Abstract
The neural computational model GrowthEstimate is introduced with focusing on new perspectives for the practical estimation of weight specific growth rate (SGR, % day–1). It is developed using recurrent neural networks of reservoir computing type, for estimating SGR based on the known data of three key biological factors relating to growth. These factors are: (1) weight (g) for specifying the age of the growth stage; (2) digestive efficiency through the pyloric caecal activity ratio of trypsin to chymotrypsin (T/C ratio) for specifying genetic differences in food utilization and growth potential, basically resulting from food consumption under variations in food quality and environmental conditions; and (3) protein growth efficiency through the condition factor (CF, 100 × g cm–3), as higher dietary protein level affecting higher skeletal growth (length) and resulting in lower CF. The computational model was trained using four datasets of different salmonids with size variations. It was evaluated with 15% of each dataset, resulting in an acceptable range of SGR outputs. Additional tests with different species indicated similarity between the estimated SGR outputs and the real SGR values, and the same ranking of wild population growth. The developed model GrowthEstimate is exceptionally useful for the precise and comparable growth estimation of living resources at individual levels, especially in natural ecosystems where the studied individuals, environmental conditions, food availability and consumption rates cannot be controlled. It is a revelation and will help to minimize uncertainty in wild stock assessment process. This will improve our knowledge in nutritional ecology, through the biochemical effects of climate change and environmental impact on the growth performance quality of aquatic living resources in the wild, as well as in aquaculture. The original GrowthEstimate software is available at GitHub repository (https://github.com/RungruangsakTorrissenManoonpong/GrowthEstimate). All other relevant data are within the paper. It will be improved for generality for future use, and required co-operations of the biodata collections of different species from different climate zones. Therefore, a co-operation will be available.
Highlights
Growth estimation is very important for studying living resources, and precise estimation of the growth rate of organisms is important for minimizing uncertainty in stock assessment
The neural computational model GrowthEstimate for estimating the weight specific growth rate (SGR, % day–1) of living resources has been developed for generality
According to the different input combinations of the datasets used for testing the model, the results indicated that the higher the input factor, the lower the SGR estimation error, and the inputs with the T/C ratio provided less error in SGR estimation
Summary
Growth estimation is very important for studying living resources, and precise estimation of the growth rate of organisms is important for minimizing uncertainty in stock assessment. The key biological factors, necessary to understand growth including genetics and suitable for the practical purposes of growth performance quality, have been studied intensively over three decades by Rungruangsak-Torrissen and her research team, by understanding a series of growth mechanisms through genetics, digestion and utilization of dietary protein, and effects of food and environments. These studies are summarized in [1,2,3,4]. The results indicate that dietary protein is the primary key nutrient for growth, regardless of eating habits (carnivores, omnivores, or herbivores), and the differences in the ability to digest the same dietary protein for utilization and deposition for optimal growth are genetically affected
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
