Improved Growth Pattern of Tiger Prawn (TP) Arthropoda in a Pond by Analytical Hierarchical Process (AHP)

Artificial intelligence (AI) is a versatile term that is a conclusive remedy to solve the problem using past rational data after deep contemplation using these terms i-e basic statistics, carving data, familiarity with common AI algorithms. Seafood especially tiger prawn export as a busi-ness will provide enormous foreign exchange to any country if the farmers overcome the corre-lated vulnerabilities in prawn farming. This research is elucidating lacking in Tiger prawn (TP) farming like curbing of Oxygen, pH, water temperature, and nutrients, etc. Moreover, hatchery statistics in terms of juveniles will depict this study's clear picture of curbed aquaculture. For normative decisions, the Analytical Hierarchical Process (AHP) is used. The problem which has been faced by local prawn farmers that there is a stagnant TP growth in ponds, the reason is the predominant sensitivity factor in TP. For this reason, they need indemnification of thirteen fac-tors with natural resources to get the plausible results to get calmness in their lives. This study will solely focus on the TP growth model, and the monitoring effect will be established by the Artificial Intelligence algorithm. This study will employ the AHP, 0-1 scaling method, data cura-tion techniques, and ecological statistics. The life of Tiger Prawn (TP) depends upon these factors mainly, a) Physical and b) Chemical parameters. Physical parameters contain environment (E) provided to TP like season (S) and temperature (T) etc. whereas the quality of Ammonia NH3 (N) from fish waste, Oxygen level (O), and water quality hard & soft (W) lies in chemicals do-main. This research will Elucidate the factors which cause conceptual muddles in the aquamarine life of TP, for this reason, Statistical tools will assess the current result, forecast the gap. AHP will analyze the domain inputs, circumspect ramification which will depict visceral factors, later results depict which pond suits the TP. In curtail, these factors will be curbed to improve the growth of TP in a control conditioned environment.

Public science data suggest that fish is the most important factor in reducing small pond biodiversity in Czechia

This project tested a public-science approach to the assessment of freshwater habitat quality via simple invertebrate sampling. We combined a mobile phone application and simple instruction to children to sample 50 ponds in Central Czechia, and we analyzed the data using a standard ecological statistics approach. Despite the limitation in scope and taxonomic precision, our data revealed the same patterns as academic studies of the same topic. Specifically, we conclude that the main cause of invertebrate community decline is fish overstocking, while diverse invertebrate communities require aquatic macrophytes. Pollution detectable by children has an effect on invertebrate community structure, but a different effect than fish has, and not as statistically robust. Importantly, almost all large ponds were found overstocked with fish; therefore they support not more diversity than small ponds, but less, and serve as ecological traps. Our findings suggest that pond conservation should focus primarily on the restoration of aquatic vegetation, and that the most effective approach will be the removal of excessive fish.

On the necessity of value assessment for anthropogenic load in ecological statistics

The problem of the cost presentation for environmental indicators by the subjects of official statistical accounting is considered. Based on the example of the Federal State Statistics Service (Rosstat), according to the subsection «Production and Consumption Wastes», a cost estimate of environmental damage from the generation, disposal and disposal of waste was carried out. Calculations have shown that the amount of such harm on an annualized basis is trillions of rubles. It is shown that the monetary value will visually see the real scale of the environmentally unfavorable impacts of the production and economic sphere. Also, compare the amount of funds invested in environmental protection and the amount of averted anthropogenic load, making timely adjustments to problematic areas of environmental protection.

Efficient Estimation of Large-Scale Spatial Capture-Recapture Models

Capture-recapture methods are a common tool in ecological statistics, which have been extended to spatial capture-recapture models for data accompanied by location information. However, standard formulations of these models can be unwieldy and computationally intractable for large spatial scales, many individuals, and/or activity center movement. We provide a cumulative series of methods that yield dramatic improvements in Markov chain Monte Carlo (MCMC) estimation for two examples. These include removing unnecessary computations, integrating out latent states, vectorizing declarations, and restricting calculations to the locality of individuals. Our approaches leverage the flexibility provided by the nimble R package. In our first example, we demonstrate an improvement in MCMC efficiency (the rate of generating effectively independent posterior samples) by a factor of 100. In our second example, we reduce the computing time required to generate 10,000 posterior samples from 4.5 hours down to five minutes, and realize an increase in MCMC efficiency by a factor of 25. We also explain how these approaches can be applied generally to other spatially-indexed hierarchical models. R code is provided for all examples, as well as an executable web-appendix.

Enhancement of Sustainable Development Statistics for the Russian Regions

Environmental and ecological statistics in Russia is still under formation. Despite the widespread recognition of the importance of providing economic growth within the environmental constraints, there continues to be significant lack of data that could be used to identify trends in the sustainable development, especially at the regional level. The authors argue that genuine savings, a complex indicator of sustainable development, could become such a statistic tool.The analysis shows that today Russia has gained extensive experience in assessing the sustainability of regional development using this indicator, but a comprehensive system for its assessment requires elaboration so that it could be established, as a regular practice in public administration.The article describes fundamental methodological issues of calculating the individual components of genuine savings through the current statistical accounting system in Russia. The paper considers conditions necessary to correctly estimate this indicator and ensure inter-regional and cross-country comparability of the results of such calculations.A version of the method for calculating genuine savings that is presented in this article requires an update of the system of statistical accounting in Russia. Practical application of this method along with the improvement of the system of statistical accounting shall allow for an adequate sustainability assessment of a particular region. This, in turn, shall provide a basis for establishing regional policies to compensate for resource depletion by investing in other types of capital.