An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach

Abstract

The natural world is constantly changing, and planetary boundaries are issuing severe warnings about biodiversity and cycles of carbon, nitrogen, and phosphorus. In other views, social problems such as global warming and food shortages are spreading to various fields. These seemingly unrelated issues are closely related, but it can be said that understanding them in an integrated manner is still a step away. However, progress in analytical technologies has been recognized in various fields and, from a microscopic perspective, with the development of instruments including next-generation sequencers (NGS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS), various forms of molecular information such as genome data, microflora structure, metabolome, proteome, and lipidome can be obtained. The development of new technology has made it possible to obtain molecular information in a variety of forms. From a macroscopic perspective, the development of environmental analytical instruments and environmental measurement facilities such as satellites, drones, observation ships, and semiconductor censors has increased the data availability for various environmental factors. Based on these background, the role of computational science is to provide a mechanism for integrating and understanding these seemingly disparate data sets. This review describes machine learning and the need for structural equations and statistical causal inference of these data to solve these problems. In addition to introducing actual examples of how these technologies can be utilized, we will discuss how to use these technologies to implement environmentally friendly technologies in society.

Keywords: Biodiversity; Environmental analysis; Machine learning; Network; Statistical inference.

Fig. 1

Various ecosystems where computational science can be applied. It shows that the multiple impacts that human activities can generate affect each ecosystem and the integrated ecosystem, and we are now at a crossroads. Carbon (C), nitrogen (N), and phosphorus (P) flows are indicated by gray, blue, and orange arrows, respectively. Computational network indicators, which serve as markers of the circulation of carbon, nitrogen, and phosphorus, are connected to various ecosystems.

Fig. 2

Relationship between carbon, nitrogen, and phosphorus flow balance and ecosystem homeostasis. The flow balance of carbon, nitrogen, and phosphorus on the left side negatively affects ecosystem homeostasis; the flow balance on the right side positively impacts ecosystem homeostasis.

Fig. 3

Concept of environmental prediction based on the accumulated machine learning database. Data accumulation is critical to better prediction performance by machine learning computations. To do that, environmental monitoring based on physical parameters is quite valuable for accumulating time-dependent data. Furthermore, occasional samplings from environments of interest allow us to elucidate biochemical data measured by NMR, MS, and NGS instruments. Even physical and biochemical data can be formatted with RDF; such a multifactored database can be computed by machine learning. In the future, computational devices, such as quantum innovation, might significantly reduce computational time, as well as device size. Therefore, “field computation (prediction)” may be possible in the fields of agriculture, fishing, livestock, and forest.

Fig. 4

Analytical step from multivariate and association analysis to machine learning and statistical causal inference (From left-top to right-bottom). (a) represents a diverse set of data. (b) Association analysis (market basket analysis), (c) Bayesian network analysis (HC: hill climbing), and (d) structural equations are shown, respectively. The stair walker is representatively showing how a person performing normal work might be able to check by portable computational device for changes in environmental factors at the field and industrial site. The multifactor environmental database can be computed and selected as casual importance factors. First, a data-driven approach can calculate all the accumulated data sets using multivariate and association analysis. The second computational step is a selection of essential factors by machine learning. Finally, causal relationships can be visualized by SEM, BayesLiNGAM, and other methods (see in the text).

Fig. 5

Impact assessment of technologies that contribute to planetary boundaries. (a) Various conditions and diagrams related to nitrogen, phosphorus, and biodiversity are shown. To make these evaluations, it is necessary to implement integrated understanding using computational science. (b) demonstrate the setting of goals for various technologies and planetary boundaries; Various methodologies are assumed for technologies aimed at environmental conservation and restoration. The current problem is that these distributed technologies are not understood in an integrated manner. In order to realistically implement these fusions, various simulations are also necessary.