Advanced Modelling Techniques Studying Global Changes in Environmental Sciences discusses the need for immediate and effective action, guided by a scientific understanding of ecosystem function, to alleviate current pressures on the environment. Research, especially in Ecological Modeling, is crucial to support the sustainable development paradigm, in which the economy, society, and the environment are integrated and positively reinforce each other. Content from this book is drawn from the 2013 conference of the International Society for Ecological Modeling (ISEM), an important and active research community contributing to this arena. Some progress towards gaining a better understanding of the processes of global change has been achieved, but much more is needed. This conference provides a forum to present current research using models to investigate actions towards mitigating and adapting to change.

This volume presents approaches and methodologies for predicting the structure and diversity of key aquatic communities (namely, diatoms, benthic macroinvertebrates and fish), under natural conditions and under man-made disturbance. The intent is to offer an organized means for modeling, evaluating and restoring freshwater ecosystems. Includes a CD-Rom illustrating the use of the modern modelling techniques.

In this book, an easily understandable account of modelling methods with artificial neuronal networks for practical applications in ecology and evolution is provided. Special features include examples of applications using both supervised and unsupervised training, comparative analysis of artificial neural networks and conventional statistical methods, and proposals to deal with poor datasets. Extensive references and a large range of topics make this book a useful guide for ecologists, evolutionary ecologists and population geneticists.

This volume presents approaches and methodologies for predicting the structure and diversity of key aquatic communities (namely, diatoms, benthic macroinvertebrates and fish), under natural conditions and under man-made disturbance. The intent is to offer an organized means for modeling, evaluating and restoring freshwater ecosystems.

In this book, an easily understandable account of modelling methods with artificial neuronal networks for practical applications in ecology and evolution is provided. Special features include examples of applications using both supervised and unsupervised training, comparative analysis of artificial neural networks and conventional statistical methods, and proposals to deal with poor datasets. Extensive references and a large range of topics make this book a useful guide for ecologists, evolutionary ecologists and population geneticists.