Assessment of Age and Growth in Fishes

Fish are the largest and most diverse group of vertebrates. They are also one of the most important protein sources for the growing human population and play an essential role in ecosystems. Given their vital ecological role and significance as a protein source for humans, the meticulous assessment and management of fish species are vital to achieving sustainability.

Fisheries science and management rely heavily on accurate assessments of age and growth in fish populations. These parameters are fundamental to understanding the life history of fish, and they play a crucial role in sustainable fisheries management, conservation strategies, and ecosystem-based approaches. Over the years, scientists have developed and improved upon various techniques for estimating the age and growth of fish. This Special Issue—a compilation of the most recent studies produced by experts on this topic—aims to provide an overview of the evolution of the age and growth assessment of fish, highlighting the transition from traditional approaches to the incorporation of modern technologies and discussing the challenges and opportunities that lie ahead.

Traditionally, the assessment of the individual ages of fish has primarily been based on the analysis of calcified structures such as otoliths, scales, and other parts of the fish skeleton (e.g., vertebrae, cleithra, opercula, and spines) [1]. Calcified structures grow throughout a fish’s life and act as a permanent record of growth, producing periodic patterns that are related to variations in growth rate induced by both environmental (biotic and abiotic) and endogenous factors, such as ontogenic events [2]. The interpretation of these patterns, referred to as growth increments, has been the cornerstone of age estimation. Calcified structure reading requires expertise and an understanding of species-specific increment deposition patterns, which can vary in complexity. Age assessment using calcified structures can be time consuming, subject to human error, and influenced by factors such as environmental conditions and fish physiology [3]. Among all the previously referred calcified structures, otoliths are the most used in the age assessment of teleost fish due to their continuous growth, even when somatic growth has completely ceased during periods of food restriction and starvation [4].

The last few years have witnessed remarkable advancements in imaging technologies and analytical methods, revolutionizing the field of the age and growth assessment of fish [5]. High-resolution imaging tools, such as micro-computed tomography (micro-CT), have enabled researchers to examine the internal structures of otoliths [6,7]. Micro-CT scans provide intricate details of growth patterns, allowing for accurate age determination and reducing the potential for error associated with direct readings. In addition to imaging, automated image analysis and machine learning algorithms have gained prominence in age estimation. These techniques offer a standardized and objective approach to interpreting growth patterns. Machine learning models can be trained to recognize and classify growth increments in calcified structures, thus minimizing subjectivity and inter-observer variability [8,9,10,11,12]. This integration of technology into age assessment not only enhances accuracy but also expedites the process [11,12], thereby facilitating the analysis of larger sample sizes and a greater diversity of species [10].

As the fields of genomics and bioinformatics continue to evolve, researchers are actively exploring innovative avenues for the age and growth assessment of fish. Moreover, there is also a need for the development of new methods that surpass the constraints of traditional methods (which focus on the analysis of growth increments in calcified structures), such as the fact that they are lethal, time-consuming, have low accuracy for some species, and cannot be applied to others. DNA methylation, a pivotal epigenetic mechanism, has emerged as a promising tool for assessing the ages of fish species, consisting of the addition of a methyl group in cytosine–guanine loci [13]. By quantifying the methylation patterns of specific genomic regions, researchers can construct an “epigenetic clock” that correlates with chronological age [13,14]. This innovative approach offers a direct and accurate means of age determination, circumventing the limitations of traditional methods [15,16,17]. Furthermore, the epigenetic clock not only enhances our understanding of fish aging processes but also holds the potential to shed light on the effects of environmental factors on age-related changes, namely, temperature and food availability [13]. Integrating genomic information with traditional methods and modern imaging techniques presents an exciting multidisciplinary approach with which to advance age estimation accuracy.

Recent advancements in modelling fish growth have ushered in a new era of precision in the assessment of age and growth among fish species. Traditional growth models often oversimplified the complex processes underlying fish growth. However, contemporary approaches, such as individual-based models (e.g., [18]) and bioenergetics models (e.g., [19]), account for the intricate interplay between biological, environmental, and ecological factors. These innovative models integrate environmental factors to create more accurate representations of growth trajectories [20]. A particularly noteworthy technique is the incorporation of the Bayesian approach, which allows for the integration of prior knowledge and uncertainty estimation into growth models [21,22]. By implementing Bayesian methods, researchers can not only enhance the predictive power of growth models but also quantify the uncertainties associated with parameter estimates, leading to more robust and reliable inferences [21,23]. By combining these improvements with the Bayesian framework, researchers are now better equipped to decipher the nuanced patterns of fish growth, thus enhancing our ability to understand population dynamics, predict responses to environmental changes, and facilitate informed fisheries management decisions. Furthermore, the application of bioinformatics and data integration holds immense potential for refining growth models. Incorporating variables such as temperature, prey availability, and habitat quality into growth models can enhance our ability to predict fish growth trajectories under changing conditions. This predictive ability is crucial for understanding the effects of climate change and anthropogenic impacts on fish populations.

While the integration of modern technologies and complementary approaches has elevated the accuracy and efficiency of age and growth assessment, challenges remain. Ensuring the availability of high-quality reference data and establishing standardized protocols are essential for cross-comparisons across studies and species. Furthermore, the accessibility of advanced imaging technologies and the expertise required to operate them can be limiting factors in some regions. In the future, fostering collaboration between researchers, fisheries managers, and policymakers will be crucial. Effective communication and knowledge exchange will facilitate the translation of scientific findings into practical management strategies that ensure the sustainability of fish populations and aquatic ecosystems.

In summary, the assessment of the age and growth of fish has evolved from traditional methods rooted in the interpretation of calcified structures to a multidisciplinary field embracing imaging technologies, machine learning, genomics and bioinformatics, and more complex and refined growth models. These advancements have not only improved our understanding of fish life histories but also equipped us with tools with which to make informed decisions regarding fisheries management and conservation. As we look toward the future, collaborative efforts, technological innovation, and ethical considerations will be pivotal in addressing challenges and unlocking new frontiers in the assessment of the age and growth of fish. This Special Issue welcomes scientific papers both within and outside the scope of the topics previously mentioned to highlight the breadth of research on the assessment of the age and growth of fish.