European hake (
La merluza europea (
European hake [
The protection of European hake spawning areas has been proposed as an effective measure for improving the size composition of catches (
Several quantitative models have been developed to support decision making in fisheries management. According to their level of complexity, the fisheries literature has investigated “simple” models such as VIT (
The InVEST suite of models includes software tools used to map and value natural goods and services that sustain human economies (
The main objective of this study is to evaluate the biological and economic benefits of closing a shallow area (an essential habitat of European hake recruits) to fishing and the consequences of various spatial management scenarios applied to the European hake fishery in the Catalan sea. The study emphasizes the importance of simulating the bioeconomic effects of such spatial management measures for decision making through the use of a management strategy evaluation (MSE) approach. MSE has been widely recognized as a valuable tool for testing the robustness of management procedures to uncertainties in the fishing system (
Our study area is located in the Catalan sea (GSA 6), including the area fished by the bottom trawl fleet from the ports of Blanes and Palamós (province of Girona, NE Spain,
In the study area, European hake is mainly exploited by bottom trawling, with 45 vessels (79% of the local fleet) with a size between 9 and 28 m length overall. It is also exploited, but to a lesser extent, by bottom longline, with 12 operational vessels (21% of the local fleet) and a ship size of between 6 and 11 m length overall. The number of fishing vessels has shown a decreasing trend in the last ten years (
We used the InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs,
InVEST uses life-history information and survival parameters to estimate the volume and the value of harvests. In the case of a population structured by age, we estimated the parameters of the population that characterize the life history of the species (recruitment, ontogenic migrations, natural mortality, maturity, weight and fertility), the subregions of interest as defined by the fishing grounds within the study area, the attributes of each subregion (fraction of exploitation and larval dispersal), the behaviour of the fishery (vulnerability to fishing), the habitat dependencies (importance and availability of nursery habitat) and optionally, economic valuation (price per unit catch).
The model is executed according to a number of time steps specified by the user and sufficient for the population to reach a state of equilibrium. The results of the model are estimates of the volume of the catch (in t) and economic value of the catches (in €) in the last step of execution and within the subregions established, in addition to estimates of abundance by age class and subregion in all years of simulation. The results of multiple executions of the model can be compared, each one representing different scenarios of habitat extension, environmental conditions and/or fishing pressure. The results can be added from the folders to the ArcGIS document.
For the present study, the definition of the inputs related to the biological data, valuation data, commercialization of the catches and data on fishing activity were obtained from literature sources, fisheries production data of the Catalan Fisheries Directorate database and spatial distribution of effort from the Spanish Ministry of Agriculture, Marine Affairs and Environment by analysing vessel monitoring data (VMS) with the QGIS software. The VMS data provided the position of all the fishing boats of Blanes and Palamós for the years 2013-2015 at an average frequency of 60 minutes. Finally, data on the migratory behaviour (ontogenic changes in spatial distribution) of European hake were obtained by associating the existing bibliographic data (
The VMS data were used to establish the extent of fishing activity in the area of interest and to determine the fishing effort at the level of each fishing zone (defined in
We modelled the European hake population as 28 subpopulations interconnected via ontogenetic migration, simulating European hake movement from nursery shallow waters to greater depths with age (ages 2, 3 and 4). The age-structured population was projected from 2015 to 2025 in annual time steps. Hence, the parameters are based on annual rates and the model progresses in one-year increments.
The European hake population dynamics is given by:
where
Harvest is assumed to occur at the beginning of the year, before the mortality from natural causes. Harvest (
where
The number of spawners is the product of the number of individuals in each age class for the entire study region and the proportion that are mature by age:
The biomass of spawners is the product of the number of individuals in each age class for the entire study region, the proportion that are mature at each age and their weight at a given age:
where
We consider the hypothesis of fast growth for European hake, with a maximum age of approximately 15 years (
Survival from natural mortality is the proportion of individuals that continue to the next age. It is calculated from the instantaneous natural mortality rates (
where
Estimating that the largest age of European hake is 15 years, the vector of natural mortality by age was calculated from the formula of Caddy, using the PROBIOM Excel spreadsheet (
The exploitation fraction (
Vulnerability to fishing may depend on size, specific behaviour of the species during a life stage, habitat use or regulations, and may change depending on fishing gear and fishing strategies. A value of 1.0 indicates that age is totally vulnerable to fishing, while values below 1 indicate vulnerability relative to fully vulnerable age. It is assumed that the vulnerability is the same in all subregions. On this basis, vulnerability is calculated by observing the values of fishing mortality applied to each age (Supplementary material Table S8). According to the stock assessment of 2015, age 2 represent the age totally vulnerable to fishing (
In the case of European hake, as for other Mediterranean fishery stocks, it is difficult to define the relationship between spawning stock biomass (SSB) and recruitment with any degree of certainty. In addition, the stock assessment figures in GSA 6 show considerable fluctuations in European hake recruitment in the last two decades, which may result from a predominance of environmental factors in the recruitment process, rather than a direct relationship between SSB and recruitment. We therefore examined the simulation results under two major hypotheses about the recruitment function assumed in the InVEST model. We first considered the hypothesis of a fixed recruitment function (H1), and then analysed the results assuming a linear recruitment (fecundity function: H2). The fecundity function assumes a constant reproductive rate for adults while the fixed recruitment function assumes that recruitment is constant and not dependent on the number of adults. The parameterization of each function is given by
Fixed recruitment function:
Fecundity function:
To determine the initial number of recruits, a virtual cohort analysis was carried out using VIT (Version 1.3). We incorporated the data of length frequency, maturity of the different age classes, the European hake catch for 2015 and the other required population parameters proportionally scaled to the study area (Supplementary material Table S9).
In the case of the linear recruitment function (fecundity), InVEST requires the age-specific fecundity values representing the number of descendants (offspring) per mature individual. These fecundity coefficients refer to the number of European hake eggs and larvae that survive and become recruits. Knowing the number of eggs per female of mature European hake (
First, based on the observations of European hake individuals in MEDITS (2012-2013) and in the commercial fishing data, we calculated the number of eggs per individual through the equation of
where
Length to age conversion (slicing) allowed us to estimate the average number of eggs for each age class, considering the MEDITS observations for ages 0, 1 and 2 and the commercial fishing observations for the higher ages (Supplementary material Fig. S1). Given the number of eggs (
where
The data presented in the literature indicate that mortality rates of eggs and larvae of demersal fish with pelagic eggs in general must be high and variable both between years and between populations. The range of mortality rates observed is 7% to 67% per day.
The combination of the sampling data of MEDITS cruises with the commercial fishing series of the longline fleet (which exploits the older age classes) allowed us to draw a distribution of the different age classes and to exploit the available observations to make an interpolation of the spatial distribution of European hake throughout the study area. The interpolation of the available data provided the spatial distribution of each age class by depth ranges (Supplementary material Fig. S1). The interpolation method used is inverse distance weighting in QGIS.
As an input required by InVEST, the “larval dispersal” represents the proportion of the cumulative larvae pool that disperses into each subregion. For models with subregions (as defined by the fishing grounds,
The migration submodel of InVEST allows European hake movements between the subregions to be integrated and was applied here with high uncertainty because no study on European hake ontogenetic migration had been conducted in our study area. To limit the uncertainty of this submodel, the results were analysed in two parts, first without considering migration and second taking into account the ontogenetic migration of European hake, and the sensitivity of the outputs to this submodel are discussed.
To map the migration of European hake in this model, we consider the migration diagram proposed by
Valuation,
where
The various parameters used in this study were implemented with a certain level of uncertainty, so we included the effect of the uncertainty in the results for a better interpretation. The sensitivity of the results to the submodels and the most uncertain parameters in this study are analysed, namely: the European hake growth parameters (±10%) and by default natural mortality, daily mortality rate (Z) in the calculation of fecundity coefficients (±10%) and, finally, the submodels of recruitment and migration. For this analysis, we proposed new input matrices each time, executed the program again and collected the results of the new simulations. The results of sensitivity analysis are presented in the form of principal component analysis (PCA).
The InVEST fisheries model facilitates comparison of fisheries production under different scenarios. In our study, we used InVEST to analyse the bioeconomic effect of a spatial management measure (MPA) and various cases of effort limitation in different fishing grounds of the study area, and then to compare the short-, medium- and long-term results of the management scenarios. The selected scenarios are consistent with the requirements of Council Regulation (EC) 1967/2006, regarding the inclusion of spatial aspects such as the establishment of protected fishing zones in order to protect nurseries and/or spawning areas for the sustainable exploitation of fishery resources in the Mediterranean Sea, and with the recommendations of the General Fisheries Commission for the Mediterranean that encourage the reduction of fishing effort in Mediterranean fisheries.
The first scenario consisted of the introduction of spatial closures in areas representing the essential habitats of European hake recruits in order to limit the fishing pressure at their level. Among the fishing areas with a high abundance of recruits, we selected the shallow fishing grounds located closer to the edge of the continental shelf (average depth ≤200 m) as the most likely to coincide with the habitat of European hake recruits. Then, we studied the effect of closing each of these six fishing zones: Vol de Terra (75 m), L’Avió (99 m), Planassa (111 m), Vol de fora (130 m), Bravada Dbf (141 m) and Cul de Rec – El Pas (182 m). The main scenario evaluated was the closure of the fishing ground Vol de Terra, which was proposed by the fishing sector during the participatory sessions organized by the MINOUW project (
The second scenario consisted of the application of reductions in the fishing effort for the trawl fleet in our study area, taking into account the distribution map of the effort in the established subregions.
The PCA carried out to analyse the effect of the two InVEST submodels that we parameterized with most uncertainty, namely, the selected recruitment function (fixed or fecundity) and the migration matrix is shown in
In the PCA (
This analysis shows that the parameters that had the greatest impact on the results were the fertility coefficients. In addition, it is noted that when one is working with a linear recruitment, varying the daily mortality rate (Z) or the growth parameters (G) of the species directly affects the fertility coefficients and generates quite different results. A low daily mortality (F+) (high fertility) or high growth parameters (G+F) (low natural mortality) lead to an increase in the assumed fertility and result in an increase in the total catch, the SSB and the abundance, especially in the long term. On the other hand, a slow growth (G–) or a higher daily mortality rate of European hake (F–) lead to a reduction of the productivity variables.
In the case of constant recruitment, the growth parameters affected the results positively when fast growth was assumed, and negatively when slow growth was assumed. However, this effect was limited compared with the sensitivity shown by the parameters under the issue of linear recruitment.
The results of the present study were analysed in two parts (with or without the migration aspect) and under two main hypotheses (constant and linear recruitment). This allowed us to consider the effect of the two submodels of InVEST parameterized with more uncertainty in the representability of the results. Firstly, we explored the effect of the closure of fishing grounds (Supplementary material Tables S7, S8) and selected those with the most important effect on the results. Then we compared the effect of the MPAs selected (A1, A2, A3, A6) with the effect of effort limitation (–10% and –20%).
In the cases analysed, we conclude that it would be beneficial to close the Vol de terra fishing ground (A1) among the other MPA scenarios (
The beneficial effect of the Vol de terra MPA on the indicators was also maintained, when the ontogenetic migration of the European hake for ages 2, 3 and 4 was considered. In this case, we observed another fishing ground (Cul de Rec – El Pas) whose closure may also be beneficial for the fishery, with similar results to those of the Vol de terra MPA (
Migration | Not considering migration Effect of Vol de Terra MPA (A1) |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|
Constant recruitment | Linear recruitment | SSB–R lineal | ||||||||
Area | Scenario 0 | MPA 1 | Scenario 0 | MPA 1 | ||||||
Ct | N | Ct | N | Ct | N | Ct | N | Ct | N | |
1 | 7% | 2% | 7% | 2% | 33% | 30% | 25% | 22% | 35% | 39% |
2 | –21% | –5% | –21% | –5% | 2% | 23% | –3% | 15% | 35% | 40% |
3 | 50% | 14% | 51% | 14% | 82% | 44% | 71% | 34% | 35% | 39% |
4 | 12% | 3% | 12% | 3% | 38% | 31% | 30% | 23% | 35% | 39% |
5 | 7% | 1% | 7% | 1% | 34% | 30% | 26% | 21% | 37% | 40% |
6 | 5% | 1% | 5% | 1% | 32% | 30% | 24% | 21% | 37% | 40% |
7 | –18% | –4% | –18% | –4% | 4% | 23% | –1% | 16% | 35% | 39% |
8 | 1% | 0% | 3% | 1% | 26% | 28% | 22% | 21% | 35% | 39% |
9 | 2% | 1% | 2% | 1% | 27% | 29% | 20% | 21% | 35% | 39% |
10 | –16% | –4% | –16% | –4% | 8% | 25% | 2% | 17% | 35% | 40% |
11 | 5% | 1% | 6% | 1% | 31% | 30% | 24% | 22% | 35% | 39% |
12 | 2% | 1% | 2% | 1% | 28% | 29% | 21% | 21% | 35% | 39% |
13 | 12% | 2% | 15% | 3% | 39% | 31% | 34% | 23% | 36% | 40% |
14 | 593% | 178% | 630% | 190% | 696% | 223% | 671% | 205% | 35% | 39% |
15 | 3% | 1% | 3% | 1% | 28% | 29% | 21% | 21% | 35% | 39% |
16 | 24% | 2% | 24% | 2% | 53% | 31% | 43% | 22% | 38% | 41% |
17 | 6% | 2% | 7% | 2% | 32% | 30% | 25% | 22% | 35% | 39% |
18 | –5% | –1% | –5% | –1% | 19% | 27% | 13% | 19% | 35% | 39% |
19 | 1% | 0% | 1% | 0% | 26% | 29% | 19% | 21% | 36% | 40% |
20 | 1% | 0% | 1% | 0% | 26% | 28% | 19% | 21% | 35% | 39% |
21 | 0% | 0% | 1% | 0% | 25% | 28% | 20% | 21% | 35% | 39% |
22 | –4% | –1% | –1% | 0% | 21% | 28% | 17% | 20% | 36% | 40% |
23 | –8% | –2% | 18% | 4% | 17% | 26% | 39% | 24% | 35% | 39% |
24 | –9% | –1% | 0% | –7% | 16% | 28% | 0% | 14% | –100% | 66% |
25 | 129% | 34% | 170% | 44% | 170% | 65% | 192% | 62% | 35% | 39% |
26 | 404% | 84% | 502% | 105% | 498% | 122% | 560% | 125% | 35% | 40% |
27 | 112% | 15% | 120% | 16% | 149% | 45% | 138% | 36% | 37% | 40% |
28 | 28% | 6% | 31% | 6% | 56% | 35% | 49% | 26% | 35% | 40% |
The closure of Vol de terra is approximately equal to a reduction in fishing effort of up to 20% in the entire area of interest (
This work represents the first application of the InVEST model to a specific case of demersal fishery in the Spanish Mediterranean. It is also the first attempt to pre-evaluate the effect of spatial management on an overexploited fishery of great interest for the fishing sector in Catalonia.
Throughout this study, we have evaluated the effect of the application of an MPA at the level of the Vol de Terra fishing ground, which seems to be an essential habitat for European hake recruits, together with other management scenarios in order to compare their efficiency and determine their effect on different biological indicators (abundance, SSB, recruitment) and economic indicators (catch or amount) of the fishery.
The InVEST model and the quality of inputs
The InVEST model has been treated with a level of uncertainty regarding the quality and availability of the information introduced (inputs). InVEST requires specific data on the biology and population dynamics of the species of interest and data on the fishing activity in the study area. This model allows us to integrate available information about the fishery and use it to explore various management scenarios (
Some submodels and parameters were implemented with more uncertainty than others. Firstly, the relation between SSB and recruitment for European hake remains undetermined throughout GSA6 given the limitation of the historical series of these data. We therefore studied two hypotheses for the recruitment function (constant and linear), analysing the impact of each function on the results, which showed qualitatively similar results. Second, the fecundity coefficients proposed in the case of linear recruitment are a source of considerable uncertainty and have a high influence on the results, as there are no scientific studies available on the survival and/or daily mortality of European hake eggs and larvae
Furthermore, in relation to the migration submodel, the migration coefficients were considered with uncertainty due to the absence of mark-recapture studies or other specific studies that explore the migratory behaviour of European hake in the Catalan Mediterranean Sea. The integration of a migration matrix in this study has a more perceptible effect on the results, especially when we consider the hypothesis of linear recruitment, owing to the added uncertainty of the migration coefficients to that of the fertility coefficients.
Subsequently, the parameters of growth and, consequently, natural mortality (resulting from the change of these parameters) affect the results to a lesser extent than the previous parameters and do not change the general conclusions of the study. The uncertainty of the results for these parameters was considered in the representation of the results with confidence intervals, but not in the case of the linear recruitment function, given the high sensitivity shown. We note that European hake tagging studies (from
All the other parameters considered in this study were obtained from the most recent bibliographic sources and/or from the scientific data available to date (MEDITS), so their effect on the results is estimated as very limited. However, additional research to clarify the dynamics of European hake recruitment through the stations at the level of the study area will improve the information on the density distribution of the recruits and thus delimit the zones of nurseries of the species for better management.
InVEST results
On top of the problem of over–exploitation, the risks of collapse or depletion of the Mediterranean stocks seem limited (
Given the multi–specific peculiarity of Mediterranean fisheries, a combination of management tools based only on technical measures and effort control has not managed to guarantee the long–term sustainability of fisheries or the conservation of important habitats (
In addition to the results of our study, many studies have shown that the spatial closure of Mediterranean nurseries can provide important benefits to fisheries in terms of increased fishing resistance and improved yields (
Outside the Spanish Mediterranean, the European hake fishery in the Gulf of Lion (GSA7) (
The analysis of another mixed European hake fishery in the Bay of Biscay using an ISIS-Fish simulation tool (
The understanding of spatial patterns in population dynamics is essential to protect critical habitats and ensure sustainable management of fisheries resources (
To identify the appropriate areas for closure against fishing, many authors have studied the spatial distribution of juvenile European hake at a regional scale and identified the main nursery areas where the highest concentrations of juveniles remain stable over the years (
The present study has shown that a spatially explicit tool such as InVEST can be very useful for pre-evaluating spatial management measures and choosing among the best possible alternatives for a fishery by integrating a limited number of non-complex data. The present work has allowed us to explore this model to guide decision making in the management of the European hake fishery, which is overexploited in the Mediterranean, and specifically at the ports of Blanes and Palamós. The results of the study coincide with other studies highlighting the benefits of spatial management and confirm the importance of protecting this fishery to sustain the stock of European hake, which is very sensitive to changes affecting juveniles.
The results of this work allow us to draw two main conclusions: First, the closure of the Vol de Terra fishing ground is the best spatial management alternative for the recovery of catches and biomass of European hake in the medium and long term, and would have a minor effect on fisheries performance in the medium term. Fishers are well aware of the importance of this area as a hake nursery, and our work helped confirm their preferences, as expressed during the participatory approach between scientists and stakeholders. According to our results, spatial closures in other fishing grounds would not attain a similar degree of stock protection. Second, the application of an MPA in this fishery would be equivalent to a reduction in fishing effort of 20% in the entire study area, but it would be easier to implement and would meet with less resistance from the sector than a fishing effort reduction measure.
This study received funding from the European Commission’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 634495 for the project Science, Technology, and Society Initiative to Minimize Unwanted Catches in European Fisheries (MINOUW).
The following supplementary material is available through the online version of this article and at the following link:
Table S1. – Main characteristics of the fishing grounds.
Table S2. – European hake growth parameters.
Table S3. – Maturity and weight by age of the European hake population.
Table S4. – Fecundity by age of the European hake population.
Table S5. – Fecundity coefficients of the European hake population.
Table S6. – Natural mortality and survivorship by age of the European hake population.
Table S7. – Exploitation fraction by subregion based on fishing effort.
Table S8. – Fishing mortality and vulnerability by age class.
Table S9. – VIT inputs.
Table S10. – InVEST migration tables.
Fig. S1. – Spatial distribution of European hake by depth. A, abundance of ages 3, 4 and 5 by depth (commercial fishing data); B, abundance of hake by age (0-3) and depth (MEDITS 2012-2013).
Fig. S2. – Larval dispersal by subregion or fishing ground according to the spatial distribution of age 0 individuals.