Funded by:
Hosted by The Marine Biological Association of the UK
An ecosystem approach to long-term
coastal observing – the western
English Channel.
Frost, M. T., Jenkins, S. R., Hinz, H., Genner, M. J., Sims, D. W., Budd,
G., Araújo, J. N., Hart, P. J. B., Southward, A. J. & Hawkins, S. J.
Workshop on Coastal Observatories.
Best practice in the synthesis of long-term observations and models
Liverpool University, October 17th – 19th, 2006.
MBA long-term observations
• long history (>100 yrs) of MBA in situ observations
1899
The Plymouth
research vessels
1902-1953
1936
“Long-Term Oceanographic and Ecological Research in the Western English Channel”.
(Southward et al., Adv. Mar. Biol., 2005)
MBA long-term observations
1975
The Plymouth
research vessels
1953-2006
“The biomass figures……are
intended to provide basic data for
following changes in the bottom
fauna in the future”
2006
(Holme, N.A. (1953)). The biomass of the bottom fauna in the English Channel off
Plymouth. JMBA. 32:1-49
Long-term monitoring
• ‘Growing concern about human influence on marine
ecosystems conflicts with our inability to separate man-made
from ‘natural’ change. This limitation results from lack of
adequate baselines and uncertainty as to whether observed
changes are local or on a broad scale. Long-term monitoring
programmes should be able to solve both these deficiencies’
(Duarte et al, 1992. Nature)
•‘long-term changes, such as those of climate change,
can best be understood using long-term data sets, which
can be costly and require long-term investment.’ (POST,
2004)
Long-term monitoring
Research definition:
“..research occurring over decades or longer”
•Monitoring definition (Parr et al):
“…the time scale which enables signals of environmental
change to be distinguished from background noise”
•practical definition:
“..any sites where there is a commitment to maintain scientific
and monitoring programmes beyond the usual length of a
scientific research programme”.
Long-term monitoring
Specifically we are interested in:
• what is the current state of the ecosystem?
• How has the ecosystem changed?
• How do interactions of climate and fishing effect ecosystems?
• short term forecasts of ecosystem state
(PML, MBA – SO10 document)
The western English Channel
Major long-term sampling
stations off Plymouth
Regular intertidal stations
From Southward et al., Adv. Mar. Biol., 2005
MBA Time Series: English Channel
Temperature and Salinity
Nutrients
Phytoplankton
Primary production
Zooplankton
Planktonic larval fish
Demersal fish
Intertidal organisms
Infaunal benthos (intermittent)
Epifaunal benthos (intermittent)
E1
E1
E1
E1
E1, L5
E1, L5
L4
various
L4
L4
n.b. There are many gaps in these series
1902-1987,
1921-1987,
1903-1987,
1964-1984
1903-1987,
1924-1987,
1913-1986,
1950-1998,
1922-1950
1899-1986
2001200120011995-2000
1995-2000
20011997-
WEC: Physical changes
• Fluctuations in sea temperature over 20th Century: both
warm and cool periods
• SST may be linked to solar activity- sunspots
(Southward, 1980) and intensity of North Atlantic
Oscillation (Sims et al., 2001; Stenseth et al., 2003)
• Acceleration of warming (~ 1 ºC) since 1987 when time
series stopped (later slide RSDAS data)
• Warmer winter minimum temperatures (< 10 ºC now rare)
• Predicted warming scenarios of 1.4 - 5.8 ºC over the next
100 years (Schneider, 2001)
Mean annual SST (ºC)
Sea-surface temperature
offshore Plymouth 1871-2000
13.5
13.0
12.5
12.0
11.5
11.0
1905 1925 1945 1965 1985 2005
Year
Data source: Met Office Hadley Centre
Grid square 50-51ºN, 4-5ºW
CPR
L5
1960
Source: Coombs &
Halliday, 2004
1965
1970
Note: work also
carried out on CPR
vs L4 (John et al,
Journal of Sea
Research. 2001)
1975
1980
J
F M A M J J A S O N D
J
F M A M J J A S O N D
Monthly abundance of pilchard eggs from CPR sampling in
the English Channel and adjacent areas and MBA station
L5 sampling off Plymouth 1958-1980
S. setosa (monthly mean x1000)
12
10
40
8
30
6
20
4
10
0
1920
2
1940
1960
Year
1980
0
2000
S. elegans (monthly mean x1000)
50
Sagitta setosa (warm water)
Sagitta elegans (cold water)
• Originally thought that changes due to < inorganic nutrients due
to reduced Atlantic inflow (Russell cycle) (leading to <PP etc)
• But now shown nutrients reduced after community changed I.e.
symptom not cause (and nutrients not reduced as dramatically as
previously thought)
Source: L5 data
Pilchard eggs
Flatfish larvae
• Climate signal for egg abundance? – lags behind temp trend by
several yrs.
• Climate signal may then propagate down (top down forcing) as
pilchard juveniles and adults prey on other smaller plankton
• can be difficult to interpret plankton signals
WEC Fish
8000
Herring - Clupea harengus
6000
5000
6000
4000
2000
0
1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
•Herring ‘replaced’ during warmer 1950s by
pilchard - never returned in abundance
Driver: over-fishing at regional scale
80000
70000
60000
50000
40000
30000
Pilchard - Sardina pilchardus
5000
3000
1000
Catch (tonnes)
•1930s (warming) stocks of herring, collapsed
Drivers: Climate + fishing?
Catch (tonnes)
Catch (tonnes)
7000
4000
3000
2000
1000
0
1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Mackerel - Scomber scombrus • Mackerel increase but quickly ‘fished down’
• Last 20 years: increase in mean annual sea
temperature = pilchard catches increased
dramatically
Drivers: climate & fishing?
20000
10000
0
1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
• evidence of climate influence from
phenological studies (squid migrate earlier in
warm years with positive NAO; Flounder
migrate to sea earlier in cooler years,)
Demersal fish - separating Fishing and climate
13.5
13.0
12.5
2
12.0
Mean CPUE [log10(x+1) transformed]
11.5
0
1913-22
1950-57
1968-79
1983-86
2001-02
16,000
2
12,000
b
8,000
1
4,000
Demersal Landings (tonnes)
Mean CPUE [log10(x+1) transformed
a
Mean SST (°C)
4
Blennius ocellaris
Buglossidium luteum
Phrynorhombus spp.
Callionymus maculatus
Cepola macrophthalma
Microchirus variegatus
Scyliorhinus canicula
Merlangius merlangus
Callionymus lyra
Trisopterus minutus
Southern
Species from
English Channel
Conger conger
Pagellus sp.
Scophthalmus rhombus
Raja sp.
Arnoglossus sp.
0
1913-22
1950-57
1968-79
1983-86
2001-02
1.6
cMECN Final Report and Genner et al, 2004)
Source:
a) non-commercial species show +ve response to increase in SST
Molva molva
Micromesistius poutassou
0.8
b) commercial
species initially show similar response (1913-22 Gadus
& 1950-57)
but
morhua
Limanda limanda
then any climate signal is overridden by fishing effects.
- Similar0 pattern
observed
in1968-79
Bristol 1983-86
Channel2001-02
but with different subset of
1913-22
1950-57
species responding (local interactions / restraints)
• Bottom-up forcing: abundance linked to temp-dependent resources?
In Situ observations: Other
• Long-term data has also been used to look at:
• nutrient cycles (Joint et al, JMBA. 1997; Jordan & Joint, ECSS. 1998).
• phytoplankton & Productivity (1964-84 main data collection)
• Work on benthos is ongoing at present (ALSF)
• Intertidal ecosystem particularly in response to climate (MarCLIM)
• Current work now on ecosystem models
“Modelling food web interactions, variation in plankton production, and
fisheries in the western English Channel ecosystem” Araujo et al (2006)
Ecosystem Models
METHODS (kind of)
• EwE (Ecopath with Ecosim) software
• Model built representing ecosystem in 1994 (warm period)
• structure / basic parameters of 1994 model used as baseline for 1973 (cold
period) model and time series data up to 1999. Building past model and running
to current allows modeller to monitor how biomasses have changed through time
– model predicted biomasses can then be compared with stock assessment
estimated biomasses – input parameters are then modified to get better fit
(tuning).
• 50 functional groups used to represent ecosystem*
• time series of biomass ‘built’ + on PCI (used to estimate biomass forcing
function driving PP) and zooplankton abundance (from CPR).
• series of model runs with and without PP and with variations in parameters to
assess relative roles of fishing, trophic interactions (v) + system productivity
• v = maximum mortality predator can inflict on prey relative to baseline
mortalities. low values = bottom-up control , high values = classic predator prey
dynamics (Lotka-Volterra)
Ecosystem Models
Results
• Best fit for model included PBF (increases accuracy of model estimates by
25% compared with fishing only) - bottom-up mechanism contributing to
production at high trophic levels.
• including V (vulnerability) also improved accuracy of model
• Biomass model of PP shows oscillations / peaks in early 1980s / late 1990s.
• zooplankton similar trend but peak at end of 1980s (coincides with small
peak in phytoplankton)
Primary producers
Microzooplankton
Mesozooplankton
Macrozooplankton
15000
250
600
250
1
12000
200
500
200
1
9000
150
400
150
300
6000
100
3000
50
0
1973 1979 1985 1991 1997
100
200
50
100
0
1973 1979 1985 1991 1997
0
1973 1979 1985 1991 1997
0
1973 1979 1985 1991 1997
Source: Araujo et al, 2006. Figure 2
Suspension feeders
Shrimps
Whelks
Echinoderms
C
Phytopla
-1.0
Zooplankton abundance...
1950 1960 1970
19801980s
1990 as
2000did
2010
•although PP kept increasing, many fish Bgroups decreased
after
Year
zooplankton
2000
1000
• zooplankton not ‘tightly controlled’ by PP but
correlated with SST.
1.0
1.0
0.5
0.5
0.0
0.0
C
0
-1000
-1000
-2000
-2000 1950
+ve + Sig.
1960
1970
1980
Year
1990
2000
2010
0
-1000
-2000
1950
1960
1970
1980
Year
1990
2000
2010
1960
1970
1980
1990
2000
2010
2.00
1.00
o
-0.5
-0.5
-1.0
-1.0 1950 1960 1970 1980 1990 2000 2010
+ve - not
Year Sig.
1950 1960 1970 1980 1990 2000 2010
Year
2000
2000
1000
1000
0
SST ( C)
B
Conclusions
Zooplankton abundance...
Zooplankton abundance... Phytoplankton colour indexx
Phytoplankton colour indexx
A
-0.5
0.00
-1.00
-2.00
1950
Year
1.2
600
0.9
1.2
0.9
400
40
990
30
8
150
0.8
24
Ecosystem
0.6Models
1.5
6
660
16
1
12
5
120
9
4
90
3
0.
0.
20
0.4
660
0.6
4
many fish
groups
also
increased
in
these
years
peaking
during
the 1980s e.g 2
0.
200
330 10
8 0.2
0.5
3
0.3
0.3
30
2
1
sole, plaice,
cod
increased
(but
catch)
increased
showing
factors
other
than
0
0
0
0
0
0
0
0
0
0
0
0
0
1973
1979
1985
1991
1997
1973
1979
1985
1991
1997
1973
1979
1985
1991
19971979
9 1985 1991 1997
1973 1979 1985 1991 1997
1973
1979
1985
1991
1997
1973 1985
1979 1991
1985 1997
1991 1997
19731985
19791991
1985
1991 1973
1997
fishing
as
important
1973
1979
1985
1991
1997
1973
1979
1973
1979
1997
1973 1979 1985 1991 1997
1973 1979 1985 1991 1997
997
0.6
Adult cod
mall
helksGadoids
2.5
1.8
2
1.5
1.5
1.2
1
0.9
Hake
sole
Small
gadoids
AdultJuvenile
plaicecrabs
Large
Gurnards
Red mullet
Scallops
Large flatfish
3
40 10
12
2.4
32 8
1.8
9
24 6
124.5 18 2
sole
Red
mullet
DabAdult
Lobsters
4 1 0.4
6
5
3
0.8
0.3
4
3.6 15
1.5
2.7 12
1
61.8 9
9
plaice
LemonJuvenile
sole
Sole
juvenile
Small
demersal
5
4
3
8
0.4 4 5
Biomass
6
(Thousands of
0.24 2 3
4
tonnes)
0.16 2
0.32 3 4
L
1.
1.
0.
0.6
2
3
0.2
2
1.2
4
16
6
2
0.4
0.
1
2
6
1
0.9 0.5
1
2
0.6
0.6
0.1
0.5
1
3
8
0.2
0.
0.08 1
3
0 3
0
0
0
0.3
0
0
0
0
0
0
0
0
1979 1985 1991 1997
1973 1979
1973
1985 1991 1997
1973 19790 1985 1991 1997
0 1979
0 19730 1973
1973 1979 1985 1991 1997 0 1973 1979 1985 1991 1997
9
1985
1991
1997
1973
1979
1985
1991
1997
1979
1985
1991 1997
0
0 1973
997
1979
1985
1991
1997
1973
1979 1973
1985 1979
19911985
19971991
1973
1979
1985
1991
1997
985 1991
1997
1997
1991
1997
1973 1973
1979 1979
1985 1985
1991 1991
1997 19971973 1979 1985 1991 1997
19731973
19791979
19851985
1991
1997
1973 1979 1985 1991 1997
997
1973 1979 1985 1991 1997
Adult plaice
l gadoids
Dab
Anglerfish Red mullet
Cod
adult
4
Lemon sole
Large
bottom
juvenile
PlaiceSole
adult
Hake
5
Large flatfish
Sole adult
Dab
8
1.5
1.8
6
1.5 1.2
Gurnards
Plaice juvenile
Lemon sole
35
1.5
328
1.2
2.421
0.9
1.814
1
0.4
15
18
1.
3
4
1.5
0.8
3
15
0.8
0.32
1.
12
2.4
3
1.2
4
12
1.2 0.9
1.
0.6
0.6 2
0.24
9
2
1.8
0.9
9
0.9 0.6
0.
0.4 1
2
0.4 1.2 0.161
70.6
6
1.2
6
0.
0.6
0.6
0.2 0
0.3
0.3
0.080
0
0
3
0.6
0.3
0.
0.23 0.6
0.31991 1997
9 1985
1973
1979
1985
1991
1997
1973
1973
1979
1985
1991
1997
1973
1979
1985
1991
1997
0
0
0 1979 1985 1991 1997
0
0
0
0
0
0
1973 1979 1985 1991
985 1991
1973 1979 1985 1991 1997
1973 1979 1985 1991 1997
0 1997
0 1997
1973 1979 1985 1991 1997
1973
1979
1985
1991
1997
1973
1979
1985
1991
1997
997
1973
1979
1985
1991
1997
1973
1979
1985
1991
1997
1973
1979 1985 1991 1997
997
1973 1979 1985 1991 1997
1973 1979 1985 1991 1997
Lemon sole
Source:Herring
Araujo Dab
et al, 2006.John
Figure
2
White
juvenile
Sprat
Dory
e adult
Large flatfish
White adult
Catches
(Thousands of
tonnes)
Gurnards
Cod juvenile
Conclusions & WEC observatory
• mixture of bottom-up and top down forcing on WEC ecosystem with climate
playing increasingly important role
• total ecosystem approach required in order to gain and understanding of
‘system drivers’ (e.g. Cushing (1961)) - observatory will aim to provide
measurements of wide range of parameters
• Linking in situ measurements to other observatory measurements enables:
• filling in gaps (e.g. temp)
E1 (50°02'N 4°22'W) Offshore Sea Surface Temperature (SST)
Satellite
data
(RSDAS)
14
12
11
10
9
E1 annual running mean
E1 5yr running mean
Satellite annual running mean
Satellite 5 yr running mean
1999
1994
1989
1984
1979
1974
1969
1964
1959
1954
1949
1944
1939
1934
1929
1924
1919
1914
1909
8
1904
SST (°C)
13
E1
restarted
in 2001
Web (Webmap server)
Western Channel Observatory
NERC datagrid interface
Data archive
(BODC / DASSH,
local SQL / Access)
Virtual Observatory
Modelling
Knowledge
Transfer
(via MECN)
Data
ERSEM
Met Office (NCOF)
Remote Observatory
in situ sampling (L4, E1, L5, buoy, etc.)
long-term time-series
Remote Sensing
scientific investigation (focus on
ecosystem based studies)
SST, Ocean Colour
Other sensors
Observatory benefits
• ground truthing for remote measurements (e.g. John, 2001 for L4:CPR). Issues
with remote measurements of productivity/chlorophyll.
• coordination and synthesis – modelling often reliant on fairly disparate datasets
(various places collected in various ways at various times).
• needs to be standardisation and methodological / technological audit trail.
• WIDER NETWORKING TO INCREASE CAPACITY FOR DATA
SYNTHESIS BEYOND WEC i.e.
• Other NERC observatories
• Other monitoring bodies (MECN)
MECN NETWORK
18 Partners:
DEFRA*
MBA
SAHFOS
PML
PEML
Dove ML
SAMS
SOS Bangor
DARD
CEFAS
FRS
POL
SOC
SMRU
JNCC*
BODC*
Met Office
EA*
Observatory benefits
•synthesis of data beyond WEC (continued)
• European (MarBEF): Largenet
e.g. Long-term pelagic stations in
Europe. (Source: Karen Wiltshire,
MECN Workshop, DEC 2005)