ocb cisco 20july09 - woods hole oceanographic institution

Eco

syst

em s

tate

Cisco Werner Brad deYoung

(and many others: M. Barange, G. Beaugrand, R. Harris, C. Moloney, I. Perry and M. Scheffer)

Regime Shifts in the Ocean: From Detection to Prediction?

DEFINITION OF REGIME SHIFT

Working definition: a regime shift is a relatively abrupt change between contrasting persistent states in an ecosystem

Claussen, et al (1999) Geophysical Research Letters26, 2037-2040.

Scheffer, et al. (2001). Nature 413: 591-596

Birth of the Sahara desert

“Simple” example

Jamaican coral reef systems

Sequence of events

Removal of fish &Eutrophication

Sea urchins #’s increase

Hurricane in ‘81 (urchins recolonized)

Pathogen

Fleshy brown algae took over

“Complicated”explanation

Loss of resilience

Overfishing

Nutrient loading

Parasite infection

Sea urchin collapse

Rock

Healthy state

Stressed state

Erosion of resilience

Environmental driver

Env

ironm

enta

l sta

teErosion of resilience

Scheffer et al. 2001. Nature 413:591-596.

• Loss of resilience

• Irreversibility

• Triggered shifts

Can we anticipate how biological and ecological systems will respond?

Scheffer, Carpenter, Walker, Foley and Folke 2001. NATURE

(a) and (b): one equilibrium state (c): three equilibrium states; two stable, one unstable

For a system on the upper branch, close to the F2bifurcation point, a slight incremental change may bring it beyond the bifurcation and induce a catastrophic shift to the lower alternative stable state.

A perturbation, if sufficiently large, may also induce a shift the lower alternative stable state.

Scheffer, Carpenter, Walker, Foley and Folke 2001. NATURE

How can we demonstrate that there are alternative attractors in real ecosystems?

Scheffer, et al. (2003) TREE.

Shifts in time series

Multimodal distributions

Dual relationships

(Response to control factor best described by 2 separate fxns)

(Spatial analogue to jumps in time series)

Review of a few other oceanic examples

• Scotian Shelf – driven primarily by fishing, cascading trophic impacts

• North Sea – combined drivers: natural=biogeographic shift and human=fishing

• North Pacific – complex natural state change(s)

Scotian Shelf – Frank et al. 2005

-30% +30%

Wei

ght,

kg

Colour display of 60+ indices

for Eastern Scotian Shelf

Red –below average

Green –above average

Grey seals - adults Pelagic fish - #’s

Pelagic:demersal #’s Pelagic:demersal wt.

Inverts - $$Pelagics - wt

DiatomsGrey seals – pups

Pelagics - $$ Greenness

Dinoflagellates Fish diversity – richness

3D Seisimic (km2) Gulf Stream position

Stratification anomaly Diatom:dinoflagellate

Sea level anomaly Volume of CIL source water

Inverts – landings Bottom water < 3 C Sable winds (Tau)

SST anomaly (satellites) chlorophyll – CPR

Temperature of mixed layer NAO

Bottom T – Emerald basin Copepods – Para/Pseudocal

Shelf-slope front position Storms

Bottom T – Misaine bank Groundfish landings

Haddock – length at age 6 Bottom area trawled (>150 GRT)

Cod – length at age 6 Average weight of fish

Community similarity index PCB’s in seal blubber

Relative F Pollock – length at age 6

Calanus finmarchicus Groundfish biomass – RV

Pelagics – landings Silver hake – length at age

Condition – KF Depth of mixed layer

Condition – JC Proportion of area – condition

RIVSUM Sigma-t in mixed layer

Oxygen Wind stress (total)

Wind stress (x-direction) Wind stress amplitude

SST at Halifax Groundfish - $$

Salinity in mixed layer Ice coverage

Wind stress (Tau) Number of oil&gas wells drilled

Nitrate Groundfish fish - #’s

Shannon diversity index –fish Seismic 2D (km)

1970 1975 1980 1985 1990 1995 2000

Grey seals, pelagic fish abundance, invertebrate landings, fish species richness, phytoplankton

Bottom temp., exploitation, groundfish biomass & landings, growth-CHP, avg. fish weight, copepods

Top Predators

(Piscivores)

Forage (fish+inverts)(Plankti-,Detriti-vores)

Zooplankton

(Herbivores)

Phytoplankton

(Nutrivores)

+

-

+

-

North Sea: Long-term changes in the ecology (hydro-climate + biology)

-10

-8

-6

-4

-2

0

2

4

6

8

1958

1961

1964

1967

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

1999

Years

c. Principal component 1 (30.08%)Regime shift

‘cold’ episodic event

‘warm’ episodic event(G. Beaugrand)

Shifts in copepod distributions in the North Atlantic:Warm-water species have extended their distribution northward by more than 10° of latitude, while cold-water species have decreased in number and extension.

(Beaugrand et al., Science, 2002)

-0. 40

0. 40. 8

- 2- 101

23- 3- 2- 1012

- 2- 1012

Secodpcpa component (31.36

SS (central North Sea)

58 62 66 70 74 78 82 86 909 49Yea rs ( 195 8-19 99)Naoaes

eaubeo species per assemblage

-0.4

0

0.4

0.8

-2

-1

0

1

2

3-3-2-10123

-2

-1

0

1

2

Seco

nd p

rinci

pal

com

pone

nt (3

1.36

%)

SST

(cen

tral N

orth

Sea

)

58 62 66 70 74 78 82 86 90 94 98Years (1958-1999)

NH

T an

omal

ies

Mea

n um

ber o

f sp

ecie

s per

ass

embl

age

Gadoid species (cod)

SST

NHT anomalies

plankton change

0%

20%

40%

60%

80%

100%

C. finmarchicus

C. helgolandicus

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Long-term changes in the abundance of two key species in the North SeaPe

rcen

tage

of

C. h

elgo

land

icus

Reid et al. (2003)

Consequences of plankton changes on higher trophic level

Mismatch between the timing of calanus prey and larval cod

Abundance of C. finmarchicus

60 65 70 75 80 85 90 95123456789101112

0.20.40.60.81.01.21.41.6 A

bundance (in log(x+1))

10

Gadoid Outburst

Abundance of C. helgolandicus

123456789101112

60 65 70 75 80 85 90 95

0.10.20.30.40.50.60.70.80.91.0 A

bundance (in log(x+1))

10

Gadoid Outburst

Beaugrand, et al. (2003) Nature. Vol. 426. 661-664.

0.2

0.4

0.6

0.8

1

1960 1970 1980 1990 2000 Year

Propn. of eggs from age 5+ codFishing mortality rate (age 3+)

But there is also an influence from fishing – how much?

Meteorological/oceanographicforcing

Ocean circulation

Biogeographic shift

Ocean conditions

Ecosystem status and function

Fishing

North Sea - dynamics

North Pacific regime shift – Hare and Mantua (2000)

Physical forcing – air temperature -but there are dozens of other such time series

Winter PDO

Euphausiid

Pink salmon (solid) lagged one year

Chinook (dashed) lagged 3 years

deYoung et al. (2007)

deYoung et al. (2007)

How predictable are regime shifts?

• Coral reefs (the “simplest” case) – We understand the causal links – We can’t predict disease outbreaks

• Fishing-dominated systems – Although fishing can be the dominant driver, its consequences

are not predictable without understanding the foodweb dynamics– Shifts may not be easily reversible

• North Pacific– We have not been able to separate drivers or where different

states are occurring– Accurate prediction not currently possible

Example North Pacific modelYamanaka, Rose, Werner et al. Ecological Modelling, 2007.

Can we model regime shifts?

NEMURO.FISH

Observations- Biologicaland Physical

NCEP 6 hourly data1948-2002

(includes interannual variability)

COCO COCO –– TokyoTokyo33--D NemuroD Nemuro

Zooplankton andZooplankton andtemperature temperature time seriestime series

Nemuro Pacific herring model

uMN

L-1

0.084

0.088

0.092

g ye

ar-1

0

40

80

o C

9.510.010.511.011.5

0.0480.0520.0560.060

Year1950 1960 1970 1980 1990 2000

0.14

0.16

0.18

(a)

(b)

(c)

(d)

(e)

Validation

Time series output

Modeled the basin-scale response in physics and lower trophic levels…

… and forced the upper trophic levels…

Obtained “shifts” in weights of individual fish at various locations in the North Pacific…

Summary• Clear evidence for regime shifts

in the ocean

• More regime shifts are likely –decreased resilience

• Limited data, systemic complexity, range of different structures

• Need sustained monitoring, experimental work, models, etc.

Resolving the impact of climatic processes on ecosystems of the North Atlantic basin and shelf seas.

BASIN is an initiative to develop a joint EU/North American ocean ecosystem research program.

BASIN: Basin-scale Analysis, Synthesis, and Integration.

Expected Result: Major impact for marine exploited resources and biogeo-chemical processes (e.g. sequestration of CO2 by the ocean).

Biological consequences expected under climatic warmingOr changes in water mass structure.

• Changes in the range and spatial distribution of species.

• Shifts in the location of biogeographical boundaries, provinces,

and biomes.

• Change in the phenology of species (e.g. earlier reproductive season).

• Modification in dominance (e.g. a key species can be replaced by

another one).

• Change in diversity.

• Change in other key functional attributes for marine ecosystems.

• Change in structure and dynamics of ecosystem with possible

regime shifts.

Research Goals

• Integrate and synthesize existing basin-wide data sets from previous programs in Europe and North America,

• Improve the current state of the art in bio-physical modelling,

• Develop hindcast modelling studies to understand the observed historical variability of the North Atlantic ecosystem,

• Construct scenarios of possible ecosystem changes in response tofuture climate variability,

• Identify data gaps that limit process understanding and contribute to uncertainty in model results,

• Specify new data needed to assess the performance of forecasts,

• Provide relevant information to resource managers and decision makers.

What did we learnabout marine food webs during the GLOBEC era?

What did we learnabout marine food webs during the GLOBEC era?

Coleen Moloney

1. marine food webs are special

We learnt that...

MEDUSAE

PLEUROBRACHIA TOMOPTERIS SAGITT

ALIMACIN

A

AMMODYTES JUV

OIKOPLEURA

LARVAL MOLLUSCA

PRORO-CENTRUM

PERIDINIUM

CERATIUM

MEROSIRA

PARALIA

THALASSI-OSIRA

COSCINO-DISCUS

GUINARDIA

PHAEOCYSTIS

BIDDULPHIA NITZSCHIA

NAVICULACHAETO-CERAS

RHIZO--SOLENIA

CUMACEAE MYSIDAE DECAPOD LARVAE

CYPRIS BALANUS LARVAE

HARPAC-TICID

PARA-CALANUS

PSEUDO-CALANUS

ACARTIA

TEMORACENTRO-PAGES

CALANUS

NYCTI-PHANES

HYPERIID AMPHIPODS

APHERUSA

PODON

EVADNE

TINTINNOPSIS

NOCTILUCARADIOLARIA

FORAM-INIFERA

ADULT HERRINGHERRINGYOUNG7-12 mm 12-42 mm 42-130 mm

predation by fish

predation by other groupsHardy (1924), adapted from Elton (1927)

Food web supporting herring in the North Sea


2. population-level processes are important in food web dynamics (target-species approach)

We learnt that...

C1-4 C5 C1-4 C5

C5 & femalenauplius nauplius

mating spawning recruitment to copepodite stage

Neocalanus plumchrus

J F M A M J J A S O N D J F M A M J J A S O N D

0

500

1000

C1-4 C5 C1-4 C5female female0

500

1000

Neocalanus flemingeri

(small form)

Neocalanus flemingeri

(large form)

female C1-3C4

C4 C5 female0

500

1000

Dep

th (m

)

modified from Tsuda et al. (1999) Mar. Biol. 135: 533

Population-level processes: adaptability

Species show remarkable flexibility in patterns of growth and reproduction

oceanexplorer.noaa.gov



3. long time series allow(ed) patterns to be identified (and processes inferred)

We learnt that...

Alaska gyre (biomass)Alaska gyre (stage ratio)VI cont. margin (stage)

Cumulative warmth-100 0 100 200 300 400

25-Mar4-Apr

14-Apr24-Apr4-May

14-May24-May

3-Jun13-Jun23-Jun

3-Jul13-Jul23-Jul2-Aug

Dates at which Neocalanus plumchrus reaches its annual biomass maximum

Alaska gyreVI cont. margin

Year1960 1970 1980 1990 2000

25-Mar4-Apr

14-Apr24-Apr4-May

14-May24-May

3-Jun13-Jun23-Jun

3-Jul13-Jul23-Jul2-Aug

warm water = early biomass peak

Mackas et al. (1998) Can. J. Fish. Aquat. Sci. 55: 1878; Mackas et al. (2007) Prog. Oceanogr. 75: 223

Changes in seasonal timing: pattern to process

Changes in timing have unknown effects through the food web

oceanexplorer.noaa.gov




4. food web structure varies on different scales

We learnt that...

myctophids

phytoplankton

krill

seals penguins

icefish

other predators

amphipods

copepods

krill abundantseals

phytoplankton

krill

penguins

icefish

other predators

myctophids

amphipods

copepods

krill scarceScotia Sea

Murphy et al. (2007) Phil. Trans. R. Soc. B 362: 113

Changes in structure: alternative food web pathways

1

23

4

5

Regions for which ecosystem regime shifts have been documented

Jarre and Shannon (in press) GLOBEC synthesis

Changes in structure: regime shifts




4. food webs can change from one state to another

5. food web dynamics vary among regions

We learnt that...

Bio

mas

s/ A

bund

ance

of t

roph

ic le

vel

IPrimary producer(phytoplankton)

IIHerbivore(copepod)

IIILevel 1 Predator

(small fish)

IVTop Predator

(large fish)

Trophic level(taxa) 'Bottom-up'

Mackas (in press, GLOBEC synthesis), based on Cury et al. (2001) and McQueen et al. (1986)

Time after perturbation

Food web dynamics: trophic controls

Continental margin areas, NE Pacific

Ware and Thompson (2005) Science 308: 1280-1284

0 2 4 60

1

2r2 = 0.87p < 0.0001n = 11

Mea

n re

side

nt fi

sh y

ield

(m

etric

tons

.km

-2)

Mean chl a concentration (mg.m-3) Mean chl a concentration (mg.m-3)

Mea

n zo

opla

nkto

n bi

omas

s (m

g [d

ry w

t].m

-3)

0 2 4 6 8

120

80

40

0

Bottom-up trophic control

Mean zoop. biomass (mg [dry wt].m-3)

Mea

n re

side

nt fi

sh y

ield

(m

etric

tons

.km

-2)

r2 = 0.79p = 0.01n = 62

4

00 40 80 120

Large scale

Small scale

Bio

mas

s/ A

bund

ance

of t

roph

ic le

vel



IIILevel 1 Predator

(small fish)

IVTop Predator

(large fish)

Trophic level(taxa) 'Bottom-up' 'Top-down'

trophic cascades




Top-down control – trophic cascades

SE US coast

Myers et al. (2008) Science 315: 1846-1850

Rel

ativ

e ab

unda

nce 0.0

0.4

0.8

1970 1980 1990 2000

Sharks

0.0

0.4

0.8

1970 1980 1990 2000

Skates

0.0

0.4

0.8

1970 1980 1990 2000

Scallops

Black Sea

Bio

mas

s

Daskalov et al. (2007)PNAS 104: 10518-10523

1950 1970 1990

4

2

0

-2

Pelagic predatory fish

1950 1970 1990

4

2

0

-2

Small planktivorous fish

1950 1970 1990

4

2

0

-2

Zooplankton

1950 1970 1990

4

2

0

-2

Phytoplankton


Bio

mas

s/ A

bund

ance

of t

roph

ic le

vel



IIILevel 1 Predator

(small fish)

IVTop Predator

(large fish)

Trophic level(taxa) 'Bottom-up' 'Top-down'

'Wasp-waist'

top-down control



bottom-up control

Trophic controls are situation-dependent

•they vary in space...

•and they vary in time...

Frank (pers. comm.), Frank et al. (2006). Ecol. Letters 9: 1096-1105.

Latitutudinal gradient in trophic controlsAnalysis of 47 systems

Hunt et al. (2002). Deep-Sea Res. II 49: 5821-5853

Cold regime

Beginning of warm regime

Warm regime

Beginning of cold regime

(Bottom-up regulation)

(Bottom-up regulation)

(Top-down regulation)

(Both top-down and bottom-up)

Zooplankton Larval survival

Abundance of piscivorous fish

Juvenile recruits

Oscillating control – Bering Sea






6. food webs should be understood from end to end

We learnt that...

Planktivore fishJuvenile fishLarval fish

Toppredators

Piscivore fish

Zooplankton

Microbial loop

Phytoplankton

carbon sequestration

marine biogeochemistry

fisheries production

maintenanceof biodiversity

marine living resource management

marine conservation

Why study marine food webs end to end?






6. food webs should be understood from end to end

7. innovation is needed to deal with the complexity of marine food webs

We learnt that...

end-to-end

food web

4Energy &

Metabolism

Feed

ing

Beha

viou

r5

3Stoichiometry

Biodiversity

& Adaptability

8

Distributions

& Movements

6

1Bi

ogeo

chem

istry genes &

genomes

species

compo-

sition

size

spectra

links &

exchanges

mass balance

tota

l bi

omas

s

nutri

ent

conc

en-

tratio

ns

food quality

indi

vidua

l di

ets

genera-

lised

patterns

ability to

adapt

efficiency

of

transfer

2

Micro-organisms

element

cycles

troph

ic co

ntro

lsLife Histories7

population dynamics

key species

Research framework for end-to-end food webs

ecosystem-level

population-level

Moloney, St John et al. (submitted)

North Pacific Coral - Jamaica North SeaDrivers Response Drivers Response Drivers Response

Complex physical climate (AO, PDO, ENSO)

Zooplankton to fish and mammals

Fishing Eutrophication

Species composition(urchins-algae-coral)

Oceanic (circulation temperature) atmospheric (NAO), fishing

Phytoplankton to fish

Time scale

Shift – 1-5 yearsPersistence –10-20 years

Shift: 1-5 yearsRegime: > 10 years

Parasite (Trigger) – 1-2 yearsErosion of resilience (10 year)

Shift – 1-2 yearsPersistence –> 20 years

Shift 1-5 years (NAO) Oceanic persistence –10 years Erosion of resilience – > 10 years -fishing

Shift – 1-5 yearsRegime: > 10 years

Spatial scale

10,000 km (basin)

1,000 -2,000 km (regional)

10-100 km 10-100 km 1000 km (fishing, oceanic) to 10,000 km (atmospheric)

1000-2000 km (extends beyond North Sea)

Detect 2 years 3-5 years < 1 year 1-2 years 2 years 2-5 years

Predict Little skill Following from detection

Erosion fishing impact is predictable Trigger – no

Probabilistic Little skill, Erosion -fishing impact is predictable

Following from detection

Manage Not possible Fishing managementafter detection - adaptation

Marine management of resilience and trigger >> prevention

Marine management -rehabilitation ( ?)

Climate – not possibleFishing -prevention

Fishing managementafter detection -adaptation

ocb cisco 20july09 - woods hole oceanographic institution

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