higher Q per individual animal, smaller

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animals may compensate by visiting fruiting plants more frequently or by having denser local

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populations (e.g. Spiegel & Nathan 2007).

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Evidence: The recent literature abounds with quantitative examples of large animals

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dispersing seeds over relatively long distances (e.g. Vellend et al. 2003, Westcott et al. 2005,

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Russo et al. 2006, Calviño-Cancela et al. 2006, Spiegel & Nathan 2007). Moreover, large

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animals have been shown to take up seeds of a large variety of plant species, irrespective of

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their dispersal morphology (e.g. Myers et al. 2004, Westcott et al. 2005). The few available

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comparisons of seed dispersal by animals of different body mass suggest that larger animals

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cause more LDD (Dennis & Westcott 2007, Jordano et al. 2007, Spiegel & Nathan 2007).

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Furthermore, we present here a meta-analysis of data on endozoochorous dispersal by birds

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which shows that seed dispersal distance increases with the body mass of the seed-dispersing

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animal as predicted from allometric relationships for P and V (Figure 6.7, Box 6.3).

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Scope: Plant species dispersed by animals, especially such dispersed endozoochorously

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(Jordano et al. 2007). The relationship does not necessarily hold for inhomogeneous

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taxonomic and/or dietary groups of animals. Although larger body mass is associated with

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higher V in both epi- and endozoochorous dispersal, it is currently unclear whether the

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allometric relationship between body mass and P found for endozoochorous dispersal (Figure

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6.7, Box 6.3), also holds for epizoochory.

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[Insert Box 6.3 here]

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G3) Migratory animals

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Mechanisms: Migratory animals, specifically birds and mammals, can transport seeds both

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externally and internally. During migration, animals move relatively fast and in a directional

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manner. They therefore have considerably higher V than equivalent non-migratory animals.

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Furthermore, migratory animals are more likely to transport seeds across dispersal barriers.

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Also, Q could be very high given the large number of migratory animals. However, general

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conclusions about the magnitude of Q and P for migratory animals still seem premature.

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Evidence: A large set of anecdotal evidence (e.g. Carlquist 1981) has been reported since

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Darwin (1859) highlighted LDD by migratory animals. Experimental approaches have mostly

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focused on seed viability and passage time (P) (e.g. Charalambidou & Santamaria 2002), and

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the expected dispersal distances were then calculated assuming that V can be estimated from

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observed migratory flight speeds (Charalambidou et al. 2003). This assumption is likely to

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lead to gross overestimation of LDD, because it ignores delays between seed uptake and the

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start of migratory flights as well as pauses in stopover sites (Sánchez et al. 2006). Laboratory

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estimates of P for captive animals may fail to represent effects of pre-migratory fasting and

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reduction of the digestive system (Clausen et al. 2002). The only available experiment

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incorporating such effects suggests that seeds dispersed by migrating ducks have relatively

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long P (Figuerola & Green 2005). However, despite the recent upsurge in research on this

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topic (Clausen et al. 2002, Charalambidou & Santamaria 2002, Figuerola & Green 2002,

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Charalambidou et al. 2003, Figuerola & Green 2005, Sánchez et al. 2006), quantitative

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estimates of V, P and Q are still largely missing for migratory animals.

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Scope: Plants fruiting during migration periods (Hanya 2005). Fleshy-fruited plants are likely

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to be dispersed by migrating passerines (Jordano 1982), while waterfowl mostly disperse

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small-seeded plants (Figuerola & Green 2002). Since studies have largely focused on aquatic

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plants dispersed internally by waterbirds; we have almost no information on other plants, on

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other groups of migrating animals, and on the importance of epizoochorous dispersal by

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migrating animals.

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G4) Extreme meteorological events

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Mechanisms: As extreme meteorological events we denote phenomena that cause highly

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energetic wind or water flow. Extreme meteorological events have exceptionally high V for

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plant seeds and are also likely to exhibit relatively long P (storms/floods carry seeds for

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longer periods than normal conditions) and high Q (storms/floods induce mass release of

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seeds). Extreme events can also damage plant seeds, thereby lowering effective seed load (Q)

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and reducing the establishment probability of transported seeds.

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Evidence: Dispersal biogeographers have long considered extreme meteorological events as

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potential LDD vectors, even at intercontinental scales (e.g. Visher 1925). Indirect evidence

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from studies of plant recruitment or genetic analyses suggests causal relationship between

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extreme events and LDD. For example, a high frequency of kelp (Pterygophora californica)

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recruitment events at relatively long (up to 4 km) distances from the nearest zoospore source

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was documented after severe winter storms (Reed et al. 1988). At far larger scales, a

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biogeographical analysis (Munoz et al. 2004) found that the floristic similarities of moss,

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liverwort, lichen, and pteridophyte floras for 27 locations across the Southern Hemisphere

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conform better with how well these locations are connected by 'wind highways' than with

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their geographic proximity. This study thus suggests that wind can be an important LDD

310

vector at the global scale. However, it does not permit to assess the relative contribution of

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standard and extreme conditions to LDD by wind. While quantitative analyses that directly

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link extreme events to plant LDD are critically missing, some insights can be drawn from a

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long-term data set on various objects that have been found as fallout debris from tornadic

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thunderstorms and could be traced back to their source location (Snow et al. 1995). Most

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plant seeds will fall into Snow et al.'s (1995) "paper" category, for which the mean transport

316

distance was 111.2 km and the maximum (of 48 records) was 338 km. Even "heavy" objects

317

(>450 g) were transported over a mean distance of 31.6 km and a maximum distance of 177

318

km (n= 30 records). For obvious reasons, Snow et al. (1995) could not trace back seeds, but

319

their data set nevertheless demonstrates the ability of tornadoes to cause LDD (Nathan et al.

320

submitted).

321 322

Scope: Extreme meteorological events probably disperse plant seeds (and larger diaspores)

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irrespective of their taxonomy and morphological classification (Nathan et al. submitted), but

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species inhabiting regions where extreme events are relatively frequent (e.g. ocean coasts and

325

islands) are more likely to be transported by this LDD mechanism.

326 327

G5) Ocean currents

328

Mechanisms: Surface ocean currents serve as efficient LDD vectors for floating seeds and for

329

rafts transporting seeds or entire plants. Oceanic currents are relatively slow (mean V = 0.1 -

330

0.3 m s-1), but their Q is essentially limitless. Moreover, seeds with good floating capacity

331

typically have a high P of several tens of days (exceptionally extending to 19 years), and for

332

rafting, P is typically one order of magnitude longer (Thiel & Haye 2006).

333

Evidence: A plethora of floating and rafting records suggests that at macro-evolutionary

334

timescales, transoceanic LDD of terrestrial plants is a recurrent phenomenon (Darwin 1859,

335

Ridley 1930, Carlquist 1981, de Queiroz 2005, Thiel & Gutow 2005, Thiel & Haye 2006).

336

But even at ecological timescales, ocean currents can transport plant propagules of various

337

non-specialized dispersal morphologies, over tens and even hundreds of kilometres. For

338

example, of all plant species colonizing the volcanic island Surtsey (35 km from Iceland) in

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339

the first decade after its emergence, 78% were recorded to arrive by oceanic currents,

340

although only 25% had apparent morphological adaptations for dispersal by water (Higgins et

341

al. 2003c).

342

Scope: The Surtsey example shows that seeds do not have to have obvious morphological

343

adaptations for floating to be long-distance dispersed by ocean currents (Higgins et al. 2003).

344

Moreover, a wide range of plant species has been recorded as rafting. These species mostly

345

originate from islands and coastal habitats (e.g. estuaries and saltmarshes) (Thiel & Gutow

346

2005, Thiel & Haye 2006).

347 348

G6) Human transportation

349

Mechanisms: Compared to nonhuman dispersal vectors, global trade and transportation by

350

humans have much higher V and longer P as well as higher Q. Human transportation also

351

increases the probability of seeds to dispersed over natural barriers (e.g. from one continent to

352

another).

353

Evidence: The rapid spread of agricultural crops across Eurasia after 8500 BC is evidence of

354

early human-mediated LDD (Diamond 2002, possible reference to Chapter 7). Over the last

355

centuries, massive human-mediated plant LDD is evident in the large number of plant species

356

that have become naturalized in regions outside their native range (Hodkinson & Thompson

357

1997, Mach & Lonsdale 2001). In most cases, human-mediated transport is given as the most

358

plausible explanation for the unprecedented scale and frequency of dispersal over very broad

359

barriers, leading to conclusions such as "Only humans could have transported scores of

360

western European species to Australia, Argentina, and western North America, and vice

361

versa, in less than 500 years" (Novak & Mack 2001). In certain cases, the available evidence

362

points to particular modes of human-mediated dispersal. For instance, of the 67 species of

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Southern African Iridaceae that have become naturalized outside their native range, 93% are

364

known to be used in horticulture, whereas this percentage is only 30% for the entire set of

365

1036 species (van Kleunen et al. 2007). At smaller scales, the identification of human-

366

mediated dispersal mechanisms is even clearer: it has been shown that LDD of seeds is caused

367

by cars (von der Lippe & Kowarik 2007), agricultural machinery (Bullock et al. 2003), and

368

livestock herds (Manzano & Malo 2006).

369

Scope: Probably most plants, especially those closely associated with human activities (e.g.

370

crop and ornamental plants, weeds and ruderals).

371 372

A vector-based perspective on the evolution and predictability of long-

373

distance seed dispersal

374

The importance of the above generalizations for LDD evolution and our ability to understand

375

and predict the large-scale dynamics of plants will depend on the answers to two fundamental

376

questions:

377

(1) Is it possible to identify all important LDD vectors? and

378

(2) How important are plant and seed traits for LDD?

379

In the following we first address these two questions from a vector-based perspective and then

380

discuss their implications for the evolution and predictability of LDD.

381 382

Is it possible to identify all important long-distance dispersal vectors?

383

The above generalizations certainly do not provide an exhaustive list of LDD mechanisms.

384

But the potential of the vector-based framework to advance our understanding of LDD will

385

depend on whether it is possible to identify all important LDD vectors for a given plant

386

species. It seems obvious that we cannot identify all vectors that might potentially disperse

16

387

seeds over long distances. This is because there seems to be an essentially limitless number of

388

vectors that could potentially cause LDD. For instance, seeds of temperate plant species could

389

be long-distance dispersed by an escaped circus elephant, and the shock wave following a

390

major asteroid impact could cause LDD of those seeds that are not destroyed by the impact.

391

Such 'freak events' occur in such an irregular manner that it seems impossible to assess

392

whether or not they will be involved in the LDD of a given plant species. The notion that

393

LDD is driven by "chance dispersal" (Carlquist 1981) seems to suggest that these freak events

394

dominate LDD. However, as pointed out by Berg (1983), we need to distinguish two types of

395

"chance dispersal": LDD can either arise from "an unusually favourable combination of the

396

regular dispersal factors" or from "an unusual coincidence involving a dispersal factor not

397

normally operating together with the taxon in question". The key distinction between these

398

two forms is whether LDD results from the rare but essentially predictable behaviour of a

399

regular dispersal vector (such as extremely turbulent wind conditions), or whether it results

400

from the rare and unpredictable involvement of an irregular dispersal vector (such as an

401

escaped elephant).

402

The distinction between regular and irregular LDD vectors is important, because it marks the

403

boundary between what can and what cannot be predicted by a vector-based approach to LDD

404

research. We can hope to obtain a quantitative understanding of regular LDD vectors (such as

405

the ones in G2-G6), but research into the mechanisms of LDD by irregular LDD vectors

406

seems futile because of the sheer number of irregular vectors. Hence, the overall contribution

407

of regular vectors to LDD defines an upper limit of what can be explained by a vector-based

408

approach (Figure 6.8).

409

The success of the vector-based approach will thus depend on the composition of a plant's

410

LDD vector spectrum: LDD can - at least in principle - be explained if it is dominated by a

411

limited number of regular vectors (Figure 6.8 a and b). On the contrary, the vector-based

17

412

approach will fail if LDD results from the joint action of an essentially unlimited number of

413

irregular vectors (Figure 6.8 c and d). The vector-based approach thus directs our attention to

414

the shape of LDD vector spectra.

415

But what do these LDD vector spectra look like for particular plant species and what is the

416

relative contribution of regular and irregular vectors to the LDD of these species? While these

417

are clearly very difficult questions, we are starting to gather information that helps to answer

418

them. Such information comes from two studies comparing mechanistic predictions for

419

regular LDD vectors with data on large-scale plant distributions. The first study is the

420

biogeographical analysis of Munoz et al. (2004) (see "G4) Extreme meteorological events"

421

above) which suggests that wind as a regular LDD vector dominates the floristic similarity

422

between cryptogam communities across the Southern Hemisphere. A second study (Schurr et

423

al. 2007) found that the combination of mechanistic models for LDD by primary and

424

secondary wind dispersal (Tackenberg 2003, Schurr et al. 2005) explains a substantial

425

proportion of the amount to which 37 species of South African Proteaceae fill their potential

426

range. In these two cases, LDD thus seems to be dominated by one regular dispersal vector. It

427

is not surprising that the dominant vector in both studies is wind: the quantitative

428

understanding of LDD mechanisms - as a prerequisite for the comparison of mechanistic

429

predictions to large-scale patterns - is most advanced for seed dispersal by wind.

430

Further quantitative analyses that also include mechanistic predictions for other regular

431

dispersal vectors are needed to assess how frequently LDD is dominated by one or a few

432

regular vectors. The limited evidence available to date does, however, suggest that LDD is not

433

always dominated by irregular vectors and that we do not always have to consider many

434

regular vectors to adequately describe LDD. If LDD is dominated by a limited number of

435

regular vectors, the vector-based framework will help to identify these vectors, to measure

436

their Q, V and P and hence to quantify their contribution to LDD.

18

437 438

How important are plant and seed traits for long-distance dispersal?

439

Another critical question for our understanding of LDD evolution and the large-scale

440

dynamics of plants is to which extent plant and seed traits control LDD. For instance, LDD

441

can only evolve adaptively if it is to some extent controlled by phenotypic traits, and if these

442

traits have in part a genetic basis. A similar question can be asked within an ecological

443

context: do interspecific differences in LDD depend in part on species traits or is LDD

444

essentially identical for all species inhabiting a given environment with a given set of LDD

445

vectors? If LDD depends on species traits, an understanding of interspecific variation in

446

biogeographical distributions and dynamics requires the quantification of these traits. If, on

447

the other hand, species traits are unimportant for LDD, all species co-occurring in a given

448

environment with a given set of LDD vectors should have the same rates of LDD. In this case,

449

the assumption of the neutral theory of biogeography (Hubbell 2001) that all species in a

450

metacommunity have the same migration rate might be a good description of reality.

451

Moreover, if species traits are unimportant for LDD, efforts to predict range dynamics under

452

environmental change can neglect variation in these traits. Instead these efforts should then

453

focus on interspecific differences in demographic processes (population growth rates,

454

probability of establishment) and on how environmental change will affect the joint spectrum

455

of LDD vectors shared by all species. To assess the consequences of the generalizations G1-

456

G6 for LDD evolution, for the large-scale dynamics of plants, and for our ability to predict

457

these dynamics under environmental change the critical question is thus to which extent plant

458

and seed traits control LDD.

459 460

As outlined above, the traits used in traditional morphological classifications have no or little

461

effect on LDD. However, this does not necessarily mean that traits in general are irrelevant -

19

462

traits other than those traditionally considered may well control LDD by particular vectors.

463

For instance, the LDD of Trillium grandiflorum seeds does not depend on the fact that they

464

are equipped with an elaiosome, but rather on their ability to survive ingestion by white-tailed

465

deer (Vellend et al. 2003). The high importance of human-mediated dispersal at very large

466

scales (G6) makes an entirely different suite of traits relevant for LDD: for example, the

467

attractiveness of Iridaceae species to gardeners seems to strongly determine their spread at

468

global scales (van Kleunen et al. 2007). These examples show how a vector-based approach

469

can identify LDD-relevant traits which escaped the traditional seed-centred approach.

470

Moreover, they show that plant and seed traits can exert some control on LDD by at least

471

certain dispersal vectors.

472

Therefore the question does not seem to be whether phenotypic traits can in principle affect

473

LDD, but rather how much of the variation in LDD they explain. It seems likely that different

474

LDD mechanisms differ in the amount of trait control (for instance phenotypic traits might

475

have a bigger effect on LDD by large animals than on LDD by tornadoes). It also seems likely

476

that, for a given LDD mechanism, the amount of trait control decreases with dispersal

477

distance. However, to give definite answers to these questions we need a quantitative

478

understanding of multiple LDD mechanisms which currently is still lacking.

479 480

Evolution of long-distance seed dispersal

481

A necessary condition for LDD to evolve in response to natural selection is that phenotypic

482

traits of plants and seeds must exert a certain degree of control over LDD. As discussed

483

above, this condition seems to be fulfilled at least for certain plant species and certain LDD

484

vectors. Yet, in comparison to environmental effects, phenotypic traits are likely to have only

485

a small effect on the LDD probability of an individual seed. Because phenotypic traits

486

themselves are also affected by environmental variation, this could be taken to mean that the

20

487

heritability of LDD (as the proportion of LDD variation explained by additive genetic

488

variation) is even smaller. However, when quantifying the heritability of LDD one has to

489

carefully distinguish between different levels at which environmental variation acts: if we

490

assume that the relevant phenotypic traits are mostly determined by the genotype of the

491

mother plant, the heritability of LDD should not be calculated from between-seed variation in

492

LDD, but rather from between-mother plant variation in the LDD of their offspring. At the

493

mother plant level, the environmental stochasticity involved in the LDD of individual seeds

494

will to some extent average out, with environmental stochasticity becoming less important the

495

more fecund individuals are. Hence, while the importance of environmental effects for LDD

496

means that the heritability of LDD is unlikely to be very high, it also seems premature to

497

conclude that LDD inevitably has a heritability close to 0. Due to the difficulties of directly

498

observing LDD (see "The challenge of studying long-distance seed dispersal" above), it seems

499

virtually impossible to quantify its heritability from direct empirical observations (as was

500

done for short-distance dispersal by Donohue et al. 2005). Instead, it seems more promising to

501

estimate the heritability of LDD by using mechanistic models of LDD vectors that propagate

502

observed phenotypic variation in dispersal traits and represent realistic levels of

503

environmental variation.

504

The fact that LDD can have strong fitness consequences means that it is likely to be exposed

505

to strong directional selection pressures. Evolutionary models show that LDD can be selected

506

for in species that expand their geographic range (Travis & Dytham 2002), are exposed to

507

specialized pests (Muller-Landau et al. 2003), face frequent catastrophic extinction from local

508

habitat fragments (e.g. van Valen 1971, Hamilton & May 1977, Comins et al. 1980; see also

509

Ronce et al. 2000, Poethke et al. 2003) or inhabit spatially autocorrelated landscapes

510

(Hovestadt et al. 2001). Selection against LDD may occur in island situations where LDD

511

carries high costs because long-distance dispersers are likely to end up in hostile

21

512

environments (Cody & Overton 1996). In metapopulations, the costs and benefits of LDD

513

may interact so that between-population selection for colonization favours increased LDD in

514

young populations whereas within-population selection favours reduced LDD as populations

515

age (Olivieri et al. 1995).

516

The short-term evolutionary response of LDD depends on the product of its heritability and

517

the strength of selection acting on it (Lynch and Walsh 1998). As discussed in the previous

518

paragraphs, LDD is likely to have rather low heritability, but it may also be exposed to strong

519

directional selection. A priori we can thus neither exclude the possibility that LDD might

520

respond quite quickly to natural selection nor the possibility that it might hardly react at all.

521

To assess the speed at which LDD can evolve adaptively, we instead have to quantify both its

522

heritability and the selective forces acting on it.

523

New avenues for studying the evolution of multivariate dispersal phenotypes are suggested by

524

the realization that most plant species have multiple LDD vectors. Seed movement over

525

different spatial scales is exposed to different selective pressures (Ronce et al. 2001), which

526

raises the question whether LDD and short-distance dispersal can evolve independently, or

527

whether they are linked through positive or negative genetic correlations (Ronce 2007). The

528

fact that LDD vectors are often different from the standard vectors driving short-distance

529

dispersal (Higgins et al. 2003c, Box 6.1) suggests that LDD and short-distance dispersal

530

might be able to evolve independently. However, seed dispersal by different vectors can still

531

be linked through phenotypic correlations between the relevant dispersal traits. For instance,

532

for seeds of a given mass, increased pappus size both reduces the seed's terminal falling

533

velocity and increases its attachment to animal furs (Couvreur et al. 2005, Tackenberg et al.

534

2006), which should lead to a positive correlation between wind dispersal and epizoochorous

535

dispersal. Such interactions between seed dispersal by different vectors are likely to be

536

mediated by quantitative, continuously varying traits (such as pappus size). However, they

22

537

can also arise from single mutations: the domestication of wild wheat and barley might have

538

been triggered by the emergence of single-gene mutants that did not shatter their seeds and

539

thus had reduced probabilities of abiotic seed dispersal but increased human-mediated LDD

540

(Diamond 2002, possible reference to Chapter 7). Changes at individual loci might thus have

541

profound consequences for the composition of Total Dispersal Kernels (TDKs) and can

542

substantially promote LDD.

543 544

Forecasting the large-scale dynamics of plants

545

The composition of TDKs may be important for the future dynamics of plants because

546

different LDD vectors are differentially suited for the spatial response of plants to

547

environmental change. For instance, many northern temperate plants produce ripe seeds in

548

summer and autumn when birds migrate southwards (Hampe et al. 2003). For poleward

549

expansions necessary under climate change, migrating birds may thus be less relevant than

550

other vectors (such as extreme storms).

551

Different LDD mechanisms are also likely to be differentially affected by environmental

552

change: climate change is predicted to increase the frequency and intensity of hurricanes,

553

severe floods and other extreme weather conditions (Easterling et al 2000, Knutson & Tuleya

554

2004, Webster et al 2005; but see Pielke et al. 2005), which should promote LDD (G4).

555

Rising levels of atmospheric CO2 raise the fecundity of certain plant species, thereby

556

increasing the seed load (Q) of their LDD vectors and hence the number of LDD events

557

(LaDeau & Clark 2001). On the other hand, the loss or decline of large and migratory animals

558

may substantially reduce LDD of many plant species (G2, G3). Certain drivers of

559

environmental change may have complex effects on LDD: for example, habitat destruction

560

and fragmentation may reduce LDD by reducing the movement of large animals (G3, Higgins

561

et al 2003b), but they may also promote LDD by creating more open landscapes (G1). To

23

562

quantitatively assess such interacting effects of environmental change on LDD, we need a

563

mechanistic understanding of the major LDD processes.

564

A quantitative description of LDD combined with knowledge on the probability of population

565

establishment after LDD (Nathan 2006) is the basis for forecasting the large-scale dynamics

566

of plants (e.g. Clark et al. 2001). However, LDD forecasts will inevitably involve uncertainty

567

that arises from three main sources, namely from model, parameter and inherent uncertainty

568

(Higgins et al. 2003a). Model uncertainty is caused by uncertainty about the identity of

569

dominant LDD vectors and by uncertainty about the exact mechanisms by which these

570

dominant vectors move seeds. This component of uncertainty can be greatly reduced by

571

research into LDD mechanisms and by the adequate modelling of these mechanisms (Higgins

572

et al. 2003a). However, the proportion of LDD accounted for by irregular LDD vectors

573

(Figure 6.8) will limit our ability to reduce model uncertainty (see " Is it possible to identify

574

all important long-distance dispersal vectors?" above). Parameter uncertainty is the result of

575

imprecise knowledge about the key parameters (Q,V and P) for each LDD vector. For most

576

LDD vectors, we seriously lack quantitative knowledge about these key parameters (see " Six

577

generalizations on long-distance dispersal mechanisms" above) meaning that parameter

578

uncertainty can be substantially reduced through the collection of empirical data. Finally,

579

inherent uncertainty is the consequence of stochasticity in LDD processes or in the models

580

used to describe them. For vectors with high seed load (Q) and fat-tailed dispersal kernels, the

581

inherent uncertainty about the location of the furthest dispersal distance is high. This leads to

582

high uncertainty about population spread rates that cannot be reduced by through an

583

improvement of models or parameter estimates (Clark et al. 2003). Despite inevitable

584

uncertainty in LDD predictions, informative quantitative forecasts of range dynamics seem

585

possible at least for certain species: the fact that LDD mechanisms can explain interspecific

586

variation in biogeographical distributions of Proteaceae (Schurr et al. 2007, see above),

24

587

suggests that mechanistic forecasts of range dynamics are within reach (Thuiller et al. 2007).

588 589

Future directions

590

In this review we have pointed out some key questions for future research on LDD of plants:

591

(1) Can we identify all important LDD vectors for a given plant species?

592

(2) What is the shape of LDD vector spectra (Figure 6.8) for different plant species?

593

(3) Which phenotypic traits of plants and seeds affect LDD?

594

(4) How important are these phenotypic traits for LDD?

595

(5) How large are the heritability of LDD and the strength of selection acting on it, and how

596

fast can LDD evolve?

597

(6) To what extent are LDD forecasts limited by inherent uncertainty that cannot be reduced

598

through improvement of model structure and parameter estimation?

599

We believe that our ability to answer these questions can be greatly advanced through the

600

wide adoption of a vector-based framework for LDD research that defines the spatial scales of

601

interest for a particular problem, identifies the dispersal vectors operating at these scales, and

602

quantifies the key parameters Q, V and P (Figure 6.3) for these vectors.

603

To quantify how Q, V and P are determined by the interaction of environmental conditions

604

(e.g. landscape openness, G1) and phenotypic traits we need to develop and/or refine

605

mechanistic models for specific LDD vectors, notably for those mentioned in G2-G6.

606

Mechanistic models for seed dispersal by animals (G2, G3) are so far restricted mostly to

607

specific animal species. However, the allometric model presented in Box 6.3 demonstrates

608

that it is possible to formulate more generic models for seed dispersal by animals. Models for

609

LDD by abiotic vectors such as extreme meteorological conditions (G4) and ocean currents

610

(G5) can build on existing models for fluid dynamics in meteorology and oceanography.

25

611

Finally, it seems possible to construct mechanistic models for LDD by human transportation

612

(G6) that use data on traffic and commodity flows.

613

The development of mechanistic models for LDD vectors will have to go hand in hand with

614

the collection of data on relevant environmental variables, vector properties and phenotypic

615

traits. For instance, the development of large databases on functional species traits (e.g.

616

Knevel et al. 2003, Poschlod et al. 2003) should interact closely with the development of

617

LDD models to ensure that the trait databases provide parameter estimates necessary to

618

forecast large-scale dynamics under environmental change. To maximize the value of trait

619

databases for conservation, it will furthermore be important to ensure that they cover the

620

geographical regions and the species that are most affected by environmental changes.

621

The development of mechanistic models for LDD vectors will also have to be integrated with

622

the collection of relevant data at large spatial scales. Large scale data can serve both to

623

validate mechanistic LDD models and to assess whether models for regular LDD vectors

624

provide an adequate description of overall LDD (see section "Is it possible to identify all

625

important long-distance dispersal vectors?" above). Large-scale information on LDD can be

626

collected by regional-scale sampling of long-distance dispersed seeds (Shields et al. 2006),

627

through technological advances in the tracking of seeds and their LDD vectors (Wikelski et al.

628

2007) and through the combination of large-scale genetic (Jordano et al. 2007) and

629

biogeographical (Munoz et al. 2004) data with information on LDD mechanisms.

630

In conclusion, we believe that the combination of new quantitative tools in a vector-centred

631

approach holds great promise for LDD research. The pace at which this field has developed in

632

recent years lets us hope that further advances in our understanding of long-distance seed

633

dispersal are not far away.

634

26

635 636

Acknowledgements We thank Jordi Figuerola, Daniel García, Arndt Hampe, Anna Kuparinen, Wim Ozinga,

637

Ophélie Ronce, Sabrina Russo, Louis Santamaría and Steve Wagstaff for helpful comments

638

and suggestions. This study has been supported through the Israeli Science Foundation (ISF

639

474/02 and ISF-FIRST 1316/05), the International Arid Land Consortium (IALC 03R/25), the

640

Israeli Nature and Parks Protection Authority, the US National Science Foundation (IBN-

641

9981620 and DEB-0453665), the German Ministry of Education and Research (BMBF) in the

642

framework of Biota Southern Africa (FKZ 54419938), the European Union through Marie

643

Curie Transfer of Knowledge Project FEMMES (MTKD-CT 2006-042261), the Minerva

644

short-term fellowship program, the Simon and Ethel Flegg Fellowship, and the Friedrich

645

Wilhelm Bessel Research Award of the Humboldt Foundation.

27

646

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Muñoz, J., Felicísimo, Á.M., Cabezas, F., et al. (2004) Wind as a long-distance dispersal vehicle in the Southern Hemisphere. Science, 304, 1144-1147. Myers, J.A., Vellend, M., Gardescu, S., et al. (2004) Seed dispersal by white-tailed deer:

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788

seed dispersal by wind. Proceedings of the National Academy of Sciences of the

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832

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844

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852

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854

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869

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36

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876

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877

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878 879

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880

implications for long- distance migration of forest herbs. Ecology, 84, 1067-1072.

881

Visher, S.S. (1925) Tropical cyclones and the dispersal of life from island to island in the

882 883 884

Pacific. American Naturalist, 59, 70-78. Von der Lippe, M. & Kowarik, I. (2007) Long-distance dispersal of plants by vehicles as a driver of plant invasions. Conservation Biology, 21, 986-996.

885

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Webster, P.J., Holland, G.J., Curry, J.A., et al. (2005) Changes in tropical cyclone number,

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duration, and intensity in a warming environment. Science, 309, 1844-1846.

890

Weir, J.E.S. & Corlett, R.T. (2007) How far do birds disperse seeds in the degraded tropical

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landscape of Hong Kong, China? Landscape Ecology, 22, 131-140.

892

Westcott, D.A., Bentrupperbäumer, J., Bradford, M.G., et al. (2005) Incorporating patterns of

893

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894

curves. Oecologia, 146, 57-67.

895

Westcott, D.A. & Graham, D.L. (2000) Patterns of movement and seed dispersal of a tropical

37

896

frugivore. Oecologia, 122, 249-257.

897

Wikelski, M., Kays, R.W., Kasdin, N.J., et al. (2007) Going wild: What a global small-animal

898

tracking system could do for experimental biologists. Journal of Experimental

899

Biology, 210, 181-186.

900 901 902 903

Williams, C.G. & Davis, B.H. (2005) Rate of transgene spread via long-distance seed dispersal in Pinus taeda. Forest Ecology and Management, 217, 95-102. Willson, M.F. (1993) Dispersal mode, seed shadows, and colonization patterns. Vegetatio, 107/108, 261-280.

38

904

Box 6.1: Polychory and the study of long-distance seed dispersal

905

The fact that most plant species are not dispersed by a single vector (haplochory) but by

906

multiple vectors (polychory) has profound consequences for the study of LDD. To illustrate

907

this, we consider the hypothetical case of a plant species producing a large number of seeds

908

(108) that are dispersed by four different dispersal vectors (Figure 6.2a). The four vectors are

909

arranged in order of decreasing seed load Q and increasing fat-tailedness of the dispersal

910

kernel they produce (Figure 6.2a). For a plant species with elaiosome-bearing seeds these four

911

vectors could for example be (1) ants (the standard dispersal vector), (2) large animals, (3)

912

strong floods and (4) transportation by humans.

913

Vector 1 transports the vast majority of seeds (95%), but its thin-tailed dispersal kernel causes

914

it to generate only short dispersal distances (Figure 6.2a). Vectors 2 through 4 generate

915

increasingly larger maximum dispersal distances although they disperse successively fewer

916

seeds (Figure 6.2a). In fact, vector 4 disperses only a tiny fraction (0.0125%) of all seeds, but

917

the maximum dispersal distance it generates is more than five orders of magnitude greater

918

than that generated by the standard vector (vector 1).

919

When plotting the relative contribution of each vector to the total dispersal kernel (TDK) it

920

becomes apparent that different vectors dominate seed dispersal over different scales (Figure

921

6.2b). In particular, the vectors dominating seed dispersal at the scales typically investigated

922

in empirical studies are very different from the vectors dominating LDD.

923

This hypothetical example illustrates the limited ability of small-scale studies to detect LDD

924

vectors, and the fallacy of extrapolating from the small-scale behaviour of the standard vector

925

to LDD. The traditional seed-centred approach will almost inevitably miss the rare seeds

926

dispersed by non-standard LDD vectors. We thus advocate a vector-centred approach that

927

specifically targets those vectors that can transport seeds at the scales of interest.

39

928

Box 6.2: How to identify long-distance dispersal vectors

929

The general model for passive dispersal (Figure 6.3) helps to identify vectors that are likely to

930

cause LDD of a given plant species. To illustrate this, we first consider the dispersal of a

931

single seed. A single seed's dispersal distance is the product of its retention time at the vector

932

(P) and the vector's displacement velocity during this time (V). For a set of seeds dispersed by

933

the same vector, V and P are two random variates that follow statistical distributions (Figure

934

6.5a). The dispersal distance as the product of these two random variates again follows a

935

statistical distribution which is called the vector's dispersal distance kernel (Figure 6.5b).

936

The area under the dispersal distance kernel that lies to the right of the LDD threshold gives

937

the per-seed probability of LDD. Formally, this per-seed LDD probability is

938

P (LDD ) =

∞

∫ f (x )dx = 1 − F (xLDD ) ,

x LDD

939

where xLDD is the LDD threshold, f is the probability density function and F the cumulative

940

density function of dispersal distance. The per-seed LDD probability thus depends on the

941

dispersal distance kernel which in turn depends on the distributions of V and P.

942

The conditions under which a vector generates a high per-seed probability of LDD fall into

943

two broad categories: in the rarer case, the median dispersal distance is close to or above the

944

LDD threshold so that dispersal events frequently exceed the LDD threshold (hatched grey

945

line in Figure 6.5b). This arises if the product of typical values for V and P is high (hatched

946

grey line in Figure 6.5a). An example for this rarer case might be provided by large animals

947

for which both typical displacement velocities and typical times of endozoochorous seed

948

retention are high (see Box 6.3). In the commoner case, the LDD threshold is far greater than

949

the typical dispersal distances, and a high per-seed LDD probability can only arise if the

950

dispersal kernel has a fat-tail (Figure 6.5b). A dispersal kernel is called fat-tailed if its tail

40

951

drops off more slowly than the tail of a negative exponential kernel (Table 6.1). In a plot of

952

log probability density against dispersal distance (Figure 6.5b), a fat tail curves away from the

953

x-axis, whereas a thin (or exponentially bounded) tail bends towards the x-axis.

954

How does the fat-tailedness of the dispersal kernel depend on the distributions of V and P? To

955

examine this, we conduct a "crossing experiment" with the two typical distributions of V and

956

P plotted in Figure 6.5a: the solid black (lognormal) distribution has a lower mean than the

957

solid grey (truncated normal) distribution, but it is leptokurtic and therefore has a higher

958

probability of generating high values. The dispersal kernel as the product of V and P is fat-

959

tailed when one or both of V and P follow the leptokurtic distribution (Figure 6.5b). In

960

contrast, if both V and P conform to the mesokurtic solid grey distribution, the resulting

961

dispersal kernel has a thin tail (Figure 6.5b). Our example thus shows that a dispersal vector

962

causes a high per-seed probability of LDD if V and/or P typically or occasionally reach high

963

values (hatched and black solid lines in Figure 6.5a and b). Figure 6.5b also shows that - as

964

the LDD threshold increases - the per-seed LDD probability of thin-tailed kernels drops far

965

more quickly than that of fat-tailed kernels: if the LDD threshold was set to 1500 m, the two

966

fat-tailed kernels would generate a higher per-seed LDD probability than the hatched grey

967

kernel.

968

To calculate the expected number of LDD events for a given vector, the per-seed probability

969

of LDD has to be multiplied with the vector's seed load (Q). Figure 6.5c shows how Q and

970

LDD probability interact: for low per-seed probabilities of LDD (thin-tailed dispersal kernels

971

with typical distances below the LDD threshold), the expected number of LDD events is

972

essentially 0 even if Q is very high. However, for high per-seed probabilities of LDD

973

(dispersal kernels that are fat-tailed or have typical distances above the LDD threshold), the

974

number of LDD events increases steeply with Q.

975

Important LDD vectors are thus those which generate a high per-seed probability of LDD

41

976

because the product of retention time (P) and displacement velocity (V) is at least

977

occasionally high. Such vectors can have a considerable contribution to LDD even if their

978

seed load (Q) is small. On the contrary, vectors for which the product of V and P has a thin

979

tail and typically lies below the LDD threshold hardly contribute to LDD.

42

980

Box 6.3: The allometry of endozoochorous seed dispersal

981

Allometric relationships between the body mass and various other quantities of organisms are

982

ubiquitous (Calder 1996). Here we use such allometric relationships to build a simple general

983

model that relates the body mass of animals to the mean dispersal distance of the seeds they

984

disperse endozoochorously.

985

The dispersal distance of a seed transported internally by an animal is the product of the gut

986

retention time P (the passage time through the animal gut) and the animal displacement

987

velocity V (Figure 6.3, Box 6.2). Because the movement velocity U of animals, rather than

988

their displacement velocity V, is usually measured, we incorporated a straightness factor c

989

which is 1 if the animal moves in a straight line and decreases towards 0 as the movement

990

path becomes less positively autocorrelated. We also accounted for the fact that animals do

991

not always move, by assuming that the time allocated to movement is a constant fraction f of

992

the gut retention time P. Hence the displacement velocity is

993

V = f c U.

994

Both the mean movement velocity U and the mean gut retention time P have strong

995

allometric relationships to animal body mass M (Robbins 1993, Calder 1996). These

996

relationships can be expressed as

997

U = U 0 M b1 and P = P0 M b 2 ,

998

where U0 and P0 are the allometric constants and b1 and b2 are the allometric exponents for U

999

and V, respectively.

1000

The expected mean dispersal distance x of seeds dispersed by an animal with body mass M is

1001

thus

1002

x = V P = fcU P = fcU 0 M b1P0 M b 2 = ( fcU 0 P0 )M b1+ b 2 .

43

(Eqn. 1)

1003

When parameterising this general mechanistic model with comparative data for the allometry

1004

of flight velocity and gut retention time in flying birds (Tucker 1973, Robbins 1993, Calder

1005

1996), we obtain

1006

x = 90432 fcM 0.5 in standard units of x [m] and M [kg],

1007

as a general mechanistic prediction for the mean distance of endozoochorous seed dispersal.

1008

To test this prediction, we extracted data from studies of seed dispersal by flying frugivorous

1009

birds (Figure 6.7). All these studies calculated seed dispersal kernels by combining empirical

1010

data on V and P, both estimated directly, and independently of M. The ten bird species

1011

included vary in body mass by two orders of magnitude and inhabit various ecosystems

1012

around the globe. A linear regression of log x against log M for these studies (Figure 6.7)

1013

estimated a scaling exponent of 0.502 that is very close to the independent expectation of 0.5

1014

(although the estimate has a broad 95% confidence interval ranging from 0.15 to 0.78). The

1015

allometric constant (f c U0 P0, see Eqn. 1) is estimated to be 1198.3 m, which together with

1016

Eqn. 2 implies that the product of the proportion of time for which birds fly (f) and the

1017

straightness factor of their movement (c) is on average 0.013.

1018

Our simple allometric model highlights the importance of large animals for LDD (G2). For

1019

large flying birds, even the mean distance of endozoochorous seed dispersal exceeds 1 km

1020

(Figure 6.7) showing that these species generate LDD frequently, as part of their ordinary

1021

behaviour. The wide confidence interval on the allometric exponent indicates that large

1022

deviations from the mean allometric trend are not uncommon. Positive deviations from the

1023

mean trend represent cases of particular importance for plant LDD.

44

(Eqn. 2)

1024

Table 6.1. An overview of the key terms used in this review (modified from Nathan et al.

1025

submitted). Term

Definition

Dispersal kernel

A probability density function characterizing the spatial distribution of seeds dispersed from a common source. The 'dispersal distance kernel' more precisely describes the distribution of seed dispersal distances (and hence the distribution of the product of displacement velocity, V, and retention time, P).

Dispersal vector

An agent transporting seeds or other dispersal units. Dispersal vectors can be biotic (e.g. birds) or abiotic (e.g. wind).

Vector displacement velocity

The speed at which a dispersal vector moves a seed away from the uptake location

(V)

(Figure 6.3).

Fat-tailed dispersal kernel

A dispersal kernel with a tail that drops off more slowly than that of any negative exponential kernel.

Haplochory

Seed dispersal by a single dispersal vector

Irregular dispersal vector

A dispersal vector acting in such a haphazard way that it cannot be predicted whether the vector will be involved in the seed dispersal of a given plant species.

Nonstandard dispersal vector

A dispersal vector different from the one inferred from traditional morphological. classifications (Higgins et al. 2003c)

Polychory

Seed dispersal by multiple dispersal vectors

Regular dispersal vector

A dispersal vector that is regularly (but potentially rarely) involved in the seed dispersal of a given plant species.

Seed passage time (P)

The time for which a seed is retained by a dispersal vector (Figure 6.3).

Seed

In a strict sense, the fertilized ovule of spermatophytes consisting of embryo, endosperm, and testa. We follow here the typical use of this term in the plant ecological literature as a synonym for a reproductive propagule.

Seed dispersal

The movement of seeds from the mother plant to a potential establishment site.

Vector seed load (Q)

The number of seeds dispersed by a particular dispersal vector.

Standard dispersal vector

A dispersal vector inferred from the phenotypic characters of the plant (e.g. the morphology of the dispersal unit) (Higgins et al. 2003c). Typically, this is the vector dispersing most seeds.

45

Total dispersal kernel (TDK)

The dispersal kernel generated by all vectors dispersing a certain plant species (Nathan 2007).

1026 1027

46

log Probability density

(a)

log Distance (x) log Probability density

(b)

1028

log Distance (x)

1029

Figure 6.1. Application of absolute (black solid arrows) and proportional (grey hatched

1030

arrows) definitions of long-distance seed dispersal (LDD) to two different dispersal distance

1031

kernels. Note that the proportional definition identifies extreme dispersal events rather than

1032

LDD events. Hence, in a species with highly restricted seed dispersal (a), the proportional

1033

definition will include dispersal distances of only a few metres, that typically are not

1034

considered as "long". On the other hand, in a species whose seeds are frequently dispersed

1035

over long distances (b), only the most extreme dispersal events meet the proportional

1036

definition, although somewhat less extreme distances might be long enough to influence

1037

large-scale dynamics.

1038

47

Q

Expected number of seeds

10

10

10

10

8

6

5

10

10

4

Vector1

Vector2

Vector3

Vector4

1

−5

10

−10

10

−2

10

2

1

4

10

6

10

10

Spatial scale (m)

1039

Contribution of vector to TDK (%)

Typical scale of empirical studies

1040

Scale of interest in landscape ecology/biogeography

100 80 60 40 20 0

−2

10

1

2

10

4

10

6

10

Spatial scale (m)

1041

Figure 6.2. (a) The total dispersal kernel (TDK, thick black line) for a hypothetical plant

1042

population dispersed by four vectors (different orange tints). The inset shows the unequal

1043

distribution of seed loads (Q) on a log scale. The arrows at the bottom indicate the range of

1044

distances of all seeds dispersed by each vector. (Figure modified from Nathan et al.

48

1045

submitted). (b) The relative contribution of each vector to the TDK as a function of spatial

1046

scale (x), showing that the vectors dominating LDD can be very different from those

1047

dominating seed dispersal at the typical scales of empirical studies.

49

1048

1049

Initiation

Transport

Vector seed load

Vector displacement velocity

Q

V

Termination

Seed passage time

P

1050

Figure 6.3. A general mechanistic model for passive dispersal (adapted from a general model

1051

for aerial transport, Isard & Gage 2001) that describes three phases of passive dispersal

1052

(boxes) through three key parameters (given below the boxes). The figure is modified from

1053

Nathan et al. (submitted).

50

(a)

1054 (b)

1055 (c)

1056 1057

Figure 6.4. Examples for the three general phases of passive dispersal (see Figure 6.3). (a)

1058

Initiation phase: Seeds of Sugarcane (Saccharum spp.) depart from their mother plants in a

1059

strong wind (© Ran Nathan). (b) Transport phase: A floating island composed of Papyrus

1060

(Cyperus papyurus L.) transports seeds, entire plants and other organisms across Lake Malawi

51

1061

(Oliver & McKaye 1982, © Kenneth McKaye). (c) Termination phase: After secondary wind

1062

dispersal over a burnt area in the South African Fynbos, a seed of the Common Sugarbush

1063

(Protea repens, L.) is trapped in burnt vegetation remains (© Frank Schurr).

52

Probability density

(a) 0.25 0.20 0.15 0.10 0.05 0.00 0

10

20

30

40

V (m/s) or P (s)

Probability density

(b) −2

10

−4

10

−6

10

0

500

1000

1500

Dispersal distance x (= V P) (m)

Expected number of LDD events

(c) 100 80 60 40 20 0 0

1064

50000

100000

Vector seed load Q

1065

Figure 6.5. Effects of vector seed load (Q), displacement velocity (V) and retention time (P)

1066

on the expected number of LDD events caused by different vectors dispersing a given plant

1067

species (different line types). (a) Distributions of V and P for different vectors. (b) Dispersal

1068

distance kernels that are the product of these distributions of V and P (note that the y-axis has

1069

log scaling). The solid black, solid grey and hatched grey kernels result from multiplying the

1070

respective distributions in (a) with themselves, and the black/grey line shows a kernel which

1071

is the product of the solid black and the solid grey distribution in (a). The black arrow

1072

indicates the LDD threshold chosen for this example. (c) The relationship between the

1073

expected number of LDD events and Q. Note that the hatched grey line increases so steeply

1074

that for Q = 100000 the expected number of LDD events exceeds 12000.

53

G1: Open landscapes (V,P) G2: Large animals (V,P) G3: Migratory animals (V) G4: Extreme meteorological events (V) G5: Ocean currents (Q,P) G6: Human transportation (Q,V,P)

10

2

10

3

4

5

10 10 Spatial scale (m)

10

6

10

7

1075 1076

Figure 6.6. A representation of six major generalizations about LDD mechanisms indicating

1077

the range of dispersal distances for which each generalization is most relevant. The brackets

1078

give the key parameters of the general dispersal model (Figure 6.3) that are predominantly

1079

affected by each mechanism.

54

1080 1081

Figure 6.7. Mean distance of endozoochorous seed dispersal versus disperser body mass in

1082

six field studies on ten flying frugivorous bird species. The line shows the fit of a regression

1083

of log mean dispersal distance ( x ) against log mean body mass (M). The estimated scaling

1084

exponent (the regression slope = 0.502) closely matches the value predicted by a generic

1085

allometric model (0.5). Numbers besides the points indicate the data source - 1: Westcott &

1086

Graham (2000), 2: Ward & Paton (2007), 3: Weir & Corlett (2007), 4: Spiegel & Nathan

1087

(2007), 5: Sun et al. (1997), 6: Holbrook & Smith (2000).

55

1088

Regular vectors dominate LDD (b) Cumulative contribution to LDD (%)

Contribution to LDD (%)

(a) 100

100

80 60 40 20 0

80 60 40 20 0

Vector

Vector

Irregular vectors dominate LDD (d) Cumulative contribution to LDD (%)

Contribution to LDD (%)

(c) 100

100

80 60 40 20 0

Vector

80 60 40 20 0

Vector

1089 1090

Figure 6.8. Two idealized types of dispersal vector spectra and their consequences for the

1091

predictability of LDD. Regular vectors are shown in grey and irregular vectors in white. (a)

1092

and (c) depict the two dispersal vector spectra by showing the relative contribution of the 20

1093

most important vectors to overall LDD. Note that the number of involved vectors is

1094

essentially unlimited. (b) and (d) show the respective cumulative contribution of vectors to

1095

overall LDD, with grey horizontal lines indicating the proportion of LDD explained by all

1096

regular vectors. If LDD is dominated by regular vectors, it can - at least in principle - be

56

1097

predicted through the quantification of these vectors. However, if LDD is dominated by

1098

irregular vectors, it is largely unpredictable.

57