The study investigates the declining patch dynamics of Cape eelgrass (Zostera capensis) in Langebaan Lagoon, South Africa, attributed to climate change and anthropogenic activities. It finds a 47% loss in seagrass beds and significant fragmentation, negatively impacting epifaunal communities, with edge areas exhibiting poorer patch quality compared to centers. This research underscores the necessity for long-term monitoring and conservation strategies for Z. capensis to support biodiversity and ecosystem stability.

1. Changing patch dynamics of Cape eelgrass Zostera capensis: impacts of loss
on epifaunal communities in Langebaan Lagoon, South Africa
Category: Capstone Project
Participants: Damaris Chenoweth, Joseph W. Galaske, Qawekazi Mkabile and Ceinwen
Smith (resource person)
Site: Klein Oesterwal, Langebaan Lagoon, Western Cape Province, South Africa
Key Words: edge effect, epifauna, fragmentation, patch quality, seagrass
Abstract
The cape eelgrass Zostera capensis is an ecosystem engineer endemic to South Africa. It
has seen a rapid decline in recent years, largely due to the increasing effects of climate
change and anthropogenic land uses. Given these losses, the present study aimed to
address the following: (1) Do total area and patch dynamics change over time? (2) Do
these trends impact patch quality? (3) How does this affect epifaunal communities? Using
satellite imagery, we calculated changes in total seagrass cover, number of patches and
the mean distances between patches from 2009 to 2015. We investigate consequences to
patch quality and epifauna by sampling seagrass bed sizes, mean leaf length, seagrass
percent cover and epifuanal presence and abundance with respect to stratum (center vs.
edge), and across intertidal zones at Klein Oesterwal beach on Langebaan Lagoon. We
discovered fragmentation and a net loss of 47% in seagrass beds. Leaf length and percent
cover were both significantly lower on the edges of a patch rather than in the center of a
patch. The mean leaf length decreased from 2014 to 2016, and differed across the
intertidal zone (high, mid, low). Lastly, leaf length was a predictor of species richness in
a patch. The difference between edge and center in patch quality influenced the
distribution, abundance and the species diversity of epifauna present in the patches. In
conclusion, seagrasses are declining and fragmenting, negatively impacting patch quality
and epifaunal communities. Long-term datasets on patch dynamics will contribute to
efficient conservation efforts for the rehabilitation of Z. capensis.
Introduction
Vital marine ecosystems such as coral reefs, seagrass beds, mangrove swamps,
continental shelves and seamounts are in decline (Airoldi et al.2008). Previous studies
have largely attributed climate change (which is associated with changes in sea level,
salinity, temperature, atmospheric carbon dioxide and UV radiation) and anthropogenic
activities (unsustainable fishing, fertilizer runoff, pollution from oil gas extraction,
untreated sewage, industrial wastes and shipping) as the main drivers of marine
ecosystem damages (Hinrichsen et al. 2011, Short and Nickles 1998). The implication of
these disturbances is that they alter the local and regional biota, near shore
geomorphology, biogeochemical cycles, community distributions, productivity and
compositions (Short and Nickles 1998).

2. Seagrasses are found in unvegetated sandflats, existing as patches or continuous
beds, and require some of the highest light levels of any plant group worldwide (Orth et
al. 2006). The fact that they have high light requirements means that they are acutely
sensitive to environmental changes, especially those that alter water clarity (Orth et al.
2006). Acting as ecosystem engineers, they provide numerous ecological services; they
stabilize sediments, their leaves trap/hold nutrients, therefore providing nutrient rich
habitats for resident communities such as anemones and limpets, they are food for
herbivores and are nurseries for invertebrates and fishes (Orth et al. 2006, Pillay et al.
2010). They, with salt marshes and mangroves, hold about 50 percent of the total organic
carbon of the ocean sediments (Hinrichsen et al. 2011, Orth et al. 2006, Ray et al. 2014).
Seagrass beds play an important role in providing substrate for some algal species, and
improve water quality by filtering suspended matter (Short and Neckles 1998, Williams
and Heck Jr. 2001, Hinrichsen et al. 2011). Combined, seagrass functions enhance the
biomass, abundance and diversity of local fauna when compared to the surrounding bare
sand (Ray et al. 2014). Studies have confirmed that these structurally complex marine
plants have partially declined due to natural disturbances such as strong storms,
hurricanes and typhoons (Duarte 2002). However, anthropogenic activity, causing direct
damage and deterioration of water quality, has been identified as the primary cause
(Airoldi et al. 2008, Duarte 2002, Ray et al. 2014). Besides seagrass loss due to
fragmentation, species composition of seagrasses changes and this can affect faunal
assemblages (Ray et al. 2014).
Duarte (2002) found that the primary source of human impact on seagrass
ecosystems is physical disturbance, which is derived from human usage of the coastal
zone for transportation, recreation and food production. In Europe, it is estimated that
between 1960 and 1995 a kilometer of coastline was developed every day; this in turn
caused huge losses of coastal wetland and seagrass habitat− between 50% and 80% of the
original area was lost for many regions (Airoldi et al.2008). In the case of the Oesterwal
beach, Langebaan in the Western Cape, Pillay et al. (2010) discovered that coastal
eutrophication (which drives algal blooms that reduce light availability), over-
exploitation of top predatory fish and coastal developments (dredging) were the main
causes of damage to seagrass beds. Dredging modifies sediment dynamics by creating
small patches of bare sand in a matrix of seagrass, dissecting contiguous seagrass into
separate patches (Boström et al.2006). Collectively anthropogenic activities can lead to
the irreversible elimination of seagrasses from coastal ecosystems (Ray et al. 2014). The
reduction of seagrasses results in habitat loss, thus causing a major reduction in species
diversity and spatial distribution, a process also described as biotic homogenization
(Airoldi et al.2008). Fragmentation in any ecosystem leads to the isolation of
subpopulations and alters interaction among species (Braschler and Baur 2016).
Populations in small patches have greater sensitivity to demographic stochasticity and
typically experience genetic variation (Braschler and Baur 2016). Patch dynamics and the
fragmentation of seagrasses are partly based on the island biogeography model, which
suggest that there is a focal habitat patch type (seagrass), which occurs in a matrix of a
less favorable habitat (bare sand) (Boström et al. 2006). As fragmentation increases the
proportion of edge to interior also increases, therefore with fragmentation a variety of
faunal responses can be expected, depending on species preference to the center or edge
of their habitat (Bell et al. 2001). Faunal densities tend to increase from the edges to the

3. interior of patches, therefore modifying species dispersal because the quality of the patch
is relatively lower (Bell et al. 2001). This could be of critical importance for conservation
in Langebaan Lagoon, especially with the predicted impact of landscape alteration (Bell
et al. 2001).
Due to the state of coastal land-use and seagrass loss, we aim to examine broad-
scale patch dynamics and impacts on patch quality and epifaunal communities. Firstly,
we intend to determine changes in total seagrass area, patch number and patch distance
among the intertidal zones (low, mid and high shore) from 2009 to 2015. Secondly we
will determine trends in patch quality both within patches and between intertidal zones.
Lastly, we will test the effects of both fragmentation and patch quality on epifaunal
presence and abundance. Since the work of Pillay et al. (2010), which provided a broad-
scale study of the Langebaan Lagoon, and Cameron-Burr et al. (2014), which narrowed
the study to just Klein Oesterwal, to our knowledge no work has been done within
Langebaan Lagoon. Detailed spatial and temporal changes of the seagrass beds in Klein
Oesterwal have not been re-examined. We aim to build on previous work with a focus on
changing seagrass patch dynamics and impacts on patch quality and the epifaunal
communities that they support. Based on Bell et al. (2001), we emphasize the importance
of understanding the role of edge effects in the restoration efforts of habitats that differ in
size and shape. Our methodology aims to show relationships between seagrass patch
dynamics and the number of species and individuals it supports (McNeill and Fairweather
2016). Our study will contribute to long-term data collection in addition to suggesting
efficient restoration and management of the seagrasses in Langebaan Lagoon.
We hypothesize that (1) cover of Z. capensis has decreased from 2009 to 2015, as
well as an increase in the fragmentation of the remaining seagrass beds. This implies
negative consequences on patch quality and epifaunal communities. (2) Patch quality,
which we define as the percent cover and leaf length of seagrass patches, will decrease
from the center of patches to the edges, and from low shore to the high shore. (3) Fauna
will be affected by changing patch quality, and patch centers will have higher species
diversity, whilst the edges have a relatively lower species diversity.
Methods
Study site
Langebaan Lagoon is a saltwater lagoon on the west coast of South Africa. The
lagoon was declared a protected area in 1985, and now falls within West Coast National
Park, in the municipality of Langebaan, Western Cape Province (Pillay et al. 2010). The
study site, Klein Oesterwal (33.7.362 S, 18.3.094 E), lies just outside of the park in an
area where human disturbance, including bait harvesting, angling, windsurfing and sail
boating, are permitted (Pillay et al. 2010). Data was collected on April 15 during the
afternoon low tide (17:00) and April 16, 17, 18, 19 during the morning low tide (06:00-
08:00). The tidal range during sampling was roughly 0.96m.
Study species
Cape eelgrass (Zostera capensis) is a seagrass endemic to South Africa and
dominates intertidal vegetation in Langebaan Lagoon (Cameron-Burr et al. 2014). Z.
capensis can exist as both continuous beds capable of spanning hundreds of square

4. meters, or as a mosaic of smaller patches among unvegetated sand flats (Pillay et al.
2010). Z. capensis beds function as ecosystem engineers; their root/rhizome system
stabilizes sediment while the leaf canopy traps nutrients, thus providing habitat and food
for a number of marine organisms, from algae to small fish (Pillay et al. 2010).
Patch size and quality
To determine if patch distance from shore affects patch quality we sampled
patches at various locations across the intertidal zone (high, mid and low shore) in
addition to quadrat location (center and edge). Zones were defined by their distance from
the highest high tide line at the top of the beach; high shore was approximately 5 meters
from the high tide line, mid shore approximately 10-20 meters and low shore 20-50 m
(Cameron-Burr et al. 2014). Four sea grass patches were sampled in the high shore, three
in the mid shore, and five in the low shore. For each patch, 3-5 measurements of length
and width were used to estimate patch area for comparison across zones. The number of
measurements depended on the size of the patch. Patch location was recorded using a
Garmin GPS model GPSMAP 64.
To assess patch quality, we measured both leaf length and percent cover in each
patch across the intertidal zone. Data for leaf length and percent cover was collected from
six 0.25 m2 quadrats, three from the center of the patch and three from the edge (Pillay et
al. 2010; Cameron-Burr et al. 2014). Using a ruler and taking the average of five
medium-sized leaves determined leaf length values for each quadrat. Medium-length was
defined as representative of 80% of the seagrass leaf lengths (Cameron-Burr et al. 2014).
To account for seasonal and daily water-level fluctuations, percent cover was determined
from the base of the seagrass bed rather than seagrass canopy cover, as has been done in
previous studies (Cameron-Burr et al. 2014).
Assessing Epifaunal Presence and Abundance
Data on epifaunal species were collected following patch quality measurements.
Data collectors initially scanned the surface of each 0.25m2 quadrat to record data on all
visually obvious species. Sediment was then carefully dug, so as to avoid disturbance to
the seagrass roots, down to a depth of roughly 5cm and sifted through by hand. Each
unique epifaunal species was then identified and recorded (Branch et al. 2010), as well as
their respective abundance. A total of 72 quadrats were sampled (36 center, 36 edge) in
the 12 patches, across all zones (low, mid, high).
Change in Patch Dynamics in Klein Oesterwal
LANDSAT images of Klein Oesterwal in 2009, 2014 and 2015 were obtained
from Google Earth Pro and geo-referenced to 1:2000 topographical map using Quantum
GIS version 2.4.0. Total area, patch count and mean patch distance of Z. capensis was
estimated for Klein Oesterwal by digitizing seagrass beds from geo-referenced images
(1:2000) for each time period. Patch distances were determined using a distance matrix
tool in QGIS. For full processing protocol see additional material in Appendix.
Data Analysis
All multivariate analyses for patch dynamics were run in Minitab Express version
1.3.0. To assess changes in patch connectedness, one-way analysis of variance (ANOVA)

5. was run to show the effect of year (2009, 2014, 2015) on patch distance. Changes in the
number of patches and total bed area were collected from the digitized seagrass beds in
QGIS. Two-way ANOVA was used to assess the effect of year (2009, 2014, 2015) and
shore position (high, mid, low) on patch size (m2). For patch quality, one-way ANOVAs
were ran to assess the effect of stratum (center vs. edge) and shore position on average
leaf length (cm) and percent cover. Two-way ANOVA was used to perceive the effect of
year (2014 vs. 2016) and shore position on average leaf length.
All epifaunal multivariate analyses were performed using PRIMER version 6.
Non-metric multidimensional scaling (MDS) ordination and cluster-analysis were used to
visually assess differences in community structure of epifauna between high, mid, and
low shore positions in 2016. MDS ordination was constructed from Bray-Curtis
similarities to show relative dispersal between samples. Epifaunal abundance data was
square root transformed to account for those species with high individual counts.
Permutational multivariate analysis of variance (PERMANOVA) was run to assess
epifaunal similarity within and between zones. Distance-based test of multivariate
dispersions (PERMDISP) was subsequently run to test for homogeneity of relative
dispersal within zones (high, mid, low).
Results
Changes in Seagrass Patch Dynamics between 2009 and 2016
Calculations from GIS show a decline in seagrass total area from 2009 to 2014 by
67.2% and a slight increase of 22% from 2014 to 2015. Additionally, the number of
patches increased from 14 in 2009 to 25 in 2015. The mean distance between patches
significantly increased from 2009 to 2015 (F= 22.83, p=0.00001; Table 1). Figure 2
shows spatial distributions of Zostera capensis in 2009, 2014 and 2015. Visually,
seagrass area appears to reduce from the high to low shores and patches appear to be
more scattered in the low shore (Figure 1). Within the smaller study site (2014 and 2016),
patch size did not differ with zone (df=2, F=1.0679413, p=0.363467433) or between
years (df=1, F=2.6431795, p=0.120467292).
Table 1. Changes in the number of patches, mean (± standard error) distance between
patches and total seagrass area at Klein Oesterwal between 2009, 2014 and 2015.
2009 2014 2015
Number of patches 14 24 25
Mean patch distance 81.71 ± 3.17 113.188 ± 3.817 92.116 ± 2.066
Total area (m2) 5205.619 1897.237 2192.979

6. Figure 1. Changes in total area of Zostera capensis at Klein Oesterwal between 2009,
2014 and 2015. Maps are based on LANSAT images with digitized seagrass beds.
Changes in Patch Quality
Mean leaf length was significantly higher in the center of a patch when compared
to edge (Table 2). Mean percent cover was also significantly higher in the center than on
the edge (Table 2). Additionally, mean leaf length differed significantly by zone, year and
with the interaction of zone and year. Length increased from the high to low zones and
decreased overall from 2014 to 2016 (df=1, F=11.676, p<0.001; Table 4). Percent cover
did not differ between zones (Table 4).
Table 2. Mean (± standard error) leaf length, percent cover and species richness
comparisons between the center and edge of a patch at Klein Oesterwal in 2016.
Center Edge df F
Mean leaf length 18.461 ± 1.450 15.492 ± 1.297 1 5.884 p=0.017*
Mean percent cover 59.710 ± 5.995 46.913 ± 5.810 1 12.966 p<0.001*
Mean species richness 4.691 ± 0.254 3.676 ± 0.254 1 7.297 p=0.009*
*Indicates significant (p<0.05) differences between center and edge.
Mean species richness represents mean species per quadrat.
Epifaunal Assemblages and Species Richness
Eighteen species were observed across all zones, the globular mud snail,
Assiminea globulus being the most abundant in the high shore (n=1204), in the mid the
most abundant was the bamboo worm, Euclyeme spp. (n=72), and the turbin shell

7. Turitella capensis in the low (n=365; Figure 2; Table 3). An MDS of faunal assemblages
showed clustering by zone with 40% similarity. Visually, the mid zone appeared less
tightly clustered than the high or low zones (Figure 3). A PERMANOVA illustrated that
all zones were significantly more similar within zones than between (p=0.001; See
Appendix). A PERMDISP additionally showed that the multivariate dispersion of the mid
zone was significantly less homogenous than that of the high and mid zones (p=0.006;
See Appendix). Similarity between edge and center was not significantly different,
however the multivariate dispersion of edge was significantly less homogenous than
center (See Appendix). Mean species richness increased from high to low zones. Mean
leaf length had a small relationship to mean species richness (Table 4).
Figure 2. Total species abundances across all zones (high, mid, low) at Klein Oesterwal
in 2016.
0 200 400 600 800 1000 1200 1400
Siphonaria compressa
Cypraeidae
Sponge spp.
Paratylodiplax edwardsii
Crab spp.
Fissurella mutabilis
Hynenosoma orbiculare
Nassarius spp.
Nassarius kraussianus
Parvulastra exigua
Littorina saxatilis
Clionella spp.
Diogenes brevirostris
Callianassa kraussi
Sargatia ornata
Euclymene spp.
Turritella capensis
Assiminea globulus
Abundance

8. Table 3. Relative species abundance (% composition) of high, mid and low zones at
Klein Oesterwal in 2016.
Common name Species High Mid Low
Eelgrass false-limpet Siphonaria compressa
Cowry shell* Cypraeidae
Sponge** Sponge spp.
Sandflat crab
Paratylodiplax
edwardsii
Crab*** Crab spp.
Cape keyhole-limpet Fissurella mutabilis
Crown crab Hynenosoma orbiculare
Dogwhelk**** Nassarius spp.
Tick shell Nassarius kraussianus
Dwarf cushion-star Parvulastra exigua
British periwinkle Littorina saxatilis
Clionella^ Clionella spp.
Common sand hermit Diogenes brevirostris
Common sandprawn^^ Callianassa kraussi
Rooted anenome Sargatia ornata
Bamboo worm^^^ Euclymene spp.
Turbin shell Turritella capensis
Globular mud snail Assiminea globulus
=0 <1 <5 <15 <50 <100
*The family name Cypraeidae was used, as cowry shell species could not be determined.
**Sponge species could not be determined.
***Crab spp. represents all crab species that were not Hynenosoma orbiculare or
Paratylodiplax edwardsii.
****Nassarius spp. represents all Nassurius genus that were not Nassarius kraussianus.
^All Clionella species.
^^Individuals were estimated by halving the number of sand prawn holes counted.

9. ^^^All Euclymene species.
Table 4. Comparisons of mean (± standard error) leaf length, mean percent cover and
mean species richness between zones at Klein Oesterwal in 2016.
High Mid Low df F
Mean leaf
length (cm)
13.481 ±
0.971
14.766 ±
1.121
17.890 ±
0.868
1 11.676 p<0.001*
Mean percent
cover (%)
28.875 ±
3.047
32.056
±3.519
29.567
±2.726
2 0.219 p=0.804
Mean species
richness
6.5 ± 0.346 9.67 ± 0.498 10.2 ± 0.182 2 5.248 p=0.031*
*Indicates significant difference.
Mean species richness represents mean species per patch.
Figure 7. Non-metric multidimensional scaling (MDS) ordination of quadrats based on
faunal assemblages at Klein Oesterwal in 2016. Each sample represents a single quadrat
with pooled data from all quadrats across the intertidal zone (high, mid, low). Quadrats
are clustered with a 40% similarity.

10. Table 5. Results from regression analyses of leaf length, percent over and patch area
against species richness at Klein Oesterwal in 2016.
*Indicates significant relationship.
Mean species richness for regressions with leaf length and percent cover were represent
mean species per quadrat. Mean species richness for regression with patch area represents
mean species per patch.
Discussion
Seagrass Decline and Fragmentation in Klein Oesterwal
The decline in total area of Zostera capensis beds observed in Langebaan Lagoon
between 2009 and 2015 is consistent with other studies that report declines in seagrasses
over the last century (Duarte 2002, Orth et al. 2006; Pillay et al. 2010; Ray et al. 2014).
The reason for the severe loss of Z. capensis in Langebaan Lagoon since 2009 is unclear,
but several studies have attributed direct human activity (bait harvesting, trampling,
boating, angling, etc.) and indirect human impacts (climate change, increased sea
temperature, increased CO2 concentration, sea level rise, changing weather patterns and
shoreline erosion) as potential sources of seagrass loss (Short and Neckles 1999; Duarte
2002; Orth et al. 2006; Pillay et al. 2010; Ray et al. 2014). Another factor not yet looked
at is the impact of avian trampling on seagrass bed decline. Large wading birds, including
the curlew sandpiper Calidris ferruginea and greater flamingo Phoenicopterus roseus,
either feed on the seagrass beds themselves or the invertebrate communities that use the
seagrass as habitat (Pillay et al. 2010). Through their foraging behavior, these wading
birds have the ability to shape seagrass patch dynamics by trampling, overturning
sediment and uprooting the beds where they feed (Pillay et al. 2010). Given the
susceptibility of seagrasses to sedimentation and burial (Pillay et al. 2010), it is likely that
avian disturbance has contributed to the decline of Z. capensis in Klein Oesterwal.
While the reasons for the decline in Z. capensis cover in Langebaan Lagoon
remain speculative, our data collected from Klein Oesterwal now show that
fragmentation of remaining seagrass habitat is also occurring over time (see Table 1).
Whilst the reasons for increased seagrass bed fragmentation also remain unclear, studies
have implicated bait harvesting as being one likely driver (Pillay et al 2010; Cameron-
Burr et al. 2014). Mud and sand prawns, popular choices for angling bait, are usually
collected with a handheld prawn pump, which turns over sediment cores from ~90cm
deep to the surface (Pillay et al. 2010). Each bait collector turns over ~200-300kg of
sediment to collect 50 prawns per day, resulting in ~5,000 tons of sediment being turned
over annually in Langebaan Lagoon (Pillay et al. 2010). Given the vulnerability of
Mean species richness
Leaf length (cm) R2=0.078 p=0.018*
Percent cover (%) R2=0.010 p=0.409
Patch area (m2) R2=0.004 p=0.846

11. seagrass beds to sediment smothering, it is probable that increasing fragmentation in
Klein Oesterwal can be at least partially attributed to such disturbance.
Impacts on Patch Quality and Epifaunal Communities
Fragmentation leads to an intensified edge effect through increasing the outer
surface area of the patch community, therefore exposing more area to higher disturbance
(Pillay et al. 2010; Cameron-Burr et al. 2014). Increasing fragmentation would therefore
have negative consequences on overall patch quality, measured in terms of leaf length
and percent cover. Likewise average leaf length declined from 2014 to 2016, supporting
the negative effect of fragmentation on patch quality. Leaf length was also shown to be
longer in the center of patches, supporting edge effects on patch leaf length. Although an
analysis of the change in percent cover from 2014 to 2016 was not possible due to
methodological differences, our results do show a change in percent cover with respect to
stratum (center vs. edge; see Figure 4). This also supports the predictions made by edge
theory, as increasing outer surface area will expose seagrass leaves to higher disturbance
from herbivory and sedimentation (Bell et al. 2001; Pillay et al. 2010; Cameron-Burr et
al. 2014).
Many motile organisms have the ability to choose preferred microhabitats to
increase survival in response to predation pressure (Ray et al. 2014). Given that percent
cover and leaf length is higher in the center of patches, edge theory predicts that epifauna
should select for the center of patches so as to avoid higher disturbance (e.g. predation)
on patch edges (Bell et al. 2001). Our results demonstrate higher species richness in the
center of a patch as compared to the edge, affirming edge effects on epifaunal
communities in Klein Oesterwal. Declining patch quality, as a result of fragmentation,
are therefore expected to have a negative impact on epifaunal presence and abundance
(Duarte 2002; Orth et al. 2006; Pillay et al. 2010; Ray et al. 2014; Cameron-Burr et al.
2014). For example, species that are largely associated with Z. capensis, the surface-
dwelling limpet Fissurella mutabilis and the dwarf cushion starfish Parvulastra exigua,
have virtually disappeared from Klein Oesterwal between 1983 and 2009 (Pillay et al.
2010). The crucial role that Z. capensis plays in supporting these marine species is
indicative of its capability to increase local heterogeneity and biodiversity in otherwise
homogenous sedimentary habitats (Orth et al. 2006; Pillay et al. 2010; Ray et al. 2014).
Implications for Management
Severe loss and fragmentation of Z. capensis, and the negative impacts on patch
quality and epifaunal communities, has important implications for conservation and
management. The current challenge is to synthesize information to enhance our
understanding of seagrass processes, threats and change in Langebaan Lagoon, and to
apply this knowledge to develop effective management strategies (Orth et al. 2006).
Management applications should be based on the foundation of seagrass knowledge
developed in Langebaan Lagoon and aimed at establishing standards to conserve and
restore Z. capensis.
A number of seagrass management strategies have objectives with quantitative
goals aimed at restoring seagrasses to target levels (Duarte 2002; Orth et al. 2006). This
allows resource managers, who often make critical decisions, to justify the expense of
public funds (Orth et al. 2006). One key example is seagrass restoration programs that

12. use transplantation. Globally, the success rate of seagrass transplantation and restoration
is ~30%, although in some regions higher success rates have been reported (Orth et al.
2006). With the severe loss and fragmentation of Z. capensis in Langebaan Lagoon (see
Table 1), we propose transplant restoration programs as one viable solution. However,
some species are so difficult to transplant that restoration is not logistically or
economically feasible (Orth et al. 2006). Given the unknown success rate and high cost
of such programs, we also suggest a small-scale pilot study be done within Langebaan
Lagoon, using Klein Oesterwal as a potential study site. Several small-scale (< 1 hectare)
restoration programs have been attempted or are being planned using both adult plants
and seeds (Orth et al. 2006), providing a reference for restoration programs within
Langebaan Lagoon. Long-term studies, comparing the functionality of transplanted areas
with that of natural populations, would be necessary before larger-scale implementation
would be feasible in Langebaan Lagoon.
We found that epifaunal assemblage and species richness significantly differed
across the intertidal zone, demonstrating higher species richness in the low shore (see
Table 4). This has implications for direct intervention programs, as our results suggest the
low shore (20-50m from the high tide line) as a higher priority for conserving total
biodiversity. Langebaan Lagoon is currently divided into 3 zones with varying
recreational and harvesting activities (Pillay et al. 2010). Zone A (municipality of
Langebaan) is defined as a multi-purpose recreational area, Zone B (Bottelray) is a
limited recreational area and Zone C (West Coast National Park) is a “sanctuary” area
where human activity is prohibited (Pillay et al. 2010). Our results warrant research into a
potential extension of the zonation, thus limiting human activity in the low shore of the
intertidal zone.
Conclusions
Seagrasses are critical components of the marine ecosystems where they exist
(Duarte 2002; Orth et al. 2006; Waycott et al. 2009; Pillay et al. 2010; Ray et al. 2014).
Despite their ecological importance, severe losses and fragmentation of Z. capensis beds
are occurring (Pillay et al. 2010), especially within the context of Klein Oesterwal. These
broad-scale processes negatively impact patch quality, and thus epifaunal communities.
Given this background, there is an urgent for the development and implementation of
effective seagrass management programs in Langebaan Lagoon. The preservation of
seagrasses and their associated ecosystem services should be an emphasized priority. We
believe that the crisis facing Z. capensis in Langebaan Lagoon can be averted with a
concerted conservation effort, and this effort will benefit not just seagrasses and their
associated epifauna but also the entirety of the coastal ecosystem.
Acknowledgements
Firstly, we thank Ceini Smith and Donovan Tye for their help in the field, as well as their
advice and guidance on this project. Secondly, we would like to thank South African
National Parks for allowing us access to resources within West Coast National Park.
Finally, we thank the Organization of Tropical Studies and Duke University for allowing
us to take part in this program.

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15. Appendix
Protocols for Geo-referencing Seagrasses in QGIS:
In Google earth Pro
 Insert place-marks into four points that were boundaries to the study area
 Save the place-marks coordinates for geo-referencing in QGIS
In QGIS:
 Load the LANDSAT images from Google Earth Pro into QGIS
To geo-reference
 Raster> Geo-referencer (pop up)>Load raster>Add points (place-mark
coordinates from the Google Earth Pro images
 Transformation settings>Transformation type_Helmert
 Target SRS>WGS 84/UTM zone 34S
 Start geo-referencing
 Close the geo-referencing pop up
 Layer>new shapefile> rename>insert CRS (same as the geo-referenced images)
 On the toolbars>Toogle editing>Add feature>digitize
 Save
To calculate area
 Right click on the new digitized shapefiles on the layers toolbar>open attribute
table>click on toogle editing mode>open field calculator>field calculator pop
up>Output field name: area>output field type: decimal number(real)>output field
width: 10> precision: 4>Function list>Geometry>$area (double click so that it
appears on expression >ok
 Export the calculated area to an excel spreadsheet
Table A2. PERMANOVA results: average similarities between/within zones with
pairwise comparisons.
Zones t df
High, Mid 4.1698 38 p=0.001
High, Low 7.2159 50 p=0.001
Mid, Low 3.6457 44 p=0.001
Table A3. PERMDISP results: average homogeneity within zones (± standard error)
(F=6.625, df1=2, df2=69, p=0.006).
Zones Average
High 34.443 ± 3.275
Mid 46.536 ± 2.442
Low 34.784 ± 1.598

16. Table A4. PERMDISP results: pairwise comparisons of average homogeneity
between zones.
Zones t
High, Mid 2.787 p=0.001
High, Low 0.099 p=0.946
Mid, Low 4.202 p=0.001
Table A5. PERMANOVA results: average similarities between/within center and
edge (t=1.4083, p=0.088).
Center Edge
Center 32.215
Edge 28.023 25.111
Table A6. PERMDISP results: average homogeneities of center and edge (F=8.915,
df1= 1, df2= 70, p=0.007).
Center 48.688 ± 1.413
Edge 54.26 ± 1.219