TDR – Biofilm Physical Factors - Roberts Bank Terminal 2 Project

Transcription

HEMMER A ENVI ROCHEM 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
THIS PAGE INTENTIONALLY BLANK
HEMMERA ENVIROCHEM INC.
ROBERTS BANK TERMINAL 2 – TECHNICAL DATA REPORT
BIOFILM PHYSICAL FACTORS
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
307071-00790 : Rev 0 : 27 January 2015
Page v
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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307071-00790-01-EN-REP-5001_Rev0.docx
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
3.
Seasonal Variation of the Fraser River Estuary .................................................... 13
METHODS ....................................................................................................................... 17
3.1
Sample Locations .................................................................................................. 18
3.2
Sample Collection ................................................................................................. 21
3.2.1
3.3
Assumptions of Database ................................................................................... 21
Analysed Variables ................................................................................................ 22
3.3.1
Spatial Data ......................................................................................................... 24
3.4
Databases ............................................................................................................. 25
3.5
Analysis ................................................................................................................. 25
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4.
3.5.1
Biofilm Biomass ................................................................................................... 25
3.5.2
Microphytobenthos Taxonomy Analysis .............................................................. 26
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
5.
Seasonal Differences .......................................................................................... 39
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 COLOCATED 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
Page x
307071-00790-01-EN-REP-5001_Rev0.docx
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 ABBREVI ATIONS
cm
Centimetre
g
Gram
kg
Kilogram
km
Kilometre
L
litre
307071-00790 : Rev 0 : 27 January 2015
Page xi
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
307071-00790 : Rev 0 : 27 January 2015
Page 1
SITE LOCATION
(
!
Roberts Bank
ROBERTS BANK
CAUSEWAY
(
!
(
!
File Location: U:\YVR\307071\00790_HEMM_CCIP BIOFL\10_Eng\16_Geomatics\MXDs\2014-06-10_TDR\2014-06-10_Ann_Variability_Figure1-2-1_Location.mxd
Canoe Passage
ROBERTS BANK
TERMINALS
Legend
Proposed Terminal and Causeway
Canada-U.S.A. Border
Aerial imagery Source: Port Metro Vancouver
B.C. FERRIES TERMINAL
0
0.5
Kilometres
1:50,000
1
±
Service Layer Credits: Content may not reflect National Geographic's current map policy. Sources: National Geographic, Esri, DeLorme, HERE, UNEP-WCMC, USGS, NASA, ESA, METI, NRCAN, GEBCO, NOAA,
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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Page 5
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
Primar y Producti vit y
As photosynthetic organisms found in the nutrient-rich environment of estuaries, microphytobenthos are
2
important primary producers (Cahoon 1999), producing an average of 50 and 875 g carbon/m /year
(Underwood and Barnett 2006); however, temporal and spatial differences in productivity do occur. For
2
instance, an annual estimate of 100 g carbon/m /year is estimated for microphytobenthos in temperate
2
waters, while 300 g carbon/m /year is suggested for tropical waters (Charpy-Roubaud and Sournia 1990).
2.1.2
Secondar y Producti vit y
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 Grow th
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).
Temperat ure
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
Turbidit y
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 Cycl e
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
2
2
160 mg Chlorophyll a/m during daytime exposure, and averages of 135 and 138 mg Chlorophyll a /m
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
Salinit y
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.
(Williams 1964)
Nitzschia closterium
(Williams 1964)
Nitzschia laevis
(Williams 1964)
Nitzschia ovalis
30 PSU (division rate).
(Saks 1982)
Nitzschia sigma
(Williams 1964)
Nitzschia thermalodies
(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 nutrientrich 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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Page 11
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/resuspension 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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Page 13
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
2
and Roberts Bank. The Fraser River drains a watershed of approximately 217,000 km (Environment
3
Canada 2013) with an annual average discharge of 3,630 m /s (Cameron 1996). In mid- to late April,
3
freshwater discharge, driven by snowmelt, begins to increase, reaching a peak >10,000 m /s in early
June. Discharge levels gradually decrease through July, August and September until a low discharge rate
3
of ~700 m /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.
(Barranguet et al. 1998,
Underwood et al. 2005,
McLachlan et al. 2009,
WorleyParsons 2015b)
Decreased productivity expected in winter
due to shorter day length and extreme low
water occurring at night when temperature
may be freezing
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.
(Brotas et al. 1998, Lauria
1998, Dransfeld 2000,
Jesus et al. 2009,
WorleyParsons 2015d)
307071-00790 : Rev 0 : 27 January 2015
Increased biofilm biomass occurs during
Spring Tides with reduced biomass during
Neap Tides.
Page 15
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.
(Pinckney and Zingmark
1991, Blanchard et al.
2006)
Higher elevations related to increased fine
sediments.
Extreme low tides occur during daylight hours
during spring and summer, enhancing
productivity potential.
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 colocated 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 m
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.
307071-00790 : Rev 0 : 27 January 2015
Page 17
2
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
Page 18
307071-00790-01-EN-REP-5001_Rev0.docx
Canoe Passage
RB2034
RB2040
RB2028
RB2003
RB2004
RB2005
RB2038
RB2032
RB2019
RB2018
RB2001
RB2002
RB2015
RB2006
RB2014
RB2013
RB2009
RB2017
RB2016
Roberts Bank
RB2007
RB2020
RB2022
RB2021
RB2011
RB2008
RB2010
RB2023
RB2024
RB2012
RB2025
(
!
ROBERTS BANK
CAUSEWAY
ROBERTS BANK
TERMINALS
(
!
(
!
File Location: U:\YVR\307071\00790_HEMM_CCIP BIOFL\10_Eng\16_Geomatics\MXDs\2014-07-15_Physical_Factors_Report\2014-07-15_Physical_Factors_Figure3.3-1_Co-Located_LocationV2.mxd
RB2029
Legend
Spring sampling
Summer sampling
Aerial imagery Source: Port Metro Vancouver
0
0.5
Kilometres
1:45,000
1
±
B.C. FERRIES TERMINAL
3.2
Sample Collection
Field teams were provided with the same sample coordinates at the beginning of each field session. Using
2
a handheld GPS, each team located the sample location and collected samples based on a 1 m
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 Dat abase
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).
307071-00790 : Rev 0 : 27 January 2015
Page 21
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
Anal ysed 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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Summary of Variables Measured by each Discipline at Co-Located Sampling
Locations
In Situ pH,
Conductivity and
Temperature
Macrofauna
Taxonomy
Meiofauna Taxonomy
Benthic Infauna
Grain Size
Nutrients and
Saturated Paste
Extracts
pH
Taxonomy
Total carbohydrate
Photopigment
Concentration
Sampling Event
(Season, Year)
Sediment Chemistry and
Quality
Biofilm
Moisture
Table 3.3-1
Spring 2013
X
X
X
X
X
X
X
X
X
X
X
X
Summer 2013
X
X
X
X
X
X
X
X
X
X
X
X
307071-00790 : Rev 0 : 27 January 2015
Page 23
Table 3.3-2
Measured Variables by Individual Studies
Biofilm
1
(2 mm deep)
Photopigment Density
•
•
•
•
a
•
•
•
•
•
•
•
•
•
•
Chlorophyll a
Chlorophyll b
Chlorophyll c2
Divinyl Chlorophyll
Phaeophytin a
β,ε-Carotene
β, β-Carotene
Alloxanthin
Canthaxanthin
Diadinoxanthin
Echinenone
Fucoxanthin
Lutein
Zeaxanthin
Taxonomic Composition to
Genus level
Sediment Chemistry and Quality
1
(100 mm deep)
Benthic Infauna
1
(50 mm deep)
Sediment Grain Size, Moisture
Content and pH
Meiofauna Taxonomy
Nutrients
Physical Surface Water
Variables
•
•
•
•
Nitrate (N)
Phosphate (P)
Potassium (K)
Sulphate (S)
Saturated Paste Extracts
•
•
•
•
•
•
•
•
Macrofauna Taxonomy
•
•
•
•
Temperature
pH
Conductivity
Salinity
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)
Biofilm Total Organic
Carbon (top 2 mm of
sediment)
Total carbohydrate
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
Anal ysis
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.
307071-00790 : Rev 0 : 27 January 2015
Page 25
Principal Component Anal ysi s
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
Microphytobent hos Taxonomy Anal ysi s
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 nonparametric 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 Anal ysis
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
pvalue
-
0.957
0.068
Physical Variables
2
Elevation (m)
x
Annual Exposure (hrs.)
-
-
0.960
0.090
-
-
0.940
0.013
√𝑥
-
0.946
0.023
Loge (x+1)
-
0.977
0.422
1
Distance to Canoe Passage (m)
1
2
Distance to Shore (m)
Biofilm Variables
2
Biofilm Total Organic Carbon (mg/m )
2
2
√𝑥
-
0.961
0.098
2
Loge (x+1)
-
0.982
0.632
Biofilm Fucoxanthin (mg/m )
Loge (x+1)
-
0.954
0.050
-
0.965
0.142
λ = 0.3
0.962
0.105
-
0.965
0.137
Biofilm Total Carbohydrate (mg/m )
Biofilm Chlorophyll a (mg/m )
2
Benthic Infauna Taxonomy
2
Total Harpacticoida Density (#/m )
2
Total Nematoda Density (#/m )
√𝑥
Box-Cox
2
Total Oligochaeta Density (#/m )
307071-00790 : Rev 0 : 27 January 2015
2
4
√𝑥
Page 27
Variable Name
Transformation
Performed
2
2
Total Polychaeta Density (#/m )
2
Total Invertebrate Density (#/m )
2
Total Invertebrate Biomass (g/m )
2
Constants
Wilks-Shapiro
(W) Statistic
pvalue
√𝑥
-
0.954
0.050
Loge (x+1)
-
0.982
0.624
Box-Cox
λ = 0.5
0.987
0.868
4
√𝑥
-
0.964
0.130
Loge (x+1)
-
0.971
0.258
Loge (x+1)
-
0.982
0.622
Box-Cox
λ = 0.2
0.981
0.590
Logite
-
0.941
0.014
Sediment Silt Content (%)
Logite
-
0.942
0.016
Sediment Sand Content (%)
Logite
-
0.983
0.693
Sediment Total Organic Carbon (%)
Logite
-
0.960
0.089
Total Macrofauna Density (#/m )
2
Total Macrofauna Biomass (g/m )
2
Total Meiofauna Density (#/m )
2
Total Meiofauna Biomass (g/m )
Sediment Variables
1
Sediment Clay Content (%)
1
Saturated Paste Extract (Adjusted
for Porewater)
Loge
0.967
0.178
1
Bromide (mg/kg)
-
0.951
0.038
Chloride (mg/kg)
1
-
0.949
0.033
Sulphate (mg/kg)
-
0.957
0.068
Phosphate (mg/kg)
Loge
0.986
0.816
Potassium (mg/kg)
-
0.968
0.190
Sulphate (mg/kg)
-
0.974
0.319
Ammonia (Total Leachable) (mg/kg)
Sediment Plant Nutrients
Notes: All Box-Cox Transformations conducted using a constant of 1. See definitions of transformations in Appendix 1.
= non-normal variables that were retained for analysis due to known influence over biofilm biomass and
microphytobenthos community composition
1
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 Anal ysis
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
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
0.167
0.487
0.670
0.118
0.319
-0.018
0.846
-0.022
0.086
0.517
0.520
0.111
0.336
0.687
0.238
0.194
Varimax Component Loading Values
2
Total Harpacticoida Density (#/m )
2
Total Nematoda Density (#/m )
2
Total Oligochaeta Density (#/m )
2
Total Polychaeta Density (#/m )
307071-00790 : Rev 0 : 27 January 2015
Page 29
PC1
PC2
PC3
PC4
7.208
6.263
5.267
1.722
28.833
25.054
6.889
0.357
0.225
21.068
0.878
0.135
0.387
0.615
0.617
0.696
0.424
0.155
0.440
0.050
0.336
0.102
0.906
0.340
0.218
0.888
0.101
Total Meiofauna Biomass (g/m )
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
Variance Explained by PC
% of Total Variance
2
Total Invertebrate Density (#/m )
2
Total Invertebrate Biomass (g/m )
2
Total Macrofauna Density (#/m )
2
Total Macrofauna Biomass (g/m )
2
Total Meiofauna Density (#/m )
2
0.114
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
r
p
Biofilm TOC
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
Elevation
Representing Factors
Justification
Annual exposure
Positive correlation (r = 0.941)
Distance from Shore
Negative correlation (r = -0.845)
Distance from Canoe Passage
Leachable Ammonia
Porewater Bromide
Porewater Sulphate (SO4)
Total Oligochaeta
n/a
Low to moderate correlations
Total Polychaeta
n/a
Porewater Chloride
307071-00790 : Rev 0 : 27 January 2015
Page 31
Variables
Total Invertebrate
Density
Representing Factors
Justification
Nematoda Density
Total Meiofauna Density
Total Meiofauna Biomass
Total Macrofauna
Density
n/a
Total Macrofauna
Biomass
n/a
% Sand
% Silt
% Clay
Sediment TOC
Phosphate
n/a
Potassium
Sulphur
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 =
2
14.879, p = 0.000, r = 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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Fucoxanthi n
Similar results to Chlorophyll a were found with Fucoxanthin. The best fit model (AICc = 35.863) contained
2
Porewater Chloride, Total Macrofauna Biomass, and % Sand (F3, 46 = 21.104, p < 0.000, r = 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
Coefficient
Standard
Error
Standardised
Coefficient
Constant
3.267644
0.191
0.000
Porewater Chloride
0.000087
0.000
0.593
Macrofauna Biomass
-0.080325
0.051
% Sand
-0.171689
0.035
Tolerance
t
p
17.064
0.000
0.980
6.135
0.000
-0.180
0.688
-1.564
0.125
-0.554
0.700
-4.846
0.000
Total Car boh ydrate
The best fit model for Total Carbohydrate (AICc = 490.979) was found to be significant (F4, 45 = 3.510,
2
p = 0.014, r = 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
Coefficient
Standard
Error
Standardised
Coefficient
121.811408
26.314
0.000
Porewater Chloride
0.007516
0.002
0.743
0.291
3.077
0.004
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
Constant
Tolerance
t
4.629
p
0.000
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Page 33
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
2
Phosphate (F5, 44 = 18.779, p = 0.000, r = 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
Coefficient
Standard
Error
Standardised
Coefficient
Constant
12.587789
1.046
0.000
Porewater Chloride
0.000068
0.000
0.412
Polychaeta Density
0.000680
0.000
Invertebrate Density
-0.186315
% Sand
Phosphate
Tolerance
t
p
12.031
0.000
0.295
2.629
0.012
0.290
0.490
2.381
0.022
0.071
-0.333
0.447
-2.616
0.012
-0.227043
0.039
-0.657
0.581
-5,876
0.000
-0.314588
0.189
-0.20
0.415
-1.661
0.104
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A
B
C
Figure 4.1-1 Scatterplot and Partial Regression Relationship of Chlorophyll a Density against Porewater Chloride (A), Polychaeta
Density (B), and % Sand Composition (C)
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A
B
Figure 4.1-2 Scatterplot and Partial Regression Relationship of Fucoxanthin Density against Porewater Chloride (A), and % Sand
Composition (B)
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A
B
Figure 4.1-3 Scatterplot and Partial Regression Relationship of Total Carbohydrate Density against Porewater Chloride (A), and % Sand
Composition (B)
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A
C
B
D
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)
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4.2
Microphytobenthos Taxonom y Anal ysis
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
Freshw ater 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 freshwaterinfluenced 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 Nutri ents 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
Benthi c 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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7.
REFERENCES
Admiraal, W. 1984. The ecology of estuarine sediment-inhabiting diatoms. Pages 269–322 in F. E. Round
and D. J. Chapman, editors. Progress in Phycological Research. Biopress, Bristol, UK.
Ahel, M., R. G. Barlow, and R. F. C. Mantoura. 1996. Effect of salinity gradients on the distribution of
phytoplankton pigments in a stratified estuary. Marine Ecology Progress Series 143:289–295.
Barranguet, C., J. Kromkamp, and J. Peene. 1998. Factors controlling primary production and
photosynthetic characteristics of intertidal microphytobenthos. Marine Ecology Progress Series
173:117–126.
Bendell-Young, L., K. Yin, C. Thomas, P. J. Harrison, T. Feeney, J. L. Arvai, C. D. Levings, and L. Ross.
2004. Biogeochemistry of the intertidal area of the Fraser River estuary. Pages 189–212 in B. J.
Groulx, D. C. Mosher, J. L. Luternauer, and D. E. Bilderback, editors. Fraser River Delta, British
Columbia: Issues of an Urban Estuary. Geological Survey of Canada, Bulletin 567.
Beninger, P. G., R. W. Elner, M. Moranais, and P. Decottignies. 2011. Downward trophic shift during
breeding migration in the shorebird Calidris mauri (Western sandpiper). Marine Ecology Progress
Series 428:259–269.
Blanchard, G. F., T. Agion, J.-M. Guarini, O. Herlory, and P. Richard. 2006. Analysis of the short-term
dynamics of microphytobenthic biomass on intertidal mudflats. Pages 85–97 in J. Kromkamp,
editor. Functioning of Microphytobenthos in Estuaries: Proceedings of the Microphytobenthos
Symposium, Amsterdam. The Netherlands. Royal Netherlands Academy of Arts and Sciences,
Amsterdam.
Blanchard, G. F., J.-M. Guarini, P. Gros, and P. Richard. 1997. Seasonal effect on the relationship
between the photosynthetic capacity of intertidal microphytobenthos and temperature. Journal of
Phycology 33:723–728.
Blanchard, G. F., J.-M. Guarini, F. Orvain, and P.-G. Sauriau. 2001. Dynamic behaviour of benthic
microalgal biomass in intertidal mudflats. Journal of Experimental Marine Biology and Ecology
264:85–100.
Blanchard, G. F., B. Simon-Bouhet, and J.-M. Guarini. 2002. Properties of the dynamics of intertidal
microphytobenthic biomass. Journal of the Marine Biological Association of the UK 82:1027–1028.
Bock, M. J., and D. C. Miller. 1995. Storm effects on particulate food resources on intertidal sandflat.
Journal of Experimental Marine Biology and Ecology:81–101.
Bray, J. R., and J. T. Curtis. 1957. An ordination of the upland forest communities of southern Wisconsin.
Ecological Monograph 27:325–349.
Brotas, V., T. Cabrita, A. Portugal, J. Serodio, and F. Catarino. 1995. Spatio-temporal distribution of the
microphytobenthic biomass in intertidal flats of Tagus Estuary (Portugal). Hydrobiologia
300/301:93–104.
Brotas, V., M. R. Plante-Cuny, and M. R. Plante-Cuny. 1998. Spatial and temporal patterns of
microphytobenthic taxa of estuarine tidal flats in the Tagus Estuary (Portugal) using pigment
analysis by HPLC. Marine Ecology Progress Series 171:43–57.
Page 48
307071-00790-01-EN-REP-5001_Rev0.docx
De Brouwer, J. F. C., and L. J. Stal. 2001. Short-term dynamics in microphytobenthos distribution and
associated extracellular carbohydrates in surface sediments of an intertidal mudflat. Marine
Ecology Progress Series 218:33–44.
Cahoon, L. B., J. E. Nearhoof, and C. L. Tilton. 1999. Sediment grain size effect on benthic microalgal
biomass in shallow aquatic ecosystems. Estuaries 22:735–741.
Cahoon, L. B. 1999. The role of benthic microalgae in neritic ecosystems. Oceanography and Marine
Biology: An Annual Review 37.
Cahoon, L. B. 2006. Upscalling primary production estimates: Regional and global scale estimates of
microphytobenthos production. Functioning of Microphytobenthos in Estuaries 21:99–108.
Cameron, E. M. 1996. Hydrogeochemistry of the Fraser River, British Columbia: seasonal variation in
major and minor components. Journal of Hydrology 182:209–225.
Charpy-Roubaud, C., and A. Sournia. 1990. The comparative estimation of phytoplanktonic,
microphytobenthic and macrophytobenthic primary production in the oceans. Marine Microbial
Food Webs 4:31–57.
Chiu, J. M. Y., V. Thiyagarajan, M. M. Y. Tsoi, and P. Y. Qian. 2006. Qualitative and quantitative changes
in marine biofilms as a function of temperature and salinity in summer and winter. Biofilms 2:183–
195.
Cloern, J. E., T. M. Powell, and L. M. Huzzey. 1989. Spatial and temporal variability in south San
Francisco Bay (USA). II. Temporal changes in salinity, suspended sediments, and phytoplankton
biomass and productivity over tidal time scales. Estuarine, Coastal and Shelf Science 28:599–
613.
Cloern, J. E. 1987. Turbidity as a control on phytoplankton biomass and productivity in estuaries.
Continental Shelf Research 7:1367–1381.
Van Colen, C., F. Montserrat, M. Vincx, P. Herman, T. Ysebaert, and S. Degraer. 2008. Macrobenthic
recovery from hypoxia in an estuarine tidal mudflat. Marine Ecology Progress Series 372:31–42.
Colijn, F., and K. S. Dijkema. 1981. Species Composition of Benthic Diatoms and Distribution of
Chlorophyll a on an Intertidal Flat in the Dutch Wadden Sea. Marine Ecology Progress Series 4:9–
21.
Compass Resource Management. 2013. Roberts Bank Terminal 2 Technical Advisory Group (TAG)
Process Report: Biofilm & Shorebirds DRAFT 1. Prepared for Port Metro Vancouver by Compass
Resource Management Ltd. Vancouver, BC.
Consalvey, M., B. Jesus, R. G. Perkins, V. Brotas, G. J. C. Underwood, and D. M. Paterson. 2004.
Monitoring Migration and Measuring Biomass in Benthic Biofilms: The Effects of Dark/far-red
Adaptation and Vertical Migration on Fluorescence Measurements. Photosynthesis Research
81:91–101.
Decho, A. W. 2000. Microbial biofilms in intertidal systems: an overview. Continental Shelf Research
20:1257–1273.
Defew, E. C., R. G. Perkins, and D. M. Paterson. 2004. The influence of light and temperature interactions
on a natural estuarine microphytobenthic assemblage. Biofilms 1:21–30.
307071-00790 : Rev 0 : 27 January 2015
Page 49
Delgado, M., V. N. de Jonge, and H. Peletier. 1991. Experiments on resuspension of natural
microphytobenthos populations. Marine Biology 108:321–328.
Denis, L., F. Gevaert, and N. Spilmont. 2012. Microphytobenthic production estimated by in situ oxygen
microprofiling: short-term dynamics and carbon budget implications. Journal of Soils and
Sediments 12:1517–1529.
Dransfeld, L. 2000. The environmental and photo-physiological control of microphytobenthos primary
production on an intertidal mudflat. Dissertation, University of Southampton.
Drinnan, R. W., and M. J. R. Clark. 1980. Fraser River Estuary Study - Water Quality. Government of
Canada, Victoria, B.C.
Du, G. ., J. H. Oak, H. Li, and I. K. Chung. 2010. Effect of light and sediment grain size on the verticall
migration of benthic diatoms. Algae 25:133–140.
Elner, R. W., P. G. Beninger, D. L. Jackson, and T. M. Potter. 2005. Evidence of a new feeding mode in
western sandpiper (Calidris mauri) and dunlin (Calidris alpina) based on bill and tongue
morphology and ultrastructure. Marine Biology 146:1223–1234.
Environment Canada. 2013. Real-time Hydrometric Data - Environment Canada. Water Office.
<http://www.wateroffice.ec.gc.ca/index_e.html>. Accessed 28 Apr 2014.
Evrard, V., K. Soetaert, C. Heip, M. Huettel, M. Xenopoulos, and J. Middelburg. 2010. Carbon and
nitrogen flows through the benthic food web of a photic subtidal sandy sediment. Marine Ecology
Progress Series 416:1–16.
Galvan, K., J. W. Fleeger, and B. Fry. 2008. Stable isotope addition reveals dietary importance of
microphytoplankton and microphytobenthos to saltmarsh infauna. Marine Ecology Progress
Series 359:37–49.
Grinham, A. R., T. J. B. Carruthers, P. L. Fisher, J. U. Udy, and W. C. Dennison. 2007. Accurately
measuring the abundance of benthic microalgae in spatially variable habitats. Limnology and
Oceanography: Methods 5:119–125.
Guarini, J. M., G. F. Blanchard, P. Gros, and S. J. Harrison. 1997. Modelling the mud surface temperature
on intertidal flats to investigate the spatio-temporal dynamics of the benthic microalgal
photosynthetic capacity. Marine Ecology Progress Series 153:25–36.
Hamilton, D. P., T. U. Chan, B. J. Robson, and B. R. Hodges. 2000. The effects of freshwater flows and
salinity on phytoplankton biomass and composition in an urban estuary, the Swan River, Western
Australia. Pages 114–119 in. Proceedings, 3rd International Hydrology and Water Resources
Symposium. Volume 1.
Harrison, P. J., J. D. Fulton, F. J. R. Taylor, and T. R. Parsons. 1983. Review of the biological
oceanography of the Strait of Georgia: pelagic environment. Canadian Journal of Fisheries and
Aquatic Sciences 40:1064–1094.
Hemmera. 2014a. Roberts Bank Terminal 2 technical data report, sediment and water quality
characterisation studies. Vancouver, BC.
Hemmera. 2014b. Roberts Bank Terminal 2 technical data report, benthic invertebrates. Vancouver, BC.
Page 50
307071-00790-01-EN-REP-5001_Rev0.docx
Herlory, O., J. Guarini, P. Richard, and G. Blanchard. 2004. Microstructure of microphytobenthic biofilm
and its spatio-temporal dynamics in an intertidal mudflat (Aiguillon Bay, France). Marine Ecology
Progress Series 282:33–44.
Herman, P. M. J., J. J. Middelburg, and C. H. R. Heip. 2001. Benthic community structure and sediment
processes on an intertidal flat: results from the ECOFLAT project. Continental Shelf Research
21:2055–2071.
Herman, P. M., J. J. Middelburg, J. Widdows, C. H. Lucas, and C. H. Heip. 2000. Stable isotopes as
trophic tracers: combining field sampling and manipulative labelling of food resources for
macrobenthos. Marine Ecology Progress Series 204:79–92.
Hillebrand, H., and U. Sommer. 1997. Response of epilithic microphytobenthos of the Western Baltic Sea
to in situ experiments with nutrient enrichment. Marine Ecology Progress Series 160:35–46.
Hillebrand, H., B. Worm, and H. K. Lotze. 2000. Marine microbenthic community structure regulated by
nitrogen loading and grazing pressure. Marine Ecology Progress Series 204:27–38.
Huettel, M., and A. Rusch. 2000. Transport and degradation of phytoplankton in permeable sediment.
Limnology and Oceanography 45:534–549.
Jesus, B., V. Brotas, L. Ribeiro, C. R. Mendes, P. Cartaxana, and D. M. Paterson. 2009. Adaptations of
microphytobenthos assemblages to sediment type and tidal position. Continental Shelf Research
29:1624–1634.
Jesus, B., C. R. Mendes, V. Brotas, and D. M. Paterson. 2006. Effect of sediment type on
microphytobenthos vertical distribution: Modelling the productive biomass and improving ground
truth measurements. Journal of Experimental Marine Biology and Ecology 332:60–74.
Kostaschuk, R. A., and J. L. Luternauer. 2004. Sedimentary processes and their environmental
significance: lower main channel, Fraser River estuary. Pages 81–92 in B. J. Groulx, D. C.
Mosher, J. L. Luternauer, and D. E. Bilderback, editors. Fraser River Delta, British Columbia:
Issues of an Urban Estuary. Geological Survey of Canada, Bulletin 567.
Kuwae, T., P. G. Beninger, P. Decottignies, K. J. Mathot, D. R. Lund, and R. W. Elner. 2008. Biofilm
Grazing in a higher vertebrate: The Western Sandpiper, Calidris Mauri. Ecology 89:599–606.
Lauria, M. L. 1998. Physical Constraints on Phytoplankton in Estuaries and Shallow Coastal Waters.
Dissertation, University of Southampton, Southampton.
Levings, C. D., and J.-B. Coustalin. 1975. Zonation of Intertidal Biomass and Related Benthic Data from
Sturgeon and Roberts Banks Fraser River Estuary British Columbia. Environment Canada.
Fisheries and Marine Service.
Levinton, J. S. 1991. Variable feeding behavior in three species of macoma (Bivalvia: Tellinacea) as a
response to water flow and sediment transport. Marine Biology 110:375–383.
Lionard, M., K. Muylaert, D. V. Gansbeke, and W. Vyverman. 2005. Influence of changes in salinity and
light intensity on growth of phytoplankton communities from the Schelde River and Estuary
(Belgium/The Netherlands). Hydrobiologia 540:105–115.
Lueangthuwapranit, C., U. Sampantarak, and S. Wongsai. 2011. Distribution and abundance of
phytoplankton: influence of salinity and turbidity gradients in the Na Thap River, Songkhla
Province, Thailand. Journal of Coastal Research 27:585–594.
307071-00790 : Rev 0 : 27 January 2015
Page 51
MacIntyre, H. L., R. J. Geider, and D. C. Miller. 1996. Microphytobenthos: The Ecological Role of the
“Secret Garden” of Unvegetated, Shallow-Water Marine Habitats. I. Distribution, Abundance and
Primary Production. Estuaries 19:186.
Majdi, N., W. Traunspurger, S. Boyer, B. Mialet, M. Tackx, R. Fernandez, S. Gehner, L. Ten-Hage, and E.
Buffan-Dubau. 2011. Response of biofilm-dwelling nematodes to habitat changes in the Garonne
River, France: influence of hydrodynamics and microalgal availability. Hydrobiologia 673:229–244.
McClatchie, S., S. K. Juniper, and G. A. Knox. 1982. Structure of a mudflat diatom community in the Avon‐
Heathcote Estuary, New Zealand. New Zealand Journal of Marine and Freshwater Research
16:299–309.
McLachlan, D. H., C. Brownlee, A. R. Taylor, R. J. Geider, and G. J. C. Underwood. 2009. Light-induced
motile responses of the estuarine benthic diatoms Navicula perminuta and Cylindrotheca
closterium (Bacillariophyceae). Journal of Phycology 45:592–599.
Miller, D. C., M. J. Bock, and E. J. Turner. 1992. Deposit and suspension feeding in oscillatory flows and
sediment fluxes. Journal of Marine Research 50:489–520.
Morrisey, D. J. 1988. Differences in effects of grazing by deposit-feeders Hydrobia ulvae (Pennant)
(Gastropoda: Prosobranchia) and Corophium arenarium Crawford (Amphipoda) on sediment
microalgal populations. II. Quantitative effects. Journal of Eperimental Marine Biology and Ecology
118:43–53.
Murugaraj, G. N., and Jeyachandran, S. 2007. Effect of salinity stress on the marine diatom Amphora
coffeaeformis (Ag.) Kuetz (Bacillariophyceae) in relation to proline accumulation. Seaweed
Resource Utilization 29:227–231.
Muylaert, K., L. Van Nieuwerburgh, K. Sabbe, and W. Vyverman. 2002. Microphytobenthos communities
in the freshwater tidal to brackish reaches of the Schelde estuary (Belgium). Belgium Journal of
Botany 135:15–26.
Muylaert, K., and K. Sabbe. 1999. Spring phytoplankton assemblages in and around the maximum
turbidity zone of the estuaries of the Elbe (Germnay), the Schelde (Belgium/The Netherlands) and
the Gironde (France). Journal of Marine Systems 22:133–149.
Nof, D. 1979. Generation of fronts by mixing and mutual intrusion. Journal of Physical Oceanography
9:298–310.
Northwest Hydraulics Consultants. 2014. Roberts Bank Terminal 2 technical data report, coastal
geomorphology study. Vancouver, BC.
Pace, M. L., S. Shimmel, and W. M. Darley. 1979. The effect of grazing by a gastropod, Nassarius
obsoletus, on the benthic microbial community of a salt marsh mudflat. Estuarine and Coastal
Marine Science 9:121–134.
Parsons, T. R., J. Stronach, G. A. Borstad, G. Louttit, and R. I. Perry. 1981. Biological fronts in the Strait of
Georgia, British Columbia, and their relation to recent measurements of primary productivity.
Marine Ecology Progress Series 6:237–242.
Patil, J. S., and A. C. Anil. 2005. Biofilm diatom community structure: Influence of temporal and
substratum variability. Biofouling 21:189–206.
Page 52
307071-00790-01-EN-REP-5001_Rev0.docx
Perkins, R. G., G. J. C. Underwood, V. Brotas, G. C. Snow, B. Jesus, and L. Ribeiro. 2001. Responses of
microphytobenthos to light: primary production and carbohydrate allocation over an emersion
period. Marine Ecology Progress Series 223:101–112.
Pinckney, J. L., and R. G. Zingmark. 1991. Effects of tidal stage and sun angles on intertidal benthic
microalgal productivity. Marine Ecology Progress Series 76:81.
Pomeroy, W. M., and C. D. Levings. 1980. Association and feeding relationships between Eogammarus
confervicolus (Amphipoda, Gammaridae) and benthic algae on Sturgeon and Roberts Banks,
Fraser River estuary. Canadian Journal of Fisheries and Aquatic Sciences 37:1–10.
Pratt, D. R., C. A. Pilditch, A. M. Lohrer, and S. F. Thrush. 2013. The effects of short-term increases in
turbidity on sandflat microphytobenthic productivity and nutrient fluxes. Journal of Sea Research.
Ross, L. R. 1998. Primary Productivity on Sturgeon Bank. Thesis, The University of British Columbia,
Department or Earth and Ocean Sciences Oceanography Division.
Roubeix, V., and C. Lancelot. 2008. Effect of salinity on growth, cell size and silification of an euryhaline
freshwater diatom: Cyclotella meneghiniana Kutz. Transitional Waters Bulletin 31–38.
Saks, N. M. 1982. Temperature, salinity and ultraviolet irradiation effects on the growth of strains of
Nitzschia ovalis. Marine Biology 175–179.
Sauer, J., K. Wenderoth, U. G. Maier, and E. Rhiel. 2002. Effects of Salinity, Light and Time on the
Vertical Migration of Diatom Assemblages. Diatom Research 17:189–203.
Scholz, B., and G. Liebezeit. 2012. Growth responses of 25 benthic marine Wadden Sea diatoms isolated
from the Solthorn tidal flat (southern North Sea) in relation to varying culture conditions. Diatom
Research 27:65–73.
Smith, D. J., and G. J. Underwood. 1998. Exopolymer production by intertidal epipelic diatoms. Limnology
and Oceanography 43:1578–1591.
Stockner, J. G., D. D. Cliff, and K. R. S. Shortreed. 1979. Phytoplankton ecology of the Strait of Geogia,
British Columbia. Journal of the Fisheries Research Board of Canada 36:657 – 666.
Sullivan, M. J., and C. A. Currin. 2000. Community structure and functional dynamics of benthic
microalgae in salt marshes. Pages 81–106 in M. P. Weinstein and D. A. Kreeger, editors.
Concepts and Controversies in Tidal Marsh Ecology. Kluwer Academic Publishers, Dordrecht.
Sutherland, T. F., R. W. Elner, and J. D. O’Neill. 2013. Roberts Bank: ecological crucible of the Fraser
River estuary. Progress in Oceanography 115:171–180.
Sverdrup, H. U., M. W. Johnson, and R. H. Fleming. 1942. The Oceans Their Physics, Chemistry, and
General Biology. Prentice-Hall, New York.
Taghon, G. L., and R. R. Greene. 1990. Effects of sediment-protein concentration on feeding and growth
rates of Abarenicola pacifica Healy et Wells (Polychaeta: Arenicolidae). Journal of Experimental
Marine Biology and Ecology 136:197–216.
Telesh, I. V., and V. V. Khlebovich. 2010. Principal processes within the estuarine salinity gradient: A
review. Marine Pollution Bulletin 61:149–155.
Terra Remote Sensing Inc. 2011. Final LIDAR Survey Operations Report: Bathymetry and Shoreline
Survey. Container Capacity Improvement Program (CCIP) - Roberts Bank, BC.
307071-00790 : Rev 0 : 27 January 2015
Page 53
Thornton, D. C., L. F. Dong, G. J. Underwood, and D. B. Nedwell. 2002. Factors affecting
microphytobenthic biomass, species composition and production in the Colne Estuary (UK).
Aquatic Microbial Ecology 27:285–300.
Trobajo, R., L. Rovira, D. G. Mann, and E. J. Cox. 2011. Effects of salinity on growth and on valve
morphology of five estuarine diatoms. Phycological Research 59:83–90.
Ulanova, A., S. Busse, and P. Snoeijs. 2009. Coastal Diatom-Environment Relationships in the Brackish
Baltic Sea1. Journal of Phycology 45:54–68.
Underwood, G. J. C., and M. Barnett. 2006. What determines species composition in microphytobenthic
biofilms? Functioning of microphytobenthos in estuaries. J. Kromkamp, editor. Functioning of
Microphytobenthos in Estuaries: Proceedings of the Microphytobenthos Symposium, Amsterdam,
The Netherlands. Royal Netherlands Academy of Arts and Sciences, Amsterdam.
Underwood, G. J. C., and J. Kromkamp. 1999. Primary production by phytoplankton and
microphytobenthos in estuaries. Advances in Ecological Research. 29:93–153.
Underwood, G. J. C., R. G. Perkins, M. C. Consalvey, A. R. M. Hanlon, K. Oxborough, N. R. Baker, and D.
M. Paterson. 2005. Patterns in microphytobenthic primary productivity: species-specific variation
in migratory rhythms and photosynthetic efficiency in mixed-species biofilms. Limnology and
Oceanography 50:755–767.
Underwood, G. J. C., J. Phillips, and K. Saunders. 1998. Distribution of estuarine benthic diatom species
along salinity and nutrient gradients. European Journal of Phycology 33:173–183.
Underwood, G. J. C., and D. J. Smith. 1998. Predicting epipelic diatom exopolymer concentrations in
intertidal sediments from sediment chlorophyll a. Microbial Ecology 35:116–125.
Underwood, G., and J. Kromkamp. 1999. Primary production by phytoplankton and microphytobenthos in
estuaries. Advances in Ecological Researches 29:93–153.
Voros, L., and J. Padisak. 1991. Phytoplankton biomass and chlorophyll-a in some shallow lakes in central
Europe. Hydrobiologia 215:111–119.
Wang, Y. . 2003. The intertidal erosion rate of cohesive sediment: a case study from Long Island Sound.
Estuarine, Coastal and Shelf Science 56:891–896.
Williams, R. B. 1964. Division rates of salt marsh diatoms in relation to salinity and cell size. Ecology
45:877–880.
WorleyParsons. 2015a. Biofilm community at Roberts Bank - analyses to support hyperspectral mapping.
Burnaby, BC.
WorleyParsons. 2015b. Roberts Bank Terminal 2 technical data report, biofilm annual variability. Burnaby,
BC.
WorleyParsons. 2015c. Roberts Bank Terminal 2 technical data report, biofilm critical shear stress.
Burnaby, BC.
WorleyParsons. 2015d. Roberts Bank Terminal 2 technical data report, biofilm regeneration. Burnaby, BC.
Wright, S. W., S. W. Jeffrey, R. F. C. Mantoura, C. A. Llewellyn, T. Bjornland, D. Repeta, and N.
Welschmeyer. 1991. Improved HPLC method for the analysis of chlorophyll a and carotenoids
from marine phytoplankton. Marine Ecology Progress Series 77:183–196.
Page 54
307071-00790-01-EN-REP-5001_Rev0.docx
Yin, K. D., R. H. Goldblatt, P. J. Harrison, M. St. John, P. J. Clifford, and R. J. Beamish. 1997. Importance
of wind and river discharge in influencing nutrient dynamics and phytoplankton production in
summer in the central Strait of Georgia. Marine Ecology Progress Series 161:173–183.
Yin, K., P. J. Harrison, S. Pond, and R. J. Beamish. 1995a. Entrainment of nitrate in the Fraser River
Estuary and its biological implications I. Effects of the salt wedge. Estuarine, Coastal and Shelf
Science 40:505 – 528.
Yin, K., P. J. Harrison, S. Pond, and R. J. Beamish. 1995b. Entrainment of nitrate in the Fraser River
Estuary and its biological implications. II. Effects of Spring vs. Neap tides and river discharge.
Estuarine, Coastal and Shelf Science 40:529 – 544.
Yin, K. 1994. Dynamics of nutrients and phytoplankton production in the Strait of Georgia estuary, British
Columbia, Canada. The University of British Columbia, Vancouver, BC.
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Appendix 1
Extended Statistical Methodology Background
307071-00790 : Rev 0 : 27 January 2015
Appendices
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.
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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
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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.
307071-00790-01-EN-REP-5001_Rev0_App1.docx
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.
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Page 3
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 pvalue based on random permutations of the data.
Page 4
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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Page 5
Appendix 2
Co-located Database Used for Biofilm Physical
Factors Analysis
307071-00790 : Rev 0 : 27 January 2015
Appendices
Taxonomy Database
Row Labels
Achnanthes spp.
Achnanthidium spp.
Akashiwo sp.
Amphidinium spp.
Amphora spp.
Biddulphia sp.
Caloneis sp.
Chaetoceros spp.
Closteriopsis sp.
Cocconeis spp.
Coscinodiscus spp.
Cyclotella spp.
Cylindrotheca spp.
Cymbella sp.
Delphineis/Rhaphoneis
Diatoma sp.
Diploneis spp.
Entomoneis sp.
Epithemia sp.
Eunotia sp.
Eunotogramma sp.
Eutreptiella sp.
Fragilaria spp.
Gomphonema sp.
Gyrosigma/Pleurosigma
Heterocapsa sp.
Heterosigma spp.
Lyrella sp.
Melosira sp.
Merismopedia sp.
Navicula spp.
Nitzschia spp.
Odontella spp.
Paralia sp.
Phaeocystis sp.
Pinnularia sp.
Prorocentrum spp.
Pseudo‐nitzschia spp.
Rhoicosphenia sp.
Rhopalodia sp.
Skeletonema sp.
Striatella sp.
Surirella spp.
Synedra spp.
Thalassiosira spp.
Torodinium spp.
Ulothrix sp.
Unknown alga spp.
Unknown centrales spp.
Unknown Cryptomonadaceae
Unknown Dinoflagellate sp.
Unknown pennales sp.
Urosolenia sp.
RB2001
RB2001F
2.71E+09 1.56E+09
RB2003
1.9E+09
4.37E+08
RB2003F RB2007
RB2007F RB2009
1.92E+08
2.88E+09
1.6E+09 1.24E+09 1.44E+09
2.56E+08
3.92E+08
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
RB2047F
2.12E+08
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+09
14580469
9383470
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+09
63891944
25272813
16923759
6224831
2.35E+08
48601564
6.39E+08
48601564
3.4E+08
2.56E+08
5.11E+08
1.96E+08
65360724
19723602
4.37E+08
13149068
1.92E+09
76553748
1.94E+08
42529860
1.28E+08
6574534
6574534
63891944
3.94E+08 2.53E+09 42529860 3.19E+09 1.64E+08
8.28E+08 8.94E+09 4.59E+08 1.04E+10 5.06E+08
8.88E+08
90260047
6943081
4.24E+08
70726157
50545627
3127823
6255647
16923759 10256111 17231464 3.55E+08 22836879
16923759
25847195
57092199
10608923
3536308
10608923
70726157
5724341 18674493 10656245 48292000 14417273 63891944 1.58E+08 8655073 81603932
30456980 33060366
59233156
12449662
12073000
63891944
44338269
22040244
96584000
1.44E+08
44338269
44080488 26468149 2297528
59233156
2.61E+08
9.19E+08
1.39E+08
60310123
18674493
36219000 25230227 31945972
4327537 69946227 38795985
2.76E+08
6892585
59233156
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
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
9467180
1.14E+08
1.23E+08
31968735
1.4E+08
68912497
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
14380494
26468149
5724341
26468149
33059955
8655073
14808289
50545627
1.11E+08
75737437
31591017
71792779
11448682
31968735
32438864
69946227
11485416
16529977
3.27E+08 12346822 3.11E+09
20829242
2.12E+08
25272813
50771277
8615732
3536308
3604318 15972986 14380494
30456980
49589932
3127823
1.48E+08
70726157
21637683
1.48E+08
2.35E+08 2.96E+08
8655073
13785171
2.68E+09
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
1.3E+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
85204617
18766941
24146000
14380494 12982610
9190114
1.04E+09
17357701
8615732
3.29E+08
51519071 49798647 10656245 48292000
63891944
1.4E+08
58321877
1.6E+08
29160938
2.4E+08
14580469 21678763
14580469
6.41E+08
21678763
4.52E+08
4E+08
21678763
43741408 21678763 80089901
21678763
1.3E+08 2.08E+09
87482815
6.41E+08
1.34E+09 9.11E+08 5.53E+09
1.24E+09 1.45E+09 6.25E+09
58321877
29160938
34023888
1.55E+08
1.41E+08
70726157
1.41E+08
10414621
63182033
12636407
30768334
46630818
43789528
92996055
92996055
12449662
36219000
15972986
11084567
2297528
30456980
9467180
1.27E+09
9467180
28401539
47085658
1.6E+08
5.7E+08
41068322
4.52E+08
88837768
74041445
72902346
79404447
11657705
53281225
91370940
57427081
49589932
11084567
6574534
19723602
37040467
22799408
2.56E+08
1.41E+08
6.74E+08
3536308
3127823
6255647
3127823
31243863
1.92E+08
65360724
7.4E+08
24300782
2.12E+09
70726157
7208636 31945972
37909220
11020122
4327537
9383470
48601564
5724341
12636407
80397913
99179864
11485416 29616578
21678763
1.08E+08
1.6E+08
87482815
6224831
1.48E+08
22836879
33847518
31124154
96584000
47918958
1.12E+08
4327537
4327537
3.67E+08
8655073
4327537
2871911
67695036
4.22E+08
10608923
7072616
31124154
1.81E+08
44424867
27711418
5542284
33253702
44080488
22040244
33060366
1.32E+08
9190114
9190114
1.18E+08
14808289
43741408
1.6E+08
80089901
1.02E+08
6.41E+08
1 of 1
Transformed Database
Site ID
RB2001
RB2001F
RB2002
RB2003
RB2004
RB2004F
RB2005
RB2006
RB2006F
RB2007
RB2007F
RB2008
RB2009
RB2010
RB2011
RB2011F
RB2012
RB2013
RB2013F
RB2014
RB2015
RB2015F
RB2016
RB2017
RB2018
RB2018F
RB2019
RB2020
RB2021
RB2021F
RB2022
RB2022F
RB2023
RB2023F
RB2024
RB2024F
RB2025
RB2025F
RB2028
RB2028F
RB2029
RB2029F
RB2032
RB2032F
RB2034
RB2034F
RB2038
RB2038F
RB2040
RB2040F
Boc‐Cox [Biofilm [Total [Total Ln[Total [Total Total Ln[Biofilm Ln[Biofilm [Total Ln[Meiofa
[Elevation ‐
Invertebra [Macrofau Ln[Macrof una Oligochae Polychaet Invert Distance [Distance Carbohydr Ln[Biofilm Chlorophy Fucoxanth Harpactic Box‐
Box‐Cox na Density auna a Density te ll a in oida Cox[Total ta Chart Annual from from ate TOC Density [Meiofaun
Biomass (#/m2)]^1 Biomass (#/m2) Canoe Shore (mg/m2)] (mg/m2) (mg_m2) (mg_m2) (#/m2)]^1 Nematoda (#/m2)]^1 (#/m2)]^1 (#/m2) a Biomass LOGITe[% LOGITe[% Datum Exposure Season
/2
+1]
(g/m2)]
+1]
/2
(#/m2)]
/4
(m)]^2
(hrs.) Pass (m) (m)]^1/2 ^1/2
+1]
+1)
/4
(g/m2) +1] +1]
(g/m2)]
Clay)
Silt)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Ln[Adjust
ed LOGITe[% LOGITe[% Ammonia Sand)
TOC)
(mg/kg)]
‐0.58191 ‐4.88532 1.194145
‐0.98904 ‐4.91255 2.033488
‐0.49457 ‐4.89885 1.576976
‐0.94405
‐4.8458 0.899604
‐1.97306 ‐4.71276 1.14636
‐2.29785 ‐4.71276 1.866526
‐1.56715
‐4.6684 1.833698
‐2.71764 ‐4.29901 2.192185
‐3.23401 ‐4.29147 2.754188
‐1.60919 ‐4.26919 1.98054
‐2.25219 ‐4.15787 1.849993
‐1.49851 ‐4.70149 1.064007
‐0.974 ‐5.06089
1.1971
0.550198 ‐5.19749 1.877239
0.581015
‐5.2933 3.010052
0.694549 ‐5.37709 1.893144
0.057653 ‐4.98401 2.514264
0.089598 ‐5.06089 1.90406
0.013232 ‐4.96931 1.803091
‐0.34515
‐5.2933 2.196385
‐1.23571 ‐4.89885 1.908073
‐1.20124 ‐4.95482 1.763024
0.164839 ‐5.49266 1.879728
‐1.07438 ‐4.89885
2.0035
‐0.56318 ‐5.21594 1.713751
‐1.0237 ‐5.16157 1.842651
2.715539 ‐6.21261 2.380547
‐0.21107 ‐5.23473 1.769989
1.835047 ‐5.80614 2.858782
1.596615 ‐5.56895 3.011157
1.468148 ‐6.07254 2.499649
1.141121 ‐5.80614 2.539981
1.816082 ‐5.94964 2.667228
1.798918 ‐5.98896 2.703757
1.11834 ‐5.56895 3.374831
0.850939 ‐5.71053 3.105189
1.379819 ‐5.68057 2.959484
1.266689 ‐5.87533 1.997539
0.68762 ‐5.71053 1.436262
0.164597 ‐5.12689 1.064617
‐0.15235 ‐5.06089 1.319892
‐0.9106 ‐5.10998 1.185995
2.426568 ‐6.50079 1.454087
1.906259 ‐6.21261 1.591738
2.787376 ‐6.72423 1.269931
3.082257 ‐6.81134 1.459734
2.610196 ‐6.64409 1.064007
2.453612 ‐6.72423 1.076012
0.339881 ‐5.39917 1.169487
‐0.03002 ‐5.31361 0.525424
Adjusted Bromide (mg/kg)
14.91443
17.53247
22.85012
19.62175
20.07722
14.65798
35.94041
28.39314
31.69705
38.04971
31.72805
36.32653
23.9726
33.85827
41.98718
29.89418
35.49223
31.19777
29.48718
35.01259
31.93833
31.42292
36.05016
33.78685
28.79581
27.71084
13.70656
27.80899
52.21843
46.63212
39.93399
43.30144
45.33333
50.89974
53.15985
41.54229
43.3657
29.10798
13.85159
12.46612
17.87709
12.02673
23.38129
13.45029
12.08333
9.919786
16.32653
10.86592
11.15254
13.48039
Adjusted Chloride (mg/kg)
4767.726
5800.866
6584.767
5460.993
6389.961
4967.427
9627.561
8330.733
9256.662
11242.83
9730.878
10510.2
7214.612
9238.845
11987.18
8994.709
9948.187
9080.78
8782.051
9924.433
9162.996
9268.775
9467.085
9546.485
8429.319
8168.675
4285.714
8005.618
13720.14
13652.85
11122.11
12870.81
12166.67
14858.61
14312.27
12139.3
11229.77
8967.136
4204.947
3983.74
5446.927
3875.278
6258.993
4152.047
3344.697
3262.032
4244.898
3547.486
3491.525
4387.255
Adjusted SO4 (mg/kg)
1002.445
1038.961
1400.491
1654.846
1447.876
879.4788
1824.953
1591.264
1486.676
2045.889
1572.238
1510.204
1301.37
1548.556
1794.872
1455.026
1554.404
1643.454
1410.256
1586.902
1519.824
1422.925
1598.746
1609.977
1623.037
1325.301
776.0618
1460.674
2662.116
2435.233
1815.182
2033.493
2000
2185.09
2936.803
2089.552
2038.835
1431.925
759.7173
644.9864
949.7207
668.1514
935.2518
625.731
488.6364
465.2406
648.9796
544.6927
701.6949
735.2941
Ln [Adjusted Adjusted Adjusted Phosphat Potassium Sulfur e (mg/kg)] (mg/kg) (mg/kg)
2.282382
374
134
1.931521
344
150
2.312535
364
197
2.424803
422
377
2.186051
555
302
2.282382
415
276
2.701361
600
256
1.824549
721
288
2.653242
651
329
2.128232
629
262
1.824549
580
384
2.701361
442
277
2.261763
510
182
2.028148
446
296
2.714695
379
175
2.360854
353
149
2.517696
459
255
2.24071
495
174
1.902108
427
205
2.379546
477
179
2.74084
527
233
2.351375
445
280
2.04122
398
250
2.484907
444
229
2.302585
373
160
2.406945
381
209
2.397895
188
79.8
2.128232
459
167
2.873565
382
215
3.015535
375
225
2.76001
389
245
2.653242
387
252
2.624669
410
199
2.80336
387
250
3.306887
426
223
3.068053
335
212
2.701361
459
230
2.549445
381
217
2.066863
269
89.8
1.704748
247
83.6
2.292535
371
125
1.84055
272
104
2.351375
245
102
2.24071
205
73.9
2.128232
167
37.7
1.589235
146
54.5
2.282382
184
58.4
1.84055
167
73.7
2.151762
241
91.6
1.740466
226
100
1 of 1
Appendix 3
Detailed Biofilm Biomass Statistical Analyses
307071-00790 : Rev 0 : 27 January 2015
Appendices
HEMMERA ENVIROCHEM
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$
HS_CLASSIFICA LABEL$
TION$
ELEVATION_CHA ANNUAL_EXPOS DISTANCE_FRO DISTANCE_FRO
RTUREM_CM_S_DATUM__M_
__HR__
ANOE_PASS__M HORE__M_
_
AREA$
BIOFILM_TOTAL_
CARBOHYDRATE_
MG_M2
BIOFILM_TOTAL_
ORGANIC_CARBO
N_MG_M2
BIOFILM_PIGME
NTS_CHLOROPHYL
L_A_MG_M
BIOFILM_PIGME TOTAL_HARPACT TOTAL_NEMATO TOTAL_OLIGOCH TOTAL_POLYCH TOTAL_INVERT_
AEAETNTICDA
DETA
S_FUCOXANTHI OIDA
A
NSITY
N_MG_M2
TOTAL_INVERT_ MACROFAUNA_T MACROFAUNA_T
BIOTAOTAOMASS
L_BENTHIC_INDI L_BIOMASS_G_M
V2
IDUAL
GRAIN_SIZE_PE
RCENT_SILT_0_063
MM_TO_
MEIOFAUNA_TOT MEIOFAUNA_TOT GRAIN_SIZE_PE
RCALAL_BENTHIC_INDIVI _BIOMASS_G_M2 ENT_CLAY_LESS
_TDUALS
HAN_4
GRAIN_SIZE_PER TOC_TOTAL_OR ADJUSTED_AMM ADJUSTED_BRO ADJUSTED_CHL
GANCONIMIDORIENT_SAND_2_0M IC_CARBON_PER A
E
DE
CEM_NT
TO_0_
ADJUSTED_SO4 ADJUSTED__PHO ADJUSTED_POTA ADJUSTED_S
SPSSHATE
IUM
22 December 2014
Page 1
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 ANNUAL_EXPOS DISTANCE_FRO DISTANCE_FRO
RTUREM_CM_S_DATUM__M_
__HR__
ANOE_PASS__M HORE__M_
_
HS_CLASSIFICA LABEL$
AREA$
BIOFILM_TOTAL_ BIOFILM_TOTAL_ BIOFILM_PIGME
ONTTICON$
ARBOHYDRATE_ RGANIC_CARBO S_CHLOROPHYL
MG_N_ML_AM2
G_M2
_MG_M
BIOFILM_PIGME TOTAL_HARPACT TOTAL_NEMATO TOTAL_OLIGOCH TOTAL_POLYCH TOTAL_INVERT_
AEAETNTICDA
DETA
S_FUCOXANTHI OIDA
A
NSITY
N_MG_M2
TOTAL_INVERT_ MACROFAUNA_T MACROFAUNA_T MEIOFAUNA_TOT MEIOFAUNA_TOT GRAIN_SIZE_PE
ALALBIOTAOTARCOMASS
L_BENTHIC_INDI L_BIOMASS_G_M _BENTHIC_INDIVI _BIOMASS_G_M2 ENT_CLAY_LESS
_TV2
HAN_4
IDUAL
DUALS
GRAIN_SIZE_PE GRAIN_SIZE_PER TOC_TOTAL_OR ADJUSTED_AMM ADJUSTED_BRO ADJUSTED_CHL
RCCGANONIMIDORIENT_SILT_0_063 ENT_SAND_2_0M IC_CARBON_PER A
E
DE
MM_CEM_TO_
TO_0_
NT
ADJUSTED_SO4 ADJUSTED__PHO ADJUSTED_POTA ADJUSTED_S
SPSSHATE
IUM
▼Hypothesis Testing: Two-sample t-test
H0: Mean1 = Mean2 vs. H1: Mean1 <> Mean2
Grouping Variable = SEASON$
Page 2
HEMMERA ENVIROCHEM
Variable
SEASON$ N
BIOFILM_TOTAL_CARBOHYDRATE_MG_M2
Fall
Spring
BIOFILM_TOTAL_ORGANIC_CARBON_MG_M2 Fall
Spring
BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2 Fall
Spring
BIOFILM_PIGMENTS_CHLOROPHYLL_A_MG_MFall
Spring
Mean Standard
Deviation
19.000 98.975 33.532
31.000 81.929 31.503
19.000 9.772 0.669
31.000 9.978 0.431
19.000 3.646 0.595
31.000 3.749 0.403
19.000 3.933 0.798
31.000 3.916 0.470
Separate Variance
Variable
SEASON Mean
$
Difference
BIOFILM_TOTAL_CARBOHYDRATE_MG_ Fall
M2
Spring
BIOFILM_TOTAL_ORGANIC_CARBON_MG Fall
_M2
Spring
BIOFILM_PIGMENTS_FUCOXANTHIN_MG Fall
_M2
Spring
BIOFILM_PIGMENTS_CHLOROPHYLL_A_ Fall
MG_M
Spring
17.045
95.00% Confidence t
df
pInterval
Value
Lower Limit Upper Limit
-2.315
36.406
1.785 36.35 0.083
8
-0.206
-0.559
0.146
27.24 0.240
1.201 4
-0.103
-0.419
0.214
28.21 0.512
0.664 2
0.017
-0.397
0.432
0.086 25.77 0.932
1
Pooled Variance
Variable
SEASON Mean
$
Difference
BIOFILM_TOTAL_CARBOHYDRATE_MG_ Fall
M2
Spring
BIOFILM_TOTAL_ORGANIC_CARBON_MG Fall
_M2
Spring
BIOFILM_PIGMENTS_FUCOXANTHIN_MG Fall
_M2
Spring
BIOFILM_PIGMENTS_CHLOROPHYLL_A_ Fall
MG_M
Spring
22 December 2014
17.045
95.00% Confidence t
df
pInterval
Value
Lower Limit Upper Limit
-1.864
35.955
1.812 48.00 0.076
0
-0.206
-0.519
0.106
48.00 0.190
1.330 0
-0.103
-0.386
0.181
48.00 0.471
0.727 0
0.017
-0.342
0.377
0.097 48.00 0.923
0
Page 3
Page 4
HEMMERA ENVIROCHEM
22 December 2014
Page 5
Page 6
HEMMERA ENVIROCHEM
▼Nonparametric: Kruskal-Wallis Test
Mann-Whitney U Test for 50 Cases
The categorical values encountered during processing are
Variables
Levels
SEASON$ (2 levels)Fall Spring
Dependent Variable BIOFILM_TOTAL_CARBOHYDRATE_MG_M2
Grouping Variable SEASON$
22 December 2014
Page 7
GroupCount Rank Sum
Fall 19
552.000
Spring 31
723.000
Mann-Whitney U Test Statistic
p-Value
Chi-Square Approximation
df
: 362.000
:
0.177
:
1.820
:
1
Kruskal-Wallis Test Statistic: 1.820
The p-value is 0.177 assuming chi-square distribution with 1 df.
Page 8
HEMMERA ENVIROCHEM
3.
PRINCIPLE COMPONENTS ANALYSIS (PCA) OUTPUTS
Matrix to be Factored
ELEVATION_C ANNUAL_EXP DISTANCE_FR DISTANCE_FR TOTAL_HARP
HARTOSUREOM_COM_SACTIC_DATUM__M_ __HR__
ANOE_PASS_ HORE__M_
OIDA
_M_
ELEVATION_CHART_DATUM__ 1.000
M_
ANNUAL_EXPOSURE__HR__
0.941
1.000
DISTANCE_FROM_CANOE_PAS 0.457
0.437
1.000
S__M_
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 0.667
0.665
0.631
-0.742
0.507
C_INDIVIDUAL
MACROFAUNA_TOTAL_BIOMAS 0.225
0.227
0.110
-0.236
0.330
S_G_M2
MEIOFAUNA_TOTAL_BENTHIC_ 0.481
0.475
0.641
-0.629
0.731
INDIVIDUALS
MEIOFAUNA_TOTAL_BIOMASS_ 0.525
0.540
0.618
-0.670
0.820
G_M2
GRAIN_SIZE_PERCENT_CLAY_ 0.480
0.456
-0.113
-0.312
0.458
LESS_THAN_4
GRAIN_SIZE_PERCENT_SILT_0 0.468
0.446
-0.047
-0.338
0.427
_063MM_TO_
GRAIN_SIZE_PERCENT_SAND_ -0.492
-0.475
-0.024
0.400
-0.479
2_0MM_TO_0_
TOC_TOTAL_ORGANIC_CARBO 0.508
0.471
0.076
-0.466
0.585
N_PERCENT
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
22 December 2014
Page 9
Matrix to be Factored (Contd.)
TOTAL_NEMA TOTAL_OLIGO TOTAL_POLYC TOTAL_INVER TOTAL_INVE
TODA
CHAEHAETT_DERT_BITA
A
NSITY
OMASS
ELEVATION_CHART_DATUM__M
_
DISTANCE_FROM_CANOE_PAS
S__M_
DISTANCE_FROM_SHORE__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
1.000
TOTAL_OLIGOCHAETA
0.368
TOTAL_POLYCHAETA
0.295
0.890
0.544
C_INDIVIDUAL
S_G_M2
MEIOFAUNA_TOTAL_BENTHIC_I 0.893
NDIVIDUALS
G_M2
GRAIN_SIZE_PERCENT_CLAY_L 0.047
ESS_THAN_4
GRAIN_SIZE_PERCENT_SILT_0_ 0.017
063MM_TO_
GRAIN_SIZE_PERCENT_SAND_2 -0.088
_0MM_TO_0_
N_PERCENT
ADJUSTED_AMMONIA
0.505
ADJUSTED_BROMIDE
0.510
ADJUSTED_CHLORIDE
0.541
ADJUSTED_SO4
0.448
ADJUSTED__PHOSPHATE
0.363
ADJUSTED_POTASSIUM
0.387
ADJUSTED_S
0.484
Page 10
1.000
0.396
0.554
0.542
0.384
1.000
0.531
0.591
0.733
1.000
0.739
0.541
1.000
0.600
0.333
0.438
0.301
0.758
0.554
0.516
1.000
0.731
0.617
0.621
0.942
0.765
0.534
0.559
0.255
0.492
0.519
0.542
0.245
0.502
-0.558
-0.590
-0.314
-0.554
0.616
0.595
0.366
0.572
0.171
0.312
0.327
0.309
0.017
0.726
0.600
0.193
0.305
0.326
0.440
0.211
0.677
0.725
0.458
0.594
0.626
0.577
0.305
0.610
0.690
0.247
0.341
0.374
0.391
0.037
0.590
0.576
HEMMERA ENVIROCHEM
MACROFAUNA
_TOTAL_BENTHIC_IN
DIVIDUAL
ELEVATION_CHART_DATUM__
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
C_INDIVIDUAL
MACROFAUNA_TOTAL_BIOMA 0.568
SS_G_M2
INDIVIDUALS
MEIOFAUNA_TOTAL_BIOMASS 0.575
_G_M2
LESS_THAN_4
_063MM_TO_
2_0MM_TO_0_
N_PERCENT
ADJUSTED_AMMONIA
0.481
ADJUSTED_BROMIDE
0.669
ADJUSTED_CHLORIDE
0.689
ADJUSTED_SO4
0.772
ADJUSTED__PHOSPHATE
0.526
ADJUSTED_POTASSIUM
0.669
ADJUSTED_S
0.692
22 December 2014
MACROFAUNA
_TOTAL_BIOMASS_G
_M2
MEIOFAUNA_
TOTAL_BENTHIC_IN
DIVIDUALS
MEIOFAUNA_
TOTAL_BIOMASS_G
_M2
GRAIN_SIZE_
PERCENT_CLAY_L
ESS_THAN_4
1.000
0.288
1.000
0.284
0.941
1.000
0.443
0.253
0.383
1.000
0.524
0.241
0.358
0.938
-0.547
-0.310
-0.429
-0.947
0.404
0.362
0.521
0.932
0.007
0.102
0.134
0.187
-0.008
0.340
0.323
0.448
0.582
0.614
0.562
0.291
0.602
0.682
0.393
0.564
0.593
0.570
0.201
0.730
0.752
-0.232
-0.134
-0.093
-0.017
-0.237
0.688
0.544
Page 11
GRAIN_SIZE_
PERCENT_SILT_0_
063MM_TO_
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
MACROFAUNA_TOTAL_BENTHI
C_INDIVIDUAL
MACROFAUNA_TOTAL_BIOMAS
S_G_M2
MEIOFAUNA_TOTAL_BENTHIC_
INDIVIDUALS
MEIOFAUNA_TOTAL_BIOMASS_
G_M2
GRAIN_SIZE_PERCENT_CLAY_
LESS_THAN_4
_063MM_TO_
2_0MM_TO_0_
N_PERCENT
ADJUSTED_AMMONIA
-0.211
ADJUSTED_BROMIDE
-0.060
ADJUSTED_CHLORIDE
-0.019
ADJUSTED_SO4
0.046
ADJUSTED__PHOSPHATE
-0.166
ADJUSTED_POTASSIUM
0.702
ADJUSTED_S
0.562
Page 12
GRAIN_SIZE_ TOC_TOTAL_O ADJUSTED_A ADJUSTED_B
PERCRGANMMONIROMIDENT_SAND_2_ IC_CARBON_P A
E
0MM_ERCETO_0_
NT
1.000
-0.912
1.000
0.137
0.003
-0.040
-0.105
0.128
-0.750
-0.611
-0.062
0.051
0.094
0.154
-0.170
0.787
0.641
1.000
0.803
0.802
0.759
0.698
0.323
0.353
1.000
0.992
0.949
0.737
0.511
0.572
HEMMERA ENVIROCHEM
ADJUSTED_CH ADJUSTED_ ADJUSTED__P ADJUSTED_PO ADJUSTE
LORISO4
HOSPTASSD_S
DE
HATE
IUM
ELEVATION_CHART_DATUM__M_
DISTANCE_FROM_CANOE_PASS_
_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_
GRAIN_SIZE_PERCENT_SAND_2_
0MM_TO_0_
TOC_TOTAL_ORGANIC_CARBON_
PERCENT
ADJUSTED_AMMONIA
ADJUSTED_BROMIDE
ADJUSTED_CHLORIDE
1.000
ADJUSTED_SO4
0.945
ADJUSTED__PHOSPHATE
0.723
ADJUSTED_POTASSIUM
0.532
ADJUSTED_S
0.594
22 December 2014
1.000
0.731
0.580
0.642
1.000
0.223
0.336
1.000
0.812
1.000
Page 13
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 23
12.93 5.18 2.07 1.18 0.97 0.59 0.37 0.35 0.29 0.20 0.18 0.15 0.10 0.09 0.06 0.06 0.05 0.03 0.03 0.02 0.01 0.01 0.00
1
9
2
3
0
3
5
1
3
2
1
0
4
1
7
5
3
6
2
8
7
1
5
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
Chi-Square
df
p-Value
:
50.000
: 2,316.766
: 300.000
:
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
4
0.215 0.024 0.283 0.211
0.212 0.025 0.270 0.201
DISTANCE_FROM_CANOE_PASS__M_
0.202 -0.247 -0.007 -0.016
-0.247 0.084 -0.179 -0.113
TOTAL_HARPACTICOIDA
0.212 0.087 -0.215 0.089
TOTAL_NEMATODA
0.176 -0.131 -0.387 0.131
TOTAL_OLIGOCHAETA
0.181 0.125 -0.146 0.082
TOTAL_POLYCHAETA
0.210 0.119 0.126 -0.025
0.232 -0.057 -0.342 0.061
0.207 0.112 -0.259 -0.376
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.226 -0.045 0.194 -0.336
MACROFAUNA_TOTAL_BIOMASS_G_M2
0.122 0.165 -0.029 -0.719
MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.229 -0.055 -0.354 0.074
MEIOFAUNA_TOTAL_BIOMASS_G_M2
0.242 0.003 -0.289 0.113
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 -0.108 0.036
Page 14
HEMMERA ENVIROCHEM
Latent Vectors (Eigenvectors)
1
TOC_TOTAL_ORGANIC_CARBON_PERCENT 0.172
ADJUSTED_AMMONIA
0.148
ADJUSTED_BROMIDE
0.198
ADJUSTED_CHLORIDE
0.207
ADJUSTED_SO4
0.215
ADJUSTED__PHOSPHATE
0.121
ADJUSTED_POTASSIUM
0.241
ADJUSTED_S
0.249
2
3
0.305 0.057
-0.296 0.061
-0.274 0.080
-0.261 0.068
-0.227 0.166
-0.289 0.242
0.116 0.076
0.051 0.067
4
0.107
-0.049
-0.063
-0.065
-0.083
-0.080
0.102
0.141
Standard Error for Each Eigenvector Element
1
2
3
4
0.028 0.064 0.086 0.244
0.028 0.064 0.091 0.275
0.040 0.040 0.065 0.146
0.022 0.051 0.049 0.068
TOTAL_HARPACTICOIDA
0.029 0.058 0.074 0.154
TOTAL_NEMATODA
0.038 0.071 0.071 0.169
TOTAL_OLIGOCHAETA
0.035 0.059 0.106 0.335
TOTAL_POLYCHAETA
0.031 0.054 0.089 0.291
0.026 0.063 0.036 0.121
0.032 0.062 0.102 0.105
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.026 0.058 0.095 0.116
0.043 0.067 0.183 0.074
MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.027 0.065 0.037 0.120
0.022 0.059 0.042 0.090
GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ 0.053 0.036 0.067 0.125
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_0.051 0.035 0.065 0.126
TOC_TOTAL_ORGANIC_CARBON_PERCENT 0.048 0.037 0.072 0.153
ADJUSTED_AMMONIA
0.049 0.042 0.078 0.144
ADJUSTED_BROMIDE
0.043 0.038 0.063 0.151
ADJUSTED_CHLORIDE
0.041 0.039 0.061 0.144
ADJUSTED_SO4
0.037 0.045 0.053 0.087
ADJUSTED__PHOSPHATE
0.051 0.053 0.078 0.125
ADJUSTED_POTASSIUM
0.025 0.048 0.070 0.211
ADJUSTED_S
0.019 0.048 0.058 0.066
22 December 2014
Page 15
Component Loadings
1
2
3
4
0.775 0.054 0.407 0.230
0.761 0.057 0.389 0.219
0.727 -0.563 -0.011 -0.017
-0.887 0.191 -0.258 -0.123
TOTAL_HARPACTICOIDA
0.763 0.199 -0.310 0.096
TOTAL_NEMATODA
0.632 -0.299 -0.557 0.143
TOTAL_OLIGOCHAETA
0.651 0.285 -0.210 0.089
TOTAL_POLYCHAETA
0.757 0.272 0.181 -0.028
0.835 -0.130 -0.493 0.066
0.745 0.256 -0.372 -0.409
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.813 -0.101 0.279 -0.366
0.438 0.376 -0.042 -0.782
MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.824 -0.126 -0.509 0.080
0.871 0.006 -0.415 0.123
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 -0.156 0.039
TOC_TOTAL_ORGANIC_CARBON_PERCENT 0.619 0.695 0.082 0.116
ADJUSTED_AMMONIA
0.533 -0.673 0.088 -0.053
ADJUSTED_BROMIDE
0.713 -0.624 0.115 -0.069
ADJUSTED_CHLORIDE
0.743 -0.594 0.098 -0.071
ADJUSTED_SO4
0.773 -0.516 0.238 -0.090
ADJUSTED__PHOSPHATE
0.434 -0.659 0.348 -0.087
ADJUSTED_POTASSIUM
0.865 0.264 0.109 0.110
ADJUSTED_S
0.895 0.116 0.096 0.153
Variance Explained by
Components
1
2
3
12.931
5.189
2.072
4
1.183
Percent of Total Variance
Explained
1
2
3
51.724
20.754
8.287
4
4.730
Rotated Loading Matrix (VARIMAX, Gamma = 1.000000)
1
2
3
4
0.570 0.683 0.127 -0.119
0.554 0.668 0.132 -0.109
0.805 0.020 0.443 0.032
-0.748 -0.499 -0.308 0.030
Page 16
HEMMERA ENVIROCHEM
Rotated Loading Matrix (VARIMAX, Gamma = 1.000000)
1
2
3
4
TOTAL_HARPACTICOIDA
0.167 0.487 0.670 0.118
TOTAL_NEMATODA
0.319 -0.018 0.846 -0.022
TOTAL_OLIGOCHAETA
0.086 0.517 0.520 0.111
TOTAL_POLYCHAETA
0.336 0.687 0.238 0.194
0.357 0.225 0.878 0.114
0.135 0.387 0.615 0.617
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.696 0.424 0.155 0.440
0.050 0.336 0.102 0.906
MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS 0.340 0.218 0.888 0.101
0.314 0.381 0.835 0.085
GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 -0.226 0.922 0.123 0.146
GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ -0.145 0.904 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 -0.088 0.897 0.238 0.129
ADJUSTED_AMMONIA
0.813 -0.141 0.259 -0.001
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_POTASSIUM
0.362 0.748 0.381 0.088
ADJUSTED_S
0.470 0.666 0.427 0.024
"Variance" Explained by Rotated
Components
1
2
3
4
7.208
6.263
5.267
1.722
Percent of Total Variance
Explained
1
2
3
28.833 25.054 21.068
22 December 2014
4
6.889
Page 17
Differences: Original Minus Fitted Correlations or Covariances
HARTOSUREOM_COM_SACTIC_DATUM__M_ __HR__
ANOE_PASS_ HORE__M_
OIDA
_M_
M_
0.140
0.217
DISTANCE_FROM_CANOE_PAS -0.068
-0.077
0.154
S__M_
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
0.039
0.036
-0.034
0.006
-0.038
0.052
0.054
-0.028
-0.016
0.016
0.023
-0.021
0.025
0.029
C_INDIVIDUAL
0.060
-0.010
-0.026
-0.017
S_G_M2
0.035
-0.033
0.005
-0.039
INDIVIDUALS
MEIOFAUNA_TOTAL_BIOMASS_ -0.009
0.011
-0.015
0.009
0.013
G_M2
GRAIN_SIZE_PERCENT_CLAY_ -0.019
-0.036
-0.009
0.014
-0.053
LESS_THAN_4
GRAIN_SIZE_PERCENT_SILT_0 -0.043
-0.057
0.016
0.016
-0.061
_063MM_TO_
GRAIN_SIZE_PERCENT_SAND_ 0.054
0.063
-0.021
-0.009
0.062
2_0MM_TO_0_
TOC_TOTAL_ORGANIC_CARBO -0.070
-0.097
0.020
-0.014
-0.012
N_PERCENT
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
Page 18
HEMMERA ENVIROCHEM
Differences: Original Minus Fitted Correlations or Covariances (Contd.)
TODA
CHAEHAETT_DERT_BITA
A
NSITY
OMASS
_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
0.181
TOTAL_OLIGOCHAETA
-0.087
0.443
TOTAL_POLYCHAETA
0.003
-0.134
0.320
0.040
-0.062
0.025
0.038
0.001
-0.057
0.014
-0.006
0.074
-0.025
0.085
0.011
-0.025
C_INDIVIDUAL
0.001
-0.010
0.015
0.000
S_G_M2
-0.060
0.021
0.038
-0.008
NDIVIDUALS
MEIOFAUNA_TOTAL_BIOMASS_ -0.035
-0.050
0.039
0.003
0.010
G_M2
-0.004
-0.057
0.003
0.000
ESS_THAN_4
0.004
-0.096
0.010
-0.027
063MM_TO_
-0.002
0.082
-0.005
0.029
_0MM_TO_0_
0.022
-0.074
-0.028
0.011
N_PERCENT
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
MACROFAUNA MACROFAUNA MEIOFAUNA_
_TOTA_TOTATOTALL_BENTHIC_IN L_BIOMASS_G _BENTHIC_IN
DIV_M2
DIVIIDUAL
DUALS
22 December 2014
MEIOFAUNA_
TOTAL_BIOMASS_G
_M2
GRAIN_SIZE_
PERCENT_CLAY_L
ESS_THAN_4
Page 19
MACROFAUNA MACROFAUNA MEIOFAUNA_
_TOTA_TOTATOTALL_BENTHIC_IN L_BIOMASS_G _BENTHIC_IN
DIV_M2
DIVIIDUAL
DUALS
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
C_INDIVIDUAL
MACROFAUNA_TOTAL_BIOMA -0.025
0.053
SS_G_M2
0.016
0.039
INDIVIDUALS
-0.021
0.002
_G_M2
GRAIN_SIZE_PERCENT_CLAY_ -0.045
0.000
0.004
LESS_THAN_4
-0.015
0.012
GRAIN_SIZE_PERCENT_SILT_0 -0.026
_063MM_TO_
0.013
-0.006
2_0MM_TO_0_
-0.034
-0.028
N_PERCENT
ADJUSTED_AMMONIA
-0.066
-0.012
-0.028
ADJUSTED_BROMIDE
-0.030
-0.025
-0.020
ADJUSTED_CHLORIDE
-0.029
-0.020
-0.017
ADJUSTED_SO4
-0.008
-0.018
-0.012
ADJUSTED__PHOSPHATE
-0.022
-0.003
0.035
ADJUSTED_POTASSIUM
0.002
-0.048
-0.031
ADJUSTED_S
0.005
0.011
-0.004
MEIOFAUNA_
TOTAL_BIOMASS_G
_M2
GRAIN_SIZE_
PERCENT_CLAY_L
ESS_THAN_4
0.053
-0.013
0.062
-0.014
0.026
0.013
-0.034
-0.003
0.036
-0.024
0.002
-0.001
0.010
-0.018
0.006
-0.007
0.050
0.017
0.021
0.003
0.053
0.020
-0.019
GRAIN_SIZE_ GRAIN_SIZE_ TOC_TOTAL_O ADJUSTED_A ADJUSTED_B
PERCPERCRGANMMONIROMIDENT_SILT_0_ ENT_SAND_2_ IC_CARBON_P A
E
063M0MM_ERCEM_TO_
TO_0_
NT
Page 20
HEMMERA ENVIROCHEM
GRAIN_SIZE_ GRAIN_SIZE_ TOC_TOTAL_O ADJUSTED_A
PERCPERCRGANMMONIENT_SILT_0_ ENT_SAND_2_ IC_CARBON_P A
063M0MM_ERCEM_TO_
TO_0_
NT
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
C_INDIVIDUAL
S_G_M2
INDIVIDUALS
G_M2
LESS_THAN_4
_063MM_TO_
0.078
2_0MM_TO_0_
-0.025
0.114
N_PERCENT
ADJUSTED_AMMONIA
0.019
-0.039
0.074
0.251
ADJUSTED_BROMIDE
0.033
-0.027
0.042
-0.011
ADJUSTED_CHLORIDE
0.037
-0.032
0.047
-0.007
ADJUSTED_SO4
0.000
0.001
0.025
-0.027
ADJUSTED__PHOSPHATE
0.054
-0.053
0.001
-0.012
ADJUSTED_POTASSIUM
0.032
-0.036
0.046
0.036
ADJUSTED_S
-0.004
0.006
-0.019
-0.046
ADJUSTED_B
ROMIDE
0.085
0.076
0.042
-0.029
0.054
0.006
LORISO4
HOSPTASSD_S
DE
HATE
IUM
22 December 2014
Page 21
ADJUSTED_CH ADJUSTED_ ADJUSTED__P
LORISO4
HOSPDE
HATE
_M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
NDIVIDUAL
G_M2
DIVIDUALS
M2
SS_THAN_4
3MM_TO_
0MM_TO_0_
PERCENT
ADJUSTED_AMMONIA
ADJUSTED_BROMIDE
ADJUSTED_CHLORIDE
0.080
ADJUSTED_SO4
0.034
0.071
ADJUSTED__PHOSPHATE
-0.031
-0.035
0.249
ADJUSTED_POTASSIUM
0.043
0.032
-0.007
ADJUSTED_S
0.000
0.001
0.004
Page 22
ADJUSTED_PO ADJUSTE
TASSD_S
IUM
0.157
-0.020
0.153
HEMMERA ENVIROCHEM
22 December 2014
Page 23
Factor Coefficients
1
2
3
4
0.095 0.155 -0.093 -0.196
0.092 0.149 -0.089 -0.186
0.106 -0.048 0.040 0.002
-0.110 -0.081 0.043 0.109
TOTAL_HARPACTICOIDA
-0.064 0.023 0.166 -0.043
TOTAL_NEMATODA
-0.059 -0.085 0.272 -0.088
TOTAL_OLIGOCHAETA
-0.059 0.046 0.120 -0.041
TOTAL_POLYCHAETA
0.038 0.097 -0.048 0.033
-0.053 -0.056 0.241 -0.020
-0.047 -0.071 0.111 0.371
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.136 0.007 -0.131 0.287
0.020 -0.076 -0.091 0.656
MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS -0.058 -0.056 0.249 -0.030
-0.057 -0.012 0.219 -0.064
GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 -0.067 0.171 -0.025 -0.046
GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ -0.037 0.155 -0.069 0.056
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ 0.036 -0.147 0.054 -0.060
TOC_TOTAL_ORGANIC_CARBON_PERCENT -0.055 0.156 0.002 -0.063
Page 24
HEMMERA ENVIROCHEM
Factor Coefficients
ADJUSTED_AMMONIA
ADJUSTED_BROMIDE
ADJUSTED_CHLORIDE
ADJUSTED_SO4
ADJUSTED__PHOSPHATE
ADJUSTED_POTASSIUM
ADJUSTED_S
1
0.135
0.143
0.137
0.159
0.185
0.019
0.034
2
3
4
-0.061 -0.011 0.019
-0.044 -0.017 0.035
-0.042 -0.010 0.039
-0.009 -0.068 0.050
-0.023 -0.123 0.031
0.113 0.006 -0.073
0.100 0.021 -0.112
Coefficients for Standardized Factor Scores
1
2
3
4
0.095 0.155 -0.093 -0.196
0.092 0.149 -0.089 -0.186
0.106 -0.048 0.040 0.002
-0.110 -0.081 0.043 0.109
TOTAL_HARPACTICOIDA
-0.064 0.023 0.166 -0.043
TOTAL_NEMATODA
-0.059 -0.085 0.272 -0.088
TOTAL_OLIGOCHAETA
-0.059 0.046 0.120 -0.041
TOTAL_POLYCHAETA
0.038 0.097 -0.048 0.033
-0.053 -0.056 0.241 -0.020
-0.047 -0.071 0.111 0.371
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.136 0.007 -0.131 0.287
0.020 -0.076 -0.091 0.656
MEIOFAUNA_TOTAL_BENTHIC_INDIVIDUALS -0.058 -0.056 0.249 -0.030
-0.057 -0.012 0.219 -0.064
GRAIN_SIZE_PERCENT_CLAY_LESS_THAN_4 -0.067 0.171 -0.025 -0.046
GRAIN_SIZE_PERCENT_SILT_0_063MM_TO_ -0.037 0.155 -0.069 0.056
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_0.036 -0.147 0.054 -0.060
TOC_TOTAL_ORGANIC_CARBON_PERCENT -0.055 0.156 0.002 -0.063
ADJUSTED_AMMONIA
0.135 -0.061 -0.011 0.019
ADJUSTED_BROMIDE
0.143 -0.044 -0.017 0.035
ADJUSTED_CHLORIDE
0.137 -0.042 -0.010 0.039
ADJUSTED_SO4
0.159 -0.009 -0.068 0.050
ADJUSTED__PHOSPHATE
0.185 -0.023 -0.123 0.031
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.
22 December 2014
Page 25
4.
PCA AND BIOFILM BIOMASS CORRELATION ANALYSIS
Number of Non-Missing Cases: 50
Means
PCA_ PCA_ PCA_ PCA_ BIOFILM_TOTAL_C- BIOFILM_TOTAL_O- BIOFILM_PIGMENT- BIOFILM_PIGMENT
1
2
3
4
ARBOHYDRATE_MG RGANIC_CARBON_ S_CHLOROPHYLL_ _-
M-
A-
S_FUCOXANTHIN_
M2
G_M2
_MG_M
MG_M2
0.000 0.000 0.000 0.000 88.407
9.900
3.922
3.710
Pearson Correlation Matrix
BIOFILM_TOTAL_C-
BIOFILM_TOTAL_O-
BIOFILM_PIGMENT-
BIOFILM_PIGMENT-
ARBOHYDRATE_MG_- RGANIC_CARBON_M- S_CHLOROPHYLL_A- S_FUCOXANTHIN_MM2
G_M2
_MG_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
Page 26
HEMMERA ENVIROCHEM
Bartlett Chi-Square Statistic : 251.732
df
:
16
p-Value
:
0.000
22 December 2014
Page 27
Matrix of Bonferroni Probabilities
BIOFILM_TOTAL_C-
BIOFILM_TOTAL_O-
BIOFILM_PIGMENT-
BIOFILM_PIGMENT-
ARBOHYDRATE_MG_- RGANIC_CARBON_M- S_CHLOROPHYLL_A- S_FUCOXANTHIN_MM2
G_M2
_MG_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
Page 28
HEMMERA ENVIROCHEM
5.
PEARSON CORRELATION MATRIX
Number of Non-Missing Cases: 50
HART-
OSURE-
_DATUM__M_ __HR__
OM_C-
OM_S-
ANOE_PASS_ HORE__M_
ACTICOIDA
_M_
M_
0.941
1.000
0.437
1.000
-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
0.493
0.487
0.651
-0.641
0.733
0.397
0.401
0.381
-0.481
0.712
0.665
0.631
-0.742
0.507
0.227
0.110
-0.236
0.330
0.475
0.641
-0.629
0.731
0.540
0.618
-0.670
0.820
0.456
-0.113
-0.312
0.458
S__M_
C_INDIVIDUAL
S_G_M2
INDIVIDUALS
G_M2
22 December 2014
Page 29
HART-
OSURE-
_DATUM__M_ __HR__
OM_C-
OM_S-
ANOE_PASS_ HORE__M_
ACTICOIDA
_M_
LESS_THAN_4
0.446
-0.047
-0.338
0.427
-0.475
-0.024
0.400
-0.479
0.471
0.076
-0.466
0.585
_063MM_TO_
2_0MM_TO_0_
N_PERCENT
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.)
TODA
CHAE-
HAET-
T_DE-
RT_BI-
TA
A
NSITY
OMASS
_
S__M_
Page 30
HEMMERA ENVIROCHEM
TODA
CHAE-
HAET-
T_DE-
RT_BI-
TA
A
NSITY
OMASS
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
1.000
TOTAL_OLIGOCHAETA
0.368
1.000
TOTAL_POLYCHAETA
0.295
0.396
1.000
0.890
0.554
0.531
1.000
0.544
0.542
0.591
0.739
1.000
0.384
0.733
0.541
0.600
0.333
0.438
0.301
0.758
0.554
0.516
1.000
0.731
0.617
0.621
0.942
0.765
0.534
0.559
0.255
0.492
0.519
0.542
0.245
0.502
-0.558
-0.590
-0.314
-0.554
0.616
0.595
0.366
0.572
C_INDIVIDUAL
S_G_M2
NDIVIDUALS
G_M2
ESS_THAN_4
063MM_TO_
_0MM_TO_0_
N_PERCENT
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
22 December 2014
Page 31
TODA
CHAE-
HAET-
T_DE-
RT_BI-
TA
A
NSITY
OMASS
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
MACROFAUNA MACROFAUNA MEIOFAUNA_ MEIOFAUNA_ GRAIN_SIZE_
_TOTA-
_TOTA-
TOTAL-
TOTAL-
PERC-
L_BENTHIC_IN L_BIOMASS_G _BENTHIC_IN _BIOMASS_G ENT_CLAY_L
DIVIDUAL
_M2
DIVIDUALS
_M2
ESS_THAN_4
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
C_INDIVIDUAL
Page 32
HEMMERA ENVIROCHEM
_TOTA-
_TOTA-
TOTAL-
TOTAL-
PERC-
DIV-
_M2
IDUAL
DIVI-
_M2
DUALS
ESS_THAN_4
1.000
SS_G_M2
0.288
1.000
0.284
0.941
1.000
0.443
0.253
0.383
1.000
0.524
0.241
0.358
0.938
-0.547
-0.310
-0.429
-0.947
0.404
0.362
0.521
0.932
INDIVIDUALS
_G_M2
LESS_THAN_4
_063MM_TO_
2_0MM_TO_0_
N_PERCENT
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_POTASSIUM
0.669
0.340
0.602
0.730
0.688
ADJUSTED_S
0.692
0.323
0.682
0.752
0.544
22 December 2014
Page 33
PERC-
PERC-
RGAN-
MMONI-
ENT_SILT_0_ ENT_SAND_2_ IC_CARBON_P A
063M-
0MM_-
ERCE-
M_TO_
TO_0_
NT
ROMIDE
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
C_INDIVIDUAL
S_G_M2
INDIVIDUALS
G_M2
LESS_THAN_4
Page 34
HEMMERA ENVIROCHEM
PERC-
PERC-
RGAN-
MMONI-
063M-
0MM_-
ERCE-
M_TO_
TO_0_
NT
ROMIDE
_063MM_TO_
1.000
2_0MM_TO_0_
-0.912
1.000
N_PERCENT
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
LORIDE
SO4
HOSP-
TASS-
HATE
IUM
D_S
_M_
TOTAL_HARPACTICOIDA
22 December 2014
Page 35
LORI-
SO4
DE
HOSP-
TASS-
HATE
IUM
D_S
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
NDIVIDUAL
G_M2
DIVIDUALS
M2
SS_THAN_4
3MM_TO_
0MM_TO_0_
PERCENT
ADJUSTED_AMMONIA
ADJUSTED_BROMIDE
ADJUSTED_CHLORIDE
1.000
ADJUSTED_SO4
0.945
1.000
ADJUSTED__PHOSPHATE
0.723
0.731
Page 36
1.000
HEMMERA ENVIROCHEM
LORI-
SO4
DE
HOSP-
TASS-
HATE
IUM
ADJUSTED_POTASSIUM
0.532
0.580
0.223
1.000
ADJUSTED_S
0.594
0.642
0.336
0.812
D_S
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
HART-
OSURE-
_DATUM__M_ __HR__
OM_C-
OM_S-
ANOE_PASS_ HORE__M_
ACTICOIDA
_M_
M_
0.000
0.000
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
S__M_
22 December 2014
Page 37
HART-
OSURE-
_DATUM__M_ __HR__
OM_C-
OM_S-
ANOE_PASS_ HORE__M_
ACTICOIDA
_M_
0.082
0.101
0.000
0.000
0.000
1.000
1.000
1.000
0.122
0.000
0.000
0.000
0.000
0.051
1.000
1.000
1.000
1.000
0.149
0.000
0.000
0.000
0.015
0.001
0.000
0.000
0.264
1.000
1.000
0.249
0.349
1.000
1.000
0.593
0.147
1.000
1.000
0.129
0.165
1.000
0.195
0.002
C_INDIVIDUAL
S_G_M2
INDIVIDUALS
G_M2
LESS_THAN_4
_063MM_TO_
2_0MM_TO_0_
N_PERCENT
ADJUSTED_AMMONIA
1.000
1.000
0.000
0.000
1.000
ADJUSTED_BROMIDE
0.149
0.141
0.000
0.000
1.000
ADJUSTED_CHLORIDE
0.059
0.085
0.000
0.000
0.941
ADJUSTED_SO4
0.001
0.001
0.000
0.000
0.424
ADJUSTED__PHOSPHATE
0.970
1.000
0.000
0.016
1.000
ADJUSTED_POTASSIUM
0.000
0.001
0.026
0.000
0.000
ADJUSTED_S
0.000
0.000
0.002
0.000
0.000
Page 38
HEMMERA ENVIROCHEM
Matrix of Bonferroni Probabilities (Contd.)
TODA
CHAE-
HAET-
T_DE-
RT_BI-
TA
A
NSITY
OMASS
_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
0.000
TOTAL_OLIGOCHAETA
1.000
0.000
TOTAL_POLYCHAETA
1.000
1.000
0.000
0.000
0.009
0.022
0.000
0.013
0.014
0.002
0.000
0.000
1.000
0.000
0.015
0.001
1.000
0.442
1.000
0.000
0.009
0.037
0.000
0.000
0.001
0.000
0.000
0.000
0.020
0.007
1.000
0.085
0.033
0.015
1.000
0.062
0.008
0.002
1.000
0.009
C_INDIVIDUAL
S_G_M2
NDIVIDUALS
G_M2
ESS_THAN_4
063MM_TO_
GRAIN_SIZE_PERCENT_SAND_2 1.000
_0MM_TO_0_
22 December 2014
Page 39
TODA
CHAE-
HAET-
T_DE-
RT_BI-
TA
A
NSITY
OMASS
0.001
0.002
1.000
0.004
N_PERCENT
ADJUSTED_AMMONIA
0.055
1.000
1.000
0.249
1.000
ADJUSTED_BROMIDE
0.047
1.000
1.000
0.002
1.000
ADJUSTED_CHLORIDE
0.015
1.000
1.000
0.000
1.000
ADJUSTED_SO4
0.332
1.000
0.411
0.003
1.000
ADJUSTED__PHOSPHATE
1.000
1.000
1.000
1.000
1.000
ADJUSTED_POTASSIUM
1.000
0.000
0.000
0.001
0.002
ADJUSTED_S
0.109
0.001
0.000
0.000
0.004
_TOTA-
_TOTA-
TOTAL-
TOTAL-
PERC-
DIVIDUAL
_M2
DIVIDUALS
_M2
ESS_THAN_4
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
Page 40
HEMMERA ENVIROCHEM
_TOTA-
_TOTA-
TOTAL-
TOTAL-
PERC-
DIV-
_M2
IDUAL
DIVI-
_M2
DUALS
ESS_THAN_4
TOTAL_POLYCHAETA
C_INDIVIDUAL
0.000
SS_G_M2
1.000
0.000
1.000
0.000
0.000
0.384
1.000
1.000
0.000
0.028
1.000
1.000
0.000
0.012
1.000
0.563
0.000
1.000
1.000
0.031
0.000
INDIVIDUALS
_G_M2
LESS_THAN_4
_063MM_TO_
2_0MM_TO_0_
N_PERCENT
ADJUSTED_AMMONIA
0.124
1.000
0.333
1.000
1.000
ADJUSTED_BROMIDE
0.000
1.000
0.003
0.006
1.000
ADJUSTED_CHLORIDE
0.000
1.000
0.001
0.002
1.000
ADJUSTED_SO4
0.000
1.000
0.007
0.005
1.000
ADJUSTED__PHOSPHATE
0.026
1.000
1.000
1.000
1.000
22 December 2014
Page 41
_TOTA-
_TOTA-
TOTAL-
TOTAL-
PERC-
DIV-
_M2
IDUAL
DIVI-
_M2
DUALS
ESS_THAN_4
ADJUSTED_POTASSIUM
0.000
1.000
0.001
0.000
0.000
ADJUSTED_S
0.000
1.000
0.000
0.000
0.013
PERC-
PERC-
RGAN-
MMONI-
063M-
0MM_-
ERCE-
M_TO_
TO_0_
NT
ROMIDE
M_
S__M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
C_INDIVIDUAL
Page 42
HEMMERA ENVIROCHEM
PERC-
PERC-
RGAN-
MMONI-
063M-
0MM_-
ERCE-
M_TO_
TO_0_
NT
ROMIDE
S_G_M2
INDIVIDUALS
G_M2
LESS_THAN_4
_063MM_TO_
0.000
2_0MM_TO_0_
0.000
0.000
N_PERCENT
ADJUSTED_AMMONIA
1.000
1.000
1.000
0.000
ADJUSTED_BROMIDE
1.000
1.000
1.000
0.000
0.000
ADJUSTED_CHLORIDE
1.000
1.000
1.000
0.000
0.000
ADJUSTED_SO4
1.000
1.000
1.000
0.000
0.000
ADJUSTED__PHOSPHATE
1.000
1.000
1.000
0.000
0.000
ADJUSTED_POTASSIUM
0.000
0.000
0.000
1.000
0.045
ADJUSTED_S
0.006
0.001
0.000
1.000
0.004
22 December 2014
Page 43
LORIDE
SO4
HOSP-
TASS-
HATE
IUM
D_S
_M_
TOTAL_HARPACTICOIDA
TOTAL_NEMATODA
TOTAL_OLIGOCHAETA
TOTAL_POLYCHAETA
NDIVIDUAL
G_M2
DIVIDUALS
M2
SS_THAN_4
3MM_TO_
0MM_TO_0_
PERCENT
Page 44
HEMMERA ENVIROCHEM
LORI-
SO4
DE
HOSP-
TASS-
HATE
IUM
D_S
ADJUSTED_AMMONIA
ADJUSTED_BROMIDE
ADJUSTED_CHLORIDE
0.000
ADJUSTED_SO4
0.000
0.000
ADJUSTED__PHOSPHATE
0.000
0.000
0.000
ADJUSTED_POTASSIUM
0.021
0.003
1.000
0.000
ADJUSTED_S
0.002
0.000
1.000
0.000
0.000
Matrix has been saved.
22 December 2014
Page 45
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 Error Std.
Tolerance df F-Ratio p-Value
Coefficient
1
2
Constant
ELEVATION_CHART- 0.031
0.037
0.146
0.327
1 0.689
0.412
0.000
0.638
0.147
1 5.910
0.020
0.014
-0.095
0.370
1 0.328
0.570
0.001
0.326
0.220
1 2.301
0.137
-0.092
0.106
-0.146
0.361
1 0.757
0.390
MACROFAUNA_TOTA- -0.085
0.049
-0.508
0.119
1 3.030
0.090
0.108
0.038
0.274
1 0.039
0.844
0.104
-0.486
0.144
1 3.347
0.075
_DATUM__M_
3
ADJUSTED_CHLORI- 0.000
DE
4
TOTAL_OLIGOCHAE- -0.008
TA
5
TOTAL_POLYCHAET- 0.001
A
6
7
L_BENTHIC_INDIVIDUAL
8
MACROFAUNA_TOTA- 0.021
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-0.190
ENT_SAND_2_0MM_TO_0_
Page 46
HEMMERA ENVIROCHEM
In
Effect
Coefficient
10
ADJUSTED__PHOSP- 0.151
0.269
0.093
0.368
1 0.313
0.579
0.002
0.103
0.091
1 0.095
0.759
HATE
11
ADJUSTED_POTASS- 0.000
IUM
Out Effect Partial
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
22 December 2014
Page 47
In
Effect
Coefficient
1
Constant
2
0.035
0.135
0.365
1 0.671
0.418
0.000
0.639
0.147
1 6.067
0.018
0.014
-0.087
0.390
1 0.300
0.587
0.001
0.322
0.222
1 2.322
0.135
-0.088
0.102
-0.138
0.380
1 0.735
0.396
0.037
-0.470
0.210
1 4.674
0.037
0.081
-0.518
0.231
1 6.255
0.017
0.262
0.087
0.378
1 0.292
0.592
0.001
0.067
0.129
1 0.059
0.810
_DATUM__M_
3
DE
4
TA
5
A
6
7
9
GRAIN_SIZE_PERC-
-0.203
10
HATE
11
IUM
Out
Effect
Partial
Correlation
8
0.274
1 0.039
0.844
L_BIOMASS_G_M2
Page 48
HEMMERA ENVIROCHEM
AIC
66.794
Step number
: 2
R
: 0.777
R-Square
: 0.603
Mallows' Cp
: 7.097
Term Removed : ADJUSTED_POTASSIUM
In Effect
Coefficient
1 Constant
2 ELEVATION_CHART- 0.029
0.035
0.136
0.365
1 0.698
0.408
0.000
0.670
0.195
1 9.061
0.004
0.012
-0.070
0.496
1 0.248
0.621
0.001
0.336
0.239
1 2.780
0.103
-0.090
0.101
-0.142
0.382
1 0.790
0.379
7 MACROFAUNA_TOTA- -0.080
0.036
-0.474
0.211
1 4.904
0.032
0.061
-0.551
0.404
1 12.670 0.001
_DATUM__M_
3 ADJUSTED_CHLORI- 0.000
DE
4 TOTAL_OLIGOCHAE- -0.006
TA
5 TOTAL_POLYCHAET- 0.001
A
6 TOTAL_INVERT_DENSITY
9 GRAIN_SIZE_PERC-
-0.216
ENT_SAND_2_0MM_22 December 2014
Page 49
In Effect
Coefficient
TO_0_
10ADJUSTED__PHOSP- 0.136
0.258
0.084
0.382
1 0.276
0.602
HATE
OutEffect
Partial
Correlation
8
0.388
1 0.001
0.971
0.129
1 0.059
0.810
L_BIOMASS_G_M2
11 ADJUSTED_POTASS- 0.038
IUM
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
In Effect
Coefficient
1 Constant
2 ELEVATION_CHART- 0.030
0.034
0.139
0.366
1 0.749
0.392
_DATUM__M_
Page 50
HEMMERA ENVIROCHEM
In Effect
Coefficient
0.000
0.651
0.201
1 8.975
0.005
0.001
0.342
0.240
1 2.962
0.093
-0.105
0.095
-0.167
0.425
1 1.243
0.271
0.036
-0.474
0.211
1 4.979
0.031
0.054
-0.516
0.505
1 14.179 0.001
0.252
0.096
0.392
1 0.384
DE
A
6 TOTAL_INVERT_DENSITY
9 GRAIN_SIZE_PERC-
-0.202
10 ADJUSTED__PHOSP- 0.156
0.539
HATE
Out Effect
Partial
Correlation
4
0.496
1 0.248
0.621
0.390
1 0.005
0.947
0.164
1 0.000
0.990
TA
8
L_BIOMASS_G_M2
11 ADJUSTED_POTASS- -0.002
IUM
AIC
63.169
22 December 2014
Page 51
Step number
: 4
R
: 0.773
R-Square
: 0.597
Mallows' Cp
: 3.692
Term Removed : ADJUSTED__PHOSPHATE
In
Effect
Coefficient
1
Constant
2
0.033
0.159
0.381
1 1.035
0.315
0.000
0.712
0.254
1 13.784 0.001
0.001
0.341
0.240
1 2.974
0.092
-0.118
0.092
-0.186
0.445
1 1.650
0.206
0.035
-0.456
0.215
1 4.771
0.034
0.051
-0.492
0.552
1 14.249 0.000
_DATUM__M_
3
DE
5
A
6
7
9
GRAIN_SIZE_PERC-
-0.192
Out
Effect
Partial
Correlation
4
0.509
1 0.355
0.555
0.392
1 0.000
0.987
0.392
1 0.384
0.539
TA
8
L_BIOMASS_G_M2
10
Page 52
HEMMERA ENVIROCHEM
Out
Effect
Partial
Correlation
HATE
11
ADJUSTED_POTASS- -0.017
0.168
1 0.012
0.912
IUM
AIC
61.623
Step number
: 5
R
: 0.766
R-Square
: 0.587
Mallows' Cp
: 2.647
Term Removed : ELEVATION_CHART_DATUM__M_
In
Effect
Coefficient
1
3
Constant
0.000
0.777
0.286
1 18.441 0.000
0.000
0.429
0.297
1 5.823
0.020
-0.127
0.091
-0.200
0.449
1 1.922
0.173
0.035
-0.465
0.215
1 4.959
0.031
DE
5
A
6
7
22 December 2014
Page 53
In
Effect
Coefficient
9
GRAIN_SIZE_PERC-
-0.205
0.049
-0.524
0.586
1 17.138 0.000
Out
Effect
Partial
Correlation
2
0.381
1 1.035
0.315
0.512
1 0.450
0.506
0.432
1 0.085
0.773
0.409
1 0.659
0.422
0.168
1 0.020
0.890
_DATUM__M_
4
TA
8
L_BIOMASS_G_M2
10
HATE
11
IUM
AIC
60.812
Step number
: 6
R
: 0.755
R-Square
: 0.569
Mallows' Cp
: 2.423
Term Removed : TOTAL_INVERT_DENSITY
Page 54
HEMMERA ENVIROCHEM
In
Effect
Coefficient
1
3
Constant
0.000
0.634
0.426
1 17.864 0.000
0.000
0.335
0.347
1 4.075
0.050
0.034
-0.392
0.230
1 3.688
0.061
-0.192
0.049
-0.491
0.607
1 15.293 0.000
Partial
DE
5
A
7
9
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_
Out
Effect
Correlation
2
0.385
1 1.290
0.262
0.586
1 1.248
0.270
-0.205
0.449
1 1.922
0.173
0.463
1 0.398
0.531
0.431
1 1.207
0.278
0.171
1 0.090
0.765
_DATUM__M_
4
TA
6
8
L_BIOMASS_G_M2
10
HATE
11
IUM
22 December 2014
Page 55
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
0.003 0.001 0.043 0.261 0.693
ADJUSTED_CHLORIDE
0.004 0.001 0.055 0.448 0.492
TOTAL_POLYCHAETA
0.007 0.006 0.642 0.000 0.346
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.001 0.000 0.002 0.001 0.997
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ 0.000 0.509 0.452 0.001 0.038
Page 56
HEMMERA ENVIROCHEM
Dependent Variable
BIOFILM_PIGMENTS_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
Tolerance t
p-Value
Coefficient
CONSTANT
3.619
0.292
0.000
.
12.406 0.000
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
Coefficient 95.0% Confidence Interval VIF
Lower
Upper
CONSTANT
3.619
3.032
4.207
.
ADJUSTED_CHLORIDE
0.000
0.000
0.000
2.349
TOTAL_POLYCHAETA
0.001
0.000
0.002
2.879
-0.135
0.003
4.353
-0.291
-0.093
1.647
22 December 2014
Page 57
Correlation Matrix of Regression Coefficients
CONST ADJUSTED_CH TOTAL_POLYC MACROFAUNA_ GRAIN_SIZE_P
ANT
LORI-
HAET-
TOTA-
DE
A
L_BENTHIC_IND ENT_SAND_2_
IV-
0MM_-
IDUAL
TO_0_
CONSTANT
1.000
ADJUSTED_CHLORIDE
0.295
1.000
TOTAL_POLYCHAETA
0.324
0.229
1.000
-0.710
-0.616
1.000
-0.259
0.370
0.170
MACROFAUNA_TOTAL_BENTHIC -0.805
ERC-
_INDIVIDUAL
GRAIN_SIZE_PERCENT_SAND_2_ -0.300
1.000
0MM_TO_0_
Analysis of Variance
Source
SS
df Mean Squares F-Ratio p-Value
Regression10.306 4 2.577
Residual
14.879 0.000
7.792 45 0.173
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
Page 58
HEMMERA ENVIROCHEM
AIC
60.951
Coefficients have been saved.
22 December 2014
Page 59
7.
FUCOXANTHIN MULTIPLE REGRESSION
▼OLS Regression
Step number : 0
R
: 0.774
R-Square
: 0.599
In
Effect
Coefficient
1
2
Constant
0.030
-0.002
0.327
1 0.000
0.989
0.000
0.740
0.147
1 7.826
0.008
0.011
-0.113
0.370
1 0.460
0.502
0.000
0.117
0.220
1 0.292
0.592
-0.077
0.085
-0.154
0.361
1 0.836
0.366
0.039
-0.249
0.119
1 0.721
0.401
0.086
-0.049
0.274
1 0.065
0.800
0.083
-0.472
0.144
1 3.113
0.086
_DATUM__M_
3
DE
4
TA
5
A
6
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-0.146
Page 60
HEMMERA ENVIROCHEM
In
Effect
Coefficient
10
ADJUSTED__PHOSP- -0.003
0.215
-0.003
0.368
1 0.000
0.988
0.001
0.205
0.091
1 0.370
0.546
HATE
11
IUM
Out Effect Partial
Correlation
none
AIC
46.114
Dependent Variable
: BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2
Minimum Tolerance for Entry into Model : 0.000
Alpha-to-Enter
: 15
: 0.150
Alpha-to-Remove : 0.150
Step number
: 1
R
: 0.774
R-Square
: 0.599
22 December 2014
Page 61
Mallows' Cp
: 9.000
In
Effect
Coefficient
1
3
Constant
0.000
0.739
0.151
1 8.218
0.007
0.011
-0.113
0.370
1 0.472
0.496
0.000
0.116
0.252
1 0.337
0.565
-0.077
0.083
-0.154
0.362
1 0.861
0.359
0.038
-0.250
0.123
1 0.764
0.387
0.081
-0.049
0.306
1 0.072
0.790
-0.146
0.077
-0.470
0.163
1 3.586
0.065
0.210
-0.003
0.373
1 0.000
0.986
0.001
0.205
0.093
1 0.392
0.535
DE
4
TA
5
A
6
7
8
L_BIOMASS_G_M2
9
10
HATE
11
IUM
Out
Effect
Partial
Correlation
2
ELEVATION_CHART- -0.002
0.327
1 0.000
0.989
_DATUM__M_
Page 62
HEMMERA ENVIROCHEM
AIC
44.114
Step number
: 2
R
: 0.774
R-Square
: 0.599
Mallows' Cp
: 7.000
In
Effect
Coefficient
1
3
Constant
0.000
0.737
0.196
1 10.893 0.002
0.011
-0.113
0.371
1 0.484
0.491
0.000
0.116
0.252
1 0.345
0.560
-0.077
0.082
-0.154
0.366
1 0.889
0.351
0.037
-0.251
0.129
1 0.833
0.367
0.078
-0.048
0.322
1 0.076
0.785
0.076
-0.470
0.164
1 3.702
0.061
DE
4
TA
5
A
6
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-0.146
Page 63
In
Effect
Coefficient
TO_0_
11
0.001
0.206
0.097
1 0.422
0.520
IUM
Out
Effect
Partial
Correlation
2
0.332
1 0.000
0.988
0.373
1 0.000
0.986
_DATUM__M_
10
HATE
AIC
42.115
Step number
: 3
R
: 0.773
R-Square
: 0.598
Mallows' Cp
: 5.072
Term Removed : MACROFAUNA_TOTAL_BIOMASS_G_M2
In
Effect
Coefficient
1
Constant
3
0.000
0.748
0.202
1 11.826 0.001
DE
Page 64
HEMMERA ENVIROCHEM
In
Effect
Coefficient
4
0.010
-0.124
0.396
1 0.637
0.429
0.000
0.129
0.268
1 0.463
0.500
-0.083
0.078
-0.165
0.390
1 1.117
0.297
0.028
-0.299
0.213
1 1.985
0.166
0.062
-0.432
0.240
1 4.681
0.036
0.001
0.251
0.130
1 0.855
0.361
TA
5
A
6
7
9
GRAIN_SIZE_PERC-
-0.134
11
IUM
Out
Effect
Partial
Correlation
2
0.379
1 0.007
0.935
0.322
1 0.076
0.785
0.393
1 0.002
0.965
_DATUM__M_
8
L_BIOMASS_G_M2
10
HATE
AIC
40.207
22 December 2014
Page 65
Step number
: 4
R
: 0.770
R-Square
: 0.594
Mallows' Cp
: 3.503
Term Removed : TOTAL_POLYCHAETA
In
Effect
Coefficient
1
Constant
3
0.000
0.679
0.258
1 12.583 0.001
0.010
-0.149
0.419
1 0.981
0.328
-0.062
0.072
-0.124
0.461
1 0.747
0.392
0.022
-0.207
0.358
1 1.621
0.210
0.062
-0.432
0.240
1 4.729
0.035
0.001
0.306
0.143
1 1.412
0.241
DE
4
TA
6
7
9
GRAIN_SIZE_PERC-
-0.134
11
IUM
Out
Effect
Partial
Correlation
2
0.457
1 0.126
0.725
0.268
1 0.463
0.500
0.341
1 0.186
0.668
_DATUM__M_
5
A
8
Page 66
HEMMERA ENVIROCHEM
Out
Effect
Partial
Correlation
L_BIOMASS_G_M2
10
0.396
1 0.011
0.918
HATE
AIC
38.755
Step number
: 5
R
: 0.766
R-Square
: 0.587
Mallows' Cp
: 2.189
In
Effect
Coefficient
1
3
Constant
0.000
0.624
0.291
1 12.027 0.001
0.010
-0.185
0.455
1 1.665
0.204
0.022
-0.210
0.359
1 1.690
0.200
0.061
-0.427
0.240
1 4.656
0.036
DE
4
TA
7
9
GRAIN_SIZE_PERC-
-0.132
22 December 2014
Page 67
In
Effect
Coefficient
11
0.001
0.292
0.143
1 1.304
0.260
IUM
Out
Effect
Partial
Correlation
2
0.463
1 0.065
0.800
0.316
1 0.083
0.774
-0.131
0.461
1 0.747
0.392
0.351
1 0.328
0.570
0.404
1 0.050
0.825
_DATUM__M_
5
A
6
8
L_BIOMASS_G_M2
10
HATE
AIC
37.616
Step number
: 6
R
: 0.758
R-Square
: 0.574
Mallows' Cp
: 1.381
Page 68
HEMMERA ENVIROCHEM
In
Effect
Coefficient
1
3
Constant
0.000
0.731
0.399
1 22.490 0.000
0.009
-0.108
0.587
1 0.717
0.401
0.021
-0.186
0.365
1 1.338
0.254
-0.182
0.043
-0.588
0.489
1 17.895 0.000
Partial
DE
4
TA
7
9
Out
Effect
Correlation
2
0.472
1 0.170
0.682
0.347
1 0.374
0.544
-0.118
0.463
1 0.625
0.433
0.501
1 1.226
0.274
0.406
1 0.018
0.895
0.143
1 1.304
0.260
_DATUM__M_
5
A
6
8
L_BIOMASS_G_M2
10
HATE
11
IUM
22 December 2014
Page 69
AIC
37.077
Step number
: 7
R
: 0.753
R-Square
: 0.568
Mallows' Cp
: 0.040
In
Effect
Coefficient
1
3
Constant
0.000
0.687
0.449
1 22.534 0.000
0.021
-0.170
0.370
1 1.138
0.036
-0.523
0.704
1 20.495 0.000
DE
7
0.292
9
GRAIN_SIZE_PERC-
-0.162
Out
Effect
Partial
Correlation
2
0.475
1 0.229
0.635
0.587
1 0.717
0.401
0.347
1 0.339
0.563
_DATUM__M_
4
TA
5
Page 70
HEMMERA ENVIROCHEM
Out
Effect
Partial
Correlation
A
6
TOTAL_INVERT_DE-
-0.153
0.525
1 1.084
0.303
0.503
1 1.349
0.252
0.431
1 0.109
0.743
0.185
1 0.357
0.553
NSITY
8
L_BIOMASS_G_M2
10
HATE
11
IUM
AIC
35.868
Step number
: 8
R
: 0.746
R-Square
: 0.557
Mallows' Cp
: -0.920
Term Removed : MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL
In
Effect
Coefficient
1
3
Constant
0.000
0.572
0.998
1 34.684 0.000
0.030
-0.456
0.998
1 22.046 0.000
DE
9
GRAIN_SIZE_PERC- -0.141
22 December 2014
Page 71
In
Effect
Coefficient
Out
Effect
Partial
Correlation
2
0.524
1 0.017
0.896
0.596
1 0.506
0.480
0.560
1 0.037
0.848
-0.150
0.525
1 1.060
0.309
0.370
1 1.138
0.292
0.688
1 2.445
0.125
0.453
1 0.008
0.929
0.186
1 0.283
0.597
_DATUM__M_
4
TA
5
TOTAL_POLYCHAET- -0.028
A
6
7
8
L_BIOMASS_G_M2
10
HATE
11
IUM
AIC
35.089
Page 72
HEMMERA ENVIROCHEM
Step number : 8
R
: 0.761
R-Square
: 0.579
Mallows' Cp
: -1.094
Term Entered : MACROFAUNA_TOTAL_BIOMASS_G_M2
In
Effect
Coefficient
1
Constant
3
0.000
0.593
0.980
1 37.635 0.000
0.051
-0.180
0.688
1 2.445
-0.172
0.035
-0.554
0.700
1 23.486 0.000
Partial
DE
8
0.125
L_BIOMASS_G_M2
9
Out
Effect
Correlation
2
0.510
1 0.015
0.903
0.596
1 0.547
0.464
0.550
1 0.000
0.995
-0.132
0.520
1 0.793
0.378
0.270
1 0.098
0.756
_DATUM__M_
4
TA
5
A
6
7
22 December 2014
Page 73
Out
Effect
Partial
Correlation
10
0.452
1 0.001
0.971
0.162
1 0.000
0.991
HATE
11
IUM
AIC
34.499
▼OLS Regression
X'X
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
0.007 0.000 0.038 0.955
ADJUSTED_CHLORIDE
0.015 0.000 0.874 0.111
0.009 0.001 0.219 0.771
Page 74
HEMMERA ENVIROCHEM
1
2
3
4
Dependent Variable
BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2
N
50
Multiple R
0.761
Squared Multiple R
0.579
Effect
Tolerance t
p-Value
Coefficient
CONSTANT
3.268
0.191
0.000
.
17.064 0.000
ADJUSTED_CHLORIDE
0.000
0.000
0.593
0.980
6.135 0.000
-0.080
0.051
-0.180
0.688
-1.564 0.125
0.035
-0.554
0.700
-4.846 0.000
Effect
Lower
Upper
CONSTANT
3.268
2.882
3.653
.
ADJUSTED_CHLORIDE
0.000
0.000
0.000
1.020
-0.080
-0.184
0.023
1.453
22 December 2014
Page 75
Effect
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ -0.172
Lower
Upper
-0.243
-0.100
1.429
CONSTA ADJUSTED_CHLO MACROFAUNA_TO GRAIN_SIZE_PER
NT
RI-
TA-
C-
DE
L_BIOMASS_G_M2 ENT_SAND_2_0M
M_TO_0_
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_ -0.456
-0.040
0.547
1.000
TO_0_
Source
SS
Residual
21.104 0.000
4.779 46 0.104
AIC
34.499
Page 76
HEMMERA ENVIROCHEM
AIC
34.499
22 December 2014
Page 77
8.
TOC MULTIPLE REGRESSION
▼OLS Regression
Step number : 0
R
: 0.835
R-Square
: 0.698
In
Effect
Coefficient
1
2
Constant
0.029
-0.042
0.327
1 0.073
0.788
0.000
0.491
0.147
1 4.578
0.039
0.011
0.012
0.370
1 0.007
0.935
0.000
0.439
0.220
1 5.480
0.024
-0.225
0.082
-0.402
0.361
1 7.532
0.009
0.038
-0.308
0.119
1 1.456
0.235
0.084
0.088
0.274
1 0.277
0.602
0.080
-0.559
0.144
1 5.799
0.021
_DATUM__M_
3
DE
4
TOTAL_OLIGOCHAE- 0.001
TA
5
A
6
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-0.193
Page 78
HEMMERA ENVIROCHEM
In
Effect
Coefficient
10
0.208
-0.159
0.368
1 1.200
0.280
0.001
0.175
0.091
1 0.357
0.554
HATE
11
IUM
Out Effect Partial
Correlation
none
AIC
42.928
Dependent Variable
: BIOFILM_TOTAL_ORGANIC_CARBON_MG_M2
Alpha-to-Enter
: 15
: 0.150
Step number
: 1
R
: 0.835
R-Square
: 0.697
22 December 2014
Page 79
Mallows' Cp
: 9.007
In
Effect
Coefficient
1
Constant
2
0.029
-0.041
0.327
1 0.074
0.787
0.000
0.489
0.148
1 4.703
0.036
0.000
0.437
0.226
1 5.697
0.022
-0.223
0.078
-0.399
0.385
1 8.118
0.007
0.037
-0.310
0.121
1 1.543
0.221
0.080
0.092
0.289
1 0.321
0.574
-0.192
0.078
-0.556
0.147
1 6.009
0.019
0.205
-0.160
0.369
1 1.245
0.271
0.001
0.187
0.121
1 0.559
0.459
_DATUM__M_
3
DE
5
A
6
7
8
L_BIOMASS_G_M2
9
10
HATE
11
IUM
OutEffect
Partial
Correlation
4
0.370
1 0.007
0.935
TA
Page 80
HEMMERA ENVIROCHEM
AIC
40.937
Step number
: 2
R
: 0.835
R-Square
: 0.697
Mallows' Cp
: 7.079
In
Effect
Coefficient
1
3
Constant
0.000
0.480
0.152
1 4.740
0.035
0.000
0.419
0.258
1 6.138
0.017
-0.224
0.077
-0.401
0.386
1 8.411
0.006
0.036
-0.321
0.124
1 1.740
0.194
0.075
0.106
0.323
1 0.490
0.488
-0.185
0.073
-0.535
0.166
1 6.431
0.015
0.201
-0.165
0.374
1 1.372
0.248
DE
5
A
6
7
8
L_BIOMASS_G_M2
9
10
22 December 2014
Page 81
In
Effect
Coefficient
HATE
11
0.001
0.198
0.125
1 0.658
0.422
IUM
OutEffect
Partial
Correlation
2
0.327
1 0.074
0.787
0.370
1 0.006
0.940
_DATUM__M_
4
TA
AIC
39.029
Step number
: 3
R
: 0.833
R-Square
: 0.693
Mallows' Cp
: 5.546
In
Effect
Coefficient
1
Constant
3
0.000
0.470
0.153
1 4.618
0.037
DE
Page 82
HEMMERA ENVIROCHEM
In
Effect
Coefficient
5
0.000
0.387
0.279
1 5.718
0.021
-0.208
0.073
-0.373
0.423
1 8.046
0.007
0.028
-0.212
0.211
1 1.301
0.260
-0.213
0.060
-0.617
0.239
1 12.493 0.001
0.194
-0.188
0.398
1 1.933
0.172
0.001
0.114
0.164
1 0.292
0.592
A
6
7
9
10
HATE
11
IUM
Out
Effect
Partial
Correlation
2
0.366
1 0.236
0.630
0.391
1 0.056
0.815
0.323
1 0.490
0.488
_DATUM__M_
4
TA
8
L_BIOMASS_G_M2
AIC
37.623
22 December 2014
Page 83
Step number
: 4
R
: 0.831
R-Square
: 0.691
Mallows' Cp
: 3.821
In
Effect
Coefficient
1
Constant
3
0.000
0.531
0.210
1 8.237
0.006
0.000
0.407
0.295
1 6.814
0.012
-0.205
0.073
-0.367
0.426
1 7.982
0.007
0.027
-0.220
0.212
1 1.428
0.239
-0.238
0.040
-0.688
0.550
1 36.256 0.000
0.190
-0.200
0.409
1 2.284
DE
5
A
6
7
9
10
0.138
HATE
Out
Effect
Partial
Correlation
2
0.366
1 0.237
0.629
0.496
1 0.212
0.648
0.424
1 0.122
0.728
_DATUM__M_
4
TA
8
Page 84
HEMMERA ENVIROCHEM
Out
Effect
Partial
Correlation
L_BIOMASS_G_M2
11
0.164
1 0.292
0.592
IUM
AIC
35.970
Step number
: 5
R
: 0.825
R-Square
: 0.681
Mallows' Cp
: 3.144
In
Effect
Coefficient
1
3
Constant
0.000
0.412
0.295
1 6.912
0.012
0.000
0.290
0.490
1 5.667
0.022
0.071
-0.333
0.447
1 6.845
0.012
0.039
-0.657
0.581
1 34.532 0.000
DE
5
A
6
TOTAL_INVERT_DE- -0.186
NSITY
9
22 December 2014
Page 85
In
Effect
Coefficient
10
0.189
-0.220
0.415
1 2.760
0.104
HATE
Out
Effect
Partial
Correlation
2
0.368
1 0.164
0.687
0.496
1 0.214
0.646
0.212
1 1.428
0.239
0.673
1 0.193
0.662
0.165
1 0.393
0.534
_DATUM__M_
4
TA
7
8
L_BIOMASS_G_M2
11
IUM
AIC
35.603
▼OLS Regression
Page 86
HEMMERA ENVIROCHEM
Eigenvalues of Unit Scaled X'X
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 22.537 70.440
1
2
3
4
5
6
CONSTANT
0.000 0.000 0.001 0.003 0.010 0.986
ADJUSTED_CHLORIDE
0.002 0.000 0.000 0.360 0.207 0.431
TOTAL_POLYCHAETA
0.005 0.012 0.798 0.078 0.000 0.105
0.000 0.000 0.001 0.002 0.036 0.961
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_0.000 0.468 0.445 0.027 0.059 0.002
ADJUSTED__PHOSPHATE
Dependent Variable
0.000 0.000 0.003 0.000 0.804 0.192
BIOFILM_TOTAL_ORGANIC_CARBON_MG_M2
N
50
Multiple R
0.825
Squared Multiple R
0.681
22 December 2014
Page 87
Effect
Tolerance t
p-Value
Coefficient
CONSTANT
12.588
1.046
0.000
.
12.031 0.000
ADJUSTED_CHLORIDE
0.000
0.000
0.412
0.295
2.629 0.012
TOTAL_POLYCHAETA
0.001
0.000
0.290
0.490
2.381 0.022
-0.186
0.071
-0.333
0.447
-2.616 0.012
0.039
-0.657
0.581
-5.876 0.000
ADJUSTED__PHOSPHATE
0.189
-0.220
0.415
-1.661 0.104
-0.315
Effect
Lower
Upper
CONSTANT
12.588
10.479
14.696
.
ADJUSTED_CHLORIDE
0.000
0.000
0.000
3.392
TOTAL_POLYCHAETA
0.001
0.000
0.001
2.042
-0.186
-0.330
-0.043
2.237
-0.305
-0.149
1.723
ADJUSTED__PHOSPHATE
-0.696
0.067
2.411
-0.315
CONST ADJUSTED_CH TOTAL_POLYC TOTAL_INVER GRAIN_SIZE_P
ANT
LORI-
HAET-
T_DE-
ERC-
DE
A
NSITY
ENT_SAND_2_0
MM_TO_0_
CONSTANT
1.000
ADJUSTED_CHLORIDE
0.662
Page 88
1.000
HEMMERA ENVIROCHEM
CONST ADJUSTED_CH TOTAL_POLYC TOTAL_INVER GRAIN_SIZE_P
ANT
LORI-
HAET-
T_DE-
ERC-
DE
A
NSITY
ENT_SAND_2_0
MM_TO_0_
TOTAL_POLYCHAETA
0.306
0.056
1.000
-0.951
-0.581
-0.331
1.000
-0.010
0.558
0.073
1.000
-0.697
-0.205
0.261
-0.229
0MM_TO_0_
ADJUSTED__PHOSPHATE
-0.520
Correlation Matrix of Regression Coefficients (Contd.)
ADJUSTED__PHOSPHATE
CONSTANT
ADJUSTED_CHLORIDE
TOTAL_POLYCHAETA
ADJUSTED__PHOSPHATE
1.000
Source
SS
Residual
18.779 0.000
4.510 44 0.102
WARNING
22 December 2014
Page 89
Case 34 is an Outlier
(Studentized Residual : -3.426)
: 0.414)
AIC
35.603
Page 90
HEMMERA ENVIROCHEM
22 December 2014
Page 91
9.
TOTAL CARBOHYDRATE MULTIPLE REGRESSION
▼OLS Regression
Step number : 0
R
: 0.774
R-Square
: 0.599
In
Effect
Coefficient
1
2
Constant
0.030
-0.002
0.327
1 0.000
0.989
0.000
0.740
0.147
1 7.826
0.008
0.011
-0.113
0.370
1 0.460
0.502
0.000
0.117
0.220
1 0.292
0.592
-0.077
0.085
-0.154
0.361
1 0.836
0.366
0.039
-0.249
0.119
1 0.721
0.401
0.086
-0.049
0.274
1 0.065
0.800
0.083
-0.472
0.144
1 3.113
0.086
_DATUM__M_
3
DE
4
TA
5
A
6
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-0.146
Page 92
HEMMERA ENVIROCHEM
In
Effect
Coefficient
10
0.215
-0.003
0.368
1 0.000
0.988
0.001
0.205
0.091
1 0.370
0.546
HATE
11
IUM
Out Effect Partial
Correlation
none
AIC
46.114
Dependent Variable
: BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2
Alpha-to-Enter
: 15
: 0.150
Step number
: 1
R
: 0.774
R-Square
: 0.599
22 December 2014
Page 93
Mallows' Cp
: 9.000
In
Effect
Coefficient
1
Constant
3
0.000
0.739
0.151
1 8.218
0.007
0.011
-0.113
0.370
1 0.472
0.496
0.000
0.116
0.252
1 0.337
0.565
-0.077
0.083
-0.154
0.362
1 0.861
0.359
0.038
-0.250
0.123
1 0.764
0.387
0.081
-0.049
0.306
1 0.072
0.790
-0.146
0.077
-0.470
0.163
1 3.586
0.065
0.210
-0.003
0.373
1 0.000
0.986
0.001
0.205
0.093
1 0.392
0.535
DE
4
TA
5
A
6
7
8
L_BIOMASS_G_M2
9
10
HATE
11
IUM
OutEffect
Partial
Correlation
2
0.327
1 0.000
0.989
_DATUM__M_
Page 94
HEMMERA ENVIROCHEM
AIC
44.114
Step number
: 2
R
: 0.774
R-Square
: 0.599
Mallows' Cp
: 7.000
In
Effect
Coefficient
1
3
Constant
0.000
0.737
0.196
1 10.893 0.002
0.011
-0.113
0.371
1 0.484
0.491
0.000
0.116
0.252
1 0.345
0.560
-0.077
0.082
-0.154
0.366
1 0.889
0.351
0.037
-0.251
0.129
1 0.833
0.367
0.078
-0.048
0.322
1 0.076
0.785
0.076
-0.470
0.164
1 3.702
0.061
DE
4
TA
5
A
6
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-0.146
Page 95
In
Effect
Coefficient
TO_0_
11
0.001
0.206
0.097
1 0.422
0.520
IUM
OutEffect
Partial
Correlation
2
0.332
1 0.000
0.988
0.373
1 0.000
0.986
_DATUM__M_
10 ADJUSTED__PHOSP- -0.003
HATE
AIC
42.115
Step number
: 3
R
: 0.773
R-Square
: 0.598
Mallows' Cp
: 5.072
In
Effect
Coefficient
1
Constant
3
0.000
0.748
0.202
1 11.826 0.001
DE
Page 96
HEMMERA ENVIROCHEM
In
Effect
Coefficient
4
0.010
-0.124
0.396
1 0.637
0.429
0.000
0.129
0.268
1 0.463
0.500
-0.083
0.078
-0.165
0.390
1 1.117
0.297
0.028
-0.299
0.213
1 1.985
0.166
0.062
-0.432
0.240
1 4.681
0.036
0.001
0.251
0.130
1 0.855
0.361
TA
5
A
6
7
9
GRAIN_SIZE_PERC-
-0.134
11
IUM
Out
Effect
Partial
Correlation
2
0.379
1 0.007
0.935
0.322
1 0.076
0.785
0.393
1 0.002
0.965
_DATUM__M_
8
L_BIOMASS_G_M2
10
HATE
AIC
40.207
22 December 2014
Page 97
Step number
: 4
R
: 0.770
R-Square
: 0.594
Mallows' Cp
: 3.503
In
Effect
Coefficient
1
Constant
3
0.000
0.679
0.258
1 12.583 0.001
0.010
-0.149
0.419
1 0.981
0.328
-0.062
0.072
-0.124
0.461
1 0.747
0.392
0.022
-0.207
0.358
1 1.621
0.210
0.062
-0.432
0.240
1 4.729
0.035
0.001
0.306
0.143
1 1.412
0.241
DE
4
TA
6
7
9
GRAIN_SIZE_PERC-
-0.134
11
IUM
Out
Effect
Partial
Correlation
2
0.457
1 0.126
0.725
0.268
1 0.463
0.500
0.341
1 0.186
0.668
_DATUM__M_
5
A
8
Page 98
HEMMERA ENVIROCHEM
Out
Effect
Partial
Correlation
L_BIOMASS_G_M2
10
0.396
1 0.011
0.918
HATE
AIC
38.755
Step number
: 5
R
: 0.766
R-Square
: 0.587
Mallows' Cp
: 2.189
In
Effect
Coefficient
1
3
Constant
0.000
0.624
0.291
1 12.027 0.001
0.010
-0.185
0.455
1 1.665
0.204
0.022
-0.210
0.359
1 1.690
0.200
0.061
-0.427
0.240
1 4.656
0.036
DE
4
TA
7
9
GRAIN_SIZE_PERC-
-0.132
22 December 2014
Page 99
In
Effect
Coefficient
11
0.001
0.292
0.143
1 1.304
0.260
IUM
Out
Effect
Partial
Correlation
2
0.463
1 0.065
0.800
0.316
1 0.083
0.774
-0.131
0.461
1 0.747
0.392
0.351
1 0.328
0.570
0.404
1 0.050
0.825
_DATUM__M_
5
A
6
8
L_BIOMASS_G_M2
10
HATE
AIC
37.616
Step number
: 6
R
: 0.758
R-Square
: 0.574
Mallows' Cp
: 1.381
Page 100
HEMMERA ENVIROCHEM
In
Effect
Coefficient
1
3
Constant
0.000
0.731
0.399
1 22.490 0.000
0.009
-0.108
0.587
1 0.717
0.401
0.021
-0.186
0.365
1 1.338
0.254
-0.182
0.043
-0.588
0.489
1 17.895 0.000
Partial
DE
4
TA
7
9
Out
Effect
Correlation
2
0.472
1 0.170
0.682
0.347
1 0.374
0.544
-0.118
0.463
1 0.625
0.433
0.501
1 1.226
0.274
0.406
1 0.018
0.895
0.143
1 1.304
0.260
_DATUM__M_
5
A
6
8
L_BIOMASS_G_M2
10
HATE
11
IUM
22 December 2014
Page 101
AIC
37.077
Step number
: 7
R
: 0.753
R-Square
: 0.568
Mallows' Cp
: 0.040
In
Effect
Coefficient
1
3
Constant
0.000
0.687
0.449
1 22.534 0.000
0.021
-0.170
0.370
1 1.138
0.036
-0.523
0.704
1 20.495 0.000
DE
7
0.292
9
GRAIN_SIZE_PERC-
-0.162
Out
Effect
Partial
Correlation
2
0.475
1 0.229
0.635
0.587
1 0.717
0.401
_DATUM__M_
4
TA
Page 102
HEMMERA ENVIROCHEM
Out
Effect
Partial
Correlation
5
0.347
1 0.339
0.563
-0.153
0.525
1 1.084
0.303
0.503
1 1.349
0.252
0.431
1 0.109
0.743
0.185
1 0.357
0.553
A
6
8
L_BIOMASS_G_M2
10
HATE
11
IUM
AIC
35.868
Step number
: 8
R
: 0.746
R-Square
: 0.557
Mallows' Cp
: -0.920
In
Effect
Coefficient
1
3
Constant
0.000
0.572
0.998
1 34.684 0.000
DE
22 December 2014
Page 103
In
Effect
Coefficient
9
0.030
-0.456
0.998
1 22.046 0.000
Out
Effect
Partial
Correlation
2
0.524
1 0.017
0.896
0.596
1 0.506
0.480
0.560
1 0.037
0.848
-0.150
0.525
1 1.060
0.309
0.370
1 1.138
0.292
0.688
1 2.445
0.125
0.453
1 0.008
0.929
0.186
1 0.283
0.597
_DATUM__M_
4
TA
5
TOTAL_POLYCHAET- -0.028
A
6
7
8
L_BIOMASS_G_M2
10
HATE
11
IUM
AIC
35.089
Page 104
HEMMERA ENVIROCHEM
Step number : 8
R
: 0.761
R-Square
: 0.579
Mallows' Cp
: -1.094
Term Entered : MACROFAUNA_TOTAL_BIOMASS_G_M2
In
Effect
Coefficient
1
Constant
3
0.000
0.593
0.980
1 37.635 0.000
0.051
-0.180
0.688
1 2.445
-0.172
0.035
-0.554
0.700
1 23.486 0.000
Partial
DE
8
0.125
L_BIOMASS_G_M2
9
Out
Effect
Correlation
2
0.510
1 0.015
0.903
0.596
1 0.547
0.464
0.550
1 0.000
0.995
-0.132
0.520
1 0.793
0.378
0.270
1 0.098
0.756
_DATUM__M_
4
TA
5
A
6
7
22 December 2014
Page 105
Out
Effect
Partial
Correlation
10
0.452
1 0.001
0.971
0.162
1 0.000
0.991
HATE
11
IUM
AIC
34.499
▼OLS Regression
X'X
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
0.007 0.000 0.038 0.955
ADJUSTED_CHLORIDE
0.015 0.000 0.874 0.111
0.009 0.001 0.219 0.771
Page 106
HEMMERA ENVIROCHEM
1
2
3
4
Dependent Variable
BIOFILM_PIGMENTS_FUCOXANTHIN_MG_M2
N
50
Multiple R
0.761
Squared Multiple R
0.579
Effect
Tolerance t
p-Value
Coefficient
CONSTANT
3.268
0.191
0.000
.
17.064 0.000
ADJUSTED_CHLORIDE
0.000
0.000
0.593
0.980
6.135 0.000
-0.080
0.051
-0.180
0.688
-1.564 0.125
0.035
-0.554
0.700
-4.846 0.000
Effect
Lower
Upper
CONSTANT
3.268
2.882
3.653
.
ADJUSTED_CHLORIDE
0.000
0.000
0.000
1.020
-0.080
-0.184
0.023
1.453
22 December 2014
Page 107
Effect
Lower
Upper
-0.243
-0.100
1.429
CONSTA ADJUSTED_CHLO MACROFAUNA_TO GRAIN_SIZE_PER
NT
RI-
TA-
C-
DE
L_BIOMASS_G_M2 ENT_SAND_2_0M
M_TO_0_
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_ -0.456
-0.040
0.547
1.000
TO_0_
Source
SS
Residual
21.104 0.000
4.779 46 0.104
Page 108
HEMMERA ENVIROCHEM
AIC
34.499
▼OLS Regression
22 December 2014
Page 109
Step number : 0
R
: 0.545
R-Square
: 0.297
In
Effect
Coefficient
1
Constant
2
2.711
-0.071
0.327
1 0.093
0.762
0.004
0.769
0.147
1 4.832
0.034
1.022
-0.129
0.370
1 0.344
0.561
0.041
0.339
0.220
1 1.408
0.243
7.682
0.084
0.361
1 0.140
0.710
3.549
-0.796
0.119
1 4.192
0.047
7.841
0.211
0.274
1 0.676
0.416
7.521
-0.586
0.144
1 2.740
0.106
19.497
0.119
0.368
1 0.288
0.595
0.113
-0.308
0.091
1 0.478
0.493
_DATUM__M_
3
DE
4
TA
5
A
6
TOTAL_INVERT_DE-
2.878
NSITY
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-12.450
10
HATE
11
IUM
Page 110
HEMMERA ENVIROCHEM
Out Effect Partial
Correlation
none
AIC
496.975
Dependent Variable
: BIOFILM_TOTAL_CARBOHYDRATE_MG_M2
Alpha-to-Enter
: 15
: 0.150
Step number
: 1
R
: 0.544
R-Square
: 0.295
Mallows' Cp
: 9.093
In
Effect
Coefficient
1
Constant
3
22 December 2014
0.003
0.753
0.151
1 4.851
0.033
Page 111
In
Effect
Coefficient
DE
4
1.010
-0.131
0.370
1 0.362
0.551
0.038
0.309
0.252
1 1.362
0.250
7.587
0.081
0.362
1 0.134
0.716
3.460
-0.816
0.123
1 4.630
0.038
7.338
0.236
0.306
1 0.967
0.331
6.997
-0.549
0.163
1 2.784
0.103
19.124
0.110
0.373
1 0.258
0.614
0.110
-0.287
0.093
1 0.436
0.513
TA
5
A
6
TOTAL_INVERT_DE-
2.776
NSITY
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-11.676
10
HATE
11
IUM
OutEffect
Partial
Correlation
2
0.327
1 0.093
0.762
_DATUM__M_
AIC
495.094
Page 112
HEMMERA ENVIROCHEM
Step number
: 2
R
: 0.541
R-Square
: 0.293
Mallows' Cp
: 7.223
In
Effect
Coefficient
1
3
Constant
0.003
0.811
0.193
1 7.366
0.010
0.967
-0.111
0.395
1 0.283
0.597
0.034
0.350
0.309
1 2.197
0.146
3.268
-0.857
0.134
1 5.729
0.021
7.082
0.255
0.321
1 1.215
0.277
6.882
-0.536
0.164
1 2.743
0.105
18.816
0.102
0.378
1 0.228
0.636
0.109
-0.279
0.093
1 0.421
0.520
DE
4
TA
5
A
7
8
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-11.397
10
HATE
11
IUM
22 December 2014
Page 113
Out Effect
Partial
Correlation
2
0.328
1 0.085
0.772
0.362
1 0.134
0.716
_DATUM__M_
6
TOTAL_INVERT_DE- 0.058
NSITY
AIC
493.261
Step number
: 3
R
: 0.538
R-Square
: 0.289
Mallows' Cp
: 5.441
In
Effect
Coefficient
1
3
Constant
0.003
0.881
0.254
1 11.669 0.001
0.954
-0.119
0.398
1 0.335
0.566
0.034
0.349
0.309
1 2.219
0.144
3.116
-0.811
0.145
1 5.637
0.022
DE
4
TA
5
A
7
L_BENTHIC_INDIV-
Page 114
HEMMERA ENVIROCHEM
In
Effect
Coefficient
IDUAL
8
6.788
0.227
0.343
1 1.048
0.312
6.780
-0.553
0.166
1 3.001
0.091
0.106
-0.320
0.097
1 0.590
0.447
L_BIOMASS_G_M2
9
GRAIN_SIZE_PERC-
-11.745
11
IUM
Out Effect
Partial
Correlation
2
0.333
1 0.055
0.816
0.366
1 0.101
0.753
0.378
1 0.228
0.636
_DATUM__M_
6
NSITY
HATE
AIC
491.538
Step number
: 4
R
: 0.532
R-Square
: 0.283
Mallows' Cp
: 3.756
22 December 2014
Page 115
In Effect
Coefficient
1 Constant
0.003
0.883
0.254
1 11.898 0.001
0.033
0.356
0.310
1 2.358
0.132
2.996
-0.762
0.155
1 5.385
0.025
6.341
0.184
0.387
1 0.787
0.380
6.611
-0.587
0.172
1 3.558
0.066
0.087
-0.456
0.143
1 1.783
0.189
DE
A
8 MACROFAUNA_TOTA- 5.624
L_BIOMASS_G_M2
9 GRAIN_SIZE_PERC-
-12.471
11ADJUSTED_POTASS- -0.116
IUM
OutEffect
Partial
Correlation
2
0.333
1 0.064
0.802
0.398
1 0.335
0.566
0.392
1 0.025
0.875
0.380
1 0.278
0.601
_DATUM__M_
4
TA
6
NSITY
HATE
Page 116
HEMMERA ENVIROCHEM
AIC
489.935
Step number
: 5
R
: 0.520
R-Square
: 0.270
Mallows' Cp
: 2.483
In Effect
Coefficient
1 Constant
0.003
0.866
0.256
1 11.576 0.001
0.033
0.319
0.320
1 1.965
0.168
2.472
-0.598
0.226
1 4.876
0.032
5.588
-0.733
0.240
1 7.778
0.008
0.079
-0.579
0.171
1 3.454
0.070
DE
A
9 GRAIN_SIZE_PERC-
-15.585
IUM
22 December 2014
Page 117
OutEffect
Partial
Correlation
2
0.384
1 0.312
0.579
0.449
1 0.061
0.805
0.068
0.443
1 0.201
0.656
0.387
1 0.787
0.380
0.416
1 0.061
0.807
_DATUM__M_
4
TA
6
8
L_BIOMASS_G_M2
HATE
AIC
488.842
Step number
: 6
R
: 0.488
R-Square
: 0.238
Mallows' Cp
: 2.291
In Effect
Coefficient
1 Constant
0.002
0.743
0.291
1 9.466
0.004
DE
Page 118
HEMMERA ENVIROCHEM
In Effect
Coefficient
1.956
-0.362
0.369
1 2.851
0.098
5.648
-0.736
0.240
1 7.667
0.008
0.077
-0.458
0.185
1 2.288
0.137
9 GRAIN_SIZE_PERC-
-15.639
IUM
Out Effect
Partial
Correlation
2
0.472
1 0.009
0.926
0.455
1 0.160
0.691
0.320
1 1.965
0.168
0.133
0.501
1 0.796
0.377
0.400
1 0.372
0.545
0.417
1 0.094
0.761
_DATUM__M_
4
TA
5
A
6
8
L_BIOMASS_G_M2
HATE
AIC
489.026
22 December 2014
Page 119
▼OLS Regression
X'X
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 14.277 19.733
1
2
3
4
5
CONSTANT
0.002 0.000 0.156 0.011 0.830
ADJUSTED_CHLORIDE
0.002 0.000 0.319 0.015 0.662
MACROFAUNA_TOTAL_BENTHIC_INDIVIDUAL 0.001 0.000 0.001 0.699 0.299
GRAIN_SIZE_PERCENT_SAND_2_0MM_TO_0_ 0.000 0.221 0.000 0.055 0.723
ADJUSTED_POTASSIUM
Dependent Variable
0.001 0.000 0.001 0.357 0.640
BIOFILM_TOTAL_CARBOHYDRATE_MG_M2
N
50
Multiple R
0.488
Squared Multiple R
0.238
Adjusted Squared Multiple R0.170
Page 120
HEMMERA ENVIROCHEM
Effect
Tolerance t
p-Value
Coefficient
CONSTANT
121.811
26.314
0.000
.
4.629 0.000
ADJUSTED_CHLORIDE
0.008
0.002
0.743
0.291
3.077 0.004
1.956
-0.362
0.369
-1.689 0.098
5.648
-0.736
0.240
-2.769 0.008
ADJUSTED_POTASSIUM
0.077
-0.458
0.185
-1.513 0.137
-0.116
Effect
Lower
Upper
CONSTANT
121.811
68.812
174.811
.
ADJUSTED_CHLORIDE
0.008
0.003
0.012
3.441
-7.242
0.637
2.712
-27.014
-4.264
4.168
ADJUSTED_POTASSIUM
-0.271
0.039
5.406
-0.116
CONST ADJUSTED_CH MACROFAUNA_ GRAIN_SIZE_P ADJUSTED_PO
ANT
CONSTANT
1.000
ADJUSTED_CHLORIDE
0.533
MACROFAUNA_TOTAL_BENTHIC -0.576
22 December 2014
LORI-
TOTA-
DE
L_BENTHIC_IND ENT_SAND_2_ IUM
ERC-
IV-
0MM_-
IDUAL
TO_0_
TASS-
1.000
-0.560
1.000
Page 121
CONST ADJUSTED_CH MACROFAUNA_ GRAIN_SIZE_P ADJUSTED_PO
ANT
LORI-
TOTA-
DE
L_BENTHIC_IND ENT_SAND_2_ IUM
ERC-
IV-
0MM_-
IDUAL
TO_0_
-0.661
0.268
1.000
-0.594
-0.060
0.812
TASS-
_INDIVIDUAL
0MM_TO_0_
ADJUSTED_POTASSIUM
-0.652
1.000
Source
SS
Regression12,706.907 4 3,176.727
Residual
3.510
0.014
40,727.478 45 905.055
WARNING
Case
9 is an Outlier
(Studentized Residual : 3.194)
: 0.356)
First Order Autocorrelation-0.119
AIC
489.026
Page 122
HEMMERA ENVIROCHEM
22 December 2014
Page 123
Appendix 4
Correlation Coefficient Matrix
307071-00790 : Rev 0 : 27 January 2015
Appendices
Elevation_Chart_Datum__M_
Annual_Exposure__Hr__
Distance_From_Canoe_Pass__M_
Distance_From_Shore__M_
Total_Harpacticoida
Total_Nematoda
Total_Oligochaeta
Total_Polychaeta
Total_Invert_Density
Total_Invert_Biomass
Macrofauna_Total_Benthic_Individual
Macrofauna_Total_Biomass_G_M2
Meiofauna_Total_Benthic_Individuals
Meiofauna_Total_Biomass_G_M2
Grain_Size_Percent_Clay_Less_Than_4
Grain_Size_Percent_Silt_0_063mm_To_
ain_Size_Percent_Sand_2_0mm_To_0_
Toc_Total_Organic_Carbon_Percent
Adjusted_Ammonia
Adjusted_Bromide
Adjusted_Chloride
Adjusted_So4
Adjusted__Phosphate
Adjusted_Potassium
Adjusted_S
Correlation Matrix.xlsx
1
0.941197659
0.457123565
‐0.844866424
0.504930808
0.35212258
0.360166615
0.716319176
0.493261592
0.397291134
0.66712133
0.224545023
0.480839262
0.525453723
0.4803681
0.468402281
‐0.491658065
0.507585067
0.367674622
0.474556559
0.502970225
0.6159702
0.408455597
0.643147098
0.737404105
Annual_Exposure__Hr__ Distance_From_Canoe_Pass__M_ Distance_From_Shore__M_
1
0.43658489
‐0.790486909
0.51174553
0.330091026
0.351321721
0.740319489
0.486997832
0.401371548
0.665090556
0.226762206
0.474693503
0.540074326
0.455731146
0.446144641
‐0.475148502
0.471274382
0.344866084
0.476390408
0.491965458
0.60510951
0.363816635
0.618755851
0.74585433
1
‐0.784289467
0.466801296
0.563760014
0.394401655
0.357845319
0.650752679
0.380979823
0.630624067
0.11037016
0.64147297
0.617675283
‐0.112991631
‐0.047474075
‐0.024068532
0.076290189
0.75833459
0.888898502
0.899834484
0.842369372
0.636417004
0.526365775
0.593060081
1
‐0.56914707
‐0.479999693
‐0.490303665
‐0.642551486
‐0.640561598
‐0.480884453
‐0.742051255
‐0.235730533
‐0.628964435
‐0.670389306
‐0.311662926
‐0.338432741
0.399846427
‐0.46596978
‐0.63597179
‐0.752489799
‐0.780792572
‐0.817657399
‐0.539042687
‐0.733765955
‐0.787252835
Total_Harpacticoida Total_Nematoda
1
0.487688938
0.574423691
0.652627819
0.732777533
0.71174056
0.507429192
0.329780655
0.730932743
0.819790411
0.457759052
0.427155018
‐0.479278576
0.585439079
0.186316457
0.377578525
0.409612047
0.439301434
0.014182865
0.638578095
0.70694676
1
0.368350207
0.294518993
0.889884035
0.543695894
0.337626945
0.110736545
0.892805462
0.762715354
0.046957318
0.017465125
‐0.087606611
0.127445742
0.504902849
0.509657932
0.54148824
0.447917655
0.363038687
0.386817668
0.484477591
Total_Oligochaeta
1
0.395689698
0.553859454
0.542346864
0.384161228
0.332516208
0.554385818
0.616701418
0.533688981
0.519499322
‐0.557903014
0.615632722
0.170919288
0.311718741
0.327266009
0.30947212
0.017113572
0.72560129
0.599888948
1 of 4
Total_Harpacticoida
Total_Nematoda
Total_Oligochaeta
Total_Polychaeta
Adjusted_Ammonia
Adjusted_Bromide
Adjusted_Chloride
Adjusted_So4
Adjusted__Phosphate
Adjusted_Potassium
Adjusted_S
Total_Polychaeta
1
0.530694068
0.590818917
0.732679399
0.437823533
0.516463642
0.621220475
0.558966393
0.541521424
‐0.590471725
0.594676421
0.192897274
0.305180301
0.326415984
0.440415939
0.210533622
0.67725611
0.725355628
1
0.738674857
0.541107357
0.300907809
0.999583832
0.942407209
0.254696196
0.245253117
‐0.314439553
0.365745427
0.457758607
0.593672204
0.625535761
0.577094029
0.304673441
0.609811302
0.689769623
1
0.60010421
0.758167474
0.73086527
0.765151114
0.4920656
0.501539458
‐0.554335938
0.571908282
0.246948921
0.340978278
0.374104983
0.39139589
0.036856265
0.590193855
0.576180376
Macrofauna_Total_Benthic_Individual Macrofauna_Total_Biomass_G_M2
1
0.567673272
0.520126665
0.575114533
0.272154008
0.380900128
‐0.421110061
0.373923193
0.480583277
0.669437032
0.68912587
0.771607309
0.526426969
0.669220792
0.691916467
1
0.288353283
0.283791867
0.442854627
0.523910176
‐0.546856019
0.404223812
0.006817904
0.102188888
0.13413378
0.186803715
‐0.007656665
0.339606872
0.322684677
1
0.940823855
0.252956478
0.240917734
‐0.309742653
0.362310408
0.447824049
0.582193084
0.614357881
0.561958322
0.291121086
0.601838046
0.682224868
2 of 4
Total_Harpacticoida
Total_Nematoda
Total_Oligochaeta
Total_Polychaeta
Adjusted_Ammonia
Adjusted_Bromide
Adjusted_Chloride
Adjusted_So4
Adjusted__Phosphate
Adjusted_Potassium
Adjusted_S
Grain_Size_Percent_Sand_2_0mm_To_0_
1
0.382586546
0.358265267
‐0.429091945
0.521214076
0.393376845
0.563814516
0.593441166
0.570406986
0.20123599
0.730125825
0.752029325
1
0.93800357
‐0.947483093
0.931910538
‐0.231528883
‐0.133799011
‐0.0926973
‐0.017245779
‐0.237268177
0.687744465
0.54429098
1
‐0.985347444
0.885789656
‐0.211226107
‐0.060393578
‐0.019458619
0.045777791
‐0.166337079
0.701530747
0.562086034
1
‐0.912029408
0.137301062
0.003093257
‐0.040262962
‐0.104556377
0.128269045
‐0.749766241
‐0.610654381
3 of 4
Toc_Total_Organic_Carbon_Percent Adjusted_Ammonia
Total_Harpacticoida
Total_Nematoda
Total_Oligochaeta
Total_Polychaeta
Adjusted_Ammonia
Adjusted_Bromide
Adjusted_Chloride
Adjusted_So4
Adjusted__Phosphate
Adjusted_Potassium
Adjusted_S
1
‐0.062224731
0.051309625
0.09447449
0.153977006
‐0.169558259
0.786839481
0.641000857
1
0.802598172
0.802166154
0.758515344
0.69812724
0.323486278
0.353290569
Adjusted_Bromide
Adjusted_Chloride Adjusted_So4
Adjusted__Phosphate
1
0.992034528
0.948881582
0.737048522
0.510720686
0.571798431
1
0.945034322
1
0.722764439 0.731289553
0.531967957 0.580124861
0.594105865 0.64201379
1
0.222723626
0.335614971
Adjusted_Potassium Adjusted_S
1
0.812345499
1
4 of 4
Appendix 5
Detailed Microphytobenthos Community
Statistical Analyses
307071-00790 : Rev 0 : 27 January 2015
Appendices
HEMMERA ENVIRCHEM INC.
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
22 December 2014
36.43
Page 1
1.2
Non-Parametric Multidimensional Scaling Analysis
22 December 2014
Page 3
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
Page 4
2.
SPRING ANALYSIS
2.1
nMDS
22 December 2014
Page 5
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
22 December 2014
Page 7
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
Name: Resem3
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
22 December 2014
Page 9
Best results
No.Vars
1
1
2
2
3
2
3
4
3
4
Corr.
0.137
0.136
0.133
0.127
0.126
0.110
0.109
0.106
0.104
0.102
Selections
5
7
5,7
5,8
5,7,8
6,7
5-7
5-8
5,7,15
5-7,14
Outputs
Plot: Graph35
Page 10
3.
SUMMER ANALYSIS
3.1
nMDS
22 December 2014
Page 11
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
22 December 2014
Page 13
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
Name: Resem4
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
22 December 2014
Page 15
Best results
No.Vars
2
3
3
3
3
4
3
4
2
4
Corr.
0.336
0.320
0.317
0.316
0.314
0.308
0.308
0.306
0.295
0.294
Selections
4,13
4,12,13
10,11,13
4,10,13
10,12,13
4,10,12,13
4,11,13
4,10,11,13
11,13
10-13
Outputs
Plot: Graph34
Page 16

TDR – Biofilm Physical Factors - Roberts Bank Terminal 2 Project

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