Open access

A Study on Economic Impact on the European Sardine Fishery due to Continued Global Warming

Written By

M. Dolores Garza-Gil, Manuel Varela-Lafuente, Gonzalo Caballero- Míguez and Julia Torralba-Cano

Submitted: 25 April 2014 Published: 22 April 2015

DOI: 10.5772/58877

From the Edited Volume

Global Warming - Causes, Impacts and Remedies

Edited by Bharat Raj Singh

Chapter metrics overview

1,742 Chapter Downloads

View Full Metrics

1. Introduction

The global warming is one of the biggest issues now facing humanity, including its repercussions on climate change. The climate change is directly or indirectly attributable to human activity and alters the composition of the atmosphere, along with the natural variability observed during comparable periods of time [1]. On a global level, human activity has generated a loss in biodiversity due, among other factors, to changes in the way we use the land, the pollution of soil and water, the diversion of water to intensively farmed ecosystems (intensive agricultural and livestock farming) and urban systems, the introduction of non-autochthonous species and the exhaustion of the ozone layer. In particular, the rate of biodiversity loss is currently greater than that of natural extinction [2]. In this context, climate change is contributing towards increasing this loss of biological biodiversity [3]. It is considered that the ongoing increase in the levels of greenhouse gases is one of the main causes, along with natural factors, of climate change [2]. The concentrations of greenhouse gases have been increased since the industrial age due fundamentally to the use of fossil fuels and to changes in soil use and cover; generating, among other effects, increases in land and sea surface temperatures, changes in precipitation (both spatial and seasonal), increases in sea levels and changes in the frequency and intensity of some climate phenomena (hurricanes, intensive precipitations, heat weaves). [4-5]

The scientific certainty that climate change is a reality [6] and, therefore, that it is necessary to live alongside it, leads us to take action aimed at reducing greenhouse gasses compatible with other action which aims to study the possibilities of adapting to these new environmental conditions that are going to characterise the planet in the coming decades. The new scenarios generated by this global phenomenon generate negative impacts on the majority of Spain’s ecosystems, and also on productive activities; but opportunities may also arise (for example, for tourism in northern Spain; [7]) and could be tapped into, if a certain level of scientific knowledge and prospective capacity with regard to the evolution of environmental conditions in the regions and their effects on natural systems over the next decades were reached.

In one of its latest reports, the Inter-governmental Panel on Climate Change (IPCC) estimates that, on a global level, the average temperature of the land surface could increase by between 1.4 and 5.8°C by the end of the 21st century (a greater increase in higher latitudes that in the tropics), that there will be an increase in the sea surface temperature, that terrestrial regions will undergo a higher increase than the oceans, that the sea will rise by between 0.09 and 0.88m, and that there will be an increase in the occurrence of extreme phenomena (heavy rainfall, hurricanes, etc.), although with regional differences (IPCC, 2013). Proximity to one or other extreme of the intervals will depend fundamentally on society’s capacity to mitigate the effects of climate change.

The different scientific reports published by national [8-9] and international [6] bodies on global warming leave no doubt as to Spain’s vulnerability to its effects. However, this knowledge is not sufficient to describe the behaviour of the various microclimates in different areas of Spain and their interaction with different ecosystems throughout the country. Northern Spain, to where the fishery that we are going to look at in this paper belongs, is located in medium latitudes, framed within an area of atmospheric circulation where western and northern winds prevail, and is the first point Atlantic perturbations reach with respect to the rest of Spain. However, at the same time, this zone is influenced by different air masses with very different thermodynamic characteristics. Specifically, masses of warm, humid air arrive, like tropical maritime masses, as well as masses of air which, as they come from higher latitudes, have the common characteristic of being cold, although with a different humidity content [10].

In the specific case of the oceans, one of their significant characteristics is that they store a much greater quantity of energy than the atmosphere, and thus the possible effects of global warming would have a greater impact on marine ecosystems than on land ecosystems [11-15]. Based on the last reports from the IPCC, the global warming will generate changes in the intensity and structure of oceanic currents, produce alterations in marine organisms and shoreline, among other effects. Such shocks on the oceans probably will have important repercussions on the fisheries [16-20]. The projected impacts of global warming on fisheries and aquaculture are negative on a global scale. These impacts of climate change and ocean acidification are generally exacerbated by other factors such as overfishing, habitat loss and pollution. This is contributing to an increase in the number of dead zones in the oceans, as well as to an increase in harmful algal blooms [6]. The fishing activity is conditioned by the natural characteristics of the marine environment. Therefore, this economic activity is located between the most affected by global warming, along with tourism and agriculture ([7], [21]-[25]). It is highly probable that global warming will generate impacts on the intensity and disposition of oceanic currents, and the effect of this, among others, will generate increasing in surface sea temperature, variations in acidification and salinity levels and variationson ([26], [6]-[7], [26]). The impacts will differ according to ecosystems and coastal or ocean zones, and will affect different groups of organisms, from phytoplankton and zooplankton to fish and algae [9]. Among these organisms, small pelagic marine species will be among those most affected by the effects form the warmin gf seas and oceans, due to their high level of instability and sensitivity to environmental impacts [27-29]. Therefore, any sea surface temperature variation will have repercussions to a greater or lesser extent on the ecosystems and these species’ reproduction levels [17], especially in warming scenarios, and so any significant modification in the biomass levels can affect fishermen’s net profits.

In the northern Spain zone, an increase in the sea surface temperature of 0.2°C per decade over the last forty years has been observed [10]. An increase has also been observed in the sea level of between 2 and 2.5cm per decade, similar data to the global increase in the Atlantic Ocean, although it has accelerated in the last twenty years [10]. Furthermore, a decrease in the intensity and duration of upwelling due to changes in wind patterns has occurred (the duration of the period favourable for upwelling has decreased significantly, by 30%, and their intensity by 45% in the last forty years; [10]). For the northern zone overall, the IPCC foresees an rise in the sea level of between 0.5 and 1.4 metres, an increase of the sea surface temperature of between 1 and 3°C and a drop in the pH of around 0.35 units [6].

The small pelagic species are target species for the majority of the Spanish fleet, in general, and for the north-west in particular. The study case chosen is the sardine fishery in zones VIIIc and IXa delimited by the International Council for the Exploration of the Sea (ICES), due to it being a hugely important pelagic species for the European fisheries sector. The potential economic impacts of global warming on this fishery will be analysed. For it, the oceantemperature is introduced into the bio-economic management problem. The sea surface allows gathering evidence of the global warming and its effects on marine ecosystems, which are the bases of fish natural growth functions. Other variables, such as the frequency and intensity of rainfall, acidity, dissolved carbon and salinity, are also prone to experience environmental changes by the climate change. However there is a high level of correlation between all of these variables: greater frequency and intensity of rainfall, acidity, salinity and dissolved carbon when the sea surface is increasing.

In this chapter, the evolution of sardine biomass is showed, based on the changes of the stock’s growth function with respect to variations in marine ecosystem characteristics through the sea surface temperature and using econometric techniques and the Theory of the Optimal Control. Correlations between the sea surface temperature and the sardine biomass are tested. The econometric techniques will allow define the significant parameters and variables that could be needed to include in the management problem. The Optimal Control Theory will allow solve the bio-economic management problem and obtain the values for the sardine biomass and catches according to the forecast scenarios for the surface sea temperature by the IPCC. In addition, we will analyse impacts on the economic yield of the fishery deriving from a possible change in the temperature conditions of the ecosystem. A strong statistical relation is showed between the sea surface temperature and the sardine biomass. In addition, the results show thatif the sea surface temperature trend in these fishing grounds continues to show warming (as it is expected), the European sardine biomass, the catches and the expected profits will drop over the medium- and long-term.

Advertisement

2. Material and method

The Ibero-atlantic sardine (Sardina pilchardus) fishery is a stock resource shared by Spain and Portugal, in the waters that surround the Iberian Peninsula between the Bay of Biscay and the Strait of Gibraltar. There are historical references to this fishery dating back to the 13th and 14th centuries, made by both Spain [30] and Portugal [31]. From its beginnings, this fishery has been significantly relevant for the fishery sectors of both countries as well as for the processing industry (salting and canning industries).

This stock is exploited majority by the purse seine gear (99% of landings [32]). The Spanish fleet using this gear in the Atlantic is composed by 491 vessels, of which 346 are involved in Cantabrian waters and the remaining 146 are fishing in the grounds of the Canary Islands and the Bay of Cadiz [33]. It is one of the more numerous fleets of the Spanish total, behind only of the artisanal fleet, and it targets small pelagic species, among which are the sardine, horse mackerel, mackerel and the anchovy ([7],[33]-[34]). The vessels involved in this fisheryare relatively homogenous vessels insofar as their technical characteristics are concerned, with an average fishing capacity of 34.2 GT, 151.8 Kw per vessel and 21m of length size. The average life of the fleet is 20 years, with a crew of 8 per vessel. The Spanish purse seine fleet in the North Atlantic grounds use nets made from synthetic materials, hydraulic haulers and electronic fish detectors. The Spanish fleet that operates in the purse seine fishery licensed to catch sardine in this fishery is 241 vessels in 2012 [35].

2.1. The sea surface temperature

The Ibero-atlantic sardine recruitment processes could be governed by localised oceanographic conditions, but also by climatic events of a global nature. The growing influence of subtropical climatic components, such as El Niño, would increase the sea surface temperature, while the frequency and intensity of the average coastal upwellings on the west coast of the Iberian Peninsula are reduced, probably meaning that such a combination would cause the fishery’s productivity to decrease [36-37]. Therefore, in this paper, we are going to establish some of the relationships between environmental alterations that could be caused by global warming, through the sea surface temperature, and the sardine fishery. We will analyse a bio-economic model which will include alterations in the sea surface temperature, and we will estimate their effects on fish population dynamics.

With respect to the sea surface temperature (SST), the annual averages have been calculated using the monthly data provided by the Oceanography Department of the Spanish Institute of Marine Research (which belongs to the Council of Scientific Research, CSIC). Figure 1 shows the results for the different maritime locations of the fishing grounds. These maritime locations range from 35°N to 45°N and from 8°W to 12°W for the Atlantic Ocean; and from 43°N to 45°N with a horizontal movement from 2°W to 8°W, which covers the entire Cantabrian coast. The data has been gathered for each grid of 1° each side, both vertically and horizontally. In order to carry out an analysis of the evolution of possible local warming in the study area, we have divided it up into three zones according to the different average temperature values observed: from the Gulf of Cadiz (in the south of Spain) to the coast of Porto, from Porto to the boundary with the Cantabrian sea in Galicia, and for the rest of the Cantabrian sea. As can be observed in Figure 1, the hottest zone is Cadiz-Porto, which exceeds the average temperature of Porto-Galicia by approximately 2°C and the Cantabrian coast by approximately 1.5°C.

Figure 1.

Sea surface temperature evolution (°C). 1987-2011

Although Figure 1 shows periods of average temperature increase (1968-1970, 1992-1996, 2000-2004) and periods where the temperature dropped (1975-1978, 1996-1998, 2003-2009), the overall trend shows an increase of the sea surface temperature for the period 1966-2011. On the other hand, in Table 1, some statistical measurements of the temperature series per maritime location are shown, this variable being highly disperse for the zone as a whole (0.35°C), greater in the Cantabrian zone (0.43°C). The minimum temperature value for the fishery overall was seen in 1978 (15.18°C), the maximum recorded in 2003 (16.76°C) And although the trend in the average temperature observed over the period 1966-2011 is upward, this increase was especially significant in the last thirty years of the period analysed: from the beginning of the decade of the 1980s. From that moment, the annual average sea surface temperature increased at a rate of 0.27°C per decade. Given that we do not know the foreseeable scenario with regard to warming for this fishing ground (the IPCC does not carry out forecasts at maritime zone level), this previous piece of data will be the one we use as a reference in the model applied, and we will assume that such a trend towards increases in SST will be maintained over the coming decades. In any case, we will devise an additional alternative scenario.

Cantabrian zone SST Porto-Galicia SST Cádiz-Porto SST Average SST
Standard deviation 0.431 0.321 0.362 0.351
Average 15.410 15.270 17.490 16.060
Variation coefficient 0.028 0.021 0.021 0.022
Minimum value 14.370 14.480 16.660 15.180
Maximum value 16.180 15.950 18.240 16.760

Table 1.

Statistical indicators for SST

Source: Own compilation from Spanish Centre for Higher Scientific Research.


2.2. The sardine stock

Similarly to most pelagic species, the sardine lives in relatively shallow water, in areas of high primary productivity (phytoplankton and zooplankton), generally situated on the edges of anti-cyclone areas and where intense upwelling phenomena that bring nutrients to the surface occur. International Council for Exploration of the Sea (ICES) scientists estimate that the northern and southern limits of sardine distribution could be related to the average water temperatures, as they need to be in waters with temperatures of between ten and twenty degrees centigrade. Therefore, it is a fishery which is particularly sensitive to the effects of climate change, and this could result in either a drop in productivity or the movement of the biomass to cooler waters. Since 1980, the ICES, as the institution in charge of evaluating the situation of the European sardine, considers the management unit to be the zone which lies between the French and Spanish maritime border and the Strait of Gibraltar. Most of the catches of these species take place in waters of the Ibero-atlantic continental platform, in the zone known as sardine stock distribution area in ICES divisions VIIIc and IXa.

The EU has regulated this fishery solely through the establishment of minimum catch sizes since 1999 (set at 11 cm). The Spanish government has limited catches per vessel and day (7,500 kg for sizes larger than 15cm and 500 for sizes of between 11 and 15cm), and has established a temporary ban during which it is not possible to fish (from February to March, inclusive, each year). For their part, various regional governments have established a weekly two-day ban in order to regulate the fishing effort in this fishery [38].

Biomass Landings
(tons) (tons)
1978 328000 145609
1979 358000 157241
1980 456000 194802
1981 554000 216517
1982 583000 206946
1983 506000 183837
1984 624000 206005
1985 723000 208439
1986 629000 187363
1987 548000 177696
1988 514000 161531
1989 528000 140961
1990 481000 149429
1991 465000 132587
1992 699000 130250
1993 875000 142495
1994 797000 136582
1995 811000 125280
1996 548000 116736
1997 466000 115814
1998 383000 108924
1999 345000 94091
2000 296000 85785
2001 408000 101957
2002 462000 99673
2003 436000 97831
2004 417000 98020
2005 386000 87023
2006 518000 96469
2007 481000 101464
2008 364000 87740
2009 276000 89571
2010 228000 80403
2011 224000 54857

Table 2.

The sardine biomass and landings in ICES Zones VIIIc and IXa. 1978-2011

Source: Own compilation from [35].


Figure 2.

The sardine biomass (tons) and landings (tons) trends. 1978-2011

Table 2 shows the sardine biomass and landings data for the period 1978-2011 on the basis of information provided by ICES. In Figure 1, the evolution of both variables over said period can be better observed. As shown in this figure, the Ibero-atlantic sardine spawning stock biomass underwent signification oscillations in said period, with a notable increase of sardine biomass in the period 1990-1992, to then undergo a significant reduction until the beginning of 2000.

These phases in biomass evolution do not seem to be reflected with the same rhythm and intensity in the total catches of this species landed. If in the second half of the decade of the 1980s the figure stood at around 150,000 tonnes, after that it continued to drop gradually, especially in the last years of the study period and in spite of the estimated increase for sardine biomass in some specific years. This could be related to the progressive drop in the number of vessels which have fished in the fishery since the beginning of the 1990s.

Figure 3.

The sardine biomass (tons) and sea surface temperature (°C). 1984-2011

On the other hand, being a pelagic species, the sardine is extremely sensitive to environmental changes in general, and to increases in ocean temperature in particular. In Figure 3 we can see the evolution of the spawning stock biomass and the average temperature of the water on the Cantabrian-north-west coast over the period 1984-2011.

It can be seen how, in general, after periods of slight increases in the sea surface temperature there is a drop in sardine biomass. This is especially significant in the periods 1994-1996 and 2003-2007. However, after periods when there was a drop in temperatures (1990-1992 and 1997-2003), an increase in the level of sardine biomass was seen. This can be proved from a statistical point of view. Table 3 shows the statistical correlation data regarding sardine biomass and average sea temperature. The negative temperature sign denotes that when variations in this variable occurred, the signs for variations in biomass went in the opposite direction.

Sardine Biomass Sardine biomass in t+1 Temperature (SST)
Sardine Biomass 1.0000
Sardine biomass in t+1 0.86293 1.0000
Temperature (SST) -0.51841 -0.69284 1.0000

Table 3.

Correlation matrix of variables

Source: Own compilation from Spanish Centre for Higher Scientific Research.


From all of the above, it can be deduced that oscillations in the sea temperature will impact on the natural productivity of this fish stock and, consequently, this will affect the generation of future economic yields through potential catches. On this basis, and in the face of an increase in the water temperature foreseeable for the northern hemisphere (as a consequence of global climate change; [6]), in this chapter we will make an estimation of its economic impact on the fishery. Prior to this, we will establish the zero scenario, our starting point, with which to make the comparison of possible future scenarios.

2.3. The economic data

Given that we do not have the economic data for the fleet whose target species is sardine as a whole, we are going to use a representative sample. The fleet sampled contributes around 18% of the total catches generated in the Ibero-atlantic sardine fishery in the period for which data was gathered (2001-2011), although this level of representation rises to 25% in the last three years of said period. Table 4 shows the evolution of the sampled vessels’ landings and the income generated by their sale at market for the period we have been able to compile data for (2001-2011).

Landings Income Average price
(tons) (euros 2011) (euros/Kg)
2001 19 303.04 11 581824.0 0.60
2002 8 548.63 5 214664.3 0.61
2003 6 474.87 4 014419.4 0.62
2004 5 930.54 4 210683.4 0.71
2005 14 546.12 12 493 800.2 0.86
2006 11 491.22 10 661 267.3 0.93
2007 13 237.87 9 632 988.3 0.73
2008 19 668.66 13 715 626.4 0.70
2009 21 511.70 11 969 763.4 0.56
2010 20 227.14 10 951 081.8 0.54
2011 16 714.95 10 741 674.3 0.64

Table 4.

Economic data: Income

Source: Own compilation from www.pescadegalicia.com.


With regard to the sample vessels’ landings a downward trend can be observed in the first years of the decade 2000-2010, coinciding with years of low biomass, after which time the trend recovered gradually, except in the final years of the series when a drop in landings was observed once again after a new reduction in sardine biomass. In general, income followed a similar trend to landings. On the basis of both variables, the average sale price for this species for each year the period comprises can be obtained. As can be seen in Table 4, the average price in 2011 constant monetary units, on the other hand, displayed an upward trend until the middle of the decade, at which time it began to fall progressively.

With respect to the exploitation costs of this fishing activity, from [39] and the vessels sampled which provided this data, we have the information relating to the cost structure of this fleet in relation with the annual value of sardine landings. As can be seen in Table 5, the largest proportion of fleet costs goes towards paying crews’ wages and salaries (this cost represents just over 55% of the average for the period overall), followed by the Gross Operating Surplus/Mixed Income-shipowner/vessel owner income – (they make up approximately 17%), and vessel costs –mainly repairs and maintenance of the vessels themselves as well as fishing gear – (representing around 14%). Furthermore, the gradual increase in fuel costs is notable, and is especially significant in the last year of the period for which data is available.

From the information shown in the last two Tables, the average unit price and cost for the overall period have been estimated, and are shown in Table 6. The price of 10-year public bonds has been used as the social discount rate for the period considered. Against this background, we are in a position to establish the bio-economic model that will be used to estimate the effects deriving from changes in the sea surface temperature on the net profits of the fishery as a whole. We will use a period of approximately fifteen years, up until 2030, as a temporary horizon, as is proposed in the Climate Change Adaptation Plan drawn up by the former Ministry for the Environment.

% 2006 2007 2008 2009 2010 2011 Average 2006-11
Value of landings 100.00 100.00 100.00 100.00 100.00 100.00 100.00
Fuel costs 5.70 7.14 7.96 7.14 5.00 7.47 6.74
Other current costs 5.37 6.43 6.23 9.09 7.22 10.33 7.44
Vessel costs 16.11 14.29 13.15 12.34 16.67 10.55 13.85
Crew costs 53.69 53.93 57.09 54.87 56.39 55.60 55.26
Gross operating surplus/Mixed income 19.13 18.21 15.57 16.56 14.72 16.04 16.71
Gross value added 72.82 72.14 72.66 71.43 71.11 71.65 71.97

Table 5.

Economic data: Cost Structure (%).

Source: Own compilation from [39] and sample.


Unit Value of parameters
Unit Price of landings €/ton 681.82
Unit cost of fishing €/ton 554.56
Discount rate % 5.00

Table 6.

Economic data: Unit values

Source: Own compilation from www.pescadegalicia.com, [39] and sample.


2.4. Methodology

Bio-economic modelling allows introducing natural, environmental and institutional variables, in addition to the strictly economic, in one analytical body. For the specific case of fishing management problem, the aim is to control the stock of fish by limiting the catch (or effort) so that the stream of net benefits over time will be optimizedand tacking into account the dynamics of fishing stock. In this way, we can determine where society can invest (or divest) the marine resource and what should be the rate of abstraction allowing to maintain their sustainable exploitation.

The bio-economic problem is represented as follows:

Maxh0πX,he-δt dtE1

s.a. X˙= dXdt=FX-h(t)˙

where denotes the net economicbenefitsobtained from fishing activity in each instant t, Xrepresents the fish stock, h denotes the harvest rate, p the unit price of landings, cdenotes the unit cost of fishing, δ the social discount rate and F(.) represents the natural growth of fishing stock (the stock dynamics without considering the harvest). The optimization problem for regulator [40] consists of determining the feasible control, h(t)=h*(t) with t ≥ 0, which optimisesthe net benefit stream while satisfying the problem’s conditions in aglobalwarming context.

Before solving the optimization problem (1), it is necessary to define previously the growth natural function X˙. The stock dynamic isstatistically tested on the basis of the data that exists on biomass and catches showed in Table 4; in addition, the sea surface temperature is included in this study case. Ordinary Least Squares (OLS) will be used to obtaining the parameters values. The Gordon-Schaefer expression is the function commonly used in the bio-economic literature [41-42], which is as follows incorporating the sea surface temperature (T):

X˙=αXt+βXt2+γTt-ht E2
Xt+1+ ht = αXt+βXt2+γTt
α 2.057 (0.076)
β -0.1048 E-5 (0.357)
γ -6048.4 (0.712)
Jb 0.5582
Q-Stat 4.6335
LM (ARCH) 0.171
R2 0.7528
R2 adjusted 0.7250

Table 7.

Econometric results for growth function of sardine stock

Note: t-ratio between brackets. Jb is the Jarque-Bera statistic of the normality test; Q-Stat is the


Ljung-Box statistic used in the correlation test; LM (Lagrange multiplier) is the one used in the heteroscedasticity test.


The equation (2) corresponds to the logistic model, where α, β and γ are parameters containing biological information on sardine stock, and T denotes the sea surface temperature. From data showed in Table 2, the results from the econometric estimation of (2) can be shown in Table 6 and so the stock dynamic is given by the following expression:

X˙=2.057 Xt-0.1048E-5Xt2-6048.4 Tt-ht E3

In this way, the Hamiltonian function in usual terms (current moment t) associated with problem (1) is given by the following expression [43]:

H X, h, t; μ=p-cht+ μ (αXt-βXt2-γTt-ht )E4

where μ denotes the shadow price of the marine resource in current terms.

The conditions necessary [43] to solve the optimization problem are given by the following mathematical expressions:

dH X, h, t; μdh= 0 ==> p-c- μ=0E5
μ˙- δμ=- dH X, h, t; μdX= - μα-2βXE6
dXdt= 0 ==> αX-βX2-γT-h=0E7

The condition (5) represents the known condition of economic efficiency. The condition (6) expresses the compensation which should exist in the optimum trajectory between the profit rate on the resource minus the social cost of not exploiting it (left side) and the total productivity of the same. Note that the natural resource is productive in two different ways: for its contribution to obtaining profits and for its contribution in the function itself of the natural growth of fish stock. And the condition (7) describes the evolution of the sardine biomass over the time.

By using expressions (5)-(7) the biomass and catch levels are obtained, X*(t) and h*(t), which will depend on the level of sea surface temperature. Once these levels are known, the losses or profits associated with the global warming scenario in relation with the fishery’s present situation can be obtained through the net economic benefit function for this stady case.

Advertisement

3. Findings

In order to obtain numerical results for the impact on profits and given that the biomass levels in our model are going to depend on the sea surface temperature in the years to come, we are going to assume three possible scenarios for this latter variable. In the first scenario, we will assume that the oceanic temperature will increase at the same rhythm as the increase observed in the last decades (0.027ºC per year). In the second scenario, we will assume that global warming intensifies and that that will affect the oceanic temperature, increasing to a greater extent than what has been seen to date (we will be estimating increases of 5% above the trend in the past decades). And in the third scenario, on the other hand, but in a context of rising temperatures, we will assume that global warming is being controlled to a greater extent (be it as a consequence of the current economic crisis or mitigation policies being carried out in the EU), and that this will give rise to a lower sea surface temperature in relation to that which would have occurred if the trend observed in the last decades continued (5% drop with regard to the past trend).

Table 8 summarises the three scenarios proposed. On the other hand, we will use up until the year 2030 as a temporary horizon, as is proposed in the Spanish government’s Climate Change Adaptation Plan ([8-9]). In any case, we will extend it some years further in order to have a wider perspective of the evolution of the fishery’s main indicators.

Description SST variation
Scenario 1 Trends last few decades Increase of 0.027ºC yearly
Scenario 2 Acceleration in global warming and increasing in SST Increase of 5% over trends last few decades
Scenario 3 Mitigation of global warming and in variation of SST Decrease of 5% over trends last few decades

Table 8.

Scenarios for trends in the sea surface temperature

Table 9 shows the scenarios forecast in relation with variations in sea surface temperatures. And the results obtained for the evolution of sardine biomass, catches and future profits the fleet could obtain are shown in Tables 10-12, respectively, for the different temperature variation scenarios contemplated.

Scenario 1 Scenario 2 Scenario 3
2015 16.679 16.726 16.650
2016 16.706 16.754 16.677
2017 16.733 16.782 16.704
2018 16.76 16.811 16.731
2019 16.787 16.839 16.758
2020 16.814 16.868 16.785
2021 16.841 16.896 16.812
2022 16.868 16.924 16.839
2023 16.895 16.953 16.866
2024 16.922 16.981 16.893
2025 16.949 17.009 16.920
2026 16.976 17.038 16.947
2027 17.003 17.066 16.974
2028 17.030 17.094 17.001
2029 17.057 17.123 17.028
2030 17.084 17.151 17.055
2031 17.111 17.179 17.082
2032 17.138 17.208 17.109
2033 17.165 17.236 17.136
2034 17.192 17.264 17.163
2035 17.219 17.293 17.190
2036 17.246 17.321 17.217

Table 9.

SST (°C) evolution under different scenarios

Source: Own compilation.


Scenario 1 Scenario 2 Scenario 3
2015 223066.5 221837.425 224304.107
2016 217004.959 215510.916 218500.843
2017 205489.328 209431.150 212885.052
2018 205489.328 203543.027 207448.530
2019 200017.684 197880.085 202184.219
2020 194726.912 192393.630 197085.849
2021 189609.726 187114.034 192147.064
2022 184659.140 181996.034 187361.753
2023 179868.449 177070.778 182724.042
2024 175234.567 172292.792 178228.286
2025 170746.824 167690.392 173872.968
2026 166401.363 163224.606 169647.338
2027 162192.709 158920.764 165548.858
2028 158115.632 154742.593 161572.884
2029 155468.200 150713.953 157714.966
2030 150336.432 146801.044 153970.843
2031 146624.964 143026.362 150336.432
2032 143026.361 139358.379 146807.821
2033 139536.448 135818.318 143381.263
2034 136151.230 132376.743 140053.166
2035 132866.886 129053.689 136820.084
2036 129679.759 129680.000 133678.718

Table 10.

Results for sardine biomass (tons) under different scenarios of SST

Source: Own compilation.


Scenario 1 Scenario 2 Scenario 3
2015 136173.574 135682.327 136666.659
2016 133735.621 133134.565 134340.834
2017 131987.354 130633.823 132055.964
2018 128994.996 128184.904 129811.690
2019 126691.327 125782.099 127607.530
2020 124431.248 123429.341 125442.928
2021 122214.141 121121.508 123317.362
2022 120039.382 118862.051 121230.297
2023 117906.334 116645.972 119181.193
2024 115813.999 114476.667 117169.503
2025 113762.218 112349.624 115194.258
2026 111750.122 110267.480 113255.500
2027 109777.040 108226.212 111352.406
2028 107842.299 106228.127 109484.401
2029 105814.331 104269.509 107650.907
2030 104085.147 102352.38 105851.348
2031 102261.395 100473.302 104085.147
2032 100473.302 98634.044 102351.731
2033 98720.203 96831.428 100650.525
2034 97001.441 95067.000 98980.963
2035 95316.361 93337.816 97342.478
2036 93664.3171 91284.140 95734.509

Table 11.

Results for the sardine catches (tons) under different scenarios of SST

Source: Own compilation.


Scenario 1 Scenario 2 Scenario 3
2015 17329449.1 17266932.9 17392199.0
2016 17019195.1 16942704.8 17096214.5
2017 16796710.7 16624460.4 16805442.0
2018 16415903.1 16312810.9 16519835.6
2019 16122738.3 16007029.9 16239334.2
2020 15835120.6 15707617.9 15963867.1
2021 15552971.6 15413923.1 15693367.5
2022 15276211.7 15126384.6 15427767.6
2023 15004760.0 14844366.4 15166998.6
2024 14738489.5 14568300.6 14910990.9
2025 14477379.9 14297613.2 14659621.3
2026 14221320.6 14032639.5 14412895.0
2027 13970226.1 13772867.7 14170707.2
2028 13724010.9 13518591.4 13932984.9
2029 13465931.7 13269337.8 13699654.5
2030 13245875.9 13025363.8 13470642.6
2031 13013785.2 12786232.4 13245875.9
2032 12786232.4 12552168.5 13025281.2
2033 12563133.0 12322767.5 12808785.9
2034 12344403.3 12098226.4 12596317.3
2035 12129960.1 11878170.5 12387803.7
2036 11919721.0 11616819.7 12183173.6

Table 12.

Results for the net profits (€) under different scenarios of SST

Source: Own compilation.


Starting with the evolution of sardine biomass (Table 10), as the sea surface temperature increases, the fish biomass decreases. The estimated biomass levels for the next twenty years are higher in the scenario of a lower increase than the one foreseen for temperature (scenario 3) and, on the other hand, they are lower in the temperature scenario with the greatest increase. This was to be expected, bearing in mind the relation observed between the sea temperature and the sardine biomass.

Along the same lines, the estimated catches evolve similarly to the fish biomass (Table 11). In particular, the fleet’s potential catches dropped as the sea surface temperature rose over the period as a whole. However, the estimated catches are greater in the scenario where temperature increases are lower (scenario 3) and lower when the temperatures are higher (scenario 2).

Lastly, and foreseeable given the forecast for the evolution of biomass and catches, the expected profits (Table 12) are higher in the scenario where the effects of climate change are mitigated (scenario 3), in which, with increasing sea surface temperatures, although lower than those proposed for the other scenarios, the profits would fall by 1.2% in the period (if the temperature were to drop 5% on the annual 0.027°C increase). In the remaining scenarios, the profits would fall more substantially: by approximately 1.25% (scenario 1) and 1.40% (scenario 2), approximately, in the period, as the annual sea surface temperature increases.

Advertisement

4. Conclusions

The scientific evidence available to date leaves no doubt that climate, the different ecosystems and the planet, as we know it now, will not be the same in the future. Knowing the possible impacts that such change will bring is fundamental if mankind is to mitigate its effects and adapt to new global warming scenarios.

In this chapter, we have estimated the economic effects of climate change on one of the most relevant socioeconomic fisheries for the Spanish fisheries sector as a whole. This has been based on the effects of global warming on the oceanic temperature and its impacts on the natural productivity of the marine species. It has been observed that after slight temperature increases, a decrease in spawning biomass occurs, and vice versa. Specifically, and after estimating the correlation between the spawning stock biomass of sardine and the sea surface temperature, it has been identified that the temperature explains around 30% of the uncertainty associated with this fishery.

With regard to the estimation of the future impact, three possible temperature evolution scenarios have been taken into account: where the observed annual trend of a 0.027°C increase in the sea surface temperature is maintained; where global warming intensifies and therefore there is a greater increase in the sea surface temperature (increases of 5% above the previous trend); and where global warming is controlled and mitigated, implying a lower increase in the sea surface temperature with respect to the trend observed in recent decades (5% decrease).

The results show that as the sea surface temperature of the Ibero-atlantic fishing grounds increases, lower levels of biomass and catches are obtained, while the economic yields would be reduced. In particular, if the current sea surface temperature increase trend is maintained, annual profits will fall by 1.3% over the period analysed (2015-2036). If global warming intensifies and this were to generate an even greater increase in the water temperature of the fishing grounds, profits would drop by around 1.4% on average in each year of the period analysed. However, if palliative measures tending towards reducing warming were introduced, giving rise to a lower increase in the sea surface temperature, which could also derive from the current economic crisis and subsequent drop in production activity, profits would fall at an annual rate of approximately 1.2%.

In short, the trends are toward higher sea surface temperatures in these zones and, as a consequence, lower levels of spawning stock biomass of sardine. It has a direct negative impact on the catches and net benefits for the fishermen involved in the fishery: the higher sea temperature, the less catches and profits will be obtained.

The results obtained in this analysis are not intended to be definitive, however they do show from a scientific point of view how global warming can impact upon a specific economic activity whose main input is a natural renewable resource. The results obtained herein make it possible to identify and quantify, under specific assumptions regarding economic parameters and climate scenarios, the direction of the economic effects of global warming on one of the fisheries most sensitive to environmental shocks, as are the pelagic fisheries. These results demonstrate, once again, the need to increase palliative action and design strategies as to how best to adapt to the possible climate scenario, at both global as well as national or local level. Better knowledge of the effects of global warming will affect future decisions on the sustainable management of marine resources and the foreseeable reduction of the pressure they are under.

Advertisement

Acknowledgments

Special thanks to researchers from Spanish Centre for Higher Scientific Research (CSIC) and Spanish Oceanographic Institute (IEO) for their comments and suggestions. Financial aid from FEDER and Xunta de Galicia (GRC2014/022) is gratefully akcnoledged.

References

  1. 1. United Nations. Framework Convention of the United Nations on Climate Change. New York: United Nations Organization; 1992.
  2. 2. IPCC. Climate change and biodiversity. Technical Issue V del IPCC. New York: UN; 2002.
  3. 3. Admunson, R. The carbon budget in soils. Annual Review of Earth & Planetary Sciences 2001; 29: 535-562.
  4. 4. Dale V.H, Joyce L.A, McNulty S, Neilson R.P. The interplay between climate change, forest, and disturbances. Science of the Total Environment 2000; 262: 201-204.
  5. 5. Lugo, A.E. Biodiversity management in the 21 st. Century. Interciencia 2000; 26: 484-488.
  6. 6. IPCC. Fifth Assessment Report. WG1. New York: UN; 2013.
  7. 7. Garza-Gil M.D., Vázquez-Rodríguez M.X, Prada-Blanco A., Varela-Lafuente M. Global warming and its economic effects on the anchovy fishery and tourism sector in North-Western Spain. In Casalegno S. (Ed) Global Warming Impacts. Case studies on the economy, human health, and on urban and natural environments. Rijeka: InTech; 2011.
  8. 8. Ministerio de Medio Ambiente. Principales conclusiones de la evaluación preliminar de los impactos en España por efecto del cambio climático. Oficina Española de Cambio Climático (OECC). Madrid: Ministerio de Medio Ambiente; 2005.
  9. 9. Ministerio de Medio Ambiente. Plan Nacional de Adaptación al Cambio Climático. Madrid: Ministerio de Medio Ambiente; 2006.
  10. 10. Pérez Muñuzuri V, Fernández Cañamero M, Gómez Gesteira J.L. Evidencias e impactos del cambio climático en Galicia. Samtiago de Compostela: Xunta de Galicia; 2009.
  11. 11. Briones R, Garces L, Ahmed M. Climate change and small pelagic in developing Asia: the economic impact on fish producers and consumers. In Hannesson R, Barange M, Herrick S (Eds) Climate change and the economics of the world’s fisheries. New York: Edward Elgar Publishing; 2006.
  12. 12. Gallagher Ch. Variable abundance and fishery movements in New Zeland squid fisheries: Preliminary findings from global and regional investigations. Workshop on economic effects of climate change on fisheries. NHH: Bergen; 2005.
  13. 13. Gómez Martín M. B. Weather, Climate and Tourism: a geographical perspective. Annals of Tourism Research 2005; 32(3): 571-591.
  14. 14. Levitus S, Antonov J.I, Boyer T.P, Stephens C. Warming of the world ocean. Sciences 2000; 287: 2225-2229.
  15. 15. Röckmann C. Rebuilding the Eastern Baltic cod stock under environmental change. Workshop on economic effects of climate change on fisheries. NHH: Bergen; 2005.
  16. 16. Herrick S, Hill K, Reiss C. An optimal harvest policy for the recently renewed United States Pacific sardine fishery. In Hannesson R, Barange M, Herrick S (Eds) Climate change and the economics of the world’s fisheries. New York: Edward Elgar Publishing; 2006.
  17. 17. McGowan J.A, Cayan D.R, Dorman L.M. Climate-ocean variability and ecosystem response in North Pacific. Science 1998; 281: 201-217.
  18. 18. Santos F. D, Miranda P. Alteraçoes climáticas em Portugal: escenarios, impactos e medidas de adaptaçao. Lisboa: Gradiva; 2006.
  19. 19. Sissener E.H, Bjorndal T. Climate change and the migratory pattern for Norwegian spring-spawning herring: implications for management. Marine Policy 2005; 29: 299-309.
  20. 20. Stenevik E.K, Sundby S. Impacts of climate change on commercial fish stocks in Norwegian waters. Marine Policy 2007; 31: 19-31.
  21. 21. Arnason R. Global warming, small pelagic fisheries and risk. In Hannesson R, Barange M, Herrick S (Eds) Climate change and the economics of the world’s fisheries. New York: Edward Elgar Publishing; 2006.
  22. 22. Brander K. Climate change and fisheries management. In Quentin R, Hilborn R, Squires D, Tait M, Williams M (Eds) Handbook of Marine Fisheries Conservation and Management. New York: Oxford University Press; 2010.
  23. 23. Hannesson R. Sharing the herring: fish migrations, strategic advantage and climate change. In Hannesson R, Barange M, Herrick S (Eds) Climate change and the economics of the world’s fisheries. New York: Edward Elgar Publishing; 2006.
  24. 24. Lam V, Cheung W, Swartz W, Sumaila R. Climate change impacts on fisheries in West Africa: Implications for economic, food and nutritional security. African Journal of Marine Science 2012; 34(1): 103-117.
  25. 25. Rice J.C, García S.M. Fisheries, food security, climate change, and biodiversity: characteristics of the sector and perspectives on emerging issues. ICES Journal of Marine Sciences 2011; 68(6):1343-1353.
  26. 26. ACIA (2004): Artic climate impacts assessment: Overview Report. Cambridge University Press, Cambridge.
  27. 27. Guisande C, Cabanas, J.; Vergara, A.; Rivero, I. (2011). Effect of climate change on recruitment success of atlantic iberian sardine (Sardina pilchardus). Marine Ecology Progress Series 223, 234-250.
  28. 28. Hollowed, A.; Barange M, Kim S, Loeng H, Peck M. Climate change effects on fish and fisheries: Forecasting impacts, assessing ecosystem responses, and evaluating management strategies. ICES Journal of Marine Science 2011; 68.
  29. 29. Jennings S, Brander K. Predicting the effects of climate change on marine communities and the consequences for fisheries. Journal of Marine Systems 2010; 79(3-4): 418-426.
  30. 30. Labarta U. Galicia Mariñeira: Historia económica y científica en estudio y explotación del mar en Galicia. Santiago de Compostela: Ed Universidad de Santiago de Compostela; 1979.
  31. 31. Pestana G. Manncial Ibero-Atlántico de sardinha. Sua avaliaçao e medidas de gestao. Lisboa: Instituto Nacional de Investigaçao das Pescas; 1989.
  32. 32. Domínguez M. La gestión de los recursos pesqueros en el marco de la PPC: Análisis de la cooperación. Vigo: Universidad de Vigo; 2003.
  33. 33. MAGRAMA. La Agricultura, la Pesca y la Alimentación en España, Informe Anual. Madrid: Ministerio de Agricultura, Alimentación y Medio Ambiente; 2013.
  34. 34. Ibermix. Identification and segmentation of mixed-species fisheries operating in the Atlantic Iberian Peninsula waters. Brussels: EC Project, FISH/2004/03-33; 2007.
  35. 35. ICES. Report of the Working Group on the Assessment of Mackerel, Horse Mackerel, Sardine and Anchovy. Headquarters. Denmark: ICES; 2013.
  36. 36. Porteiro C,; Cabanas J.M. Report of the Workshop on the Integration of Environmental Information into Fisheries Management. Strategies and Advice (anexe 6). Vigo: IEO; 2007.
  37. 37. Rosón G. Índices climáticos y su impacto en la hidrografía y dinámica marina. Santiago de Compostela: Workshop Evidencias del cambio climático en Galicia; 2008.
  38. 38. Garza-Gil M. D, Torralba-Cano J, Varela-Lafuente M. Estimating the economic effects of climate change on the European sardine fishery. Regional Environmental Change 2011; 11(1): 87-95.
  39. 39. European Commission. Economic Performance of important segments fleets. Brussels: DG XIV; several years.
  40. 40. Kamien M, Schwartz N. Dynamic Optimization. The Calculus of Variations and Optimal Control in Economics and Management. New York: North-Holland Ed; 1991.
  41. 41. Clark C. W. Mathematical Bioeconomics-the Optimal Managemet of Renewable Resources. Sussex: J. Wiley and Sons; 1976.
  42. 42. Clark C. W. Bioeconomic Modelling of Fisheries Management. New York: J. Wiley & Sons; 1985.
  43. 43. Clark C.W, Munro G. R. The economics of fishing and modern capital theory: a simplified approach, Journal of Environmental Economics and Management 1975; 5(2): 96–106.

Written By

M. Dolores Garza-Gil, Manuel Varela-Lafuente, Gonzalo Caballero- Míguez and Julia Torralba-Cano

Submitted: 25 April 2014 Published: 22 April 2015