Reg Environ Change (2016) 16:341–351
DOI 10.1007/s10113-014-0635-7
ORIGINAL ARTICLE
Direct and indirect effects of climate and fishing on changes
in coastal ecosystem services: a historical perspective
from the North Sea
Samiya A. Selim • Julia L. Blanchard
Jacob Bedford • Thomas J. Webb
•
Received: 23 July 2013 / Accepted: 14 May 2014 / Published online: 3 June 2014
Ó The Author(s) 2014. This article is published with open access at Springerlink.com
Abstract Humanity depends on the marine environment
for a range of vital ecosystem services, at global (e.g. climate regulation), regional (e.g. commercial fisheries) and
local scales (e.g. coastal defence and recreation). At the
same time, marine ecosystems have been exploited for
centuries, and many systems today are under stress from
multiple sources. Recent studies have shown how both
climate change and fishing have caused long-term changes
in the marine environment. However, there is still poor
understanding of how these changes influence change in
coastal ecosystem services. In this paper, an integrated
modelling approach is used to assess how the final delivery
of marine ecosystem services to coastal communities is
influenced by the direct and indirect effects of changes in
ecosystem processes brought about by climate and human
impacts, using fisheries of the North Sea region as a case
study. Partial least squares path analysis is used to explore
the relationships between drivers of change, marine ecosystem processes and services (landings). A simple conceptual model with four variables—climate, fishing effort,
ecosystem process and ecosystem services—is applied to
Electronic supplementary material The online version of this
article (doi:10.1007/s10113-014-0635-7) contains supplementary
material, which is available to authorized users.
S. A. Selim (&) J. L. Blanchard J. Bedford T. J. Webb
Department of Animal and Plant Sciences, University
of Sheffield, Sheffield S10 2TN, UK
e-mail: bop11sas@sheffield.ac.uk
J. L. Blanchard
e-mail: julia.blanchard@sheffield.ac.uk
J. Bedford
e-mail: jbedford1@sheffield.ac.uk
T. J. Webb
e-mail: t.j.webb@sheffield.ac.uk
the English North Sea using historic ecological, climatic
and fisheries time series spanning 1924–2010 to identify
the multiple pathways that might exist. As expected, direct
and indirect links between fishing effort, ecosystem processes and service provision were significant. However,
links between climate and ecosystem processes were weak.
This paper highlights how path analysis can be used for
analysing long-term temporal links between ecosystem
processes and services following a simplified pathway.
Keywords Marine ecosystem services Historical
ecology Path analysis Fisheries Climate change
Introduction
Drivers of change in coastal ecosystems
Coastal ecosystems have been exploited for centuries
(Worm et al. 2006; Roberts 2007; Lotze et al. 2010), and
many today are under stress from multiple sources
including overfishing, pollution and climate change
(Halpern et al. 2012). Such stresses have led to marked
changes in the structure of marine ecosystems, for instance
in terms of the collapse of fisheries and changes in food
webs (Jackson et al. 2001, Cardinale et al. 2012). Recent
studies have shown the causal links between physical
(climate) and social drivers (human impact), and changes
in marine ecosystems (Link et al. 2009; Jennings and
Brander 2010; Sumaila et al. 2011; Heath et al. 2012). The
relationship between direct exploitation and changes in the
abundance of target and nontarget species has been well
documented (Hofstede and Rijnsdorp 2011; Luczak et al.
2012; Kerby et al. 2013). Likewise, effects of rising temperatures on species and ecosystems are increasingly well
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understood (Perry et al. 2005; Dulvy et al. 2008). However,
what is still lacking is an integrated approach to assess how
these processes affect the delivery of marine ecosystem
services to coastal communities and how these changes are
influenced by the direct and indirect links between ecosystem processes, climate and human impacts.
Classification of coastal ecosystem services
Coastal ecosystems provide vital suites of ecosystem services to local communities, visitors and wider society. For
instance, they act as a source of food (fisheries), employment (fishing and tourism sectors) and recreation (tourism,
water sports and wildlife watching) (Beaumont et al. 2007).
Mace et al. (2012) present a system for classifying these
different services by considering separately ecosystem
goods and final ecosystem services (benefits) as shown for
coastal ecosystems in Fig. 1. The relative contributions of
these different ecosystem services to the full suite of services provided by a given coastal system will clearly change
over time, as both societal priorities and states of ecosystem
change. We know that ecosystem services are linked to
ecosystem function and processes (Solan et al. 2012). This
implies that different suites of ecosystem services will be
affected by the states of different ecosystem components.
However, the relationship between different services and
state of the ecosystem is poorly understood (Bennett et al.
2009; Feld et al. 2009; Cardinale et al. 2012). There is thus a
crucial link to be made between ecosystem processes and
potential ecosystem service provision.
Fig. 1 Schematic diagram, based on the framework of Mace et al.
(2012), showing how a selection of drivers can affect various
processes in coastal ecosystems and how these in turn can affect
ecosystem good and benefits (final ecosystem services). In this study,
we look at the highlighted pathway linking fishing and climate
(drivers) to SSB and recruitment of three demersal fish species
(ecosystem processes) and the consequences for delivery of the
ecosystem goods (fisheries) and ultimately on food provision and
economic livelihoods (final ecosystem services)
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S. A. Selim et al.
One approach for linking anthropogenic drivers, ecosystem function and diversity, and services is to use the
driver–pressure–state–impact–response (DPSIR) framework, but most implementations of this approach have been
theoretical (Bowen and Riley 2003; Chan and Ruckelshaus
2010; Rounsevell et al. 2010) and empirical evidence and
applications to real case studies are lacking. Overall, little
applied research exists at large spatial and temporal scales
on how these various factors are correlated and how they
interact to bring about changes in ecosystem processes and
service provision. There is thus a need to study marine
processes on a large spatial and temporal scale (Cardinale
et al. 2012; Raffaelli and Friedlander 2012) to connect
these variables to service provision.
Using history to understand the causal links
between drivers, ecosystem and services
Increasingly, researchers are turning to history to understand past ecosystem states (Jackson et al. 2001; Lotze and
Worm 2009; Szabó 2010), as it offers unique insights into
lost ecological communities. This approach has the
potential to provide a better understanding of current processes in nature and how they have been shaped by natural
climatic fluctuations and anthropogenic climate change, as
well as human activities (e.g. land use change and fishing).
Studies incorporating this historical dimension have shown
several trends that have shifted our existing perspective on
marine systems, such as the previously much higher
abundances and larger individual sizes of several species
and the rich diversity that existed in the past (Roberts
2003). A long-term perspective provides a means to
understand these links, by analysing historic time series of
physical and biological changes in a coastal ecosystem
together with social changes in the provision of certain
ecosystem services.
In this paper, we illustrate how this historical perspective can be applied to long-term time series, using a partial
least squares path modelling approach to show how climate
and fisheries can affect ecosystem processes, and through
this coastal ecosystem service provision. We used the
coastal region of North East England from Sunderland to
Grimsby to quantify provision of an exemplar ecosystem
service, the food provision and livelihoods supported by
fisheries, over the course of most of the twentieth century, a
period encompassing significant changes in environmental
and socioeconomic drivers of change. We use the excellent
records of the biological communities and climatic history
of this coastal region, as well as socioeconomic data on
fishing effort and fish landings, to examine how shifts in
environmental drivers have affected fish populations and
ultimately fisheries. In particular, we test the hypotheses
that fishing effort will negatively affect both indicators of
Direct and indirect effects of climate and fishing
fish populations (adult biomass and recruitment), despite
being positively related to landings. In contrast, we have no
clear expectation of the direction of the effects of climatic
variables on either fish populations or fisheries landings.
Methods and materials
Study region
This study focuses on the North Sea, covering the spatial
range between 52° N and 60° N and between 2° W and
4° E (Fig. 2). The North Sea has been heavily exploited by
the industrialised and densely populated nations surrounding it. It has been fished intensively since 1900, and
fishing effort has increased consistently since that time
(Rijnsdorp 1996; Jennings et al. 2002). This region has
been the focus of numerous long-term ecological surveys,
including the Continuous Plankton Recorder survey (http://
www.sahfos.ac.uk), UK government bottom trawl surveys
of demersal fish (http://www.ices.dk/marine-data/data-
Fig. 2 Study region consists of ICES rectangles IVa, b and c in the
North Sea including the fishing ports of NE England highlighted in
the black box. These include all ports from Sunderland in the north to
Grimsby in the south
343
portals/Pages/DATRAS.aspx), the Seabirds at Sea programme (jncc.defra.gov.uk/page-1547) and the North Sea
Benthos Survey (NSBS; Craeymeersch and Duineveld
2011). At present, the North Sea has regained importance
in Britain’s fisheries as, for example, in 2009, it provided
63 % of the demersal fish landed by UK vessels into the
UK and abroad (Marine Management Organization 2009).
We consider the coastline from Sunderland to Grimsby,
focusing on a single set of ecosystem goods and the
associated final ecosystem services, fisheries landings to
the Yorkshire Coast—mainly into the ports of Scarborough, Whitby, Filey, Bridlington, Grimsby and Hull
(Fig. 2).
Sources of data
Time series of fish abundance spanning the period
1924–2010 are shown in Fig. 3. Historic data on the
spawning stock biomass (SSB) and recruitment of three
demersal species, cod Gadus morhua, haddock Melanogrammus aeglefinus and whiting Merlangius merlangus,
were obtained from Pinnegar (2007). These species are
three key commercial species in terms of landings, and
they also contribute [50 % by weight of total species
composition in the central North Sea demersal fish community (Harding et al. 1986; Sparholt 1990; Hislop 1996).
The spatial resolution of this historical data set covered the
entire North Sea, and the data are aggregated according to
International Council for the Exploration of the Sea (ICES)
areas IVa, b and c (Fig. 2). Further details of how these
statistical rectangles are defined can be found on the ICES
website (http://www.ices.dk/marine-data/maps/Pages/ICESstatistical-rectangles.aspx). This coarse resolution is typical
of historical datasets and dictated the spatial resolution of
our study.
The environmental variables we use are sea surface
temperature (SST), to characterise long-term climatic
trends, extracted for the study area from ICES Surface Data
Source (http://ocean.ices.dk/data/surface/surface.htm) and
averaged for each year, and the North Atlantic Oscillation
(NAO) index, obtained from Hurrell (2013), to characterise
short-term climatic fluctuations. Monthly NAO data were
averaged across summer months (April–September) and
winter months (October–March) for each year.
We extracted fish landing data to the English East Coast
ports of Grimsby, Hull, Robin Hood’s Bay, Scarborough,
Filey, North Shields and Whitby for the three demersal fish
from ICES areas IVa, b and c from 1924 to 2010 from
ICES records (ICES 2013). Fishing effort data were
obtained from UK Sea Fisheries Statistical Archives
(http://www.marinemanagement.org.uk/fisheries/statistics/
annual_archive.htm). We digitised records of fishing effort
in terms of number of hours in the North Sea by all sail,
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S. A. Selim et al.
NAO Winter
NAO Summer
Sea Surface Temperature
13
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
1
0
-1
-2
12
11
10
9
8
Trawling
6.0
5.5
5.0
Cod SSB
Haddock SSB
Whiting SSB
6.0
5.8
5.6
5.4
5.2
5.0
4.8
5.4
5.2
5.0
4.8
4.6
4.4
Cod Recruitment
5.8
5.6
5.4
5.2
5.0
4.8
Haddock Recruitment
5.8
5.6
5.4
5.2
5.0
4.8
Whiting Recruitment
8.5
8
8.0
7
7.5
6
7.0
5
6.5
4
6.0
Cod Landings
Haddock Landings
4.5
4.5
4.0
4.0
Whiting Landings
4.0
3.8
3.6
3.4
3.2
3.0
2.8
3.5
3.5
3.0
1940
1960
1980
2000
1940
1960
1980
2000
1940
1960
1980
2000
Fig. 3 Time series of the datasets used in this study. Climate is
represented by the summer and winter NAO index and by sea surface
temperature. Fishing effort is represented by numbers of hours fished
by different types of trawlers, standardised to constant power.
Ecosystem processes are represented by SSB and recruitment of
three commercial fish species—cod Gadus morhua, haddock Melanogrammus aeglefinus and whiting Merlangius merlangus. Goods and
services are represented by landings of these three species into ports
on the east coast of England by British trawlers. All variables except
the climate indices (NAO and SST) are log-transformed
steam and motor trawls (beam and otter trawler [10 m)
from England and Wales for the period 1924–2010 and
standardised effort into smack units following Engelhard
et al. (2009). Briefly, this is done by taking the fishing
power of the original sail trawl as baseline and comparing
all other types of trawls (steam and motor) to the fishing
power of sail trawl, as recommended by Garstang (1900).
(fish landings). PLS path modelling is a variance-based
form of structural equation modelling (SEM) (Tenenhaus
et al. 2005), allowing for the simultaneous modelling of
relationships among multiple independent and dependent
variables (Gefen et al. 2000). Path modelling is mainly
used to measure both direct and indirect pathways between
variables, as well latent variables (LVs) that are unobserved or hidden, and it can deal with issues of multicollinearity when modelling complex systems, making no
strong assumptions on distribution, sample size and measurement scale (Tenenhaus et al. 2005). It is a componentbased estimation method (Vinzi et al. 2010) that contains
an inner model (structural model) which measures the
causal links between LVs (i.e. variables that are not
directly observed) and an outer model (measurement
Statistical analysis
We used Partial Least Square Path Modelling using the
PLSPM package (Sanchez 2013) in R version 2.15.3 (R
Core Team 2013) to investigate the causal relationship
between drivers of change (climate and effort), ecosystem
processes (SSB and recruitment) and ecosystem services
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Direct and indirect effects of climate and fishing
345
Temp
Climate
NAO
+−
Trawling
Demersal Landings
+
Effort
−
Services
+
Spawning Stock Biomass
Ecosystem
Recruitment
Fig. 4 Conceptual path model linking drivers of change (climate and
fishing effort), ecosystem processes and services. The inner model
consists of four latent variables (LVs, dark grey ovals). Two of these,
climate and effort, are exogenous variables (not affected by any
external factors). These affect the endogenous variables (ecosystem
processes and services) through different pathways. Each LV is
measured by its own block of manifest variables, which form the
outer model (light grey boxes). We hypothesise that climate can affect
ecosystem processes directly and services indirectly and that these
effects can be positive or negative. Ecosystem processes are
hypothesised to positively impact services, and fishing effort is
hypothesised to impact ecosystem processes negatively and services
positively
model) which shows how blocks of indicator variables
measure each LV (Fig. 4). This enables indicators to be
aggregated, while taking into account the impact of each
indicator on its LV as well as the causal relationship
between the unobserved LVs (Trinchera 2010).
Our path model is illustrated in Fig. 4, where four LVs
(climate, fishing effort, ecosystem processes and ecosystem
services) are each measured by their own block of indicators, and the hypothesised directional flows of the impact
of drivers on the ecosystem processes and ecosystem services are indicated. We hypothesised that the exogenous
(independent) variables—fishing effort and climate—have
four possible pathways linking them to the endogenous
(dependent) variables, ecosystem processes and services.
PLSPM indicators can be constructed in reflective mode or
formative mode. In our path model, climate and effort are
represented by formative indicators (e.g. temperature and
NAO ‘‘inform’’ the LV climate), whereas ecosystem and
services are represented by reflective indicators (e.g. SSB
and recruitment are shaped by the LV ecosystem). In
reflective mode, any change in the LV will cause a same
directional change in the reflective indicators. Alternatively, in formative mode, the direction of the indicator
does not have to be the same within the LV. An example of
this is the latent variable climate which is measured by SST
and NAO, which are likely to be negatively correlated
because a negative NAO is associated with mild winters.
We further separated the two ecosystem processes,
recruitment and SSB, into different LVs so as to better
assess whether climate and fishing effort had different
impacts on each. The LV services included landings of the
three demersal fish into east England ports.
Both the inner and outer models were assessed using
standard measures (Chin and Dibbern 2007) of communality and redundancy, R2 and goodness of fit (GOF). For
the outer model (measurement model), we tested whether
the chosen indicators represent and measure their LV
effectively, taking a variable weight (for formative indicator) or loading (for reflective indicator) C0.7 to indicate a
reliable construct of the indicators (Sanchez 2013). This is
equivalent to a communality (squared loading) [0.49,
indicating that 50 % of the variability in an indicator is
captured by its LV. We tested for unidimensionality (i.e.
that all indicators in a block act in the same direction) of
LVs measured in reflective mode (ecosystem and services)
using Dillon–Goldstein’s q (Sanchez 2013), which is
considered a better indicator than Cronbach’s a (Chin and
Dibbern 2007).
For the inner model, we used R2 to measure how much
of the variance in the endogenous LV is explained by its
exogenous LV (Sanchez 2013), and average communality
and redundancy to explain variability of the indicators in
relation to the amount of variance due to measurement
error (Sanchez 2013). We used path coefficients to estimate
the strength and direction of the relationship between the
exogenous and endogenous LVs (Sanchez 2013), and
Goodness of Fit (GOF, the geometric mean of the average
communality and average R2 value) to assess the overall
predictive performance of the model. Finally, we used
bootstrapping as a final check of the validity of the model
pathways and results, using the 95 % bootstrap confidence
interval to evaluate whether the parameters are significantly different from zero.
Ecosystem processes and services may not respond
instantly to the effort and climate drivers, but instead show
lagged responses. We therefore checked for lags of up to
4 years in the relationships between climate and fishing
drivers and ecosystem processes and services. Autocorrelation analysis revealed 2-year lags between NAO winter
and both cod and whiting recruitment. We therefore
included a 2-year time lag for NAO winter in our models.
Past research has shown that there might be strong
temporal autocorrelation in climate time series (Sirabella
et al. 2001).
When dealing with long time series, it is possible that
the relationship between exogenous and endogenous variables has not been constant through time. In particular, in
the North Sea, the decline in fishing effort broadly coincides with an increase in SST, suggesting a possible shift
from a regime dominated by intensive fishing, to one in
which climatic effects are more significant. To test for the
effects of this on our model, we first checked the fishing
effort time series for changes in temporal trend. Broadly
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speaking (Fig. 3), with the exception of the war years,
effort increased until the early 1970s and has declined
thereafter. Modelling postwar (1946–2010) log-transformed trawling effort as a quadratic function of year
produces an excellent fit (R2 = 0.96) with an inflexion
point in 1972, after which effort has declined markedly
(Fig S1; see supplementary material). Trends in SST are
less clear; however, if we model this time series as a twopart linear breakpoint model, the optimal year for the
breakpoint (lowest AIC, highest R2) is also 1972, with a
steeper slope (more pronounced warming) from 1972 to
2010 than in earlier years (Fig S1; see supplementary
material). Therefore, we re-ran our PLSPM under two
different ‘regimes’—a ‘fishing’ regime from 1924 to 1971
defined by heavy fishing effort and a ‘climate’ regime from
1972 to 2010 defined by a stronger climatic influence.
S. A. Selim et al.
Table 1 Outer measurement results for the three models (full time
series, fishing regime and climate regime) showing how well each
indicator measures its individual latent variable
Indicator
variable
The outer model results for the model using the full time
series are shown in Table 1. In our model, all the LVs were
measured effectively by only some the chosen indicators
(Table 1). In both SSB and recruitment, loadings were
above 0.7 for cod and whiting, but not for haddock. In the
LV services, loadings was high for all landings ([0.7).
Effort had only one indicator measuring its LV and
therefore does not have a loadings score. The LV climate is
formative, and so, we use weights rather than loadings for
evaluation. We observed a high weight for the indicator
SST (0.83), but not for NAO. Communalities were[0.5 for
all the indicators with high loadings ([0.7), and as such
more than 50 % of their variance is shared with its corresponding LV. Values of Dillon–Goldstein’s q were [0.7
for SSB, recruitment and services, so we consider the
indicators to be homogenous and unidimensional in our
model.
The overall GOF of our model using the full time series
was 0.50, showing that the predictive performance of our
model was 50 %. The inner model (showing how well the
LVs are related) has R2 values of 0.21, 0.31 and 0.55, for
SSB, recruitment services, respectively (Table 2). In other
words,[20 % of the variance in endogenous variables SSB
and recruitment, and[50 % of the variance in services, can
be explained by the exogenous variables climate and effort.
All the endogenous indicators for which it could be calculated had an average communality [0.39 indicating that
the indicators used to measure them are represented well in
the model (Table 2).
Path coefficients show the strength and direction of the
pathways, and in our full time series model (Fig. 5a),
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Fishing regime
model
Climate regime
model
Climate
NAOa winter
0.37
0.49
0.23
-0.29
-0.39
-0.11
0.83
0.81
1.00
–
–
–
Cod
0.93
0.78
0.96
Haddock
0.43
0.91
0.04
Whiting
Recruitment
0.77
0.26
0.95
NAO summer
SSTb
Effort
Total hours
trawled
SSBc
Results
Full time series
Full time series
model
Cod
0.75
0.89
0.76
Haddock
0.47
0.59
0.35
Whiting
0.82
0.63
0.9
Cod
0.90
0.99
0.97
Haddock
0.67
0.01
0.94
Whiting
0.73
-0.13
0.93
Landings
Formative indicator results are measured using weights (in italics),
and reflective indicators are measured using loadings. Results that are
significant ([0.7) are shown in bold. See text for more details
a
NAO North Atlantic oscillation
b
SST Sea surface temperature
c
SSB Spawning stock biomass
fishing effort had a significant positive effect on both ecosystem processes SSB (0.65, bootstrapped CI 0.54–0.79)
and recruitment (0.63, CI 0.43–0.78) and on ecosystem
services (fish landings, 0.87, CI 0.67–1.05). Climate had no
significant impact on either SSB or recruitment (Fig. 5a).
The impacts of SSB and recruitment on services were not
significant either (Fig. 5a). PLSPM allows the total effects
of LVs to be separated into direct and indirect effects. For
our model, indirect effects were typically much weaker than
direct effects.
‘Fishing’ versus ‘climate’ regimes
Modelling the ‘fishing’ (1924–1971) and ‘climate’
(1972–2010) regimes separately resulted in some of the
path coefficients being different in the two regimes. The
weights and loadings (Table 1) show that the LV climate is
still measured better by the formative indicator SST in both
regimes (0.81, 1.00). In the ‘fishing’ regime, the LV SSB is
measured well by cod and haddock SSB (0.78, 0.91), but
not by whiting SSB (0.26). In the ‘climate’ regime, LV
Direct and indirect effects of climate and fishing
347
Table 2 Reliability convergence of the inner model showing two measures (R2 and average communality) of the amount of variability in each
latent variable (LV) explained by the exogenous LVs climate and fishing effort, for the full time series, fishing regime and climate regime models
LV
Type
R2 full time
series
Average
communality
R2 fishing
regime
Average
communality
R2 climate
regime
Average
communality
SSB
Endogenous
0.210
0.551
0.32
0.50
0.83
0.61
Recruitment
Endogenous
0.310
0.488
0.32
0.51
0.7
0.54
Services
Endogenous
0.551
0.394
0.48
0.34
0.87
0.89
See text for more details. Results [0.5 are shown in bold
SSB is measured well by cod and whiting SSB (0.96, 0.95).
The LV recruitment is measured well by cod recruitment
only in the fishing regime (0.89) and by both cod and
whiting in the climate regime (0.76, 0.90). LV services are
measured well by only cod landing in the fishing regime
and by all three indicators in the climate regime. R2 and
average communality of the two regimes are provided in
Table 2 which shows that the inner model of the ‘climate’
regime had higher R2 and average communality than the
fishing regime model. GOF was also higher for the climate
regime model (0.69) than the GOF for the fishing regime
model (0.39).
The path coefficients and their 95 % bootstrapped CIs
for the two regimes are shown in Fig. 5b and c. The
pathways between climate and recruitment have the same
directional trend in both regimes. The path coefficients for
the pathways effort on ecosystem services were significant
in the climate regime (0.47, CI 0.19–0.73), but not in the
fishing regime (0.31, CI -0.31–0.79). SSB had a significant positive impact on services in the climate regime
(0.50, CI 0.27–0.76), but not in the fishing regime (0.10, CI
-0.68–0.69). The pathway from effort to recruitment was
significant in both regimes (fishing regime: 0.55, CI
0.29–0.76; climate regime: 0.89, CI 0.70–1.04), that from
effort to SSB was significant only in the climate regime
(0.81, CI 0.63–0.92) and additionally the SSB to services
pathway was significant in the climate regime (0.50, CI
0.27–0.76; Fig. 5b, c).
Discussion
The aim of this study was to illustrate how combining
historical data with path modelling can inform our understanding of multiple drivers of marine ecosystem processes
and ecosystem services. There is an inevitable trade-off
between the data requirements of a model and the time
period over which adequate data are available. In this initial implementation, we aimed for maximum temporal
coverage which has necessarily led to a simplified and
partial representation of the multiple complex pathways
between the physical, biological and social drivers of
change in ecosystem service provision. Nonetheless, we
have demonstrated how a partial ecosystem pathway model
can be used to test several hypotheses about the long-term
change in a natural system. Our hypotheses were that effort
would have a negative impact on both ecosystem processes, but a positive impact on services. Climate could
have either negative or positive impact on ecosystem processes. We found that effort had a positive impact on both
the SSB and the recruitment of three commercially
important demersal fish species, and these results were
significant in all the three models. SSB also had significant
positive impact on services in only the ‘climate’ regime
model, although the coefficient was positive in all three
models. The additional pathways of climate on ecosystem
processes and recruitment on services were not significant.
The fact that fishing effort had a positive impact on both
SSB and recruitment could be explained by the fact that
fishing effort increased during periods of high abundance
and availability, such that effort responds to ecosystem
processes as well as driving them. Over the 90-year period,
as effort increased, especially after 1950s, SSB and
recruitment also increased. The direction of causality is
unclear here, however, as it could be that fishers were
responding to the ‘gadoid outburst’ of the 1960s and 1970s,
when cod, haddock and whiting had some of the highest
recruitment on record (Cushing 1978; Hislop 1996).
Overall, the decline in fishing effort since the 1970s is
mostly due to the implementation of quotas and total
allowable catches beginning during the 1970s and 1980s
(Hatcher 1997). As such the correlations between effort
and landings are masked by other external factors such as
implementation of management control and policies, which
could be included in future models. Additionally, we only
had fishing effort data from British vessels fishing in the
North Sea. As such, we were unable to establish how total
fishing effort in the North Sea (including effort from foreign vessels) impacted ecosystem processes and services.
The pathways between climate and recruitment, and
climate and SSB were not significant. Past research has
shown that climate affects fish communities and that certain species have shifted polewards or moved deeper due to
rising temperatures (Brien et al. 2000; Perry et al. 2005;
123
348
S. A. Selim et al.
A
Climate
-0.14
0.03
0.87
Effort
Services
0.03
0.65
-0.13
0.63
SSB
B
Recruitment
Climate
-0.06
0.06
0.31
Effort
Services
0.10
0.49
0.07
0.55
SSB
C
Recruitment
Climate
-0.17
0.09
0.47
Effort
Services
0.50
0.81
-0.02
0.89
SSB
Recruitment
Fig. 5 Path coefficients of the inner models for all three models
showing the strength and direction of all the different pathways.
Positive pathways are highlighted in blue and negative pathways in
red. Bootstrap validation was carried out on all the models, and the
95 % confidence intervals (CI) are given below each path coefficient.
a Full time series model showing the significant positive effect of
fishing effort on both ecosystem processes and services. The other
pathways were not significant. b ‘Fishing’ regime model showing the
only significant effect was fishing effect on recruitment. c ‘Climate’
regime model showing the significant positive effects that effort had
on ecosystem processes and services, and SSB on services as well
Kerby et al. 2013). However, the interaction between climate and ecosystem processes is likely to be more complex
than the direct links between fishing effort and landings.
123
Ecosystem processes might be affected by bottom-up forcing rather than top-down control (Jackson et al. 2001;
Beaugrand 2004; Fauchald et al. 2011; Luczak et al. 2012);
hence, we would have had to include species at lower trophic levels such as zooplankton in the model to start seeing
better interaction between change in climate and ecosystem
processes. It would be possible to expand our approach to
include individual species from each trophic level, as
demonstrated for instance by Lauria et al. (2012) who analysed effects of changes in SST across trophic levels.
Additionally, although climate is well represented in our
models by a simple temperature variable (SST), future
models might need to include other indicators to effectively
measure climate, including temperature extremes, bottom
temperature or the Atlantic multidecadal oscillation (AMO).
Past studies have tried to disentangle the effects of
fishing and climate on ecosystems (Rochet et al. 2010;
Hofstede and Rijnsdorp 2011). Running the model separately for the proposed fishing (1924–1971) and climate
(1972–2010) regimes showed that only the impact of effort
on both SSB and recruitment was significantly different
under the two regimes. Fishing effort had high positive
correlation with both SSB and recruitment in the fishing
regime, but significantly higher in the climate regime. As
outlined above, this can be explained in the fishing regime
by effort tracking the rise in stocks due to ‘Gadoid outburst’ (Cushing,1978; Hislop 1996). In the climate regime,
the positive relationship between effort and stocks is likely
due to both declining effort due to management instigated
in the 1970s, partly in response to declining stocks (ICES,
2005). SSB had a stronger positive impact on services
under the climate regime than in the fishing regime.
However, recruitment had a positive impact on services
under fishing regime, but negative under climate regime,
suggesting that these ecosystem processes might affect
services in different ways, depending on how they themselves are affected by external factors. Similarly, in a study
of path coefficients differ under different regimes, Cattadori et al. (2005) showed that climate impacted red grouse
populations, but it was the trophic interactions with parasites that differed under different regimes which indirectly
affected the population.
Here, we have shown how PLSPM can be applied to
model the different drivers of change in a subset of the
ecosystem (three commercially important demersal fish
species) and for one particular type of provisioning service
(fisheries). In future models, this could be extended to
include interactions between different ecosystem components and processes (e.g. zooplankton, seabirds and benthic
invertebrates) as well as to other services (e.g. recreational
fisheries, livelihoods and climate regulation). In addition,
PLSPM has previously been applied successfully to marketing and management strategies through identification
Direct and indirect effects of climate and fishing
and measurement of LVs such as ‘satisfaction’ and ‘image’
(Hair et al. 2011). This is very pertinent to the field of
valuation of ecosystem services, in particular cultural services that can be difficult to measure and that have been
identified as a research priority (Chan and Ruckelshaus
2010). Path analysis could also be applied to regulating
services, for instance where different factors combine to
provide one particular service (e.g. disturbance prevention,
flood and storm protection), because several indicators can
measure a single LV.
More complex models will necessarily require more
comprehensive data, which will likely come at the expense
of the unusually long time series we have used in this study.
We believe that this long-term historical context of ecosystem service provision can be useful for several reasons.
First, by linking ecological and socioeconomic time series,
we can establish functional relationships between ecosystem state and ecosystem service provision (Kremen 2005;
Nelson et al. 2009; Cardinale et al. 2012). Similarly, we can
track changes in ecosystem state and ecosystem service
provision, which for instance, enables the ease (or cost) of
transitioning between different states to be estimated.
Finally, documenting the past states of ecosystems and the
services they provide can expand the range of scenarios
considered for future management and definitions of ‘good
environmental quality’ (Pinnegar et al. 2006; MSDF, EC
(2008); MacKenzie et al. 2011). This might include quantitative changes in the current suite of ecosystem services,
or qualitative changes, for instance prioritizing the recovery
of ‘lost’ ecosystem services (Bullock et al. 2011).
This study adds to the growing research assessing
human impact on landscapes and seascapes and its effect
on biodiversity and ecosystem services (Bennett et al.
2009; Nelson et al. 2009; Chan and Ruckelshaus 2010). It
builds on previous studies by adding empirical analysis
using complete time series of historical ecological and
socioeconomic data, to identify links between anthropogenic drivers, ecosystem process and ecosystem service. To
reach any solid conclusion on the overall long-term impact
of climate and fishing drivers on ecosystem processes and
services, it is clear that we would need to consider inclusion of additional variables. Future improvements to the
model and integration of long-term time series will help to
develop these links further and allow better understanding
of the complex nature of how ecosystem service provision
is influenced by changes in both natural and anthropogenic
factors.
Acknowledgments SS acknowledges the support of a NERC
Doctoral Training Grant. TJW is a Royal Society University Research
Fellow.
Open Access This article is distributed under the terms of the
Creative Commons Attribution License which permits any use,
349
distribution, and reproduction in any medium, provided the original
author(s) and the source are credited.
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