A new approach to recruitment overfishing diagnosis is presented. We hypothesize that condition of recruits should increase when recruitment failures are caused by fishing activity. This would be a consequence of the increase in trophic resource availability, because the population is smaller than that which the ecosystem could support. Temporal series of hake recruit condition were calculated from MEDITS survey data collected in Mediterranean geographical sub-areas (GSAs) 1, 6, 17 and 19 from 1994 to 2015. Multiple linear regressions were used to analyse the relationship between mean annual condition and abundance of recruits and climatic indices in each GSA. Significant correlations were only detected in GSA 6, where 69% of condition variability was explained by the negative correlation with recruit abundance, and with two climatic indices, the Western Mediterranean Oscillation and the standardized air temperature anomaly at surface from the Gulf of Lions. Despite the differences in recruit abundance among GSAs, their mean annual condition oscillated around the same basal value during most of the time series, pointing to density-dependent mortality rates as an important mechanism stabilizing hake recruitment to levels close to the carrying capacity when populations do not suffer recruitment overfishing. This pattern changed when the decreasing recruit abundance trend drove GSA 6 condition values persistently above those of the rest of the GSAs. According to our hypothesis, hake in GSA 6 is in recruitment overfishing.
Este trabajo presenta una nueva aproximación al diagnóstico de la sobrepesca de reclutamiento. Nuestra hipótesis consiste en que la condición de los reclutas debe aumentar cuando suceden fallos en el reclutamiento debidos a la actividad pesquera. Esto sería una consecuencia del incremento en la disponibilidad de recursos tróficos debido a que la población es más reducida que la que el ecosistema podría soportar. Se calcularon series temporales de condición de reclutas de merluza a partir de datos recogidos en las campañas MEDITS en las sub-áreas geográficas (GSAs) 1, 6, 17 y 19 entre los años 1994 y 2015. Se utilizó la regresión lineal múltiple para analizar la relación entre la condición media anual y la abundancia de reclutas e índices climáticos en cada GSA. Solo se detectaron correlaciones significativas en la GSA 6, dónde el 69% de la variabilidad en la condición se explica por la correlación negativa con la abundancia de reclutas, y con dos índices climáticos, la Oscilación del Mediterráneo Occidental y la anomalía estandarizada de la temperatura del aire en superficie en el Golfo de León. A pesar de las diferencias en la abundancia de reclutas entre GSAs, su condición anual media osciló alrededor de un mismo valor basal durante la mayor parte de la serie temporal, sugiriendo que las tasas de mortalidad denso-dependiente son un mecanismo importante para la estabilización del reclutamiento de merluza en niveles próximos a la capacidad de carga cuando las poblaciones no sufren sobrepesca de reclutamiento. Este patrón cambió cuando la tendencia decreciente de la abundancia de reclutas elevó los valores de condición en la GSA 6 por encima de los del resto de GSAs de forma persistente. De acuerdo con nuestra hipótesis, la merluza de la GSA 6 se encuentra en sobrepesca de reclutamiento.
The Food and Agriculture Organization adopted the definition of recruitment overfishing in
According to
There have been attempts to search for useful thresholds to determine recruitment overfishing based on biomass reference levels (
Most definitions of overfishing are related in some way to maximum sustainable yield (MSY) (
In the Mediterranean Sea, the assessment of fish stocks is based on geographical sub-areas (GSAs, Resolution GFCM/31/2007/2). In all GSAs where the European hake,
“Fish condition” is a widely used term referring to the overall physiological status or health of an individual (
In the present study we aim to explore a new approach to the assessment of recruitment overfishing based on condition of hake recruits collected during the Mediterranean International Trawl Surveys (MEDITS) since 1994. We hypothesize that recruit condition should increase when failures in recruitment occur due to fishing activity. This increase in condition must be mediated by a higher availability of trophic resources for recruits, as a consequence of a lower number of individuals: in other words, recruit abundance is reduced below the carrying capacity of the ecosystem. However, the hypothesized increase in condition could be masked by the effect of environmental variability, and more particularly by variations in climatic conditions, which have already been reported to influence hake recruitment (
Data and samples were collected during the annual MEDITS surveys from 1994 to 2015. The sampling gear used, the bottom trawl GOC73 and the sampling scheme applied in these surveys are described in detail by
The geographical analyses, which took into account all the above-mentioned GSAs, involved a total of 4252 sampling hauls performed between 2012 and 2015, the period in which hake biological data is available for all GSAs in the study region. For the temporal series analyses, only the GSAs with the longest time series of hake biological data were considered: 1, 6, 19 and the Italian side of 17. In the MEDITS programme, the sampling of individual biological parameters has been compulsory for hake in all GSAs only since 2012. However, for these four areas the biological sampling of hake has been available since the beginning of the MEDITS surveys: 1994 in GSA 17 and one year later (1995) in the rest. A total of 6685 sampling hauls (GSA1: 880; GSA6: 1690; GSA17 (Italy): 2548; GSA19: 1567) and 86171 biological samplings of hake recruits were taken into account for the time series analyses.
The standardized abundance (ind. km–2) of hake recruits (0-age group) was calculated by setting a threshold of 18 cm TL, according to the total length at the end of the age-0 class reported in the growth model for sexes combined by
In order to obtain the mean annual abundance of recruits for each GSA, their depth distribution was analysed and depths at which recruits were not usually present were excluded. The depth range included individuals from 46 to 305 m in GSAs 1 and 6; individuals from 25 to 300 m in GSA 17 (Italy); and individuals from 47 to 280 m in GSA 19. In all cases, the number of recruits retained for the analyses in each GSA within the specified bathymetric ranges accounted for more than 99% of the whole number of recruits. In the case of GSA 17, where the distribution of recruits was clearly located at the southern part of this GSA, a further restriction was applied when calculating their mean abundance, and only those samples located below latitude 44ºN were considered. In this GSA, more than 98.5% of recruits were retained for the analyses.
Size-independent measures of individual somatic condition (SC) of recruits were calculated. First, the log-transformed linear relationship between W and TL was calculated in order to obtain the predicted log(W) by sex. Differences in the log-transformed W-TL relationships between sexes were tested by applying analysis of covariance (ANCOVA) to the log-transformed data. Because no difference between sexes was found, log-transformed W-TL relationships were calculated for the whole population. Then, the residuals were calculated as the difference between the log-transformed observed and predicted W, and standardized by dividing each by the standard deviation of their predicted log(W) values. An individual that is lighter than the predicted weight for its length from the regression equation will have a negative residual, and is assumed to be in poorer condition than an individual that is heavier than the predicted weight for its length, which will have a positive residual (
Due to the minimum precision of the weight measures, 0.1 g, the SC analyses were restricted to recruits >8.5 cm. The ±0.1 g error due to scale precision thus represents less than 1% of the theoretical weight of the smallest individuals. When the analyses of SC involved more than one GSA, the individual SC was calculated considering a W-TL relationship in which all the individuals sampled in all the GSAs involved were taken into account. This allowed comparable values of SC among GSAs to be obtained.
In the calculation of the mean annual values of SC by GSA the individuals taken into account followed the same bathymetric and geographical restrictions applied to the calculation of the mean annual abundance of recruits.
The main objective of this analysis is to frame the different areas studied in the Mediterranean context. Therefore, the calculation of SC and the standardized recruit abundance was restricted to the period 2012-2015, in which the biological sampling of hake was available for all GSAs. The mean SC per haul was finally plotted in maps, excluding hauls in which <5 recruits were sampled.
In the case of recruit abundance, the higher number of samples allowed us to use a generalized additive model (GAM) to predict the abundance values in the whole study area by year. In the model, the dependent variable was the log-transformed standardized abundance of recruits per haul, whereas the explanatory variables were year included as a factor, and the smoothed effects of depth and position of the haul (latitude and longitude) by year. A Gaussian family residual distribution was applied after checking model residuals. The GAM was performed using the
Four climatic indices that have been related to Mediterranean meteorological and circulation events were used to analyse possible relationships with annual variation of hake condition. Two large-scale climatic indices, the Northwestern Atlantic Oscillation (NAOi) index and the Mediterranean Oscillation Index (MOi), and two regional climatic indices, the Western Mediterranean Oscillation index (WeMOi) and the IDEA index (IDEAi), were used.
NAOi, MOi and WeMOi are calculated from atmospheric pressure gradients in the Atlantic Ocean and/or the Mediterranean Sea. Monthly mean NAOi was obtained from the NOAA National Weather Service, Climate Prediction Center (
The IDEAi is a meso-scale index related to the temperature in the northwestern basin of the Mediterranean. This index measures the standardized air temperature anomaly at surface (1000 hPa) from the Gulf of Lions, collected with a daily resolution and averaged for the winter months (
Linear regression analysis was used to explore for temporal trends in time series of SC, recruit abundance and climatic indices in the GSAs where these data were available from 1995 to 2015 (GSAs 1, 6 and 19) and from 1994 to 2015 (GSA 17).
An overall SC mean ±95% confidence interval (CI95%) was calculated taking into account the period of the time series of SC in which the trend of any GSA had driven its values consistently above (or below) the values of the rest of the GSAs. The CI95% was used as a threshold to distinguish the values of a particular GSA that were significantly above (or below) the overall mean.
Multiple linear regression analysis was used to analyse the relationship between SC and recruit abundance and climate variability throughout the MEDITS time series. Therefore, only those GSAs with the longest time series of biological data for hake could be taken into account: GSAs 1, 6, 17 (Italy) and 19. The dependent variable was the mean annual SC of recruits in each GSA. The explanatory variables tested included the mean annual standardized abundance of recruits, and the main climatic indices potentially affecting the oceanographic condition in each area (NAOi, MOi and WeMOi were included in the models of all GSAs, whereas IDEA was only added to GSA 6 models, due to its geographic specificity). The values of NAOi, MOi and WeMOi consisted of the average of the monthly values of the 12 months previous to the start of MEDITS surveys, whereas the IDEAi value was used directly as it is only recorded during the winter. The explanatory variables were checked for collinearity. Significant correlation (positive) was found between NAOi and MOi (0.52) and WeMOi and MOi (0.43), and alternate models including/excluding NAOi and MOi were tested. A lag of 0 and 1 year was used for climatic indices aiming to detect delayed effects of climate variability on the SC of recruits. Residuals of the models were tested for autocorrelation, which was not detected in any case. Multiple linear regressions and autocorrelation analyses were performed using the R software (
The maps representing the mean SC per haul for the years 2012 to 2015 show high variability depending on the year for all GSAs (
GSA | Year | Total | |||
---|---|---|---|---|---|
2012 | 2013 | 2014 | 2015 | ||
6 | 86.9 (61) | 89.6 (67) | 97.8 (45) | 88.9 (63) | 90.3±2.4 |
7 | 89.5 (19) | 77.8 (9) | 69.2 (13) | 75 (16) | 78.9±4.3 |
5 | 64.3 (14) | 91.7 (12) | 75 (16) | 75 (16) | 75.9±5.7 |
1 | 100 (5) | 33.3 (3) | 70 (10) | 60 (10) | 67.9±13.8 |
16 | 68.3 (63) | 38.5 (65) | 18.9 (37) | 54.8 (62) | 48±10.6 |
18 | 29.4 (17) | 57.9 (19) | 47.8 (23) | 27.3 (11) | 42.9±7.4 |
9 | 66.7 (6) | 33.3 (9) | 35.7 (14) | 25 (8) | 37.8±9.1 |
17 | 39.6 (91) | 32.1 (137) | 48.4 (161) | 21.7 (143) | 35.5±5.7 |
11 | 20 (10) | 50 (10) | 5.6 (18) | 31.3 (16) | 24.1±9.4 |
19 | 42.9 (14) | 20 (15) | 0 (12) | 23.1 (13) | 22.2±8.8 |
10 | 0 (25) | 15.2 (33) | 40 (15) | 12.5 (16) | 14.6±8.4 |
8 | 0 (1) | 0 (3) | 0 (2) | 0 (1) | 0±0 |
The GAM model applied to recruit abundance showed highly significant effects of both depth (p<0.0001) and location (latitude and longitude) by year (p<0.0001), as well as lower significant mean values in 2015 (p<0.05). The model explained 59.2% of the total deviance. The maps presenting the predicted values of recruit abundance from the GAM model showed that the areas with the highest values appeared quite constant, being mainly located in the northeastern Iberian Peninsula (GSA 6), the Gulf of Lions (GSA 7), the northwestern Tyrrhenian Sea (GSA 9 and the north of GSA 10), western Sardinia (western GSA 11), southern Sicily (GSA 16) and central (Pomo Pit) and southern areas of the Adriatic Sea, in the south of GSA 17 and the north of GSA 18, respectively (
The time series of SC in the four GSA’s with the longest time series of hake biological samplings (GSAs 1, 6, 17 and 19) showed oscillations around zero (actual weight at length values equal to the expected weights at length), without a clear trend for GSA 1 and 19 during the whole time series (
The time series of recruit abundance showed no significant trend in any GSA except GSA 6 (
None of the climatic indices analysed showed any significant trend during the period 1994-2015 except WeMOi, which showed a decreasing trend (p<0.01; R2=0.37;
The multiple linear regression models showed no significant effect of recruit abundance or climatic indices on SC in GSAs 1, 17 and 19. Hence, the resulting model parameters are not tabled. On the other hand, a highly significant model (p<0.00001) was obtained in GSA 6 by including recruit abundance and WeMOi and IDEAi (
Explanatory variable | a-R2 | β | d.f. | t | p |
---|---|---|---|---|---|
Recruits abundance | 0.691 | –0.742 | 17 | 5.430 | <0.00001 |
WeMO | –0.445 | –3.541 | <0.01 | ||
IDEA | –0.449 | –3.258 | <0.01 |
The general pattern of European hake somatic condition in the western and central Mediterranean Sea for the last four years showed a large proportion of hauls with mean positive values in the westernmost areas. GSA 6 showed the highest proportions of hauls with mean positive values among all the GSAs analysed, over 90%, showing the good condition status of hake inhabiting this area compared with GSA 8 and 10, where hake showed the lowest condition. However, the evolution of condition since 1994 in the GSAs with the longest time series of condition (GSA 1, 6, 17 and 19) showed that the current general pattern is a quite recent one, not extending much further back than the period in which condition data is available for all GSAs (2011-2015). This period is included within the period 2009-2015, in which the decreasing trend in the abundance of recruits in GSA 6 showed a major drop while the increasing trend in condition in GSA 6 became steeper and drove the mean annual values above the overall mean of the period 1994-2008 (the period in which none of the GSAs showed mean values persistently above the rest).
Our analyses of the variables affecting recruit condition did not include the spawning stock biomass (SSB) resulting from the virtual population analysis models shown in
In general, during the period analysed, the condition in the four GSAs oscillated, with smaller or larger variations, above/below the mean, none of them showing persistent maximum or minimum values in comparison with the rest. This pattern in equilibrium ended in 2009, when GSA 6 clearly deviated from the mean due to its increasing trend in condition. In this area, the negative correlation detected between condition and recruit abundance, i.e. density-dependent condition, may be a response linked to the life history strategy of hake. Life history strategies were first defined on the basis of two end points, the
Hence, in the three-end-point space defined in
Our results show that despite the large differences in recruit abundance between GSA 6 and the remaining GSAs from 1995 to 2003, the oscillations of condition in the various GSAs before 2009 ranged up and down the same basal level (overall mean). We hypothesize that the compensatory reserve of hake in that period of not heavily depleted spawner population was enough to ensure high early life stage abundance in all GSAs, and thus high densities of individuals at the early stage of recruitment. At the beginning of this stage, densities would be above the carrying capacity of each GSA. Therefore, density-dependent mortality rates would affect the population, either directly due to competition for food or indirectly due to density-dependent growth caused by that competition, which would extend the small-size stages which are more exposed to predation (
In fact, climatic variability would involve a favourable environmental scenario for hake in GSA 6 during the last few decades. WeMOi showed a significant decreasing trend, with all values in the negative phase from 2002. In GSA 6, the landings of fishing resources such as sardine and anchovy have been related to WeMOi variability (
A negative correlation between the IDEA index and the abundance of recruits has already been reported in GSA 5, indicating that negative values of the IDEA index are related to favourable environmental conditions for hake recruitment (
The main objectives of the research surveys at sea in the European Data Collection Framework is “to evaluate the abundance and distribution of stocks, independently of the data provided by commercial fisheries, and to assess the impact of the fishing activity on the environment” [Article 12, Council regulation (EC) Nº 199/2008]. Our results highlight the usefulness of survey data in stock assessment and the diagnosis of the exploitation status of fish resources. These data should be directly used in fisheries assessment rather than just as tuning datasets for virtual population analyses. However, the approach presented here will be appropriate only for species whose recruitment can be detected in coincidence with the surveys at sea. We have shown that weight and length data collected during fishery monitoring surveys can allow us to detect changes in the condition of the exploited resources. This simple morphometric data should be complemented, when possible, with other biological data such as liver weight, which better assesses the condition of species such as hake, because its liver stores most of its energy/lipid reserves (
The MEDITS surveys included in the present work have been co-funded by the Instituto Español de Oceanografía and the EU through the European Maritime and Fisheries Fund (EMFF) within the National Programme of collection, management and use of data in the fisheries sector and support for scientific advice regarding the Common Fisheries Policy. The authors wish to gratefully thank the CNR-IRBIM of Mazara del Vallo for providing data for GSA 16, and COISPA for providing data for GSA 10 and GSA 18.