hosted by the marine biological association of the uk workshop on coastal observatories. best...
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Hosted by The Marine Biological Association of the UK
Workshop on Coastal Observatories.Best practice in the synthesis of long-term observations and models
Liverpool University, October 17th – 19th, 2006.
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.
Funded by:
The Plymouth research vessels 1902-1953
1899
1936
• long history (>100 yrs) of MBA in situ observations
MBA long-term observations
“Long-Term Oceanographic and Ecological Research in the Western English Channel”. (Southward et al., Adv. Mar. Biol., 2005)
MBA long-term observations
The Plymouth research vessels 1953-2006
1975
2006(Holme, N.A. (1953)). The biomass of the bottom fauna in the English Channel off Plymouth. JMBA. 32:1-49
“The biomass figures……are intended to provide basic data for following changes in the bottom
fauna in the future”
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)
From Southward et al., Adv. Mar. Biol., 2005Regular intertidal stations
The western English ChannelMajor long-term sampling stations off Plymouth
Temperature and Salinity E1 1902-1987, 2001-
Nutrients E1 1921-1987, 2001-
Phytoplankton E1 1903-1987, 2001-
Primary production E1 1964-1984
Zooplankton E1, L5 1903-1987, 1995-2000
Planktonic larval fish E1, L5 1924-1987, 1995-2000
Demersal fish L4 1913-1986, 2001-
Intertidal organisms various 1950-1998, 1997-
Infaunal benthos (intermittent) L4 1922-1950
Epifaunal benthos (intermittent) L4 1899-1986
n.b. There are many gaps in these series
MBA Time Series: English Channel
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)
Data source: Met Office Hadley CentreGrid square 50-51ºN, 4-5ºW
Sea-surface temperatureoffshore Plymouth 1871-2000
11.0
11.5
12.0
12.5
13.0
13.5
1905 1925 1945 1965 1985 2005
Year
Me
an
an
nua
l SS
T (
ºC)
11.0
11.5
12.0
12.5
13.0
13.5
1905 1925 1945 1965 1985 2005
Year
Me
an
an
nua
l SS
T (
ºC)
J F M A M J J A S O N D
19 60
19 65
19 70
19 75
19 80
J F M A M J J A S O N D
CPR L5L5
Monthly abundance of pilchard eggs from CPR sampling in the English Channel and adjacent areas and MBA station
L5 sampling off Plymouth 1958-1980
Source: Coombs & Halliday, 2004
Note: work also carried out on CPR vs L4 (John et al, Journal of Sea Research. 2001)
0
10
20
30
40
50
1920 1940 1960 1980 2000
S. s
eto
sa (
mon
thly
mea
n x
10
00)
0
2
4
6
8
10
12
Sagitta setosa (warm water)
Sagitta elegans (cold water)
S. e
lega
ns (m
onthly m
ean
x10
00)
Year
• 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)
Pilchard eggs
Flatfish larvae
Source: L5 data
• 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
Herring - Clupea harengus
0
1000
2000
3000
4000
5000
6000
7000
8000
1920 1930 1940 1950 1960 1970 1980 1990 2000Year
Ca
tch
(to
nn
es)
Mackerel - Scomber scombrus
0
10000
20000
30000
40000
50000
60000
70000
80000
1920 1930 1940 1950 1960 1970 1980 1990 2000
Year
Ca
tch
(to
nn
es)
WEC Fish
•1930s (warming) stocks of herring, collapsed Drivers: Climate + fishing?
•Herring ‘replaced’ during warmer 1950s by pilchard - never returned in abundance Driver: over-fishing at regional scale
• Mackerel increase but quickly ‘fished down’• Last 20 years: increase in mean annual sea temperature = pilchard catches increased dramaticallyDrivers: climate & fishing?
Pilchard - Sardina pilchardus
0
1000
2000
3000
4000
5000
6000
1920 1930 1940 1950 1960 1970 1980 1990 2000Year
Ca
tch
(to
nn
es)
• evidence of climate influence from phenological studies (squid migrate earlier in warm years with positive NAO; Flounder migrate to sea earlier in cooler years,)
Mean C
PU
E [
log
10(x
+1)
transfo
rmed]
0
2
4Blennius ocellarisBuglossidium luteumPhrynorhombus spp.Callionymus maculatusCepola macrophthalmaMicrochirus variegatusScyliorhinus caniculaMerlangius merlangusCallionymus lyraTrisopterus minutus
11.5
12.0
12.5
13.0
13.5
Mean S
ST
(°C
)
1913-22 1950-57 1968-79 1983-86 2001-02
a
0
1
2
0
0.8
1.6
Conger conger
Pagellus sp.
Scophthalmus rhombus
Raja sp.Arnoglossus sp.
Molva molvaMicromesistius poutassouGadus morhuaLimanda limanda
1913-22 1950-57 1968-79 1983-86 2001-02
1913-22 1950-57 1968-79 1983-86 2001-02
b
c
4,000
8,000
12,000
16,000D
em
ers
al L
andin
gs (to
nnes)
Mean CPUE [log10(x+1) transformed
a) non-commercial species show +ve response to increase in SST b) commercial species initially show similar response (1913-22 & 1950-57) but then any climate signal is overridden by fishing effects. - Similar pattern observed in Bristol Channel but with different subset of species responding (local interactions / restraints) • Bottom-up forcing: abundance linked to temp-dependent resources?
Southern Species from English Channel
Demersal fish - separating Fishing and climate
Source: MECN Final Report and Genner et al, 2004)
• 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)
In Situ observations: Other
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 ModelsResults
• 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)
Me sozooplankton
0
100
200
300
400
500
600
1973 1979 1985 1991 1997
Prim a ry producers
0
3000
6000
9000
12000
15000
1973 1979 1985 1991 1997
Microzooplankton
0
50
100
150
200
250
1973 1979 1985 1991 1997
Ma crozooplankton
0
50
100
150
200
250
1973 1979 1985 1991 1997
0
300
600
900
1200
1500
1973 1979 1985 1991 1997
Source: Araujo et al, 2006. Figure 2
Conclusions
•although PP kept increasing, many fish groups decreased after 1980s as did zooplankton
• zooplankton not ‘tightly controlled’ by PP but correlated with SST.
-1.0
-0.5
0.0
0.5
1.0
1950 1960 1970 1980 1990 2000 2010
-2000
-1000
0
1000
2000
1950 1960 1970 1980 1990 2000 2010
Zo
op
lan
kto
n a
bu
nd
an
ce
...
-2.00
-1.00
0.00
1.00
2.00
1950 1960 1970 1980 1990 2000 2010
-1.0
-0.5
0.0
0.5
1.0
1950 1960 1970 1980 1990 2000 2010Year
Ph
yto
pla
nk
ton
co
lou
r in
de
xx
-2000
-1000
0
1000
2000
1950 1960 1970 1980 1990 2000 2010
-2000
-1000
0
1000
2000
1950 1960 1970 1980 1990 2000 2010
-2.00
-1.00
0.00
1.00
2.00
1950 1960 1970 1980 1990 2000 2010S
ST
(oC
)
+ve + Sig.
+ve - not Sig.
Ecosystem Modelsmany fish groups also increased in these years peaking during the 1980s e.g sole, plaice, cod increased (but catch) increased showing factors other than
fishing as important
Adult cod
0
0.5
1
1.5
2
2.5
1973 1979 1985 1991 1997
0
0.6
1.2
1.8
2.4
3
1973 1979 1985 1991 1997
0
0.6
1.2
1.8
2.4
3
1973 1979 1985 1991 1997
0
30
60
90
120
1973 1979 1985 1991 1997
0
2
4
6
8
10
1973 1979 1985 1991 1997
Adult pla ice
0
0.9
1.8
2.7
3.6
4.5
1973 1979 1985 1991 19970
1
2
3
4
1973 1979 1985 1991 1997
0
0.6
1.2
1.8
2.4
3
1973 1979 1985 1991 1997
0
0.3
0.6
0.9
1.2
1.5
1.8
1973 1979 1985 1991 1997
0
3
6
9
12
1973 1979 1985 1991 1997
0
0.3
0.6
0.9
1.2
1.5
1973 1979 1985 1991 1997
Cod a dult
0
0.3
0.6
0.9
1.2
1.5
1973 1979 1985 1991 1997
0
0.2
0.4
0.6
0.8
1973 1979 1985 1991 1997
0
3
6
9
12
15
18
1973 1979 1985 1991 1997
0
0.1
0.2
0.3
0.4
1973 1979 1985 1991 19970
1
2
3
4
5
1973 1979 1985 1991 1997
0
0.08
0.16
0.24
0.32
0.4
1973 1979 1985 1991 1997
S ole a dult
0
0.3
0.6
0.9
1.2
1.5
1973 1979 1985 1991 1997
0
0.3
0.6
0.9
1.2
1.5
1973 1979 1985 1991 1997
0
3
6
9
12
1973 1979 1985 1991 1997
0
0.2
0.4
0.6
0.8
1
1973 1979 1985 1991 1997
P la ice a dult
0
0.6
1.2
1.8
2.4
3
1973 1979 1985 1991 1997
0
0.3
0.6
0.9
1.2
1.5
1.8
1973 1979 1985 1991 1997
0
10
20
30
40
50
1973 1979 1985 1991 1997
0
30
60
90
120
150
180
1973 1979 1985 1991 1997
0
0.2
0.4
0.6
0.8
1
1973 1979 1985 1991 1997
0
0.5
1
1.5
2
1973 1979 1985 1991 1997
Adult sole
0
1
2
3
4
5
6
1973 1979 1985 1991 1997
0
1
2
3
4
1973 1979 1985 1991 1997
Biomass (Thousands of tonnes)
Catches (Thousands of tonnes)
Source: Araujo et al, 2006. Figure 2
• 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)
Conclusions & WEC observatory
E1 (50°02'N 4°22'W) Offshore Sea Surface Temperature (SST)
8
9
10
11
12
13
14
1904
1909
1914
1919
1924
1929
1934
1939
1944
1949
1954
1959
1964
1969
1974
1979
1984
1989
1994
1999
SS
T (
°C)
E1 annual running mean E1 5yr running mean
Satellite annual running mean Satellite 5 yr running mean
Satellite data (RSDAS)
E1 restarted in 2001
Remote Observatory
Virtual Observatory
in situ sampling (L4, E1, L5, buoy, etc.)
long-term time-series
scientific investigation (focus on ecosystem based studies)
Remote Sensing
SST, Ocean Colour
Other sensors
Modelling
ERSEM
Met Office (NCOF)
Data
Data archive (BODC / DASSH, local SQL / Access)
Web (Webmap server)
NERC datagrid interface
Knowledge Transfer (via MECN)
Western Channel Observatory
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*MBASAHFOSPMLPEMLDove MLSAMSSOS BangorDARDCEFASFRSPOLSOCSMRUJNCC*BODC*Met OfficeEA*
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)