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Spatial Distributions of Native and Invasive Shrubs in a Sub-Tropical Forest
Natalia Martinez1
Carol Horvitz2
Kelley Erickson2
Matthew Palmer1
1Department
of Ecology, Evolution and Environmental Biology, Columbia University,
New York, NY 10027, USA
2Biology Department, University of Miami, Coral Gables, FL 33124, USA
12/10/2012
Ilex cassine
Schinus terebinthifolius
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Abstract
Tree invasions can negatively affect ecosystems by altering environmental conditions and
displacing native species. Trees provide structure and habitat for forest ecosystems and so exotic
tree invasions can have particularly dramatic effects on communities. The negative results of these
invasions include alteration of successional dynamics reduced diversity and relative abundance of
native species, disruption of important ecosystem functions and high public costs to manage
invasive species. Competition, dispersal and environmental heterogeneity can have significant
effects on spatial patterns of plants. Plant interactions with their immediate neighbors are most
significant, and so the spatial distribution of neighbors can influence the extent of interactions in a
system, which will influence the future composition of the system. The Everglades in South Florida
has sensitive tree islands whose successful restoration depends on native plant recruitment. There
is currently severe invasion in the Everglades by Schinus terebinthifolius. The native shrub Ilex
cassine occupies a similar niche and has been identified as an important recruit for tree island
restoration and has also been found to establish populations within Schinus thickets. This study
examines how the two are spatially distributed in sites where they coexist. Few Ilex individuals
were found at the study site and there is no significant spatial autocorrelation in either species.
Further analysis will lead to a discussion of what this indicates about the present and future state
of Schinus invasion. It is important to halt the spread of this invasive due to its level of disruption
in the already disturbed Everglades.
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Table of Contents
Abstract ……………………………………………………………………………………..………………………..………………..1
Introduction ……………………………………………………………………………………………………………………….….3
Methods………………………………………………………………………………………………………..…………….…………9
Results ……………………………………………….………………………………………………………………………….……..12
Discussion…………………………………………………………………………………………………….….…………………….19
References ………………………………………………………………..………….……………..………………………………..23
Tables and Figures:
Figure 1: Aerial photograph of site location…………………………………………………………………….……………10
Figure 2: Map showing locations of the ten transects………………………………………………………..…………11
Figure 3: Maps of Schinus and Ilex for each transect………………………………………………………….…………14
Figure 4: Graphs showing number of Schinus per plot………………………………………………………………….15
Figure 5: Graphs showing number of Ilex per plot…………………………………………………………………………16
Figure 6: Variograms for each transect……........................................................................................18
Table 1: Demographics by transect...................................................................................................13
Table 2: Showing results of Moran’s I……………………………………………………………………………….………….16
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Introduction
Invasive species can drastically affect native ecosystems by changing the ecological and
environmental conditions, particularly the species composition and biogeochemistry of the system
(Gordon 1998). Invasive plants can out-compete natives for resources, seed dispersers and
recruitment, leading to their eventual displacement (Bossard 1994). Trees provide structure and
habitat for forest ecosystems and so invasions by exotic tree species in particular can have
dramatic effects on communities. The negative results of these invasions include alteration of
successional dynamics over time, reduced diversity and relative abundance of native species,
disruption of important ecosystem functions as well as high public costs to manage their spread
(Inderjit 2005). Vacant niches, fluctuating resources, disturbance and native species which benefit
invaders can all make communities more vulnerable to invasion (Lamarque 2011). The invasive
species themselves may possess traits that provide them with particular advantage in invasion
such as rapid growth rate, the most efficient predictor of invasiveness for invasive trees (Lamarque
2011).
Spatial pattern is a crucial aspect of vegetation which has important implications not only
for plants themselves but also the organisms who interact with them such as herbivores,
pollinators and those for which plants provide a habitat (Dale 1999). It is important to recognize
that when studying vegetation the unit of analysis can be expanded from the individual to groups
of nearby individuals in patches, which provides a different set of information, and together make
up local communities (van der Maarel 1996). Patches are dynamically related to one another and
each patch in a space-time mosaic is dependent on its neighbors and develops under conditions
partly imposed by them (Watt 1947). Gap size and dynamics such as their formation and filling can
have important influences on patches, since new gaps can provide recruitment areas for different
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sets of species (van der Maarel 1996). Patch size, spacing and density influence herbivores,
pollinators and the longevity/health of the plants themselves. For example, if small patches of
trees have prolonged defoliation compared to large patches, small patches will be selected against.
These patterns can then be related to such processes.
Patches themselves can occur at different levels of densities and patterns. Dispersion is the
arrangement of points on a plane and the two alternatives to the null model of randomness are
clumped/under dispersed (the presence of one point increases the possibility of finding another in
its vicinity) and over dispersed (a points presence reduces the probability of finding another
individual nearby). Both individuals and patches can be over and under dispersed. Scale will
influence the degree to which dispersion is observed, since large clumps may only be visible from a
lower resolution, and patterns within them may only be visible at a finer scale (Rydin 1986, Dale
1999). For example, as the scale increases from a few individuals to the entire forest, it is
anticipated that populations will exhibit increased levels of aggregation, as their habitat and
resources are also often aggregated (Falster 2001). Spatial pattern can reflect past processes and
inform predictions of future processes, such as interspecific competition or population dynamics
(Falster 2001,Rydin 1986). The location of plants in space effects range of seed dispersal and
therefore the location of future generations.
Competition, dispersal and environmental heterogeneity can have significant effects on
spatial patterns of plants (Seabloom et al. 2005). Microtopography can produce spatial
heterogeneity (van der Maarel 1996). Soil properties such as amount of soil cover, compaction,
structure, nutrient, sand and moisture content can strongly influence the establishment of
seedlings and can be very heterogeneous in areas as small as one hill slope (Maestre 2003). The
spatial pattern of appropriate habitat and therefore survival can be determined through dense
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clearly defined patches (areas of high survival) and gaps (areas of low survival) (Maestre 2003).
Dispersal mechanisms produce spatial heterogeneity such as clumping in bird dispersed species
(Myster 1993). Competition can slow the invasion of a new species even if the established species
is an inferior competitor (Hart and Gardner 1997), and the distribution of a species in invaded
areas may be different from predictions based on its native distribution due to the competition it
faces in the new areas (Poll et al. 2009). Particularly if the distribution of the invader has gaps, the
native species can exist in patches and compete for resources such as seed dispersers.
Morphological factors such as size and growth pattern of plants can also have a significant impact
on spatial processes through interactions such as competition for light and nutrients (Dale 1999).
Since interactions with immediate neighbors are most significant the spatial distribution of
neighbors can influence the degree of competition experienced (Pacala 1986).
Direct resource competition between species should lead to negative cross-correlations at
small distances but possibly positive cross-correlations at greater distances, indicative of local
spatial segregation but positive association when scaling up to the size of favorable germination
patches when species share environmental preference (Dale 1999). Within species, limited
dispersal should cause a locally aggregated pattern (positive autocorrelation) (Rauschert 2012).
Indirect interactions between species, such as apparent competition, can operate at a different
spatial scale than direct interactions, and can drive patterns in different directions (Rauschert
2012). The resulting spatial structure can be studied by examining patterns of autocorrelation and
cross correlations at different spatial distances, which can be described by spatial statistics such as
correlograms or variograms (Legendre 1993). Patch patterns themselves can have important
ecological consequences and so there is much study devoted to the effects of these patterns.
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South Florida is particularly vulnerable to invasive species due to its subtropical location,
disrupted water flow and highly disturbed natural areas (Lodge 1994). It is also home to the
Everglades, an enormous flowing freshwater marsh (Loveless 1959). The Everglades is subject to
many human disturbances such as drainage, agriculture and the introduction of invasive species
(Lodge 1994). Its mosaic of habitats including hammocks, mangroves, pinelands, sawgrasses and
sloughs support an assemblage of species found nowhere else on earth, including many endemic
species (Lodge 1994). Tree islands (formed by disrupted water flow which causes soil to build up in
one location) are a common feature of the Everglades and host high levels of biodiversity and key
habitats for white-tailed deer (Odocoileus virginianus), American alligators (Alligator
mississipiensis), small mammals, reptiles, and many bird species that use tree islands for nesting,
foraging, and resting (Lodge 1994). They also regulate nutrient dynamics and are currently being
restored in the Everglades (van der Valk 2008). They have undergone many changes including
enlargement and shifts in species composition and destruction due to hydrologic alteration and
reduced numbers of alligators (Lodge 1994). As alligator abundances increase and the Everglades
Restoration plan restores some of the previous water flow, tree islands restoration efforts will fit
into these larger efforts. Due to high survivorship under both low and high water conditions, Ilex
cassine, Annona glabra and Salix caroliniana are the most suitable species for restoring tree
islands in the Everglades (van der Valk 2008).
Invasive species are a major concern on these important and sensitive tree islands. Schinus
terebinthifolius (Brazilian Pepper) is one of the major invaders in South Florida and threatens tree
islands, particularly in the East Everglades where many have been invaded (Ferriter 1997). After
Melaleuca quinquenervia, Schinus is the second most severe invader in the Everglades, removing
bird feeding habitat and forming closed forests in coastal marshes (Lodge 1994). During the past
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10 years, a dramatic increase in density of Schinus has occurred on public lands managed by the
South Florida Water Management District (Cuda 2006).
Schinus is a small evergreen tree forming dense colonies. It was introduced for horticultural
use in the United States in the early 1800’s and then widely distributed in Florida in the late 1920s
(Lass 2004). In its natural range in Argentina, Paraguay and Brazil, it is present as scattered
individuals in a variety of habitats, from sea level to over 700 m elevation (Ewel et. al., 1982). In its
native range it has not been observed to dominate the landscape as it does in southern Florida,
where it forms nearly monotypic stands in a wide range of moist to mesic sites, including tropical
hardwood hammocks, bay heads, pine rocklands, sawgrass marshes, Muhlenbergia prairies, and
the salt marsh-mangrove transition zone (Campbell et. al., 1980; Ewel, 1986). Within the Hole-in
the-Donut region of Everglades National Park, stands containing from 200 to more than 2500
Schinus trees per hectare have been found (Ewel et. al. 1982). In South Florida it thrives on
disturbed soils created by natural disturbances, such as hurricanes, and is particularly invasive in
areas disturbed by human activities, such as abandoned farmlands, roadsides, canal banks (Ewel,
1986). The Exotic Pest Plant Council has listed Schinus on its most invasive list and identified it as a
serious threat to natural areas (Cuda 2006).
Preliminary investigations on Schinus invasion (employing seed introduction and seedling
transplant experiments) in both native (undisturbed) and successional (disturbed) plant
communities in southern Florida showed that young successional communities were more
susceptible to invasion than older ones, and all successional communities were more susceptible
than undisturbed, native communities (Cuda 2006). Pineland habitats have been found to support
greater Schinus seed germination, compared to wet prairies (glades) and hammocks. Successful
invasion appears to be a function of both seed inputs to an area, the ability of introduced seeds to
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germinate and seedlings survival rates, which, if occurring at higher rates than those of native
species will lead to competition favoring the invader (Ewel et.al., 1982). Gogue et. al. (1974)
suggested that Schinus also has the ability to inhibit the growth of competing vegetation through
the production of allelopathic substances. Its potential dispersers are gray catbird, raccoon,
opossum, and robins, meaning Schinus is also a competitor for dispersal with native plants who
share many of the same dispersers.
The negative effects of Schinus on local communities extend past its role as a superior
competitor to native species, as stands provide relatively poor wildlife habitat. These stands
support lower avian species diversity and total population density when compared to native
pinelands and forest-edge habitats (Curnutt 1989). Schinus forest habitats in Everglades National
Park host few native and many non-indigenous species, such as Cuban tree frogs (Osteopilus
septentrionallis) and brown anole lizards (Anolis sagrei) (Ferriter 1997). These declines in diversity
resulting from species-rich habitat being replaced by biologically uniform habitats, stress the need
to protect native habitats from exotic pest plant encroachment.
A number of native and exotic trees (Myrsine floridana, Persea borbonia, Ilex cassine,
Nectandra coriacea, Psidium guajava) are known to establish small populations within Schinus
stands (Ferriter 1997). The native species, Ilex cassine (Dahoon Holly) is a flexible species able to
live under many conditions ranging from moist woods and cypress ponds to marsh margins and
bays (Native Plant Database 2012). It is a small evergreen tree, with stiff small leaves and red fruits
that are similar in size to those found on Schinus. Owing to its suitability for tree island recruitment,
easy identification and the fact that it has been observed to establish populations within Schinus
stands, it was chosen for comparison with Schinus (Ferriter 1997).
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This study explores the spatial distribution of Schinus and Ilex at Amelia Earhart Park, a
park with a forested area exhibiting many of the native and exotic species typical of south Florida
and also having varying levels of elevation and therefore water levels, leading to similar habitat
types of the Everglades. We used indices of dispersion to determine how the two species interact
spatially, whether Schinus is following the patterns described in the literature and how the two
species spatially coexist. Both species occupy similar niches and produce similar fruits, so if Schinus
grows in higher densities, it may attract more dispersers (such as birds and raccoons) since less
energy is needed to consume an equal amount of food in a smaller amount of space. This could
create a positive feedback loop, leading to more dispersal and establishment of Schinus, while
hindering Ilex dispersal. This could lead to gaps in Ilex patches as individuals die and fewer Ilex
seedlings are recruited, leading to an invasion of Ilex territory due to Schinus’ fast growth and
possible superior seed dispersal. Identifying the mechanisms that drive these patterns are not
within the scope of this study however since we do not have a temporal component, however it is
important to keep these drivers and potential future processes in mind.
We predicted that Schinus would be found growing in dense monospecific stands exhibit
higher spatial autocorrelation than Ilex.
Methods
In order to study the spatial relationships between the two species we first explored many
potential sites in Miami Dade County for size, presence of both species, densities of each species,
accessibility and safety. We chose the forest surrounding a mountain bike trail in Amelia Earhart
Park, located at 25.893649°, -80.283054°. The trail and forest are located in the north-west corner
of the park and the area is approximately 32 ha (Figure 1).
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Figure 1. Aerial photograph of site location, Amelia Earhart Park Mountain Bike Trails
We set a grid over a map of the plot, used the random number generator in R to produce
random coordinates on which to set transect start points, then generated random compass
directions at which to establish ten 50 m transects, as suggested by the USDA and Cooperative
State Research Education and Extension Service’s Forest Ecosystem Rapid Assessment method.
Transects were located throughout the entire 32 ha network of bike trails and their locations can
be seen in Figure 2, labeled alphabetically. We then walked down the length of each transect and
recorded the locations of Schinus or Ilex individuals within 1m either side of the transect. We
recorded whether the individuals were adults, juveniles (shorter than 1m), or fruiting. Transect D
had to be replaced because we encountered an unexpected paintball field and building.
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Figure 2.
Positions of the transects used to sample Ilex and Schinus within the mountain bike trail area of
Amelia Earhart Park. The black circles indicate the starting point of each transect and the green
circles indicate the end points. Figure 1 and 2 are roughly the same scale and this grid was laid
over the forested bike trail area in the previous photograph.
Due to the long and narrow shape of our transects we were not able to perform nearest
neighbor analyses since the nearest neighbors could be lying outside of the study area. In order to
deal with this issue we divided each of our transects into twenty five 2 x 2 m plots (Cressie 1991).
We then treated plots as the unit of analysis and used the number of individuals per plot to
analyze how the two species were distributed, using tests other than nearest neighbor analyses.
Spatial autocorrelation measures how the similarity between plots changes over distance.
This can be used to analyze how the species composition of two plots differs depending on their
physical distance apart. Moran’s I is a function that measures autocorrelation by taking the counts
of each plot, and the inverse distance matrix (describing how far apart plots are) and generates
the observed Moran's I, the expected Moran's I, the standard deviation, and a p-value. To
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generate the inverse distance matrix, we first find the distance matrix, take its inverse, and replace
the diagonal with zeros (because stations should be 0 m apart from themselves). If the p-value is
less than 0.05, we reject the null hypothesis that there is no autocorrelation in the data and it
means that the observed value of I is significantly greater than the expected value of I. The
statistical package used to conduct this test in R was ape: Analyses of Phylogenetics and Evolution
(Paradis 2004).
Variograms were also used to quantify the spatial variability of the data and identify the
scale at which the variance was highest. The semi-variogram is based on modeling the squared
differences in the z values (in this case the number of individuals per plot) as a function of the
distances between all the known points. A common hypothesis in spatial statistics is that plots
near each other will be similar and so at smaller distances there will be less variance and at larger
distances there will be a higher variance (indicating larger differences in numbers of individuals
present in this case). This test was run using the R package GeoR: A package for geostatistical
analysis (Ribeiro 2001).
We only performed these tests on transects which had more than two individuals of the
species.
Results
The abundances of both species were low and irregular. Transects supported different total
abundances of each species and different patch sizes (Figure 3). Schinus individuals were found on
9 out of 10 transects. 3 transects had relatively high numbers of individuals ranging from 13 to 14.
Ilex individuals were only found on 2 transects ; Transect A had one individual and Transect E had
33 Ilex. 31 juvenile plants were found on 7 transects. All juveniles were found within 5 meters of
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an adult. 26% of Ilex found were juveniles and 33% of Schinus were juvenile. 5 fruiting plants were
found on 2 transects.
Table 1. Demographics of Ilex and Schinus by transect
Transect/Species
# Juveniles
# Adults
# Fruiting
A/ Ilex
0
1
0
A/Schinus
5
9
0
B/Schinus
1
0
0
C/Schinus
4
9
0
E/Schinus
4
2
0
E/Ilex
9
24
3
G/Schinus
0
5
0
H/Schinus
2
2
0
I/Schinus
0
8
2
J/Schinus
0
2
0
K/ Schinus
6
8
0
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Figure 3. All individuals of both Ilex cassine (circles) and Schinus terebinthifolius (triangles)
recorded on each transect within the study area. Solid triangles and circles represent juveniles.
Transect E held the most individuals. Transect F had no individuals of either species and is not
shown.
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Figure 4. Numbers of individuals of Schinus per plot as a function of distance along each transect.
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Figure 5. Numbers of individuals of Ilex per plot as a function of distance along each transect
Transects A, C and E were found to be insignificantly autocorrelated for Schinus as the
observed I values are higher than the expected, but the p values are still higher than 0.05. Ilex
showed the same on Transect E. Schinus showed the strongest relationship on transect A.
However, none of the results were significant and so there is no strong autocorrelation in the data.
Table 2. Results of Moran’s I on transects with more than 2 individuals, showing whether
probability is low enough to reject null hypothesis of no spatial autocorrelation.
Moran's I Results
Transect
Observed
Expected
Stand. Dev
p Value
Schinus A
0.054
-0.042
0.054
0.078
Schinus B
-0.042
-0.042
0.004
0.903
Schinus C
0.042
-0.042
0.050
0.092
Schinus E
-0.012
-0.042
0.057
0.608
Schinus G
-0.079
-0.042
0.063
0.553
Schinus H
-0.066
-0.042
0.050
0.621
Schinus I
-0.037
-0.042
0.065
0.949
Schinus K
-0.037
-0.042
0.065
0.949
Ilex E
-0.012
-0.042
0.058
0.613
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There appears to be spatial correlation according to the variograms as the line is not flat for
any of the transects and so the variogram is not constant across all distances. 3 of the transects
showed correlation at short distances and an increase in variation with distance until about 35 to
40 m when variance dropped. 2 transects showed a rough decrease in variation with distance
implying that plots further away were more similar. The other transects showed cyclicity which
could be linked to underlying factors in the terrain or, more likely, limited data. However, the
transect with the highest number of individuals still exhibited cyclicity. Despite the lack of clear
direction in the trend, the range and nugget of the data can be informative. The nugget is the gap
above zero of the variance on which the line begins. It can indicate error in measurement or
evidence of sparse data. Nuggets varied for each transect and were between .082 and 2.5. The sill
represents the variance of the random field and usually appears as an asymptote with the
variograms becoming constant. Even though there are no clear sills, this is evidence of
nonstationary variables. The range indicates the distance at which data are no longer spatially
auto correlated. Since there were no clear sills we observed no clear ranges either.
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Figure 6. Variogram results for all transects with more than 2 individuals of the species in question
Discussion
Despite initial observations of what appeared to be clumping in the field, further analysis
showed that the spatial autocorrelation was not significant and so there is no strong evidence of
pattern in the dispersion of these plants. This indicates that neither of the observed species lives in
dense clusters as was predicted for Schinus. The presence of juveniles of both species means both
are likely to be in the future composition of the park depending on survival rates. Many juveniles
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had large diameters indicating that they were robust and established and likely to persist into the
next generation. Since Schinus was not found to grow in dense monospecific stands and Ilex
(among other species) were found living among them, there is opportunity for the parks
vegetation to compete with Schinus. Particularly if the Ilex population increases or Schinus’ doesn’t
increase.
However this finding could be an artifact of the sampling method and perhaps the
clumping was merely not observed. What the data do not describe however, is that despite the
data reflecting one individual of Schinus at a spatial point, the individuals are large and sprawling
and can have diameters of many meters, preventing other species from occupying that space.
What graphically may not look like a clump may indeed be a clump in space of many large
individuals whose bases are many meters away from each other but whose canopies actually
connect. We encountered this on several transects. The sprawling nature of the species made it
difficult to identify separate individuals as well. Often times branches of individuals fell into our
transect (therefore competing for light with potential natives and other individuals) but since their
base did not originate in the transect we did not record them. Since these occupy the space
between the individuals represented, they could contribute to a clumping phenomenon that was
not measured.
If there are in fact dense clumps of Schinus and the juveniles present can indicate a
probability of their persistence into the future of the forest, these dense stands can later serve as
seed sources for further expansion (Rouget 2004). Since distribution is a function of dispersal
(short- and long-distance) and habitat suitability, as the invasion progresses, more of the total area
suitable for Schinus establishment will be occupied (Rouget 2004).
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Although the study area was relatively small, within it there was much variation in habitat
type and elevation. Some areas were muddy or submerged, others were flat, dry and pine
dominated and others were very uneven and densely occupied by invasive plant
species. Dioscorea bulbifera (air potato), Casuarina equisetifolia and Melaleuca quinquenervia
were the most common invasive plants observed. This can explain some of the variation in
densities, frequencies and species composition.
Schinus recruitment depends on frugivore consumption and subsequent seed dispersal
(Panetta 1997), whereas the importance of these processes for Ilex is not known. If Schinus is
outcompeting Ilex for dispersers then perhaps Ilex was present in lower numbers as a result of
limited dispersal. Perhaps this can also explain differences in spatial patterns, as deposition of
fecal matter from mammals can release a group of many seeds at once in one place, which we
observed in the field.
In the field our team also observed, and was informed by the park manager, that Schinus
increases the damage potential of other invasive species such as air potato vines. The tangled
sprawling nature of Schinus canopies (branches often reach the ground before growing upwards
again) can raise air potato off the ground and into the canopy, after which air potato robs other
plants of sunlight by covering them with its expansive leaves. Future studies could analyze how the
presence of Schinus affects the abundance of other invasive plant species to test the degree to
which this is significant.
The sampling plan selected compromised our study by limiting the kinds of statistical
analysis we could have done and by excluding individuals whose branches were present but whose
base was not. A comparison of sampling methods using simulated species maps with varying levels
of abundance and spatial autocorrelation showed that transect sampling had the highest
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variability, returning estimates of 19-94% cover for a species with an actual cover of 50% (Goslee
2006). Transect and random methods were also likely to miss rare species entirely unless large
numbers of quadrats were sampled (Goslee 2006). In future work a different and more thorough
sampling scheme, such as setting up random circular plots covering at least 10% of the area, will
provide more robust data that can lend themselves to more kinds of analysis and therefore more
informative results. A design that allows for several spatial scales of analysis or a lower resolution
may also resolve the issue caused by Schinus occupying large areas of space and may detect the
patterns exhibited by that species. Perhaps other patches with dense Ilex populations could be
found and interactions between the two species observed. Future interesting tests would analyze
and compare gap lengths and patch sizes using new local variance methods (Dale 1999). We would
also like to test for positive cross-correlations between the two species and see if segregation from
each other could indicate the occurrence of fine scale competition. We could also test for negative
cross correlation. The dispersal kernel would also be very interesting to calculate in order to
discuss the future spread of Schinus. This study has provided a basis for what areas to set up seed
traps and areas where high abundances of the two species could be found again for sampling.
Schinus threatens the health of many ecosystems worldwide but south Florida with its
particular vulnerabilities merits more study and attention, particularly with regards to the
Everglades. Understanding the spatial structures can help to predict and understand potential
spread and inform management plans.
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