Current Scope, Case Studies and Future Directions of Ecological Informatics

Abstract

Ecological data management, analysis and synthesis as well as information processing and transfer in ecology are considered feature areas of ecological informatics. The paper presents case studies for analysis and synthesis of ecological data by means of unsupervised artificial neural networks and hybrid evolutionary algorithms to assist in computational bioindication of wetland water quality and early warning of cyanobacteria blooms in lakes and reservoirs. Integrated analysis and synthesis of ecological and genomics data, hybrid model libraries generic for ecosystem categories as well as internet-based data and model sharing are identified as key research areas of ecological informatics.

Natural Resource Management for Nonlinear Stochastic Biotic-Abiotic Ecosystems: Robust Reference Tracking Control Strategy Using Limited Set of Controllers

“…Related studies are dedicated to preventing ecosystem degradation or managing agricultural production. In preventing ecosystem degradation, analyzing ecological data could help people access critical ecological information, e.g., wetland water quality, therefore people could introduce flow regulation to recharge the drying wetland for maintaining ecological balance, or change the land use pattern to reduce pollution for improving reservoir water quality Recknagel, 2013;Yang and Chen, 2013). In managing agricultural production, a conservative planning scheme that could allow specific system violation has been proposed to solve the water quality management problem (Xu and Qin, 2013).…”

Real-time observation, early warning and forecasting phytoplankton blooms by integrating in situ automated online sondes and hybrid evolutionary algorithms

“…Because HEA can provide rules and equations to explain what factors drive the dynamics of the dependent variables (e.g. phytoplankton abundance), this algorithm has attracted much attention in forecasting the dynamics of aquatic ecosystems (Cao et al, 2006;Recknagel, 2013;Recknagel et al, 2013Recknagel et al, , 2014Wai et al, 2007).…”

“…In the past decades, a wide variety of predictive models and algorithms, including physical-process based models (Jorgensen and Nielsen, 1994;Wu and Xu, 2011), artificial neural network (Jeong et al, 2006;Recknagel et al, 2006;, and evolutionary algorithms (Recknagel, 2013;Recknagel et al, 2013Recknagel et al, , 2014, have been developed to forecast the dynamics of phytoplankton. Since algal blooms are considered as a result from an excess of nutrients, particularly nitrogen (N) and phosphorus (P) (Conley et al, 2009), most of the existing models emphasize nutrient availability (e.g.…”

A Multi-Objective Approach to Evaluate the Economic and Environmental Impacts of Alternative Water and Nutrient Management Strategies in Africa

“…Optimization algorithms have been widely used in environmental modelling (Reed et al, 2013;Jegannathan et al, 20 11;Recknagel, 2013;Nicklow et al, 2010) with an increase use of multi-objective approaches. In the case of multi-objective optimization problems, usually there is no unique optimal solution, but there is a set of solutions which are superior to the rest of solutions: these are known as Pareto-optimal or non-dominated solutions (Hans, 1988).…”