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TITLE OF PROPOSED PROJECT Shedding Light on Seed Shadows: Dispersal Patterns of Animal-Dispersed Plants
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Page 2 of
Shedding Light on Seed Shadows: Dispersal Patterns of Animal-Dispersed
Plants
Intellectual Merit: Dispersal of seeds from one area to another is a fundamental
part of the ecological and evolutionary processes that affect plants. Yet,
biologists lack a theoretical framework for predicting how far seeds will disperse.
While much recent effort has been devoted to developing such a framework for
wind-dispersed plants, little attempt has been made to do this for animaldispersed (zoochorous) plants. Here I ask two specific questions about
zoochorous seed dispersal. (1) Do general trends in dispersal patterns exist
among zoochorous plants? (2) Can these trends be related to seed or parent
plant traits? I will address these questions through two specific aims.
1) Conduct a meta-analysis of dispersal studies. Compare dispersal
patterns among plant species to search for trends.
2) Construct a mechanistic model of zoochorous dispersal. Use this model
to predict dispersal patterns. Then compare these predictions to data
collected in specific aim 1.
In specific aim 1, I will collect data on dispersal of zoochorous plants, fit
curves to these data, and use these curves to compare dispersal parameters
among species. I will then explore the ability of a number of seed, fruit, and plant
traits to explain the variation in dispersal curves among species.
In specific aim 2, I will construct a mechanistic model to predict dispersal
distribution of zoochorous plants. Many zoochorous seeds are dispersed by
passing through the guts of animals. The size of a seed or hard fruit sets the
lower bound on the size of the animals that can ingest it. Based on this
relationship, I hypothesize that seed and fruit size can be used to predict
dispersal curves of zoochorous plants. I will gather information on
relationships between seed and fruit size and minimum disperser mass, and then
use information about disperser mass to estimate fruit consumption, movement
rate, and gut passage time of dispersers. These relationships will allow me to
estimate dispersal of plants with a given seed size. I will test this model by
comparing dispersal curves generated by the model to curves fitted to data
collected in specific aim 1.
Broader Impacts: In the proposed research, I will investigate trends in dispersal
distributions of zoochorous plants. Furthermore, I will explore the utility of seed
and fruit size as predictors of dispersal distributions of these plants. I hope to use
the results of these investigations to improve the theoretical framework used to
make predictions about seed dispersal. Information on dispersal can be used to
predict spread of invasive species, effects of habitat fragmentation, rates of
natural recolonization, and rates of gene flow among populations.
The proposed research will also aid in educating future scientists. The UF
zoology department and my lab in particular, have active undergraduate research
programs. I will recruit undergraduates to assist me in the data-gathering and
analytical components of my research. The data-gathering phase will help
undergraduates gain the skills needed to read scientific literature. The analytical
portion of the study will allow undergraduates to become familiar with software
and techniques used to develop mathematical models for ecology. I will
encourage students to participate in all steps of the research process, from
reading articles to pursuing their own side projects.
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46
1
Shedding Light on Seed Shadows: Dispersal Patterns of Animal-Dispersed
Plants
1. INTRODUCTION
Biologists have been aware of the importance of seed dispersal in
determining the spatial distribution of plants since the time of Aristotle and
Theophrastus (c.a. 340 BC, Thanos, 1994). Theophrastus, for example,
observed that birds moved mistletoe seeds from one tree branch to another and
that this process determined the future locations of adult plants (Thanos, 1994).
Over two thousand years later, the importance of seed dispersal is well
established; it is fundamental to a myriad of ecological and evolutionary
processes. These include rates of population spread (Turchin, 1998), biological
invasions (e.g. Skarpaas and Shea, 2007), local population dynamics and
competition (Bolker and Pacala, 1999), susceptibility to species-specific
pathogens and predators (Janzen, 1970) and gene flow among populations
(Bacles et al., 2006).
Despite the known importance of dispersal, biologists lack a theoretical
framework for making a priori predictions about how far a seed or groups
of seeds will disperse from a parent plant (Levin et al., 2003). I believe this is
due to two trends in past dispersal research.
I) Little attempt has been made to identify generalities in dispersal patterns
across plant species.
II) Little attempt has been made to identify plant, seed, or fruit traits that
affect dispersal across plant species.
Recent studies that use mechanistic models to predict dispersal of winddispersed seeds are important exceptions to these trends (e.g. Nathan et al.,
2002; Nathan and Katul, 2005; Katul et al., 2005; Skarpaas and Shea, 2007).
These models use properties such as seed release height, seed aerodynamics,
and the fluid dynamics of wind to predict dispersal. The success of this approach
in predicting dispersal patterns in a variety of plant species (Katul et al., 2005)
suggests two things. (1) General patterns in dispersal may exist among species
with similar dispersal modes, and (2) building and testing mechanistic models
can be a fruitful way to develop a framework to predict dispersal.
Although studies of wind-dispersed seeds have provided important
insights into the study of seed dispersal, dispersal via animal vectors (zoochory)
is the most common dispersal mode in many habitats (e.g. Griz and Machado,
2001; Herrera and Pelmyr, 2002; Selwyn and Parthasarathy, 2007). The current
paradigm is that seed dispersal via zoochory is often an idiosyncratic process,
largely dependent on the identities and behaviors of animal dispersers (e.g.
Schupp et al., 2002). If this is the case, zoochorous dispersal should be expected
to vary highly from plant species to plant species and habitat to habitat. However,
quantitative comparisons of dispersal patterns across a wide variety of
zoochorous plants have not been made and past mechanistic models remain
2
largely untested. In the present study, I ask two questions about zoochorous
seed dispersal. (1) Do general trends in dispersal patterns exist among
zoochorous plants? (2) Can these trends be related to seed or parent plant
traits? I will address these questions through two specific aims.
1) Conduct a meta-analysis of data from dispersal studies. Compare
dispersal patterns among plant species using curve-fitting and
comparison techniques to search for trends.
2) Construct a mechanistic model of zoochorous dispersal. Use this model
to predict dispersal patterns. Then compare these predictions to data
collected in specific aim 1.
Ecological and evolutionary processes involve interactions at the
population level; therefore, we need a metric for quantifying dispersal at the
population level. The seed shadow describes the two dimensional pattern of
seedfall surrounding a single dispersing plant. The seed shadow can be
simplified by assuming that seeds are dispersed equally in all directions around
the parent plant (Snall et al., 2007). Seed dispersal studies typically report a
dispersal distance distribution (henceforth dispersal distribution) or associated
dispersal kernel, which describe the number and proportion of seeds falling at
each distance from the parent plant (Nathan and Muller-Landau, 2000). These
metrics assume that seed shadows are radially symmetric. The shape and scale
of the dispersal kernel is related to processes such as spatial spread (Clark,
1998), metapopulation dynamics (With and King, 1999) and site colonization
(Portnoy and Willson, 1993). In particular, whether the kernel has high or low
kurtosis (the skewed portion of the kernel tapers slowly or rapidly, respectively)
can have a powerful effect on the outcome of these processes (Hovestadt et al,
2001).
The project outlined below will contribute to basic science by further
developing the theoretical framework used to study and predict dispersal. Both
local and regional population dynamics are mediated by dispersal (Bolker and
Pacala, 1999; Freckleton and Watkinson, 2002). This reinforces the notion that
demography and dispersal interact to determine the abundance and distribution
of species in nature (Bullock et al., 2006). The results of the present study will
contribute to basic knowledge of dispersal in animal-dispersed organisms. It may
also have applications to biological problems. The rate of spread of invasive
species is highly dependent on dispersal and projections of spread can be very
sensitive to information about the shape of dispersal kernels (Higgins et al.
2001). If the modeling approach I take allows for reasonably accurate estimation
of dispersal kernels, it could be used to improve projections of spatial spread
(Skarpaas and Shea, 2007). It could also aid in managing species of
conservation concern by improving estimates of recolonization rates, gene flow
(Coulson et al., 2001; Bacles et al., 2006), and competition for limiting resources
(Bolker and Pacala, 1999).
2. DESIGN AND SCIENTIFIC SIGNIFICANCE
3
Specific Aim 1- Meta Analysis of Seed Dispersal Kernels
Objectives and hypothesis: I will compile data from published dispersal studies
along with seed, fruit and plant characteristics of study species. I will fit curves to
unmodeled data and use curve comparison techniques to compare curves
among species. I will then look for correlations between dispersal parameters
and seed, fruit, and plant traits. I hypothesize that seed size (for fleshy fruits)
and fruit size (for hard fruits) will be positively correlated with mean and
median dispersal distance and kurtosis.
Methods: In order to search for general patterns in dispersal kernels of
zoochorous plants I must first compile dispersal data. I will search specifically for
studies that meet the following criteria:
1. Study plant species must bear fruits that are primarily dispersed
animal-dispersed.
2. Studies must directly measure seed rain in the field. This includes
studies that use inverse modeling, genetic methods, or any other
method that allows identification of the source of seeds.
3. Seed and fruit mass must be known for each plant species. I will use
this information in specific aim 2. Seed mass and other plant traits
such as number of seeds per fruit and fruit mass to seed mass ratio
have been compiled in several databases (Appendix 1, Dennis et al.,
2007; C. J. Clark, unpublished data)
Once I have compiled dispersal data, I will fit dispersal kernels to
unmodeled data. A number of functions are typically used to describe the
distance that a seed will land from its parent plant (Clark et al., 2005; Bullock et
al., 2006). These include negative exponential, bounded form of an inverse
power law, Student t, Gaussian and 2Dt (Clark et al. 1999; Clark et al., 2005;
Bullock et al., 2006). The fits of these functions to observed dispersal kernels can
be compared using maximum likelihood methods (Clark et. al., 2005). In cases
where the best-fit function for a particular species has already been identified
(e.g. Clark et al., 2005), I will record parameter estimates for the fitted function.
After fitting curves to published dispersal data, I will be able to estimate
and compare distance parameters among species. In particular, I will estimate
mean and median dispersal distance among species. I will then use multiple
linear regression to determine whether seed mass and a number of other
candidate variables (e.g. fruit mass, fruit hardness, plant height) explain variation
in mean and median dispersal distance among plant species (Clark et al., 2005).
In addition to the above analysis, I will analyze differences in the shape of
dispersal kernels among species. This will require that I fit the same dispersal
function (e.g. 2Dt) to dispersal data from all species. I will compete several
functions to determine which provides the best overall fit to dispersal data for all
species using maximum likelihood methods. If either the 2Dt or exponential
4
family functions provide the best fit, the kurtosis (a number representing the
“fatness of the tail” of the distribution) can be compared among kernels (Clark,
1999). After calculating kurtosis for all species I will use linear regression to
determine whether seed mass and other explanatory variables explain variation
in kurtosis among plant species.
The results of this portion of the study will address shortcomings in current
research by (1) elucidating trends in dispersal properties among taxa and (2)
attempting to associate these patterns with seed, fruit or plant traits.
Specific Aim 2 – Construct a Mechanistic Model of Zoochorous Seed
Dispersal
Objectives and hypothesis: I will build a mechanistic model to estimate
dispersal kernels of animal-dispersed plants. I will Use this model to predict
dispersal patterns. I will then compare these predictions to data collected in
specific aim 1. I hypothesize that seed and fruit size can be used to predict
the mean and median of dispersal curves of zoochorous plants.
Model Development: One approach to estimating dispersal kernels for
zoochorous plants is to use a technique referred to as mechanistic modeling
(Levin et al., 2003; Denis and Wescott, 2007; Nathan, 2007). This technique can
be used to estimate the dispersal distribution generated by a single disperser
species (e.g. seeds dispersed by cassowaries, Dennis and Wescott, 2006) or, by
summing dispersal distributions of all dispersing species, to estimate the total
dispersal kernel of a plant (Dennis and Wescott, 2007). These models typically
incorporate (1) some measure of the proportion of the seed crop taken by
disperser species, (2) rates of movement (displacement rate) of each disperser
species and (3) time taken for seeds to pass through the gut of the each
disperser species. The frequency distribution of gut passage times is often
measured empirically for each disperser and multiplied by the displacement rate
of that disperser to get a frequency distribution of seed deposition distances from
the seed source. These distributions are then summed for all dispersers to get a
total dispersal distribution for the dispersing plant (Dennis and Wescott,2007).
This distribution can be converted to a dispersal kernel by standardizing it by the
total number of seeds dispersed.
Estimating total dispersal kernels using mechanistic modeling techniques
is labor-intensive. Gathering information about the proportion of the total seed
crop taken by each disperser requires many hours of field observation (e.g.
Dennis and Wescott, 2007). Additionally, time taken for seeds to pass through
the gut of each disperser must also be measured. Given the substantial amount
of labor involved, it is not surprising that this technique is rarely used to estimate
total dispersal kernels (reviewed by Nathan, 2007). However, estimating total
dispersal kernels using a mechanistic model might be more tractable if the
variables used to construct the model could be estimated rather than measured
directly. Biological scaling theory (Peters, 1983; Damuth, 1987; West et al.,
1997) suggests that several of the variables typically used in mechanistic models
5
can be related to the body masses of dispersers. The rates of consumption,
displacement rates, and abundances of animals (dispersers), for example, are
known to vary predictably with body mass (Peters 1983). I propose that these
relationships and several others outlined below can be used to simplify the
process of mechanistic modeling.
Minimum disperser mass. A large proportion of zoochorous seeds are
moved from one place to another inside the guts of animals. The size of a seed
or hard fruit sets the lower bound on the size of the animals that can ingest and
disperse it (Herrera and Pelmyr, 2002). All else being equal, one would expect a
larger seed to be dispersed by dispersers that are, on average, larger in body
mass than those that disperser a smaller seed because the lower limit of
disperser body mass increases with seed size (Mack, 1993). This is supported by
the observation that the mean size of fruits eaten by birds is positively correlated
with gape width and body mass (Wheelwright, 1985; Jordano, 1987) and that
species dispersed by mammals tend to be larger than those dispersed by birds
(Janson, 1983; Herrera, 1989). Given that many morphological traits are known
to vary systematically with body mass, it may be possible to predict the minimum
mass of a potential disperser based on the relationship between disperser mass,
gape width, and seed size of the dispersing plant (i.e. minimum mass Md=m0Msk,
where m0 is an empirically derived constant). If the mass of the largest disperser
is known, the size range of dispersers for a given plant species can be estimated.
Proportion of seeds taken. I will make the simplifying assumption that
the number of encounters that dispersers in the ith size class have with a
dispersing plant, Ei, in a given fruiting period is proportional to the abundance of
that species in the habitat (Ei=e0Ni, where e0 is an empirically derived constant).
Established mass-abundance scaling relationships can be used to calculate Ni
(Ni=n0Mi-3/4, where n0 is an empirically derived constant, Damuth, 1987). The
proportion of seeds taken by the ith disperser size class, Pi, can be estimated by
dividing the number of seeds taken per encounter, Ns, by the total number of
seeds produced by the parent plant, Ntotal and then multiplying this entire quantity
by Ei (Pi=Ei[Ns/Ntotal]). Ntotal can be calculated by multiplying the number of seeds
by the number of fruits per plant, which has been reported for a variety of species
(e.g. Janzen et al., 1976; Laman, 1996; Clark et al., 2006). Ns is likely related to
the feeding habits of foraging frugivores. Given that food intake rates increase
with body mass (Peters, 1983), I expect that the number of fruits and
consequently, the number of seeds taken per encounter will increase with body
mass (Ns=s0Mis, where s0 is an empirically derived constant). Because both Ei
and Ns are functions of disperser mass, M, and Ntotal is known, Pi can be rewritten
as a function of M (Pi=P(M)).
Gut passage time. My preliminary literature survey suggests that mean
gut passage time increases with increasing disperser mass (Dennis and Wescott,
2006; 2007). Mean gut passage time of the ith disperser species, Gi, is therefore
expected to increase with disperser mass (Gi=g0Mg, where g0 is an empirically
derived constant).
Displacement. The displacement of seeds from a dispersing plant can be
estimated by assuming that dispersers move by simple diffusion. The distance
6
moved by the ith size class, Fi, has a normal distribution with a mean of zero and
a variance equal to the diffusion coefficient, Di, times Gi (Fi~N(0,DiGi), Turchin,
1998). Di is a function of average foraging velocity of the ith size class, Vi
(Di1/2!Vi=v0Mi1/4, where v0 is an empirically derived constant, Peters, 1983).
Because Di and Gi are both functions of M, Fi can be rewritten as a function of M.
(Fi=F(M)).
The variables listed above can be used to generate the total dispersal
kernel for a plant species with a given seed or fruit size.
!
F(M)P(M)dM,
(1)
Where F(M)P(M) is greater than zero when M is between Md (the minimum
disperser mass derived from Md=m0Msk) and Mmax (the mass of the largest
disperser which must be known) and zero otherwise. Equation 1 will yield a total
dispersal kernel that is composed of the integral of the weighted dispersal
kernels of each disperser size class between Md and Mmax. This is a continuous
analogue of the sum used in past mechanistic models (see model development
above). I have described dispersers using size classes rather than species or
functional groups (Dennis and Wescott, 2006; 2007) and have scaling equations
to estimate variables rather than measuring them directly.
The model makes several important assumptions. (1) It assumes that all
species of the same size class have the same rates of encounter, consume the
same proportion of seeds per encounter, and have the same gut passage time
and foraging velocity. A 200g bird is assumed to behave exactly like a 200g
rodent with respect to the above variables. (2) It assumes that all species that
can consume a seed of a given size will do so with equal probability. This ignores
the possibility that some dispersers prefer some fruits over others. (3) It assumes
that the proportion of seeds taken per encounter, the gut passage times of each
species, and the foraging velocities of each species are fixed. (4) It treats size
class as a continuous random variable. This avoids the need to divide dispersers
into arbitrary discrete categories.
By conducting an extensive literature survey, I plan to gather information
on the scaling relationships mentioned above. My preliminary literature survey
indicated that sufficient data exists to estimate unknown scaling exponents (e.g.
k, g, and s) and scaling coefficients (m0, e0, n0, s0, g0 and v0).
After collecting the necessary data, I will compare dispersal kernels
generated by the above model to kernels fitted to dispersal data. Ideally I would
be able to compare mean, median, and kurtosis of these curves. While means
and medians will be comparable, comparing kurtosis may or may not be possible
depending on the degree of uncertainty about the tails of kernels generated by
the above model. However, comparing mean and median dispersal distances will
still allow me to evaluate the predictive capacity of the model.
3. PROGRESS TO DATE
7
Meta-analysis of seed dispersal kernels. I have begun compiling published
dispersal studies. I have not yet begun fitting dispersal functions to dispersal
data. I have not yet begun comparing kernels among species.
Mechanistic model of zoochorous seed dispersal. I have conducted
preliminary literature surveys to determine whether the information required to
build the proposed seed dispersal model exists. I have not yet begun compiling
and analyzing data.
4. CONCLUSION
This study seeks to identify general patterns in the dispersal kernels of
animal dispersed plants (specific aim 1). It also seeks to relate those patterns to
plant traits (specific aim 1). Willson (1993) performed the only previous metaanalysis of seed dispersal among plant species. This analysis focused primarily
on differences among plants dispersed by different vectors (i.e. animal-dispersal
versus wind-dispersal versus ballistic-dispersal). An increase in the number of
available data sets (Willson 1993 used data from nine animal-dispersed species
whereas my preliminary survey suggests that I will be able to compare at least
20-25 species) and advances in the statistical techniques (e.g. comparing
kurtosis, Clark et al., 1999) will allow me to make broader inferences about
differences in dispersal kernels among species.
The proposed model (specific aim 2) will be the first mechanistic model to
my knowledge that seeks to predict dispersal kernels of zoochorous plants based
on plant trait. Given the success and utility of mechanistic models of winddispersal (e.g. Nathan et al., 2002; Katul et al., 2005), developing similar
mechanistic models for zoochorous species seems to be a natural step in the
development of dispersal theory.
5. BROADER IMPACTS
The proposed research will contribute to a number of applied biological
problems. Invasion and spread of exotic species can be limited by dispersal
(Havel et al., 1996). The model developed here may be useful in estimating
dispersal distributions of invasive species. Incorporating this model into
projections of the spread of invasive species populations over the landscape may
greatly improve the accuracy of these projections (Kot et al., 1996).
This model may also aid in predicting rates of natural recolonization and
rates of gene flow among populations (Coulson et al., 2001; Bacles et al., 2006).
This information would be useful to restoration efforts and threatened and
endangered species management plans.
The proposed research will also aid in educating future scientists. The UF
zoology department and my lab in particular, have active undergraduate research
programs. We are currently working with three undergraduate researchers who
are pursuing diverse projects. I plan to recruit undergraduates to assist me in
both the data gathering and analytical components of my research. The data
8
gathering and model development phase will help undergraduate researchers
gain the skills needed to read scientific literature and extract pertinent
information. The analytical portion of the study will allow undergraduate
researchers to become familiar with the statistics and software used to address
spatial problems in ecology. In addition to assisting me, I will encourage students
to participate in all steps of the research process, from reading articles to
pursuing their own side projects.
9
LITERATURE CITED
Bacles, C.F.E., Lowe, A.J., & Ennos, R.A., 2006. Effective seed dispersal across a fragmented
landscape. Science 311:628.
Bullock, J. M., Shea, K., & Skarpaas, O. 2006. Measuring plant dispersal: an introduction to field
methods and experimental design. Plant Ecology 186:217-234.
Bolker, B.M. and S.W. Pacala. 1999. Spatial moment equations for plant competition:
understanding spatial strategies and the advantages of short dispersal. Am. Nat.
153:575-602.
Clark, C.J. et al., 2005. Comparative Seed Shadows of Bird-, Monkey-, and Wind-Dispersed
Trees. Ecology 86:2684.
Clark, J.S. 1998. Why Trees Migrate So Fast: Confronting Theory with Dispersal Biology and the
Paleorecord. Am. Nat. 152:204-224.
Clark, J.S. et al., 1999. Seed dispersal near and far: patterns across temperate and tropical
forests. Ecology 80:1475.
Coulson, S.J. et al., 2001. Colonization of grassland by sown species: dispersal versus microsite
limitation in responses to management. J. Appl. Ecol. 38:204-216.
Damuth, J. 1987. Interspecific allometry of population density in mammals and other animals: the
independence of body mass and population energy use. Biol. J. Linn. Soc. 31:193-246.
Dennis, A. J. & Westcott, D. W. 2006. Reducing complexity when studying seed dispersal at
community scales: a functional classification of vertebrate seed dispersers in tropical
forests. Oecologia, 149:620-634.
Dennis, A.J. & Wescott, D.A. 2007. Dispersal kernels produced by a diverse community of
vertebrates. In A. J. Dennis et al., eds. Seed Dispersal: Theory and its Application in a
Changing World. CAB International, pp. 201-228.
Freckleton, R.P. & Watkinson, A.R. 2002. Large-scale spatial dynamics of plants:
metapopulations, regional ensembles and patchy populations. J. Ecology 90:419-434.
Griz, L.M.S. & Machado, I.C.S. 2001. Fruiting phenology and seed dispersal syndromes in
caatinga, a tropical dry forest in the northeast of Brazil. J. Tropical Ecol. 17:303-321.
Herrera, C., 1989. Frugivory and seed dispersal by carnivorous mammals, and associated fruit
chracteristics, in undisturbed mediterranean habitats. Oikos, 55:50-262.
Herrera, C.M. & Pellmyr, O., 2002. Plant-Animal Interactions: an Evolutionary Approach,
Blackwell Science Ltd. pp.331.
Higgins, S.I., Richardson, D.M., & Cowling, R.M., 2001. Validation of a spatial simulation model of
a spreading alien plant population. J. Appl. Ecol 38:571-584.
Hovestadt, T., Messner, S., & Poethke, H.J. 2001. Evolution of reduced dispersal mortality and
'fat-tailed' dispersal kernels in autocorrelated landscapes. Proc. Royal Soc. B 268:385391.
Janson, C.H. 1983. Adaptation of fruit morphology to dispersal agents in a neotropical forest.
Science 219:187-189.
Janzen, D.H. 1970. Herbivores adn the number of tree species in tropical forests. Am. Nat.
104:501-528.
Janzen, D. H. et al . 1976. Two Costa Rican bat generated seed shadows of Andira inermis
(Leguminoseae). Ecology 57:1068–1075.
Jordano, P. 1987. Frugivory, external morphology and digestive system in Mediterranean Sylviic
Warblers Sylvia spp. Ibis 129:175-189.
Katul, G. et al. 2005. Mechanistic analytical models for long!distance seed dispersal by wind.
Am. Nat. 166:368-381.
Kot, M., Lewis, M.A., & van den Driessche, P. 1996. Dispersal data and the spread of invading
organisms. Ecology, 77:2027-2042.
Levin, S.A. et al., 2003. The ecology and evolution of seed dispersal: a theoretical perspective.
Ann. Rev. of Ecol. Syst. 34:575-604.
Laman, T. G. 1996. Ficus seed shadows in a Bornean rain forest. Oecologia 107:347-355.
Mack, A.L. 1993. The sizes of vertebrate-dispersed fruits: a neotropical-paleotropical comparison.
Am. Nat. 142:840-856.
10
Nathan, R. & Muller-Landau, H.C. 2000. Spatial patterns of seed dispersal, their determinants
and consequences for recruitment. TREE 15:278-285.
Nathan, R. et al. 2002. Mechanisms of long-distance dispersal of seeds by wind. Nature 418:409413.
Nathan, R. & Katul, G.G. 2005. Foliage shedding in deciduous forests lifts up long-distance seed
dispersal by wind. PNAS 102:8251-8256.
Nathan, R. 2007. Total dispersal kernels in complex dispersal systems. In A. J. Dennis et al., ed.
Seed Dispersal: Theory and its Application in a Changing World. CAB International, p.
252-276.
Peters, R.H. 1983. The Ecological Implications of Body Size, Cambridge pp.329.
Portnoy, S. & Willson, M.F. 1993. Seed dispersal curves: Behavior of the tail of the distribution.
Evol. Ecol. 7:25-44.
Schupp, E.W., Milleron, T., & Russo, S. 2002. Dissemination limitation and the origin and
maintenance of species-rich tropical forests. In D. J. Levey, W. Silva, & M. Galetti, eds.
Seed Dispersal and Frugivory: Ecology, Evolution and Conservation. CAB International,
p. 19-33.
Selwyn, M.A. & Parthasarathy, N. 2007. Fruiting phenology in a tropical dry evergreen forest on
the Coromandel coast of India in relation to plant life-forms, physiognomic groups,
dispersal modes, and climatic constraints. Flora 202:371-382.
Skarpaas, O. & Shea, K. 2007. Dispersal patterns, dispersal mechanisms, and invasion wave
speeds for invasive thistles. Am. Nat., 170:421-430.
Snall, T., O'Hara, R., & Arjas, E. 2007. A mathematical and statistical framework for modelling
dispersal. Oikos 116:1037-1050.
Thanos, C.A. 1994. Aristotle and Theophrastus on Plant-Animal Interactions. In M. Arianoutsou &
R. H. Groves, eds. Plant-Animal Interactions in Mediterranean-Type Ecosystems. Kluwer
Academic, Dordrecht, p. 3-11.
Turchin, P. 1998. Quantitative Analysis of Movement, Sunderland, MA: Sinauer Associates.
pp.396.
West, G.B., Brown, J.H., & Enquist, B.J. 1997. A general model for the origin of allometric scaling
laws in biology. Science 276:122-126.
Wheelwright, N.T. 1985. Fruit-size, gape width, and the diets of fruit-eating birds. Ecology 66:808818.
Willson, M. 1993. Dispersal mode, seed shadows, and colonization patterns. Vegetatio
107/108:261-280.
With, K.A. & King, A.W. 1999. Extinction thresholds for species in fractal landscapes. Con. Bio.
13:314-326.
Biographical Sketch
Andrew M. Hein
Professional Preparation
Auburn University
University of Florida
Zoology
Zoology
B.S., 2006
Ph.D., in progress
Appointments
2007-present: Ph.D. Student, University of Florida
Summer 2007: Research Assistant, Jim Godwin (PI), Alabama Natural Heritage Project
2006-2007:
Research Assistant (Panama), Old Dominion University and STRI
Spring 2006: Research Assistant, Craig Guyer (PI), Auburn University
2005-2006:
Undergrad Research Fellow, Craig Guyer (Advisor), Auburn University
2003-2005:
Research Assistant, Jack Feminella (PI), Auburn University
2001-2002:
Wilderness Crew (GS-4), US Forest Service, Inyo National Forest, CA
Synergistic Activities
(i) Science Education Program/Alabama Natural Heritage Project: As a research
assistant in Southern Alabama I helped give science education presentations to kids in
rural areas of Alabama. This program, originally started by Auburn University’s
Environmental Institute, was intended to expose kids to the flora and fauna of Alabama in
a scientific context. I captured animals and designed and delivered talks to elementary
and middle school students.
(ii) Tropical Conservation in Panama: During my time in Panama I worked with locals
to found a conservation NGO. This organization (AGLAC) works with local schools to
teach kids about the natural history of Panama. They also work with scientific
organizations like the Smithsonian Tropical Research Institute and the Audubon Society
to assist researchers working in central Panama. In addition to helping start AGLAC, I
built and maintain their website.
(iii) Wilderness Education: As part of my job with the US Forest Service in California I
was responsible for communicating conservation regulation and ethics to the public. I
was responsible for educating visitors about low impact camping and for assessing the
impact of use on protected wilderness areas.
Collaborators
Doctoral Advisor:
James F. Gillooly, University of Florida, USA
Year 1
FOR NSF USE ONLY
5
4
SUMMARY PROPOSAL BUDGET
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Department of Zoology, University of Florida
Proposed
PRINCIPAL INVESTIGATOR/PROJECT DIRECTOR
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AWARD NO.
Andrew Hein
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NSF-Funded
List each separately with name and title. (A.7. Show number in brackets)
CAL
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4. (
) UNDERGRADUATE STUDENTS
5. (
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TOTAL SALARIES AND WAGES (A + B)
C. FRINGE BENEFITS (IF CHARGED AS DIRECT COSTS)
TOTAL SALARIES, WAGES AND FRINGE BENEFITS (A + B + C)
D. EQUIPMENT (LIST ITEM AND DOLLAR AMOUNT FOR EACH ITEM EXCEEDING $5,000.)
(If Different)
$
0
0
0
5422
Computer, software and reference books
TOTAL EQUIPMENT
5422
E. TRAVEL
1. DOMESTIC (INCL. CANADA, MEXICO AND U.S. POSSESSIONS)
2. FOREIGN
F. PARTICIPANT SUPPORT
1. STIPENDS
$
2. TRAVEL
3. SUBSISTENCE
4. OTHER
TOTAL NUMBER OF PARTICIPANTS (
)
COSTS
G. OTHER DIRECT COSTS
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2. PUBLICATION/DOCUMENTATION/DISSEMINATION
3. CONSULTANT SERVICES
4. COMPUTER SERVICES
5. SUBAWARDS
TOTAL PARTICIPANT
6. OTHER
TOTAL OTHER DIRECT COSTS
H. TOTAL DIRECT COSTS (A THROUGH G)
I. INDIRECT COSTS (F&A) (SPECIFY RATE AND BASE)
0
0
5422
TOTAL INDIRECT COSTS (F&A)
J. TOTAL DIRECT AND INDIRECT COSTS (H + I)
K. RESIDUAL FUNDS (IF FOR FURTHER SUPPORT OF CURRENT PROJECT SEE GPG II.D.7.j.)
L. AMOUNT OF THIS REQUEST (J) OR (J MINUS K)
0
5422
0
$5422
$
M. COST SHARING: PROPOSED LEVEL $
PI/PD TYPED NAME AND SIGNATURE*
AGREED LEVEL IF DIFFERENT: $
DATE
FOR NSF USE ONLY
Andre Hein
2/18/2008
ORG. REP. TYPED NAME & SIGNATURE*
DATE
NSF Form 1030 (10/99) Supersedes All Previous Editions
*SIGNATURES REQUIRED ONLY FOR REVISED BUDGET (GPG III.C)
INDIRECT COST RATE VERIFICATION
Date Checked
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Year 2
FOR NSF USE ONLY
5
4
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ORGANIZATION
PROPOSAL NO.
DURATION (MONTHS)
Department of Zoology, University of Florida
Proposed
PRINCIPAL INVESTIGATOR/PROJECT DIRECTOR
Granted
AWARD NO.
Andrew Hein
A. SENIOR PERSONNEL: PI/PD, Co-PIs, Faculty and Other Senior Associates
NSF-Funded
List each separately with name and title. (A.7. Show number in brackets)
Person-months
CAL ACAD SUMR
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Requested By
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$
2.
3.
4.
5.
6. (
) OTHERS (LIST INDIVIDUALLY ON BUDGET EXPLANATION PAGE)
7. (
) TOTAL SENIOR PERSONNEL (1-6)
B. OTHER PERSONNEL (SHOW NUMBERS IN BRACKETS)
1. (
) POSTDOCTORAL ASSOCIATES
2. (
) OTHER PROFESSIONALS (TECHNICIAN, PROGRAMMER, ETC.)
3. (
) GRADUATE STUDENTS
4. (
) UNDERGRADUATE STUDENTS
5. (
) SECRETARIAL - CLERICAL (IF CHARGED DIRECTLY)
6. (
) OTHER
TOTAL SALARIES AND WAGES (A + B)
C. FRINGE BENEFITS (IF CHARGED AS DIRECT COSTS)
TOTAL SALARIES, WAGES AND FRINGE BENEFITS (A + B + C)
D. EQUIPMENT (LIST ITEM AND DOLLAR AMOUNT FOR EACH ITEM EXCEEDING $5,000.)
(If Different)
$
0
0
0
Laboratory Equipment
TOTAL EQUIPMENT
0
E. TRAVEL
1. DOMESTIC (INCL. CANADA, MEXICO AND U.S. POSSESSIONS)
2. FOREIGN
F. PARTICIPANT SUPPORT
1. STIPENDS
$
2. TRAVEL
3. SUBSISTENCE
4. OTHER
TOTAL NUMBER OF PARTICIPANTS (
)
COSTS
G. OTHER DIRECT COSTS
1. MATERIALS AND SUPPLIES
2. PUBLICATION/DOCUMENTATION/DISSEMINATION
3. CONSULTANT SERVICES
4. COMPUTER SERVICES
5. SUBAWARDS
TOTAL PARTICIPANT
6. OTHER
TOTAL OTHER DIRECT COSTS
H. TOTAL DIRECT COSTS (A THROUGH G)
I. INDIRECT COSTS (F&A) (SPECIFY RATE AND BASE)
0
0
0
TOTAL INDIRECT COSTS (F&A)
J. TOTAL DIRECT AND INDIRECT COSTS (H + I)
K. RESIDUAL FUNDS (IF FOR FURTHER SUPPORT OF CURRENT PROJECT SEE GPG II.D.7.j.)
L. AMOUNT OF THIS REQUEST (J) OR (J MINUS K)
0
0
0
$0
$
M. COST SHARING: PROPOSED LEVEL $
PI/PD TYPED NAME AND SIGNATURE*
AGREED LEVEL IF DIFFERENT: $
DATE
FOR NSF USE ONLY
Andre Hein
2/18/2008
ORG. REP. TYPED NAME & SIGNATURE*
DATE
NSF Form 1030 (10/99) Supersedes All Previous Editions
*SIGNATURES REQUIRED ONLY FOR REVISED BUDGET (GPG III.C)
INDIRECT COST RATE VERIFICATION
Date Checked
Date of Rate Sheet
Initials-ORG
Year 3
FOR NSF USE ONLY
5
4
SUMMARY PROPOSAL BUDGET
ORGANIZATION
PROPOSAL NO.
DURATION (MONTHS)
Department of Zoology, University of Florida
Proposed
PRINCIPAL INVESTIGATOR/PROJECT DIRECTOR
Granted
AWARD NO.
Andrew Hein
A. SENIOR PERSONNEL: PI/PD, Co-PIs, Faculty and Other Senior Associates
NSF-Funded
List each separately with name and title. (A.7. Show number in brackets)
Person-months
CAL ACAD SUMR
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Funds
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Proposer
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3.
4.
5.
6. (
) OTHERS (LIST INDIVIDUALLY ON BUDGET EXPLANATION PAGE)
7. (
) TOTAL SENIOR PERSONNEL (1-6)
B. OTHER PERSONNEL (SHOW NUMBERS IN BRACKETS)
1. (
) POSTDOCTORAL ASSOCIATES
2. (
) OTHER PROFESSIONALS (TECHNICIAN, PROGRAMMER, ETC.)
3. (
) GRADUATE STUDENTS
4. (
) UNDERGRADUATE STUDENTS
5. (
) SECRETARIAL - CLERICAL (IF CHARGED DIRECTLY)
6. (
) OTHER
TOTAL SALARIES AND WAGES (A + B)
C. FRINGE BENEFITS (IF CHARGED AS DIRECT COSTS)
TOTAL SALARIES, WAGES AND FRINGE BENEFITS (A + B + C)
D. EQUIPMENT (LIST ITEM AND DOLLAR AMOUNT FOR EACH ITEM EXCEEDING $5,000.)
(If Differ-
$ ent)
$
0
0
0
Laboratory Equipment
TOTAL EQUIPMENT
0
E. TRAVEL
1. DOMESTIC (INCL. CANADA, MEXICO AND U.S. POSSESSIONS)
2. FOREIGN
F. PARTICIPANT SUPPORT
1. STIPENDS
$
2. TRAVEL
3. SUBSISTENCE
4. OTHER
TOTAL NUMBER OF PARTICIPANTS (
)
COSTS
G. OTHER DIRECT COSTS
1. MATERIALS AND SUPPLIES
2. PUBLICATION/DOCUMENTATION/DISSEMINATION
3. CONSULTANT SERVICES
4. COMPUTER SERVICES
5. SUBAWARDS
TOTAL PARTICIPANT
6. OTHER
TOTAL OTHER DIRECT COSTS
H. TOTAL DIRECT COSTS (A THROUGH G)
I. INDIRECT COSTS (F&A) (SPECIFY RATE AND BASE)
0
0
0
TOTAL INDIRECT COSTS (F&A)
J. TOTAL DIRECT AND INDIRECT COSTS (H + I)
K. RESIDUAL FUNDS (IF FOR FURTHER SUPPORT OF CURRENT PROJECT SEE GPG II.D.7.j.)
L. AMOUNT OF THIS REQUEST (J) OR (J MINUS K)
0
0
0
$0
$
M. COST SHARING: PROPOSED LEVEL $
PI/PD TYPED NAME AND SIGNATURE*
AGREED LEVEL IF DIFFERENT: $
DATE
FOR NSF USE ONLY
Andre Hein
2/18/2008
ORG. REP. TYPED NAME & SIGNATURE*
DATE
NSF Form 1030 (10/99) Supersedes All Previous Editions
*SIGNATURES REQUIRED ONLY FOR REVISED BUDGET (GPG III.C)
INDIRECT COST RATE VERIFICATION
Date Checked
Date of Rate Sheet
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Cumulative Budget
FOR NSF USE ONLY
5
4
SUMMARY PROPOSAL BUDGET
ORGANIZATION
PROPOSAL NO.
DURATION (MONTHS)
Department of Zoology, University of Florida
Proposed
PRINCIPAL INVESTIGATOR/PROJECT DIRECTOR
Granted
AWARD NO.
Andrew Hein
A. SENIOR PERSONNEL: PI/PD, Co-PIs, Faculty and Other Senior Associates
NSF-Funded
List each separately with name and title. (A.7. Show number in brackets)
Person-months
CAL ACAD SUMR
1.
Funds
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Requested By
Granted by
NSF
Proposer
2.
3.
4.
5.
6. (
) OTHERS (LIST INDIVIDUALLY ON BUDGET EXPLANATION PAGE)
7. (
) TOTAL SENIOR PERSONNEL (1-6)
B. OTHER PERSONNEL (SHOW NUMBERS IN BRACKETS)
1. (
) POSTDOCTORAL ASSOCIATES
2. (
) OTHER PROFESSIONALS (TECHNICIAN, PROGRAMMER, ETC.)
3. (
) GRADUATE STUDENTS
4. (
) UNDERGRADUATE STUDENTS
5. (
) SECRETARIAL - CLERICAL (IF CHARGED DIRECTLY)
6. (
) OTHER
TOTAL SALARIES AND WAGES (A + B)
C. FRINGE BENEFITS (IF CHARGED AS DIRECT COSTS)
TOTAL SALARIES, WAGES AND FRINGE BENEFITS (A + B + C)
D. EQUIPMENT (LIST ITEM AND DOLLAR AMOUNT FOR EACH ITEM EXCEEDING $5,000.)
(If Differ-
$ ent)
$
0
0
0
5422
Computer, software and reference books
TOTAL EQUIPMENT
5422
E. TRAVEL
1. DOMESTIC (INCL. CANADA, MEXICO AND U.S. POSSESSIONS)
2. FOREIGN
F. PARTICIPANT SUPPORT
1. STIPENDS
$
2. TRAVEL
3. SUBSISTENCE
4. OTHER
TOTAL NUMBER OF PARTICIPANTS (
)
COSTS
G. OTHER DIRECT COSTS
1. MATERIALS AND SUPPLIES
2. PUBLICATION/DOCUMENTATION/DISSEMINATION
3. CONSULTANT SERVICES
4. COMPUTER SERVICES
5. SUBAWARDS
TOTAL PARTICIPANT
6. OTHER
TOTAL OTHER DIRECT COSTS
H. TOTAL DIRECT COSTS (A THROUGH G)
I. INDIRECT COSTS (F&A) (SPECIFY RATE AND BASE)
0
0
5422
TOTAL INDIRECT COSTS (F&A)
J. TOTAL DIRECT AND INDIRECT COSTS (H + I)
K. RESIDUAL FUNDS (IF FOR FURTHER SUPPORT OF CURRENT PROJECT SEE GPG II.D.7.j.)
L. AMOUNT OF THIS REQUEST (J) OR (J MINUS K)
0
5422
0
$5422
$
M. COST SHARING: PROPOSED LEVEL $
PI/PD TYPED NAME AND SIGNATURE*
AGREED LEVEL IF DIFFERENT: $
DATE
FOR NSF USE ONLY
Andre Hein
2/18/2008
ORG. REP. TYPED NAME & SIGNATURE*
DATE
NSF Form 1030 (10/99) Supersedes All Previous Editions
*SIGNATURES REQUIRED ONLY FOR REVISED BUDGET (GPG III.C)
INDIRECT COST RATE VERIFICATION
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InitialsORG
Budget Justification
Budget Justification:
The completion of this project will require that I boy equipment necessary to perform the computations described in the
project description. The undergraduate assistants that will be aiding me in this project will work through the URAP (undergraduate research assistantship program), which will offer them beneficial experience. Students will work on a volunteer basis.
A. Senior Personnel: None
B. Other Personnel: None
C. Fringe Benefits: None
D. Equipment: No individual item exceeds 5000 in value. Equipment includes all that is necessary to enable me to
conduct the computations outlined in the project description. This includes Mathematica software, necessary reference books and Macintosh 8 core desktop computer and accessories.
E. Travel: None
F. Participant Support: None
G. Other: None
I. Indirect Costs
Current and Pending Support
(See GPG Section II.D.8 for guidance on information to include on this form.)
The following information should be provided for each investigator and other senior personnel. Failure to provide this
information may delay consideration of this proposal.
Other agencies (including NSF) to which this proposal has been/will be submitted.
The project has no current/pending support
Investigator:
None
Support:
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Pending
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*If this project has previously been funded by another agency, please list and furnish information for immediately preceding funding period.
NSF Form 1239 (10/99)
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5
USE ADDITIONAL SHEETS AS NECESSARY
FACILITIES, EQUIPMENT & OTHER RESOURCES
FACILITIES: Identify the facilities to be used at each performance site listed and, as appropriate, indicate their capacities, pertinent
capabilities, relative proximity, and extent of availability to the project. Use “Other” to describe the facilities at any other
performance sites listed and at sites for field studies. Use additional pages if necessary.
Laboratory: Gillooly Laboratory, University of Florida
The Gillooly laboratory will provide me with space to set up the computer for data compilation and analysis.
Clinical:
Animal:
Computer: University of Florida CNS computer cluster
The cluster is available to UF students and may be used to run simulations of seed dispersal model.
Office:
Other:
MAJOR EQUIPMENT: List the most important items available for this project and, as appropriate, identify the location and
pertinent capabilities of each.
OTHER RESOURCES: Provide any information describing the other resources available for the project. Identify support services
such as consultant, secretarial, machine shop, and electronics shop, and the extent to which they will be available for the project.
Include an explanation of any consortium/contractual/subaward arrangements with other organizations.
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NSF Form 1363 (10/99)