29152840-ENVIRONMENTAL-IMPACT-ASSESSMENT-MSM3208-LECTURE-NOTES-5-Scoping-Investigation

Report
Scoping Aquatic Ecological Investigations in
EIA: Matching Experimental Designs to
Environmental Challenges
Marcus Lincoln-Smith
Great Barrier Reef Marine Park Authority, 5-8-04
Major Stages of EIA:
1. EIA approvals –
•
•
•
Predictive
Measures existing environmental indictors
Measures existing impacts (e.g. other activities; upgrades)
2. EIA post-approval –
•
•
•
•
Tests predictions
Measures environmental indicators
Distinguishes pre-existing impacts and natural variation from
new disturbance
Audit of process (rarely)
Level 1.
a. Existing information & consultation
(jetty upgrade) b. Location description and habitat inventory
Level 2.
a & b.
(maintenance c. Quantitative spatial only (dev loc; ≥ 2 controls)
dredging)
Level 3.
(small resort
tertiary o’fall)
a & b.
c1. Quantitative space/ time (dev loc; ≥ 2 refs + ≥ 2 t)
Level 4.
(major port)
a, b & c1.
d. Issue-oriented or process studies (e.g.
ecological manipulations, modeling)
Increasing cost; increasing confidence
Pre-Approval: Levels of
Investigation
Note: large projects: Levels 3 or 4 often cheaper
Level 1 Surveillance monitoring
Hypotheses usually not stated
Often open ended (nothing to compare against)
Application: generate hypotheses?
Level 2 Compliance monitoring
Hypotheses can be stated, but often not
Compare results to standard (ANZECC;
rapid assessments)
Difficult to relate to ecology and/or specific location
Application: early warning (often only big effects apparent)
Level 3 Effects monitoring (e.g. Beyond-BACI/Gradient)
Hypotheses clearly stated
Results compared to control (baseline) conditions
Application: the only real way to measure impacts compared
to natural variation
Increasing cost; increasing confidence
Post-Approval - Monitoring
Case 1: Uptake of nitrogen isotope in macroalgae,
South Coast outfalls. Client: Shoalhaven Council
δ 15 N = N15 /N14 * 1000
indicator of anthropogenic source of nutrients
Kelp (Ecklonia radiata)
Bubble weed (Phyllospora comosa)
Gerroa
Jervis Bay Region
Pacific Ocean
Crookhaven Heads
Penguin Head
Jervis Bay
Coastal location
Kinghorn Point
N
Jervis Bay
Moona Moona
Scale (km)
Plantation Point
Bay location (Marine Park)
Hyams Beach
Sydney
South Coast Outfalls – Design Tree
Location
Site (Location) Penguin Kinghorn Crookhaven
Species
Jervis Bay
Shoalhaven Coast
Head
Point
Kelp
Bubble
Weed
4
4…
Head
Plantation
Point
Kelp
Hyams
Beach
Bubble
Weed
Plants
(=replicate)
…4
4
ANOVA:
• Main effects = Location, Site (Location), Species
• Interactions = Loc x Species; Site (Loc) x
Species
Moona
Moona
Sites within Locations
(averaged across species of algae)
Mean value of δ 15N (± 1SE)
12
**
**
9
6
3
PH
KP
Coast
CH
PP
MM
Jervis Bay
HB
Comparison of locations for each species (across sites)
Coast
Mean value of δ 15N (± 1SE)
12
Jervis Bay
**
**
9
6
3
Kelp
Bubble Weed
South Coast Outfalls: Conclusions
1. Greater δ 15 N in macroalgae near outfalls than controls under preexisting treatment of effluent
2. Greater δ 15 N in either species of macroalgae from Jervis
Bay than coastal samples
Management Implications:
•
•
•
Existing impact identified – used as basis for monitoring the success
of effluent upgrade
Enabled setting of performance criteria on basis of reduction in δ 15 N
Upgrade has occurred & levels of δ 15 N have decreased as
predicted
Case 2: Bioaccumulation of contaminants in oysters in the
Hunter River, central NSW. Client: BHP Billiton
Lincoln­Smith & Cooper (2004): MPB, 48: 873 ­ 883
Oyster zone
BHP Steelworks
Shoreline, South Arm
Mean concentration (+/- SE)
of contaminant in test organism
Gradient Approach – Use linear regression
Putative
background
Distance from point source ( x 100 m)
Can be used to identify the extent of an impact
Newcastle
Sydney
Upstream
Downstream
Sampling in Hunter
Estuary (Newcastle)
Intervals = 500 m
5* = Putative
background
sites (BG)
N
1 km
n = 5 oyster composites/site
Mean concentration mg/kg, ww (n = 5, ± 1
SE)
1.0
Gradient Effects:
regressions
U/S: -ve ***; r2 = 0.90
D/S: -ve ***; r2 = 0.81
Lead
0.6
100
Copper
0.2
6
6 5 4 3
Upstream
2 1
1 2
3 4 5
6
60
SW Downstream
U/S: -ve ***; r2 = 0.70
D/S: -ve ***; r2 = 0.93
All PAHs
20
6 5 4 3
Upstream
4
2
6 5 4 3
Upstream
U/S: 0 ns; r2 = 0.09
D/S: +ve **; r2 = 0.31
2 1
1 2
3 4 5
6
SW Downstream
2 1
1 2
3 4 5
6
SW Downstream
Mean concentration (+/- SE)
of contaminant in test organism
Point Source vs Putative Background
vs References
P1, 2 = Point source;
B1, 2 = Putative background
P1 P2 B1 B2
A1 A2 B1 B2
Impact
Estuary
Reference
Estuaries
To identify the presence & magnitude
of an impact & infer estuary-wide effects
152 46’ E
▲ A
▲ B
▲ C
Sydney ■
32 55’ S
B. Hunter

152 05’ E
N
1 km
151 15’ E
33  35’ S
A. Pt Stephens
C. Hawkesbury
Multi-scale Effects:
ANOVA
Mean concentration mg/kg, ww (n = 5, ± 1 SE)
1.0
*
Lead
0.6
Copper
80
0.2
1
2
1
SW
Hunter
2
1
BG
2
1
PS
2
HW
*
*
*
40
*
References
20
6
All PAHs
1
2
SW
4
1
Hunter
2
*
1
2
SW
1
Hunter
2
BG
1
2
PS
1
2
HW
References
2
BG
1
2
PS
1
2
HW
References
Newcastle: Conclusions
1. Strong negative gradients indicated Steelworks as a point
source of some bioavailable contaminants (e.g. lead, PAHs)
2. Gradient approach also identified other potential point
sources (e.g. copper)
3. Use of external references enabled measure of “background”
conditions
Management Implications:
• Knowledge of sources of bioavailable contaminants helped focus
on specific area of concern (i.e. steelworks)
• Justified and reinforced closure of specific area of the estuary to
consumption of wild oysters
Case 3: Testing the effectiveness of a marine protected
area to replenish harvested invertebrates.
Client: GBRMPA & ACIAR; Collaborators: World Fish Centre; TNC; SIF.
(Lincoln-Smith et al. (2000), Proceedings 9th ICRS, Bali: 621–626 & GBRMPA Res. Pub. 69)
EIA in reverse – looking at the effects of removing an impact (i.e. fishing)
Target species: trochus (top shell), sea cucumbers and giant clams
Shallow terrace habitat (0 – 3.5 m)
Deep slope habitat (15 – 22 m)
7
o
S o l o m
Islands
C h o i s e Arnavon
u l
S
G
9
o
i z o
G
Reference groups for
monitoring invertebrates
I s a b e l
e o r g i a
R u s s e F l ll o r i d a
I s l a n d s
S
H
1 0 0
o
1 5 7
E
k m
I s l a
Participating communities
S a n t a
N e w
o n
G
M
a l a i t a
I s l a n d s
o n i a r a
u a d a l c a n a l
1 5 o9
E
1 6 o1
E
Temporal components: Before (3 surveys 1995), After (3 surveys 1998/9)
Spatial components:
Group
Whagena
Ysabel
Suavanao
Arnavons.........Ref 1............Ref 2...........Ref 3
Island
1
2
Site
1
2
3
4
5
6
7
8
Transect
6
6
6
6
6
6
6
6
(=replicate)
Two habitats sampled: Shallow reef terrace (0-3.5m), Deep slope (15-22m)
Analysis of data: Asymmetrical ANOVA (Winer et al. '91, Underwood '93)
Trochus niloticus
Shallow habitat
0.6
0.4
0.2
Group
Ys
ab
el
Su
av
an
ao
0
Ar
na
vo
n
W
ag
he
na
Number per 100m
2
0.8
(av. across sites)
Before
After
Trochus niloticus
Before
After
2.0
Number per 100m
2
1.6
Arnavon
Waghena
1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
1.2
0.8
0.4
0.0
2.0
1.6
Ysabel
Suavanao
1.2
0.8
0.4
0.0
17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32
Site
Total holothurians
Deep habitat
2
0
Group
Ys
ab
el
Su
av
an
ao
1
Ar
na
vo
n
W
ag
he
na
Number per 250m
2
3
(av. across sites)
Before
After
Tridacna maxima
Shallow habitat
2
1
W
ag
he
na
Ys
ab
el
Su
av
an
ao
0
Ar
na
vo
n
2
Number per 100m
3
Group
(av. across sites)
Before
After
Arnavons MCA: Conclusions
1. Successful replenishment of trochus
2. Maintenance of abundance of holothurians in MCA despite
probable ongoing exploitation outside
3. Use of reference areas identified large-scale natural (?) patterns
(e.g. giant clam)
Management Implications:
• Different times likely to be required for different species
• Marine reserve may not be the most appropriate form of
management for some species
• Its as important to know what is happening outside a reserve as
what’s happening inside
Use of experimental
design in EIA
1. Appropriate experimental designs can and should be used to improve
the reliability of decision making in EIA (design trees really help)
2. Pre- Approval Phase:
• Good designs improve predictions of effects and generation of
hypotheses
• Can be used as the “Before” part of effects monitoring
• But, need to allocate adequate time for study & evaluation (both often
rushed)
3. Post-Approval Phase:
• Surveillance & compliance monitoring can be inexpensive, but might
identify only a big (obvious) effect when its too late or expensive to fix.
• Effects monitoring
• Measures natural variation
• Provides baseline for future comparisons
• Drawback – need to have controls available

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