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Integrating ecophysiology and plankton dynamics into projected
maximum fisheries catch potential under climate change in the
Northeast Atlantic
William W. L. Cheung1, 2*, John Dunne 3, Jorge L. Sarmiento 4, and Daniel Pauly5
1
School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
2
Centre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK
3
Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, 201 Forrestal Road, Princeton, NJ 08540-6649, USA
4
Atmospheric and Oceanic Sciences Programme, Princeton University, 300 Forrestal Road, Sayre Hall, Princeton, NJ 08544, USA
5
Sea Around Us Project, Aquatic Ecosystems Research Laboratory, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
*Corresponding Author: tel: +44 1603 593647; fax: +44 1603 591327; e-mail: william.cheung@uea.ac.uk
Cheung, W. W. L., Dunne, J., Sarmiento, J. L., and Pauly, D. 2011. Integrating ecophysiology and plankton dynamics into projected maximum
fisheries catch potential under climate change in the Northeast Atlantic. – ICES Journal of Marine Science, 68: 1008 –1018.
Received 30 June 2010; accepted 3 January 2011; advance access publication 13 April 2011.
Previous global analyses projected shifts in species distributions and maximum fisheries catch potential across ocean basins by 2050
under the Special Report on Emission Scenarios (SRES) A1B. However, these studies did not account for the effects of changes in ocean
biogeochemistry and phytoplankton community structure that affect fish and invertebrate distribution and productivity. This paper
uses a dynamic bioclimatic envelope model that incorporates these factors to project distribution and maximum catch potential of
120 species of exploited demersal fish and invertebrates in the Northeast Atlantic. Using projections from the US National Oceanic
and Atmospheric Administration’s (NOAA) Geophysical Fluid Dynamics Laboratory Earth System Model (ESM2.1) under the SRES
A1B, we project an average rate of distribution-centroid shift of 52 km decade
21
northwards and 5.1 m decade
21
deeper from
2005 to 2050. Ocean acidification and reduction in oxygen content reduce growth performance, increase the rate of range shift,
and lower the estimated catch potentials (10-year average of 2050 relative to 2005) by 20– 30% relative to simulations without con-
sidering these factors. Consideration of phytoplankton community structure may further reduce projected catch potentials by 10%.
These results highlight the sensitivity of marine ecosystems to biogeochemical changes and the need to incorporate likely hypotheses
of their biological and ecological effects in assessing climate change impacts.
Keywords: biogeochemistry, climate change, fisheries catch, Northeast Atlantic, ocean acidification, oxygen, range shift.
Introduction
Climate change is causing biological and ecological changes in the
ocean (Brierley and Kingsford, 2009). Specifically, changes in the
physical (e.g. temperature, ocean current patterns) and biogeo-
chemical (e.g. acidity, oxygen content, primary productivity,
plankton community structure) conditions of the ocean result in
changes in physiology, species distribution, phenology, and
species assemblages (e.g. Edwards and Richardson, 2004;
Richardson and Schoeman, 2004;Perry et al., 2005;Hiddink and
Hofstede, 2007;Rosa and Seibel, 2008;Po
¨rtner, 2010). Such
changes may be more rapid in future (IPCC, 2007). For example,
projections from global circulation models (GCMs) suggest that
surface temperature may increase by 0.6–48C by 2090– 2099 rela-
tive to 1980– 1999 (IPCC, 2007). In addition, average surface
water pH of the ocean has dropped by 0.1 units since pre-
industrial times and an increase in atmospheric CO
2
concentration
to 800 ppmv may further reduce ocean pH by 0.3 – 0.4 units by 2100
(Caldeira and Wickett, 2003;Orr et al., 2005). Increased tempera-
ture and ocean stratification may change the extent of oxygen
minimum zones (e.g. Stramma et al., 2010). Global ocean
primary production may change in the future, although current
projections are uncertain. For example, Sarmiento et al. (2004)
suggest that global primary production may increase by 0.7–
8.1% by 2050 relative to 2005. Conversely, based on the output of
four global coupled carbon cycle-climate models, Steinacher et al.
(2010) suggest that global mean primary production may decrease
by 2–20% by 2100 relative to preindustrial conditions, and there
are large regional differences in all these simulations too.
Although the period of the estimates reported in these two
studies is different, the opposite trends in projected changes cover-
ing the similar period highlight the uncertainty of projection of the
large-scale changes in primary productivity.
A major challenge to designing effective climate change
adaptation strategies for marine ecosystems is to assess the
impacts of different scenarios of potential changes in the
ocean. Using a dynamic bioclimatic envelope model (Cheung
et al., 2008), Cheung et al. (2009) established that there
might be a high rate of species invasion in high-latitude
regions and local extinctions along the tropics by 2050 under
the Special Report on Emission Scenario (SRES) A1B.
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Furthermore, combining the projected range shift from Cheung
et al. (2009) and future primary production (Sarmiento et al.,
2004), Cheung et al. (2010) predicted that potential fisheries
catch may increase in the Arctic and Subarctic regions, but
decrease in the tropics. Similarly, Hare et al. (2010) applied a
fisheries stock assessment and spatial distribution model to
study Atlantic croaker (Micropogonias undulatus) along the
east coast of the United States and projected an increase in
spawning biomass of 30– 60% and a northward shift of 50–
100 km of the distribution centre by 2100. However, these
models mainly account for changes in physical conditions
(e.g. temperature and current patterns), salinity, and primary
production, without accounting for other important aspects
of potential ocean biochemical changes, such as ocean acidifica-
tion, expansion of oxygen minimum zones, or changes in phy-
toplankton community structure.
Recent theories and empirical evidence suggest that biogeo-
chemical changes in the ocean may have large ecological
impacts. Specifically, ocean acidification may have negative
impacts on many organisms (Guinotte and Fabry, 2008;Doney
et al., 2009). However, current studies suggest that species’
responses to more acidic water vary considerably and that the
mechanisms of biological effects of ocean acidification and their
long-term impacts are not fully understood (Dupont and
Thorndyke, 2009;Melzner et al., 2009). In addition, the expansion
of oxygen minimum zones will directly affect the aerobic perform-
ance of marine organisms (Po
¨rtner, 2010). For example, the
aerobic scope of two coral reef fish, Ostorhinchus doederleini and
O. cyanosoma, from the Great Barrier Reef was experimentally
demonstrated to decline by 33 and 47% in acidified water relative
to control water (Munday et al., 2009a). The aerobic scope of
marine ectotherms is related directly and positively to the scope
for growth (Pauly, 2010). Finally, changes in ocean conditions
not only affect the total primary productivity, but also the plank-
tonic community structure (Richardson and Schoeman, 2004;
Hays et al., 2005), which is likely to have considerable implications
for marine biodiversity and ecosystem services (Beaugrand et al.,
2010). Particularly, changes in phytoplankton size structure
affect the amount of energy transferred to higher tropic levels.
For instance, smaller phytoplanktonic cells are often grazed by
small herbivorous microzooplankton, which in turn are preyed
upon by larger planktonic consumers. The additional step in the
food chain from primary production to large zooplankton may
result in a less efficient transfer to a higher trophic level (fish).
In this study, we aim to assess the sensitivity of projected
changes in the distribution and fisheries catch potential to biogeo-
chemical changes in the ocean. We focus our study on the
exploited demersal fish and invertebrates in the Northeast
Atlantic Ocean (here defined as United Nations Food and
Agriculture Organization Statistical Area 27), i.e. on the 120
most important species (99 fish and 21 invertebrates), contribut-
ing more than 95% of the average total catch in FAO Area 27 in
the past two decades (www.seaaroundus.org). We apply a new
version of the dynamic bioclimatic envelope model, with explicit
considerations of the main physical and biogeochemical factors
affecting the exploited demersal fish and invertebrates in the
Northeast Atlantic. The model simulates changes in ecophysiology,
life history, distribution, relative abundance, and potential catch of
each studied species under scenarios of climate change.
Specifically, we focus on evaluating the sensitivity of the projected
changes in potential catch to some existing hypotheses of how
changes in ocean biogeochemistry may affect the marine
ecosystems.
Methods
Model description
The model approach involve three stages: (i) predicting current
species distribution; (ii) projecting future changes in distribution
and relative abundance; and (iii) projecting future changes in
potential catch.
Predicting current species distributions
The distribution map of each species in recent decades was derived
from an algorithm of Close et al. (2006). This algorithm estimates
the relative abundance of a species on a 30′latitude ×30′longi-
tude grid of the world ocean. Input parameters for the model
include the species’ maximum and minimum depth limits, north-
ern and southern latitudinal range limits, an index of association
to major habitat types (seamounts, estuaries, inshore, offshore,
continental shelf, continental slope, and the abyss), and known
occurrence boundaries. The parameter values of each species,
which are posted on the Sea Around Us Project website (http://
www.seaaroundus.org/topic/species/default.aspx), were derived
from data in online databases, mainly FishBase (www.fishbase
.org) and SeaLifeBase (www.sealifebase.org). We applied this
model to predict the distributions of relative abundance (normal-
ized across the grid) of the 120 species of demersal fish and invert-
ebrates. Figure 1shows the distribution of species richness
calculated using the predicted distribution of the 120 species con-
sidered here.
Projecting future species distributions
A new version of the dynamic bioclimatic envelope model was
used to project future species distribution. First, the model simu-
lated how changes in temperature, oxygen content (represented by
O
2
concentration), and pH (represented by H
+
concentration)
would affect the growth of marine fish and invertebrates. The
model algorithm was derived from the von Bertalanffy growth
function (VBGF; von Bertalanffy, 1951). Therein, growth is
viewed as the difference between two processes, i.e. growth ¼
anabolism 2catabolism, or
dB
dt=HWa−kW,(1)
where Hand kare the coefficients for anabolism and catabolism,
respectively. Anabolism scales with body weight (W) with
an exponent a,1, whereas catabolism scales linearly with
W. Solving for dB/dt¼0, we obtained H=kW1−a
1, where W
1
is the asymptotic weight.
Equation (1) can be illustrated by a “p-diagram” (Figure 2;
Kolding et al., 2008;Pauly, 2010). Figure 2suggests that relative
oxygen supply becomes increasingly limiting as fish grow,
because of the lower rate of increase in respiratory surface (and
hence oxygen supply) relative to that of increase in body size
(and hence oxygen demand; Pauly, 1981,2010). Therefore, body
growth depends on the difference between available oxygen
[aerobic scope; anabolic term in Equation (1)] and oxygen
demand for maintenance [catabolic term in Equation (1)], with
asymptotic weight being reached when the aerobic scope equals
oxygen demand.
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Equation (1) can be integrated into a generalized VBGF (Pauly,
1981):
Wt=W11−e−k(1−a)(t−t0)
1/(1−a).(2a)
Substituting K¼k(1–a) into Equation (2a), we have
Wt=W11−e−K(t−t0)
1/(1−a),(2b)
where Kis the von Bertalanffy growth parameter.
For simplification, we assume that a¼0.7, although empirical
studies indicate that agenerally varies from 0.50 to 0.95 between
fish species (Pauly, 1981,2010). Moreover, metabolism is
temperature-dependent, aerobic scope depends on oxygen avail-
ability in water, and maintenance metabolism is affected by phys-
iological stress (e.g. increased acidity). Therefore,
H/f(O2)f1(T),(3)
and
k/f2(T)f([H+]).(4)
Temperature effect on Hand kfollows the Arrhenius equation:
f1(T)/e−j1/T,(5a)
and
f2(T)/e−j2/T,(5b)
where j¼E
a
/R, with E
a
and Rthe activation energy and the
Boltzmann constant, respectively, whereas Tis the temperature
(in K). In addition, the aerobic scope of marine fish and
invertebrates decreases as temperature approaches their upper
and lower temperature limits (Po
¨rtner, 2010).
We set the values of jbased on empirically estimated metabolic
scaling exponents. A meta-analysis of the resting metabolic rate of
teleosts suggests that the within-species Q
10
of temperature ranges
from 0.45 to 3.41, with a median of 2.4 (Clarke and Johnston,
1999). We assume that the resting metabolic rate represents meta-
bolic demand for basic body functions and maintenance. Hence,
we calculated a j
2
in the Arrhenius equation (5a) for catabolism
that would result in the reported Q
10
over the possible physiologi-
cal temperature range (1– 288C) of 8. Given that anabolism is
considered to be less temperature sensitive than catabolism
(Perrin, 1995), and based on the fact that the slope of the
regression between log(K) and log(W
1
) among different popu-
lations of a species is ≈0.7 (Pauly, 2010), we estimated that j
1
should be ≈4.5.
In this model, oxygen availability depends on dissolved oxygen
(O
2
), whereas physiological stress from ocean acidification
depends on the concentration of the hydrogen ion (H
+
). We
incorporated two hypotheses of potential effects of changes in
oxygen concentration and ocean acidification on growth of
marine fish and invertebrates. For oxygen, we explored the
hypothesis that oxygen supply to the fish body is reduced linearly
with a reduction in oxygen concentration in the ocean from the
current levels. Moreover, the model accounts for the hypothesis
that increased acidity increases oxygen demand, for example, for
ionic balance regulation or increased calcium carbonate for-
mation. Some recent experimental studies suggest that the
aerobic scope of marine fish and invertebrates could be affected
by ocean acidification (Melzner et al., 2009). For example, an
2.5 times increase in CO
2
concentration might cause an
average of 33– 47% reduction in the aerobic scope of two species
of cardinal fish (family Apogonidae; Munday et al., 2009a).
However, these might be among the coral reef fish that are most
sensitive to acidification, because these species inhabit an environ-
ment without substantial changes in acidity, rendering them more
Figure 1. Current pattern of species richness of demersal fish and invertebrates in the Northeast Atlantic based on the distribution ranges of
120 major exploited species.
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sensitive to ocean acidification (Munday et al., 2009a). In addition,
sensitivity of physiological performances of marine fish and invert-
ebrates appears to be highly variable between species and taxo-
nomic groups (Melzner et al., 2009). Existing knowledge does
not allow us to predict accurately how sensitive fish and invert-
ebrate species are to ocean acidification. To examine the potential
effects of ocean acidification on species distributions and fisheries
catch potential, we therefore developed the broad-brush scenarios
of species’ sensitivity to ocean acidification; including (i) oxygen
demand of marine fish and invertebrates is insensitive to ocean
acidification; (ii) oxygen demand increases by 15% when H
+
ion concentration in the ocean is doubled from current levels;
and (iii) oxygen demand increases by 30% when H
+
ion concen-
tration in the ocean is doubled from the current levels. We recog-
nized that these scenarios are developed from limited information
on physiological responses of marine organisms to ocean
acidification and that they do not account for the differential sen-
sitivity to acidification between species. Nevertheless, we aim to
demonstrate how impacts of ocean acidification, for different
levels of sensitivity at the individual level (physiology), may
yield impacts at the population and community level and on fish-
eries catch potential.
Therefore,
H=g[O2]e−j1/T,(6a)
and
k=h[H+]e−j2/T.(6b)
The coefficients gand hwere derived from the average W
1
,K, and
environmental temperature (T
o
) of the species reported in the lit-
erature:
g=W1−a
1K
[O2]e−j1/T0,(7a)
and
h=k/(1−a)
[H+]e−j2/T0,(7b)
where H=kW1−a
1and k¼K/(1 2a) [Equations (1) and (2)].
The model predicts changes in VBGF parameters according to
changes in temperature, oxygen, and pH in the ocean relative to
the initial conditions, as
W1=H
k
1/(1−a)
,(8a)
and
K=k(1−a).(8b)
Modelling population dynamics
Based on the computed VBGF parameters, the model determined
the change in carrying capacity in each 30′latitude ×30′longitude
cell. Carrying capacity is expressed as a function of expected
biomass per recruit and recruitment. Expected biomass per
recruit was determined using a size-based population model. To
parametrize this model, the natural mortality rate (M) was pre-
dicted from Pauly’s empirical equation (Pauly, 1980):
log M=0.2107 −0.0824 log W1+0.6757 log K
+0.4627 log T′,(9)
where T′is the temperature in degrees Celsius.
The size-transition matrix (X), a matrix of probabilities of an
individual growing from a particular body-size class to other
size classes in a time-step (year), was computed from Quinn and
Deriso (1999):
u
y,l′,l=exp −(ll−[l1(1−e−K)+l′
le−K])2
2
s
2
,(10a)
Figure 2. Two p-diagrams illustrating how (a) growth and maximum
(i.e. asymptotic) size of fish depend on the balance between oxygen
supply and demand; (b) factors that increase maintenance
metabolism (e.g. increased temperature, physiological stress) or
reduced oxygen supply (e.g. hypoxia) will reduce growth and
asymptotic weight (from W
1
to W′
1
). Oxygen supply scales
allometrically with body mass, whereas oxygen demand (for
maintenance metabolism) is directly proportional to body mass.
Growth depends on the aerobic scope, i.e. on the difference between
oxygen supply and demand curves. Asymptotic size is reached when
oxygen supply is just enough to meet oxygen demand for basic body
maintenance. Hence, increase in oxygen demand or decrease in
oxygen supply will affect growth and asymptotic size.
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and
Xl′,l=
u
l′,l
l
u
l′,l
,(10b)
where l
1
is the asymptotic length, land l′the adjacent length
classes, ythe age of the fish, and
s
the variation in growth,
which is assumed to have a coefficient of variation of 20% and
is independent of length and age. Biomass per recruit (BPR) was
calculated using
BPR =
y
l
WlXl′,le−M,(11)
where
Wis the mean weight of length class l.
Many studies indicate that the ratio L
m
/L
1
is relatively con-
stant within most families of fish (Beverton and Holt, 1959;
Beverton, 1963;Mitani, 1970;Pauly, 1984;Binohlan and Froese,
2009), where L
m
and L
1
are the length at maturity and asymptotic
length. In fact, the relationship between L
m
and L
1
can be
re-expressed as:
Qm
Q1
=Lm
L1
b(1−a)
≈0.714,(12a)
where Q
m
and Q
1
are the mass-specific relative oxygen supply
when the fish reaches sexual maturation and asymptotic size,
respectively (Pauly, 1984). The exponents aand bfor the anabolic
term in Equation (1) and the length – weight relationship are
defined as previously. Pauly (1984) established that the ratio of
Q
m
to Q
1
is 1.4. Rearranging Equation (12a), we have
Lm=L1(0.714)1/[b(1−a)].(12b)
Assuming knife-edge recruitment at L
m
, the model calculated
spawning biomass per recruit (SPR) from:
SPR =
y
l
WlXl′,lmat e−Mif Ll,lm,mat =0
if Ll≥lm,mat =1.
(13)
Total larval production is directly proportional to SPR.
Initial relative recruitment strength (R) was calculated using the
initial relative abundance (A, normalized across the 30′×30′
degree resolution grid) and calculated biomass per recruit in
each cell, as BPR ¼cA/R, where cis a constant that scales from
relative abundance to absolute abundance. Hence, R¼cA/BPR
and A¼BPR R/c.
The model identified the “environmental preference profiles”
of the 120 species, defined by seawater temperature (bottom and
surface), depth, salinity, distance from sea ice, and habitat types.
Preference profiles are defined as the suitability of each of these
environmental conditions to each species, with suitability calculated
by overlaying environmental data (2001–2010) with maps of rela-
tive abundance of the species (Cheung et al., 2009). For example,
for each species, the model calculated a temperature preference
profile for the adult and prerecruit phases, based on the relative
abundance and the computed recruitment strength of the species.
Sea surface temperature was used for temperature preference pro-
files for the prerecruit phase, whereas bottom temperature was
applied to preference profiles for the adult demersal species.
Change in species’ carrying capacity (A
1
) in each spatial cell
was dependent on its calculated theoretical relative abundance
and environmental preferences. Carrying capacity is assumed to
vary positively with habitat suitability of each spatial cell and
habitat suitability depends on the species’ preference profiles to
the environmental conditions in each cell. The final carrying
capacity of a cell is therefore calculated as the product of the
habitat suitability of all the environmental conditions considered
in the model.
The model simulated changes in relative abundance of a species
by
dAi
dt=
N
j=1
Gi+Lji +Iji,(14)
where A
i
is the relative abundance of a 30′×30′cell i,Gthe intrin-
sic population growth, and L
ji
and I
ji
the settled larvae and net
migrated adults from surrounding cells ( j), respectively.
Intrinsic growth is modelled by a logistic equation:
Gi=rAi1−Ai
A1,i
,(15)
where ris the intrinsic rate of population increase. The model
explicitly represents larval dispersal through ocean currents with
an advection–diffusion – reaction model (see Cheung et al.,
2008,2009, for details).
Therefore, changes in ocean conditions are transformed by the
model into changes in life history, growth, carrying capacity,
population growth, net migration, and, hence, relative abundance
of a species, in each cell it occupies. Given the projected changes in
ocean conditions from the ocean–atmosphere-coupled GCM
under climate change scenarios, the model simulates the annual
changes in the distribution of relative abundance of each species
on the global 30′×30′grid.
We determined the rates of range shift of the 120 species in the
Northeast Atlantic Ocean from 2001 to 2055. For each simulation
year, we calculated the latitudinal and depth centroids of distri-
bution of each species, expressed as the average latitude and
depth of the centre of each spatial cell weighted by the relative
abundance of the species in the cell in the Northeast Atlantic,
respectively. Bathymetry data were based on mean depth in each
30 ×30′cell calculated using the ETOPO1 Global Relief Model
(http://www.ngdc.noaa.gov/mgg/global/global.html).
Scenarios of biogeochemical changes in the ocean
Our scenario of ocean conditions is based on outputs from the
prototype Earth System Model (ESM2.1) developed at the
Geophysical Fluid Dynamics Laboratory (GFDL) of the US
National Oceanic and Atmospheric Administration (NOAA;
Dunne et al., 2005,2007). The scenario considered here is the
SRES A1B, which assume CO
2
concentration at 720 ppm in
2100. The original resolution of the outputs from the coupled
model is 18at latitudes higher than 308N and 308S, with the resol-
ution becoming finer towards the equator. We interpolated the
physical variables from the coupled model with a resolution of
30′in latitude and longitude, using the nearest neighbour
method, thus avoiding making complicated assumptions about
the relationship between the coarser-resolution model outputs
and their downscaled values. Ocean condition fields used included
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sea surface and bottom temperature, sea ice concentration, surface
advection, surface and bottom oxygen and hydrogen ion concen-
tration, salinity, and small and large phytoplankton production
(Figure 3).
Three scenarios of predicted primary production were tested in
the model. The first and the second scenarios of primary
production data were based on outputs from ESM2.1, whereas
the third set was based on the empirical models. The first scenario
assumes that small and large phytoplankton cells contribute
equally to fish production. The second scenario assumes that a
food chain based on small phytoplankton cells is less efficient at
transferring energy to fish groups (because of the longer trophic
path length) than a food chain based on large phytoplankton
cells. Specifically, we assumed that, on average, small phytoplank-
ton cells must be consumed by an additional trophic level before (a
fraction of) their embodied energy became available to higher
trophic level groups. In addition, we assumed that the transfer effi-
ciency between trophic levels is 10%, as demonstrated by Pauly
and Christensen (1995) to be applicable to a wide range of
marine ecosystems. The third scenario was based on primary pro-
duction calculated using published empirical models and algor-
ithms (Behrenfeld and Falkowski, 1997;Carr, 2002;Sarmiento
et al., 2004). Sarmiento et al. (2004) detailed how these algorithms
were applied to project future primary production. All estimated
values of annual average primary productivity from the years
2001–2055 are scaled onto a 30′latitude ×30′longitude grid of
the world ocean (Figure 3).
Projected changes in fisheries catch potential
We simulated changes in maximum catch potential based on the
methods of Cheung et al. (2010). For each studied species,
changes in maximum catch potential were calculated based on
changes in species distributions and primary productivity. First,
we projected how the distribution of the 120 species of demersal
marine fish and invertebrates would shift under the climate
change scenario by applying the model used in this paper to simu-
late changes in the distribution of relative abundance on the 30′×
30′grid from 2005 to 2050. Second, we calculated changes in
primary production within the distribution range of the species.
We then applied the empirical method of Cheung et al. (2010)
to calculate the projected changes in catch potential in each
30′×30′cell. We also calculated how total maximum fisheries
catch potential of the studied species within the large marine eco-
systems (LMEs) of the Northeast Atlantic Ocean changes by 2050
relative to 2005 (10-year average). We conducted separate analyses
for simulations with and without consideration of ocean biogeo-
chemical effects.
Results
Our model predicted changes in life-history characteristics con-
sistent with empirically estimated growth parameters (Figure 4).
It predicted growth parameters over the environmental tempera-
ture range (5th and 95th percentiles of the water temperature
within the predicted distribution range) of each of the 120
studied species. The plot between predicted Kand L
1
is similar
to the plot using growth parameters reported in the published lit-
erature for 2015 fish species (available from FishBase: www.
fishbase.org;Pauly, 1998). The predicted intraspecific changes
are also consistent with observations, as illustrated in the
example of Atlantic cod (Figure 4).
When future scenarios of changes in biogeochemistry were
considered, growth performance of many of the studied species
was projected to decrease, relative to the scenario that growth
was affected only by temperature (Figure 5). Therefore, ocean
acidification and hypoxia result in a decrease in slope of the
log–log plot between Kand asymptotic size. In other words, the
consideration of increase in oxygen demand or reduce in supply
Figure 3. Changes in physical and biogeochemical conditions of the
ocean predicted from ESM2.1: (a) map of sea surface temperature
anomalies between 2005 and 2050 (10-year average), (b) changes in
average pH and oxygen concentration, (c) changes in the production
of small and large phytoplankton in the Northeast Atlantic, and
(d) changes in primary production by LMEs.
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from ocean acidification and hypoxia, in addition to temperature
increase, caused the model to predict a reduction in W
1
that does
not correspond to as large an increase in Kas predicted from con-
sidering temperature only.
Our model predicted that distributions of most of the 120
studied fish and invertebrates will shift northwards (Figure 6).
With the considerations of physical and biogeochemical factors,
the centroids of species distribution (calculated using the average
latitude and depth of the centre of each spatial cell weighted by
the relative abundance of the species in the cell in the Northeast
Atlantic) shifted at an average rate of 52.1 km decade
21
towards
the north and 5.1 m decade
21
towards deeper water between
2005 and 2050, under the scenario of high physiological sensitivity
to ocean acidification (i.e. 30% reduction in oxygen demand as
the H
+
ion concentration in the ocean doubles). When the
model was run with the scenarios of physical changes only
(without acidity, oxygen, and phytoplankton community struc-
ture), the latitudinal and depth centroids of the distributions
were predicted to shift at a slower rate of an average of 45.5 and
4.3 m decade
21
, respectively.
With the projected changes in the distribution of relative abun-
dance and primary production, the total maximum catch potential
may change substantially in the Northeast Atlantic region
(Figure 7). If we exclude the changes in oxygen content and pH
in the analysis, maximum catch potential increases in the LMEs
of the Subarctic (Barents Sea, Greenland Sea, Norwegian Sea,
Iceland LMEs, and other regions above 708N) by up to 80%
from 2005 to 2050 (10-year average; Figure 7). Catch potentials
in the lower latitude LMEs (Celtic-Biscay Shelf, North Sea,
Baltic, and other regions south of 708N) by 2050 relative to 2005
(10-year average) remained relatively unaffected.
When all biogeochemical factors were considered, the projected
maximum catch potential in 2050 declined substantially (Figure 7).
Under the scenario of high physiological sensitivity to ocean acidi-
fication, relative increases in catch potential from 2005 to 2050 in
the Barents Sea LME and Greenland Sea decreased from 70 and
40% (without O
2
and pH) to around 35 and 10% (with all biogeo-
chemical factors), respectively. Moreover, changes in catch potential
became negative in the other LMEs in the Northeast Atlantic.
Particularly, catch potentials were projected to decrease by up to
30% in the Celtic-Biscay Shelf, Iberian Coast, North Sea, and
Baltic Sea by 2050. Moreover, the change in maximum catch poten-
tial is sensitive to the scenario of physiological sensitivity to ocean
acidification. The scenario of intermediate sensitivity to ocean acid-
ification resulted in an intermediate level of changes in maximum
catch potential approximately mid-way between the high sensitivity
and no sensitivity scenarios (Figure 7).
The projected changes in maximum catch potential were mod-
erately sensitive to primary production predicted from different
methods (Figure 8). Without consideration of changes in oxygen
content and pH, projected changes in catch potential were
largest when total primary production in ESM2.1 was considered.
When the relative contribution of different phytoplankton cell size
was examined, percentage changes in catch potential from 2005 to
2050 declined by 10 – 20%. Projected changes in catch potential
using primary production predicted from empirical models
(documented in Sarmiento et al., 2004) were intermediate relative
to the other two sets of predictions.
Discussion
Future changes in species distributions and maximum catch poten-
tial in the Northeast Atlantic may be strongly affected by changes in
oxygen content, acidity, and phytoplankton community structure
in the ocean. Without consideration of these factors, the model
projected poleward movement of species distributions and gains
in catch potential that are consistent with previous studies
(Cheung et al., 2009,2010). The projected deepening of species dis-
tribution is moderately higher than the observed rate in the North
Sea from the 1980s to 2000s (3.1 m decade
21
;Dulvy et al., 2008),
which is reasonable, given that the projected future rates of
change in temperature and other ocean conditions are higher
than the historical rates under the climate change scenario con-
sidered in this study. However, this study suggests that the projected
impacts of climate change on species distribution and maximum
catch potential were underestimated when biogeochemical factors
were not considered. Specifically, in the model, ocean acidification
and reduction in oxygen level reduced the aerobic scope for growth
and, hence, maximum catch potential of most species in the region.
Such an effect was particularly apparent at the edge of distribution
ranges, where the temperature was close to the tolerance limits of
the species, and the fish consequently exhibited a limited aerobic
scope. This also explained the increase in the projected rate of
range shift when ocean acidification and changes in oxygen
content were considered. In addition, energy transferred from
primary production to higher trophic levels may decline when
Figure 4. Plot of log Kagainst log L
1
of (a) 120 demersal fish over
the 5th and 95th percentile of their predicted habitat temperature
(as predicted from the dynamic bioclimate envelope model); and
(b) published empirical estimates of VBGF of 2015 fish (available
from FishBase). The black triangles in (a) and (b) represent
predictions and estimates for different Atlantic cod populations.
Using Atlantic cod as an example, the predicted slope between log K
and log L
1
not significantly different (p,0.05) from the slope
calculated using observed log Kand log L
1
(Taylor, 1958).
1014 W. W. L. Cheung et al.
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the phytoplankton communities shift towards smaller size in
future. Indeed, consideration of these factors reversed the sign of
catch potential changes in some intermediate regions, such as the
North Sea and Baltic Sea, whereas in low-latitude areas, consider-
ations of these factors intensified negative trends. This will largely
affect the expected impacts and adaptation costs of countries in
the Northeast Atlantic region to climate change and may have
large implications for planning for climate change-adaptation pol-
icies (World Bank, 2009;Sumaila and Cheung, 2010).
We acknowledge that the magnitude of changes projected by
the model is uncertain. First, the model projections were affected
by uncertainties about the projected physical and biogeochemical
conditions (e.g. IPCC, 2007;Friedrichs et al., 2009). We used pro-
jections from one model ensemble member from ESM2.1; conse-
quently, our study results may be affected by the errors or biases in
this particular set of model outputs (Stock et al., 2010). Future
extension of this study will explore the sensitivity of projected eco-
logical changes between different climate model and ESM2.1 and
between different ensemble members of a model, allowing the
exploration of the sensitivity of our projections to different projec-
tions of physical and biogeochemical changes. Second, the under-
lying biological hypothesis, represented by the model structure
and input biological and ecological parameters, may be uncertain.
Specifically, we assumed that growth and maximum body size in
marine fish and invertebrates are determined primarily by avail-
ability of oxygen; the latter is related partly to the availability of
respiratory surfaces. Although this hypothesis is supported by
abundant evidence (Pauly, 2010), it has not been considered
before. Conversely, the simulated variations in growth parameters
between and within species match with empirical observations;
this provides some support to the model in representing growth
of marine fish and invertebrates. Moreover, although parameter
uncertainty may render predictions at individual species level inac-
curate, the large sample size and taxonomic and geographic cover-
age of our study allowed us to detect the signals of changes that
may otherwise be distorted by uncertainties. We did not account
for interactions between species (e.g. effects of changes in prey
availability and predation pressure on growth, population
dynamics, and distribution) and with human activities (e.g.
fishing). An analysis using a trophodynamic model suggests that
trophic interactions may significantly affect the relative abundance
of animals in a region, although the direction of changes is consist-
ent with projections from the bioclimatic envelope model
(Ainsworth et al., 2011). In future, interspecific linkages could
be incorporated to test the effects of trophic interactions on
model projections. In addition, our model did not account for
potential genetic changes as an adaptation to the changing
environmental conditions. Evolutionary processes may affect
species’ environmental tolerance, range shift, and extinction
under environmental changes (Davis et al., 2005). For example,
evolutionary mechanisms might select for genes that can facilitate
range movement (Parmesan, 2006). Conversely, the limited
Figure 5. Comparing the observed changes in growth parameters (left) with simulated changes from the dynamic bioclimate envelope model
under future scenarios of changes in ocean biogeochemistry and physical conditions (right). The slope of the plot on the right is less steep
than observed (left panel).
Figure 6. Projected mean rate of shift in latitudinal centroids of the
120 studied demersal fish and invertebrates under a scenario that
considers both physical and biogeochemical factors (including
acidity, oxygen, and phytoplankton community structure). In this
scenario, the physiological sensitivity to acidification and oxygen is
high. The black bars represent the median values, whereas the
boundary of the box represents the 25th and 75th percentiles. When
biogeochemical factors are not considered, the projected rate of
range shift declines from a median of 52 km decade
21
to
45 km decade
21
by 2050.
Effect of biogeochemical changes on fisheries catch 1015
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evidence available suggests that absolute tolerances to climate
change will not evolve fast enough for a distribution range to be
conserved (Etterson and Shaw, 2001;Jump and Pen
˜uelas, 2005;
Parmesan, 2006). Additional empirical evidence is needed before
we could determine the potential effects of evolutionary changes
under climate change.
The new version of the dynamic bioclimate envelope model pre-
sented in this paper is an attempt to fill a number of gaps in the
approaches to project climate change effects on species distri-
butions. The model allowed testing of the potential effects of pro-
jected future changes in physical and biogeochemical conditions
of the ocean. With explicit representation of ecophysiological
effects, the model took explicit account of the phenotypic plasticity
of fish to changes in environmental conditions, through changes in
body size, growth, and life history. This provides explicit linkages
between climate change effects on growth, life history, and popu-
lation dynamics of marine fish and invertebrates, thus accounting
for the potential impacts from individual animals to population
and community levels. This is a considerable advance over the con-
ventional bioclimatic envelope model (Elith and Leathwick, 2009).
Projections from this model may be treated as alternative hypoth-
eses of potential climate change impacts on biodiversity and fish-
eries catch potential in the Northeast Atlantic. Comparison with
projections from other models with different complexity, structure,
assumptions, and input data could increase the robustness of the
projections and should be carried out in the future (Morin and
Thuiller, 2009).
The model can be used to test different hypotheses of how
climate change may affect marine organisms through changes in
physical and biogeochemical conditions of the ocean. Currently,
a range of hypotheses on the biological and ecological effects of
ocean acidification on marine fish and invertebrates has been pro-
posed with equivocal empirical evidence (Guinotte and Fabry,
2008;Wood et al., 2008;Dupont and Thorndyke, 2009;Gooding
et al., 2009;Munday et al., 2009a,b). For example, some studies
suggest that ocean acidification may increase energy expenditure
on growth or body regulation (Munday et al., 2009a;Po
¨rtner,
2010), whereas others established direct increases in the mortality
of marine fish from impaired body function (Munday et al.,
2009b). Although this study assessed only one of such hypotheses
(effects on energy expenditure), the model could be modified
easily to test the sensitivity of species distributions and maximum
catch potential to different hypotheses of impacts.
In summary, we demonstrate that consideration of ocean acid-
ification, changes in oxygen content, and phytoplankton commu-
nity structure may reduce strongly the projected maximum catch
potential in the Northeast Atlantic. This may have large impli-
cations for planning climate change-adaptation strategies in the
region. This also highlights the need to increase our understanding
of these impacts and to conduct interdisciplinary research across
climate and ocean sciences, physiology, ecology, fisheries, econ-
omics, and social science. In future, the study could be extended
to address a number of knowledge gaps in the study of climate
change impacts on fish and fisheries. Particularly, the model
could be applied to assess the sensitivity of future marine biodiver-
sity and fisheries catch potential to different hypotheses about the
ecological effects of ocean biogeochemical changes. In addition,
based on historical changes in ocean condition, the model can
be applied to generate hindcasts of changes in major marine fish
and invertebrates, presenting hypotheses of climate change influ-
ences on these species in the past.
Acknowledgements
We thank A. Karpechko for his advice on regrinding ocean data, A.
Atanacio for help with the graphics, and S. Jennings for comment-
ing on an early draft of the manuscript. WWLC was partly sup-
ported by the Seedcorn Fund provided by the Centre for
Environment, Fisheries and Aquaculture Science. The Sea Around
Us project, a scientific collaboration between the University of
British Columbia and The Pew Environmental Group, provided
the species distribution ranges used in this study and funding to
DP. JS was supported by the Carbon Mitigation Initiative (CMI)
project at Princeton University, sponsored by BP and Ford Motor
Company.
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