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\begin{document}

\title{Structural and Environmental Influences on Seagrass Epifaunal
Communities: Seasonal Effects and Implications for Ecosystem Health}



\author[1]{Cloverley M. Lawrence}%
\affil[1]{South African National Parks}%


\vspace{-1em}



  
  \date{June 16, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Seagrass ecosystems play a vital role in marine environments, providing
essential services, and supporting a diversity of fauna and algae that
are critical components in marine trophic structures. However, global
and local seagrass declines due to various influences raise concerns
about the health of these ecosystems. This study investigated the
structural and environmental drivers influencing epifaunal communities
associated with the dwarf eelgrass ( \emph{Zostera capensis}), in a
temperate lagoon ecosystem. Variation in epifaunal patterns across
phenotypically distinct seagrass populations were observed. Large-leaved
morphotypes in deeper intertidal stands near the lagoon mouth exhibited
higher species diversity and richness. Conversely, small-leaved
populations in higher shore stands supported greater species abundances,
notably, two desiccation-resistant gastropods, \emph{Assiminea} sp. and
\emph{Siphonaria compressa}. Seasonal differences in epifauna were
significant, with higher abundances observed during spring and summer,
while diversity and richness peaked in autumn and winter. Several key
factors influencing epifaunal abundances were identified. The results of
structural equation modelling showed seagrass shoot densities and leaf
width to have positive direct effects, with light (turbidity) and oxygen
levels also playing important roles. Temperature, pH, and exposure
indirectly affected epifaunal abundances, however, temperature had a
direct effect on seagrass structure and significantly influenced five
out of six seagrass metrics measured. Ongoing assessments of seagrass
distribution in the lagoon indicate more significant declines in
populations near to the lagoon mouth, suggesting a simultaneous
reduction in associated epifaunal communities reliant on large-leaved
seagrass beds. As climate change-induced warming continues, further
declines in seagrass populations are anticipated, particularly in
large-leaved varieties. This trend is likely to have adverse
consequences for the associated epifaunal communities and other trophic
levels within the ecosystem. Given the significance of seagrass habitats
for food provision and sustaining livelihoods, the loss of these
ecosystems could have far-reaching consequences. Preserving seagrass
ecosystems is therefore crucial to ensuring the continued provision of
these ecosystem services.%
\end{abstract}%



\sloppy


\textbf{Structural and Environmental Influences on Seagrass Epifaunal
Communities: Seasonal Effects and Implications for Ecosystem Health}

Authors: Cloverley M. Lawrence\textsuperscript{1,2*}

\textsuperscript{1} Scientific Services, South African National Parks,
South Africa

\textsuperscript{2} Department of Biological Sciences, University of
Cape Town, Cape Town, South Africa

*Corresponding author: cloverley.lawrence@gmail.com; P. O. Box 176,
Sedgefield, 6572; +27 21 761 1130.

\textbf{Submission date:} 16 September 2023



\textbf{ABSTRACT}

Seagrass ecosystems play a vital role in marine environments, providing
essential services, and supporting a diversity of fauna and algae that
are critical components in marine trophic structures. However, global
and local seagrass declines due to various influences raise concerns
about the health of these ecosystems. This study investigated the
structural and environmental drivers influencing epifaunal communities
associated with the dwarf eelgrass (\emph{Zostera capensis} ), in a
temperate lagoon ecosystem. Variation in epifaunal patterns across
phenotypically distinct seagrass populations were observed. Large-leaved
morphotypes in deeper intertidal stands near the lagoon mouth exhibited
higher species diversity and richness. Conversely, small-leaved
populations in higher shore stands supported greater species abundances,
notably, two desiccation-resistant gastropods, \emph{Assiminea} sp.
and\emph{Siphonaria compressa} . Seasonal differences in epifauna were
significant, with higher abundances observed during spring and summer,
while diversity and richness peaked in autumn and winter. Several key
factors influencing epifaunal abundances were identified. The results of
structural equation modelling showed seagrass shoot densities and leaf
width to have positive direct effects, with light (turbidity) and oxygen
levels also playing important roles. Temperature, pH, and exposure
indirectly affected epifaunal abundances, however, temperature had a
direct effect on seagrass structure and significantly influenced five
out of six seagrass metrics measured. Ongoing assessments of seagrass
distribution in the lagoon indicate more significant declines in
populations near to the lagoon mouth, suggesting a simultaneous
reduction in associated epifaunal communities reliant on large-leaved
seagrass beds. As climate change-induced warming continues, further
declines in seagrass populations are anticipated, particularly in
large-leaved varieties. This trend is likely to have adverse
consequences for the associated epifaunal communities and other trophic
levels within the ecosystem. Given the significance of seagrass habitats
for food provision and sustaining livelihoods, the loss of these
ecosystems could have far-reaching consequences. Preserving seagrass
ecosystems is therefore crucial to ensuring the continued provision of
these ecosystem services.

\emph{Keywords:} Macrofauna, Zostera, phenotypic plasticity, structural
equation modelling, temperate lagoon, Langebaan Lagoon.

\textbf{Acknowledgements}

For contribution to conceptualisation of the research idea I wish to
acknowledge D Pillay (UCT). Many thanks to A Jarre, J Bolton and D
Pillay for comments on the thesis chapter. A special thank you to those
who assisted with field and lab work (too many to list).

\textbf{Funding Information}

Financial support from the National Research Foundation (NRF) of South
Africa is acknowledged.

\textbf{Declaration of conflicts of interest:} There are no conflicts of
interest to declare.

\textbf{1. Introduction}

Preserving and perpetuating biological diversity is grounded in the
understanding that a decline in biodiversity could disrupt ecosystem
functioning and thereby jeopardize the array of vital ecosystem services
benefiting society (Costanza et al., 1997; Hooper et al., 2012; McCann,
2000) A diverse ecosystem inherently entails functional redundancy,
where multiple species can perform specific roles. This redundancy
ensures that functional ecosystems persist even if one species faces
stressors like disease or over-exploitation. Species sharing similar
functions might exhibit varied responses to stressors, bolstering
ecosystem resilience. This, in turn, sustains ecosystem stability and
enhances the probability of recovery from stress or disturbances
(Elmqvist et al., 2003).

The preservation of biodiversity is indispensable for enhancing the
capacity of intricate systems to navigate change and to diminish
susceptibility to species loss. The higher the stability of an
ecosystem, the more significant its role in supplying critical services
to society, including carbon dioxide recycling, oxygen production, and
the maintenance of productive fisheries (Balvanera et al., 2006;
Cardinale et al., 2012; Ghilarov, 2000; Hooper et al., 2005). Examining
the diversity of fauna within marine or terrestrial systems holds
significance as it enables the exploration of various hypotheses related
to the mechanisms underlying species distributions and the preservation
of biodiversity (de Juan et al., 2013; Virta et al., 2021).

Benthic macrofauna in coastal ecosystems are primarily composed of
molluscs, amphipods, decapods and polychaetes which often play a key
role as the trophic link between primary producers and pelagic consumers
(Heck et al., 2008). In a typical seagrass food web, the main components
include seagrass plants, periphyton, algal epiphytes, detritivores,
invertebrate grazers, vertebrate grazers, meso-predators, piscivorous
predators and humans (Duffy, 2006). Seagrass structure supports
macrofaunal communities that predominantly occur as epifauna (living on
seagrass blades), and infauna (dwelling in or on sediment surfaces)
(Bologna \& Heck, 1999; Klumpp \& Kwak, 2005; Leopardas et al., 2014).
Macrofauna serve as a vital link in the food chain, connecting primary
producers with intermediate and larger predators, both directly and
indirectly associated with seagrass habitats (Baden et al., 2010; Heck
et al., 2008).

Seagrass habitats support a remarkable diversity of fauna and algae,
even though the seagrass plants themselves have relatively limited
species diversity. For instance, Orth et al. (1984) compared faunal
community densities between seagrass meadows and unvegetated areas from
several studies and discovered that seagrass meadows consistently
supported a richer assemblage of fauna compared to nearby unvegetated
areas in both tropical and temperate ecosystems. These patterns have
been observed in other studies primarily attributed to the structural
complexity of seagrass habitats, the abundance of food found within
them, and the greater stability of sediment in comparison to bare
sandflats (Bos et al., 2007; Carr et al., 2016; McCloskey \& Unsworth,
2015). However, there are exceptions to this general paradigm.

Disturbances by organisms like callianassids can influence faunal
assemblages by favoring burrowing infauna, but when seagrasses are
present, epifauna and seagrass-specific burrowers can replace them. This
ecological shift can sometimes mask differences in species diversity
indices between callianassid-dominated sandflats and seagrass habitats
(Barnes \& Barnes, 2012). However, in cases where locations lacking
callianassid bioturbators were studied, specifically in the Knysna
Estuary, South Africa, seagrass habitats were found to support fewer
individuals compared to bare sandflats. While seagrasses did support
more species, primarily due to the presence of epifauna, the numerical
differences in individuals were statistically insignificant (Barnes \&
Barnes, 2014). Unvegetated sandflats typically harbour a distinct suite
of species (Casares \& Creed, 2008). Despite often having lower species
diversity compared to vegetated areas, these unvegetated sandflats make
a significant contribution to overall diversity in nearshore coastal
environments.

The abundance of many macrofaunal species has been found to have a
positive correlation with the structure of seagrass, including biomass,
canopy and the root-rhizome system (\selectlanguage{ngerman}Ávila et al., 2015; Edgar, 1990; S.
Y. Lee et al., 2001). In \emph{Zostera capensis} (Setchell) beds in
Mozambique, macrofaunal patterns were attributed to the enhanced habitat
complexity created by the standing crop and biomass of seagrasses (Paula
et al. 2001). This pattern is also observed in smaller seagrasses with
lower structural complexity, such as \emph{Halophila decipiens} . In
Guanabara Bay, Brazil, \emph{H. decipiens} was found to support
significantly greater macrofaunal diversity, richness, and density in
seagrass habitats compared to bare sand (Casares and Creed 2008).

Seagrass stems and leaves provide a substrate for the attachment of
epiphytes, periphyton and encrusting algae. Epiphytes include both
micro- and macroalgae, while periphyton is a mucus-like layer that coats
seagrass blades comprising largely of diatoms, blue green, red, and
filamentous algae, as well as particulate material, bacteria and
microfauna (van Montfrans et al. 1984, Klumpp et al. 1992). Algae and
periphyton are highly nutritious and easily digested by grazing
invertebrates which find both food and shelter in seagrass beds
(Schneider and Mann 1991, Edgar 1992, McNeely et al. 2001). Furthermore,
the structural substrate provided by seagrasses for photosynthesizing
epiphytes significantly enhances primary productivity in seagrass
ecosystems (Borowitzka et al., 2006; Smit et al., 2005). However, an
excess of algal epiphytes can become problematic for seagrasses.

Coastal water eutrophication, primarily resulting from land-use
practices and effluent discharges, has been associated with seagrass
loss (Burkholder et al., 2007; Cardoso et al., 2004; Ruiz \& Romero,
2003). While toxic effluent can be lethal to seagrasses (Govers et al.,
2014; Koch \& Erskine, 2001; Macinnis-Ng \& Ralph, 2004; Negri et al.,
2015), an excess of nutrients in the water can promote the growth of
planktonic and benthic algae, which compete with seagrasses for
nutrients and reduce light penetration in the water column, further
inhibiting seagrass photosynthesis (Short et al., 1995). In addition,
excessive epiphytic algal growth can smother seagrasses (Nelson \& Lee,
2001; Walker \& McComb, 1992) and disrupt the balance of dissolved
carbon dioxide and oxygen (Raun \& Borum, 2013). In some cases,
long-lived epiphytes can weigh down seagrass leaves, leading to breakage
(van Montfrans et al., 1984). Climate warming, coupled with increased
coastal eutrophication, is predicted to promote algal growth, adding
further stress to seagrass ecosystems.

The influence of environmental factors, especially temperature, light
(irradiance), and salinity, on seagrass growth and distribution has been
extensively studied (Lawrence, 2023 \emph{in review} ; K. S. Lee et al.,
2007). Typically, seagrass growth exhibits a noticeable seasonal pattern
characterized by increased growth during the spring and summer months,
followed by a decline in autumn and winter (Dunton, 1990; Huong et al.,
2003; Vermaat et al., 1987). This seasonal pattern is primarily
attributed to the individual or interactive impacts of temperature and
light (Clausen et al., 2014; Nejrup \& Pedersen, 2008; Pérez-Estrada et
al., 2021).

Salinity plays a key role in influencing the distribution, biomass, and
productivity of macrofauna in estuarine and lagoon ecosystems (Al-Wedaei
et al., 2011; Vuorinen et al., 2015; Whitfield, 1992). This influence is
evident in the way species are distributed along a salinity gradient,
ranging from seawater levels at the estuary mouth, to brackish
conditions in the middle reaches, and ultimately encountering freshwater
sources in the upper reaches (Allanson \& Baird, 1999; Barnes \&
Ellwood, 2012). For instance, research conducted in 28 coastal water
bodies, including estuaries, shallow bays, and lagoons in Sussex,
England, identified seawater-derived salinity as the primary
environmental factor driving the distribution of taxa across freshwater
and saline assemblages (Joyce et al., 2005). Similarly, in the Celestun
coastal lagoon in Mexico, benthic community structure was found to be
spatially influenced by salinity along a gradient, while sediment
characteristics had a temporal impact on species diversity without
affecting abundance (Pech et al., 2007). These patterns are largely
attributed to the ionic composition of the water body, which, in turn,
is influenced by the salt composition of the soil and minerals
introduced from runoff or seepage waters (Pérez-Ruzafa et al., 2011).

Changes in salinity levels resulting from processes such as evaporation,
flooding, water abstraction, pollution, and reduced water flow have
significant implications for the structure and abundance of macrofaunal
communities (Allanson \& Baird, 1999; Van Niekerk et al., 2013). For
instance, in the Swartkops estuary in South Africa, where salinity
levels are typically similar to seawater, sediment characteristics
played a more prominent role in influencing macrobenthos (Mclachlan \&
Grindley, 1974). Following a flood event in this system, a substantial
decrease in salinity occurred, leading to alterations in the
distribution of macrobenthic organisms. These changes persisted for at
least two years following the flood (Mclachlan \& Grindley, 1974).

Teske and Wooldridge (2003) observed that endemic mud and sand fauna in
estuaries in the Eastern Cape, South Africa, were primarily influenced
by substrate characteristics, while salinity played a role in limiting
species from marine and freshwater habitats. Langebaan Lagoon shares
similarities with estuaries, despite the absence of direct freshwater
input. It is believed that groundwater seepage, supported by the
presence of freshwater-associated plants like \emph{Phragmites
australis} and \emph{Typha capensis} on the southeastern banks of the
lagoon, contributes to its unique conditions (Allanson \& Baird, 1999;
Christie, 1981). The lagoon's enclosed nature creates a relatively
stable tidal environment, with water residence times exceeding 30 days
observed in the inner basin (Largier et al., 1997). Salinity levels in
Langebaan Lagoon vary from the mouth to the head (Lawrence, 2023\emph{in
review} ), and while no effect of freshwater input was detected,
increased salinities at locations like Bottelary and Geelbek are likely
due to evaporation resulting from diurnal heating (Lawrence,
2023\emph{in review} ) as well as in the salt marsh creeks at the head
of the lagoon (Day, 1959; Flemming, 1977).

In non-quantitative surveys of \emph{Zostera} \emph{capensis} beds, Day
(1959) noted that macrofauna in Langebaan Lagoon exhibited a
distribution pattern along a salinity gradient, ranging from marine
species (mainly found in Saldanha Bay) to estuarine species
(predominantly occurring in the lagoon). He attributed this pattern to
the shelter provided by reduced wave action and its indirect impact on
the substrate, rather than temperature or salinity. Day also observed
that faunal community characteristics in Langebaan were similar to those
in other estuaries in the Western Cape, South Africa (Day, 1959).
However, specific detailed descriptions of macrofauna associated with
seagrass habitats in Langebaan, both in terms of spatial and temporal
dynamics, as well as the influence of environmental variables, are
currently lacking. Such information is crucial for enhancing our
understanding of macrofaunal diversity in this ecosystem.

The decline of seagrasses in Langebaan Lagoon has prompted an
investigation into the role of algal epiphytes and the distribution of
grazing epifauna. In this study, a specific group of macro-epifauna was
examined using visual counts, which differs from traditional techniques
used to quantify infauna. Traditional methods are more suitable for
assessing a broader range of fauna associated with both seagrass and
unvegetated habitats (Raz-Guzman \& Grizzle, 2001). Visual counts,
conducted during daylight hours, provide estimates of faunal composition
that mainly represent non-cryptic, diurnal, and sessile species (Edgar
et al., 2001). However, sampling during low tide can lead to an
underrepresentation of potentially important ecological species (Pearman
et al., 2016). Conversely, traditional coring methods may also miss
cryptic components and patterns in species distribution influenced by
succession and colonization, especially among sessile and motile
epibenthic communities (Moura et al., 2008). As a result, both
approaches categorize a subset of the overall faunal assemblage.

This study focuses on understanding the differences in epifaunal species
composition among seagrass populations in vegetated areas only. To
achieve this, a subset of species primarily involved in grazing on
seagrasses and epiphytes was examined. Visual assessment was considered
a suitable method to gauge the extent of epibenthic diversity supported
by different seagrass morphologies. Visual techniques for assessing a
subset of macrofauna in their natural habitat have been successfully
employed in previous studies (Källén et al., 2012; Q. Lee et al., 2012;
Poulos et al., 2013; Vonk et al., 2008) to address specific research
questions and uncover meaningful patterns in diversity and species
composition (Kuenen \& Debrot, 1995; Vellend et al., 2008).
Additionally, studies in other ecosystems like coral reefs and kelp
forests have primarily focused on surface fauna, as assessing infauna,
while enhancing overall diversity understanding, often involves
significant habitat destruction and is generally avoided. Consequently,
there exists a much larger body of literature on surface fauna in these
ecosystems.

The objective of this study was to assess the spatial and temporal
variations in macro-epifaunal community structure, including abundance,
richness, and diversity, within \emph{Zostera capensis} populations in
Langebaan Lagoon. The primary factors that contribute to these
variations were determined, including seagrass structural elements (such
as biomass, leaf characteristics, density, and epiphyte biomass) and
environmental factors (i.e., temperature, salinity, pH, turbidity,
oxygen levels, and chlorophyll a concentration). This research provides
a foundational understanding for further investigations into trophic
interactions, specifically grazing, as well as the impacts of
temperature on \emph{Z. capensis} (Lawrence \& Bolton, 2023).

\textbf{2. Methods}

2.1. Study Site and Sampling

Epifauna (hereafter referred to as epifauna) were assessed in
intertidal\emph{Zostera capensis} beds in Langebaan Lagoon on the west
coast of South Africa (Map 1; 18°03'E, 33°08'S). Part of the West Coast
National Park and designated a RAMSAR site for its ecological and
conservation significance, the lagoon is an important wetland and
layover for migratory birds (Christie, 1981; Compton, 2001; Day, 1959).
It is approximately 15 km long and 4 km wide and opens into a bay with a
mouth to the sea in the north. There is no freshwater inflow from
rivers, instead freshwater input occurs through groundwater seepage
during the rainy season (Whitfield, 2005). The lagoon experiences a
spring tidal range of 1.8 m at the mouth to 1.5 m at the head (Day,
1959). The benthic substrate of the lagoon mainly consists of sandy
sediment. In addition to \emph{Zostera capensis} , various algal species
and salt marsh grass (\emph{Spartina} sp.) comprise the vegetation
within and around the lagoon edge (Schils et al., 2001).

Sub-populations of seagrasses from five sites: Centre Banks, Klein
Oesterval and Oesterval (closer to the lagoon mouth), Bottelary and
Geelbek (further away from the mouth) were sampled in this study (Fig.
1). The sites were selected based on \emph{a priori} knowledge of their
position in the lagoon that displayed consistent and high seasonal and
interannual \emph{Z. capensis} coverage (Lawrence, 2023, \emph{in
review} ).

2.2. Seagrass Metrics

At each site, five randomly selected cores (Ø =10 cm, 15 cm deep) were
collected within \emph{Zostera} beds across four austral seasons
(spring, summer, autumn and winter). Core samples were rinsed with
seawater on-site then stored in labelled bags for transportation. In the
laboratory, shoot and leaf densities and average leaf lengths and widths
of the tallest 20\% of shoots, were recorded. Roots/rhizomes were then
separated from leaves, and epiphytes gently scraped off all leaves. Dry
mass was estimated for seagrass leaves and epiphytes
(60\textsuperscript{o}C, 14 hrs).

2.3. Epifauna Sampling

To assess the influence of plant structure on epifaunal diversity and
determine if different morphologically distinct seagrass populations
supported varied epibenthic diversities, invertebrate fauna present in
both the seagrass canopy and sediment surface was estimated. A visual
census method was employed to quantify the densities of epifauna within
quadrats (50 cm x 100 cm) targeting a size fraction \textgreater{}3 mm.
The surveys were conducted during spring low tide when the seagrass beds
were fully exposed. This method allowed for the quantification of a
subset of seagrass-associated fauna, primarily focusing on invertebrates
that graze on seagrass leaves and epiphyton and differs from traditional
techniques used to quantify infauna i.e., digging and sieving sediments
(Edgar et al., 2001). Traditional methods are more suitable for
evaluating a general suite of fauna associated with both seagrasses and
unvegetated habitats, which even if sampled collectively is often
analysed separately to answer specific questions ((Rodil et al., 2021).

Five quadrats were randomly placed within each seagrass bed at each of
the five sites across four seasons (n = 420). Within each quadrat, all
visible fauna present on, among, and below the seagrass leaves were
identified and counted. If any specimen could not be identified on-site,
they were collected for later identification. Species were then
categorised into phyla and functional groups using guidebooks (e.g.
(Branch, 2017) and the World Register of Marine Species
(www.marinespecies.org).

2.4. Environmental Variables

To investigate the influence of environmental variables on epifaunal
community structure, temperature, pH, turbidity, salinity, oxygen and
chlorophyll \emph{a} were recorded monthly at each site using a handheld
CTD (Conductivity Temperature Depth profiler (YSI 6820 V2-2V)). The
average of three readings taken approximately 10m apart constituted one
measurement.

2.5. Epifauna diversity indices

Epifaunal community structure was described using the diversity indices
related to the number and abundance of species recorded. Abundance was
determined as the total number of individuals (\emph{N} ) recorded
across all sites and seasons. Species richness was defined using the
Margalef index (d) that calculates the average number of species per
unit number of organisms collected (Margalef, 1989). Species diversity
was first determined after the Shannon-Wiener index (H') (Shannon 1963),
and thereafter converted to ``effective number of species'' by
calculating the exponential (exp (H)) of each diversity value after Hill
(1973). This transforms the non-linear H' to a linear form that permits
statistical comparisons that are intuitive, while the unit ``number of
species'' allows for diversity comparisons across metrics (Jost et al.
2006, Jost et al. 2010).

2.6. Statistical Analyses

Distribution of seagrass and environmental variables were explored using
multi-panel scatterplots and rank correlations to evaluate outliers,
correlations, and skewness. Strong co-linearity (± 0.7 - 0.9) was
identified between shoot and leaf densities, leaf length and width, and
temperature and pH. These variables were excluded from multivariate
analyses to avoid confounding the true degree of variability (Lipovetsky
and Conklin 2001).

Diversity indices were assessed visually using histograms and q-q plots.
Variances in the data were tested for normality (Shapiro-Wilk's test)
and homogeneity (Levene's Test) and natural log transformed where
necessary to meet assumptions for parametric analyses. Two-way factorial
ANOVAs were used to test for main and interactive effects of site
(random factor: five levels) and season (fixed factor: four levels) on
each index: abundance (\emph{N} ), richness (d) and diversity (H')
followed by Tukey's HSD tests. Univariate analyses were performed using
Sigma Stat (IBM SPSS Statistics 29).

Species abundance across seasons (fixed) and sites (random factor nested
within season) was compared using multivariate data testing in PRIMER 6
(Clarke \& Gorley, 2006). Abundance was considered an adequate metric on
which to assess the effects of seagrass structure, epiphyte biomass and
environmental factors since epifauna were largely comprised of grazers
whose densities were important in controlling epiphytes (Hovel et al.,
2002; Vonk et al., 2010). Data were 4\textsuperscript{th}-root
transformed to down-weight the influence of large variances. Visual
assessments of relationships between variables in ordination space were
made using non-metric multi-dimensional scaling (MDS) and cluster
dendograms based on Bray-Curtis similarity matrices. Non-parametric
multivariate analyses of variance were explored using PERMANOVA
(Permutational Multivariate Analysis of Variance) (Anderson et al.
2008). Within group differences as well as comparisons of species
composition between groups (i.e., beta diversity, Anderson et al. 2006)
were tested using Permutational Analysis of Multivariate Dispersions
(PERMDISP (9999 permutations)). The relative contribution of individual
taxa to the similarity among sites across seasons was evaluated using
SIMPER (similarity percentage breakdown).

To determine the environmental factors (temperature, salinity,
turbidity, pH, oxygen, chlorophyll \emph{a} and exposure) and seagrass
metrics (shoot density, leaf length, aboveground biomass, algal epiphyte
biomass) that best explained epifaunal variability, a distance-based
linear model (DistLM, selection procedure `best' and criteria `AIC') was
used. DistLM calculates the proportion of variability contributed by
each factor using multiple regression modelling to partition variation
according to selected predictor variables (Anderson et al. 2008). After
all possible model combinations were explored, the five best models that
explain variability based on smallest AIC values and adjusted
R\textsuperscript{2} derivatives are presented.

To partition the net effects of environmental variables (exogenous) and
seagrass structure (endogenous) on epifaunal abundance (endogenous) into
direct and indirect effects, maximum-likelihood estimated structural
equation models (SEM) were created in AMOS (IBM SPSS Amos version 26
Graphics). Structural equation modelling is a multivariate analysis
framework that encompasses techniques derived from factor and path
analyses (Grace, 2009). This framework enables the examination of both
direct and indirect relationships between observed and unobserved
(latent) variables. These relationships are represented by paths that
indicate the statistical dependency, and the associated parameters
specify the magnitude of the effect (direct or indirect) of independent
variables on dependent variables (observed or latent).

A full model was specified based on theory and hypothesised
relationships that (i) exogenous environmental variables: temperature,
salinity, pH and oxygen would positively or negatively influence
endogenous variables: seagrass biomass, leaf length/width, shoot
density, epiphyte biomass, chlorophyll \emph{a} (chl \emph{a} ) and
epifaunal abundance; while (ii) turbidity and exposure would negatively
influence all endogenous variables; and (iii) seagrass density, leaf
length/width, epiphytes and chl \emph{a} would positively influence
epifaunal abundance. Stepwise model selection was performed in which
variables with non-significant paths i.e. regression
coefficients\emph{p} \textgreater{} 0.05 were removed until all
remaining paths were significant (\emph{p} \textless{} 0.05) (Grace,
2009). Model fit was assessed using the chi-square value and the root
mean square error of approximation (RMSEA) as measures of
goodness-of-fit and evaluated by ensuring observed and predicted
covariance matrices were aligned. Models were then adjusted to produce a
low chi-square value with a corresponding \emph{p} value \textgreater{}
0.05 denoting observed and fitted models were not significantly
different, and an RMSEA \textless{} 0.08 indicating acceptable fit
(Grace, 2009). The final model output presents standardized coefficients
to compare the strength of direct and indirect effects.

\textbf{3. Results}

\textbf{3.1. \emph{Epifaunal Community Structure}}

Grazing invertebrates comprised 53\% m\textsuperscript{-2} of total
epifauna recorded in seagrass beds in Langebaan Lagoon (Table 3.1). This
group was dominated by the generalist microgastropod \emph{Assiminea
globulus} (31.45\% m\textsuperscript{-2}) and the pulmonate
limpet\emph{Siphonaria compressa} (16.12\% m\textsuperscript{-2}). The
keyhole limpet \emph{Fissurella mutabilis} was only found in beds in the
low shore zone comprising 3.26\% m\textsuperscript{-2} of total
epifaunal abundance (Table 3.1). Seagrass beds also supported 43.45\%
m\textsuperscript{-2} of predatory species, mainly the non-native
anemone, \emph{Sargartia ornata} (43.38\% m\textsuperscript{-2}).
Dominant at sites closer to the lagoon mouth, this species was generally
found in spring and autumn. In its native habitat (Mediterranean and
Western Europe), \emph{S. ornata} attaches to kelps and rocky shores,
however in Langebaan Lagoon it is found among \emph{Zostera} shoots
attached to loose rocks and feeds mainly on amphipods and polychaetes
(Robinson \& Swart, 2015).

The hermit crab, \emph{Diogenes brevirostris} had a dual function as
grazer and deposit feeder and comprised 2.67\% of total abundance. A
small percentage (0.83\%) of suspension feeding organisms was also
encountered, the most abundant being a solitary ascidian, \emph{Pyura
stolonifera} (0.4\%), found mainly growing in sediment between seagrass
shoots (Table 3.1).

\emph{3.1.1. Abundance}

Epifaunal abundances exhibited a distinct gradient of increase from the
mouth to the head of the lagoon (Fig. 3.1A). Bottelary displayed the
highest abundances across all seasons (136.68 m\textsuperscript{-2})
followed by Geelbek (83.78 m\textsuperscript{-2}). This was due to
elevated densities of \emph{Assiminea globulus} and \emph{Siphonaria
compressa} . Oesterval exhibited an average species abundance of 79.14
m\textsuperscript{-2}, while Centre Banks registered a slightly lower
average abundance of 68.84 m\textsuperscript{-2}. In contrast, Klein
Oesterval recorded the lowest abundance among the surveyed sites (23.66
m\textsuperscript{-2}). These sites were dominated by \emph{Sargartia
ornata} . Centre Banks supported the greatest average abundance
of\emph{Fissurella mutabilis} (26.55 m\textsuperscript{-2}), while the
hermit crab, \emph{Diogenes brevirostris} was most abundant at Klein
Oesterval (36.74 m\textsuperscript{-2}). \emph{A. globulus} was also
prevalent at high shore sites at Oesterval while \emph{F. mutabilis} was
exclusively found at sites closer to the mouth and absent at Bottelary
or Geelbek. Similarly, \emph{A. globulus} was not detected at Centre
Banks and an average of 1.26 individuals was recorded at Klein Oesterval
in winter.

Differences in abundance were significant across sites (\emph{F} =
10.80, \emph{p} \textless{} 0.001, Fig. 3.1A). Mean abundances at Centre
Banks and Klein Oesterval were similar while Oesterval, Bottelary and
Geelbek displayed homogenous means (Tukey HSD, \emph{p} \textless{}
0.05, Fig. 3.1A). Despite the high abundance (252.93) of individuals at
Centre Banks in autumn there were no significant seasonal differences in
abundance between sites (\emph{F} = 2.66, NS), however, the interaction
between site and season proved significant (\emph{F} = 6.68,
\emph{p}\textless{} 0.001).

\emph{3.1.2. Richness}

In contrast to abundance, epifaunal species richness exhibited a
distinct trend, with higher values observed in proximity to the lagoon
mouth and lower values towards the lagoon head (Fig. 3.1B). Centre Banks
displayed the highest average species richness (1.48) across all
seasons, followed by Klein Oesterval (1.27) and Oesterval (1.13). In
contrast, the number of species recorded at Bottelary and Geelbek was
comparatively lower (0.53 and 0.55 respectively). These differences were
significant across sites (\emph{F} = 8,01, \emph{p} \textless{} 0.001),
seasons (\emph{F} = 3,64, \emph{p} = 0.02) and their interaction
(\emph{F} = 3,09, \emph{p} = 0.001). Post hoc analysis revealed
significant mean differences in means between each of the sites located
closer to the mouth and the two sites at the lagoon head (Tukey
HSD,\emph{p} \textless{} 0.05, Fig. 3.1B). Seasonal variation in species
richness was primarily driven by a significant difference observed
between summer and autumn (Tukey HSD, \emph{p} = 0.04).

\emph{3.1.3. Diversity}

Species diversity had a similar pattern to richness with higher values
observed closer to the lagoon mouth and lower values at the lagoon head
(Fig. 3.1C). Klein Oesterval exhibited the highest species diversity
(2.40) followed by Centre Banks (2.28), Oesterval (2.05), and Geelbek
(1.81) -- diversity was lowest at Bottelary (1.47). These differences
were significant across sites (\emph{F} = 4.20, \emph{p} = 0.004)
attributed mainly to differences in mean diversity between Bottelary and
Centre Banks, and Bottelary and Klein Oesterval (Tukey HSD, \emph{p} =
0.006). Seasonal differences in diversity were also significant
(\emph{F} = 6.54, \emph{p} = 0.001) as well as the interaction between
site and season (\emph{F} = 2.70, \emph{p} = 0.004) indicating
variations in diversity between warmer and cooler months (Tukey
HSD,\emph{p} \textless{} 0.05).

3.2. Spatiotemporal Variations in Epifaunal Communities

Both site and season were found to have significant influence on
variation in epifaunal abundance within \emph{Zostera capensis} habitats
in Langebaan Lagoon (PERMANOVA, \emph{p} \textless{} 0.001; Table 3.2).
Site differences accounted for the largest proportion (36.83\%) of the
observed variation, which was more than twice the contribution of
seasonal differences (17.28\%). A significant portion of variation
(21.33\%) was attributed to the interaction between site and season,
while 24.56\% of epifaunal variation remained unexplained (Table 3.2).
Site differences were evident in the clear spatial separation of
epifaunal abundance in the dendogram and MDS plot showing a 60\%
dissimilarity in two groupings (Fig. 3.2 A\&B). One cluster comprised
Centre Banks, Klein Oesterval and Oesterval (closer to the mouth), and
the other Bottelary and Geelbek (closer to the head of the lagoon).

Beta-diversity differed between sites (PERMDISP: \emph{F} =
17.8,\emph{p} \textless{} 0.01), with pairwise comparisons confirming
differences in abundances between sites at the mouth and those at the
lagoon head (pairwise PERMDISP \emph{t} = 2.48, \emph{p} \textless{}
0.04). The species \emph{Siphonaria compressa} and \emph{Assiminea
globulus} contributed largely to the observed dissimilarity contributing
as much as 91\% to the observed variation (Table 3.3). \emph{Fissurella
mutabilis} , \emph{Parvulastra exigua} , \emph{Diogenes brevirostris}
and\emph{Pyura stolonifera} were the main species accounting for
\textgreater{}60\% of observed patterns at Centre Banks, Klein Oesterval
and Oesterval (Table 3.3).

Although spatially distinct seasonal patterns were not clearly evident,
the assessment of individual sites revealed seasonal groupings with
varying degrees of similarity, ranging from 25 to 75\% (Fig. 3.3). MDS
plots separated species abundances in spring from summer, autumn and
winter at Centre Banks and Klein Oesterval. In the case of Oesterval,
abundances were separated into two groups: spring and summer, and autumn
and winter (Fig. 3.3). No distinct seasonal grouping was observed for
Bottelary, while at Geelbek, the abundance in autumn appeared to be
different from the other seasons (Fig. 3.3). Generally, , there was no
significant seasonal effect observed in beta-diversity
(PERMDISP:\emph{F} = 3.24, \emph{p} = 0.09), however pairwise
comparisons revealed significant differences between epifaunal
abundances in autumn and winter (pairwise PERMDISP \emph{t} = 3.31,
\emph{p} \textless{} 0.01) and winter and summer (pairwise PERMDISP
\emph{t} = 3.19, \emph{p}\textless{} 0.01).

3.3. Influence of Environment and Seagrass Structure on Epifaunal
Abundance

All seagrass variables including epiphytic algal biomass were
significant in explaining variation in epifaunal abundance (PERMANOVA:
DistLM, \emph{p} \textless{} 0.01, Table 3.4). Initially, distance-based
linear models were run without including leaf width due to its strong
correlation with leaf length. However, a better fit was achieved when
leaf width was included. Among the seagrass variables, leaf width
contributed the highest proportion of variation in epifaunal abundance,
followed by leaf length, density, biomass, and epiphyte biomass (Table
3.4). All seagrass variables were selected in the first model that best
explained variability in epifaunal abundance determined by lowest AIC
and highest R\textsuperscript{2} values. The second best model which
included four variables (density, leaf width, epiphyte and seagrass
biomasses), and had an AIC value \textless{} 2 levels from the first
model, also adequately explained the observed variation (Table 3.4).

Environmental variables assessed independently in a DistLM marginal
test, found exposure to contribute the highest proportion of variation
followed by chl \emph{a} and turbidity (Table 3.5). Temperature,
salinity, and oxygen accounted for smaller proportions of variation
(Table 3.5). All environmental variable contributions were significant
(\emph{p} \textless{} 0.001). The top two models based on AIC and
R\textsuperscript{2} parameters, that best explained variation in
epifaunal abundance selected five (all) and four (excluding turbidity)
environmental variables respectively (Table 3.5).

The final structural equation model (SEM) employed to explore the
influence of environmental variables and seagrass metrics on epifaunal
abundance revealed several noteworthy findings. The direct effects of
temperature, salinity, pH, and exposure on abundance were found to be
non-significant (chi-square = 90.66, df = 45, \emph{p} = 0.35, RMSEA =
0.083; Fig. 3.4B). However, turbidity exhibited a strong negative direct
effect (-0.85), while oxygen displayed a positive direct effect (1.09).
Indirectly, temperature, pH, exposure, and oxygen had negative effects
on epifaunal abundance, whereas turbidity had a positive indirect effect
(Table 3.6). Although all environmental variables directly influenced
leaf length, this did not translate into indirect effects on abundance.
In contrast, leaf width was negatively influenced by temperature and
turbidity, and positively influenced by pH and exposure. It exerted a
positive direct effect, explaining 65\% of the variation in abundance
(Fig. 3.4B, Table 3.6). Similarly, shoot density had a positive direct
effect on abundance, accounting for 65\% of variation (Fig 3.4B). Shoot
densities were negatively influenced by pH, oxygen and exposure and
positively affected by turbidity (Table 3.6). No other seagrass metrics
emerged to directly influence epifaunal abundances.

\textbf{4. Discussion}

Epifaunal biodiversity in \emph{Zostera capensis} beds in Langebaan
Lagoon, as reflected by species diversity and richness, exhibited a
pattern of higher values nearer the lagoon mouth and in beds situated
below mean low water levels. In contrast, a decline in these measures
was evident upstream and in high shore beds. Species abundances
displayed a divergent trend, with lower counts observed at the mouth and
higher counts in upstream locations. Spatial and temporal disparities
significantly impacted epifaunal species abundance, primarily driven by
two species among the total of 24 recorded. Notably, these differences
were pronounced and linked to specific locations and seasons.
Environmental factors played a more substantial role, both directly and
indirectly, in explaining the observed variation than the structural
attributes of the seagrass beds.

4.1. Richness, Abundance and Diversity of Seagrass Epifauna

Despite fairly stable salinities in the lagoon, species richness and
diversity displayed a similar pattern of decline from the mouth as
observed in typical estuaries where variation in faunal communities are
largely influenced by a salinity gradient (Allanson \& Baird, 1999;
Barnes, 2010; Heymans \& Baird, 1995). However, unlike typical
estuaries, salinity levels in the lagoon increased with distance from
the mouth likely due to evaporation and the lack of distinct inflow of
freshwater. Variation in epifaunal community structure is not
necessarily attributed directly to salinity but could be an indirect
effect since salinity is often correlated with other environmental
variables (Yamada et al., 2007). Factors such as current velocity, wave
intensity, competition, and predation, which were not assessed in this
study, could account for the unexplained variation in community
structure.

In a similar enclosed bay with no freshwater input and a relatively high
salinity range (22 - 35) in Arcachon, France, macrofauna in beds of
another dwarf-eelgrass, \emph{Zostera noltei} showed similar patterns to
that observed for Langebaan Lagoon (Blanchet et al., 2004). Species
richness was lowest in assemblages furthest from the mouth and at higher
tidal levels while abundances were highest at these places. In that
study, distance from oceanic waters (km) was used as an environmental
factor and was the main attributor of differences between assemblage
groups along with tidal range (Blanchet et al., 2004). Similarly, Barnes
and Ellwood (2012) found richness and diversity to be highest in\emph{Z.
capensis} beds below mean low water levels in the marine-dominated
section of the Knysna estuary, South Africa. In that large estuarine
system species richness did not display the classic decline with
distance from the mouth, but instead had a stable fauna separated by
abrupt changes in areas where salinity levels fell below 30 (Barnes \&
Ellwood, 2012). In Arcachon, species occurring in areas of relatively
low salinities were not distinct but rather a subset of those present at
the mouth, which was not the case at Langebaan. Several species were
found only at sites closer to the mouth and not at Bottelary and
Geelbek. In contrast, a stable faunal composition was observed at Knysna
despite the wide range in salinity (\textless{}5--35). While the
observed variation in Knysna was supposedly due to changes in relative
abundance of dominant species, or the lack of marine species in upstream
localities and upper boundaries of seagrass beds (Barnes \& Ellwood,
2012), species variation in Langebaan was more likely a result of
exposure and tolerance to desiccation (this study), availability of
seagrass habitat (Angel et al., 2006) as well as the ability to survive
under periods of exposure to sun and wind during low tide (Day, 1959).

In Langebaan Lagoon, epifaunal community structure was dominated by
grazers i.e., \emph{Siphonaria compressa} , \emph{Fissurella
mutabilis}and \emph{Assiminea globulus} . \emph{A. globulus} was the
main species recorded at Bottelary and Geelbek and occurred in lower
abundances in high shore beds at Oesterval and Klein Oesterval, although
high abundances were previously observed at Oesterval in both seagrass
and saltmarsh vegetation in the high shore (Day, 1959). Regarded as a
broad niche generalist and capable of adapting to a range of
environmental conditions, this mud snail feeds mainly on bacteria and
diatoms found on sediment surfaces and occasionally on epiphytic
periphyton and is strongly averse to prolonged periods of submergence
(Angel et al., 2006). Gastropods have been documented to dominate many
estuarine systems (Allanson \& Baird, 1999) and Barnes (2013) recorded a
dominance of gastropods including 125 times greater average densities
of\emph{Assiminea} in upper-estuarine and enclosed sites compared to
elsewhere in the Knysna estuary. In Arcachon, seagrass beds at high
shore sites and sites further from the mouth were dominated by a single
gastropod, \emph{Peringia} \emph{ulvae} (Blanchet et al., 2004). The
prevalence of gastropods in high shore areas is likely attributed to
their ability to resist desiccation supported by the upward expansion of
habitat provided by seagrass and saltmarsh vegetation.

High shore sites in Langebaan were also dominated by the pulmonate
limpet \emph{Siphonaria compressa} which has been identified as South
Africa's most threatened marine invertebrate. Classified as Critically
Endangered by the IUCN (Kilburn, 1996), this limpet has an extremely
narrow habitat range and grows only in \emph{Zostera capensis} meadows
in Langebaan Lagoon and the Knysna estuary (Allanson \& Herbert, 2005).
In this study, densities of 100 m\textsuperscript{-2} were recorded at
Geelbek and Bottelary and \textless{}5 m\textsuperscript{-2} at
Oesterval. None were found at Klein Oesterval and Centre Banks, however,
Angel et al (2006) found densities between 40-80 individuals per
m\textsuperscript{2} at the lower edge of seagrass beds at Klein
Oesterval over a 30 year period. Analyses of faecal pellets revealed
that \emph{S. compressa} primarily feeds on bacteria, diatoms and
filamentous algae found on seagrass leaves (Allanson \& Herbert, 2005).

High abundances of the two dominant grazers \emph{Assiminea globulus}and
\emph{Siphonaria compressa} at the same sites suggest a lack of
competition and the exploitation of different niches. This hypothesis
was explored by Angel et al. (2006) who assessed the interaction
between\emph{A. globulus, S. compressa} and \emph{Zostera capensis}
cover in relation to low tide exposure at Langebaan. They found that
\emph{S. compressa} thrived on the lower edge of seagrass beds subjected
to shorter periods of exposure and suggested that the virtual absence of
the limpet from the upper zone was largely the result of avoiding
physiological stress from desiccation - a consequence of its small size
and thin shell. Experimental transplants of seagrass into sandbanks saw
a proliferation of \emph{S. compressa} and concluded that confinement of
the limpet to beds in the high shore was suboptimal and essentially due
to the restriction of the seagrass beds themselves to expand further
into the low shore due to sediment disturbance by the burrowing sand
prawn, \emph{Kraussillichirus kraussi} (Angel et al., 2006). The
conclusions of that experiment do not explain the absence of \emph{S.
compressa} from low shore beds at Oesterval and Centre Banks in this
study. Here, a likely reason is competition with grazing gastropods
(Orth et al., 1984) such as \emph{Fissurella mutabilis,} which is less
tolerant to desiccation and occurred in high abundances at low shore
sites close to the mouth and but not at high shore sites i.e., Geelbek
and Bottelary.

In contrast, Angel et al. (2006) recorded high and low densities
of\emph{Assiminea globulus} on the upper and lower edges of seagrass
beds respectively and noted a positive correlation with exposure. Once
again, this was attributed to disturbance of sediment and reduction of
diatoms due to bioturbation, however these snails also appeared to
prefer exposure rather than submergence (Angel et al., 2006). This
species' preference for more exposed parts of seagrass beds is likely
related to the availability of food in the form of sand-dwelling diatoms
that are abundant in the high shore zone (G.M. Branch, unpublished
data), and therefore occupies a different niche to that of
\emph{Siphonaria compressa} . Given that \emph{S. compressa} likely
feeds essentially on periphyton on seagrass leaves (Allanson \& Herbert,
2005), there was no evidence to indicate that abundance or zonation of
these two species had been influenced by competition.

Environmental conditions such as a longer tidal emergence period at
Bottelary and Geelbek correspond to a higher susceptibility of species
to desiccation. This explains the lower species richness at these sites,
and the high abundances of the desiccation resistant \emph{A.
globulus}and attests to the resilience of \emph{S. compressa} to persist
in suboptimal conditions. The cushion star \emph{Parvulastra exigua} and
keyhole limpet \emph{F. mutabilis} were previously reported to have
completely declined at Klein Oesterval (Pillay et al., 2010) but was
subsequently recorded in this study. In addition, \emph{Pyura
stolonifera} , \emph{Sargartia ornata} and \emph{Oxystele antoni} not
documented in that survey, were also found. These findings underscore
the considerable variability inherent in seagrass ecosystems. The
elevated abundances of grazers further emphasize the important trophic
role of grazing within both seagrass and intertidal ecosystems more
broadly (Asmus \& Asmus, 1985; van Der Heijden et al., 2020).

Species richness and densities recorded in this study were comparable to
the survey of macrofauna within seagrass and un-vegetated sandflats at
Klein Oesterval using the traditional method of digging and sieving
(Pillay et al. 2010). That study yielded a total 27 species and while
infauna (n = 15) was not targeted in this study, a total 13 species were
documented at Klein Oesterval - nine species were recorded in both
surveys, and an additional four were recorded in this study only. These
patterns allude to the strengths and weaknesses in sampling techniques,
many of which fail to capture various components within faunal
assemblages influenced by processes such as life history stages,
succession and colonization (Moura et al., 2008) as well as temporal
restrictions related to tidal regime, season and day/night sampling.
While traditional methods are considered comprehensive in providing
estimates of biodiversity, alternative methods including the use of a
selection of indicator species in single or multimetric indices, can be
useful to assess the ecological status of benthos in marine and
estuarine environments (Borja et al., 2011; Dauvin et al., 2016)
particularly when resources are limited. Using a subset of species has
been found to provide meaningful descriptions on faunal community
structure (Kuenen \& Debrot, 1995; Vellend et al., 2008). Importance
should therefore be placed on consistency of methods used as well as
regularity of surveys to provide informative results to address research
and management objectives (Magurran et al., 2010).

4.2. Influence of Seagrass Structure and Environmental Variables on
Epifaunal Abundance

Significant variability stemming from both site and season was evident
along with a seasonal pattern in epifaunal abundance (Figure 3.1).
Abundances were observed to be at its highest during summer, but
generally decreased in cooler months. Highest abundances of epifauna
were found in denser seagrass beds at Bottelary and Geelbek, while beds
with longer leaves near the lagoon mouth supported a richer suite of
species compared to short-leaved populations, albeit in lower densities.
Despite the absence of positive and significant correlations between
epifaunal abundance and seagrass metrics, all the assessed seagrass
variables were selected as important in elucidating the variation in
epifaunal populations. Among these variables, leaf width and length
emerged as the most influential contributors to the observed variation,
accounting for 29.78\% and 24.39\% respectively.

The abiotic environment in Langebaan Lagoon significantly influenced
variability in seagrass morphometric parameters (Lawrence 2023, \emph{in
press} ). Notably, lower temperatures were associated with larger
seagrass leaves which provide a larger surface area conducive for
epiphyte colonization (Bologna \& Heck, 1999; Terrados \& Medina-Pons,
2011). Faunal diversity in seagrass habitats tends to increase with
larger plant sizes and higher biomass, however other factors such as the
ecological characteristics of fauna, can also play a role in shaping
community structure (Heck \& Orth, 1980; Orth et al., 1984). Given that
leaves of different seagrass species differ in area per unit biomass, it
has been shown that plants with greater aboveground foliar biomass
provide more shelter, protection and available food than smaller plants
with less surface area per biomass (Gartner et al., 2013; Hansen et al.,
2010). This concept provides a plausible explanation for the observed
variations in epifaunal abundances across different sites with distinct
seagrass plant sizes and leaf areas within Langebaan Lagoon.

Several studies demonstrate similar patterns to that observed in
Langebaan. For example, in an assessment of beds with low, medium and
high densities of \emph{Zostera marina} in the United Kingdom, infaunal
diversity increased with increasing seagrass density, and significant
differences in community structure between shoot density ranges were
observed (Webster et al. 1998). Similarly, seagrass biomass was a key
regulator of macrofaunal diversity, abundance, dominance and trophic
arrangement independent of hydrodynamic and sediment properties in
Apalachee Bay, Florida (Stoner 1980). Likewise, increased habitat
complexity as a result of greater seagrass biomass was a key factor in
regulating macrofauna in \emph{Zostera capensis} beds in Mozambique
(Paula et al. 2001). \emph{Z. marina} shoot density exhibited a
significant relationship with total faunal abundance, with varying
effect magnitudes across 19 sites nested within three locations in the
southwest United Kingdom (Smale et al., 2019). In that study, higher
seagrass density generally corresponded to greater faunal abundance,
contributing to shifts in the structure of the faunal assemblage.

In the final structural equation model, several key factors were
identified to directly impact the patterns of epifaunal distribution
within Langebaan Lagoon. Notably, seagrass density, leaf width, oxygen,
and turbidity exhibited direct effects, while other environmental
variables played a substantial indirect role (Fig. 3.4). Although there
was no direct link between temperature and epifaunal abundance, and the
indirect effect was relatively modest (a 1SD increase in temperature
correlated with a 0.09SD decrease in epifaunal abundance), temperature
displayed a direct influence on both leaf length and width (Table 3.6).
It was also found to significantly predict five out of the six evaluated
seagrass metrics (Lawrence 2023, \emph{in press} ). Indirect effects
have been shown to influence community structure as significantly as
direct effects (Wootton, 2002). Consequently, the observed decline in
epifaunal abundance in the field is likely attributed to a combination
of direct physiological effects of temperature and indirect impacts on
the underlying seagrass ecosystem (Marbà \& Duarte, 2010).

Smaller and denser morphologies of \emph{Zostera capensis} experienced
higher temperatures than their large-leaved counterparts. In seagrass
beds at Bottelary and Geelbek, higher shoot and leaf densities were
observed from smaller plants with narrower leaves, resulting in greater
leaf area per unit biomass. The lower diversity of epifaunal species and
the dominance of just two species suggest a narrow ecological niche and
a high degree of adaptation to local environmental conditions at these
particular sites. In a study by Edgar and Barrett (2002) conducted in
Tasmania, it was determined that the primary factor influencing species
richness across 48 estuaries was tidal height. Unlike seasonal
variations, spatial variance played a more substantial role in
influencing species richness, which is consistent with the findings in
Langebaan Lagoon. Those authors also revealed a relationship between
species richness, faunal biomass, and factors like salinity and seagrass
biomass, particularly during low tide and shallow subtidal levels (Edgar
\& Barrett, 2002). Notably, physiological tolerance to environmental
stress, particularly in response to exposure, has been identified as a
strategy to evade the adverse impacts of predation and competition. This
phenomenon is more pronounced in diverse communities within temperate
seagrass ecosystems (Barnes \& Ellwood, 2012).

Seagrass habitats serve as a structural foundation for epifaunal
communities. Habitats characterized by structural complexity often
facilitate the co-existence of a higher number of species by buffering
the effects of competition and predation (Gilinsky, 1984; Menge \&
Sutherland, 1976; Russ, 1980). This heightened structural complexity
within habitats can indirectly regulate species interactions,
particularly by curbing predation. This is achieved through the
provision of increased refuge options for prey species, thereby reducing
capture efficacy of predators (Hammerschlag-Peyer et al., 2013; Menge \&
Sutherland, 1976). The presence of numerous refuges are linked to
heightened prey diversity, which has been shown to balance otherwise
unstable predator-prey interactions (Heck \& Orth, 1980; Orth et al.,
1984).

Enhanced species diversity is pivotal to the improved functioning and
resilience of ecosystems (Duffy et al., 2003; Unsworth et al., 2015).
While the suitability of a particular species as a food source hinges
largely on its abundance, relying on rare species for sustenance is
unlikely (Balvanera et al., 2006; Duarte, 2000). Consequently, the
preservation and enhancement of biodiversity, encompassing both species
richness and abundance, assume a critical significance in bolstering
trophic structures. This, in turn, ensures the overall vitality of
ecosystems and ensures the sustained provision of ecosystem services.

Epifaunal community structure in \emph{Zostera capensis} beds was
influenced directly by seagrass leaf size and density, and indirectly by
environmental variables. Temperature notably shapes seagrass metrics,
with warmer temperatures producing smaller, denser seagrass morphologies
(Lawrence 2023, \emph{in press} ). Persistent temperature increases may
prompt a shift to smaller populations with distinct faunal associations.
Further warming could narrow the range of larger populations, reducing
overall faunal diversity. Increasing temperatures would also mean
greater levels of evaporation. Rising temperatures also enhance
evaporation, potentially favoring more desiccation-tolerant species.
Fluctuations in seagrass abundance could heighten the risk of the
further threatened \emph{Siphonaria compressa} limpet.

Diminished epifaunal diversity in \emph{Zostera capensis} beds in
Langebaan Lagoon suggests decreased productivity and energy flow through
trophic levels, especially for fish prey. As crucial sites for
recreational and industrial fisheries in the lagoon and adjacent
Saldanha Bay, the loss of seagrass habitats implies a loss of food and
shelter for juvenile fish with critical consequences for these ecosystem
services.

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