Date Published: August 28, 2019
Publisher: Public Library of Science
Author(s): Krisna Rungruangsak-Torrissen, Poramate Manoonpong, Simon Russell Clegg.
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.
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. Since food availability and environmental conditions influence the growth and distribution of animals in nature, the biochemical data involving food utilization and growth are essential. Different biochemical techniques have been uniquely developed over thirty years during Rungruangsak-Torrissen’s career for these purposes, through genetic variations in trypsin phenotypes affecting digestion and utilization of dietary protein, feed efficiency, maintenance rations, immunity, and growth in aquatic species [1–4]. Rungruangsak-Torrissen has recently summarized in details in her book about the developed analytical techniques for different biochemical factors, including the collection of biological samples . These knowledges provide the concept for the development of the neural computational model GrowthEstimate.
At this point, the neural computational model GrowthEstimate has been developed with a combination of four datasets from salmonids of different sizes, containing key biological factors of weight (g), digestive efficiency (T/C ratio), protein growth efficiency through the condition factor (CF, 100 × g cm–3), and weight specific growth rate (SGR, % day–1) (Fig 1). Although developed from temperate species, it can also estimate the SGR of tropical species (see Table 4).
The neural computational model GrowthEstimate is developed using the combined inputs of normalized weight (g), normalized digestive efficiency (T/C ratio), and protein growth efficiency through the normalized condition factor (CF, 100 × g cm–3). The model could precisely estimate the weight specific growth rate (SGR, % day–1) of living resources, especially in natural marine and freshwater ecosystems where food availability, consumption rates, and growth rates are unknown. However, the trained model should be improved with additional datasets with similar data points from different species at different development stages. Determinations of the activities of trypsin and chymotrypsin in the pyloric caeca and small intestine for the T/C ratio study, including other biochemical techniques for studying the performance qualities of growth and maturation in aquatic living resources, are described in [4,28]. The advantages of simultaneously using different biochemical techniques developed by Rungruangsak-Torrissen and her research team are described [10,12,13,28,51], and summarized in [1–4]. The importance of the specific activity levels of trypsin and chymotrypsin for fish growth is similar to the importance of the levels of acceleration and braking capacity for car speed, respectively. A higher acceleration (trypsin specific activity) is needed to increase car speed (fish growth), and a higher braking capacity (chymotrypsin specific activity) is necessary for stopping a car (fish) at a higher speed (higher growth) [4,13,51].