Download Using leaf chemistry to better understand the

Using leaf chemistry to better understand
the ecology of seagrass in the Gippsland
Lakes
F.Y. Warry, P. Reich, R.J. Woodland, P. Cook
2013
Arthur Rylah Institute for Environmental Research
Unpublished Client Report for the Gippsland Lakes Ministerial Advisory Committee
Technical Report Series No. XXX
Technical Report Series No. XXX
Using leaf chemistry to better understand the ecology
of seagrass in the Gippsland Lakes
F. Y. Warry1,2, P. Reich1, R.J. Woodland2, P. Cook2
March 2013
1
Arthur Rylah Institute for Environmental Research
123 Brown Street, Heidelberg, Victoria 3084
In partnership with:
2
Water Studies Centre, Monash University, Clayton, Victoria 3800
Arthur Rylah Institute for Environmental Research
Department of Sustainability and Environment
Heidelberg, Victoria
Report produced by:
Arthur Rylah Institute for Environmental Research
Department of Sustainability and Environment
PO Box 137
Heidelberg, Victoria 3084
Phone (03) 9450 8600
Website: www.dse.vic.gov.au/ari
© State of Victoria, Department of Sustainability and Environment 2011
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or email [email protected]
Citation: Warry, F. Y., Reich, P, R.J. Woodland, P. Cook (2013) Using leaf chemistry to better understand the
ecological function of seagrass in the Gippsland Lakes. Arthur Rylah Institute for Environmental Research Unpublished
Client Report for the Gippsland Lakes Ministerial Advisory Committee, Department of Sustainability and Environment,
Heidelberg, Victoria
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Front cover photo: Zostera nigricaulis (J.S. Hindell).
iii
Contents
Acknowledgements ........................................................................................................................... v
Summary ........................................................................................................................................... 1
1
Introduction ............................................................................................................................ 3
2
2.1
Methods ................................................................................................................................... 7
Study Sites................................................................................................................................ 7
2.2
Monitoring Seagrass Physical Condition ................................................................................. 8
2.3
Monitoring Seagrass Leaf Chemistry....................................................................................... 8
2.3.1
2.4
Statistical Analyses ................................................................................................... 8
Seagrass Contribution to Fish Nutrition ................................................................................... 9
2.4.1
Statistical Analyses ................................................................................................... 9
3
3.1
Results ................................................................................................................................... 10
Seagrass Physical Condition and Relationships with leaf chemistry ..................................... 10
3.2
Spatial variation in seagrass leaf chemistry ........................................................................... 11
3.3
Seagrass chemical and physical condition and the Gippsland Lakes environment................ 12
3.4
Contribution of seagrass to fish nutritional support ............................................................... 14
4
Discussion.............................................................................................................................. 16
5
Key Findings ......................................................................................................................... 18
6
Recommendations ................................................................................................................ 19
References ....................................................................................................................................... 20
Appendix 1 ...................................................................................................................................... 22
Appendix 2 ...................................................................................................................................... 23
Appendix 3 ...................................................................................................................................... 24
Acknowledgements
This work was funded by the Gippsland Lakes Ministerial Advisory Committee. Thanks to T.
Daniel and A. Pickworth for field assistance and D. Hartwell for assistance in the laboratory.
Thanks to K. Morris for valuable comments on earlier versions of this report. Work was completed
in accordance with DSE Animal Ethics (AEC 07/24) and Fisheries Victoria (RP 827) permits.
v
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
Summary
Seagrass is an important component of the Gippsland Lakes ecosystem that supports numerous
ecosystem functions. Seagrass plants are particularly vulnerable to shifts in nutrient availability
and water clarity. Monitoring the condition of seagrass has conventionally measured the extent
cover or morphology of seagrass plants, but such physical based approaches mean that seagrass
decline often occurs before stress is detected and the mechanisms underpinning any changes are
poorly understood. Supplementing physical monitoring indices with measurements of leaf
chemistry may help elucidate the mechanisms underpinning physical condition changes and
relationships with threats.
The chemical composition of seagrass leaves provides a time-integrated signal of environmental
conditions and is less variable than the chemical composition of the water column. Elemental
concentrations, elemental ratios and stable isotope signatures of seagrass leaves can provide
information on the relative availability of nutrients and light and how plants respond to patterns in
the availability of these resources (e.g. growth rates).
This research aimed to supplement physical seagrass monitoring that has occurred in the
Gippsland Lakes since 2008 by: (i) investigating spatial patterns in seagrass leaf chemistry to
provide better understanding of seagrass condition and the mechanisms influencing condition with
the view to facilitate early detection of seagrass stress prior to potential decline, and; (ii)
investigate the role of seagrass in the nutritional support of fish to strengthen understanding of the
links between fish and seagrass habitats.
Seagrass (Zostera and Ruppia) and fish samples were collected from multiple sites in autumn
2012. Seagrass leaves were analysed for elemental concentrations of carbon, nitrogen and
phosphorous and stable isotope signatures of carbon (δ13C) and nitrogen (δ15N). Fish muscle
tissues were analysed for δ13C and δ15N. To assess the relative importance of seagrass to the
nutrition of fish, isotope signatures of other likely basal resources were included in isotope
modelling.
Elemental and isotopic compositions of leaves of Zostera and Ruppia across the Gippsland Lakes
were within the ranges reported for these species elsewhere in Victoria as well as for other
seagrass species from overseas. Foliar concentrations of phosphorous were at the higher end of
ranges reported elsewhere and as such N:P ratios were low. C:P and C:N ratios were also low
suggesting the supply of nutrients exceeded carbon acquisition indicating plants were not nutrient
limited.
There was significant spatial variation in leaf chemistry measurements across the Gippsland Lakes
at multiple spatial scales. Preliminary analyses indicated δ13C values decreased with proximity to
freshwater sources and with increasing potential for wind-driven mixing, which is consistent with
models of more depleted δ13C signatures under low light conditions, and also the uptake of DIC
derived from the rivers. The physical condition of seagrass was more variable over the 2009 –
2012 period at sites closer to freshwater sources, indicating that environmental conditions are more
variable, or seagrasses are more sensitive to environmental changes at these sites.
Seagrass contributed to the nutrition of multiple fish species although the extent of this
contribution varied spatially and among species. This demonstrates the importance of seagrass for
fish in the Gippsland Lakes exceeds merely the physical habitat afforded by seagrass plants. Stable
isotope modelling indicated that generally the cyanobacterium Nodularia spumigena either wasn’t
assimilated into fish biomass (muscle tissue) or was metabolised prior to fish being sampled in
autumn 2012. While these data could not definitively resolve the question of Nodularia
contribution to the nutrition of small fish in the Gippsland Lakes, they will provide useful
1
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
benchmark data for the Monash University research on impacts of Nodularia conducted in the
2012 – 2013 summer.
This study has provided an initial understanding of the spatial variation in seagrass leaf chemistry
within the Gippsland Lakes and relationships with environmental variables that capture
information about the relative availability of nutrients and light. Opportunities exist to further our
understanding leaf chemistry dynamics by investigating temporal variability and testing targeted
hypotheses about light availability.
2
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
1 Introduction
Seagrass is an important component of the Gippsland Lakes ecosystem as it supports numerous
ecosystem functions including the provision of habitat, sediment stabilisation and nutrient cycling
(Waycott et al. 2009). Seagrass is vulnerable to nutrient enrichment, sedimentation and reductions
in water clarity (Waycott et al. 2009). These threats increase with modification of landuse and
hydrology in coastal catchments, particularly because seagrasses inhabit shallow waters often in
close proximity to human activities.
Recognition of the value, vulnerability and global loss of seagrass has prompted increased seagrass
monitoring (Waycott et al. 2009). Seagrass monitoring typically focuses on physical aspects of
seagrass plants with condition characterised by, for example, measurements of percent cover and
leaf density. Remote sensing, diver visuals and underwater video techniques are often used for this
kind of physical monitoring. Underwater video techniques have been used in the Gippsland Lakes
since 2008 to efficiently monitor relative seagrass cover and distribution across broad spatial areas
(see Warry and Hindell 2012). The technique is sensitive enough to detect change in the physical
structure of seagrass, however, seagrass decline often occurs before stress is detected and the
mechanisms underpinning any change are poorly understood.
Supplementing physical monitoring of seagrass with measurements that directly capture
information on how plants function can help elucidate the mechanisms underpinning whole plant
or community level changes in condition and relationships with threats. Functional monitoring
approaches also have the potential to detect stress prior to seagrass decline. Aspects of seagrass
leaf chemistry are being increasingly monitored elsewhere to provide functional information about
seagrass condition (Fourqurean et al. 2005), the relative availability of key resources, e.g. nutrients
and light (Fourqurean et. al. 2007), and seagrass contribution to food webs (Hindell and Warry
2010). Understanding relationships between measurements of seagrass leaf chemistry and physical
condition will aid interpretation and application of both functional and physical monitoring
approaches.
The chemical composition of seagrass tissues provides a time integrated signal of environmental
conditions and is less variable than the chemical composition of the water column (Fourqurean et
al. 2005). Variation in leaf chemistry through space and time, however, is common (Fourqurean et
al. 2005), although marked spatial and temporal gradients observed in numerous estuarine
ecosystems suggest this variability reflects environmental conditions, rather than just random noise
(Fourqurean et al. 1992, 2005). Thorough understanding of natural spatial and temporal variability
in these measurements is essential for their unambiguous application (Fourqurean et al. 2005).
Scales of this natural spatial and temporal variability are considered highly location-specific
(Fourqurean et al. 2005) and understanding this variation is a necessary initial step for any new
monitoring activity so that condition shifts can be identified with confidence.
Leaf tissues were the focus of this preliminary study, as the influence of environmental variables,
e.g. nutrients and light, on seagrass chemistry has been better studied for leaves than other tissues.
The elemental content of seagrass leaves relates to the relative availability of those elements in the
environment as well as growth rates (Castejón-Silvo and Terrados 2012). This is particularly true
of nutrients imperative for growth such as nitrogen (N) and phosphorous (P). Spatial gradients in
N or P availability have been reflected in spatial patterns in N and P content of seagrass leaves
(Fourqurean et al. 1992; 2005, Figure 1). Nutrient enrichment experiments have also shown that
nutrient composition of seagrass will shift to reflect increased nutrient availability, if plants were
originally limited in the specific nutrient being added (see e.g. Bulthuis et al. 1992). Reductions in
light availability reduces photosynthetic rate and thereby the demand for nutrients, resulting in an
increase in foliar N and P (Fourqurean et al. 2007, Figure 1). Herbivory can also influence the bulk
3
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
elemental content of leaves, as herbivores may select for leaves with varying nutrient contents
(Campbell and Fourqurean 2009, Figure 1).
Elemental stoichiometry (the ratios of elements) in seagrass leaves can provide information on
nutrient limitation of plant growth and the nutritional quality of seagrass for consumers. The
universal Seagrass Redfield Ratio (SRR) is considered to be C:N:P of 550:30:1 when light and
nutrient requirements are balanced (Atkinson and Smith 1983). Deviations from this ratio may
indicate nutrient or light limitation. Sources and sinks of nutrients within aquatic ecosystems have
been identified by investigating spatial patterns in the elemental stoichiometry of seagrass
(Fourqurean et al. 1992, 2005, Figure 1).
Stable isotopes are naturally occurring isotopes that do not decay and provide a natural way to
follow element cycling (Fry 2006). The ratio of heavy to light stable isotopes (e.g. 13C/12C or δ13C;
15
N/14N or δ15N) provides a signature that will not change (or that change predictably) as elements
cycle through an ecosystem e.g. from inorganic element pools to plants to consumers (Fry 2006).
Stable isotope signatures of seagrass leaves may reflect the availability of nutrients, light and the
isotopic composition of nutrient pools (e.g. dissolved inorganic carbon and nitrogen pools). The
isotopic composition of nutrient pools may reflect their origin (e.g. anthropogenically derived
nitrogen tends to have enriched δ15N signatures). Seagrasses will fractionate the available pools of
inorganic carbon and nitrogen depending on demand relative to availability (Campbell and
Fourqurean 2009, Figure 1). Demand closely relates to light availability and its influence on
photosynthetic rate. Interspecific differences in isotopic signatures of sympatric seagrass species
can also occur and likely result from differences in plant physiology and or ecology (Campbell and
Fourqurean 2009).
Elemental concentrations, stoichiometric ratios and stable isotope signatures of seagrass leaves are
thought to relate to various environmental factors, primarily the availability of nutrients and light
(Fourqurean et al. 2005, 2007, Figure 1). Frequent, spatially explicit nutrient and light
measurements have been shown to relate to seagrass leaf chemistry elsewhere (e.g. Fourqurean et
al. 2005). In the absence of such data, environmental variables thought to contain information
about spatial patterns of nutrients and light may be a useful proxy for exploring relationships
between seagrass leaf chemistry and environmental conditions. Variables including distances from
major freshwater inputs, depth and hydrodynamic mixing likely contain information about the
delivery and dilution of freshwater and nutrients and associated impacts on light availability.
Stable isotopes are also used for investigating food web dynamics and determining the nutritional
base of aquatic consumers (Gaston and Suthers 2004). Stable isotope signatures of consumers will
reflect that of their combined sources of nutrition, thereby providing a time-integrated estimation
of assimilated diet. The value of particular vegetated habitats, such as seagrass, is often based on
comparisons of species abundance and diversity among habitats (Beck et al., 2001). Elucidating
the autotrophic sources of estuarine food webs provides an additional means of assessing the
relative value of vegetated habitats, particularly where consumers are mobile or spatially separated
from the autotrophs providing their nutritional base. Determining the likely contribution of
seagrass, relative to alternative autotrophs, to the nutrition of fish that dominate seagrassassociated assemblages will improve understanding of the functional role of seagrass within the
Gippsland Lakes ecosystem.
Monitoring of physical attributes of seagrass and fish assemblage structure was supported by the
Gippsland Lakes and Catchment Taskforce from 2008 to 2011. The Gippsland Lakes Ministerial
Advisory Committee (GLMAC) continued support for these monitoring activities in 2012. The
GLMAC also supported the supplementary work presented in this report with the objective of
improving our understanding of the ecological function of seagrass within the Gippsland Lakes by:
4
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
1. Trialling and developing monitoring approaches that target functional aspects of seagrass plants
to provide better understanding of seagrass condition and mechanisms influencing condition
changes with the view to facilitate early detection of seagrass stress prior to potential decline, and;
2. Investigating the role of seagrass in the nutritional support of fish to strengthen understanding of
the links between fish and seagrass habitats and potential impacts of fluctuations in seagrass
condition on fish assemblages.
Specifically, this research aimed to:
i.
Assess whether functional information corresponds to physical information by comparing
seagrass leaf chemistry measurements with physical condition scores derived using
underwater video during primary monitoring activities;
ii.
Assess spatial variation in the elemental concentrations, elemental stoichiometry and
isotopic values of seagrass leaves throughout the Gippsland Lakes;
iii.
Investigate relationships among environmental variables, thought to influence nutrient
availability, and both seagrass leaf chemistry and physical condition scores;
iv.
Determine the likely contribution of seagrass, relative to alternative autotrophs, to the
nutrition of some dominant fish species.
5
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
Environmental
Variable
Functional
Change
Impact on
Seagrass
↑ Demands for
critical
nutrients (N & P)
Measurement
Response
↓%N &/or %P
∆ C:N, C:P, N:P
No ∆ %N &/or %P
No ∆ C:N, C:P, N:P
↑ Light
↑ Photosynthesis
↑ size of DIC and/or
DIN pool
↑ Nutrients
∆ Composition of
DIC & DIN pool
↑ Concentrations of
N and/or P in
H2O column
↑ Growth outstrips
N supply
↑ δ15N
↑ Carbon fixation
↑ δ13C
↑ Carbon supply c.
to plant demand
(for given light)
↓ δ13C
↑ Nitrogen supply c.
to plant demand
(for given light)
↓ δ15N
Differences in sigs.
of marine and terrestrial
nutrients
∆ δ13C &/or δ15N
Potential ↑ algae &
phyto → shading
& ↓ light
↓δ13C
↑%N &/or %P
∆ C:N, C:P, N:P
↑ Availability of
N and P
No ∆ %N &/or %P
No ∆ C:N, C:P, N:P
↑ Temperature
↑ Grazing
↓ CO2 pool &
↓ iso discrimination
↑δ13C
Potential use
of HCO3-
∆δ13C
Selec. of senescent
leaves ↑ bulk plant
nutrients
↑ %N & %P
Selection of young
leaves ↓ bulk plant
nutrients
↓ %N & %P
↓ Solubility of CO2
Selection of
senescent
or young leaves
Figure 1: Conceptual diagram linking changes in environmental variables to leaf chemistry
measurements.
6
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
2 Methods
This study was undertaken during autumn (April and May) 2012. The work consisted of two
complementary components investigating: (i) the utility of leaf chemistry as a tool for monitoring
seagrass functional condition; and (ii) the role of seagrass, relative to alternative autotrophs, in the
nutrition of estuarine fish and invertebrate fauna of the Gippsland Lakes.
2.1 Study Sites
Seagrass and fish were collected from sites distributed throughout the Gippsland Lakes (Figure 2).
These sites were a subset of those sampled during annual (2009 – 2012) seagrass video monitoring
and fish assemblage surveys (see Warry and Hindell 2012). Sites for the current study represent
the spatial distribution of seagrass in the Gippsland Lakes based on these surveys (Figure 2).
Figure 2: Location of study sites within the Gippsland Lakes; triangles indicate Zostera was collected;
circles indicate Ruppia collected; crosses indicate fish were collected; site abbreviations correspond to full
names given in Appendix 1; grey dashed boxes indicate broad zones that sites fall within.
Zostera spp. is the dominant seagrass in the Gippsland Lakes and represented by two species;
Zostera nigricaulis and Zostera mulleri. Differentiating between these two species using
underwater video footage was not possible and they are grouped as ‘Zostera’ in the current work .
Ruppia spiralis (hereafter Ruppia) was present at some sites.
Sampling sites were allocated to one of four broad zones; Kalimna, Metung, Lake King South and
Lake King North (Appendix 1). Zones corresponded to broad areas of the Gippsland Lakes within
which researchers from Monash University collected water samples for analyses of the
cyanobacterium Nodularia, phyto- and zoo-plankton and particulate organic matter during the
summer of 2011 – 2012 (see section 2.3). These additional data supplemented isotopic analyses of
7
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
seagrass performed in this study to help identify the relative importance of seagrass in fish diets
within each of these zones. Two sites, Nicholson River Mouth (NRM) and Lake Victoria West
(LVW), fell outside all broad zones sampled by Monash and fish were not collected from these
two sites, so isotope values of seagrass at these sites were not used to inform food web analyses
(Appendix 1). Grouping sites into zones also provided information on the influence of spatial scale
on the variability of leaf chemistry data.
2.2 Monitoring Seagrass Physical Condition
Underwater video was used at 50 sites to document the presence/absence of seagrass and record
broad condition categories based on percent cover and blade density along each transect. These
condition categories were; 0, no seagrass observed along transect; 1, very sparse seagrass, with
only a few blades or small plants observed along transect;2, sparse seagrass throughout < 50% of
transect; 3, sparse seagrass present along > 50% of transect; 4, medium to high density seagrass
common along transect; 5, Dense seagrass present along > 50 % of transect. Sites were surveyed
annually in autumn during the period 2009 – 2012 (see Warry and Hindell 2012 for details).
2.3 Monitoring Seagrass Leaf Chemistry
Zostera was collected from 14 sites and Ruppia was collected from four sites (Figure 2). Three
replicate samples were collected from each site. Each sample consisted of several live shoots
collected with a grab sampler. Samples were stored on ice during transportation to the lab where
they were frozen at -18°C.
Only the most recent growth of seagrass leaves was prepared for analyses. Leaves were cleaned of
epibionts using a razor blade and washed in distilled water. Samples were dried to constant weight
(24 hrs at 60ºC) and ground to a fine powder.
Phosphorous content (%P) in the leaves was analysed using sulphuric acid-nitric acid digestion at
the Water Studies Centre, Monash University. Carbon and nitrogen stable isotope ratios, %C and
%N content were analysed at the Water Studies Centre, Monash University, on an ANCA GSL2
elemental analyser interfaced to a Hydra 20-22 continuous-flow isotope ratio mass-spectrometer
(Sercon Ltd., UK). The precision is ±0.1‰ for 13C and ±0.2‰ for 15N (SD for n=5). Stable isotope
data are expressed in the delta notation (δ13C and δ15N), relative to the stable isotopic ratio of
Vienna Pee Dee Belemnite (RVPDB= 0.0111797) for C and atmospheric nitrogen (RAir = 0.0036765)
for nitrogen.
δX = [(Rsample/Rstandard) – 1] × 103,
Where X is 13C or 15N and R is the corresponding ratio 13C/12C or 15N/14N. The δ values for carbon
and nitrogen were measured in tissue samples from seagrass leaves.
2.3.1
Statistical Analyses
Elemental, stoichiometric and isotope values of seagrass leaves were compared with physical
seagrass condition information derived using underwater video (see Warry and Hindell 2012),
using Spearman rank correlations. Correlations among site averaged leaf chemistry measurements,
and (i) site averaged 2012 video condition scores, and (ii) standard deviations of site averaged
video condition scores for the period 2009 – 2012, were investigated.
Spatial variations in elemental, stoichiometric and isotopic values were assessed graphically and
with nested analyses of variance (ANOVA) with site nested within zone. Both site and zone were
treated as fixed factors. Assumptions of normality and homogeneity of variances were checked
using box-plots and plots of residuals, respectively (Quinn and Keough 2002). Data that failed to
meet these assumptions were log10(x + 1) transformed and reassessed. Response variables
containing negative values were transformed using a log10(x + [1-min]) transformation.
8
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
Relationships among elemental, stoichiometric and isotope values and environmental variables
thought to influence spatial patterns in these functional values were assessed using robust (MM
type) regression analyses (Koller and Stahel 2011). Leaf chemistry measurements were regressed
against (i) distance from the mouth of the Mitchell River; (ii) distance from the entrance to the
Gippsland Lakes, and (iii) wind fetch. Distances of sites from the three major freshwater, and
nutrient inputs (Mitchell, Nicholson and Tambo rivers) were highly correlated so only distance
from the Mitchell River mouth was used in analyses. Wind fetch was calculated using wind data
from the Lakes Entrance station (www.bom.gov.au). Wind data from the 75 days preceding
sampling was used as this is considered the mean leaf turnover period for Zostera (Hemminga et
al. 1999 and references therein). Afternoon (3pm) wind measurements were used. Winds were
most frequently from the East during the period examined, and average easterly velocities were
above the overall velocity mean. Fetch was calculated as the distance directly east from the sample
site to the closest point on land. Assumptions of normality and homogeneity of variances were
checked and data transformed if required, as above.
Relationships between the standard deviations of side averaged video condition scores (2009 –
2012) and the environmental variables described above were also investigated using robust linear
regression, as above. Relationships between the 2012 video condition scores (i.e. physical
condition scores) were analysed using ordinal regression analyses as video condition score was an
ordinal response variable.
Analyses were done in R version 2.15.2 (R Core Team 2012).
2.4 Seagrass Contribution to Fish Nutrition
Stable isotope approaches were used to investigate the contribution of seagrass to fish nutrition.
Five individuals of five species representing different feeding guilds were collected from 11 sites
for stable isotope analyses (Figure 2). Samples were stored on ice during transportation to the lab
where they were frozen at -18°C.
White muscle tissue, immediately ventral to the anterior region of the dorsal fin, was used for
isotope analysis of fish, as this tissue exhibits less variability than others (Pinnegar and Polunin
2000). Tissue samples were washed in distilled water, dried to constant weight (24 hrs at 60ºC),
ground to a fine powder and analysed for carbon and nitrogen stable isotope ratios (δ13C and δ15N)
as described in section 2.2.
2.4.1
Statistical Analyses
A Bayesian mixing model (Stable Isotope Analysis in R, SIAR v4.0; Parnell et al. 2010) was used
to assess the putative contribution of primary producers to fish nutrition based on carbon and
nitrogen stable isotope values. Variance associated with consumer and source signatures as well as
uncertainty associated with trophic enrichment factors can be propagated throughout the model
(Parnell et al. 2010). Trophic enrichment factors used were mean ± standard deviation 3.4 ± 1.0 for
δ15N and 1.0 ± 0.5 for δ13C (Pinnegar and Polunin 1999). Values were derived from averages in
the literature, as specific values for the species examined here were not available.
Models were run using isotope signatures of seagrass collected during this study. Isotope
signatures of plankton (combination of zooplankton and phytoplankton), particulate organic matter
and Nodularia from samples collected during February and March by Monash University were
also incorporated into models (R. Woodland, Monash University, unpublished data). Species
specific turnover rates for fish white muscle tissue are not available for the species studied, but
estimated at approximately three months (Perga and Gerdeaux 2005). The source data from
February and March were therefore considered valid for use in determining the nutritional support
of fish sampled in April and early May. The Monash University data were collected at a broader
9
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
spatial resolution than the sample sites for this study so data from the current study were pooled
into the four broad zones described above (see section 2.1 and appendix 1).
Analyses were done in R version 2.15.2 (R Core Team 2012).
3 Results
3.1 Seagrass Physical Condition and Relationships with leaf chemistry
Seagrass physical condition scores ranged from 1 – 5 at sites used for leaf chemistry analyses.
There were no significant relationships, however, between site averaged seagrass leaf chemistry
measurements and physical condition scores based on 2012 video data. Examples are given in
Figure 3a and b. Relationships between leaf chemistry measurements and the standard deviation of
time-integrated (2009 – 2012) site means of physical condition scores were not significant (e.g.
Figure 3c and d).
a. rho = -0.10
b. rho = 0.11
5
2012 physical condition score
2012 physical condition score
5
4
3
2
1
0
4
3
2
1
0
40.0
40.5
41.0
41.5
42.0
-15.0
-13.0
%C
c. rho = 0.40
2.5
SD of 2009-2012 site mean
SD of 2009-2012 site mean
2.5
-11.0
-9.0
-11.0
-9.0
δ13C
2.0
1.5
1.0
0.5
0.0
d. rho = -0.41
2.0
1.5
1.0
0.5
0.0
40.0
40.5
41.0
%C
41.5
42.0
-15.0
-13.0
δ13 C
Figure 3: Relationships between leaf chemistry metrics and 2012 physical condition scores derived from
site averaged underwater video monitoring (a and b), and the standard deviation of time integrated means
(2009 – 2012) of site averaged physical condition scores (c and d).
10
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
3.2 Spatial variation in seagrass leaf chemistry
There was a relatively broad range in the N and P content of both Zostera and Ruppia collected
throughout the Gippsland Lakes (Table 1). The C content was relatively less variable (Table 1).
The distributions of C:N and N:P values of Zostera were close to normally distributed, however
the C:P distribution was skewed with a substantial number of low values (Appendix 2). Mean and
mode N:P values for Zostera were < 18 (the seagrass Redfield Ratio value of nutrient balance).
Distributions of stoichiometric ratios for Ruppia were difficult to interpret due to the limited
number of samples. There was significant variation in all leaf chemistry measurements both
among and within zones (Table 2, Appendix 3).
Table 1: Ranges of leaf chemistry measurements in Ruppia and Zostera samples.
measurement
Ruppia
Zostera
n= 10
n = 43
min
max
min
max
%N
1.79
2.94
2.09
5.21
%P
0.11
0.22
0.15
0.38
%C
39.83
43.05
39.07
43.73
C:N
14.65
23.99
7.50
19.82
C:P
194.88
374.57
108.31
281.90
N:P
10.10
17.89
7.04
19.29
15
δ N
3.62
9.19
-0.88
5.27
13
-14.94
-10.48
-14.35
-8.90
δ C
Table 2: Results of nested analyses of variance (ANOVA) comparing variation in log transformed leaf
chemistry measurements among broad Zones (Kalimna, Metung, Lake King North and Lake King South)
and Sites within Zones, means
Source
d.f.
%N
%P
%C
C:N
MS
P
MS
P
MS
P
MS
P
Zone
3.00
0.004
0.004
0.001
<0.001
<0.001
<0.001
0.004
0.011
Site(Zone)
8.00
0.003
0.001
0.001
<0.001
<0.001
0.072
0.005
0.011
Error
24.00
0.001
<0.001
<0.001
0.001
Table 2 cont.
Source
d.f.
C:P
δ15N
N:P
δ13C
MS
P
MS
P
MS
P
MS
P
Zone
3.00
0.063
<0.001
0.021
0.014
0.145
<0.001
0.144
<0.001
Site(Zone)
8.00
0.035
<0.001
0.013
0.025
0.077
<0.001
0.032
0.002
Error
24.00
0.004
0.005
0.007
11
0.007
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
The site-averaged δ13C and δ15N values in Zostera leaves were not significantly correlated, nor
were δ13C and N:P values (Figure 4a). Average values of %N and %P were positively correlated
(r2 = 0.88, p < 0.001; Figure 4b). There were no relationships between site averages of δ15N and
C:N or δ15N and %N (Figure 4c and d). There were no significant relationships between other
combinations of site-averaged leaf chemistry metrics.
-9
4
a.
b.
-10
%N
δ13 C
-11
-12
3
-13
-14
-15
2
5
9
13
17
0.1
0.2
N:P
5
c.
4
4
3
3
2
1
0
0
12
14
16
C:N
18
20
3
3.5
d.
2
1
10
0.4
%P
δ15 N
δ15 N
5
0.3
2
2.5
%N
Figure 4: Relationships between stable isotopic compositions and elemental content and ratios of leaves of
Zostera; each point represents the mean of the three samples collected at each site.
3.3 Seagrass chemical and physical condition and the Gippsland Lakes
environment
Robust regression analyses indicated a significant negative relationship between %C of seagrass
leaves and distance from the Mitchell River mouth (t = -2.54, p = 0.027, Figure 5a) and Gippsland
Lakes entrance (t = -3.94, p = 0.002, Figure 5b). Values of δ13C of Zostera increased significantly
with increasing distance from the Mitchell River mouth (t = 3.71, p = 0.003, Figure 5c) but
decreased with increasing wind fetch (t = -2.36, p = 0.04, Figure 5d). There were no significant
relationships between other leaf chemistry variables and distance from the Mitchell River mouth,
entrance of the Gippsland Lakes, or wind fetch.
There was no relationship between 2012 physical condition scores based on underwater video and
environmental variables (e.g. Figure 6a). The standard deviation of time-integrated site means
12
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
(2009 – 2012) did however, decrease with increasing distance from the Mitchell River mouth (t = 2.356, p = 0.043, Figure 6b).
44.0
44.0
a.
42.0
42.0
%C
43.0
%C
43.0
b.
41.0
41.0
40.0
40.0
39.0
39.0
0
10000
20000
30000
0
Distance from Mitchell (m)
-9.0
10000
20000
30000
40000
Distance from entrance (m)
-9
c.
d.
-10
δ13 C
-11
δ13 C
-11.0
-13.0
-12
-13
-14
-15.0
-15
0
10000
20000
30000
0
1000
Distance from Mitchell (m)
2000
3000
4000
Fetch (m)
Figure 5: Relationships between leaf chemistry and environmental variables; each point represents the mean
of the three samples collected at each site.
a.
b.
2.5
SD of 2009 - 2012 site mean
2012 condition score
5.0
4.0
3.0
2.0
1.0
0.0
2.0
1.5
1.0
0.5
0.0
0
10000
20000
30000
Distance from Mitchell (m)
0
10000
20000
30000
Distance from Mitchell (m)
Figure 6: Relationships between distance from the Mitchell River mouth (m) and a. site-averaged 2012
seagrass physical condition scores, and b. the standard deviation of site averaged scores over the 2009 –
2012 period.
13
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
3.4 Contribution of seagrass to fish nutritional support
Stable isotope modelling of δ13C and δ15N indicated Zostera was an important contributor to the
nutrition of some fish species in some locations. Confidence in the modelled contributions of the
autotrophs sampled in this study (seagrass, plankton, POM) to the nutrition of fish in the Lake
King North zone was very low and, as such, results are not presented here.
Zostera was the dominant source of carbon supporting the nutrition of glass shrimp collected from
sites in the Kalimna and Lake King South zones (Figure 7) and tupong (Pseudaphritis urvilli) from
sites in the Metung and Lake King South zones (Figure 8). Models indicated a combination of
sources supported nutrition of tupong from sites in the Kalimna zone. A combination of sources
supported the nutrition of smallmouth hardyhead (Atherinosoma microstoma) with macro- and
epiphytic algae likely important contributors at Kalimna sites near Lakes Entrance and plankton
(combination of phytoplankton and zooplankton) more important elsewhere (Figure 8). Numerous
sources likely contributed to the nutrition of river garfish (Hyporhamphus regularis) and species
from the family Gobidae (Figures 7 and 8).
1.0
0.6
0.4
0.6
0.0
0.2
Proportion
0.4
0.2
0.0
RUP
ZOS
POM
RUP
1.0
MAC
ZOS
PLA
POM
d. Gobidae, Metung, March
0.8
c. Gobidae, Kalimna, March
0.2
0.0
0.0
0.2
0.4
0.4
0.6
0.6
0.8
1.0
EPI
Proportion
b. glass shrimp, Lake King South, March
0.8
a. glass shrimp, Kalimna, March
0.8
1.0
Patterns of modelled source contributions were similar between models run using March 2012
(plankton and POM) and February 2012 (plankton, POM and Nodularia) data. Models indicated
Nodularia did not play a substantial role in supporting the nutrition of fish sampled in this study.
EPI
MAC
RUP
Source
ZOS
POM
EPI
MAC
RUP
ZOS
PLA
POM
Source
Figure 7: Estimated percentage contributions (mean, 75% and 95% confidence intervals) of sources
contributing to fish nutrition, derived from δ13C and δ15N using SIAR, source group abbreviations, EPI,
epiphytic algae, MAC, macroalgae, RUP, Ruppia, ZOS, Zostera, POM, particulate organic matter, PLA,
combination of phyto- and zooplankton.
14
POM
0.8
0.2
ZOS
PLA
POM
RUP ZOS
Source
POM
i. River garfish, Metung, March
0.4
0.0
0.2
0.0
MAC
MAC RUP ZOS PLA POM
0.2
0.4
0.4
0.2
0.0
EPI
EPI
0.6
0.8
h. river garfish, Lake King South, March
0.6
0.6
0.8
g. river garfish, Kalimna, March
0.0
RUP
1.0
POM
f. SMHH, Metung, March
0.8
ZOS
MAC RUP ZOS PLA POM
0.4
0.6
0.4
0.2
0.0
RUP
1.0
MAC
EPI
0.6
0.8
0.8
0.6
0.4
0.0
EPI
1.0
0.6
0.4
0.2
PLA
e. SMHH, Lake King South, March
0.2
Proportion
ZOS
1.0
RUP
POM
d. SMHH, Kalimna, March
1.0
0.0
0.2
0.0
ZOS
1.0
RUP
c. tupong, Metung, March
0.8
1.0
0.6
0.4
0.6
Proportion
0.4
0.2
0.0
MAC
1.0
EPI
Proportion
b. tupong, Lake King South, March
0.8
a. tupong, Kalimna, March
0.8
1.0
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
RUP
ZOS
PLA
Source
POM
EPI
MAC RUP ZOS PLA POM
Source
Figure 8: Estimated percentage contributions (mean, 75% and 95% confidence intervals) of sources
contributing to fish nutrition, derived from δ13C and δ15N using SIAR, source group abbreviations, EPI,
epiphytic algae, MAC, macroalgae, RUP, Ruppia, ZOS, Zostera, POM, particulate organic matter, PLA,
combination of phyto- and zooplankton, SMHH, smallmouthed hardyhead.
15
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
4 Discussion
Elemental and isotopic compositions of leaves of Zostera and Ruppia across the Gippsland Lakes
were within the ranges reported for other temperate and tropical seagrass species (Fourqurean et al.
1992, 2005, Castejón-Silvo & Terrados 2011, Burkholder et al. 2013). Isotope, %N and %C values
were also within the ranges observed for these species in riverine estuaries of Victoria, sampled as
part of the Victorian Index of Estuarine Condition (IEC; F. Y. Warry, DSE, unpublished data).
Isotope, %N and %C values of Zostera were also within ranges observed in Port Phillip Bay
during DSE Victoria’s seagrass and reefs research program (A. Hirst, DPI, unpublished data).
Leaf nutrient content has been significantly correlated to leaf and shoot morphology, including leaf
density and length (Fourqurean et al. 2007). Leaf chemistry measurements, in this study, did not
relate to the physical condition scores (based on percent cover and leaf density) derived from
underwater video monitoring conducted in April 2012 (e.g. Figure 3, Warry and Hindell 2012).
Sites where physical condition scores were more variable over time (2009 – 2012) tended to have
lower δ13C, but this relationship was not significant (Figure 3d). The general lack of relationships
between leaf chemistry measurements and physical condition scores may indicate that the rapid
assessment video technique does not capture morphological information at the level of detail
required to identify relationships with leaf chemistry measurements. It is also possible that the leaf
chemistry and physical structure of seagrass are responding to environmental changes at different
time scales.
There was significant spatial variation in all leaf chemistry measurements among sites throughout
the Gippsland Lakes. Measurements also varied among and within four broad zones within the
lakes. The spatial discrimination of leaf chemistry measurements suggests observed variability
may contain information about environmental effects on the nutrient content and physiological
status of seagrass in the Gippsland Lakes.
Values of δ13C of Zostera leaves varied among sites (Table 2, Appendix 3). In particular,
signatures of samples from the Nicholson River mouth (NRM) were depleted (more negative)
relative to other sites. Two explanations for this are possible. Firstly, depleted δ13C values may
reflect utilisation of an isotopically distinct DIC pool produced by the oxidation of terrestrially
derived organic matter. Secondly, δ13C values may be depleted as light levels are reduced which
decreases rates of photosynethis and allows greater discrimination against the heavier isotope
(Figure 1).
Significant relationships were observed between %C and δ13C and environmental variables (e.g.
distance from the mouth of the Mitchell River) thought to represent information about the relative
availability of nutrients and light. δ13C signatures increased with increasing distance from the
Mitchell River mouth and with increasing wind fetch. Freshwater inputs generally transport
nutrients and sediments into estuarine ecosystems and light availability would be expected to be
lower closer to the source of such inputs. Wind fetch represents the distance over which prevailing
winds can act to generate waves and associated hydrodynamic mixing which may increase
localised turbidity. There is an increased capacity for wind to generate waves with increasing
fetch. These patterns suggest that δ13C signatures of seagrass plants are responding to gradients of
light availability within the Gippsland Lakes. The impact of depth on the pattern of spatial
variation in δ13C samples (through its effect on light attenuation) would have been minimal as all
samples analysed in this study were collected from a similar depth range (0.5 – 1m).
There was also a decrease in the variability of physical seagrass condition (over the 2009 – 2012
periods) with increasing distance from the Mitchell River mouth. This suggests that seagrass
growing in relatively close proximity to freshwater and nutrient inputs is more dynamic. There is
16
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
potentially greater variation in the nutrient and light conditions of sites closer to freshwater inputs,
as elsewhere in the lakes marine influences likely act to dilute freshwater inputs, potentially
generating more stable environmental conditions. This, in addition to the lower δ13C signatures
observed closer to freshwater inputs suggests that the condition of seagrass at these sites is likely
more susceptible to shifts in environmental conditions, particularly light availability.
Variability in δ15N of Zostera leaves was observed among sites and zones (Table 2, Appendix 3).
This variation may result from spatial patterns in the isotopic composition of available pools of
dissolved inorganic nitrogen (DIN) and/or differing levels of fractionation of available DIN during
uptake by seagrass. Generally, δ15N values of seagrass measured in this research were at the lower
end of ranges reported for these species elsewhere in Victoria. In particular, δ15N values of plants
from sites near Lakes Entrance (NAR) and Metung (MTG, MWW) were low (Appendix 3). These
sites tend to have high water clarity, with relatively high levels of light reaching the benthos (F. Y.
Warry pers. obs.). The relatively higher light conditions suggest that bacterial nitrogen fixation
may be occurring in the rhizosphere of seagrass plants at these sites, resulting in depleted δ15N
signatures (Romero et al. 2006).
Low C:N and C:P ratios across study sites indicated neither Zostera or Rupipa were N or P limited
in April/May 2012. Low N:P ratios (< 17) indicated relatively greater availability of P than N and
%P values of leaves were at the higher end of ranges reported for seagrasses elsewhere (Table 1;
see e.g. Fourqurean et al. 1992, 2005, Burkholder et al. 2012). There was a significant relationship
between site averaged %N and %P of Zostera (Figure 4b), suggesting N and P availability varied
consistently across the Gippsland Lakes landscape or that neither nutrient was limiting.
Samples were collected in mid autumn during primary monitoring activities when the physical
structure and distribution of seagrass are thought to be at annual maxima after the summer growth
period. This period has been targeted for monitoring the physical condition of seagrass in the
Gippsland Lakes, as it maximises the potential to detect seagrass. The persistent Nodularia bloom
and relatively high freshwater inputs during the 2011-2012 summer likely reduced light
availability during this period which may have reduced photosynthetic rates, prompting decline in
condition. Primary monitoring activities detected a decline in the physical condition of seagrass at
several sites in 2012 compared with 2011 (Warry and Hindell 2012). This may represent a
temporal shift in natural seasonal cycles of growth and decline or a condition shift in response to
changed environmental conditions.
The opportunistic collection of samples for leaf chemistry analysis during primary monitoring
activities in autumn 2012 represented an efficient way to initially characterise spatial variability
and assess the potential usefulness of leaf chemistry measurements in the Gippsland Lakes.
Elemental content and stoichiometric ratios of seagrass will provide a clearer picture of nutrient
availability and plant requirements if samples are also collected during periods of rapid growth
(i.e. early to mid summer; Fourqurean et al. 2005). Nutrient limitation signals are most likely
expressed during periods of rapid growth and leaf chemistry measurements may provide the best
characterisation of landscape patterns in nutrient dynamics during such periods.
Seagrass was likely a major contributor to the nutrition of glass shrimps, tupong and gobies at
some sites. Modelling demonstrated the importance of using location-specific source values for
assessing the nutritional support of consumers. The Nodularia bloom that occurred during the
2011 -2012 summer had largely dissipated by the time sampling occurred. Modelling using
Nodularia and phytoplankton data from February suggested that generally Nodularia either wasn’t
assimilated into fish biomass (muscle tissue) or was metabolised/turned over prior to fish being
sampled in autumn 2012. While these data could not definitively resolve the question of Nodularia
contribution to the nutrition of small fish in the Gippsland Lakes, they will provide useful
17
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
benchmark data for the Monash University research on impacts of Nodularia conducted in the
2012 – 2013 summer. The current work demonstrates that seagrass is contributing to fish nutrition
and the value of seagrass habitats for fish in the Gippsland Lakes exceeds merely the physical
structure afforded by seagrass plants.
Spatial variation in Zostera leaf elemental content, stoichiometric ratios and isotopic signatures
was observed among and within four broad geographic zones in the Gippsland Lakes in autumn
2012. Similar scales of spatial variation have been observed in other estuarine systems (e.g.,
Fourqurean et al. 2007, Castejón-Silvo & Terrados 2012). Preliminary analyses indicate there may
be relationships between some aspects of leaf chemistry and environmental variables representing
information about light availability, as has been demonstrated elsewhere (e.g. Fourqurean et al.
2007).
Leaf chemistry variables have also been shown elsewhere to vary temporally (e.g. Fourqurean et
al. 2005, 2007). Research in the United States and Europe has documented strong seasonal patterns
in elemental and isotopic content of other seagrass species that is thought to relate to seasonal
changes in photoperiod, water temperatures and nutrient inputs (e.g. Fourqurean et al 2007). This
research has primarily been conducted in the northern hemisphere and/or in tropical systems,
however, where freshwater and nutrient inputs are generally higher and more consistent than in
south eastern Australia, where estuaries experience highly episodic freshwater and nutrient inputs
(Scanes et al. 2007). Seasonal differences in temperature and photoperiod are also more
pronounced at temperate latitudes. Knowledge of the temporal variation in leaf chemistry
measurements benefits robust interpretation of processes or functions underpinning observed
values.
5 Key Findings
18

Leaf elemental concentrations, stoichiometric ratios and δ13C and δ15N signatures were
within ranges previously reported for both Zostera and Ruppia elsewhere (including
Victoria).

Percent phosphorous concentrations were towards the higher end of ranges previously
reported and, as such, N:P ratios were low, consistent with the known high availability of
phosphorus in the Gippsland Lakes derived from the sediment in the deeper basins.

Low C:N and C:P ratios suggested plants were not nutrient limited.

Leaf chemistry generally did not relate to physical condition scores derived from
underwater video monitoring. This suggests that either the rapid assessment video
technique did not capture sufficiently detailed morphological information to indentify
relationships with leaf chemistry measurements, or that leaf chemistry and physical
condition are responding to environmental conditions at different time scales.

There was significant spatial variation in leaf chemistry measurements across the
Gippsland Lakes at two scales; among zones and sites (within and among zones).

Preliminary analyses indicated δ13C values decreased with proximity to freshwater sources
and with increasing potential for wind-driven mixing, which is consistent with models of
more depleted δ13C signatures under low light conditions, the increased importance of
terrestrially-derived DIC to plants growing near the tributary plume, or a combination of
these factors.
Leaf chemistry and seagrass ecological function in the Gippsland Lakes

Stoichiometric ratios and spatial patterns in isotope signatures suggest seagrasses are light
rather than nutrient limited in the Gippsland Lakes.

The physical condition of seagrass was more variable over the 2009 – 2012 period at sites
closer to freshwater sources, indicating that environmental conditions are more variable, or
seagrasses more influenced by environmental changes at these sites.

Seagrass contributed to the nutrition of fish but the extent of this contribution varied
among species and spatial zones.
6 Recommendations

Future studies of seagrass leaf chemistry in the Gippsland Lakes will need to consider
appropriate spatial scales and replication given the marked spatial variability in leaf
chemistry measurements documented in this study.

In collaboration with Monash University, an opportunity exists to investigate temporal
dynamics in leaf chemistry variables to better understand natural cycles of variability in
these measurements and their usefulness as monitoring tools. Monash University
researchers have been collecting Zostera samples (at a subset of the sites sampled in the
current study) as part of a larger body of research investigating the impacts of
phytoplankton blooms on estuarine food webs. Analyses of these samples for elemental
concentrations of C, N, P and δ13C and δ15N isotope signatures will improve interpretation
of patterns observed in the current study and may help improve understanding of patterns
of nutrient availability and seagrass plant requirements within the Gippsland Lakes.

Findings from the current study suggest seagrasses were light limited. Experiments
designed to test targeted hypotheses about the impacts of light availability on seagrass leaf
chemistry and physical condition will improve confidence in understanding patterns
variability in these chemical measurements and their ultimate usefulness as monitoring
tools.

Preliminary analyses demonstrated relationships between some leaf chemistry and
physical aspects of seagrass plants and environmental variables aiming to characterise
spatial patterns in the relative availability of nutrients and light. Better environmental data,
in particular detailed hydrodynamic models, may provide scope to better understand
relationships between seagrass structure and function and their environment. This will
improve potential of monitoring data to detect seagrass prior to decline and to understand
mechanisms underpinning condition changes.
19
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
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21
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
Appendix 1
Site information including site code, site name, broad zone within the Gippsland Lakes and
latitude and longitude in decimal degrees
22
Site Code
Site Name
Broad Zone
Latitude
Longitude
EPT
Eagle Point
Lake King North
-37.8951
147.6882
FIS
South of Flannigan Island
Kalimna
-37.8992
147.9155
FRI
Fraser Island
Kalimna
-37.8919
147.9404
GRE
The Grange
Lake King South
-37.9487
147.7539
LVC
Lake Victoria Central
Lake King South
-37.9605
147.6976
LVW
Lake Victoria - West
outside zones
-37.9837
147.6151
MTG
Metung
Metung
-37.9035
147.8604
MTW
Metung - west
Metung
-37.8987
147.8369
MWW
Metung - west, west
Metung
-37.9100
147.8176
NAR
The Narrows
Kalimna
-37.8919
147.9481
NGS
Nungumer south
Metung
-37.8953
147.8825
NRM
Nicholson River Mouth
outside zones
-37.8461
147.7358
PTK
Point King
Lake King North
-37.8963
147.7636
RIN
Rotomah Island North
Lake King South
-37.9538
147.7358
RNW
Ramond Island NW
Lake King North
-37.9014
147.7408
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
Appendix 2
5
Zostera
Proportion of observations
Proportion of observations
12
10
8
6
4
2
0
Ruppia
4
3
2
1
0
10
12
14
16
18
20
22
24
10
12
14
16
C:N
3
Zostera
10
8
6
4
2
0
24
260
300
340
380
16
18
20
Ruppia
2
1
140
180
220
260
300
340
380
100
140
180
220
C:P
C:P
5
Zostera
Proportion of observations
Proportion of observations
22
0
100
12
20
C:N
Proportion of observations
Proportion of observations
12
18
10
8
6
4
2
Ruppia
4
3
2
1
0
0
6
8
10
12
14
16
18
6
20
8
10
12
14
N:P
N:P
Frequency distributions of the C:N, C:P and N:P ratios of leaves of Zostera (n = 43) and Ruppia
(n=10) in the Gippsland Lakes.
23
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
Appendix 3
Spatial represenatation of site means and standard deviations for elemental nutrient concentrations in Zostera leaves.
Av
SD
%N
3.40
0.18
%P
0.36
0.02
NRM #
Av
SD
%N
2.67
0.12
%P
0.28
0.04
LVW #
24
Av
SD
%N
3.56
1.44
%P
0.28
0.02
%N
2.39
0.18
%P
0.21
0.03
0.05
%P
0.33
0.03
Av
SD
%N
2.84
0.23
%P
0.32
0.05
GRE #
RIN #
Av
SD
%N
2.21
0.12
%P
0.17
0.03
Av
SD
%N
3.12
0.24
%P
0.31
0.05
NGS #
RNW #
LVC #
SD
SD
3.12
PTK #
# EPT
Av
Av
%N
MTG #
MWW #
Av
SD
%N
2.27
0.09
%P
0.16
0.00
Av
SD
%N
2.87
0.26
%P
0.32
0.03
Av
SD
%N
2.50
0.06
%P
0.15
0.01
Av
SD
%N
3.00
0.47
%P
0.29
0.08
FRI # #
NAR
FIS #
Av
SD
%N
2.52
0.31
%P
0.23
0.03
0 1 2
Av
SD
%N
2.65
0.15
%P
0.20
0.02
4 Kms
¸
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
Spatial representation of site means and standard deviations for isotopic signatures in Zostera leaves, values are in ‰.
Av
SD
15N
3.33
0.51
13C
-14.3
0.03
NRM #
15N
13C
Av
SD
2.05
0.41
-11.9
# EPT
0.15
Av
SD
N
2.45
1.1
C
-11.0
0.28
GRE #
RIN #
LVC #
LVW #
Av
SD
15N
3.42
0.83
13C
-10.6
0.36
0.38
13C
-11.7
0.36
Av
SD
Av
SD
15N
4.35
1.4
N
4.23
0.11
13C
-10.9
0.61
C
-10.6
0.46
15N
2.45
0.33
13C
-11.0
0.27
SD
SD
15N
1.09
0.07
15N
1.65
0.13
13C
-11.2
0.18
13C
-11.6
0.79
Av
SD
4.56
0.11
C
-11.4
0.46
Av
SD
15N
2.97
0.33
13C
-9.58
0.09
FIS #
Av
Av
Av
N
13
SD
MTG #
MWW #
15
Av
NGS #
RNW #
13
13
SD
3.33
PTK #
15
15
Av
15N
FRI # #
NAR
4.09
0.31
13C
-9.96
0.93
0 1 2
25
Av
SD
15N
0.00
0.90
13C
-10.6
0.14
SD
15N
4 Kms
¸
Leaf chemistry and ecological function of seagrass in the Gippsland Lakes
Spatial representation of site means and standard deviations for stoiciometric ratios of elemental nutrients in Zostera leaves.
Av
SD
C:N
12.28
0.63
C:P
115.05
7.05
N:P
9.40
1.03
NRM #
Av
SD
C:N
15.51
0.66
C:P
147.91
21.25
N:P
9.54
1.31
26
Av
SD
12.38
4.36
C:P
141.02
6.59
N:P
12.74
5.77
0.20
C:P
126.40
10.84
N:P
9.61
0.96
Av
Av
SD
C:N
14.59
1.12
C:P
131.32
20.80
9.04
C:N
13.45
1.00
C:P
138.07
26.79
N:P
10.33
2.31
MTG #
MWW #
1.71
GRE #
RIN #
SD
NGS #
RNW #
LVC #
C:N
SD
13.16
PTK #
# EPT
N:P
LVW #
Av
C:N
SD
SD
C:N
16.54
0.71
C:N
18.99
1.17
C:P
269.99
11.86
C:P
269.23
5.74
N:P
16.34
0.94
N:P
14.20
0.57
Av
SD
14.13
1.17
Av
SD
C:P
126.74
10.60
Av
SD
C:N
18.20
0.98
N:P
9.05
1.48
C:N
16.85
1.10
C:P
234.67
33.79
C:P
196.63
26.30
N:P
12.99
2.56
N:P
11.75
2.14
SD
14.15
1.90
C:P
150.94
36.44
N:P
10.98
3.86
FRI # #
NAR
FIS #
Av
Av
Av
C:N
Av
C:N
C:N
16.22
1.97
C:P
177.82
20.86
N:P
10.97
Av
SD
C:N
15.38
0.72
C:P
200.68
12.99
N:P
13.04
0.24
SD
0.38
0 1 2
4 Kms
¸
Leaf chemistry and seagrass ecological function in the Gippsland Lakes
1