International Journal of Remote Sensing ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: http://www.tandfonline.com/loi/tres20 Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network Jintao Wang, Wei Yu, Xinjun Chen, Lin Lei & Yong Chen To cite this article: Jintao Wang, Wei Yu, Xinjun Chen, Lin Lei & Yong Chen (2015) Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network, International Journal of Remote Sensing, 36:13, 3317-3330, DOI: 10.1080/01431161.2015.1042121 To link to this article: http://dx.doi.org/10.1080/01431161.2015.1042121 Published online: 01 Jul 2015. Submit your article to this journal Article views: 135 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tres20 Download by: [University of Maine - Orono] Date: 17 August 2016, At: 12:56 International Journal of Remote Sensing, 2015 Vol. 36, No. 13, 3317–3330, http://dx.doi.org/10.1080/01431161.2015.1042121 Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network Jintao Wanga,b,c,d,WeiYua,b, Xinjun Chena,b,c,d*, Lin Leia,b,c,d, and Yong Chenb,e aCollege of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China; bCollaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China; cNational Engineering Research Centre for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China; dKey Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China; eSchool of Marine Sciences, University of Maine, Orono, ME 04469, USA (Received 20 September 2014; accepted 8 March 2015) Ommastrephes bartramii is a short-lived species of squid and reacts rapidly to changes in the regional environmental conditions of the fishing ground. Understanding the preferred range of key environmental variables and predicting potential resource distributions are critical to conserve and manage its resources. Commercial fishery data for the western winter–spring cohort of O. bartramii from Chinese squid-jigging vessels during 2003–2013 were used to evaluate a suitable range of three key environ- mental variables, sea surface temperature (SST), sea surface height (SSH), and chlor- ophyll-a (chl-a) concentration, and to explore potential fishing zones (PFZs) using an artificial neural network. The neural interpretation diagram and independent variable relevance analysis indicate that month, latitude, and SST had significant influences on the PFZ distribution of O. bartramii, yielding 21.78%, 23.91%, and 26.04% of contribution rates, respectively. Based on the sensitivity analyses, a high abundance of O. bartramii mainly occurred in the waters between 150°–165° E and 37°–42° N during July to August. Suitable ranges of environmental variables for O. bartramii were 11–18°C for SST, −10 to 60 cm for SSH, and 0.1–1.7 mg/m3 for chl-a concen- tration, respectively. The back-propagation network model was well developed and could be used to predict the PFZ with 80% accuracy. The actual fishing grounds coincided with the predicted PFZ, suggesting that the established model of PFZ is effective in forecasting the potential habitat of O. bartramii in the Northwest Pacific Ocean. 1. Introduction In the Northwest Pacific Ocean, the Kuroshio and Oyashio currents create a transitional zone between the subtropical and subarctic boundaries (Roden 1991), yielding a highly productive habitat for various economically important species such as the Pacific saury (Cololabis saira), anchovy (Engraulis japonicus), albacore (Thunnus alalunga), Japanese common squid (Todarodes pacificus), and neon flying squid (Ommastrephes bartramii) (Zainuddin et al. 2006; Chen, Tian, and Guan 2014). This region provides one of the most complex physical oceanographic structures in the world, with fluctuating meandering eddies, fronts, and streamers, as well as variable biological environmental conditions (Sassa, Moser, and Kawaguchi 2002). Physical and biological environments in the *Corresponding author. Email: [email protected] © 2015 Taylor & Francis 3318 J. Wang et al. Kuroshio–Oyashio transitional area dominate the climate and ecosystem in the western North Pacific Ocean, which also greatly influences fish stock abundance and distribution (Yatsu et al. 2013). O. bartramii is a short-lived species of squid (Yatsu et al. 1997) and has been commercially exploited by Japan since 1974 and later by South Korea and China, including the Taiwan Province (Wang and Chen 2005). This squid undertakes seasonal migration from the subtropical Kuroshio Current in winter to the Subarctic Oyashio Current in summer (Gong, Kim, and An 1991; Seki 1993; Murata and Nakamura 1998). An autumn cohort and a winter–spring cohort for O. bartramii have been inferred in the North Pacific based on the analyses of mantle length distribution and rates of infection by helminthic parasites (Bower and Ichii 2005). During the main fishing seasons during August to November, Chinese squid-jigging fleets mostly target the western winter–spring cohort of O. bartramii in the traditional fishing ground between 39°–45° N and 150°–165° E, accounting for a significant portion of total catches in the Northwest Pacific (Chen et al. 2008a). As an ecological opportunist, O. bartramii tends to be highly susceptible to environ- mental changes in the spawning and feeding grounds in the western North Pacific (Yatsu et al. 2000; Anderson and Rodhouse 2001). Spatial distributions of squid abundance are typically related to oceanographic conditions, such as sea surface temperature (SST), sea surface height (SSH), and chlorophyll-a (chl-a) concentration, that can be remotely monitored using satellites (Chen, Cao, et al. 2010; Chen et al. 2011). Previous studies have employed various approaches to evaluate the relationship between the environmental variables and the abundance distribution of O. bartramii. For example, the surface water temperature in the spawning and fishing grounds plays an important role in regulating population dynamics and spatial distribution of O. bartramii, especially under abnormal climatic events such as the El Niño event (Chen, Zhao, and Chen 2007; Cao, Chen, and Chen 2009; Yi and Chen 2012). A positive relationship was identified between the catch per unit effort (CPUE) of the winter–spring cohort of O. bartramii and food availability featured by chl-a (Nishikawa et al. 2014). The inter-annual variability in the CPUE of O. bartramii could be explained by the fluctuating feeding environments of the spawning ground, transitional region, and fishing grounds, all of which had significant impacts on different life-history stages of O. bartramii, including paralarvae, juveniles, and adults (Ichii et al. 2009; Wang et al. 2010; Nishikawa et al. 2014). Furthermore, the SSH was considered to be a crucial marine environmental factor in exploring the potential fishing zones (PFZs), used in the habitat modelling of O. bartramii (Chen, Tian, et al. 2010). Many methods have been developed to predict PFZ distributions such as the habitat suitability index (HSI) model, generalized linear model (GLM), and generalized additive model (GAM). Chen et al. (2009) established an integrated HSI model for the winter– spring cohort of O. bartramii based on the SST and SST with a horizontal gradient, and found that the arithmetic mean model could accurately forecast squid fishing grounds in the Northwest Pacific. Tian et al. (2009) used the GAM method to evaluate the non-linear relationship between the CPUE of O. bartramii and the environmental variables on the fishing ground and concluded that the spatial pattern of squid abundance could be well predicted. General statistical models including the linear model, piecewise-linear model, polynomial regression, exponential regression, and quantile regression have been com- monly used in the analysis of the relationships between fishing ground distribution and environmental conditions (Chen et al. 2013). However, the prediction of fishing ground for a short-lived species such as O. bartramii tends to be more difficult due to unknown mechanisms in the interactions with complex biophysical environments based on these International Journal of Remote Sensing 3319 conventional techniques. In fact, the mechanism of forming a fishing ground is complex and the dynamic interactions between fish distribution and environments tend to be non- linear. With the development of machine learning and artificial intelligence, novel meth- ods, such as expert systems, genetic algorithms, and fuzzy reasoning, were developed and have been increasingly used to explore PFZ (Chen et al. 2013). An artificial neural network with functions of self-learning, strong generalizations, and fault tolerance pro- vides an approach to evaluate complex non-linear relationships (Hush and Horne 1993; Moody and Antsaklis 1996). The artificial neural network models can be established with few assumptions on fishery and environmental data and, thus, differs from conventional statistical models and also tends to be more suitable for forecasting fishing grounds (Suryanarayana et al. 2008). Artificial neural networks have been used for predicting fish distribution