Macrophyte Communities in the Peel-Harvey Estuary: Historical Trends and Current Patterns in Biomass and Distribution

Macrophyte communities in the Peel-Harvey Estuary: Historical trends and current patterns in biomass and distribution.

Submitted by

Oliver Krumholz

This thesis is presented for the Degree of Honours in Marine Science, School of Veterinary and Life Science, Murdoch University, 2019.

Declaration

I declare that this thesis is my own account of my research and contains as its main content work which has not been previously submitted for a degree at any tertiary education institution.

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Oliver Krumholz

20th May 2019

Abstract

Estuaries are significant coastal environments and amongst the most productive ecosystems. However, anthropogenic activities have led to widespread degradation of estuaries and loss of ecosystem function. Eutrophication, a major driver for these changes, caused widespread loss of seagrass and significant blooms of macroalgae and phytoplankton. This study determined the spatial and temporal dynamics of macrophyte communities over a forty-year period (1978- 2018) in the

Peel- Harvey Estuary, a hydrologically modified eutrophic estuary in south-western

Australia. Analyses revealed a progressive decline in macroalgal biomass and an associated increase in seagrass biomass over the examined periods. The seagrass

Ruppia became the dominant macrophyte in the system and expanded into previously unvegetated areas in the southern Harvey Estuary. The observed changes in macrophyte community composition were correlated with declining total nitrogen concentrations over time in those regions of the estuary furthest from the rivers.

While these effects partly reflect improved water clarity and flushing of nutrients following the opening of an artificial channel to the ocean, they are likely also influenced by changes in river flow patterns caused by climate change. Although the overall seagrass expansion and decline in macroalgal biomass can be perceived as signals of improved estuary health, areas close to the river mouths remain exposed to higher nutrient loads. Significant accumulations of the green algae Willeella brachyclados (formerly Cladophora montagneana) in these areas underpin the potential vulnerability of the system to algal blooms and seagrass decline. Future research and monitoring is required to understand the potential threats to macrophyte communities in the Peel-Harvey Estuary from ongoing climate change.

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Table of content

Abstract……………………………………………………………………………………………………………………...……iii Acknowledgements………………………………….………………………………………………………………………v 1. Introduction………………..………………………………………………………………………………………...... ……1 1.1. Estuaries and human impacts on macrophyte communities………………………….……………1 1.2. Monitoring macrophyte communities in estuaries………………………………………...…………..5 1.3. Effects of climate change on estuaries and macrophyte communities…………………..……..7 1.4. The Peel-Harvey Estuary in Western Australia as case study…………………………….……….9 1.5. Study rationale and aims……………………………………………………………………….……………….12 2. Methods…………………………………………………………………………………………………………...…………14 2.1. Study site………………………………………………………………………………………………………..……..14 2.2. Environmental and historical macrophyte data collection………………………….…………….16 2.3. Macrophyte sampling methods……………………………………………………………………………….18 2.4. Preparation of the historical and recent data for analysis………………………………………...19 2.5. Statistical analyses………………………………………………...……………………………………...……….20 2.5.1. Historical macrophyte variability among periods…………………………………………………20 2.5.1.1. Macrophyte biomass and community composition variability…………………………20 2.5.1.2. Macrophyte patterns and water quality parameters………………………………..…….24 2.5.1.3. GIS mapping of changes in distribution among periods…..………………………..…….25 2.5.1.4. Investigation and scoping of a preliminary index for macrophyte condition….…27 2.5.2. Decadal vs seasonal variability…………………………………………………………………………...28 2.5.3. Current seasonal and regional differences……………………………………………………………29 3. Results………………………………………………………………………………….………….……………………….…32 3.1. Historical variability among periods……………………………………………………………...……….32 3.1.1. Macrophyte biomass and community composition variability……………….……………….32 3.1.2. Macrophyte patterns and water quality parameters……………………….………………..…..50 3.2. Decadal vs seasonal variability……………………………………………………………….………………51 3.3. Current seasonal and regional differences……………………………………………..………………..54 4. Discussion……………………………………………………………………….…………………………………….……62 4.1. Historical variability and the most influential environmental factors………………..………63 4.2. Decadal vs seasonal variability and current spatial and temporal patterns..……..……….71 4.3. Local and broader context of the findings……………………………………………….………………..74 4.4. Study limitations……………………………………………………………………………………..…………….76 4.5. Guidance on future research…………………………………………………….…………….…...…….……77 5. Conclusions………………………………………………………………………………………………….….………….79 6. References……………………………………………………………………………………………………..……………81 7. Appendices………………………………………………………………………………………………….….…………..91

Acknowledgements

First and foremost, I would like to thank my supervisors Dr Chris Hallett,

Dr Fiona Valesini and Dr Halina Kobryn, for their time, guidance and patience throughout the whole project. Your feedback and support was invaluable and allowed me to develop my skills and confidence in my abilities. Thank you for this.

I also want to thank my “volunteers” for participating in exiting outdoor activities and fieldwork. Steven Goynich and Ian Dapson, who did most of the scuba diving, Dr Alan Cottingham, with his incredible boat driving skills and Sorcha

Cronin-O’Reilly, who showed me the beauty of a perfectly anoxic sediment sample.

Thank you guys, it was actually a lot of fun.

To my family: thank you, too. It has been a long time, now it comes to an end. I wouldn’t have been able to do this without your support and funny comments on seagrass and the other “green stuff”.

Finally, I would like to acknowledge the financial support provided by the

Australian Research Council (ARC)- Linkage Program (LP150100451).

1. Introduction

1.1. Estuaries and human impacts on macrophyte communities

Estuaries are important coastal environments and amongst the most biologically productive ecosystems worldwide (Whittaker and Likens 1973;

Kennish 2002; Whitfield and Elliott 2012). This high productivity is the result of estuaries being exposed to complex physical and chemical oceanic, fluvial and land derived processes where high nutrient availability allows high primary production supporting complex trophic webs (McComb and Lukatelich 1995;

Kennish 2002; Perillo and Piccolo 2012).

Their high productivity, as well as the wide array of other ecosystem services provided by estuaries, has always attracted human activities and made estuaries among the most preferred areas for settlement. For example, it has been estimated that more than 75% of the world’s population will live in coastal regions by 2025 (Kennish 2002; Cohen 2003). Some of these goods and services that estuaries contribute to human well-being are the provision of food and water, coastal protection, erosion control, provision of transport and carbon sequestration (Costanza et al. 1997; Barbier et al. 2010; Borja et al. 2011).

However, human activities and settlements near estuaries have led to widespread degradation of estuarine ecosystem function and the loss of ecosystem services (Lotze et al. 2006). Anthropogenic activities in estuaries, including the extraction of resources, dredging of navigation channels, removal of fringing vegetation for housing and industrial development, alteration of shorelines, inflow of polluted water from drains and outfalls, directly impact on these ecosystems (Kennish 2002; Borja et al. 2011). Additional stressors associated

with agricultural development of estuary catchments, water diversion and the widespread use of industrial fertilisers have led to increased nutrient availability and eutrophication in many systems worldwide, resulting in algal blooms, seagrass loss and fish kills (Valiela et al. 1992; Valiela et al. 1997; Mallin et al. 2005;

Burkholder et al. 2007). These emerging problems became more apparent during the second half of the last century when accelerated industrial development and population growth resulted in increased rates of ecosystem destruction. For example, in the period since human settlement to 2006, more than 90% of the species significant for estuaries were depleted and 65% of seagrass areas were lost worldwide (Lotze et al. 2006).

Furthermore, macroalgal blooms and seagrass loss due to anthropogenic eutrophication have contributed to declines in the ecological function of many estuaries across the globe (Cardoso et al. 2004; Lyons et al. 2014). Under eutrophic conditions, macroalgae will typically outcompete and eventually replace seagrasses as they have physiological advantage with rapid growth and accelerated nutrient uptake (McGlathery 2001). Macroalgal blooms are often associated with large floating canopies of filamentous green algae (e.g. from the genera Chaetomorpha, Willeella, and Ulva) in estuaries across the globe (Lavery et al. 1991; Curiel et al. 2004; Fox et al. 2008; Han and Liu 2014; Zhang et al. 2014).

These floating mats can have detrimental effects on seagrass meadows as they cause significant light attenuation through shading, resulting in reduced seagrass growth and reproduction (Hauxwell et al. 2001).

Large accumulations of bloom-forming macroalgae can also alter the physicochemical conditions of the environment. For example, the

remineralisation of nitrogen within senescing macroalgal mats can generate ammonium concentrations that are toxic to seagrasses (Hauxwell et al. 2001). In addition, decomposing biomass on the bottom results in anoxic sediment conditions with increased sulphide levels (Han and Liu 2014). These conditions are known to reduce photosynthesis, therefore increasing the minimum light requirements for sustained seagrass growth (Hauxwell et al. 2001; Han and Liu

2014). Eventually, under continued high nutrient loading, rapid growth rates of phytoplankton will increase turbidity in the water column, reducing the available irradiance for macroalgal photosynthesis and leading to the dominance of phytoplankton, indicating severe degradation of an ecosystem (Duarte 1995).

This pathway of ecosystem degradation suggests that a subsequent decrease in water column nutrients could lead to declines in macroalgal biomass, provide seagrasses with an advantage over algal growth and enable degraded estuaries to return eventually to a seagrass-dominated system (Leston et al.

2008). While there are some documented cases of seagrass recovery from disturbance, the return towards a seagrass-dominated system does not always result necessarily in the recovery of the previously dominant seagrass species. For example, Cho et al. (2011) reported that seagrass communities in U.S estuaries previously dominated by Thalassia testudinum, Syringodium filiforme and Zostera marina were replaced by Ruppia maritima following disturbance.

Ruppia often has been perceived as a colonising genus with less ecological value. However, it also has the ability to stabilise the substrate and may therefore promote a process of natural succession to more persistent seagrass species (Cho et al. 2011; Kilminster et al. 2015). Furthermore, Ruppia has been identified as an

important ecosystem component in its own right, e.g. as a food source for waterfowl and habitat and feeding grounds for fish (Kantrud 1991; Humphries and Potter 1993). Other authors have reported that seagrasses in some systems did not recover, despite significant water quality improvements. They concluded that recruitment or other environmental factors can also play a critical role in determining the success of restoration and/or recovery of macrophyte communities (Bell et al. 2008).

Furthermore, hysteresis dynamics can prevent full recovery of macrophyte communities in estuarine systems even after the reversal of eutrophication, due to non-linear response of macrophytes and phytoplankton to altered nutrient loads (Harris 1999; Cardoso et al. 2010). The extent of the destruction and the rates of loss have stimulated significant research into the causes of estuarine health decline in some parts of the world, and have motivated subsequent attempts at management and restoration of ecosystem function and services

(Rudnick et al. 1999; Kennish 2000; Cui et al. 2009; Busch et al. 2010; Cardoso et al. 2010).

While recovery of estuarine and coastal ecosystems is not uncommon, for example Borja et al. (2010) compiled 51 examples of ecosystems that recovered or improved after the removal of anthropogenic pressures, there were only few examples of successful seagrass restoration after the reversal of eutrophic conditions. Tampa Bay in Florida showed an increase in seagrass cover after the implementation of a nutrient management strategy aiming at the overall reduction of nitrogen loading to the system (Greening and Janicki 2006). Seagrass recovery and the reduction of macroalgal blooms were also reported from the

Mondego Estuary in Portugal after the reduction of anthropogenic nutrient loads over a 10 year period (Dolbeth et al. 2007). However, the authors also concluded that a history of eutrophication potentially impacts on the resilience of the seagrass to natural stressors such as floods or storms. Gross and Hagy (2017) examined attributes connected to successful restoration of estuaries. In their study they identified nine estuaries where nutrient pollution reduction and ecosystem restoration was attempted across the globe. Only one estuarine case study achieved the restoration goal fully. Most of the other estuaries showed subsequent ecosystem decline after initial improvements. The authors attributed this to unsustained partnerships among stakeholders, top-down governance, involvement of multiple agencies and targets based on numeric water quality parameters rather than ecological function.

1.2. Monitoring macrophyte communities in estuaries

Given their important ecological functions and responses to anthropogenic disturbances, seagrasses are an integral part of many environmental monitoring programs to assess eutrophic conditions and the ecological status of estuaries (Roca et al. 2016). However, problems are frequently encountered in distinguishing between human-induced stress and that caused by the highly variable physico-chemical environments of estuaries. This problem, which impacts on the ability to establish the natural, baseline state of an estuary

(Borja et al. 2011) and has been labelled as the “Estuarine Quality Paradox”, makes it difficult to determine specific ecosystem components that may be used as robust measures of estuary health or condition (Elliott and Quintino 2007).

Seagrass responses to disturbance may be quantified via measurements of their distribution, abundance, shoot characteristics, processes such as growth and population dynamics, chemical constituents, and associated flora and fauna

(Marbà et al. 2013). Some of the metrics like depth range, shoot density and length, ground cover are obtained through in-situ assessments, whereas extractive methods are used for above and below ground biomass, leaf tissue nutrient contents and epiphyte biomass measurements (Frankovich and

Fourqurean 1997; Duarte and Kirkman 2001; Kowalski et al. 2009). While these methods may be suitable to measure the spatial relationships between environmental pressures and macrophyte response, they might not allow identification of the temporal relationships between change of pressure and response (Marbà et al. 2013). In addition, there is no uniformity in the metrics selection in assessing estuarine status. For example, Marbà et al. (2013) identified fifty-one seagrass metrics in forty-two different European monitoring programs.

Macroalgal metrics for monitoring programs are mostly confined to diversity and biomass measurements to characterize the level of degradation of estuarine and coastal ecosystems (Orlando-Bonaca et al. 2008; Zhang et al. 2014).

More recently, remote sensing methods have become important tools that allow rapid assessment of macrophyte status over large areas. Optical (aerial and satellite images) and acoustical (sonar) methods have been used to detect and discriminate seagrass and macroalgae in estuarine systems and to characterize abundance and distribution changes over larger spatial and temporal scales

(Sabol et al. 2002; Fyfe 2003; Hossain et al. 2015; Lyons et al. 2015). Therefore, biological quality elements that allow differentiation between natural rates of

change and anthropogenic impacts are to be used in estuarine ecosystem monitoring programs, management response, and restoration efforts (Borja et al.

2011).

Estuarine biotic quality elements typically include plankton, benthic invertebrates, fish, and macrophytes like seagrasses and macroalgae and are commonly used for biological indices (Ponti et al. 2009). The importance of seagrasses and macroalgae as biotic elements in estuarine ecosystems indices arises from their function as primary producer supporting complex food webs, provide shelter and structured habitat, and also have the ability to buffer excess nutrients in eutrophic conditions (Beck et al. 2001; R. Orth et al. 2006; Hardison et al. 2010). More recently, seagrasses have been linked with the important ecosystem function of carbon sequestration into sediments (Greiner et al. 2013).

1.3. Effects of climate change on estuaries and macrophyte communities

While there has been some progress to improve estuarine health by managing the causes of eutrophication (Leston et al. 2008; Greening et al. 2014;

Gross and Hagy 2017; Elliott et al. 2016), concerns have been raised around how climate change will affect estuaries, including in combination with other, more direct anthropogenic activities. Climate change encompasses climatic and hydrologic changes that may affect estuaries and their macrophyte communities, e.g. via increased CO2 levels in the surface waters, increase in mean water temperature, sea level rise, change in precipitation patterns impacting on stream flow and the occurrence of extreme weather events like storms (Brearley 2005;

Vaudrey et al. 2010; Elliott et al. 2016; Hallett et al. 2018). Macrophyte

communities can be influenced by all of these effects. For example, the atmospheric CO2 concentration impacts on water CO2 levels, with any increase being potentially beneficial for macrophyte growth (Pyke et al. 2009). On the other hand, increased CO2 concentrations can also enhance phytoplankton blooms, impacting negatively on light availability for seagrasses (Pyke et al.

2009). Increases in water temperature may result in higher metabolic rates supporting increased growth of macrophytes and phytoplankton, but also support distributional shifts of species due different thermal tolerance, and the introduction of new species (Short and Neckles 1999; Statham 2012). In addition, sea level rise will lead to increased salinity and change of available suitable habitats for seagrasses because of the highly turbid waters in estuarine systems that limits growth in deeper areas of estuaries (Short and Neckles 1999). Changes in river flow patterns also impact on salinity and stratification in estuarine systems and therefore potentially on macrophyte community composition.

Furthermore, extreme weather events like storms and floods can result in the eradication of macrophyte species in shallow estuaries, as well as increase the release of nutrients from resuspended sediments, triggering unwanted algal blooms (Lavery et al. 1991; Corbett 2010; Statham 2012). While the threats to estuarine ecosystem integrity from climate change can be significant, it appears that there is data paucity to quantify these effects (Hallett et al. 2018).

The next section will discuss the restoration attempts and management approaches taken to reverse eutrophication, reduce algal blooms and restore seagrasses in an estuary in south-western Australia.

1.4. The Peel-Harvey Estuary in Western Australia as case study

The Peel-Harvey Estuary in south-western Australia provides an informative case study of the effects of eutrophication caused by human activities in the catchment and the estuary itself, the consequent algal blooms and system degradation, and the subsequent mitigation, restoration and management actions to address these issues (McComb and Lukatelich 1995; Brearley 2005;

Mazik et al. 2016; Potter et al. 2016; Valesini et al. 2019).

Significant alterations to the estuary and its catchment started with land clearing in the river flats and the installation of a dense network of drains and channels after the arrival of the first European settlers in the early 1800s (Brearley

2005). Intensified agricultural activities including piggeries, uncontrolled application of industrial fertiliser, ongoing land clearing in the catchment which affected almost 75% of the coastal plain and a rising population resulted in increased surface water run-off carrying increased loads of nutrients and sediments. During the second half of the 20th century there was an increase of river flows by almost 100 percent as a result of the draining activities, causing a more than 20-fold increase in total phosphorous load into the estuary due to overuse of superphosphate fertiliser and leaching from the sandy soils in the coastal plain (Humphries and Robinson 1995). Furthermore, the hydrological and geomorphological (shallow, low tidal exchange and highly seasonal rainfall) properties of the estuary promoted a stratified water column in the deeper areas of the estuary (Environmental Protection Authority 2008). In addition, plant respiration at night resulted in hypoxic bottom waters, favouring the release of

previously accumulated phosphorus from the sediment (McComb and

Humphries 1992; McComb et al. 1998).

The first problems associated with the resulting eutrophication became evident in the early 1960s when large amounts of filamentous green algae floated on the surface and accumulated in shallow areas (McComb and Lukatelich 1990;

McComb and Humphries 1992; Environmental Protection Authority 2003). These issues became more even apparent in the 1970s when seagrasses started to disappear from large areas. The increased biomass from nuisance algal blooms caused by excess nutrients formed large floating mats in the estuary and ended being washed up on the shore, causing inconvenience to residents from the smell of decaying plants on the beaches (Brearley 2005). However, significant concerns to human health arose from toxic blooms of the blue-green algae Nodularia, which has the ability to fix nitrogen from the air and to grow rapidly under increased phosphorus supply (Brearley 2005). Furthermore, these algal blooms have been linked to significant fish kills caused by the algal toxins and hypoxic events from decomposing biomass (McComb and Humphries 1992).

A new management strategy to deal with the ongoing eutrophication was the construction of an additional artificial channel to the ocean from the northern Harvey Estuary at Dawesville to improve the flushing of system. The main goals were to minimise residence time of the eutrophic water in the estuary, decrease the occurrence of a stratified water column, increase the overall salinity of the system and to improve water clarity (Brearley 2005). This was envisaged to decrease the incidence of macroalgal blooms, prevent future Nodularia blooms, and support the regrowth of lost seagrasses (Humphries and Robinson 1995;

Wilson and Paling 1999; Brearley 2005; Pedretti et al. 2011; McComb and

Lukatelich 1995).

The Dawesville Channel (or “Cut”) was opened in 1994 and scientists and engineers quickly claimed success as reductions in estuarine phosphorous concentrations were recorded only one year later. Subsequent surveys and reports confirmed the initial assumptions, with a decline in macroalgal and increase in seagrass biomass, lack of Nodularia blooms, and overall improved water clarity (Wilson and Paling 1999; Environmental Protection Authority 2003;

Pedretti et al. 2011).

However, the macrophyte monitoring program that had been setup in

1978 to measure impacts of eutrophication on the estuarine macrophytes was abandoned in 2000, potentially due the perception of some stakeholders that all of the problems in the estuary were now fixed and that the Dawesville Cut provided a sustainable solution to all the problems to come (Environmental

Protection Authority 2003). Furthermore, the Environmental Protection

Authority raised concerns on the unstable ecological state of the system and its potential to revert to poorer water quality due to ongoing issues with fertiliser application in the catchment. A subsequent 2011 review of the Water Quality

Improvement Plan for the catchment showed that the plan had not been realised as scheduled and highlighted the lack of key performance indicators to measure implementation success (Peel-Harvey Catchment Council 2013). At the present time there is still no integrated catchment and estuary water quality management plan in place.

1.5. Study rationale and aims

The concerns arising from eutrophication and subsequent management attempts delivered a framework for monitoring macrophyte abundance and distribution in the estuary basins. Although this program was extensive, running from from 1978-2000 and resulting in numerous reports (e.g. McComb and

Lukatelich 1990; Lavery et al. 1991; McComb and Humphries 1992; McComb et al.

1998; Wilson and Paling 1999), none of the previous work analysed statistical relationships between any long term changes in environmental factors like salinity, temperature, turbidity and nutrients and those in macrophyte community composition and biomass.

Furthermore, effects of climate change on the seagrass and macroalgal communities in the Peel-Harvey Estuary were not considered, although significant declines in rainfall and river flows were observed since the 1970s

(Silberstein et al. 2012).

The overall aim of the present study was to characterize, for the first time, the changes in temporal and spatial distribution of the seagrasses and macroalgae in the Peel-Harvey Estuary over four decades (1978-2018), using both historical and current data. Furthermore, observed patterns in macrophyte responses were related to those in a suite of concurrent environmental (i.e. water quality) variables, to identify any significant human and or climatic influences on macrophyte distribution and biomass over this period. In addition, potential signals of macrophyte response were investigated to explore the potential utility of a simple index of macrophyte condition for the estuary.

More specifically the study aimed to: i) Characterize historical changes in macrophyte biomass and community composition among five periods spanning 1978–2018, and identify the most influential environmental drivers of any observed temporal and/or spatial changes. ii) Characterize and compare changes in the above macrophyte characteristics over inter-decadal and inter-seasonal timescales (spring 2009 vs spring 2017 vs autumn 2018). iii) Characterize the seasonal and spatial variability in macrophyte biomass, species richness and community composition, between spring 2017 and autumn

2018.

To address the above study aims a combination of macrophyte biomass data collected from the field during the current study (2017–2018) and those collected during previous studies in the Peel-Harvey Estuary (1978–2009) by

Murdoch University’s Marine and Freshwater Research Laboratory (MAFRL) were used.

2. Methods

2.1. Study site

The Peel-Harvey Estuary in south- western Australia is located about 60 km south of the Perth Metropolitan area and is the largest natural inland water body in southern Western Australia covering approximately 136 km2 (Brearley

2005). The estuary consists of two different geomorphic formations that are connected to each other by wide shallow sediment banks and a narrow channel that has been dredged regularly (Figure 1). The elongated Harvey Estuary covers an area of 61 km2 and has been classified as an inter-barrier estuary that runs parallel to a coastal sand dune system with 20 km in length and approximately 2 km wide. The Peel Inlet is an almost circular basin estuary with a diameter of about 7 km and covers an area of 75 km2 (Hodgkin and Hesp 1998). Both basins are fringed by very shallow (< 1.0 m) sediment margins and have their deepest sections in their centre with a maximum depth of only 2.5 metres.

The Peel Inlet has the only natural connection to the ocean which is permanently open, however in 1994 an artificial channel was created in the northern Harvey Estuary to improve flushing and to mitigate effects from anthropogenic eutrophication (Wilson and Paling 1999) The whole system is exposed to tides that average 0.3 m (i.e. microtidal) and is classified as river- dominated. Mixing in the estuary basins is mainly wind-driven due to the low tides and shallowness of the system (Hodgkin and Hesp 1998; Kennedy 2012).

Three river systems are connected to estuary: the Harvey River in the southern

Harvey Estuary and the Serpentine and Murray rivers in the north-eastern Peel

Inlet (Brearley 2005). The climate in the region is Mediterranean with mild wet

winters and dry hot summers causing highly seasonal river flows and resulting in large fluctuations in salinity from almost fresh to hypersaline in some parts of the estuary (Hodgkin and Hesp 1998; Tweedley et al. 2016; Hallett et al. 2018).

Figure 1. Peel-Harvey Estuary map showing the four regions (Harvey south, Harvey north, Peel west and Peel east) with the sampling and water quality monitoring sites.  shallow, + deep sites. Water quality monitoring sites: 1, 2, 4, 7, 31, 58. Additional sites for this study, not previously sampled: Pe1, Pe2, Ha1, Ha2, Ha3, Ha4. 15

2.2. Environmental and historical macrophyte data collection

Macrophyte biomass throughout the basins of the Peel-Harvey Estuary was sampled from 1978-2000, mostly biannually from at least 36 sites (McComb and Lukatelich 1990). Another macrophyte survey was conducted in spring 2009 following the previous study methodology and sites (Pedretti et al. 2009).

Furthermore, a range of physicochemical environmental factors were recorded by

MAFRL on a weekly to monthly basis at six sites in the estuary over the same time period (Hale and Paling 1999). For the current study the basins were separated into four different regions (Harvey south, Harvey north, Peel west, Peel east) with each region containing sites in both shallow (< 1.3m) and deep (> 1.3m) water (Figure 1).

The macrophyte data (mean biomass per unit area (g m-2) of dry-weight per sampled site) for the years 1978-2000 were obtained from the original MAFRL data sheets and entered into Excel spreadsheets. The environmental data and the data for the 2009 survey were available and downloaded from the Australian

Ocean Data Network (AODN) website (Australian Ocean Data Network 2014).

These data were collected from six long-term water quality monitoring sites in the Peel-Harvey Estuary, from which WQ data have been collected periodically since at least 1979 (Table 1).

Table 1. Historical water quality monitoring sites in the Peel-Harvey Estuary. Site No 1 2 4 7 31 58 Location Central Western Eastern Central Southern Northern Harvey Peel Peel Peel Harvey Harvey Start year 1977 1977 1977 1977 1979 1979

All of these WQ monitoring sites also align with macrophyte sampling sites and are located in deeper sections of the estuary (Fig. 1). The recorded measurements included Secchi depth (m) and total water column depth (m), plus measurements of the following parameters from both surface and bottom waters: pH, salinity, temperature, total nitrogen (TN), and total phosphorus (TP). The environmental data for the 2018 survey were accessed from the Water

Information Reporting (WIR) tool provided by the Department of Water and

Environment Regulation in Western Australia. In this data set the samples for salinity, temperature, and Secchi depth were taken on a fortnightly basis, whereas all nutrients were sampled once a month. To align the different parameters and data collection methods within both data sets a subset of environmental factors containing the variables temperature, salinity, Secchi depth, TN and TP were selected from the 2018 Department of Water and Environment Regulation dataset. An extra variable, quantifying the Secchi depth as a proportion of the absolute WQ site depth, was also derived for inclusion in the overall analysis.

Furthermore, two environmental data sets were created, representing i) WQ data measured directly prior to each recorded macrophyte sampling date and ii) averages of WQ data taken over an approximately eight-week period prior to each macrophyte sampling date. The eight-week time lag was used to investigate any potential delayed impacts of the selected water quality parameters on seagrass and macroalgal abundance and community composition. Finally, the values from the closest WQ monitoring sites were attributed to each individual macrophyte sampling site for each year. Data gaps for any particular year were filled by using data from the next closest WQ site. However, the year 1978 lacked

all Secchi depth measurements, whilst the year 1989 had no nutrient recordings over the selected period. Therefore, these years were removed from both the macrophyte and environmental data sets, prior to the analyses of relationships between the macrophyte patterns and those in the WQ data, as outlined below.

2.3. Macrophyte sampling methods

The contemporary sampling for this study took place over seven days in each of two sampling seasons (October 2017 and April 2018). A total of 51 sites were sampled across the two basins, including sites additional to those sampled historically to increase the spatial resolution of sampling coverage in the southern Harvey Estuary (Fig. 1). At each site, the environmental parameters of temperature, salinity and dissolved oxygen were recorded using a multiparameter probe (YSI Multiprobe 556). For sites deeper than 1.3 m, these parameters were measured at the surface and bottom of the water column. Furthermore, the overall water depth and Secchi depth were noted for each sampling site.

The sampling approach during the current study broadly followed that used in the historical studies (McComb and Lukatelich 1995; Wilson and Paling

1999; Pedretti et al. 2011), to ensure comparability of results. Samples were taken by wading in the shallow waters and by scuba diving at deeper sites. At each site, five randomly located replicate biomass samples were taken by pushing a 90 mm

Perspex core at least 10-15 cm into the sediment, to capture below ground biomass. The cores then were sealed using bungs and taken to a boat, where they were sieved through a 500 µm mesh to remove excess sediment. All remaining plant material was placed in labelled bags and put on ice for transport. At the end

of each sampling day the bags were put in the freezer for later processing. In the laboratory, replicate sample bags were thawed and each was then sorted into individual taxa, identified to the lowest possible taxonomic level. These were then oven dried at 70°C to constant weight, and the dry weights (g) of each taxon in the original replicate samples were recorded. The resulting data were used to calculate the mean biomass per unit area (g m-2), taking into account the area sampled by each core.

2.4. Preparation of the historical and recent data for analysis

Differences among historical and contemporary studies in the (i) taxonomic levels to which macrophytes were identified and (ii) spatial and temporal intensity of sampling, shaped the nature and design of the analyses used to address the aims of this study (Table 2).

Table 2. Summary of the maximum numbers of sites and taxonomic resolution used for the data analysis. Maximum Number of Examined time number of taxonomic Taxonomic spans Years Season sites groups resolution inter-period 1978-2018 autumn 43 5 taxonomic groups

decadal - seasonal 2009-2018 spring- 45 21 genus level, autumn taxonomic group seasonal 2017-2018 spring- 51 23 genus level autumn

The historical biomass data collected between 1978 and 2009 differed in taxonomic resolution among field studies, with most macrophytes identified to

different broad taxonomic resolutions and only a few genera separately noted in the biomass records. For example, until 2009 all records did not distinguish between the different seagrass genera. Similarly, most data on green algae were grouped at the broad level, with only a few taxa identified to genus level, e.g.

Cladophora and Chaetomorpha, likely reflecting the perceived environmental issues associated with these macroalgae over time (Brearley 2005). Furthermore, changing taxonomic nomenclature over the decades led to the loss of some previously recorded genera. For example, the chlorophyte genus Enteromorpha was absorbed by the genus Ulva over time and the green algal species Cladophora montagneana has been recently included in the newly recognized genus Willeella as Willeella brachyclados (Wynne 2016; Guiry and Guiry 2018). For this study, the most recent accepted nomenclature has been used.

2.5. Statistical analyses

2.5.1. Historical macrophyte variability among periods

2.5.1.1. Macrophyte biomass and community composition variability

In compiling a dataset for examining changes among periods from 1978-

2018, the following steps were taken to overcome issues associated with the variability in sampling intensity or taxonomic resolution among studies, and to ensure sufficient power for robust statistical testing. Firstly, to overcome the issues of inconsistent recording of particular macrophyte taxa and/or varying taxonomic resolution across periods, all macrophyte data were placed into meaningful taxonomic groups for these analyses (i.e. Alismatales (Seagrasses),

Chlorophyta, Rhodophyta, Phaeophyta and Charophyta). Secondly, to overcome

differences in the season(s) in which samples were collected among sampling years, data were restricted to the time of year sampled most frequently, which was predominantly autumn (March-May) and occasionally summer (January-

February). As these times of the year collectively represent the later part of the growing season for macrophytes, they were considered to represent a single

‘season’ for the purposes of these analyses. Thirdly, sites that were not sampled consistently or were added in later years were removed from the data set.

Historical late growing-season data (mid-January to May) were then averaged for each year in each region across groups of years to create four periods, thereby enabling comparison of the macrophyte community composition on a semi-decadal basis. To provide context for any long-term changes, the most recent sampling year (2018) was included as a fifth period

(Table 3). Furthermore, to capture any potential impacts from the Dawesville

Channel, periods 3 and 4 were constructed to represent those just before and after the opening of the Channel in 1994. The resulting dataset encompassed sufficient replication to permit testing for changes among periods, in the context of differences among individual estuary regions (Harvey south (HS), Harvey north (HN), Peel west (PW), and Peel east (PE) and depths (shallow (≤ 1.3 m), deep (>1.3 m).

Table 3. Summary of the years comprising each of the sampling periods from 1978-2018. Period 1 2 3 4 5 Years 1978 -1984 1985-1989 1990 -1994 1995-2000 2018 Number of 4 5 5 6 1 sampling years

The above biomass data were then subjected to a variety of univariate and multivariate analyses employing the PRIMER v7 package and PERMANOVA+ add-on (Clarke and Gorley 2015; Anderson et al. 2008), to characterize any changes in both total macrophyte biomass and community composition among periods.

For the univariate analysis of total biomass, the data were fourth-root transformed as indicated by the relevant slope of the regression line between the logarithm of the sample means against the logarithm of their standard deviations

(Clarke et al. 2014). The transformed total biomasses were then averaged across each combination of a newly created year*region*depth factor, from which a

Euclidian distance matrix was calculated. This distance matrix was subjected to a three-factor PERMANOVA (period, region, depth) with all factors being considered crossed and fixed. The components of variation (COV) values were used to assess the relative importance of each significant (P < 0.05) term.

For the community composition analysis, the biomass data for all macrophyte groups were square-root transformed to increase the contribution of generally less abundant taxa (e.g. Rhodophyta and Phaeophyta) to the overall community composition (Clarke et al. 2014). The transformation also down- weights the influence of taxa such as Chlorophyta, which display sometimes erratic growth within one growth cycle due to significant bloom events (e.g.

Lavery et al. 1991). The transformed biomasses for each taxonomic group were then averaged across each combination of year*region*depth, from which a Bray-

Curtis similarity matrix was calculated. This matrix was submitted to similar analyses to those described above. When PERMANOVA detected significant

period differences, either as a main effect or interaction, appropriate sub-sets of the distance matrix were then subjected to one-way analysis of similarities tests

(ANOSIM; Clarke and Green 1988) to more fully examine inter-period differences, as these were the main focus of this analysis. Therefore, any significant interaction between the factors ‘region’ and ‘depth’ was not explored further explored. The criterion for rejecting the null hypothesis was the same as above and the extent of any significant differences among periods was determined by the R-statistic. An R-value of one indicates that all samples within a group (e.g. period) are very similar to each other, but completely different from the samples in other groups. An R-value of zero indicates that the samples within a group are no more similar to each other than they are to those in any other groups (Clarke and Green, 1988).

Furthermore, to illustrate the macrophyte groups that were most responsible for driving any significant differences among periods, all of the biomass for each macrophyte group was averaged for a combined factor period*region and then subjected to a shade plot analysis. The taxonomic groups were displayed on this plot according to a group-average hierarchical agglomerative cluster analysis of a resemblance matrix defined between species as Whittaker’s index of association (Legendre and Legendre 2012), and positioned to optimize conformity to a serial pattern of species similarities (Clarke et al.

2014). The shade plot reflects the increasing abundance of taxa through varying shades of grey, with darker shading indicating greater biomass (on the square root-transformed scale).

To illustrate any significant trends among periods, distances among the centroids of groups of replicate samples were calculated (within each level of

‘region’ and/or ‘depth’, as indicated by the PERMANOVA output), and the resulting distance matrix subjected to non-metric multidimensional scaling ordination (nMDS). nMDS ordination plots show the graphical relationship of the rank order of dissimilarities from the Bray-Curtis matrix between pairs of samples as distance between these pairs. For example, an nMDS plot will place dissimilar pairs further away than similar pairs. A 2-d stress level of <0.2 indicates an acceptable representation of the dissimilarities in a reduced 2-d nMDS plot.

(Clarke and Gorley 2015). Furthermore, nMDS centroid plots were constructed from the similarities matrix of the factor ‘year’ for each individual region, to illustrate the differences between the years within each period, separately for each region. Centroid plots illustrate the geometric center of selected values from the Bray-Curtis matrix and reveal broader temporal or spatial patterns in community composition in an nMDS plot (Clarke and Gorley, 2015).

2.5.1.2. Macrophyte patterns and water quality parameters

The environmental data were subjected to analysis in the PRIMER v7 package. Initially, draftsman plots were used to establish if the data required transformation prior to analysis, as indicated by the presence and extent of any skewness of the values in the resulting scatterplots among each pair of environmental variables (Clarke et al. 2014; Clarke and Gorley 2015). In this case both the lagged and non-lagged TP variables required 4th-root transformation, as these data were heavily right-skewed. The non-lagged variables temperature,

Secchi depth and TN and the lagged variable TN required square root transformation. All other variables remained untransformed. Each variable was then normalized to a common scale to overcome differences in their units of measurement. This was done by subtracting the mean from each entry of a single variable and dividing by the standard deviation of that variable. As a next step, based on the output of the three-way PERMANOVA analysis of macrophyte community composition among periods, region and depths, the environmental data were then averaged for a combined year*region factor. A Euclidian distance matrix from these pre-treated, averaged environmental data and a Bray-Curtis similarity matrix from the corresponding square-root transformed macrophyte composition data were then subjected to the BEST routine in PRIMER for each individual region across the years separately for all lag and non-lag variables. The outcomes of this analysis were then visualized using nMDS ordinations, to illustrate any significant correlations between patterns in macrophyte community composition and those in the corresponding WQ variables.

2.5.1.3. GIS mapping of changes of distribution among periods

To illustrate any historical changes in distribution of the macrophyte biomass, GIS software was used to generate spatially-interpolated maps of mean macrophyte biomass throughout the estuary in each period. While a number of

GIS interpolation methods are commonly used for mapping macrophytes (e.g. inverse distance weighting, kriging, spline), the inverse distance weighted (IDW) method was selected to interpolate the field measurements as the same methods had been used for previous studies (Wilson and Paling 1999; Valley et al. 2005,

Pedretti et al. 2011). IDW estimates values within an unsampled area by using the data of a defined number of neighboring points and weighting the importance of each data point for each calculated value depending on the distance from it. This importance diminishes with increasing distance to the neighborhood points.

ArcMap V10 and ESRI Geospatial Analyst were used for the interpolation.

Initially, the data were prepared by calculating mean seagrass, Chlorophyta and total macrophyte biomass (all in g m−2 dry weight) for all sampling sites in each year across the five periods. Geolocation data, i.e. latitude and longitude

(WGS84), were appended to the data record for each site. For the import of the

Excel files into ArcMap V10, latitude and longitude were transformed into the

UTM 50 coordinate system. Each individual period was separately converted into a point feature file in the geodatabase. Boundary points with zero value for biomass were added to each of the five point feature shape files marking the most southern, western, northern and eastern points of the estuary, to allow interpolation within the defined boundaries. Also, a modified polyline of the

100k-coastline of Western Australia was added to the geodatabase. The 100k- coastline is derived from the 1:100 000 scale Australian National Topographic

Map Series (Geoscience Australia 2004). Modifications to the coastline included simplification of the line of curvature and clipping to the spatial extent of the study area.

The data were subjected to IDW interpolation with the Spatial Analyst

Tool in ArcToolbox. A ‘period one’ point feature shape file was selected and the relevant value in the Z value field was selected (e.g. total biomass). The output raster file name was defined and the raster set at 20 (i.e. 20 m pixel size). The

impacts on the significance of values from more distant points was set at 2. A larger power will exert less influence over the interpolated value in each 20 x 20 m cell. Also, the maximum numbers of nearest sample points used in the interpolation was six neighboring points, and finally, the created estuary shape file was used as an input barrier. These steps were undertaken for each of the five periodic point feature files for the selected Z value, and the resulting data frame layers were arranged on a single map.

While there were at least 35 possible neighboring sampling points available for each interpolation routine it was decided to take only the nearest six data points. Large distances between isolated sampling sites often result in concentric values around the individual sites when IDW interpolation is used

(Longley et al. 2005). In this case, after trialing different options the limitation of the nearest side numbers to six reduced the extent and numbers of these ‘bulls- eyes’. Furthermore, to accommodate the often highly seasonal and annual fluctuations in macrophyte biomass in the system, and to allow direct comparison among the individual period panels, a logarithmic biomass scale was used to create five color classes for display purposes.

2.5.1.4. Investigation and scoping of a preliminary index for macrophyte condition

A preliminary index was generated to examine potential changes in macrophyte condition in the estuary, and particularly the relative dominance of seagrasses vs macroalgae, across the five periods. The following steps were taken to create the index. Initially, the proportional contribution of seagrasses to the

combined seagrass and Chlorophyta biomass was calculated for each sample.

This proportion was then multiplied by the total combined biomass of seagrass and Chlorophyta in the sample, resulting in a weighted proportion. The weighted proportion for each sample was then converted to a scaled index value (ranging from 0-1) by subtracting it from the maximum weighted proportion value across all samples and dividing the result by the range (i.e. maximum minus minimum) of the weighted proportion values across all samples.

The resulting index values for all samples were then divided into equal quintiles, to allow classification of the macrophyte condition across all periods. A value closer to 0 indicated high Chlorophyta proportion and biomass, and therefore poorer condition. On the other hand, a value closer to 1 indicated high seagrass proportion and biomass and better macrophyte conditions. Quantile- based grading methods can show greater sensitivity to ecosystem condition than equal interval classification (Hallett 2014). To create a map and to highlight change in macrophyte condition in the estuary across the five periods, the index data were subjected to IDW interpolation similar to the method described in

2.5.1.3.

2.5.2. Decadal vs seasonal variability

The data sets for this analysis comprised historical data from spring 2009 and those collected during the current study in spring 2017 and autumn 2018, which collectively were considered for these analyses to represent three sampling occasions. To allow for robust comparison of the macrophyte biomass data across these occasions, the taxonomic resolution of the recent survey data was

broadened to match that of the 2009 data by placing five Rhodophyta genera from the 2017 and 2018 surveys into a group termed “other Rhodophyta”.

Otherwise, all other macrophytes were considered at genus level. The resulting collated data sets therefore enabled the testing and comparison of any significant changes in total biomass and community composition over decadal (i.e. 2009 vs

2017) and seasonal (spring 2017 vs autumn 2018) timescales.

Initially, as described above, the data for total biomass required a fourth- root power transformation. A Euclidian distance matrix was then calculated from the transformed total biomass data in each replicate sample. This matrix was subjected to a three-way PERMANOVA (occasion, region, depth) with all three factors considered to be crossed and fixed. Due to the outcome of the

PERMANOVA (see below), no further analysis of total biomass was undertaken.

The community composition data were firstly square-root transformed and used to construct a Bray-Curtis similarity matrix, which was then subjected to the same three-factor PERMANOVA described above. One-way ANOSIM tests and shade plots, as described in section 2.5.1.1., were then used respectively to further explore any significant differences among occasions and determine the macrophyte taxa that were most responsible for driving any such differences.

2.5.3. Current seasonal and regional differences

The full data set from the spring 2017 and autumn 2018 surveys, undertaken during the current study, comprised higher taxonomic resolution data to at least genus level. This data set was used to characterize the current extents of both seasonal and spatial variability in macrophyte total biomass,

species richness and community composition throughout the Peel-Harvey

Estuary.

Prior to univariate analysis, the total biomass data were fourth-root transformed, whilst the species richness data required only square-root transformation as indicated by the relevant slope of a regression line between the logarithm of the sample means against the logarithm of their standard deviations.

The resulting, transformed data were used to calculate separate Euclidian distance matrices for total biomass and species richness, each of which was subjected to 3-way PERMANOVA using the factors season, region and depth, with all factors fixed and crossed.

The community composition data were fourth-root transformed then used to calculate a Bray-Curtis similarity matrix. This was subjected to similar analyses as outlined in section 2.5.1., yet here equal focus was placed on the factors

‘season’, ‘region’ and ‘depth’. The outputs of this analysis were visualized via a shade plot, comprising the hierarchical agglomerative cluster analysis of a resemblance matrix of the macrophyte genera and a similarity profiles test

(SIMPROF) at a 5% significance level to identify macrophyte genera that exhibited similar patterns across the factors of interest (Clarke et al. 2014).

Furthermore, centroid nMDS plots (as outlined above) and metric-MDS plots of bootstrapped group averages and their associated confidence ellipses were created for any significant two-way interactions. Bootstrap averages and their

95% confidence ellipses are used to estimate the likely distribution of sample means by repeated resampling of a different subset of samples from the Bray-

Curtis similarities. Each resampled mean will be different, and can be projected

on an MDS ordination plot as a confidence ellipse in which the true mean value has a 95% probability of being located. The dispersion of the means in the plot shows the overall variability in values within the bootstraps (Clarke et al. 2014;

Clarke and Gorley 2015).

3. Results

3.1. Historical variability among periods

3.1.1. Macrophyte biomass and community composition variability

The mean total macrophyte biomass in the estuary declined substantially from the first period (1978-1984) to most of the following periods (1984-1989,

1990-1994, 1995-2000). This decline was less prominent in the northern Harvey

Estuary. Furthermore, all regions showed an increase in biomass from period 4 to

5 (i.e. 1995-2000 to 2018), especially in the Harvey north where total biomass in

2018 was two to eight times higher than in any of the other periods (Table 4).

Declines in biomass were most pronounced between periods 1 and 2 with biomass falling between 25 and 75% in all regions. Total mean biomass ranged between

625.75 gm-2 dry weight (Peel east, period 1) and 21.15 gm2 dry weight (Harvey south, period 3), indicating large temporal and spatial fluctuations (Table 3).

Furthermore, the large standard error (SE) values associated with many mean values reflected considerable variability in biomass among replicate samples. For example, the SE for the samples collected during the period 1 in the southern

Harvey was nearly twice than that of the mean (Table 4). Overall seagrasses and

Chlorophyta were the two main contributors to the total macrophyte biomass.

There was a general decline in Chlorophyta biomass over the five periods with a particularly large reductions from period 1 to all later periods in Peel east (~540 vs

26-134 gm-2 in periods 2-5) and Harvey south (~213 vs 4-82 gm-2 in periods 2-5).

Concurrently, seagrass biomass increased over the five periods, with that in the northern Harvey showing the largest differences from the first period to later periods (~14 vs 3-205 gm-2). While the Chlorophyta mean biomass values often

Table 4. Mean biomass (g m-2 dry-weight) and standard error (meanSE) for all macrophytes, seagrass and Chlorophyta in each period and region

1978-1984 (period 1) 1985- 1989 (period 2) 1990- 1994 (period 3) 1995-2000 (period 4) 2018 (period 5) total sea- chloro- total sea- chloro- total sea- chloro- total sea- chloro- total sea- chloro- biomass grass phyta biomass grass phyta biomass grass phyta biomass grass phyta biomass grass phyta

Harvey south 251.68453.91 35.2855.63 213.31400.11 69.4272.95 0.200.44 65.8475.59 10.4115.48 0na 3.467.43 21.1516.89 0na 18.1316.35 92.05113.26 5.4612.28 82.76119.19

Harvey north 88.8654.81 14.1111.83 67.5649.44 66.0730.43 3.312.35 57.4431.01 48.9416.42 9.828.38 14.575.89 27.8422.44 21.864.36 2.471.63 213.21247.55 205.18244.45 2.353.47

Peel west 253.71259.70 25.3132.17 210.95243.74 132.1222.05 8.637.16 115.4726.64 130.1834.47 18.279.58 70.0828.5 33.1818.48 14.375.76 10.6210.78 88.6777.43 50.7958.12 12.9539.24

Peel east 625.75501.74 37.8541.42 540.41497.24 157.09108.45 10.5110.69 134.18111.61 141.4291.02 44.9032.89 84.54102.60 60.6920.28 28.0312.64 25.9817.20 185.42168.74 103.43114.25 48.4995.59

had large SE values (nearly twice than that of the mean in Harvey south, period 1, for example), variability in seagrass biomass was less extreme (Table 4).

PERMANOVA of the total biomass data among periods (and also including region and depth as factors to remove any confounding influences), showed significant period differences as well as a significant period*region interaction (Table 5). The influence of period alone, however, was notably greater than that for the interaction, as demonstrated by their COV values. Furthermore,

PERMANOVA also showed that mean total biomass differed significantly among regions and depth, with the influence of the first of these main effects being larger than that of period (Table 5).

Table 5. Degrees of freedom (df), mean squares (MS), pseudo F-ratios, significance levels (P) and components of variation values (COV) for a three-factor period × region × depth PERMANOVA of total macrophyte biomass across five periods spanning 1978 – 2018. Bold P-values indicate significant difference in total biomass. df MS Pseudo-F P COV Period (P) 4 6.0015 10.0230 0.001 0.416 Region (R) 3 12.3010 20.5430 0.001 0.658 Depth (D) 1 16.1920 27.0420 0.001 0.537 P x R 12 1.2734 2.1266 0.020 0.293 P x D 4 0.6826 1.1400 0.331 0.073 R x D 3 0.5895 0.9845 0.394 -0.026 P x R x D 12 0.4417 0.7377 0.697 -0.200 Residual 125 0.5988 0.773

The changes in mean total biomass for each period*region combination are displayed in Fig. 2. Clearly, the largest inter-period differences occurred between periods 1 and 2 in the eastern Peel Inlet (~620 to 180 g m-2 dry-weight). The pronounced magnitude of this change relative to all other inter-period differences in other regions would have contributed substantially to the significant period*region interaction detected by PERMANOVA. 34

a) Peel west b) Peel east

2 800 - 800

g m g

600 600

weight

- 400 400

200 200

Biomass dry Biomass 0 0 1978-1984 1985-1989 1990-1994 1995-2000 2018 1978-1984 1985-1989 1990-1994 1995-2000 2018

c) Harvey north d) Harvey south 2 - 800 800

g m g

600 600

weight

- 400 400

200 200

Biomass dry Biomass 0 0 1978-1984 1985-1989 1990-1994 1995-2000 2018 1978-1984 1985-1989 1990-1994 1995-2000 2018

Figure 2. Mean biomass plot in the estuary regions including +/-SE for all periods. Biomass dry- weight g m-2. Period 1, 1978-1984; period 2, 1985-1989; period 3, 1990-1994; period 4, 1995- 2000; period 5, 2018.

The maps showing the interpolated distribution of the total biomass highlight the variability of the macrophyte biomass over the periods (Fig. 3). The highest total biomass was recorded in the south-eastern and north-western Peel

Inlet in period 1 (1978-1984) and the lowest in the southern Harvey in period 3

(1990-1994). Overall, the total biomass was greater in the Peel than in the Harvey and the Peel Inlet had a more stable total biomass than the Harvey Estuary.

However, in period 4 (1995-2000), following the opening of the Dawesville Cut, the Peel Inlet saw a marked decline in total biomass compared to the other periods.

1978-1984 1985-1989 1990-1994 1995-2000 2018

Total biomass dry-weight g m-2

< 0.1 < 1.0 < 10.0 < 100.0 < 1000.0 < 10000.0 Figure 3. Interpolated total biomass (dry-weight in g m-2) across the Peel-Harvey Estuary in each period. Period 1, 1978- 1984; period 2, 1985- 1989; period 3, 1990- 1994; period 4, 1995- 2000; period 5, 2018.

The biomass in the Harvey Estuary declined until period 3 (1990-1994) and showed an increase since then (Fig. 3).

PERMANOVA showed that macrophyte community composition, like total biomass, also differed significantly among periods and the period*region interaction. Significant differences were also found for the period*depth interaction (Table 6). The COV values indicated that the period main effect on community composition was more influential than any other significant term in the model. The period*region interaction was also relatively important, and about twice as influential as the period*depth interaction (Table 6).

Table 6. Degrees of freedom (df), mean squares (MS), pseudo F-ratios, significance levels (P) and components of variation values (COV) for a three-factor period × region × depth PERMANOVA of macrophyte community composition across five periods spanning 1978 – 2018. Bold P-values indicate significant difference in community composition.

df MS Pseudo-F P COV Period (P) 4 13190 10.667 0.001 19.577 Region (R) 3 10336 8.359 0.001 18.362 Depth (D) 1 9987.1 8.0769 0.001 12.738 P x R 12 3103.1 2.5096 0.001 15.440 P x D 4 2100.8 1.699 0.037 7.444 R x D 3 729.84 0.59024 0.866 -6.127 P x R x D 12 1168 0.9446 0.588 -4.1829 Residual 125 1236.5 35.164

To further explore the above inter-period differences in community composition, one-way ANOSIM tests for differences among periods were undertaken for each region (Table 7) and also for each water depth (Table 8). In the case of the period*region tests, significant global period differences were found in all cases, with the exception of Harvey south which was only just insignificant (P = 0.058). Harvey north showed the greatest period differences

overall, which were moderate in extent (Global R = 0.425), with those for Peel west and Peel east being only slightly less (Table 7). Among the largest pairwise differences occurred between the earliest or second earliest period, and that just after the Cut opening (1995-2000) or the most recent period (2018). Conversely, the extent of significant differences between consecutive period pairs was often relatively small, except for those periods just before and after the opening of the

Dawesville Channel (1990-1994 v. 1995-2000) in the case of the Harvey north and

Peel west regions (R = 0.576-0.582) (Table 7).

Table 7. R-statistic values for global and pair-wise comparisons from one-way ANOSIM tests of inter-period differences in macrophyte community composition among all regions. Harvey north (HS), Harvey south (HS), Peel west (PW), Peel east (PE). Significant Global tests are emboldened. Consecutive period pairs in italic.

HS HN PW PE Global R 0.088 0.425 0.400 0.348 Pair-wise R

1978-1984 v. 1985-1989 -0.068 -0.050 0.050 0.188 1978-1984 v. 1990-1994 0.177 0.174 0.216 0.445 1978-1984 v. 1995-2000 0.177 0.740 0.570 0.834 1978-1984 v. 2018 -0.260 0.724 0.543 0.595 1985-1989 v. 1990-1994 0.202 0.151 0.258 0.056 1985-1989 v. 1995-2000 -0.002 0.687 0.648 0.409 1985-1989 v. 2018 -0.324 0.493 0.667 0.415 1990-1994 v. 1995-2000 0.132 0.582 0.576 0.166 1990-1994 v. 2018 0.296 0.296 0.533 -0.078 1995-2000 v. 2018 -0.044 0.432 0.218 0.260

Although the PERMANOVA showed a significant period*depth interaction with only a small COV (7.444), a one way-ANOSIM was undertaken to test for inter-period differences for each depth category (Table 8). Significant global differences in macrophyte community composition were found for both shallow and deeper waters. The extent of these inter-period differences in shallow estuary

sites was moderate overall (Global R = 0.425), with the greatest differences occurring between 1985-1989 and either 1995-200o or 2018 (R = 0.437). Inter- period differences at deep sites, however, were mostly small, with a Global R of only 0.157 and the highest pair-wise R-value of 0.332 (1978-1984 v. 1995-2000).

This indicated that the macrophyte community in the deeper sections of the estuary has been less variable over the longer term than that in the shallow sites.

Table 8. R-statistic values for global and pair-wise comparisons from one-way ANOSIM tests of inter-period differences in macrophyte community composition in both shallow and deeper waters. Significant Global tests are emboldened. Consecutive period pairs in italic.

deep shallow Global R 0.157 0.425 Pair-wise R

1978-1984 v. 1985-1989 -0.033 0.092 1978-1984 v. 1990-1994 0.127 0.068 1978-1984 v. 1995-2000 0.332 0.359 1978-1984 v. 2018 0.243 0.134 1985-1989 v. 1990-1994 0.082 0.184 1985-1989 v. 1995-2000 0.198 0.437 1985-1989 v. 2018 0.059 0.404 1990-1994 v. 1995-2000 0.230 0.151 1990-1994 v. 2018 0.022 -0.030 1995-2000 v. 2018 0.004 0.200

To illustrate patterns in the macrophyte community response over the different periods, a centroid nMDS ordination plot of inter-period trajectories for each region was constructed (Fig. 4). The regions Harvey north, Peel west and

Peel east followed similar trajectories, with the first period close to the lower- right corner and then moving towards the left, indicating that the macrophyte community composition in these regions changed in broadly similar ways over the five periods. However, the Harvey south region followed a different trajectory from the others as it remained in the right side of the plot across all five periods.

The largest distances between consecutive periods were for period 3 and 4 in the

Harvey north and Peel west regions (R = 0.582, 0.576 respectively). While periods

4 and 5 in the Harvey south region also showed a larger distance, the one–way

ANOSIM did not show any significance for this period pair (Fig. 4).

Figure 4. Centroid nMDS ordination plot of the macrophyte community composition in each period*region combination. ̶ ̶̶ ̶ Harvey north, --- Harvey south, ̶ ̶̶ ̶ Peel west, --- Peel east. 1, 1978- 1984; 2, 1985-1989; 3, 1990-1994; 4, 1995-2000; 5, 2018.

To examine inter-period patterns in macrophyte composition in more detail, nMDS ordination plots of samples collected in each individual year, with separate trajectories for their five respective periods, were produced for each region (Fig.

5). These plots revealed substantially different inter-period patterns across the different regions, and thus the reasons underlying the significant period*region interaction. For example, in the Harvey north and Peel west regions, the macrophyte communities in periods 4 and 5 following the Dawesville Cut were highly distinct from those in all preceding periods, but this was far less obvious in

Peel east, and not at all in the case of Harvey south where there were no clear inter-period trends (Fig. 5).

a) Harvey south b) Harvey north

c) Peel west c) Peel east

c) Peel west d) Peel east

Figure 5. nMDS ordination plots of the macrophyte community composition in each year, produced for each region. The split trajectories represent the different periods to which years were assigned. - Period 1 (1978-1984);  - Period 2 ( 1985-1989);  - Period 3 (1990-1994);  - Period 4 (1995-2000);  - Period 5 (2018).

The depth-dependent patterns in the macrophyte community composition across the periods were also examined, by constructing similar nMDS ordination plots to Fig. 4, but restricted to the two depth categories (Fig.

6). The inter-period trajectories were similar for both shallow and deep waters, and remained separated from each other. This indicated that while the pattern of response in the shallow and deep macrophyte communities was similar over the periods, the composition of their communities differed from each other. The relatively weak period*depth interaction mainly reflected slight differences in the

inter-period patterns, e.g. the greater difference between periods 3 and 4 in the deeper than shallow waters (Fig. 6).

Figure 6. nMDS ordination plot of the macrophyte community composition in each period*depth combination. --- shallow, ̶ ̶ deep. 1, 1978-1984; 2, 1985-1989; 3, 1990-1994; 4, 1995-2000; 5, 2018.

nMDS plots were also constructed with separate inter-year trajectories for the five respective periods, for each water depth (Fig. 7). The ordinations for both depths showed a trend of shifting trajectories from earlier to later periods from the right to the left of the plot. Furthermore, the post-Cut periods differed quite distinctly from those prior to the Cut, as shown by the separation of the respective period trajectories. However, it appears that these differences in macrophyte community composition were less substantial in the deeper than in the shallow waters (Fig. 7).

a) deep b) shallow

Figure 7. nMDS plots of the macrophyte community composition in each depth category. The split trajectories indicate changes across the different periods. - Period 1 (1978-1984);  - Period 2 ( 1985-1989);  - Period 3 (1990-1994);  - Period 4 (1995-2000); - Period 5 (2018).

The shade plot of the pre-treated macrophyte community composition data for each period*region combination clearly showed that chlorophytes and seagrasses were the major taxa driving inter-period shifts in each region (Fig. 8).

There was a general decline in the biomass of Chlorophyta and concurrent increase in that of seagrasses over the five periods in most of the regions.

Reductions in green algal biomass were especially marked in the Peel east region, but were less pronounced in the southern Harvey which showed an initial decline over the first three periods then increased in the most recent period. Increases in seagrass biomass over time were most obvious in the Harvey north region, but little seagrass was found in the southern Harvey after the first period.

Furthermore, the shade plot indicated more inter-period patterns in the

Rhodophyta biomass across the different regions, with a marked decline in the

Harvey north and Peel west regions between 1990-1994 and 1995-2000, coinciding with the opening of the Dawesville Channel. Phaeophyta and particularly the

Charophyta represented the least biomass over all periods in all regions (Fig. 8).

Chlorophyta

Rhodophyta

Seagrass

Phaeophyta

Charophyta

Figure 8. Shade plot of the relative biomass of macrophyte groups across the five periods in each region. Hierarchical cluster analyses ordered the macrophyte groups according to their association within the period and region groups.  - Harvey south;  - Harvey north;  - Peel west;  - Peel east. Periods: 1, 1978-1984; 2, 1985-1989; 3, 1990-1994; 4, 1995-2000; 5, 2018. The intensity of shading indicates macrophyte biomass on a square-root transformed scale.

The shade plot of the macrophyte community composition for each period*depth combination showed a declining trend in Chlorophyta and increasing seagrass biomass from earlier to later periods in both depth levels, with less biomass in general in the deeper sites within each period (Fig. 9). Some exceptions from the general trend were that Chlorophyta biomass increased in

2018 and seagrass was very low in 1984-1998 in the shallow waters. Rhodophyta biomass did not exhibit an obvious pattern of change over the periods, but did show a notable decline between periods just prior to and after the Channel opening (1990-1994 vs 1995-2000) at both depth levels (Fig. 9).

D D D D D S S S S S

Seagrass

Figure 9. Shade plot of the relative biomass of macrophyte groups across the five periods in each depth category; D= deep, S= shallow. Hierarchical cluster analyses ordered the macrophyte groups according to their association within the period and depth groups. The intensity of shading indicates macrophyte biomass on a square-root transformed scale.

The trends in chlorophyte and seagrass biomass over the individual periods from 1978-2018 across all regions and depths of the estuary are summarized in Figs 10 and 11, respectively. Overall, there appeared to be a general decline in Chlorophyta biomass across the whole system over the periods. In the

Peel Inlet most of the decline occurred in the eastern region. However, period 5

(2018) showed an increase in Chlorophyta biomass for this area. In the Harvey

Estuary the Chlorophyta biomass declined from period 1 to 3 (1978-1984, 1990-

1994) with subsequent increases until 2018 and high biomass values located around the southern Harvey (Fig. 10). With the decline in Chlorophyta biomass there was a concurrent increase in seagrass (Fig. 11). Initially, period 1 (1978-1984) showed relatively high seagrass biomass and decline in biomass to the next period 2. Since then there was steady increase in seagrass biomass throughout

Peel Inlet and the northern Harvey Estuary. The southern Harvey seemed to be

almost completely depleted of seagrass biomass for most of the periods. In the more recent period (2018), however, seagrass expanded into this region (Fig. 11).

The interpolated map of the preliminary condition index values across the five periods showed the differences between seagrass- and green algal- dominated regions in the estuary (Fig. 12). During period 1 (1978-1984) there was a relatively patchy distribution of seagrass and green algal dominated areas, with a subsequent shift to a more Chlorophyta-dominated estuary in period 2 (1985-

2000). While the Peel Inlet displayed a trend of increasing seagrass dominance across the subsequent periods, the Harvey Estuary showed an ongoing shift towards green algal dominance. Period 4 (1995-2000), however, saw some seagrass dominated areas become established in the northern Harvey, whilst the southern Harvey became more Chlorophyta dominated. The most recent period 5

(2018) indicated a retraction of the green algal dominated areas into the region close to the Harvey River, whereas seagrass was dominating most of the Peel-

Harvey Estuary (Fig. 12).

1978-1984 1985-1989 1990-1994 1995-2000 2018

Chlorophyta biomass dry-weight g m-2

< 0.1 < 1.0 < 10.0 < 100.0 < 1000.0 < 10000.0 Figure 10. Interpolated Chlorophyta biomass (dry-weight in g m-2) across the Peel-Harvey Estuary in each period. Period 1, 1978- 1984; period 2, 1985- 1989; period 3, 1990- 1994; period 4, 1995- 2000; period 5, 2018.

1978-1984 1985-1989 1990-1994 1995-2000 2018

Seagrass biomass dry-weight g m-2

< 0.1 < 1.0 < 10.0 < 100.0 < 1000.0 < 10000.0 Figure 11. Interpolated seagrass biomass (dry-weight in g m-2) across the Peel-Harvey Estuary in each period. Period 1, 1978- 1984; period 2, 1985- 1989; period 3, 1990- 1994; period 4, 1995- 2000; period 5, 2018. 48

1978-1984 1985-1989 1990-1994 1995-2000 2018

Figure 12. Interpolated index score values across the Peel-Harvey Estuary in each period. Period 1, 1978- 1984, period 2, 1985- 1989; period 3, 1990- 1994; period 4, 1995- 2000; period 5, 2018.

3.1.2. Macrophyte patterns and water quality parameters

The BEST routine showed that the macrophyte community patterns from the late 1970s to 2018 were significantly matched (P = 0.01) with those in a select suite of water quality parameters, but only in the northern Harvey and western

Peel regions. Of the full suite of water quality parameters provided in the BEST analysis (i.e. salinity, temperature, TN, TP, and Secchi depth as a proportion of water depth), inter-annual patterns in the macrophyte communities were best linked, and moderately to strongly so, with those in total nitrogen for both of the above regions (Rho = 0.695 and 0.520, respectively). This was also the case when a two-month time lag was built into the water quality data, except that the extent of the match in the western Peel region was lower (Rho = 0.649 and 0.399 for

Harvey north and Peel west, respectively).

The inter-annual relationships between the patterns in macrophyte composition and total nitrogen concentrations in each region are shown in Fig.

13. Note that, since the non-lagged and lagged total nitrogen data showed very similar patterns, only the former are presented. The clear separation in the macrophyte community composition between the pre- and post-Cut years (i.e. periods 1-3 vs 4-5, respectively) was clearly linked with marked changes in the concentration of total nitrogen in the water column (as reflected by the size of the ‘bubbles’ overlaid on each macrophyte sample from each year). Thus, the small bubbles on samples from post-Cut years showed a substantial reduction in

TN concentration (Fig. 13), which were linked with the decreases in green algae and increases in seagrass observed in these regions during that time (Fig. 8).

While there was a substantial reduction in TN in 1995 (the year following the

Cut) in both regions, the macrophyte community composition showed a more delayed response in the western Peel than in the northern Harvey, marked by the separation of the year 1995 from the rest of the years within period 4 (Fig. 13b).

TN (μg-l) a) Harvey north a) Harvey north th

b) Peel west

Figure 13. nMDS ordination plot of the macrophyte community composition in each sampling year from 1979-2018 in (a) Harvey north and (b) Peel west, with total nitrogen (TN) concentration in the water column overlaid as bubbles of proportionate sizes. Bubbles are colour-coded based on the sampling period for each year. - Period 1 (1979-1984); - Period 2 (1985-1988); - Period 3 (1991-1994); - Period 4 (1995-2000); - Period 5 (2018).

3.2. Decadal vs seasonal variability

PERMANOVA showed that the total mean biomass of macrophytes did not differ significantly over decadal (spring 2009 vs spring 2017) or seasonal

(spring 2017 vs autumn 2018) time scales in the Peel-Harvey Estuary, or for any interactions involving the factor ‘occasion’ (Table 9). Total biomass, which ranged between 117 and 186 g m−2 dry-weight in spring 2009 and 2017,

respectively, differed significantly only among depths. Therefore, no further analysis of this variable was performed.

Table 9. Degrees of freedom (df), mean squares (MS), pseudo F-ratios, significance levels (P) and components of variation values (COV) for a three-factor occasion*region*depth PERMANOVA of total macrophyte biomass, with ‘occasion’ comprising decadal (spring 2009) and seasonal (spring 2017, autumn 2018) components. Bold P-values indicate significant differences in total biomass.

df MS Pseudo-F P COV Occasion (O) 2 1.0932 0.6140 0.557 -0.1370 Region (R) 3 0.6458 0.3627 0.775 -0.1990 Depth (D) 1 18.0030 10.1120 0.002 0.5439 O x R 6 0.7141 0.4011 0.884 -0.3340 O x D 2 2.9972 1.6835 0.199 0.2579 R x D 3 0.1563 0.0878 0.963 -0.3367 O x R x D 6 0.6513 0.3658 0.910 -0.4861 Residual 109 1.7804 1.3343

Macrophyte community composition (based on a more detailed taxonomic resolution than was possible with the long-term data from 1978-2018) did, however, differ significantly among the sampling occasions, although the relative influence of ‘occasion’ was lowest compared to the significant ‘region’ and

‘depth’ main effects (Table 10). No significant interactions involving ‘occasion’ were detected.

Table 10. Degrees of freedom (df), mean squares (MS), pseudo F-ratios, significance levels (P) and components of variation values (COV) for a three-factor occasion*region*depth PERMANOVA of the macrophyte community with ‘occasion’ comprising decadal (spring 2009) and seasonal (spring 2017, autumn 2018) components . Bold P-values indicate significant differences. df MS Pseudo-F P COV Occasion (O) 2 7880.2 2.8572 0.001 11.835 Region (R) 3 12739 4.619 0.001 18.668 Depth (D) 1 19871 7.2046 0.001 17.664 O x R 6 3358.8 1.2178 0.131 7.9298 O x D 2 3694.4 1.3395 0.166 7.1559 R x D 3 4364.9 1.5826 0.044 10.593 O x R x D 6 2043.4 0.7409 0.926 -12.231 Residual 109 2758 52.517

A subsequent one-way ANOSIM test among ‘occasions’ also was significant (P = 0.001), although the Global R-value indicated that the differences in macrophyte community composition were only small (R = 0.063). The pairwise tests indicated significant yet small decadal differences between spring 2009 and spring 2017 (R = 0.076), but no significant seasonal difference in community composition between spring 2017 and autumn 2018.

The corresponding shade plot showed that the main driver of this decadal change in community composition in the estuary was the seagrass Ruppia, which increased in biomass from 2009 to the late 2010s, as well as the green alga

Chaetomorpha and to a lesser extent the seagrass Zostera, which decreased over the same period (Fig. 14).

2009 2017 2018

Cystoseira Hormophysa Acetabularia Chondria Caulerpa Dictyota Halophila other Rhodo. Ruppia Willeella Laurencia Zostera Ulva Chaetomorpha Spyridia Rhizoclonium Gracilaria Lampro. Hincksia Asparagopsis Cladosiphon Figure 14. Shade plot of the pre-treated macrophyte genera biomass comprising decadal (spring 2009) and seasonal (spring 2017, autumn 2018) sampling occasions. “Other Rhodo.” = other Rhodophyta, “Lampro.” = Lamprothamnium.  - Alismatales,  - Chlorophyta,  - Rhodophyta,  - Phaeophyta,  - Charophyta. The intensity of shading indicates macrophyte biomass on a square-root transformed scale.

3.3. Current seasonal and regional differences

A total of 23 macrophyte genera were recorded throughout the Peel-

Harvey Estuary in spring 2017 and autumn 2018 (Table 11). Slightly more taxa were recorded in autumn than spring (22 and 18 genera, respectively), while the reverse was true for overall mean biomass (186.18 g m−2 in spring and 170.51 g m−2 in autumn). The most diverse macrophyte group was the Rhodophyta with 9 genera, followed by the Chlorophyta (6 genera), Phaeophyta (4 genera) and seagrasses (3 genera). In terms of biomass, however, the seagrass genus Ruppia accounted for 42% of the total recorded in spring and 47% in autumn. The most abundant Chlorophyta was Willeella with 14.2% in spring and 10.9% in autumn.

The Rhodophyta genus with the highest biomass was Chondria (2.9% in autumn), while Hormophysa was the most abundant Phaeophyta genus (3.8% of overall biomass in autumn; Table 11).

Table 11. Macrophyte genera ranked by biomass (Rk), mean biomass (푏̅, dry-weight g m-2), and percentage contribution to the mean total biomass (%) in spring 2017 and autumn 2018. Macrophyte taxa are grouped as follows:  - seagrasses;  - Chlorophyta;  - Rhodophyta;  - Phaeophyta;  - Charophyta. The top five contributors to biomass are emboldened. Spring Autumn

Genus Group Rk 푏̅ % Rk 푏̅ %  Ruppia sp 1 78.11 42.0 1 80.85 46.9  Willeella sp 2 38.90 20.9 2 27.28 15.8  Chaetomorpha sp 3 26.53 14.2 4 18.80 10.9  Halophila sp 4 9.48 5.1 3 21.11 12.2  Lamprothamnium 5 7.96 4.3 17 0.27 0.2 sp  Zostera sp 6 6.38 3.4 7 4.06 2.4  Ulva sp 7 5.00 2.7 13 0.46 0.3  Rhizoclonium sp 8 3.39 1.8 16 0.27 0.2  Spyridia sp 9 3.17 1.7 8 2.98 1.7  Gracilaria sp 10 2.50 1.3 21 0.02 0.0  Ceramium sp 11 1.48 0.8 15 0.33 0.2  Laurencia sp 12 1.37 0.7 18 0.24 0.1  Chondria sp 13 1.28 0.7 6 4.96 2.9  Hincksia sp 14 0.30 0.2 20 0.02 0.0  Dictyota sp 15 0.12 0.1 11 0.84 0.5  Caulerpa sp 16 0.11 0.1 12 0.50 0.3  Amphiora sp 17 0.06 0.0  Jania sp 18 0.04 0.0 10 0.95 0.6  Hormophysa sp 5 6.57 3.8  Cystoseira sp 9 1.42 0.8  Polysiphonia sp 14 0.34 0.2  Acetabularia sp 19 0.08 0.0 Heterosiphonia sp  22 0.00 0.0

Total 푏̅ 186.18 100.0 170.51 100.0 Numbers of genera 18 0 22 0

Total biomass was shown by PERMANOVA to differ significantly only between shallow and deep waters, and not among seasons or regions or any interaction

(Table 12). The mean total biomass in the shallow sites of the Peel-Harvey Estuary over the two seasons was approximately four times larger than in the deep sites

(240 v. 62 g m−2). The number of macrophyte genera recorded differed significantly among regions and depths (Table 13).

Table 12. Degrees of freedom (df), mean squares (MS), pseudo F-ratios, significance levels (P) and components of variation values (COV) for a three-factor season*region*depth PERMANOVA of total macrophyte biomass recorded during spring 2017 and autumn 2018 throughout the estuary. Bold P-values indicate significant differences. df MS Pseudo-F P COV Season (S) 1 0.2998 0.1383 0.724 -0.2053 Region (R) 3 1.7520 0.8083 0.493 -0.1365 Depth (D) 1 38.1670 17.6080 0.001 0.9015 S x R 3 0.2732 0.1261 0.945 -0.4122 S x D 1 2.4485 1.1296 0.282 0.1126 R x D 3 4.3983 2.0291 0.100 0.4473 S x R x D 3 0.3694 0.1704 0.919 -0.5679 Residual 84 2.1676 1.4723

Table 13. Degrees of freedom (df), mean squares (MS), pseudo F-ratios, significance levels (P) and components of variation values (COV) for a three-factor season*region*depth PERMANOVA for macrophyte genera richness recorded during spring 2017 and autumn 2018. Bold P-values indicate significant differences. df MS Pseudo-F P COV Season (S) 1 0.0010 0.0014 0.966 -0.1281 Region (R) 3 3.3184 4.6632 0.005 0.3438 Depth (D) 1 3.3507 4.7087 0.029 0.2468 S x R 3 0.1110 0.1561 0.938 -0.2334 S x D 1 0.4002 0.5624 0.463 -0.1199 R x D 3 1.0638 1.4949 0.212 0.1787 S x R x D 3 0.0618 0.0869 0.965 -0.3433 Residual 84 0.7116 0.8436

The means plot for genera richness per site in each individual region is shown in Fig. 15. Eastern Peel had the highest mean genera richness compared to the other regions. The standard-error bar also indicated significant differences in genera count compared to the northern and southern Harvey regions (Fig. 15).

Number of genera

Harvey north Peel west Peel east Harvey south

Figure 15. Mean number (+/-SE) of macrophyte genera across all sites in the four estuary regions.

The sites in shallow waters in the estuary had a slightly larger genera richness than the sites in the deeper waters (Fig. 16).

Number of genera

deep shallow deep shallow

Figure 16. Mean number (+/- SE) of macrophyte genera across all sites in deep and shallow waters.

PERMANOVA of the macrophyte community composition also showed that region and depth differences were significant, as was the two-way region*depth interaction. The region and depth main effects both had higher

COV values than that of the interaction (Table 14).

Table 14. Degrees of freedom (df), mean squares (MS), pseudo F-ratios, significance levels (P) and components of variation values (COV) for a three-factor season*region*depth PERMANOVA of macrophyte community composition recorded during spring 2017 and autumn 2018. Bold P-values indicate significant differences. df MS Pseudo-F P COV Season (S) 1 4879.7 1.8876 0.074 7.1973 Region (R) 3 11087 4.2886 0.001 19.524 Depth (D) 1 21031 8.1352 0.001 20.406 S x R 3 1137 0.4398 0.995 -11.396 S x D 1 2831.7 1.0954 0.353 3.3364 R x D 3 4534.5 1.7541 0.028 13.221 S x R x D 3 1363.7 0.5275 0.970 -14.8 Residual 84 2585.1 50.844

To further examine the above interaction term, a one-way ANOSIM was used to test for differences among the regions at each depth level. The global test showed an overall significant difference between the regions at both depths, but the differences were small (Global R= 0.148-0.226; Table 16). Additionally, each of the significant pairwise comparisons were small to moderate in extent, with the largest differences occurring between Harvey south and Peel east in the shallows

(R=0.391) and Harvey north and either Peel east or west in the deeper waters

(Table 15).

Table 15. One-way ANOSIM tests of macrophyte community composition in both the deep and shallow waters in each region. HS- Harvey south, HN- Harvey north, PW- Peel west, PE- Peel east. Significant results emboldened. deep shallow Global - R 0.226 0.148 Pair-wise R PW, HS 0.336 0.110

PW, PE 0.028 0.085 PW, HN 0.314 0.086 HS, PE 0.290 0.391 HS, HN 0.039 0.199 PE, HN 0.303 0.222

The metric-MDS plot shown in Fig. 17 reflects the variation around each region*depth combination, with ellipses in which 95% of the bootstrapped averages are expected to fall. This plot showed that there was generally greater variability (i.e. less consistency in community composition) around the deeper regions than the shallow regions, with considerable overlap of the 95% ellipses

(Fig. 17).

Figure 17. mMDS ordination plot constructed from the bootstrap averages of the macrophyte community composition in each region*depth combination. Ellipses around each group average represent the 95% confidence ellipse from 100 bootstrap per group. - Harvey south deep; - Harvey north deep; - Peel west deep; - Peel east deep;  - Harvey south shallow; - Harvey north shallow; - Peel west shallow; - Peel east shallow.

The shade plot for each region*depth combination across both seasons revealed that Chaetomorpha sp. and Ruppia sp. were the most influential genera in the shallow regions of the estuary (Fig. 18). The seagrass Ruppia sp. had the largest biomass in the shallows throughout all regions, with the highest values in the Harvey north. On the other hand, Chaetomorpha was more prevalent in

Harvey south and Peel east. Furthermore, significant biomass of Willeella was restricted to the southern Harvey region. Not surprisingly, there was less overall biomass in the deeper regions of the estuary when compared with the shallow areas. However, the biomass of the seagrasses Halophila and Zostera was relatively uniformly distributed in the estuary throughout all depth levels. Only the deep Harvey south and the shallow Peel east lacked these seagrasses. The red algae Chondria was most abundant in the deeper waters of the Peel Inlet. Overall, the Rhodophyta and Phaeophyta contributed little to the overall total biomass of the system. The greatest diversity of macrophyte genera was found in the Peel west. The cluster analysis and the SIMPROF also showed similar patterns of response between the seagrass genera Halophila and Zostera, both of which differed significantly from the pattern of Ruppia biomass throughout the system

(Fig. 18).

HSd HNd PWd PEd HSs HNs PWs PEs Heterosiphonia Amphiroa Cystoseira Caulerpa Willeella Polysiphonia Jania Zostera Halophila Spyridia Rhizoclonium Lampro. Gracilaria Hincksia Ruppia Chaetomorpha Ulva Ceramium Laurencia Dictyota Chondria Acetabularia Hormophysa Figure 18. Shade plot of the pre-treated macrophyte community biomass from spring 2018 and autumn 2018. Hierarchical cluster analyses ordered the macrophyte genera according to their association within the years. SIMPROF show similarities between groups. The intensity of shading indicates macrophyte biomass on a square-root transformed scale. Lampro.= Lamprothamnium. HSd- Harvey south deep, HNd- Harvey north deep, PWd- Peel west deep, PEd- Peel east deep, HSs- Harvey south shallow, HNs- Harvey north shallow, PWs- Peel west shallow, PEs- Peel east shallow.  - Alismatales,  - Chlorophyta,  - Rhodophyta,  - Phaeophyta,  - Charophyta.

4. Discussion

This is the first study to have examined the changes in the biomass and community composition of macrophytes (i.e. seagrasses and macroalgae) in the

Peel-Harvey Estuary over a time span of four decades, during a period where the estuarine system experienced significant change in environmental conditions arising from anthropogenic eutrophication, climate change and engineered modifications. Furthermore, for the first time, the changes in macrophyte biomass and community composition were statistically linked to concurrent patterns in water quality parameters. Statistical tests and GIS interpolation methods were used to illustrate these changes at different temporal, spatial and taxonomic resolutions.

Overall, there was a shift in the macrophyte community composition from a macroalgal to seagrass dominated system, with a notable decline of macrophyte biomass throughout the estuary over time. These changes in macrophyte community composition were correlated with reductions in the TN concentration in the water column over the examined periods. Furthermore, macrophyte biomass and community composition between the recently sampled spring and autumn seasons (2017/2018) showed no statistically significant differences.

This section will discuss (i) trends and spatial changes in macrophyte biomass and community composition among historical to contemporary periods, and the most influential environmental factors associated with these changes, and (ii) more recent and current seasonal and spatial variability of the most significant macrophyte taxa in the Peel-Harvey Estuary. The findings then will be

discussed in both the local and broader contexts, and the limitations of the study will be highlighted. Finally, recommendations for further research requirements will be given.

4.1. Historical variability and the most influential environmental factors

The results showed that the Peel-Harvey Estuary experienced significant decline in total macrophyte biomass since the start of the macrophyte monitoring in 1978. Mostly, these changes occurred between periods one and two

(1978-1984 and 1985-1990) when the biomass declined by 25-75% in each of the individual regions. This result corresponds with previous findings on trends in macrophyte biomass in this system. For example, Lavery et al. (1991) identified a steep decline in macrophyte biomass in the early 1980s. The authors attributed this to the loss of the previously dominant green alga Willeella brachyclados

(formerly Cladophora montagneana), caused by an extreme weather event and reduced light availability from phytoplankton blooms over this period. However, reduced phosphorus loading from declining rainfall and superphosphate fertilizer usage in the coastal plain catchment occurred at the same time. With some authors identifying phosphorus as limiting factor for Willeella growth in the estuary (Birch 1982; Lavery and McComb 1991; McComb and Humphries 1992), the reduced phosphorous loading from river flows also could have contributed to the observed decline macroalgal biomass. Conversely, Lavery and McComb (1991) also highlighted that estuary sediments store significant amounts of phosphate that can be released into the water column. Therefore, the causes of the disappearance of Willeella in the system in the early 1980s remain unclear.

Nutrient control of macroalgal growth in estuarine ecosystems has been discussed across the globe. For example, the growth of the green algae

Chaetomorpha linum and Ulva lactuca were regularly N-limited during summer periods in an estuary in Denmark (Pedersen and Borum 1996). Lapointe and

O’Connell (1989) found N and P limitations to the growth of the green algae

Cladophora prolifera in Harrington Sound, Bermudas. However, the authors also highlighted the primary limitation by P. Therefore, macroalgal growth can be either N and/or P limited depending on macroalgal taxon, nutrient availability and seasonal environmental factors like light and temperature (Valiela et al.

1997). Furthermore, while nutrient loads from river flows play an important role for macrophyte growth, the release of nutrients from sediment deposits have been identified as cause for macroalgal blooms in many estuaries worldwide

(Liere et al. 1982; Gomez et al. 2011; Boyle et al. Fong 2004; Gao et al. 2013).

Further declines in total macrophyte biomass occurred between the periods 3 and 4 in most of the regions, coinciding with the opening of the

Dawesville Channel in 1994. This biomass decline has previously been attributed to the improved water clarity, changes in salinity and reduced nutrient loads from increased flushing of the estuary, which accompanied the increased connectivity between estuary and ocean (Wilson et al. 1997; Wilson and Paling

1999).

While there was a general trend for declining biomass in the estuary since

1978, the most recent period 5 (2018) revealed a general increase in total biomass throughout the system since 2000. The single year of sampling that represents period 5 precludes any robust conclusions on the extent to which this increase is

likely to be representative of a continuing trend, given the potentially large annual and seasonal fluctuations of macrophyte biomass. Nonetheless, Pedretti et al. (2011) also noted a large increase in biomass between 2000 and their spring

2009 survey, supporting the argument for a trend of increased biomass from 2000 to the current 2017/2018 surveys.

Furthermore, this study has identified seagrasses and Chlorophyta as the main groups driving these biomass changes throughout the estuary. Both groups accounted for approximately 70-95% of the total biomass in the system over the examined periods. However, the decline in biomass did not occur in both groups: while the biomass of Chlorophyta broadly decreased across the five periods, that of seagrass concurrently increased, and most notably during the last period

(2018). The replacement of seagrass by macroalgae as the dominant primary producer in the system in the 1970s, linked to eutrophication, has been noted by some authors as a major feature of the Peel-Harvey Estuary (e.g. McComb and

Humphries 1992). However, this study has shown that the biomass of seagrass remained relatively high in the Harvey Estuary in the years 1978-1984, and subsequently declined. This decline occurred throughout the estuary but was more pronounced in the Harvey, leaving the southern part almost completely depleted of seagrass biomass across most of the periods.

The seagrass decline and subsequent replacement by macrophytes or phytoplankton has been directly linked to eutrophication of this system (Brearley

2005). In addition, the lack of seagrass biomass in the southern Harvey has been connected to lack of water clarity in this area caused by the high sediment load and tannin-stained water of the Harvey River during winter flows. Also, elevated

loadings of phosphorus have periodically stimulated phytoplankton blooms and caused high light attenuation in this part of the estuary (Brearley 2005).

Nutrient and freshwater inflow have been identified as factors causing phytoplankton bloom creating unfavourable light conditions for seagrass growth

(Monbet 1992). Widespread seagrass loss from phytoplankton blooms as a result of eutrophic conditions has been reported world-wide (e.g. Tampa Bay and

Chesapeake Bay, USA; Lake Nakaumi, Japan and Skive Fjord, Denmark)(Greening and Janicki 2006; Hiratsuka et al. 2007; Orth et al. 2010; Carstensen et al. 2013).

Historically, poor conditions for seagrass growth in the Peel-Harvey

Estuary were further exacerbated by water residence times in excess of 30 days prior to the opening of the Cut. These long residence times prevented the dilution of nutrients and improvements of water clarity through tidal or freshwater flushing, especially in the Harvey Estuary (Brearley 2005; Valesini et al. 2019). While water clarity and nutrient flushing were enhanced immediately after the channel opening in 1994, it appeared that these responses were initially insufficient to support seagrass recovery in the area (Wilson and Paling 1999; EPA

2003; Pedretti et al. 2011). However, the most recent surveys in 2017 and 2018 showed that seagrasses had again become established in the shallower parts of the southern Harvey, mainly comprising Ruppia with localised areas of Halophila.

While many studies have documented seagrass decline world-wide, studies on their recovery after measures to improve water clarity and reduce nutrients are less common. Engineered solutions that improved water clarity, such as the Dawesville Channel, are rare but other measures like nutrient

reduction from point and agricultural sources have led to the expansion of seagrass beds in some cases. For example, the reversal of eutrophic conditions in

Tampa Bay, Florida resulted in the expansion of seagrass beds (Greening and

Janicki 2006). Orth et al. (2006) have discussed the spread of seagrass in coastal lagoons after nutrient load reduction. In Mondego Bay, Portugal, seagrass beds expanded significantly over a 17 year period after measures to improve water quality (Cardoso et al. 2010).

However, in the present case, the immediate improvement in water quality initiated by the Dawesville Channel appeared not to be sufficient to allow seagrass recovery in some regions, suggesting that additional subsequent changes in environmental drivers may have caused the recently observed seagrass expansion. Since the early 1970s there has been a marked decrease in mean annual rainfall in the region. For example, for the Mandurah meteorological station the average rainfall from 1883-2013 was 854 mm, whereas in the years from

1975-2013 the mean was at 783 mm, and further declined to 686 mm for 2000-2013

(Fig. 19).

Figure 19. Annual rain fall (mm) for Mandurah meteorological station from 1893-2013. Horizontal lines indicate mean annual rain fall from 1893- 2013 (854mm), 1975- 2013 (783mm) and 2000- 2013 (686mm).

Silberstein et al. (2012) noted that a 16% decline in rainfall across south- western Australia since the 1970s has caused a reduction of stream-flows to reservoirs by 50%. This reduced freshwater discharge from declining rainfall, particularly over the winter period, has likely contributed to improved water clarity due to decreased loading of fine sediments and nutrients, less tannin- stained water, and shorter periods of low salinity conditions in the southern

Harvey. While the current changes in environmental conditions appeared to create more favourable conditions for seagrass growth, they have been attributed to human induced climate change (Hallett et al. 2018; Valesini et al. 2019).

The interpolated maps of the preliminary index of macrophyte condition for the estuary showed a trend towards seagrass domination for most of the regions. Under the assumption that seagrass-dominated communities are of greater value and are indicative of better estuarine health than those dominated

by Chlorophyta (Barbier et al. 2011), the Peel Inlet has shown increasing health since 1995. In particular, while the impacts of the opening of the Cut in 1994 were less pronounced in the two Peel regions, the northern Harvey has become more seagrass dominated. As mentioned previously, these changes can be attributed to improved water clarity and flushing of nutrients. In the most recent period

(2018), most of the estuary appeared to be in good health (i.e. relatively more seagrass-dominated), however, the low index values for some areas in the southern Harvey indicated ongoing environmental conditions impacting on seagrass growth and biomass (Fig. 12). Due to the proximity to the Harvey River it is likely that riverine nutrient and sediment loads continue to create more favourable conditions for green algal growth in this region.

Linking the biotic data to the environmental data showed that TN had the strongest correlation to the macrophyte community composition in the Harvey south and Peel west, with the nMDS plot showing a marked decline in nitrogen in these regions after the opening of the Dawesville Channel. While the TN reduction was substantial and shown to be significantly correlated with the observed changes in community composition in the northern Harvey and western Peel, this was not the case for the southern Harvey and eastern Peel. This suggests that the impacts of the Cut on the selected WQ variables were not as important in driving the macrophyte communities in these more distant regions, where changes in macrophyte community was potentially more influenced by the physicochemical properties of river flows. This aligns with the findings of Wilson et al. (1997), who noted a significant decline in nutrient concentration due to

increased flushing with ocean water after the Channel opening in 1994, but also noted that the areas further from the ocean remained rich in nutrients.

Furthermore, the statistical analysis did not show any correlation between

TP concentrations and changes the community composition. Furthermore, other confounding factors like salinity and water clarity did not show any impact on the overall community composition in the analysis, therefore it can be assumed that nitrogen was the most important limiting factor for the macroalgal growth in the estuary. It appears that more recently the reduced times of high river flows did not provide the required nitrogen pulse for excessive macroalgal blooms.

The importance of nutrient enrichment for macroalgal blooms has been discussed by Duarte (1995) and Valiela et al. (1997). In addition, short-term nutrient pulses result in enhanced rates of uptake and macroalgal growth

(Pedersen and Borum 1997)

Macrophyte metrics are regularly included in estuarine indices worldwide to evaluate ecological status and function and to classify system response to anthropogenic stress (e.g. Foden and Brazier 2007; García et al. 2009). For example, Foden and Brazier (2007) included metric scores of taxonomic composition, shoot density and coverage for their index, yet excluded macroalgal metrics, assuming that bloom-forming macroalgae will always manifest as reduced seagrass density. However, Hessing-Lewis et al. (2015) also highlighted the complex ecological interactions between macroalgae and seagrass and showed that macroalgal blooms were not necessarily correlated with seagrass decline. Therefore, the preliminary index outlined here, based on a simple,

weighed seagrass-green alga ratio, might be a suitable tool to be included in future assessments of ecosystem health.

4.2. Decadal vs seasonal variability and current spatial and temporal patterns

The results of the inter-decadal vs inter-seasonal comparison showed that there was an increase in seagrass biomass from 2009 to 2017/2018, mainly attributed to an increase in the biomass of the seagrass Ruppia sp. Furthermore, the green alga Chaetomorpha declined over the same time period, confirming the trends of a shift towards a more seagrass-dominated system as noted in the previous section of the discussion. This was important as period 5 only contained one year (2018) raising the question on how representative the data were due to the inherent temporal and spatial variability of the macrophyte biomass and composition in the estuary.

The current surveys of the Peel-Harvey Estuary showed no significant seasonal differences in macrophyte total biomass or community composition between the sampled spring 2017 and autumn 2018. In contrast, Wilson and

Paling (1999) noted seasonal differences, with larger biomass in autumn in this system. Yet, other authors documented different seasonal patterns for macroalgae and seagrasses in the nearby Leschenault Estuary (Hillman et al.

2000), with a seasonal peak of the macroalgae in spring whilst the seagrass biomass peaked in autumn. Therefore, it is unclear from the unreplicated seasonal sampling of the current study if the contemporary seasonal survey results represent an atypical response of the macrophyte community to unusually

stable environmental conditions, or reflect ongoing changes in the system under which the biomass is becoming relatively less dynamic over time. While climate change has caused some disturbance to these seasonal patterns, the decreasing trend in river flows and increased water temperatures, in addition to the changes in hydrology brought about by the Dawesville Channel, could be the cause for the less pronounced seasonal differences in biomass and community composition in the estuary in recent years.

In general, macrophyte growth in estuaries has been associated with variations in the physical characteristics of the water column caused by seasonal river flows and fluctuation in temperature, salinity and irradiation, among other variables (e.g. Carruthers et al. 1999; Hasegawa et al. 2007; Lirman et al. 2007; Fox et al. 2008).

There were, however, some significant differences in macrophyte composition between different regions of the estuary in 2017-18, with the greatest difference between southern Harvey and eastern Peel. This difference was mainly driven by the reoccurrence of the green algal genus Willeella. After being absent from surveys over several decades, significant amounts of Willeella were recorded, representing the second most abundant taxon in terms of biomass (>

15% of the total biomass) in the 2017/2018 surveys. Most of this biomass was concentrated in the southern Harvey. Willeella had been the dominant

Chlorophyta species in the estuary until the early 1980s, forming large floating mats, and has been highlighted as one of the key manifestations of the eutrophication of the estuary. It disappeared after a significant storm event and had not been recorded in significant quantities in the estuary until the most

recent surveys (Lavery et al. 1991; Wilson et al. 1999; Pedretti et al. 2011).

Furthermore, no clear causes had been identified for the lack of recovery of

Willeella since the 1980s, despite ongoing high nutrient levels that would support significant growth (Lavery et al. 1991). However, the current reoccurrence close to the river mouths in the Peel Inlet and Harvey Estuary likely indicated ongoing issues with eutrophication caused by high fluvial nutrient loads.

In addition, the seagrass Ruppia accounted for more than 40% of the total biomass in the system in 2017-18, and was distributed relatively evenly throughout the estuary. Ruppia was thus the major driver of the seagrass expansion that occurred in period 5 (2018) into the southern regions of the

Harvey Estuary. Given that seagrass recovery has been linked to ecosystem recovery after removal of anthropogenic eutrophication (Greening and Janicki

2006), the rise of the seagrass Ruppia in the southern Harvey may be indicating that improved water quality is now supporting healthy seagrass populations.

Water clarity and reduced nutrients in the water column have been identified as major drivers for seagrass growth (Carruthers et al. 1999; Orth et al. 2006; Bulmer et al. 2016).

The genus Ruppia is an aquatic angiosperm with large salinity tolerance that can be found in estuaries and hypersaline salt marsh pools and inland lakes across the globe (Brock 1982; Short et al. 2001; Carruthers et al. 2007). Its high phenotypic plasticity define Ruppia as a colonising species. However, colonising species have low physiological resistance to disturbance resulting in large temporal and spatial fluctuations in biomass (Kilminster et al. 2015). This raises the question of whether the currently high Ruppia biomass will remain, especially

in the southern Harvey, where the biomass of Willeella is also high and the preliminary condition index suggests ongoing dominance by macroalgae is likely.

The Environmental Protection Authority (2003) concluded that while the

Dawesville Channel had improved water quality and reduced nutrient load in the system, the rivers remained in eutrophic condition. Overall, the ecological state of the Peel-Harvey system appears to remain in a relatively unstable state, and may lack resilience to further unaccounted disturbance. This casts some doubt on the sustainability of the apparent recovery of seagrasses in the system, as there is potential for future recurrences of the significant macroalgal blooms that were documented from the pre-Cut years.

4.3. Local and broader context of the findings

The Peel-Harvey Estuary has been used as a case study of a successful restoration attempt by altering the estuary hydrology via construction of an artificial channel (e.g. Mazik et al. 2016). The Dawesville Channel aimed to improve nutrient levels in the system through increased flushing. After the opening in 1994 there were instant claims of remediation success as the nutrient levels dropped significantly (Wilson et al. 1997). This may have led to the perception that all future problems had been addressed, subsequently resulting in discontinuation of funding for monitoring of post-Cut change (Environmental

Protection Authority 2003), with lasting effects on the degree and consistency of monitoring in the system (Valesini et al. 2019). However, the comparison of nutrient levels before and after the Cut as the most important indicator for estuary recovery prevented a more holistic approach to monitor ecosystem health and also ignored potential ongoing pressures from climate change (Mazik et al.

2016). A subsequent Water Quality Improvement Plan set up by State and

Commonwealth governments assumed that the reduction of nutrient inputs from the catchment, with the specific aim of improving estuarine water quality, would also automatically improve broader ecosystem health (Environmental Protection

Authority 2008). However, in 2013 a review of the Water Quality Improvement

Plan for the catchment showed that the plan had not been realised as scheduled and highlighted the lack of key performance indicators to measure implementation success (Peel-Harvey Catchment Council 2013). As per 2017 there was still no catchment water quality management plan in place and efforts were now focused in the Peel Integrated Water Initiative (PIWI), which aims to address future water demands of the Peel region, whilst minimising land use impacts on the environment and reducing nutrients entering the estuarine system (Transform Peel 2017).

This study has shown that seagrass recovery occurred in the Peel-Harvey

Estuary following significant hydrological changes created by the Cut.

Furthermore, climate change, and specifically reduced annual and seasonal rainfall, potentially promoted the seagrass recovery. Over the same period there was an observed reduction in macroalgal biomass and blooms. These findings are in contrast to an observed worldwide accelerated trend of seagrass loss and algal blooms in estuarine systems (Waycott et al. 2009). In fact, restoration and recovery of seagrass is rare due to increasing anthropogenic pressure to estuaries worldwide (Morris et al. 2004; O'Brien et al. 2016; Park et al. 2009). There are even less examples of seagrass recovery via natural succession after reversal of eutrophication, and none of these have addressed the potential positive impacts

from an ecoengineering solution and climate change on eutrophication (e.g.

Cardoso et al. 2010; Sherwood et al. 2017; Lundquist et al. 2018). In this context, the study is one of the few world-wide examples that showed long-term trends towards natural recovery of seagrass on a relative large spatial scale despite ongoing anthropogenic pressures on the system.

4.4. Study limitations

There are several study limitations arising from the available historical data and sampling methodology. The historical data showed inconsistencies in the spatial and temporal resolution of their collection. Large areas of deep and shallow waters in the southern Harvey were not sampled over subsequent years from 1978-1984. It is unclear if more regular sampling effort (i.e. yearly) and more sampling sites, especially in the southern Harvey, would have impacted on the data analysis outcomes.

Furthermore, to achieve comparable results the historical sampling method was applied for the current study (Lavery et al. 1991, Pedretti et al. 2011).

However, the method of extractive coring potentially does not capture the true magnitude and distribution of the macrophyte biomass in the system as it depends on the subjective visual assessment of macrophyte cover by the sample collector. The estimated percentage cover dictated the targeted number of cores containing biomass. For example, a visual estimate of 40% cover should have resulted in 2 of the 5 cores at one site containing biomass. It is obvious that this methodology has some inherent sampling bias issues. In addition, while the latitude/longitude positions of the sites were the same over the years, the

determination of the exact location might have been difficult in pre-GPS times and resulted in sampling of an area rather than an exact site over the years.

However, personal observations showed that macrophyte biomass and assemblage can change dramatically over short distances.

Another problem is the lack of data since the discontinuation of the macrophyte sampling program in 2000. It is unclear if the 2009 and 2017-2018 data reflect the current and historical trends (since 2000) of the macrophyte biomass and community composition in the system, or are perhaps anomalous years in some respect.

Finally, the historically observed large accumulations of free floating macroalgal mats and wrack on the shores are not captured with the current coring method (Lavery et al. 1999, Brearley 2005). Therefore, it is unclear how much the calculated total biomass from the sampling actually reflects the total biomass of the system.

4.5. Guidance on future research

With the given study limitations and the results of the presented analyses, several themes for future research arise from this study. Overall, there is a need for the continued monitoring of the macrophyte community in the Peel-Harvey

Estuary. One reason is that any previous surveys operated under the assumption that the Dawesville Channel was the major factor influencing macrophyte dynamics in the system. While the effects of the Channel as a major perturbation have been well examined, ongoing impacts of climate change on the estuary have likely been underestimated so far. Due to the relative lack of temporal data since

2000, it cannot be determined if the current return to a more seagrass dominated system is likely to be persistent, nor can the degree to which the estuary’s macrophyte communities will be resilient to ongoing environmental pressures from progressing climate change.

Furthermore, the historical and current monitoring approach only samples macrophyte biomass and does not allow investigations of macrophyte cover or density. For example, Ruppia growth can be relatively patchy, with high biomass due to the large growth of the plant, whereas an area that is completely covered by Halophila shows significantly less biomass given its low growth form.

As cover is incorporated in many macrophyte quality indices, remote sensing methods (i.e. satellite images, echo-sounder) should be considered for future macrophyte assessments.

Finally, this study showed that it is possible to create a simple macrophyte condition index that has the ability to highlight areas of concerns for recurrent macroalgal dominance in the estuaries. Further work needs to be done to validate and refine this index.

5. Conclusions

Globally, estuaries experience significant pressures from anthropogenic activities that threaten their ecosystem function and integrity. Macrophytes are key ecological components of estuaries and have been used to assess their ecosystem health. Macroalgal blooms have been used as an indicator of eutrophication and a system under stress, whereas seagrasses provide important services to the environment and humans. There have been attempts to restore seagrass habitats worldwide, often with little success due to ongoing stressors.

The current study showed for the first time that the macrophyte community in the Peel-Harvey Estuary changed over the last four decades from macroalgal- to more seagrass dominated. These changes have been attributed to the Dawesville

Cut opening in 1994 and to ongoing climate change, which together have altered the salinity regime and improved water quality by decreasing nutrient levels and improving water clarity. While these changes were quite apparent in the regions close to the Cut, the picture in the southern Harvey and eastern Peel was different. These areas appeared to be less affected by the Cut and remained strongly influenced by the river flows, with eutrophication continuing to be a problem and causing algal blooms and hypoxia (Brearley 2005, Valesini et al.

2019).

However, this study showed also the recent expansion of seagrasses into the southern Harvey, which had been almost completely depleted of seagrasses since the mid 1980’s. This expansion likely reflects the effects of climate change and reduced river flows, which have resulted in less nutrients being flushed into the system, creating more favourable conditions for seagrasses like Ruppia and

Halophila. In some respects therefore, climate change can be thought of as having impacted positively on the estuarine health of particular regions.

However, it is unclear how climate change dynamics, with predicted further declines in rainfall and river flows, ongoing temperature and sea level rise, will impact on the macrophyte community in the system. Furthermore, there might be other, currently unaccounted pressures from accumulated interactions of environmental factors that can lead to subsequent seagrass loss and the return of the previously significant macroalgal blooms.

Ruppia has been identified as the seagrass genus that may benefit most from climate change (Richardson et al. 2018). The dominance of Ruppia as a colonising species and the return of large quantities of the green alga Willeella indicate the current delicate stability of the Peel-Harvey Estuary’s macrophyte communities.

Currently, little is known about the long-term impacts of climate change and the overall dynamics of environmental factors on ecosystems under the current increasing rates of change from accelerating greenhouse gas emissions

(Harley et al. 2012). This study highlights the requirement for continuous monitoring of the macrophyte communities in estuaries to overcome the lack of knowledge. Seagrass and macroalgal responses to changing environmental conditions mark them as key biotic elements that may support more integrative approaches to measuring estuary health and informing the effective management of estuaries world-wide, especially the Peel-Harvey Estuary and its catchment.

6. References Anderson, M., Gorley, M. and Clarke, K. 2008. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. Plymouth: PRIMER-E. Australia, Geoscience. 2004. “Geodata Coast 100K 2004.” Available: https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/61395 [Accessed 10/10/2018]. Barbier, E., Hacker, S., Kennedy, C., Koch, E., Stier, A. and Silliman, B. 2011. The Value of Estuarine and Coastal Ecosystem Services. Ecological Monographs 81 (2): 169–93. Beck, M., Heck, K., Able, K., Childers, D., Eggleston, D., Gillanders, B., Halpern, B. et al. 2001. The Identification, Conservation and Management of Estuarine and Marine Nurseries for Fish and Invertebrates. American Institute of Biological Sciences 51 (8): 633–41. Bell, S., Tewfik, A., Hall, M. and Fonseca, M. 2008. Evaluation of Seagrass Planting and Monitoring Techniques: Implications for Assessing Restoration Success and Habitat Equivalency. Restoration Ecology 16 (3): 407–16. Birch, P. 1982. Phosphorus Export from Coastal Plain Drainage into the Peel- Harvey Estuarine System of Western Australia. Australian Journal of Marine and Freshwater Research 33: 23–32. Borja, A., Basset, A., Bricker, S., Dauvin, J., Elliott, M., Harrison, T., Marques, J., Weisberg, S. and West, R. 2011. Classifying Ecological Quality and Integrity of Estuaries. In Treatise on Estuarine and Coastal Science, 1:125–62. Borja, Á., Dauer, D., Elliott, M. and Simenstad, C. 2010. Medium-and Long-Term Recovery of Estuarine and Coastal Ecosystems: Patterns, Rates and Restoration Effectiveness. Estuaries and Coasts 33 (6): 1249–60. Boyle, K., Kamer, K. and Fong, P. 2004. Spatial and Temporal Patterns in Sediment and Water Column Nutrients in a Eutrophic Southern California Estuary. Estuaries 27 (3): 378–88. Brearley, A. 2005. Ernest Hodgkin’s Swanland : The Estuaries and Coastal Lagoons of South-Western Australia. Perth: University of Western Australia Press. Brock, M. 1982. Biology of the Salinity Tolerant Genus Ruppia L. in Saline Lakes in South Australia I. Morphological Variation within and between Species and Ecophysiology. Aquatic Botany 13: 219–48. Bulmer, R., Kelly, S. and Jeffs, A. 2016. Light Requirements of the Seagrass, Zostera Muelleri, Determined by Observations at the Maximum Depth Limit in a Temperate Estuary, New Zealand. New Zealand Journal of Marine and Freshwater Research 50 (2): 183–94. Burkholder, J., Tomasko, D. and Touchette, B. 2007. Seagrasses and Eutrophication. Journal of Experimental Marine Biology and Ecology 350 (1-2): 46-72.

Busch, K., Golden, R., Parham, T., Karrh, L., Lewandowski, M. and Naylor, M. 2010. Large-Scale Zostera Marina (Eelgrass) Restoration in Chesapeake Bay, Maryland, USA. Part I: A Comparison of Techniques and Associated Costs. Restoration Ecology 18 (4): 490–500. Cardoso, P., Leston, S., Grilo, T., Bordalo, M., Crespo, D., Raffaelli, D. and Pardal, M. 2010. Implications of Nutrient Decline in the Seagrass Ecosystem Success. Marine Pollution Bulletin 60 (4): 601–8. Cardoso, P., Pardal, M., Lillebø, A., Ferreira, S., Raffaelli, D. and Marques, J. 2004. Dynamic Changes in Seagrass Assemblages under Eutrophication and Implications for Recovery. Journal of Experimental Marine Biology and Ecology 302 (2): 233–48. Carruthers, T., Walker, D. and Kendrick, G. 1999. Abundance of Ruppia Megacarpa Mason in a Seasonally Variable Estuary. Estuarine, Coastal and Shelf Science 48 (4): 497–509. Carruthers, T., Dennison, W., Kendrick, G., Waycott, M., Walker, D. and Cambridge, M. 2007. Seagrasses of South-West Australia: A Conceptual Synthesis of the World’s Most Diverse and Extensive Seagrass Meadows. Journal of Experimental Marine Biology and Ecology 350 (1–2): 21–45. Carstensen, J., Krause-Jensen, D., Markager, S., Timmermann, S. and Windolf, J. 2013. Water Clarity and Eelgrass Responses to Nitrogen Reductions in the Eutrophic Skive Fjord, Denmark. Hydrobiologia 704 (1): 293–309. Cho, H., Biber, P. and Nica, C. 2011. The Rise of Ruppia in Seagrass Beds: Changes in Coastal Environment and Research Needs. In Seagrass: Ecology, Uses and Threats, edited by Robert S. Pirog, 209–23. Nova Science Publishers, Inc. Clarke, K., and Gorley, R. 2015. PRIMER v7 : User Manual / Tutorial. Plymouth: PRIMER-E. Clarke, K., Gorley, R., Somerfield, P. and Warwick, R. 2014. Change in Marine Communities: an Approach to Statistical Analysis and Interpretation. Plymouth: PRIMER-E. Clarke, K., and Green, R. 1988. Statistical Design and Analysis for a ‘Biological Effects’ Study. Marine Ecology Progress Series 46: 213–26. Corbett, D. 2010. Resuspension and Estuarine Nutrient Cycling: Insights from the Neuse River Estuary. Biogeosciences 7: 3289–3300. Costanza, R., Arge, R., De Groot, R., Farberk, S., Grasso, M., Hannon, B., Limburg, K. et al. 1997. The Value of the World’s Ecosystem Services and Natural Capital. Nature 387 (5): 253–60. Cui, B., Yang, Q., Yang, Z. and Zhang, Z. 2009. Evaluating the Ecological Performance of Wetland Restoration in the Yellow River Delta, China. Ecological Engineering 35 (7): 1090–1103.

Curiel, D., Rismondo, A., Bellemo, G. and Marzocchi, M. 2004. Macroalgal Biomass and Species Variations in the Lagoon of Venice (Northern Adriatic Sea, Italy): 1981-1998. Scientia Marina 68 (1): 57–67. Dolbeth, M., Cardoso, P., Ferreira, S., Verdelhos, T., Raffaelli, D. and Pardal, M. 2007. Anthropogenic and Natural Disturbance Effects on a Macrobenthic Estuarine Community over a 10-Year Period. Marine Pollution Bulletin 54 (5): 576–85. Duarte, C. 1995. Submerged Aquatic Vegetation in Relation to Different Nutrient Regimes. Ophelia 41 (1): 87–112. Duarte, C., and Kirkman, H. 2001. Methods for the Measurement of Seagrass Abundance and Depth Distribution. In Global Seagrass Research Methods, edited by F. Short and R. Coles, 141–53. Amsterdam: Elsevier Science. Elliott, M., Mazik, K., Mander, L., Wolanski, E., Whitfield, A., Simenstad, C. and Valesini, F. 2016. Ecoengineering with Ecohydrology: Successes and Failures in Estuarine Restoration. Estuarine, Coastal and Shelf Science 176: 12–35. Elliott, M., and Quintino, V. 2007. The Estuarine Quality Paradox, Environmental Homeostasis and the Difficulty of Detecting Anthropogenic Stress in Naturally Stressed Areas. Marine Pollution Bulletin 54 (6): 640–45. Environmental Protection Authority. 2003. Peel Inlet and Harvey Estuary Management Strategy: Progress and Compliance by the Proponents with the Environmental Conditions Set by the Minister for the Environment in 1989, 1991 and 1993. Perth, Western Australia. Environmental Protection Authority. 2008. Water Quality Improvement Plan for the Rivers and Estuary of the Peel-Harvey System - Phosphorus Management. Perth, Western Australia. Foden, J. and Brazier, D. 2007. Angiosperms (Seagrass) within the EU Water Framework Directive: A UK Perspective. Marine Pollution Bulletin 55 (1–6): 181– 95. Fox, S., Stieve, E., Valiela, I., Hauxwell, J. and McClelland, J. 2008. Macrophyte Abundance in Waquoit Bay: Effects of Land-Derived Nitrogen Loads on Seasonal and Multi-Year Biomass Patterns. Estuaries and Coasts 31 (3): 532–41. Fox, S., Valiela, I., McClelland, J., Stieve, E. and Hauxwell, J. 2009. Macrophyte Abundance in Waquoit Bay: Effects of Land-Derived Nitrogen Loads on Seasonal and Multi-Year Biomass Patterns. Estuaries and Coasts 31 (3): 532–41. Frankovich, T. and Fourqurean, J. 1997. Seagrass Epiphyte Loads along a Nutrient Availability Gradient, Florida Bay, USA. Marine Ecology Progress Series 159: 37–50. Fyfe, S. 2003. Spatial and Temporal Variation in Spectral Reflectance: Are Seagrass Species Spectrally Distinct? Limnology and Oceanography 48 (1_part_2): 464–79. Gao, L., Wei, Q., Fu, F. and Shao, H. 2013. Influence of Outbreak of Macroalgal Blooms on Phosphorus Release from the Sediments in Swan Lake Wetland, China. Plant Biosystems 147 (4): 1175–83. 83

García, P., Zapico, E. and Colubi, A. 2009. An Angiosperm Quality Index (AQI) for Cantabrian Estuaries. Ecological Indicators 9 (5): 856–65. Gomez, E., Fillit, M., Ximenes, M. and Picot, B. 2011. Phosphate Mobility at the Sediment—water Interface of a Mediterranean Lagoon (Etang Du Méjean), Seasonal Phosphate Variation. In Oceans, Rivers and Lakes: Energy and Substance Transfers at Interfaces, 203:203–16. Greening, H., Janicki, A., Sherwood, E., Pribble, R. and Johansson, J. 2014. Ecosystem Responses to Long-Term Nutrient Management in an Urban Estuary: Tampa Bay, Florida, USA. Estuarine, Coastal and Shelf Science 151: A1–16. Greening, H. and Janicki, A. 2006. Toward Reversal of Eutrophic Conditions in a Subtropical Estuary: Water Quality and Seagrass Response to Nitrogen Loading Reductions in Tampa Bay, Florida, USA. Environmental Management 38 (2): 163– 78. Greiner, J., McGlathery, K., Gunnell, J. and McKee, B. 2013. Seagrass Restoration Enhances ‘Blue Carbon’ Sequestration in Coastal Waters. PLoS ONE. Gross, C., and Hagy, J. 2017. Attributes of Successful Actions to Restore Lakes and Estuaries Degraded by Nutrient Pollution. Journal of Environmental Management 187: 122–36. Guiry, M., and Guiry, M 2018. AlgaeBase. World-Wide Electronic Publication. National University of Ireland, Galway. Hale, J, and Paling, E. 1999. Water Quality of the Peel-Harvey Estuary: Comparison before and after the Opening of the Dawesville Channel (July 1985 to June 1999). MAFRA 99/4, Murdoch University, Western Australia. Hallett, C. 2014. Quantile-Based Grading Improves the Effectiveness of a Multimetric Index as a Tool for Communicating Estuarine Condition. Ecological Indicators 39: 84–87. Hallett, C., Hobday, A., Tweedley, J., Thompson, P., McMahon, K. and Valesini, F. 2018. Observed and Predicted Impacts of Climate Change on the Estuaries of South-Western Australia, a Mediterranean Climate Region. Regional Environmental Change 18 (5): 1357–73. Han, Q. and Liu, D. 2014. Macroalgae Blooms and Their Effects on Seagrass Ecosystems. Journal of Ocean University of China 13 (5): 791–98. Hardison, A., Canuel, E., Anderson, I. and Veuger, B. 2010. Fate of Macroalgae in Benthic Systems: Carbon and Nitrogen Cycling within the Microbial Community. Marine Ecology Progress Series 414: 41–55. Harley, C., Anderson, K., Demes, K., Jorve, J., Kordas, R., Coyle, T. and Graham, M. 2012. Effects of Climate Change on Global Seaweed Communities. Journal of Phycology 48 (5): 1064–78.

Harris, G. 1999. Comparison of the Biogeochemistry of Lakes and Estuaries: Ecosystem Process, Functional Groups, Hysteresis Effects and Interactions between Macro- and Microbiology. Marine and Freshwater Research 50 (8): 791– 811. Hasegawa, N., Hori, M. and Mukai, H. 2007. Seasonal Shifts in Seagrass Bed Primary Producers in a Cold-Temperate Estuary: Dynamics of Eelgrass Zostera Marina and Associated Epiphytic Algae. Aquatic Botany 86 (4): 337–45. Hauxwell, J., Cebrian, J., Furlong, C. and Valiela, I. 2001. Macroalgal Canopies Contribute to Eelgrass (Zostera Marina) Decline in Temperate Estuarine Ecosystems. Ecology 82 (4): 1007–22. Hessing-Lewis, M., Hacker, S., Menge, B., McConville, S. and Henderson, J. 2015. Are Large Macroalgal Blooms Necessarily Bad? Nutrient Impacts on Seagrass in Upwelling-Influenced Estuaries. Ecological Applications 25 (5): 1330–47. Hillman, K., Mccomb, A., Bastyan, G. and Paling, E. 2000. Macrophyte Abundance and Distribution in Leschenault Inlet, an Estuarine System in South-Western Australia. Journal of the Royal Society of Western Australia 83: 349–55. Hiratsuka, J., Yamamuro, M. and Ishitobi, Y. 2007. Long-Term Change in Water Transparency before and after the Loss of Eelgrass Beds in an Estuarine Lagoon, Lake Nakaumi, Japan. Limnology 8 (1): 53–58. Hodgkin, E. and Hesp, P. 1998. Estuaries to Salt Lakes: Holocene Transformation of the Estuarine Ecosystems of South-Western Australia. Marine & Freshwater Research 49 (3): 183–201. Hossain, M., Bujang, J., Zakaria, M. and Hashim, M. 2015. The Application of Remote Sensing to Seagrass Ecosystems: an Overview and Future Research Prospects. International Journal of Remote Sensing 36 (1):61-114. Humphries, P., and Potter, I. 1993. Relationship between the Habitat and Diet of Three Species of Atherinids and Three Species of Gobies in a Temperate Australian Estuary. Marine Biology: International Journal on Life in Oceans and Coastal Waters 116 (2): 193–204. Humphries, R. and Robinson, S. 1995. Assessment of the Success of the Peel-Harvey Estuary System Management Strategy - a Western Australian Attempt at Integrated Catchment Management. Water Science and Technology 32 (5–6): 255–64. Cohen, J. 2003. Human Population- The Next Half Century. Science 302: 1172–75. Harold, K. 1991. Wigeongrass (Ruppia Maritima): A Literature Review. US Fish and Wildlife, Service Fish and Wildlife Research, 58pp. Kennedy, D. 2012. Tectonic and Geomorphic Evolution of Estuaries and Coasts. In Treatise on Estuarine and Coastal Science, 1:37–59. Kennish, M. 2000. Estuary Restoration and Maintenance: The National Estuary Program. Edited by Michael J Kennish. Boca Raton: CRC Press.

Kennish, M. 2002. Environmental Threats and Environmental Future of Estuaries. Environmental Conservation 29 (01): 78–107. Kilminster, K., McMahon, K., Waycott, M., Kendrick, G., Scanes, P., McKenzie, L., O’Brien, K., et al. 2015. Unravelling Complexity in Seagrass Systems for Management: Australia as a Microcosm. Science of the Total Environment 534 (5): 97–109. Kowalski, J., Deyoe, H. and Allison, T. 2009. Seasonal Production and Biomass of the Seagrass, Halodule Wrightii Aschers. (Shoal Grass), in a Subtropical Texas Lagoon. Estuaries and Coasts 32 (3): 467–82. Lapointe, B., and O’Connell, J. 1989. Nutrient-Enhanced Growth of Cladophora Prolifera in Harrington Sound, Bermuda: Eutrophication of a Confined, Phosphorus-Limited Marine Ecosystem. Estuarine, Coastal and Shelf Science 28 (4): 347–60. Lavery, P., Lukatelich, R. and McComb, A. 1991. Changes in the Biomass and Species Composition of Macroalgae in a Eutrophic Estuary. Estuarine, Coastal and Shelf Science 33 (1): 1–22. Lavery, P., Bootle, S. and Vanderklift, M. 1999. Ecological Effects of Macroalgal Harvesting on Beaches in the Peel-Harvey Estuary, Western Australia. Estuarine, Coastal and Shelf Science 49 (3): 295-309. Legendre, P. and Legendre, L. 2012. Numerical Ecology. 3rd ed. Oxford: Elsevier. Leston, S., Lillebø, A. and Pardal, M. 2008. The Response of Primary Producer Assemblages to Mitigation Measures to Reduce Eutrophication in a Temperate Estuary. Estuarine, Coastal and Shelf Science 77 (4): 688–96. Liere, L., Peters, J., Montijn, A. and Mur, L. 1982. Release of Sediment-Phosphorus and the Influence of Algal Growth on This Process. Hydrobiological Bulletin 16 (2– 3): 191–200. Lirman, D., Deangelo, A., Serafy, J., Hazra, A., Smith, A., Hazra A. and Herlan, J. 2007. Seasonal Changes in the Abundance and Distribution of Submerged Aquatic Vegetation in a Highly Managed Coastal Lagoon. Hydrobiologia 596: 105–20. Longley, P., Goodchild, M., Maguire, D. and Rhind, D. 2005. Geographical Information Systems and Science. 2nd Ed. Chichester; Milton: Wiley. Lotze, H., Lenihan, H., Bourque, B., Bradbury, R., Cooke, R., Kay, M., Kidwell, S., Kirby, M., Peterson, C. and Jackson, J. 2006. Depletion, Degradation, and Recovery Potential of Estuaries and Coastal Seas. Science 312 (5781): 1806–9. Lundquist, C., Jones, T., Parkes, S. and Bulmer, S. 2018. Changes in Benthic Community Structure and Sediment Characteristics after Natural Recolonisation of the Seagrass Zostera Muelleri. Scientific Reports 8 (1): 1–9. Lyons, D., Arvanitidis, C., Blight, A., Chatzinikolaou, E., Guy-Haim, T., Kotta, J., Orav- Kotta, H. et al. 2014. Macroalgal Blooms Alter Community Structure and Primary Productivity in Marine Ecosystems. Global Change Biology 20 (9): 2712–24. 86

Lyons, M., Roelfsema, C., Kovacs, E., Samper-Villarreal, J., Saunders, M., Maxwell, P. and Phinn, S. 2015. Rapid Monitoring of Seagrass Biomass Using a Simple Linear Modelling Approach, in the Field and from Space. Marine Ecology Progress Series 530: 1–14. Mallin, M., McIver, M., Wells, H., Parsons, D. and Johnson, V. 2005. Reversal of Eutrophication Following Sewage Treatment Upgrades in the New River Estuary, North Carolina. Estuaries 28 (5): 750–60. Marbà, N., Krause-Jensen, D., Alcoverro, T., Birk, S., Pedersen, A., Neto, J., Orfanidis, S. et al. 2013. Diversity of European Seagrass Indicators: Patterns within and across Regions. Hydrobiologia 704 (1): 265–78. McComb, A. and Lukatelich, R. 1990. Inter-Relations between Biological and Physicochemical Factors in a Database for a Shallow Estuarine System. Environmental Monitoring and Assessment 14 (2–3): 223–38. McComb, A. and Lukatelich, R. 1995. The Peel-Harvey Estuarine System, Western Australia. In Eutrophic Shallow Estuaries and Lagoons, 5–18. McComb, A., Qiu, S., Lukatelich, R. and McAuliffe, T. 1998. Spatial and Temporal Heterogeinity of Sediment Phosphorus in the Peel-Harvey Estuarine System. Estuarine, Coastal and Shelf Science 47: 561–77. McComb, A., and Humphries, R. 1992. Loss of Nutrients from Catchments and Their Ecological Impacts in the Peel-Harvey Estuarine System, Western Australia. Estuaries 15 (4): 529. McGlathery, K. 2001. Macroalgal Blooms Contribute to the Decline of Seagrass in Nutrient-Enriched Coastal Waters. Journal of Phycology 37 (4): 453–56. Monbet, Y. 1992. Control of Phytoplankton Biomass in Estuaries: A Comparative Analysis of Microtidal and Macrotidal Estuaries. Estuaries 15 (4): 563. Morris, L., and Virnstein, R. 2004. The Demise and Recovery of Seagrass in the Northern Indian River Lagoon, Florida. Estuaries 27 (6): 915–22. Network, Australian Ocean Data. 2014. Peel-Harvey Estuarine System Overview. Available: https://catalogue.aodn.org.au/geonetwork/srv/eng/metadata.show?uuid=30ce56 c0-0d86-11dc-b244-00188b4c0af8 [accessed 10/10/2018]. O’Brien, A., Townsend, K., Hale, R., Sharley, D. and Pettigrove, V. 2016. How Is Ecosystem Health Defined and Measured? A Critical Review of Freshwater and Estuarine Studies. Ecological Indicators 69: 722-29. Orlando-Bonaca, M., Lipej, L. and Orfanidis, S. 2008. Benthic Macrophytes as a Tool for Delineating, Monitoring and Assessing Ecological Status: The Case of Slovenian Coastal Waters. Marine Pollution Bulletin 56 (4): 666–76.

Orth, R., Carruthers, T., Dennison, W., Duarte, C., Fourqurean, K., Heck, K., Hughes, A. et al. 2006. A Global Crisis for Seagrass Ecosystems. Bioscience 56 (12): 987–96. Orth, R., Marion, S., Moore, K. and Wilcox, D. 2010. Eelgrass (Zostera Marina L.) in the Chesapeake Bay Region of Mid-Atlantic Coast of the USA: Challenges in Conservation and Restoration. Estuaries and Coasts 33 (1): 139–50. Park, S., Kim, J., Kang, C., An, S., Chung, I., Kim, J. and Lee, K. 2009. Current Status and Ecological Roles of Zostera Marina after Recovery from Large-Scale Reclamation in the Nakdong River Estuary, Korea. Estuarine, Coastal and Shelf Science 81 (1): 38–48. Richardson, P., Lefcheck, J. and Orth, R. 2018. Warming Temperatures Alter the Relative Abundance and Distribution of Two Co-Occurring Foundational Seagrasses in Chesapeake Bay, USA. Marine Ecology Progress Series 599: 65–74. Pedersen, M, and Borum, J. 1996. Nutrient Control of Algal Growth in Estuarine Waters. Nutrient Limitation and the Importance of Nitrogen Requirements and Nitrogen Storage among Phytoplankton and Species of Macroalgae. Marine Ecology Progress Series 142 (1–3): 261–72. https://doi.org/10.3354/meps142261. Pedersen, M. and Borum, J. 1997. Nutrient Control of Estuarine Macroalgae: Growth Strategy and the Balance between Nitrogen Requirements and Uptake. Marine Ecology Progress Series 161: 155–63. Pedretti, Y., Kobryn, H., Sommerville, E. and Wienczugow, K. 2011. Snapshot Survey of the Distribution and Abundance of Seagrass and Macroalgae in the Peel-Harvey Estuary from November / December 2009. MAFRA 11/1. Murdoch University: Western Australia. Peel-Harvey Catchment Council. 2013. Peel-Harvey Catchment Water Quality Improvement Plan Implementation Review 2008-2011. Report prepared by the Peel-Harvey Catchment Council. Mandurah: Western Australia Perillo, G., and Piccolo, M. 2012. Global Variability in Estuaries and Coastal Settings. In Treatise on Estuarine and Coastal Science, 1:7–36. Elsevier Inc. Ponti, M., Vadrucci, M., Orfanidis, S. and Pinna, M. 2009. Biotic Indices for Ecological Status of Transitional Water Ecosystems. Transitional Waters Bulletin 3 (3): 32–90. Potter, I., Veale, L., Tweedley, J. and Clarke, K. 2016. Decadal Changes in the Ichthyofauna of a Eutrophic Estuary Following a Remedial Engineering Modification and Subsequent Environmental Shifts. Estuarine, Coastal and Shelf Science 181: 345–63. Pyke, C., Adams, M., Wardrop, D., Hershner, C., Wood, R., Secor, D., Sellner, K. et al. 2009. Potential Climate-Change Impacts on the Chesapeake Bay. Estuarine, Coastal and Shelf Science 86 (1): 1–20.

Roca, G., Krause-Jensen, D., Balsby, T., Van Katwijk, M., Marbà, N., Santos, R. et al. 2016. Response of Seagrass Indicators to Shifts in Environmental Stressors: A Global Review and Management Synthesis. Ecological Indicators 63: 310–23. Rudnick, D., Chen, Z., Childers, D. and Fontaine, T. 1999. Phosphorus and Nitrogen Inputs to Florida Bay: The Importance of the Everglades Watershed. Estuaries 22 (2): 398–416. Sabol, B., Melton, R., Chamberlain, R., Doering, P. and Haunert, K. 2002. Evaluation of a Digital Echo Sounder System for Detection of Submersed Aquatic Vegetation. Estuaries 25 (1): 133–41. Sherwood, E., Greening, H., Johansson, J., Kaufman, K. and Raulerson, G. 2017. Tampa Bay (Florida, USA): Documenting Seagrass Recovery since the 1980’s and Reviewing the Benefits. Southeastern Geographer 57 (3): 294–319. Short, F. and Neckles, H. 1999. The Effects of Global Climate Change on Seagrasses. Aquatic Botany 63 (3–4): 169–96. Short, F., Coles, R. and Pergent-Martini, C. 2001. Global Seagrass Distribution. In Global Seagrass Research Methods. Edited by Short, F and Coles, R. Elsevier B.V.: Amsterdam Silberstein, R., Aryal, S., Durrant, J., Pearcey, M., Braccia, M., Charles, S., Boniecka, L. et al. 2012. Climate Change and Runoff in South-Western Australia. Journal of Hydrology 475: 441–55. Statham, P. 2012. Nutrients in Estuaries - An Overview and the Potential Impacts of Climate Change. Science of the Total Environment 434: 213-27. Transform Peel. 2017. Peel Integrated Water Initiative. Available: http://www.peel.wa.gov.au/transformpeel/peel-water-initiative/ [accessed 10/10/2018]. Tweedley, J., Warwick, R. and Potter, I. 2016. The Contrasting Ecology of Temperate Macrotidal and Microtidal Estuaries. Oceanography and Marine Biology: An Annual Review 54: 73–171. Valesini, F., Hallett, C., Hipsey, M., Kilminster, K., Huang, P. and Henning, K. 2019. Peel-Harvey Estuary, Western Australia. In Coasts and Estuaries. The Future. Edited by Wolanski, E., Day, J., Elliott, M. and Ramachandran, R. 103–20. Burlington: Elsevier. Valiela, I., Mcclelland, J., Hauxwell, J., Behr, P., Hersh, D. and Foreman, K. 1997. Macroalgal Blooms in Shallow Estuaries: Controls and Ecophysiological and Ecosystem Consequences. Limnology and Oceanography 42 (5_part_2): 1105–18. Vaudrey, J., Kremer, J., Branco, B. and Short, F. 2010. Eelgrass Recovery after Nutrient Enrichment Reversal. Aquatic Botany 93 (4): 237- 43.

Waycott, M., Duarte, C., Carruthers, T., Orth, O., Dennison, W., Olyarnik, S., Calladine, A. et al. 2009. Accelerating Loss of Seagrasses across the Globe Threatens Coastal Ecosystems. Proceedings of the National Academy of Sciences of the United States of America 106 (30): 12377–81. Whitfield, A, and Elliott, M. 2012. Ecosystem and Biotic Classifications of Estuaries and Coasts. In Treatise on Estuarine and Coastal Science, 1:99–124. Whittaker, R. and Likens, G. 1973. The Biosphere and Man. Human Ecology 1 (4): 257-69. Wilson, C., Hale, J. and Paling, E. 1997. Water Quality of the Peel-Harvey Estuary Comparisons before and after the Opening of the Dawesville Channel (July 1991 to April 1997). MAFRA 97/6. Murdoch University, Western Australia. Wilson, C., Hale, J. and Paling, E. 1999. Macrophyte Abundance, Distribution and Composition of the Peel-Harvey Estuary: Comparison before and after the Opening of the Dawesville Channel (July 1985 to June 1999). MAFRA 99/5. Murdoch University, Western Australia. Wynne, M. 2016. The Proposal of Willeella Brachyclados (Montagne). Notulae Algarum, no. 18. Zhang, X., Zhou, Y., Liu, P., Wang, F., Liu, B., Liu, X., Xu, Q. and Yang, H. 2014. Temporal Pattern in the Bloom-Forming Macroalgae Chaetomorpha Linum and Ulva Pertusa in Seagrass Beds, Swan Lake Lagoon, North China. Marine Pollution Bulletin 89 (1–2): 229–38.

7. Appendices

Appendix 1. Seagrass and macroalgae genera and species found during the 2017/ 2018 surveys

Macrophyte groups Genera / species list Alismatales Halophila sp Lepilaena sp Ruppia megacarpa Zostera muelleri Chlorophyta Acetabularia calyculus Acetabularia peniculus Caulerpa racemosa Caulerpa scalpelliformis Chaetomorpha linum Rhizoclonium sp Ulva intestitinalis Ulva lactuca Ulva paradoxa Willeella brachyclados Rhodophyta Amphiora sp Ceramium sp Chondria fusifolia Cladosiphon filum Gracilaria flagelliformis Heterosiphonia sp Jania sp Laurencia elata Polysiphonia sp Spyridia filamentosa Phaeophyta Cystoseira trinodis Dictyota furcellata Hincksia granulosa Hormophysa triqueta Charophyta Lamprothamnium papulosum

Appendix 2

Assessing submerged aquatic vegetation response

to anthropogenic disturbance in estuaries -

global patterns and current methodologies

Oliver Krumholz

32168181

1. Introduction

Estuaries worldwide are preferred areas for human settlement due to the ecosystem goods and services they support, including the provision of food, water, fertile riparian soils and easy transport routes. However, with global population growth and industrial development since the early 1800s, estuaries have come under increasing pressure from anthropogenic activities such as dredging, removal of fringing vegetation for development, land clearing for agriculture, use of synthetic fertilisers, domestic and industrial effluent, and a general overexploitation of resources. More recently, climate change, including effects such as rising water temperatures and sea level, is causing further change for these ecosystems (Kennish 2002; Brearley 2005). This has led to degradation of estuarine integrity and loss of ecosystem services on a global scale (Kennish

2002; Orth et al. 2006; Gillanders et al. 2011)

A key and highly visible component of estuarine ecosystems is their submerged aquatic vegetation (SAV), comprising seagrasses and macroalgae. Submerged aquatic vegetation delivers important ecological functions such as providing a primary food source, supplying shelter and structured habitat, and acting as nurseries for many fish and invertebrate species and also deliver important ecosystem services like stabilising sediments and flood protection (Orth et al.

2006; Waycott et al. 2009). However, cultural eutrophication (i.e. excessive anthropogenic inputs of nutrients) and other human stressors have altered the dynamics of SAV in many estuaries across the globe (Duarte 1995; Valiela et al.

1997; Orth et al. 2006). For example, the extensive seagrass loss that is typically

associated with unwanted macroalgal blooms provides a clear signal of ecosystem degradation in eutrophic estuaries.

The measurement and assessment of SAV response to anthropogenic disturbance is therefore crucial to providing a key perspective on the health of estuarine ecosystems and inform appropriate management interventions. This review will:

1. provide a brief introduction on the characteristics and importance of estuaries and SAV;

2. discuss anthropogenic activities and the resulting pressures on estuarine ecosystems with focus on SAV;

3. outline the major impacts of human activities on ecosystem function and ecosystem services;

4. discuss different methodologies that have been applied for measuring SAV response to anthropogenic disturbance;

5. highlight the importance of estuarine management measures to restore and maintain ecosystem function and services and show how the collected data support biological indices and modelling for effective environmental management worldwide;

Finally, this review also aims to identify research gaps and problems in assessing

SAV response to human disturbance in estuaries.

2. Estuarine ecosystems

Estuaries represent the transitional zone where fresh water mixes with ocean water. Estuaries are found on all continents except Antarctica, in regions with low relief coastlines (Day et al. 2013). While most ecosystems worldwide are classified according to their biotic attributes estuaries are dominated by their physical characteristics and therefore typically classified according to their ocean connectivity, river flows, geomorphology, and tidal regime (Kennish 2002;

Tweedley et al. 2016; Roy et al. 2001; Valle-Levinson 2012). This widely varying abiotic characteristics have generated an array of definitions of an estuary in the scientific literature (Kennish 2002; Day et al. 2013; Tweedley et al. 2016). For example, Potter et al. (2010)defined estuaries as “a partially enclosed coastal body of water that is either permanently or periodically open to the sea and which receives at least periodic discharge from a river(s), and thus, while its salinity is typically less than that of natural sea water and varies temporally and along its length, it can become hypersaline in regions when evaporative water loss is high and freshwater and tidal inputs are negligible”. Furthermore, the different geomorphologies of estuaries which is determined by various temporal and spatial factor shave been used to classify estuaries. For example, many estuaries were formed during the Holocene approximately 5000 years ago when rising sea levels caused inundation of river-mouths and low lying riverine areas, resulting in the formation of distinct geomorphologies including drowned river-valley (ria), fjord, basin and barrier estuaries (Kennedy 2012; Day et al. 2013). Furthermore, the exposure of estuaries to different tidal ranges can impact on a range of

physical attributes like salinity, water residence time, turbidity and sedimentation (Roy et al. 2001; Kennish 2002; Tweedley et al. 2016). Tidal water movement has been broadly categorised into micro-, meso-, and macrotides.

Within this categorisation microtidal estuaries have a tidal range of less than 2 m, mesotidal estuaries a range of 2-4 m, and those estuaries with a tidal range > 4 m are classified as macrotidal (Davies 1964). Microtidal estuaries tend to experience a stratified water body due to different densities of fresh and salt-water under limited tidal mixing, whereas mesotidal and macrotidal systems tend to be well mixed (Archer 2013; Tweedley et al. 2016).

The continuous change of physical parameters makes estuaries some of the most productive ecosystems in the world resulting in a large variation of biotic communities (Whittaker and Likens 1973; Kennish 2002) . High primary productivity is supported by external nutrient loading from river inflows, rapid nutrient cycling and marked temporal and spatial physical gradients within estuarine systems, resulting in a high diversity of habitats often comprising SAV communities as basis for complex trophic webs (Nixon et al. 1986; Day et al. 2013)

(Nixon et al. 1986; Day et al. 2013). This high productivity makes estuaries very attractive to human exploitation.

Humans and estuaries

Estuaries provide humans with important ecosystem goods and services, which are broadly defined by the Millennium Ecosystem Assessment (MEA) as

“benefits that people obtain from ecosystems” (MEA 2005) . These benefits may be classified as provisioning, regulating, cultural or supporting services (Table 1.).

The seemingly limitless provision of ecosystem services made estuaries preferred 96

locations for human settlement. However, as a result, substantial anthropogenic activities originating from industrial, urban and agricultural development over the last 200 years have impaired the provision of ecosystem services and the ecological integrity of many estuaries worldwide (Lotze et al. 2006; Barbier et al.

2010).

______

Provisioning services

Fisheries

Seaweed and seagrass harvesting

Direct food production

Regulating services

Water quality improvement

River flood mitigation

Coastline protection from storm surges

Carbon sequestering

Habitat for rare and endangered species

Cultural services

Landscape aesthetics

Human recreation

Ecological education

Supporting services

Primary production

Serving as chemical sources, sinks, and transformers ______

Table 1. Estuary ecosystem services modified from Mitsch et al. (2015)

Ecological integrity is the ability of an ecosystem to maintain its stability and organisation over time and to show resilience when exposed to stress (Costanza

1992; Rapport et al. 1998). However, the assessment of anthropogenic impacts on ecosystem health in estuaries is inherently problematic. Due to their typically highly variable salinities, oxygen levels, turbidity and water temperatures and dynamic sediments, these systems are considered naturally highly stressed, resulting in well-adapted and resilient flora and fauna (A Borja et al. 2011). This resilience impairs the detection of anthropogenic stressors as the biota can compensate stress to a certain degree without being adversely affected.

Furthermore, it can be difficult to identify the cause of any change as natural or human induced because of the high natural variability in estuarine communities.

This problem has been called the ‘Estuarine Quality Paradox’ and impairs the ability to detect change in estuarine biota and to establish a ‘natural’ reference condition against which anthropogenic impacts can be quantified (Elliott and

Quintino 2007). To overcome this problem it is has been proposed to identify and explain the natural variability on one hand and to quantify anthropogenic stress on the other within the systems. This requires quantitative and qualitative measurements of abiotic and biotic features on different temporal and spatial scales (Borja et al. 2012).

Main human stressors of estuaries are particularly the overexploitation of resources (e.g. overfishing), urban and agricultural development, land reclamation, hydrological changes like water abstraction and diversion, dredging,

pollution and contamination from point and non-point sources, and introduced species (Lee et al. 2007; Condie et al. 2012; Statham 2012).

There also have been concerns that climate change adds additional stress to estuarine ecosystems. While climate change is a global phenomenon the impacts from it can differ locally (Rabalais et al. 2009). For example, it has been predicted for most of Australia that reduced precipitation and altered rainfall patterns will lead to increased salinity in some of the estuaries. Furthermore, reduced riverine inflow potentially decreases nutrients influx from the catchments into the system leading to more marine conditions with reduced eutrophication as well as decreased sediment transport leading to increased water clarity. This potentially reduces unwanted phytoplankton and macroalgal blooms and increases seagrass biomass in the systems (Gillanders et al. 2011; Lough and Hobday 2011). On the other hand, altered weather patterns on the east coast of the US might result in increase in annual precipitation leading to further eutrophication from erosion and nutrient inflow from adjacent agricultural areas of estuarine systems and subsequent seagrass loss due to increased turbidity (Pyke et al. 2009).

Eutrophication as a result of these anthropogenic activities has been identified as one of the main stressors to estuarine ecosystems leading to decline in estuarine integrity and ecosystem services. For example, the widespread loss of seagrass meadows in estuaries worldwide has been linked to an increase in nutrient availability caused by human activities (Short and Wyllie-Echeverria

1996; Burkholder et al. 2007). Therefore, this review will mainly discuss assessment methods for measuring impacts from anthropogenic eutrophication on SAV.

Submerged aquatic vegetation

One major biotic characteristic of many estuaries is their high primary production by abundant SAV. Moore et al. (2000)included only Charophyta and submerged vascular plants like seagrasses into the SAV group. However, there have been some inconsistencies in the definition in the literature. For example,

Dennison et al. (1993) only included vascular plants into the group, whereas

Irlandi et al. (2004)also included macroalgae into the SAV group. Therefore, for this review SAV comprises seagrasses and macroalgae.

Seagrasses are marine flowering plants that are adapted to complete their lifecycle fully submerged, and occur in estuarine ecosystems worldwide (Hillman et al. 1995; Short et al. 2001; Neri et al. 2004; Bornman et al. 2008; Haag 2010;

Zhang et al. 2014; Bulmer et al. 2016) (Hillman et al 1995; Neri et al. 2004;

Bornman et al. 2008; Haag 2010; Zhang et al. 2014; Bulmer et al. 2016). Although sexual reproduction occurs regularly, vegetative reproduction via growth of rhizomes and clonal shoots is also a strategy to maintain and expand existing meadows and to colonise new habitats (Walker et al. 2001; Cho and May 2008).

Seagrass communities are amongst the most productive ecosystems in the world

(Whittaker and Likens 1973). They fulfil a large variety of ecological functions and deliver a range of ecosystem services. For instance, seagrass meadows in estuaries maintain a large diversity of invertebrate and fish species and provide nursery grounds, shelter and food source (Beck et al. 2001; Sheaves et al. 2014;

Nagelkerken et al. 2015).

All three divisions of algae occur in estuaries. Rhodophyta and Phaeophyceae can grow as large solitary plants but also often occur as epiphytic growth on 100

seagrasses (Hardy et al. 1993; Bárbara and Cremades 1996; Mathieson et al. 2001;

Borowitzka et al. 2005). Chlorophyta on the other hand often occur in large masses of filamentous or mat forming algae in estuaries, where there are often perceived as an indicator of disturbed systems (Valiela et al. 1997). However, macroalgal productivity has also been linked to an increase in invertebrate and fish biomass in estuaries, and they can provide important habitat (Lyons et al.

2012; 2014). Macroalgae also have a significant role in nutrient cycling within estuaries, reducing the impacts of eutrophication by transferring nutrients from the water column to the sediment (Hardison et al. 2010; Lyons et al. 2014).

SAV response to anthropogenic disturbance

As stated previously anthropogenic eutrophication has been identified as major stressor on estuaries and has substantial impacts on the abundance and distribution of seagrass and macroalgae in estuaries. For example, there has been a significant decline in seagrass beds worldwide, whereas the incidence of macroalgal blooms increased at the same time. (Fletcher 1996; Hauxwell and

Valiela 2004; Waycott et al. 2009). These response to eutrophication can be attributed to distinct differences in the physiological features of seagrasses and macroalgae and their responses to disturbance.

Three main factors have been identified as cause for seagrass decline due to eutrophication:

1. Seagrasses are adapted to low nutrient environments and elevated nitrogen in the water column or sediment directly impedes seagrass growth

(Cardoso et al. 2010).

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2. Seagrasses require a high level of water clarity to survive. Eutrophication is often associated with large phytoplankton blooms that attenuate irradiation to a level that no longer sustains seagrasses. Macroalgae on the other hand have photosynthetic advantage as they still can grow at an irradiance as low as 0.1-1%, whereas seagrasses require about 11% incidental light (Duarte 1995; Valiela et al.

1997).

3. Macroalgal growth response is quicker under high nutrients loads in the water column. Free floating macroalgal mats and epiphytic growth then shade the seagrasses, depriving them of irradiation and resulting in a decline of seagrass biomass (Hauxwell et al. 2001a; Brun et al. 2003; Hauxwell and Valiela 2004; Lee et al. 2007).

Furthermore, the macroalgal response to nutrient enrichment, allows rapid increase of algal biomass over a short period of time even in low irradiance conditions outcompeting seagrass. Excessive macroalgal growth is known to impact on the integrity of seagrass beds in estuaries worldwide, resulting in decreases seagrass biomass and shoot density (Hauxwell et al. 2001b; McGlathery

2001). The problems with macroalgal blooms often get public attention when they impact on the recreational value of an estuary, with large floating masses and accumulation of decaying algal biomass on the shoreline. Furthermore, the subsequent bacterial decomposition of the algal mats often results in anoxic conditions with unpleasant odour and significantly impacts on invertebrate and fish fauna (Lavery et al. 1991; Fletcher 1996; Valiela et al. 1997; Lyons et al. 2014).

While macroalgae in nutrient enriched estuaries are predominantly green algal species from a relatively small group of taxa, significant growths of red and brown

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alga have been reported on occasion (Valiela et al. 1997; Pedretti et al. 2011).

Nonetheless, such rapid macroalgal growth can help to reduce nutrient levels in eutrophic systems by increasing the transfer rate of nutrients from the water column into the sediment (Valiela et al. 1997).

Due to the distinct seagrass and macroalgal response to anthropogenic eutrophication SAV has been widely used as a biological quality indicator to assess the ecological status of estuaries and identify and manage anthropogenic stressors. To minimize negative anthropogenic impacts on the ecological integrity of estuarine ecosystems, effective environmental management measures are required, which should be setup as feedback loops that continuously assess environmental condition as part of an adaptive management framework (Hallett et al. 2016). This requires qualitative and quantitative measurements of biotic components.

3. SAV assessment methodologies

Human impacts on SAV in estuaries have been assessed qualitatively for many years. These methods include comparison of taxonomic richness and diversity.

Most commonly, assessments are concerned with documenting a shift from a seagrass to an algal dominated ecosystem, which has been perceived as sign of eutrophication and linked to general decline of ecosystem quality (Valiela et al.

1997). For example case studies from the Mondego estuary (Portugal) and the

Canary Islands have shown a taxonomic shift from seagrass to macroalgae in response to high nutrient loads, impacting on ecosystem function and structure

(Cardoso et al. 2004; Tuya et al. 2014). Reviews of reports of macroalgal blooms 103

across the globe that impacted on seagrass beds by Valiela et al. (1997) and Han and Liu (2014) showed that chlorophytes from the genera Cladophora spp., Ulva spp., Chaetomorpha spp. and Enteromorpha spp. most commonly dominate these taxonomic shifts.

Furthermore, disturbance can also induce taxonomic shifts within seagrass communities. In Mississippi and Florida, the seagrass species Ruppia maritima expanded into areas previously occupied by Zostera spp. and Thalassia spp. following extreme weather events and salinity fluctuations (Hyun Jung et al.

2011).

Similarly, macroalgal composition and diversity are also affected by anthropogenic activities. For example, taxonomic diversity changed when the brown algae Fucus spp. disappeared and the green algae Ulva spp. became dominant after eutrophication on the west coast of Sweden (Baden et al. 1990).

Furthermore, larger canopy-forming algae can be replaced by smaller, less complex turf- or mat-forming species as a result of nutrient enrichment and urbanisation. These algae cover the substrate, preventing recolonization by canopy-forming algae resulting in alternative stable states and habitat shifts impacting on benthic fauna and fish communities (Strain et al. 2014; Orlando-

Bonaca and Rotter 2018). While changes in communities have been linked with anthropogenic eutrophication, the identification of the causes for these changes based on qualitative methods only can be challenging as the natural ecosystem dynamics in estuaries potentially allows an inherent range of stable states

(Christia et al. 2018). Therefore, the combination of qualitative and quantitative

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SAV assessment methods can provide additional information on potential anthropogenic drivers of change.

Quantitative methods like biomass, morphological features like leaf length or plant and shoot density have been used to quantify anthropogenic and natural impacts on estuarine systems (e.g. Lavery and McComb 1991; Hauxwell et al. 2001;

Lee et al. 2004). Quantitative methods allow to identify and measure change on a temporal and spatial scale. Numerous SAV assessment methods have been developed and applied over the years to monitor and assess estuarine ecosystem health and response to disturbance.

Direct gravimetric biomass measurements of dry weight or wet weight results in the removal of plants from the examined sites with coring devices or manually from quadrats and are used for temporal and spatial assessment of changes in

SAV biomass (Hemminga 1998; Pedretti et al. 2011). Biomass measurement of seagrass often includes the separation of below-ground and above-ground biomass as the shoots, leaves and the roots can respond differently to changing environmental conditions. Hemminga (1998) reports that the ratio between above- and below-ground biomass is highly variable amongst seagrasses. The ratio has been used as an indicator of seagrass health as dense below-ground biomass allows high rates of nutrient uptake and provides a carbohydrate reserve

(Olesen and Sand-Jensen 1993). However, a ratio below two impacts on the photosynthesis-to-respiration ratio, as the respiration demands of the below- ground biomass reduces net photosynthetic rates and therefore net biomass production (Hemminga 1998). An overall decrease in below-ground biomass (and an accompanying change in the above- to below-ground biomass ratio) has been

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observed in hypoxic sediments with high sulphide levels. In the same experiment, seagrass leaf growth was reduced in sediments with high sulphide levels due to decreased photosynthesis/respiration ratios (Holmer and Bondgaard 2001).

Although in situ sampling methods like coring and collecting from quadrats provide solid data they have the disadvantage that they are time consuming and expensive due to the time required for sampling in the field and the subsequent processing in the laboratory (Long et al. 1994; Lyons et al. 2015) .

Together with biomass production, the measurement of seagrass morphology, including shoot density, leaf length, width and thickness, has been used to assess anthropogenic impacts on seagrasses. For example, light availability impacts on the distribution of the photosynthetic pigments in the plants. Reduced incidental light from increased turbidity can lead to morphological changes like elongated leafs and stems in some seagrass species (Vermaat et al. 1997; Holmer and

Bondgaard 2001).

These examples illustrate the importance of measuring key metrics of SAV biomass and morphology to monitor and identify responses to anthropogenic activities. Furthermore, environmental measurements like salinity or temperature at the same time of the sampling allow to examine the impacts of changing environmental factors on seagrass and macroalgae. SAV response to anthropogenic disturbance occurs on different temporal and spatial scales. The variability in scales is adding complexity that needs to be considered and requires furthermore the integration of SAV metrics, environmental conditions and quantification of the anthropogenic disturbance in the assessment and management of estuarine SAV (O’Brien et al. 2017).

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Quantitative methods also include the measurement of nutrients, heavy metals and organic compounds in seagrass and macroalgal tissues, that allow to identify specific anthropogenic pressures on estuarine systems and its primary producers.

The quantification of nutrient concentration in SAV can deliver important information on nutrient status in estuarine systems. For example, SAV tissue nutrient level have been used to assess the level of eutrophication in estuarine systems and measure the nutrient differences influenced by changing hydrodynamics throughout the seasons (Kinney and Roman 1998). Macroalgae and seagrass will accumulate these nutrients in their tissues linked to the nutrient status on a large temporal and spatial scale in the surrounding environment. The physiological variation of the proportion of the nutrients

(C:N:P) in the plant tissue has been used as an indicator for anthropogenic nutrient enrichment (Touchette and Burkholder 2000). Duarte (1990) calculated the median C:N:P ratio with 474:21:1 for seagrass from 27 species at 30 locations worldwide. Macroalgae however, tend to have a lower C:N ratio highlighting their ability to store nutrients more effectively (Valiela et al. 1997). Decrease in the C:N proportion in seagrass leaves has been linked with increase in nutrient inflow into the system and has been considered a more reliable indicator of nutrient enrichment than the direct measurement of nutrients in the water column alone

(Touchette and Burkholder 2000). While eutrophication is mostly the result of nutrient influx from allochthonous sources, nutrient recycling from autochthonous detrital material and sediments is another nutrient supply path to aquatic vegetation in estuaries. Any increase in nutrient recycling rates can also lead to an increase in seagrass tissues nutrients (Fourqurean et al. 1997).

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To overcome problems with identifying internal and external sources of nutrients flow into estuarine systems quantification of the naturally occurring stable isotopes δ15N and δ13C in plants has been used to identify sources of N and C.

There is a variation in the tissue concentrations of these isotopes depending on the nutrient source and ecosystem processes. For example, low δ15N indicate externally sourced nitrogen from manure and chemical fertilizers (Bateman and

Kelly 2007). A recent study of four estuaries in Canada by Hitchcock et al. (2017) observed that the single parameter leaf tissue %N does not necessarily correlate with seagrass exposure to anthropogenic nitrogen in the estuaries. The study could not link an elevation in seagrass tissue nitrogen with eutrophication, but tissue %N content was strongly correlated with the salinity and temperature gradients in the system. In contrast, the δ15N leaf tissue signature appeared to deliver a more accurate representation of nitrate enrichment in these systems, and the δ15N values in macroalgae were deemed to be a reliable indicator for wastewater inflow and DIN concentrations in the water column (Cole et al.

2004).

Heavy metals in estuaries have their origin mainly from industrial effluents, urban runoff and sewage. Resuspension of sediments following dredging also increases heavy metal contamination in some systems (Ralph et al. 2006). Heavy metals accumulate in SAV following their uptake from the water column through leaves and from sediment pore-water via roots in seagrasses. Due to their lack of roots, macroalgae take up heavy metals from the surrounding water through their thalli. However, macroalgal heavy metal concentrations can also correlate with the level of contamination in the sediment due to re-mobilisation. Heavy

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metal concentration in SAV tissues shows seasonal patterns that reflect vegetative activity and growth; therefore any effective monitoring program typically requires a series of measurements over time (Villares et al. 2001; Ralph et al. 2006; Bischof and Rautenberger 2012).

Anthropogenic activities lead to the release of a large array of organic compounds into estuarine systems, including herbicides, antifouling agents, hydrocarbons and poly-aromatic hydrocarbons. High concentrations of these compounds have direct phytotoxic effects in high concentrations, but also accumulate in plant tissues from relatively low levels in the water-column and sediments. For example, seagrasses can accumulate herbicides at up to 25,000 times the background level (Ralph et al. 2006). Macroalgae also accumulate organic compounds like poly-aromatic hydrocarbons in their tissue. These compounds are transferred through the food chain to higher trophic levels via grazing (Ralph et al. 2006; Roberts et al. 2008). Overall, SAV are suitable bio monitoring tools for quantifying exposure to nutrients, contaminant inflow like heavy metals and organic compounds to estuaries that eventually impact on ecosystem health and services. However, assessment of exposure to anthropogenic stressors are only point measurements within small spatial scales that have an inherent limit due to time and money restraints. Therefore, for more accurate results on larger spatial and temporal scales remote assessment methods have been combined with qualitative and quantitative assessment methods.

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4. Remote assessment methods

Remote sensing concerns any measurements where there is no direct contact with the measurement subject, and may include passive systems like aerial photography and submerged cameras or active methods like sonar, radar or laser devices. Remote sensing combined with Geographic Information Systems (GIS) can be used to map the spatial and temporal distribution of SAV in estuaries

(Sabol et al. 2002; Dekker et al. 2006). Natural and anthropogenic disturbance plays an important role in the temporal and spatial dynamics of SAV distribution in estuaries; therefore effective mapping methods are crucial for monitoring and managing human impacts (Gullström et al. 2006).

Multispectral and hyperspectral airborne and satellite sensors have been used to image, discriminate and map the distribution of SAV in estuarine systems

(Dekker et al. 2006). Remote sensing of vegetation is based on the principle that plant pigments absorb and reflect distinct wavelengths in the visible and near infrared range of electromagnetic spectrum. These spectral properties are species specific and can be used to discriminate between different types of SAV (Silva et al. 2008). However, there are some inherent problems in using optical methods to measure distribution of SAV. Optical sensors measure the reflectance of light from submerged vegetation. Path of light through the water column is affected by the water depth, colour and turbidity, depth. Substrate properties that does not provide sufficient contrast to SAV can also affect the measurement (Green et al.

2000). While optical remote sensing is limited in the often shallow but turbid waters of many estuaries, the combination of in situ and remote sensing

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measurements have been successfully applied for evaluation of benthic features

(Liu et al. 2003). For example, hyperspectral images have been used to determine seagrass composition, percentage cover and biomass in Moreton Bay, Australia

(Phinn et al. 2008). Dekker et al. (2006) also showed that archived data derived from satellite images dating back to 1984 had sufficient resolution to identify changes in SAV communities over a 14 year period. Furthermore, calibrated aerial images including in situ irradiation measurement of substrate and SAV have also been used to map and monitor macroalgal blooms in estuaries in New Zealand and California (Nezlin et al. 2007; Lirman et al. 2008). In addition, underwater video imaging has been used where reduced water clarity precludes the use of aerial and satellite images for identifying and quantifying the distribution of SAV

(Kendall et al. 2005).

In turbid estuarine waters with limited light penetration acoustic systems have been used to assess and map the distribution of SAV. The density gradient between the SAV and the surrounding water and sediment produces a difference in the reflected acoustic signal that allows the discrimination of benthic features.

Furthermore, the difference in reflectance within SAV that results from gas within the tissue potentially allows the discrimination of different vegetation groups (Klemas 2014). Three main types of acoustic sensor have been used for assessment of benthic features including macroalgae and seagrass, i.e. single beam sounder, multi beam sounder and side scan sonar. For example, digital echo sounder systems have been used to detect submerged aquatic vegetation and to discriminate between non-vegetated areas in an estuary in Florida (Sabol et al. 2002). Furthermore, in the Indian River Lagoon, Florida, a single beam

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acoustic system employing two different frequencies was able to distinguish between seagrass, sparse and dense algal cover (Riegl et al. 2005).

Remote sensing is an important tool for monitoring and assessing changes in SAV on different temporal and spatial scales, and offers particular advantages for assessment over large areas as the acquisition of data can be quicker and more cost effective compared to comprehensive field surveys Furthermore, the combination satellite and airborne sensors with acoustic instruments has the ability to increase the accuracy of the measurements and enhance environmental management systems (Reshitnyk et al. 2014). This however, might be difficult to achieve due financial constraints experienced by researchers and environmental managers.

GIS mapping methods allow to determine the extent of the SAV distribution in a system. Furthermore, GIS methods allow to predict and map the potential habitat distribution for species within its ecological range. SAV metrics like biomass, density and occurrence data have been used to create maps of the actual distribution in estuarine systems. For example, Pedretti et al. (2011) created a map using GIS methods with the standing distribution of seagrass and macroalgae in the Peel-Harvey Estuary in south-western Australia based on biomass sampling at approximately 50 sites and spatial interpolation of the data across the whole system. Furthermore, species distribution modelling examines the relationship of organisms with the environment based on the theory that environmental factors mostly determine the distribution of species within their potential ecological niche (Guisan and Zimmermann 2000). Quantitative methods play an important role to identify environmental gradients that potentially impact on the

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distribution of the species, furthermore the collected metrics are important to validate the species distribution output. For example, habitat distribution modelling has been used to predict suitable habitats for seagrass and macroalgae in coastal areas and estuaries based on environmental factors like salinity, depth, temperature, wave exposure, sediment properties and topography (Van Der

Heide et al. 2009; Santos and Lirman 2012; Yesson et al. 2015). Furthermore, GIS methods have been used to predict seagrass habitat suitability following sealevel rise caused by climate change (Davis et al. 2016).

5. SAV indices and modelling

Qualitative and quantitative data on SAV combined with GIS methods allow to assess estuarine ecosystem integrity and services. However, to compare ecological quality of a system or between systems over relevant temporal and spatial scales the concept of biological indices has been developed. Increasingly, assessments of biological quality utilise biological indices that integrate measures of different biological ecosystem components (e.g. species and/or community level SAV metrics) together with information on relevant physico-chemical and pollution stressors (Borja et al. 2008; Martínez-Crego et al. 2010). The aim of these indices is to provide information on the ecological state and allow comparison of environmental condition on different spatial and temporal scales, such that management response is triggered if certain parameters are exceeded. These indices commonly utilise multi-metric methods, which combine measures such as species richness and abundance, cover, biomass, vegetation morphology

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nutrient content that ideally respond to anthropogenic disturbance in a known manner (Borja et al. 2012, 2013; Marbà et al. 2013).

Biological indices are often imbedded in relevant environmental legislation (Borja et al. 2008). In the USA numerous acts address the quality of water bodies, for which the federal Environmental Protection Agency (EPA) has the primary responsibility of monitoring and assessing water quality. For example, the US

EPA sets up programs that monitors the condition of coastal waters including indices of ecological condition and human use of estuaries. The European Union

Water Framework Directive (WFD) also addresses the need for integrative management of European coastal and estuarine ecosystems. Numerous indices have been developed to address the standards set by the WFD and enable the monitoring of ecological status. On the other hand, in Australia relevant legislation concerning estuaries lies predominately with the States and

Territories, resulting in different approaches to measuring and managing anthropogenic activities. Because of this there are no standardised sets of environmental indicators and indices across Australia that have been applied to assess the health of estuarine ecosystems (Borja et al. 2008; Sfriso et al. 2009;

Hallett et al. 2016).

Due to the different strategies and policies that have been applied to monitor water quality internationally, and the complexity and variability of estuarine ecosystems, numerous types of biotic indices have been developed. For example,

Martínez-Crego et al. (2010) reviewed 90 biotic indices for marine and estuarine habitats and structured them into four main types based on

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1. structural and/or functional attributes of sentinel species

2. structural attributes at the community level

3. functional attributes at the community level

4. information gathered from different communities, resulting in integrated indices.

Despite the large array of different indices reviewed, less than 8% of the reviewed papers had SAV as target communities in the first three categories. On the other hand, more than 50% of the integrated indices contained measures of

SAV. An example of an index based on the structural attributes of SAV communities is the rapid-macrophyte quality index (R-MaQI) applied in the

Venice and neighbouring lagoons in Italy. The key feature here is a dichotomous key that allows rapid assessment of the ecological status of estuarine systems.

This key includes presence-absence of seagrass and macroalgae as well as biomass and environmental features like turbidity and sediment grain size (Sfriso et al. 2009). In the USA, Jordan and Vaas (2000) presented an integrated index for

Chesapeake Bay that includes a measure of SAV coverage and water quality indices, in addition to plankton, fish and benthic macroinvertebrate metrics.

Integrated indices measure and include response of important biotic and abiotic ecosystem components to disturbance. This holistic approach potentially allows to draw links to causative factors of ecosystem degradation. However, there are some problems that can impair the validity of an index, like not having appropriate key ecosystem components included, not using appropriate temporal and lack of defined reference condition of a system (Martínez-Crego et al. 2010)

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While indices deliver information on deterioration or improvement of estuarine ecosystems, ecological models can enable predictions of future changes in estuarine health. Models are simplified depictions of the major processes and factors that drive ecological response, which are used to simulate responses to different disturbances. Simulation of aquatic ecosystem response requires the integration of a large array of components that include hydrological, hydrodynamic, biochemical and ecological processes. Furthermore, the dynamics and heterogeneity of aquatic ecosystems, and especially estuaries, necessitates the inclusion of appropriate temporal and spatial scales to achieve a model resolution that addresses the particular question (Hipsey and Hamilton 2015).

Key biotic elements such as SAV are an important part of modelling ecosystem response in estuaries due to the empirical relationship between environmental factors like nutrients or light and the growth of primary producers. These links allow to understand primary producer response and to predict future response to nutrient enrichment (Li et al. 2010). Furthermore, biotic data sets that include

SAV metrics from environmental observations play an important role in the calibration and validation of ecosystem models (Hipsey et al. 2015).

SAV metrics included in indices and models are important for measurement of ecological integrity that is required for adaptive management of ecosystems It has been proposed to integrate adaptive environmental management strategies for estuaries into conceptual frameworks that utilises a feedback loop system aiming to identify causal relationship between ecological drivers, pressure, state and response and anthropogenic impacts (Borja and

Dauer 2008). While these frameworks have been widely accepted as strategical

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concepts to promote conservation and sustainable use of estuarine ecosystems the individual strategy can differ quite substantially on national and regional level. This may be potentially due to the existing differences in national legislative frameworks for estuarine protection and management that eventually determines the use of biotic metrics and how they are included into relevant indices (Borja et al. 2008).

6. Discussion of potential future research

This review showed that qualitative and quantitative measurements of SAV have been effective assessment tools of anthropogenic disturbance for a long time.

More recently complementary methods like remote sensing and GIS became more significant due to advances in technology. The addition of these methods allows more accurate spatial and temporal assessment of SAV response to anthropogenic disturbance. While each method like satellite images or acoustic sounders have their limitations in the often shallow turbid waters of estuaries the combination of different techniques showed promising results (Reshinyk et al.

2014). This important area should be subject to further research.

Martínez-Crego et al. (2010) showed that only a small proportion of biological indices utilise SAV metrics sufficiently. Faunal indices appear to be more significant to most of the researchers despite that fact that primary production from SAV often determines the ecological value of an estuarine ecosystem. More research is needed on the significance of SAV indices and their value for ecological management systems.

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Furthermore, the review of the different methodologies that are applied worldwide to assess SAV response to anthropogenic stressors reveals that while there is a large array of established qualitative and qualitative measurement methods the use of the collected data differ quite significantly. For example the

European WFD triggered a large amount of indices that use SAV metrics for estuarine ecosystem integrity assessment and evaluation, the same in the US where indices based on EPA regulations have been developed. On the other hand, there is lack of prescriptive regulation on estuarine water and environmental quality in Australia on federal and state level. This prevented until today the establishment of biological indices that allow the evaluation between estuarine systems and bioregions. For, example in Western Australia the Swan-

Canning Estuary is subject to its own legislation with the Swan Canning Rivers

Management Act 2006, whereas the management of other estuaries in the state lies with numerous acts and management agencies preventing consistent monitoring programs (Hallett et al. 2016). This shows the requirement for further research and concerted efforts on integrated biological indices that are applicable on large spatial scales. Comparable results across geographic regions potentially increases pressure on decision makers to establish adequate homogenous legislation concerning estuarine quality.

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7. Conclusion

Coastal ecosystems like estuaries are facing increasing anthropogenic pressure. This has led to widespread ecosystem degradation and loss of ecosystem services. This review shows that SAV respond to these anthropogenic pressures and that qualitative and quantitative assessment methods allow the measurement the spatial and temporal extend of disturbance within estuarine systems. These methods allow to quantify rate and scale of change to estuarine ecosystems caused by anthropogenic activities like habitat destruction and degradation and loss of ecosystem services. Furthermore, improved methods like remote sensing and GIS round up qualitative and quantitative measurement of

SAV in estuarine ecosystems and enables to communicate data better and deal with the increasing complexity of environmental pressures arising from climate change. This review also showed while the problems with deteriorating estuarine ecosystem appear to be similar on a global scale SAV metrics and methods have been utilised inconsistently leading to a large range of indices. This is potentially due to the different legislative frameworks within each jurisdiction that prescribes more or less comprehensive monitoring and management measures on different administrative levels from local to federal governments. For example, while the European WFD led to water quality monitoring programs for estuaries including indices for SAV metrics, the lack of legislative guidance in Australia resulted in paucity of SAV indices and the inconsistent use of monitoring and management systems across the country. Furthermore, the lack of appropriate legislation for the protection of estuaries appears to prevent ongoing funding of

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monitoring and management programs in Australia. This is concerning as changes to ecosystem function are likely to accelerate in the future due to increased influence of climate change. However, the impacts like altered flow regimes, sea level and temperature rise appear to be not very conspicuous to many decision makers and managers, because they occur in “slow-motion” and might be not imminently on the “screen”. Also, due to the short life cycles in planning and politics where only the pressing problems often lead to project related funding and attention for the lifespan of the project, sustainable holistic solutions to problems are prevented with this approach. Furthermore, with the limited funding for environmental management and monitoring programs in state and federal budgets and the expected more widespread acceleration of estuarine degradation it will become increasingly difficult to allocate sufficient project based financial resources without the relevant legislation in place. To secure continuous funding in the future that allows ongoing monitoring and management programs and to develop comparable biological indices for estuarine health it is important to progress on environmental legislation that protect estuaries in Australia.

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References

Archer, Allen W. 2013. “World’s Highest Tides: Hypertidal Coastal Systems in North America, South America and Europe.” Sedimentary Geology 284–285: 1– 25. https://doi.org/10.1016/j.sedgeo.2012.12.007. Baden, Susanne, Lars-Ove Loo, Leif Pihl, and Rutger Rosenberg. 1990. “Effects of Eutrophication on Benthic Communities Including Fish : Swedish West Coast.” Ambio 13 (3): 113–22. Bárbara, I., and J. Cremades. 1996. “Seaweeds of the Ría de a Coruña (NW Iberian Peninsula, Spain).” Botanica Marina 39 (4): 371–88. Barbier, Edward B, Sally D Hacker, Chris Kennedy, Evamaria W Koch, Adrian C Stier, and Brian R Silliman. 2010. “The Value of Estuarine and Coastal Ecosystem Services.” Ecological Monographs 81 (2): 169–93. https://doi.org/10.1890/10- 1510.1. Bateman, Alison S., and Simon D. Kelly. 2007. “Fertilizer Nitrogen Isotope Signatures.” Isotopes in Environmental and Health Studies 43 (3): 237–47. https://doi.org/10.1080/10256010701550732. Beck, M W, K L Heck, K W Able, D L Childers, D B Eggleston, B M Gillanders, B Halpern, et al. 2001. “The Identification, Conservation and Management of Estuarine and Marine Nurseries for Fish and Invertebrates.” American Institute of Biological Sciences 51 (8): 633–41. Bischof, Kai, and Ralf Rautenberger. 2012. “Seaweed Responses to Environmental Stress: Reactive Oxygen and Antioxidative Strategies.” In Seaweed Biology: Novel Insights into Ecophysiology, Ecology and Utilization, edited by Christian Wiencke and Kai Bischof, 109–32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28451-9. Borja, A, A. Basset, S. Bricker, J. C. Dauvin, M. Elliott, T. Harrison, J. C. Marques, S. B. Weisberg, and R. West. 2011. “Classifying Ecological Quality and Integrity of Estuaries.” In Treatise on Estuarine and Coastal Science, 1:125–62. https://doi.org/10.1016/B978-0-12-374711-2.00109-1. Borja, Angel, Suzanne B. Bricker, Daniel M. Dauer, Nicolette T. Demetriades, João G. Ferreira, Anthony T. Forbes, Pat Hutchings, et al. 2008. “Overview of Integrative Tools and Methods in Assessing Ecological Integrity in Estuarine and Coastal Systems Worldwide.” Marine Pollution Bulletin 56 (9): 1519–37. https://doi.org/10.1016/j.marpolbul.2008.07.005. Bornman, T. G., J. B. Adams, and G. C. Bate. 2008. “Environmental Factors Controlling the Vegetation Zonation Patterns and Distribution of Vegetation Types in the Olifants Estuary, South Africa.” South African Journal of Botany 74 (4): 685–95. https://doi.org/10.1016/j.sajb.2008.05.002. Borowitzka, M.A. Michael A, Paul P.S. Lavery, and Mike van Keulen. 2005. “Epiphytes of Seagrasses.” In Seagrasses: Biology, Ecology and Conservation, 1– 21. https://doi.org/10.1007/978-1-4020-2983-7_19. Brearley, Anne. 2005. Ernest Hodgkin’s Swanland : The Estuaries and Coastal Lagoons of South-Western Australia. University of Western Australia Press. Bulmer, R. H., S. Kelly, and A. G. Jeffs. 2016. “Light Requirements of the Seagrass, Zostera Muelleri, Determined by Observations at the Maximum Depth Limit in a Temperate Estuary, New Zealand.” New Zealand Journal of Marine and Freshwater Research 50 (2): 183–94. https://doi.org/10.1080/00288330.2015.1120759.

121

Burkholder, JoAnn M, David A Tomasko, and Brant W Touchette. 2007. “Seagrasses and Eutrophication.” Journal of Experimental Marine Biology and Ecology. https://doi.org/10.1016/j.jembe.2007.06.024. Cardoso, P. G., S. Leston, T. F. Grilo, M. D. Bordalo, D. Crespo, D. Raffaelli, and M. A. Pardal. 2010. “Implications of Nutrient Decline in the Seagrass Ecosystem Success.” Marine Pollution Bulletin 60 (4): 601–8. https://doi.org/10.1016/j.marpolbul.2009.11.004. Cho, Hyun Jung, and Christopher A May. 2008. “Short-Term Spatial Variations in the Beds of Ruppia Maritima (Ruppiaceae) and Halodule Wrightii (Cymodoceaceae) at Grand Bay National Estuarine Research Reserve, Mississippi, USA.” Journal of Mississippi Academy of Sciences 53: 133–45. Christia, Chrysoula, Gianmarco Giordani, and Eva Papastergiadou. 2018. “Environmental Variability and Macrophyte Assemblages in Coastal Lagoon Types of Western Greece (Mediterranean Sea).” Water (Switzerland) 10 (2). https://doi.org/10.3390/w10020151. Cole, M.L., I. Valiela, K.D. Kroeger, G.L. Tomasky, J. Cebrian, C. Wigand, R.A. McKinney, S.P. Grady, and M.H. Carvalho Da Silva. 2004. “Assessment of a δ15N Isotopic Method to Indicate Anthropogenic Eutrophication in Aquatic Ecosystems.” Journal of Environmental Quality 33 (1): 124–32. Condie, Scott A., Donna Hayes, Elizabeth A. Fulton, and Marie Savina. 2012. “Modelling Ecological Change over Half a Century in a Subtropical Estuary: Impacts of Climate Change, Land-Use, Urbani Zation and Freshwater Extraction.” Marine Ecology Progress Series 457: 43–66. https://doi.org/10.3354/meps09718. Costanza, R. 1992. “Toward an Operational Definition of Ecosytem Health.” In Ecosystem Health: New Goals for Environmental Management, edited by R. Costanza, B.G. Norton, and B.D. Haskell, 239–56. Island Press. Davies, J.L. 1964. “A Morphogenic Approach to World Shorelines.” Zeitschrift Für Geomorphologie 8: 27–42. Day, John, Alejandro Yanez-Arancibia, W.M. Kemp, and Byron Crump. 2013. “Introduction to Estuarine Ecology.” In Estuarine Ecology, edited by John Day, Byron Crump, W.M. Kemp, and Alejandro Yanez-Arancibia, 2nd ed., 1–18. Hoboken, New Jersey: Wiley-Blackwell. Dekker, A., V. Brando, J. Anstee, S. Fyfe, T. Malthus, and E. Karpouzli. 2006. Remote Sensing of Seagrass Ecosystems: Use of Spaceborne and Airborne Sensors. Seagrasses: Biology, Ecology and Conservation. https://doi.org/10.1007/978-1- 4020-2983-7_15. Dennison, William C, Robert J Orth, Kenneth A Moore, J Court Stevenson, Virginia Carter, Stan Kollar, Peter W Bergstrom, and Richard A Batiuk. 1993. “Assessing Water Quality with Submersed Aquatic Vegetation.” Bioscience 43 (2): 86–94. http://www.jstor.org/stable/1311969. Duarte, Carlos M. 1995. “Submerged Aquatic Vegetation in Relation to Different Nutrient Regimes.” Ophelia 41 (1): 87–112. https://doi.org/10.1080/00785236.1995.10422039. Elliott, Michael, and Victor Quintino. 2007. “The Estuarine Quality Paradox, Environmental Homeostasis and the Difficulty of Detecting Anthropogenic Stress in Naturally Stressed Areas.” Marine Pollution Bulletin 54 (6): 640–45. https://doi.org/10.1016/j.marpolbul.2007.02.003.

122

Fourqurean, James W., Thomas O. Moore, Brian Fry, and James T. Hollibaugh. 1997. “Spatial and Temporal Variation in C:N:P Ratios, Δ15N, and Δ13C of Eelgrass Zostera Marina as Indicators of Ecosystem Processes, Tomales Bay, California, USA.” Marine Ecology Progress Series 157: 147–57. https://doi.org/10.3354/meps157147. Gillanders, Bronwyn M., Travis S. Elsdon, Ian A. Halliday, Gregory P. Jenkins, Julie B. Robins, and Fiona J. Valesini. 2011. “Potential Effects of Climate Change on Australian Estuaries and Fish Utilising Estuaries: A Review.” Marine and Freshwater Research 62 (9): 1115–31. https://doi.org/10.1071/MF11047. Guisan, Antoine, and E. Niklaus Zimmermann. 2000. “Predictive Habitat Distribution Models in Ecology.” Ecological Modelling 135 (2–3): 147–86. https://doi.org/10.1016/S0304-3800(00)00354-9. Gullström, Martin, Bengt Lundén, Maria Bodin, Juma Kangwe, Marcus C. Öhman, Matern S.P. Mtolera, and Mats Björk. 2006. “Assessment of Changes in the Seagrass-Dominated Submerged Vegetation of Tropical Chwaka Bay (Zanzibar) Using Satellite Remote Sensing.” Estuarine, Coastal and Shelf Science 67 (3): 399–408. https://doi.org/10.1016/j.ecss.2005.11.020. Haag, Scott Myers. 2010. “Distributional Change in Seagrass as an Ecological Indicator of Estuarine Health for Barnegat Bay – Little Egg Harbor, New Jersey .” Hallett, Chris S., Fiona Valesini, and Mike Elliott. 2016. “A Review of Australian Approaches for Monitoring, Assessing and Reporting Estuarine Condition: I. International Context and Evaluation Criteria.” Environmental Science and Policy 66: 260–69. https://doi.org/10.1016/j.envsci.2016.07.014. Hardy, F.G., S.M. Evans, and M.A. Tremayne. 1993. “Long-Term Changes in the Marine Macroalgae of Three Polluted Estuaries in North-East England.” Journal of Experimental Marine Biology and Ecology 172 (1–2): 81–92. https://doi.org/10.1016/0022-0981(93)90090-B. Heide, T. Van Der, E. T.H.M. Peeters, D. C.R. Hermus, M. M. Van Katwijk, J. G.M. Roelofs, and A. J.P. Smolders. 2009. “Predicting Habitat Suitability in Temperate Seagrass Ecosystems.” Limnology and Oceanography 54 (6): 2018–24. https://doi.org/10.4319/lo.2009.54.6.2018. Hemminga, M A. 1998. “The Root/Rhizome System of Seagrasses:An Asset and a Burden.” Journal of Sea Research 39 (3–4): 183–96. https://doi.org/https://doi.org/10.1016/S1385-1101(98)00004-5. Hillman, K, A J Mccomb, and D I Walker. 1995. “Halophila Ovalis in the Swan/Canning Estuary, Western Australia.” ELSEVIER Aquatic Botany 51: 1–54. http://ac.els-cdn.com.libproxy.murdoch.edu.au/030437709500466D/1-s2.0- 030437709500466D-main.pdf?_tid=2f87cef0-687e-11e7-bb2f- 00000aab0f6b&acdnat=1500027863_49bcc24547d0abede6713adbc766dbc8 Hipsey, Matthew R., and David P. Hamilton. 2015. “Predicting the Resilience and Recovery of Aquatic Systems: A Framework Formodel Evolution within Environmental Observatories.” Water Resouces Research, 7023–43. https://doi.org/10.1002/2015WR017175.Received. Hitchcock, Jesse K., Simon C. Courtenay, Michael R.S. Coffin, Christina C. Pater, and Michael R. van den Heuvel. 2017. “Eelgrass Bed Structure, Leaf Nutrient, and Leaf Isotope Responses to Natural and Anthropogenic Gradients in Estuaries of the Southern Gulf of St. Lawrence, Canada.” Estuaries and Coasts 40 (6): 1653– 65. https://doi.org/10.1007/s12237-017-0243-0.

123

Holmer, Marianne, and Elsebet Bondgaard. 2001. “Photosynthetic and Growth Response of Eelgrass to Low Oxygen and High Sulfide Concentrations during Hypoxic Events.” Aquatic Botany 70 (1): 29–38. https://doi.org/10.1016/S0304-3770(00)00142-X. Irlandi, E. A., B. A. Orlando, and P. D. Biber. 2004. “Drift Algae-Epiphyte-Seagrass Interactions in a Subtropical Thalassia Testudinum Meadow.” Marine Ecology Progress Series. https://doi.org/10.3354/meps279081. Jordan, Stephen J, and Pauline a Vaas. 2000. “An Index of Ecosystem Integrity for Northern Chesapeake Bay.” Environmental Science & Policy 3 (Supplement 1): 59–88. https://doi.org/10.1016/S1462-9011(00)00028-9. Kendall, Matthew S., Olaf P. Jensen, Clark Alexander, Don Field, Greg McFall, Reed Bohne, and Mark E. Monaco. 2005. “Benthic Mapping Using Sonar, Video Transects, and an Innovative Approach to Accuracy Assessment: A Characterization of Bottom Features in the Georgia Bight.” Journal of Coastal Research 216: 1154–65. https://doi.org/10.2112/03-0101R.1. Kennedy, D. M. 2012. “Tectonic and Geomorphic Evolution of Estuaries and Coasts.” In Treatise on Estuarine and Coastal Science, 1:37–59. https://doi.org/10.1016/B978-0-12-374711-2.00103-0. Kennish, Michael J. 2002. “Environmental Threats and Environmental Future of Estuaries.” Environmental Conservation 29 (01): 78–107. https://doi.org/10.1017/S0376892902000061. Kinney, Eleanor H., and Charles T. Roman. 1998. “Response of Primary Producers to Nutrient Enrichment in a Shallow Estuary.” Marine Ecology Progress Series 163: 89–98. https://doi.org/10.3354/meps163089. Klemas, V.V. 2014. “Remote Sensing of Coastal Ecosystem and Environments.” In Remote Sensing and Modeling: Advances in Coastal and Marine Resources, edited by C.W. Finkl and C. Makowski, 3–30. Springer International Publishing. Lee, Kun Seop, Sang Rul Park, and Young Kyun Kim. 2007. “Effects of Irradiance, Temperature, and Nutrients on Growth Dynamics of Seagrasses: A Review.” Journal of Experimental Marine Biology and Ecology 350 (1–2): 144–75. https://doi.org/10.1016/j.jembe.2007.06.016. Lirman, D, Ae G Deangelo, Ae J Serafy, Ae A Hazra, Ae D Smith, Hazra Ae, J Herlan, et al. 2008. “Seasonal Changes in the Abundance and Distribution of Submerged Aquatic Vegetation in a Highly Managed Coastal Lagoon.” Hydrobiologia 596: 105–20. https://doi.org/10.1007/s10750-007-9061-x. Liu, Yansui, Md Anisul Islam, and Jay Gao. 2003. “Quantification of Shallow Water Quality Parameters by Means of Remote Sensing.” Progress in Physical Geography 27 (1): 24–43. https://doi.org/10.1191/0309133303pp357ra. Long, B. G., T. D. Skewes, and I. R. Poiner. 1994. “An Efficient Method for Estimating Seagrass Biomass.” Aquatic Botany 47 (3–4): 277–91. https://doi.org/10.1016/0304-3770(94)90058-2. Lotze, Heike K, H Lenihan, B.J. Bourque, R.H. Bradbury, R.G. Cooke, M.C. Kay, S.M. Kidwell, M.X. Kirby, C.H. Peterson, and J.B.C. Jackson. 2006. “Depletion, Degradation, and Recovery Potential of Estuaries and Coastal Seas.” Science 312 (5781): 1806–9. https://doi.org/10.1126/science.1128035. Lough, Jm, and Aj Hobday. 2011. “Observed Climate Change in Australian Marine and Freshwater Environments.” Marine and Freshwater Research 62: 984–99. https://doi.org/10.1071/Mf10272.

124

Lyons, Mitchell, Chris Roelfsema, Eva Kovacs, Jimena Samper-Villarreal, Megan Saunders, Paul Maxwell, and Stuart Phinn. 2015. “Rapid Monitoring of Seagrass Biomass Using a Simple Linear Modelling Approach, in the Field and from Space.” Marine Ecology Progress Series 530: 1–14. https://doi.org/10.3354/meps11321. Martínez-Crego, Begoña, Teresa Alcoverro, and Javier Romero. 2010. “Biotic Indices for Assessing the Status of Coastal Waters: A Review of Strengths and Weaknesses.” Journal of Environmental Monitoring 12 (5): 1013. https://doi.org/10.1039/b920937a. Mathieson, A.C., C.J. Dawes, M.L. Anderson, and E.J. Hehre. 2001. “Seaweeds of the Brave Boat Harbor Salt Marsh and Adjacent Open Coast of Southern Maine.” Rhodora 103 (913): 1–46. MEA. 2005. Living Beyond Our Means: Natural Assets and Human Well-Being. Millenium Ecosystem Assessment. New York. https://www.millenniumassessment.org/documents/document.429.aspx.pdf. Moore, Kenneth A, David J Wilcox, and Robert J Orth. 2000. “Analysis of the Abundance of Submersed Aquatic Vegetation Communities in the Chesapeake Bay.” Estuaries 23 (1): 115–27. http://www.jstor.org/stable/1353229. Nagelkerken, Ivan, Marcus Sheaves, Ronald Baker, and Rod M. Connolly. 2015. “The Seascape Nursery: A Novel Spatial Approach to Identify and Manage Nurseries for Coastal Marine Fauna.” Fish and Fisheries 16 (2): 362–71. https://doi.org/10.1111/faf.12057. Neri, By Dorte Krause-jensen, Ana Luisa, Quaresma Pnrf, Alexandra H Cunha Ccmar, and Tina M Greve Fbl. 2004. “How Are Seagrass Distribution and Abundance Monitored ?” In European Seagrasses an Introduction to Monitoring and Management, 45–53. http://www.vliz.be/imisdocs/publications/67189.pdf. Nezlin, Nikolay P., Krista Kamer, and Eric D. Stein. 2007. “Application of Color Infrared Aerial Photography to Assess Macroalgal Distribution in an Eutrophic Estuary, Upper Newport Bay, California.” Estuaries and Coasts 30 (5): 855–68. https://doi.org/10.1007/BF02841339. Nixon, S W, C A Oviatt, J Frithsen, and B Sullivan. 1986. “Nutrients and the Productivity of Estuarine and Coastal Marine Ecosystems.” Journal of the Limnological Society of Southern Africa 12 (1–2): 43–71. https://doi.org/10.1080/03779688.1986.9639398. Olesen, Birgit, and Kaj Sand-Jensen. 1993. “Seasonal Acclimatization of Eelgrass Zostera Marina Growth to Light.” Marine Ecology Progress Series 94 (1): 91–99. https://doi.org/10.3354/meps094091. Orlando-Bonaca, Martina, and Ana Rotter. 2018. “Any Signs of Replacement of Canopy-Forming Algae by Turf-Forming Algae in the Northern Adriatic Sea?” Ecological Indicators 87 (January): 272–84. https://doi.org/10.1016/j.ecolind.2017.12.059. Orth, RJ, TJB Carruthers, WC Dennison, Carlos M Duarte, KJ Fourqurean, Kenneth L Heck Jr, A Randall Hughes, et al. 2006. “A Global Crisis for Seagrass Ecosystems.” Bioscience 56 (12): 987–96. https://doi.org/10.1641/0006- 3568(2006)56{[}987:AGCFSE]2.0.CO;2.

125

Pedretti, Y M, H T Kobryn, E F Sommerville, and K Wienczugow. 2011. “Snapshot Survey of the Distribution and Abundance of Seagrass and Macroalgae in the Peel-Harvey Estuary from November / December 2009 Report to Department of Water,” no. February. Phinn, Stuart, Chris Roelfsema, Arnold Dekker, Vittoro Brando, and Janet Anstee. 2008. “Mapping Seagrass Species, Cover and Biomass in Shallow Waters: An Assessment of Satellite Multi-Spectral and Airborne Hyper-Spectral Imaging Systems in Moreton Bay (Australia).” Remote Sensing of Environment 112 (8): 3413–25. https://doi.org/10.1016/j.rse.2007.09.017. Potter, Ian C, Benjamin M Chuwen, Steeg D Hoeksema, and Michael Elliott. 2010. “The Concept of an Estuary: A Definition That Incorporates Systems Which Can Become Closed to the Ocean and Hypersaline.” Estuarine, Coastal and Shelf Science 87 (3): 497–500. https://doi.org/10.1016/j.ecss.2010.01.021. Pyke, Christopher R., Mary Beth Adams, Denice Wardrop, Carl Hershner, Robert Wood, David Secor, Kevin Sellner, et al. 2009. “Potential Climate-Change Impacts on the Chesapeake Bay.” Estuarine, Coastal and Shelf Science 86 (1): 1–20. https://doi.org/10.1016/j.ecss.2009.09.026. Rabalais, Nancy N., R. Eugene Turner, Robert J. Díaz, and Dubravko Justić. 2009. “Global Change and Eutrophication of Coastal Waters.” ICES Journal of Marine Science 66 (7): 1528–37. https://doi.org/10.1093/icesjms/fsp047. Ralph, Peter J., David Tomasko, Kenneth Moore, Stephanie Seddon, and Catrionà M.O. Macinnis-Ng. 2006. “Human Impacts on Seagrasses: Eutrophication, Sedimentation, and Contamination.” In Seagrasses: Biology, Ecology and Conservation, 567–93. https://doi.org/10.1007/978-1-4020-2983-7_24. Rapport, D. J., R. Costanza, and A. J. McMichael. 1998. “Assessing Ecosystem Health.” Trends in Ecology and Evolution 13 (10): 397–402. https://doi.org/10.1016/S0169-5347(98)01449-9. Reshitnyk, Luba, Maycira Costa, Cliff Robinson, and Phil Dearden. 2014. “Evaluation of WorldView-2 and Acoustic Remote Sensing for Mapping Benthic Habitats in Temperate Coastal Pacific Waters.” Remote Sensing of Environment 153: 7–23. https://doi.org/10.1016/j.rse.2014.07.016. Riegl, Bernhard M., Ryan P. Moyer, Lori J. Morris, Robert W. Virnstein, and Samuel J. Purkis. 2005. “Distribution and Seasonal Biomass of Drift Macroalgae in the Indian River Lagoon (Florida, USA) Estimated with Acoustic Seafloor Classification (QTCView, Echoplus).” Journal of Experimental Marine Biology and Ecology 326 (1): 89–104. https://doi.org/10.1016/j.jembe.2005.05.009. Roy, P., R. Williams, A. Jones, I. Yassini, P. Gibbs, B. Coates, R. West, P. Scanes, J. Hudson, and S. Nichol. 2001. “Structure and Function of South-East Australian Estuaries.” Estuarine, Coastal and Shelf Science 53: 351–84. https://doi.org/10.1006. Sabol, Bruce M, R Eddie Jr Melton, Robert Chamberlain, Peter Doering, and Kathy Haunert. 2002. “Evaluation of a Digital Echo Sounder System for Detection of Submersed Aquatic Vegetation.” Estuaries 25 (1): 133–41. Santos, R O, and D Lirman. 2012. “Using Habitat Suitability Models to Predict Changes in Seagrass Distribution Caused by Water Management Practices.” Canadian Journal of Fisheries and Aquatic Sciences 69 (8): 1380–88. https://doi.org/10.1139/F2012-018.

126

Sfriso, Adriano, Chiara Facca, and Pier Francesco Ghetti. 2009. “Validation of the Macrophyte Quality Index (MaQI) Set up to Assess the Ecological Status of Italian Marine Transitional Environments.” In Hydrobiologia. https://doi.org/10.1007/s10750-008-9540-8. Sheaves, Marcus, Ronald Baker, Ivan Nagelkerken, and Rod M Connolly. 2014. “True Value of Estuarine and Coastal Nurseries for Fish: Incorporating Complexity and Dynamics.” Estuaries and Coasts 38 (2): 401–14. https://doi.org/10.1007/s12237-014-9846-x. Short, Frederick T., and Sandy Wyllie-Echeverria. 1996. “Natural and Human- Induced Disturbance of Seagrasses.” Environmental Conservation 23 (01): 17. https://doi.org/10.1017/S0376892900038212. Sidik, Bujang Japar, Salomao Bandeira, and Nataliya A Milchakova. 2001. “Methods to Measure Macroalgal Biomass and Abundance in Seagrass Meadows.” In Global Seagrass Research Methods, edited by F. Short and R. Coles, 223–35. Amsterdam: Elsevier Science. https://doi.org/10.1016/B978-0-444-50891- 1.50012-8. Silva, T.S.F, M.P.F Costa, J.J. Melack, and E.M.L.M. Novo. 2008. “Remote Sensing of Aquatic Vegetation: Theory and Applications.” Environmental Monitoring and Assessment. Statham, Peter J. 2012. “Nutrients in Estuaries - An Overview and the Potential Impacts of Climate Change.” Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2011.09.088. Strain, Elisabeth M A, Russell J. Thomson, Fiorenza Micheli, Francesco P. Mancuso, and Laura Airoldi. 2014. “Identifying the Interacting Roles of Stressors in Driving the Global Loss of Canopy-Forming to Mat-Forming Algae in Marine Ecosystems.” Global Change Biology 20 (11): 3300–3312. https://doi.org/10.1111/gcb.12619. Tuya, Fernando, Lydia Png-Gonzalez, Rodrigo Riera, Ricardo Haroun, and Fernando Espino. 2014. “Ecological Structure and Function Differs between Habitats Dominated by Seagrasses and Green Seaweeds.” Marine Environmental Research 98: 1–13. https://doi.org/10.1016/j.marenvres.2014.03.015. Tweedley, J. R., R. M. Warwick, and I. C. Potter. 2016. “The Contrasting Ecology of Temperate Macrotidal and Microtidal Estuaries.” Oceanography and Marine Biology: An Annual Review 54: 73–171. https://doi.org/10.1201/9781315368597-3. Valiela, Ivan, James Mcclelland, Jennifer Hauxwell, Peter J Behr, Douglas Hersh, and Kenneth Foreman. 1997. “Macroalgal Blooms in Shallow Estuaries: Controls and Ecophysiological and Ecosystem Consequences.” Limnology and Oceanography 42 (5_part_2): 1105–18. https://doi.org/10.4319/lo.1997.42.5_part_2.1105. Valle-Levinson, A. 2012. “Classification of Estuarine Circulation.” In Treatise on Estuarine and Coastal Science, 1:75–86. https://doi.org/10.1016/B978-0-12- 374711-2.00106-6. Villares, R, X Puente, and A Carballeira. 2001. “Ulva and Enteromorpha as Indicators of Heavy Metal Pollution.” Hydrobiologia 462: 221–32. Walker, D I, B Olesen, and R C Phillips. 2001. Reproduction and Phenology in Seagrasses. Global Seagrass Research Methods. Elsevier B.V. https://doi.org/http://dx.doi.org/10.1016/B978-044450891-1/50004-9.

127

Waycott, Michelle, Carlos M Duarte, Tim J B Carruthers, Robert J Orth, William C Dennison, Suzanne Olyarnik, Ainsley Calladine, et al. 2009. “Accelerating Loss of Seagrasses across the Globe Threatens Coastal Ecosystems.” Proceedings of the National Academy of Sciences of the United States of America 106 (30): 12377– 81. https://doi.org/10.1073/pnas.0905620106. Whittaker, Robert H, and Gene E Likens. 1973. “The Biosphere and Man.” Human Ecology 1 (4). http://www.jstor.org.libproxy.murdoch.edu.au/stable/pdf/4602275.pdf?refreq id=excelsior%3Aae23ac7e44f5e829bc60180dc1ad225b Yesson, Chris, Laura E. Bush, Andrew J. Davies, Christine A. Maggs, and Juliet Brodie. 2015. “The Distribution and Environmental Requirements of Large Brown Seaweeds in the British Isles.” Journal of the Marine Biological Association of the United Kingdom 95 (4): 669–80. https://doi.org/10.1017/S0025315414001453. Zhang, Xiaomei, Yi Zhou, Peng Liu, Feng Wang, Bingjian Liu, Xujia Liu, Qiang Xu, and Hongsheng Yang. 2014. “Temporal Pattern in the Bloom-Forming Macroalgae Chaetomorpha Linum and Ulva Pertusa in Seagrass Beds, Swan Lake Lagoon, North China.” Marine Pollution Bulletin 89 (1–2): 229–38.

128