A non-deterministic approach to forecasting the trophic evolution of lakes

  • Roberto Bertoni | r.bertoni@ise.cnr.it CNR-ISE, Institute of Ecosystem Study, Italy.
  • Martino Bertoni University of Basel, Switzerland.
  • Giuseppe Morabito CNR-ISE, Institute of Ecosystem Study, Italy.
  • Michela Rogora CNR-ISE, Institute of Ecosystem Study, Italy.
  • Cristiana Callieri CNR-ISE, Institute of Ecosystem Study, Italy.

Abstract

Limnologists have long recognized that one of the goals of their discipline is to increase its predictive capability. In recent years, the role of prediction in applied ecology escalated, mainly due to man’s increased ability to change the biosphere. Such alterations often came with unplanned and noticeably negative side effects mushrooming from lack of proper attention to long-term consequences. Regression analysis of common limnological parameters has been successfully applied to develop predictive models relating the variability of limnological parameters to specific key causes. These approaches, though, are biased by the requirement of a priori cause-relation assumption, oftentimes difficult to find in the complex, nonlinear relationships entangling ecological data. A set of quantitative tools that can help addressing current environmental challenges avoiding such restrictions is currently being researched and developed within the framework of ecological informatics. One of these approaches attempting to model the relationship between a set of inputs and known outputs, is based on genetic algorithms and programming (GP). This stochastic optimization tool is based on the process of evolution in natural systems and was inspired by a direct analogy to sexual reproduction and Charles Darwin’s principle of natural selection. GP works through genetic algorithms that use selection and recombination operators to generate a population of equations. Thanks to a 25-years long time-series of regular limnological data, the deep, large, oligotrophic Lake Maggiore (Northern Italy) is the ideal case study to test the predictive ability of GP. Testing of GP on the multi-year data series of this lake has allowed us to verify the forecasting efficacy of the models emerging from GP application. In addition, this non-deterministic approach leads to the discovery of non-obvious relationships between variables and enabled the formulation of new stochastic models.

Downloads

Download data is not yet available.
Published
2016-03-22
Keywords:
Deep lakes, trophic evolution, ecological modelling, genetic programing, Lake Maggiore.
Statistics
Abstract views: 2130

PDF: 498
HTML: 1340
Share it

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

How to Cite
1.
Bertoni R, Bertoni M, Morabito G, Rogora M, Callieri C. A non-deterministic approach to forecasting the trophic evolution of lakes. jlimnol [Internet]. 22Mar.2016 [cited 17Feb.2020];75(1s). Available from: https://www.jlimnol.it/index.php/jlimnol/article/view/jlimnol.2016.1374