Effects of environmental factors on the historical time serie of blackspot seabream commercial landings (1983 to 2015) in the strait of Gibraltar: A shared marine resource between the Spanish and Moroccan fleets

In the Strait of Gibraltar, the Blackspot Seabream (Pagellus bogaraveo, Brünnich 1768) is an economic resource of great commercial importance for the Spanish and Moroccan artisanal and Moroccan longline fleets. Given the great interest of the species for the fleets, it is of vital importance to know the dynamics of landings and how this can be influenced by environmental variability. From this arises the hypothesis of the present study: environmental mechanisms cause forcings in the dynamics of landings. To this end, we analysed the average annual dynamics of the time series of commercial landings of the Blackspot Seabream from 1983 to 2015 from a multivariate perspective. We applied trend, principal component (PCA) and time series clustering analyses to determine patterns and relationships between the fishery series and different oceanographic variables and climatic indices. In addition, we determined the influence of this set of variables on landings from a linear approach based on multiple linear regressions (MLRs) and generalized linear models (GLMs) and non-linear determined by generalized additive models (GAMs). The results obtained indicated the presence of common temporal patterns and the existence of significant influence between landings and ocean temperature with the current velocity modulus in specific layers and heat flux, causing lower fishing yields as we get colder waters with less intense currents. Such studies are of vital importance for the application of an ecosystem approach to the management of this resource by understanding the effect and influence of the environment on the dynamics of landings from the fishery.The authors wish to express their gratitude to Dr. Juan Gil-Herrera (Oceanography Spanish Institute, Cádiz, Spain) and Dr. Said Benchoucha and Sana el Arraf (National Institute of Fisheries Research-INRHTangier, Morocco) for providing the data set of Blackspot Seabream landings in the Spanish and Moroccan ports. Víctor Sanz-Fernández is financed by the Spanish Ministry of Science, Innovation and Universities with a FPU fellowship (FPU17/04298). Funding for open access charge: Universidad de Huelva / CBUA

Temperature patterns along the migration routes of European eel larvae towards the south of the Iberian Peninsula

This study analysed the variations in sea surface temperature patterns along the two main migration routes of European eel larvae from their spawning grounds (Sargasso Sea) to the southern coasts of the Iberian Peninsula. For this purpose, monthly time series of sea surface temperature associated with theoretical locations along the migratory routes from January 1984 to December 2006 were analysed. The results indicate that regardless of the migration route followed, the temperature pattern was characterized by two periods of maximum temperatures. Likewise, in both routes, surface temperature anomalies indicated the presence of a regime change in the mid- 1990s that significantly correlated with glass eel abundance anomalies in the south of the Iberian Peninsula. Along both routes, strong negative anomalies (mid-1980s, early-1990s and mid-1990s) were associated with positive anomalies of glass eel abundance. In contrast, from the mid-1990s, the negative anomalies of glass eel abundance were associated with a period in which the SST anomalies were clearly positive. These results support the hypothesis that SST is highly important for the recruitment of glass eels in the European coasts.Funding for open access charge: Universidad de Huelva / CBUA

Age-structure, growth and reproduction of the introduced pumpkinseed (lepomis gibbosa, l. 1758) in a tributary of the Guadalquivir river (southern Spain)

The age structure, growth and reproduction of Lepomis gibbosa (L. 1758) (= L. gibbosus) was studied from March 1993 to September 1994 in a tributary stream of the Guadalquivir River. The maximum age observed was 5+ years both in males and females. In the O+ group, seasonal growth began in February and lasted 8 months. Males and females matured during their second year of life (l+). There were no significant differences in the overall sex-ratio, which was 1: 1.1 (677 males to 745 females). Reproductive activity started in MarcWApril and lasted until AugusUSeptember. During this period, females spawned 2 batches of eggs. The relationship between fecundity (F) and fork length (&, mm) was: F=5.09 % 279 (1993) and F=85.81 L, ' 56 (1994). The maximum contribution to the fecundity of the population was observed in the 4+ female group. The reproductive effort was maximun in the 3+ group. Compared with the American pumpkinseed populations that have been studied, the lifehistory patterns of this stock are characterized by low annual growth, early maturity, reduced longevity and low fecundity

The performance of three ordination methods

The performance of robust principal component analysis (RPCA), detrended correspondence analysis (DCA) and non-metric multidimensional scaling (NMDS) with two demersal fish data sets were assessed in terms of their stability to bootstrap-generated sample variation and the method’s ability to reflect a well-known depth gradient. Stability was assessed for both species and site orderings and configurations, using scaled rank variance (SRV) and Spearman (q) and Procrustes correlations (t0). The NMDS site and species orderings showed the highest stability. DCA performed best in terms of site ordination stability, but NMDS performed best in terms of species ordination stability. In terms of matching the expected ecological variation, NMDS was the most sensitive method for the wider-depth gradient data and DCA was the most sensitive for the narrower-depth gradient data. According to the sensitivity and informative power criteria associated with the stability assessment, t0 was the best methodological approach for site ordinations, and SRV for species ordinations

Morphometric relations for body size and mouth dimensions for four fish species in the Strait of Gibraltar

Background. The deep-water longline fishery of the blackspot seabream, Pagellus bogaraveo, is an economically important fishery in the Strait of Gibraltar, which is a very complex transition ecosystem between the Mediterranean Sea and the Atlantic Ocean with an extreme spatial and temporal variability. This paper presents a series of morphometric relations for the four most important species in this fishery. Some ecological considerations about the results are also discussed. Materials and methods. The data were collected during a gear selectivity study, using different sizes of hooks baited with sardine. Relations for weight–length, length–length, and mouth dimensions for blackspot seabream, Pagellus bogaraveo (Brünnich, 1768); Atlantic pomfret, Brama brama (Bonnaterre, 1788); blackbelly rosefish, Helicolenus dactylopterus (Delaroche, 1809); and Mediterranean horse mackerel, Trachurus mediterraneus (Steindachner, 1868) were estimated and compared with the ones reported for the same species from other areas. Results. The sample size varied from 89 for T. mediterraneus to 2180 for P. bogaraveo. The fitted L–W relations explained more than 81% of the variance. For P. bogaraveo and T. mediterraneus, the estimated allometric coefficient was higher than those reported for other areas, showing a faster increase in weight, in contrast to H. dactylopterus and B. brama that showed a slower increase in weight. Moreover, linear and highly significant relations between mouth size and fish length were found for P. bogaraveo, H. dactylopterus, and T. mediterraneus. Conclusion. In this study, the first record for total length–standard length relation for H. dactylopterus is reported based on real measurements. There has been no previous studies on the relation between the different mouth size dimensions for the studied species as well as for mouth size and body length relations for P. bogaraveo and H. dactylopterus. The difference between estimated and reported coefficients might be attributed to different environmental adaptations and to the size ranges used due to the gear-size selectivity

Is the vessel fishing? Discrimination of fishing activity with low-cost intelligent mobile devices through traditional and heuristic approaches

Knowing the activity of fishing vessels accurately and in real time means a leap in quality in the management of fishing activity. This paper presents the development of a new fishing activity monitoring integral system (FAMIS) that can complement and overcome the limitations of current fishing vessel monitoring systems (VMS). FAMIS is developed on the basis of a low-cost mobile device with GPS sensors, accelerometer, gyroscope and magnetic field and integrates different statistical methods (discriminant functions) and heuristics (artificial neural networks and vectorial support machines) as techniques to classify the information recorded by the sensors of a mobile device during fishing activity. The results obtained with FAMIS indicate that, in general, heuristics have a high degree of discrimination of each of the phases of fishing operation and that, in particular, multilayer perceptrons (MLPs) are capable of correctly identifying 96.3% of towing phases using only GPS and gyro sensors

Teaching methodology for modeling reference evapotranspiration with artificial neural networks

[EN] Artificial neural networks are a robust alternative to conventional models for estimating different targets in irrigation engineering, among others, reference evapotranspiration, a key variable for estimating crop water requirements. This paper presents a didactic methodology for introducing students in the application of artificial neural networks for reference evapotranspiration estimation using MatLab c . Apart from learning a specific application of this software within their field of future professional competencies, students would get in touch with current research work in irrigation engineering, and eventual future research collaborations might be promoted.[ES] Las redes neuronales artificiales constituyen una buena alternativa a los modelos convencionales para estimar diferentes variables en ingeniería del riego, entre ellas la evapotranspiración de referencia, clave en la determinación de las necesidades de agua de riego. En este artículo se presenta una metodología didáctica para introducir al alumno en la aplicación de redes neuronales para el cálculo de evapotranspiración de referencia mediante el programa MATLAB©.Además de aprender a usar esta herramienta en una aplicación concreta dentro de su campo de competencias profesionales futuras, el alumno toma contacto con líneas actuales de investigación en el campo de la ingeniería del riego y se promueven eventuales colaboraciones de investigaciónMartí, P.; Pulido Calvo, I.; Gutiérrez Estrada, JC. (2015). Propuesta didáctica para modelizar evapotranspiración de referencia con redes neuronales artificiales en el aula. Modelling in Science Education and Learning. 8(2):27-36. doi:10.4995/msel.2015.3348SWORD273682Allen, R.G., Pereira, L.S., Raes, D., & Smith, M., (1998). Crop evapotranspiration. Guidelines for computing water requirements. FAO Irrigation and Drainage, paper 56. FAO, Roma.Bishop, C.M. (Ed.), (1995). Neural Networks for Pattern Recognition. Oxford University Press, Oxford.George H. Hargreaves, & Zohrab A. Samani. (1985). Reference Crop Evapotranspiration from Temperature. Applied Engineering in Agriculture, 1(2), 96-99. doi:10.13031/2013.26773Haykin, S. (Ed.), (1999). Neural Networks. A comprehensive foundation. Prentice Hall International Inc., New Jersey.Zanetti, S. S., Sousa, E. F., Oliveira, V. P., Almeida, F. T., & Bernardo, S. (2007). Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data. Journal of Irrigation and Drainage Engineering, 133(2), 83-89. doi:10.1061/(asce)0733-9437(2007)133:2(83

Comparación de modelos cinéticos no estructurados para fermentación de bioetanol con Saccharomyces cerevisiae

La producción alcohólica es importante para la industria de los biocombustibles. La representación con modelos cinéticos mediante simulaciones numéricas puede dar una idea para mejorar las condiciones de fermentación. En el presente trabajo se utilizaron datos experimentales reportados por Zentou et al. (2019), de un proceso de fermentación alcohólica mediante Saccharomyces cerevisiae a tres diferentes concentraciones iniciales de melaza como sustrato. Se propusieron tres diferentes modelos (Haldane-Levespiel, Haldane-Luong y Moser-Levespiel). Como objetivo principal se estimaron los parámetros cinéticos para cada modelo y se validaron por medio de simulaciones numéricas y el cálculo de los coeficientes de correlación para cada variable. En general se obtuvieron mejores ajustes que los presentados por Zentou et al. (2019). El modelo cinético de Moser-Levespiel presento el coeficiente de correlación global más alto que fue de R2=0.98.Alcoholic production is important to the biofuels industry. The representation with kinetic models through numerical simulations can give an idea to improve the fermentation conditions. In the present work, experimental data reported by Zentou et al. (2019), from an alcoholic fermentation process using Saccharomyces cerevisiae at three different initial concentrations of molasses as a substrate. Three different models were proposed (Haldane-Levespiel, Haldane-Luong and Moser-Levespiel). The main objective was to estimate the kinetic parameters for each model and validate them by means of numerical simulations and the calculation of the correlation coefficients for each variable. In general, better adjustments were obtained than those presented by Zentou et al. (2019). The Moser-Levespiel kinetic model presented the highest global correlation coefficient, which was R2=0.98

Processing acoustic images for the exploitation of fish farms

This work presents a processing methodology that uses data from a SONAR sensor located on a HROV to support the exploitation and management of fish farms.Peer Reviewe

Técnicas de predicción a corto plazo de la demanda de agua. Aplicación al uso agrícola

[ES] La demanda de agua es la magnitud de referencia en la gestión óptima de los sistemas de distribución. En este trabajo se propone la estimación de la demanda en las próximas 24 horas en un sistema de distribución de agua para riego, y se utilizan, junto con los métodos tradicionales de predicción de regresión múltiple y de modelos univariantes de series temporales (ARIMA), las Redes Neuronales Computacionales (RNCs). Se dispone de los datos de las demandas diarias de agua de las campañas de riegos 1987/88, 1988/89 y 1990/91 de la zona regable de Fuente Palmera (Córdoba). Los modelos se establecen considerando la relación de los datos presentes y pasados de la demanda, aunque también se analiza la influencia de datos climáticos (temperatura máxima, temperatura media, temperatura mínima, precipitación, humedad relativa, horas de sol y velocidad del viento). Las mejores estimaciones se consiguen con la RNC que considera como variables de entrada las demandas y las temperaturas máximas de los dos días anteriores al de estimación.Pulido Calvo, I.; Roldán Cañas, J.; López Luque, R.; Gutiérrez Estrada, JC. (2002). Técnicas de predicción a corto plazo de la demanda de agua. Aplicación al uso agrícola. Ingeniería del Agua. 9(3):319-331. https://doi.org/10.4995/ia.2002.2622SWORD31933193ÁLVAREZ, J. y S. BOLADO (1996) Descripción de los procesos de infiltración mediante redes neurales artificiales. Ingeniería del Agua, 3: 39-46.BOX, G.E.P. y G.M. JENKINS (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco, CA.CYBENCO, G. (1989) Approximation by superpositions of a sigmoidal function. Math. Controls, Signals, and Systems, 2: 303-314.COULBECK, B., S.T. TENNANT y C.H. ORR (1985) Development of a demand prediction program for use in optimal control of water supply. Systems Sci., 11: 59-66.FRENCH, M.N., W.F. KRAJEWSKI y R.R. CUYKENDAL I (1992) Rainfall forecasting in space and time using a neural network. J. Hydrol., 137: 1-31.GRIÑÓ, R. (1992) Neural networks for univariate time series forecasting and their application to water demand prediction. Neural Network World, 437-450.GUTIÉRREZ-ESTRADA, J.C., I. PULIDO-CALVO y J. PRENDA (2000) Gonadosomatic index estimates of an introduced pumpkinseed (Lepomis gibbosus) population in a Mediterranean stream, using computational neural networks. Aquat. Sci., 62: 350-363.HAIR, JR., J.F., R.E. ANDERSON, R.L. TATHAM y W.C. BLACK (1999) Análisis de regresión múltiple. En Análisis multivariante. Pretince Hall Iberia, 5ª ed., Madrid, 4: 143-226.HARTLEY, J.A. y R.S. POWELL (1991) The development of a combined demand prediction system. Civ. Engrg. Systems, 8: 231-236.HSU, K., H.V. GUPTA y S. SOROOSHIAN (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res., 31: 2517-2530.JOWITT, P.W. y C. XU (1992) Demand forecasting for wáter distribution systems. Civ. Engrg. Systems, 9: 105-121.KITANIDIS, P.K. y R.L. BRAS (1980) Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resour. Res., 16: 1034-1044.LEÓN, C., S. MARTÍN, J.M. ELENA y J. LUQUE (2000) EXPLORE-Hybrid expert system for water networks management. J. Water Resour. Planning and Mgmt., 126: 65-74.MAIDMENT, D.R., S.P. MIAOU y M.M. CRAWFORD (1985) Transfer function models for daily urban water use. Water Resour. Res., 21: 425-432.MINAI, A.A. y R.D. WILLIAMS (1990) Acceleration of back-propagation through learning rate and momentum adaptation. Int. Joint Conf. Neural Networks, 1: 676-679.MOLINO, B., G. RASULO y L. TAGLIALATELA (1996) Forecast model of water consumption for Naples. Water Resour. Mgmt., 10: 321-332.MOREU, P. (1999) Series temporales. En Estadística Informatizada. Paraninfo, 11: 93-108.NEL, D. y J. HAARHOFF (1996) Sizing municipal water storage tanks with Monte Carlo simulation. J. Water SRTæAqua, 45: 203-212.PULIDO-CALVO, I. (2001) Diseño y gestión óptimos de sistemas de impulsión y de almacenamiento de agua para riego. Tesis Doctoral, Dpto. de Agronomía, Universidad de Córdoba.RAMAN, H. y V. CHANDRAMOULI (1996) Deriving a general operating policy for reservoirs using neural networks. J. Water Resour. Plng. and Mgmt., 122: 342-347.RANJITHAN, S., J.W. EHEART y J.H. GARRET JR. (1993) Neural network-based screening for groundwater reclamation under uncertainty. Water Resour. Res., 29: 563-574.RIZZO, D.M. y D.E. DOUGHERTY (1994) Characterization of aquifer properties using artificial neural networks: neural kriging. Water Resour. Res., 30: 483-497.RÜFENATCH, H.P. y H. GUIBENTIF (1997) A model for forecasting water consumption in Geneva canton, Switzerland. J. Water SRTæAqua, 46: 196-201.RUMELHART, D.E., G.E. HINTON y R.J. Willians (1986) 'Learning' representations by backpropagation errors. Nature, 323: 533-536.SAPORTA, D. y M. MUÑOZ (1994) El consumo en redes de distribución. Predicción diaria de la demanda. En Mejora del rendimiento y de la fiabilidad en sistemas de distribución de agua. Aguas de Valencia y U.D. Mecánica de Fluidos (UPV), 2: 43-75.SHVARTSER, L., U. SHAMIR y M. FELDMAN (1993) Forecasting hourly water demands by pattern recognition approach. J. Water Resour. Planning and Mgmt., 119: 611-627.THIRUMALAIAH, K. y M.C. DEO (2000) Hydrological forecasting using neural networks. J. Hydrol. Engrg., 5: 180-189.VENTURA, S., M. SILVA, D. PÉREZ-BENDITO y C. HERVÁS (1995) Artificial neural networks for estimation of kinetic analytical parameters. Anal. Chem., 67: 1521-1525.VENTURA, S., M. SILVA, D. PÉREZ-BENDITO y C. HERVÁS (1997) Computational neural networks in conjuction with principal component analysis for resolving highly nonlinear kinetics. J. Chem. Inform. and Computer Sci., 37: 287-291.YANG, C.C., S.O. PRASHER, R. LACROIX, S. SREEKANTH, N.K. PATNI y L. MASSE (1997) Artificial neural network model for subsurface-drained farmlands. J. Irrig. And Drain. Engrg., 123: 285-292