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Multiple anthropogenic stressors as drivers of biodiversity change in aquatic systems: impacts, indicators and monitoring

By Javier Atalah B.Sc. (Hons), M. Sc. The thesis is submitted to University College Dublin for the degree of PhD in School of Biology and Environmental Science May 2009

UCD Science Education and Research Centre - West University College Dublin Belfield, Dublin 4, Ireland. Supervisor: Dr. Tasman Crowe Head of school: Prof. Thomas Bolger


Table of Contents

Table of Contents List of Figures ......................................................................................... 4 List of Tables .......................................................................................... 6 Abstract ................................................................................................... 8 CHAPTER I: GENERAL INTRODUCTION ....................................... 11 1.1. 1.1.1. 1.1.2. 1.1.3. 1.1.4. 1.2. 1.3. 1.3.1. 1.3.2. 1.4. 1.5.

Multiple stressors in aquatic systems ........................................................11 Nutrient enrichment ..............................................................................12 Sedimentation .......................................................................................15 Invasive species ....................................................................................16 Combined impacts of multiple stressors ................................................18 Environmental monitoring and indicators..................................................18 Study systems ...........................................................................................23 Rocky shores .........................................................................................23 Lakes ....................................................................................................26 Aims of the thesis .....................................................................................27 The BIOCHANGE project ........................................................................29

CHAPTER II: POLLUTION AS A DRIVER OF BIODIVERSITY CHANGE IN ROCKY SHORES: THE POTENTIAL OF MOLLUSCAN AND MACROALGAE ASSEMBLAGES FOR BIOMONITORING. ............................................................................. 30 2.1. Abstract ....................................................................................................30 2.2. Introduction ..............................................................................................31 2.3. Methods....................................................................................................33 2.3.1. Study sites .............................................................................................33 2.3.2. Water chemistry ....................................................................................36 2.3.3. Sampling of macroalgae and molluscs ...................................................36 2.3.4. Statistical Analysis ................................................................................37 2.3.4.1. Multivariate analyses .........................................................................37 2.3.4.2. Univariate analyses ............................................................................39 2.4. Results ......................................................................................................39 2.4.1. Nutrient concentration ...........................................................................39 2.4.2. Community structure .............................................................................41 2.4.3. Relationship between molluscan data and environmental variables........44 2.4.4. Univariate analyses ...............................................................................45 2.5. Discussion ................................................................................................48

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Table of Contents

CHAPTER III: IMPACTS OF INVASION BY DREISSENA POLYMORPHA (PALLAS, 1977) ON THE PERFORMANCE OF MACROINVERTEBRATE ASSESSMENT TOOLS FOR EUTROPHICATION PRESSURE IN LAKES ..................................... 54 3.1. 3.2. 3.3. 3.3.1. 3.3.2. 3.3.3. 3.4. 3.4.1. 3.4.2. 3.5.

Abstract ....................................................................................................54 Introduction ..............................................................................................56 Methods....................................................................................................59 Study sites .............................................................................................59 Sampling ...............................................................................................60 Statistical analyses ................................................................................61 Results ......................................................................................................63 Metrics ..................................................................................................63 Community structure .............................................................................67 Discussion ................................................................................................74

CHAPTER IV: COMBINED EFFECTS OF NUTRIENT ENRICHMENT, SEDIMENTATION AND GRAZER LOSS ON ROCK POOL ASSEMBLAGES ....................................................................... 79 4.1. 4.2. 4.3. 4.3.1. 4.3.2. 4.3.3. 4.3.4. 4.4. 4.4.1. 4.4.2. 4.4.3. 4.4.4. 4.4.5. 4.5.

Abstract ....................................................................................................79 Introduction ..............................................................................................80 Methods....................................................................................................83 Study site ..............................................................................................83 Experimental design ..............................................................................84 Sampling ...............................................................................................86 Data Analyses .......................................................................................86 Results ......................................................................................................87 Efficacy of nutrient enrichment .............................................................87 Effects of sedimentation and nutrients on assemblage structure .............88 Effects of sedimentation and nutrients on individual taxa ......................90 Effect of grazers and nutrients on assemblage structure .........................92 Effects of grazers and nutrients on individual taxa .................................95 Discussion ................................................................................................96

CHAPTER V: GENERAL DISCUSSION ...........................................101 5.1. 5.2. 5.3.

Monitoring and Environmental Indicators ............................................... 102 Multiple stressors and the role of experimental ecology .......................... 110 Concluding remarks ................................................................................ 113

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List of Figures

List of Figures Figure 1. Map showing the location of the eleven study sites in the west coast of Ireland. Two polluted (filled symbols) and two control sites (empty symbols) were sampled at each of the clusters Connemara and Shannon. In the Galway cluster, one polluted site and two control sites were sampled (see Methods section for details). .35 Figure 2. Mean (+SE) Nitrates (a) and ammonia (b) in each of the three clusters at polluted (filled bars) and control sites (empty bars). ................................................40 Figure 3. Non-metric multidimensional scaling (nMDS) ordination of molluscan assemblages in each cluster: Connemara (circles), Galway (triangles) and Shannon (squares) at both polluted (filled symbols) and control sites (empty symbols) on the basis of Bray-Curtis similarities of the square-root transformed data. ......................42 Figure 4. Mean (+SE) number of taxa and total abundance in each of the three clusters at polluted (filled bars) and control sites (empty bars). ...............................47 Figure 5. Map showing the location of the thirty-one study sites sampled in the Central-West area of Ireland. Invaded sites are shown by black circles (n = 20) and non-invaded (n = 11) by gray circles. ......................................................................60 Figure 6. Regressions between levels of total phosphorus and three ecological metrics: a) % sensitivity TP, b) TP score and c) Indicator taxa metric and for invaded (filled circles, n = 20) and non-invaded (empty circles, n = 11) sites. ......................65 Figure 7. Relationships between levels of total phosphorus, and a) total abundance and b) number of taxa for invaded (filled circles, n = 20) and non-invaded (empty circles, n = 11) sites. ...............................................................................................66 Figure 8. Mean (+SE) a) total abundance and b) mean number of taxa of macroinvertebrates at invaded (filled bars, n = 20) and non-invaded sites (empty bars, n = 11). ...........................................................................................................67 Figure 9. Non-metric multidimensional scaling (nMDS) ordination of macroinvertebrates assemblages in sites invaded (filled circles, n = 20) and not invaded (empty circles, n = 11) by the zebra mussel Dreissena polymorpha on the basis of Bray-Curtis similarities of a) the untransformed abundance data and b) presence absence data. The community analysis was done having first removed data on the abundance of D. polymorpha. .......................................................................68 Figure 10. Diagram of the two experimental design used in the study: a) Two-way design with Sedimentation and Nutrients, both with three levels and as orthogonal factors.; and b) Two-way design with Nutrients, and Grazers both with two levels and as orthogonal factors. A = Ambient, M = Moderate, H = High and R = Reduced. Some treatments were common to both designs. For details see text. ......................85

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List of Figures Figure 11. Mean (+SE) nitrates concentration in experimental rock pools for each of the three nutrient treatments: ‘Ambient’ (empty bar), ‘Moderate’ (grey bar) and ‘High’ (black bar). ..................................................................................................88 Figure 12. Non-metric multidimensional scaling (nMDS) ordination of assemblages on a) Ambient Sediment (empty circles), ‘Moderate Sediment’ (grey circles) and ‘High Sediment’ (black circles) and b) ‘Ambient Nutrient’ (empty squares), ‘Moderate Nutrient’ (grey squares) and ‘High Nutrient’ (black squares) rockpools on the basis of Bray-Curtis similarities of the untransformed percentage cover data (n = 4). ...........................................................................................................................89 Figure 13. Effect of sedimentation and nutrients on the percentage cover of several prominent algal taxa and grazers on experimental rock pools (mean ± SE, n = 4). ...92 Figure 14. Non-metric multidimensional scaling (nMDS) ordination of assemblages on Ambient Nutrient (circles), ‘High Nutrient’ (triangles), ‘Reduced Grazing’ (empty symbols) and ‘Ambient Grazing’ (filled symbols) rockpools on the basis of BrayCurtis similarities of the untransformed percentage cover data. ...............................93 Figure 15. Effect of grazers and nutrients on the percentage cover of several prominent algal taxa on experimental rock pools (mean ± SE, n = 4). .....................96

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List of Tables

List of Tables Table 1. Permutational ANOVAs analyses for levels of nitrates and ammonia based on Bray-Curtis similarities of the untransformed data..............................................40 Table 2. List of the 35 taxa identified and included in the analysis. .........................43 Table 3. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis dissimilarity distinguishing the two group of sites, based on the squared root transformed data............................................................................44 Table 4. Results of non-parametric multiple regression of molluscan assemblage data on individual environmental variables for (a) each variable taken individually (ignoring other variables) and (b) forward-selection of variables, where amount explained by each variable added to model is conditional on variables already in the model (i.e. those variables listed above it). %Var: percentage of variance in species data explained by that variable; Cum. (%): cumulative percentage of variance explained. ...............................................................................................................45 Table 5. PERMANOVA analysis of differences in molluscan assemblage structure based on Bray-Curtis similarities of the square-root transformed data and permutational ANOVAs analysis for the number of taxa and total abundance. ........46 Table 6. List of the taxa identified and included in the analysis, and the taxonspecific sensitivity to TP and TP optima used in the calculation of respectively, the % Sensitivity to TP and TP score metrics. ...................................................................69 Table 7. Optimum values for the 10 common taxa incorporated in the Indicator Taxa Metric. ....................................................................................................................72 Table 8. PERMANOVA analysis of differences in macroinvertebrate assemblage structure based on Bray-Curtis similarities of the untransformed and presence/absence transformed data. TP = total phosphorus......................................73 Table 9. Average abundance of several prominent taxa in invaded and non-invaded study sites, including SIMPER results for contributions from most important taxa towards the Bray-Curtis similarities distinguishing these two groups. .....................73 Table 10. Presence/absence of several taxa in invaded and non-invaded study sites contributing to the differences in assemblage composition between these two groups. ...............................................................................................................................74 Table 11. PERMANOVA of Bray-Curtis similarities based on fourth root transformed algal and invertebrate assemblage data ................................................89

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List of Tables Table 12. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis similarity distinguishing between ‘Ambient Sediment’ and sediment addition treatments (i.e. ‘Moderate Sediment’ and ‘High Sediment’ pooled together), based on the untransformed data. Av. Cover/abundance = average cover/abundance, Av. Diss. = average contribution to overall dissimilarity among samples, Diss / SD = the ratio of the average contribution to the overall dissimilarity among samples to the standard deviation of the average contribution to the overall dissimilarity, Contrib %. = percentage contribution to the overall dissimilarity among samples, Cum% = cumulative contribution to the overall dissimilarity among samples. ..................................................................................................................90 Table 13. Two-way ANOVA for percentage cover of most prominent algal groups and the abundance of the limpet Patella ulyssiponensis under sediment and nutrients manipulations. ........................................................................................................91 Table 14. PERMANOVA of Bray-Curtis similarities based on fourth root transformed algal and invertebrate assemblage data ................................................93 Table 15. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis similarity distinguishing between ‘Reduced Grazing’ and ‘Ambient Grazing’ treatments, based on the untransformed data. Av. Cover = average cover, Av. Diss. = average contribution to overall dissimilarity among samples, Diss / SD = the ratio of the average contribution to the overall dissimilarity among samples to the standard deviation of the average contribution to the overall dissimilarity, Contrib %. = percentage contribution to the overall dissimilarity among samples, Cum % = cummulative contribution to the overall dissimilarity among samples. ..................................................................................................................94 Table 16. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis similarity distinguishing between ‘Ambient Nutrient’ and ‘High Nutrient’ treatments, based on the untransformed data. Av. Cover = average cover, Av. Diss. = average contribution to overall dissimilarity among samples, Diss / SD = the ratio of the average contribution to the overall dissimilarity among samples to the standard deviation of the average contribution to the overall dissimilarity, Contrib %. = percentage contribution to the overall dissimilarity among samples, Cum% = cummulative contribution to the overall dissimilarity among samples. ..................................................................................................................94 Table 17. Two-way ANOVA for percentage cover of most prominent algal groups under grazing and nutrients manipulations. .............................................................95

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Abstract

Abstract Aquatic ecosystems are increasingly being impacted by multiple stressors caused by anthropogenic activities. Although the impacts of individual stressors on aquatic biodiversity are relatively well understood, there is a lack of knowledge of the consequences of several stressors acting simultaneously. Nutrient pollution, invasive species and sedimentation are among the major threats to aquatic biodiversity and ecosystem functioning. The EU Water Framework Directive (WFD) requires consideration of the impacts of stressors on biodiversity and to develop cost-effective tools to measure those impacts. This thesis aimed to test the impacts of several widespread stressors on the diversity of aquatic systems and to develop and test the performance of biomonitoring and ecological classification tools.

In Chapter II the effects of pollution, with emphasis on nutrient enrichment, were tested in a network of 11 intertidal sites in the west coast of Ireland. Changes in the structure and diversity of molluscan assemblages successfully discriminated between sites of differing pollution status, but macroalgal assemblages did not. Nutrient concentration in seawater accounted for more than 45% of the variability in the assemblage structure of molluscs. The use of molluscan assemblages is suggested as a cost-effective biomonitoring tool for intertidal rocky habitats.

In Chapter III, the combined effects of the invasive zebra mussel Dreissena polymorpha and nutrient enrichment on the diversity of lake macroinvertebrates was examined by sampling 31 sites. The performance of recently developed macroinvertebrate metrics of ecological quality in relation to eutrophication was also tested. The presence of D. polymorpha was associated with drastic changes in

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Abstract macroinvertebrate diversity, structure and composition. All tested metrics lost explanatory power for eutrophication pressure in invaded systems. These results imply important consequences for the management of lakes and highlight the need for the development of new assessment tools that take biological invasions into consideration.

In Chapter IV, the combined and separate effects of nutrients, sedimentation and loss of molluscan grazers in rock pools were tested experimentally. Sedimentation significantly modified macroalgal assemblage structure, due to an increase in turfing and filamentous algae and a decrease in crustose algae. Nutrients caused an increase in the cover of green ephemeral algae, which in turn was synergistically magnified by the removal of grazers. It was shown that these stressors act individually or synergistically to alter the structure and diversity of rock pool assemblages and that top-down control (by grazers) is more important than bottom-up factors (nutrients) in controlling this system.

The research in this thesis has enhanced the understanding of the impacts of multiple stressors on aquatic biodiversity. It has shown that they can act in isolation or combination as drivers of biodiversity change and can interfere with the assessment of ecological quality. This research highlighted the need to develop specialised monitoring and management strategies that take account of the range of stressors in a system and the need for a substantial body of research combining observational, experimental and modelling to underpin those strategies. Working in multiple systems and using a range of approaches is fundamental to developing a more general understanding of impacts of multiple stressors.

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Acknowledgments

Acknowledgments First, I would like to thank my supervisors Dr Tasman Crowe and Dr Mary Kelly-Quinn for their guidance, friendly support and encouragement. I would also like to thank to Environmental Protection Agency (EPA) for funding my PhD.

Thanks to the staff at the School of Biology and Environmental Science for logistical support, especially Jennifer Coughlan for her help with laboratory work. Also thanks all the students that help me with fieldwork, sample processing and identification: Letizia Cocchiglia, Elaine Keenan, Orlagh Reddington, Edel Hannigan, Amel Mackie, Maria Callanan, Gustavo Becerra. Thanks to Dr David Balata for his help in the field, literature provision and insightful discussions. Thanks to Robert French and Dr Rachel Cave for water chemistry analyses. Thanks to Dr Ken Irvine and Dr Constanze O’Toole from the freshwater lab in TCD for their inputs to the lakes study and for providing literature and data. I would also like to thank my colleagues on the BIOCHANGE project, especially Dr Steve Waldren and Louise Scally for project coordination.

I would also like to thank to all my family for their support and for visiting me in Ireland during these years. Finally and most importantly, I would like to thank to Cristina first for agreeing to come with me to Ireland, second for marrying me during the course of this thesis, third for all her fieldwork help, her unconditional support, encouragement and help in many aspects of this thesis. This thesis is dedicated to her and Tito. ..........‘gracias a la vida que me ha dado tanto’

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CHAPTER I: GENERAL INTRODUCTION 1.1.

Multiple stressors in aquatic systems

Human activities are increasing the number and intensity of anthropogenic stressors impacting most aquatic ecosystems (Vitousek et al. 1997, Halpern et al. 2008). Anthropogenic stressors can be defined as human induced changes to any environmental factor that affect populations by altering survivorship or reproduction relative to optimum conditions (Calow 1972, Folt et al. 1999).

Impacts on

populations can render species more vulernable to local and global extinction, with potentially important consequences for the structure and functioning of ecosystems (Raffaelli et al. 2003, Solan et al. 2004, Ieno et al. 2006, Raffaelli 2006, Jackson 2009). Given the potential for such changes to impact on human well being, e.g. through reduced aesthetic appeal and provision of ecosystem services such as fisheries and water purification, there is now considerable international pressure to identify and reduce the impacts of human activities on aquatic systems (Gray 1997, Vitousek et al. 1997, Worm et al. 2006).

Among the most significant stressors of aquatic systems are habitat loss (Gray 1997, Airoldi et al. 2008), overfishing (Jackson et al. 2001, Worm et al. 2006), pollution, in particular eutrophication (Nixon 1995, Andersen et al. 2006, Painting et al. 2007, Diaz and Rosenberg 2008), invasive species (Vitousek et al. 1996, Williamson 1999, Minchin et al. 2002, Allen et al. 2006, Molnar et al. 2008), sedimentation (Cooper and Brush 1993, Airoldi 2003, Balata et al. 2007a, Balata et al. 2007b), acidification (Hall-Spencer 1998, Jeziorski et al. 2008, Riebesell 2008, Zeebe et al. 2008, De'ath et al. 2009) and climate change (Suchanek 1994, Sala et al. 2000, IPCC 2007). In

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most cases anthropogenic stressors do not act in isolation. On the contrary, aquatic ecosystems are almost always subjected simultaneously to multiple stressors (Folt et al. 1999, Crain et al. 2008, Halpern et al. 2008).

Although we have good

understanding of the impacts of many individual stressors, there has been much less research conducted on the effects of multiple stressors acting simultaneously. To understand the combined effects of multiple stressors acting in aquatic systems is thus one of the most pressing challenges in ecology and conservation. Nutrient enrichment, invasive species and sedimentation are currently considered among the major threats to aquatic biodiversity and ecosystem functioning (Vitousek et al. 1997) In the sub-sections below, these stressors are described in more detail and it is discussed how they can affect ecosystems when they occur simultaneously.

1.1.1. Nutrient enrichment Inputs of nutrients into coastal and freshwater systems, through run-off from agricultural activities, industrial or urban developments have greatly increased in the last decades (Valiela et al. 1997, Howarth et al. 2000, Scheffer et al. 2001, DeBruyn and Rasmussen 2002, Painting et al. 2007, Diaz and Rosenberg 2008, Howarth 2008). Nutrient enrichment, can lead to a process known as eutrophication. The word eutrophication is derived from two Greek words: ‘eu’ meaning well and ‘trope’ which means ‘nourishment’. The use of the word in aquatic ecology refers to an increase in the concentration of nutrients in an aquatic ecosystem, enhancing the primary productivity of the system. The European Union defines eutrophication as ‘the enrichment of water by nutrients, especially nitrogen and/or phosphorus, causing an accelerated growth of algae and higher forms of plant life to produce an undesirable disturbance to the balance of organisms present in the water and to the

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quality of water concerned’ (EC 1991).

Effects of nutrient enrichment on the

structure and functioning of in a variety of aquatic systems have been widely documented (Cooper and Brush 1993, Micheli 1999, Howarth et al. 2000, Worm et al. 2000, Bokn et al. 2002, Hillebrand 2003, Clarke et al. 2006, Painting et al. 2007, Howarth 2008, Schindler et al. 2008). The detrimental effects of nutrient enrichment on aquatic ecosystems include widespread hypoxia and anoxia, increased turbidity, habitat degradation, alteration of food-web structure, loss of biodiversity, and increased frequency, spatial extent, and duration of harmful algal blooms (Valiela et al. 1997, Andersen et al. 2006, Clarke et al. 2006, Devlin et al. 2007, Howarth 2008, Pranovi et al. 2008). In an Irish context, nutrient enrichment is considered one of the major sources of water pollution in coastal and transitional waters, lakes and rivers (Toner et al. 2005). In general, the less densely populated, less developed and less intensively farmed regions along the western areas have the higher proportions of unpolluted while the eastern and south-eastern areas are most affected by water quality degradation. Although, there has been a considerable investement in waste water treatment facilities in Ireland, 18 per cent of the waste water generated only received primary treatment (Gaertner et al. 2007).

A wide range of chemical compounds can enter aquatic systems as nutrients. The number that are considered a potential source of nuisance and related to human activities is quite small, however. In this context, the nutrients that are relevant to eutrophication processes are generally restricted to inorganic nitrogen and phosphorus compounds (Schindler 1974, Allen et al. 1998, Howarth et al. 2000, Clarke et al. 2006, Schindler et al. 2008).

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Nitrogen (N) is required for the synthesise of proteins and occurs in three principal inorganic dissolved forms: ammonium (NH4+), nitrate (NO3), and nitrite (NO2). Nitrogen also occurs in dissolved organic forms, such as urea, amino acids, and peptides. Ammonium is usually the preferred form of nitrogen from a nutritional perspective because no chemical reduction is necessary for its assimilation. Nitrate and nitrate must be reduced by the enzymes nitrate reductase and nitrite reductase, respectively, making their uptake a chemical slower process (Millero 1996). The highest concentrations of dissolved nitrogen that occurs in the oceans is that of nitrates (~100 ÂľM), which is often also the most abundant form in eutrophic coastal waters. Under certain circumstances, however, ammonium can surpass nitrate in concentration (usually when nitrate is used up). Nitrite is usually the rarest of the three forms of nitrogen. The concentrations of all dissolved inorganic forms of nitrogen increase in winter and decrease in spring and summer when phytoplankton populations increase (Levinton 2001, Painting et al. 2007).

The biochemical role of phosphorus (P) is different from that of nitrogen because phosphorus is used primarily in the energy cycle of the cell (in particular as part of Adenosine triphosphate (ATP). Phosphorus occurs in water bodies as inorganic phosphate, dissolved organic phosphorus, and particulate phosphorus (Clarke et al. 2006). Phosphate is the form preferred by phytoplankton and exchanges rapidly between phytoplankton and water.

Phosphate is taken up very rapidly by

phytoplankton, and the concentration in surface waters is quite low (Painting et al. 2007). There is a general recognition that primary production in most lakes is P limited (Schindler 1974, Schindler et al. 2008), while estuaries and coastal waters are

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N limited. Although, both marine and lakes system can be either N or P limited (Howarth 2006).

In general, the sources of nutrient pollution can be classified as non-point sources and point sources (Clark et al. 1997). A point source of pollution is defined as a single identifiable, localized source that has a definite position, but limited spatial extent, e.g. a sewage outfall. Non-point sources, also know as diffuse, are generated by run-off from land use activities rather than from an identifiable or without a welldefined point source.

Another non-point source of N in aquatic systems is

atmospheric deposition (e.g. in the form of acid rain), especially in industrialized regions. The combustion of fossil fuels is large source of atmospheric N pollution.

1.1.2. Sedimentation Sediment deposition in aquatic systems is increasing worldwide as consequence of anthropogenic activities, such as urban development, deforestation, dredging, industrial and domestic discharges (Barko et al. 1991, O'Reilly et al. 1996, Vitousek et al. 1997, Airoldi and Virgilio 1998). Sedimentation in aquatic systems can lead to decreased water turbidity and deleterious effects on biodiversity over a range of scales and habitats (Airoldi 2003, Anderson et al. 2004, Balata et al. 2007a). In sedimentary systems, significant decreases in the abundance and diversity of infaunal assemblages over large spatial scales has been associated with increased sedimentation rates (Edgar and Barrett 2000). In coral reefs systems, increased sedimentation regimens has been linked to dramatic changes in species composition and abundance, and irreversible deterioration and loss of coral reefs and associated fisheries resources (de Zwaan et al. 1995, McClanahan and Obura 1997). In rocky

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shore habitats, high sedimentation rates have been associated with changes in assemblage structure, owing to either negative, positive or neutral responses of invertebrates and macroalgae to the stress imposed by sedimentation (Airoldi and Cinelli 1997, Airoldi and Hawkins 2007). In subtidal rocky habitats, sedimentation can lead to loss of beta diversity, overriding the influence of habitat complexity on beta diversity at small and large spatial scales (Balata et al. 2007a).

In freshwater systems one of the principal causes of degraded water quality and aquatic habitat is the depositing of eroded soil sediment in waterbodies. Excessive amounts of sediment resulting from natural or human-induced causes can result in the destruction of aquatic habitat and a reduction in the diversity and abundance of aquatic life (Richter 1995). Diversity and population size of fish and benthic macroinvertebrates associated with coarse substrates can be greatly reduced if the substrates are covered with sand and silt (Leveque et al. 2008).

1.1.3. Invasive species The introduction and transfer of animals and plants around the planet has been occurring for centuries. As humans have travelled the world, species have been transported along with them, for food, medicinal and ornamental purposes, along with some unintentional introductions (Williamson 1999, Leppäkoski et al. 2002, Bax et al. 2003). In some cases, these species become invasive.

There are several definitions of the term ‘invasive’ and its associated concepts (Kaiser et al. 2000, Valery et al. 2008). The term invasive implies alien or nonnative species that produce reproductive offsprings, often in large number, at

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considerable distances from parent populations and thus have the potential of spread over considerable areas (Kaiser et al. 2000). The introduction of alien or invasive species to aquatic systems is currently a major global concern (Mayer et al. 2002, Bax et al. 2003, Molnar et al. 2008). Invasive species are considered to be the second largest threat to global biodiversity, after habitat destruction (Williamson 1999).

Some invasive organisms regulate the availability of resources to other species by causing physical or state changes. Such species are termed ‘ecosystem engineers’ (Margalef and Gutierrez 1983, Jones et al. 1994) or ‘transformer species’ (Kaiser et al. 2000) and have the potential of contributing disproportionately to ecosystem functioning (Cuddington and Hastings 2004). The invasive zebra mussel, Dreissena polymorpha (Pallas 1771), is considered as an ecosystem engineer or transformer species owing to its capacity to alter the flow of organic matter and light, and to provide structurally complex habitat for other species to inhabit and seek refuge from predators (Ricciardi et al. 1997, Mayer et al. 2002). It is one of the most aggressive invaders of lakes and rivers in Europe and North America, where they are the only bivalves that attach to hard substrates. It has rapidly spread through Europe over the last decade (Minchin et al. 2002) and it has been linked to dramatic changes in benthic invertebrate diversity and abundance (Burlakova 1995, Karatayev et al. 1997, Ricciardi et al. 1997, Brodersen et al. 1998, Ward and Ricciardi 2007). By feeding on phytoplankton, zebra mussels can filter large amount of water, leading to increased water clarity and greatly enhanced benthic-pelagic coupling (Fahnenstiel et al. 1995, Carpenter et al. 1998, Minchin et al. 2003, Zhu et al. 2006). Its arrival in

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lake systems therefore has considerable potential to modify the input of existing stressors, such as nutrient pollution.

1.1.4. Combined impacts of multiple stressors In the last decades, the range of chemical, physical and biological stressors, including the ones described above, impacting aquatic systems has grown rapidly. Evaluating systems that are anthopogenically impacted by these stressors acting simultaneously is particularly challenging, because there are many possible ways they can interact with a range of potential effects. This issue raises one of the fundamental question addressed by this thesis: are stressors more harmful in combination than alone? Conceptually there are three broad models to describe the way multiple stressors can interact: additive, antagonistic or synergistic (Folt et al. 1999, Crain et al. 2008). The additive model is used when the combined effect multiple stressors is equal to the sum of their individual effects, the antagonistic model implies that the combined effect of multiple stressors is less than the sum of their individual effects and the synergistic model describes the situation when the combined effect of multiple stressors is larger than the sum of their individual effects. Folt (1999) described a variation of these three interactions models when a single dominant stressor drive the cumulative outcome, namely the comparative model.

1.2.

Environmental monitoring and indicators

In order to assess the impacts of anthropogenic multiple stressors driving biodiversity changes in aquatic sytems and to inform management responses, there is a need for reliable and cost-effective monitoring programmes and environmental indicators.

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Chapter I

Environmental indicators can be defined as a measure, index or model used to estimate the current state and future trends in physical, chemical, biological, or socioeconomic condition of the environment, along with thresholds for management action to achieve desired ecosystem goals (Fisher et al. 2001). Ideally a good ecological indicator should be linked to a specific stressor, capable of providing meaningful information to decision-making that is sufficiently sensitive and reliable, robust to confounding factors, applicable over a range of special and temporal scales, easy to measure, cost-effective, non-destructive, and scientifically and legally defensible (O'Connor and Dewling 1986, Cairns et al. 1993, UNESCO 2006, Rees et al. 2008).

Environmental indicators and standards to monitor compliance with regulatory objectives have been widely used both in marine (Borja et al. 2000, Wells et al. 2007) and freshwater systems (Johnstone and Horan 1996, Howarth et al. 2000). In recent years, the development of reliable indicator of environmental quality has been driven by several global initiatives on sustainable development (UN WSSD 2002), climate change (IPCC 2007) and the conservation of biological diversity (UN CBD 1992), and also from regional incentives such as the development of the ecosystem approach to marine environmental management (OSPAR 1992, Murawski 2007), the Marine Strategy Framework Directive (EC 2008) and the EU Water Framework Directive in relation to water quality (EC 2000).

The WFD requires that all water bodies of European member states achieve at least ‘good ecological status’ by 2015 (EC 2000). For this purpose water bodies need to be classified using ecological quality ratios (EQR), by comparing monitoring data

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with those on reference conditions (e.g. from pristine areas or using historical data before being affected by human activities). This EQR, which ranges between 0 (bad) and 1 (high), is divided then into five quality status levels (high, good, moderate, poor, and bad status), depending on the distance to reference conditions. Ecological classification uses biological, physico-chemical, hydromorphological and chemical assessments of status. Biological assessment uses numeric measures of communities of plants and animals (e.g. phytoplankton, macrophytes, macroinvertebrate and fish). Physico-chemical assessment looks at elements such as dissolved oxygen and the level of nutrients, which support the biology and other chemical, such as dangareous substances, e.g. heavy metals, solvents and pesticides. The hydromorphological assessment looks at water flow and physical habitat

One of the fundamental principles for the derivation of thresholds and ecological classification, is the identification of type-specific reference conditions for minimally impacted or pristine sites (Pollard and Huxham 1998, Bailey et al. 2004). The quantification of deviations from reference conditions requires characterisation of the relationship between a given stressor and its effect. Given that multiple stressors may modify each others’ effects and that their combined effects may be modified in turn by climate change, which may also affect reference conditions, ecological classification as required by the WFD poses a major challenge for ecologists. While there are promising advances in the development of indicators related to single stressors to support the implementation of the WFD, there is a need to improve the understanding of the assessment of systems subjected to multiple stressors.

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General Introduction

Chapter I

In last decades, there has been a shift to towards an ecosystem approach for management strategies (Murawski 2007, Johnson 2008). This requires a resource planning and management approach that recognizes the connections between land, air, water and all living organisms, including humans, their activities and institutions (Worm et al. 2006). In this context, effective biomonitoring tools should indicate ecological quality of the ecosystem as a whole, taking into consideration the interactions between the physical and ecological processes, including human society. For this purpose, is of crucial importance to generate comprehensive datasets encompassing several ecosystem components (e.g. different benthic habitats, fish communities, plankton, nutrients, etc.) across a network of sites to enable to test for multi-trophic effects and the development of bioindicators that are highly correlated with overall ecosystem quality, and the services and goods provided to human society.

Stressors are generally described as being either ‘chronic’ or ‘acute’.

Chronic

stressors act at low intensities over large temporal scales, while an acute is a discrete, usually intense event (Crowe et al. 2000). interchangeably

These terms has been used

with the terms ‘press’ or ‘pulse’ which are used to described

environmental disturbance of long and short duration respectively (Bender et al. 1984). In this context, an important aspect in the development of bioindicators is to recognise spatial and temporal variation in the action of the stressor to be evaluated. For example, outputs of sewage from tourist-oriented coastal towns are considerably greater in summer than in winter and vary from year to year depending on numbers of visitors. Different taxa respond in different ways and impacts may be very intense close to a point source, but at greater distances become apparent only at certain sites

21


General Introduction

Chapter I

depending on hydrodynamics and other factors (e.g. Bishop et al. 2002). In some cases, taxa may show a press response to a pulse disturbance, in others, there may be pulse responses to pulse disturbances and so on (Glasby and Underwood 1996). Full characterisation of the relationship between a given stressor and its effect therefore requires research across a range of taxa in a range of habitats at a range of temporal and spatial scales.

Biological monitoring tools, such as benthic invertebrates surveys, are a valuable tool to establish spatial and/or temporal variations in the ecological quality of aquatic environments (Warwick and Clarke 1993). Although chemical monitoring is useful, there are important reasons for conducting biological monitoring. Organisms have an integrating response to their environment. This means that fluctuations in water quality, which may be missed by intermittent chemical sampling and analysis, are reflected in biological assessments. It also means that some contaminants can, in effect, be detected at lower concentrations than the detection limits of chemical assays (Saqid 1992) because their biotic effects accumulate over time. Chemical monitoring will only record the contaminants that are analysed for whereas the biota may respond to many other, unmeasured pollutants which they may accumulate (Bryan et al. 1979, Widdows and Donkin 1992). Biological monitoring is also important in situations where there are a range of contaminants whose biological effects may be synergistic or antagonistic and would not be appreciated through chemical measurements. In fact, sampling a wide range of chemical contaminants may also be considerably more expensive than an appropriate biomonitoring tool (Slooff et al. 1983, Widdows and Donkin 1992). Moreover, it can be argued to maintain healthy, diverse biological communities, it is more appropriate to monitor

22


General Introduction

Chapter I

the aquatic community rather than chemical variables only (Borja et al. 2000, Clarke et al. 2006).

1.3.

Study systems

In developing understanding of effects of multiple stressors and incorporating that knowledge into cost-effective biomonitoring tools, it is valuable to take a multisystem perspective to enable common patterns and processes to be identified, as well as sources of variation. Two main study systems were considered in this thesis: rocky shores and lakes.

Both systems have received comparatively little attention in

relation to biomonitoring tools and both are tractable model systems, amenable to detailed observational and experimental research.

The present thesis was undertaken using a cross-system approach to the study of aquatic ecology, encouraging comparing aquatic systems within a wider context. This approach is essential considering the large-scale nature of many of the pressing applied issues relevant to aquatic habitats. For example, eutrophication, invasive species, the impacts of global climate change and pollutants are all matters of great current concern. Such threats will impinge on associated habitats such as freshwaters, estuaries and coastal habitats.

1.3.1. Rocky shores Intertidal rocky shores are often biologically rich environments and can include many different habitat types such as steep rocky cliffs, platforms, rock pools and boulder fields. Although they usually occupy only a narrow band at the intersection

23


General Introduction

Chapter I

of land and sea, rocky shore environments occur on many coastlines throughout the world and are important components of coastal ecosystems. Communities inhabiting rocky shores habitats constitute an important link in the marine nutrient and carbon cycles, form a base of coastal food webs and provide crucial habitat and nursery areas for many associated flora and fauna (Smith 1981, Valiela et al. 1997, Worm and Lotze 2006a).

Rocky shore environments can be characterized as diverse mosaics of overlapping and interacting patches (Sousa 1984). The species assemblages that inhabit these patches are structured and maintained by abiotic conditions (Denley and Underwood 1979), physical processes, such as disturbance (Sousa 1979, Ayling 1981, Abugov 1982, Hall and Harding 1997, Underwood 1999, Thrush and Dayton 2002, Lenz et al. 2004, Bertocci et al. 2005, Contardo-Jara et al. 2006, Atalah et al. 2007b), and biological processes, such as predation (Paine 1966, Paine 1974, Navarrete 1996, Witman and Grange 1998, Posey et al. 2006, Griffin et al. 2008, Ware et al. 2009), grazing (Jones 1948, Lubchenco and Gaines 1981, Hawkins and Hartnoll 1983, Jenkins et al. 1999, Bulleri et al. 2000, Benedetti-Cecchi et al. 2005, Worm and Lotze 2006a, Atalah et al. 2007a, Masterson et al. 2008, Noel et al. 2009), competition (Connell 1961, Menge 1976, Paine 1984, Marshall and Keough 1994, Cardoso et al. 2001, Worm et al. 2002) and colonization (Sousa 1984, Davenport and Stevenson 1998, Dial and Roughgarden 1998, Bishop and Kelaher 2007), which act at different spatial and temporal scales. They are subjected to a unique combination of both aerial and marine influences on a more or less regular, tidally defined basis. Intertidal rocky shores are usually the most accessible of marine environments, although only for a few hours around the low tide. Thus, they are relatively easy to

24


General Introduction

Chapter I

sample, compared to subtidal environments for example. This feature makes rocky shores an excellent environment for ecological research and also potentially ideal habitats for cost-effective biomonitoring to indicate ecological quality of the wider coastal environment. For example, manipulative field experiments, which are ideal for studying interactions among organisms, were pioneered in rocky shore environments and have greatly expanded our understanding of the dynamics of marine systems (Castilla 2000, Benedetti-Cecchi 2006). Accessibility has its drawbacks, however, and rocky shores are particularly exposed to human activities. Rocky shores are subject to a wide range of anthropogenic disturbances on a range of temporal and special scales (Crowe et al. 2000, Thompson et al. 2002). Anthropogenic disturbances include harvesting intertidal organisms for food such as sea urchins, gastropods or crabs (Moreno et al. 1984, Castilla 1999), pollution, trampling , sedimentation (reviewed by Airoldi 2003) and introduced species (Bax et al. 2003, Molnar et al. 2008). Impacts of these disturbances vary greatly and there is considerable experimental evidence that biological processes can be mediated by environmental factors (Underwood 1980), suggesting that multiple stressors may have unpredictable combined effects on rocky shore ecosystems.

Although, a wide range of biomonitoring tools based on macrofaunal assemblages has been proposed to monitor and assess changes in coastal areas, they have traditionally used subtidal soft-bottom assemblages e.g. AMBI (Borja et al. 2000) . Assessment using intertidal rocky shore assemblages are less common (Archambault et al. 2001, Benedetti-Cecchi and Osio 2007). Although some biomonitoring tools based on rocky shore organisms have been proposed, mostly using macroalgal assemblages (Orfanidis et al. 2001, ArĂŠvalo et al. 2007, Krause-Jensen et al. 2007,

25


General Introduction

Chapter I

Pinedo et al. 2007, Wells et al. 2007) none has been widely accepted. In an Irish context, the currently recommended system is based on the outputs of the METRIC project (Cusack et al. 2005) and involves the use a reduced macroalgal species list (Wells et al. 2007). This system has not yet been independently evaluated, or widely applied however.

1.3.2. Lakes It is estimated that there are 6,000 natural lakes in Ireland, varying in size from less than 10 ha to 17000 ha. Lakes are major resources, as they provide a range of good and services. A large proportion of the potable water requirement is derived from lakes, they also used extensively for recreational purposes, such as water sports, angling and boating activities. A considerable number of lakes support important salmonid fisheries. Good water quality is essential to sustain these resources.

Lakes are particularly prone to changes due to land use activities because the human population lives disproportionably near waterways and considerably modifying the riparian zones. Additionally, because freshwater systems are extensively used for transportation, sewage disposal sites, and water sources, a large proportion are heavily impacted by humans. This leads to a significant pressure from multiple stressors such as nutrient enrichment, sedimentation, acidification and pollution. Invasive species are also a particularly important stressor in lakes from both extensive intentional (for example, fish stocking) and unintentional (for example, boat fouling) releases of organism (Minchin et al. 2003). Eutrophication comprises the most pervasive of these anthropogenic pressures on lakes globally (Venables and Smith 2006, Schindler et al. 2008). In spite of this, current understanding of, and the

26


General Introduction

Chapter I

ability to quantify and predict, the effects of nutrient enrichment on the structure of lake littoral benthic invertebrate assemblages remains poor (Brodersen et al. 1998, White and Irvine 2003, Tolonen et al. 2005, Solimini et al. 2006, Brauns et al. 2007).

While there are assessment tools using macroinvertebrates to asses the ecological quality of running waters e.g. Q-values (Toner et al. 2005) and RIVPACS (Wright et al. 1998), there are currently no working macroinvertebrate assessment systems based in littoral communities for lakes (H책kanson 2001 , Solimini et al. 2006). Donohue et al. (in press) has recently developed three biometrics that relate littoral benthic macroinvertebrate community structure to eutrophication pressure; namely % Taxa Sensitive to total phosphorus (TP), a TP Score and an Indicator Taxa Metric. However, the performance of this metrics has not been assessed in relation to other stressor acting simultaneously in lakes, e.g. zebra mussel invasion. In this context, there is a need for testing the performance of these metrics under a range of circumstances and modify them if necessary before being adopted as part of the implementation of the WFD.

1.4.

Aims of the thesis

The primary objectives of this thesis were to assess the impacts of multiple stressors on the biodiversity of a range of aquatic systems and to generate data sets that could act as a basis for development and testing of integrative and cost-effective biomonitoring tools. In order to achieve this, and for future strategic research, a network of sites on a range of aquatic habitats differing in levels of pollution was established in the west and central areas of Ireland. Thus, the overall approach was to test specific hypotheses using field based research, including purely observational

27


General Introduction

Chapter I

studies and manipulative experiments. Three separate studies (presented as chapters in this thesis) were carried out to achieve the overall objectives. The first study, presented in Chapter II, examined the impacts of generalised pollution, particularly nutrient enrichment, on the diversity and structure of marine rocky shore assemblages at network of sites established on the west coast of Ireland. Chapter III describes a study undertaken on a network of lake sites, where the performance of ecological quality assessment metrics were tested in a range of lakes along a gradient of nutrient pressure that were either invaded or non-invaded by the zebra mussel Dreissena polymorpha. An additional aim of this study was to test the potential interactive effects of eutrophication pressure and the invasion of the zebra mussel on the structure and diversity of lake macroinvertebrate assemblages. The last data chapter, Chapter IV, describes the results of an experiment, in which the separate and combined effects of sedimentation and nutrient enrichment on algal and invertebrate assemblages in intertidal rock pools were assessed. This experiment also considered the role of grazing gastropods in controlling the growth and cover of macroalgal assemblages and its potential interaction with nutrients in structuring macrobenthic assemblages in rock pools.

The final chapter, the General Discussion, aims to draw together the three elements of research described above and to place them in the general context of current scientific research, legislation and biomonitoring programmes at an Irish and international level.

28


General Introduction 1.5.

Chapter I

The BIOCHANGE project

This thesis was part of the multidisciplinary BIOCHANGE project funded by the Environmental Protection Agency (EPA). BIOCHANGE is an integrative, multidisciplinary research framework to support national and local biodiversity policy in Ireland. Its core research directly addresses the protection and management of ecological resources in the context of anthropogenic and natural stressors and threats that might lead to environmental change. It focuses mainly on habitat fragmentation and loss, impacts on non-native species, climate change, pollution and resource management. This thesis was part of Work Package III, ‘Pollution as a driver of biodiversity change - impacts, indicators and long-term monitoring’.

29


Pollution and biodiversity on rocky shores

Chapter II

CHAPTER II: POLLUTION AS A DRIVER OF BIODIVERSITY CHANGE IN ROCKY SHORES: THE POTENTIAL OF MOLLUSCAN AND MACROALGAE ASSEMBLAGES FOR BIOMONITORING 2.1.

Abstract

The Water Framework Directive (WFD) requires consideration of the impacts of pollutants on elements of biodiversity and the development of cost-effective tools to measure those impacts.

To test the effects of urban pollution and to develop

biomonitoring tools, a network of 11 rocky intertidal sites differing in pollution status was established on the west coast of Ireland. Communities of molluscs on the lower shore were sampled at the sites and a range of physicochemical parameters was measured in order to characterize levels of pollution on the shores. Total abundance and number of taxa of molluscs were reduced in polluted sites compared to control sites. Multivariate analyses showed that the structure of the molluscan assemblages differed between polluted and control sites, discriminating between species that were more abundant at polluted sites and those that were more abundant at control sites.

Multivariate multiple regression analysis showed that nitrite,

chlorophyll-a, phosphate and ammonia levels in seawater accounted for more than 45% of the variability in the community structure of molluscs. This study suggests that molluscan assemblages could be a cost-effective tool to monitor and detect changes induced by urban pollution in coastal areas and could be successfully used as part of the implementation of the WFD.

30


Pollution and biodiversity on rocky shores 2.2.

Chapter II

Introduction

The already major impact of human activities on marine biodiversity is expected to increase during the next decades (Vitousek et al. 1997, Halpern et al. 2008). Urban, industrial and agricultural development have led to pollution of estuaries and coastal waters by a variety of effluents (Valiela 2006). Pollution can carry substantial loads of nutrients, organic matter, sediments, heavy metals and hydrocarbons, that provide single or combined pressures to coastal areas (Crowe et al. 2000). Eutrophication, in particular, is of major concern (Allen et al. 1998, Thompson et al. 2002, Valiela 2006), leading to changes in trophic web structure and alterations to benthic production. Documented effects of nutrient enrichment on rocky shores include a decline in the abundance of perennial macroalgae, mainly the brown algae Fucus vesiculosus (Kautsky et al. 1986, Schramm 1996), and increases in the cover of ephemeral algae (Worm et al. 1999). These change the structure and function of marine communities (Kautsky et al. 1992).

In recent years, the development of indicators of environmental quality has been driven by several global and regional initiatives on sustainable development (UN WSSD 2002), climate change (IPCC 2007), the conservation of biological diversity (UN CBD 1992) and integrated water resource management (WFD, EC 2000); with a shift towards using environmental indicators of anthropogenic impact within a regulatory framework. E.U Directive 2000/60/EC (The Water Framework Directive, WFD) requires considerations of impacts of pollution on elements of marine biodiversity and the development of cost-effect biomonitoring. An ideal indicator should be linked to a specific stressor, capable of providing meaningful information to decision-making that is sufficiently sensitive and reliable, robust to confounding

31


Pollution and biodiversity on rocky shores

Chapter II

factors, applicable over a range of special and temporal scales, easy to measure, costeffective, non-destructive, and scientifically and legally defensive (O'Connor and Dewling 1986, Cairns et al. 1993, UNESCO 2006, Rees et al. 2008).

Although, many biological assemblages has been used to monitor and assess changes in coastal areas, most studies have traditionally used subtidal soft-bottom assemblages (Otway et al. 1996). Assessment tools using intertidal rocky shore assemblages are less common (Archambault et al. 2001, Benedetti-Cecchi and Osio 2007). The use of macroalgal assemblages is explicitly required by the WFD and recommended by other researchers as a potential cost-effective biomonitoring tool for assessing the impacts of pollution (Orfanidis et al. 2001, ArĂŠvalo et al. 2007, Krause-Jensen et al. 2007, Pinedo et al. 2007, Wells et al. 2007). In considering alternative approaches, molluscs are frequently described as potentially reliable bioindicators (Boening 1999, Bresler et al. 2003, Espinosa et al. 2007). They inhabit almost all marine environments, often with high diversity and abundance, and their taxonomy, biology, ecology and distribution are well known in many coastal zones, including Europe. In most cases, they can be identified by use of shell features. Responses to pollution include changes in abundance and community composition and structure (Terlizzi et al. 2005b), generally reflecting patterns of overall species richness in intertidal rocky shore environments (Smith 2005). Studies that have used molluscs to assess urban pollution have been restricted mostly to subtidal environments (Terlizzi et al. 2005b) or to the use of a specific molluscan group (e.g. Patellidae, Espinosa et al. 2007).

32


Pollution and biodiversity on rocky shores

Chapter II

This study compared the diversity and structure of macroalgal and molluscan assemblages associated to Fucus serratus canopy on rocky shores subject to differing intensities of pollution. F. serratus was chosen as the focal habitat because the following features make it well suited for biomonitoring: it is widely distributed around the North-Atlantic Ocean, it is ubiquitously found on hard substrata and limited to the lower shore (ensuring limited variation in the tidal level of different sites sampled), it is easily recognised and constitutes a species-rich habitat (Hayward 1988).

In particular, the study examined the relationship between nutrient

concentrations and aspects of assemblage structure, in order to develop relevant biomonitoring techniques.

2.3.

Methods 2.3.1. Study sites

Molluscan assemblages associated with >80% coverage of Fucus serratus were sampled at eleven sites, differing in concentrations of nitrate, nitrite, ammonia and phosphate, along the West coast of Ireland (Figure 1). Sites were in rocky intertidal habitats, sheltered from wave action, within a salinity range of 31 – 34 PSU and with a substratum comprising a mix of granite bedrock and large boulders of low slope angle (>120 °). Mussel beds were absent. The overall study comprised clusters of sites in Connemara, Galway and Shannon, to account for possible regional or biogeographical differences (Figure 1). Within each cluster, two polluted and two sites considered to represent conditions of low impact (control sites) were selected, based on long term monitoring of nutrients in coastal areas (Toner et al. 2005). In Connemara, the two polluted sites were Kilkieran (53°19.2’ N, 9°43.9’ W) and Rossaveal (53°16.2’ N, 9°33.4’ W) and the two control sites were Ballynahown 33


Pollution and biodiversity on rocky shores

Chapter II

(53°14.2’ N, 9°33.2’ W) and Muighinis Island (53°17.5’ N, 09°50.3’ W). The Kilkieran site was located next to outfall of a seaweed meal production plant discharging untreated waste water directly into the bay.

Similarly, Rosaveal is

directly affected by untreated industrial effluents coming from the harbour. Within the Galway area, it was not possible to find two polluted sites with similar abiotic characteristics, and only one polluted site, Mutton Island (53°15.1 N, 9°03.0’ W), was sampled. This was located in the vicinity of an outfall discharging secondary treated sewage from Galway City. The two control sites were Parkmore (53°10.2’ N, 8°58.0’ W) and Rinville (53°16.0’ N, 9°02.5’ W). In the Shannon coastal region, the two polluted sites were Kilrush (52°37.4’ N, 09°29.3’ W) and Fenit 52°16.6’ N, 09°50.3’ W), with control sites at Rinevella Point (52°34.9’ N, 9°44.6’ W) and Carrigaholt (52°36.0’ N, 09°42.1’ W). Both polluted sites are near to outfalls from which untreated or partially treated urban waste water effluents are discharged.

In each cluster, polluted and control sites were chosen so that they were geographically interspersed. When possible, sites were chosen randomly from a larger pool of candidate sites. At each site, an area was selected randomly on the low-shore, 30 m long and 20 m wide parallel to the coast line along a conspicuous Fucus serratus fringe.

34


Pollution and biodiversity on rocky shores

Chapter II

Study sites Connemara Mweenish

Galway

Kilkieran Rossaveel Midden

Mutton Is.

Renmore Renville Parkmore

Shannon -Tralee Corrigaholt

Kilrush

Rinevella

Fenit

Polluted Unpolluted

20 km

Figure 1. Map showing the location of the eleven study sites in the west coast of Ireland. Two polluted (filled symbols) and two control sites (empty symbols) were sampled at each of the clusters Connemara and Shannon. In the Galway cluster, one polluted site and two control sites were sampled (see Methods section for details).

35


Pollution and biodiversity on rocky shores

Chapter II

2.3.2. Water chemistry In order to establish a more detailed profile of the nutrient status of the sites than was available through existing data, water samples were collected on two occasions during the winter when nutrients concentrations are likely to be maximal (Levinton 2001). At each site, triplicate water samples were taken with 500 mL opaque HDPE plastic bottles.

Sample bottles were acid washed and thoroughly rinsed with

deionised water before use, and rinsed with sample water before filling with sample. Ammonium concentrations were determined within 24 h after collection, while samples for measurement of nitrite, nitrate and phosphate were stored at –30°C for subsequent analysis. Standard spectrophotometric methods (Grasshoff et al. 1983) were used to measure levels nitrite (NO2-, µg N/L), nitrates (NO3-+NO2-, mg N/L), phosphate (PO43-, µg P/L) and ammonia (NH3, µg N/L).

Salinity (PSU) was

measured using Star-Oddi salinity loggers that were fasted to rocks at the sites for a week recording values every hour.

2.3.3. Sampling of macroalgae and molluscs Non-destructive sampling of macroalgal cover was carried out in five randomly positioned quadrats at each of the sites. Percentage cover was estimated using the point-intercept method (Murray et al. 2006) with a quadrat of 0.25 m2 and 36 intersection points. Most conspicuous taxa were identified to species level, while others were grouped into functional-form groups. The intersection data was then transformed into percentage cover. Macroalgae present in the quadrat not recorded by this method was given a cover of 1%. In the case of multi-strata growth, total percentage cover exceeded 100 %.

36


Pollution and biodiversity on rocky shores

Chapter II

Molluscs were sampled from five randomly positioned quadrats (0.25 m2) within each site. All loose molluscs found inside a quadrat were colleted and placed inside buckets. Then all specimens of the canopy-forming species (mainly Fucus serratus, but also Ascophyllum nodosum and Fucus vesiculosus) whose holdfasts were inside the quadrat were cut and placed in buckets, for later washing to remove animals. Additionally, one half of each quadrat was randomly selected and all organisms were cleaned off the rock using a paint scraper. The epifauna and the scraping samples were stored in labelled jar, preserved in 5% formaldehyde. Molluscs were later sorted and identified to species or genera. The protocols used were based on those developed by O. Mulholland and are known to be effective (O. Mulholland, unpubl. data).

2.3.4. Statistical Analysis 2.3.4.1. Multivariate analyses Differences in assemblage structure between polluted and control sites were tested using a distance-based permutational analysis of variance (PERMANOVA, Anderson 2001a, McArdle and Anderson 2001) based on Bray-Curtis similarities of the square root transformed data. The designed comprised three factors: Cluster (random, with three levels: Connemara, Galway and Shannon), Pollution (fixed, with two levels: polluted and control, and crossed with factor Cluster) and Sites (random, with two levels, and nested in factor Pollution). One cluster (Galway) only had one polluted site (see study sites section above), thus resulting in an asymmetrical design for this cluster. PERMANOVA+ (Anderson and Gorley 2007), an add-on to Version 6 of the PRIMER computer program (Clarke and Gorley 2006) was used to partition

37


Pollution and biodiversity on rocky shores

Chapter II

multivariate variability according to the full experimental design and dealt appropriately with asymmetry.

Differences in community structure among treatment levels were visualized with non-metric Multidimensional scaling (nMDS) plots on the basis of Bray-Curtis similarities of the squared root transformed data. Similarity Percentage Analysis (SIMPER, Clarke 1993) was used to identify the percentage contribution of each species (or taxon) to observed differences between communities at the polluted and control sites. The ratio Diss/SD was used to indicate the consistency with which a given species contributed to the average dissimilarity between samples from polluted and control sites. Values ≼1 indicated a high degree of consistency.

The relationship between species data and environmental variables was analysed using multivariate multiple regression (McArdle and Anderson 2001), using the DISTLM routine in the PRIMER 6 & PERMANOVA (Anderson and Gorley 2007). A marginal test was done where individual variables were fitted separately to test their relationship with the molluscan assemblage data (ignoring other variables), followed by a forward selection procedure, conditional on variables already included in the model (Anderson et al. 2004). The conditional test identifies the subset of variables that best predicts the species data. Both the conditional and marginal test were done using 4999 permutations. Tests were based on Bray-Curtis dissimilarities of the untransformed molluscan abundance data. Environmental variables used in the analyses were NO2-, NO3-, PO43- and NH3.

38


Pollution and biodiversity on rocky shores

Chapter II

2.3.4.2. Univariate analyses Univariate permutational analyses of variance (Anderson 2001b) were done on several variables to test differences between polluted and control sites. The variables were: number of taxa, total abundance, nitrate and ammonia. Univariate analyses were done using PERMANOVA, with Euclidean distances as the measure of similarity.

This is preferable to traditional ANOVA, because PERMANOVA

calculates P-values using permutations, rather than relying on tabled P-values, which assume normality.

Levene’s test based on the median was used to check the

assumption of homogeneity of variances. Additionally, as mentioned above, the PERMANOVA analysis can deal appropriately with asymmetric designs.

Total

abundance data was ln (x) transformed to remove heterogeneous variances, to achieve approximate unimodal symmetry and to avoid skewness.

2.4.

Results 2.4.1. Nutrient concentration

Significantly higher concentration of ammonia was found at polluted sites compared with controls in all three clusters (Figure 2and Table 1), this pattern was encoutered despite there was significant site-to-site variability.

Nitrate levels (NO3-) levels

varied significantly among clusters (P<0.001, Figure 2 and Table 1), but no significant differences between polluted and control sites were found in any of the three clusters.

39


Pollution and biodiversity on rocky shores

Chapter II

Table 1. Permutational ANOVAs analyses for levels of nitrates and ammonia based on Bray-Curtis similarities of the untransformed data.

Source of variation Cluster Pollution Cluster x Pollution Sites (Cluster x Pollution) Res Total Transformation

NO3+NO2 mg N/L (+SE)

0.8

df 2 1 2 5 44 54

Nitrates MS F P 9.72E+05 12.63 0.01 24354 0.45 0.59 5.33E+04 0.69 0.53 7.70E+04 1.00E+10 1.00 -7.54E-12 None

Ammonia MS F 161.20 7.87E-02 10439.00 26.13 365.14 0.18 2048.50 8.45E+06 2.42E-04

P 0.93 0.03 0.84 0.00

None

Polluted Unpolluted

0.6

0.4

0.2

0

Connemara

Galway

Shannon

Galway

Shannon

Ammonia (ug N/L) (+SE)

80 Polluted Unpolluted 60

40

20

0

Connemara

Figure 2. Mean (+SE) Nitrates (a) and ammonia (b) in each of the three clusters at polluted (filled bars) and control sites (empty bars).

40


Pollution and biodiversity on rocky shores

Chapter II

2.4.2. Community structure Eighteen morphological groups of macroalgae were found across all sites and were included in the analyses. Macroalgal assemblages did not differ significantly among clusters and pollution status; although there was significant spatial variation at the scale of sites.

Multivariate multiple regression showed that all environmental

variables measured had a significant relationship with the macroalgae data, however the explanatory power was low; assemblage variation attributable to concentration of ammonia, phosphate and nitrate were 10.8, 7.8, and 5.3%, respectively.

The

sequential model showed that all four variables together explained 30.8% of the total variation of the macroalgal data.

Thirty-five species of mollusc were found across all sites and were included in the analyses (Table 2). The molluscan assemblages differed significantly among the three geographical clusters (Table 5a) and between polluted and control sites (Table 5a). The nMDS plots (Figure 3) illustrate this pattern, showing a separation between assemblages from the three different clusters and between polluted and control sites (Figure 2). SIMPER analysis indicated that variation in the abundance of thirteen taxa contributed >75% of the difference between assemblages in polluted and control sites (Table 3). Lacuna pallidula, Rissoa parva, Bittium reticulatum, Littorina spp., Gibbula umbilicalis, G. cineraria, Nucella lapillus and Ansates pellucida tended to be more abundant at control sites, while Odostomia scalaris, Mytulis edulis, Littorina littorea, Rissoa lilacina and Cingula cingulus tended to be more abundant in polluted sites (Table 3).

Three rare species (Tricolia pullus, Lacuna vincta, Assiminea

grayana) present at control sites were not found at polluted sites.

41


Pollution and biodiversity on rocky shores

Chapter II

Stress = 0.18

Figure 3. Non-metric multidimensional scaling (nMDS) ordination of molluscan assemblages in each cluster: Connemara (circles), Galway (triangles) and Shannon (squares) at both polluted (filled symbols) and control sites (empty symbols) on the basis of Bray-Curtis similarities of the square-root transformed data.

42


Pollution and biodiversity on rocky shores

Chapter II

Table 2. List of the 35 taxa identified and included in the analysis. Species name

Family

Order

Class

Taxonomic authority

Acanthocardia tuberculata

Cardiidae

Veneroida

Bivalvia

Coen, 1915

Anomiacea sp.

Anomiidae

Pterioida

Bivalvia

Assiminea grayana

Assimineidae

Mesograstropoda

Gastropoda

Bittium reticulatum

Cerithiidae

Mesogastropoda

Gastropoda

da Costa, 1778

Odostomia scalaris

Pyramidellidae

Heterobranchia

Gastropoda

Jeffreys, 1867

Buccinum undatum

Buccinidae

Neogastropoda

Gastropoda

MacGillivray, 1843

Calliostoma zizyphinum Unidentified Polyplacophora

Trochidae

Archaeogastropoda

Gastropoda

Linnaeus, 1758

Neoloricata

Neoloricata

Polyplacophora

Cingula cingulus

Rissoidae

Mesogastropoda

Gastropoda

Montagu, 1803

Gibbula cineraria

Trochidae

Archaeogastropoda

Gastropoda

Linnaeus, 1758

Fleming, 1828

Gibbula umbilicalis

Trochidae

Archaeogastropoda

Gastropoda

da Costa, 1778

Goodallia triangularis

Astartidae

Veneroida

Veneroida

Montagu, 1803

Ansates pellucida

Patellidae

Archaeogastropoda

Gastropoda

Linné, 1758

Hiatella arctica

Hiatellidae

Myoida

Bivalvia

Linnaeus, 1767

Hinia incrassata

Buccinidae

Neogastropoda

Gastropoda

Ström, 1768

Hinia reticulatum

Buccinidae

Neogastropoda

Gastropoda

Linnaeus, 1758

Lacuna pallidula

Littorinidae

Mesogastropoda

Gastropoda

da Costa, 1778

Lacuna vincta

Littorinidae

Mesogastropoda

Gastropoda

Montagu, 1803

Littorina compressa

Littorinidae

Mesogastropoda

Gastropoda

Jeffreys, 1865

Littorina littorea

Littorinidae

Mesogastropoda

Gastropoda

Linnaeus, 1758 Linnaeus, 1758

Melarhaphe neritoides

Littorinidae

Mesogastropoda

Gastropoda

Littorina sp.

Littorinidae

Mesogastropoda

Gastropoda

Musculus sp.

Mytilidae

Mytiloida

Bivalvia

Mytilus edulis

Mytilidae

Mytiloida

Bivalvia

Linnaeus, 1758

Nucella lapillus

Muricidae

Neogastropoda

Gastropoda

Linnaeus, 1758

Unidentified Nudibranchia

Nudibranchia

Nudibranchia

Gastropoda

Ocenebra erinacea

Muricidae

Neogastropoda

Gastropoda

Linnaeus, 1758

Onchidella celtica

Onchidiidae

Systellommatophora

Gastropoda

Cuvier, 1817

Onoba semicostata

Rissoidae

Mesogastropoda

Gastropoda

Montagu, 1803

Patella vulgata

Patellidae

Archaeogastropoda

Gastropoda

Linnaeus, 1758

Rissoa lilacina

Rissoidae

Mesogastropoda

Gastropoda

Récluz, 1843

Rissoa parva

Rissoidae

Mesogastropoda

Gastropoda

da Costa, 1778

Tricolia pullus

Tricollidae

Archaeogastropoda

Gastropoda

Linnaeus, 1758

Turtonia minuta

Turtoniidae

Veneroida

Bivalvia

Fabricius O., 1780

Venerupis senegalensis

Veneridae

Veneroida

Bivalvia

Gmelin, 1791

43


Pollution and biodiversity on rocky shores

Chapter II

Table 3. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis dissimilarity distinguishing the two group of sites, based on the squared root transformed data.

Average Abundundace Polluted sites

Taxon Lacuna pallidula Rissoa parva Odostomia scalaris Bittium reticulatum Mytilus edulis Littorina littorea Littorina sp. Gibbula umbilicalis Gibbula cineraria Nucella lapillus Ansates pellucida Rissoa lilacina Cingula cingulus

0.57 1.62 1.34 0.92 1.31 1.86 2.64 1.23 0.35 0.54 0.21 0.57 0.45

Average Avearge Abundundace dissimilarity % Cummulative Diss/SD Unpolluted contributed by Contribution % sites each taxon 1.85 4.63 1.49 8.7 8.7 2.17 4.28 1.28 8.03 16.73 0.6 3.8 1.17 7.14 23.87 1.3 3.75 1.24 7.04 30.91 0.85 3.59 1.18 6.74 37.65 1 3.53 1.32 6.64 44.29 3.52 3.43 1.29 6.44 50.73 1.47 2.99 1.23 5.62 56.35 0.94 2.63 1.12 4.94 61.29 1 2.4 1.21 4.5 65.79 0.79 2.31 1.06 4.33 70.12 0.53 2.11 0.94 3.96 74.08 0.32 1.66 0.68 3.12 77.19

2.4.3. Relationship between molluscan data and environmental variables The nonparametric multivariate regression analysis showed that each of the four environmental variables (i.e. all variables measured) individually had a significant relationship with molluscan assemblage data (Table 4a), with greatest amount of variation explained by nitrite (13.0%), ammonia (12.5%) and phosphate (11.7%). The sequential model using the forward-selection method showed that the four variables together explained 45.0% of the total variation of the molluscan assemblage structure (Table 4b). The percentage of variation in the biotic data explained by ammonia and phosphates was increased after fitting the nitrite and, on the other hand, the amount of variation explained by nitrate was drastically reduced after fitting the other three variables (Table 4b).

44


Pollution and biodiversity on rocky shores

Chapter II

Table 4. Results of non-parametric multiple regression of molluscan assemblage data on individual environmental variables for (a) each variable taken individually (ignoring other variables) and (b) forward-selection of variables, where amount explained by each variable added to model is conditional on variables already in the model (i.e. those variables listed above it). %Var: percentage of variance in species data explained by that variable; Cum. (%): cumulative percentage of variance explained.

a) Marginal test Variable Nitrite Phosphate Nitrate Ammonia

% variability 12.98 11.74 8.85 12.54

F 7.91 7.05 5.14 7.60

P Cum (%) 0.0002 0.0002 0.0006 0.0002

b) Sequential test Nitrite Ammonia Phosphate Nitrate

12.98 12.66 16.91 2.47

7.91 8.86 15.02 2.25

0.0002 0.0002 0.0002 0.0298

12.98 25.65 42.56 45.03

2.4.4. Univariate analyses There was significant variation among clusters in the mean number of molluscan taxa (Table 5a and Figure 4), ranging from a maximum average of 11.8 (± 0.49 S.E.) taxa in cluster Connemara to a minimum of 7.0 (± 0.92 S.E.) taxa in cluster Galway. Similarly, there was significant spatial variation in number of taxa of molluscs at the site scale (Table 5a), ranging on average from a minimum of 6.0 to a maximum of 12.8 taxa per site. Although there was a trend for a reduced number of taxa at polluted (mean 7.3 ± 0.67 S.E.) compared with control sites (mean 10.5 ± 0.45 S.E.) in all three clusters (Figure 4), ANOVA did not detect significant differences (Table 5a).

There was significant spatial variation among clusters and between sites in the total abundance of molluscs (Table 5a and Figure 4), ranging from a maximum average of

45


Pollution and biodiversity on rocky shores

Chapter II

514.7 (± 68.7 S.E.) taxa in cluster Connemara to a minimum of 176.6 (± 42.3 S.E.). However, total abundance was significantly reduced at polluted mean (183.7 ± 36.6 S.E.) compared with control sites (mean 398.2 ± 51.6 S.E., Table 5a and Figure 4). In addition, there was significant spatial variation in total abundance of molluscs at the site scale (Table 5a), ranging on average from a minimum of 71.8 (± 6.8 S.E.) to a maximum of 598.8 (± 83.5 S.E.) individuals per site.

No changes in number of macroalgal taxa or total cover were detected in relation to pollution status or among clusters (Table 5b). Overall there were on average 12.5 (± 0.3 S.E.) macroalgae taxa per site ranging from a maximum of 16.1 to a minimum of 8.7 taxa per site, whereas polluted sites had 12.09 (± 0.4 S.E.) compared to 13 (± 0.4 S.E.) taxa in control sites. There was significant variation in both number of macroalgae taxa and total cover at the scale of sites (Table 5b).

Table 5. PERMANOVA analysis of differences in molluscan assemblage structure based on Bray-Curtis similarities of the square-root transformed data and permutational ANOVAs analysis for the number of taxa and total abundance. a) Molluscan assemblages Source of variation Cluster Pollution Cluster x Pollution Sites (Cluster x Pollution) Res Total Transformation

Multivariate df MS F P 2 11756.0 4.1 0.003 1 10700.0 4.4 0.03 2 2399.8 0.8 0.61 5 2839.0 5.9 0.00 44 484.6 54 Square root

Number of taxa MS F P 100.0 13.6 0.02 89.2 5.9 0.13 15.3 2.1 0.22 7.3 2.0 0.10 3.6

Total abundance MS F P 529.76 13.3 0.01 311.62 11.5 0.05 26.94 0.7 0.53 39.90 3.4 0.01 11.63

None

None

Number of taxa MS F P 19.5 1.1 0.42 15.6 0.2 0.67 67.6 3.8 0.11 17.9 4.8 0.00 3.8

Total abundance MS F P 1.0 0.5 0.63 1.6 0.8 0.48 2.0 1.1 0.36 1.8 6.2 0.0010 0.3

None

Arcsin

b) Macroalgal assemblages Source of variation Cluster Pollution Cluster x Pollution Sites (Cluster x Pollution) Res Total Transformation

Multivariate df MS F P 2 6930.8 2.4 0.02 1 7257.2 1.6 0.25 2 4666.0 1.6 0.15 5 2924.7 8.2 0.00 44 357.4 54 Arcsin

46


Pollution and biodiversity on rocky shores

Number of taxa (+ SE)

15

Chapter II

Polluted Unpolluted

12

9

6

3

0

Connemara

Galway

Shannon

Total abundance (+SE)

800 Polluted Unpolluted 600

400

200

0

Connemara

Galway

Shannon

Figure 4. Mean (+SE) number of taxa and total abundance in each of the three clusters at polluted (filled bars) and control sites (empty bars).

47


Pollution and biodiversity on rocky shores

2.5.

Chapter II

Discussion

Consistent differences in molluscan diversity and assemblage structure associated with pollution were found.

This indicates that molluscan assemblages can

potentially provide a robust indication of urban pollution in coastal areas, despite high inherent spatial variability.

The lack of consistent variation in algal

assemblages associated with differences in pollution status suggests that these assemblages, although explicitly required by the WFD and recommended by other workers (Orfanidis et al. 2001, ArĂŠvalo et al. 2007, Krause-Jensen et al. 2007, Pinedo et al. 2007, Wells et al. 2007), may not provide useful information for classification of ecological status of the system examined here, unless used in a different way from the approach examined here. Methods to assess ecological status based on functional groups of macroalgae may provide equivocal results because species belonging to the same group can display a completely different responses to pollution (ArĂŠvalo et al. 2007). Nevertheless, some algal taxa are known to respond reliably to pollution (e.g. some green algae respond positively to nutrient enrichment (ArĂŠvalo et al. 2007); Enteromorpha compressa tolerates high concentrations of copper (Castilla 1996), so the development of effective tools based on macroalgae should be possible.

The observed differences in molluscan assemblage structure were underpinned by changes in the relative abundance of several taxa between polluted and control sites and by differences in assemblage composition, owing to the absence of three rare species at polluted sites (Tricolia pullus, Lacuna vincta, Assiminea grayana). In the current study, polluted sites were characterised by a significant reduction in molluscan total abundance and higher abundance of the gastropods Odostomia

48


Pollution and biodiversity on rocky shores

Chapter II

scalaris and Littorina littorea and the bivalve Mytilus edulis. The pyramidellids, such as O. scalaris, are common ectoparasites in many marine communities, but little is known about their biology and life histories (Collin and Wise 1997). L. littorea is tolerant to high levels of pollution and increasingly used as a bioindicator (Oehlmann et al. 1998, Jackson 2008). M. edulis can tolerate and accumulate a wide range of contaminants, and is also used extensively as a biomonitor in coastal areas (Widdows and Donkin 1992, Boening 1999). There was also trend for a reduction of the mean number of molluscan taxa at polluted sites compared to controls. Reduced number of taxa, recognized as a symptom of nutrient pollution in hard bottom habitats (Fairweather 1990, L贸pez Gappa et al. 1990, Terlizzi et al. 2005a, Terlizzi et al. 2005b), was evident as a trend across all geographical clusters in this study, although it was not statistically significant.

The results of this study concur with several other studies that have advocated molluscs as useful, general surrogates for ecological assessment (Smith 2005, Terlizzi et al. 2005a, Terlizzi et al. 2005b, Espinosa et al. 2007). For example, Skilleter (1995) demonstrated that molluscan assemblages responded to human impacts in mangrove forests with patterns of community structure significantly correlated to the levels of habitat damage. Terlizzi et al. (2005a, 2005b) have shown that the use of molluscan assemblages were an effective method to assess the impacts of pollution in rocky subtidal habitats.

Smith (2005) showed that variation in

assemblages of prosobranch molluscs reflected patterns in the broader intertidal community and provided an accurate prediction of overall diversity.

49


Pollution and biodiversity on rocky shores

Chapter II

The observed trade off between tolerant and sensitive molluscan species, allowed the identification of potential bioindicator taxa, for example the abundance of taxa highly correlated with total abundance, pollution status or nutrient concentration. Further research to develop classification tools for rocky shores should include monitoring programmes with larger spatial and temporal scales and an increased range of pollution levels and habitat types (e.g. more exposed shores).

The

molluscan sampling of this study was undertaken during spring because a peak in molluscan abundance and species richness has been shown to occur during this period (Williams 1996). In this context, spring is recommended as a suitable time for

biological

monitoring

purposes

using

these

assemblages.

Nutrient

concentrations, however, should be measured during winter when peak concentrations a more likely to be encountered (Levinton 2001, Painting et al. 2007).

Because the habitat sampled in this study, Fucus serratus on the lower rocky shore, is widespread along the Atlantic coast of Europe and the north-east coast of America, there is a great potential of applicability of the monitoring methods at a wider geographical scale. Currently there are no ecological quality tools to assess rocky shores based on macroinvertebrates, thus the method suggested here constitutes promising progress toward the achivement of an integrative ecosystem approach in the context of the implementation of the WFD (UN CBD 1992, EC 2000, Murawski 2007, Johnson 2008).

The approach taken in this study to assess the ecological status of rocky shores was based in comparing the molluscan diversity and assemblage structure of sites with a comparatively high level of pollution with that at control sites of the type that could

50


Pollution and biodiversity on rocky shores

Chapter II

be used to define reference conditions. The WFD requires the classification of water bodies into five ecological quality classes (EC 2000). In this context, further research is required to develop more detailed classification tools based on the tolerance of molluscan taxa to pollution. For example, it would be valuable to classify taxa into a range of ecological groups based upon previous ecological models (Pianka 1970, Gray et al. 1979) and the stages of their ecological succession in a range of stressed environments (Pearson and Rosenberg 1978, Borja et al. 2000), such that taxa indicative of particular sets of conditions could be nominated and evaluated.

There is a considerable amount of literature that has shown the effects of urban pollution on the diversity and structure of rocky shore communities in both subtidal (Chapman et al. 1995, Terlizzi et al. 2005a, Terlizzi et al. 2005b) and intertidal (Archambault et al. 2001, Crowe et al. 2004, Bishop and Kelaher 2007, Espinosa et al. 2007) temperate areas.

There are many potential mechanisms that may be

responsible for these differences.

Urban pollution can increase the amount of

suspended solids in the water column, and modify the rate of sedimentation. The effects of sedimentation on rocky shore assemblages, both algae and invertebrate, are well documented (Airoldi and Virgilio 1998, Irving and Connell 2002, Airoldi 2003, Balata et al. 2007a). Abundance of key gastropods grazers can be directly reduced by sedimentation (Airoldi and Hawkins 2007).

Changes to gastropod grazer

communities often affect the structure and growth of algal assemblages (Lubchenco and Gaines 1981, Hawkins and Hartnoll 1983) and this can translate to changes in ecosystem functioning (O'Connor and Crowe 2005). Pollution can also reduce light penetration in the water column and this can influence benthic assemblage structure (Glasby 1999, Saunders and Connell 2001).

51


Pollution and biodiversity on rocky shores

Chapter II

Pollution also discharges organic and inorganic compounds and the uptake of chemicals by marine organisms has been suggested by some authors as potentially altering the abundance and structure of invertebrates assemblages (Otway et al. 1996). Of the variation in molluscan assemblages in the current study, 45% was explained by the four nutrient variables.

Significantly higher concentrations of

ammonia were measured in polluted compared with non-polluted sites. Ammonia is highly toxic to marine organisms (Randall and Tsui 2002, Chen et al. 2008). It is important to underline, however, that this was an observational study and thus the data presented do not allow the inference of causal mechanisms for the observed differences. A series of controlled experimental manipulations would be needed to unravel the underlying mechanisms causing changes in molluscan assemblages associated with pollution.

The high variation of molluscan assemblages, abundance and number of taxa within and among sites and geographical clusters likely reflects both regional biogeography and variation in the magnitude of anthropogenic pressure. The Galway and Shannon and Tralee sites are adjacent to relatively large urban centers compared with those in Connemara. It has been suggested that anthropogenic pollution can decrease or increase the spatial variation in abundance of marine assemblages and populations (Warwick and Clarke 1993, Chapman et al. 1995). Although, this study was not specifically designed to test changes in the spatial variability due to pollution, the observed extent of spatial variation at the scale of sites was not significantly different between polluted and control sites.

52


Pollution and biodiversity on rocky shores

Chapter II

In conclusion, it was shown that pollution is associated with changes in the abundance, diversity, composition and structure of rocky shore molluscan assemblages.

Similar and consistent responses were found at all three clusters

examined here, representing a broad geographical area and where the sources and amounts of pollution were quite different from each other. Intertidal habitats are easy to access and molluscs are comparatively easy to identify. Therefore it is suggested that ecological monitoring methods based on molluscs have the potential to provide means for the rapid, wide scale evaluation of rocky shores affected by impacts associated with human activities.

A limitation in assessing impacts of

pollution is often the lack of base line data, which this study has now provided and against which future monitoring can be compared.

53


Nutrient enrichment and zebra mussels

Chapter III

CHAPTER III: IMPACTS OF INVASION BY DREISSENA POLYMORPHA (PALLAS, 1977) ON THE PERFORMANCE OF MACROINVERTEBRATE ASSESSMENT TOOLS FOR EUTROPHICATION PRESSURE IN LAKES 3.1.

Abstract

Aquatic ecosystems are experiencing increasing disturbance from multiple stressors caused by anthropogenic activities. Although there is good understanding of the impacts of individual stressors, there is a lack of knowledge of the consequences of several stressors acting simultaneously. The Water Framework Directive (WFD) requires the development of tools to assess human impacts in aquatic systems that incorporate ecological measures, such as macroinvertebrates. Nutrient enrichment and invasive species are major threats to freshwater systems. The invasive zebra mussel, Dreissena polymorpha is a conspicuous invader in aquatic systems in Europe and North America, and has been linked to drastic changes in macroinvertebrate communities and lake ecology. Currently, there are no working macroinvertebrate metrics based on littoral communities to asses the ecological quality of lakes in Europe in relation to eutrophication pressure. In this study I tested three proposed ecological quality assessment tools based on macroinvertebrate assemblages (% Sensitive Taxa to total phosphorus (TP), TP Score and Indicator Taxa Metric) and two basic ecological metrics in thirty-one sites varying in nutrient pressure and in the presence or absence of D. polymorpha. There were highly significant changes in macroinvertebrate diversity, structure and composition owing to invasion by D. polymorpha. While the three metrics performed consistently well in non-invaded systems, all three metrics lost explanatory power for eutrophication pressure in 54


Nutrient enrichment and zebra mussels

Chapter III

invaded systems. These results have important consequences for the assessment of ecological status and show how lake status can be affected by the invasive zebra mussel.

55


Nutrient enrichment and zebra mussels

3.2.

Chapter III

Introduction

The magnitude and intensity of anthropogenic stressors impacting aquatic ecosystems is generally increasing (Vitousek et al. 1997, Sala et al. 2000, Halpern et al. 2008). Most ecosystems are not impacted by a single stressor, but by multiple stressors acting simultaneously (Folt et al. 1999, Crain et al. 2008, Halpern et al. 2008). While the effects of individual stressors at the species and ecosystems have received considerable attention, there is still a lack of understanding of their combined effects. The action of one stressor has potential to modify the impacts of another through additive, antagonistic or synergistic effects (Crain et al. 2008).

Tools for assessing and monitoring human impacts on aquatic systems are currently in development. In Europe, this process is driven by the EU Water Framework Directive (WFD, EC 2000). The Directive requires that tools to asses the quality of aquatic systems must incorporate ecological elements, including metrics based on benthic invertebrates. These animals are involved in key processes such as food web dynamics, productivity, nutrient cycling and decomposition (Reice and Wohlenberg 1993), forming an important link between primary producers, detrital deposits and higher trophic levels (Stoffels et al. 2005). Changes in environmental conditions in lakes are often reflected in changes in macroinvertebrate assemblages (Johnson et al. 1993).

Most assessment tools for aquatic systems have been developed with a focus on the inputs of nutrient pollution, an environmental stressor of major concern in aquatic systems (Nixon 1995, Williamson 1999, Mayer et al. 2002, Clarke et al. 2006, Solimini et al. 2006, Diaz and Rosenberg 2008). Nutrient enrichment can have a

56


Nutrient enrichment and zebra mussels

Chapter III

range of effects, including marked alterations of invertebrate community structure. Significant associations between nutrient pressures and lake littoral invertebrate assemblage structure have been found previously (Kornijow 1988, Blumenshine et al. 1997, Brodersen et al. 1998, Tolonen et al. 2005, Brauns et al. 2007). For example Blumenshine et al. (1997) found significant changes in the structure and composition of hard-bottom macroinvertebrate assemblages, and altered size frequency distributions of some insects.

The nature of the response of littoral

macroinvertebrate assemblages to nutrient enrichment has, however, been shown to be habitat dependent (Tolonen et al. 2005, Brauns et al. 2007), with the most marked effects found on stony substrata (Tolonen et al. 2005, Donohue et al. in press).

The invasive zebra Dreissena polymorpha (Pallas 1771) is one of the most aggressive invaders of lakes in Europe and North America, and has spread rapidly through Europe over the last decade (Minchin et al. 2002). The mussel functions as an ecosystem engineer by altering the flow of organic matter and light, and providing structurally complex habitat for other species to inhabit and seek refuge from predators (Ricciardi et al. 1997, Mayer et al. 2002). By feeding on phytoplankton, zebra mussels reduced the chrorophyll a:TP ratio, leading to increased water clarity and greatly enhanced benthic-pelagic coupling (Fahnenstiel et al. 1995, Carpenter et al. 1998, Minchin et al. 2003, Zhu et al. 2006). It has been linked to dramatic ecological impacts on freshwater systems, associated with increased benthic macroinvertebrate abundance and species richness, and decreased community evenness (Burlakova 1995, Karatayev et al. 1997, Ricciardi et al. 1997, Brodersen et al. 1998, Ward and Ricciardi 2007). It has been observed to have negative effects on the abundance of filter feeders or some burrowing organisms (Ricciardi et al. 1997,

57


Nutrient enrichment and zebra mussels

Chapter III

Ward and Ricciardi 2007). It therefore has strong potential to modify impacts of nutrient pollution in freshwater systems, by changing both community structure and key ecosystem processes.

Understanding the response of littoral macroinvertebrates to increased nutrients loads with and without the confounding effect of zebra mussels is essential for classifying lakes under the WFD. The need to develop cost-effective and reliable indicators that correlate well with measures of eutrophication independently of and in combination with biological invasions, is self evident.

While assessment tools using macroinvertebrates to asses the ecological quality of rivers exist in many European states (Wright et al. 1998), there are currently no working assessment systems for lakes based on littoral macroinvertebrate communities (H책kanson 2001 , Solimini et al. 2006). Donohue et al. (in press) has recently developed three biometrics that relate littoral benthic macroinvertebrate community structure to eutrophication pressure, namely % Sensitive Taxa to total phosphorus (TP), a TP Score and an Indicator Taxa Metric. These metrics were developed using data from 190 lakes located in Ireland, from species optima indicated by Canonical Correspondence Analysis (Dodkins et al. 2005). However, the performance of these metrics has not been assessed in relation to the invasion by D. polymorpha which is now widespread in Irish lakes.

In this study, the capacity of three proposed ecological classification metrics and two basic ecological metrics to indicate variation in trophic status of lakes in the center and west of Ireland was tested.

To determine whether their effectiveness was

58


Nutrient enrichment and zebra mussels

Chapter III

modified by the presence of D. polymorpha, the relationship between macroinvertebrate metrics of eutrophication and total phosphorus concentrations were compared in a number of lakes with or without zebra mussels. Additionally, the combined effects of eutrophication pressure and the invasion of the zebra mussel on the structure and diversity of lake macroinvertebrate assemblages were tested. Benthic macroinvertebrates comprise a large number of species with a range of responses to environmental pressures, are principally sedentary, allowing effective spatial analysis of pollutants or disturbances, and have sufficiently long life-cycles favourable to assessment of disturbances (Reice and Wohlenberg 1993). Given that the most marked effects of nutrient enrichment have been found on stony substrata (Tolonen et al. 2005, Donohue et al. in press), this study focussed on these habitats.

3.3.

Methods 3.3.1. Study sites

Littoral macroinvertebrates inhabiting hard substrata comprising a mixture of pebbles, gravel and bed rock were sampled at one site in each of thirty-one medium to high alkalinity lakes (>0.2 meq l-1) in the centre and west of Ireland (Figure 5). Lakes represented a range of levels of trophic state, based on total phosphorus (TP) data (Donohue pers. comm.). The number of occasions on which TP was sampled varied for each lake, but on average there were 14 sampling dates per lake between 1990 and 2004. The average TP value for the period was used for the statistical analyses. Lakes also differed as to whether or not they have been invaded by the zebra mussel D. polymorpha. Invasion status was determined by in situ visual inspection and later confirmed by presence of the D. polymorpha in the samples. Lakes sites were considered as invaded when >5 individual zebra mussels were 59


Nutrient enrichment and zebra mussels

Chapter III

found in each replicate sample and non-invaded when no zebra mussels were found in the samples. Lakes sites that fell in between this criteria (i.e. sites with 1-5 individual zebra mussels per were not included in the analyses. Some lake sites considered as non-invaded could contain D. polymorpha in another location within the lake. Therefore, invaded and non-invaded lakes are defined by the presence or absence of the zebra mussels in the vicinity of sampling sites (within a minimum of 1 km). In total, twenty invaded sites and eleven non-invaded sites were recognized. 3.3.2. Sampling All sites were sampled for littoral macroinvertebrates during spring 2007. Triplicate one-minute kick samples were obtained in ~0.5 m of water depth using a standard Freshwater Biological Association type pond net of 1 mm mesh size. Samples were preserved on site in 70% alcohol and subsequently sorted and identified to the lowest possible taxonomic level, typically species.

Figure 5. Map showing the location of the thirty-one study sites sampled in the Central-West area of Ireland. Invaded sites are shown by black circles (n = 20) and non-invaded (n = 11) by gray circles.

60


Nutrient enrichment and zebra mussels

Chapter III

3.3.3. Statistical analyses All analyses were completed using data at the level of individual sites, with abundances of macroinvertebrates summed across the three replicate kick samples to avoid pseudoreplication. Three metrics to asses the ecological status of lakes were calculated: % Sensitivity to TP, TP Score and Indicator Taxa Metric (Donohue et al. in press).

The % Sensitivity to TP, represents the general affinity of taxa either to lakes with low water column TP concentrations (i.e. scoring 1) or those without such affinity (i.e. scoring 0, Table 6). The sensitivity to TP scores are summed for each taxon found in a sample, then multiplied by 100 and divided by the number of taxa found; thus, calculating the percentage of taxa in the sample that are sensitive to TP.

TP Scores are based on TP optima, which range from 1 to 5, correspond to the ecological optimum for each taxon along a TP gradient, with taxa showing strong affinity for lakes with high water column TP concentrations scoring 5, and taxa showing strong affinity for oligotrophic conditions scoring 1.

Taxa found in a

sample that have not been given a TP optimum were not included in the calculation of the TP Score. TP optimum values for each taxon found in the sample are added and then divided by the number of taxa to calculate the TP Score for the sample.

The Indicator Taxa Metric is calculated using species optima derived from Canonical Correspondence Analysis (CCA) for the 5 common taxa, defined as taxa that were found in >25% of samples collected by (Donohue et al. in press), that showed the strongest affinity for high water column TP concentrations (i.e. highest CCA species

61


Nutrient enrichment and zebra mussels

Chapter III

optima for TP), together with the 5 common taxa with the most negative optima for TP were combined to form the Indicator Taxa Metric (Table 7). Optimum values were summed for each of the indicator taxa found in the sample to obtain the Indicator Taxa Metric score for the sample.

Additionally, two other basic ecological metrics (number of taxa and total abundance) were calculated for each of the samples. Each of the five metrics were analysed using analysis of covariance (ANCOVA) with invasion as a main effect (fixed, two levels) and TP as a covariate. Leveneâ&#x20AC;&#x2122;s test was used to check the assumption of homogeneity of variances, and the assumption of normality was checked by visual inspection of the residual plots. Total abundance and TP were log10 transformed to meet these assumptions. The homogeneity of slopes test was run before ANCOVA.

Differences in community structure between invaded and non-invaded sites were visualized with non-metric multidimensional scaling (nMDS) on the basis of BrayCurtis similarities of untransformed data. Similarity Percentage Analysis (SIMPER, Clarke 1993) was used to identify the percentage contribution of each species (or taxon) to the observed differences between assemblages of the invaded and noninvaded sites. The ratio Diss/SD was used to indicate the consistency with which a given species contributed to the average dissimilarity between samples from invaded and non-invaded sites. Values â&#x2030;Ľ1 indicated a high degree of consistency.

To test effects of invasion and total phosphorus concentrations on multivariate community structure, distance-based permutational multivariate analysis of variance

62


Nutrient enrichment and zebra mussels

Chapter III

(PERMANOVA) was used (Anderson 2001a, McArdle and Anderson 2001) based on Bray-Curtis dissimilarities of the untransformed and presence/absence transformed data. Invasion was included as a fixed factor and total phosphorus as a covariable. . The community analyses were done having first removed data on the abundance of D. polymorpha. All analyses were performed using the computer software PRIMER 6 & PERMANOVA (Clarke 1993, Anderson and Gorley 2007).

3.4.

Results 3.4.1. Metrics

The % Sensitivity to TP metric was significantly and negatively correlated with TP only for non-invaded sites (Figure 6a, r2 = 0.24). At invaded sites % Sensitivity to TP was not significantly correlated with TP (P>0.05). The test for homogeneity of slopes revealed differences in the slopes (Invasion x TP, F1,

27

= 4.52, P<0.05),

confirming differences in the nature of the relationship. TP Score and Indicator Taxa Metric, were positively and significantly correlated to TP only at non-invaded sites (r2 = 0.37 and 0.71, respectively, Figure 6b and Figure 6c). Again, the test for homogeneity of slopes revealed differences in the slopes between invaded and noninvaded sites for both metrics (Invasion x TP, F1, 27 = 4.73 and 5.51, respectively, P<0.05). The slopes of the regressions between mean number of taxa and TP, did not differ significantly between invaded and non-invaded lakes (Figure 7a, test for homogeneity of slopes: Invasion x TP, F1,27, = 0.22, P>0.05) and the ANCOVA test detected a significant effect of invasion (F1,28 = 10.81, P<0.01), with invaded sites showing a significant increase in mean number of taxa from 27.1 to 36.4 (Figure 8). Similarly, the slopes of the regressions between total abundance and TP did not vary significantly with invasion status (Figure 7b, test for homogeneity of slopes: Invasion 63


Nutrient enrichment and zebra mussels

Chapter III

x TP, F1, 27 = 0.65, P>0.05). The ANCOVA test detected a highly significant effect of invasion (F1,28 = 12.63, P<0.01), in which invaded lakes had significantly higher total abundance of invertebrates compared with non-invaded sites (Figure 8).

64


Nutrient enrichment and zebra mussels

% Sensitivity TP

60

Chapter III

a

50

40

30 Non-Invaded 2 r = 0.37, p < 0.01

Invaded 2 r = 0.002, n.s.

20 0.5

1.0

1.5

2.0

3.6

b

TP Score

3.4

3.2

3.0

2.8 Non-invaded 2 r = 0.31, p < 0.01

Invaded 2 r = 0.008, n.s.

2.6 0.5

1.0

1.5

2.0

1.2

Indicator Taxa Metric

c 0.8

0.4

0.0 Non-invaded 2 r = 0.71, p < 0.01

Invaded 2 r = 0.002, n.s.

-0.4 0.5

1.0

1.5

2.0

Total phosphorus (ug / L) (log)

Figure 6. Regressions between levels of total phosphorus and three ecological metrics: a) % sensitivity TP, b) TP score and c) Indicator taxa metric and for invaded (filled circles, n = 20) and non-invaded (empty circles, n = 11) sites.

65


Nutrient enrichment and zebra mussels

50

Chapter III

a

Number of taxa

40 30 20 10 Invaded 2 r = 0.01, n.s

Non-invaded 2 r = 0.01, n.s.

0 0.5

1.0

1.5

2.0

Total abundance (x 1000)

12

b 10 8 6 Non-invaded 2 r = 0.12, n.s.

4

Invaded 2 r = 0.07, n.s.

2 0 0.5

1.0

1.5

2.0

Total phosphorus (ug / L) (log)

Figure 7. Relationships between levels of total phosphorus, and a) total abundance and b) number of taxa for invaded (filled circles, n = 20) and non-invaded (empty circles, n = 11) sites.

66


Nutrient enrichment and zebra mussels

Chapter III

Total abundance (x 1000)

40

Number of taxa

a 30

20

10

0

5

b 4 3 2 1 0

Invaded Non-Invaded

Invaded

Non-Invaded

Figure 8. Mean (+SE) a) total abundance and b) mean number of taxa of macroinvertebrates at invaded (filled bars, n = 20) and non-invaded sites (empty bars, n = 11).

3.4.2. Community structure Overall

>93,000

individuals

were

found,

representing

131

different

macroinvertebrate taxa. Trichoptera (caddisflies) and Coleoptera (beetles) were the most diverse orders of invertebrates encountered with 30 and 24 taxa respectively. Mollusca (snails, bivalves and limpets) were the next most diverse group with 18 taxa, followed by Acari (mites) with 17 taxa. Overall, Ephemeroptera (mayflies) was the most abundant group and they made up over 30% of the total abundance of invertebrates across the data set, while the waterlouse Asellus aquaticus (Linnaeus, 1758) comprised 25% of the invertebrates.

Worms (Oligochaeta) were not identified to a lower taxonomic resolution owing to time constraints, this is why they contribute little to the taxonomic richness, even though they made up 7% of the total abundance of invertebrates.

67


Nutrient enrichment and zebra mussels

Chapter III

Stress = 0.14

a

b

Stress = 0.19

S

Figure 9. Non-metric multidimensional scaling (nMDS) ordination of macroinvertebrates assemblages in sites invaded (filled circles, n = 20) and not invaded (empty circles, n = 11) by the zebra mussel Dreissena polymorpha on the basis of Bray-Curtis similarities of a) the untransformed abundance data and b) presence absence data. The community analysis was done having first removed data on the abundance of D. polymorpha.

68


Nutrient enrichment and zebra mussels

Chapter III

Table 6. List of the taxa identified and included in the analysis, and the taxonspecific sensitivity to TP and TP optima used in the calculation of respectively, the % Sensitivity to TP and TP score metrics. Phylum/Class /Order Amphipoda

Arachnida Hydrachnidia

Family

Taxon

Corophiidae Crangonycitidae Gammaridae Argyronetidae Arrenuridae

Corophium curvispinum Crangonyx pseudogracilis Gammarus spp. Argyroneta aquatica Arrenurus claviger Arrenurus crassicaudatus Eylais extendes Feltria minuta Hydrachna globosa Diplodontus sp. Hygrobates longipalpis Limnesia fulgida Limnochares aquatica Lebertia porosa Piona nodata Piona sp. Pionacercus sp. UI Pionidae Unionicola crassipes Unionicola figuralis UI Unionicolidae Pisidium / Sphaerium sp. Eurycercus lamellatus Daphnia hyalina Dryops gracilis Macroplea sp. Chrysomelidae larvae UI Coleoptera larvae Graptodytes pictus Hygrotus quinquelineatus Hyphydrus ovatus Potamonectes sp. Elmidae larvae Limnius larvae Limnius volckmari Oulimnius sp. Gyrinus sp. Gyrinidae larvae Orechtochilus villosus Halipus sp. Halipus sp. larvae Heterocerus sp. Anacaena sp. Helophorus sp. Laccobius sp. Hydrophilidae larvae

Eylaidae Feltriidae Hydrachnidae Hydryphantidae Hygrobatidae Limnesiidae Limnocharidae Lebertiidae Pionidae

Unionicolidae

Bivalvia Cladocera Coleoptera

Sphaeriidae Chydoridae Daphniidae Callirhipidae Chrysomelidae

Dysticidae

Elmidae

Gyrinidae

Haliplidae Heteroceridae Hydrophilidae

69

Taxonomic authority

Sensitivity to TP

TP Optimum

0 1 1 0

5 2 2 4

0

4

(Fabricius, 1787) (Zetterstedt, 1828) (Linnaeus 1761)

1 0 0 1

2 4 4 2

(Panzer, 1793)

1 1 0

2 2 4

(O. F. M端ller, 1776)

1 0

2 4

0 1

4 2

Sars, 1895 Bousfield, 1958 (Clerck, 1757) Koenike, 1885 Kramer, 1875 (O.F. M端ller, 1776) Koenike 1892 (De Geer, 1778) (Hermann, 1804) Koch 1836 (Linnaeus, 1758) Thor 1900 (O.F. M端ller 1776)

(O. F. Mueller, 1776) (Koch 1836)

(O.F. Mueller, 1776) Leydig 1860 (Karsch, 1881)


Nutrient enrichment and zebra mussels

Chapter III

Table 6. Continued. Phylum/Class /Order

Family

Coleoptera

Noteridae

Diptera

Ceratopogonidae Chiromidae

Empididae Simuliidae Stratiomyidae Tabanidae Tipulidae Ephemeropter a

Caenidae

Ephemeridae Heptageniidae Gastropoda

Ancylidae Bithyniidae Hydrobiidae Lymnaeidae Neritidae Physidae Planorbidae

Valvatidae Heteroptera

Corixidae

Hirudinea

Notonectinae Erpobdellidae Glossiphoniidae

Isopoda Lepidoptera Megaloptera

Hirudinidae Asellidae Pyralidae Sialinae

Taxonomic authority

Sensitivity to TP

TP Optimum

Noterus clavicornis Noterus crassicornis Noterus sp. UI Ceratopogonidae Ceratopogonidae pupa Chironomini Orthocladiinae Pseudochironomini Tanypodinae Tanytarsini UI Dipteran pupa Empididae larvae Simuliidae larvae Stratiomyidae larvae Tabanidae larvae Tipula sp. larvae

(De Geer, 1774) (O.F. Muller, 1776)

0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0

4 4 4 4 4 4 4 4 4 4 2 2 2 2 2 4

Caenis luctuosa Caenis horaria Caenis macrura Ephemera danica Heptagenia sulphurea Heptagenia fuscogrisea Ancylus fluviatilis Succinea putris Bithynia tentaculata Bithynia leachi Potamopyrgus antipodarium Lymnaea stagnalis Lymnaea peregra Theodoxus fluviatilis Physa fontinalis Planorbis carinatus Planorbis contortus Planorbis corneus Planorbis laevis Planorbis planorbis Planorbis vortex Valvata cristata Valvata macrostoma Corixidae immature Sigara sp. Notonecta sp Erpobdella octoculata Helobdella stagnalis Glossiphonia complanata Haemopis sanguisuga Asellus aquaticus Pyralidae larvae Sialis lutaria

(Burmeister, 1839) (Linne, 1758) Stephens, 1835 Müller, 1764 (Müller, 1776) (Retzius, 1783) O. F. Müller, 1774 (Linnaeus, 1758) (Linnaeus, 1758) Leach, 1818 (Gray. 1843) (Linnaeus, 1758) (Müller) (Linnaeus, 1758) (Linnaeus 1758) Müller, 1774 (Linnaeus, 1758) (Linnaeus, 1758) Nevill, 1878 (Linnaeus, 1758) (Linnaeus, 1758) Müller, 1774 Mörch, 1864

0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

4 4 4 4 2 2 2 2 4 4 3 4 2 4 4 4 4 4 4 4 4 4 4

(Linnaeus, 1758) (Linnaeus, 1758) (Linnaeus, 1758) (Linnaeus, 1758) (Linnaeus, 1758)

0 0 0 0 1 0 0 0 1

4 4 4 4 2 4 4 4 2

Taxon

70

(Linnaeus, 1758)


Nutrient enrichment and zebra mussels

Chapter III

Table 6. Continued. Phylum/Class /Order Odonata

Oligochaeta Ostracoda Plecoptera Oligochaeta Ostracoda Plecoptera Trichoptera

Family

Taxon

Aeshnidae Corduliidae Libellulidae Zygoptera Oligochaeta Candonidae Chloroperlidae Oligochaeta Candonidae Chloroperlidae Ecnomidae Goeridae Hydroptilidae Lepidostomatidae Leptoceridae

Aeshna sp. Oxygastra curtisii Libellulidae Lestidae Oligochaeta Herpetocypris reptans Chloroperla torrentium Oligochaete Herpetocypris reptans Chloroperla torrentium Ecnomus tenellus Silo nigricornis Hydroptila sp. Lepidostoma hirtum Athripsodes albifrons Athripsodes aterrimus Athripsodes bilineatus Athripsodes cinerus Athripsodes commutatus Ceraclea fulva Mystacides azurea Mystacides longicornis Oecetis sp. Anabolia nervosa Limnephilus binotatus Limnephilus dicipiens Limnephilus flavicornis Limnephilus lunatus Limnephilus luricus Limnephilus marmoratus Molanna albicans Phryganea bipunctata

Limnephilidae

Molannidae Phryganeidae Polycentropodida e

Psychomyiidae Sericostomatidae Turbellaria

Holocentropus picicornis Polycentropus flavomaculatus Polycentropus kingi Tinodes waeneri Lype phaeopa Metalype fragilis Sericostoma personatum Turbellaria

71

Taxonomic authority

Sensitivity to TP

TP Optimum

0 0

3 3

1 0 1 1 0 1 1

2 5 2 2 5 2 2

1 1

2 2

1 1 1 1 1 1 1 1 1 0 0 0 1

2 2 2 2 2 2 2 2 2 3 5 4 2

0 0 0

4 4 4

(Stephens 1836)

1

2

(Pictet 1834) McLachlan 1881 (Linnaeus 1758) (Stephens 1836) (Pictet, 1834). (Kirby&Spence 1826)

1 1 0 1 1 1 0

2 2 4 2 2 2 4

(Dale, 1834)

(Baird, 1835) Pictet, 1841 (Baird, 1835) Pictet, 1841 (Rambur, 1842) (Pictet, 1834) (Fabricius, 1775) (Linnaeus, 1758) (Stephens, 1836) (Linnaeus, 1758) (Curtis, 1834) (Rostock, 1874) (Rambur, 1842) (Linnaeus, 1761) (Linnaeus, 1758) (Curtis, 1834) Curtis 1834 (Kolenati 1848) (Fabricius 1787) Curtis 1834 Curtis 1834 Curtis 1834 Curtis 1834 (Zetterstedt 1840)


Nutrient enrichment and zebra mussels

Chapter III

Table 7. Optimum values for the 10 common taxa incorporated in the Indicator Taxa Metric. Phylum/Class/Order

Family

Taxon

Optimum value

Amphipoda

Gammaridae

Gammarus spp.

Bivalvia

Sphaeriidae

Pisidium / Sphaerium sp.

0.1953

Coleoptera

Elmidae

Oulimnius sp.

-0.3395

Heteroptera

Corixidae

Sigara sp.

0.2343

Hirudinea

Glossiphoniidae

Hydrachnidia

-0.0523

Helobdella stagnalis

0.3859

Hydrachnidia

-0.1238

Isopoda

Asellidae

Asellus aquaticus

0.4589

Trichoptera

Leptoceridae

Athripsodes sp.

-0.2413

Trichoptera

Polycentropodidae

Polycentropus flavomaculatus

-0.1259

Turbellaria

0.2329

Turbellaria

PERMANOVA analysis showed highly significant changes in invertebrate assemblage structure owing to the invasion of zebra mussels compared with noninvaded sites (P<0.001, Table 8, Figure 9).

This pattern was true for the

untransformed and presence/absence transformed data, indicating that differences were owing both to differences in the relative abundances of taxa and the composition of the assemblages in invaded and non-invaded sites.

Six taxa

contributed almost 80% of the observed differences in the community structure between invaded and non-invaded sites. All these taxa were more abundant in invaded sites (Table 9). The waterlouse A. aquaticus and the mayfly Caenis luctuosa (Burmeister, 1839) contributed over 58% to the observed differences. Compositional changes in the assemblages between invaded and non-invaded were attributable to 11 taxa that were present only in invaded sites, and one (Caenis macrura, Stephens, 1835) that was only found in non-invaded sites (Table 10).

72


Nutrient enrichment and zebra mussels

Chapter III

The effect of TP on the macroinvertebrate community structure was almost significant (P = 0.07, Table 8), but this trend was not evident when the analysis was run on the presence/absence transformed data, indicating that the effect was only related to changes in relative abundance, rather than in composition.

Table 8. PERMANOVA analysis of differences in macroinvertebrate assemblage structure based on Bray-Curtis similarities of the untransformed and presence/absence transformed data. TP = total phosphorus. Source of variation TP Invasion TP x Invasion Residual Total Transformation

df 1 1 1 27 30

MS 3773.4 8129.2 2259.3 2147.6

F 1.76 3.79 1.05

None

P 0.07 0.00 0.39

MS 1440.8 3780.1 1615.0 1090.8

F 1.32 3.47 1.48

P 0.19 0.00 0.09

Presence/absence

Table 9. Average abundance of several prominent taxa in invaded and non-invaded study sites, including SIMPER results for contributions from most important taxa towards the Bray-Curtis similarities distinguishing these two groups.

Taxa Asellus aquaticus Caenis luctuosa Gammarus spp. Oligochaeta Crangonyx pseudogracilis Pisidium / Sphaerium spp.

Average abundance NonInvaded invaded Av.Diss Diss/SD Contrib% 1520 291 21.35 1.02 29.9 1320 308 20.16 1.06 28.24 131 130 5.29 0.74 7.41 183 68 3.9 0.81 5.47 186 47 3.72 0.77 5.21 75 25 1.41 1.04 1.98

73

Cum.% 29.9 58.15 65.56 71.03 76.24 78.22


Nutrient enrichment and zebra mussels

Chapter III

Table 10. Presence/absence of several taxa in invaded and non-invaded study sites contributing to the differences in assemblage composition between these two groups. Taxon Theodoxus fluviatilis Planorbis carinatus Sialis lutaria Planorbis planorbis Corduliidae Planorbis vortex Caenis macrura Ceratopogonidae pupa Heptagenia fuscogrisea Daphnia hyalina Noterus clavicornis Anacaena sp.

3.5.

Invaded P P P P P P A P P P P P

Non-invaded A A A A A A P A A A A A

Discussion

Nutrient enrichment and invasive species pose major threats to the natural diversity and functioning of aquatic systems (Solimini et al. 2006, Halpern et al. 2008). The simultaneous occurrence of multiple stressors has generated a major challenge to determine their independent and combined effects (Crain et al. 2008, Darling and C么t茅 2008) and to asses the ecological quality of the systems where they occur. Furthermore, because it has been shown that climate change can facilitate the spread of invasive species (Dukes and Mooney 1999, Stachowicz et al. 2002, Rocha et al. 2005), there is a potential for complex interactions of multiple stressors acting simultaneously resulting in serious alteration in the ecology and the assessment of the status of aquatics systems. Here, in a large scale observational study, it was demonstrated that the performance of the proposed metrics to assess eutrophication pressure in lakes was strongly affected by the invasion by D. polymorpha. All three metrics based on littoral macroinvertebrates (% Sensitivity to TP, TP score and Indicator Taxa Metric) consistently correlated significantly with levels of TP in the water column in non-invaded systems, but not in invaded sites. Considering the vast 74


Nutrient enrichment and zebra mussels

Chapter III

distribution and the high rate of spread of D. polymorpha in European freshwater systems, these results imply a considerable impediment in the ecological assessment of freshwater systems using macroinvertebrates as required by the WFD.

It is not unreasonable to attribute the loss of predictive power of the three tested classification metrics to the invasion of zebra mussels rather than co-varying factors. The significant shift in invertebrate community structure between invaded and noninvaded systems was related to increases in the abundance of seven taxa in invaded sites. These included taxa with strong affinity for lakes with high water column TP concentrations, such as Asellus aquaticus, Caenis luctuosa and Pisidium/Spherium spp. The positive effect on the abundance of these taxa associated with the invasion of the zebra mussel has been previously documented, although a negative effect on the abundance of Pisidium/Spherium spp. has been generally reported (reviewed by Ward and Ricciardi 2007). The invasive amphipod Crangonyx pseudogracilis was also positively related to the invasion of zebra mussels. Positive and potentially synergistic interactions among invasive species may lead to accelerated impacts on native ecosystems (Simberloff and Von Holle 1999). The overall change in the assemblage structure, specifically the increase in invertebrate abundance and number of taxa are effects generally associated with the introduction of zebra mussels (reviewed by Ward and Ricciardi 2007). Possible explanations for these changes relate to the ability of D. polymorpha, as with other ecosystem engineers, to modify the availability of resources for other species by building and modifying habitats. Mussels also change nutrient availability in littoral habitats by deposition of feces and pseudofeces (Mayer et al. 2002, Haynes et al. 2005). Benthic macroinvertebrate

75


Nutrient enrichment and zebra mussels

Chapter III

taxa respond specifically to the physically complex structure provided by aggregates of mussel shells (Botts et al. 1996).

Among the three tested metrics, the Indicator Taxa Metric best predicted concentrations of TP in non-invaded systems. The high predictive power (r2 = 0.71) strongly suggests littoral macroinvertebrates of hard substrata can provide a robust indication of nutrient pressures in lakes, despite high inherent spatio-temporal variability (White and Irvine 2003, Stoffels et al. 2005, Brauns et al. 2007). The two other metrics tested (TP score and % Sensitivity Taxa) had lower predicted power, 31 to 37% of the variation on the metrics was related to changes in phosphorus load.

One of the fundamental principles of ecological status classification under the WFD is the identification of type specific reference conditions (EC 2000). Considering the extent of the ecological alterations associated with zebra mussel invasions reported here and elsewhere, the identification of reference and status classes to asses the impacts of nutrients pressure in lakes should require their invasion status to be taken in consideration (Donohue et al. in press). Impacts of nutrients can be measured as deviations from reference conditions (e.g. Ecological Quality Ratio) and could take account of the changes in ecological parameters associated with invasion by the zebra mussel.

If new ecological metrics are to be developed, this should be

considered both in the identification of type specific reference conditions and in the calculation of the metrics.

Another option for using invasive species data in

ecological classification of aquatic systems is to modify the status class based on the presence and impact of established alien species. Assessing the ecological status of

76


Nutrient enrichment and zebra mussels

Chapter III

lakes affected by multiple stressors within the restrictions of the WFD remains a major challenge.

Even though the study was focused in a specific lake type and habitat, there is inevitably natural spatial variation in assemblages owing to abiotic and biotic characteristics of lakes (White and Irvine 2003, Brauns et al. 2007). A potential limitation of the study was that invertebrate sampling was only from one sampling occasion. Although seasonal variation in abundance and structure of lake macroinvertebrates has been reported, sampling was undertaken during spring because a peak in abundance and species richness has been shown to occur during this period (White and Irvine 2003). Spring has been recommended as a suitable time for ecological quality assessment in freshwater systems (Toner et al. 2005, Donohue et al. in press).

Previous studies have found positive relationships between nutrient pressure and macroinvertebrate total abundance (Blumenshine et al. 1997, Donohue et al. in press). There was a trend for increased abundance with increased TP concentrations, both in invaded and non-invaded sites, although these relationships were not significant. Similarly, there was a pattern of changes in assemblage structure associated with a gradient in TP concentration. Positive relationships between nutrient pressure and littoral invertebrate assemblage structure and abundance have previously been found both in hard and soft substrata (Blumenshine et al. 1997, Brodersen et al. 1998, Tolonen et al. 2005, Brauns et al. 2007, Donohue et al. in press). Both threats examined here, nutrient enrichment and invasive species, were linked to changes in macroinvertebrate community structure, through a positive

77


Nutrient enrichment and zebra mussels

Chapter III

effect on macroinvertebrate abundance. Because both of these factors have a positive effect on population abundances, it is likely their combined effect would be additive (Crain et al. 2008, Darling and C么t茅 2008), rather than synergistic (i.e. the combine effect is larger than the sum of individual effects).

The current study has yielded the first evidence that the use of ecological assessment tools can be affected by the invasion of alien species. Here it was shown that newly developed macroinvertebrates metrics to assess the ecological quality of lakes in relation to eutrophication pressure are affected by D. polymorpha. This has important practical consequences for the classification of lakes. Because of the drastic changes in lake macroinvertebrate assemblages caused by the invasion of D. polymorpha documented here and elsewhere in the literature, the performance of assessment tools relying on the abundance and composition of macroinvertebrates can be fraught with problems when used in invaded systems. The results suggest that metrics may need to be developed separately for invaded and non-invaded systems, and that the interaction between alien species and nutrient enrichment requires further investigation. Given the predicted increase in invasion associated with forecast climatic changes (Dukes and Mooney 1999, Stachowicz et al. 2002, Rocha et al. 2005), this issue is likely to become even more prevalent in the future.

78


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

CHAPTER IV: COMBINED EFFECTS OF NUTRIENT ENRICHMENT, SEDIMENTATION AND GRAZER LOSS ON ROCK POOL ASSEMBLAGES 4.1.

Abstract

Coastal areas around the world are experiencing increasing disturbance from multiple stressors caused by anthropogenic activities. Although there is good understanding of the impacts of individual stressors, there is a lack of knowledge of the consequences of several stressors acting simultaneously. Eutrophication and sediment deposition are widely recognized as major problems for the functioning of coastal systems, and they are expected to increase during the next decades. In a field experiment using rock pools as a model system, differing levels of nutrients and sedimentation were applied in a factorial experimental design that also accounted for the influence of molluscan grazers. Sedimentation significantly changed assemblage structure, mainly owing to an increase in turfing and filamentous algae and a decrease in crustose algae. Nutrients also caused an increase in the cover of green ephemeral algae, which in turn was synergistically magnified by the removal of grazers. Here it was shown that these stressors can individually alter the structure of rock pools assemblages; and that in this system, top-down control (by grazers) is more important than bottom-up factors (nutrients) in controlling macroalgal assemblage structure. The combined effect of grazers loss and nutrients was larger than the sum of their individual effects.

This study enhanced mechanistic

understanding of the impacts of multiple stressors on coastal ecosystems, which will help to inform management strategies and conservation of the marine environment.

79


Sedimentation, nutrients and grazer loss in rock pools 4.2.

Chapter IV

Introduction

Human activities are increasing the number and intensity of stressors impacting in many coastal areas (Vitousek et al. 1997, Halpern et al. 2008). Coastal marine ecosystems are subject to a wide range of stressors including habitat loss (Gray 1997, Airoldi et al. 2008), overfishing (Jackson et al. 2001, Worm et al. 2006), pollution, in particular eutrophication (Diaz and Rosenberg 2008), invasive species (Vitousek et al. 1996) and climate change (Sala et al. 2000), among many others. Importantly, these stressors do not act in isolation. On the contrary, coastal systems are almost always simultaneously subjected to multiple anthropogenic stressors (Folt et al. 1999, Crain et al. 2008, Halpern et al. 2008). While the effects of individual stressors at the species and community level are relatively well studied and documented, there is still a lack of understanding of their combined effects. To quantify and elucidate how multiple drivers can interact to change patterns of biodiversity remains one of the major challenges in modern ecology (Darling and Côté 2008).

Conceptually, the three most common models to describe the outcome of multiple stressors on biological systems are: the additive model, in which the response is equal to the sum of the effects of the individual stressors; the antagonistic model when the combined effect is less than the sum of the effects of the individual stressors; and the synergistic model in which the response to exposure to multiple stressors is greater than the sum of the effects of the individual stressors (Folt et al. 1999, Crain et al. 2008). Multiple stressors acting simultaneously also have the potential to interact, driving changes that are not at all predictable from the effects of single stressors (‘ecological surprises' sensu Paine et al. 1998) representing an

80


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

uncertainty for predictive models of biodiversity change (Mora et al. 2007, Darling and C么t茅 2008).

Eutrophication is regarded as a major problem for the functioning of aquatic systems (Valiela et al. 1997, Thompson et al. 2002, Worm and Lotze 2006b). It results as the over-enrichment of enclosed seas and estuaries with nutrients coming mainly from agricultural run-off contaminated by high fertilizer loads, and from sewage discharges. On rocky shores, for example, documented effects of nutrient enrichment include a decline in the abundance of perennial macroalgae (Kautsky et al. 1986, Schramm 1996), and increases in the cover of ephemeral algae (Worm et al. 1999), leading to changes to the structure and functioning of marine communities (Kautsky et al. 1992). The role of basal resources such as nutrients (referred to as bottom-up factors) in structuring the structure biological communities is often mediated by the interaction with top-down control, such as grazing. On many rocky shores, grazing gastropods are known to control the cover and diversity of macroalgae (Underwood 1980, Lubchenco and Gaines 1981, Hawkins and Hartnoll 1983, O'Connor and Crowe 2005, Atalah et al. 2007a). Their grazing activity can counterbalance the effects of nutrient enrichment on macroalgal growth (Guerry 2008, Masterson et al. 2008). In general, there is agreement that both factors are important in structuring assemblages, but there is still controversy on how bottom-up and top-down factors interact and influence each other and how their relative strength may vary according to environmental and biotic conditions (Menge and Sutherland 1976). In rocky shore habitats, key species of grazers are being threatened by harvesting (Moreno et al. 1984, Crowe et al. 2000), pollution (Thompson et al. 2002, Espinosa et al. 2007) and sedimentation (Airoldi and Hawkins 2007).

81


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

Sedimentation is an important environmental threat to biodiversity in most aquatic ecosystems (GESAMP 1994, Gray 1997, Crowe et al. 2000, Airoldi 2003). Coastal areas adjacent to catchments with considerable human development, intensive irrigative agriculture or erosion due to overgrazing are expected to receive increased sediment loadings in the next decades. Manipulative and observational studies in rocky substrata have demonstrated alterations in assemblage structure caused by increased loads of sediment, owing to either negative, positive or indifferent response of invertebrates to the stress imposed by sedimentation. For example, sand influenced assemblages are often dominated by opportunistic seaweeds that rapidly colonize substrata disturbed by sediment burial or scour (e.g. green ephemeral algae), and â&#x20AC;&#x153;sand lovingâ&#x20AC;? or psammophytic algae, which are capable of adjusting to sediment stress or trap and bind sediment (e.g. red filamentous, turfing forming algae) (Littler et al. 1983, Airoldi 2003).

Both nutrient enhancement and sedimentation have been shown to be important structuring forces of rocky shore communities and they have the potential to interact synergistically, antagonistically or additively to cause changes in the diversity of benthic communities. Furthermore, grazing pressure may regulate the combined or individual effects of these stressors.

The long tradition of research undertaken on rocky shores reflects in part the suitability of these environments to experimentation (Paine 1994). Changes in environmental conditions over short distances, the prevalence of evident macroscopic patterns in an essentially two-dimensional environment and the occurrence of small, usually short-living species, make rocky shores ideal systems to

82


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test ecological models aimed at explaining patterns in abundance and diversity of species experimentally (Paine 1977, Underwood 2000, Benedetti-Cecchi 2006). Here a field experiment was used with rock pools assemblages as a model system to test the individual and combined effects of nutrient enrichment and sedimentation; and the potential interaction of nutrients and grazers on the structure of rock pools macrobenthic assemblages.

4.3.

Methods 4.3.1. Study site

A field experiment was conducted from June 2008 until December 2008 in midshore rock pools along the semi-exposed coast of Ballynahown, West Galway, Ireland (53°14.2’ N, 9°33.2’ W). The shore consisted of a relatively flat outcrop of granite bedrock which contained many shallow rock pools with an average size of 0.42 m2 ± 0.05 (mean ± SE), an average volume of 9.5 L ± 1.2 (mean ± SE) and an average depth of 4.1 cm ± 0.3 (mean ± SE). Rock pools were occupied by a range of macroalgae with differing morphologies, including green ephemeral algae (e.g. Ulva spp), red filamentous algae (e.g. Ceramium spp., Gelidium spp.), turf-forming red algae (mainly, Corallina officinalis, Chondrus crispus and Mastocarpus stellatus) and encrusting coralline algae (e.g. Lithothamnium spp.). Invertebrate assemblages were dominated by gastropod grazers, including the limpet Patella ulyssiponensis, the periwinkle Littorina littorea and the flat top shell Gibbula umbilicalis.

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Sedimentation, nutrients and grazer loss in rock pools

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4.3.2. Experimental design A two factor experimental design was used with ‘Nutrients’ and ‘Sedimentation’ as orthogonal fixed factors (Figure 10). Factor nutrients had three levels: ‘Ambient Nutrients’,

corresponding

to

no

nutrient

addition;

‘Moderate

Nutrients’,

corresponding to an addition of 20 g of nutrients per L of seawater in the pools; and ‘High Nutrients’, corresponding to an addition of 40 g of nutrients per L of seawater. Nutrient addition was done as recommended by Worm et al. (2000). Coated slowrelease fertilizer pellets (Osmocote® Miracle-Gro) were used in situ to enhance nutrient levels. They consist of 19% N (NO3 and NH4), 6% P (P2O5) and 12% K2O, the latter of which is assumed to have no effect due to the high K level of seawater. Diffuser bags made of polyethylene mesh were used to contain the fertilizer and were screwed to the bottom of the rock pools. Empty nutrient diffuser bags were attached in the ‘Ambient Nutrients’ rock pools to avoid confounding. Diffuser bags were refilled every month. Factor sedimentation had also three levels: ‘Ambient Sediment’, where no sediment was added, thus corresponding to the natural sedimentation rates of the site; ‘Moderate Sediment’ where 300 mg of sediment were added per cm2 of rock pool every 15 days; and ‘High Sediment’ where 600 mg of sediment were added per cm2 of rock pool every 15 days. These amounts are equivalent to 200 and 400 g m-2 d-1, which mimicked sediments deposits likely to occur on rocky coasts close to urban areas (Connell 2005). Sediments were obtained from a sandy shore adjacent to the study site and were sprinkled onto the surface of the pools during spring low tides. The sediment used had similar grain size to the one naturally found in the study site, comprising mainly coarse and fine sand.

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Additional treatments were incorporated to enable a second analysis based on a twoway factorial design, with ‘Grazing’ and ‘Nutrients’ as fixed orthogonal factors. ‘Grazing’ had two levels (‘Ambient Grazing’ and ‘Reduced Grazing’) and ‘Nutrients’ also had two levels (‘Ambient Nutrients’ and ‘High Nutrients’), as described for the first design explained above (Figure 10).

For the ‘Reduced

Grazing’ treatments all gastropod grazers were manually removed from inside the pools and from an area of approximately 1 m around them. Manual removal was preferred over cages or fences because it avoided problems with artefacts associated with these structures. Pilot work showed that grazer removals were still effective one month after the manipulation.

All treatments for both designs were established at the same time. Experimental conditions were maintained every 2 weeks.

Each experimental treatment was

replicated four times and randomly assigned to experimental pools placed at least 3 m apart. a) Sedimentation

Nutrients

A

b) Nutrients

Grazers

A

M

A

A

M

H

A

M

H

H

A

M

H

H

R

A

R

Figure 10. Diagram of the two experimental design used in the study: a) Two-way design with Sedimentation and Nutrients, both with three levels and as orthogonal factors.; and b) Two-way design with Nutrients, and Grazers both with two levels and as orthogonal factors. A = Ambient, M = Moderate, H = High and R = Reduced. Some treatments were common to both designs. For details see text.

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4.3.3. Sampling Non-destructive sampling of macroalgal cover and sessile invertebrate abundance was carried out at the end of the experiment. Percentage cover was estimated using the point-intercept method by placing a Plexiglas quadrat of 12 x 12 cm with 36 intersection points. Macroalgae taxa present in the quadrat but not recorded by this method were assigned a cover of 1%. In the case of multi-strata growth, total percentage cover sometimes exceeded 100%. Abundance of sessile invertebrates was estimated by counting all animals within the quadrat. Three random quadrats were sampled in each pool to increase the accuracy of estimates of cover and the average value used as a single estimate of cover and abundance.

To assess the efficacy of the nutrient enrichment procedures, nutrient concentrations in the experimental rock pools were measured one month after the first nutrient addition. Triplicate surface water samples were collected ~3 h after emersion (i.e. in the middle of a low tide period), at least 10 cm away from the nutrient diffuser bag using 50 mL acid washed opaque HDPE plastic bottles. Samples for were stored at â&#x20AC;&#x201C; 30°C for subsequent analysis. Standard spectrophotometric methods (Grasshoff et al. 1983) were used to measure levels of nitrates and nitrite (NO3- + NO2- mg N/L, subsequently referred to as nitrates).

4.3.4. Data Analyses Differences in community structure among treatment levels were visualized with non-metric multidimensional scaling (nMDS) plots on the basis of Bray-Curtis similarities of square root transformed data. Differences in assemblage structure between experimental treatments were tested using distance-based permutational 86


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

analysis of variance (PERMANOVA, Anderson 2001a, McArdle and Anderson 2001) based on Bray-Curtis similarities of the square root transformed data. Significant terms were then investigated using a posteriori pair-wise comparisons with the PERMANOVA t statistic and 999 permutations. Similarity Percentage Analysis (SIMPER, Clarke 1993) was used to identify the percentage contribution of each species (or taxon) to the observed differences between communities in the different treatments. The ratio Diss/SD was used to indicate the consistency with which a given species contributed to the average dissimilarity between samples from given treatments. Values ≥1 indicated a high degree of consistency.

Two-way analyses of variance (ANOVA) were used to analyse the total percentage cover of the most prominent taxa (green filamentous, crustose coralline algae, red filamentous, Corallina offiicinalis, Patella ulyssiponensis) in response to the treatments for each experimental design.

Data from plots with ‘Ambient

Sedimentation’, ‘Ambient Nutrients’, ‘Ambient Grazers’ and ‘High Nutrients’, Ambient Grazers were used in both sets of analyses. Levene’s test was used to check the assumption of homogeneity of variances and the assumption of normality was checked by visual inspection of residual plots. Data were transformed, if necessary, to remove heterogeneous variances. If there was no suitable transformation, analyses were done on the untransformed data and interpreted with caution.

4.4.

Results 4.4.1. Efficacy of nutrient enrichment

Significant differences in levels of nitrate among the three levels of nutrient treatment were found (F2,9 = 4.53, P<0.05), although pair-wise comparisons revealed 87


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

that there were no significant differences between the moderate and high levels of nutrient treatment (Figure 11, P>0.05), due to considerable variation among replicate pools.

Nitrate + Nitrite (mg N/L)

60 50 40 30 20 10 0 Ambient

Moderate

High

Figure 11. Mean (+SE) nitrates concentration in experimental rock pools for each of the three nutrient treatments: ‘Ambient’ (empty bar), ‘Moderate’ (grey bar) and ‘High’ (black bar). 4.4.2. Effects of sedimentation and nutrients on assemblage structure There was a significant effect of sedimentation on community structure (Figure 12, Table 11), although pair-wise comparisons did not distinguish between the two levels of sedimentation (P>0.05). SIMPER analysis showed that differences were due to a consistent increase in the cover of green filamentous algae, red turfing algae, Corallina officinalis and brown filamentous algae in the treatments with sediment added (both ‘Moderate Sediment’ and ‘High Sediment’) compared to the ‘Ambient Sediment’, while there was a decrease in the cover of crustose coralline algae, grazers, the mussel Mytilus edulis and the brown crustose algae Ralfsia verrucosa (Table 12). The addition of nutrients did not significantly change the assemblage structure and there were no interactive effects of nutrients and sediments (Figure 14, Table 11).

88


Sedimentation, nutrients and grazer loss in rock pools

a

Stress = 0.11

Chapter IV

b

Stress = 0.11

Figure 12. Non-metric multidimensional scaling (nMDS) ordination of assemblages on a) Ambient Sediment (empty circles), ‘Moderate Sediment’ (grey circles) and ‘High Sediment’ (black circles) and b) ‘Ambient Nutrient’ (empty squares), ‘Moderate Nutrient’ (grey squares) and ‘High Nutrient’ (black squares) rockpools on the basis of Bray-Curtis similarities of the untransformed percentage cover data (n = 4). Table 11. PERMANOVA of Bray-Curtis similarities based on fourth root transformed algal and invertebrate assemblage data

Source Sedimentation (Se) Nutrients (Nu) Se x Nu Residual Total

df

MS

2 2 4 27 35

901.49 540.07 137.85 352.12

89

F

P

2.56 1.53 0.39

0.01 0.12 0.99


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

Table 12. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis similarity distinguishing between ‘Ambient Sediment’ and sediment addition treatments (i.e. ‘Moderate Sediment’ and ‘High Sediment’ pooled together), based on the untransformed data. Av. Cover/abundance = average cover/abundance, Av. Diss. = average contribution to overall dissimilarity among samples, Diss / SD = the ratio of the average contribution to the overall dissimilarity among samples to the standard deviation of the average contribution to the overall dissimilarity, Contrib %. = percentage contribution to the overall dissimilarity among samples, Cum% = cumulative contribution to the overall dissimilarity among samples.

Low/High Ambient Sediment Sediment

Species Crustose coralline algae Green filamentous Red turf Corallina officinalis Biofilm Red filamentous Brown filamentous Grazers Mytilus edulis Ralfsia verrucosa

Av.Cover/ Av.Cover/ Av.Diss Diss/SD Contrib% Cum.% abundance abundance 61.61 37.11 26.35 24.59 12.38 13.87 3.70 3.74 1.50 1.56

88.25 29.32 11.78 11.55 9.00 10.46 2.29 5.39 2.42 2.62

7.53 4.90 4.49 4.17 3.08 2.36 1.10 0.77 0.75 0.73

1.19 1.28 1.16 1.13 0.95 1.34 1.01 0.99 1.05 1.18

22.63 14.73 13.51 12.52 9.26 7.09 3.29 2.32 2.26 2.19

22.63 37.36 50.87 63.39 72.65 79.74 83.03 85.35 87.61 89.80

4.4.3. Effects of sedimentation and nutrients on individual taxa The cover of Corallina officinalis was significantly increased in the sedimentation treatments (Table 13, Figure 13), but pair-wise comparisons revealed no significant differences between the two level of sedimentation (P>0.005). The addition of sediment caused an increase in the mean cover of C. officinalis from 13.8% (± 2.2 S.E.) in control plots to 28.9% (± 5.3 S.E.). Nutrients had no significant effect on the cover of these red coralline algae (Table 13, Figure 13). The mean cover of crustose coralline algae was significantly reduced from 86.6% (± 2.9 S.E.) in control treatments compared to 56.8% (± 9.3 S.E.) in the sedimentation treatments (Table 13,

90


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

Figure 13), with no distinction between the two levels of sedimentation (P>0.05). The cover of green ephemeral algae was significantly increased by the addition of nutrients (Table 13, Figure 13). Pair-wise comparisons revealed assemblages under high levels of nutrient addition had greater cover of these green algae (mean 24.5% ± 2.86 S.E.) compared with the ambient nutrient levels (mean 41.2% ± 4.2 S.E., P>0.005, Figure 13). Sedimentation caused an increase in the mean cover of red turfing algae from 11.7% (± 1.0 S.E.) in controls compared to 26.1% (± 4.7 S.E.) in sedimented assemblages (Table 13, Figure 13), but there were no differences between the two levels of sedimentation (P>0.05).

Nutrient treatments had no

significant effect on the cover of red turfing algae (Table 13, Figure 13). Sedimentation had no significant effect on the cover of green ephemeral algae (Table 13, Figure 13). The abundance of the limpet Patella ulyssiponensis was significantly reduced by sedimentation (Table 13, Figure 13). The average abundance of limpets in pools with high levels of sediment added was significantly lower (mean 1.4 ind/144 cm2 ± 0.2 S.E.) than in pools with ambient sediment loading (mean 2.7 ind/144 cm2 ± 0.3 S.E.).

Table 13. Two-way ANOVA for percentage cover of most prominent algal groups and the abundance of the limpet Patella ulyssiponensis under sediment and nutrients manipulations.

C. oficinalis

Crustose Coralline

Source df MS F MS F Sedimentation (Se) 2 735.9 3.1* 2843.8 5.2** Nutrients (Nu) 2 123.3 0.5 220.8 0.4 Se x Nu 4 8.7 0.0 255.4 0.5 Residual 27 231.7 547.7 Total 35 Transformation None None

91

Green ephemeral

Red Patella filamentous ulyssiponensis

MS F MS F 0.1 0.7 871.1 3.4* 0.3 3.3* 147.9 0.6 0.1 0.6 19.5 0.1 0.1 252.7 Sqrt (x)

None

MS 6.0 0.5 2.0 1.8

F 3.3* 0.3 1.1

None


Sedimentation, nutrients and grazer loss in rock pools

50

100

Corallina officinalis 40

Mean % cover (Âą SE)

Chapter IV

Crustose coralline algae

Ambient Nutrient Moderate Nutrient High Nutrient

80

30

60

20

40

10

20

0 60

0 30

Red filamentous

Green filamentous 50

25

40

20

30

15

20

10

10

5 0

0

10 40

Grazers

Red turfing algae

2

(ind / 144 cm )

8 30

20

10

6

4

2

0

0

Ambient

Moderate

High

Ambient

Moderate

High

Sediment

Sediment

Figure 13. Effect of sedimentation and nutrients on the percentage cover of several prominent algal taxa and grazers on experimental rock pools (mean Âą SE, n = 4).

4.4.4. Effect of grazers and nutrients on assemblage structure Grazer removal caused strong and highly significant changes in assemblage structure (Table 14, Figure 14). SIMPER analyses revealed that these changes were mainly due to an increase in the cover of green ephemeral algae and a decrease in cover of live crustose coralline algae. Additionally, there was a decrease in the cover of red

92


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

turf, Corallina officinalis, red filamentous algae and biofilm in ‘Reduced Grazing’ treatments, but their contribution towards the similarities between the two treatments was less important (Table 15).

The addition of nutrients significantly changed

assemblage structure (Table 14, Figure 14). These changes were mainly caused by an increase in the cover of green filamentous algae and a decrease in the cover of crustose coralline algae (Table 16).

Table 14. PERMANOVA of Bray-Curtis similarities based on fourth root transformed algal and invertebrate assemblage data Source df MS Grazing (Gr) 1 2299.10 Nutrients (Nu) 1 783.30 Gr x Nu 1 78.49 Residual 12 279.49 Total 15

F 8.23

P 0.0004

2.80 0.28

0.02 0.91

Stress = 0.07

Figure 14. Non-metric multidimensional scaling (nMDS) ordination of assemblages on Ambient Nutrient (circles), ‘High Nutrient’ (triangles), ‘Reduced Grazing’ (empty symbols) and ‘Ambient Grazing’ (filled symbols) rockpools on the basis of BrayCurtis similarities of the untransformed percentage cover data.

93


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

Table 15. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis similarity distinguishing between ‘Reduced Grazing’ and ‘Ambient Grazing’ treatments, based on the untransformed data. Av. Cover = average cover, Av. Diss. = average contribution to overall dissimilarity among samples, Diss / SD = the ratio of the average contribution to the overall dissimilarity among samples to the standard deviation of the average contribution to the overall dissimilarity, Contrib %. = percentage contribution to the overall dissimilarity among samples, Cum % = cummulative contribution to the overall dissimilarity among samples. Reduced Grazing Taxa Green filamentous Crustose Coralline Red turf Corallina officinalis Red filamentous Biofilm Ralfsia verrucosa Brown filamentous

Ambient Grazing

Av.Cover Av.Cover Av.Diss Diss/SD Contrib% Cum.% 78.70 28.24 14.52 1.99 38.36 38.36 55.79 88.08 9.62 1.61 25.40 63.77 11.57 12.27 1.99 1.35 5.27 69.04 11.23 11.92 1.97 1.43 5.21 74.25 7.75 9.20 1.91 1.41 5.05 79.30 8.33 6.71 1.73 1.27 4.57 83.87 3.01 3.01 1.30 0.87 3.43 87.30 3.01 2.66 1.10 0.97 2.91 90.21

Table 16. SIMPER analysis results for contributions from most important taxa towards the Bray-Curtis similarity distinguishing between ‘Ambient Nutrient’ and ‘High Nutrient’ treatments, based on the untransformed data. Av. Cover = average cover, Av. Diss. = average contribution to overall dissimilarity among samples, Diss / SD = the ratio of the average contribution to the overall dissimilarity among samples to the standard deviation of the average contribution to the overall dissimilarity, Contrib %. = percentage contribution to the overall dissimilarity among samples, Cum% = cummulative contribution to the overall dissimilarity among samples. High Nutrient Taxa Green filamentous Crustose Coralline Corallina officinalis Red turf Red filamentous Biofilm Ralfsia verrucosa Brown filamentous

Ambient Nutrient

Av.Cover Av.Cover Av.Diss Diss/SD Contrib% Cum.% 66.32 40.63 11 1.38 34.41 34.41 65.97 77.89 7.04 1.18 22.04 56.45 9.03 14.12 2.13 1.36 6.65 63.1 9.37 14.47 2.12 1.27 6.64 69.74 7.58 9.38 2.05 1.81 6.41 76.15 6.37 8.68 1.74 1.25 5.45 81.59 0.81 5.21 1.3 0.77 4.06 85.65 1.85 3.82 1.17 1.05 3.67 89.33

94


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

4.4.5. Effects of grazers and nutrients on individual taxa Neither grazing or nutrients had a significant effect on the cover of Corallina.

officinalis (Figure 15, Table 17). There was a significant interactive effect of grazing and nutrients on the cover of crustose coralline algae (Figure 15, Table 17). Pair-wise comparisons showed that cover of crustose coralline algae was significantly reduced where nutrients were added only under reduced grazing conditions, with an absolute decrease of 44% (± 8.0 S.E.) relative to the controls (P < 0.05, Figure 15, Table 17). The cover of green filamentous algae was significantly increased both by reduced grazing and increased nutrients, although the effect size was synergistically magnified by the combined effect of grazer removal and nutrients (Figure 15, Table 17). Nutrient enrichment caused an absolute increase in the cover of green ephemeral of 19% (± 3.9 S.E.) respect to the control treatments. Grazer removal had a larger effect size causing an absolute cover increase of 44% (± 6.9 S.E.), while the combine effect of these two stressors elicited an absolute increase in the cover of green ephemerals of 76.2% (± 5.2 S.E.). The cover of red turfing algae was not affected by grazer removal or nutrient addition (Figure 15, Table 17). Table 17. Two-way ANOVA for percentage cover of most prominent algal groups under grazing and nutrients manipulations.

Corallina officinalis

Crustose Coralline

Green ephemeral

Red turfing

Source df MS F MS F MS F MS F Grazing (Gr) 1 1.9 0.0 4171.0 24.1 10186.0 75.9*** 1.9 0.0 Nutrients (Nu) 1 103.7 2.7 568.5 3.3 2640.8 19.7*** 103.7 2.5 Gr x Nu 1 5.4 0.1 837.2 4.8* 134.0 1.0 13.7 0.3 Residual 12 38.6 172.8 134.3 41.2 Total 15 Transformation None None None None

95


Sedimentation, nutrients and grazer loss in rock pools

120 100

Ambient Grazing Reduced Grazing

Green filamentous

Chapter IV

20

Red turfing

15 80

Mean % cover (± SE)

60

10

40 5 20 0

0 100

20

Crustose coralline

Corallina officinalis 80 15 60 10 40 5

20

0

0

Ambient Nutrient

High Nutrient

Ambient Nutrient

High Nutrient

Figure 15. Effect of grazers and nutrients on the percentage cover of several prominent algal taxa on experimental rock pools (mean ± SE, n = 4).

4.5.

Discussion

Synergy has sometimes been assumed to be the common pattern of effects of multiple stressors effects (Myers 1995, Sala et al. 2000). Empirical evidence, however, has shown that this may not always be the case (Crain et al. 2008, Darling and Côté 2008). In the current study, sedimentation and nutrients elicited separate effects on the structure and cover of macroalgal assemblages, suggesting that the additive model of multiple stressors is more appropriate than the synergistic model for the combined effect of these two stressors in this natural system. On the other hand, grazing and nutrients acted synergistically, (i.e. the combine effect the two stressors was greater than the sum of individual effects), drastically changing the population and assemblage structure. These results support the contention that the 96


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

nature of interactions between multiple stressors depends on which combinations of stressors a given system is subjected to (Crain et al. 2008).

Effects of sedimentation were characterised by a trade-off between sediment tolerant and sensitive species that drove changes in assemblage structure. Sedimentation can cause increases in the cover of sediment-tolerant species that have the ability to trap and bind sediment (Airoldi 2003, Balata et al. 2007b). Green filamentous algae, red turfing algae, Corallina officinalis and red filamentous algae form densely packed mats that tend to accumulate and trap sediment. Negative effects of sedimentation on the abundance of gastropod grazers have been demonstrated previously in numerous observational and manipulative studies (Robles 1982, Marshall and Keough 1994, Airoldi and Virgilio 1998, Airoldi and Hawkins 2007, Walker 2007). Sediment is an important source of stress for grazers that can impair their movement and attachment. It reduces their grazing activity, either directly increasing their mortality or indirectly owing to increases in cover of turf-forming algae cover that they are unable to consume (Jenkins et al. 1999). The reduction in grazing activity by sedimentation has been postulated as one of the mechanisms through which sedimentation controls algal structure on rocky shores (Airoldi and Hawkins 2007). Additionally, the cover of two types of crustose algae (crustose coralline algae and Ralfsia verrucosa) was negatively affected by sedimentation. This is most likely due to the abrasive effect of sediment when moved by wave action on these crustose forms.

Nutrient effects were only evident on the cover of green ephemeral algae. Cover of these algae was also controlled by grazing gastropods. The effects of nutrients at a community level were only evident when grazing activity was considered. Nutrient

97


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Chapter IV

enrichment and reduced grazing activity had a synergistic effect, resulting in drastic changes in the community structure, specifically in an increase in the cover of green filamentous algae and a decrease in the cover of crustose algae. The effect of grazer loss was greater than the effect of nutrient addition, indicating that a strong top-down control limited the bottom-up control effect of nutrients. The synergistic, but generally non-interactive, effects of grazing and nutrients has been empirically demonstrated before (Nielsen 2001, Hillebrand 2003, Burkepile and Hay 2006, Gruner et al. 2008, Masterson et al. 2008). The extent to which top-down control is more important than bottom-up control is context dependant (Burkepile and Hay 2006). For example, Nielsen (2001) showed that the effects of both nutrient enrichment and grazing activities were dependent on wave exposure, with reduced effects at wave exposed sites. Russell and Connell (2005) showed, in a system with weak grazing pressure, that effects of nutrient enrichment on macroalgal assemblages were dependent on the presence of canopy forming algae.

The drastic increase in cover of green ephemeral algae in rock pools with reduced grazing adds to the large body of work describing negative effects of gastropods on algal assemblages (reviewed by Lubchenco and Gaines 1981, Hawkins and Hartnoll 1983). Assemblages under reduced grazing regimes were dominated by green ephemeral algae, associated with a considerable decrease in the cover of other taxa. There are many examples in which monocultures formed by competitively superior species develop on rocky shore habitats (Lubchenco 1978, Paine 1984). In the present study, grazers were a strong interactor, preventing the development of a monoculture of green ephemeral algae, and thus indirectly affecting other taxa in the assemblage (Paine 1992). The cover of crustose coralline algae drastically decreased

98


Sedimentation, nutrients and grazer loss in rock pools

Chapter IV

in rock pools with reduced grazing, indicating an indirect negative effect of grazing on this taxon (Wootton 1992, 1993, Billick and Case 1994, Menge 1995, Anderson 1996). Grazing prevents overgrowth of crustose algae by other algae (here green filamentous algae) and creates space for settlement (Dethier 1981, Steneck 1982, McQuaid and Froneman 1993). Crustose algae have relatively slow growth rates and tend to be easily overgrown (McQuaid and Froneman 1993).

In general, the observed effects were not distinct between the levels of experimental manipulation. While treatment manipulations were done according to the physical characteristics of the rock pools and additional environmental factors where kept as constant as possible, there is inherent variability in this natural study system (e.g. pool size, depth and area, shore height, wave exposure, residence time, etc.). A field experiment approach was preferred as opposed to a laboratory experiment as it constitutes a realistic scenario where the complex ecosystem interactions and the multidimensionality of natural conditions are incorporated.

No additional ecologically comparable experimental rock pools were available at the site, so the design could not be extended to test potential third order interactions between sedimentation, nutrients and grazers. Most natural systems are subjected to more than two stressors, and as the number of stressors in a system increase, stressors interactions became more complex and often synergistic (Crain et al. 2008). Third order interactions among the stressors considered here remains to be tested.

Clarifying the simultaneous effects of multiple human-induced threats is one of the major challenges in modern ecology (Myers 1996, Sala et al. 2000, Crain et al. 2008)

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Sedimentation, nutrients and grazer loss in rock pools

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and needs to be addressed urgently to underpin measures to halt biodiversity loss. This study provides insights into the nature and magnitude of such interactions. In this field experiment, I showed that sedimentation and nutrient enhancement act separately in altering the cover of macroalgal populations. However, the combined effect of grazer loss and nutrient enhancement synergistically caused changes that induced the monopolization of the habitat by opportunistic species. There is a substantial body of literature showing that nutrient enrichment, consumer loss and sedimentation are widespread threats in marine systems (Crowe et al. 2000, Thompson et al. 2002, Halpern et al. 2008), suggesting that these types of interaction are common in nature and that biodiversity change can be driven differently from that anticipated by analyses of individual stressors. Here it was shown that all stressors have the potential to cause important changes in population and/or assemblage dynamics and their actions need to be individually reduced if â&#x20AC;&#x2DC;ecological surprisesâ&#x20AC;&#x2122; (Paine 1984) are to be avoided.

100


Chapter V

General discussion

CHAPTER V: GENERAL DISCUSSION Three independent studies were conducted in different aquatic study systems. The overarching aims that linked them were to asses the effects of multiple anthropogenic stressors on biodiversity and to test and develop tools for biomonitoring and assessment of ecological quality in these systems. In the first chapter an important ecological baseline dataset was successfully collected, including data on macroalgal and molluscan assemblages and physico-chemical parameters along a network of intertidal sites differing in levels of pollution.

Pollution in rocky shores was

associated with changes in diversity, structure and composition of molluscan assemblages.

These assemblages, associated with canopy forming algae, were

suggested as potential cost-effective bioindicators tools of generalised pollution in rocky shores. In the second chapter, a multi-lake survey in the central and western areas of Ireland was conducted to test the performance of ecological assessment metrics in relation to the invasion of the zebra mussel Dreissena polymorpha. All tested metrics successfully indicated nutrient enrichment pressure in lakes. Their effectiveness varied, however, and in each case was hindered or eliminated by the presence of invasive mussels. This was explained by drastic changes in the structure, composition and diversity of stony substratum macroinvertebrate assemblages associated with the presence of the invasive mussel. The development of ecological classification tools separately for invaded and non-invaded lakes was suggested. In the last data chapter, using a field based experiment, it was shown how widespread threats in aquatic environments, namely sedimentation, nutrient enrichment and grazer loss, can act individually or in combination to alter the structure of rock pools assemblages. It was demonstrated that top-down control (grazers) is more important

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than bottom-up factors (nutrients) in controlling macroalgal assemblage structure; and that the combined effects of two stressors, grazers loss and nutrients, is synergistic, i.e. larger than anticipated from the individual effects.

By testing these hypotheses in range of aquatic habitats it was possible to draw more general inferences about how changes in biodiversity patterns are driven by multiple anthropogenic threats and make important progress towards the development of biomonitoring and assessment ecological tools.

5.1.

Monitoring and Environmental Indicators

At an international level, policy-makers seek to develop strategies to protect, conserve and manage aquatic environments. The United Nations Convention on Law of the Sea is the international basic legal framework that governs the uses of the oceans and seas, establishing obligations to protect marine resource and use them sustainably. At a national or regional level, several initiatives have been developed recently. For example, the Oceans Policy in Australia (Commonwealth of Australia 2006); the Oceans Act in Canada (Parsons et al. 1984), the Pew Oceans Commission and the US Commission on Ocean Policy in the USA (Granek et al. 2005) and the WFD (EC 2000) and Marine Strategy Framework Directive (EC 2008) in Europe. The main objective of these initiatives is to to maintain a good environmental or ecological status for marine waters, habitats and resources.

The concept of

environmental status takes into account the structure, function and processes of marine ecosystems, bringing together physical, chemical, geographic and climatic factors, and integrates these conditions with the anthropogenic impacts and activities in the area concerned. Thus, this approach is intended to allow an assessment of the

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ecological status at the ecosystem level (â&#x20AC;&#x2DC;ecosystem-based approachâ&#x20AC;&#x2122;), rather than focusing on a single taxa or chemical level. In this context, the present study provided important progress towards the development of intregrative biomonitoring tools by incooporating biological componments at different organization (e.g. population and community) and trophic levels (e.g. primary, consumers and predators), together with the assessment of physical and chemical factors, such as nutrient enrichment, sedimentation and generalised pollution.

European states are required by the WFD to monitor and assess the overall ecological status of all transitional, coastal and lakes water bodies, and to meet good ecological status by 2015 (Gaertner et al. 2007). The purpose of ecological assessment is to measure deviations away from established reference conditions for a given habitat type. In Ireland, the marine benthic macroinvertebrate biological quality element is evaluated based on the composition and abundance of soft sediment infaunal communities, using the Infaunal Quality Index (IQI) multimetric (Cusack et al. 2005).

However, there is no consideration of rocky shore macroinvertebrate

communities for the assessment of the ecological quality of coastal waters. The only element considered in rocky shore habitats is the composition and abundance of macroalgae, with particular attention to the proportion of opportunistic and perennial species (Cusack et al. 2005, Wells et al. 2007). In this context, the use of molluscan assemblages associated with canopy forming algae as biomonitoring tools suggested in Chapter II can provide a valuable tools to complement the already considered biological elements for the classification of coastal areas. As currently there are no universally accepted biomonitoring tools for rocky shores (ArĂŠvalo et al. 2007, Ballesteros et al. 2007, Wells et al. 2007), this constitutes an important step towards

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the development of classification tools as required by the WFD. Sampling is rapid and cost-effective and molluscs can be used as surrogate measures of overall diversity (Smith 2005). The observed trade off between tolerant and sensitive molluscan species, allowed the identification of potential bioindicator taxa, for example the abundance of certain taxa highly correlated with total abundance or nutrient concentration. Further research to develop classification tools for rocky shores should include monitoring programmes with larger spatial and temporal scales in order to increase the range of pollution levels and determine pattern of spatial and temporal variability of these assemblages at a range of scales.

The research reported in this thesis had the advantage that the issue of multiple stressors and biological indicators was explored in multiple aquatic systems and using different approaches, which can benefit the development of more general understanding of impacts of anthopogenic activities on these systems. For example, eutrophication models were historically developed for freshwater environments such as lakes and reservoirs (Dillon and Rigler 1974, Vollenweider 1975, Jørgensen 1976).

Increased pressure on coastal systems has highlighted the need for

eutrophication modelling of estuarine and coastal systems, which has led to the adaptation of approaches used for freshwater, often with limited success. This is perhaps partly because coastal environments are inherently more complex and there is often no clear relationship between nutrient enrichment and eutrophication symptoms, i.e. systems with similar pressures show widely varying responses (Painting et al. 2007). However, some promising methods are being developed for lakes (e.g. Donohue et al. in press), that can be adapted for the development of ecological classification tools for marine systems.

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Canonical Correspondence


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Analysis - based Assessment System (CBAS) (Dodkins et al. 2005, Donohue et al. in press), for example utilises the multivariate optima and niche breadths of species to derive biologically scaled response metrics along environmental gradients. This method could be applied to the data presented in Chapter II, where some taxa tended to be more abundant in sites with high nutrient concentrations and others more abundant in pristine conditions, giving some indication on how this trade-off between environmental conditions can be applied to the development of classification tools.

One of the fundamental principles of ecological status classification under the WFD is the identification of type specific reference conditions (Wright et al. 1998, Ballesteros et al. 2007, Mangialajo et al. 2007). In Chapter II, the control sites established in the network of study sites could be further investigated in order to consider them as reference conditions for this specific habitat, i.e. sheltered rocky shore environments. Reference conditions must be of high ecological status and thus show 'no, or only very minor, evidence of distortion' (EC 2000). Once reference conditions are identified, the impacts of nutrients on rocky shores can be measured as deviations from this condition as Ecological Quality Ratios (Buijs 2005). Invasive species are not specifically mentioned in the WFD. However, the WFD indicates that they need to be assessed, both as environmental stressors and because they undermine naturalness, a key principle of the WFD (EC 2000). As it has been shown that most aquatic invasion can hardly be reversed (Strong and Pemberton 2001, Perrings 2002, 2005), some options for incorporating them into the implementation of the WFD include (a) the definition of a new set of reference conditions for invaded systems before the use of classification tools (b) to modify the status class

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assigned to a water body based on the presence and impact of established alien species or (c) to omit invasive species from ecological status classification altogether but to report on invasive species pressures for each water body using other indicators alongside the status classification. These alternatives are the subject of an EU-wide debate through a questionnaire distributed to all Member States, the outcome of which should provide the information needed to resolve this issue (Boon 2009).

Macroalgal assemblages, one of the four biological elements named in the WFD did not discriminate sites according to pollution status (Chapter II), suggesting that their use, although suggested by many other studies (Orfanidis et al. 2001, ArĂŠvalo et al. 2007, Krause-Jensen et al. 2007, Pinedo et al. 2007, Wells et al. 2007) and explicitly required in the WFD, may not provide useful information for classification of ecological status of the system examined here. Possible explanations may include the fact that bottom-up effect of nutrients on algal assemblages is largely limited by the strong top-down control elicited by grazers (Chapter IV). However, the use of classification tools using macroalgal assemblages requires further investigation to test their performance in different habitats, with different stressors and across a range of temporal and spatial scales (Ballesteros et al. 2007, Wells et al. 2007). Recently, new ecological status classification tools have been develop using macroalgae assemblages (Wells et al. 2007, Cusack et al. 2008). These tools are still being developed and planned to be used at a national level to implement monitoring programms in the context of WFD. They are more complex than the approach taken in the present study, as six different metrics are taken into consideration, including shore description, species richness, the ratio of algae in different ecological status groups, the proportion of red and and green algae and the proportion of opportunistic

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algae. A sliding scale is used to determine EQR and ecological status for each metric. The classification of each site is then determined by averaging the EQR values from all metrics (Wells et al. 2007, Cusack et al. 2008).

The general approach taken here, focusing in one specific habitat and season, allowed the detection of a clear biological response to stressors.

This focused

approach may be usefully adopted in monitoring programmes to avoid statistical noise owing to natural temporal and spatial variability. For example, in Chapter II the sampling effort was focused in molluscan assemblages inhabiting the lower part of sheltered rocky shores during spring and summer. In Chapter III, the survey was focused on macroinvertebrates assemblages inhabiting rocky substrata at a depth of ~ 0.5 m. This approach can be criticized for only showing a very localized community pattern, but on the other hand the objective was to develop and test in a relatively simple and robust manner the performance of ecological assessment tools, trying to keep the inherent natural variability of aquatic systems to the minimum (Irvine 2004). Furthermore, one of the qualities of a good indicator is to be cost-effective (O'Connor and Dewling 1986, Cairns et al. 1993), thus accessible environments are ideal in order to minimise cost of future sampling. Ground-truthing should involve testing whether patterns observed in these accessible environments reflects quality in less accessible habitats nearby (O'Reilly et al. 2006). Furthermore, it would be efficient to monitor ecological status in a defined season (the same season in which the assessment tool was developed), although as part of the development, it should be tested whether status indicated in one season is approximately reflected in other seasons (Borja et al. 2000, Borja et al. 2003, Toner et al. 2005).

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Ideally biomonitoring tools would be specific in helping to establish causal relationships between an exposure between to a given stressor and the ecological response. Some biomonitoring tools are specific to a given contaminant, but they are restricted to changes occurring at cellular, biochemical, or physiological levels, that can be measured using biomarkers in cells, tissues, or organs within an organism. These kinds of tools are generally less cost-effective and of restricted ecological significance, as they do not necessarily reflect changes across a range of biological organization (from intra-individual to community). In general, biomonitoring tools relating community level effects to stressors are scarce. Although, there is some evidence that physiological measurements, such as scope for growth in mussels, have been successfully related to levels of hydrocarbon contamination and overall rocky shore diversity (Crowe et al. 2004). The findings preented in this thesis are a step forward towards the establishing relationshipships between stressors (e.g. nutrient enrichment), biotic indices (e.g. indicator taxa metric) and community level effects.

Another important aim of this thesis was to generate comprehensive datasets encompassing several ecosystem components (macroinvertebrates, macroalgae, nutrients) and habitats (rocky shores and lakes) across a network of sites to enable to the development of bioindicators that are highly correlated with measures of biodiversity overall ecosystem quality. These data sets will be made available to the broader scientific community through the Irish Environmental Protection Agency data centre and will be complemented by other related studies carried out in parallel with this thesis and subsequent to it (e.g. other PhD, BSc. and MSc. projects focussed on different taxa or in different habitats nearby or in different seasons, etc.). This will allow the use of analytical tools, such as meta-anayles (Osenberg et al. 1997), to

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determine which ecosystem components or taxonomical groups provide the most efficient surrogate measures highly correlated with total biodiversity, pollution and system functioning at a range of spatial and temporal scales.

For the purpose of the WFD, the EPA is currently undertaking a monitoring of ~ 500 lakes to provide the basis for assessment of the quality of Irish lakes (Gaertner et al. 2007). The WFD explicitly requires the incorporation of macroinvertebrate communities as part of the monitoring programme. The monitoring technique and associated classification scheme is currently under development (Toner et al. 2005). In Chapter III of this thesis it was shown that littoral macroinvertebrate communities living on stony substrata were an effective tool to assess the ecological quality of non-invaded lakes. Three recently developed metrics of ecological status (Donohue

et al. in press) successfully indicated eutrophication pressure, measured as levels of total phosphorus (TP). The Indicator Taxa Metric performed best, showing a high correlation with levels of (TP): 71% of the variation of levels of TP were explained by this metric. Considering that for the calculation of Indicator Taxa Metric it is only necessary to asses the presence or absence of ten taxa (see Table 2, Chapter III), this indicator is ideal in the sense that is linked to a specific stressor, that is sufficiently sensitive and reliable, easy to measure, cost-effective, and scientifically defensible (O'Connor and Dewling 1986, Cairns et al. 1993, UNESCO 2006, Rees et

al. 2008).

The main drawback, not only of the Indicator Taxa Metric but of all the metrics tested, was that they failed to indicate ecological quality in invaded systems. Confirming that the use of bioindicators and ecological classification tools for

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systems where multiple stressors act simultaneously is an large and challenging task (Donohue et al. in press).

The misclassifying of water bodies in relation to

eutrophication pressure due to the interference with other environmental stressors, in this case biological invasions, has important implications in the management of these resources. For example, if a given water body is misclassified as being in poor ecological status due to the presence of the zebra mussel, this requires the implementation of mitigation measures in order to restore it to good status. Mitigation and restoration measures are, in general, socially and economic costly. In this sense, the combined effects of these widespread threats in aquatic systems are not only limited to their ecological impacts, but also potentially to economic and social consequences.

5.2.

Multiple stressors and the role of experimental ecology

Given that most aquatic ecosystems are subjected to more than one stressor acting simultaneously (Crowe et al. 2000, Thompson et al. 2002, Mora et al. 2007, Crain et

al. 2008, Darling and C么t茅 2008, Jackson 2009), the present study showed how they can either act in isolation or synergistically, providing more realistic scenarios than those from anticipated by single stressors analyses.

Pollution was associated with deleterious effects on the diversity and structure of assemblages of aquatic communities at large spatial scale.

On rocky shores,

differing nutrient concentrations were related to changes in the structure of molluscan assemblages. However, pollution does not only imply inputs of nutrients into aquatic systems, but also a range of other chemical, e.g. heavy metals, hydrocarbons, and physical stresses, e.g. changes in hydrodynamics, temperature and

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light penetration. Because the studies presented in Chapters II and III were purely observational, they did not allow the isolation of the effects of individual stressors on different elements of biodiversity or the establishment of a causal relationship.

Experimental ecology has a fundamental role to play in elucidating the ecological effects of multiple stressors and to provide a scientific basis for the proper management and restoration of natural aquatic ecosystems (Underwood et al. 2000). Experimental studies allow direct inference of causal relationships, the mechanistic processes and quantification of the separate and combined impacts of multiple anthropogenic threats on ecological systems.

The observational study approach

taken in Chapter II and III, did not allow the inference of causal relationships. Thus, the next logical step was to test experimentally the effect of selected stressors on elements of biodiversity. In the experiment presented in Chapter IV, interactions among important stressors of rocky shore organisms were characterised following three models frequently used in the literature to describe these interactions, namely additive, antagonistic and synergistic (Folt et al. 1999, Crain et al. 2008, Darling and C么t茅 2008). While insight into the effects of multiple stressors has been gained from laboratory experiments, the interpretation of these findings requires important caveats. Extrapolating laboratory results to the field is normally problematic, for example because interspecific interactions, such as competition or facilitation) are absent (Thompson et al. 1998, Atalah et al. in preparation). Field experiments provide a realistic scenario in which the mechanisms underlying the observed effects are likely to be robust under natural, whole community conditions (Underwood 1990).

The field experiment presented in Chapter IV quantified how multiple

stressors can simultaneously or separately act to modify rock pools assemblages. It

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has been assumed that multiplicative effects (most commonly synergies) are the common pattern of interaction between multiple stressors (Crain et al. 2008, Halpern

et al. 2008). The results of the experiment reported here (and others, e.g. Darling and Côté 2008) showed that this is not always the rule. For example, sedimentation acted independently of the effect of nutrient enrichment. Some authors have described this kind of interaction between stressors as ‘simple comparative’, expanding the three basic models mentioned above, where effect of stressors in combination is equal to the effect of the single worst of dominant stressor (Folt et al. 1999). This is a more simplistic model where a single stressor, here sedimentation, takes precedence over the other (nutrients) in determining their combined effect. On the other hand, the combined effect of nutrient enrichment and grazer loss was best described by the synergistic model. Thus, there is a wide range of possibles ways that multiple stressors can interact to modify natural systems, and the nature these interactions is mostly context dependent and stressor specific.

These findings

highlight the importance of experimental ecology to elucidate the mechanisms behind such interactions. There is considerable scope for further research in this area, because there is an urgent need for sound scientific information to help to inform strategies for management and conservation of aquatic ecosystems.

Another important role of field experimental ecology is to aid the development of ecological assessment tools. For example, to strengthen inferences concerning stress optima of selected taxa derived from observational studies, quantification of the effect of multiple stressors should be experimentally done in controlled conditions. Although in Chapter IV the effects nutrient enrichment and sedimentation were experimentally tested, it is not possible to extrapolate or directly relate these results

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to nutrient optima for the potential bioindicators highlighted in Chapter II, because the two studies were done in different habitats, namely sheltered lower rocky shore among Fucus serratus in Chapter II and mid-shore rock pools in semi-exposed rocky shores in Chapter III.

5.3.

Concluding remarks

This thesis has provided important insights into the effects of multiple stressors on the biodiversity of aquatic systems. Additionally, a valuable contribution has been made to the development and refinement of novel monitoring and classification ecological tools for the assessment of aquatic systems. Here, it was demonstrated that aquatic biodiversity is currently being jeopardized by multiple stressors that can act either independently or in combination to alter aquatic communities. Furthermore, these stressors are not only driving biodiversity changes, but they are interfering in the appropriate assessment of these inherently complex aquatic systems.

There is an vast amount of literature showing that multiple stressors, such as the ones examined here, are widespread and are increasing in their impact on aquatic systems (Crowe et al. 2000, Thompson et al. 2002, Worm et al. 2006, Halpern et al. 2008), suggesting that synergistic or additive interactions may be common (Crain et al. 2008, Darling and C么t茅 2008). Furthermore, there are many other widespread threats that were not directly considered here (e.g. ocean acidification) and superimposed on all of these are possible impacts from climate change with unpredictable effects across most systems (Suchanek 1994, Michener et al. 1997, IPCC 2007, Riebesell 2008).

There is considerable uncertainty about the relative importance and

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interactions among local stressors, such as pollution or invasive species, versus global changes in climate and aquatic chemistry that operate over very different and temporal and spatial scales (Stachowicz et al. 2002, Jackson 2009). This is an important area for new scientific research to better understand the synergies among different drivers of ecosystem change and their likely consequences. Surprisingly, however, empirical and experimental data and specially field based studies, on the interactions of multiple stressors are scarce (Brook 2008). Here it was shown that all of the stressors examined can cause changes to biodiversity and difficulties in the ecological assessment of aquatic systems. If the loss of ecosystem good and services provided by aquatic systems is to be reversed, action to reduce individual stressors one by one should be replaced by more strategic thinking, focusing on combinations of stressors shown to interact synergistically to cause the most significant impacts. Management can then be prioritised for maximum effect and minimun cost in economic and social terms. The ecological research community faces a considerable challenge in providing a sound basis for this process.

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PhD Thesis  

Multiple anthropogenic stressors as drivers of biodiversity change in aquatic systems:impacts, indicators and monitoring

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