RESUMO
Introduction: The prediction of potential fishing areas is considered one of the most immediate and practical approaches in fisheries and is an essential technique for decision-making in managing fishery resources. It helps fishermen reduce their fuel costs and the uncertainty of their fish catches; this technique allows to contribute to national and international food security. In this study, we build different combinations of predictive statistical models such as Generalized Linear Models and Generalized Additive Models. Objective: To predict the spatial distribution of PFZs of the dolphinfish (Coryphaena hippurus L.) in the Colombian Pacific Ocean. Methods: We built different combinations of Generalized Linear Models and Generalized Additive Models to predict the Catch Per Unit Effort of C. hippurus captured from 2002 to 2015 as a function of sea surface temperature, chlorophyll-a concentration, sea level anomaly, and bathymetry. Results: A Generalized Additive Model with Gaussian error distribution obtained the best performance for predicting PFZs for C. hipurus. Model validation was performed by calculating the Root Mean Square Error through a cross-validation approach. The R2 of this model was 50 %, which was considered suitable for the type of data used. January and March were the months with the highest Catch per Unit Effort values, while November and December showed the lower values. Conclusion: The predicted PFZs of C. hippurus with Generalized Additive Models satisfactorily with the results of previous research, suggesting that our model can be explored as a tool for the assessment, decision making, and sustainable use of this species in the Colombian Pacific Ocean.
Introducción: La predicción de zonas potenciales de pesca se considera uno de los enfoques más inmediatos y efectivos en las pesquerías, es una técnica importante para la toma de decisiones en el manejo de los recursos pesqueros. Ayuda a los pescadores a reducir su costo de combustible y también a disminuir la incertidumbre de sus capturas, esta técnica permite contribuir a la seguridad alimentaria nacional e internacional. En este estudio, se construyeron diferentes combinaciones de modelos estadísticos predictivos como modelos lineales generalizados y modelos aditivos generalizados. Objetivo: predecir la distribución espacial de las zonas potenciales de pesca del pez dorado (Coryphaena hippurus L.) en el Pacífico colombiano. Métodos: La variable de respuesta se expresó en escala de captura por unidad de esfuerzo, es decir, el número de individuos de C. hippurus capturados por un número total de anzuelos disponibles entre 2002 y 2015. Temperatura de la superficie del mar, concentración de clorofila, anomalía del nivel del mar y batimetría, se utilizaron como variables explicativas para los meses de estacionalidad de C. hippurus (noviembre - marzo). Resultados: El modelo con mejor rendimiento para la predicción de zonas potenciales de pesca fue un modelo aditivo generalizado con distribución de error gaussiana y función de enlace de registro, que se seleccionó en función del criterio de información de Akaike, el R2 y la desviación explicada. La validación del modelo se realizó calculando el error cuadrático medio a través de un enfoque de validación cruzada. El ajuste de este modelo fue del 50 %, lo que puede considerarse adecuado para el tipo de datos utilizados. Enero y marzo fueron los meses con mayor captura por unidad de esfuerzo y noviembre-diciembre los meses con menor. Conclusión: Las zonas potenciales de pesca previstas coincidieron satisfactoriamente con investigaciones anteriores, lo que sugiere que nuestro modelo es una herramienta poderosa para la evaluación, toma de decisiones y uso sostenible de los recursos pesqueros de C. hippurus en el Pacífico colombiano.
Assuntos
Animais , Indústria Pesqueira , Previsões , Colômbia , Sistemas de Informação GeográficaRESUMO
The Dipteran Prodiplosis longifila is a severe pest, mainly of Solanaceae, in South America and some years ago it damaged Tahiti lime crops in the United States. It is a potential invasive pest. Despite its presence in Colombia, nothing is known regarding the taxonomic identification of P. longifila or the characteristics of the damage it produces. Moreover, the current and potential distributions of this pest are unknown. To determine these factors, P. longifila was sampled in several Solanaceae- and Citrus (x) latifolia (Tahiti lime)-producing areas in Colombia. The larvae consumed tender foliage, flowers and fruits in tomato, fruits in sweet pepper, and buds in Tahiti lime. P. longifila was not found in asparagus or in potatoes. Its presence in Tahiti lime was previously unknown in Colombia. Adults recovered in the laboratory were taxonomically identified using male morphological characteristics such as the shapes of the genitalia, antenna and wing. P. longifila was found in the Andean region of Colombia. The ecological niche model for populations found in tomato suggests that P. longifila is limited in its distribution by altitude and variables associated with temperature and precipitation. The highest probability of occurrence is in areas where tomato, sweet pepper and the new host, Tahiti lime, are grown. Therefore, it is necessary to implement preventive measures, such as planting tomato materials free of P. longifila larvae, in areas where the pest is not yet present but where there is the potential for its development.
