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Modelling the effects of climate change on the distribution and production of marine fishes: Accounting for trophic interactions in a dynamic bioclimate envelope model


Abstract and Figures

Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and catches based on bioclimate envelope models are available, but to date they have not considered inter-specific interactions. We address this by combining the species-based Dynamic Bioclimate Envelope Model (DBEM) with a size-based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species' size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with pelagic species showing the greatest reductions. Goodness-of-fit to biomass data from fish stock assessments in the North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (1) predators and competitors may respond to climate change together; (2) existing parameterization of the DBEM might implicitly incorporate trophic interactions; and/or (3) trophic interactions might not be the main driver of responses to climate. Future analyses using ecologically-explicit models and data will improve understanding of the effects of inter-specific interactions on responses to climate change, and better inform managers about plausible ecological and fishery consequences of a changing environment. This article is protected by copyright. All rights reserved.
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Modelling the effects of climate change on the
distribution and production of marine fishes: accounting
for trophic interactions in a dynamic bioclimate envelope
OL I C H E R ,
*School of Environmental Sciences, The University of East Anglia, Norwich, NR4 7TJ, UK, Plymouth Marine Laboratory,
Prospect Place, The Hoe, Plymouth, PL13 DH, UK, Changing Ocean Research Unit, The University of British Columbia,
Fisheries Centre, 2202 Main Mall, Vancouver, BC V6T 1Z4, Canada, §Centre for Environment, Fisheries and Aquaculture
Science, Lowestoft, NR33 0HT, UK, Atmospheric and Oceanic Sciences Program, Princeton University, Princeton,
NJ 08544, USA
Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and
catches based on bioclimate envelope models are available, but to date they have not considered interspecific interac-
tions. We address this by combining the species-based Dynamic Bioclimate Envelope Model (DBEM) with a size-
based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species’
size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts
are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with
pelagic species showing the greatest reductions. Goodness-of-fit of biomass data from fish stock assessments in the
North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between
predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (i) predators
and competitors may respond to climate change together; (ii) existing parameterization of the DBEM might implicitly
incorporate trophic interactions; and/or (iii) trophic interactions might not be the main driver of responses to climate.
Future analyses using ecologically explicit models and data will improve understanding of the effects of inter-specific
interactions on responses to climate change, and better inform managers about plausible ecological and fishery conse-
quences of a changing environment.
Keywords: biological feedback, climate change, competition, ecosystem approach, fisheries management, model validation,
modelling, size spectrum, species interactions
Received 23 January 2013; revised version received 5 April 2013 and accepted 14 April 2013
Climate change affects ocean conditions, including tem-
perature, salinity, ice coverage, currents, oxygen level,
acidity and consequently growth, body size, distribution,
productivity and abundance of marine species, includ-
ing those that are exploited by fisheries (Perry et al.,
2005; Behrenfeld et al., 2006; Brander, 2007; P
2010; Simpson et al., 2011; Cheung et al., 2013). Over a
range of greenhouse gas emission scenarios (IPCC,
2007), changes in the marine environment are predicted
to be more rapid in the 21st century with implications
for marine ecosystems and dependent industries (Roes-
sig et al., 2004; Lam et al., 2012; Merino et al., 2012).
A range of modelling approaches have been devel-
oped to predict the potential effects of future climate
change on species distributions and abundance (Stock
et al., 2011). One class of models, species-based biocli-
mate envelope models, have been used to predict redis-
tribution of both terrestrial and aquatic species (Pearson
& Dawson, 2003; Jones et al., 2012). The Dynamic Biocli-
mate Envelope Model (DBEM) developed by Cheung
et al. (2008a,b, 2009, 2011) projects changes in marine
species distribution, abundance and body size with
Correspondence: Jose A. Fernandes, tel. +44 (0)1603591375,
fax +44 (0)1752633101, e-mail:
©2013 John Wiley & Sons Ltd 1
Global Change Biology (2013), doi: 10.1111/gcb.12231
explicit consideration of population dynamics, dispersal
(larval and adult) and ecophysiology (Cheung et al.,
2008a,b, 2009, 2011, 2013). Projections suggest that there
will be a high rate of species invasions in high-latitude
regions and a potential high rate of local extinction in
the tropics and semi enclosed seas in the 21st century
(Cheung et al., 2009). Moreover, as a result of predicted
changes in range and primary productivity, Cheung
et al. (2010) project that maximum catch potential of
exploited species is expected to decrease in the tropics
and to increase in high latitudes. However, these
projections do not account for the effects of species
interactions on redistribution and abundance, thus
introducing a source of structural uncertainty (Cheung
et al., 2010).
Rates of primary production and transfer efficiency
influence production and biomass of consumers. ‘Size-
spectrum’ models have been developed to describe
energy transfer from primary producers to consumers
of progressively larger body size (e.g. Dickie et al.,
1987) and variants of these models have been devel-
oped and applied to predict potential biomass, produc-
tion and size structure of fish in the world’s oceans
from estimates of primary production and temperature
(Jennings et al., 2008), and to predict the responses of
fish communities to fishing and climate change (Blan-
chard et al., 2011, 2012). These size-based models are
not taxonomically resolved, and this limits the range of
applications, given that species identity is usually a key
consideration for management, monitoring and regula-
tory purposes.
Here, we combine the strengths of the DBEM
(i.e. focus on identified species) with those of the size
spectrum model (i.e. focus on trophic interactions) to
predict spatial and temporal changes in species’ abun-
dance and distribution in response to predicted future
changes in temperature and primary production. Forty-
eight of the most abundant and commercially impor-
tant marine fishes in the North Atlantic, here defined as
Food and Agriculture Organization (FAO) statistical
area 27, are included. The size spectrum is used to
determine resource limits in a given geographical area
and these limits, along with habitat suitability for a
given species, determine the biomass of that species
that can be supported in this area.
Materials and methods
A modelling approach that integrates the species-based DBEM
model with the size spectrum approach, hereafter called size-
spectrum DBEM (SS-DBEM) was developed. The SS-DBEM:
(i) estimates potential biomass supported by the system; (ii)
predicts habitat suitability; and (iii) models species interac-
tions. Predictions from the SS-DBEM are then compared with
those from a DBEM model that does not incorporate species
interactions (NSI-DBEM, where NSI denotes no species inter-
Potential biomass supported at each body size class
The size-spectrum is described as a log-log relationship
between abundance and body size. The slope of the spectrum
is determined by trophic transfer efficiency and the relation-
ships between the body sizes of predators and their prey
(Borgmann, 1987; Jennings & Mackinson, 2003). The height of
the spectrum is determined by primary production and
describes the total abundance of individuals from all species
that can be supported in any defined body size class (e.g.
Boudreau & Dickie, 1992).
As predator-prey mass ratios and transfer efficiencies in
marine food chains do not depend systematically on the mean
rate of primary production or mean temperature (Barnes et al.,
2010), less energy is transferred to consumers of a given body
size when food webs are supported by smaller primary pro-
ducers (Barnes et al., 2010). Much of the variation in the body
size distribution of primary producers depends on the abso-
lute rate of primary production, with picoplankton, the small-
est phytoplankton, dominating when primary production is
low (Agawin et al., 2000). Thus, the median and mean body
sizes of phytoplankton decrease with decreasing rates of pri-
mary production (Barnes et al., 2011). To account for this, the
position of the median body mass class for phytoplankton (m)
was calculated as:
m¼ ½ð6:1PsÞ8:25=log10ð2Þð1Þ
where P
is the predicted contribution of picophytoplankton
net production to total net Primary Production (PP) as calcu-
lated using the empirical equation
Ps¼½ð12:19 log10 PP þ37:248=100 ð2Þ
derived by Jennings et al. (2008) using the data from Agawin
et al. (2000).
Once the median body mass class of phytoplankton was
defined, we calculated the consumer biomass at body size
following the approach of Jennings et al. (2008). Assumptions
about trophic transfer efficiency and the predator-prey mass
ratio (e=0.125 and l=3 respectively) followed Jennings et al.
(2008), but the spectrum was discretized using a log
series of
body mass from 2
to 2
g. Subsequent evidence suggests
that the predator-prey mass ratio may increase with body
mass and that transfer efficiency may decrease, but the
changes are not expected to affect the time-averaged slope of
the size-spectrum (Barnes et al., 2010).
Habitat suitability
The prediction of habitat suitability in SS-DBEM was based on
the algorithm implemented in NSI-DBEM (Cheung et al.,
2008a,b, 2009, 2011; Cheung et al., 2013). The NSI-DBEM
defines the relative preferences of the modelled species for
temperature and other environmental variables based on the
relationship between current distributions and gridded envi-
ronmental data. The initial distribution of relative abundance
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
2J. A. FERNANDES et al.
(representing 19702000) of the modelled marine species on a
30930latitude-longitude grid map of the world ocean is
predicted using the Sea Around Us project algorithm (Close
et al., 2006; Jones et al., 2012) based on parameters describing
range limits, association with major habitat types and known
occurrence boundaries. Parameter values for each species
were derived from data in online databases, mainly FishBase
( and SeaLifeBase (
Environmental variables incorporated into the NSI-DBEM
include sea surface temperature, sea bottom temperature,
coastal upwelling, salinity, sea-ice extent, depth and habitat
types (Cheung et al., 2011). NSI-DBEM first calculates changes
in growth and other life history traits in response to changes
in temperature and oxygen concentration based on algorithms
derived from growth and metabolic functions and empirical
equations (Cheung et al., 2011, 2013). Second, NSI-DBEM pre-
dicts size-frequency distributions for each species in each spa-
tial cell using a size-structured ‘per recruit’ model. Finally, the
model simulates spatial and temporal changes in relative
abundance within a cell based on carrying capacity of a cell,
density-dependent population growth, larval dispersal and
adult migration (Cheung et al., 2008b, 2011).
Species interactions
A new algorithm was developed to describe resource competi-
tion between different species co-occurring in a cell by com-
paring the energy (in biomass) that can be supported in the
cell (estimated with the SS model) with the energy demanded
by the species predicted to inhabit the given cell (estimated
with the NSI model). The algorithm comprises two stages: (i)
an initialization stage where competition parameters are esti-
mated; and, (ii) a recurrent stage where the competition
parameters are used to resolve conflicts between energy (bio-
mass) demands and biomass that can be supported. One
advantage of this approach is that it focuses on competition
for the energy available within a cell, thus negating the need
for a diet matrix that describes species-specific feeding interac-
tions. Data to develop such matrices are scarce at the scale of
FAO Area 27 and the persistence and emergence of feeding
interactions through time, and in response to future climate
change, is highly uncertain.
First stage. The model uses the NSI-DBEM approach to
establish an initial distribution for each species. The approach
assumes that predicted habitat suitability is a proxy for the
distribution of relative abundance of a given species. Thus,
multiplying the initial relative biomass by the estimated abso-
lute biomass from empirical data, initial species distribution is
expressed in terms of absolute biomass in each cell. Because
biomass estimates from assessment data are not available for
some of the species considered (Table 1), the initial biomass
estimates were approximated by the predicted unexploited
biomass (B
) from maximum reported fisheries catch (MC)
since 1950 and an estimate of the intrinsic growth rate (r)of
the population (Schaefer, 1954):
B1¼MSY 4=rð3Þ
Maximum sustainable yield (MSY) was calculated using the
algorithm documented in Cheung et al. (2008a) that used the
average maximum values of the catch time series of a species
as an approximated MSY. Values for r, estimated based on an
empirical equation that was dependent on asymptotic length
of the species, were obtained from FishBase (www.fishbase.
org). Although this is an approximation and not as reliable as
estimates of biomass using survey-based methods (Pauly
et al., 2013), we show that, consistent with similar findings by
Froese et al. (2012), biomass estimates from maximum catch
data were significantly correlated with those from aggregated
stock assessments (Table 1; Fig. 1). These biomass estimates
were used for model initialization only.
The initial absolute biomass estimates, based on habitat
suitability in the cells where they are distributed (Fig. 2), are
used to generate a matrix of species’ energy demand (expressed
as biomass). Matrix elements define the proportion of total
energy obtained by a species at each habitat suitability bin
and size class. The amount of energy is determined by the
average proportion of energy that a species gets in cells with
the same habitat suitability.
Energy demanded (E_D) by a species in a cell is compared
with the total biomass or energy (E_S) that can be supported
in the cell (see Table 2 for a summary of abbreviations). E_D is
determined based on the predicted habitat suitability from the
DBEM algorithm, whereas E_S is determined by the SS model.
Thus, the average proportion of energy that a species demands
in cells with the same habitat suitability can be calculated as
resoucesSpp;Suit;Size ¼E DSuit
E S ð4Þ
To convert from biomass (B) distribution to numbers (N) and
vice versa, the mean body mass (W) at each size class (i)is
used as shown below:
where nis the number of size classes considered in the model.
The initial habitat suitability value is converted using a square
root data transformation, to ensure a balanced distribution of
the cells across the habitat suitability classes, and then normal-
ized to a range from 0 to 1 relative to minimum and maximum
value of habitat suitability for each species. The model then
groups habitat suitability into six classes (bins) of values:
00.3, >0.30.4, >0.40.5, >0.50.6, >0.60.7 and >0.71. The use
of discretized bins of habitat suitability, a nonparametric
methodology, does not require the specification of explicit
distribution functions and is more computationally efficient
(Fayyad & Irani, 1993; Dougherty et al., 1995). The effects of
such discretization are minimized here by square root trans-
formation of the predicted habitat suitability, the low number
of bins and the choice of bin boundaries (Uusitalo, 2007;
Fernandes et al., 2010).
Available energy in a size class which is not demanded by
the modelled species was assigned to a group called ‘Other
groups’, because species that were not modelled explicitly
would also have an energy demand. This group has its own
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
Table 1 List of modelled fish species. Stocks that have been aggregated to provide species abundance estimates are identified by
their stock ID codes (STOCKID) in the RAM Legacy database (upper case codes). For some ICES assessed stocks not listed in the
RAM Legacy database, stock ID codes that were based on ICES Stock Summary Database were used (lower case codes)
Common name Scientific name Type Stock ID code
Albacore Thunnus alalunga Pelagic ALBANATL
American plaice/
long rough dab
Hippoglossoides platessoides Demersal
Angler Lophius piscatorius Demersal
Atlantic cod Gadus morhua Demersal CODNEAR, CODBA2224, CODBA2532, CODVIa, CODIS,
Atlantic herring Clupea harengus Pelagic HERRIsum, HERRNS, HERR2224IIIa, HERR2532, HERR30,
her-noss, hervian and her-vasu
Atlantic horse mackerel Trachurus trachurus Pelagic hom-west
Atlantic mackerel Scomber scombrus Pelagic MACKNEICES
Baltic sprat Sprattus sprattus Pelagic SPRAT22-32
Blue whiting Micromesistius poutassou Pelagic whb-comb
Boarfish Capros aper Demersal
Capelin Mallotus villosus Pelagic CAPEICE and CAPENOR
Common sole Solea solea Demersal SOLENS, SOLEVIId, SOLEIS, SOLEIIIa, SOLEVIIe, SOLECS,
Cuckoo ray Leucoraja naevus Demersal
Dab Limanda limanda Demersal
European anchovy Engraulis encrasicolus Pelagic ANCHOBAYB
European hake Merluccius merluccius Demersal HAKESOTH and HAKENRTN
European pilchard Sardina pilchardus Pelagic sar-soth
European plaice Pleuronectes platessus Demersal PLAIC7d, PLAICIIIa, PLAICNS, PLAICIS, PLAICECHW
European sprat Sprattus sprattus Pelagic SPRATNS
Flounder Platichthys flesus Demersal
Fourbeard rockling Enchelyopus cimbrius Demersal
Fourspotted megrim Lepidorhombus boscii Demersal mgb-8c9a
Greenland halibut Reinhardtius hippoglossoides Demersal GHALNEAR, GHALBSAI and GHAL23KLMNO
Haddock Melanogrammus aeglefinus Demersal HAD4X5Y, HAD5Y, HAD5Zejm, HADICE, HADNEAR,
John dory Zeus faber Demersal
Lemon sole Microstomus kitt Demersal
Ling Molva molva Demersal
Megrim Lepidorhombus whiffiagonis Demersal mgw-8c9a
Northern bluefin tuna Thunnus thynnus Pelagic ATBTUNAEATL and ATBTUNAWATL
Norway pout Trisopterus esmarkii Demersal nop-34
Golden Redfish Sebastes norvegicus Demersal GOLDREDNEAR
Pearlsides Maurolicus muelleri Pelagic
Piked dogfish/Spurdog Squalus acanthias Demersal
Pollack Pollachius pollachius Demersal
Poor cod Trisopterus minutus Demersal
Pouting/Bib Trisopterus luscus Demersal
Red bandfish Cepola macrophthalma Demersal
Saithe/Pollock Pollachius virens Demersal POLL5YZ, POLLNEAR, POLLFAPL, POLL4X5YZ and
Smallspottedcatshark Scyliorhinus canicula Demersal
Splendid alfonsino Beryx splendens Demersal
Spotted ray Raja montagui Demersal
Striped red mullet Mullus surmuletus Demersal
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
4J. A. FERNANDES et al.
resource allocation matrix based on the average habitat
suitability of the modelled species, allowing the inclusion of
resource demand from species that are not explicitly
modelled. As the species assemblage in the boundary of the
geographical domain of the model is likely to be underrepre-
sented by the modelled species, the matrix for ‘Others group’
is only computed for cells where the number of species pres-
ent is more than the square root of the total number of species
Second stage. Abundance of each species in each cell was
predicted using the algorithm in the NSI-DBEM. The model
runs uses an annual time-step for bottom-dwelling (demersal)
species and two seasonal time-steps (summer and winter) for
species in the water-column (pelagic). The energy demand of
each species is compared with energy demands of other spe-
cies co-occurring in the same cell (Fig. 2). If the energy
demanded by all organisms in the cell exceeds the energy
available, then the available energy is allocated to each species
in proportion to its energy demands. If the energy demanded
by all the species is lower than the energy available, the sur-
plus energy is allocated according to the proportional energy
demand of the species present, including the ‘Others group’. To
represent population growth that is limited by factors other
than available energy, the rate at which energy can be assimi-
lated by a species is limited as shown below:
res opSpp;Suit;W¼2std.devðE DSuitÞ
where, E_D
denotes the energy demanded in all the cells in
each bin of habitat suitability. Therefore, the amount of addi-
tional energy that can be taken by the species is limited by
two times the standard deviation ( of energy that each
species gets in the initial distribution to each habitat suitability
bin. Any energy that is left after these allocations is assumed
to be used by the ‘Others group’.
Model testing
The results from the model that includes competition were
compared with results from the NSI-DBEM and “empirical”
time series of abundance data from fish stock assessments for
the Northeast Atlantic (FAO area 27), as extracted from the
RAM Legacy Stock Assessment Database (Ricard et al., 2012; and ICES Stock
Summary Database ( To compare pro-
jected changes with observations, abundance data for each
species were normalized by dividing them by their mean
value. While the models were applied to a set of 48 fish spe-
cies, comparison with empirical data was conducted for 24
species for which data were available from the RAM Legacy
and ICES databases (Table 1). The output of the DBEM mod-
els were compared with the “empirical” time-series values for
each species and distribution of absolute error (AE) was calcu-
lated as follows:
AE ¼jpjxj7Þ
where p is the total biomass predicted in a DBEM model in
a particular year for a species j, and x is the total biomass
from the assessments. The comparison was done for the
years with available assessment data for all the 24 species
considered (19912003). To compare the performance of the
SS-DBEM and NSI-DBEM, the Percent Reduction in Error
(PRE) was calculated (Hagle & Glen, 1992; Fernandes et al.,
2009), but weighted by the maximum catch of each species
Table 1 (continued)
Common name Scientific name Type Stock ID code
Thickback sole Microchirus variegatus Demersal
Thornback ray Raja clavata Demersal
Tub gurnard Chelidonichthys lucerna Demersal
Tusk/Torsk/Cusk Brosme brosme Demersal CUSK4X
Whiting Merlangius merlangus Demersal WHITNS-VIId-IIIa, WHITVIa and WHITVIIek
Witch Glyptocephalus cynoglossus Demersal
Fig. 1 Relationship between the maximum assessed biomass
(log) and the estimated carrying capacity of fish population (B
log) for 22 species in the 27 FAO area (after removing extreme
values, the lowest and highest B
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
where AENSI is the absolute error in the NSI-DBEM model,
AESS is the absolute error in the SS-DBEM model, k the
number of species and MaxCatch the maximum catch of the
These models were also compared with empirical data
describing latitudinal and depth centroid shifts of species in
response to climate change (Dulvy et al., 2008; Cheung et al.,
2011). Distribution centroid (DC
) for each year (t) was calcu-
lated as:
Dynamic bioclimate envelop model
Cheung et al., 2008; 2009; 2011
Size distribution for each species
Size spectrum model
Jennings et al., 2008
Earth system models
Biomass supported
at each size bin
Ram Legacy Stock Assessment database
ICES Stock Summary database
Algorithm for
species interactions
Size distribution for each species
Primary production
Species interactions table
Algorithm pseudocode for species interactions
1: Calculate =
3: TotalResW, i =
4: If TotalResW, i< 1 then = ·
5: If TotalResW, i> 1 then Normalize: TotalResW, i
6: If > then =
7: Adjust biomass, abundance and size distributions based on
2: then = / E_SW,i
across cells
Total biomass
supported at
each size bin
in each cell
% resources demanded
at each size bin
in each cell
Species interactions table for each species:
i.e. % resources demanded at each
size bin by habitat suitability
SeaAroundUsBase Species
Fig. 2 Framework to calculate the matrix of energy demand at each size class for each species and to calculate the effects of species
Table 2 Summary of abbreviations
Abbreviation Description Details
DBEM Dynamic Bioclimate Envelope Model
Spp;w;iBiomass by competition resSuit
Spp;w;iE S
Spp;w;iBiomass demanded Calculated at each yearly shift
ERSEM European Regional Seas Ecosystem Model
Total biomass supported in a cell Calculated from Primary production
GFDL Geophysical Fluid Dynamic Laboratory Earth System model
I Index of cell From 0 to 250 200
NSI No species interactions
Spp;w;iActual proportion of resources by competition See Fig. 2
Proportion resources at matrix of energy demand See Eqn (4)
Proportion of resources by opportunity See Eqn (6)
Spp Index of species From 0 to 48 species
SS Size-spectrum (based interactions)
Suit Index of the habitat suitability bin Between 0 and 4 bins
W, i
Total proportion of resources demanded PSpp resSuit
W Index of the size spectrum 21 log
classes from 2
to 2
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
6J. A. FERNANDES et al.
where, B
is the predicted relative abundance in cell i,Ais the
area of the cell, Lat is the latitude at the centre of the cell and n
is the total number of cells where the species was predicted to
occur. We calculated the rate of range shift as the slope of a
fitted linear regression between the distribution centroid of
the species and time. We expressed latitudinal range shift (LS)
as poleward shift in distance from the equation:
LS ¼DS p=180 6378:2;ð10Þ
where DS is the distribution shift in degree latitude per year.
The models were run for 35 years, from 1970 to 2004, with
environmental forcing predicted from two modelling systems:
(i) the National Oceanographic and Atmospheric Administra-
tion Geophysical Fluid Dynamic Laboratory Earth System
Model (ESM) 2.1 (GFDL); and (ii) the European Regional Seas
Ecosystem Model (ERSEM). GFDL ESM2.1 is a global atmo-
sphere-ocean general circulation model (Delworth et al., 2006)
coupled to a marine biogeochemistry model (TOPAZ; Dunne
et al., 2010) which includes major nutrients and three phyto-
plankton functional groups with variable stoichiometry. For
the GFDL hindcast simulations (Henson et al., 2010), air tem-
perature and incoming fluxes of wind stress, freshwater,
shortwave and longwave radiation are prescribed as bound-
ary conditions from the CORE- version 2 reanalysis effort
(Large & Yeager, 2009). ERSEM is a biogeochemical model
that uses a functional-groups approach incorporating four
phytoplankton and three zooplankton functional groups and
decouples carbon and nutrient dynamics (Blackford et al.,
2004). Data from two different configurations of ERSEM were
applied here: on the global scale a hindcast of the NEMO-ER-
SEM model forced with DFS 4.1 reanalysis for the atmosphere
(Dunne et al., 2010) and on the regional scale a hindcast of the
POLCOMS-ERSEM model for the NW-European shelf forced
with ERA 40 reanalysis (extended with operational ECMWF
reanalysis until 2004) for the atmosphere and global ocean
reanalysis for the open ocean boundaries (more details on the
configuration can be found in Holt et al., 2012; Artioli et al.,
2012). The domain of this global model overlapped the
domain of a regional model of the North Sea area.
Results and discussion
Performance of SS-DBEM and NSI-DBEM
Predicted biomasses from SS-DBEM were generally
lower than those projected from NSI-DBEM (Fig. 3).
The reason is that the energy available from primary
producers limits species’ biomass in SS-DBEM but not
in NSI-DBEM, where species’ carrying capacity depends
mainly on the habitat suitability of the cell. The algo-
rithm in SS-DBEM explicitly modelled interspecific
competition for energy, based on size considerations,
without specifying these interactions (e.g. no diet matrix).
Our approach allows for the development of scenarios
of large-scale shift in species distribution and catches,
complementing other models that have been designed
to achieve this (Cheung et al., 2010; Metcalfe et al.,
Outputs from SS-DBEM explain slightly more of the
variation in biomass estimated from stock assessments
(FAO area 27) than those from the NSI-DBEM. The
error weighted by maximum catch predicted across
species from SS-DBEM against empirical data is 3.7%
lower than those predicted from NSI-DBEM using
GFDL environmental forcing data and 0.6% lower
using ERSEM data. GFDL might be more accurate
(Fig. 4) for the time period considered since the model
run was forced by re-analysis data such as surface
Fig. 3 Species biomass by body mass class supported in a single coastal cell (30930), used as an example. Open circles represent the
biomass that can be supported in this cell using only the size-spectrum component of the model.
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
temperature and wind fields, which is not the case for
ERSEM. However, the differences in mean absolute
error are not significant and might not hold when the
models are used for forecasting. Future work will
explore the causes of this difference, which may not
depend on the modelling itself but on input data such
as environmental forcing, or even on the adequacy of
the assessment data used for the comparison. Finally, a
lower variance in the absolute error in SS-DBEM with
respect to NSI-DBEM model (Fig. 4) is indicative of a
higher precision of simulated biomass from SS-DBEM
(Taylor, 1999). This also supports the view that the pro-
posed modelling approach is a potential advance over
models that do not account for species interactions.
Distribution shifts
Both NSI-DBEM and SS-DBEM projected poleward lati-
tudinal shift of species distributions (Fig. 5), and the
projected shifts are generally consistent between simu-
lations forced by the two sets of Earth System Model
outputs (Table 3). In addition, the projected shift of
pelagic species by the model with interactions is consis-
tently lower than if no interactions are considered
(Table 3). With ERSEM forcing, the median projected
rates of poleward shift are 63.5 km and 54.9 km over
35 years, or 18.1 and 15.7 km decade
, from NSI-
DBEM and SS-DBEM respectively. Similar to previous
analysis using NSI-DBEM, all sets of simulations show
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.2 0.4 0.6 0.8
Absolute error distribution Absolute error distribution
0 1 2 3 4 5
0 1 2 3 4 5
Error frequency
Error frequency
(0.35 ± 0.09)
(0.36 ± 0.08)
(0.347 ± 0.082)
(0.368 ± 0.095)
Fig. 4 Distribution of absolute error of predicted biomass for SS-DBEM and NSI-DBEM and the biomass estimated from stock assess-
ments for the 19912003 period in the Northeast Atlantic (FAO Area 27). The time series have been normalized between 0 and 1 before
calculating the absolute error, to ensure that species’ absolute abundances do not affect the results. The comparison is presented for
European Regional Seas Ecosystem Model (ERSEM) (left) and Geophysical Fluid Dynamic Laboratory (GFDL) (right) showing in the
legend mean and standard deviation of the absolute error. A narrower distribution of error (lower standard deviation) in SS-DBEM is
indicative of a higher precision.
1971 1977 1983 1989 1995 2001 1971 1977 1983 1989 1995 2001
Latitude distance (km)
Latitude distance (km)
Fig. 5 Predicted latitudinal shift of distribution centroids of 49 species of fishes from 1971 to 2004 using European Regional Seas Eco-
system Model (ERSEM) climatic dataset for the NSI-DBEM and SS-DBEM. The thick dark bar represents the median shift of all the spe-
cies in a year, the lower and upper boundaries of the box represent the 25% and 75% quartiles respectively. Positive value indicates
poleward shift relative to species distribution in 1971.
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
8J. A. FERNANDES et al.
a higher rate of range shift for pelagic species than
bottom-dwelling species (Cheung et al., 2009; Jones
et al., 2013). A reduction in the expected geographical
shift of particular populations as a result of ecological
interactions is consistent with the perception of
compensatory ecological processes (Frank et al., 2011).
Shifts in depth are also observed and are strongly dri-
ven by the forcing model considered. The shift in depth
is also dependent on the spatial domain considered.
For example, for demersal species in FAO Area 27, out-
puts from SS-DBEM driven by ERSEM data project a
shift to deeper waters of 1.3 m decade
. However,
when we consider North Sea only, the projected shift to
deeper waters is higher at 5.7 m decade
The slower rates of projected shifts from the
SS-DBEM relative to NSI-DBEM are consistent with
previous literature based on recent observations.
Specifically, Perry et al. (2005) projected a mean rate of
latitudinal shift of 22 km decade
from 1980 to 2004 in
the North Sea for six fish species. Comparable rates
of shift (between 18.5 and 18.8 km decade
) are
projected here for our modelled subset of species,
which includes four of these species (bib, blue whiting,
Norway pout and witch). Also, Dulvy et al. (2008) esti-
mated that bottom-dwelling species were moving into
deeper waters at an average rate of 3.1 m decade
from 1980 to 2004 (19 species of 28 species are common
between this study and Dulvy et al., 2008), which is
slower than our prediction of 5.7 m decade
. These
direct comparisons between predicted and observed
shifts need to be interpreted with caution because of
differences in the species included, the spatial domains,
and the time period considered. In addition, our simu-
lations represent average species-level changes without
consideration for stock structure, owing to incomplete
biological data to address the latter. The trend in abun-
dance or range shift of a given species may not neces-
sarily be equivalent to that of every stock of that species
(Petitgas et al., 2012).
Maximum catch
The maximum catch predicted by both DBEM models
(SS and NSI) broadly follows multi-decadal variability
in empirical estimates of total catches for the 19702004
time period in the ICES areas (Fig. 6). This is demon-
strated by maximum and minimum points in similar
years, with the highest discrepancy in years around
1985. All the time series show higher maximum values
in the first half of the time period and consistently
lower maximum values in the second half. However,
this negative trend in catches in all the time series is not
statistically significant. The empirical catch data are
aggregated catches by all species reported in ICES areas
as collected in the Eurostat/ICES database on catch sta-
tistics ( The predicted maximum
catch is based on the aggregation of the potential catch
of the 48 modelled species in ICES areas. Despite some
Table 3 Average latitudinal shift in different simulations.
NSI corresponds to simulations where the model does not
incorporate species interactions through the size-spectrum,
whereas SS denotes the use of the species interactions algo-
rithm. Geophysical Fluid Dynamic Laboratory (GFDL) and
European Regional Seas Ecosystem Model (ERSEM) corre-
spond to two different Earth System Models
Latitudinal Shift (km decade
Projection All species Demersal Pelagic
NSI-DBEM GFDL 16.7 14.1 26.0
SS-DBEM GFDL 13.7 12.6 18.4
NSI-DBEM ERSEM 18.1 15.2 28.2
SS-DBEM ERSEM 15.7 15.3 16.9
Fig. 6 Predicted changes in maximum catch compared with empirical catch data. Time series has been normalized between 0 and 1 to
compare interannual variability.
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
discrepancies, the models are able to reproduce general
trends in observed fisheries productivity in the North
East Atlantic, providing some confidence in their utility.
Catches predicted from SS and NSI approaches show
similar patterns when the most abundant and commer-
cially important species are aggregated. Further work
will focus on examining the effects of different model-
ling approaches on catch predicted for specific species,
areas (e.g. ICES areas) or size classes.
Model uncertainty
Projections from NSI- and SS-DBEM are sensitive to the
environmental variables projected by the Earth System
Models and used to force the ecological models. Earth
System Models have a number of limitations when
applied to fisheries problems (Stock et al., 2011). Their
resolution is relatively coarse to capture ecological pro-
cesses (generally ca. 1 degree in the ocean) and they
also do not capture well the coastal and continental
shelf ocean dynamics. As a result, Earth System Models
are known to systematically project lower primary
production in coastal areas (Steinacher et al., 2010).
Inter-model spread arises from diverse sources, such as
the parameters chosen for sub-grid-scale parametriza-
tions. In addition, there is overall limited availability of
reliable data to calibrate the models. Efforts to improve
the understanding and projections for primary produc-
tion are ongoing (e.g. Holt et al., 2012; Krause-Jensen
et al., 2012), which will likely contribute to improved
performance of DBEM models.
An assumption of the size-spectrum component of
the model is the linear relationship between log-abun-
dance and log-body size classes in the cell. Such an
assumption is made mainly for computational perfor-
mance. In reality, it may be violated by species’ migra-
tions that lead to energy losses and subsidies from
given cells, and by seasonal fluctuations in primary
production (Blanchard et al., 2011).
The relative abundance of individuals at size can be
modified by the overall constraints on energy availabil-
ity. In general, these have limited effect on the projec-
tions because the changes account for a small
percentage of the total abundance of species in the cell
(an average of 0.03% of abundance decrease). However,
the effects are larger and occur in more cells for whit-
ing, blue whiting, Atlantic cod, Norway pout, European
plaice, saithe and Atlantic horse mackerel.
The DBEM modelling approaches have a number of
inherent assumptions and uncertainties that may affect
the performance of the models (Cheung et al., 2009).
First, the models are based on the assumption that
the predicted current species distributions depict the
environmental preferences of the species and are in
equilibrium. Second, the underlying biological hypoth-
esis, represented by the model structure and input
parameters, may be uncertain. Moreover, the models
did not consider the potential for phenotypic and
evolutionary adaptations of the species. As these
assumptions apply to both NSI- and SS- DBEM, they do
not affect the comparison of projections between the
two models. We used theory and empirical data to
model trophic interactions. The modelling approach
does not incorporate the full range or complexity of
interactions among species. This simplification avoids
the difficulties of formalizing transient and complex
species-specific predatory interactions at large-scales. It
also requires no assumptions about the extent to which
species-specific trophic interactions that are seen today
will persist in the future. Furthermore, at the system
level, size-based processes account for much of the
variation in prey choice and trophic structure.
Survey data can provide an alternative way of vali-
dating model outputs (Simpson et al., 2011). However,
there are scale reasons why we did not pursue this type
of validation in this study. Fisheries surveys tend to
focus on particular species assemblages (e.g. pelagic or
bottom-dwelling species), and are designed to provide
a geographical and temporal snapshot that fits with the
life history of target species. As such survey data are
not directly comparable to model outputs for a large
geographical area (FAO area 27).
There are small but quantifiable improvements in
goodness-of-fit with stock assessment abundance esti-
mates, predictions of latitudinal shifts and comparisons
with predicted maximum catch and observed catches.
However, we need to be cautious about our interpreta-
tions of model performance at this stage owing to struc-
tural and parameter uncertainties, and uncertainties in
the models used to generate the environmental forcing.
The similarity of predictions might reflect incorrect
assumptions. For example, we assume that single spe-
cies models do not account for species interaction
because there is no explicit mechanism, even though
species interactions might already be implicitly incor-
porated in its parameterization (e.g. habitat suitability
calculation from observed distribution data). The simi-
larity of predictions might also be attributed to the simi-
lar effects of changing climate on many predators and
competitors and the implicit assumption of the NSI-
DBEM approach that the importance of interspecific
interactions remains the same. In addition, trophic
interactions might not be the main driver of responses
to climate at the basin scale. However, our results at the
scale of the North Atlantic basin, or aggregated ICES
areas, does not mean that trophic interactions may not
have more influence on regional and local responses.
Unfortunately, the earth system and ecological models
©2013 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12231
10 J. A. FERNANDES et al.
described in this article are too complicated to allow
comprehensive explorations of the effects of changing
model structures and parameterisation. Such explora-
tions could be achieved in the long-term by comparing
projections from the DBEMs with alternative parameter
settings for larger datasets of time series of changes in
distribution and abundance from different ocean
The main benefit of our model comes from unifying
two modelling approaches providing spatially and tem-
porally resolved species and size predictions, with full
consideration for the effects of ecological interactions.
Future development of the DBEM will also attempt to
incorporate other key biological processes that are
likely to be important in determining the responses of
marine fishes and invertebrates to climate change. Our
model has provided new insight into the effects of
ecological interactions on responses to climate and
provides a new tool for further exploring the effects of
future climate change. Predictions, in conjunction with
those from other models, will inform managers about
the range of possible ecological and fishery responses
to a changing environment, thus supporting the devel-
opment of management systems that take account of
the effects of climate change (Perry et al., 2011) and the
ongoing implementation of an ecosystem approach to
fisheries (Garcia & Cochrane, 2005; Rice, 2011). Predic-
tions of the long-term effects of climate currently need
to be considered alongside those used for operational
management, to prepare policy makers and fisheries
governance systems for changes in target fisheries and
dependent communities and economies (Perry et al.,
The research was funded by EURO-BASIN of the European
Union’s 7th Framework Program (Grant Agreement No.
264933). W. Cheung acknowledges funding support from Natu-
ral Sciences and Engineering Research Council of Canada and
National Geographic Society. The funders had no role in study
design, data collection and analysis, decision to publish, or
preparation of the manuscript.
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12 J. A. FERNANDES et al.
... Size-spectrum DBEM, devised by Fernandes et al. (2013), merges the species-based DBEM model along the size spectrum technique (SS-DBEM). The SSDBEM calculates system biomass, habitat appropriateness, and species relationships. ...
... org Woodworth-Jefcoats (2017) Fernandes, et al. (2013), http://www.sealifebase. ...
... As one travels into the western tropical Pacific and temperate latitudes, the size of the shift rises, culminating in around four species shed. Models driven by the ESM findings are reasonably consistent in their forecasts of a poleward latitudinal change in species distributions, as Fernandes et al. (2013) anticipated. Furthermore, the model's predicted movement of pelagic species is always less than if no links are incorporated. ...
... Specifically, this phenomenon has negatively affected the people who live in coastal areas as the intensity and frequency of extreme climate disasters, such as extreme weather patterns, extreme wave events, coastal flooding, and sea-level rise [1,2,3,4] has increased. In addition, the rise in ocean temperatures as another impact of climate change has resulted in a shift in fish stocks poleward since many tropical species are reaching their physiological limits leading to metabolic stress [5]. These disasters have resulted in huge economic losses, especially in tropical countries [6]. ...
... Organic carbon stock (g C/cm2) = sediment depth interval organic carbon density (5) The total organic carbon stock in sediment from a single core was revealed by summing up the organic carbon stock values at all depth intervals from the obtained samples [35]. ...
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This study aims to reveal the potential of sediment carbon in seagrass ecosystems in Karimunjawa National Park (KNP). Two seagrass sites located at two different zones in KNP were chosen as study sites i.e., Menjangan Besar (utilization zone) and Sintok (protection zone). There were nine soil cores for each 100×100 m ² site. There were three parameters used for estimating soil carbon stock i.e., compaction factor, dry bulk density, and Corg content (% soil dry weight). To collect the seagrass sediment, a PVC corer (length of 80 cm and diameter of 5.5 cm) was applied. Laboratory analysis showed that soil carbon stock at Sintok is 63.54±16.96 MgCorg/ha (mean±SE) in the top 58 cm of soil, whereas at Menjangan Besar is 65.32±11.71 MgCorg/ha (mean±SE) in the top 65 cm of soil. The result of T-test analysis showed that there was no significant difference in soil carbon stock between two sites. These seagrass soil carbon values highlight the need for implementing better management strategies in conserving seagrass ecosystems, particularly in managing zoning areas in KNP.
... One extreme example is the "borealization" of the Arctic, resulting from a physical change ("Atlantification"), which has triggered a poleward shift in species distributions and the development of a new food web (Stige & Kvile, 2017). Despite evidence that altered species interactions may be more important for population dynamics than the direct effects of, for example, climate change (Ockendon et al., 2014;Stige et al., 2019), we still have a limited understanding of their effects on population shifts and distributions (Fernandes et al., 2013). ...
... Many studies relate population displacement to the increase of change in environmental conditions, mainly temperature but also salinity, habitat, and food availability, to subsequently project the distribution of fish for the following decades (Payne et al., 2022). Our work highlights that it would be beneficial to take into account trophic interactions (Fernandes et al., 2013), as a "predatory wall" may affect the results of such population displacement projections as could bottom-up regulation . Mitigation of the effects of increasing temperature is not feasible in the short term. ...
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Climate change has a profound impact on species distribution and abundance globally, as well as local diversity which affects ecosystem functioning. Particularly, the changes of population distribution and abundance may lead to changes in trophic interactions. While species can often shift their spatial distribution when suitable habitats are available, it has been suggested that predator presence can be a constraint to climate-related distribution shifts. We test this using two well-studied and data-rich marine environments. Focusing on a pair of sympatric fishes, Atlantic haddock Melanogrammus aeglefinus and cod Gadus morhua, we study the effect of the presence and abundance of the latter on the former distribution. We found that the distribution of cod and increased abundance may limit the expansion of haddock to new areas and could consequently buffer ecosystem changes due to climate change. While marine species may track the rate and direction of climate shifts, our results demonstrate that the presence of predators may limit their expansion to thermally suitable habitats. By integrating climatic and ecological data at scales that can resolve predator-prey relationships, this analysis demonstrates the usefulness of considering trophic interactions to gain a more comprehensive understanding and to mitigate the climate change effects on species distributions.
... Concerning the introduction of trophic relationships into SDMs, two different approaches have been developed in the past years. The first one is called the Size-Spectrum Dynamic Bioclimate Envelope Model (SS-DBEM) and was first developed by Fernandes et al. (2013). The niche modeling of this approach is based on the Dynamic Bioclimate Envelope Model (DBEM) developed by Cheung et al. (2008a,b;, designated as NSI-DBEM by the authors (with NSI for no species interactions). ...
... Therefore, it does not explicitly consider the specificities of the predator-prey interactions. SS-DBEM has been declined in several ecosystems: Northeast Atlantic (Fernandes et al., 2013;Queirós et al., 2015;Fernandes et al., 2020a,b) Bay of Bengal (Fernandes et al., 2016), Kenya and Tanzania EEZs (Wilson et al., 2021), UK fisheries waters (Fernandes et al., 2017) and North Sea (Queirós et al., 2018a,b), on a variety of fish and invertebrates (Table 2). Moreover, several climate scenarios were tested using this model, like the IPCC A1B (Queirós et al., 2015;Fernandes et al., 2016), the RCP2.6 (Queirós et al., 2015;Fernandes et al., 2017Fernandes et al., , 2020a and the RCP8.5 (Queirós et al., 2015(Queirós et al., , 2018aFernandes et al., 2017Fernandes et al., , 2020aWilson et al., 2021). ...
... Carrying capacity in each cell is assumed to be a function of the unfished biomass of the population, the estimated habitat suitability and net primary production in each cell. The maximum sustainable yield of the species was approximated by the average of the top ten annual catches 67 . The average mass of an individual in the cell was simulated using a sub-model derived from a generalized von Bertalanffy growth function. ...
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Seafood is an important source of bioavailable micronutrients supporting human health, yet it is unclear how micronutrient production has changed in the past or how climate change will influence its availability. Here combining reconstructed fisheries databases and predictive models, we assess nutrient availability from fisheries and mariculture in the past and project their futures under climate change. Since the 1990s, availabilities of iron, calcium and omega-3 from seafood for direct human consumption have increased but stagnated for protein. Under climate change, nutrient availability is projected to decrease disproportionately in tropical low-income countries that are already highly dependent on seafood-derived nutrients. At 4 oC of warming, nutrient availability is projected to decline by ~30% by 2100 in low income countries, while at 1.5–2.0 oC warming, decreases are projected to be ~10%. We demonstrate the importance of effective mitigation to support nutritional security of vulnerable nations and global health equity.
... While it is widely acknowledged that climate change is impacting marine fish species distributions and size compositions (Fernandes et al., 2013;Hiddink & ter Hofstede, 2008;Jones & Cheung, 2015), this study is the first to demonstrate that changes in the spatial patterns of species richness will be different across feeding guilds in response to climate change. Species within feeding guilds that are predicted to increase in range do not simply occupy space left by species within the same guild decreasing in range (Figures 3 and 4). ...
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Many studies predict shifts in species distributions and community size composition in response to climate change, yet few have demonstrated how these changes will be distributed across marine food webs. We use Bayesian Additive Regression Trees to model how climate change will affect the habitat suitability of marine fish species across a range of body sizes and belonging to different feeding guilds, each with different habitat and feeding requirements in the northeast Atlantic shelf seas. Contrasting effects of climate change are predicted for feeding guilds, with spatially extensive decreases in the species richness of consumers lower in the food web (planktivores) but increases for those higher up (piscivores). Changing spatial patterns in predator–prey mass ratios and fish species size composition are also predicted for feeding guilds and across the fish assemblage. In combination, these changes could influence nutrient uptake and transformation, transfer efficiency and food web stability, and thus profoundly alter ecosystem structure and functioning
... The rates of ocean temperature rise and deoxygenation make urgent the development of mechanistic tools to forecast realistically their impacts from the physiology of marine organisms, to the population demographic impacts and to the consequence on marine trophic webs (Breitburg et al., 2018;Urban et al., 2016). Efforts to model the temperature impacts on marine biodiversity at the ecosystem level has so far focused mainly on the bottom-up effect of temperature on the ecosystem via changes in primary production (Lefort et al., 2015;Moullec et al., 2019) and on the distribution shift of species according to their preferred temperature (Albouy et al., 2014;Fernandes et al., 2013;Moullec et al., 2019;Serpetti et al., 2017). Mechanistic physiological response to temperature in MEM is modeled in size spectrum models (e.g., Lefort et al., 2015;Maury, 2010) and has been incorporated explicitly into few regional multispecies models (see for example (Utne et al., 2012). ...
Full text: 1 - Marine ecosystem models have been used to project the impacts of climate-induced changes in temperature and oxygen on biodiversity mainly through changes in species spatial distributions and primary production. However, fish populations may also respond to climatic pressures via physiological changes, leading to modifications in their life history that could either mitigate or worsen the consequences of climate change. 2- Building on the individual-based multispecies ecosystem model OSMOSE, Bioen-OSMOSE has been developed to account for high trophic levels’ physiological responses to temperature and oxygen in future climate projections. This paper presents an overview of the Bioen-OSMOSE model, mainly detailing the new developments. These consist in the implementation of a bioenergetic sub-model that mechanistically describes somatic growth, sexual maturation and reproduction as they emerge from the energy fluxes sustained by food intake under the hypotheses of a biphasic growth model and plastic maturation age and size represented by a maturation reaction norm. These fluxes depend on temperature and oxygen concentration, thus allowing plastic physiological responses to climate change. 3 - To illustrate the capabilities of Bioen-OSMOSE to represent realistic ecosystem dynamics, the model is applied to the North Sea ecosystem. The model outputs are confronted with population biomass, catch, maturity ogive, mean size-at-age and diet data of each species of the fish community. The model succeeds in reproducing observations, with good performances for all indicators. A first exploration of current spatial variability in species’ bioenergetic fluxes resulting from temperature, oxygen, and food availability is presented in this paper, highlighting the role of temperature. 4 - This new model development opens the scope for new fields of research such as the exploration of seasonal or spatial variation in life history in response to biotic and abiotic factors at the individual, population and community levels. Understanding such variability is crucial to improve our knowledge on potential climate change impacts on marine ecosystems and to make more reliable projections under climate change scenarios.
... • C by 2035 relative to 1980-1999 and 2.5-3.0 • C by 2100 [45]. Climate change may affect the growth, body size, and distribution of marine species [46], and shifts in the ranges of many marine species have already occurred [47,48]. It is possible that climate change will influence the population dynamics of blue sharks, and thus the effects of climate change should be considered in blue shark management strategy evaluations [49]. ...
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Blue shark (Prionace glauca) is a major bycatch species in the long-line and gill-net Pacific Ocean tuna fisheries, and the population structure is critical for fishery management. We employed generalized additive models to analyze the fork lengths of blue sharks and biological data (i.e., feeding level, sex, and genetic data), as well as environmental and spatial variables (i.e., sea surface temperature, month, longitude, and latitude) collected from 2011 to 2014 by the Chinese Thunnus alalunga long-line tuna fishery observer program. Fork length was significantly affected (p < 0.05) with location (latitude and longitude) and sex, and positively effected with sea surface temperature. No relationships were found between fork length and feeding level, month, and genetic data. We detected fork length differences among blue sharks over the range of the observed data, but the genetic data implied a panmictic population. Thus, we hypothesize that the genetic similarity was so close that it could not be well separated. Based on the precautionary principle, we recommend that the blue shark in the Pacific Ocean should be managed as two independent populations to ensure its sustainable use.
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Climate change is affecting the ocean, altering the biogeography of marine species. Yet marine protected area (MPA) planning still rarely incorporates projected species range shifts. We used the outputs of species distribution models fitted with biological and climate data as inputs to identify trends in occurrence for marine species in British Columbia (BC), Canada. We assessed and compared two ways of incorporating climate change projections into MPA planning. First, we overlaid 98 species with modelled distributions now and by the mid-21st century under two contrasting (“no mitigation” and “strong mitigation”) climate change scenarios with existing Provincial marine parks in BC, to ask which species could overlap with protected areas in the future. Second, we completed a spatial prioritization analysis using Marxan with the projected future species ranges as inputs, to ask where priority regions exist for the 98 marine species. We found that many BC marine parks will lose species in both climate scenarios that we analyzed, and that protecting 30% of important marine species will be challenging under the “no mitigation” climate change scenario. Challenges included the coarse resolution of the data and uncertainty in projecting species range shifts.
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In this paper we clearly demonstrate that changes in oceanic nutrients are a first order factor in determining changes in the primary production of the northwest European continental shelf on time scales of 5–10 yr. We present a series of coupled hydrodynamic ecosystem modelling simulations, using the POLCOMS-ERSEM system. These are forced by both reanalysis data and a single example of a coupled ocean-atmosphere general circulation model (OA-GCM) representative of possible conditions in 2080–2100 under an SRES A1B emissions scenario, along with the corresponding present day control. The OA-GCM forced simulations show a substantial reduction in surface nutrients in the open-ocean regions of the model domain, comparing future and present day time-slices. This arises from a large increase in oceanic stratification. Tracer transport experiments identify a substantial fraction of on-shelf water originates from the open-ocean region to the south of the domain, where this increase is largest, and indeed the on-shelf nutrient and primary production are reduced as this water is transported on-shelf. This relationship is confirmed quantitatively by comparing changes in winter nitrate with total annual nitrate uptake. The reduction in primary production by the reduced nutrient transport is mitigated by on-shelf processes relating to temperature, stratification (length of growing season) and recycling. Regions less exposed to ocean-shelf exchange in this model (Celtic Sea, Irish Sea, English Channel, and Southern North Sea) show a modest increase in primary production (of 5–10%) compared with a decrease of 0–20% in the outer shelf, Central and Northern North Sea. These findings are backed up by a boundary condition perturbation experiment and a simple mixing model.
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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. 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−1 northwards and 5.1 m decade−1 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 considering 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.
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We show that the distributions of both exploited and nonexploited North Sea fishes have responded markedly to recent increases in sea temperature, with nearly two-thirds of species shifting in mean latitude or depth or both over 25 years. For species with northerly or southerly range margins in the North Sea, half have shown boundary shifts with warming, and all but one shifted northward. Species with shifting distributions have faster life cycles and smaller body sizes than nonshifting species. Further temperature rises are likely to have profound impacts on commercial fisheries through continued shifts in distribution and alterations in community interactions.
The observation that the relative importance of picophytoplankton is greatest in warm and nutrient-poor waters was tested here based on a comprehensive review of the data available in the literature from oceanic and coastal estuarine areas. Results show that picophytoplankton dominate (50%) the biomass and production in oligotrophic (chlorophyll a [Chl a] 0.3 mg m 3), nutrient poor (NO 3 NO 2 1 M), and warm (26C) waters, but represent 10% of autotrophic biomass and production in rich (Chl a 5 mg m 3) and cold (3C) waters. There is, however, a strong covariation between temperature and nutrient concentration (r 0.95, P 0.001), but the number of observations where both temperature and nutrient concentrations are available is too small to allow attempts to statistically separate their effects. The results of mesocosm nutrient addition experiments during summer in the Mediterranean Sea allowed the dissociation of the effects of temperature from those of nutrients on pico-phytoplankton production and biomass and validated the magnitude at which picoplankton dominates (50%) autotrophic biomass and production obtained in the comparative analysis. The fraction contributed by picoplankton significantly declined (r 2 0.76 and 0.90, respectively, P 0.001) as total autotrophic production and biomass increased. These results support the increasing importance of picophytoplankton in warm, oligotrophic waters. The reduced contribution of picophytoplankton in warm productive waters is hypothesized here to be due to increased loss rates, whereas the dominance of picophytoplankton in warm, oligotrophic waters is attributable to the differential capacity to use nutrients as a function of differences in size and capacity of intrinsic growth of picophyto-plankton and larger phytoplankton cells.
The structure of animal communities and the energy flux through them may be characterized by biomass ratios, ecological efficiencies, and production efficiencies of the component organisms. These ratios are here interpreted in terms of the elementary processes of food intake, specific production rate, and gross growth efficiency that underlie them. The magnitude of all these processes is related to the average body mass of the organisms involved, but this well-known dependence reflects the influence of 2 different basic biological properties: 1) the metabolism-body-size relation of individuals; and 2) an ecological population factor reflected in the distribution of particle sizes within animal groups in the community, probably related to the relative sizes and distributions of predators and their prey. Both the physiological and ecological size relationships have to be recognized as scaling factors in order to transform measures of biological production of various parts of communities into common terms for comparison. By using this twofold size scaling, trophic energy flow within the community can be determined from the distribution of body sizes without the necessity of specifying trophic levels of the organisms involved.-from Authors