Download Interesting times: winners, losers, and system shifts under climate

ICES Journal of Marine Science (2011), 68(6), 1329–1342. doi:10.1093/icesjms/fsr032
Interesting times: winners, losers, and system shifts under climate
change around Australia
E. A. Fulton
CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia; tel: +61 3 62325018; e-mail: [email protected].
Fulton, E. A. 2011. Interesting times: winners, losers, and system shifts under climate change around Australia. – ICES Journal of Marine
Science, 68: 1329 – 1342.
Received 29 June 2010; accepted 13 February 2011; advance access publication 11 April 2011.
Feedback and change are basic features of ecosystems, something global change has highlighted. Changes in the physical environment
will see shifts in species ranges, community compositions, and ultimately the form and function of ecosystem and the human
societies that exploit them. What these shifts will be depends on which of the competing (and potentially counteracting)
mechanisms dominate through space and time. Moreover, changes are unlikely to be simple or linear; there will be winners, losers,
and surprises. It also means that management will be complex and non-stationary, presenting management, scientific, and statistical
challenges.
Keywords: adaptive capacity, ecosystem models, global climate change, regime shifts.
Introduction
The effect of cumulative human action on the climate and biosphere
of Earth has the potential of affecting significantly many critical
marine ecosystem services, in particular biodiversity and food
security, as highlighted by the work of Cheung et al. (2008, 2009).
IPCC (2007) scenarios for fossil-fuel emissions suggest that over
the next 60–100 years, ocean surface temperatures are likely to
warm by 1.8–48C, the sea level is predicted to rise by 0.2 –1.4 m
(Meehl et al., 2005; IPCC, 2007; Rahmstorf, 2007), storms are
likely to intensify, and ocean acidification could result in declines
in pH of 0.14–0.5 (Caldeira and Wickett, 2005; IPCC, 2007). In
addition, the projected rise in global population to 9 billion by
2050 (UN, 2009) could plausibly see 50–77% of the worlds population concentrated along coastal fringes (Small et al., 2000; CCSR,
2006), putting even more degradative pressure on coastal habitats.
Together, these shifting pressures are likely to result in significant shifts in species distributions (Cheung et al., 2009) and
altered foraging and reproductive success (Bograd et al., 2009;
Munday et al., 2009). The reduction in pH could also result in
mortality of calcifying organisms in the benthos and plankton
(Fabry et al., 2008; Hall-Spencer et al., 2008).
Since the release of the fourth assessment report by the IPCC
(2007), there has been an increasing awareness of the topic, not
only in scientific circles [where the number of papers referencing
the topic has increased by 224% (web of knowledge search 26
May 2010)], but also among conservation-orientated nongovernment organizations, government and management agencies
(many of which have initiated climate adaptation programmes or
departments), and the public [with Google searches on climate
change growing by 177% (Google trends http://www.google.
com/trends?q=climate+change)]. There is now a keen interest
among resource management groups about what ecological, economic, and social implications are associated with shifts in ecosystem structure and function induced by global change.
Until recently, most models [e.g. integrated assessment models
(IAMs)] used to explore these issues have either used simple
# Crown
copyright [2011]
mathematical relationships to deal with environmentally driven
damage to the economy or have been one-way coupled (where
the conditions from a physical model force an economic model,
or vice versa, with no dynamic feedback between them). IAMs represent key features of human systems (such as demography, energy
use, technology, emissions, land use, and the economy) and simplified representations of the climate, ecosystems, and associated
economic impacts (i.e. damage functions). The majority of
IAMs that have been used to link changes in the climate with economic outcomes have used relatively simple damage functions that
assume loss in productivity increases in proportion to some power
of temperature change (Stanton et al., 2008). Little or no consideration is given to other routes of damage (e.g. changed patterns of
rainfall, ocean acidification, sea-level rise, and inundation) or to
feedbacks that could occur through a multitude of ecological pathways. There has also been a terrestrial focus, meaning that appreciation of the effect of global change on marine systems is not as well
advanced (Richardson and Poloczanska, 2008), though many
research programmes on the topic have been initiated in the
past few years [e.g. the Bering Ecosystem Study and Bering
Sea Integrated Ecosystem Research Program (BEST-BSIERP);
Hollowed et al., 2009].
Those studies that have been undertaken in the marine realm
have often been based around bioclimate envelope models. This
kind of model gives insight into potential distributional shifts of
species based on predicted shifts in suitable (or preferred) physical
and biological conditions that have been identified from current
species distributions (Pearson and Dawson, 2003). Although
these models did not initially include interspecies relationships,
adaptation or evolution, habitat dependence, phenology, or dispersal mechanisms, they are becoming increasingly sophisticated
and the most recent marine examples do include at least some
of these processes (e.g. population and dispersal dynamics were
included in the model of Cheung et al., 2009).
A new class of model being applied to the question of the implications of global change for marine ecosystems and the societies
1330
E. A. Fulton
Table 1. List of the three model types (with seven regional-scale implementations) used in the study with brief comments on
modifications made to allow for consideration of climate change effects.
Model
Atlantis
Model type
Deterministic three-dimensional
end-to-end model with explicit
human behaviour (fisheries)
Regions covered
SE of Australia*
References
EAF et al. (2007)
InVitro
Agent-based end-to-end model with
explicit human behaviour and
multiple coastal industries
Ningaloo-Exmouth
region*
Northwest shelf
Australia*
EAF et al. (2009)
Trophodynamic two-dimensional
model with F-based human
activities (typically fisheries)
Great Barrier Reef*
Eastern Bass Strait†
Northwest shelf
Australia†
Ningaloo-Exmouth
region†
Ecospace
Gray et al. (2006)
and Little et al.
(2006)
Gribble (2003)
Bulman et al. (2006)
Bulman et al. (2006)
EAF (unpublished
data)
Modifications
To expand the anthropogenic components,
Atlantis-SE was coupled with coastal industries
and biophysical impacts model (see main text
and Supplementary material for details)
To cover the region seamlessly, the InVitro
models were coupled spatially; they were also
coupled with a coastal biophysical impacts
model
Each Ecospace model was coupled with a
nutrient-phytoplankton-zooplankton-detritus
to capture better environmentally driven
changes in production and to coastal
industries and biophysical impacts models. In
the NW, the two Ecospace models (for the
Pilbara and Gascoyne) were coupled spatially
Note that end-to-end means a model spanning physics, the foodweb, habitats, and human industries (especially fisheries).
*Models were the primary models run.
†
Models were run for comparative purposes only.
that exploit them is an end-to-end (or whole-of-system) models.
These mechanistic models attempt to integrate abiotic, biotic,
and anthropogenic processes across different scales, allowing for
dynamic two-way coupling (or interactions) between ecosystem
components from microbes to top predators and the human
sectors that exploit the system (Travers et al., 2007; Fulton
2010). These models are beginning to be coupled with regionally
downscaled ocean – atmosphere models (e.g. the models being
developed for BEST-BSIERP), though typically circulation, temperature, or production regimes are taken from these models and
used to drive the bottom end of an end-to-end model (Brown
et al., 2009). The models have usually only considered a single
driver or correlated drivers (e.g. thermally driven changes in the
environment), but cumulative impacts are likely to be critical
regarding breaching ecosystem thresholds. This paper discusses a
very early attempt to consider how multiple drivers might simultaneously act to reshape Australian marine ecosystems.
Methods
Because no single end-to-end model covers the entire expanse of
Australian waters, seven existing regional models (summarized
in Table 1 and described below) were modified to allow for representation of changes in temperature, salinity, pH, and dissolved
oxygen regimes (which in turn could drive shifts in species distributions), sea level, storm frequency and intensity, the entry or exit
of species with shifting distributions, and the location and nature
of major coastal industries and regulatory systems. Between them,
the models cover nearly two-thirds of Australia’s EEZ, across the
southeast (SE) of Australia and down the tropical coasts of both
northeast (NE) and northwest (NW) Australia (Figure 1).
Three modelling frameworks were used as a basis for the model
systems: Atlantis (Fulton et al., 2005), InVitro (Gray et al., 2006),
and Ecospace (Walters et al., 1999). All these frameworks have the
potential of covering all the main biological components of marine
ecosystems, from primary production to top predators. The
biggest differences between the models are in how they represent
the different components. Before discussing how the models
were used, a brief overview is given of the main structure and
Figure 1. Areas modelled in the current study: (A) SE of Australia
(white dashed line marks the boundary of the Eastern Bass Strait
region), (B) Great Barrier Reef and (C) northwest of Australia
(consisting of the Northwest shelf and Ningaloo-Exmouth regions—
the black dashed line marks the boundary between these two
regions).
assumptions of the models and the specific implementations
used to represent the different Australian areas. Each model is
the outcome of many years work in each region, and interested
readers are encouraged to seek extra detail in the references
provided.
Atlantis
The model for SE Australia is a modified form of Atlantis-SE
(Fulton et al., 2007), which was originally developed as the basis
for a whole-of-ecosystem management strategy evaluation in
support of a strategic restructuring of SE Australian federal fisheries. The model has also been used to look at general fisheries
and climate-related questions, such as multispecies maximum sustainable yield (MMSY; Worm et al., 2009) and the efficacy of ecological indicators of the effects of fishing (Branch et al., 2010). This
model has the broadest spatial scale (covering 3.7 million km2)
Winners, losers, and system shifts under climate change around Australia
and quite complex ecology, including a size-resolved microbial
web; nutrient, light, and space-based primary production; 37
age-structured ecological groups (from forage fish to top predators, listed in Supplementary Table A1), some resolved to the
species level; multiple genetic stocks per group; and shifting
climate-related environmental drivers of physiology and reproduction. There is also explicit representation of recreational and commercial fishing fleets, with the latter resolved to subfleets defined
by homeports, crew, and vessel sizes; and driven by social and
economic drivers that determine investment, disinvestment,
quota trading, information updating, and effort allocation.
All Atlantis models are built upon a largely deterministic,
(coarsely) spatially resolved three-dimensional biophysical submodel that tracks the nutrient (usually nitrogen and silica) flows
through the main biological groups of the system of interest on
a 6 –24-h time-step (typically). The identity and configuration of
these groups is defined based on a wide range of options, including
some form of representation of consumption, production, waste
cycling, movement, recruitment, and habitat dependence
(example formulations are given in the Supplementary material).
The physical environment (including water and substratum properties and processes) is represented using irregular polygons
matched to the major geographical and bioregional features of
the simulated marine system. The anthropogenic components of
the model represent the impact of pollution and coastal development, but are focused on the detailed dynamics of fishing fleets
and management regimes. The exact form used can again be
drawn from a wide list of options, but for Atlantis-SE includes
explicit socio-economically driven effort allocation (example formulations again given in the Supplementary material) and regulation based on gear restrictions, individual transferable quotas,
spatial and temporal zoning, discarding restrictions, size limits,
bycatch mitigation, and dynamic reference points and decision
rules.
It is quite difficult to find sufficient data to fit fully such large
models, but in this case, there has been extensive calibration ecologically (vs. 36, 20 –90-year catch history time-series and sporadic
scientific surveys) and anthropogenically (though in this case only
7 years of data were available for testing, after removing the first
10-year section of the available effort time-series as a training
dataset). Using a simple implementation of pattern-orientated
modelling (which simultaneously fits the entire model against
data from multiple datasets; Kramer-Schadt et al., 2007), bounding parametrizations were found that produce equally plausible
modelled systems given the available data and alternative possible
system structures. These parametrizations were then all carried
forward in the various simulations done for the final analyses.
InVitro
The NW region was represented by an InVitro model, formed by
combining InVitro-NWS (Little et al., 2006; McDonald et al.,
2006, 2008) and InVitro-Ningaloo (Fulton et al., 2009), which
were initially developed for evaluating multiple use management
strategies in the Pilbara and the Gascoyne regions of NW
Australia. InVitro (Gray et al., 2006) is a hybrid agent-based
modelling platform, where the behaviour and representation of
each of the biological or anthropogenic submodels or agents is
specific to their type (a classification of the agent types and
some examples are given in Supplementary Table A1). For
example, biophysically it couples classical dynamic differential
equation and metapopulation models for lower trophic levels
1331
and habitats with decision-based agents for higher trophic levels,
species of conservation concern (e.g. whales and whale sharks),
and human sectors. Similarly, anthropogenic sectors are represented in a range of ways. Impacts of commercial industries,
such as nutrient pollution, salt extraction, shipping, ports,
coastal and urban development (including residential, service,
industrial, and retail sectors), road transport systems (e.g. road
trains), and the oil and gas sector are represented using differential
equations or cellular automata (White et al., 2000) that mimic the
impacts, activities, and management strategies of these industries.
In contrast, coastal agriculture, fisheries, and tourism are much
more detailed, using true individual-based models, with individuals or small groups explicitly making decisions about site-specific
activities, based on simple economic and mechanical drivers;
Kalman filters are used in these agent types to represent learning
and updating of individual knowledge and experience (Gray
et al., 2006). Monitoring and management associated with each
of the sectors is also explicitly represented. As with Atlantis, a
form of pattern-orientated modelling was used to produce bounding parameter sets that produce plausible model system states. A
wide range of parametrizations was retained in this case, because
of a lack of long-term time-series to constrain tightly possible
parametrizations and the stochastic nature of the model, which
made it harder to distinguish between alternative parametrizations
with similar model skill.
Ecospace
The Great Barrier Reef (GBR) region was represented using a modified Ecospace model based on Gribble (2003). Ecospace is an explicitly spatial form of the Ecopath with Ecosim (EwE) modelling
approach (Walters et al., 2000; Christensen and Walters, 2004),
which uses biomass pools to represent the ecological components
of the system. EwE can include age structure of key groups—
using life-history stanzas (Walters et al., 2008)—and this was the
case for two groups in this model (mangrove jacks and small
schooling fish, updated to this form from an earlier age-structured
method used in Gribble, 2003). To model the spatio-temporal
dynamics of the system, Ecospace applies a set of differential
equations (an example of the form is given in the Supplementary
material) across a regular grid; with movement between cells
based on a gradient model between sites of good and poor
habitat or prey availability (though seasonal migration and
particle-like individual-based movement options are also available;
Walters et al., 2010). The non-spatial forms have been used widely
and they have yielded good insights into system function and fisheries management issues (Robinson and Frid, 2003). Ecospace has
not been as widely used, but given the spatial differential in potential impacts evident in the downscaled global climate model (GCM)
products, explicit spatial handling were considered critical; in particular, because it allowed for consideration of spatial dispersal (and
how this might change over time in response to environmentally
driven changes in habitat and prey fields) and variations in productivity. This variability was captured via dynamic (two-way)
coupling of ECOSPACE with a spatially resolved, environmentally
driven, nutrient –phytoplankton –zooplankton– detritus (NPZD)
model. Further details of this coupling are given below in the
section on representing biophysical change.
The GBR Ecospace model of Gribble (2003) has 32 groups
(listed in Supplementary Table A1), including two forms of detritus (with fisheries discards separate to other forms of detritus),
pelagic and benthic primary producers, planktonic as well as
1332
benthic invertebrates (with penaeid prawns pulled out to a species
level), 23 fish groups (some with age structure) and species of conservation concern, such as dugongs, large sea turtles, and seabirds.
Line, trawl, and gillnet fisheries are explicitly represented in the
model. The model was originally used to consider the implications
of alternative management options for the prawn trawl fishery
within the GBR, but has also been used to consider the implications of different levels of spatial zoning (Gribble, 2007, 2009)
and more general issues, such as MMSY (Worm et al., 2009)
and the efficacy of mean trophic level of the catch as a biodiversity
indicator (Branch et al., 2010). This model was fitted to fisheries
time-series and its spatio-temporal predictions have been validated against independent survey datasets (Gribble, 2009).
For comparative purposes, Ecospace models (modified in the
same way as for the GBR; e.g. coupled with NPZD production
models) for the SE and NW regions were run in parallel with
the Atlantis and InVitro models (these are indicated with a
dagger in Table 1). The EwE model for SE Australia is a shelf
model of Eastern Bass Strait (a well-studied area that is key to
the fisheries of the SE region and has biophysical features representative of those found across the broader region) first developed by
Bulman et al. (2006). It contains 55 living groups and two forms of
detritus (Supplementary Table A1), with a focus on the demersal
fish community. The model has been fitted formally to 10-year
time-series of data (for each of 42 groups) and has been used previously to consider a range of fisheries management issues, including ones specific to that area (Bulman et al., 2006), but also
broader ones to do with fishing of forage fish, MMSY (Worm
et al., 2009), and the usefulness of mean trophic level as a reliable
measure of marine biodiversity (Branch et al., 2010).
The NW Ecospace model is formed from a model of the Pilbara
shelf region of Australia (Bulman, 2007) and an unpublished
model of the Ningaloo Reef and Exmouth Gulf region
(Supplementary Table A1). Neither of these models has been
fitted formally to continuous time-series (because none exists),
but the spatio-temporal patterns they predict have been validated
against data from scientific surveys collected sporadically in the
region over the past 40 years. As for the SE model, these models
have been used to look at management and development issues
specific to the regions of the model, as well as more general fisheries concepts like MMSY and mean trophic level of the catch.
Ecosim (non-spatial) versions of the GBR, SE, and NW shelf
models were also used in an earlier study that considered potential
effects of climate driven changes in productivity (Brown et al.,
2009).
Representing biophysical change
The Atlantis-SE currents (in the form of bulk exchanges) and
water column properties were estimated using the
Commonwealth Scientific and Industrial Research Organisation
(CSIRO) 0.18 × 0.18 ocean forecasting model (OFAM) conditioned on the CSIRO Mark 3.5 coupled atmosphere – ocean
global circulation model [a summary of these models is provided
in Supplementary material; full details are available in Gordon
et al. (2010) and Oke et al. (2005)]. The IPCC SRES A2 emission
scenario (Nakicenovic and Swart, 2000) was used for the climate
projections. The A2 scenario was chosen because it is consistent
with the current levels of emissions (Rahmstorf et al., 2007), it is
conservative regarding the level of global economic growth (Van
Vuuren and O’Neill, 2006), and the broad-scale heterogeneity
assumed in the scenario is most consistent with the heterogeneous
E. A. Fulton
dynamics of the anthropogenic components of Atlantis-SE and the
coupled industries model. Storm events were represented via
simple-impact models (where damage was applied to appropriate
model components sitting under the footprint of the storm in proportion to the storm’s intensity); and sea-level rise was also treated
simply—via gradual changes in vertical layer depths and associated
rates of sedimentation and inundation; mechanisms to allow for
the shoreward retreat of coastal habitats were also incorporated.
These processes were parametrized from the literature
(Harmlien-Vivien and Laboute, 1986; Ellison and Stoddart,
1991; Dollar and Tribble, 1993; French, 1993; Michener et al.,
1997; Short and Neckles, 1999; Nicholls, 2002, 2004; Reed, 2002;
Scavia et al., 2002; McInnes et al., 2003; Williams et al., 2003;
Crooks, 2004; Fourqurean and Rutten, 2004; Nicholls and Lowe,
2004; Zhang et al., 2004; Cruz-Palacios and van Tussenbroek,
2005; Feagin et al., 2005; Gardner et al., 2005; Church et al.,
2006; Ericson et al., 2006; Harley et al., 2006) and conversations
with regional geomorphology experts. All these changes (summarized in Table 2) then formed the basis of the abiotic environment
that was acted on by the dynamic Atlantis components.
The same approach was taken to represent abiotic shifts, storms,
and sea-level rise in the InVitro model. For the Ecospace models,
simple NPZD models were created (using the Atlantis model framework) for the different regions. These NPZD models were
driven by downscaled abiotic drivers in the same way as in the
other models; they were also (two-way) coupled dynamically
with the Ecospace models. The coupler used a simple interpolation
from the Atlantis polygon map to the Ecospace grid. The coupler
delivers biomass values from the NPZD to Ecospace, returning predation mortality rates on the plankton groups and detrital production by the groups within the Ecospace model. This transfer
occurred during each Ecospace time-step (which is a month
long), but for numerical stability the returns were evenly smeared
over the next month’s worth of NPZD time-steps. Although
crude, this approach resulted in a production model similar in
spatial patterns, realized biomass, and production levels to that of
more sophisticated biogeochemical models (e.g. the coupled
GCM-NPZ model described in Brown et al., 2009) and better captured shifts in the planktonic and primary producer groups than
the standard Ecospace representations could, given the many changing abiotic factors that effected the lower trophic levels.
Representing change in the human systems
Fewer drivers were used to define the scenario of change from a
social and economic perspective than for the abiotic factors. The
social and economic changes seen in the simulations largely
result from the dynamics of explicit anthropogenic submodels.
However, the large contribution of exports to the Australian
economy (and the large export markets that support many
Australian industries) means that the general form of the global
economy is needed as a model driver. The A2 scenario was
chosen to provide this global context for two reasons. First, the
economic projections are conservative compared with both the
other scenarios and newer projections (Van Vuuren and O’Neill,
2006). Second, preliminary results indicated that realized rates of
economic growth in the simulations were slower than assumed
in the SRES A1 and B1 scenarios and that heterogeneity remained
between sectors and regions (although this was only within the
Australian model domains, it was assumed that this differential
could also potentially result globally, if that were explicitly represented too). This is most consistent with the SRES A2 scenario.
Winners, losers, and system shifts under climate change around Australia
1333
Table 2. Summary of abiotic and global economic conditions used to set the broad context and to drive the end-to-end models (taken
from IPCC SRES A2 scenario).
Global system feature
Global properties
Global GDP ($USa 1 000 billion year – 1)
Global population (billion)
Regional heterogeneity
Economic development
Global economic growth rate (%)
CO2 concentration (ppm)
Global temperature change (8C)
Global sea-level rise (m)
Global ocean pH
Australian properties
Sea surface temperature change (8C)
pH change
Storm frequency and intensity
Precipitation
Fires
Australian population outside modelled coastal zone (million)
Value in 2050
59–111
9.7–11.3
High
Moderate
1.0
536
+1.2–1.8
0.1–0.25
7.9
+0.8–4.0
–0.14 to –0.19
No increase in frequency, but average intensity increases
Up to 20% decrease in east– SE, potentially small increase in the north
More intense and frequent in SE
3.5–4.8
a
This is 1990 USD.
Consequently, the A2 emission scenario was chosen as the abiotic
and broad economic context (summarized in Table 2) for this
work.
Regarding dynamic model components for Atlantis-SE and
InVitro, there were quite detailed sector and regulatory models.
As discussed earlier, in InVitro, all the major coastal industries
are represented explicitly and change in these industries in
response to the biophysical changes is simply played out.
Human responses also played out in Atlantis; however, the
majority of the focus in Atlantis is on fisheries, with other
sectors represented via an impact model. To make this impact
model more responsive (i.e. more dynamic than a fixed constant
or linearly changing level of impact), it was coupled with a
dynamic stocks-and-flows representation of coastal sectors to
determine the magnitude of potential impacts (primarily in the
form of changed flows from estuaries, including the magnitude
and seasonal patterns of the river flow, but also pollutant
content and salinity levels; levels of outflows of dissolved nutrients
from catchments or outfalls; and substratum available for coastal
habitats). This simple economics-based representation of coast
industries allows for shifting demography, which effects available
labour sources and demands on services, innovation, and technological uptake (which changes costs, prices, and efficiencies) and
shifts in infrastructure (capacity, costs, and maintenance)—
readers unfamiliar with system dynamics approaches can find a
useful introduction to the concepts in Morecroft (2007). The formulations used in this study draw heavily on the representation of
coastal industries in InVitro (in turn based heavily on White et al.,
2000; Waddell et al., 2003) and the representation of technological
shifts and inter-industry structures captured in Pant (2007). In all
these representations, a temperature-dependent damage function
is used to link the biophysical and human spheres. Further feedbacks exist in the models via fish production, coastal habitats,
nutrient cycling, the status of charismatic species, and general
habitability.
In contrast to the other models, which contain detailed social
and economic submodels, the Ecospace models used simpler
fleet dynamics models and relatively static forms of management.
Given the high degree of uncertainty associated with all these
models (Atlantis, InVitro, and Ecospace), alternative parametrizations were used to capture a range of levels of vulnerability and
adaptive capacity.
Simulations and indicators
The models were run from a state representing the period 2010–
2060. This end date is arbitrary and it reflects the fact that
beyond this date the Atlantis-SE model became numerically
unstable for reasons explained further below. During this projection period, all the ecological and socio-economic components
were able to react dynamically to their circumstances—within
the constraints of the model formulations.
Given the uncertainty in the model predictions and the fact that
the models do not include dynamic (shifting value) parametrizations (or other means of representing evolution of shifts in diversity within functional groups), aggregate results rather than results
per group were considered the most reliable indicators of potential
shifts within the ecosystems (my previous experience indicates that
responses of broad types of organism are far less uncertain regarding general trends and gross magnitude of any change than specifics for individual groups). Consequently, only changes in the
relative biomass of primary producers, detritus, plankton, other
pelagic invertebrates (e.g. jellyfish and squid), benthic invertebrates (both sessile and mobile), pelagic fish, demersal fish, and
top predators are reported. Similarly, for socio-economic components, the results have been aggregated to give the relative
change in the value of small-scale fisheries (for both small
owner operators and recreational or artisanal fisheries) and
large-scale commercial fisheries (particularly operations using
large boats where the captains are not company owners).
Consideration of MMSY
As a complement to the predictions made about the future trajectories of the systems, the models were also used to calculate what
MMSY would look like in the end-state ecosystems. The approach
used was the same as documented for Worm et al. (2009), but
instead of using the model systems with a 2000–2010 climate
state as the basis for the analysis, the 2050–2060 climate state
was used instead.
1334
Results
Predicted biophysical changes
For NE and NW Australia, the models indicated increases of 5 –
22% in the relative biomass of plankton groups, 20 –105%
increases for other pelagic invertebrates, 21 –880% for pelagic
fish biomass, and a decline in demersal (often habitat-associated)
fish of 8 –35% (Table 3). The exact magnitude of these changes differed between systems, but the general patterns were similar.
Although specifics varied between systems, the general reasons
for these results are consistent—pelagic groups appeared to
benefit more often than they suffered from changed water
column properties, they were also able to move more freely to
avoid any poor conditions; in addition, their life histories often
allowed for more rapid responses and they seemed to have
greater buffer capacity in their diets, switching composition
more rapidly than is seen among the demersal groups.
Furthermore, the demersal groups had more diet components
degraded by changed environmental conditions; they therefore
had less scope for focusing on resilient prey groups. The habitat
association of many of the demersal groups also constrained
their ability to shift rapidly around patchy or poor environmental
conditions or degraded habitat.
In contrast to the consistency of the other groups, the relative
biomass of benthic invertebrates and top predators responded differently in the two systems. In the NW, these two indicators
declined—invertebrates by 12– 23% and top predators by 28 –
48%, but they increased in the NE—invertebrates by 5 –12%
and top predators by 50 –150%. In both systems, these results
are driven by cascading effects of shifts in production, trophic
interactions, and competition. The results also mask much
skewed results across individual groups. For instance, despite the
general level of change of invertebrates altogether, coral cover
decreased by 60 –85% (because of the mix of increased fragility
resulting from acidification and more variable weather conditions). Among the species of conservation concern, the
biomass of dugongs rose by 35 –125%, because seagrass benefited
from the slowing of calcifying epiphytic growth, whereas turtle
populations collapsed (with many breeding beaches suffering
inundation or storm disturbance). Moreover, although the gross
biomass of demersal fish declined, this was largely concentrated
among larger bodied or habitat-dependent groups, with the
biomass of herbivorous and small demersal fish largely unaffected.
In the SE Australian model, on average (across alternative
parametrizations of the model) the plankton, other pelagic
invertebrates, and pelagic fish all again increased (by 10– 31, 5 –
71, and 34 –282%, respectively), whereas the demersal fish
decreased by 7 –32% (Table 3), all for similar reasons to those
noted above. In this case, benthic invertebrates declined by 4 –9%
and the relative biomass of top predators increased by 45 –131%
(dominated by an increase in the great whales, which responded
to increases in plankton production). These overall results again
hide skewed and highly uncertain results for individual species
(Figure 2), as well as significant spatial variability. One particularly
interesting phenomenon was a predicted regime shift in the waters
off eastern Bass Strait and Tasmania. This region of Australia has
seen the largest changes in surface temperature over the past 60
years and is likely to see a further 2.6 –48C rise over the next 50
years under the emissions trajectory used in this analysis. Under
this level of environmental change, Atlantis-SE predicts a change
from a demersal and mesopelagically dominated system structure
E. A. Fulton
Table 3. Range of relative biomass predicted for Australian
ecosystems in 2050 under climate change (vs. state in 2010).
GBR (NE)
Group
Australia
Biophysical indicators
Detritus
0.91 –1.13
Primary producers
1.47 –1.65
Plankton
1.12 –1.22
Other pelagic
1.25 –2.05
invertebrates
Pelagic fish
1.27 –1.41
Demersal fish
0.9–0.92
Benthic invertebrates
1.05 –1.12
Top predators
1.5–2.5
Fisheries: economic indicators
Large-scale
1.91 –1.97
commercial
sectors
Small-scale and
1.09 –1.14
recreational
NW of
Australia
SE of
Australia
0.84 –1.07
0.97 –1.04
1.05 –1.15
1.20 –1.27
0.97 –1.28
0.95 –1.2
1.1–1.31
1.05 –1.71
1.21 –9.80
0.65 –0.81
0.77 –0.88
0.52 –0.72
1.34 –3.82
0.68 –0.93
0.91 –0.96
1.45 –2.31
2.29 –2.71
2.06 –2.31
0.49 –0.75
0.7–0.81
Ranges represent the average values for the aggregate indicators (calculated
over the entire model domain) for alternative model parametrizations.
to one that is increasingly pelagically dominated (Figure 3). The
shifts in nutrient patterns and thermal regime mean that there is
an increase in both benthic and planktonic production by 20–
50%, though the planktonic groups shift more towards picophytoplankton groups (whose contribution to production increases by
216%). There is a turnover in the types of benthic invertebrate
(including some invading species) that dominate, moving to
those that can cope with the changes in temperature, sources of production, detrital loads, and ocean acidification. The commercially
valuable demersal fish initially increase (by 15 –54%), because of
more favourable temperatures and increases in some prey groups,
but after 2040 they decline and are replaced by other demersal
fish groups that feed on the new benthic invertebrate fauna
coming to dominate the benthos. Similarly, squid and sharks
initially increase (by 75 –92 and 15 –20%, respectively) but then
also fall away after 2040 (the cephalopods in response to extended
periods of environmental stress that outlasts their short life histories; the sharks because of a shift in prey mix, but also because
they enter a suboptimal thermal regime). In contrast, the biomass
of mesopelagics declines continuously, decreasing by 95%,
whereas jellyfish and small pelagic forage fish increase by two- to
fourfold or more (because they are either released from competition or, for the jellyfish, better able to cope with the new environmental conditions). Beyond 2060, the model becomes numerically
unstable, because the parametrization (based on species composition and phenotypes in 2000) cannot continue to change under
the changing conditions—model reparametrization would be
required to reflect the level of biodiversity and evolutionary shifts
that would have accumulated by that point.
A comparison of these multifactor perturbation simulations
with single-factor perturbation scenarios indicated that if factors
affecting production (either via climate drivers or pollution) are
considered in isolation, then results were within 10 –15% of the
values reached in the multifactor simulations (Table 4); although
production and multifactor simulations diverge more in tropical
systems than temperate systems. In contrast, considering the
effects of acidification alone would result in a much more
pessimistic view of future ecosystems—with values in the
Winners, losers, and system shifts under climate change around Australia
1335
Figure 2. Range of results per functional group for Atlantis-SE climate change runs. The uncertainty bands indicate the range of results
observed across all modelled groups across all simulations using alternative parametrizations. The median of each range is marked for
reference.
Figure 3. Regime shift predicted for the SE Australian ecosystem—proportional community composition in 2010, 2040, and 2060.
acidification-only runs 40% lower (on average) than in the multifactor scenarios. Once again, divergence was greater in tropical
systems, where acidification could have quite devastating effects
on habitat and associated assemblages.
The form and driving mechanisms for change in the different
systems were largely consistent across model types; this engenders
confidence in the robustness of the results. Where there were
differences between the models, the trajectories and system
dynamics differed in two main ways. First, Ecospace often
predicted larger absolute responses across more individual
groups than seen in either Atlantis or InVitro; this was typically
because of stronger compensatory diet shifts in the later two
models. However, as the regime shift outlined above indicates,
once thresholds in these compensatory mechanisms were
exceeded, Atlantis (and InVitro) can display sudden and substantial changes, as different mechanisms come to dominate. The
second difference between the models was that Ecospace tended
to display less interannual variation than evident in the other
1336
E. A. Fulton
Table 4. Comparison of results from single and multifactor climate change perturbation scenarios for temperate (SE Australia) and tropical
(NE and NW) Australian ecosystem models.
Temperate
Group
Biophysical indicators
Detritus
Primary producers
Plankton
Other pelagic invertebrates
Pelagic fish
Demersal fish
Benthic invertebrates
Top predators
Tropical
Production change only
Acidification only
Production only
Acidification only
0.85 –0.98
0.90 –0.98
0.94 –0.98
0.96 –0.98
0.83 –0.98
0.88 –0.91
0.87 –0.97
0.91 –0.96
1.18 – 1.27
1.21 – 1.26
1.25 – 1.61
1.26 – 1.37
1.33 – 2.51
1.41 – 1.73
1.34 – 1.72
1.48 – 1.82
0.89 – 0.95
0.86 – 0.96
0.91 – 0.96
0.97 – 0.98
1.15 – 1.22
0.87 – 0.91
0.74 – 0.92
0.51 – 0.68
1.18 –1.42
1.13 –1.66
1.17 –1.51
1.32 –1.71
1.14 –1.31
1.59 –59.55
1.72 –3.41
1.71 –1.97
Values are the average from multifactor simulation/single-factor simulation.
models. This is partly because of its longer time-step, which tended
to have a smoothing effect on fluxes directed back to the coupled
NPZD and because the forage groups in the Ecospace models were
often more top-down controlled than in Atlantis and InVitro,
which were more constrained by the productivity of lower
trophic levels. In Atlantis and InVitro, large zooplankton,
benthic invertebrates, and the youngest age classes of finfish all
formed significant links between the upper and lower portions
of the foodwebs. In contrast, squid and forage fish groups typically
performed the linking role in Ecospace.
Economic outcomes
Economically, there are also quite uneven distribution of effects
and benefits. In all the systems modelled, the large-scale commercial fisheries see a .90% increase in the relative value of their
operations, because they have the socio-economic freedom to
respond to new target mixes, distributions, and biomasses. In
the NW and SE systems, the smaller scale (and recreational)
fishers, who have less capacity for change, see a decline of 30 –
51%. For the GBR, this small-scale sector increased by 9 –14%,
but that is only if they were willing to accept small fish, because
the trophy fish (large-bodied reef fish) they currently target are
no longer a sustainable target (the catch of those groups declining
by 22% or more). The increase in the value of the large-scale fisheries also hides differential results across sectors, with the prawn
fishery suffering a 27 –53% decline in value (mainly because of
drastically reduced landings as the target stocks decline steeply).
Comparing dynamics across model types, the fisheries in
Ecospace displayed less interannual variation and less variation
between sectors than seen in the other models. The reason was
the smoothing of interannual variation in the biophysical
models, as discussed above, and because of the simpler representation of fisheries in the Ecospace models compared with the
implementations of Atlantis and InVitro.
MMSY implications
The MMSY levels predicted for a 2060 system state was 27% lower
for the tropical systems and 12% lower for SE of Australia, with the
increase in biomass of the volatile pelagic groups unable to compensate fully for the losses of more stable demersal groups. This
is consistent with what was found for other systems considered
by Worm et al. (2009), with the majority of systems seeing a
decline in MMSY under models forced with the most likely form
of future environmental conditions (Figure 4). The only
exceptions were the East Bering Sea and California Current,
where there was very little, if any, change (,5% in both cases,
because declines in some target species is offset by substantial
increases in other, competing, commercially valuable groups).
Discussion
Climate change is expected to effect marine ecosystems via shifts
in sea-level, temperature, incident solar radiation, pH, oxygen,
stratification, surface winds, storm intensity and frequency, and
changes in coastal run-off, upwelling, and current patterns
(Hoegh-Guldberg, 2005; Hobday et al., 2006; Barange et al.,
2010). Before 2010, there had been few published predictions
of what those effects might be at an ecosystem level (i.e. simultaneously across all the interconnected aspects of an ecosystem
from primary producers to top predators and the human users
of the system). Most predictions had been based around
changes in productivity or the distribution and abundance of
single species or small closely linked groups (e.g. one to two
forage species; Ito et al., 2010). One of the few exceptions is
the global analysis by Cheung et al. (2009, 2010), which used a
sophisticated extension to bioenvelope modelling to predict the
aggregate effect of climate change on marine fisheries (Cheung
et al., 2010) and biodiversity (Cheung et al., 2009). This approach
still did not include trophic connections, but did result in similar
levels of species invasions, local extinctions, and species turnover,
as seen for the models presented here. However, although catch
predictions by Cheung et al. (2010) are similar to those for the
large-scale commercial fisheries for SE Australia, they underestimate commercial catch for the tropical systems, being closer to
the predictions for the smaller sectors. Therefore, although
there are some similarities, Cheung et al. (2010) do not give
the sense of skewed outcomes seen in the current study.
Neither do the predictions of Brown et al. (2009), which are
focused on ecosystem and fisheries predictions for large-scale
commercial fishing industries around Australia. Changes in
pelagic productivity are similar in the analysis of Brown et al.
(2009) and the current models, reflecting the fact that the
current work was in part tuned to the same GCM-NPZ. The
increase in the landings of the commercial fisheries predicted
by Brown et al. (2009) is within 15% of the average change in
overall landings predicted in this study. However, the skewed
response across groups reported here is not reflected in the
small changes to community composition recorded by Brown
et al. (2009). However, to be fair, Brown et al. (2009) do
Winners, losers, and system shifts under climate change around Australia
1337
Figure 4. Change in the exploitation rate resulting in maximum multispecies sustainable yield (MMSY) under climate change. Dashed zero
line given for reference.
clearly label it as a first step that does not include all the feedbacks and pressures that will be associated with climate change;
therefore, it should not be surprising that it misses some of the
skewness resulting from the omission of the multifactor combination of pressures included in the current study. For instance,
turtles are predicted to increase in abundance by Brown et al.
(2009), primarily driven by increases in productivity, whereas
they are predicted to collapse in this study, largely because of
the inundation of nesting beaches.
Despite the extra potential for non-linearity in the current
study, at a system-level the shifts in the ecosystems seen here do
largely tally with previous predictions. For instance, Boyd and
Doney (2002), Bopp et al. (2005) and Morán et al. (2010) also
predict that small phytoplankton groups coming to dominate.
Furthermore, the increase in production in the SE and NW and
the small decline in production along the GBR is in line with
what is already being seen in SeaWiFS data (Behrenfeld et al.,
2006)—though further data are needed to verify this finding,
because the findings of Behrenfeld et al. (2006) could be the
result of climate variability rather than climate change (Henson,
et al., 2009). The large-scale decline in corals seen in the predictions here is in line with the predictions of Hoegh-Guldberg
(1999), but is more pessimistic than the argument presented in
Hughes et al. (2003), though even they caution that the effects
of anthropogenically driven degradation of coral reefs and the
associated habitat fragmentation have undermined reef resilience
and increased their susceptibility to climate change.
Regime shifts, such as the one predicted for the SE Australian
ecosystem in this study, have been an oft-mentioned potential
outcome of climate change in many of the previous reviews
(Brander, 2007, 2009; Barange et al., 2010). Examples from the
North Atlantic (Beaugrand, 2004), Benguela (Shannon et al.,
2008), and North Pacific (Hare and Mantua, 2000; Overland
et al., 2008; Tian et al., 2008) illustrate how shifts in climatic properties could result in large-scale ecological restructuring. Similarly,
switching between alternative foodweb pathways in Antarctica
both seasonally and in high and low ice years (Atkinson et al.,
2004) indicates how rapidly, but also how widespread, these
effects can occur. Although regime shifts in the Baltic Sea
(Möllmann et al., 2009) and Black Sea (Oguz et al., 2008) highlight
how such shifts could be triggered, or at the very least exacerbated
by, anthropogenic pressures, such as pollutant-based eutrophication, overfishing, and facilitation of bioinvasion. Regime shifts
resulting directly from climate change are not expected to be ubiquitous, given that it is likely that at least some species that are lost
from systems will be replaced by functional equivalents, meaning
little, if any, net effect (Brander, 2009). However, because not all
species will shift together, this will set the stage for potentially
large-scale changes in community composition and new ecosystem
structures (Hannah et al., 2005; Hoegh-Guldberg, 2005).
Moreover, as seen in the predicted shift in the SE Australian ecosystem considered here, these changes can accrete from combinations of both slow shifts (e.g. resulting from generational-scale
range expansions) and rapid turnover (e.g. either because of
1338
shifting migrations, growth or survival rates altering the mix and
magnitude of species interactions) meaning that the pattern of
change does not have be smooth or linear (Frank et al., 2005;
Lovejoy and Hannah, 2005; Brander, 2007).
Perhaps the most consistent finding across all the models examined is that each system will have its own mix of winners and
losers; something previously acknowledged as likely in reviews,
such as those of Brander (2007, 2009). Although the current
study suggests that pelagic systems might benefit and demersal
systems decline, this generalization hides copious levels of
species and group-level detail that is highly uncertain. This is
reinforced by preliminary work (EAF, unpublished data) with a
suite of 11 Atlantis and EwE models from major large marine ecosystems around the world, where there are strongly system-specific
responses—some do match patterns for the Australian models discussed here, but for other systems all groups decline; in others the
relative contributions of the demersal system increases, whereas
the pelagic system does not. As with many things to do with ecosystems and their management, there will (generally) be no simple
rules about how global change will affect marine ecosystems. Two
exceptions to this are that it is already clear that there will be a mix
of responses across groups and systems, with some doing better
than others. Moreover, it is clear that the skewness in differential
effects (and benefits), socially, economically, and ecologically
will see the benefits rest with those groups (and sectors) with the
greatest adaptive capacity. Again, this agrees with previous
studies, such as the global economic analysis of the vulnerability
of fisheries and nations dependent on them by Allison et al.
(2009). The current study suggests that in Australian systems
this seems to benefit primary producers and pelagic ecosystem
components, which in some locations could see large-scale ecological restructuring. Economically, adaptive capacity seems to
be with larger fisheries operators. As was found by Fulton et al.
(2007), the smallest operators are most likely to suffer detrimental
effects of system-level change. Although they might have lower cost
structures, they are often more socially constrained and less able to
shift as quickly or completely as larger operators—the reason is
that the smaller operators have often made lifestyle choices or
have family or other social commitments that mean that they
cannot modify their behaviour as rapidly as “captains for hire”
working for large companies (Baelde, 2001; Marshall et al.,
2007). This pattern, of those with the least adaptive capacity
being likely to be hit the hardest, is likely to be true across
regions and nations (Brander, 2007, 2009; Allison et al., 2009),
not just the sectors considered here.
Both the differential effects of climate change seen across the
different ecosystem components and sectors simulated in the
various models considered here, as well as the consideration of
MMSY implications, indicate that modifications will have to be
made to current management methods and reference points in
response to changing marine ecosystems. Earlier reviews of potential climate effects on fisheries and management have already
identified the confounding effect other human stressors can have
on ecosystem responses to climate change (Hobday et al., 2006;
Brander, 2009; Barange et al., 2010). Many have pointed to a
need for more stringent management to reduce stress on ecosystems and give stocks more adaptive capacity (Roy and Pandolfi,
2005; Hobday et al., 2006; Brander, 2007). However, the differential effects observed here demonstrate that responses will have to
be more nuanced than simply reducing pressure, if effective, egalitarian, and economically efficient management is to be assured.
E. A. Fulton
Eide and Heen (2002) found that the management strategy is as
important, if not more so, than the climate scenario for determining the outcome in any one situation. Consequently, adaptive
capacity must be considered, because it will influence the incentives of fishers and other stakeholders to comply with changing
regulations (Fulton et al., 2011b). For instance, when recommending further cuts in fishing pressure in the fisheries considered in
the MMSY analysis, it must be acknowledged that many might
not have much scope for easily made reductions; cuts will likely
see significant political opposition, because those same fisheries
have already undergone extensive effort reductions over the past
10 years, having been restructured around more sustainable longterm effort levels (Worm et al., 2009). As with all instances of
ecosystem-based management, the form of management required
will have to vary with the system and cultures involved; recipebook applications are unlikely to be productive (Fulton et al.,
2011a). Although some generalities do exist, in the main there
are sufficient differences in the form of the responses of social
and ecological system components to climate and regulatory
changes that the exact mix of levers will have to be tailored
system-to-system.
The non-linear response of the modelled ecosystems considered here (e.g. the pattern of change 2010–2040–2060 seen
in the Bass Strait region of the SE Australian model) indicates
that not only should there be consideration of the mix of levers
to use system-to-system, but also the form of management used
will have to change as the system changes. Simply putting a
static action in place will fail for most systems. This will further
challenge existing management regimes. Currently, the best practice management methods are based on reference points, but
this equilibrium concept will have to be updated for changing productivity (Brander, 2007), stock potential, and the new ecosystem
structures. The latter two will prove particularly challenging,
because they will be hard to predict, and noise in monitoring
data will make the rate of signal detection around new states something that is relatively slow to determine relative to the cycle time
of management regimes. One option is to maintain marine zoning
that allows for the existence of reference areas. Quite apart from a
conservation role, closed areas could become the new reference
areas for judging the potential of new ecosystem structures and
the magnitude of other anthropogenic effects outside these closures (Fulton et al., 2005). However, even with such a valuable
role, effective spatial management will require a more fluid
approach to the definition of marine zoning than is typically the
case in places such as Australia, where acts of parliament are typically involved and they are therefore unlikely to be changed with
any rapidity and are likely to fall out of step with the shifting distributions of marine species. Although a daunting task, the coordinated management of large spatial areas (at the regional-level
scale that climate effects are evident) and across sectors is becoming a more common principle (Hannah et al., 2005; McDonald
et al., 2006, 2008).
Although the results presented here have provided useful
insights regarding system dynamics and management implications, they must be considered with caution. The reason is the
issues identified in the description of the results for the
Atlantis-SE model—namely the inability of a model with static
parameters to capture fully the degree and kinds of change that
will happen under the trending, but variable, pressure exerted by
global change. Without explicit consideration of shifts in biodiversity, evolution, phenotypic plasticity, and other forms of adaptive
Winners, losers, and system shifts under climate change around Australia
capacity, these models can only provide constrained insights.
Moreover, this is putting to one side the fact that the real world
will present science (and society) with surprises that we cannot
currently guess at (the “unknown unknowns” sensu Donald
Rumsfeld 2002, then United States Secretary of Defense).
Supplementary material
The following supplementary material is available at the ICESJMS
online version of this paper: the summaries and example formulations provided for the three end-to-end models are to afford
readers with an appreciation of the models, their assumptions,
and general forms. A summary of the biophysical and human
components included in the specific implementations used as a
basis for the Australian analyses presented in the main paper is
provided in Table A1. Figure A1 is an example decision tree for
animal agents in InVitro, and Figure A2 is a schematic diagram
of agent types in different InVitro-NW and how the different submodels interconnect. TEPs stands for threatened, endangered and
protected species (including whale sharks, marine mammals and
seabirds). Bathymetry can directly influence all agent types; monitors can report (for governance or output purposes) on all other
agent types; and turtle life histories are covered using multiple
agent types.
Acknowledgements
I thank the CSIRO and both PICES and ICES for funding my participation in the International Symposium on Climate Change
Effects on Fish and Fisheries held in Sendai, Japan, April 2010,
which resulted in this paper being written. I also acknowledge
funding from the Australian Fisheries Research and
Development Corporation (FRDC) on behalf of the Australian
Government and thank Miriana Sporcic, Scott Condie, and two
anonymous reviewers for comments on an earlier draft on the
manuscript. I am grateful to Cathy Bulman, Neil Gribble,
Cameron Ainsworth, Alida Bundy, John Fields, Isaac Kaplan,
Jason Link, Jodie Little, and Steve Mackinson for allowing me to
use their Ecospace models as a basis for some of the work presented here. Last, I thank Bec Gorton, Gary Griffiths, and Penny
Johnson for their technical support and fruitful discussions on
the topic, and all the domain experts who have tolerated my seemingly inane questions while drawing these models together.
References
Allison, E. H., Perry, A. L., Badjeck, M. C., Adger, W. N., Brown, K.,
Conway, D., Halls, A. S., et al. 2009. Vulnerability of national economies to the impacts of climate change on fisheries. Fish and
Fisheries, 10: 173 – 196.
Atkinson, A., Siegel, V., Pakhomov, E., and Rothery, P. 2004.
Long-term decline in krill stock and increase in salps within the
Southern Ocean. Nature, 432: 100– 103.
Baelde, P. 2001. Fishers’ description of changes in fishing gear and
fishing practices in the Australian South East Trawl Fishery.
Marine and Freshwater Research, 52: 411– 417.
Barange, M., Field, J. G., Harris, R. P., Hofmann, E. E., Perry, R. I., and
Werner, F. E. 2010. Marine Ecosystems and Global Change. Oxford
University Press, New York.
Beaugrand, G. 2004. The North Sea regime shift: evidence, causes,
mechanisms and consequences. Progress in Oceanography, 60:
245– 262.
Behrenfeld, M. J., O’Malley, R. T., Siegel, D. A., McClain, C. R.,
Sarmiento, J. L., Feldman, G. C., Milligan, A. J., et al. 2006.
1339
Climate-driven trends in contemporary ocean productivity.
Nature, 444: 752 –755.
Bograd, S. J., Schroeder, I., Sarkar, N., Qiu, X., Sydeman, W. J., and
Schwing, F. B. 2009. Phenology of coastal upwelling in the
California Current. Geophysical Research Letters, 36, L01602,
doi:10:1029/2008GL035933.
Bopp, K., Aumont, O., Cadule, P., Alvain, S., and Gehlen, M. 2005.
Response of diatoms distribution to global warming and potential
implications: a global model study. Geophysical Research Letters,
32: L19606.
Boyd, P. W., and Doney, S. C. 2002. Modelling regional responses by
marine pelagic ecosystems to global climate change. Geophysical
Research Letters, 29: 1806.
Branch, T. A., Watson, R., Fulton, E. A., Jennings, S., McGilliard, C. R.,
Pablico, G. T., Ricard, D., et al. 2010. The trophic fingerprint of
marine fisheries. Nature, 468: 431 – 435.
Brander, K. M. 2007. Global fish production and climate change.
Proceedings of the National Academy of Sciences of the USA,
104: 19709– 19714.
Brander, K. M. 2009. Impacts of climate change on marine ecosystems
and fisheries. Journal of the Marine Biological Association of India,
51: 1– 13.
Brown, C. J., Fulton, E. A., Hobday, A. J., Matear, R., Possingham, H.
P., Bulman, C., Christensen, V., et al. 2009. Ecological interactions
within marine ecosystems determine winners and losers under
climate change. Global Change Biology, doi:10.1111/j.1365 –
2486.2009.02046.x
Bulman, C. 2007. Trophic webs and modelling of Australia’s North
West Shelf. CSIRO Marine and Atmospheric Research Report.
Bulman, C., Condie, S., Furlani, D., Cahill, M., Klaer, N., Goldsworthy,
S., Knuckey, I., et al. 2006. Trophic dynamics of the eastern shelf
and slope of the south east fishery: impacts of and on the fishery.
Final Report for Fisheries Research and Development
Corporation, Project 2002/028, CSIRO.
Caldeira, K., and Wickett, M. E. 2005. Ocean model prediction of
chemistry changes from carbon dioxide emission to the atmosphere and ocean. Geophysical Research Letters, 110, C09S04,
doi:10:1029/2004JC002671.
Center for Climate Systems Research (CCSR). 2006. It’s 2025. Where
Do Most People Live? The Earth Institute at Columbia University
Report.
Cheung, W. W. L., Close, C., Lam, V. W. Y., Watson, R., and Pauly, D.
2008. Application of macroecological theory to predict effects of
climate change on global fisheries potential. Marine Ecology
Progress Series, 365: 187 –197.
Cheung, W. W. L., Lam, V. W. Y., Sarmiento, J. L., Kearney, K.,
Watson, R., and Pauly, D. 2009. Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10:
235– 251.
Cheung, W. W. L., Lam, V. W. Y., Sarmiento, J. L., Kearney, K.,
Watson, R., Zeller, D., and Pauly, D. 2010. Large-scale redistribution of maximum fisheries catch potential in the global ocean
under climate. Global Change Biology, 16: 24 – 35.
Christensen, V., and Walters, C. 2004. Ecopath with Ecosim: methods,
capabilities and limitations. Ecological Modelling, 172: 109 – 139.
Church, J. A., Hunter, J. R., McInnes, K. L., and White, N. J. 2006.
Sea-level rise around the Australian coastline and the changing frequency of extreme sea-level events. Australian Meteorology
Magazine, 55: 253– 260.
Crooks, S. 2004. The effect of sea-level rise on coastal geomorphology.
Ibis, 146 (Suppl. 1): 18 – 20.
Cruz-Palacios, V., and van Tussenbroek, B. I. 2005. Simulation of
hurricane-like disturbances on a Caribbean seagrass bed. Journal
of Experimental Marine Biology and Ecology, 324: 44 – 60.
Dollar, S. J., and Tribble, G. W. 1993. Recurrent storm disturbance and
recovery: a long-term study of coral communities in Hawaii. Coral
Reefs, 12: 223– 233.
1340
Eide, A., and Heen, K. 2002. Economic impacts of global warming. A
study of the fishing industry in North Norway. Fisheries Research,
56: 261– 274.
Ellison, J. C., and Stoddart, D. R. 1991. Mangrove ecosystem collapse
during predicted sea-level rise: Holocene analogues and implications. Journal of Coastal Research, 7: 151– 165.
Ericson, J. P., Vörösmarty, C. J., Dingman, S. L., Ward, L. G., and
Meybeck, M. 2006. Effective sea-level rise and deltas: causes of
change and human dimension implications. Global and
Planetary Change, 50: 63 – 82.
Fabry, V. J., Seibel, B. A., Feely, R. A., and Orr, J. C. 2008. Impacts of
ocean acidification on marine fauna and ecosystem processes. ICES
Journal of Marine Science, 65: 414 – 432.
Feagin, R. A., Sherman, D. J., and Grant, W. E. 2005. Coastal erosion,
global sea-level rise, and the loss of sand dune plant habitats.
Frontiers in Ecology and the Environment, 3: 359– 364.
Fourqurean, J. W., and Rutten, L. M. 2004. The impact of hurricane
Georges on soft-bottom, back reef communities: site- and speciesspecific effects in south Florida seagrass beds. Bulletin of Marine
Science, 75: 239– 257.
Frank, K. T., Petrie, B., Choi, J. S., and Leggett, W. C. 2005. Trophic
cascades in a formerly cod-dominated ecosystem. Science, 308:
1621– 1623.
French, J. R. 1993. Numerical simulation of vertical marsh growth and
adjustment to accelerated sea-level rise, North Norfolk, U. K. Earth
Surface Processes and Landforms, 18: 63 – 81.
Fulton, E. A. 2010. Approaches to end-to-end ecosystem models.
Journal of Marine Systems, 81: 171– 183.
Fulton, E. A., Fuller, M., Smith, A. D. M., and Punt, A. 2005.
Ecological indicators of the ecosystem effects of fishing: final
report. Australian Fisheries Management Authority Report, R99/
1546.
Fulton, E. A., Gray, R., Sporcic, M., Scott, R., and Hepburn, M. 2009.
Challenges of crossing scales and drivers in modelling marine
systems. Proceedings of the 18th World IMACS/MODSIM
Congress, Cairns, Australia, 13– 17 July 2009.
Fulton, E. A., Link, J. S., Kaplan, I. C., Savina-Rolland, M., Johnson, P.,
Ainsworth, C., Horne, P., et al. 2011a. Lessons in modelling and
management of marine ecosystems: the Atlantis experience. Fish
and Fisheries, doi:10.1111/j.1467-2979.2011.00412.x.
Fulton, E. A., Smith, A. D. M., and Smith, D. C. 2007. Alternative
management strategies for Southeast Australian Commonwealth
Fisheries: Stage 2: Quantitative management strategy evaluation.
Australian Fisheries Management Authority Report.
Fulton, E. A., Smith, A. D. M., Smith, D. C., and van Putten, I. E.
2011b. Human behaviour—the neglected source of uncertainty
in fisheries management. Fish and Fisheries, 12: 2– 17.
Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A., and Watkinson, A. R.
2005. Hurricanes and Caribbean coral reefs: impacts, recovery patterns, and role in long-term decline. Ecology, 86: 174– 184.
Gordon, H., O’Farrell, S., Collier, M., Dix, M., Rotstayn, L.,
Kowalczyk, E., Hirst, T., et al. 2010. The CSIRO Mk3.5 Climate
Model Centre. Australian Weather and Climate Research
(CAWCR) Technical Report, 021.
Gray, R., Fulton, E. A., Little, L. R., and Scott, R. 2006. Operating
Model Specification Within an Agent Based Framework. North
West Shelf Joint Environmental Management Study Technical
Report, 16. CSIRO, Hobart, Tasmania.
Gribble, N. A. 2003. GBR-prawn: modelling ecosystem impacts of
changes in fisheries management of the commercial prawn
(shrimp) trawl fishery in the far northern Great Barrier Reef.
Fisheries Research, 65: 493– 506.
Gribble, N. A. 2007. Ecosystem-based management of the GBR: modeling and policy initiatives. In Proceedings of MODSIM07 “Land,
Water & Environmental Management: Integrated Systems for
Sustainability”, pp. 315– 321. Ed. by L. Oxley, and D. Kulasiri.
Modelling and Simulation Society of Australia and New Zealand,
E. A. Fulton
December 2007. ISBN:978-0-9758400-4-7. http://www.mssanz.
org.au/modsim07/papers/.
Gribble, N. A. 2009. Testing the robustness of a linked-ecosystem
trophic model of the Great Barrier Reef, World Heritage Area. In
18th World IMACS Congress and MODSIM09 International
Congress on Modelling and Simulation, pp. 2115– 2121. Ed. by
R. S. Anderssen, R. D. Braddock, and L. T. H. Newham.
Modelling and Simulation Society of Australia and New Zealand
and International Association for Mathematics and Computers
in Simulation, July 2009. ISBN:978-0-9758400-7-8. http://www.
mssanz.org.au/modsim09.
Hall-Spencer, J. M., Rodolfo-Metalpa, R., Martin, S., Ransome, E.,
Fine, M., Turner, S. M., Rowley, S. J., et al. 2008. Volcanic
carbon dioxide vents show ecosystem effects of ocean acidification.
Nature, 454: 96 – 99.
Hannah, L., Lovejoy, T. E., and Schneider, S. H. 2005. Biodiversity and
climate change in context. In Climate Change and Biodiversity, pp.
295– 307. Ed. by T. E. Lovejoy, and L. Hannah. Yale University
Press, New Haven and London.
Hare, S. R., and Mantua, N. J. 2000. Empirical evidence for North
Pacific regime shifts in 1977 and 1989. Progress in
Oceanography, 47: 103– 145.
Harley, C. D. G., Hughes, A. R., Hultgren, K. M., Miner, B. G., Sorte,
C. J. B., Thornber, C. S., Rodriguez, L. F., et al. 2006. The impacts of
climate change in coastal marine systems. Ecology Letters, 9:
228– 241.
Harmlien-Vivien, M. L., and Laboute, P. 1986. Catastrophic impact of
hurricanes on atoll outer reef slopes in Tuamotu (French
Polynesia). Coral Reefs, 5: 55 – 62.
Henson, S. A., Sarmiento, J. L., Dunne, J. P. D., Bopp, L., Lima, I.,
Doney, S. C., John, J., et al. 2009. Is global warming already changing ocean productivity? Biogeosciences, 6: 10311– 10354.
Hobday, A. J., Okey, T. A., Poloczanska, E. S., Kunz, T. J., and
Richardson, A. J. (eds). 2006. Impacts of climate change on
Australian marine life: Part A. Executive summary. Report to the
Australian Greenhouse Office, Canberra, Australia.
Hoegh-Guldberg, O. 1999. Coral bleaching, climate change and the
future of the world’s coral reefs. Marine and Freshwater
Research, 50: 839– 866.
Hoegh-Guldberg, O. 2005. Climate change and marine ecosystems. In
Climate Change and Biodiversity, pp. 6506 –6518. Ed. by T. E.
Lovejoy, and L. Hannah. Yale University Press, New Haven and
London.
Hollowed, A. B., Bond, N. A., Wilderbuer, T. K., Stockhausen, W. T. T.,
A’mar, Z. T. T., Beamish, R. J., Overland, J. E., et al. 2009. A framework for modelling fish and shellfish responses to future climate
change. ICES Journal of Marine Science, 66: 1584– 1594.
Hughes, T., Baird, A. H., Bellwood, D. R., Card, M., Connolly, S. R.,
Folke, C., Grosberg, R., et al. 2003. Climate change, human
impacts, and the resilience of coral reefs. Science, 301: 929– 933.
IPCC (Intergovernmental Panel on Climate Change). 2007. Climate
Change 2007 Synthesis Report. Cambridge University Press,
New York.
Ito, S., Rose, K. A., Miller, A. J., Drinkwater, K., Brander, K., Overland,
J. E., Sundby, S., et al. 2010. Ocean ecosystem responses to future
global change scenarios: a way forward. In Marine Ecosystems
and Global Change, pp. 287 – 322. Ed. by M. Barange, J. G. Field,
R. P. Harris, E. E. Hofmann, R. I. Perry, and F. E. Werner.
Oxford University Press, New York.
Kramer-Schadt, S., Revilla, E., Wiegand, T., and Grimm, V. 2007.
Patterns for parameters in simulation models. Ecological
Modelling, 204: 553 – 556.
Little, L. R., Fulton, E. A., Gray, R., Hayes, D., Lyne, V., Scott, R.,
Sainsbury, K., et al. 2006. Multiple Use Management Strategy
Evaluation for the North West Shelf: Results and Discussion.
North West Shelf Joint Environmental Management Study
Technical Report, 18. CSIRO, Hobart, Tasmania.
Winners, losers, and system shifts under climate change around Australia
Lovejoy, T. E., and Hannah, L. (eds). 2005. Climate Change and
Marine Ecosystems. Climate Change and Biodiversity. Yale
University Press, New Haven and London.
Marshall, N. A., Fenton, D. M., Marshall, P. A., and Sutton, S. G. 2007.
How resource dependency can influence social resilience, within a
primary resource industry. Rural Sociology, 72: 359– 390.
McDonald, A. D., Fulton, E., Little, L. R., Gray, R., Sainsbury, K. J., and
Lyne, V. D. 2006. Multiple-use management strategy evaluation for
coastal marine ecosystems using in vitro. In Complex Science for a
Complex World: Exploring Human Ecosystems with Agents. Ed. by
P. Perez, and D. Batten. Anu E Press, Canberra.
McDonald, A. D., Little, L. R., Gray, R., Fulton, E., Sainsbury, K. J., and
Lyne, V. D. 2008. An agent-based modelling approach to evaluation
of multiple-use management strategies for coastal marine ecosystems. Mathematics and Computers in Simulation, 78: 401– 411.
McInnes, K. L., Walsh, K. J. E., Hubbert, G. D., and Beer, T. 2003.
Impact of sea-level rise and storm surges on a coastal community.
Natural Hazards, 30: 187– 207.
Meehl, G. A., Washington, W. M., Collins, W. D., Arblaster, J. M., Hu,
A., Buja, L. E., Strand, W. G., et al. 2005. How much more global
warming and sea level rise? Science, 307: 1769 –1772.
Michener, W. K., Blood, E. R., Bildstein, K. L., Brinson, M. M., and
Gardner, L. M. 1997. Climate change, hurricanes and tropical
storms, and rising sea level in coastal wetlands. Ecological
Applications, 7: 770– 801.
Möllmann, C., Diekmann, R., Müller-Karulis, B., Kornilovs, G.,
Plikshs, M., and Axe, P. 2009. The reorganisation of a large
marine ecosystem due to atmospheric and anthropogenic
pressure—a discontinuous regime shift in the central Baltic Sea.
Global Change Biology, 15: 1377– 1393.
Morán, X. A. G., López-Urrutia, A., Calvo-Dı́az, A., and Li, W. K. W.
2010. Increasing importance of small phytoplankton in a warmer
ocean. Global Change Biology, 16: 1137 – 1144.
Morecroft, J. 2007. Strategic Modelling and Business Dynamics: a
Feedback Systems Approach. Wiley, Chichester. 464 pp.
Munday, P., Dixson, D. A., Donelson, J. M., Jones, G. P., Pratchett, M.
S., Devitsina, G. V., and Døvingd, K. B. 2009. Ocean acidification
impairs olfactory discrimination and homing ability of a marine
fish. Proceedings of the National Academy of Sciences of the
USA, 106: 1848– 1852.
Nakicenovic, N., and Swart, R. 2000. Emissions Scenarios. Cambridge
University Press, Cambridge, UK.
Nicholls, R. J. 2002. Analysis of global impacts of sea-level rise: a case
study of flooding. Physics and Chemistry of the Earth, 27:
1455– 1466.
Nicholls, R. J. 2004. Coastal flooding and wetland loss in the 21st
century: changes under the SRES climate and socio-economic
scenarios. Global Environmental Change, 14: 69– 86.
Nicholls, R. J., and Lowe, J. A. 2004. Benefits of mitigation of climate
change for coastal areas. Global Environmental Change, 14:
229– 244.
Oguz, T., Fach, B., and Salihoglu, B. 2008. Invasion dynamics of the
alien ctenophore Mnemiopsis leidyi and its impact on anchovy collapse in the Black Sea. Journal of Plankton Research, 30:
1385– 1397.
Oke, P. R., Schiller, A., Griffin, D. A., and Brassington, G. B. 2005.
Ensemble data assimilation for an eddy-resolving ocean model.
Quarterly Journal of the Royal Meteorological Society, 131:
3301– 3311.
Overland, J. E., Rodionov, S., Minobe, N., and Bond, N. 2008. North
Pacific regime shifts: definitions, issues and recent transitions.
Progress in Oceanography, 77: 92– 102.
Pant, H. 2007. GTEM: global trade and environment model. ABARE
Technical Report.
Pearson, R. G., and Dawson, T. P. 2003. Predicting the impacts of
climate change on the distribution of species: are bioclimate
1341
envelope models useful? Global Ecology and Biogeography, 12:
361– 371.
Rahmstorf, S. 2007. A semi-empirical approach to projecting future
sea-level rise. Science, 315: 368– 370.
Rahmstorf, S., Cazenave, A., Church, J. A., Hansen, J. E., Keeling, R. F.,
Parker, D. E., and Somerville, R. C. J. 2007. Recent climate observations compared to projections. Science, 316: 709– 709.
Reed, D. 2002. Sea-level rise and coastal marsh sustainability: geological and ecological factors in the Mississippi delta plain.
Geomorphology, 48: 233– 243.
Richardson, A. J., and Poloczanska, E. S. 2008. Ocean science: underresourced, under threat. Science, 320: 1294– 1295.
Robinson, L. A., and Frid, C. L. J. 2003. Dynamic ecosystem models
and the evaluation of ecosystem effects of fishing: can we make
meaningful predictions? Aquatic Conservation: Marine and
Freshwater Ecosystems, 13: 5 – 20.
Roy, K., and Pandolfi, J. M. 2005. Responses of marine species and
ecosystems to past climate change. In Climate Change and
Biodiversity, pp. 4186– 4198. Ed. by T. E. Lovejoy, and L.
Hannah. Yale University Press, New Haven and London.
Scavia, D., Field, J. C., Boesch, D. F., Buddemerer, R. W., Burkett, V.,
Cayan, D. R., Fogarty, M., et al. 2002. Climate change impacts on
U. S. coastal and marine ecosystems. Estuaries, 25: 149– 164.
Shannon, L. J., Jarre, A., and Schwing, F. B. 2008. Regime shifts, ecological aspects. In Encyclopaedia of Ocean Sciences, pp.
4329– 4339. Ed. by J. H. Steele, S. A. Thorpe, and K. K.
Turekian. Elsevier, Amsterdam.
Short, F. T., and Neckles, H. A. 1999. The effects of global climate
change on seagrasses. Aquatic Botany, 63: 169 – 196.
Small, C., Gortnitz, V., and Cohen, J. E. 2000. Coastal hazards and the
global distribution of human population. Environmental
Geosciences, 7: 3 – 12.
Stanton, E. A., Ackerman, F., and Kartha, S. 2008. Inside the integrated
assessment models: four issues in climate economics. Stockholm
Environment Institute Working Paper, WP-US-0801.
Tian, Y., Kidokoro, H., Watanabe, T., and Iguchi, N. 2008. The late
1980s regime shift in the ecosystem of Tsushima warm current in
the Japan/East Sea: evidence from historical data and possible
mechanisms. Progress in Oceanography, 77: 127 – 145.
Travers, M., Shin, Y-J., Jennings, S., and Cury, P. 2007. Towards
end-to-end models for investigating the effects of climate and
fishing in marine ecosystems. Progress in Oceanography, 75:
751– 770.
United Nations (UN). 2009. World population prospects: the 2008
revision. United Nations Department of Economic and Social
Affairs Population Newsletter 87.
Van Vuuren, D. P., and O’Neill, B. C. 2006. The consistency of IPCC’s
SRES scenarios to recent literature and recent projections. Climatic
Change, 75: 9 – 46.
Waddell, P., Borning, A., Noth, M., Freier, N., Becke, M., and
Ulfarsson, G. 2003. Microsimulation of urban development and
location choices: design and implementation of UrbanSim.
Networks and Spatial Economics, 3: 43 – 67.
Walters, C., Christensen, C., Walters, W., and Rose, K. 2010.
Representation of multi-stanza life histories in Ecospace models
for spatial organization of ecosystem trophic interaction patterns.
Bulletin Marine Science, 86: 439 –459.
Walters, C., Martell, S. J. D., Christensen, V., and Mahmoudi, B. 2008.
An Ecosim model for exploring Gulf of Mexico ecosystem management options: implications of including multistanza life-history
models for policy predictions. Bulletin of Marine Science, 83:
251– 271.
Walters, C., Pauly, D., and Christensen, V. 1999. Ecospace: prediction
of mesoscale spatial patterns in trophic relationships of exploited
ecosystems, with emphasis on the impacts of marine protected
areas. Ecosystems, 2: 539– 554.
1342
Walters, C., Pauly, D., Chistensen, V., and Kitchell, J. F. 2000.
Representing density dependent consequences of life history strategies in aquatic ecosystems: EcoSim II. Ecosystems, 3: 70 – 83.
White, R., Engelen, G., Uljee, I., Lavalle, C., and Ehrlich, D. 2000.
Developing an urban land use simulator for European cities. In
Proceedings of the 5th EC GIS Workshop: GIS of Tomorrow, pp.
179– 190. Ed. by K. Fullerton. European Commission Joint
Research Centre.
E. A. Fulton
Williams, K., MacDonald, M., da Silveira, L., and Sternberg, L. 2003.
Interactions of storm, drought, and sea-level rise on coastal
forest: a case study. Journal of Coastal Research, 19: 1116– 1121.
Worm, B., Hilborn, R., Baum, J., Branch, T., Collie, J., Costello, C.,
Fogarty, M., et al. 2009. Rebuilding global fisheries. Science, 325:
578– 585.
Zhang, K., Douglas, B. C., and Leatherman, S. P. 2004. Global warming
and coastal erosion. Climatic Change, 64: 41 – 58.