• Dr Poonam kumari

Information gathered from fishermen is crucial for managing fish stocks sustainably. Due to a lack of fishery data, the state of fish stocks is unknown, which might jeopardise the livelihoods of those who rely on those stocks and raise the risk of overfishing. An array of potential solutions to update and modernise fisheries data systems and considerably extend data collection and analysis has become accessible as a result of recent advances in technology for collecting, managing, and analysing data important to fisheries. However, despite the widespread availability of essential consumer technologies, technologically advanced data systems are still the exception rather than the rule in fisheries management. To better understand the barriers to widespread use of high-tech data systems in fisheries management, this article describes their current state, challenges, and potential future paths. In this paper, we demonstrate that a lack of trust and cooperation between fishers and management is preventing innovation in fisheries around the world by analysing the use of data technology dependent on fisheries in a variety of contexts. We suggest a solution based on a transdisciplinary strategy for fishery management that stresses the need of multi-stakeholder problem-solving. Our suggested approach relies heavily on data feedback to ensure that fishers and managers can collect, access, and benefit from fisheries data as they work towards a common goal. Improved fisheries outcomes can be achieved by implementing a novel strategy for fisheries data systems that encourages innovation to boost data coverage, accuracy, and resolution, all while decreasing costs and facilitating adaptive, responsive, near real-time management decision-making. Introduction: The food security and lives of hundreds of millions of people around the world are at risk due to overfishing, which has increased the urgency of implementing sustainable fisheries management (Golden et al., 2016; Jackson et al., 2001; Pauly, Watson, & Alder, 2005; Szuwalski, Burgess, Costello, & Gaines, 2017; World Bank 2009). However, regardless of the fishing sector or management system, having access to accurate, consistent data about how a fishery is doing, and what, where, and how much of a species are being caught, is a fundamental component for establishing effective fishery management (Beddington, Agnew, & Clark, 2007). Higher resolution spatial and temporal fisheries data over shorter durations are needed to address growing uncertainty regarding stock status and to allow managers to adapt reference points as the environment changes as a result of anthropogenic cli-mate change and natural climate variability (Pinsky & Mantua, 2014; Szuwalski& Hollowed, 2016). This necessitates improved methods of data collecting, reporting, processing, and analysis, as well as dissemination channels that allow for responses to be made in near real time (Wilson et al., 2018). The ecological characteristics of fished stocks, such as their relative invisibility in the oceans, widespread distribution and mobility across jurisdictional boundaries, and complex interactions within marine ecosystems and the physical environment, make data collection difficult, time-consuming, and costly. Data dependent on fisheries are necessary for the effective management of all fisheries, but the specific nature of data needs and management goals varies by fishing sector (i.e. industrial fishing, small- scale fishing [SSF], and recreational and subsistence fishing), data availability (i.e. data- rich vs. data- poor), and management type (i.e. top-down, de-centralized, and informal). In the most data-rich settings, dynamic population models are used to assess stock status by fitting a lengthy time series of fishery-dependent, fishery-independent, and other crucial fishery information. When it comes to an understanding and tracking landings and stock dynamics for effective assessment and management, data-poor fisheries—the >80% of global stocks that lack adequate data for a formal stock assessment (Costello et al., 2012)—often lack the resources and capacity to do so using traditional tools and techniques (Dowling et al., 2016). Syntheses of global fisheries data have revealed a crucial truth: well-managed fisheries, in particular those that are guided by formal stock assessments, are in better condition than fisheries that are poorly managed and lack comprehensive assessments (Costello et al., 2012; Mora et al., 2009). In the end, fishery statistics are required to assist efficient management and ensure the long-term viability of fished stocks and the economic and nutritional security of those who rely on them (Pauly et al., 2005). Recent innovations and emerging technologies have the potential to contribute to fishery-dependent data systems by increasing or streamlining data collection, automating and empowering data processing and analysis, and facilitating the communication of results to relevant stakeholders. These innovations and technologies often take advantage of the widespread availability of mobile phones and tablets and the growing accessibility of cloud- based computing for data storage and artificial intelligence for analysis. More information can be gathered in less time and with greater accuracy using electronic reporting and on-board passive sensors like cameras and GPS (sometimes known as "electronic monitoring" systems). Management at more relevant spatial and temporal dimensions is possible with the use of artificial intelligence techniques like machine learning and computer vision, and the integrated processing and analysis of enormous amounts of near real- time, geo-referenced data. One-way flows of information (from fisher to management) can be transformed into a cooperative, mutually advantageous cycle of data collection, synthesis, and sharing with the help of technology, allowing fishermen to maximise their fishing based on the best available information. Fishers, whose livelihoods depend on knowing ocean dynamics, are typically the first to discover changes on the water, making the thousands of vessels operating at sea the logical first line of defencce in tracking shifting stock dynamics and environmental variability. However, high-tech fishery-dependent data systems remain the exception rather than the rule, despite the widespread availability of efficient, cost-effective technologies and the potential for technology to fulfil data demands in fisheries management. To better comprehend the barriers that have prevented the widespread implementation of technologically advanced data systems in capture fisheries, we provide a comprehensive overview of the current state of these systems and the issues they face, and we propose a solution that would direct the increased use of technology to better fisheries outcomes. For the sake of brevity, we will only address data systems that are directly related to industrial, SSF, recreational, and subsistence fishing, and are gathered and/or used on fishing vessels or at the point of landing or first point of sale. We begin with a quick summary of traditional fishery-dependent data systems, then move on to discuss the ways in which fishery-dependent data technologies have been put to use. Cost and restricted access to finance, regulatory restrictions and institutional shortfalls that inhibit innovation, and a persistent lack of trust and collaboration between managers and fishers are some of the factors we propose explain the delayed penetration of technology in fishery data systems. Finally, we argue that the adoption of technological innovations in fisheries could be facilitated by adopting a transdisciplinary approach to fisheries management, which prioritises communication and coordination among various fisheries stakeholders and the development of direct data feedbacks.

The Management Behind the Fishery Industry

"The Management Behind the Fishery Industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.335-334, November-2018, Available :http://www.jetir.org/papers/JETIR1811C96.pdf

"The Management Behind the Fishery Industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp335-334, November-2018, Available at : http://www.jetir.org/papers/JETIR1811C96.pdf