using leaf chemistry to better understand the ecology of seagrass in

Using leaf chemistry to better understand the ecology of seagrass in the Gippsland Lakes

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Unpublished Client Report for the Gippsland Lakes Ministerial Advisory Committee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

F.Y. Warry, P. Reich, R.J. Woodland, P. Cook

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

1Arthur Rylah Institute for Environmental Research 123 Brown Street, Heidelberg, Victoria 3084

In partnership with: 2Water Studies Centre, Monash University, Clayton, Victoria 3800

Arthur Rylah Institute for Environmental Research Department of Sustainability and Environment

Heidelberg, Victoria

iii

Report produced by: Arthur Rylah Institute for Environmental Research

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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 Arthur Rylah Institute for Environmental Research Unpublished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).

mailto:[email protected]

http://www.dse.vic.gov.au/

Contents

Acknowledgements ........................................................................................................................... v

Summary ........................................................................................................................................... 1

1 Introduction ............................................................................................................................ 3

2 Methods ................................................................................................................................... 7

2.1 Study Sites ................................................................................................................................ 7

2.2 Monitoring Seagrass Physical Condition ................................................................................. 8

2.3 Monitoring Seagrass Leaf Chemistry ....................................................................................... 8

2.3.1 Statistical Analyses ................................................................................................... 8

2.4 Seagrass Contribution to Fish Nutrition ................................................................................... 9

2.4.1 Statistical Analyses ................................................................................................... 9

3 Results ................................................................................................................................... 10

3.1 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

v

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.

Leaf chemistry and seagrass ecological function in the Gippsland Lakes

1

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 (δ13

C) and nitrogen (δ15

N). Fish muscle

tissues were analysed for δ13

C and δ15

N. 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 δ13

C values decreased with proximity to

freshwater sources and with increasing potential for wind-driven mixing, which is consistent with

models of more depleted δ13

C 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

Leaf chemistry and ecological function of seagrass in the Gippsland Lakes

2

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.


3

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


4

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. 13

C/12

C or δ13

C; 15

N/14

N or δ15

N) 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 δ15

N 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 seagrass-

associated 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:


5

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.


6

Figure 1: Conceptual diagram linking changes in environmental variables to leaf chemistry

measurements.

↑ Light ↑ Photosynthesis

↑ Demands for

critical

nutrients (N & P)

↓%N &/or %P

∆ C:N, C:P, N:P

No ∆ %N &/or %P

No ∆ C:N, C:P, N:P

↑ Growth outstrips

N supply↑ δ15N

↑ Carbon fixation ↑ δ13C

↑ Temperature ↓ Solubility of CO2

↓ CO2 pool &

↓ iso discrimination↑δ13C

Potential use

of HCO3-

∆δ13C

↑ Grazing

Selection of

senescent

or young leaves

Selec. of senescent

leaves ↑ bulk plant

nutrients

↑ %N & %P

Selection of young

leaves ↓ bulk plant

nutrients

↓ %N & %P

↑ Nutrients

↑ size of DIC and/or DIN pool

↑ Carbon supply c.

to plant demand

(for given light)

↓ δ13C

↑ Nitrogen supply c.

to plant demand

(for given light)

↓ δ15N

∆ Composition of

DIC & DIN pool

Differences in sigs.

of marine and terrestrial

nutrients

∆ δ13C &/or δ15N

↑ Concentrations of

N and/or P in

H2O column

Potential ↑ algae &

phyto → shading

& ↓ light

↓δ13C

↑ Availability of

N and P

↑%N &/or %P

∆ C:N, C:P, N:P

No ∆ %N &/or %P

No ∆ C:N, C:P, N:P

Environmental Variable

FunctionalChange

Impact onSeagrass

MeasurementResponse


7

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


8

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 13

C and ±0.2‰ for 15

N (SD for n=5). Stable isotope

data are expressed in the delta notation (δ13

C and δ15

N), 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 13

C or 15

N and R is the corresponding ratio 13

C/12

C or 15

N/14

N. 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.


9

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 (δ13

C and δ15

N)

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

δ15

N and 1.0 ± 0.5 for δ13

C (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

http://www.bom.gov.au/


10

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).

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).

0

1

2

3

4

5

40.0 40.5 41.0 41.5 42.0

20

12

ph

ysic

al c

on

dit

ion

sco

re

%C

0

1

2

3

4

5

-15.0 -13.0 -11.0 -9.0

20

12

ph

ysic

al c

on

dit

ion

sco

re

δ13C

0.0

0.5

1.0

1.5

2.0

2.5

40.0 40.5 41.0 41.5 42.0

SD o

f 2

00

9-2

01

2 s

ite

me

an

%C

0.0

0.5

1.0

1.5

2.0

2.5

-15.0 -13.0 -11.0 -9.0

SD o

f 2

00

9-2

01

2 s

ite

me

an

δ13C

a. rho = -0.10 b. rho = 0.11

c. rho = 0.40 d. rho = -0.41


11

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

δ15N 3.62 9.19 -0.88 5.27

δ13C -14.94 -10.48 -14.35 -8.90

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 N:P δ15N 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 0.007


12

The site-averaged δ13

C and δ15

N values in Zostera leaves were not significantly correlated, nor

were δ13

C 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 δ

15N and

C:N or δ15

N and %N (Figure 4c and d). There were no significant relationships between other

combinations of site-averaged leaf chemistry metrics.

2

3

4

0.1 0.2 0.3 0.4

%N

%P

-15

-14

-13

-12

-11

-10

-9

5 9 13 17

δ1

3C

N:P

0

1

2

3

4

5

10 12 14 16 18 20

δ1

5N

C:N

0

1

2

3

4

5

2 2.5 3 3.5

δ1

5N

%N

a.

c. d.

b.

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 δ13

C 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


13

(2009 – 2012) did however, decrease with increasing distance from the Mitchell River mouth (t = -

2.356, p = 0.043, Figure 6b).

Figure 5: Relationships between leaf chemistry and environmental variables; each point represents the mean

of the three samples collected at each site.

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.

39.0

40.0

41.0

42.0

43.0

44.0

0 10000 20000 30000

%C

Distance from Mitchell (m)

39.0

40.0

41.0

42.0

43.0

44.0

0 10000 20000 30000 40000

%C

Distance from entrance (m)

-15.0

-13.0

-11.0

-9.0

0 10000 20000 30000

δ1

3C


-15

-14

-13

-12

-11

-10

-9

0 1000 2000 3000 4000

δ1

3C

Fetch (m)

a. b.

c. d.

0.0

1.0

2.0

3.0

4.0

5.0

0 10000 20000 30000

20

12

co

nd

itio

n s

core


0.0

0.5

1.0

1.5

2.0

2.5

0 10000 20000 30000

SD o

f 2

00

9 -

20

12

sit

e m

ean


a. b.


14

3.4 Contribution of seagrass to fish nutritional support

Stable isotope modelling of δ13

C and δ15

N 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).

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.

Figure 7: Estimated percentage contributions (mean, 75% and 95% confidence intervals) of sources

contributing to fish nutrition, derived from δ13

C and δ15

N using SIAR, source group abbreviations, EPI,

epiphytic algae, MAC, macroalgae, RUP, Ruppia, ZOS, Zostera, POM, particulate organic matter, PLA,

combination of phyto- and zooplankton.

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

EPI MAC RUP ZOS PLA POM

a. glass shrimp, Kalimna, March b. glass shrimp, Lake King South, March

c. Gobidae, Kalimna, March d. Gobidae, Metung, March

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

Source


0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

Source


0.0

0.2

0.4

0.6

0.8

1.0

Source


a. glass shrimp, Kalimna, March b. glass shrimp, Lake King South, March

c. Gobidae, Kalimna, March d. Gobidae, Metung, March


15

Figure 8: Estimated percentage contributions (mean, 75% and 95% confidence intervals) of sources

contributing to fish nutrition, derived from δ13

C and δ15

N 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.

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0


a. tupong, Kalimna, March b. tupong, Lake King South, March c. tupong, Metung, March

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0


0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

Source


d. SMHH, Kalimna, March e. SMHH, Lake King South, March f. SMHH, Metung, March

g. river garfish, Kalimna, March h. river garfish, Lake King South, March i. River garfish, Metung, March

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0


0.0

0.2

0.4

0.6

0.8

1.0


a. tupong, Kalimna, March b. tupong, Lake King South, March c. tupong, Metung, March

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0


0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0


0.0

0.2

0.4

0.6

0.8

1.0


0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

Source


0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

Pro

port

ion

EPI MAC RUP ZOS POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

Source

RUP ZOS PLA POM

0.0

0.2

0.4

0.6

0.8

1.0

Source


0.0

0.2

0.4

0.6

0.8

1.0

Source


d. SMHH, Kalimna, March e. SMHH, Lake King South, March f. SMHH, Metung, March

g. river garfish, Kalimna, March h. river garfish, Lake King South, March i. River garfish, Metung, March


16

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 δ13

C, 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 δ13

C 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 δ13

C values may

reflect utilisation of an isotopically distinct DIC pool produced by the oxidation of terrestrially

derived organic matter. Secondly, δ13

C 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 δ13

C 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. δ13

C 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 δ13

C 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 δ13

C 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


17

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 δ13

C 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 δ15

N 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, δ15

N values of seagrass measured in this research were at the lower

end of ranges reported for these species elsewhere in Victoria. In particular, δ15

N 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 δ15

N

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


18

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

Leaf elemental concentrations, stoichiometric ratios and δ13

C and δ15

N 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 δ13

C values decreased with proximity to freshwater sources

and with increasing potential for wind-driven mixing, which is consistent with models of

more depleted δ13

C 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.


19

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 δ13

C and δ15

N 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.


20

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Pinnegar, J.K., Polunin, N.V.C., 1999. Differential fractionation of delta C-13 and delta N-15

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22

Appendix 1

Site information including site code, site name, broad zone within the Gippsland Lakes and

latitude and longitude in decimal degrees

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


23

Appendix 2

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.

0

2

4

6

8

10

12

10 12 14 16 18 20 22 24

Pro

po

rtio

n o

f o

bse

rvat

ion

s

C:N

0

1

2

3

4

5

10 12 14 16 18 20 22 24

Pro

po

rtio

n o

f o

bse

rvat

ion

s

C:N

0

2

4

6

8

10

12

100 140 180 220 260 300 340 380

Pro

po

rtio

n o

f o

bse

rvat

ion

s

C:P

0

1

2

3

100 140 180 220 260 300 340 380

Pro

po

rtio

n o

f o

bse

rvat

ion

s

C:P

0

2

4

6

8

10

12

6 8 10 12 14 16 18 20

Pro

po

rtio

n o

f o

bse

rvat

ion

s

N:P

0

1

2

3

4

5

6 8 10 12 14 16 18 20

Pro

po

rtio

n o

f o

bse

rvat

ion

s

N:P

Zostera

Zostera

Zostera

Ruppia

Ruppia

Ruppia


24

Appendix 3

Spatial represenatation of site means and standard deviations for elemental nutrient concentrations in Zostera leaves.

#

##

#

#

##

#

##

##

# #

¸0 2 41 Kms

NRM

EPT

LVW

LVCRIN

GRE

RNW

PTK

MWW

MTG

NGSFIS

FRINAR

0.040.28%P

0.122.67%N

SDAv

0.040.28%P

0.122.67%N

SDAv

0.020.36%P

0.183.40%N

SDAv

0.020.36%P

0.183.40%N

SDAv

0.020.28%P

1.443.56%N

SDAv

0.020.28%P

1.443.56%N

SDAv

0.030.21%P

0.182.39%N

SDAv

0.030.21%P

0.182.39%N

SDAv

0.030.17%P

0.122.21%N

SDAv

0.030.17%P

0.122.21%N

SDAv 0.030.32%P

0.262.87%N

SDAv

0.030.32%P

0.262.87%N

SDAv

0.050.32%P

0.232.84%N

SDAv

0.050.32%P

0.232.84%N

SDAv

0.030.33%P

0.053.12%N

SDAv

0.030.33%P

0.053.12%N

SDAv

0.000.16%P

0.092.27%N

SDAv

0.000.16%P

0.092.27%N

SDAv

0.050.31%P

0.243.12%N

SDAv

0.050.31%P

0.243.12%N

SDAv

0.010.15%P

0.062.50%N

SDAv

0.010.15%P

0.062.50%N

SDAv

0.030.23%P

0.312.52%N

SDAv

0.030.23%P

0.312.52%N

SDAv

0.080.29%P

0.473.00%N

SDAv

0.080.29%P

0.473.00%N

SDAv

0.020.20%P

0.152.65%N

SDAv

0.020.20%P

0.152.65%N

SDAv

#

##

#

#

##

#

##

##

# #

¸0 2 41 Kms

NRM

EPT

LVW

LVCRIN

GRE

RNW

PTK

MWW

MTG

NGSFIS

FRINAR

0.040.28%P

0.122.67%N

SDAv

0.040.28%P

0.122.67%N

SDAv

0.020.36%P

0.183.40%N

SDAv

0.020.36%P

0.183.40%N

SDAv

0.020.28%P

1.443.56%N

SDAv

0.020.28%P

1.443.56%N

SDAv

0.030.21%P

0.182.39%N

SDAv

0.030.21%P

0.182.39%N

SDAv

0.030.17%P

0.122.21%N

SDAv

0.030.17%P

0.122.21%N

SDAv 0.030.32%P

0.262.87%N

SDAv

0.030.32%P

0.262.87%N

SDAv

0.050.32%P

0.232.84%N

SDAv

0.050.32%P

0.232.84%N

SDAv

0.030.33%P

0.053.12%N

SDAv

0.030.33%P

0.053.12%N

SDAv

0.000.16%P

0.092.27%N

SDAv

0.000.16%P

0.092.27%N

SDAv

0.050.31%P

0.243.12%N

SDAv

0.050.31%P

0.243.12%N

SDAv

0.010.15%P

0.062.50%N

SDAv

0.010.15%P

0.062.50%N

SDAv

0.030.23%P

0.312.52%N

SDAv

0.030.23%P

0.312.52%N

SDAv

0.080.29%P

0.473.00%N

SDAv

0.080.29%P

0.473.00%N

SDAv

0.020.20%P

0.152.65%N

SDAv

0.020.20%P

0.152.65%N

SDAv


25

Spatial representation of site means and standard deviations for isotopic signatures in Zostera leaves, values are in ‰.

#

##

#

#

##

#

##

##

# #

¸0 2 41 Kms

0.15-11.913C

0.412.0515N

SDAv

0.15-11.913C

0.412.0515N

SDAv

0.93-9.9613C

0.314.0915N

SDAv

0.93-9.9613C

0.314.0915N

SDAv

0.09-9.5813C

0.332.9715N

SDAv

0.09-9.5813C

0.332.9715N

SDAv

0.46-11.413C

0.114.5615N

SDAv

0.46-11.413C

0.114.5615N

SDAv

0.46-10.613C

0.114.2315N

SDAv

0.46-10.613C

0.114.2315N

SDAv

0.36-10.613C

0.833.4215N

SDAv

0.36-10.613C

0.833.4215N

SDAv

0.18-11.213C

0.071.0915N

SDAv

0.18-11.213C

0.071.0915N

SDAv

0.79-11.613C

0.131.6515N

SDAv

0.79-11.613C

0.131.6515N

SDAv 0.14-10.613C

0.900.0015N

SDAv

0.14-10.613C

0.900.0015N

SDAv

0.27-11.013C

0.332.4515N

SDAv

0.27-11.013C

0.332.4515N

SDAv

0.03-14.313C

0.513.3315N

SDAv

0.03-14.313C

0.513.3315N

SDAv

0.36-11.713C

0.383.3315N

SDAv

0.36-11.713C

0.383.3315N

SDAv

0.61-10.913C

1.44.3515N

SDAv

0.61-10.913C

1.44.3515N

SDAv

0.28-11.013C

1.12.4515N

SDAv

0.28-11.013C

1.12.4515N

SDAv

NRM

EPT

LVW

LVCRIN

GRE

RNW

PTK

MWW

MTG

NGSFIS

FRINAR

#

##

#

#

##

#

##

##

# #

¸0 2 41 Kms

0.15-11.913C

0.412.0515N

SDAv

0.15-11.913C

0.412.0515N

SDAv

0.93-9.9613C

0.314.0915N

SDAv

0.93-9.9613C

0.314.0915N

SDAv

0.09-9.5813C

0.332.9715N

SDAv

0.09-9.5813C

0.332.9715N

SDAv

0.46-11.413C

0.114.5615N

SDAv

0.46-11.413C

0.114.5615N

SDAv

0.46-10.613C

0.114.2315N

SDAv

0.46-10.613C

0.114.2315N

SDAv

0.36-10.613C

0.833.4215N

SDAv

0.36-10.613C

0.833.4215N

SDAv

0.18-11.213C

0.071.0915N

SDAv

0.18-11.213C

0.071.0915N

SDAv

0.79-11.613C

0.131.6515N

SDAv

0.79-11.613C

0.131.6515N

SDAv 0.14-10.613C

0.900.0015N

SDAv

0.14-10.613C

0.900.0015N

SDAv

0.27-11.013C

0.332.4515N

SDAv

0.27-11.013C

0.332.4515N

SDAv

0.03-14.313C

0.513.3315N

SDAv

0.03-14.313C

0.513.3315N

SDAv

0.36-11.713C

0.383.3315N

SDAv

0.36-11.713C

0.383.3315N

SDAv

0.61-10.913C

1.44.3515N

SDAv

0.61-10.913C

1.44.3515N

SDAv

0.28-11.013C

1.12.4515N

SDAv

0.28-11.013C

1.12.4515N

SDAv

NRM

EPT

LVW

LVCRIN

GRE

RNW

PTK

MWW

MTG

NGSFIS

FRINAR


26

Spatial representation of site means and standard deviations for stoiciometric ratios of elemental nutrients in Zostera leaves.

#

##

#

#

##

#

##

##

# #

¸0 2 41 Kms

NRM

EPT

LVW

LVCRIN

GRE

RNW

PTK

MWW

MTG

NGSFIS

FRINAR

1.319.54N:P

21.25147.91C:P

0.6615.51C:N

SDAv

1.319.54N:P

21.25147.91C:P

0.6615.51C:N

SDAv

1.039.40N:P

7.05115.05C:P

0.6312.28C:N

SDAv

1.039.40N:P

7.05115.05C:P

0.6312.28C:N

SDAv

5.7712.74N:P

6.59141.02C:P

4.3612.38C:N

SDAv

5.7712.74N:P

6.59141.02C:P

4.3612.38C:N

SDAv2.1411.75N:P

26.30196.63C:P

1.1016.85C:N

SDAv

2.1411.75N:P

26.30196.63C:P

1.1016.85C:N

SDAv

2.5612.99N:P

33.79234.67C:P

0.9818.20C:N

SDAv

2.5612.99N:P

33.79234.67C:P

0.9818.20C:N

SDAv

1.489.05N:P

10.60126.74C:P

1.1714.13C:N

SDAv

1.489.05N:P

10.60126.74C:P

1.1714.13C:N

SDAv

1.719.04N:P

20.80131.32C:P

1.1214.59C:N

SDAv

1.719.04N:P

20.80131.32C:P

1.1214.59C:N

SDAv

0.969.61N:P

10.84126.40C:P

0.2013.16C:N

SDAv

0.969.61N:P

10.84126.40C:P

0.2013.16C:N

SDAv

0.5714.20N:P

5.74269.23C:P

1.1718.99C:N

SDAv

0.5714.20N:P

5.74269.23C:P

1.1718.99C:N

SDAv

0.9416.34N:P

11.86269.99C:P

0.7116.54C:N

SDAv

0.9416.34N:P

11.86269.99C:P

0.7116.54C:N

SDAv

2.3110.33N:P

26.79138.07C:P

1.0013.45C:N

SDAv

2.3110.33N:P

26.79138.07C:P

1.0013.45C:N

SDAv

0.3810.97N:P

20.86177.82C:P

1.9716.22C:N

SDAv

0.3810.97N:P

20.86177.82C:P

1.9716.22C:N

SDAv

3.8610.98N:P

36.44150.94C:P

1.9014.15C:N

SDAv

3.8610.98N:P

36.44150.94C:P

1.9014.15C:N

SDAv

0.2413.04N:P

12.99200.68C:P

0.7215.38C:N

SDAv

0.2413.04N:P

12.99200.68C:P

0.7215.38C:N

SDAv

#

##

#

#

##

#

##

##

# #

¸0 2 41 Kms

NRM

EPT

LVW

LVCRIN

GRE

RNW

PTK

MWW

MTG

NGSFIS

FRINAR

1.319.54N:P

21.25147.91C:P

0.6615.51C:N

SDAv

1.319.54N:P

21.25147.91C:P

0.6615.51C:N

SDAv

1.039.40N:P

7.05115.05C:P

0.6312.28C:N

SDAv

1.039.40N:P

7.05115.05C:P

0.6312.28C:N

SDAv

5.7712.74N:P

6.59141.02C:P

4.3612.38C:N

SDAv

5.7712.74N:P

6.59141.02C:P

4.3612.38C:N

SDAv2.1411.75N:P

26.30196.63C:P

1.1016.85C:N

SDAv

2.1411.75N:P

26.30196.63C:P

1.1016.85C:N

SDAv

2.5612.99N:P

33.79234.67C:P

0.9818.20C:N

SDAv

2.5612.99N:P

33.79234.67C:P

0.9818.20C:N

SDAv

1.489.05N:P

10.60126.74C:P

1.1714.13C:N

SDAv

1.489.05N:P

10.60126.74C:P

1.1714.13C:N

SDAv

1.719.04N:P

20.80131.32C:P

1.1214.59C:N

SDAv

1.719.04N:P

20.80131.32C:P

1.1214.59C:N

SDAv

0.969.61N:P

10.84126.40C:P

0.2013.16C:N

SDAv

0.969.61N:P

10.84126.40C:P

0.2013.16C:N

SDAv

0.5714.20N:P

5.74269.23C:P

1.1718.99C:N

SDAv

0.5714.20N:P

5.74269.23C:P

1.1718.99C:N

SDAv

0.9416.34N:P

11.86269.99C:P

0.7116.54C:N

SDAv

0.9416.34N:P

11.86269.99C:P

0.7116.54C:N

SDAv

2.3110.33N:P

26.79138.07C:P

1.0013.45C:N

SDAv

2.3110.33N:P

26.79138.07C:P

1.0013.45C:N

SDAv

0.3810.97N:P

20.86177.82C:P

1.9716.22C:N

SDAv

0.3810.97N:P

20.86177.82C:P

1.9716.22C:N

SDAv

3.8610.98N:P

36.44150.94C:P

1.9014.15C:N

SDAv

3.8610.98N:P

36.44150.94C:P

1.9014.15C:N

SDAv

0.2413.04N:P

12.99200.68C:P

0.7215.38C:N

SDAv

0.2413.04N:P

12.99200.68C:P

0.7215.38C:N

SDAv


1

using leaf chemistry to better understand the ecology of seagrass in

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