Post on 09-Sep-2020
Modelling ecological tipping points and road-testing management strategies for increasing marine ecosystem resilience
OCEANS & ATMOSPHERE FLAGSHIP
Éva Plagányi, Tim Skewes, Alistair Hobday CSIRO, Brisbane, Australia and Laura Blamey, William Robinson University of Cape Town
3rd International Symposium on the Effects of Climate Change on the World’s Oceans, Santos, Brazil 2015
Modelling tipping points & MSE testing | Eva Plaganyi 2 |
‘EXTREME EVENT’ RESILIENCE’: If a system is perturbed, will it bounce back or fall over?
‘PERMANENT PRESS’ RESILIENCE’: If a system is subjected to multiple stresses, can we design climate-smart strategies to build resilience and reduce the risk of collapse?
OUTLINE OF TALK
1. Examples of the use of multispecies models to advance our ability to anticipate or deal with major ecosystem shifts
Impacts of perturbations on populations (e.g. Recovery time, change in state)
Methods to detect ecological tipping points 2. Examples of how the outputs can be used to inform monitoring
and management 3. Examples of the use of management strategy evaluation (MSE)
to test the performance of alternative marine monitoring and management strategies to detect and respond to ecological changes caused by climate change
Modelling tipping points & MSE testing | Eva Plaganyi 3 |
OUTLINE
Modelling tipping points & MSE testing | Eva Plaganyi
CATS (Complex Assessment Tools)
Pla
gany
i et a
l 201
1 M
ar.
Fres
hw. R
es.
Models of Intermediate Complexity for Ecosystem assessments
4 |
OPERATING MODEL
climate drivers
supply chain
MANAGEMENT MODELe.g. quotas, usage by sectors, trade-offs
adaptive feedback
stakeholder reviewadjust
Management Strategy Evaluation
0
0.5
1
1.5
2
2.5
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2007
2009
2011
COTS
per
tow
Cou
rtesy
: AIM
S
Spatial Multi-species Operating Model (SMOM), Scotia Sea
Plagányi & Butterworth 2012 Ecol. Appl.
KRILL
KRILL FISHERY
PENGUINS Adélie
Chinstrap Gentoo
Macaroni
SEALS Antarctic fur seal
FISH Myctophids and
Perciforms
WHALES Blue Fin
Minke Humpback
FISHERY
Delay difference equations with seasonal time-step
Plagányi & Butterworth 2011
Ecol. Appl.
FISH Myctophids and
Perciforms
0
1
2
3
4
5
6
2007 2017 2027 2037
Krill
bio
mas
sBi
llion
s
0
20
40
60
80
100
2007 2017 2027 2037
Peng
uin
num
bers
Thou
sand
s
(I) Smooth interaction curve
(II) Threshold - BR
(III) Threshold - S
0
2
4
6
8
10
12
14
16
2007 2017 2027 2037
Seal
num
bers Th
ousa
nds
PERTURBATION
Resilience ? Response? Recovery ?
Breeding success impacted
Survival impacted
How does variability in one part of system propagate through ecosystem?
Modelling tipping points & MSE testing | Eva Plaganyi 7 |
Source: Plaganyi et al (2014) MEPS; Robinson et al. (2015) IJMS
0
10
20
30
40
50
60
0
1000
2000
3000
4000
5000
6000
7000
8000
1989 1991 1993 1995 1997 1999 2001 2003Ur
chin
den
sity
Abal
one
biom
ass (
t)
Abalone biomassUrchin density
A
0
5000
10000
15000
20000
25000
30000
35000
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
1987 1992 1997 2002 2007
Peng
uins
rela
tive
num
bers
bre
edin
g
Pela
gic f
ish co
mbi
ned
biom
ass (
t)
Pelagic prey biomassPenguin predator numbers
0
0.5
1
1.5
2
2.5
1994 1996 1998 2000 2002 2004 2007 2011
Cora
l cov
er (%
Acr
opor
a) a
nd
rela
tive
num
ber o
f sta
rfish
Coral coverCrown of thorns
(A) African penguin - sardine
(B) Crown of Thorns Starfish (COTS) - coral
(C) Abalone – urchins (shelter)
OBSERVED CHANGES BEST EXPLAINED* BY (III) ABRUPT CHANGE IN SURVIVAL
*AIC Model selection criterion – alternative multispecies models fitted to data
Modelling tipping points & MSE testing | Eva Plaganyi 8 |
Positive residuals: filled circles; negative residuals: open circles
There is an increase in the variance as the
penguin population starts
to decline substantially
Absolute residuals (penguins) versus relative depletion (sardine) using model output: early warning signal
Source: Plaganyi et al (2014) MEPS
9 |
Increasing variation in population numbers (such as in response to a decline in prey) may be a useful indicator that a system is approaching a tipping point
Abrupt changes in some populations can more readily be ascribed to a threshold-like response of adult survival to changing conditions, rather than breeding success or a recruitment collapse
In a nutshell
Non-linear changes in population parameters (such as survival rate) below critical prey thresholds may be contributing to the responses of predators to changes in their prey
Plag
anyi
et a
l. 20
14
OUTLINE OF TALK
1. Examples of the use of multispecies models to advance our ability to anticipate or deal with major ecosystem shifts
Impacts of perturbations on populations (e.g. Recovery time, change in state)
Methods to detect ecological tipping points 2. Examples of how the outputs can be used to inform monitoring
and management 3. Examples of the use of management strategy evaluation (MSE)
to test the performance of alternative marine monitoring and management strategies to detect and respond to ecological changes caused by climate change
Modelling tipping points & MSE testing | Eva Plaganyi 10 |
OUTLINE
Modelling tipping points & MSE testing | Eva Plaganyi 11 |
Source: Morello et al (2014) MEPS
(B) Crown of Thorns Starfish (COTS) - coral
EXAMPLE 1 – CROWN OF THORNS STARFISH (COTS)
1. Model resilience of alternative ecosystem structures
2. Tipping points to inform field management controls
Small predators
Triton – ve effect+ ve effect
Large fish
COTS Adults>15cm
COTS juv. < 15cm
Fast-growingcoral
Benthic invertebrates
MarineProtected
Area
Slow-growingcoral
Manual removal/Poison injection
COTS larvae
Nutrients
Average threshold COTS density: 7.1 (± 2.3) adult COTS ha-1 ≡ 0.028 (± 0.01) adult COTS min-1 ≡
Coral cover of ~14%
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0 0.05 0.1 0.15 0.2 0.25 0.3
Chan
ge in
COT
S nu
mbe
rs
Coral cover proportion
North reef, Lizard Island, Great Barrier Reef
COTS density corresp. to min.
Threshold of coral cover
Data from Lizard Is. (Pratchett, 2005, 2010)
Estimate an ecological threshold for COTS populations
Source: Plaganyi et al (2014) MEPS 12 | Modelling tipping points & MSE testing | Eva Plaganyi
+-
-
-
-
-
-++
Illegal fishing
Legal fishing
-
Fish
-
adults
juveniles
Rock Lobsters
Abalone
Urchins
Modelling tipping points & MSE testing | Eva Plaganyi 13 |
Source: Blamey et al (2014) Ecol. Mod.
EXAMPLE 2 – ABALONE-URCHIN-LOBSTER
1. Model resilience of alternative ecosystem structures:
Has overfishing altered the system resilience?
(C) Abalone – urchins (shelter)
0
0.2
0.4
0.6
0.8
1
1985 1990 1995 2000 2005 2010
0
0.2
0.4
0.6
0.8
1
1985 1990 1995 2000 2005 2010
Lobster
Urchin
Abalone
Urchin
Fish
Abalone
Lobster
Pro
porti
on o
f K
Year
A. With historic overfishing
B. No fishing scenario
Pro
porti
on o
f K
Urchin Abalone
Fish Lobster
Resilience to climate change
Overfishing scenario: Lobsters invade range of abalone, deplete urchins, change benthos and crash abalone population
Sustainable fishing scenario: Lobsters invade range of abalone, but are kept in check by fish hence system resilient to changes
From Blamey, Plaganyi, Branch 2014. Ecol. Mod.
OUTLINE OF TALK
1. Examples of the use of multispecies models to advance our ability to anticipate or deal with major ecosystem shifts
Impacts of perturbations on populations (e.g. Recovery time, change in state)
Methods to detect ecological tipping points 2. Examples of how the outputs can be used to inform monitoring
and management 3. Examples of the use of management strategy evaluation (MSE)
to test the performance of alternative marine monitoring and management strategies to detect and respond to ecological changes caused by climate change
Modelling tipping points & MSE testing | Eva Plaganyi 15 |
OUTLINE
CLIMATE-SMART STRATEGIES
Sea cucumber / bêche-de-mer Testing alternative management strategies:
resource impacted by fishing and climate
• Fishery: 8 bêche-de-mer species on 27 reef units (in 8 zones) in the Torres Strait, NE Australia, fished by indigenous fishers
• Medium term: 2011-2030 • Attribution. Climate change identifiable as
separate from other impacts (fishery exploitation)
Plaganyi, Skewes et al. 2013. Climatic Change
Dry
san
dfis
h >$
200
kg
Black fish2% Black teat fish
10% Leopardfish1% Prickly red fish
1%
Sand fish74%
Surf red fish12%
Modelling tipping points & MSE testing | Eva Plaganyi 17 |
Torres Straits, C
atch composition, 1993 - 2007
SEA CUCUMBER SPATIAL MULTISPECIES MODEL
• Data-poor – uncertainty re biological understanding and parameters
Location choice modelled as a simple function describing utility by zone
Modelling tipping points & MSE testing | Eva Plaganyi 18 |
SEA CUCUMBER SPATIAL MULTISPECIES MODEL
• Data-poor – uncertainty re biological understanding • Uncertainty re risks of climate change • Uncertainty re impact of climate change on population
2030 Impact
Life stage Component SST
Acid
ifica
tion
SL Curr
ents
, Tor
res
Stra
it
Stor
ms
and
Cycl
ones
Rain
fall
Phyt
opla
nkto
n pr
oduc
tivity
Seag
rass
Cora
l Ree
f
Juvenile Growth H L N N N N L L NMortality H L N N L N N M NCarrying cap. N N M N L N N L NGrowth H N N N N N N N NMortality H N N N L N N N NCarrying cap. N N M N N N N N NReproduction H N N N N N N N NGrowth H L N N N N M N NMortality H L N N N N M N NAdvection N N N N N N N N N
Climate change component
Adults
Larvae
Likelihood
Risk
Consequence
RISK MANAGEMENT NEEDS TO ACCOUNT FOR MULTI-DIMENSIONAL UNCERTAINTIES BIOLOGICAL CLIMATE
VARIABLES (and downscaling)
LIKELIHOOD OF CLIMATE IMPACTS (HIGH, MEDIUM, LOW RISK)
SEVERITY OF POTENTIAL CONSEQUENCES
Monitoring data SST & sea level rise fairly certain
High risk predictions most plausible
Growth first increases then decreases with increasing temperature
Population dynamics model
Ocean pH (acidification, bleaching, coral reef habitat)
Consider cumulative effects of high and medium risk predictions
Positive and negative effects on recruitment and larval survival
Fishing behaviour Storms & cyclone increases in intensity
Complex contributors to overall mortality rates
Future markets Phytoplankton productivity
Effect of changes in habitat
Implementation and control
Ocean currents Multispecies and ecosystem effects
19 |
‘PE
RM
AN
EN
T P
RE
SS
’ RE
SIL
IEN
CE
RISK MANAGEMENT NEEDS TO ACCOUNT FOR MULTI-DIMENSIONAL UNCERTAINTIES BIOLOGICAL CLIMATE
VARIABLES (and downscaling)*
LIKELIHOOD OF CLIMATE IMPACTS (HIGH, MEDIUM, LOW RISK)
SEVERITY OF POTENTIAL CONSEQUENCES
Monitoring data SST & sea level rise fairly certain
High risk predictions most plausible
Growth first increases then decreases with increasing temperature
Population dynamics model
Ocean pH (acidification, bleaching, coral reef habitat)
Consider cumulative effects of high and medium risk predictions
Positive and negative effects on recruitment and larval survival
Fishing behaviour Storms & cyclone increases in intensity
Complex contributors to overall mortality rates
Future markets Phytoplankton productivity
Effect of changes in habitat
Implementation and control
Ocean currents Multispecies and ecosystem effects
M1 – ave M M2 – low M H1 – h=0.7 H2 – h=0.5 R1 – High risk only
R2 – High+Medium risk
I1 – base I2 – double severity of impacts
20 | *Outside scope of study – could use multiple climate models; emission scenarios
‘PE
RM
AN
EN
T P
RE
SS
’ RE
SIL
IEN
CE
Results: local depletion per zone and species D
EP
LETI
ON
(Bsp
) RE
LATI
VE
TO
NO
FI
SH
ING
& N
O C
LIM
ATE
CH
AN
GE
b) With high and medium risk impacts
Wb
War
0
0.5
1
1.5i) H. scabra
BaCu
Da
D-C G-N
War0
0.5
1
1.5ii) H. whitmaei
Cu
Da
D-C
G-NS-R
Wb
War
0
0.5
1
1.5iii) A. Mauritiana
Ba
Cu
DaD-C
S-R
0
1
2iv) H. fuscogilva
Ba Cu DaD-C
0
1
2v) T. ananus
Ba
Cu
DaD-C
G-N S-RWb
War
0
1
2vi) A. echinites
BaCu
DaD-C G-N
S-R Wb War
0
1
2vii) A. miliaris
BaCu Da
D-C G-N S-R Wb
War0
1
2viii) B. argus
Increased risk under clim
ate change
21 |
Quantify risk (and associated uncertainty) to all 8 species in each of the 8 zones
Performance Summary - Harvest Strategies
Risk of sub-optimal management: the percentage of species for which the median 2030 spawning biomass level was less than Btarg (0.48K) Risk of depletion below Blim: percentage of species for which the lower 90% confidence limit of the 2030 RS projections was less than Blim
Harvest strategy
Risk of suboptimal
management
Risk of depletion
below BlimRisk of local
depletion
Average annual profit (US$
million)A. Current catch(status quo) 50 13 12 5.31B. No monitoring:Double catches 75 25 23 10.6Profit maximisation 50 13 12 5.31Location choice based on area and distance 50 13 16 5.31Spatial rotation (3 yr) 25 13 5 3.35Closed areas/sensitive species (Warrior, Sand 13 13 9 2.72Multi-species catch composition 13 13 6 3.08C. Adaptive feedback/monitoring:Hockey stick 38 13 9 3.65Hockey stick with spatial management 13 13 1 5.31Spatial closure (Single species in Zone) (30%K 38 13 8 5.11Spatial closure (Entire Zone) (30%K trigger) 13 13 5 3.19Spatial closure (Entire Zone) (20%K trigger) 13 13 7 4.09
22 | Modelling tipping points & MSE testing | Eva Plaganyi
Changes in Species Composition / Mixed harvest bag
Modelling tipping points & MSE testing | Eva Plaganyi
No. Common name Species name
1 Sandfish Holothuria scabra
2 Black teatfish Holothuria whitmaei
3 Surf redfish Actinopyga mauritiana
4 White teatfish Holothuria fuscogilva
5 Prickly redfish Thelenota ananus
6 Deepwater redfish Actinopyga echinites
7 Hairy blackfish Actinopyga miliaris
8 Leopardfish Bohadschia argus
Value
23 |
MSE as a risk management tool
• Climate risk assessment used as an input to dynamic model • Reference Set (rather than single model) used to capture key
uncertainties = ENSEMBLE • Demonstration of use of MSE to test the performance (and
adaptability) (especially in the face of uncertainty) of alternative harvest strategies in meeting fishery objectives, such as ensuring: • low risk of depletion (overall and local) • high probability of good catch / average profits • low risk of changing the multi-species community composition • high probability of managing through climate variability and change
• Climate-smart data poor strategy example: • 3-yr spatial rotation strategy for sea cucumbers
Modelling tipping points & MSE testing | Eva Plaganyi
MSE – Management Strategy Evaluation 25 |
A Belmont Coastal Vulnerability Theme Project
Global Understanding for local solutions: Reducing vulnerability of marine-dependent coastal communities (GULLS)
www.marinehotspots.org
NEXT STEPS – test climate-smart adaptation options
SYSTEM MODELe.g. MICE, Atlantis, EwE
climate driverse.g. data inputs or biophysical models
supply chain connecting products to consumers
ADAPTATION OPTION
adaptive feedback
stakeholder reviewadjust
Socio-economic data
Species sensitivity analyses
27 |
Model results can inform monitoring and management
1. Multispecies & ecosystem models:
2. Application examples: a. Overfishing reduces resilience b. Thresholds for management
+-
-
-
-
-
-++
Illegal fishing
Legal fishing
-
Fish
-
adults
juveniles
Rock Lobsters
Abalone
Urchins
Blam
ey e
t al.
2014
Small predators
Triton – ve effect+ ve effect
Large fish
COTS Adults>15cm
COTS juv. < 15cm
Fast-growingcoral
Benthic invertebrates
MarineProtected
Area
Slow-growingcoral
Manual removal/Poison injection
COTS larvae
Nutrients
Mor
ello
et a
l. 20
14
‘EXTREME EVENT’ RESILIENCE’: If a system is perturbed, will it bounce
back or fall over?
a. Understand responses to perturbations, recovery times, resilience
b. Increasing variation in population numbers (marine species) may be a useful indicator that a system is approaching a tipping point
28 |
Need to road-test climate-smartness of management
strategies
1. MSE can support effective risk management
2. Uncertainty in biological understanding + risk of climate change effects and their impacts
3. Even when integrating across broad range of uncertainties, possible to distinguish between performance of alternative management strategies
‘PERMANENT PRESS’ RESILIENCE’: Climate-smart strategies to build resilience to multiple stresses
Oceans and Atmosphere Flagship Dr Éva Pláganyi t +61 7 3833 5955 e eva.plaganyi-lloyd@csiro.au w www.csiro.au
WEALTH FROM OCEANS FLAGSHIP
Obrigado Thank you Plagányi, É., Punt, A., Hillary, R., Morello, E., Thebaud, O.,
Hutton, T., Pillans, R., Thorson, J., Fulton, E.A., Smith, A.D.T., Smith, F., Bayliss, P., Haywood, M., Lyne, V., Rothlisberg, P. 2014. Multi-species fisheries management and conservation: tactical applications using models of intermediate complexity. Fish Fisheries 15:1-22
Plagányi, É., Ellis, N., Blamey, L.K., Morello, E., Norman-Lopez, A., Robinson, W., Sporcic, M., Sweatman, H. 2014. Ecosystem modelling provides clues to understanding ecological tipping points. Mar Ecol Prog Ser 512: 99–113
Models of Intermediate Complexity for Ecosystem assessments
Plagányi, É.E., Skewes, T., Haddon, M. and N. Dowling. 2013. Risk management tools for sustainable fisheries management under changing climate: a sea cucumber example. Climatic Change 119:181–197
Acknowledgements:
Blamey, L.K., Plagányi, É.E. and G.M. Branch. 2014. Was overfishing of predatory fish responsible for a lobster-induced regime shift in the Benguela? Ecol. Modelling 273: 140-150
Pho
to: R
ob T
arr
With thanks: