Roberts Bank Terminal 2 – Technical Data Report · 2015-04-30 · to draw relationships between...

HEMMERA ENVIROCHEM INC.

Roberts Bank Terminal 2 – Technical Data Report Biofilm Physical Factors

307071-00790 – 01-EN-REP-5001

27 January 2015

WorleyParsons Canada Suite 600, 4321 Still Creek Drive Burnaby, BC V5C 6S7 CANADA Phone: +1 604 298 1616 Facsimile: +1 604 298 1625 www.worleyparsons.com © Copyright 2014 WorleyParsons

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HEMMERA ENVIROCHEM INC. ROBERTS BANK TERMINAL 2 – TECHNICAL DATA REPORT

BIOFILM PHYSICAL FACTORS

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Disclaimer

The information presented in this document was compiled and interpreted exclusively for the purposes stated in Section 1.2 of the document. WorleyParsons provided this report for Hemmera Envirochem Inc. solely for the purpose noted above.

WorleyParsons has exercised reasonable skill, care, and diligence to assess the information acquired during the preparation of this report, but makes no guarantees or warranties as to the accuracy or completeness of this information. The information contained in this report is based upon, and limited by, the circumstances and conditions acknowledged herein, and upon information available at the time of its preparation. The information provided by others is believed to be accurate but cannot be guaranteed.

WorleyParsons does not accept any responsibility for the use of this report for any purpose other than that stated in Section 1.2 and does not accept responsibility to any third party for the use in whole or in part of the contents of this report. Any alternative use, including that by a third party, or any reliance on, or decisions based on this document, is the responsibility of the alternative user or third party.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of WorleyParsons.

Any questions concerning the information or its interpretation should be directed to C. Martin or M. L. Lauria.

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EXECUTIVE SUMMARY

The Biofilm Physical Factors study was conducted as part of an environmental program for the proposed Roberts Bank Terminal 2 Project (Project or RBT2), and focused on analysing existing data to develop an understanding of the environmental variables that are correlated to biofilm growth at Roberts Bank. The Project, part of Port Metro Vancouver’s Container Capacity Improvement Program (CCIP), is a proposed new three-berth marine container terminal located at Roberts Bank in Delta, B.C.

The mudflats directly north of the existing Roberts Bank causeway are known to possess biofilm, which is an important source of primary production and constitutes a large source of forage for various invertebrates and vertebrates in estuarine environments. Published studies conducted in Europe have made linkages between biofilm biomass and several environmental parameters. To date, no published data have linked biofilm density and microphytobenthic composition at Roberts Bank with specific physical environmental variables.

The key objectives of this study were to 1) undertake a review of published literature to better understand how environmental variables influenced biofilm biomass and microphytobenthic composition, and 2) use an existing database collected at Roberts Bank in 2013 to assess any specific correlations observed at Roberts Bank. Published literature has shown a number of variables to influence biofilm biomass and microphytobenthos community composition. Primary factors include temperature, light, nutrients, salinity, and sediment grain size.

To assess specific relationships at Roberts Bank, statistical analyses were conducted on an existing multivariate database. The data were collected through co-located sampling efforts from three study disciplines to assess the importance of measured variables on biofilm biomass and microphytobenthic composition at Roberts Bank. As such, the statistical analyses were conducted using data that were collected for purposes beyond the scope of assessing biofilm. The intent was to utilise existing datasets to draw relationships between the physical factors driving biofilm growth on Roberts Bank. Sample size was limited to the available data and as a result, sampling distribution and frequency may have been insufficient to fully account for the natural temporal and spatial variability known to occur at Roberts Bank. Conclusions regarding the physical factors of biofilm growth at Roberts Bank are limited to the available data and associated assumptions of collection methodologies.

Field data for Biofilm, Benthic Infauna, and Sediment Chemistry and Quality studies, were filtered for specific properties including completeness and normality before being analysed. Biofilm biomass indicators (Chlorophyll a, Fucoxanthin, Total Organic Carbon, and Total Carbohydrate) were analysed with Principal Component Analysis (PCA) and Multiple Linear Regression. Additional analyses were conducted on the microphytobenthic community composition using non-parametric multi-dimensional scaling methods.

In terms of biofilm biomass, freshwater influence, as indicated by distance from Canoe Passage and Porewater Chloride levels, was observed to consistently have a negative relationship with the measured biomass indicators. Sediment grain size composition was also shown to have a significant relationship

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where all biofilm biomass indicators decreased with increased % Sand composition and increased with increased % Silt and % Clays.

An analysis of the microphytobenthic community was conducted using community composition data. A significant difference in microphytobenthic community composition occurred between spring and summer, along with a high level of spatial variability within seasons. This seasonal variability in the taxonomic composition required separate analyses for both spring and summer data, reducing the sample size used for analysis. No relationships between the microphytobenthic community composition and environmental variables were observed. However, previously published literature have indicated shifts in the microphytobenthos community composition in relation to several environmental parameters including freshwater influence and sediment grain size distribution.

This study indicates that freshwater influence (as measured by Porewater Chloride) and sediment grain size have the strongest relationship with biofilm biomass at Roberts Bank. These environmental variables have all been previously identified as influential in microphytobenthos and/or phytoplankton growth. Specific findings from this study include:

1. Biofilm biomass levels are correlated to freshwater influence as shown by a positive and significant relationship with Porewater Chloride content (salinity). Porewater chloride was significantly correlated to distance from Canoe Passage and Total Leachable Ammonia. Aside from Ammonia, no nitrogen data (nitrate and nitrite) were available for analysis due to measured values being below laboratory Detection Limits (DL). Previous research in the Fraser River estuary have established a negative relationship between nutrient availability and freshwater influence;

2. Biofilm biomass levels are correlated to sediment grain size as shown by a negative and significant relationship with % Sand. % Sand was negatively correlated to % Silt, % Clay, and sediment Total Organic Carbon (TOC), indicating an inverse relationship with biofilm biomass;

3. A positive relationship between Polychaete Density and some biofilm biomass measures (Chlorophyll a and Total Organic Carbon) were observed, while negative relationships were observed with other measures of the infauna community including Macrofauna density and biomass. The positive relationship with Polychaetes is likely due to similarities in habitat preferences and Polychaete foraging strategies rather than a cause and effect relationship; and

4. No significant effects of environmental variables on microphytobenthos taxonomy were determined due to a small sample size. However, previous literature supports a predicted effect of salinity, sediment grain size, and nutrients. Given results from a wider assessment of microphytobenthic community composition across Roberts Bank, these effects are expected, but not confirmed with the available data.



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CONTENTS 1. INTRODUCTION................................................................................................................ 1

1.1 Project Background ................................................................................................. 1

1.2 Physical Factors Overview ...................................................................................... 1

1.3 Scope of Work ......................................................................................................... 5

2. REVIEW OF EXISTING LITERATURE.............................................................................. 6

2.1 Ecological Importance of Biofilm ............................................................................. 7

2.1.1 Primary Productivity .............................................................................................. 7

2.1.2 Secondary Productivity ......................................................................................... 7

2.2 Variables Influencing Biofilm Growth ...................................................................... 8

2.2.1 Light ....................................................................................................................... 8

2.2.2 Turbidity ................................................................................................................. 9

2.2.3 Tidal Cycle ............................................................................................................. 9

2.2.4 Immersion and Exposure ...................................................................................... 9

2.2.5 Salinity ................................................................................................................. 10

2.2.6 Nutrients .............................................................................................................. 11

2.2.7 Sediment Grain Size ........................................................................................... 12

2.2.8 Predation ............................................................................................................. 13

2.3 Seasonal Variation of the Fraser River Estuary .................................................... 13

3. METHODS ....................................................................................................................... 17

3.1 Sample Locations .................................................................................................. 18

3.2 Sample Collection ................................................................................................. 21

3.2.1 Assumptions of Database ................................................................................... 21

3.3 Analysed Variables ................................................................................................ 22

3.3.1 Spatial Data ......................................................................................................... 24

3.4 Databases ............................................................................................................. 25

3.5 Analysis ................................................................................................................. 25

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3.5.1 Biofilm Biomass ................................................................................................... 25

3.5.2 Microphytobenthos Taxonomy Analysis .............................................................. 26

4. RESULTS ......................................................................................................................... 27

4.1 Biofilm Biomass Analysis ....................................................................................... 27

4.1.1 Seasonal Differences .......................................................................................... 28

4.1.2 Principal Components Analysis ........................................................................... 29

4.1.3 Multiple Regression ............................................................................................. 31

4.2 Microphytobenthos Taxonomy Analysis ................................................................ 39

4.2.1 Seasonal Differences .......................................................................................... 39

5. DISCUSSION ................................................................................................................... 41

5.1 Biofilm Biomass Indicators .................................................................................... 41

5.1.1 Freshwater Influence ........................................................................................... 41

5.1.2 Sediment Grain Size ........................................................................................... 43

5.1.3 Benthic Infauna ................................................................................................... 44

5.2 Microphytobenthos Community ............................................................................. 45

6. CONCLUSIONS ............................................................................................................... 47

7. REFERENCES ................................................................................................................. 48

Tables

TABLE 1.2-1 BIOFILM PHYSICAL FACTORS STUDY COMPONENTS AND MAJOR OBJECTIVES ......................................................................................................... 1

TABLE 2.2-1 EXAMPLES OF SPECIES-SPECIFIC OPTIMAL SALINITY LEVELS FOR BIOFILM BIOMASS (CHLOROPHYLL A) AND GROWTH (DIVISION RATE) .... 11

TABLE 2.3-1 SUMMARY OF ENVIRONMENTAL VARIABLES THAT INFLUENCE BIOFILM BIOMASS AND MICROPHYTOBENTHOS COMMUNITY COMPOSITION ....... 15

TABLE 3.1-1 SUMMARY OF THE NUMBER OF CO-LOCATED SAMPLES PER SAMPLING PERIOD WITHIN THE STUDY AREA AT ROBERTS BANK ............................... 18

TABLE 3.2-1 FIELD DATA COLLECTION DATES OF BIOFILM, SEDIMENT CHEMISTRY AND QUALITY, AND BENTHIC INFAUNA DURING 2013 CO-LOCATED SAMPLING ........................................................................................................... 21



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TABLE 3.3-1 SUMMARY OF VARIABLES MEASURED BY EACH DISCIPLINE AT CO-LOCATED SAMPLING LOCATIONS ................................................................... 23

TABLE 3.3-2 MEASURED VARIABLES BY INDIVIDUAL STUDIES ....................................... 24

TABLE 4.1-1 RETAINED VARIABLES AND TRANSFORMATIONS USED FOR PRINCIPAL COMPONENTS ANALYSIS ................................................................................. 27

TABLE 4.1-2 PRINCIPAL COMPONENTS AND COMPONENT LOADINGS FOR EACH VARIABLE IN DATABASE ................................................................................... 29

TABLE 4.1-3 CORRELATIONS BETWEEN PRINCIPAL COMPONENTS AND MEASURES OF BIOFILM BIOMASS INDICATORS ...................................................................... 31

TABLE 4.1-4 CONDENSED PHYSICAL FACTORS CONSIDERED FOR MULTIPLE REGRESSION ..................................................................................................... 31

TABLE 4.1-5 COEFFICIENTS OF MULTIPLE LINEAR REGRESSION ANALYSIS OF CHLOROPHYLL A ............................................................................................... 32

TABLE 4.1-6 COEFFICIENTS OF MULTIPLE LINEAR REGRESSION ANALYSIS OF FUCOXANTHIN ................................................................................................... 33

TABLE 4.1-7 COEFFICIENTS OF MULTIPLE LINEAR REGRESSION ANALYSIS OF TOTAL CARBOHYDRATE ............................................................................................... 33

TABLE 4.1-8 COEFFICIENTS OF MULTIPLE LINEAR REGRESSION ANALYSIS OF TOC WITHIN BIOFILM ................................................................................................. 34

Figures

FIGURE 1.2-1 OVERVIEW MAP OF ROBERTS BANK TERMINALS AND PROPOSED FOOTPRINT OF THE ROBERTS BANK TERMINAL 2 PROJECT ....................... 3

FIGURE 1.3-1 SCHEMATIC OF MICROBIAL BIOFILM WITHIN INTERTIDAL SEDIMENTS (UPDATED FROM DECHO 2000) ......................................................................... 6

FIGURE 3.1-1 CO-LOCATED SAMPLING LOCATIONS AT ROBERTS BANK IN 2013 ........... 19

FIGURE 4.1-1 SCATTERPLOT AND PARTIAL REGRESSION RELATIONSHIP OF CHLOROPHYLL A DENSITY AGAINST POREWATER CHLORIDE (A), POLYCHAETA DENSITY (B), AND % SAND COMPOSITION (C) ..................... 35

FIGURE 4.1-2 SCATTERPLOT AND PARTIAL REGRESSION RELATIONSHIP OF FUCOXANTHIN DENSITY AGAINST POREWATER CHLORIDE (A), AND % SAND COMPOSITION (B) ................................................................................... 36

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FIGURE 4.1-3 SCATTERPLOT AND PARTIAL REGRESSION RELATIONSHIP OF TOTAL CARBOHYDRATE DENSITY AGAINST POREWATER CHLORIDE (A), AND % SAND COMPOSITION (B) ................................................................................... 37

FIGURE 4.1-4 SCATTERPLOT AND PARTIAL REGRESSION RELATIONSHIP OF TOTAL ORGANIC CARBON DENSITY AGAINST POREWATER CHLORIDE (A), POLYCHAETA DENSITY (B), TOTAL INVERTEBRATE DENSITY (C), AND % SAND COMPOSITION (D) ................................................................................... 38

FIGURE 4.2-1 NMDS OF MICROPHYTOBENTHOS ASSEMBLAGE AT CO-LOCATED SAMPLING LOCATIONS ..................................................................................... 39

Appendices

APPENDIX 1 EXTENDED STATISTICAL METHODOLOGY BACKGROUND

APPENDIX 2 CO-LOCATED DATABASE USED FOR BIOFILM PHYSICAL FACTORS ANALYSIS

APPENDIX 3 DETAILED BIOFILM BIOMASS STATISTICAL ANALYSES

APPENDIX 4 CORRELATION COEFFICIENT MATRIX

APPENDIX 5 DETAILED MICROPHYTOBENTHOS COMMUNITY STATISTICAL ANALYSES



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LIST OF ABBREVIATIONS AND ACRONYMS AICc Corrected Akaike Information Criterion

ANOSIM Analysis of Similarity

B.C. or BC British Columbia

BEST Biota-Environmental Stepwise analysis

CCIP Container Capacity Improvement Program

DL Detection Limits

EPS Extracellular Polymeric Substance

FRE Fraser River estuary

GPS Global Positioning System

LiDAR Light Detection and Ranging

nMDS non-Parametric Multidimensional Scaling Analysis

PAR Photosynthetic Active Radiation

PC Principal Component

PCA Principal Component Analysis

PSU Practical Salinity Units

RBT2 Roberts Bank Terminal 2

SSC Suspended Sediment Concentration

TDR Technical Data Report

TEU Twenty-foot Equivalent Unit containers

TOC Total Organic Carbon

UK United Kingdom

USA United States of America

LIST OF UNITS AND NUMERICAL ABBREVIATIONS cm Centimetre

g Gram

kg Kilogram

km Kilometre

L litre

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m Metre

mg Milligram

mm Millimetre

nm nanometre

% Percent

s Seconds



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1. INTRODUCTION

1.1 Project Background

The Roberts Bank Terminal 2 Project (RBT2 or Project) is a proposed new three-berth marine terminal at Roberts Bank in Delta, B.C. that could provide 2.4 million TEUs (twenty-foot equivalent unit containers) of additional container capacity annually (Figure 1.2-1). The Project is part of Port Metro Vancouver’s Container Capacity Improvement Program (CCIP), a long-term strategy to deliver projects to meet anticipated growth in demand for container capacity to 2030.

Port Metro Vancouver has retained Hemmera to undertake environmental studies to inform a future effects assessment for the Project. WorleyParsons Services Canada Ltd. (WorleyParsons) was retained by Hemmera to undertake studies related to biofilm. This technical data report (TDR) describes the results of the Biofilm Physical Factors study.

1.2 Physical Factors Overview

A review of existing information and state of knowledge was completed on the reported physical factors driving biofilm productivity, focusing on key data gaps and areas of uncertainty within the RBT2 area. This TDR describes the study findings for key components identified from this gap analysis. Study components, major objectives and a brief overview are provided in Table 1.2-1.

Table 1.2-1 Biofilm Physical Factors Study Components and Major Objectives

Component Major Objective Brief Overview

Data Compilation Compile data across disciplines to provide an extensive dataset of multiple environmental variables collected at Roberts Bank

Three biological assessments were run in coordination during technical data collection: Biofilm, Benthic Infauna, and Sediment Chemistry and Quality. A subset of data points from all three studies were co-located; these co-located data were reviewed and used for detailed statistical analysis.

Biofilm Biomass Determine what environmental variables have the greatest correlation with biofilm biomass indicators at Roberts Bank

Measured photopigment density, Total Carbohydrate, and Total Organic Carbon (TOC) as indicators of biomass; a multivariate database was used to identify the environmental variables influencing biofilm biomass.

Biofilm Community Composition

Determine what environmental variables are correlated with the taxonomic composition of biofilm at Roberts Bank

Using multivariate community data, significant effects of environmental variability over biofilm taxonomic composition were tested

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1.3 Scope of Work

The Scope of Work for the Biofilm Physical Factors study is to:

• Review existing published literature and summarize previously identified environmental varaibles which have been reported to influence biofilm biomass and/or microphytobenthic community composition; and

• Analyse an existing database of environmental data (collected by three different studies) to determine what environmental variables are important for biofilm at Roberts Bank.

This report is one of five studies conducted on biofilm at Roberts Bank. Other studies include:

• Detailed imagery mapping of biofilm at Roberts Bank (WorleyParsons 2015a);

• Seasonal and spatial assessment of biofilm biomass and microphytobenthic community composition at Roberts Bank (WorleyParsons 2015b);

• Erosional threshold of biofilm at Roberts Bank (WorleyParsons 2015c); and

• Regeneration potential of biofilm at Roberts Bank follow a physical disturbance (WorleyParsons 2015d).

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2. REVIEW OF EXISTING LITERATURE

Biofilm is a three-dimensional matrix of organic and inorganic substances found on intertidal estuarine sediments. It is a thin (0.01 to 2 mm), yet dense layer, of microphytobenthos, microbes, organic detritus, and sediment in a mucilaginous matrix of Extracellular Polymeric Substances (EPS) (Kuwae et al. 2008, Compass Resource Management 2013). Comprised primarily of carbohydrates (polysaccharides), the EPS matrix provides a protective microenvironment from the rapidly changing physical and chemical conditions experienced at intertidal mudflats (Decho 2000) and is a method of attachment of sediment particles that stabilises the sediment against erosion (Wang 2003). The physical state of the EPS ranges from a gelatinous continuum to a dissolved solution (Decho 2000).

The production of biofilm and related carbohydrates is driven by microphytobenthic communities, which are typically dominated by diatoms (Admiraal 1984, Underwood and Kromkamp 1999). As microphytobenthos are photosynthetic, they are constrained by the depth of maximum light penetration, which is usually the top 2 mm of sediments (De Brouwer and Stal 2001, Herlory et al. 2004). Microphytobenthic organisms can be attached to sediment particles (MacIntyre et al. 1996), but are also known to exhibit vertical migrations within sediments spurred by changing conditions in the physical environment (e.g., light levels and water immersion/emersion) (Guarini et al. 1997, Smith and Underwood 1998). Migrations are facilitated by the secretion of EPS (Decho 2000). Figure 1.3-1 represents an updated schematic of microbial biofilm, based on Decho (2000).

Figure 1.3-1 Schematic of Microbial Biofilm within Intertidal Sediments (updated from Decho 2000)



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2.1 Ecological Importance of Biofilm

2.1.1 Primary Productivity

As photosynthetic organisms found in the nutrient-rich environment of estuaries, microphytobenthos are important primary producers (Cahoon 1999), producing an average of 50 and 875 g carbon/m2/year (Underwood and Barnett 2006); however, temporal and spatial differences in productivity do occur. For instance, an annual estimate of 100 g carbon/m2/year is estimated for microphytobenthos in temperate waters, while 300 g carbon/m2/year is suggested for tropical waters (Charpy-Roubaud and Sournia 1990).

2.1.2 Secondary Productivity

Biofilm, consisting of diatoms and associated bacteria, is concentrated in the first 2 mm of the sediment where it forms part of the basis of benthic food webs at low tide (Compass Resource Management 2013). Meiofauna and deposit feeders, comprising herbivorous and bacterivorous species, feed on the biofilm. Such predation pressure can act as a top-down control (Hillebrand et al. 2000, Herman et al. 2001). For instance, Ross (1998) observed that the community with the highest microphytobenthos productivity also had the lowest total biomass, suggesting that under high predation rates, microphytobenthos will be stimulated to grow with increased available resources (i.e., space, light, nutrient recycling from grazers), resulting in an increase in overall productivity (Blanchard et al. 2002, 2006).

Biofilm has been shown to be an important food source for higher level consumers, such as the migratory western sandpiper (Calidris mauri) (Kuwae et al. 2008). Beninger et al. (2011) suggest that western sandpipers can consume about seven times their body weight in biofilm per day, and that biofilm makes up between 45 to 59% of the volume of their total diet. As western sandpipers undergo long-distance migrations between Mexico and Alaska, there is a requirement to build up energy reserves, which are accumulated at stop-over sites. During spring migration, western sandpiper are the most abundant shorebird on the west coast of North America stopping at several large estuaries with biofilm including San Francisco Bay and the Fraser River estuary (FRE). At Roberts Bank, more than 1,000,000 western sandpipers are thought to arrive over an approximate 15-day period during their northward migration in late April and early May (Kuwae et al. 2008).

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2.2 Variables Influencing Biofilm Growth

Primary productivity of biofilm is directly related to factors that influence the growth of microphytobenthos. As photosynthetic diatoms, microphytobenthos growth and productivity is spatially restricted by a number of environmental variables including light/temperature, tidal cycle, immersion/exposure cycles, elevation, salinity, nutrient availability, sediment grain size, and predation.

2.2.1 Light

Microphytobenthos are limited by light, restricting the community to the top 2 mm of sediment (MacIntyre et al. 1996), resulting in strong species-specific responses to changes in light levels and vertical movements to maximise photosynthetic activity (Perkins et al. 2001, Sauer et al. 2002). Underwood et al. (2005) observed changes in the microphytobenthos composition at the surface of the biofilm layer over a 14 hour light exposure period. The initial surface consisted of smaller Navicula spp. and Nitzschia spp., while the larger Gyrosigma spp. and Pleurosigma spp. became more abundant later in the day. McLachlan et al. (2009) reported Navicula perminuta to exhibit upward movements in response to 430 to 510 nanometre (nm) wavelengths (blue-green light), while Cylindrotheca closterium showed no significant response. As such, different surface communities are observed at different times based on light exposure, as individual species vertically migrate to optimise growth based on light conditions (Barranguet et al. 1998). These species-specific differences in behavioural and photo-physical traits are believed to be a form of niche separation based on light availability for photosynthesis (i.e., Photosynthetic Active Radiation [PAR]) (Underwood et al. 2005).

During periods of low light, microphytobenthos will migrate towards the top of the biofilm layer to maximise photosynthetic activity, while during periods of intense light, they migrate down into the biofilm (Perkins et al. 2001). This photo-inhibitive response to high light levels can lead to a short-term decrease in total primary productivity; therefore, in natural environments, a gradual increase in productivity occurs following exposure to increasing light (i.e., sunrise), typically plateaus near mid-day, and remains high until a gradual decrease occurs as light levels are reduced (i.e., sunset) (Perkins et al. 2001).

Temperature

In the natural environment, light is intrinsically associated with temperature, making the two variables difficult to separate (Guarini et al. 1997). Using laboratory controlled conditions, maximum photosynthetic rates in microphytobenthos have been reported to progressively increase until approximately 25ºC, followed by a decrease and eventual cessation above 38ºC (Blanchard et al. 1997, Defew et al. 2004). Scholz and Liebezeit (2012) reported optimal microphytobenthic productivity between 10 and 30ºC, and noted that microphytobenthic growth generally ceases below 4ºC and above 40ºC (Scholz and Liebezeit 2012).



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2.2.2 Turbidity

Biofilm productivity rates during immersion have been reported to decrease with increasing turbidity, as measured by Suspended Sediment Concentration (SSC) (Pratt et al. 2013). Effects of turbidity are largely attributed to the reduced light during periods of immersion. Over the course of several turbidity treatments, ranging between 20 and 120 mg/L SSC, oxygen production showed a three-fold decrease, with net primary productivity predicted to be nearly zero at levels of ~120 mg/L (Pratt et al. 2013). Turbidity has also been shown to control phytoplankton biomass and productivity in the water column in estuaries (Cloern 1987, Cloern et al. 1989).

2.2.3 Tidal Cycle

Located in intertidal regions of brackish water estuaries, microphytobenthos experience dramatic daily and seasonal changes in the physical and chemical environment driven by the tidal cycle. Water column phytoplankton, and re-suspended microphytobenthos, require periods of calm water to settle on intertidal sediments. As the intertidal environment experiences large daily changes in water velocities, largely due to tidal currents, periods of calm water are limited to slack water; therefore, maximum biofilm densities have been reported during periods of slack water as opposed to more turbulent ebb and flood tides (Patil and Anil 2005). This is likely due to increased settlement during periods of minimal water movements.

The turbulence created by flood and ebb tides can also lead to re-suspension of microphytobenthos into the water column, reducing overall biofilm biomass. Dransfield (2000) reported large decreases in biofilm biomass during periods of extreme Spring Tides (when the tidal range is maximal during new or full moons). The increased tidal currents were often observed on the ebb flows, and were more pronounced during a Spring Tide that increases the bed shear along the sediment-water interface (Lauria 1998). This suggests that hydrodynamic conditions may play an important role in daily and seasonal fluctuations in biofilm biomass (Brotas et al. 1995, WorleyParsons 2015d).

2.2.4 Immersion and Exposure

The intertidal environment undergoes large changes as the tidal cycle continually immerses and exposes biofilm, inducing vertical migration. The effects of elevation (e.g., metres above Chart Datum) across the intertidal regional will have a direct effect on the immersion and exposure periods. Higher elevations have a greater exposure period allowing extended time for microphytobenthos to move closer to the surface and maximise photosynthesis (Admiraal 1984, Blanchard et al. 2001, Perkins et al. 2001, Consalvey et al. 2004, Jesus et al. 2006, 2009, Denis et al. 2012). During hot summer days, higher elevations will have the opposite effect as high light levels will lead to high sediment temperatures, stimulating downward migration on species-specific cases.

Measuring Chlorophyll a concentrations, Blanchard et al. (2006) observed an average of 160 mg Chlorophyll a/m2 during daytime exposure, and averages of 135 and 138 mg Chlorophyll a /m2 during daytime immersions and night time exposure, respectively. Similarly, Pinckney and Zingmark (1991) found daytime oxygen productivity levels at low tide to be twice those compared to daytime high tide.

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A lag time occurs between a change in light exposure and when microphytobenthos reach the surface and start to photosynthesise. Herlory et al. (2004) observed maximum densities of microphytobenthos at the biofilm surface 30 to 210 minutes following exposure to light, suggesting that with a longer exposure period (at higher elevations), microphytobenthos can maximise their photosynthetic potential through increased light exposure.

Elevation

As exposure time is directly related to elevation (e.g., metres above Chart Datum), and increased exposure leads to increased biofilm density, higher densities of biofilm generally occur in the upper intertidal region, close to shore. An additional benefit from higher elevation is the reduced energy environment. Estuarine biofilm typically occurs in relation with fine sediments on intertidal flats. The gradual slope of these flats absorbs much of the wave and tidal current energy as water travels towards shore. At Roberts Bank, where the intertidal region is several kilometres long, swells and waves travel a long distance over gradually reducing depths. The length of the intertidal zone dampens the wave forces, leading to reduced erosional forces at higher elevations, resulting in greater deposition rates of fine sediments combined with increased emersion periods.

2.2.5 Salinity

Salinity is a key driver in estuarine environments, making areas either habitable or inhabitable for individual species. Most estuarine and marine microphytobenthos species are known to have optimal salinity ranges between 10 and 30 practical salinity units (PSU) (Williams 1964, Scholz and Liebezeit 2012). Below 10 PSU, reduced productivity has been noted (Scholz and Liebezeit 2012) and 10 PSU is recognised a point where biological communities can be expected to transition between marine and freshwater species (Muylaert et al. 2002, Telesh and Khlebovich 2010). Changing salinity levels initiated by changing tidal mixing, water masses, or rain events can cause some species to suffer osmotic stress (Lionard et al. 2005), leading to changes in morphology (Trobajo et al. 2011) and reduced productivity and biomass.

Salinity changes the microphytobenthic community with general trends of increasing species richness and biomass with increasing salinity. In the Colne estuary in southwestern England, significant species-specific changes in density, diversity, and richness were observed with differing salinity for several genera, including various species of Navicula, Surrirella, and Nitzschia (Underwood et al. 1998, Thornton et al. 2002). Similar increases in microphytobenthos richness were found in the Schelde estuary (Belgium) (Muylaert et al. 2002).

Under controlled laboratory conditions, Chiu et al. (2006) found a distinct difference in biofilm communities between salinity treatments of 20, 27, and 34 PSU. During summer conditions, low salinity treatments (20 PSU) were dominated by Amphora spp. leading to differences in community composition compared to 27 and 34 PSU treatments that had greater abundances of Nitzschia spp. and Cylindrotheca (Chiu et al. 2006). Other studies have found shifts in microphytobenthos species composition along salinity gradients in the natural environment (Underwood et al. 1998, Muylaert et al. 2002, Thornton et al. 2002, Lionard et al. 2005).



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Individual species exhibit a range of salinity tolerances with varying productivity. In a study by Williams (1964), 14 pennate diatoms from a salt marsh in Georgia, USA had maximum division rates at or close to 20 PSU and elevated levels typically between 10 and 30 PSU. A more recent study by Scholz and Liebezeit (2012) in the North Sea (Germany) found the growth response of 25 microphytobenthos species to be relatively equal and at maximum values in salinities of between 20 and 35 PSU, whereas extreme salinities of 10 PSU and 40 PSU led to decreases in growth rates of most species. Published examples of optimal salinities for the growth of diatoms are presented in Table 2.2-1.

Table 2.2-1 Examples of Species-specific Optimal Salinity Levels for Biofilm Biomass (Chlorophyll a) and Growth (division rate)

Species Optimal Salinity for Biofilm Growth

Reference

Amphora coffeaeformis 35 PSU (chlorophyll a content) (Murugaraj and Jeyachandran, 2007)

Cyclotella meneghiniana 18 PSU (division rate) (Roubeix and Lancelot 2008) Cylindrotheca gerstenbergeri 6-8 PSU (division rate) (Williams 1964) Navicula sp. 17-18 PSU (division rate) (Williams 1964) Nitzschia closterium 17-18 PSU (division rate) (Williams 1964) Nitzschia laevis 17-18 PSU (division rate) (Williams 1964) Nitzschia ovalis 30 PSU (division rate). (Saks 1982) Nitzschia sigma 11-18 PSU (division rate) (Williams 1964) Nitzschia thermalodies 30-31 PSU (division rate) (Williams 1964)

2.2.6 Nutrients

In the marine environment, nutrient availability, particularly nitrogen, is known to limit primary productivity of phytoplankton (Drinnan and Clark 1980) and microphytobenthic communities (Hillebrand and Sommer 1997, Hillebrand et al. 2000). In the FRE and Strait of Georgia, nutrient availability is driven by the entrainment of deep seawater, as opposed to relatively nutrient-poor freshwater discharge, particularly in the summer (Harrison et al. 1983, Yin 1994). Nutrient entrainment is driven by several factors in the Strait of Georgia including tidal cycle, wind and wave action, and freshwater discharge rates.

As freshwater enters the estuary, the less dense freshwater floats on top of the marine water, creating a stratified water column. The seaward extent of the freshwater discharge is controlled by the tides where flood tides push freshwater back up the FRE, while ebb tides, with lower tidal heights, release freshwater into the Strait of Georgia. As the freshwater moves into the Strait of Georgia, flood tides entrain nutrient-rich water up into the water column of the estuarine plume which flows offshore. This movement is most prevalent during Spring Tides when greater water movement occurs because the difference between high and low tide is the largest (Nof 1979).

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Wind causes surface currents and upwelling to occur, increasing entrainment (Yin 1994). Strong winds and extreme Spring Tides, typical of the freshet time period, lead to increased nutrient entrainment within the FRE (Yin et al. 1995a, b). Due to the mixing pattern, larger volumes of nutrient-rich water are present at the mouth of the Fraser River during Spring Tides and the freshet, and less so during non-freshet flows and Neap Tides; therefore, the volume of freshwater discharge influences the amount of available nutrients (Yin et al. 1995b). Based on this relationship, lower salinity water in the FRE is associated with low nutrient levels, especially in the late summer (Yin 1994).

2.2.7 Sediment Grain Size

Biofilm development is typically restricted to low energy environments where erosional forces are reduced. Low energy environments are typified by fine sediments such as silts or clays, while higher energy environments have coarser sand particles. In high energy environments, regular movement of particles will cause direct physical damage to microphytobenthos (Delgado et al. 1991). This physical damage to the biofilm layer results in reduced productivity, leading to a feedback mechanism of decreased productivity and sediment stability with disturbance (Herman et al. 2001, Van Colen et al. 2008). Therefore, biofilm growth and productivity is lower on coarser sediments. Additionally changes in the microphytobenthos has been noted in related to sediment grain sizes (Colijn and Dijkema 1981, Cahoon 1999, Thornton et al. 2002, Jesus et al. 2009).

Coarser sediments have implications for vertical migration and deposition of nutrients. Fine cohesive sediments usually have a high organic matter content with high rates of bacterial mineralisation and dissolved porewater nutrients compared to coarser substrates which are more oligotrophic in nature (Underwood and Kromkamp 1999). This suggests microphytobenthos in coarse sediments may be nutrient limited (Underwood and Kromkamp 1999). As a result, fine sediments are reliant upon diffusion to exchange dissolved nutrients while sandy substrates allow flows to permeate the sediment. A shift from finer to coarser sediments necessitates a shift from epipelic to epipsammic microphytobenthos species (MacIntyre et al. 1996). Being adapted to a more permeable environment, the depth of microphytobenthos presence can be deeper; however, an overall decrease in Chlorophyll a in the sediment occurs (Huettel and Rusch 2000). In areas of predominantly sandy substrates at Sturgeon Bank, Chlorophyll a has been observed as deep as 10 cm (P. Harrison, UBC, unpublished data).

Coarser sediments have also been noted to affect vertical migration rates. Using laboratory cultures, Du et al. (2010) reported faster rates of upward migration in two different microphytobenthos in coarser (125-350 µm) sediment compared to fine (63-125 µm). It was suggested these faster rates of vertical migration were due to a combination of deeper light penetration, stimulating greater movement, as well as greater interstitial pore space providing more room for movement (Du et al. 2010). However, these results were under laboratory conditions and did not include disturbance effects from hydrodynamic forces (Du et al. 2010).



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2.2.8 Predation

The microphytobenthic community is an important primary producer in intertidal ecosystems. Biofilm communities provide a substantial source of energy for grazers and deposit-feeders (e.g., amphipods, gastropods, nematodes, crustaceans and polychaetes) and suspension feeders (e.g., bivalves and polychaetes) through re-suspension of microphytobenthos (Levinton 1991, Miller et al. 1992, Cahoon 2006). These consumers are in turn predated by larger invertebrates and larval fish. Furthermore, some larger invertebrates and fish species show preferences for microphytobenthos forage (Sullivan and Currin 2000) and additional higher order grazing has been noted by shorebirds (Elner et al. 2005, Kuwae et al. 2008).

At the Wadden Sea, Germany, Evrard et al. (2010) found some invertebrates were highly selective for diatoms and cyanobacteria with over 90% of nematode diets being derived from microphytobenthos. Other taxonomic groups, including bivalves and copepods, showed diet composition of between 10 and 90%. This grazing/predation pressure by invertebrates has the potential to influence microphytobenthos abundance.

Increases in invertebrate density have been correlated with reductions in biofilm biomass, as measured by sediment Chlorophyll a (Pace et al. 1979, Pomeroy and Levings 1980, McClatchie et al. 1982, Morrisey 1988, Bock and Miller 1995, Majdi et al. 2011). At low levels of grazer density, Chlorophyll a increases (Morrisey 1988), suggesting that there is an equilibrium between biofilm biomass and grazing/re-suspension rates (Blanchard et al. 2001, 2006). Biofilm biomass may never reach maximum levels in a stable state as the constant grazing and re-suspension caused by the immersion/exposure cycle continually disturbs the community.

Other physical variables can also influence the effects of predation. Hillebrand et al. (2000) noted that herbivory effects were negative when nutrients were limited, reducing microphytobenthos biomass and diversity; however, when nutrients were high, herbivores increased on account of the greater biomass and productivity (i.e., a greater food supply). This finding indicates that optimal growing conditions can counter the effects of predation. As microphytobenthos are removed, more resources are made available for other cells, stimulating high levels of primary productivity (Hillebrand et al. 2000). A similar finding was reported within the FRE by Ross (1998) where the highest biofilm productivity levels, nutrient levels and invertebrate densities were found in areas with the lowest biofilm biomass.

2.3 Seasonal Variation of the Fraser River Estuary

In cool temperate environments where microphytobenthos have been studied, most of the described physical factors, with the exception of sediment grain size, show wide variations with seasons. These variations are largely driven by light and tidal cycle, affecting the biogeochemical processes and creating a peak growing season in spring and summer (Dransfeld 2000, WorleyParsons 2015b).

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Day length is shorter during the fall and winter, reducing light exposure and intensity, as well as water and air temperatures. In spring, the day length and air temperature increases. Additionally, extreme low tides occur during daylight hours in spring and summer, but occur in nighttime during the fall and winter. The combination of longer exposure periods and an increase in light intensity and temperature during spring and summer increases the productivity potential of biofilm, provided there are sufficient nutrients (WorleyParsons 2015b).

Seasonal changes in the discharge of freshwater also have a large influence on the biology of the FRE and Roberts Bank. The Fraser River drains a watershed of approximately 217,000 km2 (Environment Canada 2013) with an annual average discharge of 3,630 m3/s (Cameron 1996). In mid- to late April, freshwater discharge, driven by snowmelt, begins to increase, reaching a peak >10,000 m3/s in early June. Discharge levels gradually decrease through July, August and September until a low discharge rate of ~700 m3/s is reached in winter.

The freshwater discharged into the Strait of Georgia has different physical and chemical properties. Most notably, the freshwater has lower nutrients, lower salinity, higher temperature and higher turbidity (decreased light penetration) compared to the marine waters of the Strait of Georgia (Yin 1994, Hemmera 2014a). The water masses are originally stratified with the lower density freshwater at the surface, but are gradually mixed through natural processes including the tidal cycle and wind (Yin et al. 1997, Kostaschuk and Luternauer 2004).

During the mixing process, deep marine water is drawn from the Strait of Georgia and brought into the FRE and lower reaches of the river in the form of a salt wedge; this reverses water flow and pushes freshwater back up the lower river channels. During the preceding ebb tide, the marine water retreats, releasing the freshwater into the Strait of Georgia (Bendell-Young et al. 2004). As the freshwater moves into the Strait of Georgia, it is enriched by nutrients which are entrained from the deeper marine water. This process is most prevalent during Spring Tides when greater water movement, and hence greater mixing, occurs (Nof 1979). This leads to increased nutrient entrainment within the FRE as higher nutrients are available in more saline waters (Yin et al. 1995a, b). Yin (1994) stated that lower salinity water in the FRE is associated with low nutrient levels, especially in late summer when the nitrate concentration in the Fraser River is about 2 µM.

The freshwater, compared to brackish (intermediate) and marine water is recognised as having lower nutrients as well as lower phytoplankton abundance and productivity (Harrison et al. 1983, Yin et al. 1997, Kostaschuk and Luternauer 2004). High turbidity levels, associated with the silt-laden discharge from the Fraser River, could also reduce concentrations of water column Chlorophyll a near the river mouth (Parsons et al. 1981) and reduce microphytobenthos productivity during periods of immersion (Pratt et al. 2013). Due to these relationships, changes in water masses are known to affect phytoplankton density and productivity, as well as the microphytobenthic community (WorleyParsons 2015b).



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Table 2.3-1 Summary of Environmental Variables that Influence Biofilm Biomass and Microphytobenthos Community Composition

Variable Influence Consequence within the FRE Reference

Light Increasing light levels lead to increasing potential for primary productivity until a maximum is reached where cells begin to degrade. Species-specific optima for light levels may lead to niche partitioning within the biofilm layer.

Reduced light levels during the winter due to shorter day length and low light intensity. Freshet flows increase turbidity, reducing light levels.

Decreased productivity expected in winter due to shorter day length and extreme low water occurring at night when temperature may be freezing

(Barranguet et al. 1998, Underwood et al. 2005, McLachlan et al. 2009, WorleyParsons 2015b)

Temperature Linked to light levels. Optimal temperatures for microphytobenthos and phytoplankton are recognised to be between 10 and 30ºC. Biofilm reported as absent below 4ºC. For most species, decreases in productivity are observed above 40ºC.

Warmer water temperatures during summer compared to spring and winter.

(Chiu et al. 2006, Scholz and Liebezeit 2012)

Turbidity Biofilm productivity rates decrease with increasing turbidity. Net primary productivity has been predicted to be near 0 at Total Suspended Solids levels of 120 mg/L. Turbidity also controls phytoplankton biomass and productivity in estuaries.

Increased turbidity associated with freshwater, particularly during spring freshet flows. Wind and storm events will increase turbidity through increased wave energy and sediment re-suspension.

(Cloern 1987, Cloern et al. 1989, Pratt et al. 2013)

Tidal Cycle Tidal cycle influences the volume of water with phytoplankton, which moves overtop of intertidal mudflats. During Spring Tides, water movement is maximised, leading to increased turbulence and removal of biofilm, and the decreased slack tide periods reduces the ability of water column phytoplankton to settle on sediments. In contrast, Neap Tides provide less turbulence and increased settlement potential.

Tidal cycle controls water movement, which influences phytoplankton settlement rates and microphytobenthos re-suspension potential.

Increased biofilm biomass occurs during Spring Tides with reduced biomass during Neap Tides.

(Brotas et al. 1998, Lauria 1998, Dransfeld 2000, Jesus et al. 2009, WorleyParsons 2015d)

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Variable Influence Consequence within the FRE Reference

Immersion/ Exposure Period (and elevation)

Greatest productivity occurs during daytime exposure, and generally in higher elevations. On long intertidal mudflats, higher elevations are exposed to less wave energy and have increased fine sediment composition.

Longer exposure times at higher elevation leads to higher productivity.

Higher elevations related to increased fine sediments.

Extreme low tides occur during daylight hours during spring and summer, enhancing productivity potential.

(Pinckney and Zingmark 1991, Blanchard et al. 2006)

Salinity Species-specific optimal ranges have been noted. Salinity range of between 0.5 and 10 PSU recognised as a transitional window between freshwater/marine species. Optimal salinities between 10 and 30 PSU have been previously reported for microphytobenthos.

Spring freshet decreases salinity. Decreases in biofilm biomass and productivity are expected during this time. Rain events can create small isolated decreases.

(Underwood et al. 1998, Muylaert et al. 2002, Thornton et al. 2002, Chiu et al. 2006, Scholz and Liebezeit 2012, WorleyParsons 2015b)

Nutrients Directly linked to microphytobenthos productivity. Generally, increased nutrients will lead to increased growth and productivity. Nitrogen observed to have the greatest limiting effect for estuarine and marine species.

Nutrient supply mostly from mixing of deep marine water via entrainment as opposed to freshwater inputs. During the freshet, higher salinity water contains more nutrients.

(Yin 1994, Yin et al. 1995a, b, Hillebrand and Sommer 1997)

Sediment Grain Size

Larger grain size indicative of higher energy environments (i.e., wave action). Higher energy leads to greater sediment movement, which physically damages microphytobenthos, thus reducing biofilm establishment and growth.

Decreasing percent sand composition results in increasing biofilm biomass.

(Delgado et al. 1991, Van Colen et al. 2008)

Invertebrate Density

Grazing invertebrates remove biofilm biomass, but predation can stimulate productivity due to nutrient recycling. Certain invertebrates can destabilise sediments through sediment perturbation, making conditions less optimal for biofilm establishment.

Invertebrates can stimulate productivity but also keep total biomass low. No quantified relationship between invertebrates and biofilm grazing.

(Ross 1998, Herman et al. 2000, Hillebrand et al. 2000, Blanchard et al. 2001, Evrard et al. 2010)



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3. METHODS

As part of the RBT2 Environmental Assessment, the biological, chemical and physical environment of intertidal habitat was characterised by three different studies which had different objectives. These studies included:

• Biofilm – Description and composition of biofilm at Roberts Bank (WorleyParsons 2015b);

• Sediment Chemistry and Quality – Description of physical and chemical properties of intertidal sediments in the FRE (Hemmera 2014a); and

• Benthic Infauna – Description of the biological communities contained within the intertidal sediments of the Fraser River delta (Hemmera 2014b).

The three studies measured different parameters, but operated within the same study area at Roberts Bank. All studies had a subset of randomly selected sampling locations where all studies collected data; these are referred to as co-located sampling location. Each discipline had additional sites in order to fulfill individual study objectives. The assessment of Biofilm Physical Factors only considered data for sites where sampling occurred by all three studies during the same season (co-located samples). Although co-located sampling occurred among all studies in 2012 and 2013, only data from 2013 are considered in the assessment of Biofilm Physical Factors due to differences in sampling methodologies including:

• Differences in Sampling Method: 2012 samples consisted of six cores to a depth of 1 cm per 1 m2 sampling plot, while 2013 sampling consisted of 10 cores to a depth of 2 mm. The 2012 cores were determined to be sampling too deep and not reflective of the microphytobenthic community defined for this assessment (Compass Resource Management 2013);

• Random Sampling: In 2012, core samples were collected in a non-random, haphazard fashion by field personnel. In 2013, core sample locations were selected using an alpha-numeric grid and a random number table, removing sampler bias;

• Temporal Sampling: Co-located sampling in 2012 was limited to spring, while sampling in 2013 was conducted in both spring and summer;

• Sample Distribution: In 2012, sample locations were based on stratified random grid design and included sites without established biofilm; in 2013, sample locations were randomly selected in areas of known biofilm within the Roberts Bank study area;

• Site-specific Focus: 2012 sampling was conducted across the FRE, while 2013 sampling focused on known areas of biofilm at Roberts Bank; and

• Measured Variables: The 2012 biofilm sampling was limited to photopigment density; whereas, 2013 sampling included photopigment density as well as Total Organic Carbon and Total carbohydrate, both additional indicators of biofilm biomass (Compass Resource Management 2013). Taxonomy samples were not collected at co-located sampling locations in 2012.

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A comparison of the sampling methods showed that the 2012 methods of 1 cm deep cores over-estimated the density of photopigments due to the inclusion of remnant and decomposing cells deeper in the sediment (Compass Resource Management 2013). Additionally, the use of only six sediment cores was not sufficient to provide a suitable representation given micro-scale spatial variation observed in biofilm (WorleyParsons 2015a). A minimum of eight (8) sediment cores per metre squared is recommended (Grinham et al. 2007).

3.1 Sample Locations

The study area encompassed areas of identified biofilm within the intertidal region at Roberts Bank from the BC Ferries Terminal causeway north to Canoe Passage (WorleyParsons 2015a). The number, location, and timing of sampling varied between seasons due to the changing focus of individual disciplines and methods used to select sampling locations. Figure 3.1-1 shows the sampling locations used in 2013.

As described in WorleyParsons (2015b), biofilm biomass samples were collected at all locations; microphytobenthos community samples were only collected at a subset of these locations. Therefore, a greater sample size was available for biofilm biomass compared to microphytobenthos community. The total number of co-located samples for each biofilm data type are summarised in Table 3.1-1.

Only sampling locations with complete datasets for all three studies: Biofilm, Sediment Chemistry and Quality, and Benthic Infauna measurements were considered for this assessment. Any sites missing data from the three studies were omitted from analysis.

Table 3.1-1 Summary of the Number of Co-Located Samples per Sampling Period within the Study Area at Roberts Bank

Sampling Session # Co-Located Biofilm Biomass Samples

# Co-Located Microphytobenthos Taxonomy Samples

Spring 2013 31 14

Summer 2013 19 9

Roberts Bank

Canoe Passage

RB2040

RB2038

RB2034

RB2032

RB2029

RB2028

RB2025

RB2024RB2023

RB2022RB2021

RB2020

RB2019 RB2018

RB2017

RB2016

RB2015

RB2014RB2013

RB2012

RB2011

RB2010

RB2009

RB2008

RB2007

RB2006

RB2005RB2004RB2003

RB2002RB2001

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!( B.C. FERRIES TERMINAL

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3.2 Sample Collection

Field teams were provided with the same sample coordinates at the beginning of each field session. Using a handheld GPS, each team located the sample location and collected samples based on a 1 m2 quadrant. Sample collection and handling methods are outlined in the individual study TDRs (Hemmera 2014a, b, WorleyParsons 2015b). Sampling by individual disciplines was conducted during the same sampling period; however, not all samples were collected at the exact same time (Table 3.2-1). Sampling at individual sites by all three studies occurred within five days.

All biofilm samples were collected within the top 2 mm of sediment (WorleyParsons 2015b). Sediment Chemistry and Quality samples were collected within the top 100 mm of sediment (Hemmera 2014a). Benthic Infauna samples were collected within the top 50 mm of sediment (Hemmera 2014b).

Table 3.2-1 Field Data Collection Dates of Biofilm, Sediment Chemistry and Quality, and Benthic Infauna during 2013 Co-located Sampling

Biofilm Sediment Chemistry and Quality

Benthic Infauna

Spring April 22 to 25, 2013 April 22 to 26, 2013 April 22 to 26, 2013

Summer August 17 to 22, 2013 August 20 and 21, 2013 August 20 and 21, 2013

3.2.1 Assumptions of Database

Each study designed sampling and analytical methodologies to answer different questions regarding the biological and chemical environment at Roberts Bank. Additionally, most samples were collected at different times by different field crews. The following assumptions need to be considered during the interpretation of results:

1. The different sampling depth used for Benthic Infauna (50 mm) and Sediment Chemistry and Quality (10 mm) were adequate to describe the environment experienced by biofilm (2 mm);

2. Sample collection and hold conditions (i.e., temperature and time) were adequate to preserve measured variables relative to biofilm (i.e., nutrient concentration data were not compromised due to improper temperatures or excessive hold times);

3. Time between study sampling (ranging up to five days) during the low Spring Tide event did not have a significant effect on the variability/fluctuation of the measured parameters; and

4. Analytical methods used to measure Benthic Infauna and Sediment Chemistry and Quality samples were sufficient to report variables to levels relevant to biofilm (i.e., detection limits of laboratory equipment were set within ranges that would affect biofilm).

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Given that high spatial and temporal variability is known to occur in the biological, physical, and chemical environment at Roberts Bank (Hemmera 2014a, b, WorleyParsons 2015b), the number and distribution of co-located sampling locations were not designed to specifically assess potential effects on biofilm. Given the known variability, increased sampling would have been preferable, but was not possible given the nature of this dataset. Instead, the analyses conducted are used to confirm conclusions from previously published studies.

3.3 Analysed Variables

A large number of environmental variables were analysed from the collected samples by the three studies. All data collected were used in the analysis, whether known relationships with biofilm growth had been previously established or not. A summary of the environmental variables measured during each sampling event for each discipline is provided in Table 3.3-1. A detailed list of the specific variables is provided in Table 3.3-2. Detailed reasoning and methodology for the collection and analysis of each variable is provided within individual study TDRs (Hemmera 2014a, b, WorleyParsons 2015b).

To describe biofilm biomass, four different parameters were used:

• Chlorophyll a – Chlorophyll a is the primary photosynthetic pigment of all oxygen-producing organisms and is present in algae and cyanobacteria. It is a measure of density and primary productivity potential;

• Fucoxanthin – Fucoxanthin is an accessory pigment in the chloroplasts of certain species of algae and is recognised as a marker pigment of diatoms (Wright et al. 1991). The connection of Fucoxanthin to photosynthesis makes it an indicator of primary productivity specific to diatoms, which are the primary component of the microphytobenthic community at Roberts Bank (WorleyParsons 2015a);

• Total Organic Carbon (TOC) – a measure of carbon that is available within biofilm; and

• Total Carbohydrate – the energy absorbed by Chlorophyll a (as well as accessory pigments such as Fucoxanthin) is transformed into glucose, which is a measure of total carbohydrate.

Microphytobenthos taxonomy data were based on genus level identification (WorleyParsons 2015b).



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Table 3.3-1 Summary of Variables Measured by each Discipline at Co-Located Sampling Locations

Biofilm Sediment Chemistry and Quality Benthic Infauna

Sampling Event (Season, Year)

Phot

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Summer 2013 X X X X X X X X X X X X

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Table 3.3-2 Measured Variables by Individual Studies

Biofilm (2 mm deep)1

Sediment Chemistry and Quality (100 mm deep)1

Benthic Infauna (50 mm deep)1

Photopigment Density • Chlorophyll a • Chlorophyll b • Chlorophyll c2 • Divinyl Chlorophyll a • Phaeophytin a • β,ε-Carotene • β, β-Carotene • Alloxanthin • Canthaxanthin • Diadinoxanthin • Echinenone • Fucoxanthin • Lutein • Zeaxanthin

Taxonomic Composition to Genus level Biofilm Total Organic Carbon (top 2 mm of sediment) Total carbohydrate

Sediment Grain Size, Moisture Content and pH Nutrients • Nitrate (N) • Phosphate (P) • Potassium (K) • Sulphate (S)

Saturated Paste Extracts • Ammonia (as N) • Bromide (Br) • Chloride (Cl) • Fluoride (F) • Nitrate (as N) • Nitrite (as N) • % Saturation • Sulphate (SO4)

Sediment Total Organic Carbon (top 10 cm of sediment)

Meiofauna Taxonomy Macrofauna Taxonomy Physical Surface Water Variables • Temperature • pH • Conductivity • Salinity

Notes: 1 = indicates depth of sampling for each study

3.3.1 Spatial Data

Spatial data were also determined for each sampling location using the ArcGIS (Redlands, USA) and included:

• Distance from shoreline (m) – the horizontal distance (m) of the sample location perpendicular to the nearest shoreline (defined as vegetation visible on aerial images [i.e., marsh]);

• Distance from Canoe Passage (m) – the horizontal distance (m) to the western tip of Brunswick Point, which signifies the mouth of Canoe Passage and the closest major freshwater source;



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• Elevation in Chart Datum (m) – using Light Detection and Ranging (LiDAR) remote sensing data collected by Terra Remote Sensing Inc. (2011), the elevation (m in Chart Datum) of each sampling location was determined; and

• Annual Exposure (hours) – Using the 2012 observed tides at Roberts Bank, provided by NHC Consultants, and the known elevation of each sampling location, the total time exposed in 2012 was calculated in total hours.

Spatial measurements were added to the existing database to account for potential spatial effects on biofilm biomass and microphytobenthos composition.

3.4 Databases

Any co-located sampling locations which did not have full data for Biofilm, Sediment Chemistry and Quality, and Benthic Infauna parameters were eliminated from consideration (see Table 3.1-1). After data were assessed for completeness, a data quality review was conducted. During this review, any parameters which possessed zero (0) or undefined values for more than 50% of the datapoints were removed from analysis. This occurred frequently with infauna taxonomy, where several classes of organisms were not present throughout the study area, resulting in a large proportion of 0 values. Other data removed also included chemistry data where values were below the Detection Limits (DL) of the analysis methods; this occurred with several nutrients including nitrate and nitrite.

3.5 Analysis

Data were queried from a central database managed by Hemmera and provided in Microsoft Excel format. The biofilm biomass data were analysed in Systat 13.0 (Chicago, USA) while the microphytobenthic community data were analysed in Primer-E 6.0 (Plymouth, UK). Detailed statistical methodology is provided in Appendix 1.

Two different and separate analysis streams were conducted with the available data:

1. Biofilm Biomass – this analysis stream used the entire available biofilm data set (as outlined in Table 3.3-1); and

2. Microphytobenthos Taxonomy – this analysis stream used only the subset of biofilm samples which possessed taxonomy identification data (as outlined in Table 3.3-1).

3.5.1 Biofilm Biomass

All variables were tested for normality using the Wilks-Shapiro test and applicable transformations were conducted when necessary. If variables were not able to be normalised, they were omitted from the analysis; however, some variables of interest due to the predicted influence on biofilm density were transformed to the best possible distribution and included in the analysis. Once data were sufficiently normalised, two different analyses were undertaken. Non-normal variables retained for analysis are outlined in Table 4.1-1.

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Principal Component Analysis

Principal Component Analysis (PCA) is a statistical method of identifying patterns in data while highlighting similarities and differences among variables in a larger dataset. PCA creates new groups of variables, called Principal Components (PC), which are a summary of existing data. Each PC is assigned an Eigenvalue, which represents the amount of variability in the original data captured by the new PC. As a general rule, only PCs with Eigenvalues above 1.0 are considered useful for interpretation.

The PCA calculates a component score for each variable within the PC; this score is considered to be a new variables which captures a proportion of variability within the larger dataset. The component scores were treated as new data for individual PCs and tested for significant correlations with the four biofilm biomass indicators. Results provide an inference of the key environmental variables over the tested biofilm biomass variable; therefore, to determine a statistically significant relationship, other methods are required.

Multiple Linear Regression

Multiple Regression is a common statistical analysis used in ecology to determine significant relationships between environmental variables and biological measures. Multiple regressions seek to find an equation that best predicts a dependent variable (i.e., biofilm biomass variables) given a reduced set of independent variables (i.e., environmental measurements).

Using the normalised database, a correlation analysis was conducted to identify potential issues of co-linearity. Any variables with a correlation greater than 80 % (r = 0.80) were condensed into a single variable (i.e., on intertidal mudflats, elevation decreases with distance from shore, leading to an expected high level of correlation; in this case only one of the variables was used in the analysis with the caveat that it was related to the other).

After all co-linear variables were condensed, a backwards stepwise multiple regression model was applied to the dataset. The model runs a multiple regression beginning with all available variables. Subsequent models are then run, systematically removing individual variables until the best fit model (based on the corrected Aikake Information Criterion [AICc]) was determined. The selected model indicates the best fit and the variables were tested for significance.

3.5.2 Microphytobenthos Taxonomy Analysis

Microphytobenthos taxonomic composition data were log +1 transformed to reduce the effects of abundant taxa, and pairwise Bray-Curtis similarity coefficients (Bray and Curtis 1957) were calculated for all possible combinations of samples. The resulting resemblance matrix was used to construct a non-parametric Multidimensional Scaling Analysis (nMDS). The nMDS is a dimensionless ordination plot which visually represents similarities among samples based on community composition.

The corresponding database of environmental variables were imported into Primer-E and linked to the taxonomic data based on sample name. A Biota-Environmental Stepwise (BEST) analysis was conducted to determine if specific environmental variables were correlated to changes in microphytobenthos composition.



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4. RESULTS

A total of 50 biofilm biomass samples and 23 microphytobenthos taxonomy samples, collected in both spring and summer 2013, met the criteria outlined in Section 3.4. The transformed database is presented in Appendix 2. Detailed statistical results are provided in Appendix 3.

4.1 Biofilm Biomass Analysis

Variables were visually assessed for skewed distribution and tested for normality using the Shapiro-Wilks test. If heavily skewed data were observed, they were transformed to improve linearity. If after transformation, data were still skewed, the variable was removed from the analysis. Non-normal variables of known influence were retained (i.e., Distance from Shore which was non-normal, but is known to influence biofilm presence [see Table 4.1-1]). Removal of data occurred for several variables that had a large proportion of no data or zero (0) values, including several genera of benthic infauna and several nutrients including nitrate and nitrite which were below analytical Detection Limits (DL). In total, 25 variables were considered for analysis. A list of the retained variables, and the normalising transformations performed are presented in Table 4.1-1.

Table 4.1-1 Retained Variables and Transformations Used for Principal Components Analysis

Variable Name Transformation Performed

Constants Wilks-Shapiro (W) Statistic

p-value

Physical Variables

Elevation (m) x2 - 0.957 0.068

Annual Exposure (hrs.) - - 0.960 0.090

Distance to Canoe Passage1 (m) - - 0.940 0.013

Distance to Shore1 (m) √𝑥2 - 0.946 0.023

Biofilm Variables

Biofilm Total Organic Carbon (mg/m2) Loge (x+1) - 0.977 0.422

Biofilm Total Carbohydrate (mg/m2) √𝑥2 - 0.961 0.098

Biofilm Chlorophyll a (mg/m2) Loge (x+1) - 0.982 0.632

Biofilm Fucoxanthin (mg/m2) Loge (x+1) - 0.954 0.050

Benthic Infauna Taxonomy

Total Harpacticoida Density (#/m2) √𝑥2 - 0.965 0.142

Total Nematoda Density (#/m2) Box-Cox λ = 0.3 0.962 0.105

Total Oligochaeta Density (#/m2) √𝑥4 - 0.965 0.137

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Variable Name Transformation Performed

Constants Wilks-Shapiro (W) Statistic

p-value

Total Polychaeta Density (#/m2) √𝑥2 - 0.954 0.050

Total Invertebrate Density (#/m2) Loge (x+1) - 0.982 0.624

Total Invertebrate Biomass (g/m2) Box-Cox λ = 0.5 0.987 0.868

Total Macrofauna Density (#/m2) √𝑥4 - 0.964 0.130

Total Macrofauna Biomass (g/m2) Loge (x+1) - 0.971 0.258

Total Meiofauna Density (#/m2) Loge (x+1) - 0.982 0.622

Total Meiofauna Biomass (g/m2) Box-Cox λ = 0.2 0.981 0.590

Sediment Variables

Sediment Clay Content1 (%) Logite - 0.941 0.014

Sediment Silt Content1 (%) Logite - 0.942 0.016

Sediment Sand Content (%) Logite - 0.983 0.693

Sediment Total Organic Carbon (%) Logite - 0.960 0.089

Saturated Paste Extract (Adjusted for Porewater)

Ammonia (Total Leachable) (mg/kg) Loge 0.967 0.178

Bromide1 (mg/kg) - 0.951 0.038

Chloride1 (mg/kg) - 0.949 0.033

Sulphate (mg/kg) - 0.957 0.068

Sediment Plant Nutrients

Phosphate (mg/kg) Loge 0.986 0.816

Potassium (mg/kg) - 0.968 0.190

Sulphate (mg/kg) - 0.974 0.319 Notes: All Box-Cox Transformations conducted using a constant of 1. See definitions of transformations in Appendix 1.

1 = non-normal variables that were retained for analysis due to known influence over biofilm biomass and microphytobenthos community composition

4.1.1 Seasonal Differences

Seasonal differences for each biofilm biomass indicator were assessed to determine whether potential bias could result from the different sampling periods. No significant seasonal difference was observed for Chlorophyll a (t35.771 = 0.086, p = 0.932), Fucoxanthin (t28.212 = -0.664, p = 0.512), Total Carbohydrate (H1 = 1.820, p = 0.177) or Total Organic Carbon (TOC) (t27.244 = -1.201, p = 0.240). These results indicate that no differences in mean densities of biofilm indicators occurred between spring and summer seasons.



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This was confirmed by wider seasonal sampling (WorleyParsons 2015b); therefore, spring and summer biomass indicators were run in the same analysis.

4.1.2 Principal Components Analysis

The PCA of collected data identified four PC with Eigenvalues above 1.0, which accounted for more than 81% of the variance observed in the data (Table 4.1-2). The PC are described as follows:

• Principal Component 1 – Spatial Variables and Porewater Ions/Nutrients: moderate positive effects of Elevation/Annual exposure; strong positive effects with Distance from Canoe Passage and associated variables of porewater ions/nutrients including Ammonia, Bromide, Chloride, Sulphate, Phosphate; Total Macrofauna Density.

• Principal Component 2 – Sediment Grain Size: Strong positive effects of Elevation/Annual exposure, % Fine sediments (Clay and Silt), Sediment TOC, Potassium and Sulphur; moderate positive effects of Total Oligochaeta Density, Total Polychaete Density; moderate negative effects with Distance from Shore and strong negative effects with % Sand composition.

• Principal Component 3 – Benthic Infauna: strong positive effects of Total Invertebrate Density and Biomass, Total Meiofauna Density and Biomass, and Total Nematoda Density; moderate positive effects of Distance from Canoe Passage, Total Oligochaeta Density and Sulphur.

• Principal Component 4 – Miscellaneous Benthic Infauna: Strong positive effects from Total Macrofauna Biomass; moderate positive effects from Total Invertebrate Biomass.

Table 4.1-2 Principal Components and Component Loadings for each Variable in Database

PC1 PC2 PC3 PC4

Variance Explained by PC 7.208 6.263 5.267 1.722

% of Total Variance 28.833 25.054 21.068 6.889

Varimax Component Loading Values

Elevation (m) 0.570 0.683 0.127 -0.119

Annual Exposure (hrs.) 0.554 0.668 0.132 -0.109

Distance from Canoe Passage (m) 0.805 0.020 0.443 0.032

Distance from Shore (m) -0.748 -0.499 -0.308 0.030

Total Harpacticoida Density (#/m2) 0.167 0.487 0.670 0.118

Total Nematoda Density (#/m2) 0.319 -0.018 0.846 -0.022

Total Oligochaeta Density (#/m2) 0.086 0.517 0.520 0.111

Total Polychaeta Density (#/m2) 0.336 0.687 0.238 0.194

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PC1 PC2 PC3 PC4

Variance Explained by PC 7.208 6.263 5.267 1.722

% of Total Variance 28.833 25.054 21.068 6.889

Total Invertebrate Density (#/m2) 0.357 0.225 0.878 0.114

Total Invertebrate Biomass (g/m2) 0.135 0.387 0.615 0.617

Total Macrofauna Density (#/m2) 0.696 0.424 0.155 0.440

Total Macrofauna Biomass (g/m2) 0.050 0.336 0.102 0.906

Total Meiofauna Density (#/m2) 0.340 0.218 0.888 0.101

Total Meiofauna Biomass (g/m2) 0.314 0.381 0.835 0.085

Clay (%) -0.226 0.922 0.123 0.146

Silt (%) -0.145 0.904 0.062 0.261

Sand (%) 0.099 -0.907 -0.126 -0.273

Sediment TOC (%) -0.088 0.897 0.238 0.129

Total Leachable Ammonia (mg/kg) 0.813 -0.141 0.259 -0.001

Porewater Bromide (mg/kg) 0.897 0.003 0.329 0.052

Porewater Chloride mg/kg) 0.887 0.036 0.356 0.067

Porewater Sulphate (mg/kg) 0.914 0.154 0.248 0.090

Phosphate (mg/kg) 0.861 -0.100 -0.014 -0.014

Potassium (mg/kg) 0.362 0.748 0.381 0.088

Sulphur (mg/kg) 0.470 0.666 0.427 0.024

Note: Factor loadings greater than 0.5 are bolded; the associated variable is considered to be strongly related to the given PC.

PC1 and PC2 showed significant correlations with the measured biofilm biomass indicators. Chlorophyll a was moderately correlated with PC1 (r = 0.438, p = 0.023) and strongly correlated with PC2 (r = 0.545, p = 0.001) (Table 4.1-3). Conversely, Fucoxanthin was strongly correlated with PC1 (r = 0.502, p = 0.003) and moderately correlated with PC2 (r = 0.446, p = 0.019) (Table 4.1-3). A strong correlation was observed between biofilm TOC and PC2 (r = 0.739, p < 0.000). No correlations were observed with Total carbohydrate.



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Table 4.1-3 Correlations between Principal Components and Measures of Biofilm Biomass Indicators

Chlorophyll a Fucoxanthin Total Carbohydrate

Biofilm TOC

r p r p r p r p

PC1 0.438 0.023 0.502 0.003 0.203 1.000 0.100 1.000

PC2 0.545 0.001 0.446 0.019 0.140 1.000 0.739 0.000

PC3 0.206 1.000 0.218 1.000 0.213 1.000 0.017 1.000

PC4 0.010 1.000 0.031 1.000 0.107 1.000 0.207 1.000

Note: Bold values indicate significant correlation (p < 0.05).

These results indicate that most of the variability in biofilm biomass can be attributed to variables with high loading values on PC1 and PC2. Based on the interpretation of the PC component loading values, this predominantly includes proximity to freshwater/marine influence and sediment grain size; however, results only indicate trends and are not related to the statistical significance of individual variables. In order to determine statistical significance, parametric statistics are needed and discussed in Section 4.1.3.

4.1.3 Multiple Regression

All 25 variables used in the PCA analysis were considered for the multiple regression analysis, and tested for correlation. The resulting correlation matrix is provided in Appendix 4. Any pairwise test with a correlation coefficient (r) greater than 0.80 was grouped in order to avoid analysis bias; therefore, only ten variables were considered for multiple regression analysis. Condensed physical factors considered for multiple regression are summarised in Table 4.1-4.

Table 4.1-4 Condensed Physical Factors Considered for Multiple Regression

Variables Representing Factors Justification

Elevation Annual exposure Distance from Shore

Positive correlation (r = 0.941) Negative correlation (r = -0.845)

Porewater Chloride Distance from Canoe Passage Leachable Ammonia Porewater Bromide Porewater Sulphate (SO4)

Positive correlation (r = 0.900) Positive correlation (r = 0.802) Positive correlation (r = 0.992) Positive correlation (r = 0.945)

Total Oligochaeta n/a Low to moderate correlations

Total Polychaeta n/a Low to moderate correlations

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Variables Representing Factors Justification

Total Invertebrate Density

Nematoda Density Total Meiofauna Density Total Meiofauna Biomass

Positive correlation (r = 0.890) Positive correlation (r = 1.000) Positive correlation (r = 0.942)

Total Macrofauna Density

n/a Low to moderate correlations

Total Macrofauna Biomass

n/a Low to moderate correlations

% Sand % Silt % Clay Sediment TOC

Negative correlation (r = -0.985) Negative correlation (r = -0.947) Negative correlation (r = -0.912)

Phosphate n/a Low to moderate correlations

Potassium Sulphur Positive correlation (r = 0.812)

Chlorophyll a

Based on the AICc value, the best fit multiple regression (AICc = 62.904) was found in a model containing Porewater Chloride, Total Polychaeta Density, Total Macrofauna Density, and % Sand (F4, 45 = 14.879, p = 0.000, r2 = 0.569). Significant increases in Chlorophyll a were attributable to increasing Polychaeta Density (p = 0.050) and increasing Porewater Chloride (p < 0.000), while significant decreases were attributed to increases in % Sand composition (p < 0.000). Total Macrofauna Density was included in the best fit model, but was not significant (p = 0.061). Regression coefficients are presented in Table 4.1-5. Scatterplots of significant variables are presented in Figure 4.1-1

Table 4.1-5 Coefficients of Multiple Linear Regression Analysis of Chlorophyll a

Coefficient Standard Error

Standardised Coefficient

Tolerance t p

Constant 3.619486 0.292 0.000 - 12.406 0.000

Porewater Chloride 0.000118 0.000 0.634 0.426 4.227 0.000

Polychaeta Density 0.000890 0.000 0.335 0.347 2.019 0.050

Macrofauna Density -0.065829 0.034 -0.392 0.230 -1.920 0.061

% Sand -0.192020 0.049 -0.491 0.607 -3.911 0.000

Note: bolded p-value indicates statistical significance at p < 0.05



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Fucoxanthin

Similar results to Chlorophyll a were found with Fucoxanthin. The best fit model (AICc = 35.863) contained Porewater Chloride, Total Macrofauna Biomass, and % Sand (F3, 46 = 21.104, p < 0.000, r2 = 0.579). A significant increase in Fucoxanthin was found with increasing Porewater Chloride levels (p < 0.000) while a significant decrease occurred with increases in % Sand composition (p < 0.000). No significant relationship with Total Macrofauna Biomass was observed (p = 0.125) (Table 4.1-6). Scatterplots of significant variables are presented in Figure 4.1-2.

Table 4.1-6 Coefficients of Multiple Linear Regression Analysis of Fucoxanthin



Tolerance t p

Constant 3.267644 0.191 0.000 17.064 0.000


Macrofauna Biomass -0.080325 0.051 -0.180 0.688 -1.564 0.125

% Sand -0.171689 0.035 -0.554 0.700 -4.846 0.000


Total Carbohydrate

The best fit model for Total Carbohydrate (AICc = 490.979) was found to be significant (F4, 45 = 3.510, p = 0.014, r2 = 0.238) with four variables: Porewater Chloride, Total Macrofauna Density, % Sand, and Potassium. Significant decreases in Total Carbohydrate levels were observed with increases in % Sand composition (p = 0.008), while increases were observed with increasing Porewater Chloride (p = 0.004). No significant relationships were observed with Total Macrofauna Density (p = 0.098) or Potassium (p = 0.137). Result statistics are presented in Table 4.1-7. Scatterplots of significant variables are presented in Figure 4.1-3.

Table 4.1-7 Coefficients of Multiple Linear Regression Analysis of Total Carbohydrate



Tolerance t p

Constant 121.811408 26.314 0.000 4.629 0.000


Macrofauna Density -3.302715 1.956 -0.362 0.369 -1.689 0.098

% Sand -15.638594 5.648 -0.736 0.240 -2.769 0.008

Potassium -0.116193 0.077 -0.458 0.185 -1.513 0.137


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Total Organic Carbon (Biofilm)

A significant relationship was found with the TOC of biofilm with a best fit model (AICc = 38.270) consisting of Porewater Chloride, Total Polychaeta Density, Total Invertebrate Density, % Sand, and Phosphate (F5, 44 = 18.779, p = 0.000, r2 = 0.681). A significant increase in TOC was observed with increasing Porewater Chloride (p = 0.012) and Total Polychaeta Density (p = 0.022) while a significant negative relationship was found with Total Invertebrate Density (p = 0.012) and % Sand (p < 0.000). No significant relationship occurred with Phosphate (p = 0.104) (Table 4.1-8). Scatterplots of significant variables are presented in Figure 4.1-4.

Table 4.1-8 Coefficients of Multiple Linear Regression Analysis of TOC within Biofilm



Tolerance t p

Constant 12.587789 1.046 0.000 12.031 0.000


Polychaeta Density 0.000680 0.000 0.290 0.490 2.381 0.022

Invertebrate Density -0.186315 0.071 -0.333 0.447 -2.616 0.012

% Sand -0.227043 0.039 -0.657 0.581 -5,876 0.000

Phosphate -0.314588 0.189 -0.20 0.415 -1.661 0.104




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Figure 4.1-1 Scatterplot and Partial Regression Relationship of Chlorophyll a Density against Porewater Chloride (A), Polychaeta Density (B), and % Sand Composition (C)

A B

C

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Figure 4.1-2 Scatterplot and Partial Regression Relationship of Fucoxanthin Density against Porewater Chloride (A), and % Sand Composition (B)

A B



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Figure 4.1-3 Scatterplot and Partial Regression Relationship of Total Carbohydrate Density against Porewater Chloride (A), and % Sand Composition (B)

A B

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Figure 4.1-4 Scatterplot and Partial Regression Relationship of Total Organic Carbon Density Against Porewater Chloride (A), Polychaeta Density (B), Total Invertebrate Density (C), and % Sand Composition (D)

A B

C D



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4.2 Microphytobenthos Taxonomy Analysis

Microphytobenthos taxonomy data were only collected at a subset of sampling locations. While the analysis of biofilm biomass indicators consisted of 50 different datapoints, only 23 datapoints were available for the taxonomy analysis (Table 3.1-1).

4.2.1 Seasonal Differences

The taxonomic composition of the microphytobenthos community was seasonally different (R = 0.918, p = 0.01) (Figure 4.2-1); therefore, spring and summer sites were assessed separately. This pattern was reported in the wider sampling program presented by WorleyParsons (2015b). Therefore, two separate analyses were conducted for the taxonomy analysis: spring and summer. This requirement to analyse taxonomy samples separately lowered the sample size, and ultimately lowered the overall power of the analysis, leading to low confidence in the results.

Figure 4.2-1 nMDS of Microphytobenthos Assemblage at Co-located Sampling Locations

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Spring

Using the Biota-Environment Stepwise (BEST) analysis in Primer-E, no specific pattern was observed between the spring microphytobenthos composition and the measured environmental variables (ρ = 0.137, p = 0.680) (see Appendix 5 for detailed outputs). The lack of a significant difference in the BEST routine, in light of biological descriptions of the biofilm community at Roberts Bank (WorleyParsons 2015b), indicates the available sample size (n=14) may not have been sufficient to determine underlying environmental trends within the microphytobenthic community; therefore, no statistical relationship could be determined among environmental variables and the microphytobenthic community.

Summer

As with the spring taxonomic samples, no significant effects of measured environmental variables were observed over the summer microphytobenthic community composition (ρ = 0.336, p = 0.410), again indicating the available sample size (n=9) may not have been large enough to determine trends.



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5. DISCUSSION

5.1 Biofilm Biomass Indicators

No significant difference was found in the biofilm biomass parameters between the spring and summer seasons (Section 4.1.1). This was confirmed within a wider study of biofilm biomass distribution (WorleyParsons 2015b). However, in the WorleyParsons (2015b) report, Total Carbohydrates were found to be different between spring and summer, while in the subset of data analysed for this report, no seasonal difference was found. This difference is due to the nature of the subset of data analysed, likely not including sites with elevated Total Carbohydrates due to a lack of co-located sampling effort.

From the two analysis methods (Principal Component Analysis and Multiple Linear Regression), two factors were found to consistently exhibit a significant relationship with biofilm biomass indicators. These can be broadly defined as Freshwater Influence and Sediment Grain Size.

The Principal Component Analysis showed the greatest variability in the dataset to be attributable to variables associated with freshwater including Distance from Canoe Passage and porewater ions (Chloride, Sulphate, Ammonia, Bromide, Phosphate). The secondary Principal Component included variables that are associated with wave energy including sediment grain size distribution and Elevation. Prior to conducting a Multiple Linear Regression, a correlation analysis was used to eliminate correlated variables. This step collapsed all variables related to Freshwater Influence into Porewater Chloride and all variables related to sediment grain size into % Sand.

5.1.1 Freshwater Influence

Porewater Chloride was used as an indicator of water column salinity (Sverdrup et al. 1942). The use of sediment Porewater Chloride is believed to be reflective of temporally average concentrations relative to changes in salinity within the water column, which would be expected based on mixing and the tidal cycle (Hemmera 2014a). An assessment of water and sediment quality across the Roberts Bank study area showed low Porewater Chloride values around the mouth of the Fraser River and Canoe Passage with gradual increases farther away from the large freshwater input (Hemmera 2014a). This trend was confirmed by the current data where a strong positive correlation was found between Distance from Canoe Passage and adjusted Porewater Chloride values.

The influence of freshwater on phytoplankton has been well documented in water column environments (Cloern 1987, Ahel et al. 1996, Hamilton et al. 2000, Lionard et al. 2005, Lueangthuwapranit et al. 2011), and has been shown to be an important factor in microphytobenthic communities (Underwood et al. 1998, Muylaert et al. 2002, Thornton et al. 2002, Chiu et al. 2006). Changes to the microphytobenthic assemblage can have a direct effect on primary productivity rates, which are largely site-specific. Underwood and Smith (1998) found the maximum rates of EPS production in laboratory diatom cultures ranged between 1.6 and 5.09 µg EPS/µg Chlorophyll a/day based on species. Nitzschia sigma (5.09 µg EPS/µg Chlorophyll a/day) had a significantly higher rate of production compared to Nitzschia frustulum (1.92), Navicula perminuta (2.59), and Surirella ovata (1.60).

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The microphytobenthos community within the study area was typically dominated by Nitzschia spp. and Navicula spp. with Achnanthidium spp. co- or sub- dominant depending on the location and time of year (WorleyParsons 2015b). The freshwater Achnanthidium genera was dominant or co-dominant throughout seasonal sampling near Canoe Passage, and was dominant throughout the entire study area during April sampling, when the Fraser River discharge was high (WorleyParsons 2015b). Both Nitzschia spp. and Navicula spp. were observed to have larger cell sizes at Roberts Bank, compared to more freshwater-influenced taxa, namely Achnanthidium spp. (WorleyParsons 2015b). As Chlorophyll a content is directly related to cell size (Voros and Padisak 1991), the seasonal and spatial shifts in composition along with noted changes in biomass suggest that the more marine-influenced taxa are likely to exhibit higher levels of productivity and create higher levels of biofilm biomass compared to the smaller freshwater-influenced taxa.

Correlation to Nutrients in the Fraser River

Freshwater, brackish, and marine waters are typically defined based on salinity, which is driven by chloride content; however, several other physical differences exist among the different water masses, particularly during spring freshet, including water temperature, nutrients, and turbidity (SSC). In the Fraser River estuary, a strong correlation among several parameters and salinity has been reported during Freshet. High salinities (representing marine-influenced waters) are correlated with high nutrients and low temperatures (Yin 1994).

Generally, the Fraser River has low nutrient availability compared to marine waters of the Strait of Georgia (Drinnan and Clark 1980, Harrison et al. 1983, Yin 1994). However, in the FRE, as freshwater enters the Strait of Georgia, the water masses mix, leading to the entrainment of deep marine water, rich in nutrients, including nitrogen. Nitrogen has been shown to be an essential nutrient for phytoplankton growth and productivity in the Strait of Georgia (Stockner et al. 1979). Furthermore, nitrogen has also been shown to influence microphytobenthos growth in controlled experiments in Europe (Hillebrand and Sommer 1997, Hillebrand et al. 2000). Therefore, the increased nitrogen availability characteristic of marine waters in the FRE can be expected to lead to increased biofilm biomass.

In the presented dataset, Porewater Nitrate and Nitrite data were eliminated from analysis due to measured values being below detection limits and therefore non-quantifiable (nitrate < 10 to < 70 mg/kg; nitrite < 0.1 to < 0.7 mg/kg). In the Fraser River, nitrite has generally been reported as low, historically observed at or near detection limits of 0.005 mg/L; nitrate is generally higher, ranging between 0.03 and 0.18 mg/L (Drinnan and Clark 1980). It has been noted that nitrate levels change with distance along the Fraser River with the highest levels occurring close to the mouth due to the influence of marine waters (Drinnan and Clark 1980). Seasonally, nitrate levels are highest between October and May, with low values occurring during spring freshet (Drinnan and Clark 1980).

Despite nitrate and nitrite being below detection limits, Total Leachable Ammonia measurements were above detection limits and were retained for analysis. However, this variable was significantly correlated with Porewater Chloride and removed for the multiple regression analysis due to concerns of co-linearity. Within the Roberts Bank dataset, Total Leachable Ammonia ranged between 0.69 and 11.2 mg/kg. The strong positive correlation with Porewater Chloride (r = 0.802) indicates a strong decrease with



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Freshwater Influence; however, the highest values were noted to occur at higher elevations, often immediately adjacent to the shoreline dykes (Appendix 2). These locations are closest to upland agricultural lands and possibly drainage areas, indicating a potential input from upland agricultural run-off (Hemmera 2014a).

The co-linearity of Porewater Chloride and Total Leachable Ammonia values, combined with the known relationship between nutrient availability and salinity in the Fraser River estuary (Yin 1994) makes interpretation difficult. Effects of both salinity (Williams 1964, Muylaert et al. 2002) and nutrients (Hillebrand and Sommer 1997, Hillebrand et al. 2000) have been reported to occur in isolation of each other. The database collected at Roberts Bank does not allow for the interpretation of individual effects on account of the uncontrolled environment and the known correlation of variables specific to the Fraser River estuary. Therefore, a particular influence of either variable cannot be adequately determined. The state of knowledge on phytoplankton/microphytobenthos ecology and site-specific processes of the Fraser River supports the conclusion that the extent of Freshwater Influence has a relationship to biofilm biomass at Roberts Bank.

5.1.2 Sediment Grain Size

Sediment grain size is known to correlate with several environment variables related to wave energy, including wave height and current velocity. Higher energy environments experience greater water movement, resulting in constant disturbance of sediments which prevents fine sediments from settling. This results in a less cohesive substrate of coarse particles (i.e., sand) which are likely to be disturbed periodically with specific events (i.e., storm events, Spring Tides). The increased particle movement, is known to cause direct physical damage to microphytobenthos cells (Delgado et al. 1991), reducing overall biomass and productivity of biofilm (Herman et al. 2001, Van Colen et al. 2008).

At Roberts Bank, decreasing biofilm biomass was observed with increasing coarse sediment composition (% Sand); these findings are supported in published literature (Colijn and Dijkema 1981, Cahoon et al. 1999, Thornton et al. 2002, Jesus et al. 2009). A field study conducted at Roberts Bank assessing the erosional threshold of biofilm reported a similar relationship with biofilm biomass decreasing with % Sand (WorleyParsons 2015c). Additionally, this study noted a lower erosional threshold of biofilm on coarser sediment, confirming a direct relationship between biofilm biomass, sediment grain size, and resiliency (WorleyParsons 2015c).

Based on the current knowledge of biofilm distribution at Roberts Bank (WorleyParsons 2015a), the highest densities of known biofilm occurs in the upper intertidal region. This is an area of calm water where the biomat feature reduces wave heights (Northwest Hydraulics Consultants 2014). Such an environment would allow for fine suspended sediments from the Fraser River plume to settle, as well as deposition of phytoplankton from the water column, leading to increased biofilm biomass.

The sampling methodology may have been inadequate to account for all active microphytobenthos in coarse sediments. Coarse sediments are more permeable and likely allow photosynthetically active cells to reside deeper in the sediment through a combination of increased light penetration and larger interstitial spaces. As biofilm sampling methods focused on the top 2 mm at sediment, it is possible that deeper

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microphytobenthos were not adequately sampled. Despite microphytobenthos occurring at deeper depth, overall Chlorophyll a is still known to be lower throughout the vertical sediment profile compared to finer sediments (Huettel and Rusch 2000).

5.1.3 Benthic Infauna

Inconsistent relationships between individual biofilm indicators and the infauna community were observed. Biofilm Chlorophyll a and TOC levels were found to have a positive relationship with Polychaeta Density; however, no relationships were observed between Polychaeta and Total Carbohydrate or Fucoxanthin. While Polychaeta were found to have a positive relationship with some indicators of biofilm, other infauna indicators had a negative relationship, including Total Macrofauna Density, which negatively impacted both Chlorophyll a and Total Carbohydrate levels, and Total Invertebrate Density, which decreased biofilm TOC levels. Total Macrofauna Biomass was negatively related with Fucoxanthin density.

The positive relationship between Polychaeta and biofilm biomass, namely Chlorophyll a, was expected based upon previous studies conducted at Roberts Bank. Levings and Coustalin (1975) reported that the polychaete Manayunkia aestuarina was the most abundant organism in fine substrates in the upper intertidal elevations (2.5 to 3.0 m) at Roberts Bank and Sturgeon Bank. Based on field observations, this habitat description is consistent with occurrences of biofilm at Roberts Bank (WorleyParsons 2015a). Furthermore, Sutherland et al. (2013) found biofilm indicators, including sediment Chlorophyll a, to be positively related to densities of the polychaete Polydora.

The relationship between biofilm and Polychaeta could be linked to foraging opportunity. Using stable isotope analysis, Galvan et al. (2008) found microphytobenthos and phytoplankton to be the dominant food source of three polychaete species, including M. aestuarina, which was identified as the dominant infauna at Roberts Bank by Levings and Coustalin (1975). Polychaetes possess a range of feeding strategies including deposit and suspension feeding.

The highest density biofilm areas are generally in the upper intertidal areas of Roberts Bank, mostly in the calm areas behind the ridge and runnel zone where the biomat occurs (WorleyParsons 2015a). Due to the calm nature and reduced water flow in these areas, a higher settlement rate of water column phytoplankton would be expected. If polychaetes are following a suspension feeding strategy, these areas could accommodate higher densities of organisms without decreasing the biomass of the actual biofilm. With the higher settlement rate of phytoplankton, a more optimal growth environment for polychaetes can be expected, leading to decreased sediment processing rates (Taghon and Greene 1990) and a higher carrying capacity of polychaetes as more prey is available.

Several top-down controls of biofilm biomass have been noted with different invertebrate taxa. Lower microphytobenthos abundance was observed with increasing densities of the gastropod Amphibola crenata (McClatchie et al. 1982) and Nassarius obsoletus (Pace et al. 1979). Similar observations have occurred with diverse assemblages of grazers (Herman et al. 2000, Hillebrand et al. 2000).



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5.2 Microphytobenthos Community

A significant difference in microphytobenthos community was observed between spring and summer, which was confirmed throughout a larger sampling program at Roberts Bank by WorleyParsons (2015b). This is in contrast to the biofilm biomass indicators which were not significantly different between spring and summer (Section 4.1.1 and WorleyParsons 2014b). In a wider assessment of spatial and temporal variability of the microphytobenthos community at Roberts Bank, sites in close proximity to Canoe Passage were shown to have a higher proportion of the freshwater genus Achnanthidium (WorleyParsons 2014b). The dominance of this freshwater diatom decreased during periods of reduced freshwater discharge and with distance from Canoe Passage (WorleyParsons 2015b); this indicates an influence of freshwater over the microphytobenthos community at Roberts Bank. Additionally, during this wider survey, spatial differences in the mircophytobenthos community were observed across Roberts Bank, mainly related to distance from freshwater input (WorleyParsons 2014b).

As a result of the seasonal difference in the microphytobenthos community, assessments had to be conducted within each season. This effectively reduced the total sample size from 23 samples to 14 in the spring and 9 in the summer. With this reduction in sample size, and the known variability of microphytobenthos community across Roberts Bank (WorleyParsons 2014b), no significant relationships were observed with environmental variables in either spring or summer. Although no relationship was shown between the microphytobenthos community and the environmental variables, previous relationships have been reported in published literature related to salinity, nutrients, and sediment grain size.

In the Baltic Sea, Ulanova et al. (2009) assessed the relationship between microphytobenthos communities and environmental variables from 135 different intertdal and subtidal sites along the entire 1,610 km waterbody. This study assessed microphytobenthos associated predominantly on rocky shores, and is therefore not ecologically similar to the microphytobenthos found at Roberts Bank. Using correspondence analysis, 58 taxa of common diatoms showed relationships with salinity (Ulanova et al. 2009) including a species of Achnanthidium exhibiting optimal salinities around 2 PSU. In this study, exposure to wave action and nutrients were determined to be secondary influences of assemblage composition (Ulanova et al. 2009), supporting the evidence found in the present study.

Ulanova et al. (2009) also reported a shift between freshwater-influenced diatoms and marine-influenced diatoms to occur between 5 and 6 PSU. Previous assessments of biological communities along salinity gradients have led to the Critical Salinity Theorem (Telesh and Khlebovich 2010) which predicts a transition between freshwater and marine influenced communities will occur between 0.5 and 10 PSU, which minimum species diversity values occurring between 5 and 8 PSU.

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This critical salinity theorem was also confirmed in microphytobenthos communities within the Schelde estuary in Belgium (Muylaert et al. 2002). In the community analysis, using a multivariate Redundancy Analysis, salinity and sediment grain size accounted for 53.4% and 21.1% in the variation of species abundance data, respectively (Muylaert et al. 2002); no nutrient data were collected in this study. However, a study of the phytoplankton community in the same system by Muylaert and Sabbe (1999) found salinity, Suspended Particulate Matter, temperature, nitrate and silicates were sufficient for describing shifts in composition. Minimum values is species diversity parameters were also noted between 5 and 8 PSU, agreeing with the Critical Salinity Theorem (Muylaert and Sabbe 1999).

Assessments in the Colne estuary, UK, have shown similar results. Thornton et al. (2002) reported the distribution of microphytobenthic assemblages to be related to salinity, temperature and dissolved inorganic nitrogen. Using a cluster analysis on 23 taxa which were present throughout the year with different assemblages being present based on specific environmental variables.

The reviewed literature, combined with the observations of spatial and temporal variation in the microphytobenthos community at Roberts Bank (WorleyParsons 2015b) indicate an influence of environmental variables. However, in this study, no effect was observed. This is expected to be due to the noted variability in the community combined with the low sample size of taxonomic data. Given the observed differences in biomass indicators, and the noted influence community composition can have on biomass levels (Underwood and Smith 1998, Thornton et al. 2002), an effect would be predicted. Additional sampling both spatially and within individual seasons would be required to adequate assess such relationships at Roberts Bank.



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6. CONCLUSIONS

Biofilm exists on the estuarine mudflat at Roberts Bank. This habitat is exposed to a dynamic environment influenced by several physical processes including tidal cycle, freshwater inputs, sediment deposition, and nutrient availability. As biofilm is known to fluctuate over small spatial scales, changes in physical processes and environmental variables are expected to influence biofilm biomass and microphytobenthos community composition.

The present study utilised a multivariate database compiled through co-located sampling by three different studies: Biofilm, Benthic Infauna, and Sediment Chemistry and Quality. All study sites occurred within known areas of existing biofilm at Roberts Bank and did not address regional scale differences in biofilm biomass and microphytobenthos composition. The analysis was conducted on all available data within a larger, multi-study database; therefore, sampling distribution and analytical methodologies were not specifically focused on assessing biofilm influences. Therefore, several assumptions are made regarding the representation of other study data to the representation of conditions experienced by biofilm. The analyses conducted indicate several variables which are correlated to biofilm biomass at Roberts Bank, and are all confirmed by previously published literature. These conclusions include:

1. Biofilm biomass levels are correlated to freshwater input as shown by a positive and significant relationship with Porewater Chloride content (salinity). Porewater chloride was significantly correlated to distance from Canoe Passage and Total Leachable Ammonia. Aside from Ammonia, no nitrogen data (nitrate and nitrite) were available for analysis due to measured values being below laboratory Detection Limits (DL). Previous research in the Fraser River estuary have established a negative relationship between nutrient availability and freshwater influence;

2. Biofilm biomass levels are correlated to sediment grain size as shown by a negative and significant relationship with % Sand. % Sand was negatively correlated to % Silt, % Clay, and sediment Total Organic Carbon (TOC), indicating an inverse relationship with biofilm biomass;

3. A positive relationship between Polychaete Density and biofilm biomass measures (e.g., Chlorophyll a and Total Organic Carbon) were observed, while negative relationships were observed with measures of the infauna community including Macrofauna density and biomass. The positive relationship with Polychaetes is likely due to similarities in habitat preferences and Polychaete foraging strategies rather than a cause and effect relationship; and

4. No significant effects of environmental variables on microphytobenthos taxonomy were determined due to a small sample size. However, previous literature supports a predicted effect of salinity, sediment grain size, and nutrients. Given results from a wider assessment of microphytobenthic community composition across Roberts Bank, these effects are expected, but not confirmed with the available data.

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307071-00790 : Rev 0 : 27 January 2015 Appendices

Appendix 1 Extended Statistical Methodology Background

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22 December 2014 Page 1

APPENDIX 1 STATISTICAL METHODOLOGY

1. NORMALITY TRANSFORMATION

Where a dataset was not normally distributed, it was transformed to fit a normal distribution. Transforming

data effectively changes the scale of measurement and can improve the statistical analysis of the

datasets. In particular, it allows for parametric testing. The benefits of changing the scale of measurement

include the following (Quinn and Keough 2002):

1. Make the distribution of data more symmetrical (normality);

2. Reduce the relationship between the mean and variance of data (improve homogeneity of variances);

3. Reduce the influence of outliers;

4. Improve linearity in regression analysis; and

5. Make multiplicative interaction effects additive (e.g., reduce the size of interaction effects).

For linear models, such as multiple regression, normal distributions of datasets and linearity of data are

key assumptions. Therefore, if diagnostics of datasets indicate a violation of these assumptions,

transformations were performed.

Several different transformations are applicable to biological and environmental data based on the

observed distribution of data. Examples include:

Square root (√);

Logarithmic (Log10, Loge or Log10 +1, Loge +1 );

Power (nx);

Logarithm of odds (Logit);

Angular transformation (arcsin);

Inverse (1/x); and

Reflection (Box-cox).

Some transformations can only be applied to specific data types. For instance, Logit and Arcsin

transformations are only applicable to finite data ranges (i.e., proportions and percentages) while Square

Root and Power transformations are best suited to whole numbers (i.e., abundance counts). Therefore,

specific considerations to the data type are required prior to performing transformation. These are

outlined in many statistical manuals including Quinn and Keough (2002) and Sokal and Rohlf (1995).

Details of transformations used and results of the normality tests from this study are provided in

Table 4.1-1.

307071-00790-01-EN-REP-5001_Rev0_App1.docx

2. PRINCIPAL COMPONENT ANALYSIS

Principal Component Analysis (PCA) is a statistical method of identifying patterns in data while

highlighting similarities and differences among variables in a larger dataset. PCA uses orthogonal

transformation to convert all measured, and possibly correlated, data into a number of different newly

created linear variables called Principal Components (PC); the number of PCs generated can be user

defined, but generally should equal the number of variables in the original dataset. The new PCs are a

summary of a wider dataset where the influence of each variable from the original dataset is considered.

Each new PC represents an orientation of the data. Each PC is assigned an eigenvalue that represents

the amount of variability from the original dataset captured by the PC. As a general rule, only PCs with

eigenvalues above 1.0 are considered useful for interpretation; PCs with larger eigenvalues indicate it

represents a greater amount of variation from the original dataset.

Within each new PC, loading values are determined for each of the measured variables contained within

the original database. These loading values range between -1.0 and 1.0 and reflect the correlation of

each variable to the given PC. For instance, variables with loading values above 0.5 will have a strong

and positive association with the given PC. The loading values show what variables are related to each

other and what combination of variables is driving the greatest variability in the data. These results are

not associated with statistical significance, but instead are a tool used to indicate trends in multivariate

data as well as what environmental variables are important to the designated dependent variable.

The PCA will also calculate PC component scores which are associated for each variable from the

original dataset. The component scores can be treated as new data and tested for relationship to the

dependent variables using Pearson’s correlation coefficient. Significant correlations of dependent

variables to the component scores can be interpreted as an influence of the heavily weighted variables

(based on loading values) within the PC. These findings provide an inference of the key environmental

variables over the tested biofilm biomass variables.

3. MULTIPLE LINEAR REGRESSION

Multiple Regression is a common statistical analysis used in ecology to determine significant effects on

biological communities. Multiple regression seeks to find an equation that best predicts a dependent

variable (i.e., biofilm biomass indicators) based on several independent variables (i.e., measured

environmental variables). This is achieved in an additive form where the effects of each independent

variable are summed.

When dealing with large databases, two issues can occur: Co-linearity and Variable Selection.

Co-linearity With large databases, it is likely that some variables will be correlated. For

instance, on intertidal mudflats, elevation decreases with distance away from

shore. In this scenario, elevation and distance from shore are anticipated to be

highly correlated. If both variables were to be included in a multiple regression

model, their correlation would make interpretation difficult.




Variable Selection The goal of multiple regression is to find an equation which captures the greatest

amount of variation while considering the fewest number of variables. Inclusion of

unnecessary (and co-linear) variables reduces the prediction power of the equation

and could lead to differences in significance of other variables.

Data Treatment

In order to address the issues of Co-linearity and Variable Selection, two diagnostics were used to

eliminate potential error due to model variables.

1. Correlation Analysis

All available variables were assessed for correlation by pairwise comparisons. Every variable was

tested against each other. Any variables which had a strong correlation (r > 0.80) were grouped

together into one selected variable (i.e., instead of including both elevation and distance from shore

in the model, only elevation was included with the caveat that it represented effects of distance

from shore).

2. Variable Selection

The Akaike Information Criterion (AIC) was calculated for each iteration of the model. The AIC is a

measure of model fit for a given model and provides a means for selecting the model with the least

amount of information lost.

The AIC is calculated as:

2 2 #

For datasets where the number of observations are not equal or greater to the value of the

number of variables squared, a corrected AIC (AICc) is recommended (Burnham and Anderson

2002). The AICc includes a factor which acts to reduce the possibility of overfitting the model

(i.e., including too many variables). This factor adjusts the AIC value for bias due to number of

variables and total sample size.

2 1

1

Where: k = number of model variables; and

n = number of observations.


Regression Analysis

After removal of co-linear variables, a backwards stepwise multiple linear regression was run on the

entire dataset. This approach runs a multiple regression beginning with all possible variables.

Subsequent models are run, systematically removing individual variables. The AICc and r2 values are

compared at each step to determine if a variable can be removed without losing fit of the model. This

process is repeated until a model is found with a maximum r2 value and minimum AICc. The selected

model indicates the best fit and the variables are tested for significance.

4. NON-PARAMETRIC COMMUNITY ANALYSIS

4.1 Non-Parametric Multidimensional Scaling Analysis

All community statistics were conducted in Primer-E (Plymouth, UK). Taxonomic density data were log+1

transformed to increase normality. The Bray Curtis coefficients (Bray and Curtis 1957) were calculated for

all pairwise combinations, creating a resemblance matrix of all samples. This resemblance matrix was

then plotted using a non-Parametric Multidimensional Scaling Analysis (nMDS). The nMDS is a

dimensionless ordination plot where axes are unit-less and the distance between points is relative to their

similarity. Communities plotted close to each other are considered to be similar while communities plotted

farther away are considered to be dissimilar.

4.1.1 Analysis of Similarity

To test for possible influences of season, community similarities were assessed using an Analysis of

Similarity (ANOSIM) test. The ANOSIM test uses the Bray Curtis coefficients to rank the order of

dissimilarity among defined groups. An R statistic is calculated based on the difference of the mean rank

similarity between and within groups as a factor of the total number of samples. The R statistic is a scaled

ratio between -1 and 1 with values close to zero (0) indicating high similarity. A positive value means

most similar samples are within group; negative values means most similar samples are outside of group.

To test for the significance of the R statistic, random permutations are run with samples being randomly

assigned to groups. The R statistic from the random permutations is then compared against the observed

R statistics to determine if it is significantly different compared to random groupings. If the R value is

significant, it concludes that the similarity of samples are significant (to the degree of the R value)

compared to patterns expected by random chance.

4.1.2 Biota-Environment and Stepwise Analysis

The Biota-Environment and Stepwise Analysis (BEST) analysis finds the best match between community

composition and associated environmental variables. It searches for high rank correlations between the

community assemblage similarity matrix and environmental data in a stepwise search. The results are an

optimal set of variables which possess the highest correlation with patterns in community similarities

(Clarke and Ainsworth 1993). The BEST analysis produces a rank correlation (ρ) value and associated p-

value based on random permutations of the data.




5. REFERENCES

Bray, J. R., and J. T. Curtis. 1957. An ordination of the upland forest communities of southern Wisconsin.

Ecological Monograph 27:325–349.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model inference: a practical

information-theoretic approach. 2nd edition. Springer-Verlag, New York.

Clarke, K. R., and M. Ainsworth. 1993. A method of linking multivariate community structure to

environmental variables. Marine Ecology Progress Series 92:205–219.

Quinn, G. P., and M. J. Keough. 2002. Experimental Design and Data Analysis for Biologists. Cambridge

University Press.

Sokal, R. R., and F. J. Rohlf. 1995. Biometry: The Principles and Practices of Statistics in Biological

Research. W.H. Freeman.

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Appendix 2 Co-located Database Used for Biofilm Physical Factors Analysis

Emma.Livingstone

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Taxonomy Database

Row Labels RB2001 RB2001F RB2003 RB2003F RB2007 RB2007F RB2009 RB2009F RB2011 RB2011F RB2013 RB2013F RB2016 RB2016F RB2019 RB2019F RB2020 RB2020F RB2022 RB2022F RB2024 RB2024F RB2025 RB2025F RB2026 RB2026F RB2030 RB2030F RB2031 RB2031F RB2037 RB2037F RB2038 RB2038F RB2040 RB2040F RB2041 RB2041F RB2043 RB2043F RB2045 RB2045F RB2047 RB2047FAchnanthes spp. 1.92E+08 2.88E+09 2.12E+08Achnanthidium spp. 2.71E+09 1.56E+09 1.9E+09 1.6E+09 1.24E+09 1.44E+09 6.07E+09 7.01E+08 5.66E+08 5.33E+09 4.17E+09 2.26E+09 1.69E+09 6.89E+09 2.63E+08 1.78E+09 8.63E+08 8.82E+08 2.99E+08 5.82E+09 4.22E+08 3.2E+09 1.56E+08 9.39E+08 1.12E+08 9.8E+08 3.14E+08 5.41E+08 1.44E+08 3.84E+09 8.48E+08 8.04E+08 2.27E+08 5.33E+09 2.09E+08 5.72E+09 2.16E+08 1.33E+09 6.37E+08 1.9E+09 9.04E+08 3.77E+09 6.17E+09Akashiwo sp. 14580469Amphidinium spp. 9383470Amphora spp. 4.37E+08 2.56E+08 3.92E+08 1.04E+09 59016185 2.12E+09 1.9E+08 34406058 6.77E+08 1.13E+08 1.35E+08 4.89E+08 3.65E+08 3.04E+09 99016619 1.77E+08 1.24E+08 1.39E+08 84511000 46856137 2.4E+08 14380494 25965219 1.28E+08 88676538 77140855 1.32E+08 27570342 1.61E+08 2.96E+08 3.97E+08 58321877 1.36E+09Biddulphia sp.Caloneis sp. 63891944Chaetoceros spp. 25272813 16923759 6224831Closteriopsis sp. 2.35E+08Cocconeis spp. 48601564 6.39E+08 1.96E+08 8.88E+08 90260047 4.24E+08 50545627 3127823 16923759 10256111 17231464 3.55E+08 22836879 10608923 5724341 18674493 10656245 48292000 14417273 63891944 1.58E+08 8655073 81603932 30456980 33060366 59233156 58321877 1.6E+08Coscinodiscus spp. 65360724 6943081 70726157 6255647 16923759 25847195 57092199 3536308 12449662 12073000 63891944 44338269 22040244 29160938 2.4E+08Cyclotella spp. 48601564 2.56E+08 10608923 96584000 1.44E+08 44338269 44080488 26468149 2297528 59233156 14580469 21678763Cylindrotheca spp. 3.4E+08 5.11E+08 2.61E+08 9.19E+08 1.39E+08 60310123 70726157 18674493 36219000 25230227 31945972 4327537 69946227 38795985 2.76E+08 6892585 59233156 14580469 6.41E+08Cymbella sp. 19723602 61734112 1.61E+08 1.55E+08 6.59E+08 1.58E+08 6.16E+08 3.59E+08 3.11E+08 3.7E+08 17173024 1.17E+08 46856137 14380494 1.52E+08 30456980 52936298 3.68E+08 1.65E+08 21678763Delphineis/Rhaphoneis 4.37E+08 1.92E+09 7.84E+08 2.52E+09 1.41E+09 6.07E+08 1.75E+08 4.91E+08 5743821 28290463 1.62E+08 1.57E+08 1.76E+08 86550730 99761105 3.64E+08 45950570 2.52E+08 4.52E+08 4E+08Diatoma sp. 13149068 76553748 9467180 1.14E+08 1.23E+08 31968735 1.4E+08 68912497 21678763Diploneis spp. 1.94E+08 1.48E+08 2.83E+08 31278234 2.03E+08 22836879 2.87E+08 10608923 17173024 18674493 10656245 12073000 3604318 63891944 16626851 44080488 2297528 34456248 29616578 43741408 21678763 80089901Entomoneis sp. 14380494 26468149Epithemia sp. 5724341Eunotia sp. 26468149 33059955Eunotogramma sp. 8655073Eutreptiella sp. 14808289Fragilaria spp. 50545627Gomphonema sp. 42529860 6574534 1.11E+08 75737437 31591017 71792779 11448682 31968735 32438864 69946227 11485416 16529977 21678763Gyrosigma/Pleurosigma 1.28E+08 6574534 3.27E+08 12346822 3.11E+09 20829242 2.12E+08 25272813 50771277 8615732 3536308 3604318 15972986 14380494 30456980 49589932 1.3E+08 2.08E+09Heterocapsa sp. 3127823Heterosigma spp. 1.48E+08 70726157 21637683 87482815Lyrella sp. 1.48E+08Melosira sp. 63891944 2.35E+08 2.96E+08 8655073 13785171 6.41E+08Merismopedia sp. 2.68E+09Navicula spp. 3.94E+08 2.53E+09 42529860 3.19E+09 1.64E+08 3.27E+09 6.05E+08 1.24E+10 2.84E+08 1.32E+08 6.2E+08 4.67E+09 2.78E+09 8.85E+08 3.79E+08 3.32E+08 1.09E+09 1.52E+09 4.92E+08 2.9E+08 2.4E+09 1.58E+09 1.89E+09 6.58E+08 2.92E+08 1.1E+09 1.14E+09 1.91E+09 1.44E+08 2.59E+09 2.88E+08 2.6E+08 1.14E+09 6.26E+08 1.58E+09 1.44E+09 6.62E+08 2.78E+08 8.73E+08 1.94E+09 1.41E+09 1.34E+09 9.11E+08 5.53E+09Nitzschia spp. 8.28E+08 8.94E+09 4.59E+08 1.04E+10 5.06E+08 1.3E+10 8.77E+08 2.25E+10 1.51E+09 1.01E+08 2.85E+09 6.79E+09 7.77E+09 88454846 1.96E+09 15639117 2.87E+09 1.17E+09 1.12E+09 14359553 3.91E+09 3.31E+08 3.04E+09 14145231 2.29E+08 2.37E+08 7.14E+08 6.4E+08 2.16E+08 7.51E+08 1.01E+08 21637683 7.46E+08 4.1E+08 2.07E+09 5.62E+08 1.22E+09 1.03E+08 4.82E+08 9.33E+08 8.76E+08 1.24E+09 1.45E+09 6.25E+09Odontella spp. 85204617 18766941 24146000 14380494 12982610 9190114 58321877Paralia sp. 1.04E+09 17357701 8615732 3.29E+08 51519071 49798647 10656245 48292000 63891944 1.4E+08Phaeocystis sp. 29160938Pinnularia sp. 34023888 1.55E+08 1.41E+08 63182033 30768334 46630818Prorocentrum spp. 70726157 12636407 43789528 12449662 36219000 15972986 11084567 2297528 74041445 72902346Pseudo‐nitzschia spp. 10414621 1.41E+08Rhoicosphenia sp. 92996055 30456980 79404447Rhopalodia sp. 92996055 1.6E+08 41068322 11657705Skeletonema sp. 9467180 1.27E+09 5.7E+08 88837768 4.52E+08 53281225 91370940 57427081 49589932Striatella sp. 11084567Surirella spp. 6574534 9467180 47085658 80397913 99179864 21678763Synedra spp. 19723602 37040467 28401539 22799408 6.74E+08 3536308 5724341 7208636 31945972 11020122 11485416 29616578 1.08E+08 1.6E+08Thalassiosira spp. 2.56E+08 1.41E+08 12636407 4327537 87482815Torodinium spp. 9383470 6224831Ulothrix sp. 1.48E+08Unknown alga spp. 3127823 33847518 22836879 47918958 4327537 44424867Unknown centrales spp. 48601564 31243863 6255647 31124154 96584000 1.12E+08 4327537 27711418 44080488 9190114 43741408 1.6E+08Unknown Cryptomonadaceae 3127823 2871911 5542284 22040244 80089901Unknown Dinoflagellate sp. 10608923 8655073 33060366Unknown pennales sp. 1.92E+08 65360724 7.4E+08 24300782 2.12E+09 37909220 67695036 4.22E+08 7072616 31124154 1.81E+08 3.67E+08 4327537 33253702 1.32E+08 9190114 1.18E+08 1.02E+08 6.41E+08Urosolenia sp. 70726157 14808289

1 of 1

Transformed Database

Site ID Season

[Elevation ‐Chart

Datum (m)]^2

Annual Exposure

(hrs.)

Distance from

Canoe Pass (m)

[Distance from Shore

(m)]^1/2

[Biofilm Total Carbohydrate (mg/m2)]^1/2

Ln[Biofilm TOC (mg/m2) +1]

Ln[Biofilm Chlorophyll a (mg_m2) +1)

Ln[Biofilm Fucoxanthin (mg_m2) +1]

[Total Harpacticoida (#/m2)]^1/2

Box‐Cox[Total Nematoda (#/m2)]

[Total Oligochaeta (#/m2)]^1/4

[Total Polychaeta (#/m2)]^1/2

Ln[Total Invert Density (#/m2) +1]

Boc‐Cox [Total Invertebrate Biomass (g/m2)]

[Macrofauna Density (#/m2)]^1/4

Ln[Macrofauna Biomass (g/m2) +1]

Ln[Meiofauna Density (#/m2) +1]

Box‐Cox [Meiofauna Biomass (g/m2)]

LOGITe[% Clay)

LOGITe[% Silt)

LOGITe[% Sand)

LOGITe[% TOC)

Ln[Adjusted Ammonia (mg/kg)]

Adjusted Bromide (mg/kg)

Adjusted Chloride (mg/kg)

Adjusted SO4 (mg/kg)

Ln [Adjusted Phosphate (mg/kg)]

Adjusted Potassium (mg/kg)

Adjusted Sulfur (mg/kg)

RB2001 Spring 9.507213 3609.75 1143.518 31.8767 35.07837 9.791378 3.829728 3.421327 401.2559 166.001 9.120085 245.718 13.59158 10.70765 13.63771 3.342706 13.54734 3.485213 ‐1.85864 0.021872 ‐0.58191 ‐4.88532 1.194145 14.91443 4767.726 1002.445 2.282382 374 134RB2001F Fall 9.362599 3536.25 1141.593 31.8921 98.77812 10.33846 4.116106 3.709172 347.4978 207.3818 14.78612 356.4324 14.20022 19.83614 11.91073 4.603848 14.18642 4.221336 ‐1.798 0.350769 ‐0.98904 ‐4.91255 2.033488 17.53247 5800.866 1038.961 1.931521 344 150RB2002 Spring 9.479606 3609.75 1532.857 31.41138 31.2699 9.818281 3.288402 3.110399 492.7142 0 14.68786 326.9835 13.23861 11.1933 13.99533 3.448091 13.16786 3.485213 ‐2.15322 0.068632 ‐0.49457 ‐4.89885 1.576976 22.85012 6584.767 1400.491 2.312535 364 197RB2003 Spring 11.42495 4950.5 1574.256 26.37229 31.08457 9.673204 3.300271 3.124565 1117.893 205.02 14.53668 786.2818 15.00542 19.03547 18.08269 3.916917 14.97235 6.428138 ‐1.98311 0.401011 ‐0.94405 ‐4.8458 0.899604 19.62175 5460.993 1654.846 2.424803 422 377RB2004 Spring 12.47356 5684.75 1860.687 20.03445 30.65109 10.02588 3.590163 3.38676 568.0154 221.5302 17.69831 647.6858 14.61081 13.46675 16.90973 2.973154 14.5732 5.571549 ‐1.67412 0.944775 ‐1.97306 ‐4.71276 1.14636 20.07722 6389.961 1447.876 2.186051 555 302RB2004F Fall 12.8979 5989.5 1864.663 19.91021 146.9326 10.35972 4.613436 4.073121 827.2102 313.1459 20.37457 607.6038 15.47655 20.86595 13.38269 4.122151 15.47044 6.728635 ‐1.46054 0.9459 ‐2.29785 ‐4.71276 1.866526 14.65798 4967.427 879.4788 2.282382 415 276RB2005 Spring 12.50079 5728.75 2516.017 17.95416 50.50817 10.48011 4.27847 3.948741 1134.923 270.7148 27.96913 652.5229 15.55067 21.46628 18.41889 3.366861 15.53016 7.816906 ‐1.68046 0.709738 ‐1.56715 ‐4.6684 1.833698 35.94041 9627.561 1824.953 2.701361 600 256RB2006 Spring 11.11715 4699.25 3528.569 11.40368 53.65607 10.61953 4.341855 3.862413 1098.885 259.2288 31.64265 794.6361 15.67033 24.96795 20.9279 3.97053 15.63986 8.25241 ‐1.61527 1.220783 ‐2.71764 ‐4.29901 2.192185 28.39314 8330.733 1591.264 1.824549 721 288RB2006F Fall 11.0702 4699.25 3524.422 11.4536 200.0764 10.9699 5.510723 4.664194 723.3743 406.7121 24.11828 607.6038 16.18869 21.4902 12.5146 2.671324 16.1864 8.142883 ‐1.47653 1.243325 ‐3.23401 ‐4.29147 2.754188 31.69705 9256.662 1486.676 2.653242 651 329RB2007 Spring 11.70265 5132 4210.4 11.03194 36.72277 10.33053 4.245491 3.844386 1042.493 239.2895 26.03044 427.807 15.29583 19.32645 14.73724 2.57074 15.28504 7.624783 ‐1.64534 0.715246 ‐1.60919 ‐4.26919 1.98054 38.04971 11242.83 2045.889 2.128232 629 262RB2007F Fall 11.67241 5132 4209.282 11.08817 127.8901 10.64693 4.859502 4.416066 1629.909 241.0935 21.59831 734.5901 15.77543 27.57196 15.29433 3.149611 15.76769 9.387808 ‐1.3714 0.858974 ‐2.25219 ‐4.15787 1.849993 31.72805 9730.878 1572.238 1.824549 580 384RB2008 Spring 11.23948 4794 4075.127 11.0026 99.10259 10.07272 4.424607 4.041647 694.9956 156.888 21.19713 396.5258 14.10319 11.64324 14.5876 3.108329 14.06864 4.604336 ‐1.94727 0.811836 ‐1.49851 ‐4.70149 1.064007 36.32653 10510.2 1510.204 2.701361 442 277RB2009 Spring 11.62648 5088.75 2401.489 28.61909 97.00754 10.02372 3.79459 3.606856 723.3743 212.3035 20.03137 541.9508 14.63438 12.63948 15.25019 2.853719 14.61024 5.361978 ‐1.85424 0.366203 ‐0.974 ‐5.06089 1.1971 23.9726 7214.612 1301.37 2.261763 510 182RB2010 Spring 11.68001 5132 3945.601 12.57212 126.7124 10.55806 4.683704 4.422088 942.6979 204.8576 21.7527 550.5857 14.84063 14.35994 14.97694 2.357045 14.82241 6.276685 ‐2.70118 ‐0.83346 0.550198 ‐5.19749 1.877239 33.85827 9238.845 1548.556 2.028148 446 296RB2011 Spring 6.655523 2167 3666.272 21.83154 95.45965 10.04254 3.932022 4.010057 283.7308 200.0996 22.13944 217.1861 14.54661 13.17943 15.95365 3.234848 14.51493 5.133945 ‐2.7654 ‐0.84979 0.581015 ‐5.2933 3.010052 41.98718 11987.18 1794.872 2.714695 379 175RB2011F Fall 6.642151 2167 3665.725 21.84332 70.07419 9.308977 3.289893 3.044522 634.4413 248.0943 14.53668 359.0694 14.83588 14.34458 13.87918 2.793977 14.82241 6.038326 ‐2.96794 ‐0.95341 0.694549 ‐5.37709 1.893144 29.89418 8994.709 1455.026 2.360854 353 149RB2012 Spring 8.158065 2894 3849.937 18.4494 107.0004 10.20781 4.049173 3.853758 603.9701 257.8488 18.83251 387.7041 14.89827 17.96159 17.55476 4.013489 14.86558 5.765705 ‐2.49592 ‐0.36625 0.057653 ‐4.98401 2.514264 35.49223 9948.187 1554.404 2.517696 459 255RB2013 Spring 8.396108 3005 3875.364 19.19733 99.77725 10.19359 4.205737 4.037951 601.8838 151.9191 17.22593 400.4714 13.92614 16.73281 16.29128 4.152277 13.861 4.604336 ‐2.3665 ‐0.43953 0.089598 ‐5.06089 1.90406 31.19777 9080.78 1643.454 2.24071 495 174RB2013F Fall 8.363261 2975.5 3873.616 19.25332 102.3681 10.24235 4.493568 4.135966 1134.923 295.3879 22.38174 423.3737 15.6364 24.36339 15.59311 4.164077 15.62679 7.834105 ‐2.64431 ‐0.28179 0.013232 ‐4.96931 1.803091 29.48718 8782.051 1410.256 1.902108 427 205RB2014 Spring 7.946095 2785.75 3553.961 25.61278 111.7102 10.80218 4.804349 4.388878 777.0287 161.4513 10.45536 521.2467 14.36918 13.90776 14.88245 3.436014 14.34057 5.133945 ‐2.36763 ‐0.00093 ‐0.34515 ‐5.2933 2.196385 35.01259 9924.433 1586.902 2.379546 477 179RB2015 Spring 9.330778 3499.75 3341.144 23.54734 130.963 10.3226 4.334673 4.130033 448.6177 212.3783 17.43733 428.5415 14.74951 21.16417 17.28713 4.354045 14.71378 6.276685 ‐2.08585 0.682681 ‐1.23571 ‐4.89885 1.908073 31.93833 9162.996 1519.824 2.74084 527 233RB2015F Fall 9.257581 3468.75 3340.375 23.54433 115.1571 10.37643 4.406841 4.072269 750.681 248.0158 18.83251 426.3344 14.9116 14.14645 15.25019 3.212314 14.89336 5.575994 ‐2.22682 0.713693 ‐1.20124 ‐4.95482 1.763024 31.42292 9268.775 1422.925 2.351375 445 280RB2016 Spring 6.717757 5989.5 2995.036 33.17361 92.75644 10.25033 4.099829 3.954124 634.4413 187.6061 14.88245 411.3179 14.52395 15.92997 15.55147 3.333775 14.49473 6.11663 ‐2.71433 ‐0.41903 0.164839 ‐5.49266 1.879728 36.05016 9467.085 1598.746 2.04122 398 250RB2017 Spring 8.783312 3182.75 3090.848 28.12992 134.2768 10.4797 4.287304 4.141705 347.4978 210.1061 15.79645 236.59 14.37638 16.69248 17.86609 4.208066 14.31648 4.28859 ‐2.16214 0.585045 ‐1.07438 ‐4.89885 2.0035 33.78685 9546.485 1609.977 2.484907 444 229RB2018 Spring 9.88942 3841.5 2866.151 26.4717 107.4718 10.1926 3.696848 3.90278 492.0756 151.8384 17.25661 144.065 13.70665 9.535008 15.06969 2.842785 13.64743 3.922953 ‐2.427 0.22515 ‐0.56318 ‐5.21594 1.713751 28.79581 8429.319 1623.037 2.302585 373 160RB2018F Fall 9.911571 3875.5 2864.765 26.47841 96.89338 10.17832 4.393214 3.993603 491.4361 181.085 12.67204 125.3925 13.95281 15.07411 15.6754 4.082641 13.89874 3.659247 ‐2.21498 0.563574 ‐1.0237 ‐5.16157 1.842651 27.71084 8168.675 1325.301 2.406945 381 209RB2019 Spring 7.956307 2785.75 838.8423 36.8323 61.16336 9.066856 4.228147 3.555348 200.6279 95.66734 5.007843 61.42951 11.75724 0.876943 9.120085 0.727162 11.70152 0.743492 ‐4.22606 ‐2.99476 2.715539 ‐6.21261 2.380547 13.70656 4285.714 776.0618 2.397895 188 79.8RB2020 Spring 7.405843 2508.25 3451.456 27.08399 129.7694 10.17492 3.760269 4.057681 531.4037 184.4116 14.73724 103.4013 14.48736 14.69483 12.67204 2.230219 14.47411 6.428138 ‐2.61004 ‐0.06371 ‐0.21107 ‐5.23473 1.769989 27.80899 8005.618 1460.674 2.128232 459 167RB2021 Spring 10.84738 4500.25 4446.685 8.31023 88.40721 9.597945 3.965753 3.872034 492.0756 230.0117 9.320646 66.35146 14.65659 9.931672 12.5146 2.003518 14.64596 4.883315 ‐3.72699 ‐2.04915 1.835047 ‐5.80614 2.858782 52.21843 13720.14 2662.116 2.873565 382 215RB2021F Fall 11.13562 4747.5 4442.108 8.025532 75.99481 9.115629 4.006969 3.718438 723.3743 308.2243 17.72661 250.7849 15.48084 15.97463 17.55476 2.9667 15.46272 6.463342 ‐3.67044 ‐1.78552 1.596615 ‐5.56895 3.011157 46.63212 13652.85 2435.233 3.015535 375 225RB2022 Spring 10.76594 4449 4747.348 16.34005 79.5358 9.419482 3.476305 3.448399 491.4361 331.4677 14.16432 360.8167 15.60916 18.61873 15.25019 3.633771 15.60012 6.70897 ‐3.26256 ‐1.73189 1.468148 ‐6.07254 2.499649 39.93399 11122.11 1815.182 2.76001 389 245RB2022F Fall 10.82195 4500.25 4744.957 16.25887 86.8687 9.354176 3.898938 3.761898 851.1923 368.54 14.73724 353.7757 15.93977 18.27412 18.41889 2.982753 15.92591 7.158278 ‐3.63208 ‐1.28715 1.141121 ‐5.80614 2.539981 43.30144 12870.81 2033.493 2.653242 387 252RB2023 Spring 7.322073 2483.5 5219.178 29.50681 78.9061 9.409704 4.031937 4.041647 1080.415 403.6265 21.23007 141.8654 16.36186 23.10306 14.21933 2.813035 16.35865 8.488529 ‐3.73825 ‐2.05234 1.816082 ‐5.94964 2.667228 45.33333 12166.67 2000 2.624669 410 199RB2023F Fall 7.326873 2483.5 5217.654 29.52368 72.27727 8.60298 3.572346 3.522234 723.3743 377.0742 5.007843 239.2336 15.90496 16.97016 16.29128 2.490657 15.89621 7.008165 ‐4.06076 ‐1.94555 1.798918 ‐5.98896 2.703757 50.89974 14858.61 2185.09 2.80336 387 250RB2024 Spring 13.49447 6386.5 5837.374 5.437857 118.8775 10.15641 4.175156 3.877017 636.9147 219.6446 6.590692 946.3601 14.73982 25.17241 25.56337 4.943841 14.55415 5.765705 ‐3.54925 ‐1.28515 1.11834 ‐5.56895 3.374831 53.15985 14312.27 2936.803 3.306887 426 223RB2024F Fall 13.34991 6284.75 5835.519 5.545056 132.3874 10.47646 5.228914 4.558079 919.3927 252.9798 5.955363 699.5057 15.18019 14.55299 19.01807 2.69709 15.1462 6.170812 ‐3.58651 ‐0.98305 0.850939 ‐5.71053 3.105189 41.54229 12139.3 2089.552 3.068053 335 212RB2025 Spring 6.75817 2214 6562.018 20.39392 96.26192 9.919046 4.154969 4.000034 602.4061 190.8241 20.2621 239.2336 14.31266 9.010559 13.57529 1.817622 14.29178 4.604336 ‐3.39697 ‐1.59524 1.379819 ‐5.68057 2.959484 43.3657 11229.77 2038.835 2.701361 459 230RB2025F Fall 6.925707 2525 6559.675 20.41225 77.33224 9.071533 3.834061 3.730501 634.4413 284.8648 21.45647 298.8447 15.24925 15.0738 14.83452 3.082717 15.23763 6.064333 ‐3.43256 ‐1.52649 1.266689 ‐5.87533 1.997539 29.10798 8967.136 1431.925 2.549445 381 217RB2028 Spring 4.496646 1200.75 704.5496 47.92447 67.03607 9.614158 3.491343 3.513335 283.7308 108.2119 5.007843 35.46634 12.41273 3.889992 8.14564 1.536394 12.39467 2.160845 ‐2.94854 ‐0.9341 0.68762 ‐5.71053 1.436262 13.85159 4204.947 759.7173 2.066863 269 89.8RB2028F Fall 4.502316 1200.75 704.0683 47.91633 86.61946 9.597359 3.377588 3.001217 694.9956 193.7377 17.10155 50.15699 14.16581 24.42549 10.16864 5.066449 14.15825 3.909692 ‐2.42395 ‐0.49994 0.164597 ‐5.12689 1.064617 12.46612 3983.74 644.9864 1.704748 247 83.6RB2029 Spring 3.421181 706.75 795.4866 45.57967 80.44209 10.03727 3.913222 4.028561 569.1216 118.4086 5.955363 246.9945 13.58606 10.35634 13.18138 2.892813 13.54734 4.28859 ‐2.32017 ‐0.20654 ‐0.15235 ‐5.06089 1.319892 17.87709 5446.927 949.7207 2.292535 371 125RB2029F Fall 3.34882 674.25 795.3507 45.56729 88.5017 9.838082 3.118392 3.106826 694.9956 165.8033 14.32747 214.2707 14.28 26.09137 12.09262 5.163491 14.26647 4.480484 ‐2.18696 0.456552 ‐0.9106 ‐5.10998 1.185995 12.02673 3875.278 668.1514 1.84055 272 104RB2032 Spring 3.506137 749 1310.197 51.56912 68.5714 9.689283 2.882564 3.151453 0 118.2663 17.25661 61.42951 12.83929 6.086013 10.96687 2.115725 12.80013 2.932422 ‐3.88501 ‐2.73333 2.426568 ‐6.50079 1.454087 23.38129 6258.993 935.2518 2.351375 245 102RB2032F Fall 3.498301 749 1310.7 51.5769 64.27716 8.998879 3.067122 2.837908 491.4361 204.817 14.21933 66.35146 14.16182 7.477173 8.422155 1.924117 14.15825 3.873556 ‐3.87117 ‐2.10107 1.906259 ‐6.21261 1.591738 13.45029 4152.047 625.731 2.24071 205 73.9RB2034 Spring 6.518147 2096.25 489.3733 43.31081 61.49627 9.066467 3.020913 2.889816 0 169.8463 5.007843 0 13.55063 5.36983 7.08216 0.40753 13.54734 3.485213 ‐4.29446 ‐3.06557 2.787376 ‐6.72423 1.269931 12.08333 3344.697 488.6364 2.128232 167 37.7RB2034F Fall 6.524317 2096.25 489.9407 43.32159 89.65315 9.277619 2.778819 2.972464 283.7308 169.8463 0 25.07849 13.74111 6.090958 7.08216 1.060149 13.7384 3.648069 ‐4.41998 ‐3.41099 3.082257 ‐6.81134 1.459734 9.919786 3262.032 465.2406 1.589235 146 54.5RB2038 Spring 4.597288 1245.25 959.566 49.67818 66.91373 9.494656 3.102342 2.95803 283.7308 214.7055 5.007843 202.1893 14.03991 5.521888 7.837698 0.727162 14.03689 3.485213 ‐4.09528 ‐2.90095 2.610196 ‐6.64409 1.064007 16.32653 4244.898 648.9796 2.282382 184 58.4RB2038F Fall 4.61179 1264.75 959.3572 49.68517 71.271 9.142614 2.827905 2.797891 530.8116 199.4971 14.21933 212.7981 14.10461 11.51093 9.120085 3.41556 14.09941 3.832574 ‐4.04242 ‐2.71795 2.453612 ‐6.72423 1.076012 10.86592 3547.486 544.6927 1.84055 167 73.7RB2040 Spring 6.530322 2119.25 525.0677 41.57767 71.22352 9.792504 4.000217 3.623274 200.6279 165.8033 0 253.2804 13.47488 11.35439 12.82383 3.481529 13.43612 3.485213 ‐2.66057 ‐0.61685 0.339881 ‐5.39917 1.169487 11.15254 3491.525 701.6949 2.151762 241 91.6RB2040F Fall 6.495966 2096.25 523.8869 41.58676 77.16647 9.766212 3.336837 3.158276 401.2559 118.2663 5.007843 313.2303 13.46956 11.56338 12.35097 3.448091 13.43612 3.764656 ‐2.40038 ‐0.3049 ‐0.03002 ‐5.31361 0.525424 13.48039 4387.255 735.2941 1.740466 226 100

1 of 1




Appendix 3 Detailed Biofilm Biomass Statistical Analyses

Emma.Livingstone

New Stamp

HEMMERA ENVIROCHEM ROBERTS BANK TERMINAL 2 – TECHNICAL DATA REPORT



APPENDIX 3 DETAILED BIOFILM BIOMASS STATISTICAL ANALYSES

1. BIOFILM BIOMASS SEASONAL COMPARISON Number of Variables : 34 Number of Cases : 50 SYSTAT Rectangular file U:\YVR\307071\00790_HEMM_CCIP BIOFL\10_Eng\15_I_and_E\10_Env_Mgmt\Biofilm\Tier 4\Data\Physical Factors\Tier 4\Analysis\Final Analysis\Final Physical Factors Database.syz, Created data file Tue Mar 11 08:19:54 2014 containing variables:

SITE_ID$ SEASON$ ELEVATION_CHART- _DATUM__M_

ANNUAL_EXPOSURE- __HR__

DISTANCE_FROM_C- ANOE_PASS__M_

DISTANCE_FROM_S- HORE__M_

HS_CLASSIFICATI- ON$

LABEL$ AREA$ BIOFILM_TOTAL_C- ARBOHYDRATE_MG_- M2

BIOFILM_TOTAL_O- RGANIC_CARBON_M- G_M2

BIOFILM_PIGMENT- S_CHLOROPHYLL_A- _MG_M

BIOFILM_PIGMENT- S_FUCOXANTHIN_M- G_M2

TOTAL_HARPACTIC- OIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A

TOTAL_INVERT_DE- NSITY

TOTAL_INVERT_BI- OMASS

MACROFAUNA_TOTA- L_BENTHIC_INDIV- IDUAL

MACROFAUNA_TOTA- L_BIOMASS_G_M2

MEIOFAUNA_TOTAL- _BENTHIC_INDIVI- DUALS

MEIOFAUNA_TOTAL- _BIOMASS_G_M2

GRAIN_SIZE_PERC- ENT_CLAY_LESS_T- HAN_4

GRAIN_SIZE_PERC- ENT_SILT_0_063M- M_TO_

GRAIN_SIZE_PERC- ENT_SAND_2_0MM_- TO_0_

TOC_TOTAL_ORGAN- IC_CARBON_PERCE- NT

ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E

ADJUSTED_CHLORI- DE

ADJUSTED_SO4 ADJUSTED__PHOSP- HATE

ADJUSTED_POTASS- IUM

ADJUSTED_S


2. BIOFILM BIOMASS SEASONAL COMPARISON

Number of Variables : 34 Number of Cases : 50 SYSTAT Rectangular file U:\YVR\307071\00790_HEMM_CCIP BIOFL\10_Eng\15_I_and_E\10_Env_Mgmt\Biofilm\Tier 4\Data\Physical Factors\Tier 4\Analysis\Final Analysis\Final Physical Factors Database.syz, Created data file Tue Mar 11 08:19:54 2014 containing variables: SITE_ID$ SEASON$ ELEVATION_CHA

RT- _DATUM__M_




HS_CLASSIFICATI- ON$

LABEL$ AREA$ BIOFILM_TOTAL_C- ARBOHYDRATE_MG_- M2

BIOFILM_TOTAL_O- RGANIC_CARBON_M- G_M2

BIOFILM_PIGMENT- S_CHLOROPHYLL_A- _MG_M



TOTAL_NEMATODA

TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A











ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E

ADJUSTED_CHLORI- DE

ADJUSTED_SO4 ADJUSTED__PHOSP- HATE


ADJUSTED_S

▼Hypothesis Testing: Two-sample t-test H0: Mean1 = Mean2 vs. H1: Mean1 <> Mean2 Grouping Variable = SEASON$




Variable SEASON$N Mean Standard

Deviation BIOFILM_TOTAL_CARBOHYDRATE_MG_M2 Fall 19.00098.97533.532

Spring 31.00081.92931.503

BIOFILM_TOTAL_ORGANIC_CARBON_MG_M2 Fall 19.0009.772 0.669

Spring 31.0009.978 0.431

BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2 Fall 19.0003.646 0.595

Spring 31.0003.749 0.403

BIOFILM_PIGMENTS_CHLOROPHYLL_A_MG_MFall 19.0003.933 0.798

Spring 31.0003.916 0.470

Separate Variance Variable SEASON

$ Mean Difference

95.00% Confidence Interval

t df p-Value

Lower Limit Upper Limit BIOFILM_TOTAL_CARBOHYDRATE_MG_M2

Fall 17.045 -2.315 36.406 1.78536.358

0.083

Spring

BIOFILM_TOTAL_ORGANIC_CARBON_MG_M2

Fall -0.206 -0.559 0.146 -1.201

27.244

0.240

Spring

BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2

Fall -0.103 -0.419 0.214 -0.664

28.212

0.512

Spring

BIOFILM_PIGMENTS_CHLOROPHYLL_A_MG_M

Fall 0.017 -0.397 0.432 0.08625.771

0.932

Spring

Pooled Variance Variable SEASON

$ Mean Difference

95.00% Confidence Interval

t df p-Value

Lower Limit Upper Limit BIOFILM_TOTAL_CARBOHYDRATE_MG_M2

Fall 17.045 -1.864 35.955 1.81248.000

0.076

Spring

BIOFILM_TOTAL_ORGANIC_CARBON_MG_M2

Fall -0.206 -0.519 0.106 -1.330

48.000

0.190

Spring

BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2

Fall -0.103 -0.386 0.181 -0.727

48.000

0.471

Spring

BIOFILM_PIGMENTS_CHLOROPHYLL_A_MG_M

Fall 0.017 -0.342 0.377 0.09748.000

0.923

Spring




▼Nonparametric: Kruskal-Wallis Test Mann-Whitney U Test for 50 Cases The categorical values encountered during processing are

Variables Levels SEASON$ (2 levels)FallSpring

Dependent Variable BIOFILM_TOTAL_C-

ARBOHYDRATE_MG_-M2

Grouping Variable SEASON$


GroupCountRank Sum Fall 19 552.000

Spring 31 723.000

Mann-Whitney U Test Statistic : 362.000 p-Value : 0.177 Chi-Square Approximation : 1.820 df : 1

Kruskal-Wallis Test Statistic: 1.820 The p-value is 0.177 assuming chi-square distribution with 1 df.




3. PRINCIPLE COMPONENTS ANALYSIS (PCA) OUTPUTS Matrix to be Factored ELEVATION_C

HART- _DATUM__M_





ELEVATION_CHART_DATUM__M_

1.000

ANNUAL_EXPOSURE__HR__ 0.941 1.000

DISTANCE_FROM_CANOE_PASS__M_

0.457 0.437 1.000

DISTANCE_FROM_SHORE__M_ -0.845 -0.790 -0.784 1.000

TOTAL_HARPACTICOIDA 0.505 0.512 0.467 -0.569 1.000

TOTAL_NEMATODA 0.352 0.330 0.564 -0.480 0.488

TOTAL_OLIGOCHAETA 0.360 0.351 0.394 -0.490 0.574

TOTAL_POLYCHAETA 0.716 0.740 0.358 -0.643 0.653

TOTAL_INVERT_DENSITY 0.493 0.487 0.651 -0.641 0.733

TOTAL_INVERT_BIOMASS 0.397 0.401 0.381 -0.481 0.712

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL

0.667 0.665 0.631 -0.742 0.507

MACROFAUNA_TOTAL_BIOMASS_G_M2

0.225 0.227 0.110 -0.236 0.330

MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS

0.481 0.475 0.641 -0.629 0.731

MEIOFAUNA_TOTAL_BIOMASS_G_M2

0.525 0.540 0.618 -0.670 0.820

GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4

0.480 0.456 -0.113 -0.312 0.458

GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_

0.468 0.446 -0.047 -0.338 0.427

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_

-0.492 -0.475 -0.024 0.400 -0.479

TOC_TOTAL_ORGANIC_CARBON_PERCENT

0.508 0.471 0.076 -0.466 0.585

ADJUSTED_AMMONIA 0.368 0.345 0.758 -0.636 0.186

ADJUSTED_BROMIDE 0.475 0.476 0.889 -0.752 0.378

ADJUSTED_CHLORIDE 0.503 0.492 0.900 -0.781 0.410

ADJUSTED_SO4 0.616 0.605 0.842 -0.818 0.439

ADJUSTED__PHOSPHATE 0.408 0.364 0.636 -0.539 0.014

ADJUSTED_POTASSIUM 0.643 0.619 0.526 -0.734 0.639

ADJUSTED_S 0.737 0.746 0.593 -0.787 0.707


Matrix to be Factored (Contd.) TOTAL_NEMA

TODA TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A




ANNUAL_EXPOSURE__HR__


DISTANCE_FROM_SHORE__M_

TOTAL_HARPACTICOIDA

TOTAL_NEMATODA 1.000

TOTAL_OLIGOCHAETA 0.368 1.000

TOTAL_POLYCHAETA 0.295 0.396 1.000

TOTAL_INVERT_DENSITY 0.890 0.554 0.531 1.000

TOTAL_INVERT_BIOMASS 0.544 0.542 0.591 0.739 1.000


0.338 0.384 0.733 0.541 0.600


0.111 0.333 0.438 0.301 0.758


0.893 0.554 0.516 1.000 0.731


0.763 0.617 0.621 0.942 0.765


0.047 0.534 0.559 0.255 0.492


0.017 0.519 0.542 0.245 0.502


-0.088 -0.558 -0.590 -0.314 -0.554


0.127 0.616 0.595 0.366 0.572

ADJUSTED_AMMONIA 0.505 0.171 0.193 0.458 0.247

ADJUSTED_BROMIDE 0.510 0.312 0.305 0.594 0.341

ADJUSTED_CHLORIDE 0.541 0.327 0.326 0.626 0.374

ADJUSTED_SO4 0.448 0.309 0.440 0.577 0.391

ADJUSTED__PHOSPHATE 0.363 0.017 0.211 0.305 0.037

ADJUSTED_POTASSIUM 0.387 0.726 0.677 0.610 0.590

ADJUSTED_S 0.484 0.600 0.725 0.690 0.576




Matrix to be Factored (Contd.) MACROFAUNA

_TOTA- L_BENTHIC_INDIV- IDUAL









TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA

TOTAL_INVERT_DENSITY

TOTAL_INVERT_BIOMASS


1.000


0.568 1.000


0.520 0.288 1.000


0.575 0.284 0.941 1.000


0.272 0.443 0.253 0.383 1.000


0.381 0.524 0.241 0.358 0.938


-0.421 -0.547 -0.310 -0.429 -0.947


0.374 0.404 0.362 0.521 0.932

ADJUSTED_AMMONIA 0.481 0.007 0.448 0.393 -0.232

ADJUSTED_BROMIDE 0.669 0.102 0.582 0.564 -0.134

ADJUSTED_CHLORIDE 0.689 0.134 0.614 0.593 -0.093

ADJUSTED_SO4 0.772 0.187 0.562 0.570 -0.017

ADJUSTED__PHOSPHATE 0.526 -0.008 0.291 0.201 -0.237


ADJUSTED_S 0.692 0.323 0.682 0.752 0.544


Matrix to be Factored (Contd.) GRAIN_SIZE_

PERC- ENT_SILT_0_063M- M_TO_



ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E





TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA









1.000


-0.985 1.000


0.886 -0.912 1.000

ADJUSTED_AMMONIA -0.211 0.137 -0.062 1.000

ADJUSTED_BROMIDE -0.060 0.003 0.051 0.803 1.000

ADJUSTED_CHLORIDE -0.019 -0.040 0.094 0.802 0.992

ADJUSTED_SO4 0.046 -0.105 0.154 0.759 0.949

ADJUSTED__PHOSPHATE -0.166 0.128 -0.170 0.698 0.737

ADJUSTED_POTASSIUM 0.702 -0.750 0.787 0.323 0.511

ADJUSTED_S 0.562 -0.611 0.641 0.353 0.572




Matrix to be Factored (Contd.) ADJUSTED_CH

LORI- DE

ADJUSTED_SO4

ADJUSTED__PHOSP- HATE


ADJUSTED_S





TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA











ADJUSTED_AMMONIA

ADJUSTED_BROMIDE

ADJUSTED_CHLORIDE 1.000

ADJUSTED_SO4 0.945 1.000

ADJUSTED__PHOSPHATE 0.723 0.731 1.000

ADJUSTED_POTASSIUM 0.532 0.580 0.223 1.000

ADJUSTED_S 0.594 0.642 0.336 0.812 1.000


Latent Roots (Eigenvalues) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2312.931

5.189

2.072

1.183

0.970

0.593

0.375

0.351

0.293

0.202

0.181

0.150

0.104

0.091

0.067

0.065

0.053

0.036

0.032

0.028

0.017

0.011

0.005

Latent Roots (Eigenvalues) (Contd.) 24 25 0.003 0.000

Empirical Upper Bound for the First Eigenvalue : 15.762

Chi-Square Test that All Eigenvalues are Equal

N : 50.000 Chi-Square : 2,316.766 df : 300.000 p-Value : 0.000

Chi-Square Test that the Last 21 Eigenvalues are Equal

Chi-Square : 952.514 df : 236.201 p-Value : 0.000

Latent Vectors (Eigenvectors) 1 2 3 4ELEVATION_CHART_DATUM__M_ 0.215 0.024 0.283 0.211

ANNUAL_EXPOSURE__HR__ 0.212 0.025 0.270 0.201

DISTANCE_FROM_CANOE_PASS__M_ 0.202 -0.247-0.007-0.016

DISTANCE_FROM_SHORE__M_ -0.2470.084 -0.179-0.113

TOTAL_HARPACTICOIDA 0.212 0.087 -0.2150.089

TOTAL_NEMATODA 0.176 -0.131-0.3870.131

TOTAL_OLIGOCHAETA 0.181 0.125 -0.1460.082

TOTAL_POLYCHAETA 0.210 0.119 0.126 -0.025

TOTAL_INVERT_DENSITY 0.232 -0.057-0.3420.061

TOTAL_INVERT_BIOMASS 0.207 0.112 -0.259-0.376

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.226 -0.0450.194 -0.336

MACROFAUNA_TOTAL_BIOMASS_G_M2 0.122 0.165 -0.029-0.719

MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.229 -0.055-0.3540.074

MEIOFAUNA_TOTAL_BIOMASS_G_M2 0.242 0.003 -0.2890.113

GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 0.137 0.359 0.086 0.089

GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ 0.144 0.342 0.131 -0.033

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ -0.162-0.328-0.1080.036




Latent Vectors (Eigenvectors) 1 2 3 4TOC_TOTAL_ORGANIC_CARBON_PERCENT 0.172 0.305 0.057 0.107

ADJUSTED_AMMONIA 0.148 -0.2960.061 -0.049

ADJUSTED_BROMIDE 0.198 -0.2740.080 -0.063

ADJUSTED_CHLORIDE 0.207 -0.2610.068 -0.065

ADJUSTED_SO4 0.215 -0.2270.166 -0.083

ADJUSTED__PHOSPHATE 0.121 -0.2890.242 -0.080


ADJUSTED_S 0.249 0.051 0.067 0.141

Standard Error for Each Eigenvector Element 1 2 3 4ELEVATION_CHART_DATUM__M_ 0.0280.0640.0860.244

ANNUAL_EXPOSURE__HR__ 0.0280.0640.0910.275

DISTANCE_FROM_CANOE_PASS__M_ 0.0400.0400.0650.146

DISTANCE_FROM_SHORE__M_ 0.0220.0510.0490.068

TOTAL_HARPACTICOIDA 0.0290.0580.0740.154

TOTAL_NEMATODA 0.0380.0710.0710.169

TOTAL_OLIGOCHAETA 0.0350.0590.1060.335

TOTAL_POLYCHAETA 0.0310.0540.0890.291

TOTAL_INVERT_DENSITY 0.0260.0630.0360.121

TOTAL_INVERT_BIOMASS 0.0320.0620.1020.105

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.0260.0580.0950.116

MACROFAUNA_TOTAL_BIOMASS_G_M2 0.0430.0670.1830.074

MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.0270.0650.0370.120

MEIOFAUNA_TOTAL_BIOMASS_G_M2 0.0220.0590.0420.090

GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 0.0550.0300.0670.090

GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ 0.0530.0360.0670.125

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_0.0510.0350.0650.126

TOC_TOTAL_ORGANIC_CARBON_PERCENT 0.0480.0370.0720.153

ADJUSTED_AMMONIA 0.0490.0420.0780.144

ADJUSTED_BROMIDE 0.0430.0380.0630.151

ADJUSTED_CHLORIDE 0.0410.0390.0610.144

ADJUSTED_SO4 0.0370.0450.0530.087

ADJUSTED__PHOSPHATE 0.0510.0530.0780.125

ADJUSTED_POTASSIUM 0.0250.0480.0700.211

ADJUSTED_S 0.0190.0480.0580.066


Component Loadings 1 2 3 4ELEVATION_CHART_DATUM__M_ 0.775 0.054 0.407 0.230

ANNUAL_EXPOSURE__HR__ 0.761 0.057 0.389 0.219

DISTANCE_FROM_CANOE_PASS__M_ 0.727 -0.563-0.011-0.017

DISTANCE_FROM_SHORE__M_ -0.8870.191 -0.258-0.123

TOTAL_HARPACTICOIDA 0.763 0.199 -0.3100.096

TOTAL_NEMATODA 0.632 -0.299-0.5570.143

TOTAL_OLIGOCHAETA 0.651 0.285 -0.2100.089

TOTAL_POLYCHAETA 0.757 0.272 0.181 -0.028

TOTAL_INVERT_DENSITY 0.835 -0.130-0.4930.066

TOTAL_INVERT_BIOMASS 0.745 0.256 -0.372-0.409

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.813 -0.1010.279 -0.366

MACROFAUNA_TOTAL_BIOMASS_G_M2 0.438 0.376 -0.042-0.782

MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.824 -0.126-0.5090.080

MEIOFAUNA_TOTAL_BIOMASS_G_M2 0.871 0.006 -0.4150.123

GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 0.494 0.819 0.124 0.096

GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ 0.517 0.779 0.188 -0.036

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ -0.582-0.746-0.1560.039

TOC_TOTAL_ORGANIC_CARBON_PERCENT 0.619 0.695 0.082 0.116


ADJUSTED_BROMIDE 0.713 -0.6240.115 -0.069

ADJUSTED_CHLORIDE 0.743 -0.5940.098 -0.071

ADJUSTED_SO4 0.773 -0.5160.238 -0.090

ADJUSTED__PHOSPHATE 0.434 -0.6590.348 -0.087


ADJUSTED_S 0.895 0.116 0.096 0.153

Variance Explained by Components 1 2 3 4 12.931 5.189 2.072 1.183

Percent of Total Variance Explained 1 2 3 4 51.724 20.754 8.287 4.730

Rotated Loading Matrix (VARIMAX, Gamma = 1.000000) 1 2 3 4ELEVATION_CHART_DATUM__M_ 0.570 0.683 0.127 -0.119

ANNUAL_EXPOSURE__HR__ 0.554 0.668 0.132 -0.109

DISTANCE_FROM_CANOE_PASS__M_ 0.805 0.020 0.443 0.032

DISTANCE_FROM_SHORE__M_ -0.748-0.499-0.3080.030




Rotated Loading Matrix (VARIMAX, Gamma = 1.000000) 1 2 3 4TOTAL_HARPACTICOIDA 0.167 0.487 0.670 0.118

TOTAL_NEMATODA 0.319 -0.0180.846 -0.022

TOTAL_OLIGOCHAETA 0.086 0.517 0.520 0.111

TOTAL_POLYCHAETA 0.336 0.687 0.238 0.194


TOTAL_INVERT_BIOMASS 0.135 0.387 0.615 0.617

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.696 0.424 0.155 0.440

MACROFAUNA_TOTAL_BIOMASS_G_M2 0.050 0.336 0.102 0.906

MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.340 0.218 0.888 0.101

MEIOFAUNA_TOTAL_BIOMASS_G_M2 0.314 0.381 0.835 0.085

GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 -0.2260.922 0.123 0.146

GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ -0.1450.904 0.062 0.261

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_0.099 -0.907-0.126-0.273

TOC_TOTAL_ORGANIC_CARBON_PERCENT -0.0880.897 0.238 0.129


ADJUSTED_BROMIDE 0.897 0.003 0.329 0.052

ADJUSTED_CHLORIDE 0.887 0.036 0.356 0.067

ADJUSTED_SO4 0.914 0.154 0.248 0.090

ADJUSTED__PHOSPHATE 0.861 -0.100-0.014-0.014


ADJUSTED_S 0.470 0.666 0.427 0.024

"Variance" Explained by Rotated Components 1 2 3 47.208 6.263 5.267 1.722

Percent of Total Variance Explained 1 2 3 4 28.833 25.054 21.068 6.889


Differences: Original Minus Fitted Correlations or Covariances ELEVATION_C

HART- _DATUM__M_






0.179



-0.068 -0.077 0.154

DISTANCE_FROM_SHORE__M_ -0.035 0.002 -0.036 0.095

TOTAL_HARPACTICOIDA 0.006 0.019 0.022 0.002 0.272

TOTAL_NEMATODA 0.073 0.052 -0.067 0.011 -0.121

TOTAL_OLIGOCHAETA -0.094 -0.098 0.081 -0.011 -0.053

TOTAL_POLYCHAETA 0.048 0.084 -0.038 0.020 0.080

TOTAL_INVERT_DENSITY 0.039 0.036 -0.034 0.006 -0.038

TOTAL_INVERT_BIOMASS 0.052 0.054 -0.028 -0.016 0.016


0.013 0.023 -0.021 0.025 0.029


0.061 0.060 -0.010 -0.026 -0.017


0.038 0.035 -0.033 0.005 -0.039


-0.009 0.011 -0.015 0.009 0.013


-0.019 -0.036 -0.009 0.014 -0.053


-0.043 -0.057 0.016 0.016 -0.061


0.054 0.063 -0.021 -0.009 0.062


-0.070 -0.097 0.020 -0.014 -0.012

ADJUSTED_AMMONIA -0.033 -0.046 -0.009 -0.018 -0.054

ADJUSTED_BROMIDE -0.074 -0.060 0.019 0.020 0.000

ADJUSTED_CHLORIDE -0.064 -0.063 0.025 0.009 -0.002

ADJUSTED_SO4 -0.031 -0.027 -0.009 0.017 0.035

ADJUSTED__PHOSPHATE -0.014 -0.046 -0.048 0.051 -0.070

ADJUSTED_POTASSIUM -0.111 -0.122 0.049 0.025 -0.051

ADJUSTED_S -0.036 -0.013 0.011 0.028 0.016




Differences: Original Minus Fitted Correlations or Covariances (Contd.) TOTAL_NEMA

TODA TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A







TOTAL_HARPACTICOIDA


TOTAL_OLIGOCHAETA -0.087 0.443

TOTAL_POLYCHAETA 0.003 -0.134 0.320

TOTAL_INVERT_DENSITY 0.040 -0.062 0.025 0.038

TOTAL_INVERT_BIOMASS 0.001 -0.057 0.014 -0.006 0.074


0.002 -0.025 0.085 0.011 -0.025


0.035 0.001 -0.010 0.015 0.000


0.039 -0.060 0.021 0.038 -0.008


-0.035 -0.050 0.039 0.003 0.010


0.035 -0.004 -0.057 0.003 0.000


0.034 0.004 -0.096 0.010 -0.027


-0.035 -0.002 0.082 -0.005 0.029


-0.027 0.022 -0.074 -0.028 0.011

ADJUSTED_AMMONIA 0.023 0.039 -0.045 -0.028 0.033

ADJUSTED_BROMIDE -0.053 0.056 -0.087 -0.022 -0.016

ADJUSTED_CHLORIDE -0.040 0.040 -0.094 -0.019 -0.020

ADJUSTED_SO4 -0.049 0.012 -0.050 -0.012 0.000

ADJUSTED__PHOSPHATE 0.098 0.004 -0.004 0.034 -0.024

ADJUSTED_POTASSIUM -0.036 0.100 -0.066 -0.032 -0.036

ADJUSTED_S -0.014 -0.009 0.004 -0.005 -0.022

Differences: Original Minus Fitted Correlations or Covariances (Contd.) MACROFAUNA







Differences: Original Minus Fitted Correlations or Covariances (Contd.) MACROFAUNA










TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA




0.117


-0.025 0.053


0.009 0.016 0.039


0.028 -0.021 0.002 0.053


-0.045 0.000 0.004 -0.013 0.062


-0.026 -0.015 0.012 -0.014 0.026


0.035 0.013 -0.006 0.013 -0.034


-0.039 -0.034 -0.028 -0.003 0.036

ADJUSTED_AMMONIA -0.066 -0.012 -0.028 -0.024 0.050

ADJUSTED_BROMIDE -0.030 -0.025 -0.020 0.002 0.017

ADJUSTED_CHLORIDE -0.029 -0.020 -0.017 -0.001 0.021

ADJUSTED_SO4 -0.008 -0.018 -0.012 0.010 0.003

ADJUSTED__PHOSPHATE -0.022 -0.003 0.035 -0.018 0.053

ADJUSTED_POTASSIUM 0.002 -0.048 -0.031 0.006 0.020

ADJUSTED_S 0.005 0.011 -0.004 -0.007 -0.019

Differences: Original Minus Fitted Correlations or Covariances (Contd.) GRAIN_SIZE_




ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E

ELEVATION_CHART_DATUM__




Differences: Original Minus Fitted Correlations or Covariances (Contd.) GRAIN_SIZE_




ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E

M_




TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA









0.090


-0.072 0.078


0.014 -0.025 0.114

ADJUSTED_AMMONIA 0.019 -0.039 0.074 0.251

ADJUSTED_BROMIDE 0.033 -0.027 0.042 -0.011 0.085

ADJUSTED_CHLORIDE 0.037 -0.032 0.047 -0.007 0.076

ADJUSTED_SO4 0.000 0.001 0.025 -0.027 0.042

ADJUSTED__PHOSPHATE 0.054 -0.053 0.001 -0.012 -0.029


ADJUSTED_S -0.004 0.006 -0.019 -0.046 0.006

Differences: Original Minus Fitted Correlations or Covariances (Contd.) ADJUSTED_CH

LORI- DE

ADJUSTED_SO4



ADJUSTED_S



DISTANCE_FROM_CANOE_PASS_


Differences: Original Minus Fitted Correlations or Covariances (Contd.) ADJUSTED_CH

LORI- DE

ADJUSTED_SO4



ADJUSTED_S

_M_


TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA











ADJUSTED_AMMONIA

ADJUSTED_BROMIDE



ADJUSTED__PHOSPHATE -0.031 -0.035 0.249

ADJUSTED_POTASSIUM 0.043 0.032 -0.007 0.157

ADJUSTED_S 0.000 0.001 0.004 -0.020 0.153


Factor Coefficients 1 2 3 4ELEVATION_CHART_DATUM__M_ 0.095 0.155 -0.093-0.196

ANNUAL_EXPOSURE__HR__ 0.092 0.149 -0.089-0.186

DISTANCE_FROM_CANOE_PASS__M_ 0.106 -0.0480.040 0.002

DISTANCE_FROM_SHORE__M_ -0.110-0.0810.043 0.109

TOTAL_HARPACTICOIDA -0.0640.023 0.166 -0.043

TOTAL_NEMATODA -0.059-0.0850.272 -0.088

TOTAL_OLIGOCHAETA -0.0590.046 0.120 -0.041

TOTAL_POLYCHAETA 0.038 0.097 -0.0480.033

TOTAL_INVERT_DENSITY -0.053-0.0560.241 -0.020

TOTAL_INVERT_BIOMASS -0.047-0.0710.111 0.371

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.136 0.007 -0.1310.287

MACROFAUNA_TOTAL_BIOMASS_G_M2 0.020 -0.076-0.0910.656

MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS -0.058-0.0560.249 -0.030

MEIOFAUNA_TOTAL_BIOMASS_G_M2 -0.057-0.0120.219 -0.064

GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 -0.0670.171 -0.025-0.046

GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ -0.0370.155 -0.0690.056

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ 0.036 -0.1470.054 -0.060

TOC_TOTAL_ORGANIC_CARBON_PERCENT -0.0550.156 0.002 -0.063




Factor Coefficients 1 2 3 4ADJUSTED_AMMONIA 0.135 -0.061-0.0110.019

ADJUSTED_BROMIDE 0.143 -0.044-0.0170.035

ADJUSTED_CHLORIDE 0.137 -0.042-0.0100.039

ADJUSTED_SO4 0.159 -0.009-0.0680.050

ADJUSTED__PHOSPHATE 0.185 -0.023-0.1230.031

ADJUSTED_POTASSIUM 0.019 0.113 0.006 -0.073

ADJUSTED_S 0.034 0.100 0.021 -0.112

Coefficients for Standardized Factor Scores 1 2 3 4ELEVATION_CHART_DATUM__M_ 0.095 0.155 -0.093-0.196

ANNUAL_EXPOSURE__HR__ 0.092 0.149 -0.089-0.186

DISTANCE_FROM_CANOE_PASS__M_ 0.106 -0.0480.040 0.002

DISTANCE_FROM_SHORE__M_ -0.110-0.0810.043 0.109

TOTAL_HARPACTICOIDA -0.0640.023 0.166 -0.043

TOTAL_NEMATODA -0.059-0.0850.272 -0.088

TOTAL_OLIGOCHAETA -0.0590.046 0.120 -0.041

TOTAL_POLYCHAETA 0.038 0.097 -0.0480.033

TOTAL_INVERT_DENSITY -0.053-0.0560.241 -0.020

TOTAL_INVERT_BIOMASS -0.047-0.0710.111 0.371

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.136 0.007 -0.1310.287

MACROFAUNA_TOTAL_BIOMASS_G_M2 0.020 -0.076-0.0910.656

MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS -0.058-0.0560.249 -0.030

MEIOFAUNA_TOTAL_BIOMASS_G_M2 -0.057-0.0120.219 -0.064

GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 -0.0670.171 -0.025-0.046

GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ -0.0370.155 -0.0690.056

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_0.036 -0.1470.054 -0.060

TOC_TOTAL_ORGANIC_CARBON_PERCENT -0.0550.156 0.002 -0.063

ADJUSTED_AMMONIA 0.135 -0.061-0.0110.019

ADJUSTED_BROMIDE 0.143 -0.044-0.0170.035

ADJUSTED_CHLORIDE 0.137 -0.042-0.0100.039

ADJUSTED_SO4 0.159 -0.009-0.0680.050

ADJUSTED__PHOSPHATE 0.185 -0.023-0.1230.031

ADJUSTED_POTASSIUM 0.019 0.113 0.006 -0.073

ADJUSTED_S 0.034 0.100 0.021 -0.112

Standardized Scores have been saved.


4. PCA AND BIOFILM BIOMASS CORRELATION ANALYSIS Number of Non-Missing Cases: 50

Means

PCA_1

PCA_2

PCA_3

PCA_4

BIOFILM_TOTAL_C- ARBOHYDRATE_MG_- M2

BIOFILM_TOTAL_O-RGANIC_CARBON_M- G_M2

BIOFILM_PIGMENT-S_CHLOROPHYLL_A- _MG_M


0.000 0.000 0.000 0.000 88.407 9.900 3.922 3.710

Pearson Correlation Matrix


BIOFILM_TOTAL_O-RGANIC_CARBON_M-G_M2

BIOFILM_PIGMENT-S_CHLOROPHYLL_A-_MG_M

BIOFILM_PIGMENT- S_FUCOXANTHIN_M-G_M2

PCA_10.203 0.100 0.438 0.502

PCA_20.140 0.739 0.545 0.446

PCA_30.213 0.017 0.206 0.218

PCA_40.107 0.207 0.010 0.031




Bartlett Chi-Square Statistic : 251.732

df : 16

p-Value : 0.000


Matrix of Bonferroni Probabilities


BIOFILM_TOTAL_O-RGANIC_CARBON_M-G_M2

BIOFILM_PIGMENT-S_CHLOROPHYLL_A-_MG_M

BIOFILM_PIGMENT- S_FUCOXANTHIN_M-G_M2

PCA_11.000 1.000 0.023 0.003

PCA_21.000 0.000 0.001 0.019

PCA_31.000 1.000 1.000 1.000

PCA_41.000 1.000 1.000 1.000




5. PEARSON CORRELATION MATRIX Number of Non-Missing Cases: 50


ELEVATION_CHART- _DATUM__M_






M_

1.000


DISTANCE_FROM_CANOE_PAS

S__M_

0.457 0.437 1.000

DISTANCE_FROM_SHORE__M_ -0.845 -0.790 -0.784 1.000

TOTAL_HARPACTICOIDA 0.505 0.512 0.467 -0.569 1.000

TOTAL_NEMATODA 0.352 0.330 0.564 -0.480 0.488

TOTAL_OLIGOCHAETA 0.360 0.351 0.394 -0.490 0.574

TOTAL_POLYCHAETA 0.716 0.740 0.358 -0.643 0.653

TOTAL_INVERT_DENSITY 0.493 0.487 0.651 -0.641 0.733

TOTAL_INVERT_BIOMASS 0.397 0.401 0.381 -0.481 0.712

MACROFAUNA_TOTAL_BENTHI

C_INDIVIDUAL

0.667 0.665 0.631 -0.742 0.507

MACROFAUNA_TOTAL_BIOMAS

S_G_M2

0.225 0.227 0.110 -0.236 0.330

MEIOFAUNA_TOTAL_BENTHIC_

INDIVIDUALS

0.481 0.475 0.641 -0.629 0.731

MEIOFAUNA_TOTAL_BIOMASS_

G_M2

0.525 0.540 0.618 -0.670 0.820

GRAIN_SIZE_PERCENT_CLAY_ 0.480 0.456 -0.113 -0.312 0.458








LESS_THAN_4

GRAIN_SIZE_PERCENT_SILT_0

_063MM_TO_

0.468 0.446 -0.047 -0.338 0.427

GRAIN_SIZE_PERCENT_SAND_

2_0MM_TO_0_

-0.492 -0.475 -0.024 0.400 -0.479

TOC_TOTAL_ORGANIC_CARBO

N_PERCENT

0.508 0.471 0.076 -0.466 0.585

ADJUSTED_AMMONIA 0.368 0.345 0.758 -0.636 0.186

ADJUSTED_BROMIDE 0.475 0.476 0.889 -0.752 0.378

ADJUSTED_CHLORIDE 0.503 0.492 0.900 -0.781 0.410

ADJUSTED_SO4 0.616 0.605 0.842 -0.818 0.439

ADJUSTED__PHOSPHATE 0.408 0.364 0.636 -0.539 0.014

ADJUSTED_POTASSIUM 0.643 0.619 0.526 -0.734 0.639

ADJUSTED_S 0.737 0.746 0.593 -0.787 0.707

Pearson Correlation Matrix (Contd.)

TOTAL_NEMATODA

TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A



ELEVATION_CHART_DATUM__M

_



S__M_






TOTAL_NEMATODA

TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A



TOTAL_HARPACTICOIDA







C_INDIVIDUAL

0.338 0.384 0.733 0.541 0.600


S_G_M2

0.111 0.333 0.438 0.301 0.758

MEIOFAUNA_TOTAL_BENTHIC_I

NDIVIDUALS

0.893 0.554 0.516 1.000 0.731


G_M2

0.763 0.617 0.621 0.942 0.765

GRAIN_SIZE_PERCENT_CLAY_L

ESS_THAN_4

0.047 0.534 0.559 0.255 0.492

GRAIN_SIZE_PERCENT_SILT_0_

063MM_TO_

0.017 0.519 0.542 0.245 0.502

GRAIN_SIZE_PERCENT_SAND_2

_0MM_TO_0_

-0.088 -0.558 -0.590 -0.314 -0.554


N_PERCENT

0.127 0.616 0.595 0.366 0.572






TOTAL_NEMATODA

TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A



ADJUSTED_SO4 0.448 0.309 0.440 0.577 0.391



ADJUSTED_S 0.484 0.600 0.725 0.690 0.576








M_



S__M_


TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA




C_INDIVIDUAL

1.000










MACROFAUNA_TOTAL_BIOMA

SS_G_M2

0.568 1.000


INDIVIDUALS

0.520 0.288 1.000

MEIOFAUNA_TOTAL_BIOMASS

_G_M2

0.575 0.284 0.941 1.000

GRAIN_SIZE_PERCENT_CLAY_

LESS_THAN_4

0.272 0.443 0.253 0.383 1.000


_063MM_TO_

0.381 0.524 0.241 0.358 0.938


2_0MM_TO_0_

-0.421 -0.547 -0.310 -0.429 -0.947


N_PERCENT

0.374 0.404 0.362 0.521 0.932

ADJUSTED_AMMONIA 0.481 0.007 0.448 0.393 -0.232

ADJUSTED_BROMIDE 0.669 0.102 0.582 0.564 -0.134

ADJUSTED_CHLORIDE 0.689 0.134 0.614 0.593 -0.093

ADJUSTED_SO4 0.772 0.187 0.562 0.570 -0.017

ADJUSTED__PHOSPHATE 0.526 -0.008 0.291 0.201 -0.237


ADJUSTED_S 0.692 0.323 0.682 0.752 0.544






ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E


M_



S__M_


TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA




C_INDIVIDUAL


S_G_M2


INDIVIDUALS


G_M2


LESS_THAN_4

GRAIN_SIZE_PERCENT_SILT_0 1.000








ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E

_063MM_TO_


2_0MM_TO_0_

-0.985 1.000


N_PERCENT

0.886 -0.912 1.000

ADJUSTED_AMMONIA -0.211 0.137 -0.062 1.000

ADJUSTED_BROMIDE -0.060 0.003 0.051 0.803 1.000

ADJUSTED_CHLORIDE -0.019 -0.040 0.094 0.802 0.992

ADJUSTED_SO4 0.046 -0.105 0.154 0.759 0.949

ADJUSTED__PHOSPHATE -0.166 0.128 -0.170 0.698 0.737


ADJUSTED_S 0.562 -0.611 0.641 0.353 0.572


ADJUSTED_CHLORI- DE

ADJUSTED_SO4



ADJUSTED_S




_M_


TOTAL_HARPACTICOIDA



ADJUSTED_CHLORI- DE

ADJUSTED_SO4



ADJUSTED_S

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA



MACROFAUNA_TOTAL_BENTHIC_I

NDIVIDUAL

MACROFAUNA_TOTAL_BIOMASS_

G_M2

MEIOFAUNA_TOTAL_BENTHIC_IN

DIVIDUALS

MEIOFAUNA_TOTAL_BIOMASS_G_

M2

GRAIN_SIZE_PERCENT_CLAY_LE

SS_THAN_4

GRAIN_SIZE_PERCENT_SILT_0_06

3MM_TO_

GRAIN_SIZE_PERCENT_SAND_2_

0MM_TO_0_

TOC_TOTAL_ORGANIC_CARBON_

PERCENT

ADJUSTED_AMMONIA

ADJUSTED_BROMIDE








ADJUSTED_CHLORI- DE

ADJUSTED_SO4



ADJUSTED_S


ADJUSTED_S 0.594 0.642 0.336 0.812 1.000

WARNING No Quick graph is displayed since the number of variables is greater than 20.

Bartlett Chi-Square Statistic : 2,260.029

df : 300

p-Value : 0.000








M_

0.000



S__M_

0.254 0.458 0.000

DISTANCE_FROM_SHORE__M_ 0.000 0.000 0.000 0.000

TOTAL_HARPACTICOIDA 0.055 0.044 0.190 0.005 0.000

TOTAL_NEMATODA 1.000 1.000 0.006 0.126 0.098

TOTAL_OLIGOCHAETA 1.000 1.000 1.000 0.090 0.004

TOTAL_POLYCHAETA 0.000 0.000 1.000 0.000 0.000








TOTAL_INVERT_DENSITY 0.082 0.101 0.000 0.000 0.000



C_INDIVIDUAL

0.000 0.000 0.000 0.000 0.051


S_G_M2

1.000 1.000 1.000 1.000 1.000


INDIVIDUALS

0.123 0.149 0.000 0.000 0.000


G_M2

0.027 0.015 0.001 0.000 0.000


LESS_THAN_4

0.124 0.264 1.000 1.000 0.249


_063MM_TO_

0.181 0.349 1.000 1.000 0.593


2_0MM_TO_0_

0.086 0.147 1.000 1.000 0.129


N_PERCENT

0.050 0.165 1.000 0.195 0.002




ADJUSTED_SO4 0.001 0.001 0.000 0.000 0.424



ADJUSTED_S 0.000 0.000 0.002 0.000 0.000




Matrix of Bonferroni Probabilities (Contd.)

TOTAL_NEMATODA

TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A



ELEVATION_CHART_DATUM__M

_



S__M_


TOTAL_HARPACTICOIDA







C_INDIVIDUAL

1.000 1.000 0.000 0.015 0.001


S_G_M2

1.000 1.000 0.442 1.000 0.000

MEIOFAUNA_TOTAL_BENTHIC_I

NDIVIDUALS

0.000 0.009 0.037 0.000 0.000


G_M2

0.000 0.001 0.000 0.000 0.000

GRAIN_SIZE_PERCENT_CLAY_L

ESS_THAN_4

1.000 0.020 0.007 1.000 0.085

GRAIN_SIZE_PERCENT_SILT_0_

063MM_TO_

1.000 0.033 0.015 1.000 0.062

GRAIN_SIZE_PERCENT_SAND_2

_0MM_TO_0_

1.000 0.008 0.002 1.000 0.009



TOTAL_NEMATODA

TOTAL_OLIGOCHAE- TA

TOTAL_POLYCHAET- A




N_PERCENT

1.000 0.001 0.002 1.000 0.004




ADJUSTED_SO4 0.332 1.000 0.411 0.003 1.000



ADJUSTED_S 0.109 0.001 0.000 0.000 0.004








M_



S__M_


TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA










TOTAL_POLYCHAETA




C_INDIVIDUAL

0.000

MACROFAUNA_TOTAL_BIOMA

SS_G_M2

0.005 0.000


INDIVIDUALS

0.032 1.000 0.000

MEIOFAUNA_TOTAL_BIOMASS

_G_M2

0.004 1.000 0.000 0.000


LESS_THAN_4

1.000 0.384 1.000 1.000 0.000


_063MM_TO_

1.000 0.028 1.000 1.000 0.000


2_0MM_TO_0_

0.697 0.012 1.000 0.563 0.000


N_PERCENT

1.000 1.000 1.000 0.031 0.000




ADJUSTED_SO4 0.000 1.000 0.007 0.005 1.000










ADJUSTED_S 0.000 1.000 0.000 0.000 0.013





ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E


M_



S__M_


TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA




C_INDIVIDUAL









ADJUSTED_AMMONI- A

ADJUSTED_BROMID- E

S_G_M2


INDIVIDUALS


G_M2


LESS_THAN_4


_063MM_TO_

0.000


2_0MM_TO_0_

0.000 0.000


N_PERCENT

0.000 0.000 0.000

ADJUSTED_AMMONIA 1.000 1.000 1.000 0.000



ADJUSTED_SO4 1.000 1.000 1.000 0.000 0.000



ADJUSTED_S 0.006 0.001 0.000 1.000 0.004



ADJUSTED_CHLORI- DE

ADJUSTED_SO4



ADJUSTED_S




_M_


TOTAL_HARPACTICOIDA

TOTAL_NEMATODA

TOTAL_OLIGOCHAETA

TOTAL_POLYCHAETA



MACROFAUNA_TOTAL_BENTHIC_I

NDIVIDUAL

MACROFAUNA_TOTAL_BIOMASS_

G_M2

MEIOFAUNA_TOTAL_BENTHIC_IN

DIVIDUALS

MEIOFAUNA_TOTAL_BIOMASS_G_

M2

GRAIN_SIZE_PERCENT_CLAY_LE

SS_THAN_4

GRAIN_SIZE_PERCENT_SILT_0_06

3MM_TO_


0MM_TO_0_

TOC_TOTAL_ORGANIC_CARBON_

PERCENT





ADJUSTED_CHLORI- DE

ADJUSTED_SO4



ADJUSTED_S

ADJUSTED_AMMONIA

ADJUSTED_BROMIDE





ADJUSTED_S 0.002 0.000 1.000 0.000 0.000

Matrix has been saved.


6. CHLOROPHYLL A MULTIPLE REGRESSION

▼OLS Regression

Stepwise Selection of Variables

Step number : 0

R : 0.777

R-Square : 0.604

In Effect Coefficient Standard ErrorStd. Coefficient

TolerancedfF-Ratiop-Value

1 Constant

2 ELEVATION_CHART-

_DATUM__M_

0.031 0.037 0.146 0.327 1 0.689 0.412

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.638 0.147 1 5.910 0.020

4 TOTAL_OLIGOCHAE-

TA

-0.008 0.014 -0.095 0.370 1 0.328 0.570

5 TOTAL_POLYCHAET-

A

0.001 0.001 0.326 0.220 1 2.301 0.137

6 TOTAL_INVERT_DE-

NSITY

-0.092 0.106 -0.146 0.361 1 0.757 0.390

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.085 0.049 -0.508 0.119 1 3.030 0.090

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.021 0.108 0.038 0.274 1 0.039 0.844

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.190 0.104 -0.486 0.144 1 3.347 0.075




In Effect CoefficientStandard ErrorStd. Coefficient


10 ADJUSTED__PHOSP-

HATE

0.151 0.269 0.093 0.368 1 0.313 0.579

11 ADJUSTED_POTASS-

IUM

0.000 0.002 0.103 0.091 1 0.095 0.759

Out EffectPartial Correlation


none

Information Criteria

AIC 68.744

AIC (Corrected) 77.176

Schwarz's BIC 91.688

Backward Stepwise Selection

Dependent Variable : BIOFILM_PIGMENTS_CHLOROPHYLL_A_MG_M

Minimum Tolerance for Entry into Model : 0.000

Maximum Number of Steps : 15

Alpha-to-Enter : 0.150

Alpha-to-Remove : 0.150

Step number : 1

R : 0.777

R-Square : 0.604

Mallows' Cp : 9.039

Term Removed : MACROFAUNA_TOTAL_BIOMASS_G_M2




1 Constant

2 ELEVATION_CHART-

_DATUM__M_

0.029 0.035 0.135 0.365 1 0.671 0.418

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.639 0.147 1 6.067 0.018

4 TOTAL_OLIGOCHAE-

TA

-0.007 0.014 -0.087 0.390 1 0.300 0.587

5 TOTAL_POLYCHAET-

A

0.001 0.001 0.322 0.222 1 2.322 0.135

6 TOTAL_INVERT_DE-

NSITY

-0.088 0.102 -0.138 0.380 1 0.735 0.396

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.079 0.037 -0.470 0.210 1 4.674 0.037

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.203 0.081 -0.518 0.231 1 6.255 0.017

10 ADJUSTED__PHOSP-

HATE

0.142 0.262 0.087 0.378 1 0.292 0.592

11 ADJUSTED_POTASS-

IUM

0.000 0.001 0.067 0.129 1 0.059 0.810

Out Effect Partial Correlation


8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.032 0.274 1 0.039 0.844





AIC 66.794



Step number : 2

R : 0.777

R-Square : 0.603

Mallows' Cp : 7.097

Term Removed : ADJUSTED_POTASSIUM



1 Constant

2 ELEVATION_CHART-

_DATUM__M_

0.029 0.035 0.136 0.365 1 0.698 0.408

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.670 0.195 1 9.061 0.004

4 TOTAL_OLIGOCHAE-

TA

-0.006 0.012 -0.070 0.496 1 0.248 0.621

5 TOTAL_POLYCHAET-

A

0.001 0.001 0.336 0.239 1 2.780 0.103

6 TOTAL_INVERT_DE-

NSITY

-0.090 0.101 -0.142 0.382 1 0.790 0.379

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.080 0.036 -0.474 0.211 1 4.904 0.032

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

-0.216 0.061 -0.551 0.404 1 12.670 0.001




TO_0_

10ADJUSTED__PHOSP-

HATE

0.136 0.258 0.084 0.382 1 0.276 0.602

OutEffect Partial Correlation


8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.006 0.388 1 0.001 0.971

11 ADJUSTED_POTASS-

IUM

0.038 0.129 1 0.059 0.810


AIC 64.868

AIC (Corrected)70.509


Step number : 3

R : 0.775

R-Square : 0.601

Mallows' Cp : 5.333

Term Removed : TOTAL_OLIGOCHAETA



1 Constant

2 ELEVATION_CHART-

_DATUM__M_

0.030 0.034 0.139 0.366 1 0.749 0.392






3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.651 0.201 1 8.975 0.005

5 TOTAL_POLYCHAET-

A

0.001 0.001 0.342 0.240 1 2.962 0.093

6 TOTAL_INVERT_DE-

NSITY

-0.105 0.095 -0.167 0.425 1 1.243 0.271

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.080 0.036 -0.474 0.211 1 4.979 0.031

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.202 0.054 -0.516 0.505 1 14.179 0.001

10 ADJUSTED__PHOSP-

HATE

0.156 0.252 0.096 0.392 1 0.384 0.539



4 TOTAL_OLIGOCHAE-

TA

-0.077 0.496 1 0.248 0.621

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.010 0.390 1 0.005 0.947

11 ADJUSTED_POTASS-

IUM

-0.002 0.164 1 0.000 0.990


AIC 63.169




Step number : 4

R : 0.773

R-Square : 0.597

Mallows' Cp : 3.692

Term Removed : ADJUSTED__PHOSPHATE



1 Constant

2 ELEVATION_CHART-

_DATUM__M_

0.034 0.033 0.159 0.381 1 1.035 0.315

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.712 0.254 1 13.784 0.001

5 TOTAL_POLYCHAET-

A

0.001 0.001 0.341 0.240 1 2.974 0.092

6 TOTAL_INVERT_DE-

NSITY

-0.118 0.092 -0.186 0.445 1 1.650 0.206

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.077 0.035 -0.456 0.215 1 4.771 0.034

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.192 0.051 -0.492 0.552 1 14.249 0.000



4 TOTAL_OLIGOCHAE-

TA

-0.091 0.509 1 0.355 0.555

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.003 0.392 1 0.000 0.987

10 ADJUSTED__PHOSP- 0.095 0.392 1 0.384 0.539






HATE

11 ADJUSTED_POTASS-

IUM

-0.017 0.168 1 0.012 0.912


AIC 61.623



Step number : 5

R : 0.766

R-Square : 0.587

Mallows' Cp : 2.647

Term Removed : ELEVATION_CHART_DATUM__M_



1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.777 0.286 1 18.441 0.000

5 TOTAL_POLYCHAET-

A

0.001 0.000 0.429 0.297 1 5.823 0.020

6 TOTAL_INVERT_DE-

NSITY

-0.127 0.091 -0.200 0.449 1 1.922 0.173

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.078 0.035 -0.465 0.215 1 4.959 0.031




9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.205 0.049 -0.524 0.586 1 17.138 0.000



2 ELEVATION_CHART-

_DATUM__M_

0.153 0.381 1 1.035 0.315

4 TOTAL_OLIGOCHAE-

TA

-0.102 0.512 1 0.450 0.506

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.044 0.432 1 0.085 0.773

10 ADJUSTED__PHOSP-

HATE

0.123 0.409 1 0.659 0.422

11 ADJUSTED_POTASS-

IUM

-0.021 0.168 1 0.020 0.890


AIC 60.812



Step number : 6

R : 0.755

R-Square : 0.569

Mallows' Cp : 2.423

Term Removed : TOTAL_INVERT_DENSITY






1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.634 0.426 1 17.864 0.000

5 TOTAL_POLYCHAET-

A

0.001 0.000 0.335 0.347 1 4.075 0.050

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.066 0.034 -0.392 0.230 1 3.688 0.061

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.192 0.049 -0.491 0.607 1 15.293 0.000



2 ELEVATION_CHART-

_DATUM__M_

0.169 0.385 1 1.290 0.262

4 TOTAL_OLIGOCHAE-

TA

-0.166 0.586 1 1.248 0.270

6 TOTAL_INVERT_DE-

NSITY

-0.205 0.449 1 1.922 0.173

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.095 0.463 1 0.398 0.531

10 ADJUSTED__PHOSP-

HATE

0.163 0.431 1 1.207 0.278

11 ADJUSTED_POTASS-

IUM

-0.045 0.171 1 0.090 0.765



AIC 60.951



▼OLS Regression

Eigenvalues of Unit Scaled X'X

1 2 3 4 5

3.728 1.081 0.111 0.070 0.010

Condition Indices

1 2 3 4 5

1.0001.857 5.792 7.300 19.519

Variance Proportions

1 2 3 4 5

CONSTANT 0.0030.0010.0430.2610.693

ADJUSTED_CHLORIDE 0.0040.0010.0550.4480.492

TOTAL_POLYCHAETA 0.0070.0060.6420.0000.346

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.0010.0000.0020.0010.997

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ 0.0000.5090.4520.0010.038




Dependent Variable BIOFILM_PIGMENT-

S_CHLOROPHYLL_A-

_MG_M

N 50

Multiple R 0.755

Squared Multiple R 0.569

Adjusted Squared Multiple R 0.531

Standard Error of Estimate 0.416

Regression Coefficients B = (X'X)-1X'Y

Effect CoefficientStandard ErrorStd. Coefficient

Tolerancet p-Value

CONSTANT 3.619 0.292 0.000 . 12.4060.000

ADJUSTED_CHLORIDE 0.000 0.000 0.634 0.426 4.227 0.000

TOTAL_POLYCHAETA 0.001 0.000 0.335 0.347 2.019 0.050

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL -0.066 0.034 -0.392 0.230 -1.920 0.061

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_-0.192 0.049 -0.491 0.607 -3.911 0.000

Confidence Interval for Regression Coefficients

Effect Coefficient95.0% Confidence Interval VIF

Lower Upper

CONSTANT 3.619 3.032 4.207 .



MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL -0.066 -0.135 0.003 4.353

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_-0.192 -0.291 -0.093 1.647


Correlation Matrix of Regression Coefficients

CONSTANT

ADJUSTED_CHLORI- DE

TOTAL_POLYCHAET- A



CONSTANT 1.000

ADJUSTED_CHLORIDE 0.295 1.000


MACROFAUNA_TOTAL_BENTHIC

_INDIVIDUAL

-0.805 -0.710 -0.616 1.000


0MM_TO_0_

-0.300 -0.259 0.370 0.170 1.000

Analysis of Variance

Source SS df Mean Squares F-Ratiop-Value

Regression10.3064 2.577 14.879 0.000

Residual 7.792 450.173

WARNING

Case 27 is an Outlier (Studentized Residual : 3.271)

Case 35 has large Leverage (Leverage : 0.386)

Durbin-Watson D-Statistic 1.775

First Order Autocorrelation0.105





AIC 60.951



Coefficients have been saved.


7. FUCOXANTHIN MULTIPLE REGRESSION

▼OLS Regression


Step number : 0

R : 0.774

R-Square : 0.599



1 Constant

2 ELEVATION_CHART-

_DATUM__M_

0.000 0.030 -0.002 0.327 1 0.000 0.989

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.740 0.147 1 7.826 0.008

4 TOTAL_OLIGOCHAE-

TA

-0.008 0.011 -0.113 0.370 1 0.460 0.502

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.117 0.220 1 0.292 0.592

6 TOTAL_INVERT_DE-

NSITY

-0.077 0.085 -0.154 0.361 1 0.836 0.366

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.033 0.039 -0.249 0.119 1 0.721 0.401

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.022 0.086 -0.049 0.274 1 0.065 0.800

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.146 0.083 -0.472 0.144 1 3.113 0.086






10 ADJUSTED__PHOSP-

HATE

-0.003 0.215 -0.003 0.368 1 0.000 0.988

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.205 0.091 1 0.370 0.546



none


AIC 46.114




Dependent Variable : BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2





Step number : 1

R : 0.774

R-Square : 0.599


Mallows' Cp : 9.000




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.739 0.151 1 8.218 0.007

4 TOTAL_OLIGOCHAE-

TA

-0.008 0.011 -0.113 0.370 1 0.472 0.496

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.116 0.252 1 0.337 0.565

6 TOTAL_INVERT_DE-

NSITY

-0.077 0.083 -0.154 0.362 1 0.861 0.359

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.033 0.038 -0.250 0.123 1 0.764 0.387

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.022 0.081 -0.049 0.306 1 0.072 0.790

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.146 0.077 -0.470 0.163 1 3.586 0.065

10 ADJUSTED__PHOSP-

HATE

-0.004 0.210 -0.003 0.373 1 0.000 0.986

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.205 0.093 1 0.392 0.535



2 ELEVATION_CHART-

_DATUM__M_

-0.002 0.327 1 0.000 0.989





AIC 44.114



Step number : 2

R : 0.774

R-Square : 0.599

Mallows' Cp : 7.000




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.737 0.196 1 10.893 0.002

4 TOTAL_OLIGOCHAE-

TA

-0.008 0.011 -0.113 0.371 1 0.484 0.491

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.116 0.252 1 0.345 0.560

6 TOTAL_INVERT_DE-

NSITY

-0.077 0.082 -0.154 0.366 1 0.889 0.351

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.033 0.037 -0.251 0.129 1 0.833 0.367

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.021 0.078 -0.048 0.322 1 0.076 0.785

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

-0.146 0.076 -0.470 0.164 1 3.702 0.061




TO_0_

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.206 0.097 1 0.422 0.520



2 ELEVATION_CHART-

_DATUM__M_

-0.002 0.332 1 0.000 0.988

10 ADJUSTED__PHOSP-

HATE

-0.003 0.373 1 0.000 0.986


AIC 42.115



Step number : 3

R : 0.773

R-Square : 0.598

Mallows' Cp : 5.072




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.748 0.202 1 11.826 0.001






4 TOTAL_OLIGOCHAE-

TA

-0.008 0.010 -0.124 0.396 1 0.637 0.429

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.129 0.268 1 0.463 0.500

6 TOTAL_INVERT_DE-

NSITY

-0.083 0.078 -0.165 0.390 1 1.117 0.297

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.040 0.028 -0.299 0.213 1 1.985 0.166

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.134 0.062 -0.432 0.240 1 4.681 0.036

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.251 0.130 1 0.855 0.361



2 ELEVATION_CHART-

_DATUM__M_

0.013 0.379 1 0.007 0.935

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.043 0.322 1 0.076 0.785

10 ADJUSTED__PHOSP-

HATE

0.007 0.393 1 0.002 0.965


AIC 40.207




Step number : 4

R : 0.770

R-Square : 0.594

Mallows' Cp : 3.503

Term Removed : TOTAL_POLYCHAETA



1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.679 0.258 1 12.583 0.001

4 TOTAL_OLIGOCHAE-

TA

-0.010 0.010 -0.149 0.419 1 0.981 0.328

6 TOTAL_INVERT_DE-

NSITY

-0.062 0.072 -0.124 0.461 1 0.747 0.392

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.028 0.022 -0.207 0.358 1 1.621 0.210

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.134 0.062 -0.432 0.240 1 4.729 0.035

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.306 0.143 1 1.412 0.241



2 ELEVATION_CHART-

_DATUM__M_

0.055 0.457 1 0.126 0.725

5 TOTAL_POLYCHAET-

A

0.104 0.268 1 0.463 0.500

8 MACROFAUNA_TOTA- -0.066 0.341 1 0.186 0.668






L_BIOMASS_G_M2

10 ADJUSTED__PHOSP-

HATE

0.016 0.396 1 0.011 0.918


AIC 38.755



Step number : 5

R : 0.766

R-Square : 0.587

Mallows' Cp : 2.189




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.624 0.291 1 12.027 0.001

4 TOTAL_OLIGOCHAE-

TA

-0.013 0.010 -0.185 0.455 1 1.665 0.204

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.028 0.022 -0.210 0.359 1 1.690 0.200

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.132 0.061 -0.427 0.240 1 4.656 0.036




11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.292 0.143 1 1.304 0.260



2 ELEVATION_CHART-

_DATUM__M_

0.039 0.463 1 0.065 0.800

5 TOTAL_POLYCHAET-

A

0.044 0.316 1 0.083 0.774

6 TOTAL_INVERT_DE-

NSITY

-0.131 0.461 1 0.747 0.392

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.087 0.351 1 0.328 0.570

10 ADJUSTED__PHOSP-

HATE

0.034 0.404 1 0.050 0.825


AIC 37.616



Step number : 6

R : 0.758

R-Square : 0.574

Mallows' Cp : 1.381







1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.731 0.399 1 22.490 0.000

4 TOTAL_OLIGOCHAE-

TA

-0.007 0.009 -0.108 0.587 1 0.717 0.401

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.025 0.021 -0.186 0.365 1 1.338 0.254

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.182 0.043 -0.588 0.489 1 17.895 0.000



2 ELEVATION_CHART-

_DATUM__M_

0.062 0.472 1 0.170 0.682

5 TOTAL_POLYCHAET-

A

0.092 0.347 1 0.374 0.544

6 TOTAL_INVERT_DE-

NSITY

-0.118 0.463 1 0.625 0.433

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.165 0.501 1 1.226 0.274

10 ADJUSTED__PHOSP-

HATE

0.020 0.406 1 0.018 0.895

11 ADJUSTED_POTASS-

IUM

0.170 0.143 1 1.304 0.260



AIC 37.077



Step number : 7

R : 0.753

R-Square : 0.568

Mallows' Cp : 0.040




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.687 0.449 1 22.534 0.000

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.023 0.021 -0.170 0.370 1 1.138 0.292

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.162 0.036 -0.523 0.704 1 20.495 0.000



2 ELEVATION_CHART-

_DATUM__M_

0.071 0.475 1 0.229 0.635

4 TOTAL_OLIGOCHAE-

TA

-0.125 0.587 1 0.717 0.401

5 TOTAL_POLYCHAET- 0.087 0.347 1 0.339 0.563






A

6 TOTAL_INVERT_DE-

NSITY

-0.153 0.525 1 1.084 0.303

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.171 0.503 1 1.349 0.252

10 ADJUSTED__PHOSP-

HATE

0.049 0.431 1 0.109 0.743

11 ADJUSTED_POTASS-

IUM

0.089 0.185 1 0.357 0.553


AIC 35.868



Step number : 8

R : 0.746

R-Square : 0.557

Mallows' Cp : -0.920

Term Removed : MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL



1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.572 0.998 1 34.684 0.000

9 GRAIN_SIZE_PERC- -0.141 0.030 -0.456 0.998 1 22.046 0.000




ENT_SAND_2_0MM_-

TO_0_



2 ELEVATION_CHART-

_DATUM__M_

0.019 0.524 1 0.017 0.896

4 TOTAL_OLIGOCHAE-

TA

-0.104 0.596 1 0.506 0.480

5 TOTAL_POLYCHAET-

A

-0.028 0.560 1 0.037 0.848

6 TOTAL_INVERT_DE-

NSITY

-0.150 0.525 1 1.060 0.309

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.155 0.370 1 1.138 0.292

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.225 0.688 1 2.445 0.125

10 ADJUSTED__PHOSP-

HATE

0.013 0.453 1 0.008 0.929

11 ADJUSTED_POTASS-

IUM

0.078 0.186 1 0.283 0.597


AIC 35.089






Step number : 8

R : 0.761

R-Square : 0.579


Term Entered : MACROFAUNA_TOTAL_BIOMASS_G_M2



1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.593 0.980 1 37.635 0.000

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.080 0.051 -0.180 0.688 1 2.445 0.125

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.172 0.035 -0.554 0.700 1 23.486 0.000



2 ELEVATION_CHART-

_DATUM__M_

-0.018 0.510 1 0.015 0.903

4 TOTAL_OLIGOCHAE-

TA

-0.110 0.596 1 0.547 0.464

5 TOTAL_POLYCHAET-

A

0.001 0.550 1 0.000 0.995

6 TOTAL_INVERT_DE-

NSITY

-0.132 0.520 1 0.793 0.378

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.047 0.270 1 0.098 0.756




10 ADJUSTED__PHOSP-

HATE

0.006 0.452 1 0.001 0.971

11 ADJUSTED_POTASS-

IUM

-0.002 0.162 1 0.000 0.991


AIC 34.499



▼OLS Regression


1 2 3 4

2.848 1.020 0.097 0.035

Condition Indices

1 2 3 4

1.0001.671 5.423 9.009


1 2 3 4

CONSTANT 0.0070.0000.0380.955







1 2 3 4



S_FUCOXANTHIN_M-

G_M2

N 50

Multiple R 0.761






Tolerancet p-Value

CONSTANT 3.268 0.191 0.000 . 17.0640.000


MACROFAUNA_TOTAL_BIOMASS_G_M2 -0.080 0.051 -0.180 0.688 -1.564 0.125




Lower Upper

CONSTANT 3.268 2.882 3.653 .


MACROFAUNA_TOTAL_BIOMASS_G_M2 -0.080 -0.184 0.023 1.453



Effect Coefficient95.0% Confidence IntervalVIF

Lower Upper

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ -0.172 -0.243 -0.100 1.429


CONSTANT

ADJUSTED_CHLORI- DE



CONSTANT 1.000

ADJUSTED_CHLORIDE -0.497 1.000

MACROFAUNA_TOTAL_BIOMASS_G_M2 -0.760 -0.134 1.000

GRAIN_SIZE_PERCENT_SAND_2_0MM_

TO_0_

-0.456 -0.040 0.547 1.000


Source SS df Mean SquaresF-Ratiop-Value

Regression6.577 3 2.192 21.104 0.000

Residual 4.779 460.104




AIC 34.499






AIC 34.499




8. TOC MULTIPLE REGRESSION

▼OLS Regression


Step number : 0

R : 0.835

R-Square : 0.698



1 Constant

2 ELEVATION_CHART-

_DATUM__M_

-0.008 0.029 -0.042 0.327 1 0.073 0.788

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.491 0.147 1 4.578 0.039

4 TOTAL_OLIGOCHAE-

TA

0.001 0.011 0.012 0.370 1 0.007 0.935

5 TOTAL_POLYCHAET-

A

0.001 0.000 0.439 0.220 1 5.480 0.024

6 TOTAL_INVERT_DE-

NSITY

-0.225 0.082 -0.402 0.361 1 7.532 0.009

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.046 0.038 -0.308 0.119 1 1.456 0.235

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.044 0.084 0.088 0.274 1 0.277 0.602

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.193 0.080 -0.559 0.144 1 5.799 0.021






10 ADJUSTED__PHOSP-

HATE

-0.228 0.208 -0.159 0.368 1 1.200 0.280

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.175 0.091 1 0.357 0.554



none


AIC 42.928




Dependent Variable : BIOFILM_TOTAL_ORGANIC_CARBON_MG_M2





Step number : 1

R : 0.835

R-Square : 0.697


Mallows' Cp : 9.007




1 Constant

2 ELEVATION_CHART-

_DATUM__M_

-0.008 0.029 -0.041 0.327 1 0.074 0.787

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.489 0.148 1 4.703 0.036

5 TOTAL_POLYCHAET-

A

0.001 0.000 0.437 0.226 1 5.697 0.022

6 TOTAL_INVERT_DE-

NSITY

-0.223 0.078 -0.399 0.385 1 8.118 0.007

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.046 0.037 -0.310 0.121 1 1.543 0.221

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.046 0.080 0.092 0.289 1 0.321 0.574

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.192 0.078 -0.556 0.147 1 6.009 0.019

10 ADJUSTED__PHOSP-

HATE

-0.229 0.205 -0.160 0.369 1 1.245 0.271

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.187 0.121 1 0.559 0.459



4 TOTAL_OLIGOCHAE-

TA

0.013 0.370 1 0.007 0.935





AIC 40.937



Step number : 2

R : 0.835

R-Square : 0.697

Mallows' Cp : 7.079




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.480 0.152 1 4.740 0.035

5 TOTAL_POLYCHAET-

A

0.001 0.000 0.419 0.258 1 6.138 0.017

6 TOTAL_INVERT_DE-

NSITY

-0.224 0.077 -0.401 0.386 1 8.411 0.006

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.048 0.036 -0.321 0.124 1 1.740 0.194

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.053 0.075 0.106 0.323 1 0.490 0.488

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.185 0.073 -0.535 0.166 1 6.431 0.015

10 ADJUSTED__PHOSP- -0.236 0.201 -0.165 0.374 1 1.372 0.248




HATE

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.198 0.125 1 0.658 0.422



2 ELEVATION_CHART-

_DATUM__M_

-0.043 0.327 1 0.074 0.787

4 TOTAL_OLIGOCHAE-

TA

0.012 0.370 1 0.006 0.940


AIC 39.029



Step number : 3

R : 0.833

R-Square : 0.693

Mallows' Cp : 5.546




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.470 0.153 1 4.618 0.037






5 TOTAL_POLYCHAET-

A

0.001 0.000 0.387 0.279 1 5.718 0.021

6 TOTAL_INVERT_DE-

NSITY

-0.208 0.073 -0.373 0.423 1 8.046 0.007

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.032 0.028 -0.212 0.211 1 1.301 0.260

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.213 0.060 -0.617 0.239 1 12.493 0.001

10 ADJUSTED__PHOSP-

HATE

-0.270 0.194 -0.188 0.398 1 1.933 0.172

11 ADJUSTED_POTASS-

IUM

0.000 0.001 0.114 0.164 1 0.292 0.592



2 ELEVATION_CHART-

_DATUM__M_

-0.076 0.366 1 0.236 0.630

4 TOTAL_OLIGOCHAE-

TA

0.037 0.391 1 0.056 0.815

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.109 0.323 1 0.490 0.488


AIC 37.623




Step number : 4

R : 0.831

R-Square : 0.691

Mallows' Cp : 3.821




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.531 0.210 1 8.237 0.006

5 TOTAL_POLYCHAET-

A

0.001 0.000 0.407 0.295 1 6.814 0.012

6 TOTAL_INVERT_DE-

NSITY

-0.205 0.073 -0.367 0.426 1 7.982 0.007

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.033 0.027 -0.220 0.212 1 1.428 0.239

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.238 0.040 -0.688 0.550 1 36.256 0.000

10 ADJUSTED__PHOSP-

HATE

-0.287 0.190 -0.200 0.409 1 2.284 0.138



2 ELEVATION_CHART-

_DATUM__M_

-0.075 0.366 1 0.237 0.629

4 TOTAL_OLIGOCHAE-

TA

0.071 0.496 1 0.212 0.648

8 MACROFAUNA_TOTA- 0.054 0.424 1 0.122 0.728






L_BIOMASS_G_M2

11 ADJUSTED_POTASS-

IUM

0.083 0.164 1 0.292 0.592


AIC 35.970



Step number : 5

R : 0.825

R-Square : 0.681

Mallows' Cp : 3.144




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.412 0.295 1 6.912 0.012

5 TOTAL_POLYCHAET-

A

0.001 0.000 0.290 0.490 1 5.667 0.022

6 TOTAL_INVERT_DE-

NSITY

-0.186 0.071 -0.333 0.447 1 6.845 0.012

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.227 0.039 -0.657 0.581 1 34.532 0.000




10 ADJUSTED__PHOSP-

HATE

-0.315 0.189 -0.220 0.415 1 2.760 0.104



2 ELEVATION_CHART-

_DATUM__M_

-0.062 0.368 1 0.164 0.687

4 TOTAL_OLIGOCHAE-

TA

0.070 0.496 1 0.214 0.646

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.179 0.212 1 1.428 0.239

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.067 0.673 1 0.193 0.662

11 ADJUSTED_POTASS-

IUM

0.095 0.165 1 0.393 0.534


AIC 35.603



▼OLS Regression





1 2 3 4 5 6

4.679 1.100 0.129 0.082 0.009 0.001

Condition Indices

1 2 3 4 5 6

1.000 2.062 6.031 7.576 22.53770.440


1 2 3 4 5 6

CONSTANT 0.0000.0000.0010.0030.0100.986

ADJUSTED_CHLORIDE 0.0020.0000.0000.3600.2070.431

TOTAL_POLYCHAETA 0.0050.0120.7980.0780.0000.105

TOTAL_INVERT_DENSITY 0.0000.0000.0010.0020.0360.961

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_0.0000.4680.4450.0270.0590.002

ADJUSTED__PHOSPHATE 0.0000.0000.0030.0000.8040.192

Dependent Variable BIOFILM_TOTAL_O-

RGANIC_CARBON_M-

G_M2

N 50

Multiple R 0.825







Tolerancet p-Value

CONSTANT 12.588 1.046 0.000 . 12.0310.000


TOTAL_POLYCHAETA 0.001 0.000 0.290 0.490 2.381 0.022

TOTAL_INVERT_DENSITY -0.186 0.071 -0.333 0.447 -2.616 0.012

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ -0.227 0.039 -0.657 0.581 -5.876 0.000

ADJUSTED__PHOSPHATE -0.315 0.189 -0.220 0.415 -1.661 0.104



Lower Upper

CONSTANT 12.588 10.479 14.696 .



TOTAL_INVERT_DENSITY -0.186 -0.330 -0.043 2.237


ADJUSTED__PHOSPHATE -0.315 -0.696 0.067 2.411


CONSTANT

ADJUSTED_CHLORI- DE

TOTAL_POLYCHAET- A



CONSTANT 1.000






CONSTANT

ADJUSTED_CHLORI- DE

TOTAL_POLYCHAET- A




TOTAL_INVERT_DENSITY -0.951 -0.581 -0.331 1.000


0MM_TO_0_

-0.031 -0.010 0.558 0.073 1.000

ADJUSTED__PHOSPHATE -0.520 -0.697 -0.205 0.261 -0.229

Correlation Matrix of Regression Coefficients (Contd.)

ADJUSTED__PHOSP-HATE

CONSTANT

ADJUSTED_CHLORIDE

TOTAL_POLYCHAETA



ADJUSTED__PHOSPHATE 1.000


Source SS df Mean Squares F-Ratiop-Value

Regression9.624 5 1.925 18.779 0.000

Residual 4.510 44 0.102

WARNING


Case 34 is an Outlier (Studentized Residual : -3.426)





AIC 35.603





9. TOTAL CARBOHYDRATE MULTIPLE REGRESSION

▼OLS Regression


Step number : 0

R : 0.774

R-Square : 0.599



1 Constant

2 ELEVATION_CHART-

_DATUM__M_

0.000 0.030 -0.002 0.327 1 0.000 0.989

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.740 0.147 1 7.826 0.008

4 TOTAL_OLIGOCHAE-

TA

-0.008 0.011 -0.113 0.370 1 0.460 0.502

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.117 0.220 1 0.292 0.592

6 TOTAL_INVERT_DE-

NSITY

-0.077 0.085 -0.154 0.361 1 0.836 0.366

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.033 0.039 -0.249 0.119 1 0.721 0.401

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.022 0.086 -0.049 0.274 1 0.065 0.800

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.146 0.083 -0.472 0.144 1 3.113 0.086






10 ADJUSTED__PHOSP-

HATE

-0.003 0.215 -0.003 0.368 1 0.000 0.988

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.205 0.091 1 0.370 0.546



none


AIC 46.114




Dependent Variable : BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2





Step number : 1

R : 0.774

R-Square : 0.599


Mallows' Cp : 9.000




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.739 0.151 1 8.218 0.007

4 TOTAL_OLIGOCHAE-

TA

-0.008 0.011 -0.113 0.370 1 0.472 0.496

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.116 0.252 1 0.337 0.565

6 TOTAL_INVERT_DE-

NSITY

-0.077 0.083 -0.154 0.362 1 0.861 0.359

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.033 0.038 -0.250 0.123 1 0.764 0.387

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.022 0.081 -0.049 0.306 1 0.072 0.790

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.146 0.077 -0.470 0.163 1 3.586 0.065

10 ADJUSTED__PHOSP-

HATE

-0.004 0.210 -0.003 0.373 1 0.000 0.986

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.205 0.093 1 0.392 0.535



2 ELEVATION_CHART-

_DATUM__M_

-0.002 0.327 1 0.000 0.989





AIC 44.114



Step number : 2

R : 0.774

R-Square : 0.599

Mallows' Cp : 7.000




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.737 0.196 1 10.893 0.002

4 TOTAL_OLIGOCHAE-

TA

-0.008 0.011 -0.113 0.371 1 0.484 0.491

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.116 0.252 1 0.345 0.560

6 TOTAL_INVERT_DE-

NSITY

-0.077 0.082 -0.154 0.366 1 0.889 0.351

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.033 0.037 -0.251 0.129 1 0.833 0.367

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.021 0.078 -0.048 0.322 1 0.076 0.785

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

-0.146 0.076 -0.470 0.164 1 3.702 0.061




TO_0_

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.206 0.097 1 0.422 0.520



2 ELEVATION_CHART-

_DATUM__M_

-0.002 0.332 1 0.000 0.988

10 ADJUSTED__PHOSP-

HATE

-0.003 0.373 1 0.000 0.986


AIC 42.115



Step number : 3

R : 0.773

R-Square : 0.598

Mallows' Cp : 5.072




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.748 0.202 1 11.826 0.001






4 TOTAL_OLIGOCHAE-

TA

-0.008 0.010 -0.124 0.396 1 0.637 0.429

5 TOTAL_POLYCHAET-

A

0.000 0.000 0.129 0.268 1 0.463 0.500

6 TOTAL_INVERT_DE-

NSITY

-0.083 0.078 -0.165 0.390 1 1.117 0.297

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.040 0.028 -0.299 0.213 1 1.985 0.166

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.134 0.062 -0.432 0.240 1 4.681 0.036

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.251 0.130 1 0.855 0.361



2 ELEVATION_CHART-

_DATUM__M_

0.013 0.379 1 0.007 0.935

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.043 0.322 1 0.076 0.785

10 ADJUSTED__PHOSP-

HATE

0.007 0.393 1 0.002 0.965


AIC 40.207




Step number : 4

R : 0.770

R-Square : 0.594

Mallows' Cp : 3.503




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.679 0.258 1 12.583 0.001

4 TOTAL_OLIGOCHAE-

TA

-0.010 0.010 -0.149 0.419 1 0.981 0.328

6 TOTAL_INVERT_DE-

NSITY

-0.062 0.072 -0.124 0.461 1 0.747 0.392

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.028 0.022 -0.207 0.358 1 1.621 0.210

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.134 0.062 -0.432 0.240 1 4.729 0.035

11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.306 0.143 1 1.412 0.241



2 ELEVATION_CHART-

_DATUM__M_

0.055 0.457 1 0.126 0.725

5 TOTAL_POLYCHAET-

A

0.104 0.268 1 0.463 0.500

8 MACROFAUNA_TOTA- -0.066 0.341 1 0.186 0.668






L_BIOMASS_G_M2

10 ADJUSTED__PHOSP-

HATE

0.016 0.396 1 0.011 0.918


AIC 38.755



Step number : 5

R : 0.766

R-Square : 0.587

Mallows' Cp : 2.189




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.624 0.291 1 12.027 0.001

4 TOTAL_OLIGOCHAE-

TA

-0.013 0.010 -0.185 0.455 1 1.665 0.204

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.028 0.022 -0.210 0.359 1 1.690 0.200

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.132 0.061 -0.427 0.240 1 4.656 0.036




11 ADJUSTED_POTASS-

IUM

0.001 0.001 0.292 0.143 1 1.304 0.260



2 ELEVATION_CHART-

_DATUM__M_

0.039 0.463 1 0.065 0.800

5 TOTAL_POLYCHAET-

A

0.044 0.316 1 0.083 0.774

6 TOTAL_INVERT_DE-

NSITY

-0.131 0.461 1 0.747 0.392

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.087 0.351 1 0.328 0.570

10 ADJUSTED__PHOSP-

HATE

0.034 0.404 1 0.050 0.825


AIC 37.616



Step number : 6

R : 0.758

R-Square : 0.574

Mallows' Cp : 1.381







1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.731 0.399 1 22.490 0.000

4 TOTAL_OLIGOCHAE-

TA

-0.007 0.009 -0.108 0.587 1 0.717 0.401

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.025 0.021 -0.186 0.365 1 1.338 0.254

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.182 0.043 -0.588 0.489 1 17.895 0.000



2 ELEVATION_CHART-

_DATUM__M_

0.062 0.472 1 0.170 0.682

5 TOTAL_POLYCHAET-

A

0.092 0.347 1 0.374 0.544

6 TOTAL_INVERT_DE-

NSITY

-0.118 0.463 1 0.625 0.433

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.165 0.501 1 1.226 0.274

10 ADJUSTED__PHOSP-

HATE

0.020 0.406 1 0.018 0.895

11 ADJUSTED_POTASS-

IUM

0.170 0.143 1 1.304 0.260



AIC 37.077



Step number : 7

R : 0.753

R-Square : 0.568

Mallows' Cp : 0.040




1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.687 0.449 1 22.534 0.000

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.023 0.021 -0.170 0.370 1 1.138 0.292

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.162 0.036 -0.523 0.704 1 20.495 0.000



2 ELEVATION_CHART-

_DATUM__M_

0.071 0.475 1 0.229 0.635

4 TOTAL_OLIGOCHAE-

TA

-0.125 0.587 1 0.717 0.401






5 TOTAL_POLYCHAET-

A

0.087 0.347 1 0.339 0.563

6 TOTAL_INVERT_DE-

NSITY

-0.153 0.525 1 1.084 0.303

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.171 0.503 1 1.349 0.252

10 ADJUSTED__PHOSP-

HATE

0.049 0.431 1 0.109 0.743

11 ADJUSTED_POTASS-

IUM

0.089 0.185 1 0.357 0.553


AIC 35.868



Step number : 8

R : 0.746

R-Square : 0.557





1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.572 0.998 1 34.684 0.000




9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.141 0.030 -0.456 0.998 1 22.046 0.000



2 ELEVATION_CHART-

_DATUM__M_

0.019 0.524 1 0.017 0.896

4 TOTAL_OLIGOCHAE-

TA

-0.104 0.596 1 0.506 0.480

5 TOTAL_POLYCHAET-

A

-0.028 0.560 1 0.037 0.848

6 TOTAL_INVERT_DE-

NSITY

-0.150 0.525 1 1.060 0.309

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.155 0.370 1 1.138 0.292

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.225 0.688 1 2.445 0.125

10 ADJUSTED__PHOSP-

HATE

0.013 0.453 1 0.008 0.929

11 ADJUSTED_POTASS-

IUM

0.078 0.186 1 0.283 0.597


AIC 35.089






Step number : 8

R : 0.761

R-Square : 0.579


Term Entered : MACROFAUNA_TOTAL_BIOMASS_G_M2



1 Constant

3 ADJUSTED_CHLORI-

DE

0.000 0.000 0.593 0.980 1 37.635 0.000

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

-0.080 0.051 -0.180 0.688 1 2.445 0.125

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-0.172 0.035 -0.554 0.700 1 23.486 0.000



2 ELEVATION_CHART-

_DATUM__M_

-0.018 0.510 1 0.015 0.903

4 TOTAL_OLIGOCHAE-

TA

-0.110 0.596 1 0.547 0.464

5 TOTAL_POLYCHAET-

A

0.001 0.550 1 0.000 0.995

6 TOTAL_INVERT_DE-

NSITY

-0.132 0.520 1 0.793 0.378

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-0.047 0.270 1 0.098 0.756




10 ADJUSTED__PHOSP-

HATE

0.006 0.452 1 0.001 0.971

11 ADJUSTED_POTASS-

IUM

-0.002 0.162 1 0.000 0.991


AIC 34.499



▼OLS Regression


1 2 3 4

2.848 1.020 0.097 0.035

Condition Indices

1 2 3 4

1.0001.671 5.423 9.009


1 2 3 4

CONSTANT 0.0070.0000.0380.955







1 2 3 4



S_FUCOXANTHIN_M-

G_M2

N 50

Multiple R 0.761






Tolerancet p-Value

CONSTANT 3.268 0.191 0.000 . 17.0640.000


MACROFAUNA_TOTAL_BIOMASS_G_M2 -0.080 0.051 -0.180 0.688 -1.564 0.125




Lower Upper

CONSTANT 3.268 2.882 3.653 .


MACROFAUNA_TOTAL_BIOMASS_G_M2 -0.080 -0.184 0.023 1.453




Lower Upper



CONSTANT

ADJUSTED_CHLORI- DE



CONSTANT 1.000

ADJUSTED_CHLORIDE -0.497 1.000

MACROFAUNA_TOTAL_BIOMASS_G_M2 -0.760 -0.134 1.000

GRAIN_SIZE_PERCENT_SAND_2_0MM_

TO_0_

-0.456 -0.040 0.547 1.000



Regression6.577 3 2.192 21.104 0.000

Residual 4.779 460.104







AIC 34.499




▼OLS Regression



Step number : 0

R : 0.545

R-Square : 0.297



1 Constant

2 ELEVATION_CHART-

_DATUM__M_

-0.825 2.711 -0.071 0.327 1 0.093 0.762

3 ADJUSTED_CHLORI-

DE

0.008 0.004 0.769 0.147 1 4.832 0.034

4 TOTAL_OLIGOCHAE-

TA

-0.599 1.022 -0.129 0.370 1 0.344 0.561

5 TOTAL_POLYCHAET-

A

0.049 0.041 0.339 0.220 1 1.408 0.243

6 TOTAL_INVERT_DE-

NSITY

2.878 7.682 0.084 0.361 1 0.140 0.710

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-7.266 3.549 -0.796 0.119 1 4.192 0.047

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

6.446 7.841 0.211 0.274 1 0.676 0.416

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-12.450 7.521 -0.586 0.144 1 2.740 0.106

10 ADJUSTED__PHOSP-

HATE

10.457 19.497 0.119 0.368 1 0.288 0.595

11 ADJUSTED_POTASS-

IUM

-0.078 0.113 -0.308 0.091 1 0.478 0.493






none


AIC 496.975




Dependent Variable : BIOFILM_TOTAL_CARBOHYDRATE_MG_M2





Step number : 1

R : 0.544

R-Square : 0.295

Mallows' Cp : 9.093




1 Constant

3 ADJUSTED_CHLORI- 0.008 0.003 0.753 0.151 1 4.851 0.033




DE

4 TOTAL_OLIGOCHAE-

TA

-0.608 1.010 -0.131 0.370 1 0.362 0.551

5 TOTAL_POLYCHAET-

A

0.045 0.038 0.309 0.252 1 1.362 0.250

6 TOTAL_INVERT_DE-

NSITY

2.776 7.587 0.081 0.362 1 0.134 0.716

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-7.445 3.460 -0.816 0.123 1 4.630 0.038

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

7.214 7.338 0.236 0.306 1 0.967 0.331

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-11.676 6.997 -0.549 0.163 1 2.784 0.103

10 ADJUSTED__PHOSP-

HATE

9.716 19.124 0.110 0.373 1 0.258 0.614

11 ADJUSTED_POTASS-

IUM

-0.073 0.110 -0.287 0.093 1 0.436 0.513



2 ELEVATION_CHART-

_DATUM__M_

-0.049 0.327 1 0.093 0.762


AIC 495.094






Step number : 2

R : 0.541

R-Square : 0.293

Mallows' Cp : 7.223




1 Constant

3 ADJUSTED_CHLORI-

DE

0.008 0.003 0.811 0.193 1 7.366 0.010

4 TOTAL_OLIGOCHAE-

TA

-0.514 0.967 -0.111 0.395 1 0.283 0.597

5 TOTAL_POLYCHAET-

A

0.051 0.034 0.350 0.309 1 2.197 0.146

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-7.821 3.268 -0.857 0.134 1 5.729 0.021

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

7.805 7.082 0.255 0.321 1 1.215 0.277

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-11.397 6.882 -0.536 0.164 1 2.743 0.105

10 ADJUSTED__PHOSP-

HATE

8.981 18.816 0.102 0.378 1 0.228 0.636

11 ADJUSTED_POTASS-

IUM

-0.071 0.109 -0.279 0.093 1 0.421 0.520




2 ELEVATION_CHART-

_DATUM__M_

-0.046 0.328 1 0.085 0.772

6 TOTAL_INVERT_DE-

NSITY

0.058 0.362 1 0.134 0.716


AIC 493.261



Step number : 3

R : 0.538

R-Square : 0.289

Mallows' Cp : 5.441




1 Constant

3 ADJUSTED_CHLORI-

DE

0.009 0.003 0.881 0.254 1 11.669 0.001

4 TOTAL_OLIGOCHAE-

TA

-0.552 0.954 -0.119 0.398 1 0.335 0.566

5 TOTAL_POLYCHAET-

A

0.050 0.034 0.349 0.309 1 2.219 0.144

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

-7.398 3.116 -0.811 0.145 1 5.637 0.022






IDUAL

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

6.949 6.788 0.227 0.343 1 1.048 0.312

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-11.745 6.780 -0.553 0.166 1 3.001 0.091

11 ADJUSTED_POTASS-

IUM

-0.081 0.106 -0.320 0.097 1 0.590 0.447



2 ELEVATION_CHART-

_DATUM__M_

-0.037 0.333 1 0.055 0.816

6 TOTAL_INVERT_DE-

NSITY

0.049 0.366 1 0.101 0.753

10 ADJUSTED__PHOSP-

HATE

0.074 0.378 1 0.228 0.636


AIC 491.538



Step number : 4

R : 0.532

R-Square : 0.283

Mallows' Cp : 3.756





1 Constant

3 ADJUSTED_CHLORI-

DE

0.009 0.003 0.883 0.254 1 11.898 0.001

5 TOTAL_POLYCHAET-

A

0.051 0.033 0.356 0.310 1 2.358 0.132

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-6.952 2.996 -0.762 0.155 1 5.385 0.025

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

5.624 6.341 0.184 0.387 1 0.787 0.380

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-12.471 6.611 -0.587 0.172 1 3.558 0.066

11ADJUSTED_POTASS-

IUM

-0.116 0.087 -0.456 0.143 1 1.783 0.189



2 ELEVATION_CHART-

_DATUM__M_

-0.039 0.333 1 0.064 0.802

4 TOTAL_OLIGOCHAE-

TA

-0.089 0.398 1 0.335 0.566

6 TOTAL_INVERT_DE-

NSITY

0.024 0.392 1 0.025 0.875

10 ADJUSTED__PHOSP-

HATE

0.081 0.380 1 0.278 0.601





AIC 489.935



Step number : 5

R : 0.520

R-Square : 0.270

Mallows' Cp : 2.483




1 Constant

3 ADJUSTED_CHLORI-

DE

0.009 0.003 0.866 0.256 1 11.576 0.001

5 TOTAL_POLYCHAET-

A

0.046 0.033 0.319 0.320 1 1.965 0.168

7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-5.459 2.472 -0.598 0.226 1 4.876 0.032

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-15.585 5.588 -0.733 0.240 1 7.778 0.008

11 ADJUSTED_POTASS-

IUM

-0.147 0.079 -0.579 0.171 1 3.454 0.070




2 ELEVATION_CHART-

_DATUM__M_

-0.085 0.384 1 0.312 0.579

4 TOTAL_OLIGOCHAE-

TA

-0.038 0.449 1 0.061 0.805

6 TOTAL_INVERT_DE-

NSITY

0.068 0.443 1 0.201 0.656

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.134 0.387 1 0.787 0.380

10 ADJUSTED__PHOSP-

HATE

0.038 0.416 1 0.061 0.807


AIC 488.842



Step number : 6

R : 0.488

R-Square : 0.238

Mallows' Cp : 2.291




1 Constant

3 ADJUSTED_CHLORI-

DE

0.008 0.002 0.743 0.291 1 9.466 0.004






7 MACROFAUNA_TOTA-

L_BENTHIC_INDIV-

IDUAL

-3.303 1.956 -0.362 0.369 1 2.851 0.098

9 GRAIN_SIZE_PERC-

ENT_SAND_2_0MM_-

TO_0_

-15.639 5.648 -0.736 0.240 1 7.667 0.008

11 ADJUSTED_POTASS-

IUM

-0.116 0.077 -0.458 0.185 1 2.288 0.137



2 ELEVATION_CHART-

_DATUM__M_

0.014 0.472 1 0.009 0.926

4 TOTAL_OLIGOCHAE-

TA

-0.060 0.455 1 0.160 0.691

5 TOTAL_POLYCHAET-

A

0.207 0.320 1 1.965 0.168

6 TOTAL_INVERT_DE-

NSITY

0.133 0.501 1 0.796 0.377

8 MACROFAUNA_TOTA-

L_BIOMASS_G_M2

0.092 0.400 1 0.372 0.545

10 ADJUSTED__PHOSP-

HATE

0.046 0.417 1 0.094 0.761


AIC 489.026




▼OLS Regression


1 2 3 4 5

3.865 1.035 0.070 0.019 0.010

Condition Indices

1 2 3 4 5

1.0001.932 7.417 14.27719.733


1 2 3 4 5

CONSTANT 0.0020.0000.1560.0110.830

ADJUSTED_CHLORIDE 0.0020.0000.3190.0150.662

MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.0010.0000.0010.6990.299

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ 0.0000.2210.0000.0550.723

ADJUSTED_POTASSIUM 0.0010.0000.0010.3570.640

Dependent Variable BIOFILM_TOTAL_C-

ARBOHYDRATE_MG_-

M2

N 50

Multiple R 0.488


Adjusted Squared Multiple R0.170







Tolerancet p-Value

CONSTANT 121.811 26.314 0.000 . 4.629 0.000


MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL -3.303 1.956 -0.362 0.369 -1.6890.098

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_-15.639 5.648 -0.736 0.240 -2.7690.008

ADJUSTED_POTASSIUM -0.116 0.077 -0.458 0.185 -1.5130.137



Lower Upper

CONSTANT 121.811 68.812 174.811 .


MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL -3.303 -7.242 0.637 2.712

GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_-15.639 -27.014 -4.264 4.168

ADJUSTED_POTASSIUM -0.116 -0.271 0.039 5.406


CONSTANT

ADJUSTED_CHLORI- DE




CONSTANT 1.000


MACROFAUNA_TOTAL_BENTHIC -0.576 -0.560 1.000



CONSTANT

ADJUSTED_CHLORI- DE




_INDIVIDUAL


0MM_TO_0_

-0.741 -0.661 0.268 1.000

ADJUSTED_POTASSIUM -0.652 -0.594 -0.060 0.812 1.000



Regression12,706.9074 3,176.727 3.510 0.014

Residual 40,727.47845905.055

WARNING

Case 9 is an Outlier (Studentized Residual : 3.194)



First Order Autocorrelation-0.119


AIC 489.026




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Appendix 4 Correlation Coefficient Matrix

Emma.Livingstone

New Stamp

Elevation_Chart_Datum__M_ Annual_Exposure__Hr__ Distance_From_Canoe_Pass__M_ Distance_From_Shore__M_ Total_Harpacticoida Total_Nematoda Total_OligochaetaElevation_Chart_Datum__M_ 1

Annual_Exposure__Hr__ 0.941197659 1Distance_From_Canoe_Pass__M_ 0.457123565 0.43658489 1

Distance_From_Shore__M_ ‐0.844866424 ‐0.790486909 ‐0.784289467 1Total_Harpacticoida 0.504930808 0.51174553 0.466801296 ‐0.56914707 1

Total_Nematoda 0.35212258 0.330091026 0.563760014 ‐0.479999693 0.487688938 1Total_Oligochaeta 0.360166615 0.351321721 0.394401655 ‐0.490303665 0.574423691 0.368350207 1Total_Polychaeta 0.716319176 0.740319489 0.357845319 ‐0.642551486 0.652627819 0.294518993 0.395689698

Total_Invert_Density 0.493261592 0.486997832 0.650752679 ‐0.640561598 0.732777533 0.889884035 0.553859454Total_Invert_Biomass 0.397291134 0.401371548 0.380979823 ‐0.480884453 0.71174056 0.543695894 0.542346864

Macrofauna_Total_Benthic_Individual 0.66712133 0.665090556 0.630624067 ‐0.742051255 0.507429192 0.337626945 0.384161228Macrofauna_Total_Biomass_G_M2 0.224545023 0.226762206 0.11037016 ‐0.235730533 0.329780655 0.110736545 0.332516208

Meiofauna_Total_Benthic_Individuals 0.480839262 0.474693503 0.64147297 ‐0.628964435 0.730932743 0.892805462 0.554385818Meiofauna_Total_Biomass_G_M2 0.525453723 0.540074326 0.617675283 ‐0.670389306 0.819790411 0.762715354 0.616701418

Grain_Size_Percent_Clay_Less_Than_4 0.4803681 0.455731146 ‐0.112991631 ‐0.311662926 0.457759052 0.046957318 0.533688981Grain_Size_Percent_Silt_0_063mm_To_ 0.468402281 0.446144641 ‐0.047474075 ‐0.338432741 0.427155018 0.017465125 0.519499322ain_Size_Percent_Sand_2_0mm_To_0_ ‐0.491658065 ‐0.475148502 ‐0.024068532 0.399846427 ‐0.479278576 ‐0.087606611 ‐0.557903014

Toc_Total_Organic_Carbon_Percent 0.507585067 0.471274382 0.076290189 ‐0.46596978 0.585439079 0.127445742 0.615632722Adjusted_Ammonia 0.367674622 0.344866084 0.75833459 ‐0.63597179 0.186316457 0.504902849 0.170919288Adjusted_Bromide 0.474556559 0.476390408 0.888898502 ‐0.752489799 0.377578525 0.509657932 0.311718741Adjusted_Chloride 0.502970225 0.491965458 0.899834484 ‐0.780792572 0.409612047 0.54148824 0.327266009

Adjusted_So4 0.6159702 0.60510951 0.842369372 ‐0.817657399 0.439301434 0.447917655 0.30947212Adjusted__Phosphate 0.408455597 0.363816635 0.636417004 ‐0.539042687 0.014182865 0.363038687 0.017113572Adjusted_Potassium 0.643147098 0.618755851 0.526365775 ‐0.733765955 0.638578095 0.386817668 0.72560129

Adjusted_S 0.737404105 0.74585433 0.593060081 ‐0.787252835 0.70694676 0.484477591 0.599888948

Correlation Matrix.xlsx 1 of 4

Elevation_Chart_Datum__M_Annual_Exposure__Hr__

Distance_From_Canoe_Pass__M_Distance_From_Shore__M_

Total_HarpacticoidaTotal_Nematoda

Total_OligochaetaTotal_Polychaeta

Total_Invert_DensityTotal_Invert_Biomass

Macrofauna_Total_Benthic_IndividualMacrofauna_Total_Biomass_G_M2

Meiofauna_Total_Benthic_IndividualsMeiofauna_Total_Biomass_G_M2

Grain_Size_Percent_Clay_Less_Than_4Grain_Size_Percent_Silt_0_063mm_To_ain_Size_Percent_Sand_2_0mm_To_0_

Toc_Total_Organic_Carbon_PercentAdjusted_AmmoniaAdjusted_BromideAdjusted_Chloride

Adjusted_So4Adjusted__PhosphateAdjusted_Potassium

Adjusted_S

Total_Polychaeta Total_Invert_Density Total_Invert_Biomass Macrofauna_Total_Benthic_Individual Macrofauna_Total_Biomass_G_M2 Meiofauna_Total_Benthic_Individuals

10.530694068 10.590818917 0.738674857 10.732679399 0.541107357 0.60010421 10.437823533 0.300907809 0.758167474 0.567673272 10.516463642 0.999583832 0.73086527 0.520126665 0.288353283 10.621220475 0.942407209 0.765151114 0.575114533 0.283791867 0.9408238550.558966393 0.254696196 0.4920656 0.272154008 0.442854627 0.2529564780.541521424 0.245253117 0.501539458 0.380900128 0.523910176 0.240917734‐0.590471725 ‐0.314439553 ‐0.554335938 ‐0.421110061 ‐0.546856019 ‐0.3097426530.594676421 0.365745427 0.571908282 0.373923193 0.404223812 0.3623104080.192897274 0.457758607 0.246948921 0.480583277 0.006817904 0.4478240490.305180301 0.593672204 0.340978278 0.669437032 0.102188888 0.5821930840.326415984 0.625535761 0.374104983 0.68912587 0.13413378 0.6143578810.440415939 0.577094029 0.39139589 0.771607309 0.186803715 0.5619583220.210533622 0.304673441 0.036856265 0.526426969 ‐0.007656665 0.2911210860.67725611 0.609811302 0.590193855 0.669220792 0.339606872 0.601838046

0.725355628 0.689769623 0.576180376 0.691916467 0.322684677 0.682224868












Adjusted_S

Meiofauna_Total_Biomass_G_M2 Grain_Size_Percent_Clay_Less_Than_4 Grain_Size_Percent_Silt_0_063mm_To_ Grain_Size_Percent_Sand_2_0mm_To_0_

10.382586546 10.358265267 0.93800357 1‐0.429091945 ‐0.947483093 ‐0.985347444 10.521214076 0.931910538 0.885789656 ‐0.9120294080.393376845 ‐0.231528883 ‐0.211226107 0.1373010620.563814516 ‐0.133799011 ‐0.060393578 0.0030932570.593441166 ‐0.0926973 ‐0.019458619 ‐0.0402629620.570406986 ‐0.017245779 0.045777791 ‐0.1045563770.20123599 ‐0.237268177 ‐0.166337079 0.128269045

0.730125825 0.687744465 0.701530747 ‐0.7497662410.752029325 0.54429098 0.562086034 ‐0.610654381












Adjusted_S

Toc_Total_Organic_Carbon_Percent Adjusted_Ammonia Adjusted_Bromide Adjusted_Chloride Adjusted_So4 Adjusted__Phosphate Adjusted_Potassium Adjusted_S

1‐0.062224731 10.051309625 0.802598172 10.09447449 0.802166154 0.992034528 1

0.153977006 0.758515344 0.948881582 0.945034322 1‐0.169558259 0.69812724 0.737048522 0.722764439 0.731289553 10.786839481 0.323486278 0.510720686 0.531967957 0.580124861 0.222723626 10.641000857 0.353290569 0.571798431 0.594105865 0.64201379 0.335614971 0.812345499 1





Appendix 5 Detailed Microphytobenthos Community Statistical Analyses

Emma.Livingstone

New Stamp

HEMMERA ENVIRCHEM INC. ROBERTS BANK TERMINAL 2 – TECHNICAL DATA REPORT



APPENDIX 5– COMMUNITY STATISTICS OUTPUTS

1. SEASONAL ANALYSIS

1.1 Bray Curtis Similarity Index

RB2001 RB2001F RB2003 RB2007 RB2007F RB2009 RB2011 RB2011F RB2013 RB2013F RB2016 RB2019 RB2020 RB2022 RB2022F RB2024 RB2024F RB2025 RB2025F RB2038 RB2038F RB2040

RB2001

RB2001F 44.59

RB2003 79.07 37.94

RB2007 54.70 35.45 58.73

RB2007F 38.08 64.52 32.92 39.30

RB2009 38.64 24.91 45.47 76.75 30.52

RB2011 58.78 32.18 62.34 77.17 28.64 59.82

RB2011F 42.17 56.40 36.80 43.73 65.45 33.05 30.56

RB2013 57.12 37.59 60.79 54.90 32.96 43.66 49.82 34.50

RB2013F 29.71 60.26 26.10 31.51 74.99 24.34 23.41 59.83 26.85

RB2016 51.24 36.40 55.44 60.86 32.23 40.06 64.99 32.16 67.04 26.25

RB2019 63.16 39.53 81.35 71.49 34.38 60.88 64.85 36.82 78.19 27.69 70.85

RB2020 65.81 35.82 56.88 62.48 31.62 52.39 75.78 32.56 67.58 25.66 64.83 58.81

RB2022 53.07 55.17 70.63 63.10 48.64 54.45 58.69 53.04 67.28 39.21 62.63 85.16 52.38

RB2022F 41.61 64.50 35.69 42.95 72.81 32.61 29.99 71.81 33.93 59.68 31.59 36.25 31.99 53.14

RB2024 55.86 60.07 46.06 53.95 53.45 42.88 58.97 55.50 58.94 43.70 67.21 63.07 67.30 78.46 54.75

RB2024F 41.38 56.37 35.65 43.13 58.90 33.85 39.06 62.10 34.43 55.54 32.69 36.34 42.12 52.43 54.47 55.33

RB2025 55.98 51.78 49.10 44.80 37.70 35.38 58.41 46.50 58.88 39.19 57.22 51.24 77.47 56.48 46.24 71.16 47.48

RB2025F 35.17 73.00 30.49 44.94 71.54 36.60 33.54 62.60 29.73 65.33 27.95 31.22 35.92 46.56 72.32 47.65 68.76 41.31

RB2038 50.82 55.03 65.92 50.30 49.76 43.14 55.68 52.63 62.62 41.38 50.65 66.74 60.15 79.62 60.34 63.73 44.30 71.66 47.07

RB2038F 36.09 74.09 31.49 29.86 60.36 21.19 27.26 56.31 30.69 61.77 28.88 32.17 29.19 38.80 63.52 39.66 56.58 41.93 71.14 40.22

RB2040 53.78 46.35 44.56 63.10 49.54 52.64 56.46 52.89 67.71 40.39 63.26 59.79 63.75 62.50 51.75 79.59 43.07 56.53 44.87 51.01 29.66

RB2040F 33.55 79.72 29.38 35.87 65.59 28.00 32.93 60.60 28.86 61.40 27.65 30.15 35.30 44.06 67.18 45.92 67.95 40.43 86.34 45.58 82.21 36.43

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1.2 Non-Parametric Multidimensional Scaling Analysis


1.3 Analysis of Similarity (ANOSIM) ANOSIM: One-Way Analysis Resemblance worksheet Name: Resem1 Data type: Similarity Selection: All Factor Values Factor: Season Spring Summer Factor Groups Sample Season RB2001 Spring RB2003 Spring RB2007 Spring RB2009 Spring RB2011 Spring RB2013 Spring RB2016 Spring RB2019 Spring RB2020 Spring RB2022 Spring RB2024 Spring RB2025 Spring RB2038 Spring RB2040 Spring RB2001F Summer RB2007F Summer RB2011F Summer RB2013F Summer RB2022F Summer RB2024F Summer RB2025F Summer RB2038F Summer RB2040F Summer Global Test Sample statistic (Global R): 0.918 Significance level of sample statistic: 0.1% Number of permutations: 999 (Random sample from 817190) Number of permuted statistics greater than or equal to Global R: 0 Outputs Plot: Graph8




2. SPRING ANALYSIS

2.1 nMDS

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2.2 Environmental Database (transformed and normalized)

Elevation Annual

Exposure

Distance from Canoe

Pass Distance

from Shore % Clay % Silt % Sand TOC Leachable Ammonia

Porewater Bromide

Porewater Chloride

Porewater SO4 Phosphate Potassium Sulphur

RB2001 0.210592 -0.04892 -1.00147 0.460272 1.121136 0.718342 -0.78308 0.852824 -0.94239 -1.1019 -1.01901 -0.84601 -0.38672 -0.18271 -0.65329

RB2003 1.002102 0.775393 -0.77595 -0.0014 0.970471 1.044267 -1.06084 0.917235 -1.3102 -0.74319 -0.81785 0.200229 0.025844 0.204613 2.222606

RB2007 1.108681 0.886982 0.60426 -1.28806 1.379311 1.314397 -1.57098 1.856943 0.039634 0.661033 0.859864 0.827332 -0.83326 1.874949 0.861585

RB2009 1.079447 0.860391 -0.34283 0.187044 1.126462 1.014344 -1.08381 0.56671 -0.9387 -0.41166 -0.309 -0.36663 -0.44645 0.914708 -0.08521

RB2011 -0.82836 -0.93595 0.319371 -0.38225 0.023575 -0.03098 0.10886 0.187934 1.325254 0.961071 1.075851 0.424783 0.865602 -0.14236 -0.16806

RB2013 -0.16034 -0.42073 0.428845 -0.6032 0.506413 0.321698 -0.26805 0.56671 -0.05587 0.138909 0.232504 0.18196 -0.50744 0.793669 -0.17989

RB2016 -0.80448 1.414187 -0.03207 0.569049 0.085396 0.339327 -0.21034 -0.13696 -0.08626 0.508665 0.344598 0.110263 -1.08531 0.010951 0.719566

RB2019 -0.32913 -0.55553 -1.16099 0.875917 -1.74443 -1.87489 1.745999 -1.31026 0.539149 -1.19394 -1.15887 -1.20905 -0.0521 -1.68359 -1.29474

RB2020 -0.54039 -0.72614 0.206899 0.058288 0.211623 0.644769 -0.49865 0.283387 -0.2233 -0.11932 -0.07947 -0.11116 -0.83326 0.503176 -0.26274

RB2022 0.749182 0.467063 0.885391 -0.84285 -0.5782 -0.78927 0.789274 -1.082 0.687879 0.804617 0.824835 0.457353 0.996871 -0.06167 0.660391

RB2024 1.796366 1.658269 1.456097 -1.75726 -0.9252 -0.40523 0.520978 -0.26128 1.780776 1.812439 1.750519 2.256062 2.58106 0.23689 0.400022

RB2025 -0.78897 -0.90705 1.835499 -0.50283 -0.74089 -0.6718 0.721527 -0.4432 1.262106 1.066115 0.856075 0.816019 0.826977 0.503176 0.482866

RB2038 -1.61829 -1.50265 -1.09778 1.953351 -1.58613 -1.79424 1.665203 -2.01345 -1.1049 -0.99429 -1.17072 -1.41285 -0.38672 -1.71587 -1.54801

RB2040 -0.87641 -0.96531 -1.32527 1.27393 0.150467 0.169267 -0.07609 0.015408 -0.97318 -1.38856 -1.38932 -1.32831 -0.7651 -1.25592 -1.15509

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2.3 Biota-Environment Stepwise (BEST) BEST Biota and/or Environment matching Data worksheet Name: Data14 Data type: Environmental Sample selection: All Variable selection: All Resemblance worksheet Name: Resem3 Data type: Similarity Selection: All Parameters Rank correlation method: Spearman Method: BIOENV Maximum number of variables: 5 Resemblance: Analyse between: Samples Resemblance measure: D1 Euclidean distance Variables 1 Elevation 2 Annual Exposure 3 Distance from Canoe Pass 4 Distance from Shore 5 % Clay 6 % Silt 7 % Sand 8 TOC 9 Leachable Ammonia 10 Porewater Bromide 11 Porewater Chloride 12 Porewater SO4 13 Phosphate 14 Potassium 15 Sulfur Global Test Sample statistic (Rho): 0.137 Significance level of sample statistic: 68% Number of permutations: 99 (Random sample) Number of permuted statistics greater than or equal to Rho: 67


Best results No.Vars Corr. Selections 1 0.137 5 1 0.136 7 2 0.133 5,7 2 0.127 5,8 3 0.126 5,7,8 2 0.110 6,7 3 0.109 5-7 4 0.106 5-8 3 0.104 5,7,15 4 0.102 5-7,14 Outputs Plot: Graph35




3. SUMMER ANALYSIS

3.1 nMDS

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3.2 Environmental Database (transformed and normalized)

Elevation Annual

Exposure

Distance from Canoe

Pass Distance

from Shore % Clay % Silt % Sand TOC Porewater Ammonia

Porewater Bromide

Porewater Chloride

Porewater SO4 Phosphate Potassium Sulphur

RB2001F 0.23554 0.091608 -1.08408 0.539341 1.198497 1.043324 -0.9751 0.709877 0.219842 -0.86478 -0.79407 -0.62661 -0.5911 -0.09854 -0.4743

RB2007F 1.049227 1.070156 0.32513 -0.91442 1.673192 1.520434 -1.89503 1.750437 -0.02588 0.374562 0.4264 0.389135 -0.81991 1.913928 2.069296

RB2011F -0.7228 -0.74804 0.075435 -0.16286 -0.10335 -0.18106 0.251017 0.069379 0.031906 0.214457 0.197782 0.16588 0.327241 -0.02179 -0.48517

RB2013F -0.1165 -0.25226 0.170934 -0.34384 0.256773 0.449468 -0.24517 0.631623 -0.08868 0.178924 0.131741 0.080605 -0.65401 0.609235 0.123556

RB2022F 0.749632 0.682753 0.571205 -0.55309 -0.84236 -0.49437 0.576246 -0.52219 0.89809 1.384976 1.40151 1.267697 0.952655 0.268139 0.634449

RB2024F 1.640165 1.777046 1.072179 -1.30176 -0.79165 -0.20888 0.364912 -0.39037 1.654966 1.231394 1.174339 1.374474 1.83993 -0.17529 0.199647

RB2025F -0.62291 -0.52851 1.404837 -0.26286 -0.62035 -0.71907 0.667694 -0.6176 0.171702 0.145818 0.18922 0.121878 0.730635 0.216975 0.253997

RB2038F -1.43805 -1.30132 -1.1678 1.782701 -1.29896 -1.83762 1.532104 -1.78806 -1.06232 -1.4468 -1.49386 -1.56805 -0.78568 -1.60789 -1.30368

RB2040F -0.7743 -0.79143 -1.36784 1.216793 0.528207 0.427777 -0.27667 0.156905 -1.79962 -1.21855 -1.23307 -1.20501 -0.99976 -1.10477 -1.0178

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3.3 Biota-Environment Stepwise (BEST) BEST Biota and/or Environment matching Data worksheet Name: Data15 Data type: Environmental Sample selection: All Variable selection: All Resemblance worksheet Name: Resem4 Data type: Similarity Selection: All Parameters Rank correlation method: Spearman Method: BIOENV Maximum number of variables: 5 Resemblance: Analyse between: Samples Resemblance measure: D1 Euclidean distance Variables 1 Elevation 2 Annual Exposure 3 Distance from Canoe Pass 4 Distance from Shore 5 % Clay 6 % Silt 7 % Sand 8 TOC 9 Porewater Ammonia 10 Porewater Bromide 11 Porewater Chloride 12 Porewater SO4 13 Phosphate 14 Potassium 15 Sulfur Global Test Sample statistic (Rho): 0.336 Significance level of sample statistic: 41% Number of permutations: 99 (Random sample) Number of permuted statistics greater than or equal to Rho: 40


Best results No.Vars Corr. Selections 2 0.336 4,13 3 0.320 4,12,13 3 0.317 10,11,13 3 0.316 4,10,13 3 0.314 10,12,13 4 0.308 4,10,12,13 3 0.308 4,11,13 4 0.306 4,10,11,13 2 0.295 11,13 4 0.294 10-13 Outputs Plot: Graph34

Roberts Bank Terminal 2 – Technical Data Report · 2015-04-30 · to draw relationships between...

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Transcript of Roberts Bank Terminal 2 – Technical Data Report · 2015-04-30 · to draw relationships between...