mechanistic insights into the effects of climate change on larval cod

Mechanistic insights into the effects of climate change onlarval codTROND KR I ST IANSEN1 * , CHARLES STOCK 2 , KENNETH F . DR INKWATER 1 and

ENRIQUE N. CURCHITSER3

1Institute of Marine Research, Bergen, 5817 Norway, 2NOAA Geophysical Fluid Dynamics Laboratory, Princeton University,

Forrestal Campus, 201 Forrestal Road, Princeton, NJ 08540-6649, USA, 3Department of Environmental Sciences/Institute of

Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ 08901 USA

Abstract

Understanding the biophysical mechanisms that shape variability in fisheries recruitment is critical for estimating the

effects of climate change on fisheries. In this study, we used an Earth System Model (ESM) and a mechanistic indivi-

dual-based model (IBM) for larval fish to analyze how climate change may impact the growth and survival of larval

cod in the North Atlantic. We focused our analysis on five regions that span the current geographical range of cod

and are known to contain important spawning populations. Under the SRES A2 (high emissions) scenario, the ESM-

projected surface ocean temperatures are expected to increase by >1 °C for 3 of the 5 regions, and stratification is

expected to increase at all sites between 1950–1999 and 2050–2099. This enhanced stratification is projected to decrease

large (>5 lm ESD) phytoplankton productivity and mesozooplankton biomass at all 5 sites. Higher temperatures are

projected to increase larval metabolic costs, which combined with decreased food resources will reduce larval weight,

increase the probability of larvae dying from starvation and increase larval exposure to visual and invertebrate preda-

tors at most sites. If current concentrations of piscivore and invertebrate predators are maintained, larval survival is

projected to decrease at all five sites by 2050–2099. In contrast to past observed responses to climate variability in

which warm anomalies led to better recruitment in cold-water stocks, our simulations indicated that reduced prey

availability under climate change may cause a reduction in larval survival despite higher temperatures in these

regions. In the lower prey environment projected under climate change, higher metabolic costs due to higher temper-

atures outweigh the advantages of higher growth potential, leading to negative effects on northern cod stocks. Our

results provide an important first large-scale assessment of the impacts of climate change on larval cod in the North

Atlantic.

Keywords: climate change, cod, earth system modeling, fish, Gadus morhua, individual-based modeling, mechanistic modeling,

phytoplankton production, recruitment, zooplankton

Received 30 August 2012 and accepted 19 November 2013

Introduction

Global climate change is expected to alter the physical

environment as well as the biological structure and

functioning of the world’s ocean ecosystems (Rosen-

zweig et al., 2007; Doney et al., 2012). Changes in physi-

cal features, such as ocean temperature, stratification,

and currents, will have considerable impacts on marine

ecosystems (Hays et al., 2005; Doney et al., 2012). For

example, projected reductions in primary productivity

(Steinacher et al., 2010) could have effects throughout

the foodweb including the recruitment of fish stocks

(Brander, 2007a). Climate change is also expected to

affect fish stocks by causing major geographic shifts in

species distribution and abundance over the next

50–100 years (Barker & Knorr, 2007; Brander, 2007b;

Cheung et al., 2009b). Together these changes could

have major direct and indirect impacts on fisheries

recruitment, although considerable uncertainty exists

with respect to the magnitude of these changes and the

mechanisms that underlie them (Brander, 2007a).

Observations and climate model simulations suggest

that climate change impacts in the North Atlantic will

include several physical changes that could affect bio-

logical productivity. Observed sea surface temperatures

in the North Atlantic over the twentieth century indi-

cate a warming trend everywhere with exception of the

far northwestern Atlantic (Deser et al., 2010b). Climate

model simulations from the fourth IPCC assessment

report project these trends to continue under high CO2

emissions scenarios (Steinacher et al., 2010; Capotondi

et al., 2012). Warming and decreased surface salinity

*Present address: National Oceanic and Atmospheric Administra-

tion, Silver Spring, Maryland, USA

Correspondence: Trond Kristiansen, tel. +4797701109, fax

+4755238531,

e-mail: [email protected]

© 2013 John Wiley & Sons Ltd 1559

Global Change Biology (2014) 20, 1559–1584, doi: 10.1111/gcb.12489

Global Change Biology

are projected to increase surface ocean stratification

over most of the North Atlantic during the second half

of the twenty-first century (Capotondi et al., 2012).

Enhanced stratification limits the vertical exchange of

cold nutrient-rich water to the surface layer and is pro-

jected to decrease primary productivity in most areas of

the North Atlantic (Steinacher et al., 2010). The promi-

nent exceptions to this trend occur in extremely high-

latitude ecosystems, where increased stratification and

warming is projected to increase primary productivity

by alleviating strong light limitation and reducing ice

cover (Bopp et al., 2001; Steinacher et al., 2010; Doney

et al., 2012).

These physical changes can have diverse impacts on

cod recruitment across the North Atlantic basin. Atlan-

tic cod (Gadus morhua) is a benthic fish species with a

pelagic larval stage that is distributed across the North

Atlantic in more than 25 different cod populations

(Drinkwater, 2005). Past natural climate variability in

the North Atlantic basin has caused oscillating cold and

warm conditions in cod spawning grounds (Drink-

water, 2006; Sundby & Nakken, 2008). Warm conditions

have been found to have a positive effect on survival

and recruitment of Atlantic cod through enhanced

zooplankton productivity, faster growth rates, and ele-

vated survival (Ottersen et al., 2001). However, the

response to ocean warming may depend on the geo-

graphical location of the population, the availability of

prey resources, and the species involved (Drinkwater,

2005; Cheung et al., 2009a, 2013; Kristiansen et al.,

2011). For example, increased ocean temperatures have

resulted in stronger recruitment of Barents Sea stocks

of cod, haddock, and herring (Ottersen & Loeng, 2000),

while increased temperatures in the North Sea have

had negative effects on cod recruitment through

changes in the prey composition, distribution, and

abundance (Beaugrand et al., 2003). In the Barents Sea,

warmer temperatures were associated with an

increased inflow of water rich in zooplankton, which

increased growth and survival through the larval phase

(Sundby, 2000). However, in the North Sea, warmer

temperatures caused a shift in zooplankton species

composition that was lower quality for larval fish,

leading to starvation (Beaugrand et al., 2003).

Although past conditions in the North Atlantic

provide key insights into interactions between cod and

climate, we still have limited knowledge about how the

physical and biological changes that result from climate

change may affect cod recruitment. Cod production, or

growth and recruitment, is strongly linked to the mor-

tality rate during larval and juvenile stages (Sundby

et al., 1989). Successful feeding, growth, and survival

during this period depends on the combination of

physical and biological environmental conditions

such as turbulence, prey abundance, light level, and

predator abundance (Leggett & Deblois, 1994) among

others. For example, although warmer temperatures

increase fish metabolic rates (e.g., K€oster et al., 2003;

O’Connor et al., 2007) enabling higher and faster

growth potential, they also require higher prey abun-

dance to sustain higher metabolic rates. In addition, the

overlap in time and space between larval fish and phy-

toplankton and zooplankton (Cushing, 1990; Edwards

& Richardson, 2004; Durant et al., 2007) can have a

significant impact on fisheries recruitment patterns.

Because no single variable determines the survival of

larval fish, understanding future dynamics will require

considering several mechanisms that all have an effect

on fish growth, feeding, and survival (Clark et al., 2003;

Beaugrand & Kirby, 2010; Kristiansen et al., 2011). While

empirical relationships between fish populations and

environmental variables (e.g., bioclimate envelope mod-

els or niche models) provide the basis for initial assess-

ments of future responses (e.g., Cheung et al., 2009a),

such approaches allow for only limited exploration of

the underlying interactions between processes. For

example, the behavioral response of larval fish to verti-

cal variations in food abundance can only be considered

using models that account for the dynamic mechanistic

interactions between larvae and the biophysical condi-

tions of the ocean (DeAngelis & Gross, 1992). In addi-

tion, the static parameterization of niche models may

limit their robustness for climate change applications as

ecosystems evolve into novel states. The continued

development and application of ecosystem models that

can account for ecology and physiology is essential for

improving confidence in projections of climate change

impacts on living marine resources (Stock et al., 2011).

In this study, we forced a highly mechanistic individ-

ual-based cod larvae model (Fiksen & MacKenzie, 2002;

Kristiansen et al., 2007, 2009b) with climate change pro-

jections from a global Earth System Model (ESM). This

combination of models provided a powerful tool for

elucidating the mechanisms underlying the response of

cod larvae to climate changes. Our modeling frame-

work quantified physical changes, the planktonic eco-

system response, and the larval behavioral response

within five habitats, or areas, in the North Atlantic that

are historically important spawning and nursery

grounds for larval and juvenile stages of Atlantic cod

and span the current geographical range of the species.

Using our modeling framework, we analyzed how sur-

vival through the larval stages may be affected by

large-scale climate change from 1950 to 2099 and how

changes in survival may differ across habitats in the

North Atlantic. We also assessed the relative impor-

tance of temperature changes and productivity changes

in driving the predicted responses.

© 2013 John Wiley & Sons Ltd, Global Change Biology, 20, 1559–1584

1560 T. KRISTIANSEN et al.

Materials and methods

This study combined projected changes in physical and plank-

tonic ecosystem dynamics from an ESM with an IBM for cod

larvae to project how changes in climate will affect survival

and growth rates of larval cod. A zooplankton model was also

used to translate changes in primary production projected by

the ESM into changes in the larval cod prey field. The IBM is a

two-dimensional (time and depth) vertical model and uses

physical and plankton forcing specific to five different regio-

nal domains. Each of these domains encompasses large inter-

nal spatial variability that we did not capture with this

analysis where we focused only on variation with time and

depth. An overview of each of the model components is pro-

vided below and the relationships between the components

are summarized in Fig. 1. This is followed by a description of

the implementation of the modeling framework for each study

site and the experiments conducted.

The earth system model (NOAA GFDL ESM2.1)

This study used the Geophysical Fluid Dynamics Laboratory

(GFDL) prototype Earth System Model (ESM2.1). This model

is built on the physical components of GFDL’s CM2.1 physical

climate model (Delworth et al., 2006), with added marine

biogeochemistry and land vegetation dynamics. The ocean

- Temperature- dependent growth

- Temperature andfood dependent growth

- Metabolism

Stomach Update:- weight - stomach

IBM: Mechanistic feeding module

IBM: Bio-energetics module

Encounter Ingest

Capture Approach

IBM: Predation module

IBM: Vertical behavior module

Forcing: ESM + zooplankton models

Calculate mortality from starvation, invertebrates, and visual predators

Calculate vertical behavior based on ingestion and mortality rates

PreyLightWindTemp.

Phytoplanktonproduction rates

Chl-a

Forcing data for IBM

Zooplankton modelEarth System Model

GFDL ESM v.2.1

Fig. 1 A schematic of the modeling setup consisting of the Earth System Model (ESM), a prey model, and the individual-based model.

Temperature, wind (used to calculate turbulence), and current velocity data were used to force the individual-based model directly.

The phytoplankton values estimated by the ESM model were used as input to a prey model to estimate abundance of zooplankton at

12 different prey stages. The prey items were used as input to a mechanistic individual-based model that simulated the feeding ecology

and bioenergetics of larval cod. Growth was either food-limited or temperature-limited (growth saturated) depending on how much

prey the larva consumed in one time-step and the amount of food in the stomach.


CLIMATE CHANGE EFFECTS ON FISHERIES 1561

component of ESM2.1 is the Modular Ocean Model (Griffies

et al., 2005) with 1° 9 1° longitude/latitude horizontal resolu-

tion except along the equator where resolution is refined to

1/3°. Fifty vertical levels are resolved, with 10 m resolution

over the top 200 m. The TOPAZ (Tracers of Ocean Phyto-

plankton with Allometric Zooplankton) model provided the

biogeochemical dynamics. TOPAZ includes major macro- and

micronutrients (N, P, Si, and Fe) and three types of phyto-

plankton (<5 lm ESD small phytoplankton, >5 lm ESD large

phytoplankton, and diazotrophs). Phytoplankton growth and

chlorophyll to carbon ratios are based on the algorithm of Ge-

ider et al. (1997). Particle remineralization and sinking is mod-

ulated by biogenic and lithogenic minerals (Dunne et al., 2005,

2007) and zooplankton grazing is modeled implicitly (Dunne

et al., 2005). In the North Atlantic, historical ocean-ice hind-

casts using the MOM4p1-TOPAZ coupling have successfully

captured North Atlantic bloom dynamics as observed by

SeaWiFS (Henson et al., 2009a) and interannual variations in

bloom dynamics observed over the past 50 years in the Con-

tinuous Plankton Recorder (CPR) time series (Henson et al.,

2009b). However, these simulations did not fully capture

plankton regime shifts observed in the CPR record (Henson

et al., 2009b).

The ESM simulations used herein are identical to earlier

work (Rykaczewski & Dunne, 2010; Polovina et al., 2011). The

simulations were initialized using data from the World Ocean

Atlas 2001 (Conkright et al., 2002) and Carbon Dioxide Infor-

mation Analysis Center (Key et al., 2004). The model was spun

up for 1000 years with a pre-industrial CO2 concentration of

286 ppm (Delworth et al., 2006). Simulations were then made

based on the A2 scenario of the IPCC Special Report on Emis-

sions Scenarios (Nakicenovic et al., 2000). This scenario calls

for CO2 to increase to 850 ppm in 2100 and is considered a

high carbon emission scenario.

Focus on a single scenario from a single model prevented a

full exploration of response uncertainty across models and

scenarios. The intent of this study, however, was to elucidate

the potential mechanistic responses of larval cod to character-

istic climate-change-driven changes in stratification, tempera-

ture, and productivity across the North Atlantic shared by

most climate projections (Steinacher et al., 2010; Capotondi

et al., 2012). The projected trends will be compared against an

ensemble of earth system projections from the fifth IPCC

assessment.

Six output variables were taken from ESM2.1 to force the

zooplankton and larval cod modules: temperature, primary

production from large and small phytoplankton, chlorophyll,

and wind, and surface light. Profiles of temperature and phy-

toplankton conditions in the upper 60 m of the water column

were used to simulate a dynamically changing environment

where the larval fish feeds and grows. The wind conditions at

the surface were used to estimate the turbulence level of the

water column (MacKenzie & Leggett, 1993), while chlorophyll

values were used to determine the attenuation of light with

depth (Riley, 1956). For temperature, the difference between

the modeled (1950–1999) climatology and the World Ocean

Atlas (Locarnini et al., 2010) was used to bias correct ESM pro-

jections (Table 1). The 1950–1999 average ocean temperature

estimated by the ESM model for the upper 30 m of the water

column was within 1 °C of WOA values for all sites except the

Georges Bank/Gulf of Maine (GB/GOM) region, where it is

3.2 °C too warm (Table 1). This warm bias is shared by many

coarse resolution climate models (Randall et al., 2007) and is

generally attributed to the separation of the Gulf Stream in the

models being too far to the north. The magnitude of seasonal

changes in temperature was reasonably captured by the

model (Table 1), so no bias correction for seasonal tempera-

ture changes was applied. The modeled average (2002–2011)

primary productivity (g C m�2 d�1) was within the range of

observed 14C-based methods or satellite-based estimates (see

ESM vs. Eppley-VGPM; Table 2, Fig. S4), so no bias correction

was used.

Zooplankton model

A zooplankton model was created to simulate a generic spe-

cies with six nauplii (I–VI) and six copepodite (I–VI) stages,

where the abundance and distribution was calculated based

on the primary production estimates obtained from the ESM.

The zooplankton model estimated the mesozooplankton bio-

mass (Z, mg C m�3) based on estimates of mesozooplankton

production and mortality. The mesozooplankton production

estimate was derived from small and large phytoplankton

primary productivity (PPSP and PPLP, converted to mg C

m�3 day�1) taken from the ESM. Consumption by microzoo-

plankton was assumed to be the dominant loss mechanism for

small phytoplankton (Calbet & Landry, 2004) and consump-

tion by mesozooplankton was assumed to be the dominant

loss mechanism for large phytoplankton and microzooplank-

ton. Therefore, microzooplankton was not considered a prey

for larval fish, but only regarded as a prey resource for meso-

zooplankton. Assuming a zooplankton gross growth efficiency

(gge) of 0.25 (Straile, 1997) then yielded the following estimate

of mesozooplankton production (PZ, mg C m�3 day�1):

PZ ¼ gge2 � PPSP þ gge� PPLP ð1Þ

PZ was added to the total mesozooplankton biomass in the

water column (Z). Mesozooplankton mortality (mZ, mg

C m�3 day�1) was assumed to be temperature and density

dependent (Ohman & Hirche, 2001):

mZ ¼ QðT�T0Þ=1010 �mZ0 � Z2 ð2Þ

Here, T was the temperature, Q10 = 2.0 was the factor by

which the mortality rate change for a 10 °C rise in water tem-

perature, T0 = 6.4 (Ohman & Hirche, 2001), and mZ0 was set to

be consistent with observed mesozooplankton mortality rates

(0.2 day�1, Ohman et al. (2004)) at typical mesozooplankton

concentrations in the North Atlantic. The density-dependent

(i.e., quadratic) formulation was assumed to arise from a com-

bination of mesozooplankton predators aggregating over

areas of increased mesozooplankton biomass and adult meso-

zooplanton consuming earlier life stages (Ohman & Hirche,

2001). This formulation produced average zooplankton bio-

mass that was comparable with observed values (see Table 2

for details; Moriarty & O’Brien, 2012). The zooplankton



biomass was divided into zooplankton length intervals of

133 lm ranging from 100 to 1600 lm according to the

algorithm described in Daewel et al. (2008). The result is a

generic prey item consisting of 12 different size categories

from nauplii to copepodite.

The individual-based model

The IBM was configured for Atlantic cod and has been thor-

oughly tested against several observational datasets in prior

studies. For example, the IBM was able to reproduce growth

and feeding patterns as observed in a macrocosm over the

course of the first 47 days posthatching. The IBM adequately

reproduced observed growth patterns when the observed

environment (prey resources, temperature, and turbulence)

was used as input to the IBM (Kristiansen et al., 2007). The

IBM has also been tested against a very detailed observational

dataset on GB and proved to be able to simulate growth, feed-

ing, behavior, and prey selection comparable with observa-

tions for two separate years 1993 and 1994 (Kristiansen et al.,

2009b). The IBM consisted of 4 modules: (1) a feeding module,

(2) a growth module, (3) a predator module, and (4) a behav-

ioral module. These modules consisted of a number of func-

tions that were estimated sequentially (Fig. 1). All processes

were estimated for each time-step as responses may vary with

time of day, the depth position in the water column, and with

Table 1 Column two shows the climatological temperature from the World Ocean Atlas 2009 (Locarnini et al., 2010), while column

three shows the calculated climatology (1950–1999) from Earth System Model (ESM) for the five areas Georges Bank, West

Greenland, Iceland, North Sea, and Lofoten. Column four shows the temperature difference (DT, °C) between the model (ESM) and

the observed (WOA). DT was used to bias correct the ESM temperature data that forced the individual-based model (IBM) used in

this study. Data for both WOA and ESM were depth averaged for the upper 30 m of the water column over the domain (Fig. 1).

Columns 6 to 9 show the seasonal and annual temperature (°C) and salinity (psu) from the World Ocean Atlas (climatology) and

the ESM averaged between 1950 and 1999. The World Ocean Atlas shows standard deviations between stations found within the

regions and therefore indicates a different standard deviation compared to the standard deviation show for the ESM values, which

depicts variability between years.

Location

Clim

(WOA, °C)Clim

(ESM, °C)

DT(ESM-

WOA, °C) Period

WOA

temp (°C)WOA

salt (psu)

ESM temp

(°C; biascorrected) ESM salt (psu)

Georges

Bank

10.2 13.39 3.2 Annual

average

10.24 � 0.58 32.84 � 0.09 10.62 � 3.52 35.67 � 0.25

JFM 5.85 � 0.05 32.88 � 0.04 6.42 � 1.16 35.68 � 0.22

AMJ 8.25 � 0.56 32.73 � 0.11 13.47 � 2.59 35.52 � 0.24

JAS 14.98 � 1.72 32.80 � 0.14 13.34 � 1.95 35.70 � 0.25

OND 11.87 � 0.10 32.97 � 0.07 9.38 � 1.75 35.78 � 0.21

West

Greenland

3.9 4.4 0.5 Annual

average

3.90 � 0.02 34.31 � 0.06 1.42 � 2.70 34.15 � 0.31

JFM 2.89 � 0.03 34.60 � 0.02 �0.99 � 1.08 34.39 � 0.23

AMJ 2.99 � 0.03 34.49 � 0.04 4.34 � 1.81 34.25 � 0.26

JAS 5.92 � 0.16 34.09 � 0.13 2.79 � 1.87 33.95 � 0.27

OND 3.80 � 0.05 34.06 � 0.05 �0.46 � 1.21 34.02 � 0.24

Iceland 8.72 7.87 0.86 Annual

average

8.73 � 0.1 35.18 � 0.0 7.10 � 1.96 35.36 � 0.08

JFM 7.29 � 0.02 35.17 � 0.0 5.61 � 0.50 35.41 � 0.03

AMJ 8.14 � 0.12 35.18 � 0.0 9.58 � 1.40 35.35 � 0.09

JAS 10.89 � 0.22 35.21 � 0.01 7.52 � 1.58 35.32 � 0.10

OND 8.59 � 0.02 35.16 � 0.0 5.69 � 0.35 35.38 � 0.06

North Sea 9.88 9.28 �0.6 Annual

average

9.88 � 0.33 34.60 � 0.10 9.24 � 3.29 27.50 � 0.29

JFM 5.87 � 0.02 34.64 � 0.09 5.22 � 1.21 27.48 � 0.24

AMJ 8.72 � 0.46 34.58 � 0.12 11.66 � 1.86 27.27 � 0.19

JAS 14.23 � 0.86 34.55 � 0.10 12.14 � 1.59 27.53 � 0.25

OND 10.71 � 0.01 34.61 � 0.10 7.94 � 1.90 27.74 � 0.26

Lofoten 7.53 6.70 �0.83 Annual

average

7.53 � 0.20 34.42 � 0.09 8.35 � 2.33 34.59 � 0.20

JFM 5.7 � 0.07 34.50 � 0.04 6.01 � 0.56 34.73 � 0.15

AMJ 6.43 � 0.16 34.54 � 0.06 10.76 � 1.90 34.66 � 0.20

JAS 10.14 � 0.79 34.37 � 0.16 9.61 � 1.52 34.49 � 0.19

OND 7.87 � 0.08 34.27 � 0.09 7.01 � 0.88 34.49 � 0.16



larval ontogeny. The mechanistic approach relied on a realistic

representation of the physical and biological environment, a

fundamental understanding of the biology of the prey and the

larvae, and the interaction between these components.

Feeding module. The feeding module calculated the number

of prey items encountered, approached, captured, and

ingested by each larva within one time-step. The encounter

rate was estimated based on the larva’s ability to visually per-

ceive the prey and the prey density (Aksnes & Giske, 1993).

The prey differed in length and weight from smaller nauplii to

larger copepodites where the smallest prey items were visu-

ally hard to detect but easy to catch, and the larger prey were

more visible but harder to catch (Kristiansen et al., 2009b).

These prey items were visually detected by the larva using a

functional response that depended on (1) the eye sensitivity of

Table 2 Table showing average (variable period see below) observed vs. average (1958–1999) modeled zooplankton biomass log10(mg C m�3), and annual and seasonal average modeled phytoplankton productivity (g C m�2 d�1)

Location Period

Zooplankton biomass log10 (mg C m�3) Phytoplankton production (g C m�2 d�1)

ESM COPEPOD ESM Eppley-VGPM

Georges

Bank

Annual

average

1.1 � 0.13 (n = 470) 0.85 � 0.22 (n = 5628) 0.79 � 0.034 (n = 144) 0.73 � 0.046 (n = 114)

JFM 0.24 � 0.06 0.27 � 0.12

AMJ 1.19 � 0.16 0.73 � 0.26

JAS 1.25 � 0.09 1.04 � 0.36

OND 0.47 � 0.18 0.61 � 0.26

West

Greenland

Annual

average

0.68 � 0.14 (n = 470) 0.65 � 0.29 (n = 72) 0.40 � 0.014 (n = 144) 0.35 � 0.029 (n = 85)

JFM 0.04 � 0.02 0.04 � 0.04

AMJ 0.75 � 0.31 0.47 � 0.26

JAS 0.54 � 0.08 0.42 � 0.16

OND 0.16 � 0.08 0.10 � 0.11

Iceland Annual

average

1.1 � 0.15 (n = 470) 0.45 � 0.078 (n = 43) 0.40 � 0.007 (n = 144) 0.54 � 0.077 (n = 85)

JFM 0.01 � 0.00 0.05 � 0.05

AMJ 0.86 � 0.5 0.43 � 0.27

JAS 0.69 � 0.06 0.91 � 0.41

OND 0.12 � 0.08 0.10 � 0.11

North Sea Annual

average

0.74 � 0.06 (n = 470) n/a (n = 0) 0.54 � 0.010 (n = 144) 0.58 � 0.028 (n = 95)

JFM 0.14 � 0.06 0.11 � 0.13

AMJ 0.91 � 0.08 0.68 � 0.24

JAS 0.89 � 0.05 0.63 � 0.22

OND 0.26 � 0.12 0.36 � 0.15

Lofoten Annual

average

0.80 � 0.11 (n = 470) 0.37 � 0.26 (n = 165) 0.43 � 0.011 (n = 144) 0.64 � 0.055 (n = 76)

JFM 0.01 � 0.01 0.00 � 0.00

AMJ 0.89 � 0.42 0.67 � 0.30

JAS 0.75 � 0.14 0.72 � 0.27

OND 0.08 � 0.07 0.18 � 0.23

For both the modeled and observed phytoplankton, the values were averaged for the period 2002–2011. Both observed and modeled

average and standard deviation values (n equals number of samples) were calculated based on annual average values for the

described time periods over the geographical regions show in Fig. 2. The seasonal averages of observed and modeled phytoplank-

ton productivity were estimated by averaging over the seasons January, February, March (JFM), April, May, June (AMJ), July,

August, September (JAS), and October, November, December (OND). All modeled values were taken from the ESM model.

Observed zooplankton within each of the 5 regions were extracted from the COPEPOD database (Moriarty & O’Brien, 2012; http://

www.st.nmfs.noaa.gov/plankton/) and averaged over the entire period when observations were available: 1958–2001 for Georges

Bank, 1963 for West Greenland and Iceland, North Sea, and 1958–2005 for Lofoten. Eppley-VGPM (Eppley, 1972; Behrenfeld & Fal-

kowski, 1997) primary production estimates were extracted for each region, annually averaged, before average and standard devia-

tion values for 2002–2011 were calculated. The Eppley-VGPM estimates were calculated using observed chlorophyll and

temperature data (MODIS), SeaWiFS PAR, and estimates of euphotic zone depth were available from the Oregon State University

(www.oregonstate.edu/ocean.productivity/index.php). Also see Fig. S4 for a seasonal comparison between phytoplankton produc-

tion estimates from ESM and Eppley-VGPM.



the larva that improved through ontogeny, (2) the image size

of the prey, and (3) the light level (Aksnes & Utne, 1997). The

light conditions at the depth of the larva were estimated based

on how the surface light attenuated with depth, as well as

beam attenuation (loss of light) between the predator and the

prey. The attenuation coefficient was calculated assuming a

background attenuation of 0.1 m�1, which increased depend-

ing on the chlorophyll level in the surface layer (Riley, 1956).

The chlorophyll and light values were taken from the ESM

model. The biological characteristics of the prey, such as its

contrast to the background and its image area, affected the

visibility to the larva (Fiksen & MacKenzie, 2002).

The movement of the larva during feeding was modeled as

a pause-travel pattern, where the larva searched for prey in

the visible half-sphere in front of its snout during pause

(O’Brien et al., 1989). If a prey was located within the field of

perception, or a prey item was moved into the field of percep-

tion due to turbulence, the larva would move to attack posi-

tion. The probability of attack success (PCA, [0,1]) could be

simplified and represented as a linear function:

PCA ¼ max 0:0;minð1:0;�16:7 � LpreyLfish

þ 3

2Þ

� �ð3Þ

Here, Lprey (mm) was the length of the prey, and Lfish (mm)

was the length of the larval or juvenile fish (Lfish 2 [3,20]

(mm)). As the larva grew and developed, its ability and

success in capturing larger prey items increased.

Growth and metabolism module. The feeding module added

the total ingested biomass captured during one time-step to

the food biomass already stored in the larval gut, where it was

used to estimate growth rate. If the larva captured enough

prey biomass required to grow at the physiological maximum,

the growth was restricted by temperature alone (Folkvord,

2005). If the larva captured less than required, the growth rate

was determined by the total energy available in the gut after

all other costs (routine and active metabolism, digestion) were

accounted for (Kristiansen et al., 2007, 2009b). The tempera-

ture- and weight-dependent maximum specific growth rate

(Folkvord, 2005) was:

SGRðw;TÞ ¼ a� b lnðwÞ � c lnðwÞ2 þ d lnðwÞ3 ð4Þ

Here, SGR was the specific growth rate (% day�1), as a func-

tion of temperature (T) and dry mass of the larva (w, mg),

parameterized by a = 1.08 + 1.79T, b = 0.074T, c = 0.0965T,

d = 0.0112T. The instantaneous growth rate g (day�1) wasg¼ðlnðSGR�dtÞ100þ1Þ=dt . Routine metabolism R = 57.12�e�7w0.9�exp (0.088�T)

(R, mg day�1) of larval cod was parameterized from Finn et al.

(2002) as a function of larval weight (dry-weight, w, mg) and

temperature (T). Metabolism increased when the larvae were

active, and we defined active metabolism as Ractive = 2.5R for

larval body length SL ≥5.5 mm and Ractive = 1.4R for larval

body length SL <5.5 mm (Lough et al., 2005). Active metabo-

lism (Ractive) was used when light was above a threshold

(0.01 lmol s�1 m�2). When light level was too low for feeding

(no activity), active metabolism equaled routine metabolism

(Ractive = R). The dry-weight biomass of food in the gut

required to grow at the maximum, temperature- and weight-

dependent rate was therefore:

Dmax ¼ ððexpðg � dtÞ � 1Þ � wt�1 þ Ractive � dtÞ=A ð5Þ

where A (dimensionless) was the assimilation efficiency

(Buckley & Dillmann, 1982), dt was the time-step length, wt�1

was the larval weight calculated at the previous time-step,

and Ractive was active metabolism. If the stomach content (st,

mg) was lower than required (Dmax, mg) to attain SGR, growth

was food-limited and constrained by the food available in the

stomach. The available biomass of food in the stomach at the

current time-step (st) was a function of the ingested material

(i), the remaining stomach content from last time-step (St�1),

and the food biomass extracted for growth, respiration, and

lost to egestion (D 2 [0, Dmax]):

St ¼ St�1 �Dþ i ð6Þ

Finally, the larval weight (wt) at the current time-step (t)

was given by

wt ¼ wt�1 � expðg � dtÞ if Dmax � Stwt�1 þ StA� Ractive � dt if Dmax [ St

�ð7Þ

Predation module. Mortality rates from predation were esti-

mated from two sources; (1) visual predation from larger fish

and (2) size-dependent (body length) invertebrate predation

(e.g., jellyfish). Our baseline model for visual predation was

that described by Fiksen et al. (2002) and Fiksen & Jørgensen

(2011) and assumed a fixed density of piscivores (0.00015

fish m�3). A second case where piscivore density varied with

mesozooplankton biomass was also considered in recognition

that piscivore biomass could also vary over the long time-

scales considered in this study. Visual predation from pisci-

vores changed with the light intensity and therefore varied

through the day and with depth (Fig. S2). Predation rate from

fish (mf, h�1) was assumed to be proportional to the square of

the visual range (Fiksen et al., 2002) mf = 0.05�P2, where P

(mm) was the light- and prey size-dependent perception dis-

tance of the piscivores [the coefficient 0.05 summarized all fac-

tors such as fish density and escape probability; see Fiksen

et al. (2002) for details]. Mortality from invertebrate predators

(mn, h�1), mn = 0.01�SL�1.3, decreased with larval length (SL,

mm) and was constant with depth and time of the day

(McGurk, 1986). Total predation rate from both visual and

invertebrate predators (mz, h�1) mz = mn + mf was thus a func-

tion of light, density of piscivores, and larval body length. If

the body mass of the larva decreased to 80% or less of the

expected value at any length, then the larva starved and

probability of survival was set to zero (Fiksen et al., 1998).

Vertical behavior module. Between each time-step, the larva

was allowed to move to a new depth position. The choice of

depth level was estimated as a trade-off between the local

ingestion rate and predation rate from piscivore and inverte-

brate predators. The algorithm for estimating vertical behavior

assumed that the larva could ‘sense’ the ingestion and preda-

tion rates within swimming distance above and below current



depth position. The larva moved to the depth that minimized

predation and maximized ingestion rates (Kristiansen et al.,

2009a). If the larva had less than 30% gut fullness, the risk of

starvation was considered higher than the risk of being eaten

and the larva would move to well-lit areas of the water col-

umn to feed (for details, see Kristiansen et al., 2009a). For each

time-step, the larva could swim upward or downward a cer-

tain distance determined as a function of its body length (Peck

et al., 2006). If the larva swam the maximum distance, an extra

activity cost of 10% of the routine metabolism was added to

the total metabolism. If the larva swam a shorter distance, the

activity cost was scaled relative to the maximum cost (Vikebø

et al., 2007). We did not consider if vertical velocities from the

ESM would affect the larval vertical migration.

Model runs. We released a cohort of 10 individual larval fish

with body length 5 � 0.2 mm every 7 days throughout the

year for 150 years (1950–2099) and tracked the growth and sur-

vival rates of each individual for the first 30 days of their lives

after hatching. The time-step of the model was 1 h. We con-

ducted experiments releasing 10, 50, and 100 individuals and

found no significant difference between experimental results

except in the time it took to do the simulations. The total

amount of larva released for each region for the 1950–2099 sim-

ulations was 78,000. This allowed us to analyze growth and

survival patterns for the critical first 30 days of larval life

under past and future climate conditions. The long-term sur-

vival for larval fish depended on both the larval survival and

the health of the larvae after 30 days (Leggett & Deblois, 1994).

Thus, in addition to analysis of changes in larval survival, we

also show the weight at age 30 days. These simulations were

conducted for both a baseline ‘constant piscivore’ case and a

case where piscivore biomass was assumed to vary in propor-

tion to the mesozooplankton biomass. As climate change may

lead to changes in the timing of the optimal environmental

conditions for spawning, we modeled growth and survival

throughout the year. Because the spawning period varies con-

siderably in time between the Atlantic cod stocks considered

here (ICES, 2005), we decided to present the results (growth,

survival) as annual averages to make comparison between

regions easier. However, as the peak spawning currently occurs

during spring (February – May) for all cod stocks, we also

decided to present average values for this specific period.

To assess the direct impact of changes in temperature on cod

larvae relative to changes in primary production on larval

growth and survival, we repeated the baseline simulations

using input data from the ESMwhere the temperature had been

detrended. The detrending involved estimating the linear

regression function for the temperature predictions (2000–

2099), subtracting the regressed value from the predicted tem-

perature, and adding the temperature climatology (1950–1999).

This removed the temperature trend while preserving the vari-

ability. The difference between simulations including long-

term changes in temperature and phytoplankton production

and those including only changes in productivity was used to

assess the magnitude and direction of the direct temperature

effect on metabolism at each site. The detrended temperature

was also used as input to the zooplankton productionmodel.

Assessing the model responses to past climate varia-

tions. Before analyzing the projected changes in cod larvae

dynamics due to climate change, we assessed the consistency

of model results with past basin-scale variations. Specifically,

temperature anomalies were (1) positively correlated with

recruitment for Atlantic cod stocks near the cold-water limit of

the species range (i.e., warm anomalies favor recruitment at

Lofoten, Iceland), (2) uncorrelated for mid-temperature stocks,

and (3) negatively correlated for stocks near the warm-water

limit of the species range (Planque & Fr�edou, 1999; Drink-

water, 2005). While the response of an ecosystem to climate

change may be fundamentally different than the response to

past climate variations, the extent to which the model

produces results consistent with the observed gradient in tem-

perature-recruitment sensitivity would increase confidence in

future projections.

Century-scale climate change simulations are designed to

capture multidecadal climate trends associated with green-

house gas accumulation in the atmosphere and simulations

also resolve many modes of internal climate variability (e.g.,

the North Atlantic Oscillation and the Atlantic Multidecadal

Oscillation). The exact timing of changes in the states of the

modes, however, will not match those in the historical record

because only remote forcing (e.g., the concentration of green-

house gases) links the simulations to a specific year. Thus,

following the recommendations of Stock et al. (2011), we orga-

nized the comparison of the modeled 1950–1999 conditions

and observations by temperature anomaly rather than on a

year-by-year basis.

Observations of the number of cod recruits were available

from 1979 to 2000 for GB, 1963–2001 for Lofoten and Iceland,

and 1964–2001 for the North Sea. Data on 0-group abundance

(juveniles aged 5–6 months) were available for Lofoten from

1965 to 2001, for Iceland from 1970 to 2001, for GB 1978–1987,

and for the North Sea from 1991 to 2001. Data for all regions

were obtained from ICES (http://www.ices.dk), except for

West Greenland where neither recruitment nor 0-group data

were available. Environmental conditions at the time of

observation were represented by the annual average tem-

perature anomaly of the upper 50 m of the water column

for each region based on the Simple Ocean Data Assimila-

tion database (SODA, http://www.atmos.umd.edu/�ocean/). SODA is a global re-analysis of the ocean climate

(Carton & Giese, 2008) from 1958 to 2002 based on an

0.5° 9 0.5° ocean model. The model uses assimilation to

constrain simulations to observed temperatures and salini-

ties, which were derived principally from the World Ocean

Atlas (Locarnini et al., 2010). SODA provides a realistic

time series of the ocean conditions at the time of observa-

tions. Temperature anomalies were calculated by subtract-

ing the 1961–1990 climatology from the annual average.

For this analysis, we used the 1961–1990 climatology rather

than 1950–1999 because the earliest start of the recruitment

time series was 1963. Previously observed temperature was

correlated with observed number of recruits and 0-group.

As recruitment is defined as the number of 1-year-old fish

in the North Sea and Georges Bank and the number of

3-year-old fish in Lofoten and Iceland (i.e., because colder



water results in slower maturation), temperatures were

lagged by 1 and 3 years, respectively.

Our IBM simulated the life of fish for the first 30 days after

hatching, which is a significantly different age than the recruit-

ment data, but less different from age of 0-group. However,

recruitment is indicative of larval survival probability and size

at age. Larger larvae have a higher probability of reaching the

age of recruitment because faster growth means that they can

outgrow the prey size of their predators and spend less time at

the larval stage where they are vulnerable to predation. Larger

larvae also have improved mobility and behavior that favors

feeding, enabling them to feed on larger prey and increase

their chance of prey capture. Size is also a good indicator of

prey resources and growth conditions, particularly during

spring. We therefore defined a fitness index that combined

weight at age 30 days after hatching multiplied by survival

probability to compare against the observed number of recruits

and 0-group, while recognizing that differences between

30-day fitness estimated by the model and recruitment/0-

group abundance may contribute to model-data discrepancies.

Results

Changes in the physical properties of the North Atlantic

Results from ESM2.1 projected ocean temperatures to

increase over most of the North Atlantic above 35°N,

with the prominent exception of the pronounced cooling

in the Northwest Atlantic south of Greenland (Figs 2a–eand 3a–b). Of the 5 areas considered, Lofoten, Georges

Bank, and the North Sea showed increasing tempera-

tures with time, Iceland was largely unchanged, and

West Greenland cooled (Table 3). Changes were modest

during the 2000–2049 period before reaching magni-

tudes of 1–2 °C by 2050–2099. The temperature changes

predicted by ESM2.1 were similar in magnitude and

spatial structure to other climate models (Capotondi

et al., 2012) including cooling in the vicinity of West

Greenland, which has been linked to changes in ocean

circulation and mixing (Xie et al., 2010).

The North Atlantic basin above 35°N generally

became less saline (Fig. 3c–d). The freshening was

caused by a combination of increased ice melt and an

increasing excess of precipitation relative to evapora-

tion at high latitudes (Capotondi et al., 2012). The com-

bination of increased ocean temperature with fresh

surface water strengthened the stratification in the

northern parts of the North Atlantic, creating a shal-

lower annual mean mixed layer depth (Fig. 3e–f).

Changes in biological productivity of the North Atlantic

Results from the ESM2.1 projected a decrease in net pri-

mary productivity (NPP) of 6% on average between

1950–1999 and 2000–2049 and 16% by 2050–2099 over

the North Atlantic above 35°N (Fig. 4c). However, frac-

tional decreases in large phytoplankton productivity,

which are prominent during the spring and fall blooms

in the region, were much more pronounced, particu-

larly during the 2050–2099 period (Fig. 4b, Table 3).

There was a clear spatial overlap between areas where

the mixed layer depth (Fig. 3e–f) decreased and areas

with sharp declines in large phytoplankton production.

Production of small phytoplankton increased modestly

in all areas by 2050–2099 with the exception of a modest

decrease in the GB/GOM region (Fig. 4a, Table 3).

These increases, however, were not large enough to

prevent an overall decline in primary production. The

changes in large and small phytoplankton production,

combined with changes in ocean temperature, reduced

the total modeled abundance (averaged over the full

year) of zooplankton by 32, 58, 9, 17, and 35% in the

2050–2099 period relative to 1950–1999 at Georges

Bank, West Greenland, Iceland, North Sea, and Lofoten,

respectively (Table 3). These strong regional changes in

NPP and associated planktonic productivity were near

the upper end of the range of projected global mean

impacts (2–20% decreases in NPP (Steinacher et al.,

2010)), but consistent with the strong regional decreases

predicted by these same models under the A2 scenario

in the North Atlantic from 30°N to 80°N.

Assessment of the larval response to past climatevariations

The modeled larval fitness index exhibited a significant

positive correlation with temperature at Lofoten

(Fig. 5 h, r = 0.3, P = 0.07) and Iceland (Fig. 5d,

r = 0.33, P = 0.041), sites near the cold-water limit of

the Atlantic cod range. This was consistent with signifi-

cant positive correlations found in observation-based

recruitment and 0-group abundance for these sites

(Fig. 5 g and c). The modeled temperature variability

was also consistent with observations at these sites –with Lofoten experiencing larger fluctuations than

Iceland.

In the North Sea, near the warm-water limit of the

range of Atlantic cod, the positive relationship between

temperature and fitness found for Lofoten and Iceland

disappeared and was replaced with an insignificant

negative trend (Fig. 5e, r = �0.11, P = 0.5). This was

consistent with a similarly modest but statistically

insignificant trend in 0-group abundance in the North

Sea (Fig. 5f, r = �0.19, P = 0.57). The modeled fitness

could not fully explain the significant negative trend

seen in recruitment, but we note that 0-group abun-

dance was likely more closely related to 30-day fitness

(see Methods).



The main discrepancy between the model and obser-

vation-based recruitment estimates occurred in the GB

region (Fig. 5a and b), where the modeled 30-day fit-

ness showed a robust positive correlation (r = 0.5,

P = 0.0014) with temperature anomalies, while recruit-

ment estimates suggested an insignificant correlation

typical of a mid-temperature stock (r = �0.28,

P = 0.19). However, the 0-group abundance data

showed a positive correlation, although insignificant

(r = 0.41, P = 0.24) with temperature anomalies. Corre-

lations between modeled fitness and 0-group abun-

dance with temperature anomalies were therefore

a

b c

d

eGreenland

UK

Canada

°C

Dep

th (

m)

Time (month/year)

(a)

(b)

(c)

(d)

(e)

Fig. 2 Map of the North Atlantic indicating the five spawning locations of interest used in this study; (a) Georges Bank, (b) West

Greenland, (c) Iceland, (d) the North Sea, and (e) Lofoten. Also shown are the time-depth (upper 35–45 m) vertical profiles of the Earth

System Model predicted temperature for the period July 2003–June 2097. The vertical profiles were smoothed with a 5-year running

mean.



comparable in direction and strength. However,

discrepancies between modeled fitness and the recruit-

ment response along with the influence of the delayed

Gulf Stream separation in coarse climate models on

water mass properties in the GB region (Table 1) sug-

gest cautious interpretation of GB projections. The

robust contrasts between historical temperature

responses for Lofoten/Iceland relative to the North Sea,

in contrast, are encouraging.

Future changes in specific growth and survival rate oflarval cod

Under the assumption of constant piscivore and inver-

tebrate biomass, predicted larval survival rates after the

first 30 days posthatching showed that the survival

potential decreased during the twenty-first century at

all five spawning grounds relative to the 1950–1999climatology (Fig. 6). In fact, the annual mean average

survival probability during the 2050–2099 period

decreased by 22–54% relative to survival probability

during 1950–1999 in the North Atlantic (Table 3). The

average change in the annual mean survival probability

by 2050–2099 across all sites was �34%, while for the

peak season, survival decreased by 26%.

During the 2000–2049 period, when basinwide

temperature and plankton productivity changes were

generally modest, larval weights and survival were

maintained at levels comparable to 1950–1999 at the

Georges Bank, Iceland and the North Sea sites (Fig. 6).

The primary exception to this was West Greenland,

where pronounced projected increases in stratification

and decreases in plankton productivity already showed

significant impact on larval weight and survival, partic-

ularly in the autumn. Decreased autumn survival was

associated with an increase in starvation mortality and

invertebrate predation associated with low larval

weight, and occurred despite a pronounced drop in

piscivore predation reflecting a reduction of larvae in

well-lit regions close to the ocean surface (Fig. 7). Lofo-

ten exhibited a similar though less pronounced

response than Greenland.

By 2050–2099, the more pronounced increases in

ocean temperatures and stratification and larger

decreases in plankton productivity began to signifi-

cantly affect larval dynamics at all sites. Varying reduc-

tions in growth rates and body weight (Fig. 6, S3,

Table 4) of larval cod during early spring and autumn

across all regions (Table 4) resulted in increased starva-

tion and mortality from invertebrates (Fig. 7, Table 5)

during these periods. Later in the spring and in the

summer, elevated prey encounter rates due to season-

ally stronger irradiance combined with high ocean tem-

peratures (Tables 3 and 4), enabled high growth rates

Table 3 Table showing the relative change between the climatology (1950–1999) and the periods 2000–2049 and 2050–2099 for

variables calculated by the Earth System Model (ESM) and the individual-based model (IBM) model. Temperature anomaly (°C)indicates change relative to climatology, where the minimum to maximum temperature range is indicated in parenthesis. Also

shown are the relative change in extreme temperature ranges (minimum and maximum temperature), the relative change (%) of

small and large phytoplankton production rates, and zooplankton abundance between time periods 2000–2049, and 2050–2099 and

the climatology of 1950–1999. Light grey cells indicate positive values, while white indicates negative, or no change. Positive values

in the relative change in extreme temperature ranges indicate that the minimum or maximum range of temperature is increasing

relative to climatology, while negative values indicate the opposite.

Location

Period

relative to

1950–1999

ESM IBM

Temp (°C)(min–max)

Change

temperature

extreme

min, max

Small

phytoplankton

productivity (%)

Large

phytoplankton

productivity (%)

Zooplankton

abundance (%)

Average

survival

through year

(peak season –

February to

May) (%)

Georges

Bank

2000–2049 0.1 (9.1–19.6) 0.30, 0.00 �5.7 �14.4 �12.2 �7.0 (�12.0)

2050–2099 1.7 (10.6–21.6) 1.70, 2.00 �8.7 �37.7 �31.7 �36.0 (�29.0)

West

Greenland

2000–2049 �1.0 (0.3–8.7) �1.30, �0.70 4.5 �32.9 �30.1 �22.0 (�21.0)

2050–2099 �1.4 (�0.4 to 8.4) �2.00, �1.00 5.1 �64.4 �57.8 �37.0 (�35.0)

Iceland 2000–2049 �0.1 (6.0–11.8) �0.10, �0.00 4.9 �2.2 �0.9 �1.0 (�12.0)

2050–2099 0.2 (6.4–11.8) 0.30, 0.00 16.6 �9.3 �9.1 �22.0 (�22.0)

North Sea 2000–2049 0.2 (4.9–14.5) 0.30, 0.20 �0.7 1.9 2.4 �4.0 (�4.0)

2050–2099 1.5 (6.2–15.8) 1.60, 1.50 0.1 �20.9 �16.6 �26.0 (�21.0)

Lofoten 2000–2049 0.1 (4.0–11.9) �0.20, 0.40 7.9 �14.2 �13.8 �21.0 (�0.1)

2050–2099 1.0 (4.9–12.4) 0.70, 0.80 15.8 �34.3 �34.3 �54.0 (�25.0)



and near prior to climate change weights were main-

tained at all sites except West Greenland (Fig. 6,

Table 4). However, high feeding rates during these

periods could only be maintained with increased forag-

ing in well-lit surface waters (Fig. 6, right column),

making larvae more susceptible to visual predation

(Fig. 7, middle column) and often led to periods of

increased mortality rates from piscivores. The annual

average mortality from starvation increased strongly at

all sites by 2099 relative to climatology (Table 5).

(a)

(c)

(b)

(d)

(e) (f)

psu

% %

psu

°C°C

Fig. 3 The Earth System Model modeled 50-year average temperature (a, b), salinity (c, d), and mixed layer depth (MLD, e, f) anoma-

lies for a, c, e) 2000–2049 and b, d, f) 2050–2099 relative to the 1950–1999 climatology. Also shown are the 9 °C and 10 °C contour lines

for temperature and the 32–36 psu contour lines for salinity. The anomalies are in degrees celsius for temperature, psu for salinity, and

percentage change for MLD.



%

%

%

(a)

(b)

(c)

Fig. 4 Percent change between 1950–1999 and 2050–2099 in (a) small phytoplankton production, (b) large phytoplankton production,

and (c) total phytoplankton production. Large phytoplankton is a major food resource for larval and juvenile stage fish and a necessary

requirement for fish survival. Circles indicate center of our five study locations.



Nor

mal

ized

obs

erve

d r

ecru

its (

squa

res)

and

0-g

roup

(ci

rcle

s)

Temperature anomaly (°C)

Warm(a)

(c)

(e)

(g)

Cold (b)

(d)

(f)

(h)

Cold Warm

Nor

mal

ized

mod

eled

fitn

ess

(wei

ght

• su

rviv

al p

roba

bilit

y)

ModelObservations



Sensitivity simulations where the piscivore abun-

dance decreased in proportion to declines in meso-

zooplankton abundance suggested that declines in

piscivore biomass proportional to declines in mesozoo-

plankton could enable cod to maintain survival rates at

most sites (data not shown). While the productivity of

the North Atlantic was projected to decrease, prey

resources did generally remain high enough to main-

tain comparable growth and survival if cod larvae were

able to exploit high prey regions in well-lit parts of the

water column without large predation risks. Sensitivity

simulations assuming a decrease in piscivore predation

led to an average survival probability increase by 14%

and 59% for the periods 2000–2049 and 2050–2099 rela-

tive to simulations assuming constant piscivore preda-

tion. The lone exception to this was the West Greenland

site, where very large productivity declines led to mod-

erate decreases in larval survival (~10%) despite

decreased piscivore biomass.

Effects of temperature change due to climate change vs.climate variability

Removing the observedwarming trend (Table 1, Fig. S1)

from the North Sea and Georges Bank led to significant

increases in survival at these already relatively warm-

water sites (+7% and +11% during the 2050–2099 period,Table 6). Warm-water anomalies at warm-water sites

tend to lead to decreased recruitment and higher meta-

bolic costs (Drinkwater, 2005). By subtracting the trends,

this led to cooler temperatures, and hence, the increase

in survival compared to the case with the trend

included. This response is consistent with past-observed

responses to warm-water anomalies at warm-water

sites (Planque & Fr�edou, 1999; Beaugrand et al., 2003;

Drinkwater, 2005) and reflects lower metabolic costs and

reduced starvation mortality in cooler waters. The sur-

vival probability at Iceland showed little change after

removing the long-term temperature trend because

projected temperature changes at Iceland were very

small.

The response to removing the direct impact of long-

term temperature trends on cod larvae in colder regions

(i.e., Lofoten and West Greenland) was opposite of that

expected from past observed responses to climate vari-

ability (Table 6). Removing the warming signal at Lofo-

ten led to an increase in cod survival in 2050–2099,contrary to the response to warm/cold variability

described in Ottersen & Loeng (2000). Likewise, remov-

ing the cooling signal from the already cold West

Greenland site led to decreased survival (�14.6%)

when the response to past variation suggests that pre-

venting further cooling of such a cold-water system

should enhance recruitment (Planque & Fr�edou, 1999).

In both of these cases, the model suggested that strong

declines in prey abundance predicted under climate

change limited the advantages of higher maximum

growth in warmer temperatures from being realized.

Discussion

Our results suggest that projected climate changes will

decrease phytoplankton and zooplankton production

and increase ocean temperatures, reducing larval cod

survival probability in the North Atlantic by ~22–54% if

piscivore and invertebrate abundance are maintained.

Reduced phytoplankton production decreased zoo-

plankton abundance, resulting in reduced larval body

weight and slower developmental rates that increased

susceptibility to invertebrate predation and the proba-

bility of mortality from starvation. We also found that

in contrast to past observed responses to climate vari-

ability in which warm anomalies led to better recruit-

ment in cold-water stocks, our simulations suggested

that reduced prey availability and warmer tempera-

tures under climate change had a negative impact on

larval survival throughout the range of Atlantic cod.

The results of ESM2.1 projections and mesozooplank-

ton submodels under a high CO2 emissions scenario

projected declining productivity and substantial

(~30%) declines in mesozooplankton biomass between

1950–1999 and 2050–2099. These projected trends are

Fig. 5 Observed (left column) temperature anomalies and normalized recruitment (squares, left column) and 0-group abundance (cir-

cles) for cold (blue) and warm (red) years compared with modeled (right column) temperature anomalies and normalized fitness

(weight multiplied by survival probability) for cold (blue) and warm (red) years for the regions Georges Bank (a, b), Iceland (c, d), the

North Sea (e, f), and Lofoten (g, h). Smaller circles and squares indicate annual values, while larger blue and red circles and squares

indicate the average regression of cold and warm values, respectively. The grey lines indicate the slope of the linear correlations. The

temperature anomalies were calculated relative to the 1961–1990 climatology. The Spearman rank correlations between observed tem-

perature and number of recruits (left column, squares) were r = �0.28 (P = 0.19), r = 0.37 (P = 0.016), r = �0.42 (P = 0.008), and

r = 0.64 (P = 0.0005) for the Georges Bank, Iceland, North Sea, and Lofoten, respectively. Correlations between 0-group abundance and

temperature (left column, circles) were generally higher with r = 0.41 (P = 0.24), r = 0.31 (P = 0.08), r = �0.19 (P = 0.57), r = 0.54

(P = 0.0006) for Georges Bank, Iceland, North Sea, and Lofoten, respectively. Correlations between temperature and the fitness index

(right column) were r = 0.5 (P = 0.0014), r = 0.33 (P = 0.041), r = �0.11 (P = 0.5), and r = 0.3 (P = 0.07), for Georges Bank, Iceland,

North Sea, and Lofoten, respectively.



Wgt

(m

g)

Cha

nge

in fr

actio

n of

indi

vidu

als

abov

e 15

m

Sur

viva

l pro

babi

lity

(%)

(a)

(d)

(g)

(j)

(m)

(b) (c)

(e) (f)

(h) (i)

(l)(k)

(n) (o)

Fig. 6 The modeled survival probability, weight, and change in fraction of occurrence of larval fish above 15 m depth, averaged over

50-year period: 1950–1999, 2000–2049 (green lines), and 2050–2099 (red lines) for (a, b, c) Georges Bank, (d, e, f) West Greenland, (g, h, i)

Iceland, (j, k, l) the North Sea, and (m, n, o) Lofoten. The shaded areas indicate the standard deviations within each 50-year period and

the inter-annual variability for each variable. Positive values in the right-hand panels (c, f, i, l, o) indicate higher frequency in the upper

15 m, while negative values indicate the opposite.



Table

4Annual

andseasonal

averag

edvalues

ofmodeled

weightincrease

over

thefirst30

day

sin

thelife

oflarval

cod(%

),av

erag

edaily

specificgrowth

rate

(%d�1),an

d

averag

etemperature

(°C)experiencedover

30day

sbylarval

codfortheperiods19

50–199

9,20

00–204

9,an

d20

50–209

9.Theseasonal

averag

eswerecalculatedbyav

erag

ingover

themonthsofJanuary,Feb

ruary,an

dMarch

(JFM),April,May

,an

dJune(A

MJ),July,August,Sep

tember

(JAS),an

dOctober,Novem

ber,Decem

ber

(OND)

Area

Period

Chan

ge(%

)in

weightover

first30

day

sSpecificgrowth

rate

(%d�1)

Tem

perature

(°C)experiencedbylarvae

Annual

averag

eJFM

AMJ

JAS

OND

Annual

averag

eJFM

AMJ

JAS

OND

Annual

averag

eJFM

AMJ

JAS

OND

Geo

rges

Ban

k19

50–199

946

9.46

92.71

583.13

948.98

252.96

5.44

0.84

8.21

10.14

2.52

9.43

6.6

7.25

12.52

11.34

Geo

rges

Ban

k20

00–204

944

1.29

59.95

577.47

899.87

231.08

5.17

0.06

8.20

9.93

2.44

9.4

6.88

7.35

11.74

11.59

Geo

rges

Ban

k20

50–209

948

6.21

57.38

803.33

1004

.97

95.24

4.18

�0.7

9.02

9.16

�0.7

10.87

8.29

8.7

13.24

13.18

WestGreen

land

1950–199

916

4.46

�2.07

171.73

461.77

29.35

2.08

�2.02

3.88

7.31

�0.84

3.59

1.16

2.92

6.69

3.58

WestGreen

land

2000–204

910

8.39

011

1.63

310.89

14.65

1.24

�1.82

2.56

5.37

�1.1

2.69

0.02

1.89

5.75

3.1

WestGreen

land

2050–209

953

.85

2.17

78.17

130.09

4.66

0.15

�1.69

1.51

2.47

�1.65

1.9

�0.64

1.12

4.21

2.89

Icelan

d19

50–199

961

5.81

�2.16

949.44

1406

.04

110.09

3.47

�4.06

8.33

10.88

�1.21

8.22

7.01

7.78

10.14

7.94

Icelan

d20

00–204

957

6.07

�3.71

778.97

1385

.614

3.7

3.44

�3.99

7.19

10.99

�0.35

8.08

6.94

7.49

9.82

8.03

Icelan

d20

50–209

963

3.67

�4.15

884.06

1535

.93

117.11

3.28

�4.29

7.45

11.1

�1.02

8.45

7.35

7.91

10.19

8.32

NorthSea

1950–199

967

3.24

93.72

942.01

1425

.82

212.75

5.82

0.71

9.83

11.4

1.2

9.87

6.01

8.2

14.05

11.21

NorthSea

2000–204

971

1.95

74.76

924.82

1562

.75

269.96

5.76

�0.11

9.53

11.68

1.8

10.09

6.52

7.99

14.02

11.82

NorthSea

2050–209

972

6.95

70.07

1156

.11

1486

.87

172.4

5�0

.63

10.22

10.44

�0.18

11.27

7.62

9.21

15.26

12.98

Lofoten

1950–199

937

8.09

0.88

538.71

990.23

15.11

2.3

�3.29

6.92

8.62

�2.79

7.03

5.29

5.85

9.46

7.53

Lofoten

2000–204

936

4.95

�0.3

485.91

1005

.19

11.27

2.1

�3.27

6.27

8.59

�2.79

7.14

5.24

5.66

9.55

8.12

Lofoten

2050–209

943

3.23

�1.53

673.84

1090

.08

4.03

1.77

�3.69

7.01

7.62

�3.51

86.2

6.58

10.3

8.94



(a)

(d)

(g)

(j)

(m)

(b)

(e)

(h)

(k)

(n)

Cha

nge

pisc

ivor

e (f

ish)

pre

dtio

n (%

)

Cha

nge

inve

rteb

rate

pre

datio

n (%

)

Cha

nge

in p

roba

bilit

y of

dyi

ng fr

om s

tarv

atio

n (%

)(c)

(f)

(i)

(l)

(o)

Fig. 7 Percent change in probability of dying from starvation, cumulative (integrated exposure to predators over 30 days) invertebrate

(body length dependent) and piscivore (visual dependent) predation between 2000–2049 (green lines) and 2050–2099 (red lines) relative

to climatology 1950–1999 for (a, b, c) Georges Bank, (d, e, f) West Greenland, (g, h, i) Iceland, (j, k, l) the North Sea, and (m, n, o) Lofo-

ten. The shaded areas indicate the standard deviations within each 50-year period and the interannual variability for each variable.



Table

5Annual

andseasonal

averag

edvalues

oftheprobab

ilityofdeath

from

starvation(%

),av

erag

emortalityrate

from

invertebratesan

dpiscivores(h

�1)fortheperiods

1950–199

9,20

00–204

9,an

d20

50–209

9.Theseasonal

averag

eswerecalculatedbyav

erag

ingover

themonthsofJanuary,Feb

ruary,March

(JFM),April,May

,June(A

MJ),July,

August,Sep

tember

(JAS),an

dOctober,Novem

ber,Decem

ber

(OND)

Area

Period

Probab

ility(%

)ofdeath

from

starvation

Averag

emortalityrate

(h�1)from

invertebrates

Averag

emortalityrate

(h�1)from

piscivores

Annual

averag

eJFM

AMJ

JAS

OND

Annual

averag

eJFM

AMJ

JAS

OND

Annual

averag

eJFM

AMJ

JAS

OND

Geo

rges

Ban

k19

50–199

923

.32

48.28

00

44.62

1.38

E-06

1.68

E-06

1.22

E-06

1.11

E-06

1.53

E-06

9.20

E-07

6.40

E-07

1.06

E-06

1.23

E-06

7.30

E-07

Geo

rges

Ban

k20

00–204

923

.67

56.85

00

37.58

1.40

E-06

1.72

E-06

1.22

E-06

1.13

E-06

1.56

E-06

9.20

E-07

6.00

E-07

1.09

E-06

1.25

E-06

7.20

E-07

Geo

rges

Ban

k20

50–209

934

.71

66.92

04.93

67.77

1.46

E-06

1.74

E-06

1.18

E-06

1.18

E-06

1.72

E-06

9.10

E-07

5.90

E-07

1.21

E-06

1.27

E-06

5.90

E-07

West

Green

land

1950–199

939

.99

94.13

2.69

0.78

63.72

1.58

E-06

1.79

E-06

1.50

E-06

1.26

E-06

1.75

E-06

8.70

E-07

5.00

E-07

1.17

E-06

1.37

E-06

4.50

E-07

West

Green

land

2000–204

942

.85

94.8

5.7

2.64

68.53

1.64

E-06

1.79

E-06

1.60

E-06

1.40

E-06

1.78

E-06

8.20

E-07

4.70

E-07

1.10

E-06

1.27

E-06

4.50

E-07

West

Green

land

2050–209

951

.41

93.54

7.48

9.78

93.28

1.71

E-06

1.79

E-06

1.67

E-06

1.60

E-06

1.79

E-06

7.00

E-07

4.70

E-07

9.90

E-07

9.10

E-07

4.30

E-07

Icelan

d19

50–199

949

.22

100

20.66

076

.13

1.44

E-06

1.79

E-06

1.25

E-06

1.05

E-06

1.69

E-06

1.09

E-06

4.80

E-07

1.68

E-06

1.70

E-06

4.90

E-07

Icelan

d20

00–204

948

.78

100

26.79

067

.94

1.44

E-06

1.79

E-06

1.30

E-06

1.04

E-06

1.65

E-06

1.08

E-06

4.50

E-07

1.55

E-06

1.78

E-06

5.40

E-07

Icelan

d20

50–209

949

.73

100

26.53

0.43

71.51

1.45

E-06

1.79

E-06

1.29

E-06

1.04

E-06

1.69

E-06

1.11

E-06

4.60

E-07

1.60

E-06

1.87

E-06

5.10

E-07

NorthSea

1950–199

926

.84

51.06

00

57.12

1.37

E-06

1.68

E-06

1.16

E-06

1.07

E-06

1.58

E-06

1.20

E-06

6.10

E-07

1.75

E-06

1.80

E-06

6.30

E-07

NorthSea

2000–204

927

.41

58.57

00

52.04

1.37

E-06

1.71

E-06

1.17

E-06

1.06

E-06

1.55

E-06

1.20

E-06

5.70

E-07

1.66

E-06

1.89

E-06

6.70

E-07

NorthSea

2050–209

932

.32

66.33

00.77

64.29

1.42

E-06

1.73

E-06

1.14

E-06

1.16

E-06

1.65

E-06

1.19

E-06

5.60

E-07

1.80

E-06

1.79

E-06

6.00

E-07

Lofoten

1950–199

952

.12

99.22

12.76

2.6

93.4

1.53

E-06

1.79

E-06

1.31

E-06

1.21

E-06

1.78

E-06

1.01

E-06

4.50

E-07

1.56

E-06

1.70

E-06

3.50

E-07

Lofoten

2000–204

954

.57

99.06

14.29

8.5

93.79

1.54

E-06

1.79

E-06

1.35

E-06

1.21

E-06

1.78

E-06

1.02

E-06

4.20

E-07

1.50

E-06

1.80

E-06

3.80

E-07

Lofoten

2050–209

959

.18

100

14.71

18.71

99.15

1.55

E-06

1.79

E-06

1.32

E-06

1.28

E-06

1.79

E-06

1.06

E-06

4.20

E-07

1.65

E-06

1.83

E-06

3.70

E-07



consistent with past analyses (Deser et al., 2010a; Stein-

acher et al., 2010; Capotondi et al., 2012) and results

from other earth system simulations contributed to the

IPCC’s fifth assessment report (Fig. 8). The predicted

decreases can be attributed to increased stratification

over the North Atlantic, which limited nutrient supply

to the surface layer and reduced primary productivity,

particularly large phytoplankton. However, there were

large regional differences in primary productivity

changes (Fig. 4) and regional details differ across mod-

els (Fig. 8). Full characterization of possible regional-

scale responses will require full sampling of these

possibilities. A few areas may experience greater phyto-

plankton and zooplankton production. This scenario is

most likely in the Arctic, where ice-melt and increased

stratification could alleviate the effects of strong light

limitation and lengthen the growing season (Fig. 4).

In general, the opportunity for fast growth and devel-

opment of larval codmay be reduced in the future across

the North Atlantic spawning grounds due to reduced

primary productivity and elevated temperatures. Our

results suggest that larval cod survival probability the

first month after hatching could decrease at all major

spawning grounds in the North Atlantic. We find that

reductions in survival probability had two main causes:

(1) smaller weight and length, leading to increased mor-

tality from starvation or invertebrate predation (particu-

larly during winter and autumn) or (2) increased

invertebrate and piscivore predation due to smaller

larvaemovingmore frequently into the surface layer.

During winter and autumn, slower growth rates

caused by reduced phytoplankton production and

zooplankton abundance slowed development and

prolonged the period when larvae were small and had

limited mobility. These conditions increased the proba-

bility for larval cod to be eaten by invertebrates and to

die from starvation because they were unable to locate

prey resources. In all regions, larvae compensated for

the loss of prey during spring by moving more fre-

quently to the upper 15 m of the water column to

search for prey. The more frequent presence of larval

cod in the surface layer during autumn increased their

predation by piscivores because they became more visi-

ble in the surface layer. A different pattern was found

on West Greenland, and Lofoten during autumn where

larval cod had strongly reduced presence in the upper

15 m of the water column by 2099. This surface avoid-

ance could have been due to warmer temperatures that

increased the energy required to satisfy metabolism

combined with lack of prey resources. Overall, we

observed reduced survival probability across all

regions, although the reasons for the mortality

depended on season and region.

These results are in accordance with the growth-mor-

tality hypotheses, which suggest that larval fish that are

able to locate food, grow fast, increase in weight and

length and minimize the duration of the larval stage

Table 6 Percentage relative difference in integrated survival

between simulations using detrended Earth System Model

(ESM) temperature and regularly modeled ESM temperature.

Survival was integrated throughout the year and averaged

over 50-year time periods. Positive values indicate that the

survival was higher in the results of the detrended simula-

tions, while negative values suggest that survival was higher

in the results where regular predicted ESM temperatures were

used

Location

Period relative

to 1950–1999

Difference in survival

between simulations

using detrended and

regular temperature (%)

Georges Bank 2000–2049 0.6

2050–2099 10.7

West Greenland 2000–2049 0.8

2050–2099 �14.6

Iceland 2000–2049 �1.2

2050–2099 �0.4

North Sea 2000–2049 4.8

2050–2099 6.5

Lofoten 2000–2049 �0.3

2050–2099 8.4

Fig. 8 To show that the trends and patterns predicted by the GFDL ESM2.1 model were consistent with other climate models, we com-

piled the model output from seven climate models available from the IPCC data portal (http://cmip-pcmdi.llnl.gov). We downloaded

the RCP8.5 scenarios from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We focused on comparing the projected sea

surface temperature (CMIP5 name: TOS) and integrated diatom production (CMIP5 name: INTPDIAT) model results with the output

from the GFDL ESM2.1 model. The seven models we used were NorESM (Norway), GFDL-ESM2M, GFDL-ESM2G (USA), MPI-ESM

(Germany), IPSL (France), INMCM4 (Russia), and BCC-CSM1 (China). Here, left column shows comparison between the model results

of the GFDL ESM 2.1 model used in this study with the CMIP5 models for sea surface temperature (SST [°C]). Right column shows the

annual vertically integrated production of diatom (INTPDIAT [g C m�2 day�1]). As the ESM2.1 only contains total primary production,

no direct comparison was made. However, the trend in primary production seen in ESM 2.1 (Table 3) is comparable with the trend of

diatom production in CMIP5 models. SST and INPDIAT is shown in (a, b) for Georges Bank, (c, d) West Greenland, (e, f) Iceland (g, h)

the North Sea, and (i, j) Lofoten. Both the SST and INTPDIAT have been smoothed with a 10-year running mean. The thick red line

indicates the average of the seven CMIP5 models, while the grey-shaded area indicates the standard deviation across models. The thick

orange line shows the GFDL ESM 2.1 solution used in this study.



SS

T a

nom

alie

s (°

C)

Year

mg

C m

–2 d

–1

Year

(b)

(d)

(f)

(h)

(j)

(a)

(c)

(e)

(g)

(i)



will have a higher chance of survival (Leggett & Deb-

lois, 1994). Observations of pelagic stages of bluefish

(Pomatomus saltatrix) found individuals that were larger

at age and had faster growth also had higher survival

(Hare & Cowen, 1997). In addition, strong size-selective

mortality during the larval stage has been observed in a

number of species such as sprat (Meekan et al., 2006),

Japanese anchovy (Takasuka et al., 2004), and European

anchovy (Allain et al., 2003).

Behavior is known to have a major impact on larval

survival (Lima & Dill, 1990; Fuiman & Cowan, 2003), so

we parameterized our IBM with optimal behavior, a

behavioral response based on generations of adapta-

tions (Fuiman & Cowan, 2003; Fitzpatrick et al., 2005).

Behavior was implemented according to internal state

(e.g., hunger level, body weight) and external stimuli

(e.g., predators, prey concentrations) (Fiksen et al.,

2007). Larvae were programmed to optimally maximize

ingestion and minimize risk of predation by adjusting

vertical position. Although such behavior could reduce

starvation, it could also increase visibility to predators.

As larvae grow and develop, their swimming ability

increases through ontogeny (Peck et al., 2006) allowing

larger individuals to more quickly adjust their depth

position to accommodate changes in the environment.

One benefit of mechanistic IBMs is to facilitate

the analysis of multiple, dynamic processes both

individually and in aggregate. We used a physical

model and an IBM to differentiate the effects of future

temperature trends from future changes in prey pro-

duction on metabolism, growth, and survival by

repeating the simulations with temperature detrended

(Table 1). The detrended temperature (Fig. S1) pre-

served the physical variability and seasonality in tem-

perature, while removing the future predicted

temperature anomalies. Removing the temperature

trend allowed us to examine the effect of variability

in ocean temperature relative to the effect of changes

in the prey abundance on larval cod survival. We

found that in regions where the average temperature

was already high (≥9–10 °C) such as in the North Sea

and Georges Bank, future predicted temperature

increases had a negative effect on both growth and

survival because prey abundance was also decreasing.

In order for growth and survival rates to remain equal

to climatological values (1950–1999), the increase in

temperature would have to be accompanied by

increased prey abundance. Sensitivity simulations con-

ducted using the detrended temperature instead of the

regular temperature profiles, decreased the tempera-

ture-dependent metabolic cost for larval cod in the

North Sea and Georges Bank and subsequently

reduced mortality from starvation and increased sur-

vival probability (Table 6).

For colder regions, such as Lofoten, the situation was

more complex. The zooplankton abundance was ade-

quate to sustain the increased metabolic cost due to

only small variations in temperatures in the spawning

grounds in Lofoten and Iceland during the 2000–2049period, which lead to increased larval cod growth and

survival relative to the detrended simulations. How-

ever, during the 2050–2099 period, primary productiv-

ity and zooplankton abundance in Lofoten decreased

significantly and the temperature increased, which neg-

atively affected larval growth and survival rates

(Table 1).

Our results suggest that there are fundamental differ-

ences between the ecological consequences of warm

anomalies associated with past climate variations and

persistent warming associated with climate change.

Earlier studies on the effect of climate variability on cod

(Planque & Fr�edou, 1999; Drinkwater, 2005) found that

warmer ocean temperatures had a positive effect on

recruitment for cold-water stocks (e.g., Lofoten) and

negative for warm water stocks (e.g., Georges Bank,

North Sea). Past warm anomalies can be indicative of

advection of prey abundant water masses into spawn-

ing habitats (Sundby, 2000), which can promote the

feeding and growth of larval fish. However, climate

change-driven ocean warming is associated with green-

house gases creating increases in net downward radia-

tion flux at the ocean surface, which impacts ocean

advection and stratification. In combination with

geographically varying degrees of freshening, this

warming acts to inhibit the vertical exchange of nutri-

ents, thereby reducing productivity and preventing lar-

val cod from deriving any benefit from the increased

growth capacity that warming at high-latitude sites

might provide. The fundamentally different response

of cod larvae to changes in temperature associated with

climate variability and those associated with climate

change highlights the limitations of purely empirical

approaches for predicting climate responses and the

value of mechanistic approaches such as the modeling

framework used here.

Modeling ecosystem dynamics is complex and cer-

tain assumptions and simplifications of reality are nec-

essary. Here, we assumed that the present prey species

composition and size-structure of zooplankton at each

habitat would persist into the future, but the abun-

dances would change according to zooplankton pro-

ductivity and ocean temperature. This representation of

the ecosystems was a simplification because functional

and structural ecological changes are already taking

place both in marine and terrestrial systems as a conse-

quence of climate change (Edwards & Richardson,

2004; Hays et al., 2005; Menzel et al., 2006; Brander,

2007a). Our modeling simulations also considered



generic prey items and did not account for changes in

lipid content, reductions in average sizes of prey and

changes in species, but only considered changes in

abundance. In addition, the zooplankton model

assumed static size-spectra of mesozooplankton, which

may not be representative for all seasons in the North

Atlantic. The static approach may underestimate the

seasonal variability of the prey field and fail to intro-

duce possible changes in the size-structure caused by

ecological changes due to climate change.

Invertebrate mortality was estimated using a func-

tional relationship between length and mortality rate

based on observations and should on average be rep-

resentative for the exposure of newly hatched larval

fish to invertebrates (McGurk, 1986). However, the

simulations also used a fixed density of piscivore pre-

dators, while in nature, the abundance of visual pre-

dators change with time and space (Garrison et al.,

2000). Unfortunately, parameterizing spatio-temporal

variations in predation rates requires huge amounts of

data that we currently do not have available. Nonethe-

less, new IBM model developments connects zoo-

plankton/fish interactions and are able to realistically

simulate the seasonal variability in abundance, distri-

bution, and size composition of both zooplankton and

fish populations (Hjøllo et al., 2012). These develop-

ments may enable future modeling efforts to avoid the

limitation of assuming static, generic prey items, as

well as including more realistic predator fields. Size-

based or size-resolving models may also provide alter-

native solutions for capturing changes or shifts in the

zooplankton composition (e.g., Ji et al., 2013). Such

model improvements may also help researchers

quantify the potential impacts of projected changes in

mesozooplankton abundance and distribution, as well

as help assess possible shifts in prey species and

changes in the size structure of the zooplankton

community.

Changes in the temporal-spatial patterns of zoo-

plankton will be critical for understanding how climate

change may affect current important spawning sites for

fish. It is likely that new reachable habitats or areas

may allow higher survival rates due to improved feed-

ing conditions, while current habitats may cease to pro-

vide adequate biotic and abiotic conditions for

spawning and survival. We know that past warm and

cool periods have shifted spawning grounds north and

south along the coast of Norway (Sundby & Nakken,

2008) suggesting that climate change may also lead to

shifts in spawning habitats. Modeling frameworks for

exploring these more detailed aspects of mesozoo-

plankton and larval fish population dynamics are

under development but have not been implemented in

global-scale ESM frameworks.

Survival probability was calculated for all seasons of

the year, but the timing when larval cod are present in

the water column varies with regions and is usually

limited in time to specific seasons. For example, spawn-

ing mainly occurs during late autumn and the end of

May on Georges Bank, but in Lofoten, light, prey, and

temperature conditions limits spawning to the period

from late February to mid-May. For all of the five

regions considered here, peak spawning occurs during

late winter and early spring (February–May) (ICES,

2005). Nonetheless, the IBM predicted potentially high

survival rates during periods when there has been no

observed major spawning, e.g., in Lofoten and Iceland

during autumn. Larval cod survival probability could

potentially be high during these periods as predicted,

but survival at the later juvenile stages is likely low due

to low food resources during late autumn and winter

(Heath & Lough, 2007; Kristiansen et al., 2011).

The coarse resolution (~1° latitude/longitude in the

ocean) of the vast majority of present physical climate

models and ESMs prevented explicit resolution of

many detailed aspects of coastal hydrodynamics (Stock

et al., 2011). Our projected impacts should be inter-

preted as a function of broad-scale climate-driven

changes in radiation, ocean stratification, and associ-

ated ecological responses. Future work should explore

whether unresolved local physical and biological

dynamics at each site may significantly modify these

results and whether the extent of regional-scale varia-

tions is consistent across climate models.

Sensitivity runs indicated that there was little differ-

ence in larval survival during the 2050–2099 period

compared with the 1950–1999 climatology if piscivore

biomass declined in proportion to projected declines in

mesozooplankton biomass. The risk of predation was

sufficiently reduced for larval cod larvae to maintain

survival probabilities at 4 of 5 sites (the exception being

West Greenland). A decline in piscivore biomass, how-

ever, would imply an overall decline in fisheries bio-

mass that would presumably decrease the overall

number of larval cod larvae unless cod achieved a

selective or competitive advantage relative to other fish

species under climate change. A full exploration of

feedbacks between changes in piscivore abundance,

adult cod, and overall larval survival would require

‘nesting’ larval cod dynamics within a fisheries food

web model. While significant progress has been made

in linking climate dynamics, planktonic ecosystems and

fish in food web models (e.g., Fulton et al., 2011), signif-

icant challenges remain (Rose et al., 2010).

Our results suggest climate change impacts on the

physics and biology of North Atlantic ecosystems can

have major consequences for productivity from lower

trophic levels up to larval fish feeding, growth, and



survival. Even small changes in food web structure can

lead to nonlinear responses, which are not always pre-

dictable based on static bioenvelope approaches. This

study has shown that predicting changes in population

dynamics benefits from a mechanistic approach that

takes into account both physical and biological changes

and accounts for feedbacks in the system. Our compara-

tive study of the survival patterns across the North

Atlantic revealed that even though increased ocean tem-

peratures can potentially have positive effects on larval

growth, decreased zooplankton abundance projected

under climate change will offset any potential benefits

because larvae will have smaller body weight and

length and be more vulnerable to invertebrate predation

and starvation. While the improvements discussed pre-

viously may refine projections, the large-scale climate

factors considered herein suggest that the overall sur-

vival probabilities for larval cod may decrease across

the North Atlantic, which may have major impacts on

the recruitment of Atlantic cod in the future.

Acknowledgements

T. K. was financially supported by the Norwegian ResearchCouncil projects MEECE (#122105), MENU II (#190286), andARCWARM (#178239). K. D. was supported by theMENU II pro-ject. We thank John Dunne and Jasmin John from NOAA’s Geo-physical Fluid Dynamics Laboratory for providing the EarthSystem Model simulation used for this study. T. K. would like tothank Elizabeth R. Selig for invaluable help and support in finish-ing this study.We also thank ToddD. O’Brien at NOAAFisheriesfor providing us with zooplankton observations from the COPE-PODdatabase (http://www.st.nmfs.noaa.gov/plankton/).

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Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Figure S1. Time-depth (upper 35–45 m) vertical profiles ofthe ESM predicted detrended temperature for the period July2003–June 2097 for (a) Georges Bank, (b) West Greenland,(c) Iceland, (d) North Sea, and (e) Lofoten. The verticalprofiles were smoothed with a 5-year running mean.Figure S2. (a) shows the mortality rate (h�1) and (b) irradi-ance (light, lE m�2 s�1) as experienced by a 5-mm larvalcod on days of the year January 31st (circled line) and May1st (solid lines) for North Sea, Lofoten, Georges Bank,Iceland, and West Greenland waters.Figure S3. The modeled daily specific growth rate (%d�1)averaged over 50 year periods: 1950–1999, 2000–2049, and2050–2099 for (a) Georges Bank, (b) West Greenland, (c)Iceland, (d) North Sea, and (e) Lofoten.Figure S4. Seasonal phytoplankton production from Eppley-VGPM (blue) and ESM (red) for (a) Georges Bank, (b) WestGreenland, (c) Iceland, (d) North Sea, and (e) Lofotenrespectively.



mechanistic insights into the effects of climate change on larval cod

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