Egg quality in fishes has been a topic of research in aquaculture and fisheries for decades as it represents an important life history trait and is critical for captive propagation and successful recruitment. A major factor influencing egg quality is proper yolk formation, as most fishes are oviparous and the developing offspring are entirely dependent on stored egg yolk for nutritional sustenance. These maternally derived nutrients consist of proteins, carbohydrates, lipids, vitamins, minerals, and ions that are transported from the liver to the ovary by lipoprotein particles including vitellogenins. The yolk composition may be influenced by broodstock diet, husbandry, and other intrinsic and extrinsic conditions. In addition, a number of other maternal factors that may influence egg quality also are stored in eggs, such as gene transcripts, that direct early embryonic development. Dysfunctional regulation of gene or protein expression may lead to poor quality eggs and failure to thrive within hours of fertilization. These gene transcripts may provide important markers as their expression levels may be used to screen broodstock for potential spawning success. In addition to such intrinsic factors, stress may lead to ovarian atresia or reproductive failure and can impact fish behavior, fecundity, and ovulation rate. Finally, postovulatory aging may occur when eggs become overripe and the fish fails to spawn in a timely fashion, leading to low fertility, often encountered during manual strip spawning of fish.

The power of citizen science to contribute to both science and society is gaining increased recognition, particularly in physics and biology. Although there is a long history of public engagement in agriculture and food science, the term ‘citizen science’ has rarely been applied to these efforts. Similarly, in the emerging field of citizen science, most new citizen science projects do not focus on food or agriculture. Here, we convened thought leaders from a broad range of fields related to citizen science, agriculture, and food science to highlight key opportunities for bridging these overlapping yet disconnected communities/fields and identify ways to leverage their respective strengths. Specifically, we show that (i) citizen science projects are addressing many grand challenges facing our food systems, as outlined by the United States National Institute of Food and Agriculture, as well as broader Sustainable Development Goals set by the United Nations Development Programme, (ii) there exist emerging opportunities and unique challenges for citizen science in agriculture/food research, and (iii) the greatest opportunities for the development of citizen science projects in agriculture and food science will be gained by using the existing infrastructure and tools of Extension programmes and through the engagement of urban communities. Further, we argue there is no better time to foster greater collaboration between these fields given the trend of shrinking Extension programmes, the increasing need to apply innovative solutions to address rising demands on agricultural systems, and the exponential growth of the field of citizen science.

Recent technological advancements have revolutionized research capabilities across the biological sciences by enabling the collection of large data that provides a broader picture of systems from the cellular to ecosystem level at a more refined resolution. The rapid rate of generating these data has exacerbated bottlenecks in study design and data analysis approaches, especially as conventional methods that incorporate traditional statistical tests and assumptions are not suitable or sufficient for highly dimensional data (i.e., more than 1,000 variables). The application of machine learning techniques in large data analysis is one promising solution that is increasingly popular. However, limitations in expertise such that the results from machine learning models can be interpreted to gain meaningful biological insight pose a great challenge. To address this challenge, a userfriendly machine learning workflow that can be applied to a wide variety of data types to reduce these large data to those variables (attributes) most determinant of experimental and/or observed conditions is provided, as well as a general overview of data analysis and machine learning approaches and considerations thereof. The workflow presented here has been beta-tested with great success and is recommended to be incorporated into analysis pipelines of large data as a standardized approach to reduce data dimensionality. Moreover, the workflow is flexible, and the underlying concepts and steps can be modified to best suit user needs, objectives, and study parameters.

Aquaculture is the farming of aquatic organisms and it is the major source of seafood produced globally. The majority of seafood products consumed in the United States, however, are imported from foreign countries. Benjamin J. Reading, Ph.D., and doctoral candidate Linnea K. Andersen share how there is a great window of opportunity to expand the domestic aquaculture industry as a whole to support a reliable and sustainable seafood industry that will help support food security, agricultural development and best environmental practices in the 21st Century.