Ontario Benthos Biomonitoring Network
Participants’ Training
Updated April 2006
Standard Report (OBBN Vision)
Clear Lake Inflow, 22-May-2005
Longitude: -74.7° Latitude: 45.0°
Sampled by: Jones & Dmytrow
Summary Statistics
Reference Sites (nref=15) Test Site*
mean
St. Dev.
mean
-0.84
0.43
0.09
CA1(abundance)
CA2(abundance)
Richness
% Chironomidae
% EPT
-0.34
13
34
62
0.41
4.16
12.31
8.90
2.74
13
33
46
*values in bold typeface are beyond 2 st. dev. from the reference-site mean
Hypothesis-test Results
D
F
Pnon-central
7.52
94.31
0.03
Test Site Atypical
Index Contributions*
D
F
P
CA1(abundance)
6.52
110.28
0.015
CA2(abundance) Richness % Chironomidae % EPT
3.38
1.53
5.04
4.22
100.73
2.3
65.81
6.4
0.014
0.177
0.038
*values in bold typeface are beyond 2 st. dev. from the reference-site mean
0.032
Stream reference sites with test-site like
Index Contribution
collection method, gear type, mesh size,
collection season, and flow permanence were
selected based on similarity (Euclidean
distance) to the following test-site habitat
features: dominant substrate, elevation,
latitude, longitude, and catchment area.
Euclidean distances for reference sites ranged
from 5 to 72. Total Euclidean distance for 15
reference sites and 5 attributes was 494
Instructors
Chris Jones, Ministry of Environment, Benthic Biomonitoring Scientist and
OBBN coordinator (Lead Instructor)
Nicole Dmytrow, Saugeen Conservation, OBBN Assistant Coordinator
(Sampling, Benthos Identification)
Ron Reid, Ministry of Environment, Benthos Scientist (Sampling, Benthos
Identification)
Michelle Bowman, University of Toronto (RCA bioassessment calculations:
Test Site Analysis)
Desired Outcome
Participants understand the purpose and
administration of the OBBN, and demonstrate
competence with its methods.
This course is part of OBBN’s quality assurance plan:
certification is one way of protecting the credibility of the
Network.
The OBBN is part of the Canada-wide Canadian Aquatic
Biomonitoring Network (CABIN). We are working on
standard training and certification requirements for CABIN.
Participants’ Certification
• 2 types of certificates (Participant, Trainer)
• To be certified, participants must:
– Pass a general multiple-choice test
– Correctly identify 40 of 44 benthos specimens to the coarse
OBBN 27-group level
• In addition to above, trainers must:
– Assist with teaching the course
– List at least 2 diagnostic characters for each specimen on the
benthos identification test (without consulting references)
Student Instructors
Rebecca Crockford, District of Muskoka
Lynette Dawson, Quinte Conservation
Gerry Sullivan, Otonabee Region Conservation Authority
Angela Wallace, Gartner Lee
Biomonitoring Knowledge vs. Degree of
OBBN Involvement
Degree of Network involvement vs. OBBN Knowledgea
16
14
y = 1.3454x + 6.8247
R2 = 0.274
# of Answers
12
10
8
Correct
6
Don't Know
4
y = -1.2307x + 6.5219
2
2
R = 0.2557
0
1
2
3
4
5
6
Ordinal Degree of involvement
Equation
C = 6.82 + 1.35(ODI)
DK = 6.52 - 1.23(ODI)
C = 10.5 + 0.072(MI)
DK = 3.29 - 0.0716(MI)
2
R
0.27
0.26
0.11
0.12
Regression results (C = number of correct answers, DK = number of questions
answered ‘Don’t Know’, ODI = ordinal degree of OBBN involvement; all listed
comparisons are significant at the =0.05 level)
Agenda: Day 1
Welcome to the Course
Purpose
Background (Need for Biomonitoring; Benthos as Indicators;
Benthos facts; Complementarity of Stressor- and Effect-based
Monitoring; OBBN Components, Principles, and Status Update)
Chris Jones,
Gerry Sullivan
The Reference Condition Approach
(RCA Overview; Definition of Reference Site;
OBBN Reference Site Sampling Strategy; Criteria for minimally
impacted; Spot the reference site; Example of RCA
Bioassessment)
Chris Jones,
Angela Wallace
Protocol
Chris Jones,
(Standardization vs. Flexibility, Collection Procedures For Lakes, Lisa Campbell
Streams, Wetlands, Processing Methods, Archiving,
Habitat Characterization)
Sampling: Kennisis River and Lake of Bays
(Student trainers as group leaders)
Nicole Dmytrow, Chris Jones, Ron
Reid, Gerry Sullivan, Angela Wallace,
Lisa Campbell, Lynette Dawson,
Rebecca Crockford
Sieve Samples
Nicole Dmytrow
Agenda: Day 2
Benthos Picking (random sub-sampling to obtain ~100count sample)
Nicole Dmytrow, Chris Jones, Ron
Reid
Gerry Sullivan, Angela Wallace, Lisa
Campbell, Lynette Dawson, Rebecca
Crockford
Benthos Identification (OBBN 27-group Level)
- Diagnostic features of each group (slide show)
- Examples from the DESC reference collection
(demonstration)
- Practice using specimens collected yesterday (hands-on)
Chris Jones, Nicole Dmytrow,
Rebecca Crockford, Lynette Dawson
Practice identification skills
Chris Jones, Nicole Dmytrow
Students to identify specimens in front of class
(microscope projection), highlighting diagnostic
characters
Chris Jones
Agenda: Day 3
Assessment: Is Test Site Within Normal Range?
-Summary Metrics
-Hypothesis Testing (TSA)
Michelle Bowman, Chris
Jones
Review
Gerry Sullivan, Angela
Wallace, Lisa Campbell,
Lynette Dawson,
Rebecca Crockford
Chris Jones, Nicole
Dmytrow
Certification Test (Optional)
Chris Jones, Nicole
Dmytrow
Take-up test, general discussion, and wrap-up
Chris Jones
Biomonitoring Rationale
• Legislation & policy stress protection of biota
– Biological definitions of impairment and adverse
impact in Ontario
– “biological integrity” in U.S. Water Pollution Control
Act
– The EU Water Framework Directive requires both
“good ecological status” and “good chemical status”
of surface water
• Management stresses protection/rehabilitation of biota:
– Target setting
– Performance evaluation
(Roux et al. 1999, Jones et al. 2005b, Jones 2006)
Biomonitoring Rationale II
“Biomonitoring is required … because the
consequences of environmental stress can
only be determined by an appraisal of the
biota”.
Wright (2000)
What are
Benthos?
• Bottom-dwelling
aquatic
invertebrates
• Include animals
like insects,
worms, mollusks,
crustaceans, and
mites
Caddisfly of the
family Helicopsychidae
Mayfly of the family
Ephemerellidae.
Why Use Benthos As Bioindicators?
•
•
•
•
•
•
•
•
•
Benthos are excellent indicators of
aquatic ecosystem health.
Abundant and widespread
Nobody cares
Easily and inexpensively sampled
Sedentary (unlike fish)
Long lived (months to years)
Many species with different
tolerances
Respond to both water and
sediment chemistry
Readily archived for future
reference
Provide early-warning
Stream benthos collection in the Raisin River watershed
(Rosenberg & Resh 1993, 1996; Mackie 2001)
Complementarity of Stressor- and Effectbased Monitoring
Stressor-based Approach
Effect-based Approach
Monitoring
focus
Stressors causing environmental
change, i.e., chemical and physical
inputs
Effects (responses) of natural and/or
anthropogenic disturbances, e.g.,
changes in the structure and function of
biological communities
Management
focus
Water quality regulation: controlling
stressors through regulations
Aquatic ecosystem protection:
managing ecological integrity
Primary
indicators
Chemical and physical habitat variables, Structural and functional biological
e.g., pH, dissolved oxygen, copper
attributes (e.g., relative taxa
concentration
abundances, frequency of deformities)
Assessment
end points
Degree of compliance with a set
criterion or discharge standard
Adapted from Roux et al. (1999)
Degree of deviation from a benchmark
or desired biological condition
Biology
Benthos data, Pretty River,
October 1996; reference site
data, 1997-2000
95% confidence
ellipse
Stressor and Effectbased Approaches
are Complementary
CA2
Mad R.
Noisy
Nottawasaga
Pine 2
Pine 1
Sheldon
Pretty
CA1
Chemistry
= Ontario Water Quality Objective
Zinc Data: 1997 - 2001
Phosphorus Data:
1997 - 2001
25
20
0.1
ug/l
mg/L
0.08
0.06
0.04
15
10
0.02
5
0
0
Pretty River @ hwy. 26, Collingwood
Pretty River @ Hwy. 26, Collingwood
Pretty River, Highway 26,
Collingwood, Ontario
Technical Issues
The application of benthos
biomonitoring has been limited
by a number of technical issues.
• Unlike water chemistry, no
guidelines or “biocriteria”exist
• Complex; many confounding
factors: biota respond to things
other than stressor of interest
• No standard sampling protocol
• Taxonomy requires special
expertise
• Experts disagree on
hypothesis-testing procedures
and interpretation
• Cost
OBBN Background
OBBN: a collaborative lake-, stream-, and wetland-bioassessment network
Leads: Ontario Ministry of Environment and Environment Canada
(EMAN), but part of national CABIN program
Purposes
1.
2.
3.
4.
Evaluate aquatic
ecosystem condition
Measure effectiveness
of programs
Provide biological
complement to
Provincial Water Quality
Monitoring Program
Support development of
biocriteria for aquatic
ecosystem condition
Aquatic mite
Barriers to Biomonitoring in Ontario
Standard
Protocol
Data
Sharing
Training
Implementation
Status
http://obbn.eman-rese.ca
• On-line
• Printed
manual
subject to
Ministry
approval
• Train-the-trainer
• Integration with North American
Benthological Society Taxonomic
Training Certification Program (NABS
TCP)
Protocol
• Collaborative projects
required to develop
Research
OBBN products
• Current focus is on
understanding sources
of variance and
evaluating methods
OBBN
• National integration
Database • Launched 31 Oct. 2005
• ~30 organizations have
accounts
Analytical • Query tool, data exporter,
automated
Software
bioassessment-hypothesis
test, reporting module
• spring 2006 release date
OBBN Partners
OBBN Leads
Technical Advisory Committee
• Ontario Ministry of
Environment
• Environment Canada’s
Ecological Monitoring
and Assessment
Network
• Universities
• Conservation Authorities
• Ontario’s Ministries of
Environment and Natural
Resources
• Environment Canada
• Trout Unlimited
• District of Muskoka
Certified
Participants
• All Sectors
OBBN Partner Roles
OBBN Leads
• Coordinate 5
program
components
• Provide technical
advice and
equipment
• Research
Technical Advisory
Committee
• Technical guidance
and review
• Research
• Program Priorities
• Problem Solving
Partners
• Sampling (for their
own purposes and
to collaborate on
regional, provincial,
and national
reporting)
• Data-sharing
• Research
Data-sharing Agreement
I understand and accept that as a partner in the Canadian
Aquatic Biomonitoring Network, data entered into this system is
freely shared among all Network participants.
I further understand and accept that CABIN and its partners put
no restrictions on, and do not regulate, how data is used by
network members.
Although I have made every attempt to ensure the quality of the
data I enter into the database, I make no guarantee about the
accuracy of that data, and assume no liability associated with its
use.
OBBN Socio-economics and Demography
Highest Level of Education Achieved
(n=38)a
Percent of Responses
Age (n=37)
40%
50%
40%
30%
20%
30%
20%
10%
10%
0%
0%
20-29
30-39
40-49
50-59
60-69
CD
Percentof Responses
Employment Status (n=38)b
UUG
UGD
Vocational Sector (n=38)c
100%
35%
30%
25%
20%
15%
10%
5%
0%
80%
60%
40%
20%
0%
U
R
PT
FT
Other
PS
Gov
CA
Acad
Ed
NGO
f Responses
OBBN participants’ socio-economic status and demography (aCD = college diploma;
Years in Current Job (n=34)
Years Residing in Present
UUG = university undergrad. degree; UGD = university
grad.degree; bU =Community
unemployed;
(n=38)
50%
R = retired;
PT = part-time; FT = full-time; cPS = private sector; Gov = government; CA =
40%
conservation
authority; Acad = academic; Ed =40%
education; NGO = non-government or
30% organization
30%
non-profit
MotivesMotives
of ofParticipation
Participation
a
R (n=36)
MO (n=37)
Very
Important
PE (n=38)
Somewhat
Important
GRR (n=37)
Not
Important
GE (n=34)
AMD (n=38)
*AEC (n=39)
TE (n=37)
0%
20%
40%
60%
80%
100%
Percent of Responses
Motives of OBBN participation (R = research; MO = meeting others with common
interests; PE = performance evaluation (i.e., evaluating performance of water
management programs; GRR = guiding rehabilitation or restoration; GE = Guiding
enforcement; AMD = Assessing or managing biodiversity; AEC = Assessing/managing
ecological condition; TE = Training/education)
Follow-up action (n=29)
Full control (5)
Analysis and interpretation (n=32)
4
Developing and refining methods (n=32)
3
Perspectives *
on Network *
Implementation
(I)
2
Choice of data shared (n=34)
No Control (1)
Choice of sampling sites (n=35)
• 88% categorized
participantgovernment
relationship type as
partnership or
collaboration
0%
10%
20%
30%
40%
50%
60%
70%
Percent of Responses
Participants’ perceived control or influence over
components of the OBBN
Types of government-participant relationships in monitoring
programs (adapted from Savan et al. 2004).
Who determines monitoring protocol?
Who selects sites to be monitored?
Who determines analytical methods,
interpretation, and data distribution?
Who determines follow-up action?
Relationship Type (based on degree of participant control)a
Control
Partnership
Collaboration Co-optation
Participants
Shared
Shared
Government
Participants
Participants
Shared
Government
Participants
Participants
Shared
Government
Participants
Participants,
then government
Shared
Government
Benthos: From Snot Globules to Jewelry
Anterior view of water-boatman head (Corixidae)
Caddisfly larva (Hydropsychidae)
Mayflies
True Flies
Black Flies
Caddisflies
Leeches
Dragonflies &
Damselflies
Biocriteria and the Reference
Condition Approach
Biocriteria
“Healthy is Variable.”
–Dr. Robert Bailey, University of Western Ontario
• 2 equally healthy sites may have
different biological assemblages
• Need to determine what normal is
• Biomonitoring conundrum: Is an
observed difference greater than
expected by chance? Is it biologically
meaningful?
• Biocriteria are critical values for
hypothesis tests
• The “normal range” is a pragmatic
biocriterion
(Kilgour et al. 1998, Bowman & Somers 2005)
Stream
Sample
Date
Partner
HYDRACARINA
Trhypochthoniidae
EPHEMEROPTERA
Baetidae
Ephemerellidae
PLECOPTERA
Leuctridae
Capniidae
Perlodidae
Chloroperlidae
TRICHOPTERA
Rhyacophilidae
Hydropsychidae
COLEOPTERA
Elmidae
DIPTERA
Chironomidae
Ceratopogonidae
Tipulidae
Simulidae
Empididae
Total:
2
1
81
1
49
2
1
1
6
0
1
0
5
1
2
2
1
3
11
20
20
3
4
0
1
135
29
2
6
2
0
122
Biocriteria
“Healthy is Variable.”
–Dr. Robert Bailey, University of Western Ontario
• 2 equally healthy sites may have
different biological assemblages
• Need to determine what normal is
• Biomonitoring conundrum: Is an
observed difference greater than
expected by chance? Is it biologically
meaningful?
• Biocriteria are critical values for
hypothesis tests
• The “normal range” is a pragmatic
biocriterion
(Kilgour et al. 1998, Bowman & Somers 2005)
Stream
Baxter
Baxter
Sample
Riffle 1
Riffle 2
Date
16-Aug-04 16-Aug-04
Partner
ORCA
ORCA
HYDRACARINA
Trhypochthoniidae
2
1
EPHEMEROPTERA
Baetidae
81
49
Ephemerellidae
1
2
PLECOPTERA
Leuctridae
1
1
Capniidae
1
0
Perlodidae
6
5
Chloroperlidae
0
1
TRICHOPTERA
Rhyacophilidae
2
1
Hydropsychidae
2
3
COLEOPTERA
Elmidae
11
20
DIPTERA
Chironomidae
20
29
Ceratopogonidae
3
2
Tipulidae
4
6
Simulidae
0
2
Empididae
1
0
Total:
135
122
Experimental Designs for Bioassessments
Has the
impact
occurred?
Is when and
where
known?
Is there a
control area? Experimental Design Name
Yes
Yes
Yes
Spatial Study (Control-Impact)
No
Impact from Spatial Pattern
Yes
Reference Condition Approach
No
Modern Analog Approach
Yes
Optimal Impact Study (BACI)
No
Temporal (Before-After)
Yes
Monitoring for When
No
Monitoring for Where
No
No
Yes
No
(Adapted from Green 1979 [Bowman and Somers 2005]; see also Underwood 1997)
History of the RCA
• A product of researchers working on the common
challenge of studying an environment where an impact
had (or was likely to have) occurred, but when and
where the impact occurred were not known
• UK: RivPACS, Australia: AusRivAS, Canada: BEAST
• U.S.: Rapid-Bioassessment Procedures
(Wright et al. 2000, Bailey et al. 2004, Barbour et al. 1999, Bowman and Somers 2005)
Reference Condition Approach (RCA)
Multiple, minimally impacted control sites define the normal
range of biological conditions to be expected at a test site
Reference site
Test site
“Long-term monitoring programs…provide the measures of normal (reference data) against
which the abnormal is judged. It is impossible to convince a court that something is wrong if
‘right’ is not defined.” – MOEE Biomonitoring Review Committee, 1994
RCA Steps
The RCA has the following 5 steps (Bailey et al. 2004):
1. Minimally impacted reference sites are randomly selected and their
biological communities and habitats are characterized.
2. Reference sites are grouped according to the similarity of their biological
assemblages and/or habitats (depending on the approach used, a model
that predicts a test site’s reference-state assemblage type, hence its
reference-site group membership, may be built using a set of natural-habitat
or physiographic attributes that are known to distinguish assemblage types).
3. A test site is sampled to characterize its biological community and habitat.
4. Appropriate reference sites are selected to define the normal or expected
test-site condition.
5. Statistically test the bioassessment null hypothesis (i.e., that the test site is
in reference condition).
Sample benthos and habitat at a variety of
randomly selected, minimally impacted reference sites
Summarize the biological condition of reference sites. Group
reference sites having similar biological communities.
Build a statistical model that predicts group membership based “niche variables”
(physiographic variables that account for separation between groups)
Sample the biological community of a test site and characterize its niche
attributes. Summarize biological condition using a set of metrics
Use physiographic model to predict test site to a
reference group.
RCA
Steps
Suitable reference site
group available?
No
Yes
Establish normal range of biological condition for test
site using appropriate reference site group (ref±2SD)
Site likely unimpaired.
Resample periodically
and confirm reference
group selection
Yes
Biological condition of
test site is within normal range?
No
Site may be impaired.
Confirm reference group
selection and resample.
If same result,
investigate for causes of
impairment
RCA Messiness
-Different definitions of minimal impact,
reference site classification methods,
summarization and hypothesis-testing
procedures (e.g., Wright et al. 2000, Linke
et al. 2005).
-Different researchers have different
approaches to each step (Bowman and
Somers 2005)
RCA Step-1 Challenges : Reference
Sites and Minimal Impact
1.
Minimally impacted reference sites are randomly selected and
their biological communities and habitats are characterized.
• “Sites that are not disturbed by human activities are ideal reference sites; however,
land-use practices and atmospheric pollution have so altered the landscape and
quality of water resources … that truly undisturbed sites are rarely available (Barbour
et al. 1996). ”
• Standard criteria for minimal impact don’t exist
• It is particularly difficult to find reference sites for large waterbodies and for any
waterbodies in areas where climate and geography favour agriculture or urban
development
• randomly selecting reference sites may be difficult because of their restricted and
aggregated spatial distribution, and because of their remote location and difficult
access (Hughes 1995).
Reference Site Criteria: Wyoming
Different weights for different attributes
Different thresholds for different eco-regions
(U.S. EPA 1996)
OBBN: Qualitative Definition
of Minimal Impact
CRITERIA FOR “MINIMALLY IMPACTED”
Well downstream of significant point sources
Minimal regulation of water level (minimal affect from dams and impoundments)
Extensive naturally vegetated buffer
Well forested catchment
Minimal development or urban land use in catchment
Minimal agricultural land use in catchment
Minimal impervious cover and artificial drainage in catchment
Minimal anthropogenic acidification (i.e. pH matches expectation based on local geology)
Water chemistry better than regulatory guidelines, e.g. Ontario Ministry of Environment PWQO’s (REF
PWQO)
(Jones et al. 2004)
RCA Step-1 Challenges:
What is a reference site?
OBBN Approaches to RCA Step 1
• Sample a wide range of sites (but also ensure relevance to test sites)
• Reserve at least 10% of annual sampling effort for reference site resampling (same sites each year, or different sites in different years, or a
combination of the two strategies)
• Ideally, sample enough reference sites to adequately describe the normal
ranges of different types of waterbodies (~30 sites per group; Bowman
and Somers 2005)
• Where insufficient reference sites exist, estimate normal range using best
available sites, modeling, and applying best professional judgment.
Remember:
• We don’t know how many assemblage types there are
• Try to sample some unusual sites (e.g. large rivers, clay plain streams)
OBBN Approaches to RCA Step 1
• Standard methods required: location, taxa counts, habitat data
• OBBN Coordinators provide QC checks on reference-site samples;
confirmed taxa enumerations and physiographic data returned to
collector
• Depending on question, impacted sites may be used in
bioassessments; however, minimally impacted sites are always
useful for determining relative condition
Use of Impacted Control Sites
?
?
Urban control site
Urban mine-impacted test site
Minimally impacted reference site
CA2
(Hypothetical Data)
(e.g., Reynoldson
et al. 2005)
CA1
Use of Impacted Control Sites
!
Urban control site
Urban mine-impacted test site
Minimally impacted reference site
CA2
(Hypothetical Data)
(e.g., Reynoldson
et al. 2005)
CA1
Send
Reference
Site
Samples
(But Not
Like This)
RCA Challenges, Steps 2-4 : Sampling
Methods, Classification and Prediction
2.
3.
4.
Reference sites are grouped according to the similarity
of their biological assemblages and/or habitats
(depending on the approach used, a model that
predicts a test site’s reference-state assemblage type,
hence its reference-site group membership, may be
built using a set of natural-habitat or physiographic
attributes that are known to distinguish assemblage
types).
A test site is sampled to characterize its biological
community and habitat.
Appropriate reference sites are selected to define the
normal or expected test-site condition.
RCA Challenges, Steps 2-4
• No agreement on sampling methods (collection, sample processing,
taxonomic resolution)
• No agreement on data summarization (multivariate, multi-metric,
hybrid)
• Difficult to know a priori which habitat attributes (and scale) to
measure
• Numerous questions about classification:
–
–
–
–
Method (a priori vs a posteriori, statistical methods)?
# of groups?
# of sites per group?
Habitat measures to match ref and test sites?
Grouping reduces residual
variation among reference
sites and increases power
of assessment BUT:
• It goes against our
knowledge that
communities change
continuously across
environmental gradients
• How many groups are
there?
(Gerritsen et al. 2000)
CA2
Why Classify?
CA1
Reference Gp. 1
Reference
Reference
Gp. 2
Sites Gp. 3
Reference
Test
Test Site
Grouping reduces residual
variation among reference
sites and increases power
of assessment BUT:
• It goes against our
knowledge that
communities change
continuously across
environmental gradients
• How many groups are
there?
(Gerritsen et al. 2000)
CA2
Why Classify?
CA1
Reference Gp. 1
Reference
Reference
Gp. 2
Sites Gp. 3
Reference
Test
Test Site
Different Approaches to Classification
2 main ways to group sites: a priori and a posteriori
Grouping
method
a priori
a posteriori
Groups based on assumptions about
factors that determine community
composition (e.g., ecoregion); May
under- or over-estimate # of groups
because assumptions about
deterministic factors may be incorrect;
within- and between-group variance may
not be optimal
Biological community
composition dictates
group; # of groups tends to
make more biological
sense
Prediction Easy; if you know the habitat attributes
you know the group
Can be tricky because not
all between-group variation
can be explained and
because deterministic
factors may not be
adequately measured
Messiness in Classification
A
CA
NMDS
B
C8
D6
D8 D5 D7
C9
C2
D4
D9
B4
C5C6
C1C7
D3
C3
C4
B7
D8
D1
D2
A8
B3 B5
B9
A9
A6
A4
A2
A3
A1
D9
B1
B8
A7
C9
D7
D5 D6
B6
A7
D1
C4C2 C3
B6
B7
D3
B8
D4
D2
B9
A6
A4A2 A3
A9
B2
B4
C6 C1
C5
C7
C8
A8
B5
B1
B3
B2
A1
A5
A5
TWINSPAN
TWINSPA
C
UPGMA
D
C8
C9
C2
D4
D9
D6
D8 D5 D7
C1
D3
C3
C4
B3 B5
B9
A9
B7
B6
B2
A8
B1
B8
A7
A1
B3
B9
A9
A6
A4
A2
A3
A1
B5
B2
A5
Ward's
E
K-means
F
C8
D6
D8 D5 D7
C8
C9
C2
D4
D9
B4
C5C7
C6
C1
D6
D8 D5 D7
D3
C3
C4
B8
A7
A6
A4
A2
A3
A1
B9
B4
C5C7
C6
C1
D3
C3
C4
B7
B6
D1
D2
B1
B3 B5
C2
D4
D9
B6
D2
A9
C9
B7
D1
A5
D3
C3
C4
D2
A5
A8
B4
C5C7
C6
C1
D1
B1
B8
A7
C2
D4
D9
B6
D2
A6
A4
A2
A3
C9
B7
D1
A8
C8
B4
C5C7
C6
D6
D8 D5 D7
B2
A8
B1
B8
A7
A6
A4
A2
A3
A9
B9
B3
B5
B2
A1
A5
Different reference-site classification methods will result in different models of
reference condition (e.g., Wright et al. 2000, Bowman and Somers 2005)
PCO2
PCO2
Further Messiness in Classification
PCO1
PCO1
A 2-axis Principle Coordinates Analysis ordination plot showing a seemingly appropriate set
of 22 reference sites defining an assemblage type (left), and an alternate classification
(right) of two groups of 20 sites that results from adding additional data for an assemblage
type that was under-represented in the solution shown at left. Ellipses represent 90%
confidence bounds for each assemblage type. Hypothetical data: Group 1 sites (diamond
symbols) were simulated as randomly distributed variables (mean PCO1 = 1, mean PCO2 =
3.5); group 2 (squares) had mean PCO1 = 4 and PCO2 = 1. The standard deviations for PCO1
and PCO2 values was 1 for both groups.
OBBN Approach, Steps 2-4
• Balance standardization with flexibility
• Classification-free reference-site matching: Nearest Neighbour
• Sampling is more than just collecting bugs: in data-driven approach, niche
variables used to select reference sites for test sites
• Habitat characterized with site-, reach-, and catchment-scale measures
• To summarize biotic composition, a variety of indices should be used,
because each summarizes and emphasizes different patterns in the
assemblage. Further guidance may be given as we learn more about
responses to stressors in different parts of the province
• Analytical software defaults will reflect current knowledge and
recommendations
• Selecting reference sites will be automated by OBBN/CABIN database
• Refining models is a research priority
Classification vs. Nearest Neighbour
Predictor 2
Classification
Approach
Predictor 1
Predictor 2
(Simulated Data)
Nearest-Neighbour or
Classification-free Approach
Predictor 1
RCA Challenges, Step 5
5. Statistically test the bioassessment null hypothesis
(i.e., that the test site is in reference condition).
• How much deviation from normal is ecologically significant? What level of
confidence is required?
• Hypothesis-testing methods differ in the way they implicitly define “health” or
biological integrity, in their assumptions, in their manner of quantifying
biological condition and effects, in the format of their outputs, and in the
predictability of their response to stress (Norris and Hawkins 2000)
– U.K. and Australia: Ratio of expected-to-observed taxa richness, (e.g., Davies
2000 and Moss 2000)
– U.S.: Multi-metric scores, with biocriteria set using regional reference sites (e.g.,
Barbour and Yoder 2000);
– Canada: Ordination-axis-scores compared against confidence ellipses for
reference sites (e.g., Reynoldson et al. 2000).
Ecologically Significant Effect
• When testing bioassessment hypotheses
(H0: test
site normal), critical effect size must be defined a priori
• Central test (H0: no difference) not biologically
meaningful or management-relevant
• OBBN-recommended: 95% of reference site distribution
…but need to consider Type I (false positive) & Type II
(false negative) error rates and their consequences
(Bowman and Somers 2005, Jones et al. 2004)
Biocriteria
Messiness: Error
Rate and Effect Size
Considerations
Null Hypothesis
Decision
True
False
Reject H0
Type I error
(false
positive, α)
Correct
decision
Accept H0
Correct
decision
Type II error
(false
negative, )
(From Bailey et al. 2004)
Biocriteria: Summary of Key Points
• Biocriteria: critical values for testing bioassessment null hypothesis (H0:
test site normal)
• Confidence in bioassessment decision (i.e., pass or fail) depends on how
well we model normal range, and therefore how well we estimate
probabilities of false positives and false negatives
• Setting biocriteria means trade-offs between Type-I and Type-II error rates:
consider the consequences of these errors (management responses and
costs)
• There is no magic α-level
• Determining Type-II error rate requires a set of observations that are known
to deviate from normal by a specified effect size (this requires simulated
data)
(Bailey et al. 2004, Jones et al. 2004, Bowman and Somers 2005)
OBBN Approach, RCA Step 5
• Use data from the same season
• Test Site Analysis (TSA; Bowman and Somers 2005,
2006a, and 2006b) is recommended method for testing
bioassessment null hypothesis; Represents a
convergence of multivariate and multi-metric methods:
– Multiple indices are used to summarize composition
– A non-central multivariate equivalence test (e.g., McBride 1993)
is calculated using all indices and considering redundancies
among the summary indices (test statistics include D, F, and p)
– Why not just count-up individual passes and fails?
– If the site fails, a discriminant analysis is done to describe the
effect size associated with each of the indices used in the
equivalence test thereby characterizing the test-site’s response
signature.
• OBBN recommends
95th percentile of
reference-site
distribution as
biocriterion (but need
to consider error rates
and power appropriate
for specific studies
• This step will
ultimately be
automated by OBBN
database
Summary Index 2
OBBN Approach, RCA Step 5
Reference
Test
Centroid
Summary Index 1
(Simulated Data)
Does our Site Pass?
Cumulative Probability
100%
90%
80%
Percentile
70%
60%
50%
40%

30%
20%
10%
0%
(Simulated Data)
Bray-Curtis Distance
RCA Bioassessment Example
10
8
6
4
columns
2
Chironomidae
Black
Nott
M isc. Diptera
Isopoda
Hirudinea
Gastropoda
-4
Decapoda
-3Silver

M ites
Anisoptera
-2
Amphipoda
0
Noisy
0Keast
Oligochaeta
-2
-4
-6
-8
50% Ellipse
Tipulidae
Centre
Walker's
Simulidae
Ephemeroptera
Trichoptera
-1
rows
Nott2
75% Ellipse
Plecoptera
1
Sheldon
Pine Sheldon 2
M egaloptera
Coleoptera
Ceratopogonidae
Turbellaria
95% Ellipse
2
3
4
99.9 Ellipse
RCA Bioassessment Example
3
2
Tipulidae
1
Turbellaria
Simulidae
WillowM isc. DipteraDecapoda
Boyne River
Coleoptera
Pine
Trichoptera mad River Everett
Amphipoda Lepidoptera
Plecoptera
North Saugeen
Anisoptera River
Teesw
ater River Chironomidae
Zygoptera
Hemiptera Pine River (Mulmur)
0
Ephemeroptera
-2
-1
0
1 M egaloptera 2
Pelecypoda
-4
-3
Ceratopogonidae
-1
Penetangore South
Isopoda
Hirudinea
Gastropoda
-2

Tabanidae
Penetangore North
Oligochaeta
-3
M ites
-4
columns
3
4
row s
50% Ellipse
75% Ellipse
95% Ellipse
Sampling Methods
Sampling Protocols

Standardization vs. Flexibility
Biomonitoring
Component
Recommendation
Study Design
Reference Condition Approach
Benthos
Collection Method
Mesh Size
Time of Year
Travelling-Kick-and-Sweep (where possible); replication in lakes and
wetlands, sub-sampling in streams
500 m
Any season; assessment comparisons use data from the same season
Picking
In lab (preferred) or in field (optional); preserved (preferred) or live
(optional), microscope (preferred) or visually unaided (optional); random
sub-sampling using Marchant Box (preferred) or Bucket Method
(optional) to provide a minimum 100-animal count per sample
Mix of 27 Phyla, Classes, Orders and Families (minimum); Family
(preferred); Genus/Species (optional, recommended for reference sites)1
Test Site Analysis (TSA; see Appendix 9): Mahalanobis distance (e.g.,
Legendre and Legendre 1998) calculated across selected summary metrics;
non-central significance test to determine if biological distance between
test site and reference site group mean is larger than a specified effect size;
if the null hypothesis (H0: │Dtest – Dreference mean │≤ critical effect size) is
rejected, use discriminant function analysis to identify metrics contributing
most to the separation between the test site and reference condition
Taxonomic Level
Analysis
(Bioassessment
Hypothesis
Testing)
Protocol Instruction Format
1. Sampling unit/inference
2. Replication
3. Benthos collection methods
General Comments:
1. Some protocols require evaluation and may be updated
2. There may be situations in which protocols will not work as
written. In this case, adapt as necessary
3. If time or property access limit ability to apply techniques, collect
what you can. Some information is better than none
4. Obtain landowner permission
5. Avoid sensitive times (e.g., fish spawning) and sensitive habitats
6. Adjust sampling effort if experience shows a habitat to have
exceptionally high or low benthos densities
(Excerpt from Protocol Manual)
Sub-sampling vs. Replication
• Sub-sampling: “In some experimental situations, several
observations may be made within the experimental unit
… such observations are made on sub-samples of
sampling units. Differences among sub-samples within
an experimental unit are observational differences rather
than experimental unit differences”
• Replication: “When a treatment appears more than once
in an experiment, it is said to be replicated.”
(Steel and Torrie 1980)
Lakes
• Sampling Unit
• Replication
• Collection method
Replicate #1
Lake Segment
(sampling unit)
Transect
1 m depth contour
Replicate #2
• Sampling unit is
“lake segment”
• 10 minute
traveling kick
and sweep
along transects
• 3 replicates
collected
Replicate #3
Streams
Cross Section A-B
• Sampling unit
• Alternate
definitions
(pg. 21)
A
B
A
Top of both banks approximately
same height from water surface
Channel Mid Line
Thalweg
Cross-over Point
Sampling Reach
Boundary
Flow Direction
B
• Replication & collection methods
Streams
• Samling unit
encompasses 2
riffles and 1 pool
(often meander
sequence)
• 2 transect
subsamples in
riffles, one in pool
• ~ 3 minute, 10 m
kick
r
le o
Riff -over
s
cros
Pool
r
le o
Riff -over
s
cros
Pool
le or
Riff over
scros
w
Flo
Optional Transect
Sampling
Location
Sampling Reach
Boundary
Transect Traveling
Kick and Sweep
Applying Traveling Kick and Sweep in
Flow
Large or Small Streams
Pool
Transect
Supplementary
Transect
Riffle
w
Flo
Current Speed Distribution
2
1
3
4
5
Transect
Sampled portion
of transect
Stratum boundary
Riffle
Streams:
Grab Sampling
Optional
Transect
or
e
l
f
f
Ri -over
ss
cro
Ekman, Ponar or other
grab sample
Sampling Reach
Boundary
Pool
r
Riffle o
ver
cross-o
Flo
w
Pool
r
le o r
f
f
i
R -ove
s
cros
•
•
•
Sampling unit encompasses 2 riffles and one pool
(meander sequence)
2 transects in riffles, 1 transect in pool
Each subsample is a composite of 3 (or more) grabs
Wetlands
• Sampling Unit
• Replication
• Collection Methods
1 m depth
contour
Wetland Segment (replicate)
Traveling Kick Transect
Stovepipe Core Sample
Jab and Sweep Sample
2 m depth
contour
Wetlands: Selecting Collection
Method
Water
Depth
0.15-1 m
0.05-1 m
<0.05 m or
saturated
soils
Substrate
Type
Stable (e.g.,
sand/gravel)
Soft (e.g.,
organic, muck)
Soft to
moderately
stable
Plant
Density
Low
Recommended
Gear
D-net
moderate D-net
Recommended
Technique
Traveling kick
and Sweep
Jab and Sweep
Any
Core
Stovepipe Corer
Summary of Collection Methods
Collection Method
Traveling kick and sweep; standard method for wadeable habitats
Grab samples (Ekman Dredge, Ponar Grab, or similar); option for deep
water sites
Jab and Sweep; option for wadeable, sparsely vegetated, soft
sediments
Coring; option for deep or very shallow water (especially in shallow
wetland soils)
Artificial substrate; option for atypical habitats or special studies
Streams Lakes Wetlands
  
O
O
O
O
O
O
O
O
Sampling Groups
1
2
3
Gerry Sullivan
Christine Spedalieri
Chris Brown
Trevor Middel
Ben Jewiss
Cassandra Borm
Angela Wallace
Nancy Harrtrup
Rebecca Scobie
John Haselmayer
Suzanne Partridge
Alana Nunn
Lisa Campbell
Robin Tapley
Valerie Stevenson
Scott Parker
Liisa Kearney
Julie Hordowick
4
5
Lynette Dawson
Beth Gilbert
Marnie Guindon
Diana Tyner
Debbie DePasquale
Vince D'Elia
Rebecca Crockford
Rajesh Bejankiwar
Erin McGauley
Sara Kelly
Carolyn Paterson
Josh Hevenor
Sample Processing
• Sieve
• Sub-sample
– Marchant Box (preferred)
– Bucket method
• Sort carefully (Optional:
microscope or magnifier)
• Identify and tally (taxonomic
level matches training)
• 100 count (minimum)
• Preserve and archive sample
Sample Processing: Transporting
to Lab
•
•
•
•
Sieve in net in field
Release non-benthos
Keep live samples cool
Label transport containers inside and out
(date, location, sample number, etc.)
Sample Processing: Sieving
• Must be done to remove fines
• Preliminary done in field, thorough done in
lab
• 0.5 mm mesh sieve
• Remove large pieces (rocks, wood)
Sample Processing: Sub-sampling
& Picking
•
•
•
•
Need random sub-samples
100-count but sort entire last sub-sample
Consider suction device if using Marchant Box
If using Bucket Method, estimate portion picked
by weight or volume
• A bit of soap will sink floaters
• Screen for fast moving
• Sort thoroughly
Benthos ID: 27 Group Level
Sample Processing: Preservation
• Formalin or Alcohol can be used
• Small volumes can be discharged to septic
system or municipal sewage system
• Safe storage
• Avoid poisonous denatured alcohols
• Replace formalin with alcohol after a
couple of days
Habitat Characterization
Done for 2 reasons:
1. Niche Attributes
2. Diagnosis
niche variable a natural habitat (often physiographic)
variable that accounts for a significant portion of the
difference in biological condition between reference site
groups
diagnostic useful in determining cause (often of
biological impairment)
Habitat Characterization (Table 10, Pg. 37)
Measured at site
Location (latitude &
longitude)
Organic matter, areal
coverage
Measured remotely (GIS)
Drainage area
Base Flow Index
Elevation
Riparian vegetation
Water temperature
Canopy cover (%)
Dissolved oxygen, pH, Aquatic macrophytes
conductivity, alkalinity and algae
Maximum Depth
Bank full width (m)
Maximum hydraulic
head
Instantaneous
discharge (m3/s)
Wetted width
Perennial or
intermittent
(presence of
standing water)
Dominant substrate
classes
Basin relief
Mean annual lake
evaporation
Length of main
channel Mean annual
precipitation
Mean Annual Run-off
Mean Annual
Snowfall
Maximum Watershed
Elevation
Mean Elevation
Maximum Flow
Distance
Minimum Watershed
Elevation
Mean Slope of
Watershed
Catchment Perimeter
Shape factor
Slope of main channel
Tributary density
Catchment land cover
(areal proportions of 28
land cover types)
Order
Aspect
Area
Perimeter
Fetch
TSA
Insert TSA Section:
Michelle Bowman
General Discussion/Review
Certification Test
• Test is optional
• Passing grade for both multiple-choice and benthos identification tests is
90%
• For benthos identification test:
– Participants can use references
– Trainers are not permitted to use references, and a correct answer
includes both the taxonomic group and at least 2 diagnostic
characters
• Students cannot be immediately certified without a passing grade, but
arrangements can be made for a re-test (you do not have to redo the
course to re-take the test)